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Factors Influencing the Attitude of Filipino Consumers to Shop Online amidst COVID-19 Pandemic

• Creencia, Steffany Leigh L. 1
• Cruz, Angelo C. 1
• Legaspi, Monique M. 1
• Paredes, Charles Daniel C. 1
• 1. Far Eastern University

Description

          With the COVID-19 pandemic hitting the Philippines for the past two years, and as a result the e-commerce expanded and became more significant, a research of online customer behavior is particularly important. The goal of this research was to study the factors influencing the attitude of Filipino consumers to shop online amidst COVID-19 pandemic. This study used a qualitative research approach, and the data was collected through focus group discussion that consists of 15 students from Far Eastern University. The research revealed that there are numerous factors that influence people to shop online, and e-commerce has different advantages and disadvantages for Filipino consumers. The research also concluded that while there are issues with online shopping, Filipino consumers to continue to utilize it as it provides them with a lot of advantages.

Factors Influencing the Attitude of Filipino Consumers to Shop Online amidst COVID-19 Pandemic.pdf

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Online shopping and rising internet penetration to lead philippines e-commerce at 17% cagr through 2025, forecasts globaldata, media enquiries.

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09 Dec, 2021 Online shopping and rising internet penetration to lead Philippines e-commerce at 17% CAGR through 2025, forecasts GlobalData

Driven by growing consumers’ preferences for online shopping and increasing internet penetration, the Philippines e-commerce market has been registering growth during the last few years. The COVID-19 pandemic has further accelerated this trend. Consequently, e-commerce value is expected to grow at a strong CAGR of 17% between 2021 and 2025, forecasts GlobalData, a leading data and analytics company.

According to GlobalData’s E-Commerce Analytics, e-commerce transaction value in Philippines grew by 11.8% to be valued at PHP229.8bn ($4.8bn) in 2020. It is estimated to increase by 15.1% in 2021 to reach PHP264.5bn ($5.5bn).

Shivani Gupta, Banking and Payments Senior Analyst at GlobalData, comments: “While sectors such as travel and accommodation were affected due to lockdown and travel restrictions, a strong growth was seen in online purchases of retail goods such as grocery and electronics. This helped drive the growth in e-commerce sales.”

The pandemic augmented the e-commerce activities in the country as wary consumers are staying home to avoid exposing themselves to disease vectors. This trend will continue with rise in new online shoppers and proliferation of online sellers in the country. According to the Department of Trade and Industry (DTI) in the Philippines, the number of online sellers increased from 1,700 in March 2020 to 93,318 in January 2021.

The government is also taking various initiatives to boost the e-commerce sales. In January 2021, it launched the ‘Philippine e-Commerce Roadmap 2022’ to promote e-commerce adoption among small-medium enterprises (SMEs) and boost consumer confidence in online shopping. The government is looking to increase ecommerce contribution of GDP from 3.4% in 2020 to 5.5% in 2022.

As Filipino consumers continue to embrace online shopping, it will support the growth of electronic payments. According to GlobalData’s 2021 Financial Services Consumer Survey* for online purchases, alternative payment solutions such as Gcash, PayMaya, and PayPal are the most preferred payment method in Philippines, collectively accounting for 30.6% of total e-commerce payments in 2021. They are closely followed by cash which has 29.8% share while payment cards account for 23.5%.

Gupta concludes: “Rising consumers’ preferences for online shopping, increasing number of online merchants, proliferation of alternative payment solutions and government support will support e-commerce sales in the Philippines. It is forecasted to grow at a CAGR of 17.0% over the next five years to reach PHP495.0bn (US$10.3bn) by 2025.”

*GlobalData’s 2021 Financial Services Consumer Survey was carried out in Q1 and Q2 2021. Over 52,742 respondents aged 18+ were surveyed across 42 countries.

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The impact of COVID-19 on the evolution of online retail: The pandemic as a window of opportunity

Levente szász.

a Faculty of Economics and Business Administration, Babeș-Bolyai University, 400591, Cluj-Napoca, Teodor Mihali str, 58-60, Romania

Csaba Bálint

b National Bank of Romania, 030031, Bucharest, Lipscani str. 25, sector 3, Romania

Ottó Csíki

Bálint zsolt nagy, béla-gergely rácz, dénes csala.

c Department of Engineering, Lancaster University, Engineering Building, Lancaster University, Lancaster, LA1 4YW, United Kingdom

d Economics Observatory, School of Economics, University of Bristol, Beacon House, Queens Road, Bristol, BS8 1QU, United Kingdom

Lloyd C. Harris

e Alliance Manchester Business School, University of Manchester, Booth St W, Manchester, M15 6PB, United Kingdom

Associated Data

Data will be made available on request.

Pandemic-related shocks have induced an unexpected volatility into the evolution of online sales, making it difficult for retailers to cope with frequently occurring, drastic changes in demand. Relying on a socio-technical approach, the purpose of this paper is to (a) offer a deeper insight into the driving forces of online sales during the pandemic, and (b) investigate whether pandemic-related shocks accelerate the long-term growth of online retail. Novel, high-frequency data on GPS-based population mobility and government stringency is used to demonstrate how time spent in residential areas and governmental restrictions drive the monthly evolution of online sales in 23 countries. We deconstruct these effects into three main phases: lure-in, lock-in, and phase-out. Lastly, using time series analysis, we show that the pandemic has induced a level shift into the long-term growth trend of the online retail sector in the majority of countries investigated.

1. Introduction

The outbreak of the pandemic caused by the spread of a novel type of coronavirus, SARS-CoV-2, has induced an unprecedented shock to the global economy in terms of its speed and encompassing nature, having a significant impact on virtually all countries and economic sectors. During the pandemic, businesses and consumers have been forced continuously to adapt to the immediate and drastic changes brought about by this crisis. Furthermore, there is a general consensus that there will be long lasting global effects and the world economy will return to a “ new normal ” ( Roggeveen and Sethuraman, 2020 ; Sneader and Singhal, 2021 ).

As with similar health-related and economic crises in the past, it is widely accepted that online retail represents a sector that plays a crucial role ( Li et al., 2020 ; Guthrie et al., 2021 ), providing vital access for customers to essential products ( Kirk and Rifkin, 2020 ; Martin-Neuninger and Ruby, 2020 ). Given its significant role, the present paper focuses on the evolution of online retail during the COVID-19 pandemic and analyses the short-term and potential long-lasting effects of this crisis.

Most of the existing papers studying the interaction between the early-stage of the pandemic and the online retail sector report that in several countries the outbreak of COVID-19 led to an unprecedented surge in online retail demand (e.g., Gao et al., 2020 ; Hobbs, 2020 ; Hwang et al., 2020 ). These observations are supported by commentators suggesting that in 2020 the “share of e-commerce in retail sales grew at two to five times the rate before COVID-19” ( Lund et al., 2021 ). However, only a few studies acknowledge that, beyond the general upswing, the pandemic has increased the volatility of online sales evolution. Furthermore, literature offers little guidance on which factors can explain these changes in online sales during a crisis when traditional market mechanisms do not function as usual. In response, therefore, this paper aims to use large-scale, longitudinal data covering 23 different countries and multiple waves of the pandemic to investigate the drivers of short-term online retail evolution during COVID-19 .

While some researchers have tentatively begun to explore these short-term effects (e.g., Chang and Meyerhoefer, 2021 ; Eger et al., 2021 ), the longer-lasting implications of the pandemic on the online retail sector have yet to be studied empirically. Most scholars emphasize the need to investigate whether the pandemic has truly altered the evolution trajectory of online retail or if the current crisis is merely a single shock after which the sector will return to its traditional evolutionary path as consumers and retail businesses return to their “old habits” in the post-pandemic period ( Sheth, 2020 ; Eger et al., 2021 ; Reardon et al., 2021 ; Schleper et al., 2021 ). Consequently, given the uncertainty of what the “ new normal ” might bring for the online retail market, this paper also intends to use the most recent time series to investigate whether the pandemic has altered the long-term evolution of the sector .

In pursuing the two objectives (investigation of short-term drivers and long-term trend implications), this paper adopts Geels’ (2002) multi-level perspective (MLP) as a theoretical lens to investigate technological transitions in a complex socio-technical context. We interpret the pandemic as a force capable of opening a “window of opportunity” ( Dannenberg et al., 2020 ). Such windows constitute powerful tensions created at the level of the socio-technical landscape that bring a unique possibility for a technological novelty to break through and become more dominant in mass markets ( Geels, 2004 ). Hence, we explore the interplay between the window of opportunity opened by COVID-19 and the growth of the online retail sector. More specifically, we aim to investigate (a) the short-term driving forces behind the exponential evolution of the online retail sector during the pandemic, and (b) whether the pandemic has truly created a window of opportunity for a positive shift in the long-term evolution of online retail. Along with pursuing these objectives we also aim to provide a theoretical contribution to the literature on windows of opportunity, a central concept that has received only limited attention in previous MLP studies ( Geels, 2011 ; Dannenberg et al., 2020 ). In this regard, our paper aims to offer a more detailed insight into how a technological transition path might behave during such a period and to provide a means to evaluate the potential long-term effect of windows of opportunity.

