research article on online shopping

Online Shopping

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• Yi Cai 2 &
• Brenda J. Cude  

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This chapter provides an overview of recent research related to online shopping and the conceptual frameworks that have guided that research. Specifically, the chapter addresses research related to who shops online and who does not, what attracts consumers to shop online, how and what consumers do when shopping online, and factors that might slow the growth in consumer online activities. The chapter reports on research related to the online shopping process, including consumer perceptions of privacy and security, as well as online information search. Directions for future research are suggested.

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Cai, Y., Cude, B.J. (2008). Online Shopping. In: Xiao, J.J. (eds) Handbook of Consumer Finance Research. Springer, New York, NY. https://doi.org/10.1007/978-0-387-75734-6_9

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ORIGINAL RESEARCH article

Changing trends of consumers' online buying behavior during covid-19 pandemic with moderating role of payment mode and gender.

• 1 Management Studies Department, Bahria University, Karachi, Pakistan
• 2 Faculty of Engineering Sciences and Technology, Hamdard University, Karachi, Pakistan

It was not long ago when technological emergence fundamentally changed the landscape of global businesses. Following that, business operations started shifting away from traditional to advance digitalized processes. These digitalized processes gave a further boost to the e-commerce industry, making the online environment more competitive. Despite the growing trend, there has always been a consumer market that is not involved in online shopping, and this gap is huge when it comes to consumers from developing countries, specifically Pakistan. On contrary, the recent COVID-19 pandemic has brought drastic changes to the way consumers used to form their intention and behave toward digitalized solutions in pre COVID-19 times. Evidence shows that the global e-commerce industry has touched phenomenal growth during COVID-19, whereas Pakistan's e-commerce industry still holds a huge potential and has not fully boomed yet. These facts pave new avenues for marketers to cater to this consumer market for long-term growth. Hence, the study provides insights into how consumers' online buying behavior has transformed during the COVID-19 pandemic in the context of Pakistan. The study presents a framework based on the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). Furthermore, the moderating role of gender and payment mode has also been examined. For the analysis of variables, the partial least squares (PLS) method was used to conduct structural equation modeling (SEM) by collecting data from 266 respondents. The results show a significant and positive impact of perceived benefits, perceived ease of use, perceived enjoyment, and social influence on consumers' intention, but they also show an insignificant impact of gender and payment mode as a moderating variable on PEOU-BI and BI-AB, respectively. The results are of utmost significance for Pakistani businesses, marketers, and e-traders to streamline their business practices accordingly. Lastly, the proposed framework demonstrates new directions for future research to work upon.

Introduction

In developing countries like Pakistan, the Internet brought much convenience to businesses, specifically in the twenty-first century. Due to the current COVID 19 pandemic, there has been a drastic change in the way consumers have shifted toward online buying. It is evident that post-COVID circumstances have left a significant impact on the e-commerce industry ( Rashid et al., 2022 ). This has caused global e-commerce sales projection to reach $7.4 trillion by 2025 ( Statista, 2022 ). On the contrary, South Asian countries show a low share of just 1.4% in global e-commerce business compared to their population share in the world.

Pakistan's e-business has shown drastic improvements ever since the pandemic struck. As per SBP (FY20), registered e-commerce merchants have increased, and markets have expanded to Rs. 234.6 billion with 55.5% yearly. These situations have raised doubts about Pakistan's digital connectivity, which shows a huge potential for growth and untapped areas for e-traders in Pakistan. Despite these statistics, the e-commerce market in Pakistan is still in its infancy stage. It has been evident that there are a whole lot of consumer bases that are not involved in online shopping ( Ahmed et al., 2017 ). The reason for this lack of involvement in online shopping is unknown. Domestically, there is a research and development gap that causes inconsistency in theoretical and empirical evidence on factors that may shape an individual's online buying behavior in the Pakistani market. Therefore, it is pertinent to fill this gap by examining Pakistani consumers' psychological and behavioral beliefs. Globally, there has been plenty of research studies conducted proposing valuable conceptually, theoretically, and empirically tested frameworks that intend to explain antecedents of consumers' intentions toward online buying behavior.

These studies examined the online behavior of consumers in numerous dimensions and postulated perceptions behind online shopping behavior and attributes ( Jarvenpaa and Todd, 1996 ; Chang and Kannan, 2006 ), consumer information process styles, online store layouts, ( Park and Kim, 2003 ), behavioral and normative beliefs about technology adoption ( Karahanna et al., 1999 ; Limayem et al., 2001 ; Foucault and Scheufele, 2002 ) risks related to online shopping ( Jarvenpaa et al., 1999 ; Akhlaq and Ahmed, 2015 ; Haider and Nasir, 2016 ; Pappas, 2016 ), and technology-oriented factors affecting online purchase intention ( van der Heijden et al., 2003 ; Prashar et al., 2015 ).

Overall, the connotations of previous studies tended toward two dimensions: (a) “product and shopping attributes” that are customer-specific and (b) “technological attributes” that are website-/technology-specific. None of the available studies has covered integrated attributes of both dimensions “customer-specific” and “technology-specific” in a single framework to study insights of consumers' behaviors for online buying. Hence, there is a need to address underlying factors that may shape consumers' intention and actual behavior to opt for online purchases. Based on these arguments, the present study proposes a comprehensive framework comprised of factors impacting consumers' online buying behavior during the pandemic.

In this regard, the theoretical foundation of this study is built upon the Technology Acceptance Model ( Davis, 1989 ) (TAM), which is an extension of the Theory of Planned Behavior (TPB) ( Ajzen, 1985 ). TAM is a widely used and highly influential model of user's acceptance of “technology.” As the present study examines the buying behavior of an online consumer, it tends to predict how consumers' perceived benefits, perceived ease of use, perceived enjoyment, and social influence have an impact to form consumers' intention and behavior to purchase online.

The results of this study would be of interest to a diverse research audience, including the academia, marketers, advertisers, policymakers, governments, and businesses. For the academia, new theoretical literature has been presented with the inclusion of potent constructs obtained from the technological model (TAM), psychological, and behavioral model (TPB) along with the normative notion of social influence on consumers' behavioral intention and behavior. Marketers may devise strategies to encourage their consumers to opt for online purchases, whereas advertisers may use appealing and creative content to promote them. In addition, policymakers may enact laws to encourage e-trading, and the government may facilitate Pakistani e-traders by releasing funds to build and maintain an advanced IT infrastructure. Lastly, the study would be of optimum significance for businesses that may work on their website designs and processes and maintain a website infrastructure to aid consumers according to their changing shopping preferences.

Literature Review

Perceived benefits and behavioral intentions.

