impacting factors for online shopping a literature review

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Início Números Vol.2 nº4 / nº3 Nº3 Artigos Drivers of shopping online: a lit...

Drivers of shopping online: a literature review

Consumers are increasingly adopting electronic channels for purchasing. Explaining online consumer behavior is still a major issue as studies available focus on a multiple set of variables and relied on different approaches and theoretical foundations. Based on previous research two main drivers of online behavior are identified: perceived benefits of online shopping related to utilitarian and hedonic characteristics and perceived risk. Additionally, exogenous factors are presented as moderating variables of the relationship between perceived advantages and disadvantages of internet shopping and online consumer behavior.

Entradas no índice

Keywords: , texto integral, 1. introduction.

1 The increasing dependence of firms on e-commerce activities and the recent failure of a large number of dot-com companies stresses the challenges of operating through virtual channels and also highlights the need to better understand consumer behavior in online market channels in order to attract and retain consumers.

2 While performing all the functions of a traditional consumer, in Internet shopping the consumer is simultaneously a computer user as he or she interacts with a system, i.e., a commercial Web site. On the other hand, the physical store has been transformed into Web-based stores that use networks and Internet technology for communications and transactions.

3 In this sense, there seems to be an understanding that online shopping behavior is fundamentally different from that in conventional retail environment, (Peterson et al ., 1997) as e-commerce relies on hypertext Computer Mediated Environments (CMEs) and the interaction customer-supplier is ruled by totally different principles.

4 Understanding the factors that explain how consumers interact with technology, their purchase behavior in electronic channels and their preferences to transact with an electronic vendor on a repeated basis is crucial to identify the main drivers of consumer behavior in online market channels.

5 Online consumer behavior research is a young and dynamic academic domain that is characterized by a diverse set of variables studied from multiple theoretical perspectives.

6 Researchers have relied on the Technology Acceptance Model (Davis, 1989: Davis et al ., 1989), the Theory of Reasoned Action (Fisbein and Ajzen, 1975), the Theory of Planned Behavior (Ajzen, 1991), Innovation Diffusion Theory (Rogers, 1995), Flow Theory (Czikszentmihalyi, 1998), Marketing, Information Systems and Human Computer Interaction Literature in investigating consumer’s adoption and use of electronic commerce.

7 While these studies individually provide meaningful insights on online consumer behavior, the empirical research in this area is sparse and the lack of a comprehensive understanding of online consumer behavior is still a major issue (Saeed et al ., 2003).

8 Previous research on consumer adoption of Internet shopping (Childers et al ., 2001; Dabholkar and Bagozzi, 2002; Doolin et al ., 2005; Monsuwé et al .; 2004; O´Cass and Fenech, 2002) suggests that consumers’ attitude toward Internet shopping and intention to shop online depends primarily on the perceived features of online shopping and on the perceived risk associated with online purchase. These relationships are moderated by exogenous factors like “consumer traits”, “situational factors”, “product characteristics” and “previous online shopping experiences”.

9 The outline of this paper is as follow. In the next section an assessment of the basic determinants that positively affect consumers’ intention to buy on the Internet is carried out. Second, the main perceived risks of shopping online are identified as factors that have a negative impact on the intention to buy from Internet vendors. Third, since it has been argued that the relationship between consumers’ attitude and intentions to buy online is moderated by independent factors, an examination of the influence of these factors is presented. Finally, the main findings, the importance to professionals and researchers and limitations are summarized.

2. Perceived benefits in online shopping

10 According to several authors (Childers et al ., 2001; Mathwick et al ., 2001; Menon and Kahn, 2002;) online shopping features can be either consumers’ perceptions of functional or utilitarian dimensions, or their perceptions of emotional and hedonic dimensions.

11 Functional or utilitarian perceptions relate to how effective shopping on the Internet is in helping consumers to accomplish their task, and how easy the Internet as a shopping medium is to use. Implicit to these perceptions is the perceived convenience offered by Internet vendor whereas convenience includes the time and effort saved by consumers when engaging in online shopping (Doolin, 2005; Monsuwé, 2004).

12 Emotional or hedonic dimensions reflect consumers’ perceptions regarding the potential enjoyment or entertainment of Internet shopping (Doolin, 2005; Monsuwé, 2004).

13 Venkatesh (2000) reported that perceived convenience offered by Internet Vendors has a positive impact on consumers’ attitude towards online shopping, as they perceive Internet as a medium that enhances the outcome of their shopping experience in an easy way.

14 Childers et al . (2001) found “enjoyment” to be a consistent and strong predictor of attitude toward online shopping. If consumers enjoy their online shopping experience, they have a more positive attitude toward online shop ping, and are more likely to adopt the Internet as a shopping medium.

15 Vijayasarathy and Jones (2000) showed that Internet shopping convenience, lifestyle compatibility and fun positively influence attitude towards Internet shopping and intention to shop online.

16 Despite the perceived benefits in online shopping mainly associated with convenience and enjoyment, there are a number of possible negative factors associated with the Internet shopping experience. These include the loss of sensory shopping or the loss of social benefits associated with shopping (Vijayasarathy and Jones, 2000).

17 In their research, Swaminathan et al . (1999) found that the lack of social interaction in Internet shopping deterred consumers from online purchase who preferred dealing with people or who treated shopping as a social ex perience.

3. Perceived risk in online shopping

18 Although most of the purchase decisions are perceived with some degree of risk, Internet shopping is associated with higher ri sk by consumers due to its newness and intrinsic characteristics associated to virtual stores where there is no human contact and consumers cannot physically check the quality of a product or monitor the safety and security of sending sensitive personal and financial information while shopping on the Internet (Lee and Turban, 2001).

19 Several studies reported similar findings that perceived risk negatively influenced consumers’ attitude or intention to purchase online (Doolin, 2005; Liu and Wei, 2003; Van der Heidjen et al ., 2003).

20 Opposing results were reported in two studies (Corbitt et al ., 2003; Jar venpaa et al ., 1999). The authors found that perceived risk of Internet shopping did not affect willingness to buy from an online store. One of the reasons for this contradictory conclusion might be due to the countries analyzed, respectively New Zealand and Australia, where individuals could be more risk- taken or more Internet heavy-users.

21 In examining the influences on the perceived risk of purchasing online, Pires at al. (2004) stated that no association was found between the fre quency of online purchasing and perceived risk, although satisfaction with prior Internet purchases was negatively associated with the perceived risk of intended purchases, but only for low-involvement products. Differences in perceived risk were associated with whether the intended purchase was a good or service and whether it was a high or low-involvement product. The perceived risk of purchasing goods through the Internet is higher than for services. Perceived risk was found to be higher for high-involvement than for low-involvement-products, be they goods or services.

22 Various types of risk are perceived in purchase decisions, including prod uct risk, security risk and privacy risk.

23 Product risk is the risk of making a poor or inappropriate purchase deci sion. Aspects involving product risk can be an inability to compare prices, being unable to return a product, not receiving a product paid for and product not performing as expected (Bhatnagar et al ., 2000; Jarvenpaa and Todd, 1997; Tan, 1999; Vijayasarathy and Jones, 2000).

24 Bhatnagar et al . (2000) suggest that the likelihood of purchasing on the Internet decreases with increases in product risk.

25 Other dimensions of perceived risk related to consumers’ perceptions on the Internet as a trustworthy shopping medium. For example, a common perception among consumers is that communicating credit card information over the Internet is inherently risky, due to the possibility of credit card fraud (Bhatnagar et al ., 2000; George, 2002; Hoffman et al ., (1999); Jarvenpaa and Todd, 1997; Liebermann and Stashevsky, 2002).

26 Previous studies found that beliefs about trustworthiness of the Internet were associated with positive attitudes toward Internet purchasing (George, 2002; Hoffman et al ., (1999); Liebermann and Stashevsky, 2002).

27 Privacy risk includes the unauthorized acquisition of personal information during Internet use or the provision of personal information collected by companies to third parties.

28 Perceived privacy risk causes consumers to be reluctant in exchanging personal information with Web providers (Hoffman et al ., 1999). The same authors suggest that with increasing privacy concerns, the likelihood of purchasing online decreases. Similarly, George (2002) found that a belief in the privacy of personal information was associated with negative attitudes toward Internet purchasing.

4. Exogenous factors

29 Based on the previous literature review, four exogenous factors were reported to be key drivers in moving consumers to ultim ately adopt the Internet as a shopping medium.

4.1. Consumer traits

30 Studies on online shopping behavior have focus mainly on demographic, psychographics and personality characteristics.

31 Bellman et al . (1999) cautioned that demographic variables alone explain a very low percentage of variance in the purchase decision.

32 According to Burke (2002) four relevant demographic factors – age, gen der, education, and income have a significant moderating effect on consum ers’ attitude toward online shopping.

33 In studying these variables several studies arrived to some contradictory results.

34 Concerning age, it was found that younger people are more interested in using new technologies, like the Internet, to search for comparative information on products (Wood, 2002). Older consumers avoid shopping online as the potential benefits from shopping online are offset by the perceived cost in skill needed to do it (Ratchford et al ., 2001).

35 On the other hand as younger people are associated with less income it was found that the higher a person’s income and age, the higher the propen sity to buy online (Bellman et al ., 1999; Liao and Cheung, 2001).

36 Gender differences are also related to different attitudes towards online shopping. Although men are more positive about using Internet as a shop ping medium, female shoppers that prefer to shop online, do it more frequently than male (Burke, 2002; Li et al ., 1999).

37 Furthermore Slyke et al . (2002) reported that as women view shopping as a social activity they were found to be less oriented to shop online than men.

38 Regarding education, higher educated consumers have a higher propen sity to use no-store channels, like the Internet to shop (Burke, 2002). This fact can be justified as education has been positively associated with individ ual’s level of Internet literacy (Li et al ., 1999).

39 Higher household income is often positively correlated with possession of computers, Internet access and higher education levels of consumers and consequently with a higher intention to shop online (Lohse et al ., 2000).

40 In terms of psychographics characteristics, Bellman et al . (1999) stated that consumers that are more likely to buy on the Internet have a “wired life” and are “starving of time”. Such consumers use the Internet for a long time for a multiple of purposes such as communicating through e-mail, reading news and search for information.

41 A personality characteristic closely associated with Internet adoption for shopping is innovativeness defined as the relative willingness of a person to try a new product or service (Goldsmith and Hokafer, 1991).

42 Innovativeness seems to influence more than frequency of online purchasing. It relates to the variety of product classes bought online, both in regard to purchasing and to visiting Web sites seeking information. (Blake et al ., 2003). In this sense innovativeness might be a fundamental factor determining the quantity and quality of online shopping.

4.2. Situational factors

43 Situational factors are found to be factors that affect significantly the choice between different retail store formats when consumers are faced with a shopping decision (Gehrt and Yan, 2004). According to this study, the time pressure and purpose of the shopping (for a gift or for themselves) can change the consumers’ shopping habits. Results showed that traditional stores were preferred for self-purchase situations rather than for gift occasions as in this case other store formats (catalog and Internet) performed better in terms of expedition. As for time pressure it was found that it was not a significantly predictor of online shopping as consumers when faced with scarcity of time responded to temporal issues related to whether there is a lag of time between the purchase transaction and receipt of goods rather than whether shopping can take place anytime.

44 Contradictory results were reported by Wolfinbarger and Gilly (2001). According to this study important attributes of online shopping are convenience and accessibility. When faced with time pressure situations, consumers engaged in online shopping but no conclusions should be taken on the effect of this factor on the attitude toward Internet shopping.

45 Lack of mobility and geographical distance has also been addressed has drivers of online shopping as Internet medium offers a viable solution to overcome these barriers (Monsuwé et al ., 2004). According to the same au thors the physical proximity of a traditional store that sells the same prod ucts available online, can lead consumers to shop in the “brick and mortar” alternative due to its perceived attractiveness despite consumers’ positive attitude toward shopping on the Internet.

46 The need for special items difficult to find in traditional retail stores has been reported a situational factor that attenuates the relationship between attitude and consumers’ intention to shop online (Wolfinbarger and Gilly, 2001).

4.3. Product characteristics

47 Consumers' decisions whether or not to shop online are also influenced by the type of product or service under consideration.

