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103 Online Shopping Topic Ideas & Essay Examples

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• Online Shopping: Benefits and Drawbacks Essay The last major advantage of online shopping is that it assists the customer to find the best deal on a product.
• Online Shopping vs. Traditional Shopping The advent of internet shopping in the late nineties created a revolution in the retail industry. It is possible to know about the sizes, features, and costs of products in online and traditional shopping.
• Traditional vs. Online Shopping Traditional shopping involves shoppers physically entering a brick-and-mortar store or shopping mall to select items of their choice, pay for them in cash or by credit card, and either take delivery personally or have them […]
• Advantages of Online Shopping In addition to this, the number of people adapting to online shopping is expected to grow, due to the numerous benefits associated with it.
• International Students Attitudes Towards Online Shopping The researcher strived to answer three key questions, which sought to find out students’ attitudes towards online shopping, the nationality of students who make the largest number of online purchases, and the barriers that prevent […]
• Influence of Online Shopping Apps on Impulsive Buying Olsen et al.go further and confirms that online shopping apps have increased impulse buying due to the wealth of information they provide the consumer.
• Product Reviews in Online Shopping The paper will discuss strategies used by online retailers in their product reviews as well as describe a research study that can be used to explore the relationship between customer comments and their buying habits.
• Amazon’s Online Shopping and Innovative Delivery The company started as an online seller of books, but later, Amazon became the platform for a variety of goods and services to sell.
• Online Shopping as a Method of Supply Online shopping is the method of selling goods and services that allows individuals to sell goods directly over the internet. This mode of operation is better than the use of door-to-door sales people who can […]
• Secure Online Shopping System Model on Customer Behavior The aim is to find respondents who are the potential, if not actual customers of our online products who fall within the category of youths and young adults described in the introduction.
• Online Shop Business Plan One of the major aims of a supply chain management is to ensure that the goods used in manufacture are of the right quality and quantity; this goes ahead as it is reflected in the […]
• Online Shopping and Its Advantages The decision of a customer to buy a product from a specific website depends on the reputation of the company and brand, which owns it.
• Online Shopping Characteristics and Effectiveness Background information on online shopping will be presented, and the way on how to succeed in online shopping will be discussed. What are the details of online shopping DMC students should be aware of?
• Consumer Attitudes Towards Online Shopping Since the online environment gives consumer a wider choice of products and product platforms from where to make their purchases, this study seeks to establish the exact consumer behaviour portrayed in an e-commerce environment and […]
• Drawbacks and Benefits of Online Shopping One of the benefits of online shopping is that it makes the customer have quick access to items that are identical regardless of where he or she does the shopping for them.
• Amazon’s Success: Online Shopping Psychology One of the many factors contributing to Amazon’s success is its thorough understanding of its consumers, which is shown in the layout of its website and the numerous innovations it has brought to online retail.
• Saudi Women’s Perspective on Online Shopping Owing to the existence of different sites, the researcher examined the growth and expansion of the e-commerce segment in the nation.
• Consumer Behavior in Online Shopping: A Study of Aizawl The article shows the effective use of credibility of the authors, appropriate structure and organization, regional relevance of the cited literature, and functional illustrative material.
• Consumer Behavior in Online Shopping On the one hand, earlier studies argue that purchase intention is the key motivator for the consumers. Qualitative method is based upon judgment and intuition of the experts in the matter and consumers.
• The Effects of Online Shopping on Customer Loyalty For example, the study by Afrashteh, Azad, and Tabatabaei Hanzayy is dedicated to the concept of online shopping and the use of this electronic marketing technique to influence customer loyalty in conditions of the state […]
• Jordan’s Furniture Company and Online Shopping First of all, I would like to point out that Jordan’s Furniture is a furniture retailer in the Commonwealth of Massachusetts, the U.S.A.
• How Motivation Influences Online Shopping The Balanced Buyer: In this cluster, about a third of the sample was moderately driven by the desire to seek variety.
• UK Consumer Attitudes Towards Online Shopping It means that delivery represents a vital component of the overall purchasing or service reception experience and contributes to the development of customer loyalty.
• Online Shopping Impact on the Global Retail Industry While the significance and the convenience of e-commerce are indisputable, it is important to study its impact on the traditional retail industry around the world to identify the challenges, which it has to withstand.
• Secure Online Shopping System Integration Therefore, the new service called SOSS, which is proposed in the management of the online ticketing business, will form part of the actual customer safety guarantee service.
• Peacock Fashion Company’s Online Shops The purpose of the paper will be to determine the characteristics and feelings of online shoppers as related to online fashion shopping in United Kingdom market.
• Online Shopping Impact on the Fashion & Design Industry In this report, the aim will be to determine the impact of online shopping on the fashion and design industry. The increased profitability of this industry means that the individual firms have the capacity to […]
• Consumer Science: Online Shopping in the United Arab Emirates In an attempt to identify these factors, the present study uses a mixed-methods methodology to show the importance of online shopping and how this concept has changed consumer habits on shopping in the UAE. The […]
• Online Shopping Platform for La Donna Boutique By using online services, La Donna cost of production will be reduced because it will be selling goods directly to the customers and this will make producers to get rid of costly intermediaries. The e-commerce […]
• Service Marketing: Online Shopping Competition Their website allows customers to register with them and be able to do their shopping from the comfort of their homes.
• Online Shopping and Purchase Decision The above is a detailed explanation of the buying process for an online product specifically E-reader from Kindle. The customer will then evaluate the alternatives and make a purchase decision.
• The Era of Online Shopping Today, online shopping has become a great phenomenon thanks to the rapid development of internet security technologies and a similar pace in the penetration of the World Wide Web.
• Online Shopping vs. Brick-And-Mortar Shopping
• The Need for Accelerated Knowledge Management Within Internet Banking and Online Shopping
• Using Online Shopping Codes to Save Money
• Online Shopping Increases Consumption Rate
• The Advantages and Disadvantages of Online Shopping
• The Consumer Society: Advertising and Online Shopping
• Understanding Egyptian Consumers’ Intentions in Online Shopping
• Online Shopping Services for Consumers and Businesses
• Online Shopping Will Replace Traditional Shopping
• Visiting Malls While Online Shopping Is Fun
• The Relationship Between Marketing Mix and Buying Decision Process on the Online Shopping in Thailand
• The Advantages and Risks of Online Shopping
• Walmart Online Shopping Information System
• The Most Famous Online Shopping Website In China
• Perceived Risk and Online Shopping Intention: A Study Across Gender and Product Type
• The Benefits and Disadvantages of Online Shopping
• Online Shopping Reviewers Are Not All That They Seem
• Analyzing Customer Satisfaction: Users Perspective Towards Online Shopping
• Australian Customers and Online Shopping
• Antique Motorcycle Online Shopping Options
• Relationship Between Convenience, Perceived Value, and Repurchase Intention in Online Shopping in Vietnam
• The Development and Validation of the Online Shopping Addiction Scale
• Television Advertising and Online Shopping
• Assessing Benefits and Risks of Online Shopping in Spain and Scotland
• Online Shopping: Effectiveness and Convenience
• The Legal Issues Surrounding Online Shopping
• Taobao Established Shopping From Home With Online Shopping
• The Pros and Cons of Online Shopping vs. Brick and Mortar Stores
• Why People Like Online Shopping
• Online Shopping Lifts Aramex Profits by 4% and Rent Cap Removal Hits Abu Dhabi
• What Influences Online Shopping Of Individuals From European Countries
• Perceived Value, Transaction Cost, and Repurchase-Intention in Online Shopping: A Relational Exchange Perspective
• Online Shopping Unexpected Impacts Are We Gaining More or Less
• Differentiation Between Traditional and Online Shopping
• Popular Websites For Online Shopping
• The Online Shopping Industry Has Changed The World
• Online Shopping: Product Availability and Logistics
• The Interactions Between Online Shopping and Personal Activity Travel Behavior: An Analysis With a Gps-Based Activity Travel Diary
• Statistics and Facts About Online Shopping
• Analysing Online Shopping Behaviour of Google Merchandising Store Customers
• How Effect of Freight Insurance on Consumers’ Attitudes Toward Online Shopping?
• Does Online Shopping Cause Us to Spend More Money?
• Does Freight Insurance Work in Online Shopping?
• What Are the Pros and Cons of Online Shopping?
• How Do E-Servicescapes Affect Customers’ Online Shopping Intention?
• What Are the Moderating Effects of Gender and Online Shopping Experience?
• How Online Shopping Behaviour Is a Priority Issue for Many?
• How Does Online Shopping Cause You to Spend More Money?
• How Has Online Shopping Become a Convenient and Efficient Time?
• What Effects Repurchase Intention of Online Shopping?
• What Influences Online Shopping of Individuals From European Countries?
• Why Are More Customers Switching to Online Shopping From Traditional Coursework?
• Why Do People Like Online Shopping?
• What Is the Cheapest Online Shopping Site?
• What Is Called Online Shopping?
• How Many Types of Online Shopping Are There?
• Is Online Shopping Cheaper Than In-Store?
• What Are the Disadvantages of Online Shopping?
• What Is the Advantage and Disadvantage of Online Shopping?
• Why Is Online Shopping Better?
• What Is the Importance of Online Shopping?
• How Is Online Shopping Helpful?
• What Are the Factors Influencing Online Shopping?
• Do Consumers Prefer Online Shopping?
• How Does COVID Affect Online Shopping?
• What Are the Benefits of Online Shopping?
• How Does Online Shopping Affect the Consumer?
• What Is the Theory of Online Shopping?
• How Has Online Shopping Changed the Way We Shop?
• How Does Online Shopping Affect the Economy?
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Why Do People Shop Online? A Comprehensive Framework of Consumers’ Online Shopping Intentions and Behaviors

Venkatesh, V., Speier-Pero, C., and Schuetz, S.W. “Why Do People Shop Online? A Comprehensive Framework of Consumers’ Online Shopping Intentions and Behaviors,” Information Technology & People, 2022, forthcoming.