Previous studies have provided many findings and devoted considerably to delivering benefits to consumers to stimulate their shopping intentions. Research has clearly defined the concept of consumer benefits and the significance of hedonic and utilitarian benefits for them ( Babin et al., 1994 ; Holbrook, 1994 ; Jones et al., 2006 ; Wang et al., 2013 ). Consumers derive practical benefits from the performance of a product or a service after achieving a task ( Kim, 2002 ). Furthermore, recent studies conducted by Widyastuti et al. (2020) stated the perception of perceived benefits, whereas Yew and Kamarulzaman (2020) and Bangkit et al. (2022) found a significant positive impact of perceived benefits on online consumer behavior. In the same line, a study conducted by Jeong et al. (2003) on “online shoppers of the hotel industry” found that for customers, the most critical factor that influences their “behavior intention” is the satisfaction level of available information, dimensions, and attributes provided by a website. Chang and Kannan (2006) stated in their study that website quality has positively influenced consumers' purchase intention. Bai et al. (2008) found significantly positive empirical results in online usability, functionality, customer satisfaction, and behavior intentions. The study further stated that consumers perceive all these dimensions as valued, increasing their purchase intentions. As Babin and Babin (2001) stated that consumers who efficiently complete shopping tasks would show stronger repeated purchase intentions.

In addition, Teo (2002) , Xia et al. (2008) , Nazir et al. (2012) , and Manu and Fuad (2022) shared similar findings where consumers derive attributes of perceived benefits through online shopping; it provides the required information on a product or a service, saves time, low prices, and convenience in the availability of products that are not locally available. Online shopping is getting popular in Pakistan because of its ease of use and the comfort it brings to consumers without much effort ( Iqbal and Hunjra, 2012 ). Furthermore, research highlights that consumers seek internet shopping valuable for price reviews and comparisons, search and deal evaluation convenience, low prices, selection variety, information on product features, latest awareness of brands and fashion trends ( Sorce et al., 2005 ; Zhou and Zhang, 2007 ; Jiang et al., 2013 ; Jhamb and Gupta, 2016 ). Teo (2006) indicates that consumers expect benefits like sufficient product information, convenience, online security, and easy contact with vendors. Moreover, while shopping online, consumers also expect prompt delivery of a product, a reliable supply chain, and return transaction policies ( Dawn and Kar, 2011 ).

H1: Perceived benefits significantly impact the behavioral intention for online purchases.

Moderating Role of Gender

In various marketing and consumer behaviors, demographic variables, specifically the impact of gender, have been taken into different contexts. In some studies, overall demographics are used as antecedents of TAM variables ( Porter and Donthu, 2006 ). Others have used them to moderate the effect of the predictor and criterion relationship in technological acceptance ( Chang and Kannan, 2006 ). Previously, research studies have accepted that there is a significant role of gender in technology acceptance ( Yousafzai and Yani-de-Soriano, 2012 ); a study further shows that men have a more strong and significant impact on perceived usefulness and behavioral intention in relation to technology acceptance and women have more impact on perceived ease of use and behavioral intention. This study is in line with Davis (1989) , Clegg and Trayhurn (2000) , and Venkatesh et al. (2003) . In conclusion, it has been argued that men are more tech-savvy, task-oriented and adopt technology to avail themselves benefits of online shopping. However, for acceptance of technology, women tend to show more computer anxiety than men ( Venkatesh and Morris, 2000 ; Karavidas et al., 2005 ; Zhang, 2005 ).

H2: Gender moderates the effect of perceived benefits on the behavioral intention for online purchase.

H3: Gender moderates the effect of perceived ease of use on the behavioral intention for online purchase.

Perceived Ease of Use (PEOU) and Behavioral Intention (BI)

Perceived ease of use is best defined by Davis (1989 , 1993) as one of TAM's basic constructs. PEOU is defined as a degree to which a person believes using a particular system is effortless ( Davis, 1989 ). Al-Azzam and Fattah (2014) postulated that perceived ease of use refers to a consumer who believes that using the Internet for shopping is free of effort and involves minimal friction in using and handling websites. Apart from the vital role of “ease of use” in technology acceptance, it has also been proposed for website usability and efficiency while shopping online ( Monsuwe et al., 2004 ). Considering these findings, it can be claimed that if there is an ease in usage and effortlessness in handling technology, consumers are more likely to adopt a system while purchasing online. Hence, one's intention to purchase online increases ( Venkatesh, 2000 ; Xia et al., 2008 ). Many other researchers have confirmed a strong sign and a direct relationship between perceived ease of use and the behavioral intention of a person ( Teo et al., 1999 ; Venkatesh and Bala, 2008 ; Ingham et al., 2015 ).

The study further implies that if a consumer has an increased experience, they adjust themselves to system-specific ease of use and reflect on their interaction with repeated usage of the system, which influences the behavioral intention to shop online. Few latent dimensions merely shape “ease of use” including site characteristics, navigation, and download speed ( Zeithmal et al., 2002 ). However, the most significant role in shaping “ease of use” is played by two dimensions elaborated by Venkatesh (2000) ; these include computer self-efficacy, computer anxiety, and computer playfulness; “computer self-efficacy” relates to the general use of computer or skills needed to operate a system; “computer anxiety” refers to a person's fear of using a computer when required, whereas “computer playfulness” is a degree to which a consumer's cognitive ability makes them feel less effortful and underestimates the complexity of system usage for online interaction. Increased usage experience contributes to unique attributes of perceived enjoyment concerning user system specification; it makes a more enjoyable experience for users. ( Venkatesh, 2000 ; Monsuwe et al., 2004 ).

H4: PEOU significantly impacts behavioral intention for online purchase.

Perceived Enjoyment (PE) and Behavioral Intention (BI)

Researchers have explained enjoyment as how online shopping is perceived to be enjoyable or fun for a consumer. Various researchers have theoretically and empirically proved the role of intrinsic motivation in online shopping ( Davis et al., 1992 ; Venkatesh and Speier, 1999 ; Venkatesh, xbib2000 ). Intrinsic motivation has been taken as a construct of perceived enjoyment in many studies ( Monsuwe et al., 2004 ). Davis et al. (1992) introduced the third belief in TAM, perceived enjoyment. He proposed that perceived enjoyment directly impacts the behavioral intention of an online consumer. In addition, studies conducted in the past two decades have shed some light to state the role of perceived enjoyment in the behavioral intention of a consumer ( Koufaris, 2002 ; Cyr et al., 2006 ; Chang and Chen, 2008 ; Marza et al., 2019 ; Bangkit et al., 2022 ). Triandis (1980) reports that emotions like fun, joy, and pleasure influences human behavior. According to self-determination theory ( Deci, 1975 ), if a person is intrinsically involved in online shopping and personally determined, they enjoy doing it. Kuswanto et al. (2019) investigated variables impacting the online behavior of university students in Indonesia and highlighted that the online shopping behavior of consumers significantly gets influenced by enjoyment, social influence, and perceived risk.