2. Literature review

2.1. the impact of covid-19 on online retail.

Given the crucial role of online retail channels during a pandemic, researchers have examined a variety of ways in which COVID-19 has influenced online shopping. As COVID-19 was first identified in China, initial studies investigated how the outbreak of the crisis has reshaped the retail landscape in China with emphasis on the increasing importance of online channels ( Gao et al., 2020 ; Guo et al., 2020 ; Hao et al., 2020 ; Li et al., 2020 ; Jiang and Stylos, 2021 ). These studies focused on how the outbreak of the pandemic influenced online shopping ( Gao et al., 2020 ; Guo et al., 2020 ), and how online channels helped the population to cope with the emerging health-crisis ( Li et al., 2020 ; Hao et al., 2020 ).

Given the narrow focus of initial studies, authors called for further research in other countries better to understand the global impact of the pandemic on online retail ( Gao et al., 2020 ; Li et al., 2020 ; Jiang and Stylos, 2021 ). Subsequent studies taking this research avenue offered a good cross-section globally by covering multiple different countries but investigated almost exclusively the short-term impacts of COVID-19 on online retail, using data from the first wave of the pandemic ( Table 1 ). Moreover, observers typically argued that the major driving forces behind the exponential proliferation of online channel use in the context of COVID-19, can be grouped in two distinct, but intertwined categories: (a) governmental regulations and restrictions, and (b) pandemic-induced changes in customer behavior. In line with this observation, Shankar et al. (2021) also contend that “many shoppers move a large portion of their business online during the COVID-19 outbreak either by choice or due to regulation …” . Therefore, the next two subsections review the studies that attribute the changes in online sales to one of these two factors.

Summary of the literature on the impact of COVID-19 on the evolution of online retail.

2.1.1. Studies highlighting the impact of changing customer behavior

Adopting a behavioral perspective, Chang and Meyerhoefer (2021) illustrated how the first wave in Taiwan (where no strict stay-at-home orders or business closures were imposed) has shifted consumers’ attention towards online channels. In the early weeks of the pandemic the surge in the number of confirmed cases increased both sales and the number of customers of online food commerce. The change in customer behavior was also induced by the media, as COVID-19 related press articles and Google searches also positively correlated with online food sales.

In a similar manner, Sheth (2020) argued that the pandemic had several powerful and immediate effects on consumer behavior: while facing constraints, consumers improvised and replaced old habits with new ones, such as switching to online retail channels, enabling thereby the “store to come home”. In line with this, Jiang and Stylos (2021) proposed that individual pressures during lockdowns force consumers to create a “new retail purchasing normality” involving higher digital engagement and increased online purchases. Consultancy papers also supported this view. A multi-country survey conducted by McKinsey & Company demonstrated that the pandemic induced a major shift in consumer behavior, at least two thirds of customers having tried new, mostly online forms of shopping ( Sneader and Singhal, 2021 ).

In terms of shifting consumer behavior, Tran (2021) proposed that fear of the pandemic can also drive online purchasing intentions aiming to improve the health safety of the consumer and the surrounding community. Researchers focusing on the second wave of the pandemic ( Chopdar et al., 2022 ; Eger et al., 2021 ) also connected the fear of the virus to increased online shopping. One exception is identified by Mehrolia et al. (2021) , concluding that a considerable majority of Indian customers decided not to order food through online channels during the first wave of the pandemic due to the fear connected to food delivery.

Hao et al. (2020) focused on a different aspect of customer behavior. Their study points out that panic buying (i.e., ordering more than the short-term necessity of the household due to fear), which is a common consumer response during disasters, is more associated with online food retail channels than with traditional channels. Following this idea, Guthrie et al. (2021) use the react-cope-adapt model ( Kirk and Rifkin, 2020 ) to illustrate that during the first month of the pandemic in France consumers reacted by panic buying, dramatically increasing the online purchasing of essential products. This period was followed by coping with the crisis which led to an increase of online orders related to non-essential products. The adapt phase was supposed to show a sustained modification of online purchasing behavior. However, due to limited data available, the authors concluded that long-term behavior changes require further investigation.

2.1.2. Studies highlighting the impact of government regulations

During the pandemic, several governmental restrictions had an immediate impact on online retail. For example, Martin-Neuninger and Ruby (2020) and Hall et al. (2021) identify government-related factors, namely the lockdown period and travel restrictions, as primary reasons behind the surge in online shopping in New Zealand. Hobbs (2020) also argued that initial stay-at-home and distancing orders issued in Canada led to an uptake of the online food retail: while online grocery deliveries were already used by early adopters in the pre-pandemic era, during the outbreak many late-adopter customers tried this channel for the first time. Jílková and Králová (2021) reported similar phenomena in the Czech Republic for all generational cohorts. In summary, unexpected regulations imposed by governments determined an immediate increase in demand for online shopping: existing customers started to use online channels more frequently, while new customers, including older and less tech-savvy generations, turned to online channels for the first time ( Hwang et al., 2020 ; Pantano et al., 2020 ).

From the retailer’s perspective, Reardon et al. (2021) provided several case examples of Asian and Latin American food industry firms strengthening their e-commerce business models or reconfiguring their entire food supply chains as a response to early-stage lockdown policies. Based on a survey among small Belgian retailers, Beckers et al. (2021) found that restrictions have doubled online orders during the first wave of the pandemic. To match the increase in demand, half of the retailers not using online channels before the pandemic opened one during the first months of COVID-19. Based on a literature review, Kirk and Rifkin (2020) also predicted that in order to conform to social distancing regulations, online retail coupled with contactless distribution methods would substantially gain ground during the pandemic. However, results related to the long-lasting effects of the pandemic on online retail are still “speculative in nature” ( Hobbs, 2020 ). Many of the customers who made the shift due to the restrictions might continue to utilize online channels in the long run. Other customers might return to traditional channels as soon as possible ( Beckers et al., 2021 ; Mehrolia et al., 2021 ). Thus, whether online retail can capitalize on the pandemic in the long run is still a subject of debate.

2.1.3. Summary and research questions

A summary of the key studies is provided in Table 1 in chronological order, highlighting the short-term drivers (i.e., government regulations and/or customer behavior, beside the papers narrowly focusing on the effect of the pandemic itself) and potential long-term implications related to the growth of the online retail sector.

Based on the literature, we derive two main conclusions that serve as basis for our research questions. First, as demonstrated in Table 1 , there is a plethora of mostly anecdotal, non-empirically-based evidence that during the pandemic (and beside the pandemic itself) two major factors, i.e., government restrictions and consumer behavior changes, drove a significant initial surge in online shopping. Second, extant studies failed to offer insights into how these factors drive online sales during the entire period of the current pandemic ( Schleper et al., 2021 ). Therefore, we cover the full period of COVID-19 to date and provide more conclusive empirical evidence on how these two factors influence the evolution of online retail.

RQ1. How do changes in customer behavior and government regulations drive the evolution of online retail during the pandemic?

Moreover, the long-term implications of this change in online retail use have remained, so far, a subject of anecdotal speculation ( Table 1 ). However, changes to the retail sector might become a constant in the “ new normal ”, and further research is needed “to understand the short-term and long-term impact of the pandemic on consumer behavior and provide guidance on how retailers should cope with those changes” ( Roggeveen and Sethuraman, 2020 ). Hobbs (2020) suggested that COVID-19 prompted sceptics and late-adopters to use online retail channels, and these new customers are likely to continue to shop online even after the pandemic. More cautious voices, however, asked the question whether the pandemic has “swung the pendulum too far and too fast towards online shopping” ( Gauri et al., 2021 ), which may potentially result in an unsustainable boost to online retail. Thus, the extent to which this shift will lead to a fundamental leap in the long-term role of online retailing is unknown.

RQ2. What trend-shifting impact does the pandemic have on the long-term evolution of online retail?

In answering RQ1 and RQ2 we also aim to extend the scope of existing research ( Table 1 ) in four different aspects. Given that COVID-19 is a global phenomenon, we aim to cover a larger geographical region compared to the majority of previous studies focusing on a single country. Second, in contrast with existing research mostly investigating a single branch of the online retail sector, we propose to analyze the online retail sector as a whole, covering the sales of all types of products. Third, we integrate novel measures into the analysis that have emerged during this pandemic (mobility indicators, government stringency index) to be able better to explain the evolution of the online retail sector during this crisis. Fourth, we investigate a longer period before and during the pandemic than previous studies to infer long-term implications.

2.2. A socio-technical approach to study the evolution of online retail during COVID-19

The multi-level perspective (MLP) has been established as insightful in studying COVID-19 related developments in the online retail sector ( Dannenberg et al., 2020 ). Consequently, we use the MLP as a theoretical lens to study the short and long-term evolution of online retail. Geels (2002) argues that the central tenet of MLP is that technological transitions are not only dependent on the development of the technology itself, but also pivot on the broader socio-technical context. In line with this view, technological transition represents a change from one socio-technical configuration (regime) to other: beyond the substitution of an older technology with a newer one, such transitions include changes in other socio-technical dimensions such as infrastructures, policies, user practices, and markets ( Geels, 2002 , 2004 ).