48 The lack of physical contact and assistance as well as the need to “feel” somehow the product differentiates products according to their suitability for online shopping.

49 Relying on product categories conceptualized by information economists, Gehrt and Yan (2004), reported that it is more likely that search goods (i.e. books) can be adequately assessed within a Web than experience goods (i.e. clothing), which usually require closer scrutiny.

50 Grewal et al . (2002) and Reibstein (1999) referred to standardized and fa miliar products as those in which quality uncertainty is almost absent and do not need physical assistance or pre-trial. These products such as groceries, books, CDs, videotapes have a high potential to be considered when shopping online.

51 Furthermore in case of certain sensitive products there is high potential to shop online to ensure adequate levels of privacy and anonymity (Grewal et al ., 2002). Some of these products like medicine and pornographic movies are raising legal and ethical issues among international community.

52 On the other hand, personal-care products like perfume or products that required personal knowledge and experience like cars or computers, are less likely to be considered when shopping online (Elliot and Fowell, 2000).

4.4. Previous online shopping experiences

53 Past research suggests that prior online shopping experiences have a direct impact on Internet shopping intentions. Satisfactory previous experiences decreases consumers’ perceived risk levels associated with online shopping but only across low-involvement goods and services (Monsuwé et al ., 2004).

54 Consumers that evaluate positively the previous online experience are motivated to continue shopping on the Internet (Eastlick and Lotz, 1999; Shim et al ., 2001; Weber and Roehl, 1999).

5. Conclusion

55 Relying on an extensive literature review, this paper aims to identify the main drivers of online shopping and thus to give further insights in explaining consumer behavior when adopting the Internet for buying as this issue is still in its infancy stage despite its major importance for academic and professionals.

56 This literature review shows that attitude toward online shopping and in- tention to shop online are not only affected by perceived benefits and perceived risks, but also by exogenous factors like consumer traits, situations factors, product characteristics, previous online shopping experiences.

57 Understanding consumers’ motivations and limitations to shop online is of major importance in e-business for making adequate strategic options and guiding technological and marketing decisions in order to increase customer satisfaction. As reported before consumers´ attitude toward online shopping is influenced by both utilitarian and hedonic factors. Therefore, e-marketers should emphasize the enjoyable feature of their sites as they promote the convenience of shopping online. As personal characteristics also affect buyers´ attitudes and intentions to engage in Internet shopping e-tailers should customize customers´ treatment. Furthermore, the e-vendor should assure a trust-building relationship with its customers to minimize perceived risk associated to online shopping. Adopting and communicating a clear privacy policy, using a third party seal and offering guarantees are mechanisms that can help in creating a reliable environment.

58 Some limitations of this paper must be pointed out as avenues for future. The factors identified as main drives of shopping online are the result of a literature review and there can always be factors of influence on consumers´ intentions to shop on the Internet that are not included because they are addressed in other studies not included in this review. However there are methodological reasons to believe that the most relevant factors were identified in this context. A second limitation is that this paper is the result of a literature review and has never been tested in its entirety using empirical evidence. This implies that some caution should be taken in applying the findings that can be derived from this paper Further research is also needed to determine which of the factors have the most significant effect on behavioral intention to shop on the Internet.

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Para citar este artigo

Referência do documento impresso.

Ana Teresa Machado , «Drivers of shopping online: a literature review» ,  Comunicação Pública , Vol.2 nº4 / nº3 | 2006, 39-50.

Referência eletrónica

Ana Teresa Machado , «Drivers of shopping online: a literature review» ,  Comunicação Pública [Online], Vol.2 nº4 / nº3 | 2006, posto online no dia 30 outubro 2020 , consultado o 29 maio 2024 . URL : http://journals.openedition.org/cp/8402; DOI : https://doi.org/10.4000/cp.8402

Ana Teresa Machado

Escola Superior de Comunicação Social Instituto Politécnico de Lisboa

[email protected]

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Peer-reviewed

Research Article

A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis

Roles Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation School of Economics and Management, Zhengzhou University of Light Industry, High-tech District, Zhengzhou City, Henan Province, China

Roles Conceptualization, Funding acquisition, Project administration, Supervision

* E-mail: [email protected]

Affiliation School of Politics and Public Administration, Soochow University, Gusu District, Suzhou City, Jiangsu Province, China

Roles Data curation, Funding acquisition, Project administration

Roles Formal analysis, Funding acquisition, Project administration

• Qiwei Wang, 
• Xiaoya Zhu, 
• Manman Wang, 
• Fuli Zhou, 
• Shuang Cheng

• Published: May 18, 2023
• https://doi.org/10.1371/journal.pone.0286034
• Peer Review
• Reader Comments

The coronavirus disease 2019 pandemic has impacted and changed consumer behavior because of a prolonged quarantine and lockdown. This study proposed a theoretical framework to explore and define the influencing factors of online consumer purchasing behavior (OCPB) based on electronic word-of-mouth (e-WOM) data mining and analysis. Data pertaining to e-WOM were crawled from smartphone product reviews from the two most popular online shopping platforms in China, Jingdong.com and Taobao.com . Data processing aimed to filter noise and translate unstructured data from complex text reviews into structured data. The machine learning based K-means clustering method was utilized to cluster the influencing factors of OCPB. Comparing the clustering results and Kotler’s five products level, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. This study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM. The definition and explanation of these categories may have important implications for both OCPB and e-commerce.

Citation: Wang Q, Zhu X, Wang M, Zhou F, Cheng S (2023) A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis. PLoS ONE 18(5): e0286034. https://doi.org/10.1371/journal.pone.0286034

Editor: Ahmad Samed Al-Adwan, Al-Ahliyya Amman University, JORDAN

Received: April 19, 2023; Accepted: May 5, 2023; Published: May 18, 2023

Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Henan Province Philosophy and Social Science Planning Project (grant number. 2020CZH012), the Henan Key Research and Development and Promotion Special (Soft Science Research) (grant number. 222400410126), the Jiangsu Province Social Science Foundation Youth Project (grant number. 21GLC012) and the Doctor Fund of Zhengzhou University of Light Industry (grant number. 2020BSJJ022, 2019BSJJ017). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

A prolonged quarantine and lockdown imposed by the coronavirus disease 2019 (COVID-19) pandemic has changed the human lifestyle worldwide. The COVID-19 pandemic has negatively impacted various sectors such as manufacturing, import and export trade, tourism, catering, transportation, entertainment, especially retail and hence the global economy. Consumer behavior has gradually shifted toward contactless services and e-commerce activities owing to the COVID-19 [ 1 ].

Consumers are relying on e-commerce more than ever to protect their health. Recent advances in information technology, digital transformation, and the Internet helped consumers to encounter the COVID-19 to meet the needs of the daily lives, which led to an increase in the importance of e-commerce and changes in consumers’ online purchasing patterns [ 2 ]. When consumers shop online, their behavior is considered non-traditional, and is illustrated by a new trend and current environment. To analyze the influencing factors of online consumer purchasing behavior (OCPB), it is necessary to consider several factors, such as the price and quality of a product, consumers’ preferences, website design, function, security, search, and electronic word-of-mouth (e-WOM) [ 3 ]. As the current website design and payment security have become a user-friendly and guaranteed system compared with a decade ago, some factors are no longer considered as essential. By contrast, greater diversity and complexity have become the main characteristics of the influencing factors. Furthermore, under the traditional sales model, consumers’ purchase decisions were simple, while online consumers have more options in terms of shopping channels and decision choices. Meanwhile, in recent years, consumers’ preferences have gradually shifted from standardized products to customized and personalized. In line with these changes, information technology and data science, such as big data analytics, data mining from e-WOM, and machine learning (ML), adaptively analyze data regarding online consumers’ needs to obtain more accurate data.

Since the concept of big data was proposed in 2008, it has been applied and developed lasting 14 years, emerging as a valuable tool for global e-commerce recently. However, most enterprises have failed to seize the benefits generated from big data. In the context of big data, a huge number of comments were posted regarding e-malls (Amazon, Taobao, etc.) and online social media (blogs, Bulletin Board System, etc.). For instance, Amazon was the first e-commerce company to establish an e-WOM system in 1995, which provided the company with valuable suggestions from online consumers. E-WOM has greater credibility and persuasiveness, compared with traditional word of mouth (WOM), which is limited by various subjective factors. Moreover, e-WOM has the advantage of containing not only structured data (e.g., ratings) but also unstructured data (e.g., the specific content of consumer reviews). However, e-WOM provides product-related information that cannot be directly transformed to a research objective. Thus, an innovative method of big data analytics needs to be utilized to explore the influencing factors of OCPB, which shows the advantage of interdisciplinary applications.

The research problems are to explore the factors influencing OCPB through e-WOM data mining and analysis and explain the most important influencing factors for online consumers that are likely to exist in the future within the context of the COVID-19. The study fulfills the literature gaps on exploring influencing factors of OCPB from the perspective of e-WOM. The study makes a significant contribution to the consumer study because its findings can adequately identify the influencing factors of OCPB. It also provides the theoretical and managerial implications of its findings including how e-commerce platforms can use such data to adapt their platforms and marketing strategies to diverse situations.

The remainder of this is organized as follows. Section 1 presents the introduction. Section 2 discusses the literature review and hypotheses. Section 3 provides the methodology, including data mining and analysis. Section 4 describes the results, including K-means results, performance metrics, hypotheses results, and a theoretical model. Sections 5 and 6 provide discussion and conclusion, respectively.

2. Literature review and hypotheses

2.1 influencing factors of ocpb.

Online shopping has an increasing sales volume each year, which has become huge challenges for offline retailers. Venkatesh et al. [ 4 ] found that culture, demographics, economics, technology, and personal psychology were the main antecedents of online shopping, and the main drivers of online shopping were congruence, impulse buying behavior, value consciousness, risk, local shopping, shopping enjoyment, and browsing enjoyment by a comprehensive model of consumers online purchasing behavior. Within the context of COVID-19, OCPB is positively impacted by attitude toward online shopping [ 5 ]. Melović et al. [ 6 ] focused on millennials’ online shopping behavior and noted that the demographic characteristics, the affirmative characteristics, risks and barriers of online shopping were the key influencing factors. Based on the stimulus-organism-response (SOR) theory model, consumers’ actual impulsive shopping behavior is impacted by arousal and pleasure [ 7 ]. Furthermore, the influencing factors of consumers’ purchase behavior toward green brands are green perceived quality, green perceived value, green perceived risk, information costs saved, and purchase intentions by perceived risk theory [ 8 ]. The positive and negative effects of corporate social responsibility practices on consumers’ pro-social behavior are moderated by consumer-brand social distance, although it also impacts consumer behavior beyond the consumer-brand dyadic relationship [ 9 ]. Green perceived value, functional value, conditional value, social value, and emotional value may impact green energy consumers’ purchase behavior [ 10 ]. Recipients’ behavior and WOM predict distant consumers’ behavior [ 11 ]. Moreover, consumer behavior is significantly impacted by financial rewards, perceived intrusiveness, attitudes toward e-mail advertising, and intentions toward the senders [ 12 ]. Store brand consumer purchase behavior is positively impacted by store image perceptions, store brand price-image, value consciousness, and store brand attitude [ 13 ]. A meta-analysis summarizes the influencing factors of consumer behavior, household size, store brands, store loyalty, innovativeness, familiarity with store brands, brand loyalty to national brands, price consciousness, value consciousness, perceived quality of store brands, perceived value for money of store brands, and search versus experience positively impact consumer behavior, whereas price–quality consciousness, quality consciousness, price of store brands, and the consequences of making a mistake in a purchase negatively impact consumer behavior [ 14 ].

