literature review on online and offline shopping

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Drivers of shopping online: a literature review

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

Entradas no índice

Keywords: , texto integral, 1. introduction.

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

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

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

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

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

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

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

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

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

2. Perceived benefits in online shopping

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

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

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

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

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

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

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

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

3. Perceived risk in online shopping

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

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

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

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

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

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

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

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

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

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

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

4. Exogenous factors

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

4.1. Consumer traits

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

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

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

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

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

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

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

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

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

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

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

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

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

4.2. Situational factors

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

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

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

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

4.3. Product characteristics

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

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

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

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

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

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

4.4. Previous online shopping experiences

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

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

5. Conclusion

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

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

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

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

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

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Ana Teresa Machado , «Drivers of shopping online: a literature review» ,  Comunicação Pública [Online], Vol.2 nº4 / nº3 | 2006, posto online no dia 30 outubro 2020 , consultado o 15 abril 2024 . URL : http://journals.openedition.org/cp/8402; DOI : https://doi.org/10.4000/cp.8402

Ana Teresa Machado

Escola Superior de Comunicação Social Instituto Politécnico de Lisboa

[email protected]

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• Published: 03 February 2024

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

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

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

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• Business and management

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

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Giang-Do Nguyen & Thu-Hien Thi Dao

Introduction

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

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

O2O and O4O.

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

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

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

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

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

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

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

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

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

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

Literature review

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

Online commerce

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

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

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

Theoretical development and research hypotheses

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

Research model.

Effort expectancy (EFE)

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

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

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

Performance expectancy (PFE)

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

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

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

Social influence (SOI)

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

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

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

Facilitating conditions (FCC)

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

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

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

Innovativeness (INO)

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

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

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

Continuance Intention (COI)

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

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

Moderating effects of innovativeness

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

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

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

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

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

Methodology

Instrument development.

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

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

Data collection

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

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

Analysis and results

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

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

Measurement model

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

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

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

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

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

Structural model

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

PLS Analysis Result.

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

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

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

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

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

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

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

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

Theoretical Contribution

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

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

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

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

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

Managerial Implication

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

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

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

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

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

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

Limitations and future research directions

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

Data availability

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

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The impact of COVID-19 pandemic restrictions on offline and online grocery shopping: New normal or old habits?

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• Philipp Brüggemann   ORCID: orcid.org/0000-0001-5755-6497 1 &
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The COVID-19 pandemic is changing future trends in retailing and e-commerce immensely. Recent research revealed a considerable increase in online grocery shopping (OGS) since the COVID-19 pandemic started. In addition, current statistics indicate a steady increase in OGS over the coming years. Despite this, less is known about whether consumers’ behavior is evolving to a ‘new normal’ or returning to ‘old habits’ after pandemic restrictions are withdrawn. To address this research gap, we operationalize and empirically analyze offline and online purchasing behavior before, during, and after pandemic restrictions. To this end, we use an extensive household panel dataset of 17,766 households reporting their purchases before, during and after the first lockdown in Germany in 2020. Our findings on offline purchase patterns show that while more than 10% of the consumers avoided brick-and-mortar retail during the lockdown, almost all of them returned afterwards. Looking at online purchase patterns, we find high volatility in OGS for both separate and combined purchase patterns. The combined analysis of purchase patterns (online and offline), reveal that households that avoided brick-and-mortar stores during the lockdown did not switch (completely) to the online channel. Based on our findings that consumers are still in reach of brick-and-mortar retailers we suggest offline retailers act now to retain their customers, e.g., by offering competitive benefits in their stores. OGS operators should urgently analyze the customer churn revealed in this analysis and derive measures to retain them. They do not seem to have succeeded in retaining their customers and keeping them loyal to the online channel during the entire observation period. Even worse, they also failed to convince consumers to use OGS who stayed at home due to the lockdown. The fact that a total of 96.75% of the observed consumers did not practice OGS at all shows that OGS in Germany was in 2020 still in its infancy. However, as current statistics forecast a further substantial increase in OGS over the coming years, our results are increasingly relevant for brand managers, brick-and-mortar retailers and OGS providers in Germany and beyond.

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The Impact of Pandemic Restrictions on Offline and Online Grocery Shopping Behavior - New Normal or Old Habits?

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How attitudes and purchases differ between also-online versus offline-only grocery shoppers in online and offline grocery shopping

Philipp Brüggemann & Koen Pauwels

Avoid common mistakes on your manuscript.

1 Introduction

The COVID-19 pandemic has changed the world of retailing dramatically over a short period of time [ 1 ]. On the one hand, these changes pose immense challenges for brand managers and retailers. On the other hand, it also provides new opportunities for retailing and e-commerce [ 2 ]. For instance, consumers’ habits have been strongly restricted by governmental policies, especially by lockdowns. Brick-and-mortar retail was limited to the most necessary during this period. While grocery stores predominantly stayed open, other retail sectors did not. The resulting decline in sales and even insolvencies of entire businesses will change whole business sectors [ 3 – 5 ]. These transformations will, in turn, also affect the future of grocery shopping, e.g., in increasing opportunities to buy groceries online [ 6 – 8 ]. While the consequences of the COVID-19 pandemic are dramatically and existentially challenging, it also creates scope for new practices and emerging business models. Roggeveen and Sethuraman [ 1 ] suggest that online grocery shopping (OGS) will increase as a result of the COVID-19 pandemic. Additionally, they expect changes in consumer behavior in the future. Recent statistics support this expectation, as worldwide data show an increase in OGS turnover, especially since the COVID 19 pandemic [ 9 ]. Furthermore, latest forecasts based on these data point to a continuous increase in OGS over the coming years [ 9 ].

Within the COVID-19 pandemic OGS offers new opportunities to gain competitive advantages [ 10 ]. However, companies need to know how to act on these developments. Despite its high relevance, there is still a lack of research in analyzing different consumers’ purchase patterns before, during, and after pandemic restrictions. In particular, understanding the impact of pandemic restrictions (e.g., lockdown) on consumer buying behavior is important to help brand managers and retailers rapidly respond to these immense changes. In fact, “ignoring trends can give rivals the opportunity to transform the industry” [ 11 ].

Numerous studies have already confirmed the increase in use of OGS during the COVID-19 pandemic worldwide [ 12 – 22 ]. Additionally, Brüggemann and Pauwels [ 23 ] found significant differences between also-online and offline-only grocery shoppers in both consumers’ attitudes and purchase behavior. However, it has not yet been investigated how offline and online grocery purchases are affected by pandemic restrictions on household level. Verhoef et al. [ 24 ] synthesize empirical and conceptual research on effects of COVID-19 on retailing. The authors state that COVID-19 changes consumer needs and behavior and that retailers need to know how to respond. In addition, the authors conclude that more structured methods should be used to examine future trends in OGS due to the pandemic. With our research, we provide such a structured method for analyzing changes in consumer behavior. Taken toghether, we identify a lack of research on whether a ‘new normal’ will emerge or whether consumers will return to ‘old habits’ in both offline and online grocery shopping.

For instance, consumers who were already familiar with OGS before COVID-19 pandemic may also shop online during and after pandemic restrictions. Furthermore, it is also conceivable that consumers who previously purchased offline-only might also shop online during pandemic restrictions, but return to their old habits afterwards. Conversely, some consumers might continue to purchase online after pandemic restrictions, at least to some extent.

With this research we provide new insights on how consumers behave before, during, and after pandemic restrictions, both when shopping for groceries offline and online. For this purpose, we use household panel data from 2020. We observe offline and online purchases before, during, and after the first lockdown of the COVID-19 pandemic in Germany from March 22, 2020 to May 5, 2020. We examine changes in offline and online purchases of 17,766 households by conducting structured analyses of household purchasing behavior. This approach provides for the first time sophisticated insights into how consumers’ offline and online grocery shopping behavior evolved before, during, and after pandemic restrictions. Taken together, we address the following research question:

How does consumer purchase behavior evolve during and after pandemic restrictions in terms of online and offline grocery shopping?

2 Theoretical background

2.1 covid-19 and pandemic restrictions.

The COVID-19 virus first emerged in China in December 2019 and has spread rapidly throughout the world. The virus has killed over 1.6 million people and sickened over 76 million people [ 25 ]. In Germany the first COVID-19 case was detected in January 2020. To limit the spread of the virus, multiple interventions have been put in place. In addition to mandatory masks and social distancing, temporary lockdowns were imposed. The first lockdown in Germany was implemented from March 22, 2020 to May 5, 2020. During this time people were forced to stay at home. Only with a valid reason (e.g., for work, grocery shopping, or doctor’s appointment) it was allowed to leave one’s home. The supermarkets remained open during the lockdown to ensure households’ supply [ 26 ].

