research questions on online shopping

• 33 Online Shopping Questionnaire + [Template Examples]

Online shopping is increasingly becoming the most preferred shopping option across the globe. In 2019 alone, an estimated 1.92 billion people purchased goods or services online. This has created a need to offer good customer service to online shoppers, which is why you need an online shopping questionnaire. 

An online shopping questionnaire helps you to study users’ behaviors, experiences, and preferences as they shop items from your e-commerce store. In this article, we will discuss 33 questions you should include in your online shopping questionnaire to help you understand your customers’ needs. 

Online Shopping Survey Questions  

The types of questions listed in your online shopping survey must reflect the aims and objectives of the data collection process. Also, be sure to ask good survey questions that allow respondents to freely communicate their thoughts and perceptions without boxing them into a corner. 

Close-ended questions, open-ended questions , and dichotomous questions are examples of good survey questions that you can add to your online shopping survey. 

Close-Ended Questions

A close-ended question is a type of survey question that restricts respondents to a set of answer-options to choose from. In other words, the researcher provides options for you to choose from in response to the question. Close-ended questions help you to gather quantitative data . 

11 Close-ended Questions for an Online Shopping Questionnaire 

How often do you shop on this site, choose 2 products you buy frequently on this site..

• Accessories
• Perfumes and Oils
• Skincare products

What is the biggest challenge you face with shopping online?

• Slow checkout time
• Lack of products I want
• Slow webpage response time 

How likely are you to recommend this site to other online shoppers?

• Very likely
• Somewhat likely
• Very unlikely

What is your biggest concern about online shopping?

• Breach of personal information
• Breach of payment details
• Poor internet connection

How much do you spend on online shopping every month?

• Less than 100 USD
• $100 – $500
• $500 – $1000
• More than 1000 USD 

How many times do you shop online in a month?

• More than thrice 

Which payment method do you prefer for online shopping?

• Payment Gateways
• Bank transfer

How would you rate your overall online shopping experience?

How likely are you to return to this webpage for your online shopping.

• Highly unlikely 

What is your gender?

• Others. Please state
• Prefer not to say

Open-Ended Questions

This is a type of survey question that does not limit respondents to predetermined answers. Open-ended questions allow you to fully communicate your ideas and perceptions in response to a question. You can describe them as free-form survey questions. 

Examples of Open-Ended Questions in an Online Shopping Questionnaire   

• Describe your online shopping experience with us . This question allows your customer to provide a holistic view of their overall customer experience with your organization. 
• Describe a negative experience you had while shopping online . This question allows customers to highlight any areas needing improvement in your online store. 
• Describe a positive experience you had while shopping . Let customers identify strong points when it comes to online shopping with your brand.
• Why do you shop online with us? With this question, you would be able to identify the unique selling points of your brand across different customer segments. 
• How old are you ? This question helps you to understand who your customers are; that is, the different age groups that your brand appeals to. 

• Which products do you buy regularly? The responses to this question will help you to identify fast-moving products and to categorize your stock accordingly. 
• Have you experienced any difficulty with adding products to your online cart? This question allows respondents to provide specific feedback on definite aspects of your online shopping operations. 
• What do you think about the pricing of our products ? Use this question to collect feedback on product pricing to avoid overcharging or under-charging your customers. 
• What do you think about the quality of our products? This question allows you to collect first-hand feedback from customers in terms of the quality of your product(s).  
• What other online store do you shop on? The answers to this question make it easy for you to identify your competition. You can leverage this data to create a better customer experience for your clients. 
• What major challenges have you encountered while shopping on our site ? This question allows you to identify and address customer dissatisfaction easily. After identifying the challenges faced, you should work on providing sustainable solutions to them. 

Dichotomous Question

A dichotomous question is a type of close-ended survey question that provides respondents with 2 opposite answer options for them to choose from. Common answer options in dichotomous questions include true/false, yes/no, fair/unfair, to mention a few. 

Dichotomous Question Samples for an Online Shopping Questionnaire

Did you enjoy the online shopping experience on our website.

This question allows customers to provide feedback on the overall shopping experience. 

Do you always shop on our website?

This simple question allows you to track consumer retention for your organization. 

Would you recommend our website to others?

Positive responses to this question serve as an indicator of a good customer experience. 

Our website provided the best online shopping experience.

Just like the first question in this section, this question helps you to gather feedback on your overall online shopping experience. 

Do you have any challenges with our checkout method?

Get direct feedback from customers about your e-commerce checkout process on your website. 

Our product prices are affordable.

This question allows you to gather feedback from customers about product pricing. 

Have you ever had a bad experience while shopping with us?

This question allows you to track and address customer dissatisfaction. 

Do you have any concerns about your data privacy while shopping online?

I always use the credit card option for my online shopping transactions..

This question prompts customers to indicate preferred payment options. 

I always use the bank transfer method for online shopping transactions.

Just like the question above, customers can provide responses here that allow you to identify preferred payment methods for your e-commerce store. 

Do you always shop online?

Responses to this question provide insight into customers’ behaviors and preferences. 

Can’t find your preferred Online Shopping survey template? ithCreate yours for free with the easy-to-use Formplus builder

How to Create an Online Shopping Questionnaire with Formplus  

With Formplus, you can create a smart online shopping questionnaire and either add the form to your website or share it with your customers using our multiple form sharing options. Formplus makes it easy for you to collect and process data from your customers, and this helps to improve customer experience and consumer satisfaction for your organization. 

Follow these steps to create your online shopping questionnaire from scratch using Formplus. 

• If you do not have a Formplus account, visit www.formpl.us to sign up for your Formplus account. If you have a Formplus account, visit the aforementioned website and click on the “Access Dashboard” button to gain access to your personalized Formplus dashboard. 

• Once you have access to your Formplus dashboard, click on the “create new form” button to start building your online shopping survey. You’d find this button at the top left corner of your dashboard. 

• Alternatively, you can modify any of the existing Formplus templates to suit your data collection needs. All you need to do is click on the “template” option on the dashboard navigation bar and then, follow the prompt. 
• Now, you should be in the form builder. This is where you create your online shopping form. Start by adding the form title to the builder’s title bar. 

• Next, go to the form fields section located on the left side of the form builder. There, you’d find more than 30 form field options including digital signature fields, payment fields, date-time validation, and so on. You can add any of these fields to your form by simply clicking on them or drag and drop the field from the builder’s inputs section. 

• After adding the fields, click on the pencil icon just beside each field to access the form fields editing section. Here, you should add your question(s) and/or options. 

• When you’ve added and modified all preferred fields accordingly, click on the save icon just at the top right corner of the builder. This automatically saves the form and takes you to the builder’s section. 

• The Customize section is where you can change the look and feel of your online shopping survey. You can add preferred background images to your form, embed your organization’s logo, change the form font, or even customize the form layout using CSS. 

• To add the online shopping survey to your website simply, go to the form builder’s “Share” page. You’d find it on the builder’s navigation panel right at the top corner.
• Click on the “embed” tab on the sidebar.
• You’d see 4 options here: Use as Pop-up, Use as iFrame embed, Embed in Facebook Page, and Embed in WordPress site.  

• Click on “use as iFrame embed”  and copy the displayed code.

• Paste the code at the appropriate place to add it to your site. 
• If you have a WordPress website, you can embed the form by choosing the “Embed in WordPress site” option, copy the shortcode, and paste it inside your WordPress editor.

• Copy the form link and share it with respondents. 

Importance of an Online Shopping Survey  

E-commerce businesses, especially, should prioritize online shopping surveys because these data collection tools are key to business optimization, improved customer experience, brand loyalty, and increasing revenue. Here are 6 ways that online shopping surveys can make a difference in your business. 

• Understand Consumer Behaviour: With an online shopping survey, you’d have a better understanding of your customers’ online shopping behaviors with specific insights into their preferences, challenges, and experiences. This allows you to place them into distinct customer segments as part of market research. 
• Seamless Data Collection: An online shopping survey is a fast, easy, and convenient method of data collection . Unlike paper forms and other traditional survey methods, an online shopping survey can be filled on the go which allows you to gather real-time information from respondents, instantly. 
• With a smart online survey, you’d find it easier to highlight current trends and patterns in consumers’ behaviors. 
• Improved Customer Experience and Satisfaction: It helps you to immediately identify and address any challenges faced by your customers and to resolve these challenges accordingly. If you embed the survey into your e-commerce website, customers complete the questionnaire once they are done shopping on your webpage. 
• Optimized Marketing Plans and Strategies: The data gathered via an online shopping survey can help you create a well-defined marketing plan and strategy for your organization. Having a clear knowledge of who your customers are and what different customer segments prefer typically empowers you to create specifically tailored adverts that appeal to each segment.
• It improves your organization’s response time to customers’ complaints. 

Research shows that consumers spend an average of 5 hours shopping online every week and 92% of consumers shop online at least once a year . This, once again, emphasizes how much online shopping has become integral to our everyday lives. 

If you want to create unforgettable online shopping experiences for your target audience, you must understand customers’ experiences and expectations. An online shopping questionnaire is a simple but effective data tool that helps you to gather objective data from consumers. 

Connect to Formplus, Get Started Now - It's Free!

• compatibility questionnaires
• online questionnaire sample
• online shopping
• online store
• online surveys
• busayo.longe

You may also like:

Survey or Questionnaire? 15 Differences & Similarities

Guide on differences between survey and questionnaires. + Survey Questionnaire tips, tools, advantages template and examples

4 Types of Questionnaire + Free Question Examples

For many years, questionnaires have been a popular data collection tool during research. Questionnaires are very diverse and

Highest-Paying Survey Sites on the Web

In this post, we will highlight how the paid survey companies operate and we will also list out sites with legit paid surveys.

Pilot Testing: Meaning, Importance + [Question Example]

This article will discuss what a pilot test is, best practices for implementing pilot tests and how Formplus can help create stellar...

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home QuestionPro QuestionPro Products

45+ Proven eCommerce survey questions to ask your customers

Having an eCommerce store limits your opportunities to interact with your customers too often.  However, customer feedback and experience data can make a huge difference in taking your business to the next level. Know what your buyers feel and gain actionable insights with eCommerce survey questions to increase your sales.

The best way to boost the conversion rate is to ask your customers, ‘How can we make it better?’ Design their online shopping experience such that they feel driven to purchase again. Know from them what makes it an excellent experience for them and what irritates them.

Analyzing the data will help you decide if the product quality needs to be improved or website navigation. Collecting data through an eCommerce survey questionnaire enables you to make decisions based on the inputs from the people that matter most – customers.

LEARN ABOUT: Testimonial Questions

Top eCommerce survey questions to include in your website questionnaire

Several factors collectively contribute to the success of an online business. It is essential that these individuals parts are paid attention while working on the overall focus to drive sales.

To understand your strengths and weaknesses, ask your customers questions on various e-commerce processes. You can use these free survey questions for eCommerce or use a ready-made survey template to evaluate the potential of e-commerce services.

Free evaluation of the potential of e-commerce services survey template

Pre-Purchase eCommerce survey questions

Not all visitors buy products from your online store. Some of them just browse the store and leave. It is important to learn more about their expectations to offer them a better experience next time. It is also essential to understand if they are likely to visit again when they want to shop online again. Below are some of the pre-purchase eCommerce survey questions you can ask your visitors and get their feedback.

It is highly likely that many customers would not like to answer your eCommerce website survey. However, try to get minimum possible feedback even from such visitors. Ask a simple Net Promoter Score (NPS) question on how likely are they to recommend your website to others.

• Considering your  complete  experience with our company, how likely would you be to recommend us to a friend or colleague?

Below are some of the general questions on the ecommerce website. They give you an idea of the overall experience of the visitors. You can also ask these questions in the beginning so that even if the respondent leaves the survey without completing it, you have some data in hand.

• How satisfied did you feel based on your overall experience?
• On a scale of 1-10, how likely are you to recommend us to your friend or colleague?
• Please let us know how we can improve your experience.

The website is an inevitable component of an online business. It is important to know what your visitors think about your e-commerce site. While you can always analyze the traffic data, feedback straight from the horse’s mouth definitely helps.

• How did you learn about our website?
• Social Media
• Search Engine
• Recommendation from a friend
• Who are your shopping for?
• What were you looking for?
• Did you get what you were looking for?
• Are you going to return later?
• What led you to visit our website?
• Researching product information
• Interested in buying products
• Looking for contact information
• Know more about the company
• Did you like the design?
• How easy was it to navigate through the site?
• Please rate the below parameters as compared to our competitors.
• Website performance
• Product catalog
• Product information
• Shipping options
• Payment experience
• Online help
• What other information would you like on this page?

Product reviews and ratings

Many customers make purchase decisions based on product ratings. See if your customers are satisfied with the number of reviews per product.

• On a scale of 0-10, how much do you trust the product reviews?
• How helpful do you find ratings while making a purchase decision?
• On a scale of 0-10, how much does an overall product rating affect your purchase decision?

Product catalog and quality

An e-commerce website sells products and services online. Know if your visitors are happy with the range of products and their quality.

• Did you find enough range of products?
• Did you find enough product details?
• Do you generally find various alternatives for the same product?

Post-Purchase eCommerce survey questions

Below are the sample eCommerce survey questions you can ask your visitors after they convert into the customers. Learn more about their experience at various touchpoints and find ways to boost your sales.

• How satisfied are you with the quality of products?
• How do you rate the quality of our products as compared to our competitors?
• How satisfied are you with the availability of products?
• If your preferred product is not available, do you get acknowledged when it is back in stock?
• What other products would you like to see in our online store?

Online shopping survey questions

The purchase experience can be a game-changer for an e-commerce website. Know if your customers had a pleasant experience while buying products and services.

• How safe did you feel while sharing your card details?
• How was the checkout experience overall?
• Did you experience a hassle-free payment experience?
• On a scale of 0-10, how likely are you to buy from us again?

An eCommerce store may have a great look and feel, a customer gets happy when they actually see their products in hand. Know if they have any concerns about the shipping process with the below questions.

• Did you receive your product within the expected timeline?
• Would you like to enroll in paid services to get products earlier?
• Did you receive your product at the shipping address?
• Did you receive your bill at the billing address?
• How easily could you update your address details?
• Please rate your experience with the delivery personnel.

Customer support

The customer support team can leave a long-lasting impression on your customers. Know if your staff was polite and friendly to them.

• On a scale of 1-10, how was your experience with the customer support executive?
• Did the customer executive solve your query?
• How helpful was the customer support staff?

Branding and Marketing

Collect data from an e-commerce website survey and use it to offer the right products to your customers. It may be possible that they weren’t able to find the right product on your website. However, marketing communication can educate them more about the product benefits and suggest the ones they need.

• Please select the reason for purchasing this product. Select all that apply.
• Preferred brand
• Locally owned, made or sourced
• Sentimental value
• Product quality
• Adherence to laws
• Transparency
• Would you like to share your contact details to learn more about our discounts and sale?
• Would you like to know more about our membership benefits?
• Would you like to enroll in a paid membership to avail priority services?

Feedback on vendors

Many online stores like Amazon, eBay, etc source products from different vendors and offer a platform to sell their products. Get feedback from your customers for your vendors and pass on the same to them. It will also help you decide if you should continue allowing the same vendors to sell on your website.

• How satisfied are you with the vendor options we offer?
• How satisfied are you with the quality of products from this vendor?
• How helpful are the vendor details we offer?
• What more information would you want about the vendor?

Asking too many questions to your customers might demotivate them from responding to your eCommerce survey. So, ask questions randomly to your sample audience. You can randomly display one or more questions within a block. You can also limit the number of questions by randomizing the blocks.

Keep a mix of different types of questions. The survey should have the right proportion of multiple-choice questions , open-ended questions , and closed-ended questions.

LEARN ABOUT: User Interface Survey Questions & Vendor Satisfaction Survey

How to distribute eCommerce survey questions?

Assume you have 200 customers who have agreed to answer eCommerce survey questions. Split your respondents into a group of 50 or 100. You can set quotas to divide the audience into groups and control the data quality. Then, say, let the 100 customers answer the website, product catalog, ratings, and customer support questions. The other 100 respondents will see questions related to purchasing experience, shipping, brand recognition, and vendor feedback. 

Using a survey tool , you can distribute a survey through email, QR code, website, social media sites, or survey app. To send a online questionnaire through email, import all contacts into the application, and embed a link in the invitation email.    LEARN ABOUT: Consumer Survey

LEARN ABOUT:  Social Communication Questionnaire

How to use data collected from eCommerce survey questions to increase your sales?

• Customer segmentation : Use the data to segment your customers into various groups based on behavioral segmentation , demographic, and psychographic segmentations . Filter survey results on location, device, custom variables , question, distribution list, etc. to learn more about their buying behavior.
• Marketing : Data collected from the survey questions for eCommerce can help you send tailored marketing communication to your customers. Send them offers on products that match their interest. You can up-sell and cross-sell to those who agree to receive marketing emails and calls. The survey data can also give you insights into the right target audience for your next promotional campaign.
• Know your customer : Learn more about your customers’ interests and the ever-changing consumer market. The times are so dynamic now that it requires one to be always aware of the statistics and make decisions backed by numbers. 
• Analyze website data : Know what your visitors like the most about your website, the most queried products, and the improvement areas. After all, a great user experience converts into a purchase.
• Identify trends : Generate reports and perform trend analysis of the survey data . Find out if there is a consistent spike or drop in the demand for a specific product. Analyze if there are too many complaints about your eCommerce website’s performance, design, products, vendors, or others. See if any customer service executive has received a lot of negative feedback from the customers. Dig into the details and work on resolving issues to improve the customer’s journey. You can later conduct a customer satisfaction survey and measure the improvement in scores.
• Forecast demand : Apply machine learning and artificial intelligence algorithms to forecast demand. It will help you better plan your inventory so that, the next time potential customers browse through an eCommerce store, they don’t see a ‘Not in stock’ message.

LEARN ABOUT: Product Survey Questions

MORE LIKE THIS

Data Information vs Insight: Essential differences

May 14, 2024

Pricing Analytics Software: Optimize Your Pricing Strategy

May 13, 2024

Relationship Marketing: What It Is, Examples & Top 7 Benefits

May 8, 2024

The Best Email Survey Tool to Boost Your Feedback Game

May 7, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.8(9); 2022 Sep

Evaluating the impact of social media on online shopping behavior during COVID-19 pandemic: A Bangladeshi consumers’ perspectives ☆

Md rukon miah.

a Department of Marketing, Comilla University, Cumilla, Bangladesh

Afzal Hossain

b Department of Business Administration, Trust University, Barishal, Bangladesh

Rony Shikder

Meher neger, associated data.

Data will be made available on request.

Background of the study

Nowadays, the business pattern is changing globally. The business organization is influenced customers to purchase their necessary goods and services from online businesses. The online-based business takes promotional activities through social media platforms like Facebook, Twitter, Instagram, and Pinterest.

The aim of the research was to investigate the impact of social media on online shopping behavior during the COVID-19 pandemic in the context of Bangladeshi consumers.

Research methods

Quantitative type research was applied and the study used descriptive research design. A standardized questionnaire was used to collect 350 data points from Bangladeshi consumers using an online purposive sampling method. A partial least square structural equation modeling (PLS-SEM) approach was used to evaluate the data and test the hypotheses.

PLS-SEM analysis method demonstrated that celebrity endorsement, promotional tools, and online reviews had a positive significant impact on online shopping behavior during the COVID-19 pandemic in the perspective of Bangladesh.

The research paper provides practical guidelines for online-based business organizations on how to effectively use social media platforms for business target advertising and promotional activities. The customers are also motivated to purchase through social media because of positive online reviews and trustworthy celebrity endorsements.

Online shopping; Social media, Bangladeshi consumers, COVID-19 pandemic, PLS-SEM.

1. Introduction

With the expansion and spread of the 2019 novel coronavirus (2019-nCoV), also known as the severe acute respiratory syndrome coronavirus 2, a new public health crisis is threatening the world (SARS-CoV-2). In December 2019, the virus was revealed in bats and conveyed to humans via anonymous intermediary species in Wuhan, Hubei Province, China. To date (05/03/2020), there have been roughly 96,000 recorded cases of coronavirus disease 2019 (COVID-2019) and 3300 recognized deaths. The disease is spread through inhalation or contact with polluted droplets, with a 2 to 14-day incubation period. Fever, cough, sore throat, dyspnea, weariness, and malaise are common symptoms. Most people have a minor case of the common symptoms. Most people have a minor case of the condition. However, certain people (typically the elderly and those with comorbidities) may develop complications ( Singhal, 2020 ). The global proliferation of coronavirus has had a number of negative effects on human health ( Jajodia et al., 2020 ; Rajendran et al., 2020 ). Most enterprises have been adversely impacted by COVID-19, and as a consequence, they have been compelled to implement multiple measures to limit the proliferation of the coronavirus while also harming their organizational performance and effectiveness ( Bartik et al., 2020 ; Donthu and Gustafsson, 2020 ; Sohrabi et al., 2020 ). To contain the spread, people should exercise social detachment, self-isolation, and reduce travel, which also led to a significant decrease in institutional and business output ( Nicola et al., 2020 ). The global COVID-19 epidemic has severely affected societies and economies around the world and has hit various sectors of society in various ways. This unprecedented situation has far-reaching consequences for consumers’ daily lives and has dramatically changed how businesses operate and how consumers behave ( Donthu and Gustafsson, 2020 ; Yuen et al., 2020 ). The current situation, after the first wave and the beginning of the second wave of the COVID-19 epidemic in Europe, has forced many consumers to reconsider their established shopping and shopping habits or even learn new ones ( Sheth, 2020 ). Nowadays, social media is playing a significant role in the online marketing environment for buying products from online stores rather than traditional themed stores with the help of an internet connection. In the current situation, social media is a relatively new trend. The most popular social networking sites like Facebook, Twitter, LinkedIn, Pinterest, and Google contribute to the majority of activities such as messaging, chatting, gambling, and blogging. Consumers typically participate actively on social media and spend long hours on Facebook and Twitter, creating content and sharing it. Companies that are aware of these issues are moving towards various activities to attract customers, increase their level of awareness and make the most of the opportunities offered through social media. Accordingly, firms conduct strategic campaigns that overlap with customer structures and brand values to increase the level of social brand recognition. Digital and social media marketing allows companies to accomplish their marketing aims at relatively low cost ( Ajina, 2019 ; Yadav, 2016 ).

Individuals and families who buy a company's goods for personal consumption are denoted as consumers ( Kotler, 2004 ). Consumer behavior refers to the actions that consumers participate in when buying, consuming, and disposing of products and services. Consumer behavior is the study of how people shop, what they shop for, when they shop, and why they shop. When a customer needs to make a purchase, they will go through the steps of acknowledgement, information search, evaluation, purchase, and feedback ( Blackwell et al., 2006 ). Finally, the customer will select a product or brand to consume from a variety of options available in the market. These factors, on the other hand, have an impact on consumer purchasing behavior. When it comes to consumer buying choice behavior, it's critical to identify the many sorts of consumers who have different buying decision behaviors based on their level of involvement and capacity to discern significant differences between brands. The term “buying participation” is defined by Hawkins and Mothersbaugh (2010) as the level of interest a buyer has in purchasing a product or service. Retail managers and marketers must keep records of shifts in consumer buying behavior and attitudes in order to identify which strategies they should implement ( Verma and Gustafsson, 2020 ). Pantano et al. (2020) argue that customers have re-examined their buying habits even while recognizing advantages from previously unknown services. On the one hand, social media is a rich source of information about a company's consumer views; on the other hand, it promotes social interaction among consumers, which results in increased trust and, thus, changes in customer preferences' purchasing behavior ( Hajli, 2014 ).

Online shopping behavior involves the process of purchasing goods and services through the internet ( Sun et al., 2019 ). After collecting product information, the consumer selects an item according to its requirements and transaction criteria for the selected product, evaluates the product along with other available options, and gains post-press experience ( Kotler, 2000 ). Online shopping behavior is related to the psychological state of the customer buying online ( Li and Zhang, 2002 ). Social networking sites have been widely used by people for their professional and personal use in the era of global communication. According to E-marketer (2013) , companies for various marketing activities such as marketing research, branding, customer relationship management, sales promotion, and service and service delivery have gradually adopted various studies as well as social networking sites that ensure the positive effects of social development in marketing strategy media.

The World Wide Web has persuaded people around the world to make small changes in their behavior and attitudes. Because of these blessings, online shopping has emerged, which affects the lives of ordinary citizens. Online shopping has started in Bangladesh, but consumers are still not very accustomed to shopping online. Customers are becoming familiar with the internet and its benefits. Online shopping is becoming popular and a priority among a group of customers to get better quality offers related to information, benefits, and cost choice. Like other young Asians, Bangladeshi youth are experimenting with new ways of shopping that have led to the rise and popularity of online shopping in Bangladesh.

Nowadays, customers' purchasing patterns are changing globally, and they are purchasing goods and services through online shopping. Customers were heavily influenced by social media to shop online. During COVID-19, customers didn't go to shopping malls frequently because of lockdown, isolation, and fear of being affected by the coronavirus ( Eger et al., 2021 ). Business organizations can motivate customers to purchase through online shopping via social media platforms like Facebook, Twitter, Instagram, and Pinterest. Marketers have a great advantage on social media because they can influence or create awareness about goods and services and motivate them to purchase via online shopping. Business organizations can use social media platforms to influence their existing and potential customers to purchase their necessary goods and services through online shopping or online business platforms ( Chaturvedi and Gupta, 2014 ). Customers have been influenced by organizations via live streaming, celebrity endorsements, online reviews of customers, and promotional tools like target advertising ( Geng et al., 2020 ; Schouten et al., 2020 ). During the corona pandemic, the marketers took home delivery services to the customers ( Wang et al., 2021 ). Good online reviews have influenced potential customers to purchase through online shopping ( Mo et al., 2015 ). Online shopping behavior will benefit both customers and marketers ( Berman, 2012 ). Nowadays, in our society, some customers are so busy that they don't have the available time to purchase their necessary products or services. That's why they are not able to go to the market practically within a short time. They prefer to order any kind of commodity or service via online shopping. At present, customers want a relaxed environment on social media for shopping. Marketers provide target advertising via social media like Facebook, Twitter, and so on ( Luo et al., 2019 ). Thus, social media marketing tools are more useful than other marketing communication mixes. Word of mouth from celebrities and positive customer reviews encourages other customers to shop online.

This study was conducted on social media due to several factors that influence buying behavior. Purchasing online remittances has become an interesting and new topic for researchers around the world. People's buying patterns are changing. Online social media is a tool that has only recently developed and developed rapidly in the last few years, and it might have the problem of a lack of studies in all countries since it is at an early stage in the field of social commerce ( Huang and Benyoucef, 2015 ; Hossain et al., 2019 ). There are a lot of social media users in Bangladesh and they prefer to shop online, but there is still a lack of research on the trend of social media impact when buying a product online. Thus, by doing this research, marketers can focus on the areas that have the most impact on their online buying behavior. The purpose of the study is to understand the buying behavior of online shoppers.

After reviewing most of the related literature on social media that influences online shopping, it is clear that most researchers tried to assess the influence of social media (live streaming, celebrity endorsements, promotional tools, and online reviews) on buying behavior, purchase intention, purchase decision, customer satisfaction, and online shopping behavior from the perspectives of customers all over the world, but this research has been tried to focus on investigating the influence of social media on online shopping behavior during the COVID-19 pandemic from the perspectives of Bangladesh, which remained an unexplored field. This research provides an insight on the influence of live streaming, celebrity endorsements, promotional tools, and online reviews on online shopping behavior during the COVID-19 pandemic of citizenship customers' level in eminent Bangladeshi purchasers' and sellers' experiences, which will help policy makers and stakeholders formulate better digital marketing strategies in Bangladesh, as well as the research field in the perspectives of the COVID-19 pandemic.

The broad objective of the research was to investigate the influence of social media on online shopping behavior during the COVID-19 pandemic in the context of Bangladeshi consumers. Specific objectives are: to assess the behavior pattern of consumers towards online platforms; to explore the impacts of the COVID-19 pandemic on buying behavior; and to study the effect of live streaming, celebrity endorsements, promotional tools, and online reviews on the online shopping behavior of consumers during the coronavirus pandemic in the context of Bangladesh.

The theory behind the study and the terminology and propositions that will be used to achieve the research objective will be explained. Furthermore, the interrelated association of dependent and independent variables will also be deliberated upon following past studies. The key research questions of the study are stated as follows: Is there any significant relationship between live streaming and online shopping behavior?; How is celebrity endorsement relevant to online shopping behavior?; How are promotional tools relevant to online shopping behavior?; and what are the relationships between online reviews and online shopping behavior?

The research paper is allocated into several sections. Initially, the literature review is provided based on a past study. Secondly, the conceptual model and hypotheses developed have been demonstrated. Thirdly, research methodologies that are applied to the current research are described. Fourthly, the paper is presented with the results and interpretations. Fifthly, the discussions, conclusion, and implications sections incorporate the consequences of the present research and its linkups with the previous studies. At the end of the segment, the shortcomings and potential directions of the research are stated.

2. Literature review

2.1. theoretical background, 2.1.1. social influential theory.

According to Kelman (1958) , SIT (Social Influential Theory) is defined as individuals' beliefs, attitudes, and consequent activities or manners that are impacted on other people over three procedures: compliance, identification, and internalization. Persuasion is expected to happen when people receive influence and accept the persuaded conduct to increase rewards and evade punishments. Hence, satisfaction resulting from compliance is because of the social effect of acquiescent influence. Identification might be said to occur when individuals embrace persuasion with the purpose of making or sustaining a preferred and useful connection to other people or a group. Internalization is expected to happen when individuals receive influence and later observe that the gratified of the persuaded performance is pleasing in which the content designates the attitudes as well as actions of others. Influencers perform their functions as a third party who can meaningfully form the company's purchasers' opinions, choices, and actions. Any person can be an influencer by influencing customers to purchase goods and services within a community ( Gillin, 2007 ). Information transferred from one person to another person influences customers through word of mouth. Celebrity people's behavior influences customers through talking about the company ( Sernovitz et al., 2012 ).

2.1.2. Information processing theory

How people collect, illustrate, and use information to make decisions is the main concept of Human Information Processing Theory ( Newell and Simon, 1958 ; Norman, 1968 ; Reitman, 1965 ). Information process theory conceptualizes how individuals take care of ecological occasions, encode data to be learned, relate it to what they know, store new information in their memory, and retrieve it depending on the situation ( Shuell, 1986 ), cited in Schunk (2012) . Researchers have shown that buyers' decisions are formed by the manner in which humans' process information ( Huber and Seiser, 2001 ). In this study, online shopping behavior also depends on the buyer's decision. Information is one of the most important things that influences the consumer's purchasing pattern. When consumers gather or collect information from online reviews and celebrity endorsements, they will be motivated to purchase the products or services.

2.1.3. Social exchange theory

SET was developed initially to investigate human behavior ( Homans, 1958 ) and was later applied to comprehend hierarchical behavior ( Blau, 1964 ; Emerson, 1962 ). The Social Exchange Theory states that individuals and organizations are assisted to maximize their rewards and limit their expenses ( Salam et al., 1998 ). Individuals regularly anticipate proportional advantages, like individual warmth, trust, appreciation, and monetary return, at the point when they act as indicated by social norms. Accordingly, relational cooperation from a money-saving perspective is an exchange where actors obtain benefits. From a cost-benefit perspective, they communicate individually, which aids in exchange where the actor gains an opportunity ( Blau, 1964 ). In the present day, SET has been adopted in social networking research. So, this theory is suitable for this study because it depends on online shopping behavior. Based on psychology, SET accepts the fundamental ideas of modern economics as a foundation for analyzing human behavior and connections in order to determine the complexity of social structures. At the time of promoting, companies require a cost to get a customer's attractions in order to retain the customer. Hence, if the research is used promotional tools more, such as advertising, personal selling, and sales promotion, as a result, it's possible to get customer attention whenever they are motivated or influenced, at which time they will purchase goods and services online. Promotional tools and live streaming are both related to human behavior and easily affect online shopping behavior.

2.2. Live streaming

The coronavirus pandemic calamity knocked out the world and affected all sides of our lives, including customers' preferences, habits, and shopping behaviors. During the corona pandemic times, e-shops were stimulated on social media ( Ali et al., 2021 ). Day by day, live streaming has been popular. Numerous merchants on social commerce display places have embraced it because of its ability to increase their company's sales performance. Live streaming shopping is a new form of social commerce that has already been developed and implemented by social commerce merchants ( Adoeng et al., 2019 ; Taobangdan and Taobao, 2019 ). The live presentation helps a businessman influence the online customer to purchase products. Live streaming has transformed the out-of-date social business model in different ways. In outdated online shopping, customers can only know about goods and services via text and pictures. Otherwise, live streaming allows online sellers to show real-time videos of the products and also let customers know about the product's overall features and quality ( Wongkitrungrueng and Assarut, 2018 ). In traditional social commerce, shoppers could only ask about product-related topics, but in modern times, consumers can ask the question via screen and streamers can give the answer in real-time ( Wongkitrungrueng and Assarut, 2018 ). Live streaming shopping creates a real-time stream between sellers and buyers. Online shoppers can watch the live presentations of products that influence customers to purchase that product. Customers' any confusion about products can be reduced through visual presentations of products ( Chen et al., 2017 ; Kim and Park, 2013 ; Zhou et al., 2018 ). The increasing popularity of visual presentations highly influences customers to buy the products ( Yu et al., 2018 ). While customers' engagement with live presentations of products is positively impacted on customer minds about products, it is also a stimulus to shop for those products ( Wongkitrungrueng and Assarut, 2018 ). Despite the fact that buyer commitment has been identified as a significant antecedent persuading purchaser buying in online spending ( Prentice et al., 2019 ), only a few studies have measured the previous circumstances and outcomes of purchaser assignation according to live streaming shop. Live streaming broadcasting makes use of one or more pieces of equipment that can instantly show images and sounds to other locations, allowing users to observe their existence ( Chen and Lin, 2018 ). Live streaming shopping is a new social media form with a high HCI that raises customer awareness of products. Preceding live-streaming lessons have chiefly concentrated on video games and e-sports ( Cheung and Huang, 2011 ; Sjoblom and Hamari, 2017 ). Many customers increase their capacity to buy through live streaming shopping by gaining new perspectives and asking pertinent questions ( Lu et al., 2018 ). Live streaming can show images as well as sounds from one place to a different place instantly ( Chen and Lin, 2018 ). Live streaming purchasing is an extremely noticeable form of merchandise demonstration through online videos. When customers make purchase decisions, they need clear information about products and also want to see the products visibly through the live presentation. It gives the clients an intellect of engagement. Besides, the richness of live streaming spending makes it stress-free to fascinate buyers. Consequently, consumers observe immersion ( Yim et al., 2017 ). Besides, live presentations can communicate complete videos to consumers, as well as the sellers can show how to use the merchandise through live streaming, which permits the product to be visualized ( Li, 2019 ; Javadi et al., 2012 ). In live presentations, sellers and customers interact with each other through live streaming, and customers watch the seller's voice, movement, and product features. So, customers know that the sellers are real people because of the live presentation via social media. Live streaming allows companies to broadcast their products' different items via live presentations. Furthermore, live presentations can prompt captivation, which can lead to a logic of immersion ( Shin, 2017 ). Online shopping and e-commerce have developed an innovative and lucrative business model. Here, buyers and sellers are both connected with live presentations, with buyers asking product-related questions to sellers and also watching the product and product features ( Attfield et al., 2011 ). Visual presentation shopping is being subjected to extraordinary growth. On the other hand, interest in the live-stream market is in its embryonic stage. Different celebrities talk about products and motivate them through live presentations ( Ma, 2021 ). Day by day, with the increase of online shopping, many companies provide live help or visual presentations through test chatting, instant messaging, and live product presentations. Businesses and customers can conduct real-time human-to-human communications for e-commerce Web sites ( Qiu and Benbasat, 2005 ). E-retailers are taking on innovative arithmetic advertising tactics to deliver more accurate information to their consumers. In real-time business, live video streaming allows sellers and consumers to interact ( Zhang et al., 2019 ). Nowadays, consumers have become familiar with visual presentations and product features online and have finally purchased those products that they like. Consumers are motivated to purchase products through live presentations ( Yin, 2020 ).

