research project on online shopping

Online Shopping

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• Yi Cai 2 &
• Brenda J. Cude  

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

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Segmenting Customers Based on Key Determinants of Online Shopping Behavior

Information Seeking Behaviour in Online Shopping

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

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

Drivers of shopping online: a literature review

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

Entradas no índice

Keywords: , texto integral, 1. introduction.

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

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

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

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

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

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

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

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

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

2. Perceived benefits in online shopping

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

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

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

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

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

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

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

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

3. Perceived risk in online shopping

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

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

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

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

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

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

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

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

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

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

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

4. Exogenous factors

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

4.1. Consumer traits

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

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

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

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

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

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

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

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

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

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

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

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

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

4.2. Situational factors

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

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

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

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

4.3. Product characteristics

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

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

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

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

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

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

4.4. Previous online shopping experiences

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

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

5. Conclusion

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

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

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

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

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Para citar este artigo

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

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Ana Teresa Machado , «Drivers of shopping online: a literature review» ,  Comunicação Pública [Online], Vol.2 nº4 / nº3 | 2006, posto online no dia 30 outubro 2020 , consultado o 16 agosto 2024 . URL : http://journals.openedition.org/cp/8402; DOI : https://doi.org/10.4000/cp.8402

Ana Teresa Machado

Escola Superior de Comunicação Social Instituto Politécnico de Lisboa

[email protected]

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• Published: 09 October 2023

Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea

• Jiam Song   ORCID: orcid.org/0000-0002-7975-0909 1 ,
• Kwangmin Jung   ORCID: orcid.org/0000-0002-5615-8865 2 &
• Jonghun Kam   ORCID: orcid.org/0000-0002-7967-7705 1  

Humanities and Social Sciences Communications volume  10 , Article number:  669 ( 2023 ) Cite this article

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A Correction to this article was published on 30 October 2023

This article has been updated

The COVID-19 pandemic has changed the level of the received risk of the public and their social behavior patterns since 2020. This study aims to investigate temporal changes of online search activities of the public about shopping products, harnessing the NAVER DataLab Shopping Insight (NDLSI) data (weekly online search activity volumes about +1,800 shopping products) over 2017–2021. This study conducts the singular value decomposition (SVD) analysis of the NDLSI data to extract the major principal components of online search activity volumes about shopping products. Before the pandemic, the NDLSI data shows that the first principal mode (15% of variance explained) is strongly associated with an increasing trend of search activity volumes relating to shopping products. The second principal mode (10%) is strongly associated with the seasonality of monthly temperature, but in advance of four weeks. After removing the increasing trend and seasonality in the NDLSI data, the first major mode (27%) is related to the multiple waves of the new confirm cases of corona virus variants. Generally, life/health, digital/home appliance, food, childbirth/childcare shopping products are associated with the waves of the COVID-19 pandemic. While search activities for 241 shopping products are associated with the new confirmed cases of corona virus variants after the first wave, 124 and 190 shopping products are associated after the second and third waves. These changes of the public interest in online shopping products are strongly associated with changes in the COVID-19 prevention policies and risk of being exposed to the corona virus variants. This study highlights the need to better understand changes in social behavior patterns, including but not limited to e-commerce activities, for the next pandemic preparation.

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Introduction.

The COVID-19 pandemic has spread out since early 2020. The corona virus is contagious and has transformed to variants. The global community has been suffered with multiple waves of new confirmed cases of the corona virus variants. The herd community of the corona virus consists of the natural infected groups and vaccinated groups. However, the herd immunity for COVID-19 is required to prevent multiple variants from Alpha through Omicron (Moghnieh et al. 2022 ). Particularly, the absence of vaccines for corona virus and its variants has exacerbated the pandemic over the world. The variants have decreased the probability to form the herd immunity.

The spread of corona virus variants has significantly increased public risk perception, thereby leading people to avoid in-person activities (Dryhurst et al. 2020 ). Markets have responded to such changes in socioeconomic landscape by rapidly adapting digital transformations, which consequently boosted online platforms relating to shopping. The public have become preferred to online shopping, rather than in-person shopping, particularly when the number of infected people increases (Grashuis et al. 2020 ; Li et al. 2020 ; Mouratidis and Papagiannakis, 2021 ; Pham et al. 2020 ). This shift of the public’s lifestyle provides an opportunity to understand the impact of the COVID-19 pandemic on socioeconomic change via big social monitoring data relating to online information seeking activities.

The impact of the COVID-19 pandemic can be examined by comparing socioeconomic activities before and after COVID-19 pandemic. However, the long-lasting pandemic crisis makes it difficult to investigate the time-varying impact of the COVID-19 pandemic. Few literature has considered temporal changes of the impact of COVID-19 through its multiple waves due to the cost of collecting relevant data and the time-consuming data preprocessing. Online social monitoring data enables us to investigate the impact of the multiple waves of the corona virus variants and relevant prevention policies on online socioeconomic activities, which are costly-efficient and real-time monitoring. Recent studies have investigated changes in online activity patterns during the COVID-19 pandemic (Gu et al. 2021 ; Lampos et al. 2021 ; Nasser et al. 2021 ). However, the socioeconomic impact of the multiple waves of the corona virus variants remains unknown.

During the COVID-19 pandemic, online shopping patterns has been investigated in various ways. A previous study discussed a chance to die or modify old purchasing habits from in-person activities and to create new habits (Sheth, 2020 ). The new habits are likely to be influenced by socioeconomic constraints, such as public policy, technology and changing demographics. Another study proposed this behavior pattern change during the COVID-19 pandemic introducing the “react”, “cope”, and “adapt” phases of the Reacting Coping Adapt (RCA) framework (Kirk and Rifkin, 2020 ). At the “react” phase, the public change their purchasing behavior based on pandemic risk perception as a social response to dynamic social distancing policies. At the “coping” phase, they start adopting new purchasing pattern based on the public policy level. At “adapt” phase, they establish/stabilize new purchasing pattern and less reactive to the pandemic situation (Guthrie et al. 2021 ; Kirk and Rifkin, 2020 ). The RCA framework has been validated by the online shopping patterns in France before, during, and after the COVID-19 pandemic (Guthrie et al. 2021 ). The application of this RCA framework to other countries and social behaviors is still lacking.

Nowadays, the Internet service providers monitor and record online search activities through data logging and analyze these online search activity data to detect changes in the user’s interest and optimize the search algorithm for most relevant information to their interest in a timely manner. For example, increased online search activities about a specific shopping product hint an emerging demand of the shopping product, which is a practical information for inventory and supply chain management.

Online social network data, such as Twitter, have been already used to predict stock market price change (Almehmadi, 2021 ). Online information search activity data, such as Google Trends, have been used to forecast the near-term values of economic indicators (Carrière‐Swallow and Labbé, 2013 ; Choi and Varian, 2012 ), private consumption (Vosen and Schmidt, 2011 ), and epidemics (Carneiro and Mylonakis, 2009 ; Teng et al. 2017 ). Recently, the utility of these data has been examined in investigating spatiotemporal changes of social response to natural disasters, such as earthquakes and droughts (Gizzi et al. 2020 ; Kam et al. 2021 ; Kam et al. 2019 ; Kim et al. 2019 ). However, These social monitoring big data have been underutilized to investigate the changes of socioeconomic activities during the multiple waves of the corona virus variants.

The NAVER Shopping website is the most popular online shopping platform among the citizens of the Republic of Korea with online sales valued at about 2.7 billion KRW in the third quarter of 2021 (2.3 million USD) ( https://www.wiseapp.co.kr/insight/detail/89 ). Online shopping activities via the NAVER shopping website can capture major modes of online shopping activities of the Koreans. For example, increased online search activities relating to a specific shopping product hint an emerging demand of the NAVER’s users relating to the shopping product (Woo and Owen, 2019 ). Rumors about an emerging topic can affect the public’s social behavior patterns via social media (Alkhodair et al. 2020 ). However, the quality of social monitoring data determines an appropriate analysis spatial scale, and a careful design of data preprocesses is necessary for quality control (Wilcoxson et al. 2020 ). Recently, it has been found that the public interest in nationwide natural disasters and global pandemics can reduce the impact of rumors on social media and online seeking activities because the rumors can be verified by the direct and indirect experience of the public from the disaster or pandemic (Park et al. 2022 ; Kam et al. 2021 ).