Figure  1 shows the three time intervals in 2020 that are relevant for this study. In previous research on purchase patterns before, during, and after pandemic restrictions, Brüggemann and Olbrich [ 27 ] utilized data from two years. Here, the observed time periods are of different lengths. On the one hand, this also includes households that purchase irregularly. On the other hand, there is a risk that these households randomly did not shop during the lockdown. Therefore, in this research we consider periods of equal length for the periods before, during, and after the lockdown. Based on this, we define the first observation period from February 10, 2020 to the start of the first lockdown on March 22, 2020. The second period includes the first lockdown, which lasted until May 5, 2020. Finally the third interval considers the period after the lockdown until July 14, 2020. As a result, we obtain three observation periods, each of six weeks in length.

Periods before, during, and after pandemic restrictions

German consumers were able to shop in brick-and-mortar stores during the entire observation period. Given the unchanged accessibility of those stores, we are particularly interested in how this lockdown nevertheless affect grocery shopping, especially with regard to the choice between online and offline channels. By differentiating the three observation periods, we can investigate how resilient households are to changes in distribution channels. To investigate this in a well-founded manner, we first review the relevant literature on grocery retailing in the following section.

2.2 Grocery retailing

Grocery retailing, as well as research in the field, has a long tradition [e.g., 28 – 30 ]. However, retailing is still constantly evolving. According to Guha et al. [ 31 ], the increased use of artificial intelligence will transform commerce, e.g., in order to increase both in-store and online sales, to improve the effectiveness of supply chains, or to make the payment more efficient. As well as current technological developments, the COVID-19 pandemic is also a driver of change. For example, Roggeveen and Sethuraman [ 1 ] expect that the COVID-19 pandemic will cause consumers to become accustomed to new ways of shopping, such as OGS and home delivery. In addition, the authors state that people may be more likely to work at home in the future and thus be more likely to book online sports courses or to buy an in-home bike, for example. Overall, a major change in consumer behavior when purchasing groceries is expected as a result of the COVID-19 pandemic [ 32 , 33 ].

Both the accelerating development and the use of new technologies in retail are also playing increasingly important roles in the channel structure. Whereas a few decades ago products were sold almost mainly via brick-and-mortar stores or catalogs, consumers have become accustomed to online ordering and home delivery. This development is especially facilitated by technical progress. Well-known examples of large e-commerce companies in the retail sector are Amazon and Alibaba [ 34 ].

For grocery shopping, the online channel has also been discussed in numerous publications over the past decades [ 35 – 41 ]. However, before the COVID-19 pandemic, the development of OGS was slow [ 42 – 44 ]. There is consensus in the literature that COVID-19 has accelerated this development, at least short-term [ 13 , 45 , 46 ]. However, it is not implied that this strong increase in OGS will continue after the COVID-19 pandemic.

According to East [ 47 ], it is likely that the post-pandemic increase in share of OGS will return to pre-pandemic levels, especially for discount purchasing. Gruntkowski and Martinez [ 48 ] found that the COVID-19 pandemic led to a reduction of consumers expected risk regarding OGS. Furthermore, Tyrväinen and Karjaluoto [ 49 ] used a meta-analysis to examine OGS before and after the COVID-19 pandemic. They find that perceived usefulness and attitudes before the COVID-19 pandemic have a strong influence on intention to use OGS. The authors conclude that the increasing adoption of OGS is not due to higher expected usefulness or more positive attitudes, but that consumers were driven to use OGS by the COVID-19 pandemic. Despite this extensive meta-analysis, the results are limited and further research is needed. For example, the meta-analysis data is based only on questionnaires, not on purchase data. In addition, the authors did not identify any publication that looked at the same households both before and after COVID-19 restrictions.

Shen et al. [ 46 ] investigate pandemic effects on OGS usage as well. They studied online purchase patterns before, during, and after COVID-19. The authors identified a sharp increase in OGS use among consumers during the COVID-19 pandemic. After the pandemic, consumers still report a higher use of OGS than before, though Shen et al.’s [ 46 ] data suggests a decrease in usage, especially in the long-term. However, these results are limited, too. The findings are merely based on survey data, and most of the respondents are habitants of the same city in the United States.

2.3 Research framework and contribution

After shedding light on previous literature, we find that a broad analysis of online and offline grocery shopping that considers equal periods before, during, and after pandemic restrictions with real purchase data does not yet exist. Nevertheless, it is enormously relevant for companies to obtain information on changes in consumer preferences and buying behavior in online and offline channels at an early stage in order to respond accordingly.

With this study, we address this research gap by examining purchases by households before, during, and after the first lockdown in Germany. Our contribution is to identify different purchase patterns online and offline before, during, and after the first lockdown in Germany in order to determine whether different consumer groups react differently to such restrictions. We additionally broaden our contribution by looking at the online and offline purchase patterns combined. This allows us to examine how the same household behaves online and offline before, during and after the pandemic. In the next sections, we provide information about the data operationalization and descriptive statistics and describe the resultung purchase patterns.

3 Empirical analysis

3.1 data operationalization and descriptive statistics.

We use household panel data from 2020 provided by the GfK . Footnote 1 The data includes purchases from the product groups chocolate bars, coffee, hair shampoo, and laundry detergent from more than 30,000 German households. The market research institute GfK compiles their household panel according to several criteria (e.g., household size, household location, social status, nationality, number of children,…). Thus, they provide a representative data set for German households with regard to these criteria [ 50 ]. Moreover, it is recorded whether a purchase were made offline or online. Online and offline channels can belong to the same retailer or to different retailers. We use this differentiation because we are particularly interested in the decision of households to shop offline or online.

Figure  2 shows the volume-based share of OGS per month based on the household panel data we use for this empirical analysis. Here, we find a comparatively strong increase in the volume-based share of OGS during the lockdown. However, the figure also shows that after the first lockdown the volume-based share of OGS decreases. Overall, the volume-based share of OGS seems to be higher after the lockdown than before. The information provided by this figure is interesting but limited. We cannot see here if/how the households’ purchase patterns change before, during, and after the lockdown. This is where our empirical analysis comes in.

Volume based share of OGS per month (calculation by the authors)

We analyze the purchase data under consideration of the first lockdown in Germany from March 22, 2020 to May 5, 2020. To allow for a reliable analysis, we consider identical periods before, during, and after the lockdown. Since the lockdown lasted six weeks, we consider the purchases six weeks before and six weeks after this lockdown (see Fig.  1 ). Additionally, only households that purchased both before and after the lockdown are included in the analysis to handle the problem of panel mortality. We can thus observe 17,766 households reporting their offline and online purchases during the eighteen-week observation period. It should be emphasized that this reduction of the basic sample (from more than 30,000 households to 17,766 households) may affect the representativeness regarding all German households described above.

Table 1 provides some key figures for total purchases (offline and online) as well as for offline and online purchases separately. First of all, it can be noted that we examine purchases over a period of six weeks in each period. In the case of total purchases (offline and online), it is noticeable that some households have not made any purchases during the lockdown (from 17,766 to 15,873). The quantity sold, volume and value fell slightly during the lockdown, but slightly rose again afterwards. Value per quantity decreased slightly during the lockdown, but also increased to the pre-lockdown level after the lockdown. Overall, value per kilogram is even higher after the lockdown than before. The offline purchases show a very similar picture, not least because they also largely determine the total purchases due to their high proportion.

Looking at online purchases, we see that the number of households buying online increases during the lockdown (from 208 to 284) and remains almost at this level after the lockdown (283). The quantity sold, volume and value show that after the increase during the lockdown, value decrease slightly again. Overall, these descriptive data indicate a moderate increase in OGS due to the lockdown. Interestingly, the value per quantity and the value per kilogram decrease during this period. Compared to offline purchases, it can be seen that both value per quantity and value kilogram are considerable higher online than offline. In order to be able to consider product group-specific differences in the later discussion of the empirical results, we also provide information on the structure of the data in terms of the four product groups we use. Table 2 shows the shares of the four product groups in the data we use.

For total purchases (offline and online) and offline purchases, the shares are mostly stable before, during and after the lockdown, with the exception of coffee. For coffee, the data suggest a slight increase in the share of coffee in the data during and after the lockdown. For online purchases, the share of coffee is surprisingly relatively high. Here, a positive trend can be seen during and after the lockdown. This also partly explains the significantly higher value per quantity and value per kilogram as coffee is comparatively more expensive than chocolate bars, hair shampoo, and laundry detergent. Furthermore, a positive trend can also be seen in the data for online purchases of hair shampoo. These findings must be considered when interpreting the results. In the next section, we present the operationalization of the purchase patterns.

3.2 Purchase patterns

Figure  3 shows the procedure for separating the household panel data to calculate online, offline, and combined purchase patterns. In the first investigation, we separate households between online and offline purchases, derive online and offline purchase patterns, and analyze them separately, respectively for the three periods before, during, and after the lockdown. In the second investigation, we combine selected offline and online purchase patterns to observe the behavior of households online and offline simultaneously.