60 Pages Posted: 6 Apr 2022

Viswanath Venkatesh

Virginia Polytechnic Institute and State University - Pamplin College of Business

Cheri Speier-Pero

Michigan State University - The Eli Broad College of Business and The Eli Broad Graduate School of Management

Sebastian Schuetz

City University of Hong Kong (CityU) - Department of Information Systems

Date Written: March 17, 2022

Purpose: Consumer adoption of online shopping continues to increase each year. At the same time, online retailers face intense competition and few are profitable. This suggests that businesses and researchers still have much to learn regarding key antecedents of online shopping adoption and success. Based on extensive past research that has focused on the importance of various online shopping antecedents, this work seeks to provide an integrative, comprehensive nomological network. Approach: We employ a mixed methods approach to develop a comprehensive model of consumers online shopping behavior. To that end, in addition to a literature review, qualitative data are collected to identify a broad array of possible antecedents. Then, using a longitudinal survey, the model of consumer shopping intentions and behaviors is validated among 9,992 consumers. Findings: We identified antecedents to online shopping related to culture, demographics, economics, technology and personal psychology. Our quantitative analysis showed that the main drivers of online shopping were congruence, impulse buying behavior, value consciousness, risk, local shopping, shopping enjoyment, and browsing enjoyment. Originality: The validated model provides a rich explanation of the phenomenon of online shopping that integrates and extends prior work by incorporating new antecedents.

Keywords: Online shopping, Chisnall model, consumer behavior, longitudinal, mixed methods

Suggested Citation: Suggested Citation

Viswanath Venkatesh (Contact Author)

Virginia polytechnic institute and state university - pamplin college of business ( email ).

VA United States

HOME PAGE: http://vvenkatesh.com

Michigan State University - The Eli Broad College of Business and The Eli Broad Graduate School of Management ( email )

East Lansing, MI 48824-1121 United States

City University of Hong Kong (CityU) - Department of Information Systems ( email )

83 Tat Chee Avenue Kowloon Hong Kong

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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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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 02 setembro 2024 . URL : http://journals.openedition.org/cp/8402; DOI : https://doi.org/10.4000/cp.8402

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Consumers’ rational attitudes toward online shopping improve their satisfaction through trust in online shopping platforms

• Published: 02 September 2024

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• Yaxing Lan   ORCID: orcid.org/0009-0001-0275-8451 1 &
• Guofang Liu   ORCID: orcid.org/0000-0002-2502-2244 1  

Currently, online shopping has become one of the main consumption methods, with online retail sales reaching 13.79 trillion yuan in 2022. However, not all consumers are satisfied with their online shopping experiences. This study proposed that consumers’ rational attitudes toward online shopping were an important influencing factor for their satisfaction. Additionally, consumers’ trust in online shopping platforms is a mediator in the above relationship. Two studies were conducted to investigate this proposition. In Study 1, participants’ rational attitudes were first operationalized by a procedure to approve their decisions. Then, their rationality, trust in online shopping platforms, and consumer satisfaction were measured. It was found that participants’ rational attitudes improved their satisfaction through the mediating role of their trust in online shopping platforms. Study 2 further examined the hypotheses by providing participants with either budget alert information or no information. The results showed that such alert information increased participants’ rationality and supported the findings of Study 1. Based on the results, rational consumers are more likely to be satisfied with their consumption, and trust is a key mechanism. Therefore, online shopping platforms and retailers should make efforts to improve consumers’ rational attitudes and protect their rights and interests to obtain consumers’ trust and a win‒win result between themselves and consumers.

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Lan, Y., Liu, G. Consumers’ rational attitudes toward online shopping improve their satisfaction through trust in online shopping platforms. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06622-0

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ORIGINAL RESEARCH article

Online consumer satisfaction during covid-19: perspective of a developing country.

• 1 Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai, China
• 2 School of Management, Zhejiang Shuren University, Hangzhou, China
• 3 Faculty of Management Sciences, Riphah International University, Faisalabad Campus, Punjab, Pakistan
• 4 Department of Professional Psychology, Bahria University, Islamabad, Pakistan

A conceptual model based on the antecedents and consequences of online consumer satisfaction has been proposed and empirically proved in this study. Data were collected during Smart Lockdown of COVID-19 from 800 respondents to observe the difference between perceived and actual, and direct and indirect e-stores. Confirmatory factor analysis was used to observe the validity of the data set. The structural equation modeling technique was used to test the hypotheses. The findings indicated that consumers feel more satisfied when they shop through direct e-store than indirect e-store, whereas their perception and actual experience are different. Implications have also been added to the study.

Introduction

Online shopping is the act of buying a product or service through any e-stores with the help of any website or app. Tarhini et al. (2021) stated that shopping through online channels is actively progressing due to the opportunity to save time and effort. Furthermore, online shopping varies from direct e-store and indirect e-store about their perception against the actual experience. Developing countries still face various conflicts and issues while promoting and utilizing e-commerce to the maximum compared with the developed countries ( Rossolov et al., 2021 ). In the developing countries, the difference between the perception and actual experience of the consumers varies when buying from indirect e-store compared to the direct e-store. On the contrary, as the world has been suffering from the COVID-19 pandemic, it has brought drastic changes globally in many sectors, business being one of them. De Vos (2020) stated that a large-scale lockdown was imposed worldwide to prevent the virus from spreading.

To survive, switching traditional shopping or trade toward digital was one factor that captured the attention across the globe on a larger scale. In April 2020, Walmart reported a 74% increase in online sales even though they faced a low customer walk-in at stores ( Nassauer, 2020 ; Redman, 2020 ). This upsurge of swift adoption of online channels has led this research to ask a few questions. First, what will be the difference between the perceived and the actual product purchased online? A recent study has documented that consumers bear actual risk after shopping through online channels ( Yang et al., 2020 ). Research reported that 30% of the products through online channels get returned and are not according to their perception ( Saleh, 2016 ). The same author also showed that the return and complaint rates are getting higher when consumers shop through an online channel.

Second, is there any difference between the perceived and the actual product purchase online from a direct e-store or an indirect e-store? Direct e-store means the online brand store, for example, Walmart, and indirect e-store means third-party stores such as Amazon, Alibaba, Jingdong (JD), and Daraz. The direct e-store strives hard to maintain a clear, potent perception in the mind of its buyer ( Grewal et al., 2009 ). Kumar and Kim (2014) stated that a brand strengthening its relationship with its consumer satisfies its needs through the actual product or services. In the literature ( Olotewo, 2017 ; Rossolov et al., 2021 ), it is stated that the shopping patterns of buyers from direct and indirect e-stores vary greatly, especially in the developing countries. In this way, when shopping through a direct e-store, consumers may easily recognize the difference in buying from a direct and indirect e-stores ( Mendez et al., 2008 ).

Third, a conceptual framework from a consumer perspective, antecedents and consequences of customer satisfaction, has been proposed and empirically proved. The literature ( Alharthey, 2020 ) discussed different risk types in online shopping. Three main types of risk, perceived uncertainty, perceived risk, and price, are addressed in this model. To the best of the knowledge of the authors, no such investigation directed specific circumstances, particularly in the developing countries. Therefore, it is necessary to look for the antecedents and consequences of customer satisfaction to promote online shopping in the developing countries. The degree of consumer satisfaction defines his/her experience and emotions about the product or service purchased through the online channel. Recent studies ( Guzel et al., 2020 ; Mamuaya and Pandowo, 2020 ) stated that the intention of the consumers to repurchase and their electronic-word-of-mouth (e-WOM) depends on their degree of satisfaction. In light of these heavy investments in online shopping, there is an exciting yet unexplored opportunity to comprehend better how the purchasing experiences of consumers through online channels influence their satisfaction level.

The study contributed to the current marketing literature in several ways. First, this study has highlighted that the perceived risk is very high when shopping through online channels, mainly the indirect e-stores. Therefore, the managers should develop strategies that reduce the perceived risk for the online consumer to shop more. Second, the study also disclosed that the perceived uncertainty in shopping through the online channel is high. While shopping online, the website design, graphics, and color scheme make the product more attractive than the actual one. Therefore, the managers must balance the visual appearance of the product on the website with the actual appearance of the product. This would increase the confidence and satisfaction of the consumer. Third, this study has also revealed that people are more satisfied while shopping from direct e-stores than indirect e-stores. Because the focal brands officially sponsor the direct e-stores, they pay more attention to their quality to retain consumers and maintain their brand reputation. Fourth, an indirect e-store works as a third party or a retailer who does not own the reputation of the product. This study exhibited the difference between the perception of the consumer being very high and the actual experience of using that product being quite different when shopping from the indirect channel. Last but not the least, this study is the first to report pre- and post-purchase consumer behavior and confirmed the perceived and the actual quality of a product bought from (i) direct e-store and (ii) indirect e-store.