According to the MLP, technological transitions are shaped by the interaction between developments unfolding on three analytical levels ( Geels, 2002 , 2004 , 2011 ):

• • Technological niches represent the micro-level of the MLP. Niches are quasi-protected spaces where radical innovations are developed (e.g., R&D laboratories, subsidized development projects, or specific user categories supporting emerging innovations). They are unstable socio-technical configurations where innovations are carried out by a limited number of actors. Processes in the niche are gradually linked together and stabilize in time into a dominant design that allows for the radical innovation to break through to the next level.
• • Socio-technical regimes represent the meso-level of the MLP. Regimes refer to “the semi-coherent set of rules that orient and coordinate the activities of social groups” ( Geels, 2011 ) creating thereby a “deep structure” that ensures the stability of the current socio-technical system. Nevertheless, the semi-coherence of these rules allows for a dynamic stability which enables further incremental innovation, with small adjustments accumulating into stable technological transition paths. A socio-technical regime is formed by the co-evolution of different sub-regimes, each with its own set of rules and dynamics: user and market, technological, science, policy, and socio-cultural sub-regimes. According to Geels (2004) , the socio-technical regime can be understood as the meta-coordination of the different sub-regimes that determines technology adoption and use.
• • The socio-technical landscape represents the macro-level of the MLP. The landscape provides a wider, technology-external context for the interactions of actors within the niche and the socio-technical regime. Actors cannot influence elements of the landscape on the short-run, and changes at the landscape level take place usually slowly, representing longer-term, deep structural tendencies (e.g., macroeconomic processes, cultural patterns, political trends).

An important implication of the MLP is that the future evolution of a (new) technology does not only depend on the processes within the niche, but also on the interactions between different levels; including the regime and landscape levels. Geels and Schot (2007) contend that the general pattern of technology transition involves all three levels: (1) niche innovations align and gain internal momentum, (2) landscape developments put pressure on existing regimes, and (3) regimes destabilize creating an opportunity for niche innovations to break through to mass markets.

In terms of the interplay between COVID-19 and online retailing, another important concept linked to the MLP is the “window of opportunity”. Geels (2002) argues that windows of opportunity are created when tensions appear in the current socio-technical regime or when landscape developments put a pressure on the current regime for internal restructuring. These tensions loosen the rules of the socio-technical regime and create opportunities for technologies to escape the niche-level and become more deeply embedded in the regime. Competition with the existing technology becomes more intensive, triggering wider changes in the regime, where the new technology may replace the old one in the long run ( Geels, 2004 ).

Dannenberg et al. (2020) conclude that COVID-19 represents a critical landscape development that puts pressure on the socio-technical configuration of the retail sector. In line with our literature review, they suggest that two sub-regimes were particularly affected: policy regime (government regulations) and, user and market regime (sudden change in customer behavior). The authors further argue that these two major changes have opened a window of opportunity for online grocery retail to gain substantial market share. In this regard, RQ1 aims to investigate how the developments within these two dimensions influence the evolution of the online retail sector during the opening up of a window of opportunity ( Fig. 1 ). Given that, to date, the MLP offers little insight into the evolution of a technology during a window of opportunity ( Dannenberg et al., 2020 ), answering RQ1 should enrich this theoretical framework by explicating the forces that drive technology transitions during tensions in the landscape and the socio-technical regime (i.e., during a window of opportunity).

COVID-19 and the trajectory of online retail evolution (adapted from: Geels, 2002 ; Dannenberg et al., 2020 ).

Concerning the long-term impact of this window of opportunity, we investigate whether it enables the online retail sector to gain a significantly higher share of the whole retail sector on the long run (technology trajectory in Fig. 1 ) to the detriment of offline channels ( Helm et al., 2020 ). However, in the long run, MLP is not necessarily about mapping “winning” technologies that entirely replace/reconfigure existing regimes: it is just as possible that the breakthrough of a new technology will lead to a symbiosis with incumbent socio-technical regimes ( Geels, 2002 ; Genus and Coles, 2008 ). Thus, in our case, the question is more about the relative share of online retail and physical retail within the retail sector (cf. omnichannel retailing, Gauri et al., 2021 ). Beside speculation, current literature offers little guidance in this regard. Dannenberg et al. (2020) suggest that even if the pandemic has led to an upswing of online shopping, there is no indication for a fundamental long-term shift from physical to online retail. The authors, however, base their assumptions on a limited set of data, both from a temporal (March–May 2020) and from a geographical/sectoral perspective (German grocery retail). On the other hand, many other authors advocate a breakthrough of online retail as a result of taking advantage of the window of opportunity created by the pandemic (e.g., Chang and Meyerhoefer, 2021 ; Hobbs, 2020 ; Tran, 2021 ). Answering RQ2 is designed to explicate and illuminate further this debate.

3. Data and variables

3.1. data used in short-term analysis (rq1).

To investigate RQ1, we use as dependent variable the monthly evolution of online retail sales during the pandemic (Feb 2020–Jan 2022) in European countries. We rely on Beckers et al. (2021) who define online retail channel use as the selling of goods via mail, phone, website, or social media. Therefore, we adopt NACE-level retail trade data published by Eurostat using the index of deflated turnover (i.e., turnover in real terms, 2015 = 100) for the “Retail sale via mail order houses or via Internet” sector. Seasonally and calendar adjusted time series data is used to assess the monthly changes during the pandemic in this sector, shortly denoted from now on “online retail” ( ΔOnline_retail ). In terms of countries, the Eurostat database was deemed the most suitable to study our research questions as it provides online retail data for 23 European countries (20 countries of the European Union, plus Norway, UK, and Turkey, covering thereby all major economies from Europe). This sample offers a rich variety of pandemic-related contexts: each of these countries was hit by the pandemic to a different extent and the reaction of authorities was also fairly diverse ( Hale et al., 2021 ). Fig. 2 illustrates the evolution of the ΔOnline_retail variable in these countries.

Monthly changes in online retail turnover during the pandemic in the countries investigated.

To investigate this volatile evolution, two novel measures are used as explanatory variables that have been introduced recently as a response to the need to track social phenomena more frequently and more precisely during the pandemic.

The first variable is a proxy of changes in general customer behavior: population mobility . Shankar et al. (2021) argue that during a period characterized by dramatic and frequent changes in shopping behaviors, high-frequency, mobile GPS data can offer better information for retailers. Therefore, we integrate into our analysis the mobility data provided by Google® through their Community Mobility Reports ( Google, 2021 ), comprising several types of mobilities grouped by the destination/location of the mobility. Based on Beckers et al. (2021) who argue that COVID-19 has temporarily put an end to hypermobility cutting short consumers’ physical range around their homes, we select the residential component ( ΔResidential ) from the different forms of mobility, arguing that the changes in residential mobility (i.e., amount of time spent at home) could be the strongest component to explain changes in online shopping. Given that there might be some time needed for online shopping behavior to adjust to changes in mobility, the one-month lagged version of the variable is also used in our model ( ΔResidential(-1) ).

The second explanatory variable incorporated in our analysis is related to government restrictions . We use data from the Oxford COVID-19 Government Response Tracker, more precisely the values of the COVID-19 Stringency Index which aggregates the stringency of lockdown-type governmental measures, such as school closures, travel restrictions, bans on public gatherings, workplace closures, etc. ( Hale et al., 2021 ). This represents the most suitable proxy to measure the type of regulations connected by previous literature to online channel use during the pandemic ( Table 1 ). The index provides a multi-country panel of daily frequency, measured as a percentage value; 100% representing the highest level of stringency. To match the frequency of the dependent variable, the monthly change of the index is computed as explanatory variable ( ΔGovernment_stringency ). The one-month lagged variant is also introduced in the analysis ( ΔGovernment_stringency(-1) ).

Beside the two novel explanatory variables generated during the pandemic, we integrate several control variables in our analysis. These variables assess the income and purchasing power of the population (GDP/capita and unemployment level in each country), the level of urbanization (density of the population in each country), the level of education (percentage of the population attending tertiary education), the pervasiveness of online channels (Internet penetration), and the actual pervasiveness of online shopping (Online retail share in the retail sector) ( Hortaçsu and Syverson, 2015 ). Data for all countries analyzed are retrieved from the Eurostat database. The unemployment variable has a monthly frequency ( Δ Unemployment ), while the other variables ( GDP/capita, Internet penetration, Tertiary education, Population density, Online retail share ) change on a yearly basis. Descriptive statistics for the monthly variables are provided in Table 2 . The correlation matrix is included in Appendix A.

Descriptive statistics of the main variables included in the short-term analysis.