Furthermore, a study conducted by Akhlaq and Ahmed (2015) has also proposed perceived enjoyment as a significant construct backed by an intrinsic motivation that positively impacts consumers' intention to shop online. Findings on Pakistani consumers reported by Cheema et al. (2013) show that perceived enjoyment has a significant and positive impact on online shopping intention and holds a 42% contribution to the model. Apart from intrinsic motivations, another latent dimension, exploration and curiosity to use a system, is also prominent in investigating the online shopping context. The empirical evidence reported by Teo (2002) shows that interest in online browsing is related to curiosity about knowing various products and brands available to purchase online. According to Teo's study, around 50% of the respondents browsed even if they did not intend to purchase.

H5: Perceived enjoyment mediates the relationship between perceived ease of use and behavioral intention for online purchase.

Causal Nature of Perceived Ease of Use (PEOU) and Perceived Enjoyment (PE)

There are differences in research findings that confirm the causal relationship between perceived ease of use and perceived enjoyment ( Sun and Zhang, 2006a , b ). In some studies, perceived enjoyment has been considered as an antecedent of perceived ease of use ( Venkatesh, 1999 , 2000 ; Agarwal and Karahanna, 2000 ; Venkatesh et al., 2002 ). In other studies, it has been confirmed as a consequence of perceived ease of use ( Deci, 1975 ; Davis et al., 1992 ; Teo et al., 1999 ; van der Heijden et al., 2003 ). It has been claimed that an easier-to-use system is more enjoyable ( Igbaria et al., 1995 ). For an empirical discussion of this inconsistent argument regarding the causal relationship between perceived ease of use and enjoyment, Sun and Zhang (2006a , b) conducted information system-based research in a utilitarian context using a covariance-based statistical method to find a causal relationship. They concluded that perceived enjoyment and perceived ease of use have overall dominance in the model in a utilitarian system environment. The present study aims to confirm this causal relationship by considering perceived enjoyment due to perceived ease of use. The study tends to measure a consumer's buying behavior via technology ( Davis et al., 1992 ; van der Heijden et al., 2003 ).

H6: Perceived ease of use significantly impacts perceived enjoyment for online purchase.

Social Influence (SI) and Behavioral Intention (BI)

“Social influence” (SI), an antecedent of the subjective norm (SN), is a crucial construct of TPB and TAM ( Davis, 1989 ) that has originated from the Theory of Reasoned Action (TRA) ( Fishbein and Ajzen, 1975 ). TRA states that a person's behavioral intention (BI) has a significant and positive relationship with subjective norms ( Karahanna et al., 1999 ). One's social circle may influence a person to behave in a particular manner ( Ajzen, 1985 ). According to classic internalization studies, when someone incorporates the referent's influence in adopting a system, the person perceives the referent's belief as their own belief ( Kelman, 1958 ; Warshaw, 1980 ). Wei et al. (2009) mentioned in their study Rogers (1995) ' proposition of social influence; he stated that social influence can be defined as two forms: mass media and interpersonal influence. Mass media or external influence includes newspapers, reports, academic journals, published articles, magazines, television, radio, and other applicable mediums, whereas interpersonal influence comes from family, peers, friends, social networks, and electronic word of mouth (EWOM) ( Bhattacherjee, 2000 ; LaRose and Eastin, 2002 ; Rao and Troshani, 2007 ; Pietro et al., 2012 ).

Venkatesh and Davis (2000) stated that people incorporate social influence to gain status and acceptance in their social setting. Studies by Ketabi et al. (2014) and Kuswanto et al. (2019) further highlighted the role of social norms and social influence on consumers, respectively; it has been stated that in certain situations the reference group of a person, specifically “friends,” strongly influences the behavior of an individual. In their qualitative study, Wani et al. (2016) also identified “social influence” and “e-word of mouth” as critical factors. Their study further elaborates that the opinions of consumers, peers, friends, and colleagues matter a lot while purchasing online. Even in the last shopping stage, just before check-out, if a consumer reads any comment about a product or a service, it undoubtedly impacts one's decision ( Park et al., 2011 ).

H7: Social influence significantly impacts behavioral intention for online purchase.

Behavioral Intention (BI) and Actual Behavior (AB)

Plenty of studies have used TPB and TAM to determine an individual's intention to engage in a particular behavior ( Ajzen, 1985 ; Pavlou and Marshall, 2002 ; Delafrooz et al., 2010 ; Tsai et al., 2011 ; Zaidi et al., 2014 ; Bauerová and Klepek, 2018 ). Behavioral intention is the focal point of TRA, TPB, and TAM. According to the extended TAM model postulated by Venkatesh and Davis (2000) , the relationship between intention to use and actual usage was significant and strongly mediated the effect of perceived benefits, perceived ease of use, and subjective norms on actual usage of an online consumer. Limayem and Hirt (2003) elaborated in their study that there are some “facilitating conditions” ( Triandis, 1979 ) that moderate the relationship between behavioral intention and actual behavior. Even if a person intends to act in a certain way, one cannot without those facilitating conditions. These conditions align with Ajzen's “perceived behavioral conditions,” but the point of difference is that Ajzen's perceived behavioral control is subjective, whereas Triandis' facilitating conditions are objective. In the present study, the behavioral intention was considered to examine the significance of perceived intentions of a person on actual buying behavior while purchasing online.

Payment Mode

According to a report by Yousuf (2018) [Asian Development Bank (ADB)], 95% of Pakistan's e-commerce transactions come from cash on delivery (COD), and the remaining 5% comes from electronic payment with debit/credit cards. Another study states that only 31% of Pakistani tend to pay online for shopping and that cybercrimes and lack of trust in payment systems are the main reasons for their choice. ( CIGI, 2017 ). An increase in the online payment rate includes uncertain security and privacy issues that may influence consumers' buying behavior in e-markets ( Pang et al., 2016 ). As Valois et al. (1988) stated, some factors may affect the strength of the relationship between intention and actual behavior. However, in the current perspective of the COVID-19 pandemic, it has been stated by Pollak et al. (2022) that the market has become adaptable to non-standard situations within a short period. This implies that there have been specific changes observed in consumers' behaviors and preferences during crisis times. Considering the facts and figures on the “payment mode” of Pakistan's e-commerce and the overall concept of “facilitating conditions” from Triandis' theory, the study tends to imply that the “online payment method” (OPM) is a moderator to determine the impact of payment method on the relationship between intentions and actual behavior.

H8: Payment mode moderates the effect of behavioral intention on actual behavior for online purchase.

H9: Behavioral intention mediates the impact of perceived benefits, perceived ease of use, social influence, and perceived enjoyment on actual behavior for online purchase.

Based on hypotheses this study builds the framework to study four variables namely, perceived benefits, perceived ease of use, perceived enjoyment, and social influence along with mediating role of behavioral intention, whereas moderating role of Gender and payment mode is also under examination for the study ( Figure 1 ).