2.3. Celebrity endorsement

There are many social media platforms, for instance, Facebook, Twitter, Snapchat, and Instagram. Day by day, social media continues to rise speedily in popularity. Celebrity people are using different social media platforms and distributing different information about products to customers. The celebrity of Instagram is influencing consumers' online purchasing behaviors ( Gupta et al., 2020 ). Through social media, online information sharing in the communal sphere has not only promoted the customers' buying choices. Celebrity people provide information about goods and services to actual and potential customers ( Lee et al., 2008 ; Ashfaq and Ali, 2017 ). Along with the diverse investigators, the practice of celebrity endorsements supports in structure the products' identification as well as generates optimistic insolence ( Petty et al., 1983 ), improves the prospect of buying ( Friedman and Friedman, 1979 ), nurtures trademark trustworthiness, and completely influences positive word of mouth ( Bush et al., 2004 ). Celebrity endorsements have a significant impact on consumers' purchase decisions ( Ohanian, 1990 ). In the same way, Instagram celebrity has a momentous impact on consumers' online shopping behaviors ( Kutthakaphan and Chokesamritpol, 2013 ). Most celebrities have a more positive impact on consumers' minds about the products than less credible celebrities. Credible celebrity people influence consumers' online shopping behavior ( Aziz et al., 2013 ). Celebrity people created a brand different from another one because consumers can easily select their preferred products. Through social media advertisements ( Meng et al., 2020 ). celebrity endorsements have an effect on customers' buying behavior. Celebrity images might have an effect on positive and negative consumer attitudes. A celebrity's usefulness depends on their trustworthiness and credibility in an online advertisement. A celebrity's good image can have a positive effect on product acceptance ( Ibok, 2013 ). A celebrity can easily motivate consumers towards purchasing products because people believe infamous people. Through social media, a famous personality created awareness about products with customers. They can positively influence customers' opinions of the brand ( Rai and Sharma, 2013 ). Celebrity endorsement is one kind of promotional activity that attracts customers to specific products. Different companies use different celebrities to promote the awareness of their products to customers, and customers might be motivated to purchase those products. Customers purchased products based on the credibility of celebrities ( Khatri, 2006 ). The influence of superstars' post-legitimacy, observational learning, sentimentality polarization, and impulse purchasing propensity reins in the dormant state-trait theory. Security is influencing consumers' online shopping behavior through social media ( Zafar et al., 2021a ). Normally, followers consider that celebrity posts are authentic; that's why they easily influence consumers to make online purchases ( Wilcox and Stephen, 2013 ). On social media, celebrities share their opinions and advertisements that highly stimulate potential buyers to purchase products ( Chung and Cho, 2017 ; Xiang et al., 2016 ). Celebrity advertisements have so many advantages and disadvantages. Celebrity advertisements can be used to achieve a company's competitive advantage ( Han and Yazdanifard, 2015 ). With regard to a celebrity's values, occupation, ethnicity, and other characteristics, the customer ought to never be curious about why this star is certifying the merchandise ( Meng et al., 2021 ; Gan and Wang, 2015 ). Generally, the research should be focused on celebrities' groups or pages where customers are replaying or commenting on celebrities' posts as well as their peers' social communication. Some celebrities have a large number of followers; they maintain an online community. Business organizations give priority to social media celebrities in their marketing strategy to motivate online shopping behavior ( Pemberton, 2017 ). Consumers follow the celebrity's posts and pursue their lifestyle, with clothing, makeup, fashion, the destination of holidays, even restaurant choice. Organizations try to use such celebrities for effective social media marketing promotions ( Hennig-Thurau et al., 2013 ; Kumar and Mirchandani, 2013 ). Celebrity followers always enquire for recommendations from business organizations. Celebrities' any business-related posts that stimulate consumers' online purchasing behavior ( Wilcox and Stephen, 2013 ).

2.4. Promotional tools

Technological changes are occurring in eye flashes and values are changing over time. Customers' buying habits change rapidly, and the fortunes of different companies vary. Online marketing has been seen as a new form of marketing and has given companies new opportunities to do business. According to Dehkordi et al. (2012) , e-commerce and e-marketing show that internet marketing is easier than conventional marketing ( Dehkordi et al., 2012 ). Leena Jeenefa noted that there are several notable relationships between purchasing behavior and the effects of media advertising ( Jenefa, 2017 ). Reza Jalilvand and Samiei (2012) evaluates how advertisers use social media to make their products popular. The reason for the promotional price promotion is that the consumer does not have the rational mindset to think about whether it is worth buying more at that moment, and this also increases online purchasing behavior ( Agyeman-Darbu, 2017 ). Some social media stated that if consumers buy two, they will get one free, and this also leads to the consumer having a strong positive feeling. Ibok (2013) found that young people feel more comfortable when choosing and buying products online than in physical shopping options. Social media helps them save time and effort examining product information. Privacy, trust, and protection play an important role in social media networking sites. Online advertising businesses use electronic marketing tools to create marketing strategies, advertising theories, and customer buying behavior due to potential market segmentation. According to Eyre et al. (2020) , online advertising includes contextual ads on examining banner ads, rich media ads, social network advertising, online classified advertising, and marketing email like spam. Advertising is defined as the definition of any personal meaning related to product ideas and information in the media to create a brand image ( Kotler and Amstrong, 2010 ). For many years, television, radio, newspapers, and magazines were the only means and channels of advertising, but nowadays, online advertising is becoming the main driving force in many advertising initiatives and efforts ( Kotler and Amstrong, 2010 ). Content is one of the most important features of e-advertisement. It delivers written information regarding particular products or services to online users. Customers are rapidly adopting online shopping day by day due to a busy lifestyle. Undoubtedly, as a developing country, Bangladesh has a lot of potential customers for online businesses. Bangladesh is one of the countries that uses social media the most. It is important to know what causes online buying behavior on social media.

2.5. Online reviews

Purchase intention can be used to measure the possibility of a consumer buying a certain product. When deciding to buy a product, most customers are influenced by comments and ratings from online reviews, and they take a positive or negative view of the product. Social media enabled through mobile devices can be accessed everywhere, instead of not only increasing access to information but also allowing people to create content and strengthen their voices around the world ( Labrecque et al., 2013 ). Social media is playing a crucial role in sharing opinions and product knowledge with consumers and, as a result, having an impact on other consumers ( Lim et al., 2016 ). According to Zhang et al. (2019) , the availability of online reviews plays an important role in online shopping behavior compared to other things. The availability of online reviews refers to the large number of online reviews that are sufficiently available online for the consumer's decision-making process ( Zhang and Zhu, 2010 ). Social media users have realized that a good number of online reviews point to online shopping behavior among customers. Good online shops create an opportunity to search for any product ( Zhang and Zhu, 2010 ). Furthermore, the availability of online reviews makes online shopping appreciate the quality and motivates the customer to try it for the first time ( Cui et al., 2010 ). A good number of customer reviews will have a positive impact on other users on social media, and it can be effective for the online shopping industry to increase sales volume through social media reviews ( Geetha et al., 2018 ). In addition, many researchers have found that a large number of online reviews can influence a potential customer when they choose a product through social media. Significantly, if consumers respond positively to a good number on social media sites, they are more likely to choose their favorite product than cheap ones ( Geng et al., 2020 ). For example, the availability of online reviews on social media should create an opportunity to try a new product, and potential customers may be the priority in their selection criteria ( Geetha et al., 2018 ). Numerous empirical studies across different industries have already investigated the influence of the number of review attributes from a variety of perspectives. For example, the number of reviews ( Dellarocas et al., 2007 ; Ghose and Ipeirotis, 2010 ), the response to negative reviews for online product management ( Kim et al., 2015 ), the positive online product reviews ( Ye et al., 2009 ), and the overall valence of a set of reviews of a product ( Spark & Browning, 2011 ). Consumers consider the internet as a tool to obtain information as a part of the decision-making process before purchasing products. The number of online reviews needs to have a positive impact on potential customers of unfamiliar products ( Zhang and Zhu, 2010 ). As a result, the brand availability of online-spread products increases because customers share their experiences on social media pages. A product review site assesses consumers on their own and how they feel about product quality, service systems, and their overall environment. For this reason, the behavioral motive of the customer should change when they decide to choose a product from the review site ( Gan and Wang, 2015 ). An online review is similar to a traditional face-to-face communication messenger. It is considered a new form of recommendation ( Helm et al., 2010 ). Zhang and Zhu (2010) indicate that the reviews' perceptual information and reasoning power are an important determinant of customer behavioral will, although the source is not credible. So online review materials still play an important role in consumer decision-making because good reviews from one customer can lead to another customer purchasing the product. Additionally, many prior studies have examined whether the availability of online reviews has a significant influence on consumers' product selection when they search for other reviews on social media ( Zhang et al., 2019 ; Cui et al., 2010 ). It has also been noted that the availability of online reviews has been verified as an effective tool for conducting research questions on consumer product selection ( Zhang et al., 2019 ).

2.6. Online shopping behavior

Businesses turned to alternatives and took up online marketing because of COVID-19 pandemic. Online marketing is a significant method for streamlining business processes, reducing managerial costs and turnaround time, maintaining social distance, staying at home, protecting against viruses, and illuminating associations with customers and business partners ( Hossain, et al., 2022 ; Hossain and Khan, 2018 ). At present, online shopping is becoming more popular all over the world, especially for retailers and customers. Online shopping creates opportunities for both online retailers and customers ( Kuester and Sabine, 2012 ; Hossain et al., 2018b ). Customer research has shown that customer assessments dispatched online and the allotment of information or particular views have become enormously influential means of communication. Online reviews have taken over business organizations through social media (Facebook, Snapchat, Twitter, and Instagram) ( Doh and Hwang, 2009 ; Lee et al., 2011a ; Jalilvand and Samiei, 2012 ; Huete-Alcocer, 2017 ). Different types of online reviews have improved the customers' internet shopping performance. Satisfied customers are giving online reviews through social media that influence other consumers' online shopping ( Fu et al., 2020 ). Nowadays, several customers are purchasing social media. Many business organizations have opted to take advantage of opportunities obtainable through social media networks to gain more consumers ( Kaplan and Haenlein, 2014 ). Live streaming stimuli motivate consumer cognitive states that influence consumer online shopping behavior ( Xu et al., 2020 ). The business organization has promoted social media advertising to attract online shoppers to purchase products online ( Mumtaz et al., 2011 ). Targeted advertising by businesses on social media (Facebook, Instagram, and so on). Business organizations know about customers' choices, preferences, and information through social media. They are doing e-advertising based on customers' preferable products and are influencing customers to purchase those products. An organization is able to run different advertising for different categories of customers, and an organization can set their target price ( Iyer et al., 2005 ). Companies can transfer information about products through online advertising. Consumers can visually watch their preferred products via advertising. Entrepreneurs use celebrity endorsements to promote their company's products, and it is increasing consumer purchase intentions. Consumers purchase products online and the created appeal of a statement by a celebrity might influence a customer's product image ( Wang et al., 2013 ).

This research has been prepared during COVID-19. In the research has applied three types of theories, such as social influence theory, information processing theory, and social exchange theory. In previous research, researchers have used online reviews as well as celebrity endorsements as factors under both social influence theory and information processing theory. For the first time at COVID-19, the research has applied these factors under the social influence theory and information processing theory, which have never been used before. The research paper has used social exchange theory. This theory identifies that promotional tools influence customers to buy their necessary goods and services through online shopping. The previous researchers didn't show social media impacts on online shopping behavior during COVID-19. The research has applied those factors during the COVID-19 time period, which made research paper unique from previous research. During COVID-19, The research was used technical tools that had never been applied to that type of theory before. The research paper has analyzed by SmartPLS version 3.0 and used a structural equation model..

3. Conceptual model and hypotheses development

According to Zhang et al. (2019) , by reducing psychological distance and perceived uncertainty, a live streaming strategy can improve a customer's online purchase intention. Chandrruangphen et al. (2022) find out vendors to concentrate on significant live streaming attributes to develop trust with their clients and increase their customers' intentions to watch and buy. The literature and researcher findings suggest that offering live presentations enables sellers to introduce items in a novel way, which might improve customers' moods and sentiments towards the product. So, customers should feel more confidence in the seller and his/her items because of live streaming. Thus, it is expected that:

Hypothesis 1 (H1) : Live streaming has a significant impact on online shopping behavior.

Park and Lin (2020) develop and test an integrative model of online celebrity endorsement by exploring compatibility impacts on customers. Meng et al. (2021) find that the feelings of audiences towards online celebrities can influence a buyer's willingness to buy products suggested by the online superstar. The literature and researcher findings suggest that celebrity endorsements represent attractiveness, believability, and celebrity-product compatibility, which have positive effects on a buyer's attitude towards products and brands as well as purchase intention. As a result, celebrity endorsement may increase users' desire to purchase any product. Therefore, it is expected that:

Hypothesis 2 (H2) : Celebrity endorsement has a positive influence on online shopping behavior.

Ashraf et al. (2014) found that sales promotion played a more significant role in the development of consumer buying behavior. Yahya et al. (2019) and Shamout (2016) revealed in their study that coupons, discounts, free delivery, and other promotional tools have a positive impact on consumer buying decisions. The literature and researcher findings suggest that sales promotion has a huge impact on consumer buying behavior, such as purchase time, product brand, product quantity, brand switching, and so on. Again, sales promotion can be used by marketers to create a long-term customer relationship, which can help them increase their sales. Based on the previous discussion, it is expected that promotional tools will have a positive relationship with purchase intention ( Siddique and Hossain, 2018 ). Thus, it is expected that:

Hypothesis 3 (H3) : Promotional tools have a positive influence on online shopping behavior.

According to Nuseir (2019) and Ventre and Kolbe (2020) , organizations should seek to increase customers' sharing of their positive online opinions in order to improve attachment and encourage online shopping. When the reviews contain detailed information about the product, consumers deem online reviews to be more credible ( Jimenez and Mendoza, 2013 ). The literature and researcher findings suggest that consumer opinion and peer reviews are among the top factors to consider for online shopping behavior. Thus, online sentimental reviews grab more attention from consumers and affect them positively when purchasing products. Therefore, it is expected that:

: Online reviews have a significant impact on online shopping behavior.

In this study, four independent variables (live streaming celebrity endorsements, promotional tools, and online reviews) and one dependent variable (online shopping behavior) have been recognized. Based on the previous literature and discussions, the conceptual framework ( Figure 1 ).

Research model.

4. Research methods

4.1. research design.

The research design was applied when the collection of data and analysis of data processed by combining them were used in the research ( Jahoda et al., 1951 ). This study is based on the quantitative survey method, with data collected using a structural questionnaire. To test the hypothesis, the study was conducted based on an online convenience and judgmental sampling survey. This study applied a descriptive study and collected respondents' attitudes and behaviors about social media's impact on online shopping behavior.

4.2. Methods of research data collection

The study collected data from respondents in written form. The study confirmed that informed consent was obtained from all participants for our research paper. The research paper applied primary and secondary data to prepare the study and make it more presentable. Primary data was collected via a survey and developed questionnaire. Business market research might use a questionnaire technique to collect consumer and customer opinions ( Wang and Feng, 2012 ). Online surveys are used to learn about the impact of social media on internet shopping behavior. Primary data was collected from respondents by developing a Google form and sharing that form with other respondents via Facebook, WhatsApp, e-mail, and so on. In particular, the questionnaire was developed for those people who connected with social media like Facebook, Twitter, Pinterest, YouTube, WhatsApp, and so on.

This research paper also used secondary data that was collected from different articles, books, and newspapers. The research was collected secondary data by penetrating electronic databases, including Research Gate, Google Scholars, and Emerald Insight. The research was collected secondary data by searching top journals like the Journal of Marketing Analytics, the Journal of Business Research, the Journal of Consumer Research, and so on.

4.3. Method of sampling

4.3.1. sampling unit.

People who have the equivalent attitudes and behavior in the direction of an entire group of people ( Sekaran and Bougie, 2016 ). These people use social media and their age is above 15 years old. They are considered the population of this study. So, the population is unfamiliar with this research paper. For this research paper, there is no earmarked sampling unit among the total population. In this study, the population is considered students, managerial-level people, businessmen, and teachers.

4.3.2. Sampling technique

Respondents for this study were chosen using an online purposive sampling technique and non-probability sampling methods. This research data was collected during the corona pandemic. The researcher collected data by distributing the questionnaire through Google Form Link and sharing this link with different convenient people. Non-probability sampling has been used because it is less time-consuming and less costly to prepare a sampling frame. Among the numerous ways of non-probability sampling, purposive sampling technique has been used because they are cheerfully available, generate a relatively low cost, and are convenient.

4.3.3. Sample size

The purposive sampling method is applied to collect (N = 350) respondents' opinions through a developed questionnaire. The sample (N = 350) was collected from the Dhaka, Sylhet, Khulna, and Chattogram divisions among eight divisions of Bangladesh.

4.4. Measurement scale of dependent and independent variable

The study used the Likert Scale (5 ratings). The Likert Scale is used for individual responses and measures the dependent variable and independent variable about the impact of social media on online shopping behavior during the coronavirus pandemic. The Likert Scale has five stages, and each statement in the form was measured by 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree.

4.4.1. Measurement instruments

As illustrated in Table 1 , the study used four constructs of social media to examine online shopping behavior during the COVID-19 pandemic. Live streaming factors include social sharing, hedonic consumption, cognitive assimilation, and impulsive consumption. The celebrity endorsement factor includes the number of shares, authenticity, positive sentiments, and recognizable celebrity. Promotional tools factor includes price discount, sales promotion, buy one get one, surroundings influence. Online review factors include the reviewer's reputation, the review's reliability, good customer rating, and argument quality.

Table 1

Origin of constructs and measured variables.

4.5. Data analysis

The smartPLS software version 3.0 was applied to examine the data collected via questionnaire. The conceptual model of the study was verified using structural equation modeling (SEM). For sample distribution, percentile measures and frequency distribution were primarily used in this study. The study's descriptive statistics were tested using mean as well as standard deviation measures. Collinearity statistics were used to test for multicollinearity among the independent variables. Besides, the reliability of the data or scale items was ascertained using Cronbach's alpha coefficients and composite reliability (CR). Discriminant validity was also used to test the Fornell-Larcker Criterion and the Heterotrait-Monotrait ratio (HTMT) among the independent and dependent variables.

4.6. Quality of data assurance

Enumerators and overseers were knowledgeable about this research objective, scale, data collection technique, and questionnaire. On a daily basis, the data collected is appropriately administered by superintendents and the data comprehensiveness and reliability are tested before the data is input to SmartPLS version 3.0 for more treatment as well as analysis.

5. Results and interpretations

5.1. descriptive analysis.

The study used mean and standard deviation scores to explore all of the aspects. The constructs were ranked in accordance with their enumerated mean standards. As shown in Table 2 , online reviews had the highest mean score (M = 4.1164), while celebrity endorsements had the lowest mean score (M = 3.4829). Most of the factors produced medium mean scores. Therefore, the factor mean scores recommend that among all perspectives, there be no higher variation.

Table 2

Descriptive statistics analysis.

5.2. Multicollinearity test

The study used a multicollinearity test to measure the independent variables that were highly correlated among themselves. The estimated path coefficients were affected by the predictor constructs of collinearity. Tolerance values below 0.10 and variance inflation facet values above 5 specify the existence of inter predictor constructs' collinearity ( Hair et al., 2019 ). As illustrated in Table 3 , all tolerance and VIF values have an acceptable range in collinearity statistics. So, it was recommended that multicollinearity wouldn't affect the independent variable's capability to take to mean the outcome variable.

Table 3

Multicollinearity test.

5.3. Measurement model analysis (outer model)

Hair et al. (2019) define "the measurement model as a constituent of a theoretical path model that holds the pointers and their associations with the factors; also called the outer model in PLS-SEM." In this study, confirmatory factor analysis (CFA) is applied to square in the event the variables are loaded on their relevant constructs ( Hair et al., 2019 ). In this study, SmartPLS software version 3.0 was applied to conduct structural equation modelling ( Ringle et al., 2015 ).

5.3.1. Unidimensionality

In the present constructs, the unidimensionality component designates that every measurement item has a satisfactory equal factor loading according to the corresponding latent construct. Hair et al. (2019) claim that each factor has a measurement variable with a least factor loading of 0.70. According to Table 4 , online reviews (OR1) and online shopping behavior (OSB6) have factor loadings of 0.674 and 0.663, respectively. However, OR1 and OSB6 factor loading values are close to 0.70. So, the research can be recommended that the unidimensionality measurement model has been recognized.

Table 4

Measurement model summary.

5.3.2. Construct reliability tests

The researcher used Cronbach's alpha and composite reliability (CR) to test the internal consistency. The recommended values of composite reliability (CR) and Cronbach's alpha are equal to or greater than 0.70, which is considered satisfactory to good for research ( Hair et al., 2019 ). As illustrated in Table 4 , all of the CR and Cronbach's alpha values have a satisfactory level. So, the researcher recommended that the constructs be reliable for further research.

5.3.3. Convergent validity tests

The average variance extracted (AVE) is 0.50 or greater than 0.50, assuring the convergent validity of the latent constructs ( Hair et al., 2019 ). As illustrated in Table 4 , all the average variance extracted (AVE) values are greater than 0.50 in this study because of the appropriateness of the constructs for further research.

5.3.4. Discriminant validity tests

Discriminant validity implies that each construct is empirically distinct from the other cross-loading that exists among the latent constructs. The correlation coefficients and square root of average variance extracted (AVE) among the constructs are associated to create discriminant validity ( Hair et al., 2019 ). According to Table 5 , the diagonal numbers are higher than the inter-construct resemblances presented off-diagonally. However, the discriminant's legitimacy is gained for the research constructs.

Table 5

Discriminant validity: Fornell-Larcker Criterion.

5.4. Measurement model analysis (Inner model)

The study measurement model recommended that all the measurement models be valid, then analyze the structural model relationship ( Hair et al., 2019 ). The researcher makes an assessment which one accepts and rejects via significant and insignificant relationships that can be identified by structural model analysis. Besides, the researchers used a bootstrapping procedure with a subsample of 500 to assess the size of the path coefficients ( Ringle et al., 2015 ).

Figure 2. Structural model.

The structural model analysis includes the paths, path coefficients, t values, p values, and path coefficient results. A two-tailed t-test with a level of significance of 5% was used to test the hypotheses that had been developed. The coefficients are statistically significant if the measured t-value is greater than the critical value of 1.96. According to Table 6 and Figure 2 , the path coefficients of three latent constructs, including celebrity endorsement, promotional tools, and online reviews, had a positive and significant association with online shopping behavior at p < 0.05. Here, the researchers mention that hypotheses H2, H3, and H4 are accepted. However, hypothesis H1 has no significant and positive relationship with online shopping behavior. Accordingly, H1 live streaming was rejected. According to Table 6 and Figure 2 , the celebrity endorsement perspective's highest path coefficient (β2 = 0.452) specifies that if celebrity endorsement were to grow by one standard deviation unit, online shopping behavior could increase by 0.452 standard deviation units if all other independent perspectives continued constant.

Table 6

Structural model estimates.

Note: p∗< 0.05, based on the two-tailed test; t = 1.96.

6. Discussions

In the Bangladeshi setting, the research aimed at understanding the impact of social media on online shopping behavior during the COVID-19 pandemic. It has been found that most researchers explored the influence of social media on purchase intention, behavioral intention, satisfaction, purchase decision, and loyalty ( Hossain et al., 2020 ; Gupta et al., 2020 ; Fu et al., 2020 ; Zhou et al., 2018 ; Jenefa, 2017 ). However, there was less focus and thus fewer studies into the impact of social media on online shopping behavior during the COVID-19 pandemic in the context of Bangladeshi consumers.

According to the findings of the above analysis, three social media factors out of four had a significant and positive impact on online shopping behavior during the COVID-19 pandemic from the perspective of Bangladeshi consumers. Besides, the rest of the factors of social media have no significant positive relationship with the online shopping behavior of consumers during the COVID-19 pandemic in the country. The celebrity endorsement factor (β2 = 0.452, t = 10.233), promotional tools factor (β3 = 0.215, t = 3.809), and online reviews factor (β4 = 0.207, t = 4.901) are significantly and positively related to the online shopping behavior of Bangladeshi consumers during the COVID-19 pandemic at p < 0.05.

From the above findings, the study found that those three independent variables, like celebrity endorsements, promotional tools, and online reviews, have a significant positive relationship with the dependent variable, online shopping behavior. Based on the analysis, the researcher found that the independent variable live streaming has no significant positive relationship with the dependent variable online shopping behavior. Here, the live streaming was not supported at a significant value of 0.380, which is higher than the p value of 0.05. The study recommended that live streaming has no significant positive relationship with online shopping behavior. Based on the research, celebrity endorsement's significant value was notated at 0.000, which is lower than the p-value of 0.05. This indicates that celebrity endorsement has a significant positive relationship with consumers' online shopping behavior. Xiang et al. (2016) ; Zafar et al., 2021a ; and Ahmed et al. (2015) , also supported that celebrity endorsement has a positive impact on consumers' online shopping behavior. Based on the analysis, the researchers found that promotional tools have a positive connection with consumers' online shopping behavior. Here, the significant value of 0.00 is lower than the p-value of 0.05. Based on the study, online reviews were significant at a significant value of 0.00, which is smaller than the p-value of 0.05. This suggests that online reviews have a significant positive relationship with customers' online shopping behavior. According to Zhang and Zhu (2010) ; Fu et al. (2020) , also supported that online reviews have a strong relationship with customers' online shopping behavior.

7. Conclusion and implications

During the COVID-19 pandemic, customers are purchasing their necessary products through an online platform. Customers are learning about new products being launched in the market through social media. Customers are safely purchasing their products through online shopping behavior during the corona pandemic. The study has been conducted with the objective of exploring the impact of social media on online shopping behavior during the COVID-19 pandemic from the perspective of Bangladeshi consumers. Different aspects of social media are important tools to guide consumers' online shopping behavior during the coronavirus pandemic in Bangladesh. This research studies the influence of live streaming, celebrity endorsements, promotional tools, and online reviews on consumers’ online shopping behavior during the coronavirus pandemic in the context of Bangladesh. The results of the research has revealed that celebrity endorsement, promotional tools, and online reviews had a positive significant impact on online shopping behavior in the perspectives of Bangladesh. In contrast, live streaming had no significant positive relationship with the online shopping behavior of consumers during the COVID-19 pandemic. The research paper provides practical guidelines for online-based business organizations on how to effectively use social media platforms for business target advertising and promotional activities. Customers are also motivated to purchase through social media because of positive online reviews and trustworthy celebrity endorsements.

7.1. Theoretical implications

Day by day, people are becoming more accustomed to online shopping during the corona pandemic. Most people have connected with social media like Facebook, Twitter, Pinterest, YouTube, WhatsApp, and so on. Social media has a positive impact on online shopping behavior. Customers are watching different advertisements via social media, and they are motivating consumers to shop online. The study has proven that celebrity endorsements, promotional tools, and online shopping have a significant positive impact on online shopping behavior. In the meantime, with the development of social media, the influences on online shopping are increasing. During the coronavirus pandemic, social media-based marketing has also attracted the attention of enterprises. However, there has recently been little research studying the relationship between social media and online shopping behavior. To compensate for the gap, this research has been based on the impact of social media on online shopping behavior. Live streaming has no significant relationship with online shopping during the COVID-19 pandemic. On the other hand, celebrity endorsement has a significant positive connection with online shopping behavior. Besides, promotional tools and online reviews have a positive impact on online shopping behavior during the corona pandemic. Business organizations are highly focused on social media-based promotional activities. Consumers have adjusted their online shopping behavior during the COVID-19 pandemic.

7.2. Practical implications

Introducing celebrity endorsements, promotional tools, and online reviews of social media constructs have a positive connection with online shopping behavior during a COVID-19 pandemic. The research paper yields several practical suggestions for social commerce sellers and e-commerce-based organizations. First, the research results illustrated that celebrity endorsements have a positive relationship with customers' online shopping behavior, which includes attractive celebrities, celebrities, and recognizable celebrities. Hence, social commerce sellers who have not until now accepted celebrity endorsements for promotion should adopt celebrity endorsements that help increase the consumer's online shopping behavior. When famous or attractive celebrities talk about products and live streaming products, then customers are stimulated to purchase those products through the online market. Celebrity endorsers should have clear knowledge about product features before motivating them to purchase those products via online shopping.

Second, the research results showed that promotional tools constructed by social media have a significant positive connection with online shopping behavior. E-commerce sellers should promote promotional activities to increase the sales volume of online shopping. Besides, they should have used re-targeting advertising via social media to enhance online shopping behavior.

Third, the study also found that online reviews have a significant positive relationship with online shopping behavior during the corona pandemic. Potential customers' positive reviews or good ratings influence potential customers’ online shopping behavior. To connect with current and potential customers, e-commerce business sellers should have Facebook pages, Twitter accounts, Instagram accounts, and so on. The social media seller requests that customers give reviews about their product features, price, and quality via social media. Actual customers' positive reviews are highly motivated by other actual and potential customers' purchases through an online business.

8. Limitations and future research

In the study, the main objective was to investigate the major influencing factors that impact consumers' online shopping behavior during COVID-19 outbreaks. The research paper has several limitations. For instance, in the literature, there are several antecedents of the impact of social media on online shopping behavior, but in this study, the researchers only used four antecedents, like live streaming, celebrity endorsement, promotional tools, and online reviews. Future research should add more antecedents in their research paper with four antecedents. Second, this study used an online purposive sampling technique to investigate the impact of social media on consumers' online shopping behavior. The research will be recommended that for future research, they should use experimental methods to measure customers’ online buying behavior through social media. Third, due to the COVID-19 pandemic outbreaks, data was collected from respondents through an online survey using a self-administered questionnaire. For that reason, in some cases, it was not possible to know more properly about the respondents. Field-level surveys and face-to-face interview methods should be used to collect data for further research to address the problem of false information and data. Fourth, current research is based on quantitative information but may differ in results when applying qualitative information. Future research should apply a combination of quantitative and qualitative data analysis.

Declarations

Author contribution statement.

Md Rukon Miah: Conceived and designed the experiments; Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Afzal Hossain: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper; and Corrected proof.

Rony Shikder: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Tama Saha: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Meher Neger, PhD: Conceived and designed the experiments; Analyzed and interpreted the data; Overall Supervision of the Study.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Declaration of interest’s statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

☆ This article is a part of the "Business and Economics COVID-19 Special Issue.