3.2. Data used in long-term analysis (RQ2)

To evaluate the trend-shifting potential of the pandemic in the online retail sector, the same retail trade data is used as for the short-term analysis, covering however a longer period of time between Jan 2000 and Jan 2022 ( Online_Retail ). To offer an overview of the long-term evolution of our focal variable, we present a boxplot containing data for all countries aggregated to annual averages, normalized on a 0–100 scale ( Fig. 3 , left). Primary visual inspection suggests that two periods can be distinguished in terms of the dynamism of the sector (2000–2010 characterized by slower growth pace versus 2011–2021 showing stronger momentum), while the relatively higher values of the last two boxplots indicate that it is beneficial to investigate whether the pandemic has induced a level shift into the evolution of online retail.

Long-term evolution of online retail turnover (left) and online retail market share (right) in the countries investigated (normalized: min = 0, max = 100).

Furthermore, to assess whether the online retail sector could exploit the window of opportunity opened by the pandemic, we compute another variable as a proxy measuring the share of online retail in total retail sales. For this purpose, we calculate the ratio between the indices of deflated turnover of online retail and the “Retail trade, except of motor vehicles and motorcycles” sector, this latter being a proxy for total retail sales ( Online_Retail_Ratio ≈ Online_Retail/Total_Retail ) ( Fig. 3 , right). The ratio approach is also consistent with theory (symbiotic technologies: Geels, 2002 ) and previous research ( Hortaçsu and Syverson, 2015 ).

4. Analysis and results

4.1. short-term analysis (rq1), 4.1.1. panel regression analysis.

To illuminate the impact of mobility and government restrictions on the monthly evolution of online sales, we have elected to implement a panel regression model. We have performed three random-effects and three cross-section fixed-effects panel regressions. We opted for the panel specification because it enables us to harness the rich structure of our data and to account for the unobserved heterogeneity present in the data. We perform 2 × 3 = 6 regressions because of the different methodology (fixed vs. random effects), and the 3 combinations resulting from including only the government stringency variables, only the residential mobility variables, and both. Five control variables were nearly collinear in the fixed effects case; therefore Table 3 presents only the estimates for these variables in the random effects case. Our main specification is the following:

where C i j t and β ( C ) j are the independent variables and their coefficients, i is the index of countries, t of time, and j of the equation variables.

Regression models.

Notes: t-values in parentheses; *significant at 0.05; **significant at 0.01.

Results of the fixed effects specifications of our panel regression model (equations 1 to 3) indicate that our first variable of interest, residential mobility, and its one-period lag, have a significant impact on the monthly change in online retail sales, both variables having the expected positive sign. The same can be pointed out for the government stringency and lag variables. However, when we include both residential mobility and government stringency, only the first remains significant, due to high collinearity between the two explanatory variables. The results are similar in the random effects case (equations 4 to 6). The goodness-of-fit statistics (adjusted R-squared, F-statistic) are quite high for panel regressions, indicating that the explanatory variables introduced in the panel explain a large proportion of the variation of the monthly change in online retail sales.

Thus, results altogether indicate that both residential mobility and government stringency are significant predictors of online retail channel use: as residential mobility increases (i.e., people spend more time at home) and, alternatively, as government stringency increases (i.e., anti-COVID-19 measures become stricter) the use of online retail channels increases. Furthermore, the impact of all control variables is insignificant, meaning that mobility and government stringency indicators provide a better explanation for the variation of online retail sales during the pandemic than traditional variables that have been used to explain the evolution of the online retail sector in pre-pandemic periods.

4.1.2. Detailed analysis of short-term effects

While panel regression results show that both residential mobility and government stringency are good predictors of the evolution of online sales, relationships between variables are rarely perfectly linear. Therefore, we provide a more detailed analysis on the interplay between these variables. Fig. 4 illustrates the monthly evolution of online sales (vertical axis) together with the monthly percentage change in residential mobility (horizontal axis) for the entire period of the pandemic, each dot representing one country in one month.

Monthly evolution of online sales and residential mobility during the pandemic in the countries investigated.

Beside the general positive relationship between the two variables, the scatter plot also indicates that three different forces can be identified that shape the evolution of online retail sales during the pandemic. First, there are periods in which mobility is restricted more and more to residential areas, and consumers adapt by significantly increasing their monthly spending on online retail channels (as high as +30–50% during the first wave of the pandemic). This process is exactly what was expected during the pandemic: as the mobility range of people is restricted primarily to their homes, they turn to online retail channels more frequently. This process is termed the “lure-in” phase. Typical months during which the lure-in phase was dominant were Mar 2020, Apr 2020, Oct–Nov 2020, Nov 2021, and Jan 2022 ( Fig. 5 ).

Monthly evolution of online sales and residential mobility during different phases of the pandemic in the countries investigated.

However, it is also observable that when consumers are not confined to residential areas and start increasing their mobility outside their homes (i.e., residential mobility decreases), a decrease in online spending does not follow automatically, as people tend to continue to use, or even increase the usage of, online retail channels. Additionally, in many cases a large drop in residential mobility is paired with no significant change in online retail sales. These cases are labelled as the “lock-in” phase, which means that temporarily consumers remain users of online channels even if their mobility would allow them to use offline channels more intensively. Thus, mobility restrictions have an immediate (lure-in), but also a lagged (lock-in) impact on online retail channel use, in line with the significance of lagged variables in our panel regression model ( Table 3 ). The most typical months in which several European countries went through this lock-in phase were May 2020, Jun 2020, Feb 2021, Mar 2021 ( Fig. 5 ). This phase is not as consistent on a monthly basis as the lure-in phase, several countries experiencing a negative change in online channel use, concurrently with the decrease of residential mobility.

Lastly, there is also a “phase-out” period denoting cases where online retail use decreases, while time spent at home generally decreases. During these months a part of the former online shopping volume of customers is most probably replaced by (or allocated back to) offline channels. Furthermore, in some rare instances residential mobility has a slight increase, while consumers still decrease their online spending. Predominantly phase-out months include Jul 2020, May–Jul 2021, Dec 2021 ( Fig. 5 ).

The same three phases can be observed if the residential mobility indicator on the vertical axis is replaced by the government stringency index ( Fig. 6 , Fig. 7 ). In summary, there is a clear lure-in phase which was noticeable especially during the beginning of the first and second wave of the pandemic (Mar–Apr, 2020; Oct–Nov 2020): sudden drops in mobility and severe governmental restrictions clearly prompt customers to shop online. This effect has some “stickiness” (lock-in phase) because as governmental restrictions are eased, certain customers continue to use (or even increase the use of) online retail channels. Nevertheless, after a relatively short period the lock-in effect fades and customers drop their online shopping volume significantly (phase-out), countervailing to some extent the argument of the pandemic-induced upward boost of the online retail sector. Thus, while illuminating in other respects, this analysis, in itself, is unhelpful regarding the longer-term implications of the pandemic for the online retail sector. The next section aims to address this deficiency.

Monthly evolution of online sales and government stringency during the pandemic in the countries investigated.

Monthly evolution of online sales and government stringency during different phases of the pandemic in the countries investigated.

4.2. Long-term analysis (RQ2)

To investigate the potential trend-shifting impact of the pandemic in the online retail sector, a two-step approach is applied. First, to establish a basis for comparison, we analyze the 20-years trend of the sector without considering the specific effect of the pandemic. Second, based on the long-term trend established, we focus on the period of the pandemic, and use outlier detection methods to estimate whether the pandemic has induced a level shift in the long-term trend of the sector.

4.2.1. Long-term trend analysis

Online retail sales and online retail market shares show an increasing tendency during the last 20+ years ( Fig. 3 ). While the retail sector as a whole had a slight increasing tendency during this period, the average annual growth rate of the online retail sector was clearly higher. This difference is most visible during the last ten years when the online retail sector has been constantly on an increasing trajectory, thereby raising its market share within the total retail sector. Thus, the online retail sector has been benefitting from continuous market share gains with a relatively lower growth pace in the early period (2001–2010), and with rapid increases in the last period (2011–2021). These differences are illustrated in Fig. 8 .

Average annual growth rates in the retail sector in European countries (%).

Next, we use unit root tests to statistically demonstrate that there is an underlying long-term growth trend in the data ( Chatfield and Xing, 2019 ), both in terms of monthly online retail turnover ( Online_Retail ) and in terms of online retail market share ( Online_Retail_Ratio ). Applying the most widely used Augmented Dickey-Fuller (ADF) test, we aim to show that there is a systematic, persistent stochastic trend in the time series (i.e., an upward tendency in our case). Unit root test results confirm that in most of the countries investigated the null hypothesis of one unit root cannot be rejected: the p-values are above 0.05 in 23 cases out of 24 in case of the Online_Retail variable and in 21 cases out of 24 for Online_Retail_Ratio . Thus, for the vast majority of countries neither Online_Retail , nor Online_Retail_Ratio is stationary, indicating that there is an (upward) long-term stochastic trend in the time series. Furthermore, unit root test results also imply that any positive or negative shock (such as the pandemic) during the period investigated has a persistent effect on the trend. Nevertheless, further investigation is needed to determine whether this shock applies for the pandemic period as well.