Recent studies found a relationship between decision making and consumer behavior patterns at the individual level during the COVID-19 pandemic (Birtus and Lăzăroiu, 2021 ; Smith and Machova, 2021 ; Vătămănescu et al. 2021 ). Statewise sentimental alterations have been also found from the public’s complaints about water pollution during the COVID-19 pandemic (Liu et al. 2023 ). However, the impact of the COVID-19 pandemic and associated prevention policies on national-level social behavior pattern remains unknown. Online social monitoring data provides a unique opportunity to examine the relationship between decision making and consumer behaviors as response to changes of the COVID-19 pandemic prevention policies.

This study aims to investigate the impact of multi-year COVID-19 pandemic, using the NAVER DataLab Shopping Insight (NDLSI) data that provided by the NAVER Corporation. The data provides online search activity volumes relating to +1,800 shopping products at the nation level, which can detect an emerging change of online purchasing activities of the Koreans. The NAVER Corporation has operated the online search engine since 1999 and is the most popular internet search engine platform in South Korea. It had 1.2 billion visits from August through October 2022, and 94% of these visits solely from the Republic of Korea ( https://www.similarweb.com/website/naver.com/#traffic ). The NAVER Coporation provides weekly online search activity volume data of 1,800 shopping times since 2017 via the NDLSI platform. Such big social monitoring data provide a unique research opportunity to examine the COVID-19 impact on online shopping activities of the Koreans within the RCA framework by answering the following questions:

What are the major components of the dynamic patterns of online search activities before and after COVID-19?

How have the social behavior patterns related to online shopping search activities changed along multiple waves of corona virus variants?

Which prevention policies are key factors of the temporal changes of online shopping search activities during the COVID-19 pandemic?

To answer these questions, this study extracts the major modes of information seeking behavior patterns relating to shopping products from the NDLSI data (2017–2021) via the singular value decomposition algorithm-based Principal Component Analysis (PCA). Furthermore, the RCA framework is validated by the major modes of the NDLSI data during the multiple waves of the COVID-19 pandemic. The PCA analysis of the NDLSI data will advances the current understanding about changes in e-commerce before and after the two-year long COVID-19 pandemic.

Data and methods

Naver datalab shopping insight (ndlsi) data.

The NDLSI data includes the number of clicks on 1,837 shopping products from the NAVER Shopping platform. This study uses the NDLSI data that provide 214-week online search activities relating to 1,837 shopping products (July 31, 2017 through August 30, 2021). Weekly relative search activity volumes of the NDLSI data range from 0 to 100 (normalized by the maximum number of clicks during the search period and multiplied by 100). The NDLSI data is classified at the three levels: 11 categories for the first level, 204 categories for the second level, and 1,837 items for the third level (see Table S1 . in Supplementary Material). These categories of shopping products are provided from NAVER shopping platform, which are based on the merchant category codes (MCCs) that a credit card issuer to uses to categorize the transactions consumers complete using a particular card. The MCCs is used to classify merchants and businesses by the type of goods or services provided in order to keep a track of transactions. Recently, changes in credit/debit card spending in the MCCs have been analyzed during the COVID-19 pandemic (Darougheh, 2021 ; Dunphy et al. 2022 ). The first level categories include Fashion clothing, Fashion Miscellaneous Goods, Cosmetics/Beauty, Digital/Home Appliance, Furniture/Interior, Childbirth/Childcare, Food, Sports/Leisure, Life/Health, Leisure/Life convenience, and Duty-free shops. The category and product names are provided in Korean. In this study, the category and product names are translated in English via the Google Translator.

Six COVID-19 metrics

This study uses the six COVID-19 metrics from the Center for Systems Science and Engineering at Johns Hopkins University (JHU CSSE) COVID-19 dataset (Dong et al. 2020 ). The six COVID-19 metrics include new confirmed cases, stringency index, residential index, vaccination index, new death cases, and fatality. New confirmed/death cases are the number of the corresponding case of the Koreans over the study period. The stringency index is estimated based on the nine metrics: school closures, workplace closures, cancellation of public events, restrictions on public gatherings, closures of public transport, stay-at-home requirements, public information campaigns, restrictions on internal movements, and international travel controls. The stringency index shows the strictness of the government prevention policies in quantitative method (Dong et al. 2020 ). The value ranges from 0 (lowest stringency) to 100 (highest stringency). Higher stringency values represent more strict prevention policies. The residential index shows the number of people who spend more time at home after the COVID-19 pandemic than before. The vaccination index is a partial vaccinated index that represents the percent of who have vaccinated at least once. The fatality index is the ratio of the number of the number of new death cases to the number of new confirmed cases. While these daily six metrics are available, this study computes and uses the weekly sums of new confirmed cases and the weekly averages of the other five COVID-19 metrics, which is a consistent temporal scale with the NDLSI data analysis. The Korea Meteorological Administration (KMA) provides the historical meteorological data of the Republic of Korea through the Open MET Data Portal platform ( https://data.kma.go.kr/cmmn/main.do ). In this study, weekly temperature averages of the 95 stations in the Republic of Korea are computed to extract the seasonality of the regional climate system.

Singular value decomposition (SVD)-based principal component analysis

In the machine learning field, the principal component analysis (PCA) is a popular unsupervised learning method. The PCA technique is known as a data compressing technique to extract key features of the high dimension data. Singular value decomposition (SVD) algorithm can be used to extract the PCA major modes (Vosen and Schmidt, 2011 ; Wilks, 2011 ). The SVD algorithm-based PCA decomposes a covariance matrix into three matrixes if the A matrix has m x n dimension (n < m; Eq. 1 ). These matrixes include the U matrix (an m by m matrix), the Σ matrix (a m diagonal matrix) and the V transpose matrix (an n by n matrix). The Σ matrix is a diagonal matrix which have one to one correspondence with the U matrix. The U matrix shows the orthogonal eigenvectors, which are known as the principal components (PCs).

In this study, the SVD algorithm is employed to the covariance matrix of the NDLSI data over the five different periods. The five periods include the period before the COVID-19 pandemic (July 31, 2017–December 31, 2019), Wave 1 (July 31, 2017–May 25, 2020), Wave 2 (July 31, 2017–October 19, 2020), Wave 3 (July 31, 2017–March 1, 2021), and Wave 4 (July 31, 2017–August 31, 2021) of the corona virus variants. Here, the waves are defined based on the surges of the new confirm cases. To explore shopping products with an increasing/decreasing interest of the public during each wave of the COVID-19 pandemic, the SVD analysis period for the wave of interest covers before the emergence of the next wave, which includes the overlapped analysis period of the previous wave. It enables us to investigate the impact of the wave of interest on the public interest relating to shopping products compared to that of the previous wave.

Two major modes are found before the COVID-19 pandemic: the increasing line trend and the seasonality pattern of the online search activities. Before employing the SVD-based PCA analysis to the NDLSI data, the linear trend and the seasonality are removed from the NDLSI matrixes over the period of Wave 1, 2, 3 and 4. The detrended NDLSI data over the different periods enable us to investigate changes of online search activities relating to shopping products over the multiple waves of COVID-19. Not available values in the NDLSI data were replaced with zeros. The U and V matrixes are the same eigenvectors of the covariance matrix and the Σ matrix includes the eigenvalues. The Σ matrix’s diagonal values show the quantitative contribution of the corresponding vector to the total variance of the covariance matrix.

