literature review on online shopping behaviour

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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.

Bibliografia

Ajzen, I. (1991) The theory of planned behavior: some unresolved issues. Organizational Behavior and Human Decisions Processes , 50 (2), pp. 179-211.

Bellman, S., Lohse, G., and Johnson, E. (1999) Predictors of online buying behavior. Communica tions of the Association for the Comptuting Machinery , 42 (12), pp. 32-38.

Bhatnagar, A., Misra, S., and Rao, H. R. (2000) On risk, convenience and internet shopping behavior. Communications of the Association for Computing Machinery , pp. 43 (11), 98-105.

Blake, B. F., Kimberly, A. N., and Colin, M. V. (2003) Innovativeness and variety of internet shopping. Internet Research , 13 (3), pp. 156-169.

Burke, R. R. (2002) Technology and the customer interface: what consumers want in the physical and virtual store. Journal of the Academy of Marketing Science , 30 (4), pp. 411-432.

Childers, T. L., Carr, C. L., Peck, J., and Carson, S. (2001) Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing , 77 (4), pp. 511-535.

Corbitt, B. J., Thanasanki, T., and Yi, H. (2003) Trust and e-commerce: a study of consumer perceptions. Electronic Commerce Research and Applications , 2, pp. 203-215.

Csikszentmihalyi, M. (1988) Optimal experience: psychological studies of flow in cousciousness . U.K, Cambridge University Press.

Dabholkar, P. A. and Bagozzi R. P. (2002) An attitudinal model of technology-based self-service: moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science , 30 (3), pp. 184-201.

Davis, F. D. (1989) Perceived usefulness, perceived ease of use and user acceptance of information techonology. MIS Quaterly , 13 (4), pp. 319-340.

Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989) User acceptance of computer technology: a comparation of two theoretical models. Management Science , 35 (8), pp. 982-1002.

Doolin, B., Dillon, S., Thompson, F., and Corner, J. L. (2005) Perceived risk, the internet shopping experience and online purchasing behavior: a New Zeland perspective. Journal of Global Information Management , 13 (2), pp. 66-88.

Eastlick, M. A. and Lotz, S. L. (1999) Profiling potential adopters of an interactive shopping medium. International Journal of Retail and Distribution Management, pp. 27 (6/7), 209-223.

Elliot, S. and Fowell, S. (2000) Expectations versus reality: a snapshot of consumer experiences with internet retailing. International Journal of Information Management, 20 (5), pp. 323- 336.

Fishbein, M., and Ajzen, I. (1975) Belief, attitude, intention and behavior: an introduction to theory and research . Reading, MA, Addison-Wesley.

Gehrt, K. C. and Yan, R-N. (2004) Situational, consumer, and retail factors affecting internet, catalog, and store shopping. International Journal of Retail and Distribution Management , 32 (1), pp. 5-18.

George, J. F. (2002) Influences on the intent to make internet purchases. Internet Research , 12 (2), pp. 165-180.

Goldsmith, R. E. and Hofacker, C. F. (1991) Measuring consumer innovativeness. Journal of the Academy of Marketing Science , 19 (3), pp. 209-221.

Grewal, D., Iyer, G. R., and Levy, M. (2002) Internet retailing: enablers, limiters and market con sequences. Journal of Business Research .

Hoffman, D. L., Novak, T. P., and Peralta, M. (1999) Building consumer trust online. Communica tion of the Association of Computing Machinery , 42 (4), pp. 80-85.

Jarvenpaa, S. and Todd, P. (1997) Consumer reactions to electronic shopping on the world wide web. International Journal of Electronic Commerce , 1 (2), pp. 59-88.

Jarvenpaa, S., Tractinsky, N., and Vitale, M. (1999) Consumer trust in an internet store. Informa tion Technology and Managemet , 1 (1/2), pp. 45-72.

Lee, M. K.-O. and Turban, E. (2001). A trust model for consumer internet shopping. International Journal of Electronic Commerce , 6 (1), 75-91.

Li, H., Kuo, C., and Russel, M. G. (1999) The impact of perceived channel utilities, shopping orientations, and demographics on the consumer’s online buying behavior. Journal of Com- puter-Mediated Communications , 5 (2).

Liao, Z. and Cheung, M. T. (2001) Internet based e-shopping and consumer attitudes: an empirical study. Information and Management , 38 (5), pp. 299-306.

Liebermann, Y. and Stashevsky, S. (2002) Perceived risks as barriers to internet and e-commerce usage. Qualitative Market Research , 5 (4), pp. 291-300.

Liu, X. and Wei, K. K. (2003) An empirical study of product differences in consumers’ e-commerce adoption behavior. Electronic Commerce Research and Applications , 2, pp. 229-239.

Lohse, G. L., Bellman, S., and Johnson, E. J. (2000) Consumer buying behavior on the internet: findings from panel data. Journal of Interactive Marketing , 14 (1), pp. 15-29.

Mathwick, C., Malhotra, N. K. and Rigdon, E. (2001) Experiential value: conceptualisation, measurement and application in the catalog and internet shopping environment. Journal of Re- tailing , 77 (1), pp. 39-56.

Menon, S. and Kahn, P. (2002) Cross-category effects of induced arousal and pleasure on the internet shopping experience. Journal of Retailing , 78 (1), pp. 31-40.

Monsuwé, T. P., Dellaert, G. C.and de Ruyter, K. (2004) What drives consumers to shop online? A literature review. International Journal of Service Industry Management , 15 (1), pp. 102-121.

O’Cass, A. and Fenech, T. (2002) Web retailing adotion: exploring the nature of Internet users web retailing behavior. Journal of Retailing and Consumer Services , 13 (2), pp. 151-167.

Peterson, R. A., Balasubramaniam, S., and Bronnenberg, B. J. (1997) Exploring the implications of the internet for consumer marketing. Journal of the Academy of Marketing Science , 25 (4), pp. 329-346.

Pires, G., Staton, J., and Eckford, A. (2004) Influences of the perceived risk of purchasing online. Journal of Consumer Behavior , 4 (2), pp. 118-131.

Ranganathan, C. and Ganapathy, S. (2002) Key dimensions of business-to-consumer web sites. Information and Management , 39 (6), pp. 457-465.

Ratchford, B. T., Talukdar, D., and Lee, M.-S. (2001) A model of consumer choice of the internet as an information source. International Journal of Electronic Commerce , 5 (3), pp. 7-21.

Reibstein, D. J. (1999) Who is buying on the Internet, 1999? Working Paper, The Wharton School, University of Philadelphia, PA.