Literature Review

Theoretical review.

Literature shows that when consumers get influenced to buy a particular product or service, some underlying roots are based on their behavior ( Wai et al., 2019 ). Appraisal theory significantly explains consumer behavior toward shopping and provides an opportunity to analyze the evaluation process (e.g., Roseman, 2013 ; Kähr et al., 2016 ; Moors et al., 2017 ; Ul Haq and Bonn, 2018 ). This research, aligned with the four dimensions of appraisal theory as the first stage, clearly defines the agency stage that either of the factors is responsible for customer satisfaction. The second stage explains that consumer's degree of satisfaction holds great importance and refers to novelty in the literature. The third stage of the model briefly explains the feelings and emotions of the consumers about the incident, aligning with the certainty phase. The last step explains whether the consumers have achieved their goal or are not aligned with the appetitive purpose.

Cognitive appraisal researchers stated that various emotions could be its root cause ( Scherer, 1997 ); it could be the reaction to any stimulus or unconscious response. On the contrary, four dimensions of appraisal theory are discussed in this research ( Ellsworth and Smith, 1988 ; Ma et al., 2013 ). Agency (considering themselves or objects are answerable for the result of the circumstance) ( Smith and Ellswoth, 1985 ; Durmaz et al., 2020 ); novelty (assessing the difference between the perception of an individual and his actual experience) ( Ma et al., 2013 ); certainty (analysis of the apparent probability of a specific outcome and its effect on the emotions of the buyer) ( Roseman, 1984 ), and appetitive goal (judging the degree to what extent the goal has been achieved) ( Hosany, 2012 ).

Hypotheses Development

Perceived risk and consumer satisfaction.

Perceived risk is the perception of shoppers having unpleasant results for buying any product or service ( Gozukara et al., 2014 ). Consumers who buy a specific product or service strongly impact their degree of risk perception toward buying ( Jain, 2021 ). Buyers who tend to indulge in buying through online channels face perceived risk characterized by their perception compared to the actual uncertainty involved in it ( Kim et al., 2008 ). Literature ( Ashoer and Said, 2016 ; Ishfaq et al., 2020 ) showed that as the risk of buying is getting higher, it influences the degree of consumers about information about their buying, either purchasing from the direct or indirect e-shop. Johnson et al. (2008) stated that consumer judgment that appears due to their experience strongly impacts their satisfaction level. Jin et al. (2016) said that as the ratio of risk perception of their consumer decreases, it enhances customer satisfaction. Thus, from the above arguments, it is hypothesized as follows:

H 1 : Perceived risk has a significant negative impact on consumer satisfaction—direct vs. indirect e-store; perceived vs. actual experience .

Perceived Uncertainty and Consumer Satisfaction

Uncertainty is defined as a time that occurs in the future that comprises the predictable situation due to the asymmetry nature of data ( Salancik and Pfeffer, 1978 ). Consumers may not expect the outcome of any type of exchange conducted as far as the retailer and product-oriented elements are concerned ( Pavlou et al., 2007 ). Therefore, uncertainty initiates that retailers may not be completely predictable; on the contrary, consumers tend to analyze and understand their actions about decision making ( Tzeng et al., 2021 ). Thus, the degree of uncertainty involved in buying through online channels influences that degree of customer satisfaction. In addition, when the performance of any particular product or service matches the degree of expectations, he gets satisfied and, hence, repeats his decision of buying ( Taylor and Baker, 1994 ). But if the product quality fails to meet the requirements, it negatively affects the degree of satisfaction ( Cai and Chi, 2018 ).

H 2 : Perceived uncertainty has a significant negative impact on consumer satisfaction—direct vs. indirect e-store; perceived vs. actual experience .

Price Value and Consumer Satisfaction

Oliver and DeSarbo (1988) suggested that the price value is the proportion of the result of the buyer to the input of the retailer. It is defined as an exchange of products/services based on their quality against a price that is to be paid ( Dodds et al., 1991 ). Consumers look for a higher value in return; consumers are willing to pay a higher price ( Pandey et al., 2020 ). Yet, it leads to higher dissatisfaction when they receive a lower degree of profitable products. Besides, the buyers associate such type of product/service they use as less favorable or not according to their needs and desires. Hence, the buyers regret their decision-making degree for choosing that particular product ( Zeelenberg and Pieters, 2007 ). Aslam et al. (2018) indicated that a product/service price influences the satisfaction of a buyer. Afzal et al. (2013) recommended that the price is among those factors that hold great significance for the degree of satisfaction of the consumer. If the price value of any product/service differs from consumer to consumer, consumers tend to switch brands. Hence, it is hypothesized that:

H3 : Price value has a significant positive impact on consumer satisfaction—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Satisfaction With Consumer Delight, Consumer Regret, and Outrage

Satisfaction is defined as how a consumer is pleased with a particular brand or view about a product/service that matches requirements. It is an essential factor that triggers when the product or service performance exceeds the expectation and perception of the customers ( Woodside et al., 1989 ). The decision of the buyer significantly affects their satisfaction toward the product or service ( Park et al., 2010 ). If buyers are satisfied with the product/service they purchased online, this degree of satisfaction significantly affects their repurchase intention and WOM ( Butt et al., 2017 ). Tandon (2021) stated that a consumer satisfied with the product/service would get delighted. Consumer satisfaction, when exceeding the expectations, leads to consumer delight ( Mikulić et al., 2021 ). Mattila and Ro (2008) recommended that the buyer gets disappointed by anger, regret, and outrage. It also defines that negative emotions have a significant effect on the purchasing intention of the consumers. Oliver (1989) stated that unsatisfied buyers or products that do not fulfill the needs of the customers can create negative emotions. Sometimes, their feelings get stronger and result in sadness and outrage. Bechwati and Xia (2003) recommended that the satisfaction of the consumers influences their behavior to repurchase; outraged consumers due to dissatisfaction sometimes want to hurt the company. Besides deciding to purchase, consumers mostly regret their choices compared to other existing choices ( Rizal et al., 2018 ). Hechler and Kessler (2018) investigated that consumers who are outraged in nature actively want to hurt or harm the company or brand from which they got dissatisfied or hurt. Thus, it is proposed that:

H 4 : Consumer satisfaction has a significant negative impact on (a) consumer delight, (b) consumer regret, (c) consumer outrage—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Delight and E-WOM

Oliver et al. (1997) recommended that a degree of delight in a buyer is termed as a positive emotion. Consumers purchase a product/service that raises their degree of expectation and gets them delighted ( Crotts and Magnini, 2011 ). Delighted buyers are involved in sharing their experiences with their friends and family and spreading positive WOM to others ( Parasuraman et al., 2020 ). Happy buyers generally share their opinions while posting positive feedback through social media platforms globally ( Zhang, 2017 ). A positive WOM of the buyer acts as a fundamental factor in spreading awareness about the product/service and strongly impacts other buyers regarding buying it ( Rahmadini and Halim, 2018 ). Thus, it is proposed that:

H5 : Consumer delight has a significant positive impact on E-WOM—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Delight and Repurchase Intention

Delighted consumers tend toward brand loyalty; thus, they increase their buying intention of the service or product ( Ludwig et al., 2017 ; Ahmad et al., 2021 ). Customers can understand the objective of loyalty in purchasing a similar product or a new one from the same company. Delighted consumers tend to indulge in a higher degree of an emotional state that leads them to higher purchase intentions; it eliminates the switching of brands ( Parasuraman et al., 2020 ). Kim et al. (2015) stated that consumers delighted with a product or service of a brand become loyal to it, and the possibility of switching brands gets very low. Research ( Loureiro and Kastenholz, 2011 ; Tandon et al., 2020 ) shows that delighted consumers are more eager to purchase the same product again. Hence, it is proposed that:

H6 : Consumer delight has a significant positive impact on his repurchase intention—direct Vs. indirect e-store; Perceived Vs. actual experience

Consumer Regret and E-WOM

Regret is considered a negative emotion in reaction to an earlier experience or action ( Tsiros and Mittal, 2000 ; Kumar et al., 2020 ). Regret is when individuals frequently feel pity, disgrace, shame, or humiliation after acting in a particular manner and afterward try to amend their possible actions or decisions ( Westbrook and Oliver, 1991 ; Tsiros and Mittal, 2000 ). Regret is that specific negative emotion the buyers feel while making a bad decision that hurts them; their confidence level is badly affected. They blame themselves for choosing or creating a terrible decision ( Lee and Cotte, 2009 ). Li et al. (2010) suggested that buyers quickly start regretting and find their way to express their negative emotions. When they feel betrayed, they tend to spread negative WOM (NWOM) as a response to their anxiety or anger. Jalonen and Jussila (2016) suggested that buyers who get dissatisfied with their selections get involved in negative e-WOM about that particular brand/company. Earlier research says that buyers suffering from failure to buy any product/services tend to participate actively and play a role in spreading NWOM due to the degree of regret after making bad choices. Whelan and Dawar (2014) suggested that consumers sense that business has treated them unreasonably, and many consumers complain about their experience, resulting in e-WOM that may reduce consumer repurchase intention. Thus, it can be stated that:

H7 : Consumer regret has a significant negative impact on e-WOM—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Regret and Repurchase Intention

Regret has a substantial influence on the intentions of the consumers to not entirely be measured by their degree of happiness ( Thibaut and Kelley, 2017 ). Results may not be evaluated by matching the structured degree of expectation but are also linked to alternatives reachable in the market. Therefore, such sort of evaluation and assessments will probably influence repurchase intention. For example, suppose the skipped reserve overtakes the picked alternative. In that case, the customer might change the replacement for the future purchase, regardless of whether the individual is profoundly happy with the picked option ( Liao et al., 2017 ). According to the researchers, there is a negative relationship between regret and consumer repurchase intention ( Liao et al., 2017 ; Durmaz et al., 2020 ). Furthermore, Unal and Aydin (2016) stated that perceived risk negatively impacts regret, influencing the repurchase intention of the consumers. Thus, it can be stated that:

H8 : Customer's regret has a significantly negative influence on his repurchase intention—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Outrage and E-WOM

The disappointment of the consumers is a negative response to a product or a service ( Anderson and Sullivan, 1993 ). Outrage is the negative emotion a consumer experience when he purchases something totally against his requirements ( Lindenmeier et al., 2012 ). Besides, when the perception of the buyer is infringed, such behaviors occur. According to Torres et al. (2019) , enraged consumers get involved in communicating their outrage through e-WOM. Outraged consumers actively hurt the firm or brand from which they got hurt ( Hechler and Kessler, 2018 ). Consumers give e-WOM online reviews to decrease the negative emotions from the experiences of the consumer and re-establish a calm mental state to equilibrium ( Filieri et al., 2021 ). Thus, such consumers tend to give negative comments about the brand or product, which failed to match their expectations. NWOM has been characterized as negative reviews shared among people or a type of interpersonal communication among buyers concerning their experiences with a particular brand or service provider ( Balaji et al., 2016 ). Hence, it is hypothesized that:

H9 : Consumer outrage has a significant negative impact on e-WOM—direct vs. indirect e-store; perceived vs. actual experience .

Consumer Outrage and Repurchase Intentions

Repurchase intentions are characterized as the expressed trust of a buyer that they will or will not purchase a specific product and service again in the future ( Malhotra et al., 2006 ). Establishing relations with buyers should result in the repurchase. Negative disconfirmation ensues dissatisfaction or a higher level of outrage ( Escobar-Sierra et al., 2021 ). When a service/product fails and is not correctly addressed, the negative appraisal is overstated. Hence, “it may be more difficult to recover from feelings of victimization than to recover from irritation or annoyance” typically associated with dissatisfaction ( Schneider and Bowen, 1999 , p. 36). Therefore, consumers get outraged from buying such a product that fails to match their perception. When the experience of a consumer prompts a negative disconfirmation, the purchaser will also have a higher urging level through outrage. Therefore, consumers will probably have negative intentions to repurchase and do not want to indgule in making the same decision repeatedly ( Wang and Mattila, 2011 ; Tarofder et al., 2016 ). Therefore, it is proposed that:

H10 : Consumer outrage has a significant negative impact on repurchase intention—direct vs. indirect e-store; perceived vs. actual experience .

Methodology

This research explores the difference between the perception of the consumers and the actual online shopping experience through direct and indirect e-stores. It was an experimental design in which online shopping was studied in the developing countries. Data were collected from those individuals who shop from online channels; direct e-store and indirect e-store. Taking care of COVID-19 standard operating procedures, only 50 respondents were gathered two times, every time in a university auditorium after obtaining the permission from the administration. The total capacity of the auditorium was 500, as the lockdown restrictions were lifted after the first wave of the coronavirus.

Data Collection Tool

A questionnaire was used for the survey. The questionnaire was adapted in English to guarantee that the respondents quickly understood the questions used. A cross-sectional study technique was used for this research. A cross-sectional study helps in gathering the data immediately and collects data from a large sample size. The total number of distributed questionnaires was 1,250, out of which 800 were received in the usable form: 197 incomplete, 226 incorrect, and dubious responses, and 27 were eliminated. Thus, a 64% response rate was reported. Research showed that a 1:10 ratio is accepted ( Hair et al., 1998 ) as far as the data collection is concerned; for that instance, this study data fell in the acceptable range.

Indirect E-Store

Consumers who prefer to shop through online channels were gathered in an auditorium of an institute. Only those consumers were eligible for this experiment, who themselves buy through e-stores. A few products were brought from an indirect e-store, and later on, those products were shown to the respondents from the website of that indirect e-store. After showing products, we asked the respondents to fill the survey as per their perception of the product. Then we asked them to fill out another questionnaire to ascertain the difference between the perception and actual experience when purchasing from an indirect e-store. Once all the respondents completed the survey, we have shown them the actual products they have selected by seeing the website of the indirect e-store.

Direct E-Store

The second experiment was carried out on those consumers who shop from direct e-stores. For that purpose, a few popular reviewed clothing articles were purchased from the e-store. As in the case of an indirect e-store, respondents were also shown these articles from the websites of these direct e-stores. We then asked the respondents to fill the survey to confirm their perception of the products. Once all the respondents completed the survey, we showed them the actual product and asked them to fill out another questionnaire according to their actual purchasing experience from the direct e-store. The primary purpose of this experiment was to compare buying from direct e-store and indirect e-store.

Construct Instruments

The total number of items was 34, which were added in the earlier section of the questionnaire. These items were evaluated with the help of using a five-point Likert scale that falls from strongly disagree (1) to strongly agree (5). The items used in the study were empirically validated. Table 2 carries the details of the items of the questionnaire. The price value was evaluated using three items used by Venkatesh et al. (2012) . The perceived uncertainty was one of the independent variables that carry four items derived from Pavlou et al. (2007) . Perceived risk was the third independent variable used, held three items; thus, its scale was derived from Shim et al. (2001) . Wang (2011) validated consumer satisfaction carrying three items; consumer delight was measured by a 3-item scale proposed by Finn (2012) ; consumer regret was measured by the scale proposed by Wu and Wang (2017) . It carries a three-item scale. Consumer outrage was measured by Liu et al. (2015 ); it has six items. Repurchase intention was measured through a scale adapted from Zeithaml et al. (1996) , which carries four items. e-WOM was validated by the scale adapted from Goyette et al. (2010) ; it has five items.

Demographics of the Respondents

A total of 800 questionnaires were filled, and the respondents expressed their perception and actual experience from direct e-store and indirect e-store. Respondents belonged to different age groups from 18 to 50 years and above. There were 49% women and 51% men who took part in filling this survey. The income level of the respondents was grouped in different categories from “above 10,000 to above 50,000. The majority (56%) of the respondents were single, and 44% were married (Details can be viewed in Figure 1 ; Table 1 ). Data for both direct and indirect e-store was collected equally; 50% each to compare each category better.

Figure 1 . Proposed conceptual framework.

Table 1 . Demographics of the respondents.

Reliability and Validity

Reliability evaluates with the help of composite reliability (CR). All CR values fall into the range of 0.7–0.9, which is acceptable ( Hair et al., 2011 ). Convergent and discriminant validity has been observed through confirmatory factor analysis as recommended by some researchers ( Fornell and Larcker, 1981 ; Hair et al., 2010 ).

Convergent Validity

Convergent validity is evaluated with the help of two standards mentioned in the literature earlier, factor loading and average variance extracted (AVE), both the values should be >0.5 ( Yap and Khong, 2006 ). The values are mentioned in Table 2 .

Table 2 . Reliability and convergent validity.

Discriminant Validity

Discriminant validity is evaluated based on two conditions that are required to evaluate it. First, the correlation between the conceptual model variables should be <0.85 ( Kline, 2005 ). Second, the AVE square value must be less than the value of the conceptual model ( Fornell and Larcker, 1981 ). Table 3 depicts the discriminant validity of the construct of the study.

Table 3 . Discriminant validity.

Multi-Group Invariance Tests

Multi-group confirmatory factor analysis was conducted as the pre-requisites for the measurement model. The multi-group analysis was used to investigate a variety of invariance tests. Different invariance tests were performed to guarantee the items working precisely in the same manner in all the groups. In this research, the following are the model fit indexes, that is, CMIN/dF =2.992 CFI = 0.915, TLI = 0.906, and RMSEA = 0.071. Byrne (2010) and Teo et al. (2009) stated that CFI gives more accurate results, especially when comparing variables in different groups.

Hypotheses Testing

Scanning electron microscope technique was used to run and test the proposed hypotheses for the conceptual model. First, all the hypotheses proposed were checked, from which eight were initially accepted. Later, the multi-group test was utilized to test the proposed hypotheses and compare the shopping experience from direct e-store with indirect e-store and consumer perception with actual experience. Table 4 explains this in detail.

Table 4 . Hypotheses results.

Discussion and Implications

This research offers a remarkable number of facts for practitioners. This study can benefit marketing strategists by reducing the perceived risk, decreasing the intensity of perceived uncertainty, stabilizing the price, enhancing consumer satisfaction, promoting delighting consumers, accepting the negative behavior of the consumers, consumer retention, and establishing a positive e-WOM.