Spearman’s rank correlation

Spearman’s rank correlation is a non-parametric metric to find a relationship between two variables based on their ranks (Spearman, 1904 ). This study uses Spearman’s rank correlation because online search activities of most items in the NDLSI data do not have normal distribution. Spearman’s rank correlation efficiency ranges from −1 (negative perfect relation between two variables) to +1 (positive perfect). In this study, Spearman’s rank correlation is used to trace the user’s interest in shopping products that are associated with the wave of corona virus variants. Furthermore, Spearman’s rank correlation is computed between the COVID-related metrics and NDLSI data to examine which socio-economic factors associated on e-commerce search activities. First, Spearman’s rank correlation coefficients are computed between the first principal component (PC1; one time series) and the search activities of +1,800 shopping products (>1,800 time series) during the periods of Wave 1 through 4. Furthermore, the distribution of Spearman’s rank correlation coefficient is constructed by the kernel density estimate (KDE) method from the Joyplot python package ( https://github.com/leotac/joypy ). Shopping products with a high coefficient have increased from Wave 1 through 4 (Fig. S 1 ). In this study, 0.45 of Spearman’s coefficient is a threshold value to detect up to 20% of associated item with the PC1 mode with the COVID-19 pandemic.

Quantile-Quantile plot (QQ plot)

The number of the PC-associated shopping products affect the construction of the reliable correlation distributions with the COVID-19 metrics. A Quantile-Quantile (QQ) plot is a common visualization method to determine whether two data sets are came from same distributions or not. Despite different numbers of the COVID-19 associated shopping products during each wave period, the QQ plot can detect the stability of the correlation distribution shape. The QQ plot is based on the ranks of each data, which gives an advantage that the two dataset still can be compared in the QQ plot even though the sample sizes of the two datasets are different. The one-to-one line is a reference line of the QQ plot. When the quantile line of the two data is close to the reference line, the two sample data are from the same distribution (Nist, 2006 ). In this study, the QQ plots are constructed for the sensitive analysis of the stability of the correlation distribution shape to shopping products numbers. This analysis can determine how many shopping products are needed to generate the reliable distributions of its correlation with the waves of corona virus variants (Figs. S 2 and S 3 ).

Principal components of NDLSI data

Before the COVID-19 pandemic (hereafter, Wave 0), the first and second Principal Component (PC) modes (PC1 and PC2, respectively) explained around 15% and 10% of the total variance, respectively. PC1 was a monotonic increasing trend of online search activities for shopping products. PC2 was strongly associated with the seasonality of weekly mean temperature, however the seasonal cycle of online search activities relating to shopping products was four weeks ahead of the seasonality of the temperature (Fig. 1A, B ). Based on Spearman’s rank correlation coefficients with the PC1 and PC2, the top 10 shopping products showed that these two major modes captured well an increasing trend of shopping product-specific e-commerce and the seasonality of online search relating to shopping items during Wave 0 (Fig. 1C–F ).

Weekly time series of Principal Component 1 (PC1) of NDLSI data before COVID-19 ( A ) and PC2 ( B ) with heatmap of correlation coefficients of top 10 correlated items. Associated online search activities of top 10 shopping products with the PC1 time series ( A ): Positive ( C ) and negative correlation ( D ). Associated online search activities of top 10 shopping products with seasonality ( B ): Summer- and winter-related shopping products in ( C ) and ( D ), respectively.

Results showed that the top 10 PC1-related shopping products included toothpaste, table tennis shoes, and cleaning tissue, packed lunch, and hair spray. Shopping products with a negative correlation coefficient with the PC1 mode included (car) hands free, sea fishing, Random Access Memory (RAM), and Network Attached Storage (NAS). Shopping products with a positive (negative) correlation coefficient with the PC2 mode (the seasonality of temperature in advance of four weeks) were summer (winter) season shopping products. Based on the correlation coefficients with PC2, summer season shopping products included fan, parasol, yeolmu kimchi (a type of kimchi for summer), and tarp. Winter season shopping products included brooch, beanie, and neck cape. These PC2-based items were the well-known popular shopping products for summer and winter, respectively, confirming that the PCA technique is useful to extract and interpret key features in the NDLSI data when the principal major mode is associated with a certain temporal pattern (herein, the seasonality of temperature).

Flow of PC1 related items during the COVID-19 pandemic

Results from the PCA analysis of the detrended NDLSI data showed that PC1 resembled the new confirm cases of COVID-19 over the four waves of the corona virus variants (Fig. 2 ). The percent of explained variance by the PC1 mode increased from the first wave (20%) through the fourth wave (27%), which means that associated shopping products with the corona virus variants increased during the COVID-19 pandemic. The first-level category shopping products associated with the PC1 mode showed temporal changes from Wave 1 through 4 (Fig. 3 ). For visualization, the Sankey diagram was constructed, which has been often used as an efficient visualization for changes of the flow/volume of the data (Lupton and Allwood, 2017 ).

Weekly time series of the PC1 mode of the detrended NDLSI data up to Wave 1 through Wave 4 (gray dash lines) along South Korea’s COVID-19 new confirmed cases (a sky line).

Sankey diagram of COVID-19 associated shopping products during the four waves.

Based on the result of the explained variance by the PC1 mode (around 20% of the total variance), changes in online search activities relating to shopping products with the correlation coefficient, 0.45, or higher (close to 20% of total items) were analyzed. Overall, life/health, digital/home appliance items showed a large percentage during the study periods). Outdoor activity-related category items, including cosmetics/beauty, fashion clothing and fashion miscellaneous goods, account for small portions than other category items. Associated items with the corona virus variants have increased from Wave 1 through Wave 4 by more than twice (from 327 to 714). After the first wave, new 241 shopping products showed the correlation coefficient, 0.45 or above. This inflow of online search activities were associated with shopping items in the categories of life/health (25%), digital/home appliances (15%), and food (15%) (Fig. 4 ).

Percentages of the first-level shopping product categories of inflow after Wave 2, 3, and 4.

After Wave 2, the inflow of the 125 items included life/health (29%), digital/home appliance (19%), and childbirth/childcare (12%) items with decreased item numbers (125 items). After Wave 3, the inflow of 190 items included life/health (22%), digital/home appliance (17%), and childbirth/childcare (19%) items. Interestingly, duty-free shopping products and leisure/life convenience items first appeared after the Wave 2 and 4, respectively. The leisure/life convenience category items included work out class (fitness/personal training and Pilates) abroad travel items (abroad travel package, airline ticket, Wi-Fi/ Universal Subscriber Identity Module (USIM)). Increasing online search activities relating to work out class may be come from a concern about health due to a restrict quarantine policy. Increased interest in abroad travel cases after Wave 4 suggests that the public in South Korea might have a low perceived risk of the COVID-19 pandemic and begin to consider that the pandemic is over.

To investigate the temporal change of the third-level (product-specific) category shopping products associated with the waves of the corona virus variants, changes in the correlation coefficients of the top 10 items were selected for each waves (Fig. 5 ). The results showed that 31 shopping products were associated with the PC1 component throughout the four waves. More than 32% shopping products were in the category of life/health shopping products. These 31 items can be classified into two groups: the items with a higher and lower correlation coefficient over time. The first group items included minidisc player monitor arms, webcam, interphone box, fabric, handicraft supplies/subsidiary materials, character card/ticket, processed snacks, cooking oil/oil, bread, tuning supplies, craft, feed, seeds/seedlings, water aperture, gravel/sands/soil, landscape tree/sapling. These first group shopping products showed a persistent increase in the correlation coefficient through the multiple waves. The second group items included gas range, microwave, toothbrush, hula hoop. These second group shopping products showed a decrease in the correlation coefficient (Fig. 5 ).

The numbers of the Wave 1 through 4 heatmaps are Spearman’s rank correlation coefficients of the shopping products with the PC1 mode. The Wave 2 to 4 heatmap depict the percent changes of Spearman’s rank correlation coefficients compared with the correlation coefficients after Wave 1 (( Corr X – Corr 1 )/ Corr 1 ) * 100, where X depicts the wave occurrence order (X = 2, 3, and 4).

Weekly time series of the COVID-19 new confirmed cases ( A ), the stringency ( B ), residential index ( C ), vaccinated rate ( D ), new deaths by the corona virus ( E ), and fatality ( F ).