Rogers, E. M. (1985) Diffusion of innovations . New York: Free Press.

Saeed, K. A., Hwang, Y., and Yi, M. Y. (2003) Toward an integrative framework for online con sumer behavior research: a meta-analysis approach. Journal of End User Computing , 15 (4), pp. 1-26.

Shim, S., Eastlick, M. A., Lotz, S. L., and Warrington, P. (2001) An online prepurchase intentions model: the role of intention to saerch. Journal of Retailing , 77 (3), pp. 397-416.

Slyke, C. V., Comunale, C. L., and Belanger, F. (2002). Gender differences in perceptions of web-based shoping. Communications of the Association for Computing Machinery , 45 (7), 82-86.

Swaminathan, V., Lepkowska-White, E., and Rao, B. P. (1999) Browsers or buyers in cyberspace? An investigation of factors influencing electronic exchanges. Journal of Computer-Mediated Communication , 5 (2).

Tan, S. J. (1999) Strategies for reducing consumers’ risk aversion in internet shopping. Journal of Consumer Marketing , 16 (2), pp. 163-180.

van der Heidjen, H., Verhagen, T., and Creemers, M. (2003) Understanding online purchase intentions: Contributions from technology and trust perspectives. European Journal of Infor- mation Systems , 12, pp. 41-48.

Vijayasarathy, L. R. and Jones, J. M. (2000) Print and internet catalog shopping: assessing atti tudes and intentions. Internet Research , 10 (3), pp. 191-202.

Weber, K. and Roehl, W. S. (1999). Profiling people searching for and purchasing travel products on the world wide web. Journal of Travel Research , 37, 291-298.

Wolfinbarger, M. and Gilly, M. C. (2001) Shopping online for freedom, control, and fun. California Management Review , 43 (2), pp. 34-55.

Wood, S. L. (2002) Future fantasies: a social change perspective of retailing in the 21 st century. Journal of Retailing , 78 (1), pp. 77-83.

Para citar este artigo

Referência do documento impresso.

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

Referência eletrónica

Ana Teresa Machado , «Drivers of shopping online: a literature review» ,  Comunicação Pública [Online], Vol.2 nº4 / nº3 | 2006, posto online no dia 30 outubro 2020 , consultado o 19 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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Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors

1 Henley Business School, The University of Reading, Greenlands, Henley-on-Thames, Oxfordshire, UK

ARUN DHANDAYUDHAM

2 Consultant in Addictions Psychiatry, CRI – Northamptonshire, Northampton, UK

Background: Compulsive and addictive forms of consumption and buying behaviour have been researched in both business and medical literature. Shopping enabled via the Internet now introduces new features to the shopping experience that translate to positive benefits for the shopper. Evidence now suggests that this new shopping experience may lead to problematic online shopping behaviour. This paper provides a theoretical review of the literature relevant to online shopping addiction (OSA). Based on this selective review, a conceptual model of OSA is presented. Method: The selective review of the literature draws on searches within databases relevant to both clinical and consumer behaviour literature including EBSCO, ABI Pro-Quest, Web of Science – Social Citations Index, Medline, PsycINFO and Pubmed. The article reviews current thinking on problematic, and specifically addictive, behaviour in relation to online shopping. Results: The review of the literature enables the extension of existing knowledge into the Internet-context. A conceptual model of OSA is developed with theoretical support provided for the inclusion of 7 predictor variables: low self-esteem, low self-regulation; negative emotional state; enjoyment; female gender; social anonymity and cognitive overload. The construct of OSA is defined and six component criteria of OSA are proposed based on established technological addiction criteria. Conclusions: Current Internet-based shopping experiences may trigger problematic behaviours which can be classified on a spectrum which at the extreme end incorporates OSA. The development of a conceptual model provides a basis for the future measurement and testing of proposed predictor variables and the outcome variable OSA.

INTRODUCTION

Shopping has been defined as: “the process of browsing and/or purchasing of items in exchange for money” ( www.businessdirectory.com ). It is a process that consists of a number of stages including the search for product information, the processing and assimilation of information in order to evaluate alternative product options, as well as the actual purchase act. A shopping episode may include some or all of these stages and so may or may not include the actual act of purchase. Shopping is today considered both a functional or utilitarian activity as well as a social or leisure activity with hedonistic features ( Hirschman & Holbrook, 1982 ). The enjoyment element has been enhanced by the introduction of large shopping malls offering a range of activities including shopping, eating and entertainment. Authors such as Langrehr (1991) have identified a shift in beneficial experience for the shopper from the purchase item itself to the experience of the shopping process. The highly experiential and sensory nature of shopping provides rewards in itself to the individual, separate from the rewards of the purchase act.

Over the past decade the shopping process has been altered by the advent of the Internet. Internet or online shopping offers a range of benefits in terms of both the information search stage of shopping ( Rose & Samouel, 2009 ) as well as the act of purchase. Cheung, Chan & Limayem (2005) identify a range of internal and external factors that influence consumer purchase behaviour online. These include internal characteristics such as the individual shopper’s attitude to the Internet medium, personal motivations, perceptions of risk and personal innovativeness as well as external benefits that derive from the medium itself such as: convenience, ease of use, perceived usefulness, control and enjoyment ( Cheung et al., 2005 ; Gefen, 2003 ; Wolfinbarger & Gilly, 2001 ). The effect of these benefits has been a steady increase in consumer use of online shopping and the consequential value to e-retailers. Given the ease with which the online shopper can now access e-retail websites and purchase online, the penetration of online shopping within the general population is increasing in the UK and other developed countries. Current estimates are that 60% of the UK adult population now take part in online shopping activity (OECD, 2012) and 2012 saw a year-on-year growth of 16% in online sales against an overall increase in retail growth of 4% (Mintel, 2013).

In the UK statistics make dismal reading in terms of negative behaviour leading to addictions. It is estimated that 20% of the population smoke; 200,000 people are in treatment for heroin dependency; over 20% of the adult population is clinically obese and a similar percentage of the population drink alcohol over the recommended limits ( Parsons, 2010 ). Non-chemical addictive behaviours have been referred to as “excessive appetites” and significantly documented in terms of their epidemiology, etiology and comorbidity ( Orford, 2006 ). Research identifies that negative or problem-based behaviours can develop in relation to both consumption ( Faber, O’Guinn & Krych, 1987 ; Hirschman, 1992 ) and buying (Black, 1996 , 2007a , 2007b ; O’Guinn & Faber, 1989 ; Workman & Paper, 2010 ).