4.2.2. Outlier detection during the pandemic

Outlier detection is used to determine whether the pandemic has caused a level shift in the Online_Retail , and especially in the Online_Retail_Ratio time series. For this purpose, we use ARIMA 1 models with specific dummy regressors on both time series, implemented in JDemetra+ which is a proprietary software developed by the National Bank of Belgium in cooperation with the Deutsche Bundesbank and Eurostat. The software has been officially recommended since 2015 to the members of the European Statistical System and the European System of Central Banks as a tool for seasonal adjustment and other connected time series issues, such as outlier detection. In general, outliers are represented by abrupt changes in a time series caused by unexpected natural or socioeconomic effects, such as the pandemic. Three main types of outliers can be identified ( Fig. 9 ): (a) additive outlier (AO), which changes the time series for one period only, returning to the original trend afterwards, (b) level shift (LS) that causes a permanent (upward or downward) change in the level of a time series, and (c) transitory change (TC) whose effect of changing the time series is faded out over a limited number of periods ( IMF, 2018 ). Here, we specifically look for LS type outliers: a positive LS would suggest that online retail turnover and its market share registered a sudden increase during the pandemic, and that therefore the pandemic has accelerated the underlying growth trend of online retail.

Level shift versus other outlier types (source: IMF, 2018 ).

JDemetra+ uses the traditional TRAMO 2 methodology ( Gómez and Maravall, 1996 ; Findley et al., 2017 ) where TRAMO is designed to perform outlier detection as well. 3 Although this is a widely used framework in economics and connected disciplines, its applications in retailing are quite scarce which offers us the possibility to shed additional light on the effect of the pandemic on the online retail sector. In particular, TRAMO uses regression models with ARIMA errors as follows:

where z t is the original data series, β = ( β 1 , … β n ) is a vector of regression coefficients, y t = ( y 1 t , … y n t ) represents n regression variables (in our case LS, AO and TC outliers), while x t is the disturbance that follows the general ARIMA process.

Using the TRAMO method, we analyze the full Jan 2000–Jan 2022 time period for outliers in each country involved in the analysis, complemented by the aggregated time series on the EU-27 level. Both Online_Retail and Online_Retail_Ratio time series were analyzed for all three types of outliers. However, in light of RQ2, only LS type outliers are listed in Table 4 that were identified during 2020. It should be noted that 2021 LS outliers are not (yet) taken into consideration here because they are situated at the end of our time series data (i.e., further data is needed by TRAMO to determine whether a 2021 LS will remain significant and persist in the long run). In contrast, LS outliers in 2020 have already proven that they induced a persistent upward shock into the long-term trend of the online retail sector. Table 4 lists all significant level shifts (p < .05) detected during 2020. Full results are presented in Appendix B .

Level shift (LS) detection during the pandemic.

The results of LS detection indicate that at the level of the EU-27, as well as in most of the countries investigated there was at least one positive LS in the online retail trend during the first year of the pandemic. This strongly suggests that COVID-19 has induced a boost both to online retail turnover and to its market share, supporting the window of opportunity concept. Out of the 23 countries analyzed, only 9 where had no significant LS. However, these cases represent smaller European countries, the largest ones (Germany, France, Italy, Spain, UK) all experiencing positive significant LSs. Furthermore, some of the countries (Italy, Lithuania, Norway) experienced multiple significant LSs during 2020 which further strengthens our conclusion related to the long-term implications of the pandemic. While there are two anomalous negative LSs in the Online_Retail_Ratio as well ( Table 4 ), we suggest that these do not contradict our results, as these are all overcompensated by multiple positive LSs in the same countries (Italy and Norway), the magnitude of which is significantly higher than that of the negative LSs. Nevertheless, these negative LSs could be a sign of a significant “phase-out” effect, as discussed in the short-term analysis.

5. Summary and discussion

Two important gaps were addressed in this paper: (RQ1) how can factors related to consumer behavior (mobility) and regulations (government stringency) explain the volatile evolution of online retail sales during the pandemic, and (RQ2) what long-term trend-shifting effects can be identified during the pandemic in the evolution trajectory of online retail.

First, our results confirm that the two indicators proposed to estimate changes in consumer behavior ( Residential mobility ) and in government regulations ( Government stringency ) can significantly explain the hectic short-term evolution of the online retail sector during the pandemic. Released for the first time during the pandemic, these two indicators are significantly above and beyond the explanatory power of traditional variables used to predict online channel use in pre-pandemic periods. The more people are confined to residential areas, and the stricter government restrictions are, the more customers turn to online channels. These results offer empirical support to previous studies that proposed that changes in mobility ( Shankar et al., 2021 ) and pandemic-related government regulations ( Hwang et al., 2020 ) could provide a better measure to estimate changes in online sales.

Second, using these newly introduced variables, our study goes beyond demonstrating the simple linear relationship between these variables and online retail turnover, to describe in more detail how online shopping habits change during the pandemic. This is a novel approach compared to existing studies that simply argue that the pandemic is linked to the increased use of online channels (e.g., Chang and Meyerhoefer, 2021 ; Hwang et al., 2020 ; Eger et al., 2021 ). Using government stringency and mobility data, we offer a more nuanced understanding of how online shopping behavior evolves during the different stages of the pandemic, an issue currently hotly debated in the literature ( Kirk and Rifkin, 2020 ; Guthrie et al., 2021 ; Schleper et al., 2021 ). Three different phases are distinguished in this paper: (1) a lure-in phase; (2) a temporary lock-in phase; and (3) a phase-out period. Furthermore, the same phases seem to repeat during different waves of the pandemic, starting with a strong lure-in phase, followed by a mix of lock-in and phase-out periods.

Third, using advanced outlier detection methods, we show that the faster growth trend that characterized online retail in the past decade has experienced a new positive level shift during 2020 in most of the countries investigated. In only a couple of months during the pandemic, online retail has gained extra market share against offline retail that in normal circumstances would have probably taken several years. Thus, our empirical findings confirm the predictions of some researchers (e.g., Chang and Meyerhoefer, 2021 ; Tran, 2021 ), and actively address the questions posed by other researchers (e.g., Sheth, 2020 ; Guthrie et al., 2021 ), by establishing that the pandemic has indeed induced a persistent upward shift into the growth trajectory of online retail. These level shifts were especially visible in the larger economies of Europe. Thus, our results are concordant with several other studies that suggest that many firms managed to quickly overcome infrastructural challenges and build up the necessary online capacities ( Guo et al., 2020 ; Beckers et al., 2021 ; Reardon et al., 2021 ), while customers will continue to use online retail channels more intensively in post-lockdown periods as well ( Hobbs, 2020 ; Eger et al., 2021 ; Hall et al., 2021 ). Even if some customers return to traditional shopping channels ( Hobbs, 2020 ; Sheth, 2020 ), our results indicate that for a large segment of customers the pandemic-induced shock outweighs the potential phase-out effect, shifting their long-term orientation towards online channels.

6. Conclusion

This paper analyzed short-term drivers (RQ1) and long-term implications of the pandemic (RQ2) in the online retail sector, relying on the MLP’s socio-technical approach as a theoretical lens. COVID-19 is operationalized within the MLP as an exogeneous landscape event that induced a shock on the regime level. This shock opened a window of opportunity for online retail to exponentially grow and significantly increase its share against traditional retail channels.

6.1. Theoretical implications

Our research shows that during a window of opportunity created by a landscape event, forces within the socio-technical regime that shape the long-term trajectory of a technology change radically. Geels and Schot (2007) argue that strong landscape pressures (such as a pandemic) destabilize actual socio-technical regimes creating tensions that open windows of opportunity for technologies to emerge. Our short-term analysis related to RQ1 offers additional insights into how these regime tensions function. Panel regression results indicate that during unstable periods (when windows of opportunity are created by landscape pressures), certain sub-regimes take over the force that shapes technological transitions, while other sub-regimes become negligible. In our study, the policy regime (strict government restrictions) and the user preferences and market regime (reorientation of shopping behaviors due to reduced mobility) were responsible for creating the tension on the regime-level. Conversely, other sub-regimes on the same level, such as technological regimes (e.g., technical infrastructure used in online retail), science regime (e.g., technical knowledge used to operate online transactions), and socio-cultural regimes (e.g., distrust of certain segments of the population in online retail), had no significant impact on the way online retail was evolving. Thus, we propose that windows of opportunity are created when one or more particular regimes exert pressures that take over the place of other regimes in creating the forces that shape technological transitions. When a window of opportunity is open, these new forces remain dominant and might even alter other regimes.

Second, our long-term analysis suggests that COVID-19 can be regarded as a shock-type landscape development that creates tension in the current socio-technical regime to create a window of opportunity for online retail. Results of our long-term analysis suggest that the quasi-stable socio-technical regime of the last decades enabled a gradual and constant growth of online retail in Europe, attaining continuously increasing market shares throughout the years. However, as the pandemic generated a window of opportunity for this sector, online retail was able to capitalize on this opportunity in most countries, receiving a significant boost to its previous growth tendency.