• Adoeng W., Kalangi J.B., Wangke S.J. A comparative analysis of E-advertisement between jd. Id and shopee customers in Manado. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi. 2019; 7 (3):3379–3388. [ Google Scholar ]
• Agyeman-Darbu K. Retailing of Consumer Goods in Kumasi Metropolis. The Mediating Effect of Customer Service (Doctoral Dissertation) 2017. The impact of sales promotion on consumer purchasing behaviour. [ Google Scholar ]
• Ahmed R., Seedani S., Ahuja M., Paryani S. 2015. Impact of celebrity endorsement on consumer buying behavior. Available at SSRN 2666148. [ Google Scholar ]
• Ajina The perceived value of social media marketing: an empirical study of online word-of-mouth in Saudi Arabian context. Entrepreneurship Sustain. Issues. 2019; 6 (3):1512–1527. [ Google Scholar ]
• Ali Taha V., Pencarelli T., Škerháková V., Fedorko R., Košíková M. The use of social media and its impact on shopping behavior of Slovak and Italian consumers during COVID-19 pandemic. Sustainability. 2021; 13 (4):1710. [ Google Scholar ]
• Ashfaq M., Ali M. Impact of celebrity endorsement on consumer buying behavior in FMCG sector of Pakistan. Oman Chap. Arab. J. Busin. Manag. Rev. 2017; 34 (5627):1–12. [ Google Scholar ]
• Ashraf M.G., Rizwan M., Iqbal A., Khan M.A. The promotional tools and situational factors’ impact on consumer buying behaviour and sales promotion. J. Publ. Adm. Govern. 2014; 4 (2):179–201. [ Google Scholar ]
• Attfield S., Kazai G., Lalmas M., Piwowarski B. WSDM Workshop on User Modelling for Web Applications. 2011, February. Towards a science of user engagement (position paper) pp. 9–12. [ Google Scholar ]
• Aziz S., Ghani U., Niazi A. Impact of celebrity credibility on advertising effectiveness. Pakistan J. Comm. Social Sci. (PJCSS) 2013; 7 (1):107–127. [ Google Scholar ]
• Bakewell C., Mitchell V. Generation Y female consumer decision-making styles. Int. J. Ret. Distrib. Manag. 2003; 31 (2):95–106. [ Google Scholar ]
• Blackwell R.D., Miniard P.W., Engel J.F. tenth ed.s. Thomson/Sount; Masao, OH: 2006. Consumer Behavior. [ Google Scholar ]
• Bartik A., Bertrand M., Cullen Z., Glaeser E.L., Luca M., Stanton C. How are Small businesses adjusting to COVID-19? Early evidence from a survey. SSRN Electron. J. 2020; 20 (12):1–36. [ Google Scholar ]
• Berman S.J. Digital transformation: opportunities to create new business models. Strategy & Leadership. 2012; 40 (2):16–24. [ Google Scholar ]
• Blau P. John Wiley & Sons; New York: 1964. Exchange, and Power in Social Life. [ Google Scholar ]
• Bush A.J., Martin C.A., Bush V.D. Sports celebrity influence on the behavioral intentions of generation Y. J. Advert. Res. 2004; 44 (1):108–118. [ Google Scholar ]
• Chandrruangphen E., Assarut N., Sinthupinyo S. The effects of live streaming attributes on consumer trust and shopping intentions for fashion clothing. Cogent Busin. Manag. 2022; 9 (1) [ Google Scholar ]
• Chaturvedi D., Gupta D. Sachin, Effect of Social Media on Online Shopping Behaviour of Apparels in Jaipur City-An Analytical Review. 2014. Effect of Social Media on Online Shopping Behaviour of Apparels in Jaipur City-An Analytical Review. March 2014) [ Google Scholar ]
• Chen A., Lu Y., Wang B. Customers’ purchase decision-making process in social commerce: a social learning perspective. Int. J. Inf. Manag. 2017; 37 (6):627–638. [ Google Scholar ]
• Chen C.C., Lin Y.C. What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telematics Inf. 2018; 35 (1):293–303. [ Google Scholar ]
• Cheng Y.H., Ho H.Y. Social influence's impact on reader perceptions of online reviews. J. Bus. Res. 2015; 68 (4):883–887. [ Google Scholar ]
• Cheung G., Huang J. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2011, May. Starcraft from the stands: understanding the game spectator; pp. 763–772. [ Google Scholar ]
• Chung S., Cho H. Fostering par asocial relationships with celebrities on social media: implications for celebrity endorsement. Psychol. Market. 2017; 34 (4):481–495. [ Google Scholar ]
• Cui B., Tung A.K., Zhang C., Zhao Z. Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. 2010, June. Multiple feature fusion for social media applications; pp. 435–446. [ Google Scholar ]
• Dehkordi G.J., Rezvani S., Rahman M.S., Nahid F.F.N., Jouya S.F. A conceptual study on E-marketing and its operation on firm's promotion and understanding customer's response. Int. J. Bus. Manag. 2012; 7 (19):114. [ Google Scholar ]
• Dellarocas C., Zhang X.M., Awad N.F. Exploring the value of online product reviews in forecasting sales: the case of motion pictures. J. Interact. Market. 2007; 21 (4):23–45. [ Google Scholar ]
• Doh S.J., Hwang J.S. How consumers evaluate eWOM (electronic word-of-mouth) messages. Cyberpsychol. Behav. 2009; 12 (2):193–197. [ PubMed ] [ Google Scholar ]
• Donthu N., Gustafsson A. Effects of COVID-19 on business and research. J. Bus. Res. 2020; 117 :284–289. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Eger L., Komárková L., Egerová D., Mičík M. The effect of COVID-19 on consumer shopping behaviour: generational cohort perspective. J. Retailing Consum. Serv. 2021; 61 [ Google Scholar ]
• E-marketer E-marketer, eMarketer in Review – Key 2013 Trends, Coverage Areas and Platform Growth. 2013. https://www.emarketer.com/newsroom/index.php/emarketer-review-key-2013-trends-coverage-areas-platform-growth
• Emerson R.M. Power- dependence relations. American Sociology Review. 1962; 27 (1):31–41. [ Google Scholar ]
• Eyre R., De Luca F., Simini F. Social media usage reveals recovery of small businesses after natural hazard events. Nat. Commun. 2020; 11 (1):1–10. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Friedman H.H., Friedman L. Endorser effectiveness by product type. J. Advert. Res. 1979; 19 (5):63–71. [ Google Scholar ]
• Fu H., Manogaran G., Wu K., Cao M., Jiang S., Yang A. Intelligent decision-making of online shopping behavior based on internet of things. Int. J. Inf. Manag. 2020; 50 :515–525. [ Google Scholar ]
• Gan C., Wang W. Uses and gratifications of social media: a comparison of microblog and WeChat. J. Syst. Inform. Technol. 2015; 17 (4):351–363. [ Google Scholar ]
• Geetha V., Rajkumar V.S., Arunachalam L. Impact of social media sites on students purchase intention in online shopping: an empirically study. Int. J. Mech. Prod. Eng. Res. Dev. 2018; 8 :927–938. [ Google Scholar ]
• Geng R., Wang S., Chen X., Song D., Yu J. Content marketing in e-commerce platforms in the internet celebrity economy. Indust. Manag. Data Syst. 2020; 120 (3):464–485. [ Google Scholar ]
• Ghose A., Ipeirotis P.G. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Trans. Knowl. Data Eng. 2010; 23 (10):1498–1512. [ Google Scholar ]
• Gillin P. Linden Publishing; 2007. The New Influencers: A Marketer’s Guide to the New Social media. [ Google Scholar ]
• Gupta Y., Agarwal S., Singh P.B. To study the impact of instafamous celebrities on consumer buying behavior. Acad. Market. Stud. J. 2020; 24 (2):1–13. [ Google Scholar ]
• Hair J.F., Black W.C., Babin B.J., Anderson R.E. Cengage Learning EMEA- United Kingdom; 2019. Multivariate Data Analysis: A Global Perspective. [ Google Scholar ]
• Hajli N. A study of the impact of social media on consumers. Int. J. Mark. Res. 2014; 56 (3):387–404. [ Google Scholar ]
• Han O.W., Yazdanifard R. The review of the effectiveness of celebrity advertising that influence consumers perception and buying behavior. Global J. Manag. Bus. 2015 [ Google Scholar ]
• Hawkins D.I., Mothersbaugh D.L. eleventh ed. McGraw-Hill Irwin; New York, NY: 2010. Consumer Behavior: Building Marketing Strategy. [ Google Scholar ]
• Helm S., Eggert A., Garnefeld I. Handbook of Partial Least Squares. Springer; Berlin, Heidelberg: 2010. Modeling the impact of corporate reputation on customer satisfaction and loyalty using partial least squares; pp. 515–534. [ Google Scholar ]
• Hennig-Thurau T., Hofacker C.F., Bloching B. Marketing the pinball way: understanding how social media change the generation of value for consumers and companies. J. Interact. Market. 2013; 27 (4):237–241. [ Google Scholar ]
• Homans G. Social behavior as exchange. Am. J. Sociol. 1958; 63 (6):597–606. [ Google Scholar ]
• Hossain A., Chowdhury M.H.K., Shamsuzzaman H.S., Fahim M., Khan M.Y.H. Banking service in Bangladesh: the impact of service marketing Mix on purchase intention of university students. Strat. Change. 2020; 29 (3):363–374. [ Google Scholar ]
• Hossain A., Hasan S., Begum S., Sarker M.A.H. Consumers’ Online Buying Behaviour during COVID-19 Pandemic Using Structural Equation Modeling. Transntl. Market. J. 2022; 10 (2):311–334. [ Google Scholar ]
• Hossain A., Jamil M.A., Rahman M.M. Exploring the key factors influencing consumers’ intention, satisfaction and loyalty towards online purchase in Bangladesh. Int. J. Econ. Finan. Res. 2018; 4 (7):214–225. [ Google Scholar ]
• Hossain A., Khan M.Y.H. Green marketing mix effect on consumers buying decisions in Bangladesh. Mark. Manag. Innov. 2018; 10 (4):298–306. [ Google Scholar ]
• Hossain A., Neger M., Chowdhury M.H.K. Analyzing the impact of social media, promotional efforts and reference groups on consumers buying behavior of eco-friendly products in Bangladesh. Int. J. Sci. Bus. 2019; 3 (1):126–135. [ Google Scholar ]
• Hossain A., Rahman, Md. L. & Hasan, M.M. Consumers’ internet shopping decision toward fashion apparels and its impact on satisfaction in Bangladesh. Bus. Ethics Leadership. 2018; 2 (4):74–82. [ Google Scholar ]
• Huang Z., Benyoucef M. User preferences of social features on social commerce websites: an empirical study. Technol. Forecast. Soc. Change. 2015; 95 :57–72. [ Google Scholar ]
• Huber O., Seiser G. Accounting and convincing: the effect of two types of justification on the decision process. J. Behav. Decis. Making. 2001; 14 (1):69–85. [ Google Scholar ]
• Huete-Alcocer N. A literature review of word of mouth and electronic word of mouth: implications for consumer behavior. Front. Psychol. 2017; 8 :1256. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ibok N.I. Factors determining the effectiveness of celebrity endorsed advertisements: the case of Nigerian telecommunication industry. Am. J. Bus. Manag. 2013; 2 (3):233–238. [ Google Scholar ]
• Iyer G., Soberman D., Villas-Boas J.M. The targeting of advertising. Market. Sci. 2005; 24 (3):461–476. [ Google Scholar ]
• Jahoda M., Deutsch M., Cook S.W. Vol. 1, Basic processes. Vol. 2, Selected techniques. Dryden Press 1; 1951. Research methods in social relations with special reference to prejudice. [ Google Scholar ]
• Jajodia A., Ebner L., Heidinger B., Chaturvedi A., Prosch H. Imaging in corona virus disease 2019 (COVID-19)—a Scoping review. Europ. J. Radiol. Open. 2020; 7 (1):1–6. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Jalilvand M.R., Samiei N. The effect of electronic word of mouth on brand image and purchase intention: an empirical study in the automobile industry in Iran. Market. Intell. Plann. 2012 [ Google Scholar ]
• Javadi M.H.M., Dolatabadi H.R., Nourbakhsh M., Poursaeedi A., Asadollahi A.R. An analysis of factors affecting on online shopping behavior of consumers. Int. J. Market. Stud. 2012; 4 (5):81. [ Google Scholar ]
• Jenefa L. Impact of digital advertisement on garments buying behavior. Int. J. Transform. Operat. Market. Manag. 2017; 1 (1) [ Google Scholar ]
• Jiménez F.R., Mendoza N.A. Too popular to ignore: the influence of online reviews on purchase intentions of search and experience products. J. Interact. Market. 2013; 27 (3):226–235. [ Google Scholar ]
• Kaplan A., Haenlein M. Collaborative projects (social media application): about Wikipedia, the free encyclopedia. Bus. Horiz. 2014; 57 (5):617–626. [ Google Scholar ]
• Kelman H.C. Compliance, identification, and internalization three processes of attitude change. J. Conflict Resolut. 1958; 2 (1):51–60. [ Google Scholar ]
• Khatri P. Celebrity endorsement: a strategic promotion perspective. Indian Media Stud. J. 2006; 1 (1):25–37. [ Google Scholar ]
• Kim S., Park H. Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance. Int. J. Inf. Manag. 2013; 33 (2):318–332. [ Google Scholar ]
• Kim W.G., Lim H., Brymer R.A. The effectiveness of managing social media on hotel performance. Int. J. Hospit. Manag. 2015; 44 :165–171. [ Google Scholar ]
• Kotler P. millennium edition. 2000. Marketing Management. Boston. [ Google Scholar ]
• Kotler P. John Wiley & Sons; 2004. Ten Deadly Marketing Sins: Signs and Solutions. [ Google Scholar ]
• Kotler P., Armstrong G. Pearson education; 2010. Principles of Marketing. [ Google Scholar ]
• Kuester S. Vol. 110. University of Mannheim; 2012. MKT 301: Strategic Marketing & Marketing in Specific Industry Contexts; pp. 393–404. [ Google Scholar ]
• Kumar V., Mirchandani R. Increasing the ROI of social media marketing. IEEE Eng. Manag. Rev. 2013; 41 (3):17–23. [ Google Scholar ]
• Kutthakaphan R., Chokesamritpol W. 2013. The Use of Celebrity Endorsement with the Help of Electronic Communication Channel (Instagram): Case Study of Magnum Ice Cream in Thailand. [ Google Scholar ]
• Labrecque L.I., vor dem Esche J., Mathwick C., Novak T.P., Hofacker C.F. Consumer power: evolution in the digital age. J. Interact. Market. 2013; 27 (4):257–269. [ Google Scholar ]
• Lee J., Park D.H., Han I. The effect of negative online consumer reviews on product attitude: an information processing view. Electron. Commer. Res. Appl. 2008; 7 (3):341–352. [ Google Scholar ]
• Lee J., Park D.H., Han I. The different effects of online consumer reviews on consumers' purchase intentions depending on trust in online shopping malls: an advertising perspective. Intern. Res. 2011 [ Google Scholar ]
• Li C.Y. How social commerce constructs influence customers' social shopping intention? An empirical study of a social commerce website. Technol. Forecast. Soc. Change. 2019; 144 :282–294. [ Google Scholar ]
• Li N., Zhang P. Consumer online shopping attitudes and behavior: an assessment of research. AMCIS 2002 proceedings. 2002; 74 https://aisel.aisnet.org/amcis2002/74 [ Google Scholar ]
• Lim Y.J., Osman A., Salahuddin S.N., Romle A.R., Abdullah S. Factors influencing online shopping behavior: the mediating role of purchase intention. Procedia Econ. Finance. 2016; 35 :401–410. [ Google Scholar ]
• Lu Z., Xia H., Heo S., Wigdor D. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 2018, April. You watch, you give, and you engage: a study of live streaming practices in China; pp. 1–13. [ Google Scholar ]
• Luo J., Pan X., Wang S., Huang Y. Identifying target audience on enterprise social network. Indust. Manag. Data Sys. 2019; 119 (1):111–128. [ Google Scholar ]
• Ma Y. To shop or not: understanding Chinese consumers’ live-stream shopping intentions from the perspectives of uses and gratifications, perceived network size, perceptions of digital celebrities, and shopping orientations. Telematics Inf. 2021; 59 [ Google Scholar ]
• Meng L.M., Duan S., Zhao Y., Lü K., Chen S. The impact of online celebrity in live streaming E-commerce on purchase intention from the perspective of emotional contagion. J. Retailing Consum. Serv. 2021; 63 [ Google Scholar ]
• Meng X., Zhang W., Li Y., Cao X., Feng X. Social media effect, investor recognition and the cross-section of stock returns. Int. Rev. Financ. Anal. 2020; 67 [ Google Scholar ]
• Mo Z., Li Y.F., Fan P. Effect of online reviews on consumer purchase behavior. J. Serv. Sci. Manag. 2015; 8 (3):419. [ Google Scholar ]
• Momtaz H., Islam M.A., Ariffin K.H.K., Karim A. Customers’ satisfaction on online shopping in Malaysia. Int. J. Bus. Manag. 2011; 6 (10):162. [ Google Scholar ]
• Neger M., Uddin B. Factors affecting consumers’ internet shopping behavior during the COVID-19 Pandemic: evidence from Bangladesh. Chin. Bus. Rev. 2020; 19 (3):91–104. [ Google Scholar ]
• Newell A.J.C.S., Simon H.A. Elements of a theory of human problem-solving. Psychol. Rev. 1958; 65 :151–166. [ Google Scholar ]
• Nicola M., Alsafi Z., Sohrabi C., Kerwan A., Al-Jabir A., Iosifidis C., Agha M., Agha R. The socio-economic implications of the coronavirus pandemic (COVID-19): a review. Int. J. Surg. 2020; 78 (1):185–193. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Norman D.A. Toward a theory of memory and attention. Psychol. Rev. 1968; 75 :522–536. [ Google Scholar ]
• Nuseir M.T. The impact of electronic word of mouth (e-WOM) on the online purchase intention of consumers in the Islamic countries–a case of (UAE) J. Islam. Market. 2019; 10 (3):759–767. [ Google Scholar ]
• Ohanian R. Construction and validation of a scale to measure celebrity endorsers' perceived expertise, trustworthiness, and attractiveness. J. Advert. 1990; 19 (3):39–52. [ Google Scholar ]
• Pantano E., Pizzi G., Scarpi D., Dennis C. Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. J. Bus. Res. 2020; 116 :209–213. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Park H.J., Lin L.M. The effects of match-ups on the consumer attitudes toward internet celebrities and their live streaming contents in the context of product endorsement. J. Retailing Consum. Serv. 2020; 52 [ Google Scholar ]
• Park S., Nicolau J.L. Asymmetric effects of online consumer reviews. Ann. Tourism Res. 2015; 50 :67–83. [ Google Scholar ]
• Pemberton C. 2017. Energize Your Influencer Marketing - Smarter with Gartner. https://www.gartner.com/smarterwithgartner/energize-your-influencer-marketing (Accessed 5 12 2018) [ Google Scholar ]
• Petty R.E., Cacioppo J.T., Schumann D. Central and peripheral routes to advertising effectiveness: the moderating role of involvement. J. Consum. Res. 1983; 10 (2):135–146. [ Google Scholar ]
• Prentice C., Han X.Y., Hua L.L., Hu L. The influence of identity-driven customer engagement on purchase intention. J. Retailing Consum. Serv. 2019; 47 :339–347. [ Google Scholar ]
• Qiu L., Benbasat I. Online consumer trust and live help interfaces: the effects of text-to-speech voice and three-dimensional avatars. Int. J. Human-Comp. Inter. 2005; 19 (1):75–94. [ Google Scholar ]
• Rai S.K., Sharma A.K. Celebrity attributes and influence on consumer behaviour: a study of Shekhawati region of Rajasthan. Pacific Bus. Rev. Int. 2013; 5 (11):57–64. [ Google Scholar ]
• Rajendran D.K., Rajagopal V., Alagumanian S., Santhosh Kumar T., Sathiya Prabhakaran S.P., Kasilingam D. Systematic literature review on novel corona virus SARS-CoV-2: a threat to human era. Virus Disease. 2020; 31 (2):253–261. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Reitman W.R. John Wiley & Sons; New York: 1965. Cognition and Thought: an Information-Processing Approach. [ Google Scholar ]
• Reza Jalilvand M., Samiei N. The effect of electronic word of mouth on brand image and purchase intention: an empirical study in the automobile industry in Iran. Market. Intellig. Plan. 2012; 30 (4):460–476. [ Google Scholar ]
• Ringle C.M., Wende S., Becker J.M. SmartPLS GmbH; Boenningstedt: 2015. SmartPLS.“SmartPLS 3. [ Google Scholar ]
• Salam A.F., Rao H.R., Pegels C.C. Paper Presented at Americas Conference on Information Systems. 1998. An investigation of consumer- perceived risk on electronic commerce transactions: the role of institutional trust, and economic incentive in a social exchange framework. Baltimore, MD, USA. [ Google Scholar ]
• Schouten A.P., Janssen L., Verspaget M. Celebrity vs. Influencer endorsements in advertising: the role of identification, credibility, and Product-Endorser fit. Int. J. Advert. 2020; 39 (2):258–281. [ Google Scholar ]
• Schunk D.H. 2012. Learning Theoriesan Educational Perspective. [ Google Scholar ]
• Sekaran U., Bougie R. Research methods for business: a skill building approach. John Wiley & Sons. 2016 [ Google Scholar ]
• Sernovitz A. How Smart Companies Get People Texas; 2012. Word of Mouth Marketing. [ Google Scholar ]
• Shamout M.D. The impact of promotional tools on consumer buying behavior in retail market. Int. J. Bus. Soc. Sci. 2016; 7 (1):75–85. [ Google Scholar ]
• Sheth J. Impact of Covid-19 on consumer behavior: will the old habits return or die? J. Bus. Res. 2020; 117 :280–283. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Shin D.H. The role of affordance in the experience of virtual reality learning: technological and affective affordances in virtual reality. Telematics Inf. 2017; 34 (8):1826–1836. [ Google Scholar ]
• Shuell T.J. Cognitive conceptions of learning. Rev. Educ. Res. 1986; 56 :411–436. [ Google Scholar ]
• Siddique Z.R., Hossain A. Sources of consumers awareness toward green products and its impact on purchasing decision in Bangladesh. J. Sustain. Dev. 2018; 11 (3):9–22. [ Google Scholar ]
• Singhal T. A review of coronavirus disease-2019 (COVID-19) Indian J. Pediatr. 2020; 87 (4):281–286. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sjöblom M., Hamari J. Why do people watch others play video games? An empirical study on the motivations of Twitch users. Comput. Hum. Behav. 2017; 75 :985–996. [ Google Scholar ]
• Sohrabi C., Alsafi Z., O’Neill N., Khan M., Kerwan A., AlJabir A., Iosifidis C., Agha R. World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19) Int. J. Surg. 2020; 76 (1):71–76. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sparks B.A., Browning V. The impact of online reviews on hotel booking intentions and perception of trust. Tourism Manag. 2011; 32 (6):1310–1323. [ Google Scholar ]
• Sun Y., Shao X., Li X., Guo Y., Nie K. How live streaming influences purchase intentions in social commerce: an IT affordance perspective. Electron. Commer. Res. Appl. 2019; 37 [ Google Scholar ]
• Taobangdan, Taobao Taobao Live Streaming Ecological Development Report. Report. 2019 [ Google Scholar ]
• Ventre I., Kolbe D. The impact of perceived usefulness of online reviews, trust and perceived risk on online purchase intention in emerging markets: a Mexican perspective. J. Int. Consum. Market. 2020; 32 (4):287–299. [ Google Scholar ]
• Verma S., Gustafsson A. Investigating the emerging COVID-19 research trends in the field of business and management: a bibliometric analysis approach. J. Bus. Res. 2020 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wang J.S., Cheng Y.F., Chu Y.L. Effect of celebrity endorsements on consumer purchase intentions: advertising effect and advertising appeal as mediators. Human Fact. Ergon. Manuf. Service Indust. 2013; 23 (5):357–367. [ Google Scholar ]
• Wang Y., Feng H. Customer relationship management capabilities: Measurement, antecedents and consequences. Manag. Decis. 2012; 50 (1):115–129. [ Google Scholar ]
• Wang X.C., Kim W., Holguín-Veras J., Schmid J. Adoption of delivery services in light of the COVID pandemic: who and how long? Transport. Res. Pol. Pract. 2021; 154 :270–286. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wilcox K., Stephen A.T. Are close friends the enemy? Online social networks, self-esteem, and self-control. J. Consum. Res. 2013; 40 (1):90–103. [ Google Scholar ]
• Wongkitrungrueng A., Assarut N. The role of live streaming in building consumer trust and engagement with social commerce sellers. J. Bus. Res. 2020; 117 :543–556. [ Google Scholar ]
• Xiang L., Zheng X., Lee M.K.O.O., Zhao D. Exploring consumers’ impulse buying behavior on social commerce platform: the role of parasocial interaction. Int. J. Inf. Manag. 2016; 36 (3):333–347. [ Google Scholar ]
• Xu X., Wu J.H., Li Q. What drives consumer shopping behavior in live streaming commerce? J. Electron. Commer. Res. 2020; 21 (3):144–167. [ Google Scholar ]
• Yadav B.S. The role of social media communication in the branding of educational hubs. IUP Journal of Soft Skills. 2016; 10 (4):51. [ Google Scholar ]
• Yahya S.F.H., Hashim N.A., Bahsri N., Dahari N.A. The effect of sales promotion strategy on online fashion shopping behavior among employee of Sahawan Sdn bhd. Global Bus. Manag. Res. 2019; 11 (2):1–13. [ Google Scholar ]
• Ye Q., Law R., Gu B. The impact of online user reviews on hotel room sales. Int. J. Hospit. Manag. 2009; 28 (1):180–182. [ Google Scholar ]
• Yim M.Y.C., Chu S.C., Sauer P.L. Is augmented reality technology an effective tool for e-commerce? An interactivity and vividness perspective. J. Interact. Market. 2017; 39 :89–103. [ Google Scholar ]
• Yin S. International Conference on Human-Computer Interaction. Springer; C ham: 2020, July. A study on the influence of E-commerce live streaming on consumer’s purchase intentions in mobile internet; pp. 720–732. [ Google Scholar ]
• Yu E., Jung C., Kim H., Jung J. Impact of viewer engagement on gift-giving in live video streaming. Telematics Inf. 2018; 35 (5):1450–1460. [ Google Scholar ]
• Yuen K.F., Wang X., Ma F., Li K.X. The psychological causes of panic buying following a health crisis. Int. J. Environ. Res. Publ. Health. 2020; 17 (10):3513. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zafar A.U., Qiu J., Li Y., Wang J., Shahzad M. The impact of social media celebrities' posts and contextual interactions on impulse buying in social commerce. Comput. Hum. Behav. 2021; 115 [ Google Scholar ]
• Zhang M., Qin F., Wang G.A., Luo C. The impact of live video streaming on online purchase intention. Serv. Ind. J. 2019; 40 (9–10):656–681. [ Google Scholar ]
• Zhang X.M., Zhu F. Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. J. Market. 2010; 74 (2):133–148. http://www.hbs.edu/faculty/Pages/item. aspx?num=45146 05 17th 2016. [ Google Scholar ]
• Zhou L., Wang W., Xu J.D., Liu T., Gu J. Perceived information transparency in B2C e-commerce: an empirical investigation. Inf. Manag. 2018; 55 (7):912–927. [ Google Scholar ]

A study on factors limiting online shopping behaviour of consumers

Rajagiri Management Journal

ISSN : 0972-9968

Article publication date: 4 March 2021

Issue publication date: 12 April 2021

This study aims to investigate consumer behaviour towards online shopping, which further examines various factors limiting consumers for online shopping behaviour. The purpose of the research was to find out the problems that consumers face during their shopping through online stores.

Design/methodology/approach

A quantitative research method was adopted for this research in which a survey was conducted among the users of online shopping sites.

As per the results total six factors came out from the study that restrains consumers to buy from online sites – fear of bank transaction and faith, traditional shopping more convenient than online shopping, reputation and services provided, experience, insecurity and insufficient product information and lack of trust.

Research limitations/implications

This study is beneficial for e-tailers involved in e-commerce activities that may be customer-to-customer or customer-to-the business. Managerial implications are suggested for improving marketing strategies for generating consumer trust in online shopping.

Originality/value

In contrast to previous research, this study aims to focus on identifying those factors that restrict consumers from online shopping.

• Online shopping

Daroch, B. , Nagrath, G. and Gupta, A. (2021), "A study on factors limiting online shopping behaviour of consumers", Rajagiri Management Journal , Vol. 15 No. 1, pp. 39-52. https://doi.org/10.1108/RAMJ-07-2020-0038

Emerald Publishing Limited

Copyright © 2020, Bindia Daroch, Gitika Nagrath and Ashutosh Gupta.

Published in Rajagiri Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Today, people are living in the digital environment. Earlier, internet was used as the source for information sharing, but now life is somewhat impossible without it. Everything is linked with the World Wide Web, whether it is business, social interaction or shopping. Moreover, the changed lifestyle of individuals has changed their way of doing things from traditional to the digital way in which shopping is also being shifted to online shopping.

Online shopping is the process of purchasing goods directly from a seller without any intermediary, or it can be referred to as the activity of buying and selling goods over the internet. Online shopping deals provide the customer with a variety of products and services, wherein customers can compare them with deals of other intermediaries also and choose one of the best deals for them ( Sivanesan, 2017 ).

As per Statista-The Statistics Portal, the digital population worldwide as of April 2020 is almost 4.57 billion people who are active internet users, and 3.81 billion are social media users. In terms of internet usage, China, India and the USA are ahead of all other countries ( Clement, 2020 ).

The number of consumers buying online and the amount of time people spend online has risen ( Monsuwe et al. , 2004 ). It has become more popular among customers to buy online, as it is handier and time-saving ( Huseynov and Yildirim, 2016 ; Mittal, 2013 ). Convenience, fun and quickness are the prominent factors that have increased the consumer’s interest in online shopping ( Lennon et al. , 2008 ). Moreover, busy lifestyles and long working hours also make online shopping a convenient and time-saving solution over traditional shopping. Consumers have the comfort of shopping from home, reduced traveling time and cost and easy payment ( Akroush and Al-Debei, 2015 ). Furthermore, price comparisons can be easily done while shopping through online mode ( Aziz and Wahid, 2018 ; Martin et al. , 2015 ). According to another study, the main influencing factors for online shopping are availability, low prices, promotions, comparisons, customer service, user friendly, time and variety to choose from ( Jadhav and Khanna, 2016 ). Moreover, website design and features also encourage shoppers to shop on a particular website that excite them to make the purchase.

Online retailers have started giving plenty of offers that have increased the online traffic to much extent. Regularly online giants like Amazon, Flipkart, AliExpress, etc. are advertising huge discounts and offers that are luring a large number of customers to shop from their websites. Companies like Nykaa, MakeMyTrip, Snapdeal, Jabong, etc. are offering attractive promotional deals that are enticing the customers.

Despite so many advantages, some customers may feel online shopping risky and not trustworthy. The research proposed that there is a strong relationship between trust and loyalty, and most often, customers trust brands far more than a retailer selling that brand ( Bilgihan, 2016 ; Chaturvedi et al. , 2016 ). In the case of online shopping, there is no face-to-face interaction between seller and buyer, which makes it non-socialize, and the buyer is sometimes unable to develop the trust ( George et al. , 2015 ). Trust in the e-commerce retailer is crucial to convert potential customer to actual customer. However, the internet provides unlimited products and services, but along with those unlimited services, there is perceived risk in digital shopping such as mobile application shopping, catalogue or mail order ( Tsiakis, 2012 ; Forsythe et al. , 2006 ; Aziz and Wahid, 2018 ).

Literature review

A marketer has to look for different approaches to sell their products and in the current scenario, e-commerce has become the popular way of selling the goods. Whether it is durable or non-durable, everything is available from A to Z on websites. Some websites are specifically designed for specific product categories only, and some are selling everything.

The prominent factors like detailed information, comfort and relaxed shopping, less time consumption and easy price comparison influence consumers towards online shopping ( Agift et al. , 2014 ). Furthermore, factors like variety, quick service and discounted prices, feedback from previous customers make customers prefer online shopping over traditional shopping ( Jayasubramanian et al. , 2015 ). It is more preferred by youth, as during festival and holiday season online retailers give ample offers and discounts, which increases the online traffic to a great extent ( Karthikeyan, 2016 ). Moreover, services like free shipping, cash on delivery, exchange and returns are also luring customers towards online purchases.

More and more people are preferring online shopping over traditional shopping because of their ease and comfort. A customer may have both positive and negative experiences while using an online medium for their purchase. Some of the past studies have shown that although there are so many benefits still some customers do not prefer online as their basic medium of shopping.

While making online purchase, customers cannot see, touch, feel, smell or try the products that they want to purchase ( Katawetawaraks and Wang, 2011 ; Al-Debei et al. , 2015 ), due to which product is difficult to examine, and it becomes hard for customers to make purchase decision. In addition, some products are required to be tried like apparels and shoes, but in case of online shopping, it is not possible to examine and feel the goods and assess its quality before making a purchase due to which customers are hesitant to buy ( Katawetawaraks and Wang, 2011 ; Comegys et al. , 2009 ). Alam and Elaasi (2016) in their study found product quality is the main factor, which worries consumer to make online purchase. Moreover, some customers have reported fake products and imitated items in their delivered orders ( Jun and Jaafar, 2011 ). A low quality of merchandise never generates consumer trust on online vendor. A consumer’s lack of trust on the online vendor is the most common reason to avoid e-commerce transactions ( Lee and Turban, 2001 ). Fear of online theft and non-reliability is another reason to escape from online shopping ( Karthikeyan, 2016 ). Likewise, there is a risk of incorrect information on the website, which may lead to a wrong purchase, or in some cases, the information is incomplete for the customer to make a purchase decision ( Liu and Guo, 2008 ). Moreover, in some cases, the return and exchange policies are also not clear on the website. According to Wei et al. (2010) , the reliability and credibility of e-retailer have direct impact on consumer decision with regards to online shopping.

Limbu et al. (2011) revealed that when it comes to online retailers, some websites provide very little information about their companies and sellers, due to which consumers feel insecure to purchase from these sites. According to other research, consumers are hesitant, due to scams and feel anxious to share their personal information with online vendors ( Miyazaki and Fernandez, 2001 ; Limbu et al. , 2011 ). Online buyers expect websites to provide secure payment and maintain privacy. Consumers avoid online purchases because of the various risks involved with it and do not find internet shopping secured ( Cheung and Lee, 2003 ; George et al. , 2015 ; Banerjee et al. , 2010 ). Consumers perceive the internet as an unsecured channel to share their personal information like emails, phone and mailing address, debit card or credit card numbers, etc. because of the possibility of misuse of that information by other vendors or any other person ( Lim and Yazdanifard, 2014 ; Kumar, 2016 ; Alam and Yasin, 2010 ; Nazir et al. , 2012 ). Some sites make it vital and important to share personal details of shoppers before shopping, due to which people abandon their shopping carts (Yazdanifard and Godwin, 2011). About 75% of online shoppers leave their shopping carts before they make their final decision to purchase or sometimes just before making the payments ( Cho et al. , 2006 ; Gong et al. , 2013 ).

Moreover, some of the customers who have used online shopping confronted with issues like damaged products and fake deliveries, delivery problems or products not received ( Karthikeyan, 2016 ; Kuriachan, 2014 ). Sometimes consumers face problems while making the return or exchange the product that they have purchased from online vendors ( Liang and Lai, 2002 ), as some sites gave an option of picking from where it was delivered, but some online retailers do not give such services to consumer and consumer him/herself has to courier the product for return or exchange, which becomes inopportune. Furthermore, shoppers had also faced issues with unnecessary delays ( Muthumani et al. , 2017 ). Sometimes, slow websites, improper navigations or fear of viruses may drop the customer’s willingness to purchase from online stores ( Katawetawaraks and Wang, 2011 ). As per an empirical study done by Liang and Lai (2002) , design of the e-store or website navigation has an impact on the purchase decision of the consumer. An online shopping experience that a consumer may have and consumer skills that consumers may use while purchasing such as website knowledge, product knowledge or functioning of online shopping influences consumer behaviour ( Laudon and Traver, 2009 ).

From the various findings and viewpoints of the previous researchers, the present study identifies the complications online shoppers face during online transactions, as shown in Figure 1 . Consumers do not have faith, and there is lack of confidence on online retailers due to incomplete information on website related to product and service, which they wish to purchase. Buyers are hesitant due to fear of online theft of their personal and financial information, which makes them feel there will be insecure transaction and uncertain errors may occur while making online payment. Some shoppers are reluctant due to the little internet knowledge. Furthermore, as per the study done by Nikhashem et al. (2011), consumers unwilling to use internet for their shopping prefer traditional mode of shopping, as it gives roaming experience and involves outgoing activity.

Several studies have been conducted earlier that identify the factors influencing consumer towards online shopping but few have concluded the factors that restricts the consumers from online shopping. The current study is concerned with the factors that may lead to hesitation by the customer to purchase from e-retailers. This knowledge will be useful for online retailers to develop customer driven strategies and to add more value product and services and further will change their ways of promoting and advertising the goods and enhance services for customers.

Research methodology

This study aimed to find out the problems that are generally faced by a customer during online purchase and the relevant factors due to which customers do not prefer online shopping. Descriptive research design has been used for the study. Descriptive research studies are those that are concerned with describing the characteristics of a particular individual or group. This study targets the population drawn from customers who have purchased from online stores. Most of the respondents participated were post graduate students and and educators. The total population size was indefinite and the sample size used for the study was 158. A total of 170 questionnaires were distributed among various online users, out of which 12 questionnaires were received with incomplete responses and were excluded from the analysis. The respondents were selected based on the convenient sampling technique. The primary data were collected from Surveys with the help of self-administered questionnaires. The close-ended questionnaire was used for data collection so as to reduce the non-response rate and errors. The questionnaire consists of two different sections, in which the first section consists of the introductory questions that gives the details of socio-economic profile of the consumers as well as their behaviour towards usage of internet, time spent on the Web, shopping sites preferred while making the purchase, and the second section consist of the questions related to the research question. To investigate the factors restraining consumer purchase, five-point Likert scale with response ranges from “Strongly agree” to “Strongly disagree”, with following equivalencies, “strongly disagree” = 1, “disagree” = 2, “neutral” = 3, “agree” = 4 and “strongly agree” = 5 was used in the questionnaire with total of 28 items. After collecting the data, it was manually recorded on the Excel sheet. For analysis socio-economic profile descriptive statistics was used and factors analysis was performed on SPSS for factor reduction.

Data analysis and interpretation

The primary data collected from the questionnaires was completely quantified and analysed by using Statistical Package for Social Science (SPSS) version 20. This statistical program enables accuracy and makes it relatively easy to interpret data. A descriptive and inferential analysis was performed. Table 1 represents the results of socio-economic status of the respondents along with some introductory questions related to usage of internet, shopping sites used by the respondents, amount of money spent by the respondents and products mostly purchased through online shopping sites.

According to the results, most (68.4%) of the respondents were belonging to the age between 21 and 30 years followed by respondents who were below the age of 20 years (16.4%) and the elderly people above 50 were very few (2.6%) only. Most of the respondents who participated in the study were females (65.8)% who shop online as compared to males (34.2%). The respondents who participated in the study were students (71.5%), and some of them were private as well as government employees. As per the results, most (50.5%) of the people having income below INR15,000 per month who spend on e-commerce websites. The results also showed that most of the respondents (30.9%) spent less than 5 h per week on internet, but up to (30.3%) spend 6–10 h per week on internet either on online shopping or social media. Majority (97.5%) of them have shopped through online websites and had both positive and negative experiences, whereas 38% of the people shopped 2–5 times and 36.7% shopped more than ten times. Very few people (12%), shopped only once. Most of the respondents spent between INR1,000–INR5,000 for online shopping, and few have spent more than INR5,000 also.

As per the results, the most visited online shopping sites was amazon.com (71.5%), followed by flipkart.com (53.2%). Few respondents have also visited other e-commerce sites like eBay, makemytrip.com and myntra.com. Most (46.2%) of the time people purchase apparels followed by electronics and daily need items from the ecommerce platform. Some of the respondents have purchased books as well as cosmetics, and some were preferring online sites for travel tickets, movie tickets, hotel bookings and payments also.

Factor analysis

To explore the factors that restrict consumers from using e-commerce websites factor analysis was done, as shown in Table 3 . A total of 28 items were used to find out the factors that may restrain consumers to buy from online shopping sites, and the results were six factors. The Kaiser–Meyer–Olkin (KMO) measure, as shown in Table 2 , in this study was 0.862 (>0.60), which states that values are adequate, and factor analysis can be proceeded. The Bartlett’s test of sphericity is related to the significance of the study and the significant value is 0.000 (<0.05) as shown in Table 2 .

The analysis produced six factors with eigenvalue more than 1, and factor loadings that exceeded 0.30. Moreover, reliability test of the scale was performed through Cronbach’s α test. The range of Cronbach’s α test came out to be between 0.747 and 0.825, as shown in Table 3 , which means ( α > 0.7) the high level of internal consistency of the items used in survey ( Table 4 ).

Factor 1 – The results revealed that the “fear of bank transaction and faith” was the most significant factor, with 29.431% of the total variance and higher eigenvalue, i.e. 8.241. The six statements loaded on Factor 1 highly correlate with each other. The analysis shows that some people do not prefer online shopping because they are scared to pay online through credit or debit cards, and they do not have faith over online vendors.

Factor 2 – “Traditional shopping is convenient than online shopping” has emerged as a second factor which explicates 9.958% of total variance. It has five statements and clearly specifies that most of the people prefer traditional shopping than online shopping because online shopping is complex and time-consuming.

Factor 3 – Third crucial factor emerged in the factor analysis was “reputation and service provided”. It was found that 7.013% of variations described for the factor. Five statements have been found on this factor, all of which were interlinked. It clearly depicts that people only buy from reputed online stores after comparing prices and who provide guarantee or warrantee on goods.

Factor 4 – “Experience” was another vital factor, with 4.640% of the total variance. It has three statements that clearly specifies that people do not go for online shopping due to lack of knowledge and their past experience was not good and some online stores do not provide EMI facilities.