A range of terminology has developed in the domain including: “compulsive buying” ( O’Guinn & Faber, 1989 ; Workman & Paper, 2010 ), “impulsive purchasing” (Baumeister, 2002 ); “compulsive consumption” ( Faber et al., 1987 ; Hirschman, 1992 ); “impulsive spending patterns” ( Vohs & Faber, 2003 ) and shopping addiction ( Sussman, Lisha & Griffiths, 2010 ). The consequences of such behaviour include high levels of debt; negative emotions such as depression or feelings of frustration, shame, guilt and alienation; legal problems; and relationship break-down ( Lejoyeux & Weinstein, 2010 ; O’Guinn & Faber, 1989 ).

There is evidence emerging that problematic shopping behaviour is now occurring online ( Chen, Tarn & Han, 2004 ). The subject of Internet-based problematic shopping behaviour is therefore an important area of academic research for two reasons. First, given the rapid growth in e-re-tailing, there is currently limited research that identifies the predictive factors of such behaviour ( Sun & Wu, 2011 ). Second, isolation of the predictors of such addictive behaviour would raise awareness amongst the medical profession, e-retailers and consumer groups of this emerging condition. Both of these requirements are met by this paper which presents the theoretical support for a conceptual model of online shopping addiction. The objective of the paper is two-fold. First it reviews the literature in relation to problematic buying behaviour. Second it develops a model that hypothesises seven predictors of the behaviour drawn from prior literature in both the clinical and consumer behaviour literatures.

A selective review of two fields of literature was undertaken: consumer behaviour and clinical addiction. The literature search was conducting using the following key databases: EBSCO, ABI Pro-Quest, Web of Science – Social Citations Index, Medline, PsycINFO and Pubmed. The following key terms were used: “addiction”, “Internet addiction”, “online addiction”, “technology addiction”, “compulsive buying”, “compulsive shopping”, “shopping addiction”, “impulsive buying”, “shopping behaviour”, “online shopping”, “problem online shopping behaviour”, “disordered shopping behaviour”, “ pathological shopping behaviour”. Analysis of the articles was conducted by the authors using the criteria as expressed in the title and objectives of the paper. By this method the authors identified the nature of existing research in the field, epistemological assumptions and methodological approaches. This classification provided a framework through which to analyse the literature.

SHOPPING BEHAVIOUR

Behaviours are reinforced via the rewards that they elicit. In some respects this aspect of human behaviour is central to survival such as the rewarding nature of food ( Davenport, Houston & Griffiths, 2012 ). Reward sensitivity has been identified as a component of personality and therefore individuals vary in terms of the degree to which they are sensitive to rewards in their environment and the degree to which they are able to control their responses to such rewards. The rewards of shopping have been recognised to extend beyond the actual act of purchase and may include pleasure afforded by the shopping process, the attention and praise of others as well as relief from anxiety or stress ( Davenport et al., 2012 ). Individuals exhibiting problematic buying behaviour have been found to have higher levels of anxiety in response to external and/or internal stimuli and excessive shopping sessions or “binges” have been found to provide quick and ready relief of such anxiety ( O’Guinn & Faber, 1989 ). The terms “compulsion” and “addiction” are both used (often interchangeably) within the literature relating to problematic shopping behaviour, and more recently to online shopping behaviour and so we explore both here.

Addictive behaviour

Addictive behaviour is a term applied to excessive behaviour that has negative consequences. The word “addiction” is most often used by clinicians to refer to a condition that involves intense preoccupation with the behaviour and leads to physiological changes particularly in the brain. It is characterised by a loss of control and negative outcomes for the individual either psychologically, physically or socially ( Sussman et al., 2010 ). Psychiatrists use the criteria contained in the “Diagnostic and Statistical Manual, Fourth Edition” ( American Psychiatric Association, 2000 ), which is internationally recognised and accepted, to diagnose any disorder with a mental health component and which provides 7 criteria for identification of an addiction dependency.

An addiction is conceptualized as a disorder involving both impulsivity and compulsivity. Impulse control disorders are characterised by two features. Firstly the inability to resist an impulse, drive or temptation even if it is harmful to the individual. Secondly there is a period of tension or arousal prior to the act, relief during the act and regret or guilt after the act ( Benson, 2008 ). People with an impulse control disorder are less likely to have any insight into their behaviour and their ability to resist the behaviour is diminished ( Benson, 2000 ).

Compulsive behaviour

A compulsion is part of the addictive process. The American Psychiatric Association ( 1985 , p. 234) defines compulsions as “repetitive and seemingly purposeful behaviours that are performed according to certain rules or in a stereotyped fashion”. Such repetitive behaviour is often extreme and takes a ritualistic form. It has the purpose of relieving some form of anxiety or tension within the individual but may result in inappropriate or disruptive consequences ( O’Guinn & Faber, 1989 ; Ullman & Krasner, 1969 ).

Problematic buying behaviour

La Rose & Eastin ( 2002 , p. 549) differentiate between “impulsive”, “compulsive” and “addictive” buying as different forms of “unregulated consumer behaviour”. Explanations of compulsive and addictive behaviour have been developed across a range of theoretical approaches including biological (compulsion as an illness or disease), psychological (personality or trait based explanations), or social (social/cultural explanations) ( Hirschman, 1992 ). Compulsive and addictive behaviours have been investigated in relation to consumption which includes both the purchase and use of goods and services ( Hirschman, 1992 ). “Compulsive buying disorder” has been recognised and has been estimated to have a prevalence of 5.8% in the US general adult population ( Black, 2007a ) and is associated with ways to relieve negative feelings via the reward of short-term gratification ( Christenson et al., 1994 ). As previously stated, the reward element may be derived beyond the actual act of purchase and include aspects of the buying process itself and/or post purchase attention and pleasure ( Davenport et al., 2012 ; Faber, Christenson, de Zwaan & Mitchell, 1995 ; O’Guinn & Faber, 1989 ). The comorbidity of compulsive buying with mood disorders such as depression, eating disorders, obsessive-compulsive disorders, substance use disorder and personality disorders have been reported ( Lejoyeux & Weinstein, 2010 ).