Based on protection motivation theory and theory of planned behavior (TPB), consumers are more likely to use online shopping channels than offline channels during the COVID-19 pandemic [ 15 ]. The TPB is also adapted to explain the influencing factors of consumers’ behavior in different areas. For instance, the attitude, perceived behavioral control, policy information campaigns, and past-purchase experiences significantly impact consumers’ purchase intention, whereas subjective and moral norms show no significant relationship based on the extended TPB [ 16 ]. Although green purchase behavior has different antecedents, only personal norms and value for money have fully significant relationships with green purchase behavior, environmental concern, materialism, creativity, and green practices. Functional value positively influences purchase satisfaction, physical unavailability, materialism, creativity, and green practices, and negatively influences the frequency of green product purchase by extending the TPB [ 17 ]. Meanwhile, Nimri et al. [ 18 ] utilized the TPB in green hotels and showed that knowledge and attitudes, as well as subjective injunctive norms, positively impacted consumers’ purchase intention. Yi [ 19 ] observed that attitude, social norm, and perceived behavioral control positively impacted consumers’ purchase intention based on the TPB. The factors of supportive behaviors for environmental organizations, subjective norms, consumer attitude toward sustainable purchasing, perceived marketplace influence, consumers’ knowledge regarding sustainability-related issues, and environmental concern are the influencing factors of consumers sustainable purchase behavior [ 20 ]. Consumers’ green purchase behavior is impacted by the intention through support of the TPB [ 21 ].

2.2 Influencing factors of emergency context attribute

Consumers exhibited panic purchase behavior during the COVID-19, which might have been caused by psychological factors such as uncertainty, perceptions of severity, perceptions of scarcity, and anxiety [ 22 ]. In the reacting phase, consumers responded to the perceived unexpected threat of the COVID-19 and intended to regain control of lost freedoms; in the coping phase, they addressed this issue by adopting new behaviors and exerting control in other areas, and in the adapting phase, they became less reactive and accommodated their consumption habits to the new normal [ 23 ]. The positive and negative e-WOMs may have significant influence on online consumers’ psychology. Specifically, e-WOM that conveys positive emotions (pride, surprise) tends to have a greater impact on male readers’ perception of the reviewer’s cognitive effort than female readers, whereas e-WOM that conveys negative emotions (anger, fear) has a greater impact on cognitive effort of female readers than male readers [ 24 ]. When online consumers believe their behavioral effect is feasible and positive, while their behavioral decision is related to the behavioral outcome [ 25 ]. Traditionally, there are five stages of consumer behavior that include demand identification, information search, evaluation of selection, purchase, and post-purchase evaluation. In addition, online purchase behavior involved in the various stages can be categorized into: attitude formation, intention, adoption, and continuation. Most of the important factors that influence online purchasing behavior are attitude, motivation, trust, risk, demographics, website, etc. “Internet Adoption” is widely used as a basic framework for studying “online buying adoption”. Psychological and economic structures associated with the IT adoption model can be used as the online consumer’s behavior models for innovative marketers. The adoption of online purchasing behavior is explained by different classic models of attitude behavior [ 26 ]. Consumer behaviors represented by customer trust and customer satisfaction, influence repurchase and positive WOM intentions [ 27 ]. Return policy leniency, cash on delivery, and social commerce constructs were significant facilitators of customer trust [ 28 ]. Meanwhile, seller uncertainty was negatively influenced by return policy leniency, information quality, number of positive comments, seller reputation, and seller popularity [ 29 ]. Social commerce components were a necessity in complementing the quality dimensions of e-service in the environment of e-commerce [ 30 ]. Perceived security, perceived privacy and perceived information quality were all significant facilitators of online customer trust and satisfaction [ 31 ].

E-service quality, consumer social responsibility, green trust and green perceived value have a significant positive impact on green purchase intention, whereas greenwashing has a significant negative impact on green purchase intention. In addition, consumer social responsibility, green WOM, green trust and green perceived value positively moderated the relationship between e-service quality and green purchase intention, while greenwashing and green participation negatively moderated the relationships [ 32 ]. Large-scale online promotions provide mobile users with a new shopping environment in which contextual variables simultaneously influence consumer behavior. There is ample evidence suggesting that mobile phone users are more impulsive during large-scale online promotion campaigns, which are the important contextual drivers that lead to the occurrence of mobile users’ impulse buying behavior in the “Double 11” promotion. The results show that promotion, impulse buying tendency, social environment, aesthetics, and interactivity of mobile platforms, and available time are the key influencing factors of impulse buying by mobile users [ 33 ]. Environmental responsibility, spirituality, and perceived consumer effectiveness are the key psychological influencing factors of consumers’ sustainable purchase decisions, whereas commercial campaigns encourage young consumers to make sustainable purchases [ 34 ]. The main psychological factors affecting consumers’ green housing purchase intention include the attitude, perceived moral obligation, perceived environmental concern, perceived value, perceived self-identity, and financial risk. Subjective norms, perceived behavioral control, performance risk, and psychological risk are not included. Meanwhile, the purchase intention is an important predictor of consumers’ willingness to buy [ 35 ]. The perceived control of flow and focus will positively affect the utilitarian value of consumers, while focus and cognitive enjoyment will positively impact the hedonic value. Moreover, utilitarian value has a greater impact on satisfaction than hedonic value. Finally, hedonic value positively impacts unplanned purchasing behavior [ 36 ]. Utilitarian and hedonic features achieve high purchase and WOM intentions through social media platforms and also depend on gender and consumption history [ 37 ].

Therefore, we present the following hypothesis:

• Hypothesis 1 (H1): Perceived emergency context attribute is the influencing factor of OCPB.

2.3 Influencing factors of perceived product attribute

Product quality and preferential prices are the major factors considered by online consumers, especially within the context of the COVID-19. Specifically, online shopping offers lower price, more choices for better quality products, and comparison between them [ 1 ]. Under the circumstance of online reviews, an original equipment manufacturer (OEM) selling a new product carefully decides whether to adopt the first phase remanufacturing entry strategy or to adopt the phase 2 remanufacturing entry strategy under certain conditions. Meanwhile, the OEM adopts penetration pricing for new and remanufactured products, when the actual quality of the product is high. Otherwise, it adopts a skimming pricing strategy, which is different from uniform pricing when there are no online reviews. Online reviews significantly impact OEM’s product profits and consumer surplus. Especially when the actual quality of the product is high enough, the OEM and the consumer will be also reciprocal [ 38 ]. Online reviews reduce consumers’ product uncertainty and improve the effect of consumer purchase decisions [ 39 , 40 ]. Uzir et al. [ 41 ] utilized the expectancy disconfirmation theory to prove that product quality positively impacts customer satisfaction, while product quality and customer satisfaction are mediated by customer’s perceived value. Product quality and customer’s perceived value will have greater influence with higher frequency of social media use. Nguyen et al. [ 42 ] studies consumer behavior from a cognitive perspective, and theoretically develops and tests two key moderators that influence the relationship between green consumption intention and behavior, namely the availability of green products and perceived consumer effectiveness.

Both sustainability-related and product-related texts positively influence consumer behavior on social media [ 43 ]. Online environment, price, and quality of the products are significantly impacted by OCPB. Godey et al. [ 44 ] explained the connections between social media marketing efforts and brand preference, price premium, and loyalty. Brand love positively impacts brand loyalty, and both positively impact WOM and purchase intention [ 45 ]. Brand names have a systematic influence on consumer’s product choice, which is moderated by consumer’s cognitive needs, availability of product attribute information, and classification of brand names. In the same choice set, the share of product choices with a higher brand name will increase and be preferred even if it is objectively inferior to other choices. Consumers with low cognitive needs use the heuristic of “higher is better” to select options labeled with brand names and choose brands with higher numerical proportions [ 46 ].

• Hypothesis 2 (H2): Perceived product attribute is the influencing factor of OCPB.

2.4 Influencing factors of perceived innovation attribute

Product innovation increases company’s competitive advantage by attracting consumers, whereas the enhancement of innovative design according to consumer behavior accelerates the development of sustainable product [ 47 , 48 ]. The innovation, WOM intentions and product evaluation can be improved positively by emotional brand attachment and decreased by perceived risk [ 49 ]. Based on the perspective of evolutionary, certain consumer characteristics, such as buyer sophistication, creativity, global identity, and local identity, influence firms’ product innovation performance, which can increase the success rate of product innovation, and enhance firms’ research and development performance [ 50 ]. However, technological innovation faces greater risk as it depends on market acceptance [ 51 ]. Moreover, electronic products rely more on technological innovation compared with other products, which maintain the profit and market [ 52 ]. The technological innovation needs to apply logical plans and profitable marketing strategies to reduce consumer resistance to innovation. Thus, Sun [ 53 ] explains the relationship between consumer resistance to innovation and customer churn based on configurational perspective, whereas the results show that response and functioning effect are significant but cognitive evaluation is not.

Based on the perspective of incremental product innovation, aesthetic and functional dimensions positively impact perceived quality, purchase intention, and WOM, whereas symbolic dimension only positively impacts purchase intention and WOM. By contrast, aesthetic and functional dimensions only positively impact perceived quality, whereas symbolic dimension positively impacts purchase intention and WOM. Furthermore, perceived quality partially mediates the relationship between aesthetic and functional dimensions and purchase intention and WOM by incremental product innovation, whereas perceived quality fully mediates the relationship between aesthetic and functional dimensions and purchase intention and WOM by radical product innovation [ 54 ]. Contextual factors, such as size of organizations and engagement in research and development activity, moderate the relationship between design and product innovation outcomes [ 55 ]. For radical innovations, low level of product innovation leads to more positive reviews and less inference of learning costs. As the functional attribute of radical innovations is not consistent with existing products, it is difficult for consumers to access relevant product category patterns and thus transfer knowledge to new products. The product innovation of aesthetics, functionality, and symbolism positively impact willingness to pay, purchase intention, and WOM through brand attitude [ 56 ]. This poor knowledge transfer results in consumers feeling incapable of effectively utilizing radical innovations, resulting in greater learning costs. In this case, product designs with low design novelty can provide a frame of reference for consumers to understand radical innovations. However, incremental product innovation shows no significant difference between a low and high level of design newness [ 57 ].

• Hypothesis 3 (H3): Perceived innovation attribute is the influencing factor of OCPB.

2.5 Influencing factors of perceived motivation attribute

The research has proven that almost all consumers’ purchases are motivated by emotion. Under this circumstance, an increase in online consumers’ positive emotions increases, their purchase frequency, whereas an increase in online consumers’ negative emotions reduces their purchase frequency. Additionally, user interface quality, product information quality, service information quality, site awareness, safety perception, information satisfaction, relationship benefits and related benefit factors have negative impacts on consumers’ online shopping emotionally. Nevertheless, only product information quality, user interface quality, and safety perception factors have positive effects on online consumer sentiment [ 58 ]. E-WOM carries emotional expressions, which can help consumers express the emotions timely. Pappas et al. [ 59 ] divides consumers’ motivation into four factors, namely entertainment, information, social-psychological, and convenience, while emotions into two factors, namely positive and negative. Specifically, according to complexity and configuration theories, a conceptual model by a fuzzy-set qualitative comparative analysis examines the relationship between a combination of motivations, emotions, and satisfaction, while results indicate that both positive and negative emotions can lead to high satisfaction when combing motivations.

From the perspective of SOR theory, consumers’ motivation is greatly influenced by self-consciousness, while conscious cognition plays the role of intermediary. First, after being stimulated by the external environment, online consumers will form “cognitive structure” depending on their subjectivity. Instead of taking direct action, they deliberately and actively obtain valid information from the stimulus process, considering whether to choose the product, and then react. Second, the stimulation stage in the retail environment can often attract the attention of consumers and cause the change of their psychological feelings. This stimulation is usually through external environmental factors, including marketing strategies and other objective influences. Third, organism stage is the internal process of an individual. It is a consumers’ cognitive process about themselves, their money, and risks after receiving the information they have seen or heard. Reaction includes psychological response and behavioral response, which is the decision made by the consumer after processing the information [ 60 ]. Based on literature review, 10 utilitarian motivation factors, such as desire for control, autonomy, convenience, assortment, economy, availability of information, adaptability/customization, payment services, absence of social interaction, and anonymity and 11 hedonic motivation factors, such as visual appeal, sensation seeking/entertainment, exploration/curiosity, escape, intrinsic enjoyment, relaxation, pass time, socialize, self-expression, role shopping, and enduring involvement with a product or service, are refined [ 61 ]. Consumers’ incidental moods can improve online shopping decisions impulsivity, while decision making process can be divided into orientation and evaluation [ 62 ]. Sarabia‐Sanchez et al. [ 63 ] combine K-means cluster and ANOVA analyses to explore the 11 motivational types of consumer values, which are achievement, tradition, inner space, universalism, hedonism, ecology, self-direction (reinforcement, creativity, harmony, and independence), and conformity.