Factor 5 – Fifth important factor arisen in the factor analysis was “Insecurity and Insufficient Product Information” with 4.251% of the total variance, and it has laden five statements, which were closely intertwined. This factor explored that online shopping is not secure as traditional shopping. The information of products provided on online stores is not sufficient to make the buying decision.

Factor 6 – “Lack of trust” occurred as the last factor of the study, which clarifies 3.920% of the total variance. It has four statements that clearly state that some people hesitate to give their personal information, as they believe online shopping is risky than traditional shopping. Without touching the product, people hesitate to shop from online stores.

The study aimed to determine the problems faced by consumers during online purchase. The result showed that most of the respondents have both positive and negative experience while shopping online. There were many problems or issues that consumer’s face while using e-commerce platform. Total six factors came out from the study that limits consumers to buy from online sites like fear of bank transaction and no faith, traditional shopping more convenient than online shopping, reputation and services provided, experience, insecurity and insufficient product information and lack of trust.

The research might be useful for the e-tailers to plan out future strategies so as to serve customer as per their needs and generate customer loyalty. As per the investigation done by Casalo et al. (2008) , there is strong relationship between reputation and satisfaction, which further is linked to customer loyalty. If the online retailer has built his brand name, or image of the company, the customer is more likely to prefer that retailer as compared to new entrant. The online retailer that seeks less information from customers are more preferred as compared to those require complete personal information ( Lawler, 2003 ).

Online retailers can adopt various strategies to persuade those who hesitate to shop online such that retailer need to find those negative aspects to solve the problems of customers so that non-online shopper or irregular online consumer may become regular customer. An online vendor has to pay attention to product quality, variety, design and brands they are offering. Firstly, the retailer must enhance product quality so as to generate consumer trust. For this, they can provide complete seller information and history of the seller, which will preferably enhance consumer trust towards that seller.

Furthermore, they can adopt marketing strategies such as user-friendly and secure website, which can enhance customers’ shopping experience and easy product search and proper navigation system on website. Moreover, complete product and service information such as feature and usage information, description and dimensions of items can help consumer decide which product to purchase. The experience can be enhanced by adding more pictures, product videos and three-dimensional (3D), images which will further help consumer in the decision-making process. Moreover, user-friendly payment systems like cash on deliveries, return and exchange facilities as per customer needs, fast and speedy deliveries, etc. ( Chaturvedi et al. , 2016 ; Muthumani et al. , 2017 ) will also enhance the probability of purchase from e-commerce platform. Customers are concerned about not sharing their financial details on any website ( Roman, 2007 ; Limbu et al. , 2011 ). Online retailers can ensure payment security by offering numerous payment options such as cash on delivery, delivery after inspection, Google Pay or Paytm or other payment gateways, etc. so as to increase consumer trust towards website, and customer will not hesitate for financial transaction during shopping. Customers can trust any website depending upon its privacy policy, so retailers can provide customers with transparent security policy, privacy policy and secure transaction server so that customers will not feel anxious while making online payments ( Pan and Zinkhan, 2006 ). Moreover, customers not only purchase basic goods from the online stores but also heed augmented level of goods. Therefore, if vendors can provide quick and necessary support, answer all their queries within 24-hour service availability, customers may find it convenient to buy from those websites ( Martin et al. , 2015 ). Sellers must ensure to provide products and services that are suitable for internet. Retailers can consider risk lessening strategies such as easy return and exchange policies to influence consumers ( Bianchi and Andrews, 2012 ). Furthermore, sellers can offer after-sales services as given by traditional shoppers to attract more customers and generate unique shopping experience.

Although nowadays, most of the vendors do give plenty of offers in form of discounts, gifts and cashbacks, but most of them are as per the needs of e-retailers and not customers. Beside this, trust needs to be generated in the customer’s mind, which can be done by modifying privacy and security policies. By adopting such practices, the marketer can generate customers’ interest towards online shopping.

Conceptual framework of the study

Socioeconomic status of respondents

KMO and Bartlett’s test

Cronbach’s α

Agift , A. , Rekha , V. and Nisha , C. ( 2014 ), “ Consumers attitude towards online shopping ”, Research Journal of Family, Community and Consumer Sciences , Vol. 2 No. 8 , pp. 4 - 7 , available at: www.isca.in/FAMILY_SCI/Archive/v2/i8/2.ISCA-RJFCCS-2014-017.php

Akroush , M.N. and Al-Debei , M.M. ( 2015 ), “ An integrated model of factors affecting consumer attitudes towards online shopping ”, Business Process Management Journal , Vol. 21 No. 6 , pp. 1353 - 1376 , doi: 10.1108/BPMJ-02-2015-0022 .

Alam , M.Z. and Elaasi , S. ( 2016 ), “ A study on consumer perception towards e-shopping in KSA ”, International Journal of Business and Management , Vol. 11 No. 7 , p. 202 .

Alam , S. and Yasin , N.M. ( 2010 ), “ What factors influence online brand trust: evidence from online tickets buyers in Malaysia ”, Journal of Theoretical and Applied Electronic Commerce Research , Vol. 5 No. 3 , pp. 78 - 89 , doi: 10.4067/S0718-18762010000300008 .

Al-Debei , M.M. , Akroush , M.N. and Ashouri , M.I. ( 2015 ), “ Consumer attitudes towards online shopping: the effects of trust, perceived benefits, and perceived web quality ”, Internet Research , Vol. 25 No. 5 , pp. 707 - 733 , doi: 10.1108/IntR-05-2014-0146 .

Aziz , N.N.A. and Wahid , N.A. ( 2018 ), “ Factors influencing online purchase intention among university students ”, International Journal of Academic Research in Business and Social Sciences , Vol. 8 No. 7 , pp. 702 - 717 , doi: 10.6007/IJARBSS/v8-i7/4413 .

Banerjee , N. , Dutta , A. and Dasgupta , T. ( 2010 ), “ A study on customers’ attitude towards online shopping-An Indian perspective ”, Indian Journal of Marketing , Vol. 40 No. 11 , pp. 36 - 42 .

Bianchi , C. and Andrews , L. ( 2012 ), “ Risk, trust, and consumer online purchasing behaviour: a Chilean perspective ”, International Marketing Review , Vol. 29 No. 3 , pp. 253 - 275 , doi: 10.1108/02651331211229750 .

Bilgihan , A. ( 2016 ), “ Gen Y customer loyalty in online shopping: an integrated model of trust, user experience and branding ”, Computers in Human Behavior , Vol. 61 , pp. 103 - 113 , doi: 10.1016/j.chb.2016.03.014 .

Casalo , L. , Flavián , C. and Guinalíu , M. ( 2008 ), “ The role of perceived usability, reputation, satisfaction and consumer familiarity on the website loyalty formation process ”, Computers in Human Behavior , Vol. 24 No. 2 , pp. 325 - 345 , doi: 10.1016/j.chb.2007.01.017 .

Chaturvedi , D. , Gupta , D. and Singh Hada , D. ( 2016 ), “ Perceived risk, trust and information seeking behavior as antecedents of online apparel buying behavior in India: an exploratory study in context of Rajasthan ”, International Review of Management and Marketing , Vol. 6 No. 4 , pp. 935 - 943 , doi: 10.2139/ssrn.3204971 .

Cheung , C.M. and Lee , M.K. ( 2003 ), “ An integrative model of consumer trust in internet shopping ”, ECIS 2003 Proceedings , p. 48 .

Cho , C.H. , Kang , J. and Cheon , H.J. ( 2006 ), “ Online shopping hesitation ”, Cyberpsychology and Behavior , Vol. 9 No. 3 , pp. 261 - 274 , doi: 10.1089/cpb.2006.9.261 .

Clement , J. ( 2020 ), “ Worldwide digital population as of April 2020 ”, available at: www.statista.com/statistics/617136/digital-population-worldwide/ ( accessed 18 June 2020 ).

Comegys , C. , Hannula , M. and Váisánen , J. ( 2009 ), “ Effects of consumer trust and risk on online purchase decision-making: a comparison of Finnish and United States students ”, International Journal of Management , Vol. 26 No. 2 , available at: www.questia.com/library/journal/1P3-1874986651/effects-of-consumer-trust-and-risk-on-online-purchase

Forsythe , S. , Liu , C. , Shannon , D. and Gardner , L.C. ( 2006 ), “ Development of a scale to measure the perceived benefits and risks of online shopping ”, Journal of Interactive Marketing , Vol. 20 No. 2 , pp. 55 - 75 , doi: 10.1002/dir.20061 .

George , O.J. , Ogunkoya , O.A. , Lasisi , J.O. and Elumah , L.O. ( 2015 ), “ Risk and trust in online shopping: experience from Nigeria ”, International Journal of African and Asian Studies , Vol. 11 , pp. 71 - 78 , available at: https://iiste.org/Journals/index.php/JAAS/article/view/23937

Gong , W. , Stump , R.L. and Maddox , L.M. ( 2013 ), “ Factors influencing consumers’ online shopping in China ”, Journal of Asia Business Studies , Vol. 7 No. 3 , pp. 214 - 230 , doi: 10.1108/JABS-02-2013-0006 .

Huseynov , F. and Yildirim , S.O. ( 2016 ), “ Internet users’ attitudes toward business-to-consumer online shopping: a survey ”, Information Development , Vol. 32 No. 3 , pp. 452 - 465 , doi: 10.1177/0266666914554812 .

Jadhav , V. and Khanna , M. ( 2016 ), “ Factors influencing online buying behavior of college students: a qualitative analysis ”, The Qualitative Report , Vol. 21 No. 1 , pp. 1 - 15 , available at: https://nsuworks.nova.edu/tqr/vol21/iss1/1

Jayasubramanian , P. , Sivasakthi , D. and Ananthi , P.K. ( 2015 ), “ A study on customer satisfaction towards online shopping ”, International Journal of Applied Research , Vol. 1 No. 8 , pp. 489 - 495 , available at: www.academia.edu/download/54009715/1-7-136.pdf

Jun , G. and Jaafar , N.I. ( 2011 ), “ A study on consumers’ attitude towards online shopping in China ”, International Journal of Business and Social Science , Vol. 2 No. 22 , pp. 122 - 132 .

Karthikeyan ( 2016 ), “ Problems faced by online customers ”, International Journal of Current Research and Modern Education (IJCRME) , Vol. 1 No. 1 , pp. 166 - 169 , available at: http://ijcrme.rdmodernresearch.com/wp-content/uploads/2015/06/23.pdf

Katawetawaraks , C. and Wang , C.L. ( 2011 ), “ Online shopper behavior: influences of online shopping decision ”, Asian Journal of Business Research , Vol. 1 No. 2 , pp. 66 - 74 , available at: https://ssrn.com/abstract=2345198

Kumar , M. ( 2016 ), “ Consumer behavior and satisfaction in e-commerce: a comparative study based on online shopping of some electronic gadgets ”, International Journal of Research in Commerce and Management , Vol. 7 No. 7 , pp. 62 - 67 , available at: https://ijrcm.org.in/article_info.php?article_id=6785

Kuriachan , J.K. ( 2014 ), “ Online shopping problems and solutions ”, New Media and Mass Communication , Vol. 23 No. 1 , pp. 1 - 4 , available at: www.academia.edu/download/34229456/Online_shopping_problems_and_solutions

Laudon , K.C. and Traver , C.G. ( 2009 ), E-Commerce Business. Technology. Society , 5th ed ., Prentice Hall .

Lawler , J.P. ( 2003 ), “ Customer loyalty and privacy on the web ”, Journal of Internet Commerce , Vol. 2 No. 1 , pp. 89 - 105 , doi: 10.1300/J179v02n01_07 .

Lee , M.K. and Turban , E. ( 2001 ), “ A trust model for consumer internet shopping ”, International Journal of Electronic Commerce , Vol. 6 No. 1 , pp. 75 - 91 , doi: 10.1080/10864415.2001.11044227 .

Lennon , S.J. , et al. ( 2008 ), “ Rural consumers’ online shopping for food and fiber products as a form of outshopping ”, Clothing and Textiles Research Journal , Vol. 27 No. 1 , pp. 3 - 30 , doi: 10.1177/0887302X07313625 .

Liang , T.P. and Lai , H.J. ( 2002 ), “ Effect of store design on consumer purchases: an empirical study of on-line bookstores ”, Information and Management , Vol. 39 No. 6 , pp. 431 - 444 , doi: 10.1016/S0378-7206(01)00129-X .

Lim , P.L. and Yazdanifard , R. ( 2014 ), “ Does gender play a role in online consumer behavior? ”, Global Journal of Management and Business Research , Vol. 14 No. 7 , pp. 48 - 56 , available at: https://journalofbusiness.org/index.php/GJMBR/article/view/1570

Limbu , Y.B. , Wolf , M. and Lunsford , D.L. ( 2011 ), “ Consumers’ perceptions of online ethics and its effects on satisfaction and loyalty ”, Journal of Research in Interactive Marketing , Vol. 5 No. 1 , pp. 71 - 89 , doi: 10.1108/17505931111121534 .

Liu , C. and Guo , Y. ( 2008 ), “ Validating the end-user computing satisfaction instrument for online shopping systems ”, Journal of Organizational and End User Computing , Vol. 20 No. 4 , pp. 74 - 96 , available at: www.igi-global.com/article/journal-organizational-end-user-computing/3849

Martin , J. , Mortimer , G. and Andrews , L. ( 2015 ), “ Re-examining online customer experience to include purchase frequency and perceived risk ”, Journal of Retailing and Consumer Services , Vol. 25 , pp. 81 - 95 , doi: 10.1016/j.jretconser.2015.03.008 .

Mittal , A. ( 2013 ), “ E-commerce: it’s impact on consumer behavior ”, Global Journal of Management and Business Studies , Vol. 3 No. 2 , pp. 131 - 138 , available at: www.ripublication.com/gjmbs_spl/gjmbsv3n2spl_09.pdf

Miyazaki , A.D. and Fernandez , A. ( 2001 ), “ Consumer perceptions of privacy and security risks for online shopping ”, Journal of Consumer Affairs , Vol. 35 No. 1 , pp. 27 - 44 , doi: 10.1111/j.1745-6606.2001.tb00101.x .

Monsuwe , T.P.Y. , Dellaert , B.G.C. and Ruyter , K.D. ( 2004 ), “ What drives consumers to shop online? A literature review ”, International Journal of Service Industry Management , Vol. 15 No. 1 , pp. 102 - 121 , doi: 10.1108/09564230410523358 .

Muthumani , A. , Lavanya , V. and Mahalakshmi , R. ( 2017 ), “ Problems faced by customers on online shopping in Virudhunagar district ”, International Journal of Science Technology and Management (IJSTM) , Vol. 6 No. 2 , pp. 152 - 159 , available at: www.ijstm.com/images/short_pdf/1486214600_S184_IJSTM.pdf .

Nazir , S. , Tayyab , A. , Sajid , A. , Ur Rashid , H. and Javed , I. ( 2012 ), “ How online shopping is affecting consumers buying behavior in Pakistan? ”, International Journal of Computer Science Issues (IJCSI) , Vol. 9 No. 3 , p. 486 .

Nikhashem , S.R. , Yasmin , F. , Haque , A. and Khatibi , A. ( 2011 ), “ Study on customer perception towards online-ticketing in Malaysia ”, In Proceedings For 2011 International Research Conference and Colloquium , Vol. 1 , No. 1 , pp. 320 - 338 .

Pan , Y. and Zinkhan , G.M. ( 2006 ), “ Exploring the impact of online privacy disclosures on consumer trust ”, Journal of Retailing , Vol. 82 No. 4 , pp. 331 - 338 , doi: 10.1016/j.jretai.2006.08.006 .

Roman , S. ( 2007 ), “ The ethics of online retailing: a scale development and validation from the consumers’ perspective ”, Journal of Business Ethics , Vol. 72 No. 2 , pp. 131 - 148 , doi: 10.1007/s10551-006-9161-y .

Sivanesan ( 2017 ), “ A study on problems faced by customers in online shopping with special reference to Kanyakumari district ”, International Journal of Research in Management and Business Studies , Vol. 4 No. 3 , pp. 22 - 25 , available at: http://ijrmbs.com/vol4issue3SPL1/sivanesan.pdf

Tsiakis , T. ( 2012 ), “ Consumers’ issues and concerns of perceived risk of information security in online framework. The marketing strategies ”, Procedia – Social and Behavioral Sciences , Vol. 62 No. 24 , pp. 1265 - 1270 , doi: 10.1016/j.sbspro.2012.09.216 .

Wei , L.H. , Osman , M.A. , Zakaria , N. and Bo , T. ( 2010 ), “ Adoption of e-commerce online shopping in Malaysia ”, In 2010 IEEE 7th International Conference on E-Business Engineering , IEEE , pp. 140 - 143 .

Yazdanifard , R. and Godwin , N.W. ( 2011 ), “ Challenges faced by customers: Highlighting E-shopping problems ”, Paper presented at international Conference on Economics, Business and Marketing Management (CEBMM 2011) , Shanghai, China , available at: http://www.researchgate.net/profile/Assc_Prof_Dr_Rashad_Yazdanifard/publication/268507745_Challenges_faced_by_customers_Highlighting_E-shopping_problems/links/546d4ade0cf26e95bc3cb0a1/Challenges-faced-by-customers-Highlighting-E-shopping-problems.pdf ( accessed 20 March 2020 ).

Further reading

Grabner-Kräuter , S. and Kaluscha , E.A. ( 2003 ), “ Empirical research in on-line trust: a review and critical assessment ”, International Journal of Human-Computer Studies , Vol. 58 No. 6 , pp. 783 - 812 .

Nurfajrinah , M.A. , Nurhadi , Z.F. and Ramdhani , M.A. ( 2017 ), “ Meaning of online shopping for indie model ”, The Social Sciences , Vol. 12 No. 4 , pp. 737 - 742 , available at: https://medwelljournals.com/abstract/?doi=sscience.2017.737.742

Corresponding author

Related articles, we’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

103 Online Shopping Topic Ideas & Essay Examples

🏆 best online shopping topic ideas & essay examples, 👍 good online shopping topic ideas to research, 📌 most interesting online shopping topics to write about, ❓ research question about online shopping.

When it comes to choosing an essay topic, online shopping has plenty ideas to offer. That’s why we present to you our online shopping topic list! Here, you will find best hand-picked essay titles and research ideas.

But that’s not all of it! In addition to our shopping essay topics, we also offer free sample papers. Check them out!

• Online Shopping vs. Traditional Shopping The advent of internet shopping in the late nineties created a revolution in the retail industry. It is possible to know about the sizes, features, and costs of products in online and traditional shopping.
• Traditional vs. Online Shopping Traditional shopping involves shoppers physically entering a brick-and-mortar store or shopping mall to select items of their choice, pay for them in cash or by credit card, and either take delivery personally or have them […]
• Influence of Online Shopping Apps on Impulsive Buying Olsen et al.go further and confirms that online shopping apps have increased impulse buying due to the wealth of information they provide the consumer.
• Advantages of Online Shopping In addition to this, the number of people adapting to online shopping is expected to grow, due to the numerous benefits associated with it.
• International Students Attitudes Towards Online Shopping The researcher strived to answer three key questions, which sought to find out students’ attitudes towards online shopping, the nationality of students who make the largest number of online purchases, and the barriers that prevent […]
• Drawbacks and Benefits of Online Shopping One of the benefits of online shopping is that it makes the customer have quick access to items that are identical regardless of where he or she does the shopping for them.
• Consumer Behavior in Online Shopping On the one hand, earlier studies argue that purchase intention is the key motivator for the consumers. Qualitative method is based upon judgment and intuition of the experts in the matter and consumers.
• Product Reviews in Online Shopping The paper will discuss strategies used by online retailers in their product reviews as well as describe a research study that can be used to explore the relationship between customer comments and their buying habits.
• How Motivation Influences Online Shopping The Balanced Buyer: In this cluster, about a third of the sample was moderately driven by the desire to seek variety.
• Online Shopping Characteristics and Effectiveness Background information on online shopping will be presented, and the way on how to succeed in online shopping will be discussed. What are the details of online shopping DMC students should be aware of?
• Online Shopping as a Method of Supply Online shopping is the method of selling goods and services that allows individuals to sell goods directly over the internet. This mode of operation is better than the use of door-to-door sales people who can […]
• The Era of Online Shopping Today, online shopping has become a great phenomenon thanks to the rapid development of internet security technologies and a similar pace in the penetration of the World Wide Web.
• Amazon’s Success: Online Shopping Psychology One of the many factors contributing to Amazon’s success is its thorough understanding of its consumers, which is shown in the layout of its website and the numerous innovations it has brought to online retail.
• Saudi Women’s Perspective on Online Shopping Owing to the existence of different sites, the researcher examined the growth and expansion of the e-commerce segment in the nation.
• Consumer Behavior in Online Shopping: A Study of Aizawl The article shows the effective use of credibility of the authors, appropriate structure and organization, regional relevance of the cited literature, and functional illustrative material.
• The Effects of Online Shopping on Customer Loyalty For example, the study by Afrashteh, Azad, and Tabatabaei Hanzayy is dedicated to the concept of online shopping and the use of this electronic marketing technique to influence customer loyalty in conditions of the state […]
• Jordan’s Furniture Company and Online Shopping First of all, I would like to point out that Jordan’s Furniture is a furniture retailer in the Commonwealth of Massachusetts, the U.S.A.
• Survey Analysis: Phones vs. PC in Online Shopping The findings of the survey indicate that the majority of female online shoppers prefer using mobile phones to make purchases; both computer and mobile apps are used to shop online.
• Online Shopping and Its Advantages The decision of a customer to buy a product from a specific website depends on the reputation of the company and brand, which owns it.
• Amazon’s Online Shopping and Innovative Delivery The company started as an online seller of books, but later, Amazon became the platform for a variety of goods and services to sell.
• UK Consumer Attitudes Towards Online Shopping It means that delivery represents a vital component of the overall purchasing or service reception experience and contributes to the development of customer loyalty.
• Online Shopping Impact on the Global Retail Industry While the significance and the convenience of e-commerce are indisputable, it is important to study its impact on the traditional retail industry around the world to identify the challenges, which it has to withstand.
• Secure Online Shopping System Integration Therefore, the new service called SOSS, which is proposed in the management of the online ticketing business, will form part of the actual customer safety guarantee service.
• Peacock Fashion Company’s Online Shops The purpose of the paper will be to determine the characteristics and feelings of online shoppers as related to online fashion shopping in United Kingdom market.
• Online Shopping Impact on the Fashion & Design Industry In this report, the aim will be to determine the impact of online shopping on the fashion and design industry. The increased profitability of this industry means that the individual firms have the capacity to […]
• Consumer Science: Online Shopping in the United Arab Emirates In an attempt to identify these factors, the present study uses a mixed-methods methodology to show the importance of online shopping and how this concept has changed consumer habits on shopping in the UAE. The […]
• Online Shopping: Benefits and Drawbacks Essay The last major advantage of online shopping is that it assists the customer to find the best deal on a product.
• Online Shopping Platform for La Donna Boutique By using online services, La Donna cost of production will be reduced because it will be selling goods directly to the customers and this will make producers to get rid of costly intermediaries. The e-commerce […]
• Secure Online Shopping System Model on Customer Behavior The aim is to find respondents who are the potential, if not actual customers of our online products who fall within the category of youths and young adults described in the introduction.
• Service Marketing: Online Shopping Competition Their website allows customers to register with them and be able to do their shopping from the comfort of their homes.
• Online Shopping and Purchase Decision The above is a detailed explanation of the buying process for an online product specifically E-reader from Kindle. The customer will then evaluate the alternatives and make a purchase decision.
• Online Shop Business Plan One of the major aims of a supply chain management is to ensure that the goods used in manufacture are of the right quality and quantity; this goes ahead as it is reflected in the […]
• Consumer Attitudes Towards Online Shopping Since the online environment gives consumer a wider choice of products and product platforms from where to make their purchases, this study seeks to establish the exact consumer behaviour portrayed in an e-commerce environment and […]
• Online Shopping vs. Brick-And-Mortar Shopping
• The Need for Accelerated Knowledge Management Within Internet Banking and Online Shopping
• Using Online Shopping Codes to Save Money
• Online Shopping Increases Consumption Rate
• The Advantages and Disadvantages of Online Shopping
• The Consumer Society: Advertising and Online Shopping
• Understanding Egyptian Consumers’ Intentions in Online Shopping
• Online Shopping Services for Consumers and Businesses
• Online Shopping Will Replace Traditional Shopping
• Visiting Malls While Online Shopping Is Fun
• The Relationship Between Marketing Mix and Buying Decision Process on the Online Shopping in Thailand
• The Advantages and Risks of Online Shopping
• Walmart Online Shopping Information System
• The Most Famous Online Shopping Website In China
• Perceived Risk and Online Shopping Intention: A Study Across Gender and Product Type
• The Benefits and Disadvantages of Online Shopping
• Online Shopping Reviewers Are Not All That They Seem
• Analyzing Customer Satisfaction: Users Perspective Towards Online Shopping
• Australian Customers and Online Shopping
• Antique Motorcycle Online Shopping Options
• Relationship Between Convenience, Perceived Value, and Repurchase Intention in Online Shopping in Vietnam
• The Development and Validation of the Online Shopping Addiction Scale
• Television Advertising and Online Shopping
• Assessing Benefits and Risks of Online Shopping in Spain and Scotland
• Online Shopping: Effectiveness and Convenience
• The Legal Issues Surrounding Online Shopping
• Taobao Established Shopping From Home With Online Shopping
• The Pros and Cons of Online Shopping vs. Brick and Mortar Stores
• Why People Like Online Shopping
• Online Shopping Lifts Aramex Profits by 4% and Rent Cap Removal Hits Abu Dhabi
• What Influences Online Shopping Of Individuals From European Countries
• Perceived Value, Transaction Cost, and Repurchase-Intention in Online Shopping: A Relational Exchange Perspective
• Online Shopping Unexpected Impacts Are We Gaining More or Less
• Differentiation Between Traditional and Online Shopping
• Popular Websites For Online Shopping
• The Online Shopping Industry Has Changed The World
• Online Shopping: Product Availability and Logistics
• The Interactions Between Online Shopping and Personal Activity Travel Behavior: An Analysis With a Gps-Based Activity Travel Diary
• Statistics and Facts About Online Shopping
• Analysing Online Shopping Behaviour of Google Merchandising Store Customers
• How Effect of Freight Insurance on Consumers’ Attitudes Toward Online Shopping?
• Does Online Shopping Cause Us to Spend More Money?
• Does Freight Insurance Work in Online Shopping?
• What Are the Pros and Cons of Online Shopping?
• How Do E-Servicescapes Affect Customers’ Online Shopping Intention?
• What Are the Moderating Effects of Gender and Online Shopping Experience?
• How Online Shopping Behaviour Is a Priority Issue for Many?
• How Does Online Shopping Cause You to Spend More Money?
• How Has Online Shopping Become a Convenient and Efficient Time?
• What Effects Repurchase Intention of Online Shopping?
• What Influences Online Shopping of Individuals From European Countries?
• Why Are More Customers Switching to Online Shopping From Traditional Coursework?
• Why Do People Like Online Shopping?
• What Is the Cheapest Online Shopping Site?
• What Is Called Online Shopping?
• How Many Types of Online Shopping Are There?
• Is Online Shopping Cheaper Than In-Store?
• What Are the Disadvantages of Online Shopping?
• What Is the Advantage and Disadvantage of Online Shopping?
• Why Is Online Shopping Better?
• What Is the Importance of Online Shopping?
• How Is Online Shopping Helpful?
• What Are the Factors Influencing Online Shopping?
• Do Consumers Prefer Online Shopping?
• How Does COVID Affect Online Shopping?
• What Are the Benefits of Online Shopping?
• How Does Online Shopping Affect the Consumer?
• What Is the Theory of Online Shopping?
• How Has Online Shopping Changed the Way We Shop?
• How Does Online Shopping Affect the Economy?
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2024, February 29). 103 Online Shopping Topic Ideas & Essay Examples. https://ivypanda.com/essays/topic/online-shopping-essay-topics/

"103 Online Shopping Topic Ideas & Essay Examples." IvyPanda , 29 Feb. 2024, ivypanda.com/essays/topic/online-shopping-essay-topics/.

IvyPanda . (2024) '103 Online Shopping Topic Ideas & Essay Examples'. 29 February.

IvyPanda . 2024. "103 Online Shopping Topic Ideas & Essay Examples." February 29, 2024. https://ivypanda.com/essays/topic/online-shopping-essay-topics/.

1. IvyPanda . "103 Online Shopping Topic Ideas & Essay Examples." February 29, 2024. https://ivypanda.com/essays/topic/online-shopping-essay-topics/.

Bibliography

IvyPanda . "103 Online Shopping Topic Ideas & Essay Examples." February 29, 2024. https://ivypanda.com/essays/topic/online-shopping-essay-topics/.

• Apple Topics
• Woolworths Research Ideas
• Mobile Technology Paper Topics
• Google Paper Topics
• Yahoo Research Topics
• Laptop Ideas
• Nike Topics
• Walmart Topics

• Bangalore, India
• +91-9916527695
• [email protected]

Ultimate 70-Question Online Shopping Survey for E-Commerce Success

70 Online Shopping Questionnaire for E-commerce Businesses

In the era of digitalization, online shopping has become a common method of acquiring goods and services. It offers convenience, variety, and accessibility to customers throughout the world. With the proliferation of online stores, however, customer satisfaction has become a major concern for businesses. This article examines the key online shopping questionnaires that contribute to customer satisfaction in online shopping.

General E-Commerce Online Shopping Questionnaire

There is no one “best” general e-commerce online shopping questionnaire as it largely depends on the specific research objectives and target audience. However, here are some key elements to consider when creating a general e-commerce online shopping questionnaire:

• 1How often do you shop online?
• What is your preferred payment method for online purchases?
• How satisfied are you with the website’s user interface?
• How easy was it to find the products you were looking for?
• Were there any issues with the checkout process?
• Would you recommend this website to others?
• How satisfied were you with the customer service?
• Did the website provide enough information about the products?
• How long did it take for your order to arrive?
• How likely are you to make another purchase from this website?

Overall, a well-designed general e-commerce online shopping questionnaire can provide businesses with valuable insights into their customers’ online shopping behavior, preferences, and satisfaction levels. This information can be used to improve the online shopping experience, optimize marketing and sales strategies, and increase customer loyalty and retention.

Online Shopping Questionnaire On The Products

• How often do you purchase products from our website?
• Which product category do you usually purchase from?
• How satisfied are you with the quality of our products?
• Are there any specific products that you think we should add to our collection?
• How likely are you to recommend our products to someone else?
• How frequently do you purchase products from our competitors?
• What do you think about the pricing of our products?
• How satisfied are you with the variety of products available on our website?
• Do you feel that our products meet your expectations?
• Would you like to see more product reviews or ratings on our website?

Online Shopping Questionnaire About The Website

• How frequently do you visit our website for online shopping?
• How easy is it to navigate through our website?
• Were you able to find the products you were looking for easily?
• How would you rate the load time of our website?
• Are our product descriptions clear and informative?
• How would you rate the overall design of our website?
• Did you experience any issues while placing your order? If yes, please elaborate.
• How satisfied were you with the checkout process?
• Were you able to track your order easily?
• Is there anything you would suggest we improve on our website to enhance your online shopping experience?

The website for an eCommerce business is a valuable asset, and even if the products are of high quality, a poorly functioning or difficult-to-use website will cause visitors to quickly leave. In other words, the website plays a crucial role in attracting and retaining customers, and it must be optimized for functionality and user experience to ensure that customers stay and engage with the business.

Online Shopping Questionnaire Based On Customer Behavior

• How often do you shop online?
• What motivates you to shop online instead of visiting a physical store?
• How long do you typically spend browsing products before making a purchase?
• What factors influence your decision to purchase from a particular website?
• Have you ever abandoned a shopping cart before completing a purchase? If so, why?
• How often do you read customer reviews before making a purchase?
• How likely are you to recommend our website to a friend or family member?
• Do you prefer to shop on a mobile device or a desktop/laptop computer?
• Have you ever purchased on our website using a mobile device?
• How important is the ability to track your order and receive updates throughout the shipping process to you?

Gaining insight into users’ attitudes toward online shopping can provide valuable information about their purchasing behavior. While many people feel secure using their credit cards to shop online, there may be exceptions. Thus, it’s important to identify which payment methods customers are comfortable using to ensure a positive shopping experience.

Post-Purchase Online Shopping Questionnaire

• How was your overall shopping experience on our website?
• How satisfied were you with the quality of the products you purchased?
• Did the products you received match the descriptions on our website?
• How satisfied were you with the shipping/delivery process?
• Was there anything you found confusing or difficult during the checkout process?
• Did you have any issues with payment or billing?
• Was customer service able to assist you with any questions or issues you had?
• Is there anything else you would like to share about your experience shopping with us?

Online Shopping Questionnaire For Customer Support

Neglecting the quality of customer support can lead to customer dissatisfaction, and churn, and can even deter potential new customers. It is imperative to prioritize and provide adequate attention to customer support. One way to gauge customer satisfaction is by using metrics such as Net Promoter Score (NPS), Customer Satisfaction (CSAT), and Customer Effort Score (CES) through targeted survey questions to determine the level of customer satisfaction with the business.

• Was it easy to get in touch with our customer support team?
• Did our customer support staff solve your query or concern on time?
• How would you rate the professionalism and knowledge of our customer support staff?
• Was our customer support staff able to address all of your questions and concerns?
• Did our customer support staff provide clear and helpful solutions to your problems?
• How satisfied are you with the level of support you received from our customer support staff?
• Did our customer support team exceed your expectations?
• What improvements would you suggest for our customer support services?
• Did our customer support team follow up with you after resolving your query or concern?
• How likely are you to recommend our online store based on your experience with our customer support team?

Shipping-Related Online Shopping Questionnaire

Picture ordering a product and discovering that it will take two weeks for it to arrive. It’s important to take prompt and efficient action to ensure that the product is delivered on time, and not to be indifferent about it. It’s the online shopping company’s responsibility to ensure timely delivery of the product.

• Did you receive your order on time?
• Were you satisfied with the shipping options offered during checkout?
• Did you receive regular updates regarding the status of your shipment?
• Were you satisfied with the condition of your order upon delivery?
• Was the shipping cost reasonable?
• Did you encounter any issues with the shipping process?
• Was the shipping carrier used for your order reliable?
• Were the shipping and handling fees clearly communicated during checkout?
• How important is fast shipping to you when making an online purchase?
• Would you be willing to pay extra for expedited shipping in the future?

Why Online Shopping Survey is Important

Online shopping surveys are an essential tool for businesses and organizations to understand the preferences, behavior, and expectations of their customers who shop online. With the exponential growth of e-commerce in recent years, businesses must have a deep understanding of the online shopping experience to make informed decisions that can help grow their business.

Here are some of the key reasons why online shopping surveys are essential:

Gain insights into customer preferences: Online shopping surveys allow businesses to gain insights into customer preferences by asking specific questions about product categories, pricing, payment options, and delivery methods. By collecting this information, businesses can tailor their offerings to better meet customer needs and preferences.

Understand customer behavior: Online shopping surveys can help businesses understand how their customers behave when they shop online. For example, businesses can gain insights into the frequency of purchases, the average order value, and the time spent browsing different product categories. By understanding customer behavior, businesses can optimize their online store to improve customer engagement, increase sales, and build customer loyalty.

Identify areas for improvement: Online shopping surveys can help businesses identify areas for improvement in their online shopping experience. By asking customers about their pain points and frustrations, businesses can address these issues and improve their online store’s functionality and design to enhance the customer experience.

Measure customer satisfaction: Online shopping surveys can help businesses measure customer satisfaction with their online shopping experience. By collecting feedback from customers, businesses can identify areas where they are excelling and areas where they need to improve to meet customer expectations.

Gather feedback on new products: Online shopping surveys can help businesses gather feedback on new products they are considering launching. By asking customers about their preferences and opinions on new products, businesses can make informed decisions about product development and marketing.

In conclusion, customer surveys play a vital role in understanding the needs, expectations, and preferences of customers in the context of online shopping. By asking targeted questions about various aspects such as product quality, website usability, customer support, and delivery times, businesses can gain valuable insights to improve their offerings and provide a better customer experience. The data collected from customer surveys can help businesses make informed decisions that drive customer satisfaction, loyalty, and revenue. Thus, conducting customer surveys is an essential tool for any online shopping company that wishes to succeed and stay ahead of the competition.

Share this article

Our recent blogs.