Online, offline and combined purchase patterns

For both offline and online purchases, we derive eight purchase patterns to observe consumers’ purchase behavior before, during, and after the lockdown. To identify the different purchase patterns, we code them by attributing to each household ‘0’ for no purchases and ‘1’ for at least one purchase in each observation period (before, during, and after lockdown). For instance, a coding of 1-0-0 means that the related household only purchased before the lockdown and neither during nor after. Figure  4 illustrates the calculation of purchase patterns using three exemplary purchases from three different households.

Calculation of online and offline purchase patterns

Using this procedure, we obtain eight online and eight offline purchase patterns. The following Table 3 provides an overview and detailed descriptions of the 16 resulting purchase patterns. In the next sections, the empirical findings on online and offline purchase patterns are examined separately. This is followed by an empirical analysis of the combined purchase patterns.

3.3 Empirical findings

3.3.1 offline purchase patterns.

The offline purchase patterns before, during, and after the lockdown are shown in Table 4 . Based on these purchase patterns, different descriptions are specified (see second column). The majority of consumers purchased offline before, during, and after the lockdown (88.88%; 15,777). During the lockdown, 10.65% (1890) did not purchase offline. The fact that these consumers return to brick-and-mortar retail after the lockdown shows that they (at least partially) return to their ‘old habits’ and do not completely change their behavior to OGS.

3.3.2 Online purchase patterns

Table 5 shows the results regarding online purchase patterns. Here, the empirical results show a much more differentiated behavior than in offline purchasing behavior. Among the (also-)online purchasing households, 18.86% (109) purchased online only before the lockdown (‘online 1-0-0’). Exclusively during the lockdown, 29.58% (171) of (also-)online grocery shoppers purchased online (‘online 0-1-0’). In other words, nearly one-third of these households started shopping online during the first lockdown and stopped afterwards. Thus, the lockdown appears to have been a reason for these households to try OGS. However, they did not continue to use OGS afterwards.

After the lockdown, 25.95% (150) of (also-)online grocery shoppers chose OGS for the first time (‘Online 0-0-1’). On the one hand, this suggests that a lockdown increases the number of online shopping households only in the short term. On the other hand, there still is a substantial amount of fluctuation among OGS. Only a few households purchased (also-)online before and after the lockdown, but not during (6.06%; 35) (‘Online 1-0-1’). Other consumers started using OGS (8.48%; 49) (‘Online 0-1-1’) or stopped using it since the lockdown (2.60%; 15) (‘Online 1-1-0’). Only 8.48% (49) of (also-)online purchasing households bought groceries online before, during, and after the lockdown (‘Online 1-1-1’). In comparison with offline purchases, the (also-)online purchasing households represent only 3.25% (578).

Taken together, more than half of online shoppers try OGS, but then buy offline only again (51.04%; 295). Footnote 2 Furthermore, almost all of the observed households shop offline (again) after the lockdown (99.58%; 17,691). Footnote 3 While 35.99% (208) Footnote 4 of the observed (also-)online shopping households shopped groceries online before the lockdown, 48.96% (283) Footnote 5 shopped for groceries online afterwards. Overall, our results of the online grocery purchase patterns show strong dynamics in the use of OGS.

3.3.3 Combined purchase patterns

Beyond the separated analyses of offline and online purchase patterns, this section looks at combined purchase patterns. This allows us to observe how the households behave both online and offline before, during, and after pandemic restrictions. Regarding offline purchase patterns, the previous investigation suggests, that two patterns are almost solely relevant. Thus, in the further analysis, we focus on these two offline purchase patterns.

First, we consider households that made offline purchases continuously throughout the observation period (‘offline 1-1-1’). These households comprise 88.88% (15,777) of offline purchasing households. Second, we consider households that did not shop at brick-and-mortar stores during the lockdown (‘offline 1-0-1’). These households comprise 10.65% (1890) of offline buying households.

The previous results have revealed strong dynamics in the use of OGS. Therefore, in the further analysis we consider all eight online purchase patterns and combine each of them with the two offline purchase patterns mentioned above (‘offline 1-1-1’ and ‘offline 1-0-1’). Thus, sixteen combined purchase patterns emerge. The combined purchase patterns, their descriptions, quantity and share are shown in Table 6 .

The combination of purchase patterns shows that the majority of households shop groceries steadily offline and not at all online. This segment comprises 86.38% (15,346) of the observed households (‘offline 1-1-1 and online 0-0-0’). The second largest segment consists of households that did neither shop in brick-and-mortar stores nor online groceries during the lockdown (‘offline 1-0-1 and online 0-0-0’). This group comprises 10.37% (1842) of all observed households. This means that even the households that did not shop offline during the lockdown also predominantly did not switch to the online channel. This shows that the lockdown hardly caused households to switch from offline to online.

Only 0.89% (159) of the households used OGS during the lockdown and neither before nor after (‘offline 1-1-1 and online 0-1-0’). However, these households use OGS only in a complementary way, as they continued to shop offline.

Furthermore, 0.61% (109) of the observed households started shopping online after the lockdown (‘offline 1-1-1 and online 0-0-1’). However, these households continue to shop offline as well. The empirical results show that 0.50% (89) of the observed households have not shopped online since the lockdown (‘offline 1-1-1 and online 1-0-0’). These households have thus stopped using OGS during the observation period and have returned to purchase exclusively offline. Interestingly, only 0.12% (21) households made both online and offline purchases in each period (‘offline 1-1-1 and online 1-1-1’). Further purchase patterns with comparatively small numbers of cases can be seen in Table 6 . In the next chapter we will discuss the implications of our findings.

4 Implications

4.1 offline purchase patterns.

The results of this study clearly show that German grocery shopping habits we observed differ between offline and online channels. Regarding offline purchase patterns, we found that almost all of the observed households purchase groceries from brick-and-mortar retailers after the lockdown (99.58%; 17,691). Footnote 6 It is particularly relevant for retailers that 10.65% (1890) of the observed households avoided brick-and-mortar retail stores during the lockdown, but returned to buy groceries in brick-and-mortar stores afterwards. Hence, offline grocery shopping is mainly affected by a lockdown in the short term and consumers are still in reach of brick-and-mortar retailers. Our household panel data only show a comparatively strong increase in the share of OGS during the lockdown (see Fig.  2 ). Moreover, current forecasts for Germany and worldwide show a substantial increase in OGS over the coming years, at least until 2027 [ 9 , 52 ]. This indicates a risk that offline retailers will increasingly lose market shares to online grocery retailers. Thus, brick-and-mortar retailers should now focus on generating and communicating competitive advantages over online providers (e.g., via price, experience, or service) as long as this is feasible for them. For instance, consumers can haptically experience the products in brick-and-mortar retail, which is hardly possible in online stores [ 52 ].

After all, if retailers lose households to online grocery retailers, influencing and winning back these consumers will be much more challenging. Offline retailers should also think about adding an online channel in order to reduce their disadvantages compared to online providers (e.g., shopping at anytime and anywhere as well as home delivery).

4.2 Online purchase patterns

Compared to the offline purchase patterns, we found considerably more divergent online purchase patterns. Since 51.04% (295) Footnote 7 of (also-)online purchasing consumers bought groceries online before or during the lockdown but not afterwards, OGS providers should critically evaluate their customer retention measures. OGS providers need to identify these consumers as well as possible reasons for their suspended OGS use.

However, we also find consumers who started using OGS during the observation period. For instance, 34.43% (199) Footnote 8 of (also-)online grocery shoppers began shopping for groceries online during or after the lockdown. This high fluctuation presents both threats and opportunities for brand managers as well as for retailers. For instance, there is a risk that actual customers will either switch to competitive online suppliers or satisfy their needs in brick-and-mortar stores (again). The high dynamic of OGS can also persuade new households to shop for groceries online. The fact that more than 96% of households made purchases exclusively offline throughout the observation period shows that OGS was not very widespread in Germany in 2020. However, this shows the immense untapped potential for OGS. As current forecasts predict a further increase in the share of OGS in grocery retailing over the coming years, both in Germany and worldwide [ 9 , 51 ], OGS providers should work now on improving customer loyalty in order to benefit from this trend in the long term.

4.3 Combined purchase patterns

The combined purchase patterns reveal that households who avoided brick-and-mortar stores during the lockdown hardly switched to OGS. Thus, 10.37% (1842) of all households neither shopped offline nor started OGS during the lockdown. This means that almost none of these households started buying groceries online during or after the lockdown (0.11%; 20). Footnote 9 For OGS providers, this shows how tightly households are still holding on to their ‘old habits’ instead of trying new ways of shopping. Therefore, this insight is relevant for both OGS providers and brick-and-mortar retailers.