Reducing Risks

Certain factors play a role in antecedents of consumer satisfaction; they are particularly those that resist consumers to shop from any online channel, neither direct e-store nor indirect e-store. Perceived risk, perceived uncertainty, and the price are some of those antecedents that play a significant role in affecting the degree of satisfaction of the consumers, resulting in either to retain a consumer or to outrage a consumer. This study aligns with the existing literature. Tandon et al. (2016) ; Bonnin (2020) and Pandey et al. (2020) showed that consumers seek to shop from an e-store without bearing any risk. Consumers feel more confident about an e-store when the perceived risk is less than shopping from traditional ones as consumers want to feel optimistic about their decision. Second, e-vendors should ensure that the quality of a product is up to the mark and according to the consumer needs. Therefore, vendors should offer complete details about the product/service and its risks to the consumers. Moreover, this study suggests that e-stores must align the visuals of a product with its actual appearance. This would help them to increase customer satisfaction and confidence in the e-store.

Focus on Consumer Satisfaction

Consumer satisfaction is the deal-breaker factor in the online sector. Literature ( Shamsudin et al., 2018 ; Hassan et al., 2019 ) showed that organizations prioritize their consumers by fulfilling their requirements and required assistance. As a result, consumers are more confident and become satisfied consumers in the long run. This study adds to the literature that the degree of satisfaction of the consumers plays an essential role in shopping from an e-store. Consumers feel more confident in shopping from a direct e-store than an indirect e-store as the difference in the perception of consumers and the actual experience varies. Therefore, online vendors should focus on satisfying their consumers as it plays a remarkable role in retaining consumers.

Value Consumer Emotions

Online, retaining, and satisfying consumers are the most vital factor that directly affects the organization. This research aligns with the existing literature ( Jalonen and Jussila, 2016 ; Hechler and Kessler, 2018 ; Coetzee and Coetzee, 2019 ); when the retailer successfully fulfills its requirements, the consumer gets delighted repeating his choice to repurchase. On the other hand, if the online retailer fails to serve the consumer, the consumer regrets and, in extreme cases, becomes outraged about his decision. The negative emotions of the consumers threaten the company from many perspectives, as the company loses its consumer and its reputation in the market is affected. Therefore, first, market practitioners should avoid ignoring the requirements of consumers. Second, online vendors should pay special attention to the feedback of the consumers and assure them that they are valued.

Consumer Retention

The ultimate goal is to retain its consumers, but e-vendors should make proper strategies to satisfy their consumers as far as the online sector is concerned. The earlier studies of Zhang et al. (2015) and Ariffin et al. (2016) contributed to the literature that consumer satisfaction is a significant aspect in retaining a consumer. This research has also suggested that the satisfaction of the consumers plays a vital role in retaining them. Moreover, online shoppers provide the fastest spread of the right WOM about the product/ service. Second, consumers should feel valued and committed to vendors.

Pre- and Post-buying Behavior

This study contributed to a conceptual model that deals with consumer pre- and post-purchase behavior from the direct and indirect e-stores. With the help of experimental design, this study has reported its finding, highlighted how a satisfied customer is delightful and shares e-WOM, and showed repurchase intention. However, if the customer is not satisfied with the flip of a coin, he may feel regretted or outraged and cannot share e-WOM or have a repurchase intention.

Conclusions

This research concludes that online shopping has boomed during this COVID-19 pandemic period, as the lockdown prolonged in both the developed and the developing countries. The study further supports the difference between shopping from a direct e-store and an indirect e-store. The perception of the consumers shopping from direct e-store is more confident, and their degree of satisfaction is much higher, as the actual experience of the consumers aligns with their perceptions. Instead, consumers feel dissatisfied or outraged to choose an indirect e-store for shopping. Indirect e-store makes false promises and guarantees to its buyers, and eventually, when the consumers experience the product, it is against their perception.

This research fills the literature gap about the antecedents that lead to online shopping growth in the developing countries. This study aligns with Hechler and Kessler's (2018) earlier research, which stated that dissatisfied consumers threaten the reputation of the organization. Furthermore, Klaus and Maklan (2013) , Lemon and Verhoef (2016) suggested that handling the experience and satisfaction of the buyers plays a significant role in surviving among its competitors. Grange et al. (2019) recommended that e-commerce develops and attracts consumers by fulfilling their needs and requirements quickly. This study aligned with the existing literature by adding factors influencing the shopping preferences of the consumers from an e-store.

Limitations and Future Research

Despite its significant findings, this research has some limitations and scope for future research. First, this research only examined a few risks involved in online shopping. Future research studies should analyze other risks, for example, quality risk and privacy risk. Second, this study focused on shopping through direct e-stores and indirect e-stores. Future research can implement a conceptual model of a specific brand. Third, this study can be implemented in other sectors, for example, tourism, and hospitality. Fourth, it may be fascinating to look at other fundamentals, such as age, gender, education, relation with the retailer, or the degree of involvement with online shopping to differentiate other factors.

The proposed framework can be utilized in other developing countries, as every country faces different problems according to its growth and development. The model can be examined among specific direct e-stores to compare new customers and loyal customers. Future studies can explore indirect relationships along with adding mediators and moderators in the proposed model.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by This study involving human participants was reviewed and approved by the Ethics Committee of the Department of Management Sciences, Riphah International University, Faisalabad Campus, Faisalabad, Pakistan. The participants provided their written informed consent to participate in this study. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

AS contributed to the conceptualization and writing the first draft of the research. JU contributed to visualizing and supervising the research. All authors who contributed to the manuscript read and approved the submitted version.

Conflict of Interest

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

Publisher's Note

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

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Keywords: consumer perception, online shopping, actual experiences, customer satisfaction, direct shopping, perceived risk, delight, outrage

Citation: Rao YH, Saleem A, Saeed W and Ul Haq J (2021) Online Consumer Satisfaction During COVID-19: Perspective of a Developing Country. Front. Psychol. 12:751854. doi: 10.3389/fpsyg.2021.751854

Received: 02 August 2021; Accepted: 30 August 2021; Published: 01 October 2021.

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Copyright © 2021 Rao, Saleem, Saeed and Ul Haq. 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: Junaid Ul Haq, junaid041@yahoo.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.

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Please note you do not have access to teaching notes, what motivates consumers to be in line with online shopping: a systematic literature review and discussion of future research perspectives.

Asia Pacific Journal of Marketing and Logistics

ISSN : 1355-5855

Article publication date: 28 April 2022

Issue publication date: 9 March 2023

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

Design/methodology/approach

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

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

Originality/value

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

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

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

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Factors Affecting E-Shopping Behaviour: Application of Theory of Planned Behaviour

Honglei tang.

1 School of Economics and Management, Huzhou University, Huzhou 310013, China

Zeeshan Rasool

2 School of Economics and Management, Shaanxi University of Science and Technology, Xi'an 710021, China

Mohsin Ali Khan

3 National University of Modern Languages Multan Campus, 60000, Pakistan

Ahmad Imran Khan

4 Putra Business School, University of Putra, UPM, Serdang, 43400 Selangor, Malaysia

Farooq Khan

5 The Women University Multan, Multan, Pakistan

Anum Afzal Khan

Syed arslan abbas.

6 Faculty of Business, Law and Social Sciences, Birmingham City University, UK

Associated Data

The data are associated in the manuscript.

E-shopping is a rapidly growing phenomenon among different individuals who intend to shop online. However, a trust deficit in the E-shopping environment has always been a critical issue in the brick-and-click mode of shopping, being one of the main reasons for E-cart abandonment in E-commerce. This empirical study is aimed at investigating the perceived effect of website trust on E-shopping intentions and behaviour, drawing upon the theory of planned behaviour (TPB). Data were collected through self-administered questionnaires from working adults who shop for garments online. Structural equation modelling was used to evaluate the model fit and assumptions. Our findings suggest that website trust and E-shopping attitude play substantial roles in building E-shopping intentions and actual behaviours. Both are the significant predictors of the behaviour mediated by E-shopping intentions. However, E-shopping intentions did not mediate between subjective norms and E-shopping behaviour, when working adults decide to purchase garments online.

1. Introduction

E-shopping is the process whereby customers directly buy goods or services from a seller in real time, without an intermediary service, over the Internet [ 1 ]. It is a form of E-commerce, which has become prosperous in communities where Internet-enabled devices have made online shopping easier for customers. The way that consumers purchase or check for appropriate items can easily shift. At present, customers are testing sites in a wide variety of ways, such as to gather information, compare product features and pricing to their near alternatives, and then pick the best available choices [ 2 ]. During the past two decades, the number of E-shopping retailers has increased significantly, indicating that, in the future, retailers will rely on this mode of shopping [ 3 ]. The manner in which retailers advertise and connect with their consumers has changed, as well as providing buyers and retailers with a global marketplace [ 4 ]. In Pakistan, the E-commerce boom has sparked the interest of Pakistani producers in concentrating on regions with high consumer opportunity [ 5 ].