O’Guinn and Faber (1989) point out that no single factor explains the etiology of compulsive buying behaviour. Rather they identify a range of factors including levels of arousal (e.g., low boredom or high excitement); release of anxiety or stress; sensation seeking; external environmental stimuli (e.g., the media) or relief from a negative affective state such as low self-esteem. Other identified factors include personality traits (e.g., impulsiveness, instant gratification) and demographics with compulsive shopping strongly linked to women ( Workman & Paper, 2010 ). Black ( 2007a , 2007b ) cites survey studies in which the prevalence rate of compulsive buying disorder amongst women is as high as 80% to 94% although there are suggestions that such findings may be an artefact of sampling methods. However, gender differences have been identified with men more likely to be addicted to drugs, gambling and sex ( Holden, 2001 ), whilst women are more likely demonstrate disorders in relation to eating and shopping (referred to as “mall disorders”) ( Davenport et al., 2012 ). Legendary female excessive shoppers have included two US First Ladies: Mary Todd Lincoln and Jacqueline Kennedy Onassis, Imelda Marcos (Former First Lady of the Philippines) and Princess Diana in the UK ( Black, 1996 , 2007a ).

O’Guinn and Faber (1989) note that, our view of the severity and consequences of compulsive shopping is dictated by how the behaviour is perceived by society. They suggest that at one extreme level compulsive buying may be viewed by society as a “crime” whilst at another merely a “bad habit”. An obsessive shopper may be able to accommodate their behaviour within their everyday life and whilst it may appear worrying to others, it may not necessarily create negative consequences for the individual.

Lejoyeux & Weinstein (2010) suggest that similarities exist between compulsive buying and addiction in terms of the clinical characteristics. However, Sussman et al. (2010) provide a clear distinction between compulsive and addictive behaviour by identifying the characteristic differences. An addiction will typically involve a lot of time spent on the part of the individual thinking about engaging in the behaviour and is therefore typified by intense preoccupation that is beyond the need to release immediate anxiety as typically found in compulsive disorders. Rather addiction is characterised by loss of control or an inability to freely decide whether to engage in the behaviour or not. The individual is unable to predict when the behaviour may occur, how long it will last or when it will stop. Finally an addiction will have negative long-term effects for the individual that may include detrimental effects upon finances, social relationships, the ability to work effectively or to lead their lives appropriately. Edwards (1993) proposes that such shopping behaviours range across a continuum from normal behaviour where purchase is according to the individual’s needs and wants through to addictive with a severe lack of control. Given the recognised negative consequences of online shopping addiction for consumers, we focus upon this far end of the continuum in our work. Taking the Sussman et al. (2010) definition of addiction, we now move to discuss its application within online shopping.

DEVELOPMENT OF A CONCEPTUAL MODEL OF ONLINE SHOPPING ADDICTION

It has been recognised that a society is vulnerable to addiction at the stage when a new substance or behavioural activity is first introduced into the culture. For example, the introduction of alcohol into native cultures that have no prior exposure to the substance demonstrate higher prevalence of addiction to the substance compared to those in established societies ( Orford, 2006 ). Our exposure time to the Internet is approximately 15 years and therefore adaptation to online behaviour is still in its infancy and it can be argued consumers are vulnerable to its influence. New technologies have the ability to influence subjective experiences so powerfully as to make them potentially addictive activities ( Orford, 2006 ). The Internet is one such technology and there are several subtypes of Internet related problem behaviours that have emerged such as online sexual addiction, social media addiction, online gaming and gambling addictions that combine both pre-established addictions with Internet addiction ( Griffiths, 1995 ). It has been questioned whether Internet addictions actually do exist or whether the Internet is the medium through which pre-existing addictive behaviour is carried out. This view is proposed by authors such as Griffiths (2000) who suggests that technological addictions should be viewed as a subset of behavioural addictions which of themselves demonstrate the core elements of addiction. Therefore true Internet addictions may not be highly prevalent ( Widyanto & Griffiths, 2007 ).

The literature in relation to problematic online shopping behaviour is currently limited and most often discussed within the context of broader Internet dependency or addictions ( Chen et al., 2004 ). Sun and Wu (2011) link problematic online buying behaviour to addiction to the Internet itself. They conclude that emotional instability and materialism have positive effects upon Internet addiction, which in turn positively influences impulsive online buying. Materialism and impulsiveness have been linked to technology addiction for example in young people and cell-phone use ( Roberts & Pirog, 2012 ). La Rose and Eastin (2002) found evidence of unregulated online buying amongst college students and evidence for the role of poor self-regulation in influencing this behaviour. They propose that this irrational, lack of control, element of online shopping can be a stronger determinant of online shopping behaviour than rational, economic considerations.

Other studies have focused upon the consequences of OSA such as that by Lo and Harvey (2012) who look at the ways in which compulsive shoppers differ to normal shoppers in terms of their buying process and their emotional responses to the consequences of their buying. The study utilised an experimental design which involved the observation of online shoppers as they performed various shopping tasks such as searching, adding items to the shopping cart and payment. Data was captured both during the shopping process and following the experience which showed distinct differences in compulsive and non-compulsive shoppers. For example compulsive shoppers did not check product information in such detail and were less concerned about over-spending identified by credit card usage. The authors concluded that the compulsive shopper is addicted to the process of shopping itself, experiences cravings to shop but tends to ignore the consequences of satisfying such cravings.

Given the limited understanding of the predictors of OSA, we propose that insights will be found in both existing theories of addictive/compulsive buying behaviour and by identifying specific features of the online retail medium which may encourage OSA. The model therefore incorporates recognised predictors of addictive behaviours in general: low self-esteem, low self-regulation, negative emotional state and female gender as well as predictors specific to the online retail medium: enjoyment, social anonymity and cognitive overload. Figure 1 below presents our conceptual model of OSA and we discuss the theoretical support for the hypothesised relationships between OSA and the 7 proposed predictors.

A conceptual model of online shopping addiction (OSA)

Low self-esteem

The effect of low self-esteem is consistently reported in terms of compulsive and addictive behaviour ( Davenport et al., 2012 ; Hirschman, 1992 ; O’Guinn & Faber, 1989 ). Compulsive buying is an attempt to relieve feelings of low self-esteem ( Jacobs, 1986 ). Low self-esteem is relieved by the reward or outcome of the repetitive behaviour. Davenport et al. (2012) point out that it may not be the goods purchased but involvement in the buying process that relieves anxiety and feelings of low self-esteem. We therefore hypothesise that individuals with low self-esteem will find the process of online shopping provides relief from such feelings and therefore this factor will have a direct effect upon OSA.