Top 5 Benefits of a Mystery Shopping Program – Hs Brands Asia

The Future of Customer Experience Management

8 Benefits of Market Research for Small Businesses by Hs Brands Asia

5 ways to Measure Customer Satisfaction

Best AI Apps to Help Mystery Shoppers in their Tasks

Brand Protection Services and Solutions for Your Business

Get in touch with us, get in touch with us.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 03 February 2024

From storefront to screen: an in-depth analysis of the dynamics of online for offline retailing

• Hyeon Jo   ORCID: orcid.org/0000-0001-7442-4736 1 &
• Youngsok Bang   ORCID: orcid.org/0000-0003-2664-8414 2  

Humanities and Social Sciences Communications volume  11 , Article number:  209 ( 2024 ) Cite this article

792 Accesses

1 Altmetric

Metrics details

• Business and management

Within the rapidly changing online sphere, the significance of online for offline (O4O) commerce platforms in directing consumer choices is evident. The purpose of this research is to examine the factors that influence consumer shopping motives within the context of O4O commerce. The value of this study lies in its enhancement of our understanding of how various factors within the O4O model impact consumer decision-making processes. This offers significant insights for businesses and marketers, enabling them to strategize more effectively for customer engagement and retention. The study analyzed a dataset of 272 consumers who were familiar with O4O platforms, utilizing the Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology, specifically conducted through the SmartPLS software program. The results revealed that effort expectancy has a connection with continuance intention but remains unrelated to shopping intention. In contrast, performance expectancy was influential in both continuance and shopping intentions. Social influence showed a strong relationship with continuance intention, yet lacked significance with shopping intention. Facilitating conditions primarily directed continuance intention, without influencing shopping intention. The study also validated the relationship between continuance intention and shopping intention, highlighting innovativeness as a key moderator in the bond between social influence and continuance intention. These insights offer valuable perspectives for industry professionals, elucidating factors that drive consumer interactions on O4O commerce platforms.

Similar content being viewed by others

Enhancing customer value co-creation and stickiness in social commerce: integrating PLS-SEM and NCA for deeper insights into customer-to-customer dynamics

Retail mix instruments influencing customer perceived value and customer engagement: a conceptual framework and research agenda

Factors influencing continuance intention to use mobile banking: an extended expectation-confirmation model with moderating role of trust

Introduction.

The ever-accelerating pace of technological advancements, particularly in the information technology (IT) sector, has significantly reshaped the consumer shopping experience and retail environment (Oláh et al. 2019 ). This digital revolution has given rise to innovative business models such as online-to-offline (O2O) and omnichannel retail strategies (Chen et al. 2016 ; Lee et al. 2022 ). O2O commerce is a business strategy that draws potential customers from online channels to make purchases in physical stores (Pan et al. 2019 ). Omnichannel refers to a multichannel approach to sales that aims to provide the customer with a seamless shopping experience, whether the customer is shopping online from a desktop or mobile device, or in a bricks-and-mortar store (Verhoef et al. 2015 ). These models are characterized by their integrative nature, allowing consumers to transition effortlessly between online and offline shopping channels, thereby enhancing shopping convenience and enriching the overall customer experience (Piotrowicz and Cuthbertson 2014 ). These retail strategies have attracted considerable scholarly attention and have been the focus of extensive empirical investigation (Hsieh 2017 ; Juaneda-Ayensa et al. 2016 ; Schiessl et al. 2023 ). Despite the plethora of research on O2O and omnichannel strategies, there is an emergent trend in the retail sector that remains largely underexplored: the online for offline (O4O) model (Son 2019 ). The O4O model represents a paradigm shift in retailing as it leverages online capabilities to augment the physical shopping environment, creating a new retail dimension that warrants scholarly investigation.

O4O is a recent business model that leverages online data and technologies to enhance offline shopping experiences and improve business performance in brick-and-mortar stores (Sohn et al. 2023 ). This model represents a paradigm shift from traditional and online retail practices, emphasizing a synergistic blend of digital and physical customer interactions (Jo 2023b ). In O4O, online platforms are not just a sales channel but are used as powerful tools to optimize offline customer experiences. Online companies utilize their digital strengths, including data analytics, personalization technologies, and mobile applications, to advance into the offline market (Son 2019 ). Characteristics that distinguish O4O from other models, such as O2O, include the following. First, O4O is not merely about connecting online and offline channels but deeply integrating them to create a seamless and enhanced shopping experience. Online data, such as customer preferences and behavior patterns, are utilized to tailor the offline shopping environment and services (Kim et al. 2018 ). Second, unlike O2O, which often involves making reservations, orders, and reception of offline services online, O4O emphasizes improving the in-store customer experience using advanced IT solutions. This might include personalized recommendations, automated checkout systems, or innovative in-store navigation systems (Cui and Yang 2020 ). Overall, O4O represents a symbiotic relationship between the digital and physical retail world, where online technologies and data are harnessed to reinvent and elevate the offline shopping experience. Figure 1 illstrates the O2O and O4O models.

O2O and O4O.

Many companies are adopting O4O strategies to generate new sales in physical stores by applying online service technology. Musinsa, a South Korean online fashion commerce company, has started offering O4O services by linking its online and offline platforms to customers purchasing its products (MaeilBusinessNewsKorea 2020 ). Customers can order products from the online store by 7 pm and collect them from the physical store the same day. If they choose the pick-up service (known as Mutan), they can retrieve their products from lockers installed outside the store, even after business hours. Amazon epitomizes the O4O standard with its Amazon Go initiative and new bookstores. Amazon Go is a grocery store that dispenses with cashiers or checkout lines by optimally integrating IT within the brick-and-mortar setup (Özdemir and Hekim 2018 ). Amazon’s new offline bookstore in New York utilizes vast amounts of data from Amazon users to display books that have garnered positive online reviews (Kim et al. 2022a ). Freshippo, part of the Alibaba Group, is an offline grocerant where visitors can use a mobile app to obtain product information and recipe ideas (Yoo et al. 2020 ). Customers can order via the app both within and outside the store, enhancing the efficiency of in-store shopping. These companies have successfully enhanced customer satisfaction and boosted sales in physical stores by effectively leveragin online IT.

The unified theory of acceptance and use of technology (UTAUT) has proven to be an efficient and robust model for predicting and explaining technology acceptance and usage behavior across diverse contexts and technologies (Huang 2023 ; Khashan et al. 2023 ; Venkatesh et al. 2003 ). The UTAUT is a model that posits four key determinants of technology use intention and behavior: performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al. 2003 ). This paper employs the theory for several reasons. First, UTAUT integrates eight prominent theories, including the technology acceptance model (TAM) (Davis 1989 ) and the theory of planned behavior (TPB) (Ajzen 1991 ), providing a comprehensive framework for understanding the adoption and use of technology (Venkatesh et al. 2003 ). Its inclusiveness and robustness make it particularly well-suited for studying new and evolving technologies, such as the O4O business model. Second, UTAUT identifies four core determinants of user acceptance and usage behavior—performance expectancy, effort expectancy, social influence, and facilitating conditions. These constructs encapsulate a broad range of factors influencing technology acceptance, enabling an in-depth exploration of consumer behavior in the O4O context (Dwivedi et al. 2019 ; Venkatesh et al. 2003 ). Lastly, empirical evidence supports the predictive power of UTAUT. The model explains around 70% of the variance in behavioral intention to use and about 50% in technology use, outperforming individual legacy models (Dwivedi et al. 2019 ; Venkatesh et al. 2003 ). This robustness adds confidence to the applicability of UTAUT in predicting and explaining consumers’ continuance and shopping intentions in the O4O environment. By applying the UTAUT model in the context of O4O, this research aims to provide valuable insights into consumer behavior within this emerging business model.

Existing literature has indicated that personal innovativeness plays an influential role in shaping consumers’ technology acceptance behavior (Agarwal and Prasad 1998 ; García de Blanes Sebastián et al. 2022 ; Lee 2019 ; Mezghani 2018 ; Senali et al. 2022 ; Wu and Yu 2022 ). Innovativeness refers to an individual’s predisposition to be open to new ideas and to adopt innovations earlier than other members of a social system (Rogers 2010 ). Given the emerging nature of the O4O model, consumer innovativeness could significantly influence the adoption and continued use of this technology. Moreover, exploring the moderating influence of innovativeness introduces an additional level of intricacy and subtlety to the research. Prior studies indicate that consumer innovativeness can interact with other factors to influence technology adoption and usage behavior, acting as a moderator (Thakur and Srivastava 2014 ; Yoon and Rolland 2012 ). For instance, highly innovative individuals may perceive less risk in trying new technologies, potentially enhancing the effect of performance expectancy on adoption intention. As a result, the investigation of the moderating influence of innovativeness within the O4O context has the potential to shed light on the distinct impacts of UTAUT constructs on technology acceptance, offering a more profound and intricate understanding of the subject matter. The primary objective of this study is to explore the O4O business model and its impact on consumers’ shopping intentions and behaviors. To attain this objective, this paper poses the following research questions:

RQ1: How do the main factors of UTAUT (effort expectancy, performance expectancy, social influence, and facilitating conditions) and innovativeness influence continuance and shopping intentions in the O4O context?

RQ2: What is the moderating effect of innovativeness on the relationship between UTAUT factors and consumers’ continuance intentions within the O4O model?

RQ3: How does the continuance intention of consumers influence their shopping intention in the O4O context?

The primary objective of this study is to explore and understand the influence of the UTAUT factors and user innovativeness on the continuance and shopping intentions in the context of the O4O retail model. Ultimately, the purpose of this research is to provide empirical insights that can aid businesses employing the O4O model in enhancing the effectiveness of their strategies to boost customer retention and shopping intentions, thereby improving their overall performance.

This research seeks to address the gaps in current studies and make novel contributions in several respects. First, it examines the behavior of O4O users—an area yet to be extensively studied—by employing the UTAUT (Venkatesh et al. 2003 ). Most recent studies on e-commerce have focused predominantly on O2O and omnichannel (Gu et al. 2019 ; Kang and Namkung 2019 ; Park and Kim 2021 ; Piotrowicz and Cuthbertson 2014 ). Considering O4O is a relatively new concept, adopted primarily by advanced companies, empirical analysis is essential. UTAUT, capable of explaining both the adoption and use of technology, is apt for understanding O4O. Since O4O places more emphasis on the offline store experience compared to O2O, the findings of this study will offer new and meaningful implications. Second, this research introduces the innovativeness of O4O users as a novel variable and examines its moderating effect. To use O4O, consumers must familiarize themselves with not only the app functions on mobile phones but also the various IT installations in physical stores. During this process, the innovativeness of users may significantly influence overall decision-making. Exploring the moderating effect of innovativeness on UTAUT factors will clarify the specific mechanisms of impact for each construct. Lastly, this study considers both continuance intention and shopping intention. While previous IT-related studies primarily set continuance intention as an explanatory variable (Jo 2023a ; Marinković et al. 2020 ; Santosa et al. 2021 ), marketing studies mainly used purchase or shopping intention as the final variable (Chang and Chen 2021 ; Hanjaya et al. 2019 ; Lăzăroiu et al. 2020 ; Li et al. 2020a ; Pillai et al. 2020 ). As O4O prompts consumers to purchase in physical stores through IT, this study conducts an integrated analysis, encompassing both continuance intention and shopping intention.

Future research could explore a broad range of potential areas. These include extending the context of the study beyond the O4O retail model to include other digital and omnichannel retail models; incorporating additional moderating variables; conducting longitudinal studies to gain insights into the evolution of user perceptions over time.

The rest of the article is structured as follows: The section “Literature review” reviews the literature, the section “Theoretical development and research hypotheses” introduces research models and hypotheses, the section “Methodology” discusses the development of scales and research subjects, the section “Analysis and Results” presents the analysis results, the section “Discussion” details the discussion of the results, and finally, the section “Conclusion” outlines implications, limitations, and future research directions.

Literature review

The UTAUT framework is used to comprehensively explain the acceptance and utilization behavior of IT users (Venkatesh et al. 2003 ). According to the theory, behavioral intention for IT is determined by performance expectancy, effort expectancy, and social influence. In turn, behavioral intention and facilitating conditions lead to user behavior. In particular, the user’s gender, age, experience, and voluntariness of use moderate the effects of exogenous variables on behavioral intention and use behavior. Since then, UTAUT2 has been newly revised and introduced to account for consumer behaviors (Venkatesh et al. 2012 ). Hedonic motivation, price value, and habit were added to the existing UTAUT. Voluntariness of use was excluded from the moderating variables. Unlike the TAM (Davis 1989 ), expectation-confirmation model (ECM) (Bhattacherjee 2001 ), and information system (IS) success model (DeLone and McLean 2003 ), UTAUT and UTAUT2 have the advantage that they can comprehensively explain the process of both adopting and using IT. For these reasons, those theories have been extensively employed in many contexts in the field of IT and marketing (Balakrishnan et al. 2022 ; Patil et al. 2020 ; Tam et al. 2020 ).

Online commerce

A vast body of previous research has introduced the UTAUT to explain consumer behavior such as acceptance and use of IT in the purchasing environment (Escobar-Rodríguez and Carvajal-Trujillo 2014 ; Juaneda-Ayensa et al. 2016 ; Li et al. 2020b ; Madan and Yadav, 2018 ; Mosquera et al. 2018 ). Earlier works on online shopping behavior primarily focused on e-commerce around 2010 (Chiemeke and Evwiekpaefe 2011 ; Min et al. 2008 ; Uzoka, 2008 ). Recent studies around 2020 have paid attention to mobile commerce (Madan and Yadav 2018 ; Salimon et al. 2021 ). Santosa et al. ( 2021 ) reflected all factors in UTAUT2 except hedonic motivation to clarify the preceding factors of continuance intention of 40–74 age users in the context of digital payment. They unveiled that all 6 proposed variables impact continuance intention via satisfaction. Madan and Yadav ( 2016 ) explored antecedents of behavioral intention to accept mobile wallets by introducing the constructs in UTAUT. They validated that behavioral intention is shaped by performance expectancy, social influence, facilitating conditions, perceived value, perceived risk, and perceived regulatory support. Afterward, Madan and Yadav ( 2018 ) proposed a theoretical framework based on UTAUT2 to describe the behavioral intention and actual use of mobile shopping. They revealed that behavioral intention is determined by facilitating conditions, hedonic motivation, perceived critical mass, and personal innovativeness. They also found that there are partially significant differences in path coefficients according to age and gender. Doan ( 2020 ) identified the leading factors of online purchase intention by adopting UTAUT. It was demonstrated that all four constructs in UTAUT are the vital precursors of purchase intention. Salimon et al. ( 2021 ) incorporated TAM, UTAUT, and Technology-Organization-Environment (TOE) framework to clarify the factors affecting the adoption of small and medium businesses. The authors mentioned that self-efficacy, computer anxiety, and m-commerce knowledge are the key predictors of adoption.

As a multitude of companies uses various channel provision strategies by using advanced mobile devices, research on omnichannel has been actively conducted. Juaneda-Ayensa et al. ( 2016 ) integrated TAM, UTAUT, and UTAUT2 to elucidate consumer purchase intentions in an omnichannel context. They newly suggested personal innovativeness and perceived security as the leading factors of purchase intention. It was found that purchase intention was significantly affected by performance expectancy, effort expectancy, and personal innovativeness. Mosquera et al. ( 2018 ) employed UTAUT2 to predict behavioral intention and use behavior toward smartphones in an omnichannel environment. They figured out that behavioral intention is significantly influenced by performance expectancy, hedonic motivation, and habit. Moreover, it was found that user behavior is formed by behavioral intention and habit. The authors investigated the moderating roles of age on the hypothesized paths. There were partially significant differences between millennials and non-millennials. Kazancoglu and Aydin ( 2018 ) conducted a qualitative exploratory study to explicate consumers’ purchase intention toward omnichannel shopping. They discovered 12 themes about the intentions toward omnichannel. The six themes of them were similar to the components in UTAUT2. The authors also drew 6 additional factors (perceived trust, anxiety, perceived risk, situational factors, privacy concerns, and need for interaction) as the precursors of intention to shop in omnichannel. Park and Kim ( 2021 ) cast light on consumers’ personality traits in explaining the adoption behavior toward omnichannel. They proposed service integration, information integration, information consistency, and perceived effectiveness as the contributors to use intention. The significance of path coefficients was shown to be different according to the level of need for cognition of consumers. Among the suggested constructs, only perceived effectiveness has a significant correlation with use intention in all groups. Kim et al. ( 2022b ) included personal innovativeness in the UTAUT model combined with task-technology-fit to understand the consumers’ omnichannel behavior. They demonstrated that all four UTAUT factors positively influence usage intention through task-technology-fit. Demographic variables such as gender, age, and income did not moderate the effects of predictors on user behavior.

In summary, previous studies have explained consumer behavior in various omnichannel contexts using UTAUT. However, existing works did not focus on O4O and did not verify the moderating effect of the innovativeness of customers. The current study examines how innovativeness moderates the effects of factors of UTAUT on the continuance intention and shopping intention of consumers in the O4O domain.

Theoretical development and research hypotheses

Figure 2 illustrates the research model employed in this study. The paper proposes that effort expectancy, performance expectancy, social influence, facilitating conditions, and innovativeness influence both continuance intention and shopping intention within the context of O4O platforms. Additionally, it posits that continuance intention has an impact on shopping intention. Lastly, the current study suggests that innovativeness moderates the effects of effort expectancy, performance expectancy, social influence, and facilitating conditions on continuance intention.

Research model.

Effort expectancy (EFE)

Effort expectancy is conceptualized as the extent of ease related to consumers’ use of various touchpoints during the buying experience (Juaneda-Ayensa et al. 2016 ). According to UTAUT, effort expectancy is a critical determinant of user acceptance and use of technology (Venkatesh et al. 2003 ). As such, when considering the context of O4O, the ease and effortlessness of using the platform should play a pivotal role in influencing consumers’ continuance intention and shopping intention. Existing studies have demonstrated the impact of effort expectancy on continuance intention in various contexts (Marinković et al. 2020 ; Santosa et al. 2021 ; Tam et al. 2020 ). Marinković et al. ( 2020 ) found effort expectancy to significantly influence continuance intention in mobile shopping environments. Likewise, Santosa et al. ( 2021 ) confirmed the relationship between effort expectancy and continuance intention in digital payment. Regarding shopping intention, Chatterjee et al. ( 2019 ) demonstrated that effort expectancy significantly influences shopping intention in the context of accomodance. Doan ( 2020 ) also confirmed a significant relationship between effort expectancy and shopping intention in the context of e-commerce. If customers perceive that using an O4O platform is straightforward, they are more likely to continue using it, hence a possible positive relationship between effort expectancy and continuance intention. Similarly, if the O4O platform is effortless to use, consumers may be more likely to shop through the platform as they could navigate, find products, and complete transactions with ease. Given the above, this research puts forward the conjectures that effort expectancy significantly influences both the continuance intention and shopping intention of consumers in the context of O4O.

H1a. Effort expectancy has a positive and significant effect on continuance intention.

H1b. Effort expectancy has a positive and significant effect on shopping intention.

Performance expectancy (PFE)

Performance expectancy means the extent to which utilizing technology helps the user in carrying out specific tasks (Venkatesh et al. 2012 ). It is essentially the consumer’s perceived usefulness of the system, which has been identified as a critical determinant of technology acceptance and use in UTAUT. In the context of O4O, consumers’ performance expectancy of the platform can greatly influence their continuance intention. Several studies have found a significant relationship between performance expectancy and continuance intention across different domains. Individuals exhibiting a greater degree of performance expectancy are generally more inclined to perpetually engage with mobile applications (Tam et al. 2020 ). The expectancy of performance enhances the frequency of customers utilizing their smartphones while in stores (Mosquera et al. 2018 ). Furthermore, performance expectancy has been identified as a significant factor in increasing purchasing intentions (Juaneda-Ayensa et al. 2016 ), as well as shopping intentions (Ertz et al. 2022 ). If consumers perceive the O4O platform to be useful and beneficial in aiding their shopping process, they are more likely to continue using it, implying a potential positive association between performance expectancy and continuance intention. Furthermore, when consumers perceive the platform as valuable in enhancing their shopping experience, such as offering superior product information or comparison features, they may be inclined to actualize their shopping intentions on the platform. Therefore, the current study suggests that performance expectancy serves as the key determinant of continuance intention and shopping intention.

H2a. Performance expectancy has a positive and significant effect on continuance intention.

H2b. Performance expectancy has a positive and significant effect on shopping intention.

Social influence (SOI)

The concept of social influence pertains to the degree to which individuals believe that significant others in their lives endorse the usage of a specific technology (Escobar-Rodríguez and Carvajal-Trujillo, 2014 ). This factor plays a crucial role in technology acceptance models, particularly in the UTAUT framework. According to Venkatesh et al. ( 2003 ), social influence affects users’ acceptance and use of technology by altering their perceptions about what is considered standard or expected behavior within their social groups. Social influence has been shown to enhance the behavioral intention towards adopting m-commerce (Yang 2010 ; Yang and Forney 2013 ). Additionally, the sustained intention of m-shopping users is significantly driven by social influence (Yang and Forney 2013 ). When individuals feel a greater level of influence from their social surroundings, their propensity to purchase tends to increase (Venkatesh et al. 2012 ). In the context of O4O, where online platforms enhance offline shopping experiences, consumer behaviors are likely influenced by their social networks. If a consumer’s peers or social circle positively perceive and use O4O platforms, they are more likely to continue using them and have higher shopping intentions. Therefore, it is hypothesized that social influence significantly impacts both the continuance intention and shopping intention in the O4O context.

H3a. Social influence has a positive and significant effect on continuance intention.

H3b. Social influence has a positive and significant effect on shopping intention.

Facilitating conditions (FCC)

Facilitating conditions are defined as the extent to which consumers perceive the availability of resources and support that assist them in a specific behavior, such as using O4O platforms (Brown and Venkatesh 2005 ). A positive relationship exists between facilitating conditions and the application of technology in shopping scenarios (Mosquera et al. 2018 ). When consumers perceive facilitating conditions to be favorable, they are more likely to use shopping apps (Hew et al. 2015 ; Marriott et al. 2017 ). A stronger intention to shop is observed among consumers who perceive a more comprehensive range of facilitating conditions (Madan and Yadav 2018 ). Based on the insights from these studies, this research anticipates that facilitating conditions enhance the degree of both continuance intention and shopping intention. When consumers find that supportive measures, like customer service and technical support, are in place to aid in the use of the O4O platform, they are likely to have higher continuance intentions. This implies that facilitating conditions could be positively associated with continuance intention. Furthermore, the ease and smoothness of the shopping process, facilitated by these supportive measures, may also lead to increased shopping intentions, indicating a potential positive relationship between facilitating conditions and shopping intention in the O4O environment. Based on the above, this research posits that facilitating conditions amplify the extent of continuance intention and shopping intention.

H4a. Facilitating conditions have a positive and significant effect on continuance intention.

H4b. Facilitating conditions have a positive and significant effect on shopping intention.

Innovativeness (INO)

Innovativeness is described as the extent to which an individual is open to adopting new products and exploring new experiences (Midgley and Dowling 1978 ). This characteristic has been recognized as a critical determinant of IT users’ intentions to continue using a technology or service (Jo 2023a ). In the retail context, consumer innovativeness has been identified as a crucial driver of the intention to use services (Hwang et al. 2019 ). Furthermore, a high level of consumer innovativeness has been linked to greater purchase intentions in an omnichannel setting (Juaneda-Ayensa et al. 2016 ; Ryu 2019 ). Consumers who are more innovative are more likely to continuously use O4O platforms because of their natural curiosity and desire to utilize emerging technologies. This tendency extends to their shopping intention as well. O4O platforms, by providing an innovative shopping method, are more likely to stimulate the shopping intentions of innovative consumers. Consequently, within the O4O context, it is plausible to suggest that consumer innovativeness has a significant relationship with both continuance intention and shopping intention, adhering to the underlying principles of the UTAUT model.

H5a. Innovativeness has a positive and significant effect on continuance intention.

H5b. Innovativeness has a positive and significant effect on shopping intention.

Continuance Intention (COI)

Continuance intention is an individual’s perceived likelihood of persisting in using an IS to achieve a specific goal. Increased continuance intention among users is directly linked to higher actual usage of the technology (Kim 2018 ). Furthermore, consumers who discern a greater utility in a shopping platform tend to display stronger purchase intentions (Fu et al. 2018 ). The more consumers engage with O4O services, the more they are presented with opportunities to purchase, which consequently amplifies their shopping intention. When consumers exhibit a high continuance intention, it implies they find the O4O platform valuable and are willing to continue using it. This continual use exposes consumers to more purchasing opportunities within the O4O platform, thus promoting their shopping intentions. Therefore, within the context of O4O, this study posits that a strong continuance intention significantly enhances shopping intention, aligning with the inherent logic of behavioral intention in the UTAUT framework.

H6. Continuance intention has a positive and significant effect on shopping intention.

Moderating effects of innovativeness

Previous research using the UTAUT model to study consumer behavior typically focused on age and gender as the primary moderating variables (Madan and Yadav 2018 ; Mosquera et al. 2018 ). However, as shopping IT becomes integral to the purchasing process in the e-commerce domain, innovativeness could be a critical factor influencing purchasing decisions. Notably, consumers who exhibit high levels of innovativeness tend to seek and adopt new solutions within multichannel contexts (Konuş et al. 2008 ). Their innovativeness significantly influences purchase intention in omnichannel settings (Juaneda-Ayensa et al. 2016 ). Therefore, it is proposed that innovativeness may moderate the relationships between UTAUT constructs (effort expectancy, performance expectancy, social influence, facilitating conditions) and continuance intention. Consumers with a high degree of innovativeness may perceive less effort, expect better performance, be more influenced by social context, and see more facilitating conditions in the O4O context, thereby enhancing their continuance intentions. This leads to the assumption that innovativeness could play a significant role in moderating these relationships.

H7a. Innovativeness significantly moderates the effect of effort expectancy on continuance intention.

H7b. Innovativeness significantly moderates the effect of performance expectancy on continuance intention.

H7c. Innovativeness significantly moderates the effect of social influence on continuance intention.

H7d. Innovativeness significantly moderates the effect of facilitating conditions on continuance intention.

Methodology

Instrument development.

To ensure the validity of the proposed constructs, the measurement indicators were meticulously derived from established literature within the fields of IS and marketing. The measures were adjusted to work in the O4O construct. For instance, the indicators for effort expectancy were adopted from Venkatesh et al. ( 2012 ), reflecting the ease of learning and using O4O platforms. Similarly, performance expectancy items were sourced from the same study, focusing on the productivity and usefulness of O4O. Social influence items, also from Venkatesh et al. ( 2012 ), evaluate the perceived support and approval from significant others regarding the use of O4O. Facilitating conditions items were adapted from Polites and Karahanna ( 2012 ), emphasizing the resources and compatibility necessary for O4O usage. The innovativeness construct drew upon Agarwal and Prasad ( 1998 ), capturing the willingness to experiment with new technologies. For continuance intention, the items were sourced from Bhattacherjee ( 2001 ), reflecting the desire to persist in using O4O services. Finally, the shopping intention construct was based on items from Pillai et al. ( 2020 ), focusing on the propensity to use O4O services for shopping purposes. Table A1 presents all the measurement items for the constructs.

The questionnaire was initially developed by the authors. It was then translated from English into Korean by a Korean expert fluent in English. The response results of the survey were translated into English again. The two English versions of the questionnaire had only slight differences that were adjusted by the author. All variables except for demographic information and frequency were gauged using a 7-point Likert scale. Academic and industry professionals in IS and marketing thoroughly refined it, assuring content validity. Before distributing the questionnaires, a pilot study of 15 participants was carried out (Akter et al. 2010 ). Their feedback played a valuable role in completing the final questionnaire by revising the logical arrangement, ambiguity of terms, and simplicity of sentences.

Data collection

The data collection process for this research involved gathering information from O4O users to empirically validate the analytical model. The data was collected over two distinct time periods, each utilizing different methods. During the first data collection period, a market research organization was employed to conduct the data collection. This organization is a reputable survey institute in South Korea and possesses an online panel consisting of 1.3 million individuals. In the third week of March 2022, an online link containing the survey was distributed to the O4O users through this organization’s online panel. For the second data collection period, data were collected using a convenience sampling method in June 2023 by the authors. The convenience sampling method involves selecting participants who are readily available and willing to participate. By utilizing this method and collecting data at a different time from the first period, the aim was to enhance the generalizability of the research findings. Prior to participating in the survey, respondents were presented with a question on the first page of the online questionnaire, asking whether the results of the analysis through the survey could be published in an academic journal. Only those who agreed to the publication of the results were allowed to proceed and participate in the survey. This step ensured that the respondents were genuinely interested in contributing to the study. After data collection, insincere or unreliable responses were eliminated from the dataset, resulting in a final sample size of 272 responses.

The demographic distribution of the final sample is presented in Table 1 , which provides information on the gender, age, and education level of the participants. Among the final samples, 35.3% were male, and 64.7% were female. In terms of age, the majority of respondents were in their 20 s (39.7%), followed by those in their 30 s (27.9%), 40 s (22.8%), 50 s (7.0%), 60 s (2.6%), and other age categories. Regarding education, the highest proportion of participants had an undergraduate degree (61.8%), followed by high school (23.9%) and graduate degrees (14.3%).

Analysis and results

The research model was analyzed using partial least squares structural equation modeling (PLS-SEM) through the SmartPLS 4 software. PLS-SEM was chosen as an appropriate statistical tool for a few critical reasons. First, PLS-SEM is particularly suitable for this study given the nascent and less explored nature of the O4O phenomenon. As O4O platforms are not yet widely used among consumers, acquiring a large sample size can pose a considerable challenge. This limitation is important because traditional covariance-based SEM methods often require large sample sizes to produce reliable results (Hair et al. 2017 ). PLS-SEM, in contrast, is a more robust method for smaller sample sizes and is well-suited to exploratory research in emerging fields. It does not impose strict requirements on sample size (Hair et al. 2021 ). This makes it an ideal technique for analyzing data from relatively under-researched and underused platforms like O4O, where it may be difficult to obtain a large sample size. Second, as it is a variance-based method, PLS-SEM is particularly useful when the goal of the research is predicting key target constructs or identifying key “driver” constructs (Hair et al. 2019 ). In this study, it was essential to identify the constructs that significantly influence continuance intention and shopping intention in the O4O context, thus making PLS-SEM the ideal choice. Third, PLS-SEM makes no assumptions about data distribution and can efficiently handle complex models, including those with second-order constructs and formative measurement models (Hair et al. 2019 ). Given the complexity of our research model and the incorporation of the moderating effect of innovativeness, the use of PLS-SEM was justified. Lastly, PLS-SEM is known for its robustness against potential multi-collinearity issues among predictors (Hair et al. 2019 ), which was essential considering the multiple constructs being analyzed simultaneously in this research. Based on the above series of evidence, this study utilized PLS-SEM for analysis.

The validation of the research model proceeded in two stages: (1) An evaluation of the measurement model, and (2) an evaluation of the structural model.

Measurement model

The present study confirmed the convergent validity, reliability, and discriminant validity of the measurement model. The factor loadings ranged from 0.771 to 0.945 and were all statistically significant at the p  = 0.001 levels, strongly presenting a satisfactory level of convergent validity (Bagozzi et al. 1991 ). Scale reliability was assessed using composite reliability (CR) and Cronbach’s alpha. Cronbach’s alpha and CR estimates of all of the constructs exceeded the recommended minimum value of 0.7 (Nunnally 1978 ), suggesting high construct reliability. Finally, the square root of the AVE of each construct was compared to the correlation between the construct and other constructs to examine discriminant validity. All the square roots of AVE are higher than the off-diagonal entries in the corresponding columns and rows, achieving discriminant validity. Table 2 describes the test results of reliability and validity.

The discriminant validity of the constructs in our study was assessed using both the Fornell and Larcker ( 1981 )’s criterion and the Heterotrait-Monotrait (HTMT) ratio of correlations (Henseler et al. 2015 ). According to the Fornell-Larcker criterion, the square root of the AVE for each construct (shown on the diagonal in Table 3 ) should be greater than its highest correlation with any other construct, ensuring that each construct is more strongly related to its indicators than to others. Our results meet this criterion, as demonstrated in Table 3 .

Further, the HTMT criterion was applied as an additional measure of discriminant validity (Henseler et al. 2015 ). Table 4 shows the HTMT values for each pair of constructs. As recommended by Henseler et al., HTMT values less than 0.90 provide evidence of discriminant validity, which our constructs satisfy, thereby reinforcing the distinctiveness of the constructs in our study.

This paper assessed the overall model. Model fit estimates were as follows. χ2 was 833.223. The normed fit index (NFI) was 0.837, which is lower but close to the threshold of 0.9 (Afthanorhan 2013 ). The standardized root mean square residual (SRMR) of the measurement model was 0.066, which is less than the acceptable limit of 0.08 (Bentler and Bonett 1980 ). Considering the above measures, the measurement model shows a good model fit.

Multi-collinearity diagnostics were conducted to verify that there was no high intercorrelation between the predictor variables, which could cause problems in the path analysis. The variance inflation factor (VIF) was used as a measure to assess the severity of multi-collinearity. As suggested by Hair et al. ( 2006 ), a VIF value greater than 5.0 indicates a problematic level of multi-collinearity. As shown in the table, the VIF values for all the construct items ranged from 1.525 (INO1) to 4.200 (COI2), which were all below the critical threshold of 5.0. Therefore, it can be concluded that there were no significant multi-collinearity issues in this research. These findings ensured the reliability and validity of the path analysis results.

Structural model

This study carried out structural equation modeling (SEM) to evaluate the hypotheses. It applied bootstrapping with 5,000 subsamples to verify the proposed hypotheses and path coefficients. The analysis (SEM) results are shown in Fig. 3 .

PLS Analysis Result.

Path analysis was utilized to test the hypotheses, with the results detailed in Table 5 . The findings are summarized as follows. H1a proposed that effort expectancy would positively affect continuance intention. The analysis revealed a positive effect ( β  = 0.115 , p  = 0.045), thereby supporting H1a. H1b suggested that effort expectancy would positively influence shopping intention, but the results did not support this hypothesis ( β  = −0.031 , p  = 0.521). H2a and H2b posited that performance expectancy would positively affect continuance intention and shopping intention, respectively. Both these hypotheses were supported ( β  = 0.209 , p  = 0.003 for H2a; β  = 0.161 , p  = 0.010 for H2b). H3a stated that social influence would positively influence continuance intention, and this was supported by the results ( β  = 0.168 , p  = 0.002). However, H3b, which suggested that social influence would positively affect shopping intention, was not supported ( β  = 0.210, p  = 0.117). H4a and H4b proposed that facilitating conditions would positively affect continuance intention and shopping intention. H4a was supported ( β  = 0.435 , p  = 0.000), whereas H4b was not ( β  = 0.030, p  = 0.647). H5a and H5b suggested that innovativeness would positively influence continuance intention and shopping intention. Neither hypothesis was supported ( β  = 0.068, p  = 0.165 for H5a; β  = 0.079, p  = 0.192 for H5b). H6, which proposed that continuance intention would positively affect shopping intention, was supported ( β  = 0.497 , p  = 0.000). H7a through H7d hypothesized the moderating effects of innovativeness on the relationships between each of the four constructs (effort expectancy, performance expectancy, social influence, and facilitating conditions) and continuance intention. H7a, H7b, and H7d were not supported, as indicated by their respective coefficients ( β  = −0.052 , p  = 0.495 for H7a; β  = 0.085 , p  = 0.258 for H7b; β  = −0.024 , p  = 0.736 for H7d). H7c was marginally supported ( β  = −0.095 , p  = 0.091 for H7c). Overall, the conceptual framework described approximately 73.8% of the variability in continuance intention and 71.0% of the variability in shopping intention.

The purpose of this study was to shed light on the determinants of shopping intention in the O4O context. To achieve this, the researchers made modifications to the UTAUT model.