Online grocery shop operator need to understand that changing consumer habits is a process that often can only be accomplished over an extended period of time. Hence, OGS providers are faced with the difficulty to persuade households to try OGS. Targeted incentives should be created and communicated, e.g., welcome gifts or additional benefits such as automatic shopping lists or deposit acceptance. The high volatility in the use of OGS should be considered by the providers of OGS in their strategy to attract additional customers. On the one hand, households must be convinced of the use of OGS and its advantages through communication (e.g., time savings due to home delivery). On the other hand, however, the customers acquired must also be tied to OGS in the long term. It seems the latter has not been successful during the observation period, since many households discontinue OGS.

For brick-and-mortar retailers, the data indicate that the households are sticking to or returning to their ‘old habits’. Moreover, our results for the combined purchase patterns reinforce the results of the separate purchase patterns. Offline retailers still have access to households through their stores, given that almost all customers at least returned to offline shopping during the observation period. Retailers need to act now and convince customers of the benefits of brick-and-mortar stores in the future. The results of the combined purchase patterns clearly show that households do not immediately switch to the online channel even during a lockdown. Therefore, the need for brick-and-mortar stores is demonstrated and their existence justified. Furthermore, as OGS become more widespread worldwide [ 51 ], the pressure on brick-and-mortar stores will increase. For this reason, retailers should pay particular attention to customer loyalty.

5 Concluding remarks

5.1 summary.

This study contributes to a better understanding of how consumer purchasing behavior evolves during and after pandemic restrictions on online and offline grocery shopping. During the first lockdown in Germany, 10.37% (1842) of the consumers avoided brick-and-mortar stores. The offline purchase patterns are characterized by ‘old habits’, since after the lockdown almost all of the observed consumers visit brick-and-mortar stores again. Thus, consumers did not completely switch from offline to online channel as a result of pandemic restrictions. Our results provide valuable insights for brick-and-mortar retailers, at least in Germany. Our empirical results indicate that it is still possible for retailers to influence customers in their own stores, since almost all of the consumers observed still shop (also-)offline. Particularly against the backdrop of the predicted increase in OGS over the coming years in Germany and worldwide [ 9 , 51 ], brick-and-mortar retailers should now develop strategies to retain their customers in the long term. They can seek to do this, for example, by gaining competitive advantages over online retailers (e.g., through price, experience, or service) or by adding an online channel. If consumers are accustomed to online channels and increasingly purchase their groceries online, it will become much more challenging for brick-and-mortar retailers to reach out to these consumers. Fundamentally, it is key for these companies to anticipate and act on these game-changing trends to shape new standards and be successful in the long term [ 11 ]. While our 2020 data for Germany show a high momentum in OGS with still relatively low shares of OGS (see Fig.  2 ), the general trend in Germany and worldwide points to an increase in OGS in the coming years [ 9 , 51 , 53 ]. As a result, the competitive pressure on brick-and-mortar stores will continue to rise, making it particularly necessary for them to improve customer retention.

Our findings on OGS show an ongoing process of change due to a high level of dynamism in online purchase patterns. During the observation period, the buying behavior of (also-)online purchasing consumers changes in different ways. Some consumers started OGS before or during the lockdown, but then switched back to brick-and-mortar stores. Other consumers started OGS before or during the lockdown and maintained this shopping behavior afterwards. These findings indicate a ‘new normal’–at least for certain consumer segments.

Looking at combined online and offline purchasing behavior, we find that households not using offline stores during the lockdown did not switch to the online channel. So there is no entire shift from the offline to the online channel due to pandemic restrictions. Moreover, we also find high volatility in OGS among combined purchase patterns. Online grocery shop operators do not seem to have succeeded in retaining their customers during the observation period. Even worse, they also failed to convince consumers to use OGS who stayed at home due to the lockdown.

In conclusion, this study provides valuable insight into consumer purchasing behavior in Germany both offline and online before, during, and after pandemic restrictions. In particular, brand managers and retailers should take these findings to consider changes in consumers’ behavior in more detail in order to derive measures to increase loyalty. In addition, these new insights provide deeper knowledge of consumer behavior between offline and online channels, as well as consumer responses to crises.

Additionally, the empirical results indicate that it will be crucial for offline retailers to retain their customers, since those who have switched completely to OGS can hardly be reached by offline retailers without enormous efforts. Fortunately, however, we have for now hardly found any households that exclusively use the online channel to purchase groceries.

For online grocery shop operators, it is key to not only continue to attract new customers, but to retain existing customers. The possibly high acquisition costs for new customers (e.g., due to ads, welcome gifts, or discounts) are pointless if customers are not bound to the provider in the long term. In this case the provider will not be able to compete in the long term.

This research differs from previous publications in several aspects. While recent publications, e.g., by Tyrväinen and Karjaluoto [ 49 ], Gruntkowski and Martinez [ 48 ], Shen [ 46 ], Younes et al. [ 54 ], Gomes and Lopez [ 55 ] and Eriksson and Stenius [ 56 ] are based on survey data, we provide an empirical analysis based on real purchase data. This allows us to observe purchases of the same households before, during and after the pandemic to show the evolution of offline and online grocery shopping. Morever, to the best of our knowledge, this research is the first analyzing offline and online channel choices before, during, and after the pandemic using an extensive dataset. In terms of content, we provide several new insights into how German households’ behavior changed differently online and offline in 2020.

5.2 Limitations and further research directions

Even this study has some limitations. First, it cannot be clearly proven that the changes in the purchasing behavior of offline and online purchase patterns are caused by the COVID-19 pandemic. The processes of change in consumer purchasing behavior may be also driven (at least in part) by digitization, increasing online offers, and changing demands.

Second, we cannot draw a detailed picture of shifts in demand between offline and online purchases. Looking at Fig.  2 , we can see a comparatively strong increase in the still low volume-based market share of OGS during the lockdown. Moreover, Table 1 shows an increase in the number of online shopping households, quantity sold, volume and value over time. However, while this study focuses on consumer behavior in terms of offline and online channel use, further research should, for example, analyze volume-based purchase patterns in more detail.

Third, Table 2 reveals some differences in the proportions of the observed product groups between online and offline channels. Furthermore, Table 2 shows, for example, that before, during, and after the lockdown, online purchases of coffee increased proportionately. Further research should therefore analyze how online and offline grocery shopping develops differently in terms of product groups and what impact this evolution has on channel performances. Thus, further research may run cross-sectional analyses to reveal differences between products groups.

Fourth, the data in this analysis are exclusively from German households. However, further analysis should go beyond that and compare the results with data from other countries. Thus, further research should complement both cross-national and country-specific insights to OGS research.

Fifth, our data only covers the period up to 2020. Recent statistics show that OGS turnover in Germany increased by 21.21% from 2020 to 2021. Interestingly, the data from 2021 to 2022 shows a slight decrease in OGS turnover of 1.14%. From 2022 to 2027, the annual growth rate in Germany is predicted to exceed on average 15.85% [51]. Worldwide, a very similar annual turnover growth rate of 14.92% is predicted from 2021 to 2027 [ 9 ]. Overall, these key figures show the very dynamic development of OGS both for Germany and worldwide. Moreover, these key figures underline the relevance of this topic, for both online grocery store operator and brick-and-mortar retailer. Based on this, we expect our results to be highly relevant for Germany and beyond. However, in further research, the development of online and offline grocery shopping need to be analyzed with more recent data.

Finally, further research should investigate whether consumers with different purchase patterns differ, e.g., in terms of consumer characteristics and demographics, attitudes towards price consciousness or brand preference as well as purchase behavior and attitudes towards organic or fair trade products.

The source of the data is GfK Consumer Panels & Services .

Considered purchase patterns: ‘Online 1-0-0’, ‘Online 0-1-0’, and ‘Online 1-1-0’.

Considered purchase patterns: ‘Offline 0-0-1’, ‘Offline 1-0-1’, Offline 0-1-1- ‘, and ‘Offline 1-1-1’.

Considered purchase patterns: ‘Online 1-0-0’, ‘Online 1-0-1’, ‘Online 1-1-0’, and ‘Online 1-1-1’.

Considered purchase patterns: ‘Online 0-0-1’, ‘Online 1-0-1’, Online 0-1-1- ‘, and ‘Online 1-1-1’.

Considered purchase patterns: ‘offline 0-0-1’, ‘offline 1-0-1’, ‘offline 0-1-1’, and ‘offline 1-1-1’.

Considered purchase patterns: ‘online 1-0-0’, ‘online 0-1-0’. and ‘online 1-1-0’.

Considered purchase patterns: ‘online 0-0-1’ and ‘online 0-1-1’.

Considered purchase patterns: ‘offline 1-0-1 and online 0-0-1’ and ‘offline 1-0-1 and online 0-1-1’.