Figure 1 . Theoretical framework.

Methodology

Instrument development.

This study conducted an online survey for data collection. In this regard, an adapted instrument from previous studies was used and tested for reliability and validation. Questionnaires were sent to respondents for collection of their responses on a five-point Likert scale, which ranged from 1 (Strongly Disagree) to 5 (Strongly Agree). It is in line with previous studies conducted in the context of online consumer buying behavior ( Davis et al., 1992 ; van der Heijden et al., 2003 ; Sorce et al., 2005 ; Yang et al., 2015 ).

Furthermore, for unit analysis, eight items of perceived benefits were adapted from Teo (2002) , Swinyard and Smith (2003) , Sorce et al. (2005) , and Forsythe et al. (2006) . Five items of perceived ease of use were adapted from Gefen et al. (2003) and Cheema et al. (2013) . Four items of perceived enjoyment were adapted from Teo (2002) and Cheema et al. (2013) . Five items of social influence were adapted from Davis (1985) and Karaiskos et al. (2012) . Three items of behavior intention were adapted from Limayem and Hirt (2003) ) and Karaiskos et al. (2012) . In addition, three items of actual behavior were adapted from Karaiskos et al. (2012) . Lastly, the items of payment mode were adapted from Hasan and Gupta (2020) .

Furthermore, the questionnaires contain two sections; the first section contains demographic variables of an individual including age, gender, and education level, whereas the second section contains the independent, moderating, and mediating variables of the study.

Sample and Procedures

For data collection of the present study, 350 questionnaires were sent out to respondents who were online buyers. The questionnaires were developed with questions regarding whether respondents are online buyers, how long they have been into online buying, and what is the occurrence of their buying patterns. In addition, the questionnaire link shared with the respondents included a note stating that this study seeks responses from online buyers only and that respondents who were not online buyers were not required to record responses.

A self-administered questionnaire was sent out using “an online survey”. A questionnaire link was sent out to the respondents via social media platforms and email. Out of 350, a total of 266 responses were received, and no data were missing from the 266 responses as the questionnaires were designed by utilizing close-ended questions to choose from the list, and fields were marked required. According to the gender category, of those who participated in the research, 51.5% were men and 48.5% were women. The remaining demographic details are shown in Table 1 .

Table 1 . Respondents' profile.

Evaluation Method

The partial least squares (PLS) method was used to conduct the structural equation modeling (SEM) approach to evaluate the present study. Hair et al. (2012) stated that PLS is a second-generation evaluation technique that measures and tests structural modeling, component factor analysis (CFA), and regression. Thus, extensive pre-analysis and data validation were conducted using Smart-PLS for the present study.

Result Analysis

Common method bias.

Kock (2015) stated that the occurrence of variance inflator factor (VIF) should be less than or equal to 3.3. For this study, all VIF values were in the range of 1.67- 2.6, showing that the model was considered free of common method bias because no such thing was observed. To further testify the model, Harman's single-factor test ( Podsakoff and Lee, 2003 ) was conducted to examine if the model was free from common method biases. According to the requirement, if the total variance extracted by one factor exceeds 50%, this shows the presence of common method biases in the study. However, the present study shows that the total variance extracted by one aspect is 27.288, less than 50%. Also, the inter-correlation of all the constructs of this study is less than 0.9 ( Pavlou and El Sawy, 2006 ). Hence, the outcomes indicate that common method bias is not an issue in this study.

Measurement Model

An assessment of reliability and validity was conducted to evaluate and reduce measurement errors. It has been stated as a required test to reduce measurement errors while assessing for internal consistency, discriminant, and convergent validities ( Hair et al., 2012 ). Furthermore, these tests have been evaluated by assessing the values of Cronbach's alpha (α), factor loadings, average variance extracted (AVE), and composite reliability (CR). The acceptable value of CFA should be 0.7 at minimum ( Hair et al., 2012 ). Along similar lines, the present study shows that the CFA values are above 0.7 and are acceptable to show the internal consistency of the data ( Table 2 ). Furthermore, for all the constructs, the values of AVE and CR are above 0.5 and 0.8, respectively ( Fornell and Larcker, 1981 ); these values show acceptable convergent validity. Table 2 shows that Cronbach's alpha, CR, and AVE of actual behavior are 0.789, 0.875, and 0.701, respectively. The alpha (α), CR, and AVE values of behavioral intentions are reported as 0.752, 0.858, and 0.668. Perceived benefits are 0.809, 0.867 and 0.568. In addition perceived ease of use-values are 0.838, 0.885, and 0.606.; perceived enjoyment values are 0.756, 0.845, and 0.577. Lastly, the three items of social influence show Cronbach α, CR, and AVE values of 0.757, 0.861, and 0.673, respectively. Furthermore, all the hypotheses (except for the moderator payment mode) show that their discriminant validity ( Table 3 ) meets the requirement suggested by Fornell and Larcker (1981) ; that is, the square root of each construct's AVE should be higher than its correlation with the remaining constructs.

Table 2 . Convergent validity of measurement model.

Table 3 . Measurement model and discriminant validity.

Structural Model

To assess the results, the estimated path coefficient of the structural model is analyzed. The results of variables and constructs are shown in Figure 2 . The analysis shows that there is a positive and significant impact of perceived behavior, perceived ease of use, perceived enjoyment, and social influence on behavioral intention for online shopping, as their values are H 1 : β = 0.32, p < 0; H 4 : β = 0.156, p < 0.001; H 5 : β = 0.217, p < 0.001; H 7 : β = 0.217, p < 0.001, respectively. However, perceived ease of use also shows a significant and positive impact on perceived enjoyment given that H 6 : β = 0.595, p < 0.001.

Figure 2 . Structural model.

PLS has been used to test moderating and mediating impacts, and special consideration has been given to assess relevant effects in a single model; PLS made it more sophisticated and allowed not to follow a causal step approach to evaluate mediating and moderating effects, whereas considering mediating and moderating effects with PLS is straightforward, and the outcomes give deep insights into advanced mediation and moderation analyses more accurately ( Chin, 2010 ; Streukens et al., 2010 ; Nitzl et al., 2016 ). Hence, relevant effects have been assessed overall in a single model. To discuss the moderating roles of the model, it is evident from the results that gender shows significant moderation in perceived behavior and behavioral intention relationship: H 2 : β = −0.164, p = 0.006. In contrast, there is an insignificant moderation impact of gender on perceived ease of use and behavioral intention relationship: H 3 : β = 0.036, p = 0.501.