The results of the study affirm that effort expectancy significantly impacts continuance intention but does not influence shopping intention. Aligned with previous studies’ findings (Alsyouf and Ishak 2018 ; Chiu and Wang 2008 ; Gupta et al. 2020 ; Tam et al. 2020 ; Venkatesh et al. 2003 ), this result suggests that ease of use increases the likelihood of continued technology usage. This suggests that usability or user-friendliness is crucial for retaining users on an O4O platform. If consumers have to put forth less effort to understand how to navigate and use the platform, they are more likely to continue using it over the long term. However, the fact that effort expectancy does not significantly influence shopping intention is somewhat surprising. The fact contrasts with the findings of Juaneda-Ayensa et al. ( 2016 ), who highlighted the importance of effort expectancy in influencing purchase intention. A possible explanation is that usability alone may not be sufficient to drive purchasing behavior. Although users may find the platform easy to use, this does not necessarily translate into an increased likelihood of making purchases. Shopping intention could be influenced more by other factors such as perceived value, trust, product assortment, and price. This is an important distinction for managers and developers of O4O platforms. While user-friendliness is crucial for retaining users, it may not be enough to convert users into customers. This implies the need for a comprehensive strategy that not only enhances the usability of the platform but also addresses other factors that influence shopping intention. The finding underlines the importance of a multifaceted approach to optimizing the user experience on O4O platforms.

Performance expectancy was found to affect both continuance intention and shopping intention. This finding supports previous research that has confirmed the significant impact of performance expectancy on continuance intention (Chiu and Wang 2008 ; Gupta et al. 2020 ; Hutabarat et al. 2021 ; Kim et al. 2022b ; Tam et al. 2020 ) and purchase intention (Juaneda-Ayensa et al. 2016 ). In the O4O commerce context, this could translate to the belief that using the O4O platform would streamline the shopping process, provide a wider range of product choices, offer better prices, or facilitate more convenient transactions. As a result, if consumers perceive high performance expectancy, they are more likely to continue using the O4O platform and show higher shopping intentions. This dual impact suggests that performance expectancy is a critical determinant in both the retention of users and the facilitation of purchases on the platform. Theoretically, this finding reinforces the role of performance expectancy as delineated in the UTAUT, confirming its relevance in the O4O context. From a managerial perspective, these findings underscore the importance of enhancing the performance of O4O platforms. Businesses operating in this domain need to focus on improving the tangible benefits that these platforms can deliver. This could involve increasing the efficiency of the platform, offering a diverse range of products and services, ensuring competitive pricing, and simplifying the transaction process. This performance-focused approach can contribute to both maintaining a stable user base and driving more purchases on the platform, thereby maximizing the potential for revenue growth.

The observation that social influence significantly affects continuance intention is consistent with existing research indicating that users’ intentions to persist with a technology are substantially shaped by their social surroundings (Chen et al. 2012 ; Hutabarat et al. 2021 ; Li and Lee 2022 ; Xiao and Wang 2016 ; Yang and Forney 2013 ). This underscores the idea that in the digital realm, users often look to others in their social networks when deciding whether to continue using a service. However, the finding that social influence does not significantly affect shopping intention adds a unique dimension to our understanding of the relationship between social influence and user behavior. Contrary to studies suggesting that social influence is a significant determinant of purchase intentions in online contexts (Hu et al. 2019 ; Teo et al. 2018 ), our results indicate that this may not always be the case, especially within the O4O context. This distinction may arise from the unique characteristics of O4O services, suggesting that while social factors might encourage users to continue using the service, they do not necessarily translate into an increased propensity to make purchases. This observation offers critical implications for both theory and practice. Theoretically, it emphasizes the context-dependent nature of the role of social influence, thus calling for further exploration of this construct within varying technology use contexts. Practically, it suggests that service providers should differentiate their strategies for boosting continuance intentions and shopping intentions. While fostering a positive social environment can enhance continuance intentions, other factors may need to be prioritized to stimulate shopping behavior.

Facilitating conditions were found to significantly impact continuance intention, but not shopping intention. This echoes previous findings (Bakar et al. 2013 ; Bhattacherjee et al. 2008 ; Erwanti et al. 2018 ; Mosquera et al. 2018 ; Sharma and Saini 2022 ; Yang and Forney 2013 ; Zhou, 2011 ), indicating that support and resources available to technology users encourage continued use of the platform, but may not necessarily influence shopping intention. Facilitating conditions are defined as the extent to which consumers perceive that an organizational and technical infrastructure exists to support the use of the system. In this case, the discovery indicates that when consumers perceive that sufficient resources and support (like user-friendly interface, technical support, and comprehensive guides) are in place, they are more likely to continue using the O4O platform. However, these conditions, despite encouraging continued use, do not appear to directly encourage consumers’ shopping intentions. From a theoretical perspective, this aids in refining our understanding of the importance of facilitating conditions within the UTAUT framework. While previous studies have suggested that facilitating conditions impact both behavioral intentions and use behavior (Venkatesh et al. 2003 ), this research presents a more complex relationship in the O4O context, underscoring the need for further investigation. From a managerial perspective, the observation emphasizes the importance of providing robust facilitating conditions for retaining users on the O4O platform, such as user-friendly design and readily available technical support. However, it also suggests that to encourage shopping intentions, businesses might need to look beyond these facilitating conditions and focus on other aspects such as user personalization, product offerings, and promotional activities.

The study also found that continuance intention significantly influences shopping intention. This finding indicates that when consumers have a high continuance intention - i.e., they plan to keep using the O4O platform - they are more likely to develop strong shopping intentions. In other words, the more comfortable and satisfied users are with the platform, the more likely they are to make purchases. From a theoretical standpoint, this finding aligns with extant research that suggests a positive relationship between continuance intention and actual behavior (Bhattacherjee 2001 ). However, it extends this by demonstrating that in the O4O context, the “actual behavior” can include shopping intentions, which is a more specific type of behavior. From a managerial perspective, this suggests that increasing user satisfaction and fostering a high continuance intention can be an effective strategy for stimulating shopping intention. Operators of O4O platforms can thus focus on ensuring high-quality user experience to cultivate continued usage and, consequently, boost shopping behavior. This may involve various strategies such as optimizing user interface, providing prompt customer service, or ensuring reliable operations.

The findings that innovativeness does not moderate the effects of effort expectancy, performance expectancy, and facilitating conditions on continuance intention, but marginally and negatively moderates the effect of social influence on continuance intention, presents intriguing insights. Theoretically, it sheds light on the intricate interplay of consumer innovativeness and social influence in the context of continuance intention in O4O platforms. This suggests that while innovativeness does not influence how effort expectancy, performance expectancy, and facilitating conditions affect continuance intention, it can alter the impact of social influence. More specifically, the higher a consumer’s innovativeness, the less they are affected by social influence when it comes to their intention to continue using the O4O platform. From a managerial perspective, this points to the importance of considering consumer innovativeness in strategic decision-making. It implies that while innovations in the platform can stimulate continued use, for highly innovative consumers, peer opinions and social norms might be less influential. This further suggests the importance of a tailored approach, where strategies are adjusted based on consumer innovativeness. The negative coefficient ( β  = −0.095) indicates that as the innovativeness of a consumer increases, the effect of social influence on continuance intention decreases, albeit marginally. This could be interpreted as innovative consumers being less influenced by their social environment in their decision to continue using the O4O platform, possibly because they are more comfortable with technology and therefore rely less on others’ opinions. However, the fact that this is a marginal result ( p  < 0.1) indicates a need for further research. Future studies could explore this relationship further, possibly investigating why innovativeness only marginally moderates the effect of social influence on continuance intention, and under what conditions this might change.

Theoretical Contribution

This study significantly contributes to innovation management, offering a wealth of innovative insights. It thoroughly investigates the complex interactions between UTAUT constructs and their impact on continuance and shopping intentions within the O4O service domain. By reaffirming the robustness of UTAUT as a theoretical framework in forecasting these intentions, the research transcends conventional understanding, shedding light on the in-depth dynamics that emerge in the O4O landscape (Venkatesh et al. 2003 ). One pivotal discovery is that while effort expectancy considerably influences continuance intention, it does not similarly affect shopping intention. This finding creates a point of divergence from earlier research conducted by scholars such as Chatterjee et al. ( 2019 ) and Doan ( 2020 ), who opined that effort expectancy was critical for shopping intentions. The discrepancy arising in this context, thus, underscores the potential influence of technology and context specificity on the role of effort expectancy. Perceived ease of use might not directly influence shopping intentions in an O4O context. However, it enhances the user’s inclination to continue using the platform, thus indirectly impacting shopping intentions through the mediating role of continuance intention. Such an insight redefines our understanding of how effort expectancy works in the e-commerce realm, particularly O4O services. Therefore, future studies should take into account the unique characteristics of the O4O context and the importance of continuance intention, bringing a fresh perspective into the current body of knowledge.

The second major theoretical contribution of this study lies in its successful expansion of the UTAUT framework by integrating the construct of consumer innovativeness. This incorporation has injected fresh perspectives into the discourse on innovation management, thereby advancing it. Predominantly, the focus of past studies has remained confined to technological and organizational innovation (Acikgoz et al. 2022 ; Alalwan et al. 2018 ; Lee, 2019 ). However, this research presents a shift in emphasis by underlining the immense relevance of consumer innovativeness. The novelty and importance of this innovative approach cannot be understated. It underscores the role of consumer innovativeness in modifying the effects of UTAUT constructs on continuance intention. More specifically, it highlights how consumer innovativeness can significantly moderate the impact of social influence on continuance intention. This provides compelling evidence of the dynamic role of consumer characteristics in shaping the outcomes of technology adoption models. Consequently, this extension of the UTAUT model encourages scholars to look beyond organizational and technological factors in innovation management. It signifies that consumer attributes, such as innovativeness, are crucial determinants of innovation outcomes in the increasingly consumer-centered digital marketplace. Thus, the examination of consumer innovativeness in the context of O4O services, as carried out in this study, paints a fuller picture of innovation management that accounts for the critical role of consumers.

In its third theoretical contribution, this study offers a thought-provoking discovery - consumer innovativeness only marginally moderates the effect of social influence on continuance intention. The intriguing aspect of this finding is that it stands in contrast with the results from earlier studies which have established a potent influence of social factors on the adoption of innovative products (Hölsgens 2022 ; Liang et al. 2022 ). The study suggests that the influence of social factors may be diminished among consumers with higher innovativeness, particularly in the realm of O4O services. This unexpected result presents a distinct opportunity for academics to reexamine and rethink the interaction between consumer innovativeness and social influence in the context of technology adoption and persistent usage. The traditional notion that social influence uniformly impacts adoption behaviors might not be applicable to innovative consumers within O4O services. Thus, this new comprehension opens up exciting pathways for future research. It indicates the need for an in-depth investigation into how consumer characteristics, specifically innovativeness, interact with social influences in moderating adoption and continuance behaviors.

As a fourth contribution, the findings of this study underscore the pivotal role that continuance intention plays in molding shopping intention within the context of O4O services. This relationship has been relatively under-explored in the body of existing academic literature. Much of the existing research in this field has primarily focused on separately discerning the factors that determine adoption intention (Park and Kim 2021 ), continuance intention (Song and Jo 2023 ), and purchase intention (Kim et al. 2022b ; Sombultawee and Wattanatorn 2022 ). The emphasis has been less on examining post-adoption behaviors such as continuance intention and shopping intention within a single framework. This study breaks new ground by demonstrating that continuance intention holds significant predictive power in determining shopping intention. In doing so, it provides a fresh perspective and adds an intriguing facet to our understanding of consumer behavior within O4O contexts. The determination of shopping intention in the O4O context by continuance intention suggests that the mere adoption of O4O services is not the end-point for consumers. Instead, their ongoing usage intention impacts their shopping behaviors within the platform, adding complexity to our understanding of their buying behaviors. Moreover, this revelation aligns with the growing acknowledgment in innovation management literature that continued usage or post-adoption behaviors are as significant as initial adoption behaviors, if not more (Chen et al. 2018 ; Jahanmir et al. 2020 ; Wang et al. 2022 ). This could drive future research to delve deeper into the dynamics of post-adoption behaviors in the realm of O4O services, thereby further expanding our understanding of innovation management in consumer behavior.

Finally, by integrating the UTAUT within an O4O context, this research provides a significant theoretical expansion of the theory’s scope of application. This study’s discovery, that facilitating conditions exert substantial influence over continuance intention but not shopping intention, challenges prior findings in the field (Venkatesh et al. 2012 ). This unexpected finding indicates a potential reliance of the facilitating conditions’ function on the contextual intricacies and the particular technology being studied, highlighting the importance of considering contextual factors in understanding their impact. Consequently, this revelation warrants further academic scrutiny into the nuanced role and impact of facilitating conditions across distinct settings and with various technologies. This study’s emphasis on the O4O context, a rapidly growing and dynamic area of commerce, provides a unique perspective within the literature on innovative commerce. Unraveling the determinants of both continuance and shopping intentions within an O4O context significantly broadens our understanding of user behavior within this evolving landscape.

Managerial Implication

The research findings present a multitude of practical implications for different stakeholders, including marketers, managers, service providers, and users.

First, the research findings underscore significant implications for marketers and managers operating within the O4O sector. The study reveals that effort expectancy is a substantial determinant of continuance intention, but not shopping intention, in O4O services. This understanding presents marketers with opportunities to refine their strategies. It implies that the perceived effort or ease of use involved in utilizing O4O services significantly influences a user’s decision to continue using the service. Hence, marketers should prioritize efforts to simplify the user interface and the overall usage process of their platforms, which can in turn enhance users’ continuance intentions. However, influencing shopping intentions extends beyond just ensuring ease of use. Marketers need to consider other salient factors, such as performance expectancy, when strategizing to enhance shopping intentions. As suggested by previous studies, the factor also holds considerable sway in shaping a user’s shopping intention (Ertz et al. 2022 ; Juaneda-Ayensa et al. 2016 ). Thus, a balanced focus on these elements can yield a more comprehensive and effective marketing strategy.

Second, the pivotal role of performance expectancy in affecting both continuance and shopping intentions is brought to light by this research. It suggests that if consumers perceive an O4O service as instrumental in effectively achieving their objectives, they are more likely to maintain the use of such service (Tam et al. 2020 ) and undertake purchases (Jayasingh et al. 2022 ; Zhang et al. 2023 ). This is a critical insight for service providers, who should take strides to ensure their platforms are efficient, reliable, and add real value for their consumers. Providers should make it a priority to continually enhance the features and services on offer (Prassida and Hsu 2022 ). They should focus on user-centric designs and improvements that can heighten user satisfaction and, consequently, enhance user performance. By doing so, service providers can fulfill the performance expectations of users, encouraging continued usage and increasing the propensity for shopping, thus driving business growth and customer retention.

Third, the significant role of social influence in shaping both continuance and shopping intentions is underscored. This implies that businesses can leverage the social network of users to influence user behaviors. By implementing a robust referral program, businesses can tap into users’ social circles to attract new users (Dose et al. 2019 ). Encouraging social sharing of products, experiences, or reviews can help businesses boost their reach and impact in online communities, possibly reinforcing the shopping intentions of existing and potential users (Cheung and Thadani 2012 ). Moreover, creating a sense of community within the service platform can not only enhance users’ intention to continue using the service but also stimulate shopping behavior by creating a sense of trust and mutual support among users. The power of social influence, therefore, should not be underestimated in strategic planning.

Fourth, the findings regarding social influence’s impact on continuance intention but not on shopping intention present valuable insights for managers in the O4O industry. Managers should leverage the power of social influence to retain users on their platforms, emphasizing the creation of strong user communities, encouraging peer-to-peer interactions, and possibly implementing a robust referral program (Muller and Peres 2019 ). However, they must also recognize that while social influence may encourage users to continue using the platform, it does not necessarily translate into increased shopping intentions. Therefore, to drive shopping behavior, managers must focus on other influential factors, such as performance expectancy and facilitating conditions, in tandem with building a positive social environment. This multi-faceted approach can help not only retain users but also stimulate active engagement and purchases on the platform.

Finally, the study unveils the influential role of consumer innovativeness in shaping the impact of UTAUT constructs, notably social influence, on continuance intention. This indicates that businesses should be mindful of the differing levels of innovativeness among their users, tailoring their strategies to address the varying needs and expectations of these different user segments (Li et al. 2022 ; Shah et al. 2022 ). Specifically, companies might want to debut advanced features or services that can cater to the preferences of highly innovative users, those who are inclined to adopt and appreciate novel products and experiences. This would not only serve their penchant for novelty but also bolster their continued usage of the platform, thereby potentially leading to increased shopping intentions. On the other hand, for users with lower levels of innovativeness, companies could provide extensive guidance and support to foster their comfort and familiarity with the service, which could enhance their continuance intention. Consequently, this bifurcated approach could enable businesses to effectively cater to a broader user base, optimizing user retention and shopping intentions.

Limitations and future research directions

This paper has the following limitations and suggests several research directions. First, this research mainly dealt with the technological factors of O4O. There may be a combination of economic and personal factors that can influence shopping behavior. Thus, future research needs to reflect both intrinsic and extrinsic variables in addition to technological components to improve the generality of the result. Second, the survey was conducted in only one country. If the term O4O and its business model become more prevalent around the world, researchers will need to collect samples from various countries in the future. Finally, the current work did not consider the type of O4O store. Since O4O is classified into several forms according to business purposes, future studies should contain this aspect.

Data availability

The data used in this study are available from the corresponding authors upon reasonable request.

Acikgoz F, Filieri R, Yan M (2022) Psychological predictors of intention to use fitness apps: the role of subjective knowledge and innovativeness. Int J Human–Computer Interaction 39:1–13

Afthanorhan W (2013) A comparison of partial least square structural equation modeling (PLS-SEM) and covariance based structural equation modeling (CB-SEM) for confirmatory factor analysis. Int J Eng Sci Innovative Technol 2(5):198–205

Google Scholar  

Agarwal R, Prasad J (1998) A conceptual and operational definition of personal innovativeness in the domain of information technology. Inf Syst Res 9(2):204–215

Article   Google Scholar  

Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Akter S, D’Ambra J, Ray P (2010) Service quality of mHealth platforms: development and validation of a hierarchical model using PLS. Electron Mark 20(3):209–227. https://doi.org/10.1007/s12525-010-0043-x

Alalwan AA, Baabdullah AM, Rana NP, Tamilmani K, Dwivedi YK (2018) Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technol Soc 55:100–110

Alsyouf A, Ishak AK (2018) Understanding EHRs continuance intention to use from the perspectives of UTAUT: Practice environment moderating effect and top management support as predictor variables. Int J Electron Healthc 10(1-2):24–59

Bagozzi RP, Yi Y, Phillips LW (1991) Assessing construct validity in organizational research. Adm Sci Q 36(3):421–458. https://doi.org/10.2307/2393203

Bakar AA, Razak FZA, Abdullah WSW (2013) Assessing the effects of UTAUT and self-determination predictor on students continuance intention to use student portal. World Appl Sci J 21(10):1484–1489

Balakrishnan J, Abed SS, Jones P (2022) The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services? Technol Forecast Soc Change 180:121692

Bentler PM, Bonett DG (1980) Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 88(3):588

Bhattacherjee A (2001) Understanding information systems continuance: an expectation-confirmation model. MIS Q 25(3):351–370. https://doi.org/10.2307/3250921

Bhattacherjee A, Perols J, Sanford C (2008) Information technology continuance: a theoretic extension and empirical test. J Computer Inf Syst 49(1):17–26

Brown SA, Venkatesh V (2005) Model of adoption of technology in households: a baseline model test and extension incorporating household life cycle. MIS Q 29:399–426

Chang Y-W, Chen J (2021) What motivates customers to shop in smart shops? The impacts of smart technology and technology readiness. J Retail Consum Serv 58:102325. https://doi.org/10.1016/j.jretconser.2020.102325

Chatterjee D, Dandona B, Mitra A, Giri M (2019) Airbnb in India: comparison with hotels, and factors affecting purchase intentions. Int J Cult Tour Hosp Res 13(4):430–442. https://doi.org/10.1108/IJCTHR-05-2019-0085

Chen S-C, Yen DC, Hwang MI (2012) Factors influencing the continuance intention to the usage of Web 2.0: An empirical study. Computers Hum Behav 28(3):933–941. https://doi.org/10.1016/j.chb.2011.12.014

Chen X, Wang X, Jiang X (2016) The impact of power structure on the retail service supply chain with an O2O mixed channel. J Operational Res Soc 67(2):294–301

Chen Y, Yang L, Zhang M, Yang J (2018) Central or peripheral? Cognition elaboration cues’ effect on users’ continuance intention of mobile health applications in the developing markets. Int J Med Inform 116:33–45

Article   PubMed   Google Scholar  

Cheung CMK, Thadani DR (2012) The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decis Support Syst 54(1):461–470. https://doi.org/10.1016/j.dss.2012.06.008

Chiemeke S, Evwiekpaefe A (2011) A conceptual framework of a modified unified theory of acceptance and use of technology (UTAUT) Model with Nigerian factors in E-commerce adoption. Educ Res 2(12):1719–1726

Chiu C-M, Wang ETG (2008) Understanding Web-based learning continuance intention: The role of subjective task value. Inf Manag 45(3):194–201. https://doi.org/10.1016/j.im.2008.02.003

Article   MathSciNet   Google Scholar  

Cui C, Yang S-B (2020) The Impact of O4O Selection Attributes on Customer Satisfaction and Loyalty: Focusing on the Case of Fresh Hema in China. Knowl Manag Res 21(3):249–269. https://doi.org/10.15813/kmr.2020.21.3.014

Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340

DeLone WH, McLean ER (2003) The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Syst 19(4):9–30

Doan T (2020) Factors affecting online purchase intention: A study of Vietnam online customers. Manag Sci Lett 10(10):2337–2342

Dose DB, Walsh G, Beatty SE, Elsner R (2019) Unintended reward costs: the effectiveness of customer referral reward programs for innovative products and services. J Acad Mark Sci 47(3):438–459. https://doi.org/10.1007/s11747-019-00635-z

Dwivedi YK, Rana NP, Jeyaraj A, Clement M, Williams MD (2019) Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Inf Syst Front 21(3):719–734. https://doi.org/10.1007/s10796-017-9774-y

Ertz M, Jo M-S, Kong Y, Sarigöllü E (2022) Predicting m-shopping in the two largest m-commerce markets: The United States and China. Int J Mark Res 64(2):249–268

Erwanti N, Widodo AP, Nurhayati OD (2018) Determinants of continuance intention to use on-demand mobile service: GO-JEK case. Int J Computer Sci Issues (IJCSI) 15(6):9–15

Escobar-Rodríguez T, Carvajal-Trujillo E (2014) Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tour Manag 43:70–88

Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement Error. J Mark Res 18(1):39–50. https://doi.org/10.2307/3151312

Fu S, Yan Q, Feng GC (2018) Who will attract you? Similarity effect among users on online purchase intention of movie tickets in the social shopping context. Int J Inf Manag 40:88–102

García de Blanes Sebastián M, Sarmiento Guede JR, Antonovica A (2022) Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants [Original Research]. Front Psychol 13. https://doi.org/10.3389/fpsyg.2022.993935

Gu W, Bao P, Lee J-H (2019) A study on the continuance intention of O2O fresh agricultural products e-commerce. J Ind Distrib Bus 10(10):35–44

Gupta A, Yousaf A, Mishra A (2020) How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model. Int J Inf Manag 52:102094

Hair J, Anderson R, Tatham BR (2006) Multivariate data analysis, 6th edn. Pearson Prentice Hall, Upper Saddle River, NJ

Hair JF, Hult GTM, Ringle CM, Sarstedt M (2021) A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications

Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019) When to use and how to report the results of PLS-SEM. Eur Bus Rev 31(1):2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hair JF, Sarstedt M, Ringle CM, Gudergan SP (2017) Advanced issues in partial least squares structural equation modeling. Sage Publications

Hanjaya SM, Kenny SK, Gunawan SF (2019) Understanding factors influencing consumers online purchase intention via mobile app: perceived ease of use, perceived usefulness, system quality, information quality, and service quality. Mark Sci Res Organ 32(2):175–205

Henseler J, Ringle CM, Sarstedt M (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci 43:115–135

Hew J-J, Lee V-H, Ooi K-B, Wei J (2015) What catalyses mobile apps usage intention: an empirical analysis. Ind Manag Data Syst 115(7):1269–1291

Hölsgens R (2022) Introducing the adopter perspective in social innovation research. Innov: Eur J Soc Sci Res 35(2):245–264. https://doi.org/10.1080/13511610.2021.1964351

Hsieh J-K (2017) The role of customers in co-creating m-services in the O2O model. J Serv Manag 28(5):866–883. https://doi.org/10.1108/JOSM-03-2016-0062

Hu X, Chen X, Davison RM (2019) Social support, source credibility, social influence, and impulsive purchase behavior in social commerce. Int J Electron Commer 23(3):297–327

Huang Y-C (2023) Integrated concepts of the UTAUT and TPB in virtual reality behavioral intention. J Retail Consum Serv 70:103127

Hutabarat Z, Suryawan IN, Andrew R, Akwila FP (2021) Effect of performance expectancy and social influence on continuance intention in OVO. J Manaj 25(1):125–140

Hwang J, Lee J-S, Kim H (2019) Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: The moderating role of gender and age. Int J Hospitality Manag 81:94–103

Jahanmir SF, Silva GM, Gomes PJ, Gonçalves HM (2020) Determinants of users’ continuance intention toward digital innovations: are late adopters different? J Bus Res 115:225–233

Jayasingh S, Girija T, Arunkumar S (2022) Determinants of omnichannel shopping intention for sporting goods. Sustainability 14(21):14109, https://www.mdpi.com/2071-1050/14/21/14109

Jo H (2023a) Understanding the key antecedents of users’ continuance intention in the context of smart factory. Technol Anal Strategic Manag 35(2):153–166

Jo H (2023b) What drives recommendation intention and loyalty of online for offline (O4O) consumers? Sustainability 15(6):4775, https://www.mdpi.com/2071-1050/15/6/4775

Juaneda-Ayensa E, Mosquera A, Sierra Murillo Y (2016) Omnichannel customer behavior: key drivers of technology acceptance and use and their effects on purchase intention. Front Psychol 7:1117

Article   PubMed   PubMed Central   Google Scholar  

Kang J-W, Namkung Y (2019) The information quality and source credibility matter in customers’ evaluation toward food O2O commerce. Int J Hospitality Manag 78:189–198. https://doi.org/10.1016/j.ijhm.2018.10.011

Kazancoglu I, Aydin H (2018) An investigation of consumers’ purchase intentions towards omni-channel shopping. Int J Retail Distrib Manag 46(10):959–976. https://doi.org/10.1108/IJRDM-04-2018-0074

Khashan MA, Elsotouhy MM, Alasker TH, Ghonim MA (2023) Investigating retailing customers’ adoption of augmented reality apps: integrating the unified theory of acceptance and use of technology (UTAUT2) and task-technology fit (TTF). Marketing Intelligence & Planning 41

Kim B (2018) Understanding the role of conscious and automatic mechanisms in social networking services: a longitudinal study. Int J Hum–Computer Interact 34(9):805–818. https://doi.org/10.1080/10447318.2017.1392079

Kim JK, Jeong CG, Li Q, Choi IY (2022a) The demand effect analysis of head books and tail books in book recommendation networks. Expert Syst 39(2):e12847

Kim R, JM, Lee H, Choi J, Lee J, Kim S, Lee S, Seo Y, Kwon J (2018) Trend Korea 2018. Mirae Book Publishing Co

Kim S, Connerton TP, Park C (2022b) Transforming the automotive retail: Drivers for customers’ omnichannel BOPS (Buy Online & Pick up in Store) behavior. J Bus Res 139:411–425. https://doi.org/10.1016/j.jbusres.2021.09.070

Konuş U, Verhoef PC, Neslin SA (2008) Multichannel shopper segments and their covariates. J Retail 84(4):398–413

Lăzăroiu G, Neguriţă O, Grecu I, Grecu G, Mitran PC (2020) Consumers’ decision-making process on social commerce platforms: online trust, perceived risk, and purchase intentions. Front Psychol 11:890

Lee MS (2019) Effects of personal innovativeness on mobile device adoption by older adults in South Korea: the moderation effect of mobile device use experience. Int J Mob Commun 17(6):682–702

Lee PTY, Feiyu E, Chau M (2022) Defining online to offline (O2O): a systematic approach to defining an emerging business model. Internet Res 32(5):1453–1495. https://doi.org/10.1108/INTR-10-2020-0563

Li G, Zhang T, Tayi GK (2020a) Inroad into omni-channel retailing: Physical showroom deployment of an online retailer. Eur J Operational Res 283(2):676–691

Li J, Guo F, Xu J, Yu Z (2022) What influences consumers’ intention to purchase innovative products: evidence from China [Original Research]. Front Psychol 13. https://doi.org/10.3389/fpsyg.2022.838244

Li L-Y, Lee L-Y (2022) Factors affecting consumer behavior in omni-channel marketing of the fitness industry: motivation and attitude towards O2O channels. J Cases Inf Technol (JCIT) 24(1):1–10

Li Y, Li X, Zhang Z, Zhang G, Gong M (2020b) Understanding consumers online furniture purchase behavior: an updated UTAUT perspective. J For Econ 35(4):267–303

Liang S-Z, Hsu M-H, Chen W-H (2022) Psychological factors behind innovation adoption: affordance actualisation model. J Comput Inform Syst 1–15. https://doi.org/10.1080/08874417.2022.2148141

Madan K, Yadav R (2016) Behavioural intention to adopt mobile wallet: a developing country perspective. J Indian Bus Res 8(3):227–244

Madan K, Yadav R (2018) Understanding and predicting antecedents of mobile shopping adoption: A developing country perspective. Asia Pac J Mark Logist 30(1):139–162. https://doi.org/10.1108/APJML-02-2017-0023

MaeilBusinessNewsKorea (2020) Musinsa Terrace, Online and Offline Differentiated O4O Service, Available at: https://www.mk.co.kr/news/economy/view/2020/06/587347/ (Accessed on July 26 2022)

Marinković V, Đorđević A, Kalinić Z (2020) The moderating effects of gender on customer satisfaction and continuance intention in mobile commerce: a UTAUT-based perspective. Technol Anal Strategic Manag 32(3):306–318. https://doi.org/10.1080/09537325.2019.1655537

Marriott HR, Williams MD, Dwivedi YK (2017) What do we know about consumer m-shopping behaviour? Int J Retail Distrib Manag 45(6):568–586

Mezghani K (2018) Effects of personal innovativeness on IS Managers’ Intentions to switch toward cloud ERP in Saudi SMEs. Electron J Inf Syst Evaluation 21(1):46–61

Midgley DF, Dowling GR (1978) Innovativeness: the concept and its measurement. J Consum Res 4(4):229–242

Min Q, Ji S, Qu G (2008) Mobile commerce user acceptance study in China: a revised UTAUT model. Tsinghua Sci Technol 13(3):257–264

Mosquera A, Juaneda-Ayensa E, Olarte-Pascual C, Pelegrín-Borondo J (2018) Key factors for in-store smartphone use in an omnichannel experience: millennials vs. nonmillennials. Complexity 2018:1057356. https://doi.org/10.1155/2018/1057356

Muller E, Peres R (2019) The effect of social networks structure on innovation performance: A review and directions for research. Int J Res Mark 36(1):3–19

Nunnally JC (1978) Psychometric theory, 2nd edn. Mcgraw Hill Book Company

Oláh J, Kitukutha N, Haddad H, Pakurár M, Máté D, Popp J (2019) Achieving sustainable e-commerce in environmental, social and economic dimensions by taking possible trade-offs. Sustainability 11(1):89, https://www.mdpi.com/2071-1050/11/1/89

Özdemir V, Hekim N (2018) Birth of industry 5.0: Making sense of big data with artificial intelligence,“the internet of things” and next-generation technology policy. Omics: J Integr Biol 22(1):65–76

Pan Y, Wu D, Luo C, Dolgui A (2019) User activity measurement in rating-based online-to-offline (O2O) service recommendation. Inf Sci 479:180–196

Park J, Kim RB (2021) The effects of integrated information & service, institutional mechanism and need for cognition (NFC) on consumer omnichannel adoption behavior. Asia Pac J Mark Logist 33(6):1386–1414. https://doi.org/10.1108/APJML-06-2018-0209

Patil P, Tamilmani K, Rana NP, Raghavan V (2020) Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. Int J Inf Manag 54:102144

Pillai R, Sivathanu B, Dwivedi YK (2020) Shopping intention at AI-powered automated retail stores (AIPARS). J Retail Consum Serv 57:102207. https://doi.org/10.1016/j.jretconser.2020.102207

Piotrowicz W, Cuthbertson R (2014) Introduction to the special issue information technology in retail: toward omnichannel retailing. Int J Electron Commer 18(4):5–16

Polites GL, Karahanna E (2012) Shackled to the status quo: the inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Q 36:21–42

Prassida GF, Hsu P-Y (2022) The harmonious role of channel integration and logistics service in Omnichannel retailing: The case of IKEA. J Retail Consum Serv 68:103030

Rogers EM (2010) Diffusion of innovations . Simon and Schuster

Ryu JS (2019) Consumer characteristics and shopping for fashion in the omni-channel retail environment. Asian J Bus Environ 9(4):15–22

Salimon MG, Kareem O, Mokhtar SSM, Aliyu OA, Bamgbade JA, Adeleke AQ (2021) Malaysian SMEs m-commerce adoption: TAM 3, UTAUT 2 and TOE approach. J Sci Technol Policy Manag 14(1):98–126

Santosa AD, Taufik N, Prabowo FHE, Rahmawati M (2021) Continuance intention of baby boomer and X generation as new users of digital payment during COVID-19 pandemic using UTAUT2. J Financial Serv Mark 26(4):259–273

Schiessl D, Korelo J, Dias HBA (2023) How online shopping experiences shape consumer webrooming behavior. Mark Intell Plan 41(1):16–30. https://doi.org/10.1108/MIP-08-2021-0254

Senali MG, Iranmanesh M, Ismail FN, Rahim NFA, Khoshkam M, Mirzaei M (2022) Determinants of intention to use e-wallet: personal innovativeness and propensity to trust as moderators. Int J Human–Computer Interaction 1–13. https://doi.org/10.1080/10447318.2022.2076309

Shah SK, Tang Z, Gavurova B, Oláh J, Acevedo-Duque Á (2022). Modeling consumer’s innovativeness and purchase intention relationship regarding 5G technology in China [Original Research]. Front Environ Sci 10. https://doi.org/10.3389/fenvs.2022.1017557

Sharma S, Saini JR (2022) On the role of teachers’ acceptance, continuance intention and self-efficacy in the use of digital technologies in teaching practices. J Furth High Educ 46(6):721–736

Sohn K, Sung C, Zhang Q, Kwon O (2023) The online for offline mobile retail business strategy: sustainable multichannel services. Int J Mob Commun 21(2):225–248

Sombultawee K, Wattanatorn W (2022) The impact of trust on purchase intention through omnichannel retailing. J Adv Manag Res 19:513–53

Son J-Y (2019) A study of the influence of online digital character experience on offline related products purchasing intention-focused on kakao friends O4O (online for offline) product portfolio. J Korea Contents Assoc 19(2):296–304

Song HG, Jo H (2023) Understanding the continuance intention of omnichannel: combining TAM and TPB. Sustainability 15(4):3039

Tam C, Santos D, Oliveira T (2020) Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Inf Syst Front 22(1):243–257. https://doi.org/10.1007/s10796-018-9864-5

Teo LX, Leng HK, Phua YXP (2018) Marketing on Instagram: Social influence and image quality on perception of quality and purchase intention. Int J Sports Mark Spons 20(2):321–332

Thakur R, Srivastava M (2014) Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Res 24(3):369–392

Uzoka F-ME (2008) Organisational influences on e-commerce adoption in a developing country context using UTAUT. Int J Bus Inf Syst 3(3):300–316

Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540

Venkatesh V, Thong JYL, Xu X (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q 36(1):157–178. https://doi.org/10.2307/41410412

Verhoef PC, Kannan PK, Inman JJ (2015) From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing. J Retail 91(2):174–181

Wang N, Xie W, Ali A, Brem A, Wang S (2022) How do individual characteristics and social capital shape users’ continuance intentions of smart wearable products? Technol Soc 68:101818

Wu W, Yu L (2022) How does personal innovativeness in the domain of information technology promote knowledge workers’ innovative work behavior? Inf Manag 59(6):103688. https://doi.org/10.1016/j.im.2022.103688

Xiao X, Wang T (2016) The implications of social influence theory on continuance intention for social networking among Chinese university students. J Organ End Use Comput (JOEUC) 28(4):55–72

Yang K (2010) Determinants of US consumer mobile shopping services adoption: implications for designing mobile shopping services. J Consum Mark 27(3):262–270. https://doi.org/10.1108/07363761011038338

Yang K, Forney JC (2013) The moderating role of consumer technology anxiety in mobile shopping adoption: differential effects of facilitating conditions and social influences. J Electron Commer Res 14(4):334

Yoo SR, Lee SW, Jeon HM (2020) The role of customer experience, food healthiness, and value for revisit intention in Grocerant. Sustainability 12(6):2359

Yoon C, Rolland E (2012) Knowledge-sharing in virtual communities: familiarity, anonymity and self-determination theory. Behav Inf Technol 31(11):1133–1143

Zhang M, Hassan H, Migin MW (2023) Exploring the consumers’ purchase intention on online community group buying platform during pandemic. Sustainability 15(3):2433, https://www.mdpi.com/2071-1050/15/3/2433

Zhou T (2011) Understanding mobile Internet continuance usage from the perspectives of UTAUT and flow. Inf Dev 27(3):207–218

Download references

Author information

Authors and affiliations.