These two shopping product groups might originate from the different social response to the strictness of prevention policies. During the first wave, the government forced the public to stay at home to minimize the risk of being exposed to the corona virus. However, the prevention policies became less strict at Wave 4 to account for the fatigue of the public from the multi-year pandemic and revive local business and industry sectors. While the first group items have become more associated with the waves of the corona virus variants, the second group items no longer showed a high correlation coefficient with the corona virus variants.

Association with the six COVID-19 metrics

A surge of new confirmed cases of corona virus variants can influence social behavior patterns relating to e-commerce in a different way due to a different level of the COVID-19 prevention policy and the easy access of online shopping activities. In this study, Spearman’s rank correlation coefficients between the six COVID-19 metrics and the NDLSI data are computed to investigate potential causes of changes in online search activity volumes of shopping products (Fig. 6 ).

The six COVID-19 metrics showed different correlation distributions with the six COVID-19 metrics (Fig. 7 ). As the sensitivity test of the correlation distribution shape to the number of shopping products, the Quantile-Quantile (QQ) plots have been made along the different shopping times (see Figs. S 2 and S 3 ). According to the QQ plots, the top 50 items were chosen to construct the correlation distributions of the top 50 shopping products with the vaccination index. The correlation coefficients were widely distributed, indicating a relatively weak association with online search activities relating to the shopping products (Fig. 7A ). The correlation distributions with the stringency and fatality indices showed a low variance with high correlation coefficients above 0.8. The correlation distribution with the residential index showed a relatively low correlation coefficients than those with the stringency and fatality indices. New confirmed and death cases showed a relatively high variance than the correlation distributions with the fatality and stringency data. The categories of the top 50 shopping products included life/health (20%), digital/home appliance (16%) and food (16%), shopping products (Fig. 7B ).

Distributions of Spearman’s rank correlation coefficient of top 50 items related to the COVID-19 pandemic with six COVID-19 metrics ( A ), and the pie chart of first category percentage of items of top 50 items ( B ).

To investigate associations of online search activities relating shopping products with the six COVID-19 metrics, the Spearman’s rank correlation coefficients with the 31 PC1-associated items associated with the COVID-19 pandemic were computed (Fig. 8 ). New confirmed cases, stringency, residential index, new death cases and fatality showed a high correlation coefficient with the most of top 10 shopping products. The vaccination index showed no significant correlation coefficient with the top 10 shopping products. Gas range, baby walker and toothbrush items showed a relatively low correlation with the COVID-19 metrics than other shopping products. Online search activities relating to these shopping items showed a decreasing correlation during COVID-19 pandemic (see Fig. 5 ), that is, these items no longer show a significant effect of the COVID-19 pandemic after the Wave 4.

Heatmap of Spearman’s rank correlation coefficient between COVID-19 metrics and the 31 shopping products.

Overall, the stringency and fatality metrics generally have high association with the changes in online search activity patterns for shopping product. Stringency can be regarded as how government control public strictly. Fatality shows seriousness of pandemic. The results indicate that consumer behavior response sensitively to extent of restriction policies and seriousness of pandemic.

This study used the NDLSI data about the online search activity volumes for shopping products, not real purchasing data. Using the data of online search activities can provide an evidence on emerging purchasing patterns of the public in the next regime, implying that the public might tend to purchase items that have been most searched in the previous timeframe (Chen et al. 2017 ). Lately, credit card data Footnote 1 and bar cord data Footnote 2 include the records of actual purchase activities. Integrating the actual purchase data and online search activity data can provide more practical guidelines and plans for socio-economic changes not only during the COVID-19 pandemic, but also the post pandemic period.

This study revealed that the public interest in online shopping products had been changed not only after the first wave of the COVID-19 pandemic but also during the following three waves. These dynamic patterns of the public interest in online shopping products were possibly explained by the RCA framework (Kirk and Rifkin, 2020 ). The RCA framework consists of reacting, coping, and adapting phases, and significant changes in social behavior patterns are expected during a transition period from one to another phase. The first wave was a typical ‘react’ phase because people responded to the pandemic situation. A large inflow volume after Wave 2 (241 items) indicated a coping phase. The new confirmed cases were relatively low during Wave 2 (see a line colored in sky in Fig. 2 ) compared with those during other waves. Inflow of online search activities relating shopping products was the minimum after Wave 2. This finding suggests that a transition from a ‘react’ to ‘coping’ phase might occur between Wave 2 and Wave 3. After Wave 3, the public coped with the long-term pandemic. During Wave 4, the categories related with outdoor activities show a low percentage, indicating a low level of the public interest in outdoor activities due to the COVID-19 quarantine policy. The result that the inflow of online search activities relating to leisure/life convenience items (workout class, abroad travel) at Wave 4 indicates that the public became less reactive to the wave of the corona virus variants, which hints an emerging signal of a low perceived risk of the COVID-19 pandemic after Wave 4. Therefore, the ‘adapt’ phase transition is expected after Wave 4.

Understanding the public’s purchasing patterns amid a global crisis via big social monitoring data is critical from the risk management perspective. Risk control (e.g., self-protection) and financing (insurance) strategies can be improved for the next global crisis by understanding and predicting changes in social behaviors. This study found that the shopping products with an increased interest of the public have been changed during the two year-long COVID-19 pandemic, which can be explained by different stages of the RCA framework. The social behavior patterns found by this study had been also reported from the observed reacting and coping consumer behaviors in mass media and online and reacting public behavior to social distance during the COVID-19 pandemic (Guthrie et al. 2021 ; Kirk and Rifkin, 2020 ; Tintori et al. 2020 ). Specifically, better understanding and predicting of which products can help markets manage inventory of shopping products that are in an emerging high/low demand throughout different regimes of the crisis. This study found that associations of these products were more clear when they were used for self-protection measures (e.g., facial masks in the COVID-19 pandemic).

Governments and authorities can accordingly implement changes in the public’s actions to prevent potential market failures that, for example, self-protection measures may not be sufficiently supplied, or big market players use their power to dominate necessity markets (Stiglitz, 2021 ). These responses from the public and private sectors can be optimized with prevention plans in a timely manner of different waves of the crisis by analyzing big social monitoring data. This study found changes in the interest and demand of the shopping products related to self-protection measures during the COVID-19 pandemic, which hints how to facilitate big social monitoring data to mitigate the adverse effects of daily infections. Furthermore, this information can help insurance industries manage systematic risks that cannot be fully controlled by individuals or other industry sectors, which can offer risk transfer measures (Alonso et al. 2020 ; Harris et al. 2021 ; Peiffer-Smadja et al. 2020 ; Rita et al. 2019 ). This study also found a strong association between changes in online search activities of the public relating to shopping items and perceived risk, which was previously found in the travel insurance purchasing patterns (Al Mamun et al. 2022 ; Tan and Caponecchia, 2021 ). This information can give an insight for how to increase the public’s willingness to prepare for the next pandemic.

Search engine optimization (SEO) algorithms for searching items have been developed, particularly in the e-commerce sector to increase the customer’s satisfaction and loyalty (Husain et al. 2020 ; Liu et al. 2008 ; Pratminingsih et al. 2013 ). Some online search engine platforms collect the data of the user’s online activities and optimize the customized recommendation algorithm that could give more relevant result of searching. Especially, e-commerce sites, such as Amazon, have developed this customized SEO algorithm to increase a chance to purchase the products (Heng et al. 2018 ; Linden et al. 2003 ). In this study, the observational evidence of the COVID-19 impact on online search activities about shopping products was reported, which was also found in online shopping pattern for apparel (Watanabe et al. 2021 ). The SEO algorithms developed by the data before the COVID-19 pandemic increased the user’s complaint by three times (Dahiya et al. 2021 ), implying that the COVID-19 pandemic was an unprecedented event since the advent of Internet that supposedly cause a drastic context difference. Therefore, the SEO algorithms are needed to update until the data after the pandemic is sufficient. Furthermore, the expected continued growth of online commerce industries requires the coping strategies to adapt an increasing trend of not only pandemics but also other disasters such as climatic extremes, pandemic, war, and terror.