Low self-regulation

Difficulties with control are strongly linked to compulsive and addictive behaviour ( Baumeister, 2002 ; Hirschman, 1992 ; Workman & Paper, 2010 ). Baumeister (2002) defines self-control as the individual’s capacity to alter their own state or responses to stimuli and uses the term interchangeably with self-regulation. Self-regulation has been recognised within the consumer behaviour literature. Vohs and Faber (2003) view self-regulation as being managed by a limited set of resources that the individual draws on to control their responses. These resources include cognitions, emotions or behaviours. They propose that each act of self-control depletes the resources and therefore reduces the individual’s overall capacity for self-regulation. Sun and Wu (2011) recognise the relationship between Internet addiction and lack of self-control and suggest that a continuous exposure to the online environment that encourages lack of self-control depletes the individual’s resource capacity for self-regulation. La Rose and Eastin (2002) identify that whilst there are features of online retail websites that encourage self-regulation (e.g., shopping carts to contain products before purchase and therefore time to consider; search engine opportunities to search and evaluate information; or past purchase history to trigger awareness of buying behaviour) these were found to be outweighed by retail website features such as advertising pop-ups, timed discount offers, vivid interactive graphical displays of products, or ‘one click’ purchases, all of which encourage purchase and weaken self-regulation. In their study La Rose and Eastin (2002) found a direct relationship between deficient self-regulation and online shopping activity and we similarly include this effect within our conceptual model.

Negative emotional state

Emotion has been identified as a factor in the continued use of technology ( Oritz de Guinea & Markus, 2009 ). Baumeister (2002) suggests that at times of emotional distress an individual is more likely to loosen self-control and act in an impulsive way in order relieve such feelings. Shopping has been recognised to ease anxiety and stress and therefore a shopper in a negative emotional state is more likely to act impulsively and excessively ( Davenport et al., 2012 ). We hypothesise that the ease of access and instant gratification of the e-retail medium provides an ideal environment for reduction of negative emotional state. Therefore negative states are a driver of OSA.

Psychologically enjoyment has been identified as a hedonistic emotion which positively motivates physical activity and has been linked to website experience ( Lin, Gregor and Ew-ing, 2008 ). Davenport et al. (2012) identify “reward sensitivity” as an influence upon compulsive buying proposing that an individual who is highly sensitive to rewards will respond to enjoyable stimuli such as food or shopping. Hedonic motivations have been identified in relation to shopping ( Arnold & Reynolds, 2003 ). Enjoyment has been found to be a determinant of an individual’s likelihood to continue to shop ( Hart, Farrell, Stachow, Reed & Cadogan, 2007 ) and pleasure is identified as a rewarding aspect of compulsive buying behaviour ( Davenport et al., 2012 ). Similarly enjoyment has been identified as a motivator of online shopping ( Wolfinbarger & Gilly, 2001 ). Lejoyeux and Weinstein ( 2010 , p. 249) report that positive feelings of pleasure or excitement (often referred to as “a high” or “a rush”) are associated with compulsive buying. We therefore incorporate this into our model and hypothesise that enjoyment is a key factor in the development of OSA.

Pratarelli and Browne (2002) provide evidence that certain addictions may vary by gender. There is consistent evidence that women are more likely to demonstrate compulsive and addictive buying behaviour ( Black 2007a ; Christenson et al., 1994 ; Davenport et al., 2012 ; McElroy, Phillips & Keck, 1994 ). This may be explained by the fact that in Western countries females are socialized and expected to do most of the household shopping. From an early age young females learn that shopping and buying are activities that can be used to feel better and therefore can be used to combat negative mood states ( Benson, 2008 ). Whilst excessive Internet usage has been found to be male dominated particularly in the area of gaming and gambling ( Mottram & Fleming, 2009 ), we hypothesise that the female gender variable is likely to be a stronger predictor of OSA than being male, in line with prior clinical findings.

Social anonymity

As stated, shopping is traditionally a social activity involving personal interaction with others (other shoppers and/or retail staff). Online shopping is (most often) solitary and a key feature is the social anonymity of the shopping environment. Lejoyeux and Weinstein (2010) suggest that compulsive buying may be prompted by the online retail environment because of the social anonymity that allows the shopper to keep their buying behaviour private from others, such as their family. Added to the effect of anonymity, Sun and Wu (2011) suggest that an appeal of online shopping is that the individual may feel less inhibited about their shopping when they are not visible to others. Disinhibition has been identified as a distinctive feature of Internet behaviour encouraging many forms of inappropriate behaviour such as intimate self-disclosure on social media sites; “flaming” or negative comment about others; through to activities such as bullying or the use of pornography sites ( Joinson, 2007 ). Such behaviours come about due to two factors. First, the lack of concern by the individual about self-presentation (how they appear to others) and second how others may judge them due to reduced social cues in the virtual environment ( Joinson, 2007 ). We hypothesise that the social anonymity of the online environment and subsequent disinhibi-tion encourages inappropriate excessive shopping behaviour due to the absence of the regulation of normal shopping environmental cues such as the response behaviours of other shoppers or retail staff.

Cognitive overload

Compulsive buying behaviour has been linked to arousal and the effect of the external environment ( O’Guinn & Faber, 1989 ). Online retail sites satisfy the need for arousal among shoppers by the dynamic nature of the medium. This includes devices such as graphic displays, interactive dialogue and “pop ups” providing product information or notification of special offers. Such frequent and constantly changing stimuli provide repeated stimulation and temptation potentially creating cognitive overload for the individual. Increases in the cognitive load of the individual in one area can overwhelm self-control in another leading to lack of willpower. As discussed earlier, self-control is managed by a limited set of resources and authors such as Muraven & Baumeister (2000) have applied a ‘limited resources model’ to explain the concept of self-control. They propose that the resource needed for an individual to exert self-control is limited and the resource is partially depleted by the act of self-control itself. Increased cognitive load, which similarly depletes resources, has been found to make temptation harder to resist ( Fudenberg & Levine, 2006 ). We therefore hypothesise that the cognitive stimulation of online retail websites will increase cognitive load leading to lack of self-control and so have an effect upon OSA.

The dependent variable: Online shopping addiction

Our start point to identify the components of OSA and therefore definition criteria is to look at the literature regarding technological addiction. The Internet as a form of technology addiction is now well researched ( Chen et al., 2004 ; Leung, 2004 ) particularly in the areas of online gambling and gaming ( Griffiths, 2009 ; Wood & Griffiths, 2007 ). Griffiths ( 1995 , p. 15) first introduced the term “technology addictions” which he defines as “non-chemical (behavioural) addictions which involve human-machine interaction”. As stated, such addictions may combine with other categories of addictive behaviour such as gambling, sex or shopping. In developing a definition of technological addiction, Griffiths ( 1995 ) cites Marlatt, Baer, Donovan and Kivlahan (1988) in terms of defining addictive behaviour as characterised by loss of control, an inability to withdraw from the behaviour despite attempts, and long-term negative consequences. Griffith (1995) draws on the clinical criteria for established addictions (DSM4) to develop an understanding of technological addiction and development of criteria. The application to Internet behaviour is supported by subsequent studies ( Meerkerk, Van den Eijnden, Vermulst & Garrelsen, 2009 ) and we similarly propose that the Griffiths technology addiction criteria be adapted to online shopping to measure the components of OSA.