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Brüggemann, P., Olbrich, R. The impact of COVID-19 pandemic restrictions on offline and online grocery shopping: New normal or old habits?. Electron Commer Res 23 , 2051–2072 (2023). https://doi.org/10.1007/s10660-022-09658-1

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DOI : https://doi.org/10.1007/s10660-022-09658-1

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Asia Pacific Journal of Marketing and Logistics

ISSN : 1355-5855

Article publication date: 28 April 2022

Issue publication date: 9 March 2023

This study conducts a systematic literature review to synthesize the extant literature primarily on “online shopping consumer behavior” and to gain insight into “What drives consumers toward online shopping”.

Design/methodology/approach

The authors followed guidelines for systematic literature reviews with stringent inclusion and exclusion criteria. The review is based on 79 research papers published from 2000 to 2020 in 21 reputed peer-reviewed international journals. The papers were analyzed and synthesized based on their defining characteristics, methodologies, major constructs and themes addressed.

The literature synthesis indicated that consumers have to make a trade-off between 11 perceived benefits and six perceived sacrifices to improve their net perceived value before making the final decision to adopt online shopping. It is important to decode these factors as they could improve both the functional and recreational value of the shopping experience for online consumers, resulting in an improvement in conversion rates from a prospect to the final purchase at e-stores. This could improve turnover as well as profits for the e-tailers.

Originality/value

This study pioneers to consolidate these factors through the lens of the value adoption model. This study also suggests insightful directions for further research perspectives in the online context from both consumers' and retailers' perspectives.

• Online shopping
• Perceived value
• Perceived sacrifices
• Systematic literature review
• Value-based adoption model (VAM)

Srivastava, A. and Thaichon, P. (2023), "What motivates consumers to be in line with online shopping?: a systematic literature review and discussion of future research perspectives", Asia Pacific Journal of Marketing and Logistics , Vol. 35 No. 3, pp. 687-725. https://doi.org/10.1108/APJML-10-2021-0777

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The impact of COVID-19 pandemic restrictions on offline and online grocery shopping: New normal or old habits?

Philipp brüggemann.

FernUniversität in Hagen, Universitätsstraße 11, 58097 Hagen, Germany

Rainer Olbrich

The COVID-19 pandemic is changing future trends in retailing and e-commerce immensely. Recent research revealed a considerable increase in online grocery shopping (OGS) since the COVID-19 pandemic started. In addition, current statistics indicate a steady increase in OGS over the coming years. Despite this, less is known about whether consumers’ behavior is evolving to a ‘new normal’ or returning to ‘old habits’ after pandemic restrictions are withdrawn. To address this research gap, we operationalize and empirically analyze offline and online purchasing behavior before, during, and after pandemic restrictions. To this end, we use an extensive household panel dataset of 17,766 households reporting their purchases before, during and after the first lockdown in Germany in 2020. Our findings on offline purchase patterns show that while more than 10% of the consumers avoided brick-and-mortar retail during the lockdown, almost all of them returned afterwards. Looking at online purchase patterns, we find high volatility in OGS for both separate and combined purchase patterns. The combined analysis of purchase patterns (online and offline), reveal that households that avoided brick-and-mortar stores during the lockdown did not switch (completely) to the online channel. Based on our findings that consumers are still in reach of brick-and-mortar retailers we suggest offline retailers act now to retain their customers, e.g., by offering competitive benefits in their stores. OGS operators should urgently analyze the customer churn revealed in this analysis and derive measures to retain them. They do not seem to have succeeded in retaining their customers and keeping them loyal to the online channel during the entire observation period. Even worse, they also failed to convince consumers to use OGS who stayed at home due to the lockdown. The fact that a total of 96.75% of the observed consumers did not practice OGS at all shows that OGS in Germany was in 2020 still in its infancy. However, as current statistics forecast a further substantial increase in OGS over the coming years, our results are increasingly relevant for brand managers, brick-and-mortar retailers and OGS providers in Germany and beyond.

Introduction

The COVID-19 pandemic has changed the world of retailing dramatically over a short period of time [ 1 ]. On the one hand, these changes pose immense challenges for brand managers and retailers. On the other hand, it also provides new opportunities for retailing and e-commerce [ 2 ]. For instance, consumers’ habits have been strongly restricted by governmental policies, especially by lockdowns. Brick-and-mortar retail was limited to the most necessary during this period. While grocery stores predominantly stayed open, other retail sectors did not. The resulting decline in sales and even insolvencies of entire businesses will change whole business sectors [ 3 – 5 ]. These transformations will, in turn, also affect the future of grocery shopping, e.g., in increasing opportunities to buy groceries online [ 6 – 8 ]. While the consequences of the COVID-19 pandemic are dramatically and existentially challenging, it also creates scope for new practices and emerging business models. Roggeveen and Sethuraman [ 1 ] suggest that online grocery shopping (OGS) will increase as a result of the COVID-19 pandemic. Additionally, they expect changes in consumer behavior in the future. Recent statistics support this expectation, as worldwide data show an increase in OGS turnover, especially since the COVID 19 pandemic [ 9 ]. Furthermore, latest forecasts based on these data point to a continuous increase in OGS over the coming years [ 9 ].

Within the COVID-19 pandemic OGS offers new opportunities to gain competitive advantages [ 10 ]. However, companies need to know how to act on these developments. Despite its high relevance, there is still a lack of research in analyzing different consumers’ purchase patterns before, during, and after pandemic restrictions. In particular, understanding the impact of pandemic restrictions (e.g., lockdown) on consumer buying behavior is important to help brand managers and retailers rapidly respond to these immense changes. In fact, “ignoring trends can give rivals the opportunity to transform the industry” [ 11 ].

Numerous studies have already confirmed the increase in use of OGS during the COVID-19 pandemic worldwide [ 12 – 22 ]. Additionally, Brüggemann and Pauwels [ 23 ] found significant differences between also-online and offline-only grocery shoppers in both consumers’ attitudes and purchase behavior. However, it has not yet been investigated how offline and online grocery purchases are affected by pandemic restrictions on household level. Verhoef et al. [ 24 ] synthesize empirical and conceptual research on effects of COVID-19 on retailing. The authors state that COVID-19 changes consumer needs and behavior and that retailers need to know how to respond. In addition, the authors conclude that more structured methods should be used to examine future trends in OGS due to the pandemic. With our research, we provide such a structured method for analyzing changes in consumer behavior. Taken toghether, we identify a lack of research on whether a ‘new normal’ will emerge or whether consumers will return to ‘old habits’ in both offline and online grocery shopping.

For instance, consumers who were already familiar with OGS before COVID-19 pandemic may also shop online during and after pandemic restrictions. Furthermore, it is also conceivable that consumers who previously purchased offline-only might also shop online during pandemic restrictions, but return to their old habits afterwards. Conversely, some consumers might continue to purchase online after pandemic restrictions, at least to some extent.

With this research we provide new insights on how consumers behave before, during, and after pandemic restrictions, both when shopping for groceries offline and online. For this purpose, we use household panel data from 2020. We observe offline and online purchases before, during, and after the first lockdown of the COVID-19 pandemic in Germany from March 22, 2020 to May 5, 2020. We examine changes in offline and online purchases of 17,766 households by conducting structured analyses of household purchasing behavior. This approach provides for the first time sophisticated insights into how consumers’ offline and online grocery shopping behavior evolved before, during, and after pandemic restrictions. Taken together, we address the following research question:

How does consumer purchase behavior evolve during and after pandemic restrictions in terms of online and offline grocery shopping?

Theoretical background

Covid-19 and pandemic restrictions.

The COVID-19 virus first emerged in China in December 2019 and has spread rapidly throughout the world. The virus has killed over 1.6 million people and sickened over 76 million people [ 25 ]. In Germany the first COVID-19 case was detected in January 2020. To limit the spread of the virus, multiple interventions have been put in place. In addition to mandatory masks and social distancing, temporary lockdowns were imposed. The first lockdown in Germany was implemented from March 22, 2020 to May 5, 2020. During this time people were forced to stay at home. Only with a valid reason (e.g., for work, grocery shopping, or doctor’s appointment) it was allowed to leave one’s home. The supermarkets remained open during the lockdown to ensure households’ supply [ 26 ].

Figure  1 shows the three time intervals in 2020 that are relevant for this study. In previous research on purchase patterns before, during, and after pandemic restrictions, Brüggemann and Olbrich [ 27 ] utilized data from two years. Here, the observed time periods are of different lengths. On the one hand, this also includes households that purchase irregularly. On the other hand, there is a risk that these households randomly did not shop during the lockdown. Therefore, in this research we consider periods of equal length for the periods before, during, and after the lockdown. Based on this, we define the first observation period from February 10, 2020 to the start of the first lockdown on March 22, 2020. The second period includes the first lockdown, which lasted until May 5, 2020. Finally the third interval considers the period after the lockdown until July 14, 2020. As a result, we obtain three observation periods, each of six weeks in length.