This study provides an insight about how social big monitoring data can help authorities to better understand the social response to COVID-19 via near real-time social monitoring data. In this study, the NDLSI data about the online search activity volumes relating to shopping products, not real purchasing data, were used. The NDLSI data analysis provided a possible evidence on an emerging change in the public’s purchasing patterns at the shopping product level. Previously, it was found that the public tended to purchase shopping products that have been most searched in the previous timeframe (Chen et al. 2017 ). Associations between the public interest in shopping products and purchase records can be explored using credit card data Footnote 3 and barcode data Footnote 4 . These data have been used to investigate changes in spending associated with stringent nonpharmaceutical interventions during the COVID-19 pandemic (Horvath et al. 2023 ). Integrating the actual purchase data and online search activity data can give more practical guidelines and plans for socio-economic changes during not only the COVID-19 pandemic, but also the post pandemic period. Furthermore, the e-commerce sector can harness social big monitoring data to develop their strategic plans for supply chain management for the next pandemic.

This study also explored associations of changes of online search activity patterns with the COVID-19 metrics. The results showed that the COVID-19 metrics, except for vaccination, were strongly associated with changes in online search activity patterns relating to shopping products. The stringency index was a reliable indicator of the strictness of the government’s response to the COVID-19 pandemic and had a significant impact on social behavior patterns, which is in line with the findings of Makki et al. ( 2020 ) that the timing and duration of the stringency implementation are key factors to prevent the spread of the corona virus variants. Furthermore, a recent study found that policy perceptions affect the practice of volunteered prevention behaviors, such as mask waring and social distancing (Lee et al. 2021 ). They found that the perceived policy stringency was associated with actual risk and political ideology, causing noncompliance in communities during the COVID-19 pandemic.

The proposed methods in this study have some limitations. For example, the results based on the correlation analysis provide potential, not actual, triggers of changes in the social behavior patterns during the COVID-19 pandemic, which have previously known as the caveat of the correlation analysis (Haley and Drazen, 1998 ; Stigler, 2005 ). The findings of this study about potential triggers however can help design more effective and efficient interview and survey questionnaires to investigate true triggers of changes in the public interest in shopping products. Combined information from big social monitoring and survey/interview data will create new knowledge about the dynamics of social behavior patterns and help develop a reliable social behavior prediction modeling.

Conclusions

This study succeeded to extract the major modes of the public’s interest in shopping products and investigate changes in online search activities relating to associated shopping products with the COVID-19 pandemic. The SVD algorithm-based PCA analysis of the NDLSI data showed the dynamic patterns of online search activities relating to shopping products during the two year-long COVID-19 pandemic. Before the COVID-19 pandemic, an increasing trend and seasonality of online search activity volumes about shopping products are the major mode of the NDLSI data. After the COVID-19 pandemic, the impact of COVID-19 on online search activities relating to shopping products were various during the four waves of the corona virus variants, particularly when the objective risk was dramatically increased. Changes of the online search activity patterns were associated with the change of the COVID-19 prevention policy and objective risk of being exposed to the corona virus variants. This study attempted to explain the changes of these online search activity patterns within the RCA framework by identifying the react, coping, and adapt phases.

This study highlights the utility of online social monitoring data in developing strategic plans for preparation, mitigation, and recovery policies for the next pandemic. Furthermore, the findings of this study can guide how to design interview and survey questionnaires to investigate actual drivers of social behavior changes during the COVID-19 pandemic. Integrated studies using online social monitoring data and survey and interview data will advance the current knowledge and prediction skill of social behavior changes, which can provide actionable information to mitigate its adverse effects for the sustainable development of our communities Kim et al. ( 2019 ), Spearman ( 1904 ).

Data availability

The data used in this study are available at Harvard Dataverse: https://doi.org/10.7910/DVN/JT8RCK .

Change history

30 october 2023.

A Correction to this paper has been published: https://doi.org/10.1057/s41599-023-02297-3

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Acknowledgements

We thank the NAVER DataLab for making available the NAVER DataLab Shopping Insight (NDLSI) data. This study was supported by a grant from the National Research Foundation of Korea (NRF-2021R1A2C1093866).

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JS: conceptualization, data curation, investigation, formal analysis, methodology, software, validation, visualization, writing—original draft, writing—review and editing. JK: conceptualization, methodology, funding acquisition, project administration, resources, supervision, writing—review and editing. KJ: analysis, validation, writing—review and editing.

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Song, J., Jung, K. & Kam, J. Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea. Humanit Soc Sci Commun 10 , 669 (2023). https://doi.org/10.1057/s41599-023-02183-y

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CONCEPTUAL ANALYSIS article

Service marketing in online shopping platform: psychological and behavioral dimensions.

A correction has been applied to this article in:

Corrigendum: Service Marketing in Online Shopping Platform: Psychological and Behavioral Dimensions

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• 1 Business School, Huaiyin Institute of Technology, Huai'an, China
• 2 Doctoral Program, I-Shou University, Taiwan, China
• 3 Management School, University of Liverpool, Liverpool, United Kingdom
• 4 North China University of Water Resources and Electric Power, Zhengzhou, China
• 5 Department of Business Administration, Cheng Shiu University, Kaohsiung, Taiwan
• 6 Center for Environmental Toxin and Emerging-Contaminant Research, Cheng Shiu University, Kaohsiung, Taiwan
• 7 Super Micro Mass Research and Technology Center, Cheng Shiu University, Kaohsiung, Taiwan

This conceptual analysis critically discusses how service marketing is workable for online shopping platforms and how important service-related and influenced factors played their roles the aforementioned issue. The concepts of service, service marketing, and related factors were re-visited, or at least reflected, in the new context of online platforms. Mostly, we framed the essence and importance of those discussed factors from the psychological and behavioral angles. Implications for theory, practices, and policy-making were offered seriously.

Introduction

Recent years have witnessed the huge interests in the trending research on service marketing ( Rust and Huang, 2014 ). The researchers have highlighted that service is one of the most important section of marketing that could be felt by consumers before the consumption, and thus could influence the purchase decision directly ( Rust and Huang, 2014 ). With the development of service marketing, this concept has been extended from physical to virtual marketplace in various forms ( Schultz et al., 2013 ). When people reach nearly all the shopping platforms, the concept of “service” goes beyond the traditional understanding and increasingly covers other new conceptual elements ( Shareef et al., 2016 ; Al-kumaim et al., 2021 ).

Nevertheless, with the development of online shopping, it is increasingly difficult to evaluate which high-quality standards online shopping platforms should have. Such unclear standards caused interest of researcher, to systematically examine how service marketing could be applied in the online shopping platforms to contribute to platform performance. Doing so, a conceptual analysis provided by this paper can contribute in three ways. First, we could explicate the erratic and fuzzy standards to judge the effectiveness of service in online platform contexts, which help improve the comprehensiveness and conceptualization for further (qualitative and quantitative) studies. Second, for practitioners of online shopping, the review and analytical arguments of this study could provide theoretical support about how to develop the quality service mechanisms (procedures, rules, references, etc.). Third, for policymakers, a clearer description about the standards of service marketing in online shopping platforms can facilitate reasonable policies formation to deal with both the civil and commercial disputes related to the services of online shopping platforms.

Service marketing has become one of main subfields of marketing. The value of service marketing lies in the expectations and reactions of consumers ( Fan and Dong, 2021 ). The services may be offered directly or indirectly to the consumers in business to consumer (B2C) or business to businesses (B2B) ( Rust and Huang, 2014 ). With increasing popularity, the online shopping platforms have shown their potentials to replace the traditional shopping mechanisms. Generally, the traditional marketing mix is also working in the online shopping platforms, for which the consumers are still influenced by 4Ps (Product, Price, Place, and Promotion) even if the effects of “place” has been weakened to some extents ( Rust and Huang, 2014 ). Namely, the traditional 4Ps are still able to be extended by service marketing in the online platforms. However, as an invisible form of marketing, it is more difficult to be managed when it is applied on a virtual network platform. Customer service is crucial for online shopping platforms that demands high level of customer loyalty based on customer perceptions of the platform's service quality.