• Hypothesis 4 (H4): Perceived motivation attribute is the influencing factor of OCPB.

3. Materials and methods

3.1 research design.

Given the present study’s objective to identify the influencing factors of OCPB, we analyzed e-WOM using big data analysis. To obtain accurate data of the influencing factors on OCPB, smartphones were the main object of data crawling. The rationale behind this choice is as follows. First, the time people spend using their smartphones is gradually increasing. Nowadays, smart phones are not only used for telephone calls or text messages, but also for taking photographs, recording video, surfing the web, online chatting, online shopping, and other such uses [ 64 ]. Second, smartphones have become a symbol of personal identification, as users’ using fingerprint or facial scans are frequently used to unlock devices, conduct online transactions, and make reservations, etc. Finally, smartphones’ software and hardware are updated frequently, so they may be considered high-tech products. Therefore, smartphones were chosen as the research object to determine which influencing factors affect OCPB.

Fig 1 shows the e-WOM data mining process and methods used. A dataset obtained from Taobao.com and Jingdong.com was collected by utilizing a Python crawling code, additional details of which are provided in Section 2.3. Section 2.4 addresses issues regarding language complexity. Moreover, Section 2.5 refers to the clustering of the influencing factors of OCPB through the K-means method of ML.

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3.2 Data collection

The data were crawled from the e-commerce platforms Jingdong.com and Taobao.com by utilizing Python software. Jingdong and Taobao are the most powerful and popular platforms in China having professional e-WOM and user-friendly review systems. Specifically, the smartphone brands selected for analysis were Apple, Samsung, and Huawei because these three smartphone companies occupy the largest percentage of the smartphone market.

The authors determined that the analysis of the influencing factors of OCPB would be more persuasive and realistic by choosing smartphone models with high usage rate and liquidity. Thus, products reviews were crawled for the purchase of newly launched smartphones from Apple, Samsung, and Huawei in 2022. Specifically, to guarantee high-quality data, reviews from Taobao flagship stores and Jingdong directly operated stores were selected. However, we only collected reviews’ text content instead of images, videos, ratings, or rankings, the rationale was to ensure the reliability of data and meet research objectives. For instance, some e-commerce sellers attempt to increase their sales volume through deceitful methods, such as by faking ratings, rankings, and positive comments. Furthermore, online sellers and e-commerce companies (rather than consumers) often decide which smartphones are highest-rated and highest-selling. Finally, nowadays, the content of online reviews is not limited to text, as they also involve pictures, videos, and ratings, which have limited contribution in analyzing influencing factors of OCPB. Thus, the analyzed data regarding e-WOM in reviews was limited to text content.

In addition, to accurately reflect the real characteristics of OCPB during the COVID-19 pandemic, the study period ranged between February and May, 2022 (4 months). During that 4-month period, consumers exhibited a preference for buying products from e-commerce platforms. Specifically, the number of text reviews for the aforementioned types of smartphones was 51,2613 and 44,3678 in Taobao and Jingdong, respectively, for a total of 956,291 reviews.

3.3 Textual review processing method

As the crawled data exhibited noise, several data cleaning methods were adopted to filter noise and transform unstructured data of complex contextual review into structured data. Fig 1 shows the main procedures of the reviews’ pre-processing and the details are as follows.

First, to identify the range of sentences and for further data processing, sentences were apportioned using Python’s tokenizer package.

Second, this study employed Python’s Jieba package to perform word segmentation. The Jieba package is the Python’s best Chinese word segmentation module, comprising three modes. The exact mode was used to segment the sentences as accurately as possible, so they may be suitable for textual context analysis. The full mode was used to scan and process all words in each sentence, although it had a relatively high speed, it had a low capacity to resolve ambiguity. Additionally, the search engine mode segmented long words a second time, which allowed for the improvement of the recall rate, and was suitable for engine segmentation based on Jieba’s exact mode.

Third, stop words were deleted by referring to a stop words list. These included conjunctions, interjections, determiners, and meaningless words, among others. Finally, Python’s Word-to-vector (Word2vec) package was imported in the next step. Word2vec is an efficient training word vector model proposed by Mikolov [ 65 , 66 ]. The basic starting point was to match pairs of similar words. For instance, when “like” and “satisfy” appeared in a same context, they showed a similar vector, as both words had a similar meaning. Kim et al. [ 67 ] stated that a word could be considered a single vector and real numbers in the Word2vec model. In fact, most supervised ML models could be summarized as f ( x )−> y . Moreover, x could be considered a word in a sentence, while y could be considered this word in the context. Word2vec aimed to decide whether the sample of ( x , y ) could match the laws of natural language. Namely, after the process of Word2vec, the combination of word x and word y could be reasonable and logical or not. Table 1 shows the results of text processing.

https://doi.org/10.1371/journal.pone.0286034.t001

3.4 Influencing factors analysis by K-means

ML styles are divided into supervised and unsupervised algorithms. This study mainly utilized unsupervised algorithms to analyze the clusters of influencing factors of OCPB. Unsupervised algorithms consist in the clustering of unknown or unmarked objects without a trained sample [ 68 ]. This study utilized K-means to cluster the influencing factors.

For a given sample set, the K-means algorithm divides the sample set into k clusters according to the distance between samples. The main algorithm’s logic is to make the points in the cluster as close as possible, and to make the distance between the clusters as large as possible. Assuming that clusters can be divided into ( C 1 , C 2 ,…, C k ), the Euclidean distance of E is shown in Eq 1 .

The main procedures of K-means were the following.

Step 1 consisted of inputting the samples D = { x 1 , x 2 ,… x m }, K is the number of clusters, and appears as C = { C 1 , C 2 ,… C k }.

In Step 2, K samples were randomly selected from data set D as the initial K centroid vectors: { μ 1 , μ 2 ,… μ k }.

For Step5, it was necessary to repeat Steps 3 and 4, until all the centers μ remained steady. The final clustering result can be shown as C = { C 1 , C 2 ,… C k }.

The main procedures of K-means, according to Jain [ 69 ], are shown in Table 2 .

https://doi.org/10.1371/journal.pone.0286034.t002

4.1 K-means results

Based on the main procedures of K-means ( Table 2 ), the results are presented in Figs 2 – 4 .

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Four clusters of influencing factors of OCPB can be clearly identified in the analyses of the Jingdong dataset, Taobao dataset, and combined Jingdong and Taobao dataset. After checking the context of four clusters, even though small differences were found, their influence was found to be negligible for our analyses. Thus, Fig 4 was chosen as the benchmark of influencing factors of OCPB. In Section 4.3, the explanation and analysis of influencing factors of OCPB will be presented.

4.2 Performance metrics

First, performance metrics of sum of the square errors (SSE) and silhouette coefficient were adapted to verify the clustering results of K-means.

When the number of clusters does not reach the optimal numbers K, SSE decreases rapidly with the increase of the number of clusters, while SSE decreases slowly after reaching the optimal numbers, and the maximum slope is the optimal numbers K.

Where C i is the i th cluster, p is the sample point in C i (the mean value of all samples in C i ), and SSE is the clustering error of all samples, which represents the quality of clustering effect.

Fig 5 indicates that the SSE decreases rapidly when K equals the number of four.

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The range of sc i is between -1 and 1, the clustering effect is bad when sc i is below zero, whereas the clustering effect is good when sc i is near 1 conversely.

Based on Fig 6 , it is obviously to show that the silhouette coefficient reaches highest when K equals the number of four. Therefore, the results of the SSE and the silhouette coefficient jointly prove the number of K is four.

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4.3 Hypotheses results

Based on the K-means analysis, this section presents the influencing factors identified in the data from Jingdong and Taobao, which indicate the influencing factors influencing OCPB.

The first cluster comprises the perceived emergency context attribute, such as logistics, expressage, delivery, customer service, promotion, and reputation.

The second cluster comprises the perceived product attribute, such as appearance, brand, hand feeling, color, cost-performance ratio, price, design, and usability.

The third cluster comprises the perceived innovation attribute, such as photograph, quality and effects, screen quality, audio and video quality, pixel density, image resolution, earphone capabilities, and camera specifications.

The fourth cluster comprises the influencing factors, such as processing speed, operation, standby time, battery, system, internal storage, chip, performance, and fingerprint and face recognition, which cannot represent the perceived motivation attribute.

The results match the findings of Zhang et al. [ 70 ] to some extent, who identified 11 smartphone attributes based on online reviews: performance, appearance, battery, system, screen, user experience, photograph, price, quality, audio and video, and after-sale service. In addition, other scholars have explained the relationship between feature preferences and customer satisfaction [ 71 , 72 ], usage behavior and purchase [ 73 , 74 ], importance and costs of smartphones’ features and services [ 75 ], brand effects [ 76 ], and purchase behavior of people of different ages and gender groups [ 77 – 79 ]. Thus, H1, H2 and H3 are supported, while H4 is not supported according to the results of the K-means analysis.

4.4 Theoretical framework and validity of OCPB influencing factors

Kotler’s five product level model states that consumers have five levels of need comprising the core level, generic level, expected level, augmented level, and potential level. First, the core benefit is the fundamental need or want that consumers satisfy by consuming a product or service. Second, the generic level is a basic version of a product made up of only those features necessary for it to function. Third, the expected level includes additional features that the consumer might expect. Fourth, the augmented level refers to any product variations or extra features that might help differentiate a product from its competitors and make the brand a preferred choice amongst its competitors. Finally, a potential product includes all augmentations and improvements that a product might experience in the future [ 80 ].

In contrast with these levels, this study proposed the four influencing factors of OCPB. Based on Table 3 , first, the perceived emergency context H1 is not included in Kotler’s five products level, while the influencing factor expresses the significant characteristics of OCPB compared with Kotler’s model. Second, the perceived product attribute H2 could be considered the core and generic level. Third, the perceived innovation attribute H3 could be considered the potential level. Fourth, the results of H4 mainly reflects additional or special function of product, which meets the definition of the expected and augmented level. To refine the theoretical framework, H4 changes to the perceived functionality attribute by combing the explanation of the expected and augmented level, instead of the perceived motivation attribute. The details are shown in Fig 7 .

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Fig 7 shows the four influencing factors of the theoretical framework of OCPB. Specifically, according to Kotler’s five products level, the perceived product attribute is the necessary influencing factor of OCPB, which meets the core drive and basic requirement. For instance, the core drive of purchasing of a smartphone is the core function of communication, and then the appearance, brand, color, etc. The perceived functionality attribute is the additional influencing factor of OCPB, which meets the expected and augmented requirement. For instance, when smartphones are in the same price range, consumers prefer to choose a smartphone belonging to better quality, smarter design, or better functionality. Moreover, the perceived innovation attribute is the attractive influencing factor of OCPB, which reflects the potential level. For instance, most consumers are the Apple fans mostly because the Apple products offer innovative usage experience and different technology elements yearly. Finally, the perceived emergency context attribute is the adaptive influencing factor of OCPB, which shows the main distinction with Kotler’s five products level. Further, because of the COVID-19, consumers only have online channel to purchase product under a prolonged quarantine and lockdown. Thus, in the emergency context, consumers primarily consider whether the product can be purchased in the e-commerce platform, whether the product can be delivered normally, or whether the packaged has been disinfected fully.

5. Discussion

Traditional consumer behavior is mainly affected by psychological, social, cultural, economic, and personal factors [ 81 , 82 ]. Park and Kim [ 83 ] conducted an empirical study to identify the key influencing factors that impact OCPB, which include service information quality, user interface quality, security perception, information satisfaction, and relational benefit. Further, Sata [ 84 ] conducted an empirical study and found that price, social group, product features, brand name, durability and after-sales services were important to consumers’ buying behavior when choosing a smartphone for purchase. Simultaneously, some studies have utilized big data technology to explore OCPB, exploring online consumers’ attitude toward products in different countries, and identified product features. However, these studies do not identify the influencing factors of OCPB and ignore e-WOM. To better explain OCPB influencing factors, e-WOM should be integrated into the theoretical framework and used in practical applications. Thus, this study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM.

5.1 Theoretical implications

First, perceived emergency context attribute is the influencing factor of OCPB. Because of the COVID-19, e-commerce is the priority choice for consumers under circumstances of prolonged quarantine and lockdown, and then considering logistics and delivery. Furthermore, customer service, packaging, promotion, and reputation are critical to online consumers.