Periods before, during, and after pandemic restrictions

German consumers were able to shop in brick-and-mortar stores during the entire observation period. Given the unchanged accessibility of those stores, we are particularly interested in how this lockdown nevertheless affect grocery shopping, especially with regard to the choice between online and offline channels. By differentiating the three observation periods, we can investigate how resilient households are to changes in distribution channels. To investigate this in a well-founded manner, we first review the relevant literature on grocery retailing in the following section.

Grocery retailing

Grocery retailing, as well as research in the field, has a long tradition [e.g., 28 – 30 ]. However, retailing is still constantly evolving. According to Guha et al. [ 31 ], the increased use of artificial intelligence will transform commerce, e.g., in order to increase both in-store and online sales, to improve the effectiveness of supply chains, or to make the payment more efficient. As well as current technological developments, the COVID-19 pandemic is also a driver of change. For example, Roggeveen and Sethuraman [ 1 ] expect that the COVID-19 pandemic will cause consumers to become accustomed to new ways of shopping, such as OGS and home delivery. In addition, the authors state that people may be more likely to work at home in the future and thus be more likely to book online sports courses or to buy an in-home bike, for example. Overall, a major change in consumer behavior when purchasing groceries is expected as a result of the COVID-19 pandemic [ 32 , 33 ].

Both the accelerating development and the use of new technologies in retail are also playing increasingly important roles in the channel structure. Whereas a few decades ago products were sold almost mainly via brick-and-mortar stores or catalogs, consumers have become accustomed to online ordering and home delivery. This development is especially facilitated by technical progress. Well-known examples of large e-commerce companies in the retail sector are Amazon and Alibaba [ 34 ].

For grocery shopping, the online channel has also been discussed in numerous publications over the past decades [ 35 – 41 ]. However, before the COVID-19 pandemic, the development of OGS was slow [ 42 – 44 ]. There is consensus in the literature that COVID-19 has accelerated this development, at least short-term [ 13 , 45 , 46 ]. However, it is not implied that this strong increase in OGS will continue after the COVID-19 pandemic.

According to East [ 47 ], it is likely that the post-pandemic increase in share of OGS will return to pre-pandemic levels, especially for discount purchasing. Gruntkowski and Martinez [ 48 ] found that the COVID-19 pandemic led to a reduction of consumers expected risk regarding OGS. Furthermore, Tyrväinen and Karjaluoto [ 49 ] used a meta-analysis to examine OGS before and after the COVID-19 pandemic. They find that perceived usefulness and attitudes before the COVID-19 pandemic have a strong influence on intention to use OGS. The authors conclude that the increasing adoption of OGS is not due to higher expected usefulness or more positive attitudes, but that consumers were driven to use OGS by the COVID-19 pandemic. Despite this extensive meta-analysis, the results are limited and further research is needed. For example, the meta-analysis data is based only on questionnaires, not on purchase data. In addition, the authors did not identify any publication that looked at the same households both before and after COVID-19 restrictions.

Shen et al. [ 46 ] investigate pandemic effects on OGS usage as well. They studied online purchase patterns before, during, and after COVID-19. The authors identified a sharp increase in OGS use among consumers during the COVID-19 pandemic. After the pandemic, consumers still report a higher use of OGS than before, though Shen et al.’s [ 46 ] data suggests a decrease in usage, especially in the long-term. However, these results are limited, too. The findings are merely based on survey data, and most of the respondents are habitants of the same city in the United States.

Research framework and contribution

After shedding light on previous literature, we find that a broad analysis of online and offline grocery shopping that considers equal periods before, during, and after pandemic restrictions with real purchase data does not yet exist. Nevertheless, it is enormously relevant for companies to obtain information on changes in consumer preferences and buying behavior in online and offline channels at an early stage in order to respond accordingly.

With this study, we address this research gap by examining purchases by households before, during, and after the first lockdown in Germany. Our contribution is to identify different purchase patterns online and offline before, during, and after the first lockdown in Germany in order to determine whether different consumer groups react differently to such restrictions. We additionally broaden our contribution by looking at the online and offline purchase patterns combined. This allows us to examine how the same household behaves online and offline before, during and after the pandemic. In the next sections, we provide information about the data operationalization and descriptive statistics and describe the resultung purchase patterns.

Empirical analysis

Data operationalization and descriptive statistics.

We use household panel data from 2020 provided by the GfK . 1 The data includes purchases from the product groups chocolate bars, coffee, hair shampoo, and laundry detergent from more than 30,000 German households. The market research institute GfK compiles their household panel according to several criteria (e.g., household size, household location, social status, nationality, number of children,…). Thus, they provide a representative data set for German households with regard to these criteria [ 50 ]. Moreover, it is recorded whether a purchase were made offline or online. Online and offline channels can belong to the same retailer or to different retailers. We use this differentiation because we are particularly interested in the decision of households to shop offline or online.

Figure  2 shows the volume-based share of OGS per month based on the household panel data we use for this empirical analysis. Here, we find a comparatively strong increase in the volume-based share of OGS during the lockdown. However, the figure also shows that after the first lockdown the volume-based share of OGS decreases. Overall, the volume-based share of OGS seems to be higher after the lockdown than before. The information provided by this figure is interesting but limited. We cannot see here if/how the households’ purchase patterns change before, during, and after the lockdown. This is where our empirical analysis comes in.

Volume based share of OGS per month (calculation by the authors)

We analyze the purchase data under consideration of the first lockdown in Germany from March 22, 2020 to May 5, 2020. To allow for a reliable analysis, we consider identical periods before, during, and after the lockdown. Since the lockdown lasted six weeks, we consider the purchases six weeks before and six weeks after this lockdown (see Fig.  1 ). Additionally, only households that purchased both before and after the lockdown are included in the analysis to handle the problem of panel mortality. We can thus observe 17,766 households reporting their offline and online purchases during the eighteen-week observation period. It should be emphasized that this reduction of the basic sample (from more than 30,000 households to 17,766 households) may affect the representativeness regarding all German households described above.

Table ​ Table1 1 provides some key figures for total purchases (offline and online) as well as for offline and online purchases separately. First of all, it can be noted that we examine purchases over a period of six weeks in each period. In the case of total purchases (offline and online), it is noticeable that some households have not made any purchases during the lockdown (from 17,766 to 15,873). The quantity sold, volume and value fell slightly during the lockdown, but slightly rose again afterwards. Value per quantity decreased slightly during the lockdown, but also increased to the pre-lockdown level after the lockdown. Overall, value per kilogram is even higher after the lockdown than before. The offline purchases show a very similar picture, not least because they also largely determine the total purchases due to their high proportion.

Descriptive statistics about key performance indicators

Looking at online purchases, we see that the number of households buying online increases during the lockdown (from 208 to 284) and remains almost at this level after the lockdown (283). The quantity sold, volume and value show that after the increase during the lockdown, value decrease slightly again. Overall, these descriptive data indicate a moderate increase in OGS due to the lockdown. Interestingly, the value per quantity and the value per kilogram decrease during this period. Compared to offline purchases, it can be seen that both value per quantity and value kilogram are considerable higher online than offline. In order to be able to consider product group-specific differences in the later discussion of the empirical results, we also provide information on the structure of the data in terms of the four product groups we use. Table ​ Table2 2 shows the shares of the four product groups in the data we use.

Descriptive statistic about the proportions of the product groups used

For total purchases (offline and online) and offline purchases, the shares are mostly stable before, during and after the lockdown, with the exception of coffee. For coffee, the data suggest a slight increase in the share of coffee in the data during and after the lockdown. For online purchases, the share of coffee is surprisingly relatively high. Here, a positive trend can be seen during and after the lockdown. This also partly explains the significantly higher value per quantity and value per kilogram as coffee is comparatively more expensive than chocolate bars, hair shampoo, and laundry detergent. Furthermore, a positive trend can also be seen in the data for online purchases of hair shampoo. These findings must be considered when interpreting the results. In the next section, we present the operationalization of the purchase patterns.

Purchase patterns

Figure  3 shows the procedure for separating the household panel data to calculate online, offline, and combined purchase patterns. In the first investigation, we separate households between online and offline purchases, derive online and offline purchase patterns, and analyze them separately, respectively for the three periods before, during, and after the lockdown. In the second investigation, we combine selected offline and online purchase patterns to observe the behavior of households online and offline simultaneously.

Online, offline and combined purchase patterns

For both offline and online purchases, we derive eight purchase patterns to observe consumers’ purchase behavior before, during, and after the lockdown. To identify the different purchase patterns, we code them by attributing to each household ‘0’ for no purchases and ‘1’ for at least one purchase in each observation period (before, during, and after lockdown). For instance, a coding of 1-0-0 means that the related household only purchased before the lockdown and neither during nor after. Figure  4 illustrates the calculation of purchase patterns using three exemplary purchases from three different households.