Unfortunately, there is still a lack of commonly accepted answer about what standards could be used for online platforms. Due to such vagueness, various online shopping platforms are unable to create their own high-quality service marketing clearly and effectively. Even if their service quality is improving, with deep understanding of service, the cost of these attempts is not only capital investment but also the patience and evaluation of the consumers ( Chang et al., 2016 ).

Against such background, this article tends to reference related literature to conduct an integrative conceptual analysis for the issues mentioned above.

The Conceptual Analysis

Most of the extant studies direct to a common goal of explaining why online shopping platforms should improve service. In the earlier research, the importance of service and service marketing did not receive enough attention, for some studies even believed that the online shopping did not incorporate service concepts. However, with development of service, this claim has been overturned by further studies who claimed that the service quality could increase the retention rate of consumers. In addition, the studies form Hsieh and Tsao (2014) also stressed that the high-quality service of online shopping platform would reduce the perceived risk of consumers and further increase the consumer loyalty.

After that, the researcher discussed the factors which could influence the service marketing in online shopping platforms to find out the method to improve service quality. Afterward, claimed customer commitment would also influence the consumer perception of services of online shopping (Singh and Pandy, 2016), which has been proved by other studies. Finally, for consumers, the perceived services would also be different according to the different values ( Blasco-Arcas et al., 2014 ; Chang et al., 2016 ).

Finally, the researcher collected related opinions about how online shopping platforms could improve the service quality. In some studies, it was mentioned that the consumer satisfaction of service quality is highly depended on the ease of use of website pages rather than the personalization. Moreover, with the development of online shopping portals, the uses of shopping platforms are not only limited for purchasing but also used as communication channels at present. Therefore, Lazarus et al. (2014) put forward that online shopping platforms, especially for some platforms that highly rely on the mobile channels, some factors should be given priority to the consideration, such as convenience, security, and emotional values. The last but not the least, Chang et al. (2016) and Zheng et al. (2017) found out that the coupon proneness and value consciousness play important roles in explaining the e-loyalty.

Service Marketing

In the existing studies, the importance of service marketing has been accepted widely by scholars and practitioners.

With the forming of unique concept of service, service marketing has become an important subject of marketing. According to Haynes and Grugulis (2014) , the service is believed to include four main characteristics, such as involving intangibility, inseparability, perishability, and variability, which is a commonly accepted opinion. First, service is a concept that lacks physical form, which means that service does not interact with consumers through any conventional senses. The value of service is created by the consumption or experience, as its ownership could not be transferred, so that the quality of service is unable to be evaluated before purchasing. Inseparability simply means that the production and consumption are inseparable, so that the service marketing is also influenced by this characteristic that the process of service marketing is highly contacted and labor-intensive. Through the impact of inseparability, the companies that focus on the service marketing are easier to be influenced by the capital for labor and human error. Moreover, service is ephemeral and unable to be stored. In other words, the supply of service could not have buffer between the supply and demand, because all supply should be provided timely. The last characteristic of service is the variability, also known as heterogeneity, which states that the services are inherently variable in quality and substance. Namely, service quality is difficult to manage and there are fewer opportunities to standardize the service marketing delivery. According to the abovementioned characteristics, the unique characteristics of services give rise to the problems and challenges of service marketing that are rarely paralleled in product marketing. These challenges and problems would be further discussed as follow.

The Classification of Service

In current theories, the framework of service marketing has been controversial according to various standards to distinguish different types of services. The first classification is related to who or what is being processed, involving people processing, mental stimulus processing, possession processing, and information processing ( Lazarus et al., 2014 ). This type of classification mainly relates to the sources of core value generated with service. In addition, there is another method to classify service marketing according to the degree of customer interaction, involving high low contact services ( Yahyaoui et al., 2015 ).

However, the quality of services is difficult to judge before purchasing ( Yahyaoui et al., 2015 ), so that economists believe that nearly all of service marketing could be classified with a processual framework of “Search → Experience → Credence (SEC)” ( Girard and Dion, 2010 ). According to this framework, most products fall into the search goods category, those which possess attributes that can be evaluated prior to purchase or consumption. Experience goods mean products or services that can be accurately evaluated only after the product has been purchased and experienced, which could be seen as another side of search products. It is different to the above two classifications, credence claims are the goods that are difficult or impossible to evaluate even after consumption has occurred ( Girard and Dion, 2010 ). These goods are called credence products because the quality evaluations of the consumers depend entirely on the trust given to the product manufacturer or service provider ( Girard and Dion, 2010 ). According to these different classifications, the forms of service marketing operated in online shopping platforms are also different, so that the content of this section would be used in further discussion below.

Service and Service Marketing in Online Shopping Platforms

Due to the virtuality of online shopping platform, the importance of service and service marketing did not pay enough attention, to which some earlier research even believed that the online shopping did not have the concept of service. Service goes with different face in physical vs. online world. In a previous study, the researchers claimed that the online shopping is a new concept that could subvert over the position of traditional shopping and get rid of the limitation of services provided by humans. Some researchers also put forward a similar opinion that the online shopping based on machinery and procedures did not have the ability to exchange emotion with consumers, so that consumers would not have demands and expectation emotionally, which could create a fair competitive channel because the consumer would only focus on the quality of products (see also Wei, 2021 ). This claim seems reasonable according to the background of era, but it has been denied at present. In the further analytical study, they found out the optimal service level on the “fulfillment and responsiveness” function for the risk averse uniquely exists. Customer loyalty is more positively correlated to the service level, which could cause the largest optimal service level. Moreover, the business organizations do not have to worry about the increase of cost on service-construction would reduce profits, because it was found out that the optimal service level is independent of the profit target, which states that the profits of organizations would not be reduced by the improvement of service. In other words, if the organization could achieve nearly optimal service level, no matter how organizations create its targets, the service quality would only increase the retention rate of consumers.

Based on the above discussion, in nature, physical vs. online service marketing differ in the following ways. First, the medium of service marketing delivery is different (e.g., Majeed et al., 2020 ). While service marketing delivery can be done through touchable medium, which can be stored, transferred, and removed in physical ways, and online service marketing can only be passed via a virtual medium, which has higher level of dangers to be easily copied or removed. The different medium of service marketing delivery also affects the ways the stakeholders interact via the marketing medium ( Méndez-Aparicio et al., 2017 ). Also, the customers experience differently ( Wong et al., 2018 ). Second, the physical and online service marketing differ in the scope and speed of marketing outcomes. The range of influences can be broader by online service marketing, and for online service marketing the marketers could see the results of their strategies faster. Third, also due to the aforementioned difference in scope and speed, the online service marketing relatively relies on the technological advantages, such as artificial intelligence and big data, which offer more instant feedback for service marketing modification during the marketing contacts. The need for precision is higher in online service marketing as compared with the physical service marketing.

The Perceived Risk Determines Online Loyalty

As mentioned above, the service quality is more related to the consumer perception than profit target, but it also means that the service of online shopping highly relates to every factor that could be perceived by the consumers, such as risks. The study of Hsieh and Tsao (2014) is highly representative among all the studies discussed about perceived risks, in which they analyzed the role of perceived risk within the services of online shopping platform in-depth. To discuss the reason why it is necessary to mention perceived risk, Lazarus et al. (2014) provided a statement that perceived risk has a significant negative effect on online loyalty, which states low perceived risk means higher online consumer loyalty. In the study of Hsieh and Tsao (2014) , they mentioned that system quality and information quality do not have significant negative effects on the perceived risk, which seems an opposite opinion with Lazarus et al. (2014) . However, Lazarus et al. (2014) only claimed that the security should have a priority to consideration, but they did not mention it would influence the perceived risk. Therefore, the study of Hsieh and Tsao (2014) is a supplementary explanation of Lazarus et al. (2014) . Moreover, Hsieh and Tsao (2014) found out that e-service quality has a significant negative effect on perceived risk, which means high-quality service of online shopping platform would reduce the perceived risk of consumers. Managerially, this paper offered a conceptual foundation for service marketing tactics for online platform.