Third, as a more general research implication for retail, our study demonstrates that high-frequency indicators that emerged during the pandemic, such as data on population mobility and on government stringency can be used to better assess fundamental socio-economic processes during crises. These two types of indicators provide a more complex, real-time assessment of ongoing socio-economic processes, making them more suitable to make predictions or explain phenomena in a volatile context.

6.2. Practical implications

Through demonstrating that mobility and government stringency has a positive impact on the evolution of online sales, we offer an important tool to retail practitioners to monitor and anticipate potential large variations in online demand. While mobile GPS data has already been used to track retail store traffic, our analysis suggests that tracking customer movements outside brick-and-mortar stores can also provide an anchor during volatile times. Such high-frequency, near-real-time data could become the primary input for managers to keep up with sudden pandemic-related developments, and potentially with post-pandemic shopping behavior changes as well.

Online retailers that have already capitalized on this pandemic should also take into consideration that a sudden pandemic-related growth in sales could be followed by a temporary lock-in phase. However, retailers should continue to work on keeping (newly acquired) customers, as a phase-out period might rapidly occur. Conversely, our long-term analysis, suggests that actors in the online retail sector should expect that, on average, the phase-out effect is outweighed by the pandemic-induced boost in online sales, creating much potential on the long-run for online retailers to capture the benefits of the positive level shift in the growth trajectory of the sector.

6.3. Limitations and further research

A first set of limitations is related to the nature of data employed in our study. While Eurostat provides the most reliable macroeconomic data, comparable across countries, on the evolution of the (online) retail sector, aspects of the data were not ideal. Several countries had missing data on the most recent values of the online retail turnover index, and some European countries (e.g., Switzerland) could not be involved in our study at all. While all largest retail markets have been included in our sample, results of the study can nevertheless not be universally generalized beyond the 23 countries involved in the analysis.

In respect of GPS-based mobility and government stringency data, we have shown that these variables are suitable to explain the large variations in online retail sales during the pandemic. However, whether and to what extent these data can be used to keep up with developments in the online retail sector beyond the pandemic remains unknown but represents a promising direction for future research.

Another set of limitations stems from the results described in this paper. While our outlier detection could empirically demonstrate the pandemic-induced level shift in the long-term evolution of the online sector, statistically significant shifts were not observed in all the countries investigated. It remains an important future research avenue to explain why some countries, including the largest European economies, experienced level shifts during the pandemic, while others have not.

Lastly, this paper focused on the evolution of the online retail sector, explaining its volatile evolution during the pandemic and demonstrating how the sector could take advantage of the window of opportunity created by COVID-19. Our results could provide a starting point for investigating other technologies and solutions, such as video conferencing, home delivery or VR-solutions, to evaluate whether and to what extent they have capitalized on pandemic-induced opportunities, thereby shaping how the “ new normal ” might look like in a post-pandemic world.

Acknowledgement

This research was partially supported by the PN-III-P1-1.1-TE-2019-1773 research project for young independent research teams funded by UEFISCDI Romania.

1 Autoregressive Integrated Moving Average.

2 Time series Regression with ARIMA noise, Missing values and Outliers.

3 A comprehensive description of the procedure and its technical implementation in JDemetra+ is provided by Eurostat’s website .

Appendix A. – Correlation matrix

*significant at the p < .001 level.

Appendix B. Complete results of outlier detection

Outlier detection with TRAMO in the Online_Retail time series (Jan 2000–Jan 2022)

Content of cells: (a) type of outlier: LS – level shift, TC – transitory change, AO – Additive outlier; (b) month of occurrence in parentheses; (c) magnitude of outlier [t-value].

Outlier detection with TRAMO in the Online_Retail_Ratio time series (Jan 2000–Jan 2022)

Data availability

• Beckers J., Weekx S., Beutels P., Verhetsel A. COVID-19 and retail: the catalyst for e-commerce in Belgium? J. Retailing Consum. Serv. 2021; 62 [ Google Scholar ]
• Chang H.H., Meyerhoefer C.D. COVID‐19 and the demand for online food shopping services: empirical Evidence from Taiwan. Am. J. Agric. Econ. 2021; 103 (2):448–465. [ Google Scholar ]
• Chatfield C., Xing H. Chapman and hall/CRC; 2019. The Analysis of Time Series: an Introduction with R. [ Google Scholar ]
• Chopdar P.K., Paul J., Prodanova J. Mobile shoppers’ response to Covid-19 phobia, pessimism and smartphone addiction: does social influence matter? Technol. Forecast. Soc. Change. 2022; 174 [ Google Scholar ]
• Dannenberg P., Fuchs M., Riedler T., Wiedemann C. Digital transition by COVID‐19 pandemic? The German food online retail. Tijdschr. Econ. Soc. Geogr. 2020; 111 (3):543–560. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Eger L., Komárková L., Egerová D., Mičík M. The effect of COVID-19 on consumer shopping behaviour: generational cohort perspective. J. Retailing Consum. Serv. 2021; 61 [ Google Scholar ]
• Findley D.F., Lytras D.P., McElroy T.S. Detecting seasonality in seasonally adjusted monthly time series. Statistics. 2017; 3 [ Google Scholar ]
• Gao X., Shi X., Guo H., Liu Y. To buy or not buy food online: the impact of the COVID-19 epidemic on the adoption of e-commerce in China. PLoS One. 2020; 15 (8) [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gauri D.K., Jindal R.P., Ratchford B., Fox E., Bhatnagar A., Pandey A., Navallo J.R., Fogarty J., Carr S., Howerton E. Evolution of retail formats: past, present, and future. J. Retailing. 2021; 97 (1):42–61. [ Google Scholar ]
• Geels F.W. Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Res. Pol. 2002; 31 (8–9):1257–1274. [ Google Scholar ]
• Geels F.W. From sectoral systems of innovation to socio-technical systems: insights about dynamics and change from sociology and institutional theory. Res. Pol. 2004; 33 (6–7):897–920. [ Google Scholar ]
• Geels F.W. The multi-level perspective on sustainability transitions: responses to seven criticisms. Environ. Innov. Soc. Transit. 2011; 1 (1):24–40. [ Google Scholar ]
• Geels F.W., Schot J. Typology of sociotechnical transition pathways. Res. Pol. 2007; 36 (3):399–417. [ Google Scholar ]
• Genus A., Coles A.M. Rethinking the multi-level perspective of technological transitions. Res. Pol. 2008; 37 (9):1436–1445. [ Google Scholar ]
• Gómez V., Maravall A. Banco de España. Servicio de Estudios; 1996. Programs TRAMO and SEATS: Instructions for the User (Beta Version: September 1996) [ Google Scholar ]
• Google L.L.C. 2021. Google COVID-19 Community Mobility Reports. https://www.google.com/covid19/mobility/ [ Google Scholar ]
• Guo H., Liu Y., Shi X., Chen K.Z. The role of e-commerce in the urban food system under COVID-19: lessons from China. China Agric. Econ. Rev. 2020; 13 (2):436–455. [ Google Scholar ]
• Guthrie C., Fosso-Wamba S., Arnaud J.B. Online consumer resilience during a pandemic: an exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. J. Retailing Consum. Serv. 2021; 61 [ Google Scholar ]
• Hale T., Angrist N., Goldszmidt R., Kira B., Petherick A., Phillips T., et al. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker) Nat. Human Behav. 2021; 5 (4):529–538. [ PubMed ] [ Google Scholar ]
• Hall M.C., Prayag G., Fieger P., Dyason D. Beyond panic buying: consumption displacement and COVID-19. J. Serv. Manag. 2021; 32 (1):113–128. [ Google Scholar ]
• Hao N., Wang H., Zhou Q. The impact of online grocery shopping on stockpile behavior in Covid-19. China Agric. Econ. Rev. 2020; 12 (3):459–470. [ Google Scholar ]
• Helm S., Kim S.H., Van Riper S. Navigating the ‘retail apocalypse’: a framework of consumer evaluations of the new retail landscape. J. Retailing Consum. Serv. 2020; 54 [ Google Scholar ]
• Hobbs J.E. Food supply chains during the COVID‐19 pandemic. Can. J. Agric. Econ. 2020; 68 (2):171–176. doi: 10.1111/cjag.12237. [ CrossRef ] [ Google Scholar ]
• Hortaçsu A., Syverson C. The ongoing evolution of US retail: a format tug-of-war. J. Econ. Perspect. 2015; 29 (4):89–112. [ Google Scholar ]
• Hwang E., Nageswaran L., Cho S. 2020. Impact of COVID-19 on Omnichannel Retail: Drivers of Online Sales during Pandemic. SSRN 3657827. [ Google Scholar ]
• IMF . Statistics Department; 2018. Quarterly National Accounts Manual . International Monetary Fund. [ Google Scholar ]
• Jiang Y., Stylos N. Triggers of consumers’ enhanced digital engagement and the role of digital technologies in transforming the retail ecosystem during COVID-19 pandemic. Technol. Forecast. Soc. Change. 2021; 172 [ Google Scholar ]
• Jílková P., Králová P. Digital consumer behaviour and eCommerce trends during the COVID-19 crisis. Int. Adv. Econ. Res. 2021:1–3. [ Google Scholar ]
• Kirk C.P., Rifkin L.S. I'll trade you diamonds for toilet paper: consumer reacting, coping and adapting behaviors in the COVID-19 pandemic. J. Bus. Res. 2020; 117 :124–131. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Li J., Hallsworth A.G., Coca‐Stefaniak J.A. Changing grocery shopping behaviours among Chinese consumers at the outset of the COVID‐19 outbreak. Tijdschr. Econ. Soc. Geogr. 2020; 111 (3):574–583. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lund S., Madgavkar A., Manyika J., Smit S., Ellingrud K., Meaney M., Robinson O. McKinsey Global Institute; 2021. The Future of Work after COVID-19. [ Google Scholar ]
• Martin-Neuninger R., Ruby M.B. What does food retail research tell us about the implications of coronavirus (COVID-19) for grocery purchasing habits? Front. Psychol. 2020; 11 doi: 10.3389/fpsyg.2020.01448. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mehrolia S., Alagarsamy S., Solaikutty V.M. Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression. Int. J. Consum. Stud. 2021; 45 (3):396–408. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pantano E., Pizzi G., Scarpi D., Dennis C. Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. J. Bus. Res. 2020; 116 :209–213. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Reardon T., Heiman A., Lu L., Nuthalapati C.S., Vos R., Zilberman D. “Pivoting” by food industry firms to cope with COVID‐19 in developing regions: e‐commerce and “copivoting” delivery intermediaries. Agric. Econ. 2021; 52 (3):459–475. doi: 10.1111/agec.12631. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Roggeveen A.L., Sethuraman R. How the COVID-19 pandemic may change the world of retailing. J. Retailing. 2020; 96 (2):169–171. [ Google Scholar ]
• Schleper M.C., Gold S., Trautrims A., Baldock D. Pandemic-induced knowledge gaps in operations and supply chain management: COVID-19's impacts on retailing. Int. J. Oper. Prod. Manag. 2021; 41 (3):193–205. [ Google Scholar ]
• Shankar V., Kalyanam K., Setia P., Golmohammadi A., Tirunillai S., Douglass T., Hennessey J., Bull J.S., Waddoups R. How technology is changing retail. J. Retailing. 2021; 97 (1):13–27. [ Google Scholar ]
• Sheth J. Impact of Covid-19 on consumer behavior: will the old habits return or die? J. Bus. Res. 2020; 117 :280–283. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sneader K., Singhal S. McKinsey and Company; 2021. The Next Normal Arrives: Trends that Will Define 2021- and beyond. [ Google Scholar ]
• Tran L.T.T. Managing the effectiveness of e-commerce platforms in a pandemic. J. Retailing Consum. Serv. 2021; 58 doi: 10.1016/j.jretconser.2020.102287. [ CrossRef ] [ Google Scholar ]