CONCLUSIONS

This article reviews literature on compulsive and addictive shopping and the emergent literature in relation to problematic online shopping behaviour. The contribution of this review is that it fills a gap in the literature in terms of the identification of potential predictors of online shopping addiction. Seven predictor variables are proposed to influence the likely development of OSA which includes: low self-esteem, low self-regulation; negative emotion, enjoyment, gender, social anonymity and cognitive overload . The dependent variable of OSA is predicted to have six component features that include: salience, euphoria, tolerance, withdrawal symptoms, conflict and relapse. Development of the model helps both clinicians and retailers to recognise the pre-conditions for the development of addictive consumer behaviour when shopping online. Whilst not all of the proposed predictors of OSA are within the control of e-retailers, the research seeks to shed light on an important aspect of consumer retail behaviour. Further research is called for in order to development measurement scales and testing of the proposed conceptual model.

Acknowledgements

The authors would like to acknowledge the support given to the authors by Professor Mark Griffiths of Nottingham Trent University since their initial interest in the subject of online shopping addiction.

Funding sources

No financial support was received for this study.

Authors’ contributions

The literature review and early development of the conceptual model was undertaken by AD under the supervision of SR as part of the completion of his research dissertation for his MBA programme at Henley Business School. The subsequent development of the conceptual model and preparation of the journal article was undertaken jointly by both authors. Both authors had full access to the literature and its analysis throughout the study.

Conflict of interest

The authors declare no conflict of interest.