Likewise, in South Asia, E-commerce has grown substantially in recent years; however, E-commerce in the region is still far below its potential [ 6 ]. In the Pakistani E-shopping context, most stores in Pakistan have developed websites where customers can shop online and can make payments through the use of debit/credit cards. Unfortunately, the general public in Pakistan has expressed a lack of confidence toward the goods that are presented to them online.

Pakistani adults, however, seem versatile and have increasingly engaged in online shopping, particularly for online food orders [ 7 ]. Although Pakistani communities have limited awareness of and trust deficit problems with E-shopping, individuals still find it to be a simple shopping source [ 8 ]. Digital tools provide consumers with easy access to information about the price, design, packaging, and characteristics of products. The ease of online shopping is an important aspect for people [ 9 ].

Despite its ease and convenience, a major problem associated with E-shopping is trust deficit, in which the buyers question the credibility of E-vendors and their E-shopping mediums [ 10 ]. Shopping cart abandonment due to trust deficit is a big challenge for E-vendors in the online shopping environment. While this phenomenon is rare in conventional brick-and-mortar offline environments, it is common in E-commerce. It has been statistically postulated that E-shoppers abandon their shopping carts nearly 69.6% of the time, costing E-vendors approximately US$ 61 billion in lost sales per annum [ 6 ]. “Trust deficit in E-shopping is one of the major reasons behind query abandonment” [ 11 ], resulting in a huge loss of revenues for E-vendors. Globally, garments are the most abandoned products (i.e., approximately 40%) among E-shoppers [ 12 ]. The cart abandonment rate for clothes/apparels has been found to be the highest among other commodities and has been gradually increasing since 2006. The Dawn online Pakistani newspaper report revealed that the risk of fraud and misleading practices has put a damper on E-commerce optimism [ 13 ]. “Customers fear that the actual product shown on the website might not be delivered” [ 14 ].

Hence, the current study intends to investigate the significance of website trust in the online shopping sphere, in order to assess how extensively trust can influence the behaviour of E-shoppers. E-commerce is a medium which allows consumers to purchase directly from producers or retailers by using an Internet web browser or social networking site (SNS). This direct contact between sellers and consumers has been enabled by the transition of the Internet to delivering information in global interconnection scenario. This work focuses on examining the confidence factor (as a perceived behavioural control) in E-shopping websites, along with two main histories of the expected theory of behaviour (i.e., behaviour and subjective norms). This factor (i.e., website trust) may enable or deter the performance of certain behaviours while using an E-shopping medium. The theoretical underpinnings of this study are based on the theory of planned behaviour (TPB). In psychology, TPB is a theory that links the beliefs and behaviour of an individual. The theory states that intentions toward attitude, subject norms, and perceived behavioural control together shape an individual's behavioural intentions and behaviours. TPB describes better that psychological activity is not necessarily voluntary and regulated by the individual. In the absence of confidence in E-shopping media, one does not have volitional influence over their actions, despite having two other primary determinants (attitudes and subjective norms). Trust is also the reciprocal confidence, through which the other party exploits the vulnerabilities of others in the course of an interaction [ 15 ]. A lack of confidence may lead to hesitation in E-commerce. In previous studies, trust has been established as a key element in effective online companies [ 16 , 17 ].

There has been some research in Pakistan, given this changing trend, to investigate the factor of confidence in Internet technology as a platform for shopping [ 7 ]. This phenomenon will continue to rise in the future, due to the convenience factor of E-shopping. The significant roles of expectations and the expected actions in embracing E-shopping, therefore, need to be examined.

In the Pakistani context, garments have been observed to be one of the most preferred products to buy on the Internet, as compared to purchasing cell phones, laptops, or other electronic devices. Although the Pakistani community has limited knowledge about online shopping and also has trust deficit issues regarding the credibility of online shopping stores, people still consider it an easy source of shopping (Sulaiman et al., 2007). They may believe that the online shopping stores will not deliver the actual product that they advertise on their networking sites or even get the desired products tangibly in return for their online payments (Hassan et al., 2014). At the same time, a vast majority also consider that visiting an outlet physically is an exhaustive and cumbersome procedure, compared to online shopping, where they can get information regarding price, design, packing, and features through some digital interface while sitting in their bedroom (Phau et al., 2013). However, the transition from conventional to online shopping has been difficult in this region. The reason for this is not only the lack of trust of online shoppers in online shopping (e.g., that the vendor will not provide exactly what they advertise on their official site) and the expectation to be satisfied with their purchases (Hassan et al., 2014).

Our research questions are as follows:

RQ1 . Does the E-shopping attitude of a working adult influence their E-shopping behaviour?

RQ2 . Do the subjective norms of working adults determine their E-shopping behaviour?

RQ3 . Does website trust affect the E-shopping behaviour of the working adults?

RQ4 . Do E-shopping intentions mediate the relationship between E-shopping behaviour and its antecedents?

2. Literature Review

2.1. subjective norms.

Subjective norms refer to the perceived social pressure to perform (or not to perform) a certain behaviour. The literature on subjective norms has indicated that the influence of subjective norms can provide equivocal results. Previous studies have concluded that someone who aims to follow people's expectations and wants to be the same would certainly have good subjective standards in E-shopping behaviour [ 18 ].

In the E-shopping literature, “however, there have been conflicting reports of subjective norms” [ 19 ]. Past studies have shown an important positive impact on consumer buying intentions by subjective norms [ 20 , 21 ]. However, studies have also found a negative effect [ 22 ] or even no effect of subjective norms on the E-shopping intentions of customers. In the early stages of Internet adoption, a research by “trust and privacy” found that, in contrast to other technologies, such as telephone or email, arbitrary norms played no significant part [ 23 ].

The inconsistent findings within the subjective norm literature call for further research, in order to understand the generalizability of subjective norms in different contexts.

2.2. E-Shopping Attitude

Attitude is defined as a person's overall evaluation of a concept. Two types of attitude can be identified: attitudes toward objects and attitudes toward behaviours. As this study measures the attitudes of working adults toward E-shopping, attitudes toward behaviours are more relevant to the context of this study. An attitude toward behaviour refers to the “degree to which a person has a favourable or unfavourable evaluation or appraisal of the behaviour in question” [ 24 ], whereas a customer's attitude toward E-shopping refers to a “customers psychological state in terms of making purchases over the Internet” [ 25 ].

The psychological nature of customers in the context of an online shopping decision affects their attitude toward E-shopping. A research on the E-shopping behaviours of British and American consumers has also shown that E-shopping is a determinant of online shopping. Likewise, consumer research on E-shopping behaviour accepts that attitude represents a description of the positive or negative self-appraisal of a client's behaviour, values, feelings, and patterns during online transactions [ 26 ]. The better the behaviour of an individual is, in relation to the behaviour predicted, the more likely the person wants to participate in the behaviour.

2.3. Website Trust

Trust is a multidimensional concept which is complex in nature, and so, one may find a number of definitions of trust even in the literature relating to a similar context. Trust is the mutual assurance that, during an exchange, no party will exploit the vulnerabilities of another. Trust is the willingness of a person or group to be vulnerable to the actions of other group of people, based on expectations that the other will do a certain action benefitting the trust. Trust also refers to the belief of an individual in the trustworthiness of others, which can be determined by their perceived integrity, benevolence, and competence. Eventually, trust can be conceptualized as “the degree to which one can believe and rely upon promises made by others”. So, in context of online shopping, where the state of vulnerability of the user is quite high due to the dynamic disposition of cyberspace, trust has been theorized as a factor directly contributing to attitude [ 27 ]. Trust can be theorized as a belief that another individual or group will not behave opportunistically, for example, that a vendor will deliver exactly what has been promised [ 28 ]. Apart from various definitions, trust is usually considered essential in online shopping environments, as it consists of various kinds of potential risks which are associated to cyberspace.

As far as trust in the E-commerce domain is concerned, it leads to a belief that permits customers to voluntarily open themselves to actions of the E-sellers, after taking into account the E-seller's worth. This relates to the construct of trust as a belief encompassing goodwill and believability or honesty [ 29 ]. The E-commerce environment is uncertain, and thus, trust is more complex and important than in traditional commerce.

Trust is known as the confidence that an individual or group places in some entity; regardless of whether an individual's trust turns out to be well-employed or not, trust is instigated by the individual.

As recommended above, trust in an online business or transaction can occur as various trust (or trustee) relationships, but we confine our definition to be specific with one kind of trust association, that is, the trust that occurs for an individual toward a particular online shopping website. In this study, the object in our model is a website or an SNS that is browsed by consumers for transactional and/or informational purposes. Websites and social networking sites (SNSs) are referred to as the basic Internet technology that enables customers to interact with a website or the people behind the website. In the modern day, a website possesses both features, as it works as a storefront and also acts as a salesperson in the offline world. So, online trust was conceptualized as per the requirements of the current study, that is, “an attitude of confident expectation in an online situation of risk that one's vulnerabilities will not be exploited” [ 31 ].

The intention behind conducting this quantitative study was to evaluate all three determents of the behavioural theory (TPB) to know which antecedent is more influential to build behaviour while making purchase decision. Secondly, how can we increase the interest of the working adults to shop their garments by using some online shopping medium? This study intended to know the most influential factor among attitude, subjective norms, and website trust that leads to form intentions and then behaviours which eventually encourage or discourage the consumers to shop their garments online. This study helps to better answer the raised questions regarding consumers' behaviours that which factor highly motivates them to shop their garments online or otherwise?