Second, perceived product attribute is the influencing factors of OCPB. The basic features of product, such as appearance, brand, hand feeling, price, and design, positively attract online consumers. Elegant appearance, famous brand, better hand feeling, lower price, and better design would be more impactful to OCPB.

Third, perceived innovation attribute is the influencing factor of OCPB. For smartphone, online consumers would show more interest in the innovation of speed, operation, standby time, chip, etc. Scientific and technological innovation for most products could improve the level of OCPB. Thus, the guarantee and improvement of functionality of a product could create more opportunities for online consumers to make purchasing decisions.

Fourth, according to Kotler’s five products level, perceived product attribute satisfies the characteristics of core drive and basic, while the perceived innovation attribute satisfies the characteristics of the potential level. Because hypothesis of perceived motivation attribute is not supported. Based on the analyzing results, the perceived functionality attribute is refined instead of the perceived motivation functionality attribute, which satisfies the expected and augmented. Meanwhile, the perceived emergency context attribute is not included, which shows the main difference with Kotler’s five products level.

5.2 Managerial implications

The influencing factors of OCPB were clustered into four categories: perceived emergency context, product, innovation, and function attributes. The definition and explanation of these categories may have important managerial implications for both OCPB and e-commerce. First, the findings of this study suggest that e-commerce enterprises should pay more attention to improving the quality, user experience, and additional design features of their products to arouse the interest of OCPB. However, this may be difficult for e-commerce enterprises because achieving these goals requires updating the software and hardware constantly, which involves significant investment. For most scientific and technical corporations, making heavy investments is not particularly difficult, however, service-type enterprises and small and medium enterprises may have insufficient funds to afford such heavy investments. This is the main reason that most online consumers buy products from famous brands instead of small and medium enterprises. Therefore, to improve their situation, both types of companies could jointly develop products or services, for instance, small and medium enterprises may purchase patents from large enterprises, jointly researching and developing products, or large enterprises could share their achievements at a price.

Second, the pandemic has accelerated the spread of e-commerce considerably, changing consumers’ shopping style in the process. Accordingly, e-commerce enterprises should adapt their marketing strategies, especially as the COVID-19 pandemic is still ongoing, due to the rapid development of the economy and its dynamic environment. For instance, e-commerce platforms should realize that changes in OCPB will continue to contribute to the growth of the e-commerce market. Moreover, e-commerce enterprises should combine their online presence with brick-and-mortar stores. Even more importantly, e-commerce enterprises should successfully operate their supply chain to adapt to the implementation of lockdown measures and the closing of manufacturing factories. Consumers should exercise caution when facing e-commerce enterprises’ adaptive financial policy, such as interest-free rates, which may cause financial burden.

Third, e-commerce enterprises should offer a simple and smooth shopping experience, clearly display practical information, increase the value of goods (by improving the quality, design, and performance of products or services) and improve their brand image for online consumers. However, e-commerce enterprises sometimes rely on certain fraudulent methods to increase their sales volume, such as falsifying positive e-WOM and deleting negative feedback, as was identified during the data processing stage. Therefore, online consumers should select online stores cautiously to avoid buying products of poor quality or performance.

Fourth, nowadays, technology is constantly evolving at an accelerated rate, particularly in the smartphone industry, as companies launch new products with innovative functions each year. Thus, e-commerce enterprises should strive to innovate to secure their position in the market. In addition, consumers should reconsider the need to experience the state-of-the-art products because these may have high prices.

6. Conclusion and limitations

In conclusion, during the COVID-19 pandemic, consumers highly preferred to buy products online, because most brick-and-mortar stores were closed due to lockdowns and social distancing measures. Additionally, with the rapid development of e-commerce, online shopping has become the most popular shopping style because it allows consumers to not only save time and money, but also review e-WOM before purchasing a product. Moreover, e-WOM is much more reliable compared with traditional WOM. Thus, this study proposed a theoretical framework to explore and define the influencing factors of OCPB based on e-WOM data mining and analyzing. The data were crawled from Jingdong and Taobao, while the data process was also fully demonstrated. Comparing the results, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. Moreover, perceived emergency context attribute is the main difference compared with Kotler’s five products level, while perceived product attribute meets the core and generic level, perceived functionality attribute meets the expected and augmented level, and perceived innovation attribute meets the potential level.

However, this study still has certain limitations. First, the data were crawled from Chinese e-commerce websites, hence, they may not be generalized in contexts where the influencing factors and dimensions may vary compared with other countries or regions. Second, this study only explored and defined the antecedents of OCPB. Data should be added from Western e-commerce websites. Moreover, the present study’s results should be compared with Western studies to generate a more comprehensive view of the antecedents of OCPB. Future studies should explore the underlying mechanisms influencing OCPB.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0286034.s001

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International Journal of Service Industry Management

ISSN : 0956-4233

Article publication date: 1 February 2004

While a large number of consumers in the US and Europe frequently shop on the Internet, research on what drives consumers to shop online has typically been fragmented. This paper therefore proposes a framework to increase researchers’ understanding of consumers’ attitudes toward online shopping and their intention to shop on the Internet. The framework uses the constructs of the Technology Acceptance Model (TAM) as a basis, extended by exogenous factors and applies it to the online shopping context. The review shows that attitudes toward online shopping and intention to shop online are not only affected by ease of use, usefulness, and enjoyment, but also by exogenous factors like consumer traits, situational factors, product characteristics, previous online shopping experiences, and trust in online shopping.

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Perea y Monsuwé, T. , Dellaert, B.G.C. and de Ruyter, K. (2004), "What drives consumers to shop online? A literature review", International Journal of Service Industry Management , Vol. 15 No. 1, pp. 102-121. https://doi.org/10.1108/09564230410523358

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Copyright © 2004, Emerald Group Publishing Limited

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What makes an online review credible? A systematic review of the literature and future research directions

• Open access
• Published: 05 December 2022
• Volume 74 , pages 627–659, ( 2024 )

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• K. Pooja   ORCID: orcid.org/0000-0001-7735-8308 1 &
• Pallavi Upadhyaya   ORCID: orcid.org/0000-0003-4523-2051 2  

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Online reviews of products and services are strategic tools for e-commerce platforms, as they aid in consumers’ pre-purchase decisions. Past research studies indicate online reviews impact brand image and consumer behaviour. With several instances of fake reviews and review manipulations, review credibility has become a concern for consumers and service providers. In recent years, due to growing webcare attitude among managers, the need for maintaining credible online reviews on the e-commerce platforms has gained attention. Though, there are several empirical studies on review credibility, the findings are diverse and contradicting. Therefore, in this paper, we systematically review the literature to provide a holistic view of antecedents of online review credibility. We examine variables, methods, and theoretical perspective of online review credibility research using 69 empirical research papers shortlisted through multi-stage selection process. We identify five broad groups of antecedents: source characteristics, review characteristics, consumer characteristics, interpersonal determinants in the social media platform and product type. Further, we identify research issues and propose directions for future research. This study contributes to existing knowledge in management research by providing the holistic understanding of the “online review credibility” construct and helps understand what factors lead to consumers’ belief in the credibility of online review. The insights gained would provide managers adequate cues to design effective online review systems.

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1 Introduction

Online reviews of products and services have become an integral component of product information on e-commerce platforms and are often used as strategic instrument to gain competitive advantage (Gutt et al. 2019 ). They are influential in marketing communications and help shoppers identify the products (Chen and Xie 2008 ) and make informed pre-purchase decisions (Hong and Pittman 2020 ; Eslami et al. 2018 ; Klaus and Changchit 2019 ; Reyes- Menendez et al. 2019 ). In the absence of physical interaction with the product, they aid consumers to take decisions based on experiences shared by previous users on the e-commerce platform (Klaus and Changchit 2019 ). Reviews facilitate the free flow of consumer-generated content that help managers promote their products or brand or company (Smith 2011 ). The products that get at least 5 reviews have a 270% higher conversion rate compared to the products with no reviews (Collinger et al. 2017 ).

With the growing popularity of online reviews, there is an overwhelming interest among researchers to understand the characteristics of reviews and reviewer that contribute to the credibility of online reviews (Cheung et al. 2009 ; Chih et al. 2020 ; Fang and Li 2016 ; Jimenez and Mendoza 2013 ; Liu and Ji 2018 ; Mumuni et al. 2019 ; Qiu et al. 2012 ; Tran and Can 2020 ; Yan et al. 2016 ). The credibility of online information and digital media is often contested, due to the lack of quality control standards and ambiguity concerning the ownership of the information with the convergence of information and media channels (Flanagin and Metzger 2007 ). As all online reviews cannot be trusted (Johnson and Kaye 2016 ) and when sources are uncertain (Lim and Van Der Heide 2015 ) consumers often use cues to assess review credibility. The credibility issue also arises due to review manipulation practices by asking the reviewers to write a positive review in favour of the brand and to write a negative review attacking the competitor's product, by incentivizing the reviewer (Wu et al. 2015 ).

Recent meta-analysis studies on electronic word of mouth (eWOM) communications have focused on factors impacting eWOM providing behaviour (Ismagilova et al. 2020a ), the effect of eWOM on intention to buy (Ismagilova et al. 2020b ), the effect of source credibility on consumer behaviour (Ismagilova et al. 2020c ), factors affecting adoption of eWOM message (Qahri-Saremi and Montazemi 2019 ) and eWOM elasticity (You et al. 2015 ). Moran and Muzellec ( 2017 ) and recently Verma and Dewani ( 2020 ) have proposed four-factor frameworks for eWOM Credibility. Zheng ( 2021 ) presented a systematic review of literature on the classification of online consumer reviews.

Even though there are literature reviews and meta-analysis on eWOM, they address different research questions or constructs in eWOM and no attempt to synthesise the antecedents of online review credibility, in the context of products and services has been made. Xia et al. ( 2009 ) posit that all eWOM are not formulated equally and classify eWOM as “many to one” (e.g., No of ratings, downloads calculated by computers), “many to many” (e.g., Discussion forums), “one to many” (e.g., Text-based product reviews), and “one to one” (instant messaging). Studies confirm that the effort to process and persuasiveness of different forms of eWOM vary (Weisfeld -Spolter et al. 2014 ). Senecal and Nantel ( 2004 ) argue that consumers spend significantly more time and effort to process online reviews than any other form of eWOM. Hence understanding credibility of the online reviews and the factors that influence credibility is important for managers of e-commerce platforms.

Our objective in this paper is three-fold: First, we revisit, review, and synthesize 69 empirical research on online review credibility that focuses on textual online reviews of products and services (“one to many” form of eWOM). Second, we identify the antecedents of review credibility. Finally, we identify gaps and propose future research directions in the area of online reviews and online review credibility. From theoretical perspective, this systematic review synthesises the antecedents of review credibility, in the context of online reviews of products and services. As in past literature, eWOM and online reviews are interchangeably used, we carefully analysed both the eWOM credibility and online review credibility and selected studies that focused on reviews of products and services. Studies on sponsored posts on social media, blogs, the brand initiated eWOM communication were excluded. From managerial perspective, this study would aid managers of e-commerce platforms, a holistic view of review credibility and aid in the design of online review systems.

1.1 Defining online review credibility

Mudambi and Schuff ( 2010 ) define online reviews as “peer-generated product evaluations, posted on company or third-party websites”. Person-to-person communication via the internet is eWOM. An online review is a form of eWOM. There are various channels of eWOM such as social media, opinion forums, review platforms, and blogs. Past literature posits that credible eWOM is one that is perceived as believable, true, or factual (Fogg et al. 2001 ; Tseng and Fogg. 1999 ).

The perception a consumer holds regarding the veracity of online review is considered as the review credibility (Erkan and Evans 2016 ). Several research studies (Cheung et al. 2009 ; Dong 2015 ) define credible online reviews as a review that the consumers perceive as truthful, logical, and believable. Past research defines credibility to be associated with consumers’ perception and evaluation and not as a direct measure of the reality of reviews (Chakraborty and Bhat 2018a ). The credibility of online reviews is described as consumers’ assessment of the accuracy (Zha et al. 2015 ) and validity of the reviews (Chakraborty and Bhat 2017 ).