• American Psychiatric Association. Diagnostic and statistical manual of mental disorders. Washington, DC: American Psychiatric Association; 1985. [ Google Scholar ]
• American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed. Washington, DC: American Psychiatric Association; 2000. [ Google Scholar ]
• Arnold M. J., Reynolds K. E. Hedonic shopping motivations. Journal of Retailing. 2003; 79 :77–95. [ Google Scholar ]
• Baumeister R. F. Yielding to temptation: Self-control failure, impulsive purchasing, and consumer behavior. Journal of Consumer Research. 2002; 28 :670–676. [ Google Scholar ]
• Benson A. L. I shop, Therefore I am: Compulsive buying and the search for self (Oniomania, ergo sum: The complete guide to compulsive buying disorders) Northvale, NJ: Jason Aronson; 2000. [ Google Scholar ]
• Benson A. L. To buy or not to buy. Boston, MA: Trumpeter; 2008. [ Google Scholar ]
• Black D. W. Compulsive buying: A review. Journal of Clinical Psychiatry. 1996; 57 (8):50–55. [ PubMed ] [ Google Scholar ]
• Black D. W. Compulsive buying disorder: A review of the evidence. The International Journal of Neuropsychiatric Medicine. 2007a; 12 (2):124–132. [ PubMed ] [ Google Scholar ]
• Black D. W. A review of compulsive buying disorder. World Psychiatry. 2007b; 6 :14–18. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• The Business Dictionary: http://www.businessdictionary.com/def-inition/shopping.html . Accessed: November 15, 2012.
• Chen K., Tarn J. M., Han B. T. Internet dependency: Its impact on online behavioural patterns in e-commerce. Human Systems Management. 2004; 23 :49–58. [ Google Scholar ]
• Cheung C. M. K., Chan G. W. W., Limayem M. A critical review of online consumer behavior: Empirical research. Journal of Electronic Commerce in Organizations. 2005; 3 (4):1–19. [ Google Scholar ]
• Christenson G. A., Faber R. J., de Zwaan M., Raymond N. C., Specker S. M., Eckern M. D. Compulsive buying: Descriptive characteristics and psychiatric co-morbidity. Journal of Clinical Psychiatry. 1994; 55 :1–35. [ PubMed ] [ Google Scholar ]
• Davenport K., Houston J. E., Griffiths M. D. Excessive eating and compulsive buying behaviours in women: An empirical pilot study examining reward sensitivity, anxiety, impulsivity, self-esteem and social desirability. International Journal of Mental Health and Addiction. 2012; 10 (4):474–489. [ Google Scholar ]
• Edwards E. A. Development of a new scale for measuring compulsive buying behaviour. Financial Counselling and Planning. 1993; 4 :67–84. [ Google Scholar ]
• Faber R. J., O’Guinn T. C., Krych R. Compulsive consumption. In: Wallendorf M., Anderson P., editors. Advances in Consumer Research. Vol. 14. Provo, UT: Association for Consumer Research; 1987. pp. 132–135. [ Google Scholar ]
• Faber R. J., Christenson G. A., de Zwaan M., Mitchell J. Two forms of compulsive consumption: Comorbidity of compulsive buying and binge. The Journal of Consumer Research. 1995; 22 (3):296–304. [ Google Scholar ]
• Fudenberg D., Levine D. K. A dual-self model of impulse control. The American Economic Review. 2006; 96 (5):1449–1476. [ PubMed ] [ Google Scholar ]
• Gefen D. TAM or just plain habit: A look at experienced online shoppers. Journal of End User Computing. 2003; 15 (3):1–13. [ Google Scholar ]
• Griffiths M. Technological addictions. Clinical Psychology Forum, 1995 Feb;:14–19. [ Google Scholar ]
• Griffiths M. Does Internet and computer “addiction” exist? Some case study evidence. CyberPsychology & Behavior. 2000; 3 (2):211–218. [ Google Scholar ]
• Griffiths M. The use of online methodologies in data collection for gambling and gaming addictions. International Journal of Mental Health Addiction. 2009 doi:10.1007/s11469-009-9209-1. [ Google Scholar ]
• Hart C., Farrell A. M., Stachow G., Reed G., Cadogan J. W. Enjoyment of the shopping experience: Impact on customers’ repatronage intentions and gender influence. The Services Industry Journal. 2007; 27 (5):583–604. [ Google Scholar ]
• Hirschman E. C. The consciousness of addiction: Toward a general theory of compulsive consumption. Journal of Consumer Research. 1992; 19 :155–179. [ Google Scholar ]
• Hirschman E. C., Holbrook M. B. Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing. 1982 Summer; 48 :92–101. [ Google Scholar ]
• Holden C. Behavioural addictions: Do they exist? Science. 2001; 294 :980–982. [ PubMed ] [ Google Scholar ]
• Jacobs D. F. A general theory of addictions: A new theoretical model. Journal of Gambling Behavior. 1986 Spring-Summer; 2 :15–31. [ Google Scholar ]
• Joinson A. N. Disinhibition and the Internet. In: Gackenbach J., editor. Psychology and the Internet. Burlington, MA: Elsevier; 2007. [ Google Scholar ]
• Langrehr F. W. Retail shopping mall semiotics and hedonic consumption. Advances in Consumer Research. 1991; 18 :428–433. [ Google Scholar ]
• La Rose R., Eastin M. S. Is online buying out of control? Electronic commerce and consumer self-regulation. Journal of Broadcasting and Electronic Media. 2002; 46 (4):549–564. [ Google Scholar ]
• Lejoyeux M., Weinstein A. Compulsive buying. The American Journal of Drug and Alcohol Abuse. 2010; 36 :248–253. [ PubMed ] [ Google Scholar ]
• Leung L. Net-generation attributes and seductive properties of the Internet as predictors of online activities and Internet addiction. CyberPsychology & Behavior. 2004; 7 (3):333–348. [ PubMed ] [ Google Scholar ]
• Lin A., Gregor S., Ewing M. Developing a scale to measure the enjoyment of web experiences. Journal of Interactive Marketing. 2008; 22 (4):40–57. [ Google Scholar ]
• Lo H-Y., Harvey N. Effects of shopping addiction on consumer decision-making: Web-based studies in real time. Journal of Behavioral Addictions. 2012; 1 (4):162–170. [ PubMed ] [ Google Scholar ]
• Marlatt G. A., Baer J. S., Donovan D. M., Kivlahan D. R. Addictive behaviors: Etiology and treatment. Annual Review of Psychology. 1988; 39 :223–252. [ PubMed ] [ Google Scholar ]
• McElroy S. L., Phillips K. A., Keck P. E. Obsessive compulsive spectrum disorder. Journal of Clinical Psychiatry. 1994; 55 (10):33–51. [ PubMed ] [ Google Scholar ]
• Meerkerk G. J., Van den Eijnden R. J. J. M., Vermulst A. A., Garrelsen H. F. L. The Compulsive Internet Use Scale (CIUS): Some psychometric properties. CyberPsychology & Behavior. 2009; 12 (1):1–6. [ PubMed ] [ Google Scholar ]
• Mintel Report. Merry mobile and multichannel Christmas for UK retailers. 2013 Retrieved from http://www.mintel.com/ press-centre/retail-press-centre/merry-mobile-and-multichan-nel-christmas-for-uk-retailers . [ Google Scholar ]
• Mottram A. J., Fleming M. J. Extraversion, impulsivity, and online group membership as predictors of problematic Internet use. CyberPsychology & Behavior. 2009; 12 (3):319–321. [ PubMed ] [ Google Scholar ]
• Muraven M., Baumeister R. F. Self-regulation and depletion of limited resources: Does self-control resemble a muscle? Psychological Bulletin. 2000; 126 (2):247–259. [ PubMed ] [ Google Scholar ]
• OECD. Internet Economy Outlook Report. 2012 Retrieved from http://www.oecd-ilibrary.org/science-and-technology/oecd-internet-economy-outlook-2012_9789264086463-en . [ Google Scholar ]
• O’Guinn T. C., Faber R. J. Compulsive buying: A phenomenological exploration. Journal of Consumer Research. 1989; 16 :147–157. [ Google Scholar ]
• Orford J. Excessive appetites. 2nd ed. Chichester, England: John Wiley & Sons; 2006. [ Google Scholar ]
• Oritz de Guinea A., Markus M. L. Why break the habit of a lifetime? Rethinking the roles of intention, habit, and emotion in continuing information technology use. MIS Quarterly. 2009; 33 (3):433–444. [ Google Scholar ]
• Parsons P. 21st century addiction. FOCUS, Issue 221 (October) 2010 [ Google Scholar ]
• Pratarelli M. E., Browne B. L. Confirmatory factor analysis of internet use and addiction. Cyberpsychology & Behavior. 2002; 5 (1):53–64. [ PubMed ] [ Google Scholar ]
• Roberts J. A., Pirog S. F., III A preliminary investigation of materialism and impulsiveness as predictors of technological addictions among young adults. Journal of Behavioral Addictions. 2012; 2 (1):56–62. [ PubMed ] [ Google Scholar ]
• Rose S., Samouel P. Internal psychological versus external market-driven determinants of the amount of consumer information search amongst experienced online shoppers. Journal of Marketing Management. 2009; 25 (1–2):171–190. [ Google Scholar ]
• Sun T., Wu G. Trait predictors of online impulsive buying tendency: A hierarchical approach. Journal of Marketing Theory and Practice. 2011; 19 (3):337–346. [ Google Scholar ]
• Sussman S., Lisha N., Griffiths M. Prevalence of the addictions: A problem of the majority or the minority? Evaluation & the Health Professions. 2010; 34 (3):3–56. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ullman L. P., Krasner L. A psychological approach to abnormal behavior. Englewood Cliffs: Prentice Hall; 1969. [ Google Scholar ]
• Vohs K., Faber R. Self-regulation and impulsive spending patterns. Advances in Consumer Research. 2003; 30 :125–126. [ Google Scholar ]
• Widyanto L., Griffiths M. Internet addiction: Does it really exist? (Revisited) In: Gackenbach J., editor. Psychology and the Internet. 2nd ed. Elsevier, MA: Academic Press; 2007. [ Google Scholar ]
• Wolfinbarger M., Gilly M. C. Shopping online for freedom, control and fun. California Management Review. 2001; 43 (2):34–55. [ Google Scholar ]
• Wood R. T. A., Griffiths M. D. Online data collection from gamblers: Methodological issues. International Journal of Mental Health Addiction. 2007; 5 :151–163. [ Google Scholar ]
• Workman L., Paper D. Compulsive buying: A theoretical framework. The Journal of Business Inquiry. 2010; 9 (1):89–126. [ Google Scholar ]