In addition, the moderation impact of payment mode also shows insignificance on behavioral intention and actual behavior relationship: H 8 : β = 0.033, p = 0.247). Lastly, the model has demonstrated a significant and positive impact of behavioral intention on the actual buying behavior of online consumers: H 9 : β = 0.604, p < 0.001). Therefore, H 1 , H 2 , H 4 , H 5 , H 6 , H 7 , and H 9 are supported, whereas H 3 and H 8 are rejected based on the results. Detailed findings are shown in Table 4 .

Table 4 . Structural model results (hypothesis testing).

According to the criteria, the value of R 2 must be greater than 0.2, as proposed by Hair et al. (2016) . The present study shows an acceptable value of R 2 , which is 0.554. Furthermore, the value of Q 2 has also been examined using Stone-Geisser's blindfold technique; this technique can be used to examine function fitting and cross-validation. However, this procedure is stated as a sample reuse procedure by Mikalef et al. (2017) . If the value of Q 2> 0, it implies that the model has a predictive relevance ( Hair et al., 2012 ). The Analysis shows that the behavioral intention (Q 2 =0.371), perceived enjoyment (Q 2 = 0.2), and actual behavior (Q 2 = 0.376) variables show reasonable predictive relevance, demonstrating that their values are above 0.

Discussions and Implications

The recent COVID-19 pandemic has changed the landscape of business processes and how they used to function. Prolonged lockdowns resulted in responses to the pandemic causing closures of several companies. However, it brought a new wave of online shopping all over the global market. Interestingly, when businesses went bankrupt and started the closure of their processes, the online market thrived and expanded by over 30–50% ( Financial Times, 2021 ). This shifted the relevance and significance of the research domain once more toward examining key components shaping one's intentions and behavior in a certain way. Thus, the present study seeks to investigate determinants impacting, moderating, and mediating consumers' online buying behavior during the COVID-19 pandemic. The findings suggest several contributions in consumer behavior, advertising, social media, digitalized marketing, academia, and practical aspects of consumers' intention and behavior.

Theoretical Implications

The present study has examined and concluded the determinants impacting the way consumers' online behavior has changed during COVID-19 in the context of Pakistan. For this purpose, the study has developed an integrated model based on the foundation of the well-established Technology Acceptance Model and Theory of planned behavior. First, the results of this research have validated the established scales of measuring consumers' online behavior in South Asian countries, specifically Pakistan. Second, the significant impact of perceived benefits, perceived enjoyment, ease of use, and social influence shows the generalizability and predictive power of TAM and TPB to measure consumers' behavior during the COVID-19 pandemic. This contributes to the academia and research and development in the stated domain so that further research could be carried out with the inclusion of constructs obtained from the technological model (TAM), psychological, and behavioral model (TPB) along with the other notion of social influence on consumers' behavioral intention leading to shaping ones' actual technology usage behavior. The study holds novelty as the context is different from that of routine consumers' online behavior; this implies insights into how consumers' intentions have changed drastically to opt for online buying. Interestingly, according to pre-COVID times, some consumers showed reluctance to go for online buying considering facilitating conditions, i.e., payment mode ( Triandis, 1977 , 1980 ; Pang et al., 2016 ). However, during the COVID-19 pandemic, the same broader consumer base shifted drastically to opt for online buying. The study reveals a new research realm to extend relevant theoretical paradigms to examine the impact of the external environment on consumers' buying intention and behavior.

Second, the integrated model with the role of mediation and moderation implies that theory predicts consumers' intention across situations; the present study has shown its generalizability during the time of a pandemic. This paves the way for further theoretical contribution in “crisis times” by introducing key determinants in cross-cultural and longitudinal analyses.

Practical Implications

Based on the results of this study, the following practical implications have been proposed:

First, the study provides supporting evidence of perceived benefits sought by consumers when buying online. It implies that when a consumer enjoys buying online, it influences their intention to choose purchase behavior in the long run. As consumers find it convenient, businesses need to work on enhancing website design and logistics systems to make shopping more user-friendly and prompt. Interactive and appealing website designs will make one's online experience enjoyable by providing superior images and photos of products/services, proper availability of product/service descriptions, and previous reviews on the same or related products. On the contrary, a complicated website and delays in distribution and logistics will obliterate the purpose of the “convenience” sought by consumers.

Second, perceived ease of use has shown a significant positive impact on perceived enjoyment and intention, indicating that perceived enjoyment mediates the effect of perceived ease of use on behavioral intention. It reveals that a consumer enjoys more when there is more ease for them to use technology. Hence, it stimulates one's intention toward online buying. Therefore, businesses need to work on their online service portals, availability of mobile phone website options, online check-out counters, guest check-out counter chatbots, and advanced navigation options from one product to another to make it effortless and user-friendly to enhance consumers' shopping experience.

Third, the role of gender has been studied widely to understand how gender as a moderator plays its role specifically while managing or using tech-oriented systems ( Venkatesh et al., 2003 ; Yousafzai and Yani-de-Soriano, 2012 ). In the present study, in Pakistan, it is evident from the results that gender plays a significant role in the relationship between perceived benefits and behavioral intention, whereas it has an insignificant role in the relationship between perceived ease of use and behavioral intention. For the moderating role of gender in the relationship between perceived benefits and behavioral intention relationship, the coefficient is negatively significant, which means that although the relationship shows significance, it is weaker in nature.

The insignificant and weak moderating role of gender in the relationship between perceived ease of use and behavioral intention, and in that between perceived benefits and behavioral intention, reveals that the studied relationships are not affected by the gender of a consumer. One's perceived ease of use and perceived benefits may tend toward forming online intention regardless of what gender the person belongs to. Businesses must employ strategies considering gender-neutral online portals, website designs, and online shopping services regarding technology's ease of use and perceived benefits.

In addition, findings of payment mode have shown an interesting insight that payment mode has no impact on consumers' online buying. According to previous studies, the trust factor related to online privacy had been a vital issue, and consumers did not want to shop online because of fraudulent cases, specifically in developing countries. However, the present study reveals that COVID-19 circumstances left consumers with no choice but to adhere. This shift brought a considerable consumer market to the e-commerce sector, which was first-time online buyers ( Statista, 2022 ). When consumers start trusting online payment structures in Pakistan, businesses need to make sure they take payment security as a priority, develop transaction systems, and secure their electronic payments via enhanced “secure electronic transaction” (SET) protocol. Lastly, social influence has also shown a key finding to positively impact buying intention. Businesses and marketers need to utilize such behaviors by involving more powerful bloggers/influencers who are famous among the public. These influencers may use social-friendly content in effortless ways to buy online. In this way, businesses may move from a more traditional way to a more personal level with their consumers. This may also include the creation of personal blogs, putting up fewer formal posts on social media handles, and decreasing the gap between brands and consumers by going through the personalization process.