Philippine E-Journals

Home ⇛ smcc higher education research journal (business administration journal) ⇛ vol. 3 no. 1 (2021), factors affecting consumer satisfaction of online business.

Kaye Abian | Ian Alvin Gales | Alfen C. Nitsche | Benjiemen T. Dalagan

Today, online shopping is a great innovation, making life easier and more convenient for everyone. The study aimed to determine the factors affecting the level of consumer satisfaction of online businesses. This research study used Thomassen’s model that allows the researcher to determine the factors that affect the customers’ satisfaction and what factors greatly influence their satisfaction. Five factors contributed to this research study: fast transaction, mode of payment convenience, price and values, customer service, and product quality. is research used a descriptive survey method of distributing questionnaires to 393 respondents. Questionnaires were distributed among all online consumers in the selected eleven barangays in Nasipit, Agusan del Norte. Based on the findings, there is no significant difference in the online business level of consumer satisfaction as to their profile. The factor that greatly affects consumer satisfaction of online business is fast transaction resulted in highly satisfied. It was recommended that online sellers continue and maintain their fast transactions and find more ways to improve their payment system to pay easily and conveniently.

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The Philippines experienced the highest increase in the use of shopping apps (53 percent) in Southeast Asia. The total sessions in shopping apps in the country reached 4.9 billion. The study also said that ecommerce players in countries that underwent strict social distancing are expected to report an increase in mobile app usage and downloads.

2| The Philippines posted the highest increase in “basket size” in the region

Overall, Southeast Asians are spending more money online this year compared to the first half of 2019. While the average basket size (or the amount added to an online cart) increased in each country, the Philippines had the highest increase (57 percent) compared to its neighbors.

In iPrice’s platform alone, Filipino users now spend about P1,311 per month on average. 

iPrice also analyzed web traffic data from SimilarWeb to get an idea of which sites that are thriving and which are failing, as well as product categories that are gaining demand, and how ecommerce players are adapting to the unexpected pandemic. Here are some insights: 

3| Fewer people are browsing fashion retail sites

Fashion ecommerce sites have taken the biggest hit in the region as clothing and apparel become less of a priority while people are staying home. Each country’s fashion websites recorded by iPrice and SimilarWeb have experienced a collective decrease in web traffic. 

In the Philippines, fashion sites’ web traffic collectively decreased by 30 percent compared to last year’s.

2| Meanwhile, electronics sites are booming

Electronics sites increased by a 59 percent during the first half of 2020. iPrice sees this as proof that Filipinos are increasing hungry for information about gadgets during the lockdown period. Compared to other countries in Southeast Asia, the Philippines had the highest web traffic increase in electronics sites.

Strangely, electronics sites in other countries (except Singapore) even experienced a decrease in web traffic.  

3| The most ordered item in Southeast Asia are sports and outdoor products

Out of all the other categories, sports and outdoor products obtained the highest growth in average orders, posting an increase of 34 percent compared to last year. The average amount spent on these products is $28 (P1,361). According to iPrice, because of restrictions on outdoor and sports activities, this is a clear sign that consumers are adapting. 

4| Ecommerce sites are doing are switching up their promos to attract more customers

Because of shifting consumer trends, ecommerce players are trying new things to entice more customers to buy their products. Giants like Lazada PH and Shopee PH offered “free shipping” promos across the year and added a number of promos on essential goods, too. Local player Beauty MNL, meanwhile offered numerous discounts/promos on vitamins and supplements.

To see the full Map ef Ecommerce Philippines report,  click here.

All data on the total visits on desktop & mobile web in this study were taken from global traffic figures from the respective websites as of June 2020 from?SimilarWeb. The following industries were not included in this list: e-ticketing, financial services, rental services, insurance, delivery service, food & beverage, meta-search, couponing, cashback websites & e-commerce who solely provides classified ads/P2P services. SimilarWeb introduced a major algorithm update on September 2019 which affected the total visits recorded by all websites for 2019. The number of sessions in a quarter based on quarterly sessions on Android Shopping Apps.  

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Online purchase behavior during COVID-19 Philippines 2020, by gender

Impacts of covid-19 pandemic on the online purchase behavior among consumers in the philippines as of may 2020, by gender.

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Rakuten Insight

Philippines

May 7 to 30, 2020

13,467 respondents

16 years and older

among 9,061 female and 4,406 male respondents

Online survey

Original survey question: how did your online purchase behavior change during the COVID-19 pandemic?

Other statistics on the topic

Key Figures of E-Commerce

• Online share of retail trade in selected European countries 2014-2022

Financial Services

• Most used payment methods in e-commerce worldwide in 2023, with a forecast for 2027

B2C E-Commerce

• Leading online marketplaces in Europe 2023, by monthly visits

Shopping Behavior

• Share of European consumers that shop online vs. offline each week 2023

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Statistics on " Digital shopping behavior in Europe "

• E-commerce share of enterprises' revenue in the EU-27 2011-2023
• E-commerce revenue share of companies in Europe 2022, by country
• European e-shoppers online purchase frequency 2023, by product category
• Attitudes towards online shopping in the UK 2024
• Attitudes towards online shopping in France 2024
• Attitudes towards online shopping in Germany 2024
• Attitudes towards online shopping in Italy 2024
• Attitudes towards online shopping in Spain 2024
• Attitudes towards online shopping in the Netherlands 2024
• Online vs. offline purchases by category in the UK 2022
• Online vs. offline purchases by category in France 2022
• Online vs. offline purchases by category in Germany 2022
• Online vs. offline purchases by category in Italy 2022
• Online vs. offline purchases by category in Spain 2022
• Online vs. offline purchases by category in the Netherlands 2022
• Share of shoppers buying groceries online in the UK 2023, by generation
• Share of shoppers buying groceries online in France 2023, by generation
• Share of shoppers buying groceries online in Germany 2023, by generation
• Share of shoppers buying groceries online in the Netherlands 2023, by generation
• Share of shoppers buying groceries online in Austria 2023, by generation
• Highest valued e-commerce startup companies in Europe 2023
• Top online stores in the UK 2023
• France: Top 10 online stores
• Top online stores in Germany 2023
• Top online stores in Italy 2023
• Top online stores in Spain 2022, by net sales
• Netherlands: Top 10 online stores 2023
• Mobile payment market size in 10 countries in Europe in 2020, with a 2025 forecast
• Top POS and e-commerce payment methods for small-ticket items in Europe 2022
• Top POS and e-commerce payment methods for big-ticket items in Europe 2022
• Biggest contactless payment brands at POS in Germany 2024
• Biggest contactless payment brands at POS in the UK 2024
• Biggest contactless payment brands at POS in France 2024
• Biggest buy now, pay later (BNPL) apps in Germany 2015-2023, based on downloads
• Biggest buy now, pay later (BNPL) apps in the UK 2015-2023, based on downloads