2 Research methods

This paper uses the systematic literature review method (Linnenluecke et al. 2020 ; Moher et al. 2009 ; Neumann 2021 ; Okoli 2015 ; Snyder 2019 ) to synthesize the research findings. Liberati et al. ( 2009 ) explains systematic review as a process for identifying, critically appraising relevant research and analyzing data. Systematic reviews differ from meta-analysis with respect to methods of analysis used. While meta-analysis focuses primarily on quantitative and statistical analysis; systematic reviews use both quantitative and qualitative analysis and critical appraisal of the literature. In a systematic review, pre-specified protocols on inclusion and exclusion of the articles are used to identify the evidence that fits the criteria to answer the research question (Snyder 2019 ). In this paper, we follow the steps proposed by Okoli ( 2015 ) for conducting the systematic review process and the recommendations given by Fisch and Block ( 2018 ) to improve the quality of the review. The purpose of our systematic literature review is to identify and synthesize the antecedents of online review credibility.

The study uses journal articles from two popular research databases (Scopus and Web of Science) to conduct a systematic search of articles on review credibility/eWOM credibility. As online reviews are interchangeably used with other related concepts such as eWOM, user-generated content, and online recommendations in the literature, we used a diverse pool of sixteen keywords (refer Fig.  1 ) for the initial search. The keywords were identified through an initial review of literature and articles having these terms in the title, abstract, and keywords were chosen. Initial search and document retrieval were done in January 2022. Studies published till October 2022 were later updated in the paper. A set of filters using inclusion and exclusion criteria were applied to arrive at a focused set of relevant papers. The full-length empirical articles in English language, related to business management and allied areas were included for systematic review. Using multiple phases of filtering and reviewing (refer Fig.  1 ), we shortlisted the final list of 69 empirical papers that used either review credibility or eWOM credibility as a construct with a focus on reviews of products and services. In line with previous systematic reviews (Kuckertz and Brändle 2022 ; Nadkarni and Prügl 2021 ; Walter 2020) we excluded work in progress papers, conference papers, dissertations or books from the analysis.

Systematic review process

2.1 Descriptive analysis of empirical research on online review credibility

The 69 empirical research articles included 36 experimental design studies and 33 cross-sectional survey-based studies. Figure  2 summarises the review credibility publication trends in the last decade with their research design choices.

Research designs of Review credibility articles

Research on review credibility has used samples from diverse geographical regions, the highest number of studies being in the USA, China, and Taiwan (refer to Table 1 ). Table 2 and Table 3 summarizes the sample and analysis methods used in these studies. Even though online review is commonly used in tourism and hospitality, there are only six studies examining review credibility.

3 Theoretical perspectives in review credibility literature

Most of the empirical research (88 percent) on review credibility has used theories to explain the antecedents of review credibility. A total of 48 different theories have been invoked in explaining various dimensions of review credibility antecedents.

We observed five broad groups of theories from the underlying 48 theories that contribute to understanding the different aspects of online review credibility assessment by consumers. We discuss them in the following sections.

3.1 Information processing in online review

Several theories provide a lens to understand ways in which individual consumes or processes the information available in the online reviews. The popular theories discussed in the review credibility literature such as the elaboration likelihood model, heuristic—systematic model, accessibility—diagnosticity theory, and attribution theory describe how an individual processes information.

Building on the elaboration likelihood model (ELM) several studies have examined characteristics of online review content such as argument quality (Cheung et al. 2009 ; Hussain et al. 2018 ; Thomas et al. 2019 ), review sidedness (Cheung et al. 2012 ; Brand and Reith 2022 ), review consistency (Brand et al. 2022 ; Brand and Reith 2022 ; Cheung et al. 2012 ; Thomas et al. 2019 ), and source credibility (Cheung et al. 2012 ; Hussain et al. 2018 ; Reyes- Menendez et al. 2019 ). These dimensions are also examined using the heuristics-systematic model (HSM). These two theories are similar in their function as both ELM and HSM posit two routes (the central vs. peripheral route and the systematic vs. heuristic route) for judging the persuasiveness of messages (Chang and Wu 2014 ). In literature, the elaboration likelihood model has received more empirical support compared to the heuristics systematic model. The yale persuasive communication theory covers a wider array of factors that can affect the acceptance of the message (Chang and Wu 2014 ). This theory has been adopted by studies to evaluate the relationship between these factors with review credibility.

The psychological choice model posits that the effectiveness of online reviews gets influenced by environmental factors like product characteristics and consumer’s past experience. These factors influences the credibility assessment by the consumer and purchase decision based on their interaction with the online reviews.

Consumers’ use of information for judgment also depends upon the accessibility and diagnosticity of the input as proposed in accessibility-diagnosticity theory. This theory helps in understanding the utilization of information by individuals and posits that the information in hand has more value than information stored as a form of memory (Tsao and Hseih 2015 ; Chiou et al. 2018 ). The attribution theory helps in understanding the nature of the causal conclusion drawn by the consumers in the presence of negative and positive information (Chiou et al. 2018 ).

Overall, the theories related to information processing have contributed well to understanding the influence of strength of the message, argument, valence, source reputation, consistency, persuasiveness, and diagnosability.

Theories such as media richness theory (Tran and Can 2020 ) and language expectancy theory (Seghers et al. 2021 ) provided insights into the relevance of the quality of the information shared in online reviews. Several other theories focus on the information adoption process (ex. Information adoption mode, informational influence theory, dual-process theory). For example, cognitive cost theory has been used to explain review adoption due to the effect of different levels of cognitive involvement of the consumer when they are exposed to reviews from different platforms simultaneously (Yan et al. 2016 ).

The contribution of technology acceptance model (TAM) to the review credibility literature is operationalized in the study by Liu and Ji ( 2018 ). Hussain et al. ( 2018 ) uses TAM to complement ELM in the computer-mediated communication adoption process.

We observe that the theories in information processing in the online review have provided a theoretical lens to understand the role of the quality of the information in the online review credibility assessment.

3.2 Trust in online reviews

Studies have examined the trust formation and perception of the trustworthiness of the source of the information in online reviews using the theoretical lens of trust transfer theory and source credibility theory. Virtual communities do not support the face-to-face interaction between sender and receiver of the message. Therefore, the receiver has to rely on cues such as the reputation of the source, credibility of the source, and the reviewer profile. These cues are observed as some of the antecedents of review credibility. Trust transfer theory contributes to our understanding of how online reviews shared on a trusted e-commerce website makes the consumer consider that review is credible compared to the review shared on a website that is not trustworthy (Park and Lee 2011 ). Source credibility theory suggests trustworthiness and expertise of the source of the review have a positive relationship with review credibility (Mumuni et al. 2019 ; Shamhuyenhanzva et al. 2016 ). These theories note that when a person perceives the origin of online review as trustworthy, he would be more likely to consume the information.

3.3 Socio-cultural influence in online reviews

Individuals’ innate values or beliefs help shape their behaviour. As online reviews are more complex social conversations (Kozinets 2016 ) there is a need to gain perspectives on how these conversations differ in terms of country and culture (Bughin et al. 2010 ). The theories such as culture theory, and Hall’s categorization provide a lens to examine the influence of culture on online review consumption and assessment of review credibility (Brand and Reith 2022 ; Chiou et al. 2014 ; Luo et al. 2014 ).

In general, attention paid to understanding the influence of cultural factors on online reviews is very limited (Mariani et al. 2019 ; Gao et al. 2017 ). However, much attention has been given to understanding the role of social influence through the use of theories like social influence theory, role theory, social identity theory, social information processing theory, socio-cognitive systems theory, and value theory. The most prominent theory related to this theme is the social influence theory. Social influence theory emphasizes the social pressure faced by consumers to form a decision based on online reviews (Jha and Shah 2021 ). Social identity theory posits that an individual may reduce uncertainty by choosing to communicate with other people who share similar values and social identities (Kusumasondjaja et al. 2012 ).

Social information processing theory posits the importance of the closeness between review writer and reader on social networking as an alternative cue, in the absence of physical interaction (Lim and Van Der Heide 2015 ). The social standings of an individual in terms of the number of friends on social networks (Lim and Van Der Heide 2015 ), nonverbal cues such as profile photos (Xu 2014 ), and their impact on review credibility have been studied using this theory. In a nutshell, these theories explain individuals’ belief that gets shaped due to the influence of the social groups and how it impacts the credibility of the review.

3.4 Consumer attitude and behaviour towards online reviews

Consumers attitude towards computer-mediated communications and online reviews have been examined in past studies (Chakraborty and bhat 2017 ; Chih et al. 2020 ; Hussain et al. 2018 ; Isci and Kitapci 2020 ; Jha and Shah 2021 ) using several theoretical frameworks. Theories such as attitude—behaviour linkage, cognition-affection-behaviour (CAB) model, expectancy-disconfirmation theory (EDT), needs theory, regulatory focus theory, search and alignment theory, stimulus- organism-response model, theory of planned behaviour, yale attitude change model, associative learning theory were used in literature to examine the factors that influence the formation of the attitude and behaviour towards online reviews. These factors and their relationship with credibility evaluation have been studied by the yale attitude change model (Chakraborty and Bhat 2017 , 2018b ), and the stimulus-organism-response model (Chakraborty 2019 ). Jha and Shah ( 2021 ) adapted attitude-behavior linkage theory to study how the exposure to past reviews acts as an influence to write credible reviews.

The consumer’s expectation about product experience and credibility assessment is studied using theories like expectancy-disconfirmation theory (Jha and Shah 2021 ), needs theory (Anastasiei et al. 2021 ), and regulatory focus theory (Isci and Kitapci, 2020 ; Lee and Koo, 2012 ). Overall, these theories have contributed to the advancement of the understanding of the holistic process involved in consumer attitude formation and behaviour in online reviews.

3.5 Risk aversion

The theories such as category diagnosticity theory, prospect theory, uncertainty management theory, and uncertainty reduction theory provide a theoretical lens to examine how consumers rely on credible information to avoid uncertain outcomes. Hong and Pittman ( 2020 ) use category diagnosticity theory and prospect theory to hypothesize negative online reviews as more credible than positive reviews. An individual who focuses on reducing loss perceives negative online reviews as more diagnostic and credible. Kusumasondjaja et al. ( 2012 ) also argue that consumers try to avoid future losses by spending effort to find credible information before making a decision. With the help of these underlying assumptions, studies have used perspectives drawn from theories to understand the loss-aversion behaviour and higher perceived diagnostic value of negative information. Prospect theory suggests consumers attempt to avoid risks or loss and expect gain. Consumers avoid choosing the experience which has more negative online reviews because of the risk and loss associated with the negativity of the reviews (Floh et al. 2013 ). The risk aversion-related theories have contributed to understanding the consumers’ quest for credible information in negative reviews.

4 Antecedents of online review credibility

Literature on review credibility reveals varied nomenclature and operationalisation of antecedents of review credibility. However, we can broadly categorize review credibility antecedents into five broad groups: source characteristics, message characteristics, consumer characteristics, social/interpersonal influence, and product type (Refer to Fig.  3 ).

Anteeedents of review credibility

We discuss these antecedent themes along with the major constructs in each theme in the following sections. In the final section, we also summarise the theoretical perspectives in each antecedent themes.

4.1 Source characteristics

Literature reveals that several characteristics of the source influence the credibility perception and evaluation of review by consumers. Chakraborty and Bhat ( 2017 ) define a source as the person who writes online reviews. Researchers have operationalized the source characteristics primarily through reviewers’ knowledge and reliability (Chakraborty and Bhat 2017 ); reviewer characteristics such as identity disclosure, level of expertise, review experience, and total useful votes (Liu and Ji 2018 ). In several studies (Cheung et al. 2012 ; Chih et al. 2013 ; Mumuni et al. 2019 ; Newell and Goldsmith 2001 ; Reyes- Menendez et al. 2019 ; Yan et al. 2016 ), expertise and trustworthiness of the reviewer is one of the most common conceptualizations of source credibility. Cheung and Thadani ( 2012 ) define source credibility as the “message source’s perceived ability (expertise) or motivation to provide accurate and truthful (trustworthiness) information”.