Calculation of online and offline purchase patterns

Using this procedure, we obtain eight online and eight offline purchase patterns. The following Table ​ Table3 3 provides an overview and detailed descriptions of the 16 resulting purchase patterns. In the next sections, the empirical findings on online and offline purchase patterns are examined separately. This is followed by an empirical analysis of the combined purchase patterns.

Offline and online purchase patterns

Empirical findings

Offline purchase patterns.

The offline purchase patterns before, during, and after the lockdown are shown in Table ​ Table4. 4 . Based on these purchase patterns, different descriptions are specified (see second column). The majority of consumers purchased offline before, during, and after the lockdown (88.88%; 15,777). During the lockdown, 10.65% (1890) did not purchase offline. The fact that these consumers return to brick-and-mortar retail after the lockdown shows that they (at least partially) return to their ‘old habits’ and do not completely change their behavior to OGS.

Offline grocery purchase patterns

1 = purchases, 0 = no purchases; with purchases ‘before-during-after’ lockdown; n = 17,766

Online purchase patterns

Table ​ Table5 5 shows the results regarding online purchase patterns. Here, the empirical results show a much more differentiated behavior than in offline purchasing behavior. Among the (also-)online purchasing households, 18.86% (109) purchased online only before the lockdown (‘online 1-0-0’). Exclusively during the lockdown, 29.58% (171) of (also-)online grocery shoppers purchased online (‘online 0-1-0’). In other words, nearly one-third of these households started shopping online during the first lockdown and stopped afterwards. Thus, the lockdown appears to have been a reason for these households to try OGS. However, they did not continue to use OGS afterwards.

Online grocery purchase patterns

After the lockdown, 25.95% (150) of (also-)online grocery shoppers chose OGS for the first time (‘Online 0-0-1’). On the one hand, this suggests that a lockdown increases the number of online shopping households only in the short term. On the other hand, there still is a substantial amount of fluctuation among OGS. Only a few households purchased (also-)online before and after the lockdown, but not during (6.06%; 35) (‘Online 1-0-1’). Other consumers started using OGS (8.48%; 49) (‘Online 0-1-1’) or stopped using it since the lockdown (2.60%; 15) (‘Online 1-1-0’). Only 8.48% (49) of (also-)online purchasing households bought groceries online before, during, and after the lockdown (‘Online 1-1-1’). In comparison with offline purchases, the (also-)online purchasing households represent only 3.25% (578).

Taken together, more than half of online shoppers try OGS, but then buy offline only again (51.04%; 295). 2 Furthermore, almost all of the observed households shop offline (again) after the lockdown (99.58%; 17,691). 3 While 35.99% (208) 4 of the observed (also-)online shopping households shopped groceries online before the lockdown, 48.96% (283) 5 shopped for groceries online afterwards. Overall, our results of the online grocery purchase patterns show strong dynamics in the use of OGS.

Combined purchase patterns

Beyond the separated analyses of offline and online purchase patterns, this section looks at combined purchase patterns. This allows us to observe how the households behave both online and offline before, during, and after pandemic restrictions. Regarding offline purchase patterns, the previous investigation suggests, that two patterns are almost solely relevant. Thus, in the further analysis, we focus on these two offline purchase patterns.

First, we consider households that made offline purchases continuously throughout the observation period (‘offline 1-1-1’). These households comprise 88.88% (15,777) of offline purchasing households. Second, we consider households that did not shop at brick-and-mortar stores during the lockdown (‘offline 1-0-1’). These households comprise 10.65% (1890) of offline buying households.

The previous results have revealed strong dynamics in the use of OGS. Therefore, in the further analysis we consider all eight online purchase patterns and combine each of them with the two offline purchase patterns mentioned above (‘offline 1-1-1’ and ‘offline 1-0-1’). Thus, sixteen combined purchase patterns emerge. The combined purchase patterns, their descriptions, quantity and share are shown in Table ​ Table6 6 .

For offline purchase patterns, only 1-1-1 and 1-0-1 patterns are considered. The offline purchase patterns not considered include 99 households (see “missing”)

The combination of purchase patterns shows that the majority of households shop groceries steadily offline and not at all online. This segment comprises 86.38% (15,346) of the observed households (‘offline 1-1-1 and online 0-0-0’). The second largest segment consists of households that did neither shop in brick-and-mortar stores nor online groceries during the lockdown (‘offline 1-0-1 and online 0-0-0’). This group comprises 10.37% (1842) of all observed households. This means that even the households that did not shop offline during the lockdown also predominantly did not switch to the online channel. This shows that the lockdown hardly caused households to switch from offline to online.

Only 0.89% (159) of the households used OGS during the lockdown and neither before nor after (‘offline 1-1-1 and online 0-1-0’). However, these households use OGS only in a complementary way, as they continued to shop offline.

Furthermore, 0.61% (109) of the observed households started shopping online after the lockdown (‘offline 1-1-1 and online 0-0-1’). However, these households continue to shop offline as well. The empirical results show that 0.50% (89) of the observed households have not shopped online since the lockdown (‘offline 1-1-1 and online 1-0-0’). These households have thus stopped using OGS during the observation period and have returned to purchase exclusively offline. Interestingly, only 0.12% (21) households made both online and offline purchases in each period (‘offline 1-1-1 and online 1-1-1’). Further purchase patterns with comparatively small numbers of cases can be seen in Table ​ Table6. 6 . In the next chapter we will discuss the implications of our findings.

Implications

The results of this study clearly show that German grocery shopping habits we observed differ between offline and online channels. Regarding offline purchase patterns, we found that almost all of the observed households purchase groceries from brick-and-mortar retailers after the lockdown (99.58%; 17,691). 6 It is particularly relevant for retailers that 10.65% (1890) of the observed households avoided brick-and-mortar retail stores during the lockdown, but returned to buy groceries in brick-and-mortar stores afterwards. Hence, offline grocery shopping is mainly affected by a lockdown in the short term and consumers are still in reach of brick-and-mortar retailers. Our household panel data only show a comparatively strong increase in the share of OGS during the lockdown (see Fig.  2 ). Moreover, current forecasts for Germany and worldwide show a substantial increase in OGS over the coming years, at least until 2027 [ 9 , 52 ]. This indicates a risk that offline retailers will increasingly lose market shares to online grocery retailers. Thus, brick-and-mortar retailers should now focus on generating and communicating competitive advantages over online providers (e.g., via price, experience, or service) as long as this is feasible for them. For instance, consumers can haptically experience the products in brick-and-mortar retail, which is hardly possible in online stores [ 52 ].

After all, if retailers lose households to online grocery retailers, influencing and winning back these consumers will be much more challenging. Offline retailers should also think about adding an online channel in order to reduce their disadvantages compared to online providers (e.g., shopping at anytime and anywhere as well as home delivery).

Compared to the offline purchase patterns, we found considerably more divergent online purchase patterns. Since 51.04% (295) 7 of (also-)online purchasing consumers bought groceries online before or during the lockdown but not afterwards, OGS providers should critically evaluate their customer retention measures. OGS providers need to identify these consumers as well as possible reasons for their suspended OGS use.

However, we also find consumers who started using OGS during the observation period. For instance, 34.43% (199) 8 of (also-)online grocery shoppers began shopping for groceries online during or after the lockdown. This high fluctuation presents both threats and opportunities for brand managers as well as for retailers. For instance, there is a risk that actual customers will either switch to competitive online suppliers or satisfy their needs in brick-and-mortar stores (again). The high dynamic of OGS can also persuade new households to shop for groceries online. The fact that more than 96% of households made purchases exclusively offline throughout the observation period shows that OGS was not very widespread in Germany in 2020. However, this shows the immense untapped potential for OGS. As current forecasts predict a further increase in the share of OGS in grocery retailing over the coming years, both in Germany and worldwide [ 9 , 51 ], OGS providers should work now on improving customer loyalty in order to benefit from this trend in the long term.

The combined purchase patterns reveal that households who avoided brick-and-mortar stores during the lockdown hardly switched to OGS. Thus, 10.37% (1842) of all households neither shopped offline nor started OGS during the lockdown. This means that almost none of these households started buying groceries online during or after the lockdown (0.11%; 20). 9 For OGS providers, this shows how tightly households are still holding on to their ‘old habits’ instead of trying new ways of shopping. Therefore, this insight is relevant for both OGS providers and brick-and-mortar retailers.