The Consumer Loyalty Equal to Success

It is clear that the studies of Hsieh and Tsao (2014) mentioned that the service is related to the consumer loyalty, but they did not further explain how important consumer loyalty is in the operation of organizations. In fact, it has been mentioned by other researchers that the importance of consumer loyalty directly influenced the success of companies in long-term. The consumer loyalty, or called as customer retention, plays an irreplaceable role in the operation of online shopping platforms, but the situation is that only a few of managers could really understand its significances. First of all, the direct influence of consumer loyalty is to increase the repurchase intention that the high evaluation of online services or high consumer loyalty could largely increase the possibility of repurchasing. According to the results of model based on the Information Systems (IS) use theory and social exchange theory, it was found that the shopping habit increases the influence of emotional evaluation on continuance, while habit weakens the impact of rational evaluation on continuance intention. Some researchers similarly conclude that trust and perceived benefits are the key predictors of the consumer attitudes toward online shopping, as 28% of the variation in online shopping attitudes was caused by the perceived benefits and trust. In other words, a higher brand loyalty or repurchase rate could reduce the impacts of rational evaluation, and further increase fault tolerance in competition and the rate of success.

Therefore, according to some research, the service quality will increase the retention rate of consumers directly, which is independent of the profit target. In addition to the high-quality services, the perceived risk of consumers played the important role when organizations intend to increase consumer loyalty, and consumer loyalty directly influenced the success of companies in long-term.

The Factors to Determine the Quality of Service in the Online Shopping Platforms

Personal privacy.

According to the existing studies, most of the researchers believe safety to be one of the most important factors that would influence the consumer perception of services. As a virtual trading platform, online shopping platforms are more prone to the security issues, which directly influence the service quality of platforms. In a previous study, it was mentioned that perceived Web security and personal privacy concerns can influence the consumer acceptance of online shopping. This opinion was accepted by some researchers in their study which claimed that the users form perceptions of security control that strongly determine how much trust they put in online services. However, security control is a difficult measure to observe the credence quality of online services that Internet users cannot easily assess through the research or experience. Moreover, in addition to qualitative analyses, the importance of safety is also supported by some quantitative data. For example, a survey was created and 120 questionnaires were distributed among the students and the general public. The results showed that people already shopping online prefer to purchase online in future, because of the most important privacy factors. Therefore, according to the current studies, the personal privacy is one of most influential factors to influence the consumer perception of service marketing in online platforms.

Commitment for Customers

In addition to safety, some studies claimed customer commitment would influence the consumer perception of services of online shopping. It was found out that different attitudes of consumers toward online shopping shows that they would still prefer the traditional shopping pattern because of various traits related to promises, such as value, trust, and comfortable. At the same time, the key antecedents and consequences of marketing in online retailing highly rely on four mediators, involving trust, commitment, relationship quality, and relationship satisfaction. A novel multiple-criteria decision-making (MCDM) approach is used to solve the decision of service quality for shopping platform services. They also claimed that similarity and seller expertise were found to have the strongest impact on the relational mediators. Indeed, the security control perceptions of the users arise solely from their predispositions, but online service providers can influence the consumers. Thus, existing studies concluded that the commitment of supplier in online platforms would highly influence the attitude of consumers because word of mouth was the most critical outcome of relationship marketing efforts.

Values of Consumers

However, it was showed that the determinants of online shopping acceptance differ among the service types, but the previous studies have limited the generalizability of their results to a few products at best. The “fulfillment and responsiveness” function is significantly related to the customer loyalty in online shopping platforms. However, they still identified a research gap about whether the customer loyalty would be influenced by the individual values. In the research of Chang et al. (2016) , the researchers intended to discuss this research gap, and researchers used 866 samples to explore the relationships among the intrinsic motivation, extrinsic motivation, flow, cognitive attitudes, perceived satisfaction, and purchase intention of online shopping of the consumers from a cognitive attitude perspective. The results indicated that hedonic value, utilitarian value, security, and privacy significantly affected the cognitive attitudes (i.e., cognitive trust and perceived risk). The results of Shu-Hao Chang et al. (2016) initially proved that the individual values of consumers would influence the effects of consumer loyalty, which the perceived services would also be different according to different values. Blasco-Arcas et al. (2014) claimed that the online cues related to customer to customer (C2C) interactions and coproduction in the engagement platform determine the customer co-creation experiences ( Razmus, 2021 ; Siddique et al., 2021 ). For example, if the customers perceive that they are co-creating the experience, their purchase intentions increase ( Razmus, 2021 ; Siddique et al., 2021 ).

Thus, for the discussion about what factors would affect the service in online shopping platforms, some most important factors have been found out by existing study, such as personal privacy, commitment for customers, and values of consumers. Compared with the other factors, the personal privacy is the most influential factors to influence the consumer perception of service marketing in online platforms. In addition, the role of commitment of supplier in online platforms is that it highly influences the attitude of consumers, because word of mouth is the most critical outcome of relationship marketing efforts. Finally, even if the values of consumers are less influenced by online platforms, individual values are still able to influence the effects of consumer loyalty, due to different perceived standards.

Online Platform Service Quality Improvement

The ease of use.

As discussed before, there were three main factors that would highly influence the consumer perception of service in online shopping platforms. Thus, in this section, researcher intends to analyse how service marketing could be applied in online shopping platforms. In fact, this is not a new topic in academia; a research model was developed to examine the relationship among the service quality dimensions and overall service quality, customer satisfaction, and purchase intentions. The result seems outdated at present due to fast development of online shopping in past 10 years, but it still explained how early scholars understood service marketing of online shopping platforms. In this study, the researcher collected data from a survey of 297 online consumers to test the research model. The analytical results showed that the dimensions of website design, reliability, responsiveness, and trust affect overall service quality and customer satisfaction. Moreover, service quality is significantly related to the customer purchase intentions. However, the personalization dimension is not significantly related to overall service quality and customer satisfaction. In other words, in earlier period, the consumer satisfaction of service quality is highly depended on the ease of use of website pages rather than the personalization.

The Security of Shopping

With the development of online shopping, the uses of shopping platforms are not only limited to the purchasing but also as communication channels at present. Therefore, the uses of online shopping platforms have been classified into social networking rather than pure functional websites. As a result, Lazarus et al. (2014) used to mentioned that the interaction-centric capabilities for engaging consumers are the basis of “co-creation capabilities” of a firm. These capabilities can be used as the strategic tools to develop competitive advantage for service firms under service-dominant logic. Past literature has indicated that the consumption value is an important factor in consumer decision-making on whether to adopt online shopping. However, the previous studies of the indirect effects of personal characteristics on the adoption of online shopping have emphasized solely the importance of utilitarian values, but none have investigated the indirect effects of consumption values that include both utilitarian and hedonic aspects. As a result, in this research, the researchers discussed how online shopping platforms could use the consumption values and personal characteristics to carry out high-quality services. The results showed that online shopping platforms, especially for some platforms that highly rely on mobile channels, some factors should be given priority to consideration, such as convenience, security, and emotional values.

The Role of Promotion

As a type of shopping method, the promotion could play a positive role to increase the consumer satisfaction. Moreover, in the later research from Chang et al. (2016) , 866 samples were collected and analyzed using the structural equation modeling for validation of the proposed model. They found out that the cognitive attitudes significantly affected the perceived satisfaction and purchase intention, respectively. Flow significantly and positively influenced the cognitive trust and purchase intentions, respectively. The cognitive trust is the mediators between motivations and perceived satisfaction/purchase intention. For how could improve the perceived satisfaction and purchase intention, in addition to the traditional or mentioned methods, Zheng et al. (2017) used a sample of 537 users of an online shopping platform to advance the theoretical understanding of e-loyalty by exploring the roles of coupon proneness and value consciousness in the context of online shopping platforms. They found out that coupon proneness and value consciousness play important roles in explaining the e-loyalty.

In other words, the quality of service operation on online platforms is highly depended on the ease of use of website pages rather than the personalization. However, with the development of technology, the demands of security are also increased in the past few years.