In Pakistani context, garments are observed one of the preferred buying on Internet as compared to purchasing cell phones, laptops, or other electronic devices. Although Pakistani community has limited knowledge about online shopping and they also have a trust deficit issues on credibility of online shopping stores, people still consider it as an easy source of shopping (Sulaiman et al., 2007). They may think that online shopping stores do not deliver the actual product what they put in front of them on their networking sites or even they get the desired products tangibly in return of their online payments (Hassan et al., 2014). At the same time, a vast majority also consider to visit an outlet physically as an exhaustive and cumbersome procedure as compared to online shopping where they can get information regarding price, design, packing, and features through some digital interface while sitting in their bedrooms (Phau et al., 2013). For Pakistan, however, the transition from conventional to online shopping has been more difficult than the region. The reason is not only the lack of trust of online shoppers in online shopping that the vendor does not provide exactly what they put in front of them at their official sites and expect them to be satisfied with their purchases (Hassan et al., 2014). In current times, a phenomenal growth has observed in online shopping that surely indicates that in future, this mode of shopping will be the prime focus of the retailers. These indicators show that in near and far future, there is an enormous market potential of E-commerce growth is laying vacant for the current players and as well as for the new comers. Now the consumers are potentially more interested in adopting online shopping for their convenience which mold the producers and retailers to pay more focus on this area for growth and expansion of their businesses. The ease of online shopping forms as an emerging trend in Gen Y. The acceptance of online shopping has raised the retailers' interest for focusing on this area (Lim et al., 2015). According to Vijayasarathy and Jones (2001), by using Internet, buyer/produce interactive online shopping technology enabled buyers to have an opportunity to compare among desired products before making their purchase, whereas the second largest benefit of online shopping for consumers is to gain information about products and services besides accomplishing their purchases. E-commerce is a medium which allows the consumers to purchase directly from producers or retailers by using some Internet web browser or some social networking site (SNS). This sellers and consumers' direct contact did happen because of the transition of Internet for delivering information in global interconnection scenario.

Collectively, corporate reputation is conceptualized as the degree to which people or firms in the industry believe that a firm is honest and concerned about its customers. Therefore, perceived website reputation is characterized as “the degree of website popularity to which a consumer perceives”. However, website popularity and credibility are a mix of various other essential characteristics of an online website, such as legitimacy, uniqueness, visibility, transparency, and consistency. In online business, the good repute of a website plays a vital role in its significance and profitability. From the point of view of consumers, reputable and credible websites are more likely to be acknowledged among customers than unknown ones [ 32 ]. In fact, the websites which have significant reputation are probably more convincing than websites with low or no perceived trustworthiness. Therefore, consumer trust in a website is also affected by the opinions of their associates or referral cues, regarding the repute of the website.

Online shopping websites need to manage their image, as it is a valuable asset which usually yields high profitability. In context of online shopping, website image refers to the perception that the customers have in their mind regarding the website. It can also be defined as what customers perceive and what comes in their mind when they think about the website or see its logo [ 33 ]. Customer perception is the pivotal point that describes how a customer perceives the actions and procedures of an online shopping website. With regard to web-based shopping, perceived website image is also linked with some of the website physical and behavioural aspects, such as website visual appeal, layout, functionalities, the manners in which it collaborates with the customers, the variety of goods and service it offers, and, finally, its operational excellence for transactions.

2.4. E-Shopping Intentions

Intentions are presumed to be an indicator of the extent to which people are willing to approach a certain behaviour and how many attempts they will try, in order to perform that certain behaviour. A lack of intention to purchase goods online is the main obstacle in the development of electronic commerce [ 34 ]. Purchasing intention is a core aspect of consumer cognitive activity in the purchase of a particular product by a consumer [ 35 ]. Generally, when an individual has favourable attitudes or subjective standards or a highly perceived influence over their actions, their intention to enact an action will be stronger [ 36 ].

Although intention has been determined as a salient predictor of actual behaviour to shop online, it does not always translate into purchase action [ 37 ]. Based on TPB, perceived behavioural control determines the decision of an online shopper after online behavioural intention sinks in. A study on E-shopping intentions and behaviour found trust to be a major indicator in the replacement of perceived behavioural control, significantly influencing E-shopping intentions and behaviour [ 38 ]. Purchase intention may have a positive influence on actual online purchasing, and further investigation of the relationship between trust and intention in future studies has been recommended [ 39 ]. E-shopping behaviour is directly determined by E-shopping intentions, which are influenced by the E-shopping environment.

As per recommendations and a step ahead from the base study where the sample was centric to undergraduate and postgraduate students of one of the renowned postgraduate institutions in Perlis, Malaysia, this study has taken the “working adults” as a study subject for originating the behavioural intent of the working class that contrary to the students has better power to purchase and sovereign in making their decisions and they do not depend upon other family earning heads. Additionally, website trust has taken as an additional construct in online shopping scenario for measuring its impact as a perceived behavioural control. Hence, the above criterion is to get ensured by using some screening questions that the respondents have easy access over digital media. They are employed somewhere and free to make their purchase decisions and thus have experienced of buying their garments in past through some social networking site (SNS) or online shopping store. Intention refers to the willingness of an individual to perform certain behaviours (Chen, SheenLou, 2006). It also refers to the strength of intention that how strong it is, in performing a certain behaviour (Venkatesh et al., 2003). Researchers, for instance Hennington et al. (2009), have paid attention to behavioural intention. TAM is one of the better accepted models for understanding desire and willingness to use a technology (Schepers & Wetzels, 2007). As per Yu et al. (2005), perceived ease of use and perceived usefulness both define the individual's attitude significantly, which mean customer's feelings toward using online shopping.

Venkatesh et al. (2003) have found that the attitude has no direct effect on intention. Meanwhile, according to the most renowned theories like TRA (Fishbein & Ajzen, 1975), TAM (Davis et al., 1989), and TPB (Ajzen, 1991), attitude has more significant positive impact on Intention, whereas many other researchers who conducted their researches on similar framework underscored the attitude's strong impact on behavioural intent (e.g., Cheong & Park, 2005; George Joey, 2002; Jiang, Chen, & Wang, 2008; Kumar & Ghodeswar, 2015). Nakagawa and Gouvêa (2010) and Gouvea (2010) suggested that attitude is a determinant related to intention to adopt E-shopping. Venkatesh et al. (2003) have revealed in his study that how significantly the intentions determine usage behaviour. In such context, outcome of the aforesaid statements may summarize as, attitude has a significant positive influence on intentions. In other words, consumer's favourable and positive emotions toward online shopping resulted in increases of consumers' willingness for online shopping.

2.5. Theoretical Model

For better understanding of the research hypothesis, Figure 1 presents the theoretical model, aimed at investigating the relationships among the study constructs in the case of online garment shopping.

Diagrammatical representation of relationship between study variables (theoretical model).

As per the theoretical framework of the research ( Figure 1 ), hypotheses were developed to investigate the research objectives. Hypotheses are used to designate the logical relationship that is imagined between two or more variables in a formal statement, which can be tested through some statistical operation. Icek Ajzen's theory of planned behaviour states that subjective norms signify the perceived social influence for performing (or not performing) a behaviour. It is the impact of an individual's normative beliefs that motivate them to approve (or not) a specific behaviour. More precisely, it refers to an individual's perception about whether society think they should involve in given behaviour or not. Therefore, we developed the following hypotheses:

Hypothesis 1a (H1a). Subjective norms affect the garment E-shopping behaviour of consumers.

Hypothesis 1b (H1b). Subjective norms affect the garment E-shopping intentions of consumers.

Hypothesis 1c (H1c). Subjective norms affect the garment E-shopping behaviour of consumer, through the mediating role of E-shopping intentions.

Chih-Chung and Chang [ 40 ] analysed six past studies that measured the attitude toward online shopping and confirmed that all studies showed a significant positive influence of online shopping attitude on online purchase intention and behaviour. According to TRA (Fishbein & Ajzen, 1975), TAM (Davis et al., 1989), and TPB (Ajzen, 1991), attitude has a significantly positive effect on behavioural intent. Numerous researchers have confirmed this relationship (e.g., Bruner & Kumar, 2003; Chang & Wang, 2008; Chen, Sheen, & Lou, 2005; Chen, Sheen & Lou, 2006; Cheong & Park, 2005; George, 2002; Lin, 2007; Shin, 2007). Hence, it was hypothesised that

Hypothesis 2a (H2a). Attitude affects garment E-shopping behaviour.

Hypothesis 2b (H2b). Attitude affects garment E-shopping intentions.

Hypothesis 2c (H2c). Attitude affects garment E-shopping behaviour, through the mediating role of E-shopping intentions.

Chen and Tan (2004), Yu et al. (2005), Wu and Chen [ 30 ], Cho and Fioritto (2009), and Lee and Park [ 32 ] have established that trust has a significantly positive influence on behaviour. So, it was hypothesised that

Hypothesis 3a (H3a). Website trust affects garment E-shopping behaviour.

Hypothesis 3b (H3b). Website trust affects garment E-shopping intentions.