Limitations and Future Study

There is a significant scope to examine and investigate the external factors that impact consumers' shift toward online buying, specifically during crises like pandemics. Data have been collected from educated online users who are tech-savvy. However, education level and how users are learning technologies are constantly changing. Future research in this domain may be conducted with larger populations regardless of their educational status. Additionally, payment mode was one of the external factors used as a moderator to investigate its impact on online buying behavior; future research may include longitudinal studies to see if consumers' behavior persists across situations for payment mode or changes with difficult times like the COVID-19 pandemic. It will be significant to investigate diverse external factors that moderate one's intention-behavior relationship in particular times and changes over the period.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: perceived benefits, perceived ease of use, perceived enjoyment, social influence, behavioral intention, actual behavior, gender, payment mode

Citation: Sajid S, Rashid RM and Haider W (2022) Changing Trends of Consumers' Online Buying Behavior During COVID-19 Pandemic With Moderating Role of Payment Mode and Gender. Front. Psychol. 13:919334. doi: 10.3389/fpsyg.2022.919334

Received: 13 April 2022; Accepted: 24 June 2022; Published: 10 August 2022.

Reviewed by:

Copyright © 2022 Sajid, Rashid and Haider. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sana Sajid, sanasajid2010@yahoo.com

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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

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

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Stay up to date:, internet of things.

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

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

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

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

A consumer pivot to digital and devices

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

Have you read?

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

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

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

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

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

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

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

Medicines and groceries on demand

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

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

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

A decline in brand loyalty

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

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

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

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

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

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

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

Healthy, hygienic and sustainable

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

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

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

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A study on factors limiting online shopping behaviour of consumers

Rajagiri Management Journal

ISSN : 0972-9968

Article publication date: 4 March 2021

Issue publication date: 12 April 2021

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

Design/methodology/approach

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

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

Research limitations/implications

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

Originality/value

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

• Online shopping

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

Emerald Publishing Limited

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

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

Introduction

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

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

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

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

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

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

Literature review

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

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

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

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

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

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

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

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

Research methodology

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

Data analysis and interpretation

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

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

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

Factor analysis

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

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

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

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

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

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

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

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

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

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

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

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

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

Conceptual framework of the study

Socioeconomic status of respondents

KMO and Bartlett’s test

Cronbach’s α

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Further reading

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

Corresponding author

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New technologies are impacting a wide range of Americans’ commercial behaviors, from the way they evaluate products and services to the way they pay for the things they buy

Table of contents.

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

Americans are incorporating a wide range of digital tools and platforms into their purchasing decisions and buying habits, according to a Pew Research Center survey of U.S. adults. The survey finds that roughly eight-in-ten Americans are now online shoppers: 79% have made an online purchase of any type, while 51% have bought something using a cellphone and 15% have made purchases by following a link from social media sites. When the Center first asked about online shopping in a June 2000 survey, just 22% of Americans had made a purchase online. In other words, today nearly as many Americans have made purchases directly through social media platforms as had engaged in any type of online purchasing behavior 16 years ago.

But even as a sizeable majority of Americans have joined the world of e-commerce, many still appreciate the benefits of brick-and-mortar stores. Overall, 64% of Americans indicate that, all things being equal, they prefer buying from physical stores to buying online. Of course, all things are often not equal – and a substantial share of the public says that price is often a far more important consideration than whether their purchases happen online or in physical stores. Fully 65% of Americans indicate that when they need to make purchases they typically compare the price they can get in stores with the price they can get online and choose whichever option is cheapest. Roughly one-in-five (21%) say they would buy from stores without checking prices online, while 14% would typically buy online without checking prices at physical locations first.

Although cost is often key, today’s consumers come to their purchasing decisions with a broad range of expectations on a number of different fronts. When buying something for the first time, more than eight-in-ten Americans say it is important to be able to compare prices from different sellers (86%), to be able to ask questions about what they are buying (84%), or to buy from sellers they are familiar with (84%). In addition, more than seven-in-ten think it is important to be able to try the product out in person (78%), to get advice from people they know (77%), or to be able to read reviews posted online by others who have purchased the item (74%). And nearly half of Americans (45%) have used cellphones while inside a physical store to look up online reviews of products they were interested in, or to try and find better prices online.

The survey also illustrates the extent to which Americans are turning toward the collective wisdom of online reviews and ratings when making purchasing decisions. Roughly eight-in-ten Americans (82%) say they consult online ratings and reviews when buying something for the first time. In fact, 40% of Americans (and roughly half of those under the age of 50) indicate that they nearly always turn to online reviews when buying something new. Moreover, nearly half of Americans feel that customer reviews help “a lot” to make consumers feel confident about their purchases (46%) and to make companies be accountable to their customers (45%).

But even as the public relies heavily on online reviews when making purchases, many Americans express concerns over whether or not these reviews can be trusted. Roughly half of those who read online reviews (51%) say that they generally paint an accurate picture of the products or businesses in question, but a similar share (48%) say it’s often hard to tell if online reviews are truthful and unbiased.

Finally, this survey documents a pronounced shift in how Americans engage with one of the oldest elements of the modern economy: physical currency. Today nearly one-quarter (24%) of Americans indicate that none of the purchases they make in a typical week involve cash. And an even larger share – 39% – indicates that they don’t really worry about having cash on hand, since there are so many other ways of paying for things these days. Nonwhites, low-income Americans and those 50 and older are especially likely to rely on cash as a payment method.

Among the other findings of this national survey of 4,787 U.S. adults conducted from Nov. 24 to Dec. 21, 2015:

• 12% of Americans have paid for in-store purchases by swiping or scanning their cellphones at the register.
• Awareness of the alternative currency bitcoin is quite high, as 48% of Americans have heard of bitcoins. However, just 1% of the public has actually used, collected or traded bitcoins.
• 39% of Americans have shared their experiences or feelings about a commercial transaction on social media platforms.

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

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

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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

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The next horizon for grocery e-commerce: Beyond the pandemic bump

Over the past 24 months, e-commerce in the North American grocery industry has continued to mature and scale. The pandemic served as an accelerator for grocery e-commerce, with much of the sector experiencing the equivalent of more than five years of growth in just five months .

About the research

We recently completed extensive research that included surveys of grocery CEOs, functional and operations executives, and consumers (see sidebar, “About the research”). Our surveys confirmed that consumers will continue to favor e-commerce as one of many ways to shop. However, many grocers don’t believe they have the necessary capabilities to manage this channel. In this article, we examine the actions organizations must take to win in e-commerce.

E-commerce takes hold

About the authors.

This article is a collaborative effort by Vishwa Chandra, Prabh Gill, Sajal Kohli , Varun Mathur, Kumar Venkataraman, and Janice Yoshimura.

The industry is now on the edge of the next transformation in e-commerce: grocery executives expect e-commerce penetration to more than double for their own organizations in the next three to five years, to an average of 23 percent (Exhibit 1).