Source credibility is used as a single construct in several studies (Abedin et al. 2021 ; Chih et al. 2013 ; Cheung et al. 2009 , 2012 ; Mumuni et al. 2019 ; Reyes-Menendez et al. 2019 ; Yan et al. 2016 ; Luo et al. 2014 ). Studies have also conceptualized its sub-dimensions such as source trustworthiness (Chih et al. 2020 ; Lo and Yao 2018 ; Shamhuyenhanzva et al. 2016 ; Siddiqui et al. 2021 ; Thomas et al. 2019 ; Tien et al. 2018 ); reviewer expertise (Anastasiei et al. 2021 ; Fang 2014 ; Fang and Li 2016 ; Jha and Shah 2021 ) and reviewers’ authority (Shamhuyenhanzva et al. 2016 ), as separate antecedents to review credibility. Mumuni et al. ( 2019 ) posited that reviewer expertise and reviewer trustworthiness as two distinct constructs. Chih et al. ( 2020 ) define source trustworthiness as the credibility of the information presented by the message sender. Thomas et al. ( 2019 ) operationalize reviewer expertise as a peripheral cue and found that the amount of knowledge that a reviewer has about a product or service is influential in consumer’s perception of review credibility. Information presented by professional commentators who are perceived as experts in the specific field was found to have a positive influence on credibility (Chiou et al. 2014 ).

Source cues help in assessing the credibility and usefulness of the information shared in product reviews (Liu and Ji 2018 ). Reviews written by the source whose identity is disclosed have higher credibility compared to the reviews written by unidentified sources (Kusumasondjaja et al. 2012 ). However, in case of positive reviews with disclosed identity of the sponsor the review, credibility is negatively affected (Wang et al. 2022 ). Zhang et al. ( 2020 ) found that suspicion about the identity of the message sender influences negatively on the message’s credibility. Past studies found that when the number of friends of a reviewer (Lim and Van Der Heide 2015 ) and a number of trusted members of the reviewer (Xu 2014 ) are high in the online review community, reviews of such reviewers are considered as more credible. If a reviewer involves very actively in writing the review, the number of reviews posted by the reviewer provides evidence to the reader that the reviews written by such reviewers are credible (Lim and Van Der Heide 2015 ). The consumer also believes online reviews to be credible when they perceive the reviewer as honest (Yan et al. 2021) and caring (Yan et al. 2021). The source characteristics as antecedents of review credibility are summarized in Table 4 .

Several studies also define the source with the characteristics of the platform where the review is published. Consumers’ trust on the website (Lee et al. 2011 ) and the reputation of the website (Chih et al. 2013 ) were found as antecedents of the review credibility. If a consumer perceives an online shopping mall as trustworthy, he would believe that reviews posted in shopping mall as credible (Lee et al. 2011 ). Chih et al. ( 2013 ) posit that in addition to the source credibility (reviewer expertise), consumers evaluate the quality of contents of a website based on website reputation, which in turn leads to higher trust on the website and higher perceived credibility of the review. Website reputation is defined as the extent to which consumers perceive the platform where the review is published to be believable and trustworthy (Chih et al. 2013 ; Thomas et al. 2019 ; Tran and Can 2020 ; Guzzo et al. 2022 ; Majali et al. 2022 ). Bae and Lee ( 2011 ) found that consumer-developed sites were perceived as more credible than marketer-developed sites. Similarly, Tsao and Hsieh ( 2015 ) found that review quality as perceived by consumers had a higher impact on review credibility on independent platforms than on corporate-run platforms. Ha and Lee ( 2018 ) found that for credence service (eg. Hospital), the provider-driven platform and reviews were more credible and for experience goods (eg. Restaurant), consumer-driven platforms were perceived as more credible.

4.2 Review characteristics

Several characteristics of the message or the review are found to influence the review credibility on online review platforms (presented in Table 5 ). A product with a large number of reviews provides evidence of higher sales and popularity of the product (Flanagin and Metzger 2013 ; Hong and Pittman 2020 ; Reyes- Menendez et al. 2019 ). When online review for a product or service is higher, it directly influences the review credibility (Hong and Pittman 2020 ; Reyes- Menendez et al. 2019 ; Thomas et al. 2019 ; Tran and Can 2020 ).

If the reviewer agrees with most of online reviews or recommendations of others those reviews are considered as consistent reviews (Chakraborty and Bhat 2017 , 2018b ; Chakraborty 2019 ). The consistent online reviews were found to have higher credibility (Abedin et al. 2021 ; Baharuddin and Yaacob 2020 ; Brand and Reith 2022 ; Chakraborty and Bhat 2017 , 2018b ; Chakraborty 2019 ; Cheung et al. 2009 , 2012 ; Luo et al. 2014 ; Tran and Can 2020 ). Fang and Li ( 2016 ) found out that receiver of the information actively monitors the consistency of the information while perceiving the credibility of review. The degree of agreement in aggregated review ratings on the review platform creates consensus among the reviewers (Qiu et al. 2012 ). Information evolved from such consensus is perceived as highly credible (Lo and Yao 2018 ; Qiu et al. 2012 ). However, a few studies (Cheung et al. 2012 ; Luo et al. 2015 ; Thomas et al. 2019 ) have reported contradicting findings and argue that when the involvement of consumers is low and consumers are knowledgeable, review consistency has an insignificant impact on the review credibility.

Past studies have found strong evidence on the impact of review argument quality (Anastasiei et al. 2021 ; Baharuddin and Yaacob 2020 ; Cheung et al. 2012 ; Thomas et al. 2019 ; Tran and Can 2020 ; Tsao and Hsieh 2015 ) and review quality (Bambauer-Sachse and Mangold 2010 ; Chakraborty and Bhat 2017 , 2018b ; Chakraborty 2019 ; Liu and Ji 2018 ) and argument strength (Cheung et al. 2009 ; Fang 2014 ; Fang and Li 2016 ; Luo et al. 2015 ) on review credibility. Concreteness in the argument also positively impacts the review credibility (Shukla and Mishra 2021 ).

According to Petty et al. ( 1983 ), the strength of the argument provided in the message represents the quality of the message. Cheung et al. ( 2009 ) define argument strength as the quality of the information in the online review. Chakraborty and Bhat ( 2017 ) present review quality as the logical and reliable argument in the online review. Recent studies (Thomas et al. 2019 ; Tran and Can 2020 ) considered accuracy and completeness as dimensions of argument quality.

Review attribute helps in classifying the review as an objective review or subjective review based on the information captured (Lee and Koo 2012 ). Jimenez and Mendoza (2013); Gvili and Levy ( 2016 ) operationalize the level of detail as the amount of information present in the review about a product or service. Past studies have found evidence for the positive relationship between different attributes of reviews such as review objectivity (Luo et al. 2015 ; Abedin et al. 2021 ), level of detail (Jimenez and Mendoza 2013 ), review attribute (Lee and Koo 2012 ), message readability (Guzzo et al. 2022 ), persuasiveness of eWOM messages (Tien et al. 2018 ), interestingness (Shamuyenhanzva et al. 2016 ), graphics (Fang and Li 2016 ) and suspicion of truthfulness (Zhang et al. 2020 ) with review credibility. Vendemia ( 2017 ) found that the emotional content of information in the review also influences the review credibility. While assessing the review credibility, the utilitarian function of the review (Ran et al. 2021 ) and message content (Siddiqui et al. 2021 ) play an important role.

Several studies confirm that review valence influences review credibility (Lee and Koo 2012 ; Hong and Pittman 2020 ; Lo and Yao 2018 ; Manganari and Dimara 2017 ; Pentina et al. 2018 ; Pentina et al. 2017 ; vanLohuizen and Trujillo-Barrera 2019 ; Kusumasondjaja et al. 2012 ; Lim and Van Der Heide 2015 ; Chiou et al. 2018 ). Chiou et al. ( 2018 ) explain review valence is negative or positive evaluation of the product or service in online reviews. Review valence is often operationalized in experimental research at two levels: positive reviews vs negative reviews. Several studies report that negative reviews are perceived to be more credible than positive reviews (Chiou et al. 2018 ; Kusumasondjaja et al. 2012 ; Lee and Koo 2012 ; Lo and Yao 2018 ; Manganari and Dimara 2017 ). Negative reviews present a consumer’s bad experience, service failure or low quality and they create a loss-framed argument. Tversky and Kahneman ( 1991 ) explain that loss-framed arguments have a greater impact on the behaviour of consumer than gain-framed arguments. Contradictory to these findings, a few studies found that positive reviews are more credible than negative reviews (Hong and Pittman 2020 ; Pentina et al. 2017 , 2018 ). Lim and Van Der Heide ( 2015 ) found that though negative reviews impact greatly on consumer behavior it is perceived to be less credible.

Several studies (Chakraborty 2019 ; Cheung et al. 2012 ; Luo et al. 2015 ) have observed the impact of review sidedness (positive, negative or two-sided reviews) on review credibility and found that two-sided reviews are perceived as more credible. Further, Cheung et al. ( 2012 ) found that when consumers’ expertise level was high and involvement level was low, review sidedness had a stronger impact on review credibility.

Star ratings are numerical evidence of product performance (Hong and Pittman 2020 ). Star rating represents the average rating of all the review ratings therefore it helps to assess the conclusions in general (Tran and Can 2020 ). Rating evaluation needs a low amount of cognitive effort while processing the review information (Thomas et al. 2019 ). Past studies have found star ratings (Hong and Pittman 2020 ), aggregated review scores (Camilleri 2017 ), product or service ratings (Thomas et al. 2019 ; Tran and Can 2020 ), review ratings (Luo et al. 2015 ), and recommendation or information rating (Cheung et al. 2009 ) act as peripheral cues influencing the review credibility.

4.3 Consumer characteristics

Receiver is the consumer of the review and consumer needs, traits, motivation, knowledge, and involvement have been found to influence the review credibility. Chih et al. ( 2013 ) posit that online community members have two types of needs: functional need (need to find useful product information) and social need (need to build social relationships with others). These needs motivate consumers to use online reviews and form perceptions of review credibility. Consumers refer to online reviews to understand the product's pros, cons, and costs (Hussain et al. 2018 ); reduce purchase risk, and information search time (Schiffman and Kanuk 2000 ).

Past research studies indicate consumer’s motivation to obtain more information on purchase context (Chih et al. 2013 ), self-worth reinforcement (Hussain et al. 2018 ), opinion seeking from other consumers (Hussain et al. 2018 ), and prior knowledge of the receiver on the product (Cheung and Thadani 2012 ; Wang et al. 2013 ), influences review credibility. When the online reviews are congruous to the consumer’s knowledge and experiences, the message is perceived to be credible (Chakraborty and Bhat 2017 , 2018b ; Chakraborty 2019 ; Cheung et al. 2009 ). Chiou et al. ( 2018 ) found that high-knowledge consumers find reviews less credible. Studies in the past have also used prior knowledge of consumers as a control variable (Bae and Lee 2011 ) and moderating variable (Doh and Hwang 2009 ) when studying other factors. Bambauer-Sachse and Mangold ( 2010 ) found that knowledge on manipulations on product reviews influenced consumers' product evaluations, negative reviews, in particular, and when they come from a highly credible source.

Lim and Van Der Heide ( 2015 ) observed differences in the perceived credibility of users and non-users of the review platform and found an interaction effect between users’ familiarity with the review platform and reviewer profile (number of friends and number of reviews) characteristics of review credibility. Consumer experience with online reviews affects their perception of review credibility (Guzzo et al 2022 ). Izogo et al ( 2022 ) posit that consumer experiences such as sensory, cognitive and behavioral experience also influences review credibility. Consumer motivation, beliefs, and knowledge, as antecedents in literature, are summarised in Table 6 .

Cheung et. al ( 2012 ) posited that the influence of source and message characteristics on review credibility depends on two characteristics of the consumer: involvement and expertise. The authors found that level of involvement and knowledge of consumers moderate the relationships between review characteristics (review consistency and review sidedness) source credibility, and review credibility. Consumers process the information through central route, when making high involvement decisions and carefully read the content (Lin et al. 2013 ; Park and Lee 2008 ). When consumers have low involvement decisions, they are more likely to use peripheral cues and pay lesser attention to the review content, resulting in low eWOM credibility. Xue and Zhou ( 2010 ) found that consumers with high involvement decisions trusted negative reviews. In a recent study, Zhang et al. ( 2020 ) found that personality traits such as dispositional trust can trigger suspicion about the truthfulness of the message and may in turn, impact review credibility.