Headquarters, HJ Institute of Technology and Management, 71 Jungdong-ro 39, Bucheon-si, Gyeonggi-do, 14721, Republic of Korea

School of Business, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea

Youngsok Bang

You can also search for this author in PubMed   Google Scholar

Contributions

H.J. wrote the original manuscript of this study. Y.B. was responsible for the revision of the article.

Corresponding author

Correspondence to Youngsok Bang .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

This study was approved by an institutional review board of HJ Institute of Technology and Management. The research was conducted in compliance with the National Health and Medical Research Council’s ethical standards for research involving human participants.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Jo, H., Bang, Y. From storefront to screen: an in-depth analysis of the dynamics of online for offline retailing. Humanit Soc Sci Commun 11 , 209 (2024). https://doi.org/10.1057/s41599-024-02723-0

Download citation

Received : 12 October 2023

Accepted : 18 January 2024

Published : 03 February 2024

DOI : https://doi.org/10.1057/s41599-024-02723-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

The pandemic has changed consumer behaviour forever - and online shopping looks set to stay

More and more consumers are ordering goods online. Image:  REUTERS/Danish Siddiqui

.chakra .wef-1c7l3mo{-webkit-transition:all 0.15s ease-out;transition:all 0.15s ease-out;cursor:pointer;-webkit-text-decoration:none;text-decoration:none;outline:none;color:inherit;}.chakra .wef-1c7l3mo:hover,.chakra .wef-1c7l3mo[data-hover]{-webkit-text-decoration:underline;text-decoration:underline;}.chakra .wef-1c7l3mo:focus,.chakra .wef-1c7l3mo[data-focus]{box-shadow:0 0 0 3px rgba(168,203,251,0.5);} Simon Torkington

.chakra .wef-9dduvl{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-9dduvl{font-size:1.125rem;}} Explore and monitor how .chakra .wef-15eoq1r{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;color:#F7DB5E;}@media screen and (min-width:56.5rem){.chakra .wef-15eoq1r{font-size:1.125rem;}} Internet of Things is affecting economies, industries and global issues

.chakra .wef-1nk5u5d{margin-top:16px;margin-bottom:16px;line-height:1.388;color:#2846F8;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-1nk5u5d{font-size:1.125rem;}} Get involved with our crowdsourced digital platform to deliver impact at scale

Stay up to date:, internet of things.

• Consumer shift to digital channels will remain after the pandemic -PwC report.
• Customer loyalty has plummeted, with buyers switching brands at unprecedented rates.
• The use of smartphones for online shopping has more than doubled since 2018.

Billions of people affected by the COVID-19 pandemic are driving a “historic and dramatic shift in consumer behaviour” – according to the latest research from PwC.

The consulting and accounting firm's June 2021 Global Consumer Insights Pulse Survey reports a strong shift to online shopping as people were first confined by lockdowns, and then many continued to work from home. Other trends in this shift towards digital consumption include online shoppers being keen to find the best price, choosing more healthy options and being more eco-friendly by shopping locally where possible.

Another significant finding from the report is that consumers do not think they’ll go back to their old ways of shopping once the pandemic is over.

A consumer pivot to digital and devices

More than 8,600 people across 22 territories took part in PwC’s survey. They were asked how often, in the past 12 months, they had bought clothes, books and electronics using a range of shopping channels.

Have you read?

Covid-19 pandemic accelerated shift to e-commerce by 5 years, new report says, these charts show how covid-19 has changed consumer spending around the world.

The chart below illustrates their answers, and shows a shift to digital and a growing trend for shopping using connected devices such as smartphones, tablets and smart voice assistants such as Amazon Echo, Google Home and Samsung SmartThings.

More than 50% of the global consumers responding to the June 2021 survey said they had used digital devices more frequently than they had six months earlier, when they had taken part in a prior PwC survey. The report also finds the use of smartphones for shopping has more than doubled since 2018.

COVID-19 has exposed digital inequities globally and exacerbated the digital divide. Most of the world lives in areas covered by a mobile broadband network, yet more than one-third (2.9 billion people) are still offline. Cost, not coverage, is the barrier to connectivity.

At The Davos Agenda 2021 , the World Economic Forum launched the EDISON Alliance , the first cross-sector alliance to accelerate digital inclusion and connect critical sectors of the economy.

Through the 1 Billion Lives Challenge , the EDISON Alliance aims to improve 1 billion lives globally through affordable and accessible digital solutions across healthcare, financial services and education by 2025.

Read more about the EDISON Alliance’s work in our Impact Story.

Medicines and groceries on demand

A survey of US consumers by McKinsey & Company gives a more detailed breakdown of the shift to digital shopping channels and the kinds of purchases consumers are making.

The survey found a 15-30% overall growth in consumers who made purchases online across a broad range of product categories. Many of the categories see a double-digit percentage growth in online shopping intent, led by over-the-counter medicines, groceries, household supplies and personal care products.

And McKinsey noted that “consumer intent to shop online [post-pandemic] continues to increase, especially in essentials and home-entertainment categories”.

A decline in brand loyalty

With consumers shopping from their sofas and home offices, another trend flagged up by McKinsey is a marked decline in brand loyalty.

In total, 75% of US consumers have tried a new shopping behaviour and over a third of them (36%) have tried a new product brand. In part, this trend has been driven by popular items being out of stock as supply chains became strained at the height of the pandemic. However, 73% of consumers who had tried a different brand said they would continue to seek out new brands in the future.

What is the World Economic Forum doing to manage emerging risks from COVID-19?

The first global pandemic in more than 100 years, COVID-19 has spread throughout the world at an unprecedented speed. At the time of writing, 4.5 million cases have been confirmed and more than 300,000 people have died due to the virus.

As countries seek to recover, some of the more long-term economic, business, environmental, societal and technological challenges and opportunities are just beginning to become visible.

To help all stakeholders – communities, governments, businesses and individuals understand the emerging risks and follow-on effects generated by the impact of the coronavirus pandemic, the World Economic Forum, in collaboration with Marsh and McLennan and Zurich Insurance Group, has launched its COVID-19 Risks Outlook: A Preliminary Mapping and its Implications - a companion for decision-makers, building on the Forum’s annual Global Risks Report.

Companies are invited to join the Forum’s work to help manage the identified emerging risks of COVID-19 across industries to shape a better future. Read the full COVID-19 Risks Outlook: A Preliminary Mapping and its Implications report here , and our impact story with further information.

Healthy, hygienic and sustainable

The trend towards online shopping has also seen consumers focus on staying healthy during long periods in lockdown. McKinsey notes a desire to reduce touchpoints to ensure greater hygiene with the shopping experience.

One enterprise in the US has tapped into these trends to provide a service for shopping online at a range of farm shops local to the buyer. To qualify for the FarmMatch scheme, farmers must grow their food using sustainable methods.

As the world navigates its way out of the pandemic, the way we all act as consumers has been changed fundamentally by COVID-19. The research points to this change becoming permanent, leaving retailers and manufacturers with the challenge of attracting and retaining consumers in an 'omnichannel' world, where customer loyalty is hard-won.

Don't miss any update on this topic

Create a free account and access your personalized content collection with our latest publications and analyses.

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

The Agenda .chakra .wef-n7bacu{margin-top:16px;margin-bottom:16px;line-height:1.388;font-weight:400;} Weekly

A weekly update of the most important issues driving the global agenda

.chakra .wef-1dtnjt5{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;} More on Health and Healthcare Systems .chakra .wef-17xejub{-webkit-flex:1;-ms-flex:1;flex:1;justify-self:stretch;-webkit-align-self:stretch;-ms-flex-item-align:stretch;align-self:stretch;} .chakra .wef-nr1rr4{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;white-space:normal;vertical-align:middle;text-transform:uppercase;font-size:0.75rem;border-radius:0.25rem;font-weight:700;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;line-height:1.2;-webkit-letter-spacing:1.25px;-moz-letter-spacing:1.25px;-ms-letter-spacing:1.25px;letter-spacing:1.25px;background:none;padding:0px;color:#B3B3B3;-webkit-box-decoration-break:clone;box-decoration-break:clone;-webkit-box-decoration-break:clone;}@media screen and (min-width:37.5rem){.chakra .wef-nr1rr4{font-size:0.875rem;}}@media screen and (min-width:56.5rem){.chakra .wef-nr1rr4{font-size:1rem;}} See all

Antimicrobial resistance is a leading cause of global deaths. Now is the time to act

Dame Sally Davies, Hemant Ahlawat and Shyam Bishen

May 16, 2024

Inequality is driving antimicrobial resistance. Here's how to curb it

Michael Anderson, Gunnar Ljungqvist and Victoria Saint

May 15, 2024

From our brains to our bowels – 5 ways the climate crisis is affecting our health

Charlotte Edmond

May 14, 2024

Health funders unite to support climate and disease research, plus other top health stories

Shyam Bishen

May 13, 2024

How midwife mentors are making it safer for women to give birth in remote, fragile areas

Anna Cecilia Frellsen

May 9, 2024

From Athens to Dhaka: how chief heat officers are battling the heat

Angeli Mehta

May 8, 2024

Just one more step to your free trial.

.surveysparrow.com

Already using SurveySparrow?  Login

By clicking on "Get Started", I agree to the Privacy Policy and Terms of Service .

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Enterprise Survey Software

Enterprise Survey Software to thrive in your business ecosystem

NPS® Software

Turn customers into promoters

Offline Survey

Real-time data collection, on the move. Go internet-independent.

360 Assessment

Conduct omnidirectional employee assessments. Increase productivity, grow together.

Reputation Management

Turn your existing customers into raving promoters by monitoring online reviews.

Ticket Management

Build loyalty and advocacy by delivering personalized support experiences that matter.

Chatbot for Website

Collect feedback smartly from your website visitors with the engaging Chatbot for website.

Swift, easy, secure. Scalable for your organization.

Executive Dashboard

Customer journey map, craft beautiful surveys, share surveys, gain rich insights, recurring surveys, white label surveys, embedded surveys, conversational forms, mobile-first surveys, audience management, smart surveys, video surveys, secure surveys, api, webhooks, integrations, survey themes, accept payments, custom workflows, all features, customer experience, employee experience, product experience, marketing experience, sales experience, hospitality & travel, market research, saas startup programs, wall of love, success stories, sparrowcast, nps® benchmarks, learning centre, apps & integrations, testimonials.

Our surveys come with superpowers ⚡

Blog Customer Experience

70+ Online Shopping Questionnaire for Ecommerce Businesses

Kate williams.

Table Of Contents

• 70+ Online Shopping Questionnaire
• General e-commerce online shopping questionnaire
• questionnaire on the products
• questionnaire about the website
• questionnaire based on customer behavior

Post-purchase online shopping questionnaire

• questionnaire for customer support

Shipping-related Online shopping questionnaire

Importance of online shopping surveys.

In an online shopping survey, the questions you ask and the question types you use have a huge bearing on the kind of data you will get. When you ask good questions, you will end up with good answers.  In this article, we will check out the online shopping questionnaire sample, and the importance of online shopping surveys. 

Here’s an online shopping questionnaire created just for you!

Feel free to make use of it. Sign up with your email. Tweak the survey the way you want and start sending it out with your brand’s label.

Online Shopping Questionnaire Template

General e-commerce online shopping questionnaire.

• “Were you satisfied with the overall experience?”
• “Would you buy from us again?”
• “Did you find the shopping experience pleasurable?”
• “Was the eCommerce website easy to navigate?”
• “Which are the areas where you would like us to improve?”
• “Was it easy to choose the products?”
• “Were you able to find enough information about the product on the website?”
• “Do you have any suggestions to improve the website experience?”
• “On a scale of 1-10, would you recommend our eCommerce website to friends or family?”
• “Would you like to suggest other products to be added to the site?”
• “How did you find our website?”
• “What are the channels you use to discover similar products like ours?” 

The above survey questionnaire about online shopping is a general eCommerce one to understand the experience of the customer with your brand. The answers to the above questions will give you deep insights into how your eCommerce shopping site is running. It will help you with your lead-generation activities. 

Let’s imagine someone saying they will not buy from your brand again (question #2). You can send them a follow-up question requesting the reason for their stance. You could also try to buy their goodwill by offering a free product or an attractive discount on their next purchase. Make sure that you solve any current issue they are facing. 

Online shopping questionnaire on the products

• “Why do you prefer our product over our competitor’s?”
• “Have you ordered from our shopping site before?”
• “Were you able to find various products to choose from?”
• “Did you find sufficient information about the product listed on the site?”
• “Which were the other products that you were considering before finally buying this product?”
• “Does our product meet your personal/business requirements?”
• “On a scale of 1 to 10, how would you rate the design of the product?”
• “On a scale of 1 to 10, how would you rate the features in the product?”
• “Are you happy with your decision to buy this product?”
• “What is your favorite feature about the product?
• “What is your least favorite feature about the product?”
• “Do you find our product easy to use?”
• “What are the pain points that our product solves for you?”
• “Are you happy to pay the retail price of this product?”
• “Is our product better than that of the competitors?”
• “What made you choose our product?”
• “If given a chance to make the same purchase again, would you buy our product?”
• “What are the features in our product that you feel can be removed?”
• “Would you like us to add more features and functionality into the product?”
• “What are the must-have features that you expect in a product similar to ours?”
• “What was your most important consideration while buying the product?”
• “Did you find ways to ease your confusion (if any) when finally deciding to buy the product?”

In other words…

The final quality of the product has a huge bearing on the purchasing decision of the buyers. But, it would be unwise to ignore many other factors that depend on this decision. You need to make it easy for the customers to buy from you. The product description should be written in an unambiguous manner. Taking feedback from your customers will give you a goldmine of information that will help to create a better product. 

You will understand what matters to your customers. Getting customer feedback will make you more customer-centric. It will change the way you design the products. It will reflect how you present the final product to the customer. Remember this, every single aspect of the product matters. 

Online shopping questionnaire about the website

• “Do you enjoy our website’s navigation?”
• “Were you able to find products with ease?”
• “Did the search bar work the way it was supposed to?”
• “How often do you visit our website?”
• “Do you visit our website because of any other prompts?”
• “What are the things about our website you would like us to change?”
• “Do you like the colors we used in the website?”
• “Is the content in the website evenly spaced for the products to stand out?”
• “Did it take a lot of time for the website to load?”
• “Have you ever had a bad experience on our website?”
• “What are your recommendations to improve our website?”
• “Would you recommend our website to friends and family if they are looking for products similar to the ones we stock?”
• “Is our website better than our competitors?”

The eCommerce website is a valuable piece of real estate. No matter how good the products you manufacture are, if the website is not functional or easy to use, then the visitors would pop out as soon as they came in. 

Online shopping questionnaire based on customer behavior

Let us look at a few questionnaires on consumer buying behavior online shopping.

• “Are you comfortable making payments online?”
• “What issues did you face when shopping online on our website?” 
• “How was the shopping experience at our website?”
• “How was your checkout experience at the online store?”
• “Do you feel safe shopping online?”
• “Have you ever faced a bad payment-related experience while shopping online?”
• “What payment options do you prefer on the online store?”

Understanding how the users feel about buying online will give you an idea about their shopping behavior. People feel safe these days shopping online using their credit cards, but there will be anomalies. Therefore, you need to figure out the payment options that they are all right using. 

Sending an online survey immediately after they make a purchase will give you an idea about your overall experience with your brand. It will give you pointers on areas where there can be an improvement. You can use the data you gather from the surveys to improve your shopping experience. 

• “Are you happy with the products lined up for the category you were searching for?”
• “How was your overall shopping experience?”
• “On a scale of 1 to 10, how would you rate your shopping experience?” 
• “Did you purchase all the products that you wanted?”
• “How easy was it to finalize the item you wanted?”
• “What can we do to improve the shopping experience?”
• “What made you abandon the cart?”

Online shopping questionnaire for customer support

If your customer support is not great, then you will end up alienating them, and increase churn. Not to mention the fact that you will turn away new customers. Giving adequate attention to customer support is the bare minimum that you can do. There are Net Promoter Score (NPS), Customer Satisfaction (CSAT), and Customer Effort Score (CES) questions to gauge how satisfied your customers are with you. 

Let us look at a few questionnaires on online shopping customer satisfaction

• “Was it easy to get in touch with the customer support team?”
• “Did they spend adequate time listening to your issue?”
• “On a scale of 1 to 10, how would you rate our customer support?”
• “Where do you think we can improve on the customer support front?”
• “How fast did they resolve your issue?”
• “Did the customer support team respond immediately to your complaint?”
• “Would you recommend our customer support staff to friends or family?”

Imagine ordering a product only to find that it will take a fortnight for the delivery to happen. You don’t want to have a lackadaisical attitude when it comes to delivering the product. It is the responsibility of the online shopping company to ensure that the product gets delivered on time. 

• “Did you get the product delivered on time?”
• “Are you satisfied with the shipping and delivery experience, on a scale of 1 to 10?”
• “Did the agent call you during the drop?”
• “Was the logistics partner’s behavior professional?”
• “Would you recommend our shipping and delivery services to friends or family?”
• “Where do you think we should improve our delivery services?”
• “What did you like about our shipping and delivery?”
• “Did the website give you timely updates on when you will receive the product?”

Create Appealing Online Shopping Questionnaire with SurveySparrow

Sign Up with SurveySparrow for Free & Star Creating Survey in Few Minutes!

Please enter a valid Email ID.

• 14-Day Free Trial
• • Cancel Anytime
• • No Credit Card Required
• • Need a Demo?

eCommerce businesses have a lot to learn about their customers’ needs, requirements, and motivation to buy, which keep varying. The results from the survey can help in optimizing the website, improving the shopping experience, changing business tactics, etc. 

Let us look at why eCommerce stores should regularly conduct surveys:

1. Helps with data collection

Online shopping surveys are great for collecting data about customers, their motivations, purchasing behavior, requirements, and so on. You will get to know about their challenges, experiences, and preferences. You can gather information in real-time using which you can make immediate changes to your business. 

2. Better customer experience

When a customer buys from you one time and never shops from you again, you will never know what happened. Conducting a survey as soon as they shop from you or a few months after they use your service is a great way to find out if they were dissatisfied with anything. You can take remediation steps immediately to address them. It might work in your favor as they will be pleased that you solved the frictions that they faced while shopping. They might even recant on their decision of not shopping from you again. 

3. Helps improve your marketing strategy

You can use the data gathered from the online shopping survey to understand if your marketing strategy is on point. If you get to know that a majority of your target market is on Instagram, through the survey, there’s your cue to change your strategy. You will have a better understanding of your customers. It will also help you create content for each segment so that you hit the right nerves. 

Here is a few online shopping questionnaires you can ask:

• What are the social media sites you use to search for products?
• Where do you access most content from?

4. Understand why your product isn’t selling

Not having the expected sales from your online store can be terrifying. You need to know why sales aren’t happening. What better way than asking your website visitors directly. 

You can ask the following online shopping questionnaire:

• What stopped you from buying the product?
• What did you dislike about the product? 

5. Understand why people leave your site

Sending an exit-intent pop-up survey when a user leaves the webpage would be an easy way to collect information about why people leave the site. Ask questions to customers who were almost ready to buy, but backed out from doing so. 

• Didn’t like our product? We’d love to know why.
• What can we do to make you buy from us?
• May we know why you exited the shopping cart?

6. Understand your pricing strategy

For online stores that are still unclear about their pricing or want to know what customers think about it, no better way than surveys. You don’t want to scare away potential buyers by overpricing. Nor do you want to underprice your products. Ask the right questions to understand if your pricing is right. 

Here are a few online shopping questionnaire you can ask:

• Are you satisfied with our pricing?
• Are we better priced when compared against competitors?

7. Helps gather testimonials

People trust when others like them recommend products or services. They are more likely to listen to them than advertisements on TV. Online shopping surveys are also a great opportunity for online stores to gather testimonials in a subtle manner. 

• How has our product helped you solve your problems?
• Would you miss our products if we shut shop today?
• Please tell us how you feel about your entire experience with us.

8. Find product market fit

Do your potential customers want the product that you are selling? Is there a market for it? Understanding the demand for your product is pivotal, as it can help you with managing your inventory too. 

• What’s the most important feature you use in our product? 
• Is there an indispensable part about our product? 

9. Measure word-of-mouth recommendations

The NPS survey tells whether your customers would recommend your products or not. For a customer to talk about your products to others, they have to be really fond of the product. Finding out those who give a score of 9 or 10 (called Promoters) will help you identify those who can be made ambassadors for your brand. 

• Why did you give this score?
• What can we do to make things right for you?

10. Get your communication right:

The way you communicate with your existing and potential customers matters a lot. Does it address their pain points? Are your points clear and concise or do you keep rambling? Does your communication address the best parts of your products? Asking the right questions will help you figure out how your marketing messages work for you. 

• What do you like most about our brand?
• Do you enjoy our advertisements?
• How has been our after-sales service? 
• If you could describe the brand in a single word, what would it be?
• What do you think is unique about our brand?

Wrapping up

Consumers spend hours researching about the product before they open their purse strings. Getting a peek into their minds will clearly help your brand. To create remarkable shopping experience for your customers, you need to have a clear understanding of their expectations, pain points, needs, and requirements. Using the right online survey tool and asking the right online shopping questionnaire will help you gather information that can catapult your brand to the next level. Online shopping survey questions examples in the article can be used in your online shopping surveys. 

If you are looking for the an online survey tool to gather relevant data of shoppers, SurveySparrow is the tool to choose. It has a myriad of eclectic features that make data gathering a breeze. Get in touch with us, and we will show you how our online survey tool can help you. 

Content Marketer at SurveySparrow

You Might Also Like

How to use surveys in seo analysis, 11 best customer onboarding tools to check out in 2024, 60+ must ask 360 review questions for performance evaluation, everything about delighting customers. you’ll find them here..

Leave us your email, we wont spam. Promise!

Start your free trial today

No Credit Card Required. 14-Day Free Trial

Request a Demo

Want to learn more about SurveySparrow? We'll be in touch soon!

Get Access to the Checklist of Online Shopping Questionnaire

Create online shopping surveys with surveysparrow for free..

14-Day Free Trial • No Credit card required • 40% more completion rate

Hi there, we use cookies to offer you a better browsing experience and to analyze site traffic. By continuing to use our website, you consent to the use of these cookies. Learn More

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

• Online Shopping and E-Commerce
• 1. Online shopping and purchasing preferences

Table of Contents

• 2. Online reviews
• 3. New modes of payment and the ‘cashless economy’
• Acknowledgments
• Methodology

America has long been a nation of shoppers, and that is as true online as it is in the physical world. The earliest modern e-commerce transactions date to just 1994, but by 2015 Americans were spending nearly $350 billion annually online – or roughly 10% of all retail purchases, excluding automobiles and fuel. Pew Research Center surveys of digital commerce tell a similar tale. When the Center first asked about online shopping in a survey conducted in June 2000, just 22% of Americans indicated that they had ever made a purchase online. But in the intervening years that figure has increased nearly fourfold: Today, 79% of Americans say they make purchases online.

And in an era of widespread social media use and smartphone access, many Americans are incorporating these devices and platforms into their purchasing behaviors. Roughly half (51%) of Americans report making online purchases using their cellphones, while 15% have purchased something by following a link on social media sites such as Facebook or Twitter.

A substantial majority of Americans are online shoppers, but for most this behavior is a relatively infrequent occurrence. Some 15% of Americans say that they make purchases online on a weekly basis (4% do so several times a week, while 10% do so about once a week) and 28% shop online a few times a month. On the other hand, nearly six-in-ten Americans say they buy online less often than a few times a month (37%) or they never make any online purchases (20%).

And while each of these online shopping behaviors are relatively common across a wide range of demographic groups, younger adults in particular are especially likely to utilize cellphones and social media platforms to engage in commercial activity. Some 90% of 18- to 29-year-olds ever buy items online, while 77% have purchased something using their cellphones and 24% have bought something by following a link on social media. By contrast, a majority (59%) of those 65 and older ever generally make online purchases–but only 17% have bought something using their cellphones and just 5% have done so through a social media link.

Two-thirds of online shoppers generally prefer buying from physical stores, although pricing differences are ultimately what drives most Americans’ purchasing decisions

Despite the large share of Americans who engage in online shopping and the potential conveniences that come with buying online, a majority of online shoppers indicate that – all things being equal – they actually prefer to do their shopping in physical stores. Some 65% of online shoppers indicate that, when given the choice, they generally prefer to buy from physical locations; 34% indicate that they generally prefer to buy online.

As might be expected, the most dedicated online shoppers tend to express a relatively pronounced preference for shopping online as opposed to shopping in physical stores. Among Americans who make online purchases on a weekly basis, 62% indicate that they generally prefer to buy online, while 37% generally prefer to buy from physical stores. But among those who buy online on a monthly basis, 42% prefer online shopping while 58% prefer buying from physical locations. And among those who make online purchases even less frequently, just 18% prefer buying online – with 82% indicating that they prefer to shop in physical stores.

But even as many online shoppers express preferences for physical stores in the abstract, their ultimate decision of where to buy something often comes down to price. When asked a second question about their relative preferences for online and in-person shopping that incorporates pricing, fully 65% of online shoppers indicate that if they needed to make a purchase they would probably compare the price they could get online with the price they could get from physical stores and choose whichever one offered them the best deal. Another 21% of online shoppers say they would likely buy from stores without looking at prices online, while 14% indicate they would buy online without looking at prices in physical stores.

Users who frequently shop online are substantially more likely to say that they would typically choose to make purchases by simply buying online without visiting stores: 28% of weekly online shoppers say that they would likely do this if they needed to buy something, compared with 17% of monthly online shoppers and just 6% of those who buy online less often. But even among these frequent online shoppers, 62% say that they would typically compare the price they could get online and the price they could get in physical stores and choose whichever one is cheapest.

When buying something for the first time, Americans especially value the ability to compare prices and ask questions

Americans take a number of factors into consideration when shopping for something that they haven’t purchased in the past – especially the ability to compare prices from multiple sellers and to ask questions about what they are buying. When asked about the importance of seven different factors when buying something for the first time, 86% of Americans say that the ability to compare prices from several different sellers is either extremely (42%) or somewhat (44%) important, while a similar share say that being able to ask questions is extremely (42%) or somewhat (41%) important.

Other factors that Americans consider important include the ability to buy from stores or sellers they are familiar with (34% of Americans describe this as extremely important); the ability to read ratings or reviews that other people have posted online (32%); the ability to look at or try out the product in person (30%); and the ability to get advice or recommendations from people they know (23%). The ability to buy online – without having to make a trip to the store – ranks as the least important factor: just 42% of Americans say that this is at least somewhat important to them when buying something for the first time, and only 10% describe it as extremely important.

Regardless of their demographic characteristics, when buying something for the first time most Americans assign greater importance to being able to look at or try the product in person than they do to being able to buy online without making a trip to the store. However, frequent online shoppers are one of the few groups who place more importance on being able to buy online. Nearly three-quarters (72%) of weekly online shoppers say that being able to buy online without having to make a trip to the store is important to them when buying something for the first time (20% say it is extremely important). Meanwhile, a slightly smaller share (66%) says it is important to be able to try something out in person (with 15% saying this is extremely important).

Many Americans are using their cellphones while inside physical stores to help with purchase decisions or to get a better price

Today cellphone ownership is nearly ubiquitous, and roughly two-thirds of Americans have smartphones. And as the reach of these mobile devices have expanded, many consumers are using them to augment and assist with their physical and in-person purchasing experiences.

The survey asked about four different ways that people might utilize their mobile phones while making purchasing decisions inside physical stores and found that calls for advice and assistance are especially common: Nearly six-in-ten Americans (59%) say that they have used their cellphones to call or text someone while inside a store to discuss purchases they are thinking of making. Just under half (45%) have used their phones while inside a store to look up online reviews or to try and find a better price online for something they are thinking of purchasing. And a relatively small share of Americans (12%) have used their cellphones to physically pay for in-store purchases.

As noted above, a majority of Americans under the age of 50 have used cellphones to purchase something online – and this group is also especially likely to utilize their cellphones while making in-store purchasing decisions. Fully 70% of 18- to 49-year-olds have used their cellphones to call or text someone from inside a store to ask for purchasing advice, while 62% have used their phones to look up online reviews of something they were thinking of purchasing or to see if they could find a better price online. And nearly one-in-five (18%) have swiped their phones at the register to pay for purchases.

Sign up for our weekly newsletter

Fresh data delivery Saturday mornings

Sign up for The Briefing

Weekly updates on the world of news & information

• Emerging Technology
• Personal Finances
• Platforms & Services

Online shopping has grown rapidly in U.S., but most sales are still in stores

On alternative social media sites, many prominent accounts seek financial support from audiences, majority of americans aren’t confident in the safety and reliability of cryptocurrency, for shopping, phones are common and influencers have become a factor – especially for young adults, payment apps like venmo and cash app bring convenience – and security concerns – to some users, most popular.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Social Media's Role in Reshaping Online Shopping, According to Retailers

Updated: May 16, 2024

Published: May 14, 2024

Social buying. Everyone and their mama is doing it — or maybe it‘s just me and my family. I’m consistently tagged in posts (thank you, cousin) about adorable gifts, must-have outfits, and the like.

Now, I’m a content marketer who knows when I’m being sold to, but even I get lured by social posts with irresistible products. And I know I’m not alone — as of 2024, over 110 million Americans (roughly 42% of internet users) are fellow social buyers.

So, if you’re a brand selling products to consumers and you’re not already using social selling, 2024 is a superb year to start.

Not convinced?

Let’s explore the social commerce landscape, best practices, and fun examples of brands already seeing success. Plus, I’ll share insights from experts I talked to about the future (and present-day) of social commerce.

HubSpot uses the information you provide to us to contact you about our relevant content, products, and services. HubSpot will share the information you provide to us with the following partners, who will use your information for similar purposes: Search Engine Journal, Litmus, Rock Content. You can unsubscribe from communications from HubSpot at any time. For more information, check out HubSpot's Privacy Policy . To unsubscribe from Search Engine Journal's communications, see Search Engine Journal's Privacy Policy . To unsubscribe from Litmus's communications, see Litmus's Privacy Policy . To unsubscribe from Rock Content's communications, see Rock Content's Privacy Policy .

The State of Marketing in 2024

HubSpot's Annual Inbound Marketing Trends Report

• Top Marketing Channels
• AI in Marketing
• Managing Privacy
• The Future of Marketing

You're all set!

Click this link to access this resource at any time.

Social Media and Online Shopping — Today's Landscape

7 social media online shopping trends, tips for making the most of your social media.

Salespeople. Marketers. Brands. They’re all jumping aboard the social selling bandwagon for good reason. Global social commerce sales could reach an astounding $2.9 trillion by 2026 .

I know it’s a staggering number, but forecasts aren’t always enough to convince the gatekeepers of our selling and marketing budgets, are they?

So, let’s look at some facts and numbers straight from the horse’s mouth (buyers and brands):

• Salespeople reveal, “Our highest quality leads come from social media , so we’ll prioritize this channel.”
• Consumers say, “ 36% of us use social media to find new products, plus 28% of us Gen Z and Millennials purchased directly from social media apps within the past three months.”
• 80% of social media marketers agree that “consumers are buying our products directly from social apps more than they purchase from our brand websites or third-party resellers.”
• 87% percent of brands confirmed that “social selling has been effective for their business.”
• Instagram says that “ 71% of Gen Z are likely to buy directly from [Instagram] compared to 68% for YouTube and TikTok.”

And if that’s not enough to convince you, check out this chart illustrating how well sales improved year over year for brands using social selling.

In a nutshell, social media commerce is on the rise, widely accepted by young consumers, and drives sales for brands.

What’s the secret behind the success and rapid growth of social media selling? Well, there isn’t one. Like any other marketing channel, you must monitor competitors and test different strategies.

But to give you a leg up, I gathered the top trends I’m seeing based on responses from experts and my own research.

1. Seamless In-App Shopping Experiences

As I noted above, consumers are buying from brands directly on social media platforms, so it makes sense to build a seamless in-app shopping experience for your customers.

No one wants to jump through hoops to make a purchase they thought would take only a few seconds.

But since you don’t have control over the development of these apps, or how well they’ll work for your customers, be sure to choose platforms already two steps ahead.

For example, I see social networks like Facebook, Instagram, and TikTok enhancing in-app shopping. Facebook has a marketplace and shops you can use to build your digital storefront.

(In our recent study, we found this feature to be highly important to 36% of marketers.)

Instagram also has shopping features that could be used by over 46 million American social buyers in 2024. Both Facebook and Instagram allow users to checkout directly on the platform.

TikTok Shop is also available, but has been slow to gain traction in the U.S. In the summer of 2023, it generated $3 million to $4 million daily.

If you decide to use the platform, know that users can shop from multiple brands at once and add products to a single shopping cart.

But don’t rely on platforms to deliver seamless social media shopping experiences. I recommend taking it further by creating shoppable social posts. You can also use Likeshop.me to tie your shop to your social posts.

World Market wins with shoppable Instagram posts.

Like all the decor you see in a photo-rific post on Instagram? You can buy everything in one sitting. Below is an example of a highly shoppable post from World Market created using Likeshop.me.

Image Source

This shopping feature turns your Instagram posts and TikToks into mini-shops where you can tag and add products for shoppers to explore (and more importantly, purchase).

Gift Delivery also saw great success using shoppable videos.

“ By integrating direct purchase links into our video content, we've made it seamless for customers to buy products as soon as they see them featured,” shares Billy Parker , Gift Delivery’s managing director.

Parker continues that preliminary campaigns with this feature yielded “a 20% uptick in sales attributed to shoppable video content alone.”

Parker also notes that “the success of these campaigns lies in their ability to not only showcase products in action but also in the convenience they offer, significantly shortening the customer journey from discovery to purchase.”

Are you wondering which platforms you should focus on?

The top social networks offering the highest ROI (according to 1,000+ social media marketers) include:

• Instagram (33%).
• Facebook (25%).
• YouTube (18%).
• TikTok (12%).
• X/Twitter (6%).

2. Short-Form Product Videos to Drive Engagement and Sales

Product demos, teasers, and similar videos are a money-maker on social media for 66% of video marketers . The beauty of this trend is that it’s short and sweet, and allows you to toot your own horn.

According to 36% of video marketers, three minutes or less is all you need. Done right, 40% of video marketers state that videos help customers understand your product better.

But how do you create engaging videos that feature your product without it coming off as an ad?

One option is to get an influencer involved. Tying social proof into the video makes it less sales-y — even more so if you partner with a small, trusted content creator (more on that later).

Examples of short video content you can create include:

• Behind the scenes (BTS). Show you’re human and relatable.
• Product teasers. Showcase a new feature or product.
• How-tos. Share a quick tip to improve a process using your product.
• User-generated content (UGC). Demonstrate how others are using your product.
• Highlight reels & montage. Show the multiple benefits of your product in action.
• Customer reviews. Leverage customer success stories as social proof.
• FAQs. Answer questions about your product.
• Influencer collabs. Partner with an influencer to feature your product in their content naturally.

You get the idea. So what does short video content look like in the real world? Let’s take a look.

Irresistible Me lets its hair down on TikTok.

Irresistible Me is a beauty company specializing in hair extensions and wigs.

“TikTok is where we let our hair down — literally! It’s all about fun, quick, engaging content,” says Irresistible Me’s Marketer Kate Ross. “We jump on trends, create challenges, and use TikTok shopping features to link back to our products. It’s like the energetic party everyone wants to be at.”