Executives are even more bullish on e-commerce’s upside potential, noting that penetration could nearly triple to as high as 35 percent (nearly $600 billion versus about $150 billion at 11 percent penetration). Our research suggests continued support for e-commerce from consumers, who indicated a positive net intent to buy more groceries online (click and collect as well as delivery) in 2022 (Exhibit 2).

The main drivers of e-commerce’s growth during COVID-19 were safety and convenience, but our research found consumers also value the channel’s unique features—such as product comparisons, assortment, and personalized promotions. In parallel, consumers increasingly prefer home delivery (a rise from 48 percent in December 2020 to 63 percent a year later, which translates to an approximately $100 billion market today) and appreciate its product and service enhancements, including speed, reliability, assortment breadth, and flexibility (Exhibit 3).

We are also seeing consumers demonstrate different preferences for how their digital orders are filled based on need and occasion, a shift that reflects continued maturity in consumers’ approach to online grocery (Exhibit 4). Their use of different options based on occasion (Exhibit 5) compels retailers to offer a full portfolio of e-commerce options (such as same-day delivery, two-hour delivery, instant delivery, and click and collect). As demand spreads across different trips, the result is smaller baskets.

This degree of channel shifting within the grocery sector has precedents. Over the past couple of decades, the emergence and adoption of new offerings and channels have spurred significant changes in consumer behavior. For example, the rise of mass merchants with 150,000-square-foot stores created a different in-store experience than the one offered by the traditional neighborhood store. The mass-merchant category now accounts for about 26 percent of the market. Similarly, club retailers encouraged consumers to buy in bulk, and the rapid growth of discount and value grocery, featuring a predominantly private-label offering, defied the conventional wisdom that consumers wanted only consumer-packaged-goods (CPG) brands. Each of these “new” offerings has been accompanied by changing consumer behavior.

Keeping pace with e-commerce growth

As consumers have shifted toward e-commerce, two-thirds of retailers don’t feel well prepared to meet the dual challenges of delivering on growth while achieving profitability. Our research revealed that retailers feel some trepidation. Two-thirds of respondents expect to lose some share in the shift to digital, and more than half believe it will be difficult to attract the necessary talent to support digital growth (Exhibit 6). Meanwhile, grocers are considering how to allocate capital across multiple parallel efforts, including supply chain resilience, store remodels, digitalization, and talent acquisition.

To enhance their capabilities in the short term, grocers have responded by implementing three specific strategies.

First, some grocers are building partnerships with technology companies. To expand fulfillment capabilities, grocers such as Ahold Delhaize, Wakefern, and H-E-B have partnered with microfulfillment center (MFC) technology players like Dematic, Takeoff Technologies, and Swisslog. Google and Microsoft are also working with grocers to introduce artificial intelligence in replenishment and commerce (for example, to enable consumers to build grocery lists while shopping online).

Second, grocers continue to rely on third parties to manage costs and expand their e-commerce offerings. Instacart became a leader through its early market entry, but it has been joined by players such as Shipt and DoorDash. The latter handles fulfillment for Albertsons, alongside Instacart and Uber. Grocers are also using partnerships to provide new and innovative value propositions to customers. In Europe, for example, Morrisons has partnered with Deliveroo to make deliveries in as little as ten minutes.

Last, the shift to e-commerce is also challenging how retailers think about capabilities across the e-commerce value chain, from in-store digitalization and pricing and promotion to trade spending and media and advertising. The role of the store will continue to be significant, with grocers investing in digitalization to improve the in-store experience for consumers—for example, through self-checkout and grab and go.

How grocers can win in e-commerce—delivering on both growth and profitability

To excel in the next horizon of e-commerce, grocers need to develop an integrated value proposition that meets consumer needs while protecting their own profitability.

Our research found consumers are looking to save money, be healthier, build on their (rediscovered) joy of cooking, and find the best promotions more easily. For each of these needs, an evolved digital presence (both app- and web-based) can help grocers highlight their assortment, personalize their promotions, and engage consumers in a more meaningful manner—something that a purely brick-and-mortar offering cannot do. Organizations, especially retailers that have underinvested in the past, are planning to make aggressive investments in their digital capabilities to support these tasks.

However, simply redefining the value proposition will not be enough. To draw more consumers to e-commerce, retailers must offer lower costs, reduce minimum order requirements, protect quality and freshness, and enhance the breadth and discoverability of their assortments (Exhibit 7).

To deliver on the dual objective of growth and profitability, grocers need to take a range of simultaneous actions:

Engage customers meaningfully in their omnichannel journeys and invest in user experience

Omnichannel has become table stakes. After spending the past few years building this core offering, grocers are now focusing on retention efforts by forging personal relationships with customers to increase basket size through upselling and increased frequency of trips, both online and in store. Grocers are also experimenting with new ways to engage shoppers in omnichannel. For example, mobile scan–based product information and scan-and-go commerce are changing the way shoppers interact with grocers in-store and on apps. Establishing and maintaining a social connection with consumers and reaching out daily will be important for grocers hoping to move from share of stomach to share of mind. A social-first, video-rich capability will also be a must-have. E-grocer Weee, for example, which specializes in products for Asian and Hispanic shoppers, uses gamified, video-rich social media offerings to nurture a highly engaged customer base.

To draw more consumers to e-commerce, retailers must offer lower costs, reduce minimum order requirements, protect quality and freshness, and enhance the breadth and discoverability of their assortments.

The convergence of value propositions across the industry is raising the bar on user experience in e-commerce. Consumers increasingly value the ability to find products quickly and build their baskets while shopping online. Grocers are responding by investing in e-commerce capabilities and forming partnerships with technology companies to improve the user experience. For example, Albertsons and Google have partnered to create in-store shoppable maps with dynamic hyperlocal features, AI-powered conversational commerce, and predictive grocery-list building.

At the same time, retailers must enhance the in-store experience through continued investments in store technology. Solutions include self-checkout, digital shelf tags, and payments innovation to improve personalization and efficiency.

All of these offerings will have the dual objective of enabling growth while increasing profitability. However, focused investments will be needed to build both the talent bench and the core technology infrastructure. Successful grocers will seek to attract the right talent to their organizations and address the legacy technology debt from the past couple of decades.

Successful grocers will seek to attract the right talent to their organizations and address the legacy technology debt from the past couple of decades.

Build a distinct—but connected—capability in e-commerce category management

Because e-commerce is set to account for a significant share of overall business, retailers are starting to be more deliberate about standing up channel-specific management capabilities and getting sharper on assortment choices (breadth and depth, online versus offline), pricing, and online-only promotions, among other factors. Grocers need to make investments in data, analytics, and IT infrastructure to get a deeper understanding of their online business performance—for example, the effectiveness of online promotions and digital shopping trends by consumer segment. They must also dedicate resources to building their organizational muscle through efforts such as upskilling merchants. These capabilities should be integrated into a broader omnichannel category management strategy, which can provide a holistic and thoughtful merchandising experience anchored in a single view of the customer.