4.4 Interpersonal influence in the social media

Earlier research shows that interpersonal influence (Chu and Kim 2011 ) and tie strength (Bansal and Voyer 2000 ) positively influences online reviews. Consumers perceive online reviews as more credible when social status and cognitive dissonance reduction can be achieved through online forums (Chih et al. 2013 ). The previous studies have considered these factors under the theme related to source or communicator of the message (Verma and Dewani 2020 )). However, the constructs tie strength and homophily represent an interpersonal relationship between the communicator and the reader. Therefore, we discuss them separately. Tie strength is considered to be higher in an online community when the members have close relationships with other members and frequently communicate with each other. Consumers who have similar tastes and preferences share information in brand communities and enjoy meeting other members in a meaningful way (Xiang et al. 2017 ). Reviews are found to be more credible when review writers get exposed to past reviews written by others (Jha and Shah 2021 ). The exposure to past reviews moderates the relationship between disconfirmation and perception of online review credibility (Jha and Shah 2021 ). The recommendations of the members on social networking sites have also been found to be influencing the credibility of online reviews (Siddiqui et al. 2021 ).

Consumers’ perceptions of their similarity to the source of message are believed to impact their credibility assessment (Gilly et al. 1998 ; Wangenheim and Bayon 2004). Brown and Reingen ( 1987 ) define similarity or homophily as the “degree to which individuals are similar to sources in terms of certain attributes”. Herrero and Martin ( 2015 ) found that hotel consumers would perceive reviews more credible when there is a similarity between users and content creators. Source homophily is found to have an impact on review credibility in the e-commerce context as well (Abedin et al. 2021 ). Similarity of the source is often described in terms of interests of consumers and content generators. Xu ( 2014 ) posits that when a greater number of trusted members for reviewers are present on the website, it increases trust, thereby impacting the perceived credibility of the review. (Table 7 ).

4.5 Product type

The type of the product (search or experience product) is found to impact user’s evaluation of review credibility (Bae and Lee 2011 ; Jimenez and Mendoza 2013 ) and review helpfulness (Mudambi and Schuff 2010 ). Experience products differ from search products. They require more effort in retrieving product’s attribute-related information online and often require direct experience to assess the product features accurately. Bae and Lee ( 2011 ) found that when review originates from the consumer-owned online community, consumers find review credible for experience products. Tsao and Hsieh ( 2015 ) found that the credibility of eWOM is stronger for credence products than search products. Credence goods are those whose qualities cannot be confirmed even after purchase, such as antivirus software and sellers often cheat consumers due to information asymmetry and charge higher prices for inferior goods.

Jimenez and Mendoza ( 2013 ) found differences in consumers’ evaluation of review credibility for search and experience products. The study found that for search products detailed reviews were considered more credible and for experience products, reviewer agreement impacted review credibility (Jimenez and Mendoza 2013 ). Chiou et al. ( 2014 ) found that the review credibility was perceived differently for elite (eg: Classical musical concerts) and mass (eg: movies) cultural offerings. The study posited that when consumers read reviews of elite cultural offerings, and it originates from professionals, it is perceived as more credible. (Table 8 ).

4.6 Summary of antecedent themes and theoretical perspectives

Review characteristics, followed by source characteristics, are the most researched themes in terms of the number of studies and theories used (refer to Fig.  4 ). It indicates the wide coverage of different theoretical perspectives examined in these two areas. Consumer characteristics, interpersonal determinants in social media, and product type were less researched antecedent themes and lesser examined through a theoretical lens.

Anteeedent themewise articles and theories

The most popular theories in review credibility literature are the elaboration likelihood model, social influence theory, accessibility- diagnosticity theory, attribution theory, and theory of reasoned action. Contribution from these theories was noted in at least four antecedent themes identified in our study. Table 9 summarizes the theories used in each antecedent theme identified in the current review.

5 Review credibility: future research directions

Though there is ample research on online review credibility, there are several gaps in understanding the aspects of consumer behavior in online review evaluation and mitigation of issues with credibility. We identify six research issues that need further investigation and empirical evidence.

5.1 Research issue 1: review credibility in a high-involvement decision-making context

Several studies have examined credibility of reviews in experience products such as movies (Chiou et al. 2014 ; Flanagin and Metzer 2013 ), restaurants (Ha and Lee 2018 ; Pentina et al. 2017 ; vanLohuizen and Trujillo-Barrera 2019 ), hotels (Lo and Yao 2018 ; Manganari and Dimara 2017 ), and search goods such as audiobooks (Camilleri 2017 ), consumer electronics (Bambauer-Sachse and Mangold 2010 ; Chiou et al. 2018 ; Lee et al. 2011 ; Lee and Koo 2012 ; Tsao and Hsieh 2015 ; Xu 2014 ), few studies (Jimenez and Mendoza 2013 ; Doh and Hwang 2009 ; Xue and Zhou 2010 ; Bae and Lee 2011 ) have examined both experience and search products.

However, most of the products involve low to medium involvement of consumers and there is a gap in understanding online review usage, credibility, and impact in the context of high involvement decisions. There are several online review platforms on high involvement goods and services such as cars (eg: carwale, auto-drive), and destination holiday planning (TripAdvisor). Consumers often use online reviews to reduce purchase risk. As purchase risks are higher in high involvement decisions, consumers would spend more time searching online to evaluate the product. It is also necessary to understand to what extent consumers trust online reviews in a high involvement decision context, which often combines online information, reviews, and offline experiences (eg: visit to a car dealership for a test drive). Previous studies on consumer involvement (Hussain et al. 2018 ; Lin et al. 2013 ; Park and Lee 2008 ; Reyes-Menendez et al. 2019 ; Xue and Zhou, 2010 ) have operationalized involvement as a multi-item construct that captures the level of involvement of consumers, using consumers’ response. Experimental design studies, using high involvement goods and their reviews would help to establish causal relationships, in high involvement goods context. As an exception, one of the recent studies by Isci and Kitapci ( 2020 ) uses experimental design using automobile products as the stimuli for the experiment. However, as observed in our analysis, there are scarce studies in high involvement decision making context.

5.2 Research issue 2: mitigation of low credibility of the online review

While extant literature is available on factors affecting review credibility and its impact on brand and consumer behavior, there is limited literature and discussion on how companies can mitigate the impact of low credibility of reviews and improve trust. More evidence and empirical research is required to demonstrate effectiveness of measures that firms can take to build credibility and improve trust. As reviews are an important component of product information in e-commerce websites and reviews are used to form pre-purchase decisions, research on mitigation of poor credibility would be useful. For example, while past research shows that reviews on marketer-developed sites are perceived less credible for experience products than consumer-developed sites (Bae and Lee 2011 ). There is a need to study strategies that marketers can use to gain the trust of consumers.

5.3 Research issue 3: mitigating impact of negative online reviews

Past studies have indicated that consumers pay more attention to negative reviews (Kusumasondjaja et al. 2012 ; Lee and Koo 2012 ; vanLohuizen and Barrera 2019 ; Yang and Mai 2010 ), and trust (Xue and Zhou 2010 ; Banerjee and Chua 2019 ) more than positive reviews. Negative reviews are found to be persuasive and have a higher impact on brand interest and purchase intention (Xue and Zhou 2010 ). There are also limited studies discussing the ways to mitigate the impact of negative reviews and strategies to deal with them in a wide variety of contexts. While extant literature is available on review characteristics such as review sidedness, review valence, and its impact on review credibility (Refer to Table 5 ), there is little empirical evidence on strategies to deal with negative reviews. An exception is a study by Pee ( 2016 ), that addressed this issue by focusing on marketing mix and suggested that managing the marketing mix can mitigate the impact of negative reviews. However, more research is needed to equip marketers with mitigation techniques and fair strategies to deal with negative reviews.

5.4 Research issue 4: credibility of brand initiated online reviews

Brand-initiated eWOM often incentivizes consumers to share the content with their friends and it is unclear whether such initiatives are perceived as less credible. Brands use a variety of strategies to promote products on social media and facilitate person-to-person communications of brand content such as referral rewards, coupons, and bonus points (Abu-El-Rub et al. 2017 ). Incentivized reviews can easily manipulate consumers as their motive is not to provide unbiased information to make an informed decision (Mayzlin et al. 2014 ).

These practices followed by the service providers, or the vendors could jeopardize the trust consumers have towards them. More research in this area would provide insights into the best social media marketing practices that are considered credible. Future research must focus on guiding marketers on ethical and credible practices in social media marketing and managing online reviews.

5.5 Research issue 5: presence of fake online reviews

Unlike incentivized reviews, deceptive opinion spams are written to sound real and to deceive the review readers (Ott, Cardie and Hancock 2013 ; Hernández Fusilier et al. 2015 ). Spammers use extreme language when it comes to praising or criticizing (Gao et al. 2021 ). These spammers are active on several social media and review platforms. As technology is continuously evolving deceptive opinion spam has found a way through the use of artificial intelligence. The social media platforms like Twitter and Facebook have experienced the rise of bot or automated accounts. This trend is even entering into online review systems and is a threat to the online review system Tousignant ( 2017 ). A study conducted by Yao et al. ( 2017 ) argues that the reviews generated by bots are not only undetectable but also scored as useful reviews. This is a serious issue as the whole purpose of online review platforms is to provide information that would lead an individual to make an informed decision, but these fake reviews severely damage the credibility of review site (Munzel 2016 ). In recent years, researchers started contributing to this area and have proposed models to detect fake reviews in different platforms such as app stores (Martens and Maalej 2019 ), online review platforms (Singh and Kumar 2017 ), and filtering fake reviews on TripAdvisor (Cardoso et al. 2018 ). However, presence of fake reviews can make the review users skeptical towards using the reviews. Future research must focus on the role of artificial intelligence in online review systems and its impact on consumers’ assessment of online review credibility. Research into tools to detect and curb the spread of fake reviews is needed to improve credibility of reviews.

5.6 Research issue 6: new forms of online reviews

Rapid technological developments have resulted in new digital formats of online reviews such as video and images. Past experimental design studies have primarily used stimuli in the form of textual reviews. As consumers use more and more multimedia data and engage in platforms such as Youtube.com or Instagram.com, research is required to examine the online review credibility and practices using new forms of reviews.

6 Theoretical contribution and managerial implications and conclusions

This paper makes three important theoretical contributions. First, it provides a consolidated account of antecedents, mediators and moderators of the construct online review credibility identifies five broad groups of antecedents. Second, this paper also makes a maiden attempt to map the antecedent themes to the theoretical frameworks in the literature. This mapping provides a holistic understanding of theories that examine various facets of online review credibility. In the process, we also identify theoretical lenses that are less investigated. Third we identify research gaps and issues that needs further investigation in the area of online review credibility. Some of the areas of future research include mitigation strategies for negative reviews and credibility of reviews in purchase of high-involvement product or service. Emergence of new forms of multimedia reviews, fake reviews and sponsored reviews have also triggered the need to push research beyond simple text reviews. Future research could use theoretical lens that have been less explored to investigate research issues in review credibility. There is a need to advance online review credibility research beyond the popular theoretical frameworks such as elaboration likelihood model, social influence theory, accessibility- diagnosticity theory, attribution theory, and theory of reasoned action.

The paper has several managerial implications. The lower credibility of reviews poses threat to its relevance in digital marketing and electronic commerce. Therefore, managers of electronic commerce must strive to adopt practices to preserve the trust and integrity of online reviews. Our review indicated five groups of antecedents of online review credibility: source characteristics, review characteristics, consumer characteristics, interpersonal characteristics in social media, and product type. Managers cannot control completely all the factors on the social media. However, by appropriately designing the e-commerce platform with the elements that influence credibility, managers will be able to improve their marketing communications. Awareness of review characteristics that impact review credibility would help managers to choose more appropriate measures to deal with negative and positive reviews. Managers must adopt a social media marketing strategy that is suitable to the context of the review and type of product.

Data availability

The dataset was generated by two licensed databases and thus cannot be made accessible.

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Pooja, K., Upadhyaya, P. What makes an online review credible? A systematic review of the literature and future research directions. Manag Rev Q 74 , 627–659 (2024). https://doi.org/10.1007/s11301-022-00312-6

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