Other statistics that may interest you Digital shopping behavior in Europe

• Basic Statistic E-commerce share of enterprises' revenue in the EU-27 2011-2023
• Basic Statistic Online share of retail trade in selected European countries 2014-2022
• Premium Statistic E-commerce revenue share of companies in Europe 2022, by country
• Premium Statistic European e-shoppers online purchase frequency 2023, by product category
• Premium Statistic Most used payment methods in e-commerce worldwide in 2023, with a forecast for 2027

Attitudes towards online shopping

• Premium Statistic Attitudes towards online shopping in the UK 2024
• Premium Statistic Attitudes towards online shopping in France 2024
• Premium Statistic Attitudes towards online shopping in Germany 2024
• Premium Statistic Attitudes towards online shopping in Italy 2024
• Premium Statistic Attitudes towards online shopping in Spain 2024
• Premium Statistic Attitudes towards online shopping in the Netherlands 2024

Online vs. in-store shopping behavior

• Premium Statistic Share of European consumers that shop online vs. offline each week 2023
• Premium Statistic Online vs. offline purchases by category in the UK 2022
• Premium Statistic Online vs. offline purchases by category in France 2022
• Premium Statistic Online vs. offline purchases by category in Germany 2022
• Premium Statistic Online vs. offline purchases by category in Italy 2022
• Premium Statistic Online vs. offline purchases by category in Spain 2022
• Premium Statistic Online vs. offline purchases by category in the Netherlands 2022

Online grocery shopping

• Premium Statistic Share of shoppers buying groceries online in the UK 2023, by generation
• Premium Statistic Share of shoppers buying groceries online in France 2023, by generation
• Premium Statistic Share of shoppers buying groceries online in Germany 2023, by generation
• Premium Statistic Share of shoppers buying groceries online in the Netherlands 2023, by generation
• Premium Statistic Share of shoppers buying groceries online in Austria 2023, by generation

Leading online retailers in Europe

• Premium Statistic Leading online marketplaces in Europe 2023, by monthly visits
• Premium Statistic Highest valued e-commerce startup companies in Europe 2023
• Basic Statistic Top online stores in the UK 2023
• Premium Statistic France: Top 10 online stores
• Premium Statistic Top online stores in Germany 2023
• Basic Statistic Top online stores in Italy 2023
• Basic Statistic Top online stores in Spain 2022, by net sales
• Basic Statistic Netherlands: Top 10 online stores 2023

Online payment

• Premium Statistic Mobile payment market size in 10 countries in Europe in 2020, with a 2025 forecast
• Premium Statistic Top POS and e-commerce payment methods for small-ticket items in Europe 2022
• Premium Statistic Top POS and e-commerce payment methods for big-ticket items in Europe 2022
• Premium Statistic Biggest contactless payment brands at POS in Germany 2024
• Premium Statistic Biggest contactless payment brands at POS in the UK 2024
• Premium Statistic Biggest contactless payment brands at POS in France 2024
• Premium Statistic Biggest buy now, pay later (BNPL) apps in Germany 2015-2023, based on downloads
• Premium Statistic Biggest buy now, pay later (BNPL) apps in the UK 2015-2023, based on downloads

Further related statistics

• Premium Statistic Where consumers shop for clothing and footwear in the United Kingdom (UK) 2013
• Premium Statistic Channels consumers use for their main grocery shop in the United Kingdom (UK) 2013
• Premium Statistic Furniture shopping: Consumers' delivery preferences in the United Kingdom (UK) 2013
• Premium Statistic AstraZeneca's expenditure on research and development 2006-2023
• Basic Statistic Consumer electronics: Online purchasing in Great Britain 2020, by demographic
• Premium Statistic India: leading e-commerce categories 2013, by sales
• Premium Statistic Growth rate of attendance to leading Latin American amusement parks 2019-2022
• Premium Statistic Leading online retailers in Southeast Asia 2020, by monthly visits
• Basic Statistic Per capita consumer spending in India 1974-2012
• Basic Statistic Travel arrangements: Online purchasing in Great Britain 2019, by demographic

Further Content: You might find this interesting as well

• Where consumers shop for clothing and footwear in the United Kingdom (UK) 2013
• Channels consumers use for their main grocery shop in the United Kingdom (UK) 2013
• Furniture shopping: Consumers' delivery preferences in the United Kingdom (UK) 2013
• AstraZeneca's expenditure on research and development 2006-2023
• Consumer electronics: Online purchasing in Great Britain 2020, by demographic
• India: leading e-commerce categories 2013, by sales
• Growth rate of attendance to leading Latin American amusement parks 2019-2022
• Leading online retailers in Southeast Asia 2020, by monthly visits
• Per capita consumer spending in India 1974-2012
• Travel arrangements: Online purchasing in Great Britain 2019, by demographic

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Filipino Consumers’ Behavior towards Online Shopping

This paper discusses what are the Filipino consumers’ attitude towards online shopping and seeks to understand the factors affecting the decisions of these consumers on why they chose to shop online. It will also aim to delve into both the benefits and the problems of using online shopping to the consumers. This paper will recommend a course of action that can be taken to improve strategies in online shopping. Thus, by following this plan of action, this would be a great help to the marketers, entrepreneurs and businessmen to compete in the market and to make their products more compatible to the consumers’ needs and wants.

Related Papers

suhana mohezar

Rommel De Gracia

Vijaya Sooria Sangaran Kutty

With the emergence of the World Wide Web (www), consumer and retailers can buy or sell products over an electronic network primarily the internet. This phenomenon is known as E- commerce or Online Shopping. This simple idea has attracted millions of retailers and consumers which lead to a substantial growth in the industry. Consumers can choose from a variety of product or services and place an order from anywhere at any time and get it delivered to their doorsteps. Previous research in this field shows that consumers are still hesitant to trust online shopping websites as they doubt that the information shown in the website cannot be trusted and consumers feel hesitant to provide their personal information and payment information as they believe online shopping is not safe. The key factors in terms of marketing communication online are the marketers’ ability to produce authentic and relevant information and use communication symbols or trust signs which enables the consumers to trust the website. Hence in this paper the researchers seek to investigate the method of shopping which is preferred among undergraduates and to explore the reasoning behind it. Additionally, this paper seeks to identify the factors that influence the purchasing decision of consumers when shopping online. The findings will help marketers understand how to better communicate with consumers online via text, audio, visual symbols and signs.

IOSR Journals

nazish fatemah

The main purpose of this research was to investigate the effect of sales promotion and natural environment that is casual factor in consumer buying behavior. The survey found that there 403 was an insignificant relationship between coupons and buying behavior. On the other hand the buy-one-get-one free, Physical surrounding has a significant relationship with the purchasing behavior. The consequences of this research will help marketers to recognize the most excellent kind of promotional tools that significantly influence the purchasing behavior of consumers. Traders can develop their business plan more effectively through the results of this study and plans help entrepreneurs and traders to gain a competitive benefit over their competitors and enable businesses to gain maximum profit.

Ted Waitforit

RA A Rather

hafizah mansor

American-Eurasian Network for Scientific Information

Dr. Muhammad Khalilur Rahman

"Background: Today, global market is targeted by so many established retailers, because they are looking for new potential market expansion. Malaysian hypermarkets are growing drastically challenging in the past decades; as a result customer satisfaction and customer loyalty has become a critical phenomenon. Objective: The main purpose of this study is to measure customers’ loyalty through satisfaction towards retail marketing strategy in Malaysian hypermarkets perspective. Methodology: In this study, resource based theory has been exploited for developing conceptual research framework. The data has been collected by distributing self-administered questionnaire among 350 consumers involving different races in Malaysian hypermarkets in Kuala Lumpur through convenience sampling method. The data analysis has been conducted using descriptive statistics as well as exploratory and confirmatory factor analysis. Subsequently, by using AMOS through structural equation modeling, 292 valid cases have been selected to test the hypothesized relationship among the variables in the research conceptual model. Results: The results revealed that service quality, product quality, price strategy and store attribute have significant relationship with customer satisfaction and importantly customer satisfaction has direct relationship to customer loyalty. Conclusion: The study concludes that customer satisfaction is the predecessor to customer loyalty in Malaysian hypermarket setting. Hence, the establishment of customer satisfaction has a great meaning to the retailers due to its predictive power. Finally, it is hoped that this study could facilitate improving customer loyalty through customer satisfaction in Malaysian hypermarkets. Keywords: Retail marketing strategy, Malaysian hypermarkets, Service quality, Product quality, Customer satisfaction, Customer loyalty. ""

rebecca odame-lartey

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