As consumers continue the shift toward buying through mobile apps, grocers are starting to use the full suite of e-merchandising levers—such as product placement, product recommendations, personalized promotions, and digital media—to monetize their digital assets  with consumer goods companies. The launch of retail media networks (such as Instacart’s new Carrot Ads platform) allows retailers to capture a greater share of marketing spending from brands beyond what they have traditionally captured. This source will be a key driver of profitability for grocers in the coming years.

Making this shift will not be easy, and our survey indicates that retailers recognize this challenge. Retailers and CPG companies have deep and complex ways of optimizing trade promotions and advertising in the brick-and-mortar channel. There are dozens of mechanisms through which CPGs and retailers invest in advertising and trade, and ROI is often hard to track and measure. Both retailers and CPGs will need to lean on digital capabilities to optimize their investments for greater impact on revenue and profitability.

Develop a portfolio of fulfillment options that are aligned to individual markets’ needs

As demand for online grocery continues to scale, grocers are going to have to revisit how and where they fulfill orders. The network of the future for grocers will encompass a mix of automated MFCs, manual dark stores, and store fulfillment. Matching the right fulfillment option to each specific location based on a market’s demand profile and service promise will be critical.

Retailers are conducting pilots with automated MFCs and manual dark stores. Many grocers are now locating MFCs close to their customers to improve speed at a lower cost. Both aggregate demand and consistency of demand are key factors in ensuring ROI. Grocers are also implementing centralized fulfillment centers to handle larger order volumes and support next-day delivery in highly concentrated geographies.

In parallel, grocers are experimenting with new last-mile models (for example, autonomous vehicles with precise delivery slots) and tech-enabled logistics optimization to lower costs while maintaining service levels.

While automation will be a key lever for retailers to increase efficiency and speed, grocers will need to make at-scale investments to build out a comprehensive network along with a focused effort to drive volume at each node. Since the benefits of automation will accrue to all participants in the industry, there is an opportunity for collaboration among grocers, technology companies, marketplaces, and CPG companies to rapidly scale these networks.

Use e-commerce as a way to innovate and harness the broader ecosystem

Grocers are approaching e-commerce as an opportunity to push the boundaries of their current offerings. Some retailers are deploying e-commerce to strengthen their current assortments (for example, to push private brands and prepared meals) and to promote new offerings (such as meal kits, partnerships with dark kitchens and local restaurants, and expansion into catering services to capture new meal occasions).

In response, grocers need to define their operating models to fully harness their own capabilities while participating in third-party ecosystems to serve customers through different missions. Retailers should also seek to engage consumers where they are spending their time; whether on social channels, on content sites (for example, Eater magazine online), or in the metaverse, grocers need to be there.

Grocers must also quickly determine which components of their end-to-end e-commerce value chain they want to fully own as a core capability and what partners can provide. The answer will vary across the value chain as retailers assess where they can compete with distinctive offerings and where they have the requisite capabilities and resources. Efficiency and speed will be critical factors in deciding whether to invest in in-house solutions or partner with a third party. The market is likely to be segmented into large retailers with the resources to develop efficient in-house capabilities and smaller companies that must rely on third parties.

Implications for other industry players

While many of these recommendations are applicable to all grocery players, the rapid growth of e-commerce has significant additional implications for various players within the broader ecosystem. Besides Amazon, players such as Cornershop by Uber and DoorDash also offer marketplaces for shoppers. Investments continue to pour into instant delivery, with multiple players including Instacart, Gopuff, Gorillas, and JOKR now testing and offering delivery in less than 30 minutes. More first-party services are also emerging: Gopuff and DashMart by DoorDash are now playing in this space with their own warehouse-based grocery-delivery models.

Digital-native third-party marketplaces have notched significant growth in the past few years. They now have an opportunity to use their technical capabilities to ensure their retail partners have access to the best digital technology and user experiences. Another priority will be improving efficiency and reducing costs to customers through the accelerated adoption of technology (such as microfulfillment), increased batching of grocery e-commerce orders on delivery milk runs, and shared resources in delivery across vehicles and drivers. Marketplaces can also unlock additional value pools (such as advertising) that used to flow to media players outside the sector—for example, by luring spending from traditional media channels such as television ads to grocery marketplace advertising via retail media networks.

Pure-play, first-party online grocers have the opportunity to make headway by deploying different delivery models (such as a scheduled, milk-run approach), expanding their offerings to address more need states and occasions, and further distinguishing themselves from traditional competitors (for example, through subscription models). They can also differentiate their offerings by assortment authority (including breadth, depth, and brands covered) and experiment with adopting social-first, video-first offerings to engage consumers.

Despite the substantial growth of online grocery and the increased number of players, the market truly is on the verge of its next transformation. Executives should recognize that the leaders of today are not guaranteed to be winners tomorrow. Retailers that take decisive action and make strategic investments today will be well positioned to carve out a profitable position for the future.

Download the full report here .

Vishwa Chandra is a partner in McKinsey’s San Francisco office, Prabh Gill is an associate partner in the Vancouver office, Sajal Kohli is a senior partner in the Chicago office, where Kumar Venkataraman is a partner and Janice Yoshimura is a consultant; Varun Mathur is an associate partner in the Austin office.

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

Published: May 14, 2024

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

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

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

Not convinced?

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

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Social Media and Online Shopping — Today's Landscape

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

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

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

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

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

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

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

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

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

1. Seamless In-App Shopping Experiences

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

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

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

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

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

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

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

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

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

World Market wins with shoppable Instagram posts.

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

Image Source

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

Gift Delivery also saw great success using shoppable videos.

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

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

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

Are you wondering which platforms you should focus on?

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

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

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

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

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

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

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

Examples of short video content you can create include:

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

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

Irresistible Me lets its hair down on TikTok.

Irresistable Me is a hair extension boutique that makes short videos on TikTok.

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

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

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

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

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

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

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

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

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

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

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

So how can brands put this to use?

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

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

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

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

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

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

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

4. Social Media Becomes a Top Search Channel

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

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

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

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

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

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

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

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

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

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

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

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

5. Live Streaming Continues to Grow

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

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

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

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

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

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

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

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

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

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

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

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

Aldo uses live shopping mixed with influencers to drive engagement.

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

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

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

6. Augmented Reality is Enhancing Shopping Experiences

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

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

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

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

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

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

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

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

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

They could even share the looks with their friends.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Craft engaging, authentic live sessions.

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

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

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

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

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

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

Use giveaways to increase reach for quizzes and improve personalization.

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

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

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

Don’t just generate customers — grow a community.

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

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

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

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

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

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

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

Community & Connection = Clicks & Conversions

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

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

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

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

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

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