Here’s an example of a TikTok using user-generated content, or should I say influencer-generated content, with Audrey Boos .

@irresistibleme_hair The curls are unreal😱@audrey🛸 #fyp #foryou #foryoupage #curlyhair #curlyextensions #irresistiblemehairextensions #viral #extensions #trendy ♬ original sound - Stan :)

The video did well, with over 2K likes, 700+ bookmarks, and nearly 100 comments.

“TikTok has been huge for us. We’ve been getting creative, jumping into challenges, and teaming up with influencers who just get what we’re all about,” continues Ross. “It’s all about fun videos that show off what you can do with our products. This approach has brought a bunch of new faces to our site and helped us stand out in a pretty crowded market.”

3. More Team-Ups With Nano- and Micro-Influencers to Build Trust

I’m seeing fewer big influencers and more micro-influencers in my feeds lately. And I kinda like it. Okay, I really like it. Like most, I enjoy seeing real and relatable content creators.

It appears more brands are taking this approach, too, which is better for their bottom line — it reduces the marketing spend and potentially boosts their revenue.

Roughly 67% of influencer marketers work with micro-influencers and 24% team up with nano-influencers. The top social platforms they plan to do most of their partnerships on are:

• Instagram (27%).
• Facebook (19%).
• TikTok (15%).

So far, 47% of marketers report successful micro-influencer partnerships. This is not surprising when 21% of social media users between 18 and 54 buy products based on influencer recommendations.

So how can brands put this to use?

Glossier uses UGC to show how everyday women use its products.

Glossier , a renowned makeup company, regularly partners with nano- and micro-influencers. The following IG reel shows Sky Mejias applying its lip products. It’s a mix of a tutorial and social proof to get followers to give the items a try.

          View this post on Instagram                       A post shared by Glossier (@glossier)

The video generated 320K views and nearly 7K likes, so we know it got good reach. This influencer is considered a nano-influencer since she has just over 3,500 followers.

It’s also promising that 1 in 3 Gen Zers bought from an influencer-founded brand in the past year. This proves how much our younger generation of buyers trusts influencers.

“Micro-influencers have been our secret weapon. We've seen incredible engagement from collaborations that feel genuine and personal,” shares Ross. “One campaign that stands out involved partnering with a micro-influencer who shared her journey from short to long hair using our extensions. Her story resonated with many, leading to a spike in visits and sales.”

Ross shares that they also leveraged AI: “What's cool is how we can test using AI to match our products with the right influencers, ensuring their audience aligns with our target customers.”

4. Social Media Becomes a Top Search Channel

Gen Z and millennials continue to break the mold, this time with how they find brands and products. The old way: Google, Bing, and Yahoo. The new way? TikTok and Instagram.

Our State of Social Media Marketing 2024 report shows that 36% of Gen Z and 22% of millennials search social media more than they do search engines.

To conform to this new trend, brands must treat social media posts like they would SEO content.

“I can confidently say hashtags and reels are among our top performing Instagram strategies,” shares Michael Nemeroff , co-founder of Rush Order Tees . “We use targeted keywords as hashtags for our posts. However, we specifically prioritize keywords that still have less than 100k uses as hashtags to increase our chances of reaching more narrow, niche audiences.”

The Ordinary and its partner influencers use keyword-focused hashtags.

The best way to demonstrate the keyword-focused trend is to do it. So, I typed #acneskincare into Instagram and found the following reel by Joy Mercy Michael .

          View this post on Instagram                       A post shared by Joy Mercy Michael | Mrs.Bivin (@lovedbymercybivin)

What makes this post work? It’s 100% user-generated content. It’s unsponsored and naturally refers her viewers to The Ordinary’s product (among a few others in the description, making it feel more authentic).

And since she tagged the brand in the post, it’ll reach its audience too. It also helps that she has over 100K followers.

Pro tip: Since it’s not just your own posts customers will find featuring your products, I recommend selecting a hashtag directly related to your product.

By promoting this hashtag in every post, you increase the likelihood that customers will use it too, which in turn increases the odds of prospects finding your products.

The more of your posts users see in the results, the higher the odds they’ll click on one.

5. Live Streaming Continues to Grow

Publishing images, reels, and carousels on social media keeps your audience engaged. But there’s nothing like the experience of interacting with a brand and other shoppers in real time.

Live streaming allows retailers to connect with customers and potential buyers on a more personal level, which humanizes your brand and offers the attention they need during the customer journey.

I believe brands should do more Q&A-style lives to invite viewers to interact and get answers that may keep them from hitting the buy button. The stream could feature an employee or an influencer.

Hallmark Timmins , a Canadian gift shop, partners with the latter.

“My brand has tested live-stream shopping events and found sales conversions to be three to four times higher than traditional social media posts,” explains Shawn Stack , Founder of Hallmark Timmins.

Stack continues that, “Viewers seem to find the real-time, interactive nature of live streams highly engaging, and the option to buy with one click reduces purchase friction.

We've also built personal connections between our influencers and their viewers, who regularly tune in to not just shop but also chat and get style advice.”

Your stream doesn’t have to be all sales. It can be a product demonstration or a Q&A session. If you have a product line, hire models or influencers to use the items so your audience can see how it works/looks before buying.

But don’t turn your stream into an infomercial. Instead, use “quiet selling,” where models wear shoppable items viewers can purchase during the stream. There’s no overt selling — just valuable discussions.

In a recent HubSpot study, we found that 27% of marketers want to use platforms that offer live-streaming features.

Are you wondering if live streaming actually works? According to CivicScience data, 25% of Gen Zers and 14% of millennials have purchased from live shopping streams.

Additionally, by 2026 live shopping sales will make up 5% of ecommerce in the U.S.

Aldo uses live shopping mixed with influencers to drive engagement.

Canada is already seeing success with live streaming. For instance, Aldo launched a successful live shopping pilot, partnering with influencers Mimi Cuttrell and Nate Wyatt to showcase its spring 2021 collection.

The interactive livestream allowed viewers to explore products from home, achieving a 308% engagement rate and driving 17,000 page views to Aldo's website in the following five days.

I expect to see this trend become mainstream in America soon, especially with social commerce on the rise.

6. Augmented Reality is Enhancing Shopping Experiences

The pandemic normalized shopping for and purchasing everything entirely online — even houses and cars.

Brands that took notice are adopting augmented reality (AR) to attract shoppers who enjoy the convenience of online shopping, but still want the in-store shopping experience.

This AR shopping experience works by overlaying a digital product image on a real-world image of a store or the customer's home (or face). Like that lamp? Use your smartphone or tablet to see how it’d look on your bedroom nightstand.

Peeping that pair of glasses? Mirror yourself in selfie mode wearing the shades to see if they’re your style.

It’s the same for hair products. “We’re currently working on implementing Augmented Reality (AR) on our website,” shares Ross, “so that customers can see how they’d look in different hair extensions or wigs without leaving their couch.”

It’s a smart move — it gives shoppers what they want, increases sales, and reduces returns.

I predict brands will drive traffic to their website using AR experiences on social media. However, many will create these tools within their apps and websites to keep consumers shopping in their online stores.

American Eagle partnered with Snapchat for “Dress Yourself” AR and VR experience.

In 2021, fashion brand AE used Snapchat to launch its Dress Yourself AR campaign — a unique experience where customers could use their self-facing camera to try on and shop various looks within its back-to-school collection.

They could even share the looks with their friends.

AE also partnered with Bitmoji to create a first-of-its-kind virtual reality clothing line that customers could purchase on Snapchat and wear on their avatars.

This wasn’t its first dabble in the metaverse — AE also launched a virtual store on Snapchat during the holiday season of 2020. After raking in $2 million, it chose to go all in, hiring an in-house metaverse team .

Now, it’s a matter of when other retail brands will follow suit.

Ready to dive head first into some of these social commerce trends? Before you do, be sure to read the following best practices I gathered from retailers and marketing experts.

Use interactive content to engage and collect first-party data.

Posting on social media can help with brand recognition. But if you’re trying to sell on social media platforms, engagement is the name of the game.

You can use a mix of videos to drive views and interest, but there’s another way I found to be quite effective: quizzes.

These are not just your typical “take this quiz to see what type of dog you are” kind of content. I’m talking about quizzes that tie directly into a purchase.

I believe this is a game changer — it got me to purchase a face wash cream from IL MAKIAGE (and they got me with an upsell for its cream before checking out, too).

According to PopSmash , a Shopify quiz app tool, quizzes have helped:

• A haircare brand increase Shopify store conversions by 41%.
• A cosmetic brand increase ad revenue by 200%.
• A home goods brand increase their average order value by 60%.

“Instead of trying to sell directly on social media, we've found success in targeting engagement that sells for us,” explains Gabe Mays , founder of PopSmash. “For example, when posting about products, we have merchants share a link to a product recommendation quiz where users can find the best variant of that product for them.”

According to Mays, this works better because people are on social to be entertained, not buy. The quiz engages them while helping them discover the best products for them and can drive conversions.

The opt-in rate: Out of those who comment on a social post, around 30% will take the quiz and opt-in.

Craft engaging, authentic live sessions.

Live streaming is a growing trend, but it won’t work well if your streams are … well, boring. It’s tempting to jump in and showcase your products, but remember — consumers want to be entertained, not sold to.

As I stated earlier, you shouldn’t create infomercials. Use themes, trends, and edutainment content to attract viewers and then quiet sell to them with shoppable items in the video.

I’d also recommend teaming up with influencers across platforms like Twitch, YouTube, and Kick (the new kid on the block).

Then, when a sales event comes around — such as during the holidays or a new product launch — you can partner with influencers to showcase the goods.

“For Mother's Day, we did something special,” shares Ross. “We teamed up with moms who are also influencers to chat about something many moms go through but don't always talk about: hair loss after having a baby. These amazing moms shared their own stories … which helped a lot of our followers feel understood and less alone.”

These influencers didn't just talk about the problem, though. Through their videos, they also showed how Irresistible Me’s hair extensions could help. “What made this campaign a hit was how real and open it was,” continues Ross. “Plus, offering a special deal for Mother‘s Day was the cherry on top. It was all about connecting, sharing real stories, and showing that there’s a simple way to feel great about your hair again.”

Use giveaways to increase reach for quizzes and improve personalization.

“The new key approach we‘ve found (especially for DTC brands) is not to just think of ’social selling' as selling since often users are on social to be entertained, not to shop,” says Mays. He says that you have to first engage them, and then take an “oh by the way, maybe you'll like this” approach.

According to Mays, giveaways like this activate your social audience, who drive organic engagement and funnel it to the quiz. The quiz captures contact details (e.g., name, email) and product preferences to get them into a higher-converting channel like email or SMS.

Mays advises, “The key thing here is that ‘social selling’ isn't just about trying to drive sales in the moment, but giving yourself leverage (personalization and contact data) to consistently drive longer-term sales.”

Don’t just generate customers — grow a community.

At least 20% of people have joined and participated in an online community. Some of them belong to communities created by their favorite brands.

It’s a fun way to connect with customers, get feedback, and share products and information they care about.

It’s about building relationships and loyalty — and hopefully, brand advocates — to increase your brand awareness and sales.

Our research shows that in 2024, 86% of social media marketers will prioritize building an active online community.

“One major trend is community-driven curation and influencer marketing. Our ‘DoDo Crews’ program taps into passionate communities, giving them tools to share looks and inspirations directly with their followers,” shares Mark Sheng , project engineer at DoDo Machine .

Sheng shares that, “Early results show a 25% bump in conversion when shoppers discover products through these trusted sources.”

Sheng’s advice is to put the community at the center. Facilitate authentic connections among brands, creators, and shoppers. Use trusted voices and native video. Social shopping should feel like genuine sharing between friends.

Community & Connection = Clicks & Conversions

Social selling isn‘t about shoving products down people’s throats. It‘s about fostering genuine connections and cultivating communities of passionate fans.

The brands winning are those making their customers feel like they’re sharing between friends (or at least, trusted advisors).

User-generated content, influencer partnerships, community curation — these are what will continue to drive social sales. When trusted voices do the selling for you, it turns a promotion into a friendly recommendation.

Tie in immersive tech like AR try-ons and shoppable videos to meet customers exactly where they are: scrolling on social, ready to be entertained and inspired to spend.

Brands putting community first will unlock clicks, purchases, and meaningful loyalty. They're the ones who understand the future of social commerce is all about human-to-human connection, not brand-to-consumer broadcasting.

Don't forget to share this post!

Related articles.

290+ Social Media Holidays for Your 2024 Content Calendar [+Template]

Which Social Media Channels are Gaining and Losing Steam in 2024? [New Consumer and Platform Data]

How to Use AI For a More Effective Social Media Strategy, According to Ross Simmonds

The Future of Social Media [Research]: What Marketers Need to Know

Is TikTok Becoming the Next QVC? All About TikTok Live Shopping

The Most Effective Types of Content on Social Media in 2024 [New Data]

57 TikTok Stats to Know in 2024

70+ Social Media Demographics for Marketers in 2024

The 7 Types of Social Media and Pros & Cons of Each (Research)

11 Social Media Trends Marketers Should Watch in 2024 [New Data]

HubSpot uses the information you provide to us to contact you about our relevant content, products, and services. HubSpot will share the information you provide to us with the following partners, who will use your information for similar purposes: Litmus, Rock Content, Search Engine Journal. You can unsubscribe from communications from HubSpot at any time. For more information, check out HubSpot's Privacy Policy . To unsubscribe from Litmus's communications, see Litmus's Privacy Policy . To unsubscribe from Rock Content's communications, see Rock Content's Privacy Policy . To unsubscribe from Search Engine Journal's communications, see Search Engine Journal's Privacy Policy .

Data from over 1,400 marketers across the globe.

Marketing software that helps you drive revenue, save time and resources, and measure and optimize your investments — all on one easy-to-use platform

Every product is independently selected by (obsessive) editors. Things you buy through our links may earn us a commission.

The Trash and Treasures of Temu

How are these headphones $4.98 and every other question you have about the chaotic new everything store..

This article was featured in One Great Story , New York ’s reading recommendation newsletter. Sign up here to get it nightly.

Maybe you first clocked it during the Super Bowl, when its “Shop Like a Billionaire” jingle aired not once, not twice, but six different times. Maybe the site has been haunting you on Facebook and Instagram with targeted ads for products that are implausibly priced (a six-pack of bras, $16.58) and at times delightfully niche (a silicone nose model for piercing practice, $3.98). Or maybe you went to visit your parents one day only to find their home and garage teeming with cheap gadgets from China that they bought from a fun new app. Temu, which is both an app and a website, launched Stateside in September 2022. As of December 2023, it serves approximately 30 million daily users in the U.S. and was the most downloaded free app of last year. But while Temu may feel like a new kind of retail experience, it’s really just a turbocharged amalgamation of things we’ve seen before: the scale of Amazon and vastness of its merchandise selection, the aggressive advertising of Wish, the treasure hunt of a Ross or Marshalls, and the mobile gamification of commerce à la Candy Crush. And in a time of high inflation — as venture-subsidized start-ups shutter and even dollar-stores prices rise — Temu can feel like the last affordable shopping destination left.

What exactly is it?

At its core, Temu is just a giant marketplace where manufacturers and suppliers — around 80,000 of them, mostly based in China — can showcase their goods and sell them directly to consumers abroad. It’s up to the vendors to list their inventory on Temu’s site, but Temu will manage almost everything else: setting the prices, customer service, dealing with Customs, and handling payments and returns.

So it’s just Amazon but based in China?

Many of the manufacturers that sell stuff on Temu also supply goods to Amazon (and stores like Target and Walmart). Still, there are a few key differences between the two businesses. While Amazon owns some of its inventory — maybe purchased from a wholesaler or stocked as part of the company’s private label — Temu doesn’t own the products it sells. Amazon has been optimized for fast delivery, erecting a vast infrastructure of trucks and warehouses. Temu has optimized for lower prices. Its delivery system, which relies mostly on third-party shipping services like USPS, typically takes a week or longer, but the prices on average are noticeably lower than Amazon’s. John Deighton, a professor at Harvard Business School who studies consumer behavior, believes Temu’s “long-term strategy is to really hurt Amazon.”

How does it make money if everything’s so cheap?

By cutting out the intermediary steps between factories and consumers, Temu claims it can price products lower than most retailers. But experts hypothesize that Temu is most likely subsidizing at least part of the cost of its products in order to gain market share. John Deighton, a professor of consumer behavior at Harvard Business School, says he has seen an analysis of Temu’s financials that suggests no matter how much stuff it’s selling, it can’t be making up for what it’s losing. “It’s not just a loss; it’s a hopeless loss,” he says. “It’s on a scale that no amount of volume is going to redeem.” China Merchants Securities, a brokerage firm, estimated in 2022 that Temu is losing between $588 million and $954 million a year. Juozas Kaziukėnas, founder of e-commerce intelligence firm Marketplace Pulse, calls the strategy a “shock-imposed buy”: When the price of something is so unbelievably low that customers have no choice but to hit “purchase.”  

Who is paying for this, then?

One reason Temu can afford to lose so much money is that its parent company, PDD Holdings, is a Chinese e-commerce giant whose revenue for the fiscal year 2023 was $34.88 billion. PDD Holdings is notorious for its brutal “996” work culture, in which employees work from 9 a.m. to 9 p.m. six days per week. According to the newspaper Nikkei Asia, PDD employees use pseudonyms at work and are discouraged from socializing with one another. The company has a strict clock-in, clock-out system, in which being even one minute late results in the deduction of one hour’s pay. PDD’s work culture was particularly scrutinized after the deaths of two of its employees. The first, in 2020, involved a 22-year-old who suddenly died while walking home at 1:30 a.m. after working late; the other, in January 2021, occurred when an engineer on leave jumped off the 27th story of an apartment building. An anonymous venture capitalist in San Francisco says of PDD, “It’s just one of the most insanely hardworking, brutal cultures, even in China. The intensity is unparalleled.” (When asked about the PDD work culture, a Temu spokesperson said, “We take pride in our energetic and creative team.”)

Is the website supposed to give me a headache?

Temu’s app and website feel like a cross between a carnival and a Pennysaver catalogue on steroids: There are lightning deals and limited-time offers, countdown timers and random-prize draws, all of them dangling the possibility of a rebate or discount (many of which expire after 60 minutes). You can play games like Fishland, in which feeding a certain number of fish wins you a free product, and Lucky Flip, in which you can earn rewards by matching symbols on cards. Occasionally, a picker wheel will pop up, tempting you to spin to win a discount. But as novel as this may seem to Americans, this chaotic, arcadelike shopping experience is not new to Chinese e-commerce — AliExpress has been using similar tactics for nearly a decade.

Is any of this stuff good?

Temu is dizzying: more than 3 million product listings, divided into 33 categories further split into dozens of subcategories. To figure out what people are buying, I reached out to both avid consumers and professional product reviewers. Many use the site as a place to buy unglamorous sundries they’d normally get from Amazon. One 20-something Goldman Sachs analyst uses it to purchase items for her pet: enrichment toys, treat dispensers, and water bottles. A venture capitalist says he ordered blackout curtains on the app when he realized they were far cheaper on Temu ($10) than on Amazon ($20).

Dorothy, a Chicago-based lawyer, first heard about Temu at her favorite dollar store when buying craft supplies. “I overheard some of the crafty mavens in the aisle talking. ‘Girl, you got to go on Temu,’” she says. She has spent $5,643.27 on Temu since January 2023 and found success in a few categories. “The jewelry is fantastic,” Dorothy says. She recently bought her daughter a customized 925-sterling-silver necklace (“A Sex and the City necklace like Carrie had”), which was received enthusiastically. She has also been pleased with purchases that are functional but can be disposed of quickly, like press-on nails and silk flowers for crafts, and she’ll also buy things she wants to try once but doesn’t wish to spend a lot of money on: silver lamé leggings and 89-cent clip-on bangs she keeps around so that when she suddenly has the impulse to get bangs, she can “put those on and stop myself.”

What’s with all the knockoffs?

The site abounds with close and not-so-close replicas of beloved products: There are copies of the viral Bogg bag , Yeezy Runners , Crocs , the 40-ounce Stanley tumbler , and even the Apple Watch . The site is also saturated with facsimiles of popular makeup and skin-care products, like a Tarte Shape Tape look-alike ($1.79) and something that resembles the Dior Lip Glow Oil ($2.69). The quality of the knockoffs can be a toss-up, though; Dorothy, for instance, is partial to a pair of $20 fake Ferragamo shoes that she calls her “fake-a-gamos,” but had to give away an Izod-polo-shirt look-alike because the logo looked like a “drunk alligator.”

Do I really dare use Temu lip oil?

“Once you start testing the formula, the texture, I would say it’s very different from the original product,” says TikTok influencer Demi Ngai , who has tried many of Temu’s beauty offerings. The most noticeable difference, she says, is the “overwhelming fragrance” of the Temu version. Dorothy’s review is a bit more blunt; Temu’s makeup, in her experience, is “garbage.”

Are any of the knockoffs actually good dupes?

Austin Evans, who reviews gadgets on his YouTube channel , says if he could recommend one tech accessory on Temu, it would be the Razer-mouse dupe, which often costs less than $20 (the price of a real one ranges from $30 to $180). Evans has also found that Temu’s cheap Bluetooth earbuds, the ones that look like AirPods, are “surprisingly good.”

Are the electronics safe?

There have been reports of Temu-related electrical snafus. In early 2024, a Swiss daily newspaper found that local police had received several reports of cheap electrical goods, such as chargers and batteries, that had caused major fires. Although the origin for these items is unclear, an officer on the Aargau police department’s TikTok account later named Temu as a “cheap platform” whose electrical goods may not adhere to certain standards and certifications. And earlier this year, a consumer-watchdog group in the U.K. tested eight heaters — three from Temu and five from TikTok Shop — and found that six were unsafe and posed fire hazards and explosion threats. Shortly after the report was released, Temu removed the heaters from its site. “We expect our sellers to meet the standards required by the markets they sell to,” a spokesperson said. “We adopt a comprehensive approach to vetting the merchants who sell products through our platform.”

Didn’t I just see some of this stuff on Etsy?

Many artists and small-business owners have found copies of their work being sold on the website. Jessi Roberts, founder of a Texas-based accessory and apparel business called Cheekys, hadn’t heard of Temu until her customers began sending her screenshots of the site, which listed counterfeit versions of her earrings — and even used her own product images. “They will pull every picture off our website,” she says. Even now, her customers will tag her in a Temu ad or comment on Temu’s Instagram post when they spot counterfeits, but their loyalty can backfire: “The problem is that the second they start talking about Temu or commenting on their post, Temu’s ads start coming to them hot and heavy.” Roberts and her lawyer, Andrea Sager, say they have submitted takedown requests for copied products dozens of times, most of which have been futile — only after Time published an article about her stolen designs did Roberts receive a message from a Temu employee letting her know that they had removed the counterfeit products. Sager, who represents small-business owners, says she is constantly on Temu’s website looking for her clients’ work. (“Temu has strict policies against sellers who infringe on intellectual-property rights,” a spokesperson said. “When we receive reports of infringement, we promptly investigate each case and take appropriate action.”)

What are the weirdest things Temu sells?

Many of the oddest wares I came across on Temu call to mind the Japanese term chindōgu , which literally translates to “unusual tool” and denotes a bewildering but impressive Japanese invention that solves an ultraspecific problem: butter in the form of a glue stick, a fan attachment for chopsticks to cool down noodles, a toilet-roll dispenser affixed to a headband for chronic nose-blowers. One day, on a whim, I typed the word silicone into the search bar, which yielded a seven-piece set of soft silicone body parts for piercing practice , silicone foot models, a silicone back-scrubber belt , sex dolls that looked like AI avatars in three dimensions , and an “artificial booty shaper” that was basically a pair of flesh-colored underwear made of silicone with extra padding on the butt (and a butt crack to make it look more realistic).

What happens when you buy something?

To find out, I placed two orders. My total haul, which was 29 items chosen through a mix of recommendations, curiosity, and personal need, came out to $120.08. From the moment I hit “purchase” to when the packages arrived from China, I received 12 emails updating me on their every move. Each order came in a large white plastic mailing bag. Inside, many of the items appeared to be in their original factory packaging, labeled with the manufacturer’s name, address, and batch number.

Is there anything a buyer should be wary of?

We asked people to share their biggest Temu flops. If there’s anything to stay away from, Evans says, it’s video games, especially those for the Nintendo Switch. Even if they look very realistic, there’s a chance that they could be counterfeit. Installing them, he has heard, can get your Switch console banned. Dorothy’s Temu fail was a nearly five-foot-tall stuffed giraffe she bought for her grandson — it was also one of the more expensive items she has ever bought on the site, at $63. When the giraffe arrived, it wouldn’t stand on its legs, so her grandson had no choice but to play with what resembled a wounded animal.

My own experience confirmed that no matter what category you’re looking at, Temu can be hit or miss. The first order, which took seven days to arrive, contained wireless earbuds, a compact desktop vacuum cleaner, ear-protection covers, silicone socks, white foam slippers, a neck fan, a gooseneck phone holder, and a Montessori tooth model, which I had hoped would convince my toddler to brush his own teeth. The silicone socks were covered in a fine white powder; the neck fan’s cooling power was weak. While Evans had spoken highly of the electronics, the wireless earbuds simply refused to connect to my phone, and I could not figure out how to use the vacuum cleaner.

But when my second package came, ten days after the order date, I was pleasantly surprised. There were still some duds, like a heated eyelash curler that performed worse than the non-heated ones that I already own and a pair of silicone stick-on nipple covers that were not as sticky as I would have liked. Still, I was satisfied with the quality of the wet bag for diapers and clothes, the tabi socks , a pair of grippy socks that can be used for Pilates , and this precision pin-tail comb with a stainless-steel tip , which has a substantial weight to it and is very similar to this Y.S. Park one I have that costs ten times more.

Okay, but was anything you ordered actually good ?

YouTuber Matt Shaver told me he’d had good luck with Temu’s jeans, which inspired me to try some for myself. After some browsing, I landed on this $19.11 pair — both because I liked their baggy shape and contrast stitching, and also because they had the highest percentage of cotton (95 percent cotton, 5 percent polyester). These jeans ended up being my most shocking discovery: comfortable, snug around the waist but baggy through the legs (which is how I like jeans to fit), and remarkably reminiscent of the much beloved Rudy Jude utility jeans and these double-knee painter pants by Stan Ray . Upon closer examination, some of the seamwork reveals the lower quality, but the jeans are no worse than what you would buy at any other apparel chain that makes its clothes in China (like Gap or Zara). My other Temu win was this two-in-one box cutter and thermal-paper corrector , which works a bit like magic: You swipe it across a label taped to a box that you want to reuse and the label’s text disappears.

Is there any way to decrease my odds of buying pure junk?

On a Reddit thread , one user shared a hack for finding quality products on Temu: Simply locate an item with excellent reviews on Amazon, upload the image to Google Lens, and “you will almost always find links to the exact same item on Temu for a much lower price.” If you’re looking at a Temu listing that doesn’t have quite enough information, you can do the same trick in reverse to read the product’s Amazon reviews.

How much Temu are Americans buying?

In December, research firm Cargo Facts Consulting aggregated data showing that Temu shipped around 4,000 tons of goods per day. In the same way that Temu has bypassed typical wholesalers to work directly with factories, the company has also done something unprecedented in the air-cargo industry: working directly with commercial airlines themselves rather than exclusively through freight forwarders, which it’s able to do because of its massive volume of goods. Sunandan Ray, CEO of freight-forwarding company Unique Logistics International, claims that, to expand shipping capacity, Temu has even gone directly to charter-flight operators — which would mean the company is effectively hiring an entire plane for its own purposes. “The airlines themselves cannot cope with this traffic, so Temu needs other options,” says Ray. Temu is shipping such an enormous volume of goods to the U.S. that it has helped spur the recovery of the air-cargo industry, which had flagged during the pandemic. Niall van de Wouw, the Chief Airfreight Officer at freight analytics company Xeneta , compares Temu’s effect on the air-cargo industry to the role PPE like N95 masks played at the start of the pandemic in 2020. “It’s nearly impossible to have a conversation about air freight in Asia-Pacific and not mention Temu or Shein,” he says.   

How are USPS drivers feeling about Temu?

Sean Fogelson, a former USPS delivery person in Cincinnati who works as a comedian, heard about Temu in June 2023 after orders started taking over his delivery load: “It just kept coming, and I’m like, What the hell is this shit, man? ” He coined the term Temu tired, which was the basis of a TikTok video that went viral.

Fogelson was especially frustrated with Temu’s packaging — the way the company would put an entire order’s worth of items in a single bag (as opposed to a sturdier box). On the USPS sub-Reddit, others agreed, harping especially on the “shrink wrap”–like packaging that often turns the parcel into an unwieldy, irregularly shaped object. Schlepping several of these packages around can be particularly burdensome for carriers because they can’t always fit them into their satchels.

Is the company spending a fortune on import taxes?

No. In fact, it’s spending very little by taking advantage of a consumer loophole known as the de minimis value, which applies to shipments so small they don’t warrant taxes or duty. In the U.S., shipments that contain merchandise with a value under $800 do not need to pay duties. The average Temu order size is $25, according to a Wired report , so the majority fall under the exemption.

According to a May 2023 report delivered to Congress, Temu and Shein were likely responsible for more than 30 percent of incoming international shipments falling under the de minimis provision. In 2022, U.S. Customs and Border Protection cleared 685 million de minimis shipments. The report points out that Temu’s business model relies on this provision, which allows the company to circumvent compliance with forced-labor restrictions, customs duties, and facing “the same level of customs scrutiny that other retailers might face” since de minimis shipments also bypass inspection. In 2022, Gap, for instance, paid $700 million in import taxes, H&M paid $205 million, and Shein and Temu paid zero.

What about the labor conditions?

Because Temu does not own its products or operate its own factories, the origins of its goods can be difficult to trace. Still, both the U.S. government and experts have claimed that Temu is laissez-faire when it comes to keeping its supply chains free of slave labor. According to a report published by Congress in June 2023, Temu admitted that it does not prohibit third parties from selling products originating from Xinjiang, where Uyghurs have been abused. And the tech firm Ultra analyzed shipping data and concluded that many of the products listed on Temu’s sister platform, Pinduoduo, some of which were also listed on Temu, came from companies in Xinjiang. (“We strictly prohibit the use of involuntary labor and expect our business partners and sellers to ensure they are compliant with platform rules and the law,” a spokesperson said.)

Are Shein and Temu friends?

Even though their offerings are different, there is certainly some overlap in what the two companies sell — enough to fuel an acrimonious rivalry. In December 2022, just three months after Temu launched Stateside, Shein filed a lawsuit against Temu, accusing the company of paying social-media influencers to make “false and deceptive statements” about Shein and creating fake Shein accounts on Twitter that “[tricked] consumers into believing Temu [was] associated with the brand.” (Temu said it “strongly and categorically rejects all allegations.”) In July 2023, Temu hit back with its own suit against Shein, accusing the fast-fashion retailer of “[forcing] manufacturers to sign loyalty oaths certifying that they will not do business with Temu.” (Both lawsuits were dropped in October.)

On December 13, 2023, Temu filed another lawsuit against Shein. The 100-page complaint accused Shein of using “mafia-style intimidation” against suppliers who “[dared] to work with Temu,” which included “physical detention … personal threats, and illegal seizures of merchants’ personal devices” to access Temu’s confidential information and trade secrets. (One supplier’s representatives, according to the lawsuit, were held in a small room at Shein’s office for up to ten hours.) In pages of corporate trash talk, Temu alleges that Shein did this to persuade suppliers to sign exclusivity contracts and that Shein had illegally seized IP rights. The lawsuit also purports that Shein bombarded Temu with fake copyright takedown requests — 33,000 over ten months — and blatantly copied Temu’s games and “arcade-style” look by poaching its marketing employees. Read the entirety of the lawsuit here .

Is it tee-mo o or teh-moo ?

In its 2023 Super Bowl ad, the company referred to itself as tee-moo (allegedly derived from its motto, “Team up, price down”). Then, in the 2024 Super Bowl commercial, the pronunciation switched to teh-moo. Michael Gross, managing director of the agency hired to produce the ad’s music, said Temu gave them no explanation; the company’s only instruction was to make sure the new pronunciation was heard several times throughout the jingle.

“Team up, price down” — what does that mean?

Group buying, known as tuán gòu in Chinese, involves offering discounted rates for bulk purchases, a concept Temu employs to incentivize consumers’ buying behavior. This is a common practice in China, where community leaders such as grocery-store owners and housewives will gather groups of buyers (often through messenger services like WeChat) who want to go in on a wholesale order together — whether that be groceries or household items like toilet paper. Collective purchases like this give the group access to lower prices. More recently, Chinese e-commerce websites like Temu have applied group-buying tactics both by selling products in bulk at low prices and by offering referral programs through which customers can unlock discounts by sharing links with friends and family.

If I start shopping on Temu, will I be able to stop?

As far as we know, no formal studies have been conducted that showed a link between Temu and a shopping addiction. Still, there are several Facebook groups for “Temu addicts,” and Reddit posts abound with individuals complaining that their mother-in-law or husband — or even themselves — is addicted to Temu. Ngai, the TikTok influencer, describes the experience as being “led down this rabbit hole. It’s Amazon but on steroids. It was just this endless scroll that you could go on for days.” Part of the hook, several shoppers told us, is the sense of embarking on a treasure hunt. “Temu very much feels like the 2024 version of the dollar store when I want to walk around and buy a couple of random things. It’s fun. It’s a little problematic but, largely speaking, appears to be pretty solid,” says Evans, the gadget reviewer. Dorothy, the super-user, is a dedicated shopper of Ross and Primark but says that after a long day at work, there’s something particularly soothing about scrolling through Temu “to see if there’s anything special today.”

I heard Temu is especially popular among boomer women. Why?

The site’s most loyal shoppers are those in the 59-and-older demographic — which has provoked everything from disdain and alarm to pure exasperation from younger generations (a recent Business Insider headline: “Is Your Mom a ‘Temu Victim’?”). Several experts have attributed this to the site’s design, which looks more accessible than Amazon’s. Another has suggested that older shoppers, if they’re less tech-savvy, may be more vulnerable to the psychological manipulations of Temu’s casinolike design. Nostalgia may also be at play: One older shopper told Business Insider that Temu’s offerings remind him of what he used to find on late-night infomercials. Another shopper said the site echoes the now-extinct experience of coming across strange gadgets and knickknacks in the checkout line of Bed Bath & Beyond.

What is this Boston address that shows up when I Google Temu?

Most of the business, including the entire engineering team, is based in China. But Temu is also registered as WhaleCo, Inc., in Massachusetts and headquartered in Boston at 31 Saint James Avenue, Suite 355. Little is known about the operations of the American office. “Temu chose Boston for its office in the U.S. because it offers access to skilled talent and convenient global transportation links,” a company spokesperson said. Deighton’s hypothesis is that the office serves as “an advertising buying station,” i.e., a small team of people whose sole function is to bid on Google AdWords and the like — though this is only a guess. “Somebody has to be placing those orders, and someone’s negotiating quantity discounts with Yahoo and so on,” he says.

The Strategist is designed to surface the most useful, expert recommendations for things to buy across the vast e-commerce landscape. Some of our latest conquests include the best acne treatments , rolling luggage , pillows for side sleepers , natural anxiety remedies , and bath towels . We update links when possible, but note that deals can expire and all prices are subject to change.

• remove interruptions
• newsletter pick
• the strategist
• new york magazine
• retail rabbit hole
• strategist investigates
• one great story

Every product is independently selected by (obsessive) editors. Things you buy through our links may earn us a commission.

Deal of the Day

Micro sales, greatest hits, most viewed stories.

• 10 Things That Delighted Us: From Electric Mops to Plum Sykes’s Perfect Pencil
• The Best Running Watches, According to Runners
• I’ve Worn (and Danced All Night in) These Heels at 7 Weddings
• The 11 Very Best Shampoos
• The 17 Very Best Protein Powders
• The Best Father’s Day Gifts for Every Type of Dad

What is your email?

This email will be used to sign into all New York sites. By submitting your email, you agree to our Terms and Privacy Policy and to receive email correspondence from us.

Sign In To Continue Reading

Create your free account.

Password must be at least 8 characters and contain:

• Lower case letters (a-z)
• Upper case letters (A-Z)
• Numbers (0-9)
• Special Characters (!@#$%^&*)

As part of your account, you’ll receive occasional updates and offers from New York , which you can opt out of anytime.