A study on factors limiting online shopping behaviour of consumers

Rajagiri Management Journal

ISSN : 0972-9968

Article publication date: 4 March 2021

Issue publication date: 12 April 2021

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

Design/methodology/approach

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

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

Research limitations/implications

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

Originality/value

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

• Online shopping

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

Emerald Publishing Limited

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

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

Introduction

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

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

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

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

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

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

Literature review

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

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

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

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

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

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

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

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

Research methodology

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

Data analysis and interpretation

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

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

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

Factor analysis

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

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

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

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

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

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

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

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

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

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

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

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

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

Conceptual framework of the study

Socioeconomic status of respondents

KMO and Bartlett’s test

Cronbach’s α

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further reading

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

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

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The Factors Affecting Online Buying Behavior of Consumers During Crises: Literature Review

• Conference paper
• First Online: 28 March 2023
• Cite this conference paper

• Maryam Shaaban 13 ,
• Allam Hamdan 14 &
• Ramzia Albakri 15  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 621))

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The aim of this research paper is to understand the consumer buying behavior online during the crises. It is important to distinguish the factors that affect their online buying behavior. Many researchers have identified that consumer buying behavior is impulsive, it evaluates how emotion, thoughts and preference vary from consumer to consumer. Nowadays, purchasing any product and services is clearly different from the past-days and it is extremely influenced by digital marketing as a successful tool for increasing good advantage. The difference is that the consumers in the 21 st century are more sophisticated, and better concerned with innovative technology and internet network. Whereas they search for any product and services detailed information easily and feedback from different consumers/users. In order to achieve in today’s world and the rapidly developing market, marketers need to understand everything about their consumers such as; what they want, what they need, what they work and finally how they want to spend their money and time. Many factors influence consumers in his/her decision-making process, purchasing behavior and the selection of specific brand or merchant. By indicating and understanding the factors the marketers will have good chance to develop their strategy to attract and maintain their recent consumers and potential consumers as a real strength to better meet the need of their consumers.

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Zendehdel, M., Paim, L.H., Delafrooz, N.: The moderating effect of culture on theconstruct factor of perceived risk towards online shopping behaviour. Cogent Bus. Manag. 3 (1), 1223390 (2016). https://doi.org/10.1080/23311975.2016.1223390

Article   Google Scholar  

Rodrmguez-Ardura, I., Ryan, G., Gretzel, U.: Special issue of qualitative approaches to emarketing and online consumer behaviour: Guest editors’ introduction. J. Theor. Appl. Electron. Commer. Res. 7 (2), 7–8 (2012). https://doi.org/10.4067/s0718-18762012000200004

Patel, J., Malpani, A.: Impact of Covid19 on e-marketing: an early stage review. Int. J. Adv. Sci. Technol. 29 (12), 1774–1779 (2020)

Google Scholar  

Pärson, G., Vancic, A.: Changed buying behavior in the COVID-19 pandemic the influence of price sensitivity and perceived quality (2020)

Panda, R., Swar, B.N.: Online shopping: an exploratory study to identify the determinants of shopper buying behaviour. IJBIT 7 (1) (2014)

Saxena, E., Gupta, D.D.: Factors influencing online shopping behaviour: a review of motivating and deterrent factors. Mark. Rev. 18 (1), 3–24 (2018). https://doi.org/10.1362/146934718x15208754808171

Tyagi, A.: A study on factors affecting consumer buying behaviour. Int. J. Bus. Res. 11 (2018)

Chawla, M., Khan, M.N., Pandey, A.: Online buying behavior: a brief review and update. AIMA J. Manag. Res. 9 (2/4) (2015)

Ramya, N.A.S.A.M., Ali, S.M.: Factors affecting consumer buying behavior. Int. J. Appl. Res. 2 (10), 76–80 (2016)

Bhat, I.A., Rather, N.A., Mir, F.A.: An empirical study on factors affecting consumer satisfaction. Int. J. Res. Commer. Manag. 4 (3) (2013)

Lim, Y.J., Osman, A., Salahuddin, S.N., Romle, A., Abdullah, S.: Factors influencing online shopping behavior: the mediating role of purchase intention. Procedia Econ. Finan. 35 , 401–410 (2016). https://doi.org/10.1016/S2212-5671(16)00050-2

Bahl, R., Kesharwani, S.: Factors affecting online shopping behaviour. Glob. J. Enterp. Inf. Syst. 10 (2) (2018). https://doi.org/10.18311/gjeis/2018/22292

Mansoor, D., Jalal, A.: The global BUSINESS crisis and Consumer BEHAVIOR: Kingdom of Bahrain as a case study. Int. J. Bus. Manag. 6 (1) (2011). https://doi.org/10.5539/ijbm.v6n1p104

Njuguna, J.M.: Influence of e-marketing on consumer buying behavior in supermarkets in Nairobi country (2018)

Loxton, M., Truskett, R., Scarf, B., Sindone, L., Baldry, G., Zhao, Y.: Consumer behaviour during CRISES: preliminary research on how Coronavirus has Manifested consumer panic BUYING, herd mentality, changing discretionary spending and the role of the media in influencing behaviour. J. Risk Financ. Manag. 13 (8), 166 (2020). https://doi.org/10.3390/jrfm13080166

Uzun, H., Poturak, M.: Factors affecting online shopping behavior of consumers. Eur. J. Soc. Hum. Sci. 3 (2014)

Jin, L.Y., Osman, A.B., Bin, A.B., Halim, M.S.: Perceived usefulness and trust towards consumer behavior: a perspective of consumer online shopping. J. Asian Sci. Res. 4 (10) (2014)

Drossos, D.A., Maragoudakis, M., Kokkinaki, F.: Buying behavior on daily-deal sites: the role of face value, product involvement, information, and website quality. J. Internet Commer. 14 (2), 200–232 (2015). https://doi.org/10.1080/15332861.2015.1011568

Katawetawaraks, C., Wang, C.L.: Online shopper behavior: influences of online shopping decision. Asian J. Bus. Res. 1 (2) (2011). https://doi.org/10.14707/ajbr.110012

Kumar, A.: Factors influencing consumer behaviour. JETIR 6 (8) (2019)

Qazzafi, S.: Consumer buying decision process toward products. Int. J. Sci. Res. Eng. Dev. 2 (5) (2019)

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Maryam Shaaban

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Shaaban, M., Hamdan, A., Albakri, R. (2023). The Factors Affecting Online Buying Behavior of Consumers During Crises: Literature Review. In: Alareeni, B., Hamdan, A., Khamis, R., Khoury, R.E. (eds) Digitalisation: Opportunities and Challenges for Business. ICBT 2022. Lecture Notes in Networks and Systems, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-031-26956-1_16

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