Discussions

This research aimed to discuss the role of service marketing in online shopping platforms and in-depth discuss the importance of service marketing in the marketing of online shopping platforms. However, even if the concept of “service” has had a clear definition, it is still a visible product that its value only depends on the gap between the consumer expectation and real experience. Moreover, as one of subfields of marketing, service marketing and product marketing are always combined in the marketing strategies, so that the discussion of service marketing in online platforms cannot be fully independent of the influence of product marketing. Thus, another disadvantage of this type of studies is that the performance of service marketing is highly influenced by the products, as there are different products that are sold on online shopping platforms. In other words, invisibility of service marketing and dependency with product marketing caused the research aim difficult to be achieved.

In the current study, the importance of services for online shopping platforms has been discussed in-depth, in which the researchers not only analyzed how service influence the success of organizations but also what method could be used by these organizations. Service marketing, as one of main subfields of marketing, is more difficult to be managed when it is applied on a virtual network platform. However, with the development of service, this concept has been paid more attention than before at present, so business organizations need to put enough attention on the improvement of service quality.

Theoretical Implications

First, as an academic literature review, this paper reviewed numerous representative studies in past 15 years, so that this paper could give other researcher a guide of future projects. In addition, service marketing is becoming increasingly popular in recent years, as most of the marketing scholars are interested in this field. Therefore, the topic area of this study is a hot topic that would not be outdated in a short time.

Even if this paper conceptually analyzed the extant literature systematically, there are still some possibilities for the future studies. These research gaps are divided into the guesses that have not been proved and the questions put forward by the current study. The first opportunity is related to the questions mentioned left by earlier studies. Previous studies mentioned that online shopping based on machinery and procedures did not have the ability to exchange emotion with consumers; however, it was found if an organization could achieve nearly optimal service level, the service quality would only increase the retention rate of consumers. However, the previous studies did not explain the optimal service level or provide the framework to test the optimal service level. Even if a researcher has found some factors that could influence the service quality, it is still unable to answer this question. Thus, the first opportunity is to know how online shopping platform could create the optimal service level or how these organizations divide into different levels.

Second, as early as 2005, Guang and Fen have found that the consumer satisfaction of service quality is highly dependent on the ease of use of website pages rather than the personalization. Even in 2015, Assarut and Eiamkanchanalai mentioned that online shopping platforms should provide priority to consider some factors, such as convenience. Both these studies did not discuss the role of personalization, but some research (e.g., Koch and Benlian, 2015 ; Zobov et al., 2016 ; Oberoi et al., 2017 ) claimed personalization has become increasingly important at present, so the second opportunity is to prove whether personalization has become more important than some factors, especially in the current era.

Third, the most important contribution of this study is that it provided experiences about long-term and voluminous literature review, laid the foundation of future research. Moreover, the topic of this project is about service marketing and online shopping platform, created a systemic review about the concepts about these fields in-depth.

Fourth, the current conceptual analysis discussed the psychological and behavioral aspects of the important issues separately. Future studies are encouraged to discuss, or empirically examine, the issues that are at the intersection of the two major perspectives. As individual psychology and behaviors could be mutually influential, more interesting phenomena and issues of service marketing in online shopping context might be explored by integrating the two perspectives.

Fifth, future research should take the role played by culture into account when investigating the service marketing in online shopping context. Here, culture not only refers to that in physical settings (societal, social, organizational, etc.) but also that embedded in virtual and neuro-psychological worlds. In the age of internet and more modernized technologies, the way culture forms and functions are very different from that in traditional business settings. Whether in physical or virtual settings, however, one thing stays for sure is that culture could affect the human cognition, interactions, decisions, and actions. But in the virtual world, culture becomes more difficult to capture and measure. So, it would be quiet challenging but contributively if scholars of online service marketing could bring the updated concept of culture into related studies.

Last, the performance of service marketing is highly influenced by the products as different products are sold on platforms. In other words, the invisibility of service marketing and dependency with product marketing caused the research aim difficult to be achieved.

Practical Implications

For related practitioners of online shopping, the results of this study could provide theoretical support about how to develop quality of service and how to increase the effectiveness of service marketing. The literatures reviewed in this paper involved the influential factors, operating method of service marketing, impacts of service marketing on consumers, and the methods of improvement, which intended to help online platforms to find out a method to create high-quality service. However, even if the concept of “service” has had a clear definition, it is still a visible product that its value only depends on the gap between the consumer expectation and real perception. This paper explained the related concepts of service in details, which could help practitioners to further understand the service and its importance. Moreover, with the development of technology, the online shopping is able to replace more and more functions of real stores, to become mainstream shopping method in the future. In addition, the findings of this research could contribute in changing online shopping platform to be more humane and more advanced, which could even change the current business philosophy of these platforms to pay more attention on service marketing.

Policy Implications

For policymakers, a clearer analysis of service marketing standards in online shopping platform context contributes to policy regulations for online shopping platforms' service-related disputes. Service marketing, as one of main subfields of marketing, is more difficult to be managed when it is applied on a virtual network platform. Thus, it is easier to have disputes between online retailers and consumers, due to service problems and lack of related laws to regulate the market. However, this paper provides a clear description about how consumers require the service quality of online shopping, so it helps the current market to establish the market standards.

Concluding Remarks

With the development of service marketing, this concept has not been limited only on real places, which it has be extended to online communication and been reflected by various forms. However, with the development of online shopping, it is increasingly difficult to evaluate what standards high-quality online shopping platforms should have. In this conceptual analysis, we discussed how service marketing is working in online shopping platforms and in-depth discuss how important role service has played in the marketing of online shopping platforms. In addition, in the review of relevant literatures, we created a logic comparison among the different opinions, through discussion of the findings of current studies.

Author Contributions

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

This work was supported by Social Science Fund of Jiangsu Province, China (Project No. 18GLB007).

Conflict of Interest

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

Publisher's Note

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

Acknowledgments

F-ST acknowledges a distinguished visiting professorship from the North China University of Water Resources and Electric Power, Zhengzhou, China.

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Keywords: service marketing, online shopping, platform, psychological antecedents, behavioral antecedents

Citation: Wang Y, Qi M, Parsons L and Tsai F-S (2021) Service Marketing in Online Shopping Platform: Psychological and Behavioral Dimensions. Front. Psychol. 12:759445. doi: 10.3389/fpsyg.2021.759445

Received: 16 August 2021; Accepted: 15 September 2021; Published: 21 October 2021.

Reviewed by:

Copyright © 2021 Wang, Qi, Parsons and Tsai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yong Wang, wangyong@hyit.edu.cn ; Fu-Sheng Tsai, fusheng_tsai@hotmail.com

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

A study on factors limiting online shopping behaviour of consumers

Rajagiri Management Journal

ISSN : 0972-9968

Article publication date: 4 March 2021

Issue publication date: 12 April 2021

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

Design/methodology/approach

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

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

Research limitations/implications

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

Originality/value

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

• Online shopping

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

Emerald Publishing Limited

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

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

Introduction

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

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

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

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

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

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

Literature review

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

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

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

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

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

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

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

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

Research methodology

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

Data analysis and interpretation

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

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

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

Factor analysis

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

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

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

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

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

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

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

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

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

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

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

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

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

Conceptual framework of the study

Socioeconomic status of respondents

KMO and Bartlett’s test

Cronbach’s α

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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 .

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

Table of contents.

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

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

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

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

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

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

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

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

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

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

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

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Mini Project Report On ONLINE SHOPPING SYSTEM

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Top difficulties in using AR in online shopping in the U.S. in 2024

A survey conducted in the United States in March 2024 reveals the main difficulties customers face when using augmented reality (AR) while online shopping. Technical issues or glitches were pointed out as the top challenges, with 18 percent and 17 percent, respectively. Over two-fifths of the respondents stated that they have never tried using this technology to buy products online, and roughly 14 percent stated that they have not encountered any problems when using it.

Main challenges encountered in using augmented reality (AR) in online shopping environments in the United States as of March 2024

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Statistics on " eCommerceDB - Top online stores in the United States "

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