Online grocery shop operator need to understand that changing consumer habits is a process that often can only be accomplished over an extended period of time. Hence, OGS providers are faced with the difficulty to persuade households to try OGS. Targeted incentives should be created and communicated, e.g., welcome gifts or additional benefits such as automatic shopping lists or deposit acceptance. The high volatility in the use of OGS should be considered by the providers of OGS in their strategy to attract additional customers. On the one hand, households must be convinced of the use of OGS and its advantages through communication (e.g., time savings due to home delivery). On the other hand, however, the customers acquired must also be tied to OGS in the long term. It seems the latter has not been successful during the observation period, since many households discontinue OGS.

For brick-and-mortar retailers, the data indicate that the households are sticking to or returning to their ‘old habits’. Moreover, our results for the combined purchase patterns reinforce the results of the separate purchase patterns. Offline retailers still have access to households through their stores, given that almost all customers at least returned to offline shopping during the observation period. Retailers need to act now and convince customers of the benefits of brick-and-mortar stores in the future. The results of the combined purchase patterns clearly show that households do not immediately switch to the online channel even during a lockdown. Therefore, the need for brick-and-mortar stores is demonstrated and their existence justified. Furthermore, as OGS become more widespread worldwide [ 51 ], the pressure on brick-and-mortar stores will increase. For this reason, retailers should pay particular attention to customer loyalty.

Concluding remarks

This study contributes to a better understanding of how consumer purchasing behavior evolves during and after pandemic restrictions on online and offline grocery shopping. During the first lockdown in Germany, 10.37% (1842) of the consumers avoided brick-and-mortar stores. The offline purchase patterns are characterized by ‘old habits’, since after the lockdown almost all of the observed consumers visit brick-and-mortar stores again. Thus, consumers did not completely switch from offline to online channel as a result of pandemic restrictions. Our results provide valuable insights for brick-and-mortar retailers, at least in Germany. Our empirical results indicate that it is still possible for retailers to influence customers in their own stores, since almost all of the consumers observed still shop (also-)offline. Particularly against the backdrop of the predicted increase in OGS over the coming years in Germany and worldwide [ 9 , 51 ], brick-and-mortar retailers should now develop strategies to retain their customers in the long term. They can seek to do this, for example, by gaining competitive advantages over online retailers (e.g., through price, experience, or service) or by adding an online channel. If consumers are accustomed to online channels and increasingly purchase their groceries online, it will become much more challenging for brick-and-mortar retailers to reach out to these consumers. Fundamentally, it is key for these companies to anticipate and act on these game-changing trends to shape new standards and be successful in the long term [ 11 ]. While our 2020 data for Germany show a high momentum in OGS with still relatively low shares of OGS (see Fig.  2 ), the general trend in Germany and worldwide points to an increase in OGS in the coming years [ 9 , 51 , 53 ]. As a result, the competitive pressure on brick-and-mortar stores will continue to rise, making it particularly necessary for them to improve customer retention.

Our findings on OGS show an ongoing process of change due to a high level of dynamism in online purchase patterns. During the observation period, the buying behavior of (also-)online purchasing consumers changes in different ways. Some consumers started OGS before or during the lockdown, but then switched back to brick-and-mortar stores. Other consumers started OGS before or during the lockdown and maintained this shopping behavior afterwards. These findings indicate a ‘new normal’–at least for certain consumer segments.

Looking at combined online and offline purchasing behavior, we find that households not using offline stores during the lockdown did not switch to the online channel. So there is no entire shift from the offline to the online channel due to pandemic restrictions. Moreover, we also find high volatility in OGS among combined purchase patterns. Online grocery shop operators do not seem to have succeeded in retaining their customers during the observation period. Even worse, they also failed to convince consumers to use OGS who stayed at home due to the lockdown.

In conclusion, this study provides valuable insight into consumer purchasing behavior in Germany both offline and online before, during, and after pandemic restrictions. In particular, brand managers and retailers should take these findings to consider changes in consumers’ behavior in more detail in order to derive measures to increase loyalty. In addition, these new insights provide deeper knowledge of consumer behavior between offline and online channels, as well as consumer responses to crises.

Additionally, the empirical results indicate that it will be crucial for offline retailers to retain their customers, since those who have switched completely to OGS can hardly be reached by offline retailers without enormous efforts. Fortunately, however, we have for now hardly found any households that exclusively use the online channel to purchase groceries.

For online grocery shop operators, it is key to not only continue to attract new customers, but to retain existing customers. The possibly high acquisition costs for new customers (e.g., due to ads, welcome gifts, or discounts) are pointless if customers are not bound to the provider in the long term. In this case the provider will not be able to compete in the long term.

This research differs from previous publications in several aspects. While recent publications, e.g., by Tyrväinen and Karjaluoto [ 49 ], Gruntkowski and Martinez [ 48 ], Shen [ 46 ], Younes et al. [ 54 ], Gomes and Lopez [ 55 ] and Eriksson and Stenius [ 56 ] are based on survey data, we provide an empirical analysis based on real purchase data. This allows us to observe purchases of the same households before, during and after the pandemic to show the evolution of offline and online grocery shopping. Morever, to the best of our knowledge, this research is the first analyzing offline and online channel choices before, during, and after the pandemic using an extensive dataset. In terms of content, we provide several new insights into how German households’ behavior changed differently online and offline in 2020.

Limitations and further research directions

Even this study has some limitations. First, it cannot be clearly proven that the changes in the purchasing behavior of offline and online purchase patterns are caused by the COVID-19 pandemic. The processes of change in consumer purchasing behavior may be also driven (at least in part) by digitization, increasing online offers, and changing demands.

Second, we cannot draw a detailed picture of shifts in demand between offline and online purchases. Looking at Fig.  2 , we can see a comparatively strong increase in the still low volume-based market share of OGS during the lockdown. Moreover, Table ​ Table1 1 shows an increase in the number of online shopping households, quantity sold, volume and value over time. However, while this study focuses on consumer behavior in terms of offline and online channel use, further research should, for example, analyze volume-based purchase patterns in more detail.

Third, Table ​ Table2 2 reveals some differences in the proportions of the observed product groups between online and offline channels. Furthermore, Table ​ Table2 2 shows, for example, that before, during, and after the lockdown, online purchases of coffee increased proportionately. Further research should therefore analyze how online and offline grocery shopping develops differently in terms of product groups and what impact this evolution has on channel performances. Thus, further research may run cross-sectional analyses to reveal differences between products groups.

Fourth, the data in this analysis are exclusively from German households. However, further analysis should go beyond that and compare the results with data from other countries. Thus, further research should complement both cross-national and country-specific insights to OGS research.

Fifth, our data only covers the period up to 2020. Recent statistics show that OGS turnover in Germany increased by 21.21% from 2020 to 2021. Interestingly, the data from 2021 to 2022 shows a slight decrease in OGS turnover of 1.14%. From 2022 to 2027, the annual growth rate in Germany is predicted to exceed on average 15.85% [51]. Worldwide, a very similar annual turnover growth rate of 14.92% is predicted from 2021 to 2027 [ 9 ]. Overall, these key figures show the very dynamic development of OGS both for Germany and worldwide. Moreover, these key figures underline the relevance of this topic, for both online grocery store operator and brick-and-mortar retailer. Based on this, we expect our results to be highly relevant for Germany and beyond. However, in further research, the development of online and offline grocery shopping need to be analyzed with more recent data.

Finally, further research should investigate whether consumers with different purchase patterns differ, e.g., in terms of consumer characteristics and demographics, attitudes towards price consciousness or brand preference as well as purchase behavior and attitudes towards organic or fair trade products.

Open Access funding enabled and organized by Projekt DEAL.

Declarations

On behalf of all authors, the corresponding author states that there is no conflict of interest.

1 The source of the data is GfK Consumer Panels & Services .

2 Considered purchase patterns: ‘Online 1-0-0’, ‘Online 0-1-0’, and ‘Online 1-1-0’.

3 Considered purchase patterns: ‘Offline 0-0-1’, ‘Offline 1-0-1’, Offline 0-1-1- ‘, and ‘Offline 1-1-1’.

4 Considered purchase patterns: ‘Online 1-0-0’, ‘Online 1-0-1’, ‘Online 1-1-0’, and ‘Online 1-1-1’.

5 Considered purchase patterns: ‘Online 0-0-1’, ‘Online 1-0-1’, Online 0-1-1- ‘, and ‘Online 1-1-1’.

6 Considered purchase patterns: ‘offline 0-0-1’, ‘offline 1-0-1’, ‘offline 0-1-1’, and ‘offline 1-1-1’.

7 Considered purchase patterns: ‘online 1-0-0’, ‘online 0-1-0’. and ‘online 1-1-0’.

8 Considered purchase patterns: ‘online 0-0-1’ and ‘online 0-1-1’.

9 Considered purchase patterns: ‘offline 1-0-1 and online 0-0-1’ and ‘offline 1-0-1 and online 0-1-1’.

Contributor Information

Philipp Brüggemann, Email: [email protected] .

Rainer Olbrich, Email: [email protected] .