Hypothesis 3c (H3c). Website trust affects garment E-shopping behaviour, through the mediating role of E-shopping intention.

Lim et al. [ 3 ] also revealed that purchase intention exerts a significantly positive impact on online shopping behaviour. Thus, we hypothesised the following:

Hypothesis 4 (H4). E-shopping intention influences garment e-shopping behaviour.

3. Research Methodology

3.1. research design.

In the survey questionnaire, data comprising two parts were obtained. The first segment consisted of surveys and information about the Internet use, familiarity, and experience of the respondents. The second component consisted of five-point Likert measurements, which varied from extremely disagree to strongly agree.

A data screening procedure was applied to the data collected through the self-administrated questionnaire. First, missing data, outliers, normality, and interitem correlations were computed for each item. Second, the adequacy of covariances and Cronbach's alpha were computed for each construct. Lastly, a confirmatory factor analysis was conducted for each of the constructs.

After the data screening process, the conceptual model was translated into an AMOS model, consisting of a measurement part (confirmatory factor analysis, CFA) and a structural equation part (structural equational modelling, SEM). In order to evaluate the complex structural relations, the model consisted of complex structural relations between variables and so, a structural equational modelling (SEM) technique was used. SEM allows for the construction of a coherent dependent link chain between a number of structures to be observed simultaneously, while taking measurement errors into account [ 41 ]. Consequently, the methodology indicates the manner in which the observed variables refer to the latent constructs and their unified dependency.

3.2. Research Measures

We adopted measurement scales from the literature. These multi-item instruments have already been tested for measuring the same concepts and were found to be valid and reliable for use in a similar study setting.

Subjective norms refer to the “perceived social pressure to perform or not to perform the behaviour in question.” The first four items (Quests. # 1−4) of the questionnaire were measuring subjective norms, as adopted from [ 42 ]. These items were measured using a five-point Likert scale, with answers from “strongly disagree” to “strongly agree.” The subjective norm scale indicated a fair level of reliability, with Cronbach's alpha ( α ) of 0.747, at the level also endorsed by Robert A. Peterson (1994) in his study “A Meta-Analysis of Cronbach's Coefficient alpha,” in which he took various constructs from numerous past studies that discussed the personality and behavioural traits of individuals. He found, in his meta-analysis, that the average mean of Cronbach's alphas varies across studies, depending upon the nature and the environment of the study. The average mean of the Cronbach's alpha ( α ) for subjective norms, after considering 289 studies, was 0.76.

Attitude toward behaviour is operationally defined as the “degree to which individuals have favourable or unfavourable appraisal of the behaviour of interest.” Questions no. five to eight measured the impact of online shopping attitude on purchase intentions and behaviour. These four items (Quests. # 5−8) were adopted from Taylor and Todd (1995). A five-point Likert scale, with answers from “strongly disagree” to “strongly agree,” was used for measuring these four questions. Online shopping attitude scale indicated a good level of reliability, with Cronbach's alpha ( α ) of 0.804 ( Table 1 ).

Measurement model result summary.

Note. AVE: average variance extracted.

Trust implies the “degree to which one can believe and rely upon promises made by others.” Trust in the E-commerce sphere relates to the belief that encourages customers to voluntarily open themselves to actions through the E-seller, after considering the seller's worth. This relates to the formation of trust as a belief encompassing goodwill and believability. To measure the trust of respondents in websites, we adopted 5 items (Quests. # 9−13) from [ 43 ]. These five items were also measured on a five-point Likert scale, with answers from “strongly disagree” to “strongly agree.” The website trust scale had a good level of reliability, with a Cronbach's alpha ( α ) of 0.847 ( Table 1 ).

Purchase intentions refer to the “willingness of a person to buy the required products or services.” In the E-commerce context, online purchase intention can be theorized as “a situation when an individual need to purchase the required products or services through the website.” Four items (Quests. # 14−17) were employed for measuring the online purchase intentions of the respondents, as adopted from [ 44 ], which were again measured using a five-point Likert scale. The online purchase intention scale possessed a good level of reliability, with a Cronbach's alpha ( α ) of 0.855 ( Table 1 ).

Consumer shopping behaviour can be conceptualized as “the sum of a consumer's attitudes, preferences, intentions and decisions regarding the consumer's behaviour in the marketplace when purchasing a product or service.” For measuring online shopping behaviour, 17 items (Quest. # 18−34) were used, adopted from [ 45 ]. All items were gauged using a five-point Likert scale with answers from “strongly disagree” to “strongly agree.” The online shopping behaviour scale also showed a good level of reliability, with a Cronbach's alpha ( α ) of 0.847 ( Table 1 ).

3.3. Procedure/Data Collection

The current study was designed considering population statistics with a considerable sample size ( N = 500) from 2019 to 2020, in order to gain statistically valid results. Using closed-ended questionnaires, E-shoppers who had used the Internet as a shopping tool in previous times went through the quantitative study process. Working adults who were 20 years or older and who had shopped for garments online were taken as the unit of analysis. A purposive (nonprobabilistic) sampling technique was employed for extracting the sample, as the target population had particular traits (i.e., age, working status, and past E-shopping experience). The data presented in this paper were collected from five hundred respondents through self-administered questionnaires. A total of 470 questionnaires were returned producing a 94.5% response rate, and 439 were considered adequate for data analysis after data screening—33 questionnaires had some missing values and so were excluded from the final sample. Overall, 93.4% of questionnaires were adequate for further analysis.

3.4. Participants

A population is an overall collection of individuals or a subject, which is the prime focus of the scientific query, which sample is extracted to measure the variables of interest. The population normally refers to a particular community that observes common binding characteristics or traits.

As per the predefined objectives of the study, the target population of concern was working adults in Pakistan, who were purposively selected as the subject of analysis. The target population of the study included both males and females who purchased their garments online, through online store/website or social networking site (SNS).

A total of 439 E-shoppers responded to the survey, consisting of 360 male and 79 female working adults. The group composition of the participants included different rates of sex, age, marital status, and education. All respondents were asked to choose whether or not to comply with the statements of questions from a list of responses.

4. Data Analysis and Discussion

4.1. sample demographics/respondent profile.

In Table 2 , the mean values shown indicate the central tendency of the data, where the acquired values were clearly spread around a central value which perfectly described the data. The low standard deviation values indicate that the data points tended to be close to the mean of the data set, showing relatively close agreement among respondents and little variation about the answers of the questions. The mean and standard deviation values verified the significance of the data, and that there were very small chances of error.

Measurement of study variables.

The mean values shown in Table 3 demonstrate the central tendency of the data, where the acquired values were clearly spread around the central values. The standard deviation is a measurement of how widely the responses are spaced. From the above table, it can be that standard deviation was very small, indicating that the data points tended to be close to the mean of the data set. Hence, it can be assumed that there was relatively close agreement among the respondents mentioned in Table 4 . All answers were relatively close to the mean, with just a little variation, proving that the data were very significant and there was a very low chance of error. Therefore, the responses acquired using the items of the online shopping behaviour subscale direction significantly varied from each other.

Demographic distribution of sample ( N = 439).

Note. Descriptive statistics ( N = 439), their classifications, and frequencies.

Descriptive statistics of the study.

4.2. Multivariate Normality

Skewness and kurtosis tests verified the multivariate normality and ensured that the results of the study could not be skewed by significant variations in the main data. The results showed that the data were uniformly distributed, as neither the skewing values (peakness) nor kurtosis (flatness) surpassed their normal range [ 46 ].

As we dealt with a sample for further analysis in this study (i.e., it is a population in terms of the EFA in Table 5 ), the Principal Axis Factoring method with direct oblique rotation was carried out using the 34 items. In Exploratory Factor Analysis (EFA), the factors are permitted to be correlated with one another in promax rotation. The factor pattern matrix contained the coefficients for the linear combination of the variables.

Frequencies.

Note. Skewness value > +1 or <−1: balanced distribution and kurtosis value < 1: flat distribution.

The abovementioned Table 6 presents the factor pattern matrix, which contains the coefficients for the linear combination of the variables.

Factor pattern matrix.

4.3. Validation of the Measurement Model

A measurement model was used to link the observed variables with the latent constructs, while the instrument scores and the concepts that they are meant to measure were linked through confirmatory factor analysis (CFA). Before undertaking the confirmatory factor analysis, the convergent and discriminant validities of study instruments were assessed, to determine how thoroughly these constructs gauged the intended concepts.

The convergent validity obtained from the six factors with low factor loads in the measurement model and other loadings in the factor exceeding the threshold value (0.70) was demonstrated using the criterion provided by [ 47 ]. Construct reliabilities exceeding 0.70 were considered and, after removing the poor factor loadings, the average variance extracted (AVE) was upgraded to an acceptable level (i.e., ≥0.50, ranging 0.51–0.62), as shown in Table 1 . So, all factors satisfied the discriminant validity and were precise in nature (i.e., truly measuring the characteristics being represented by the variables).

To ensure the uniformity and stability of the measures, the internal and the composite reliabilities were measured. The Cronbach's alpha test confirmed the internal consistency and reliability of the concepts, with values ranging from 0.75 to 0.86 (Wollack, Cohen, & Wells, 2003). The Composite reliability values, ranging from 0.80 to 0.93, were also above the proposed level (0.70) [ 48 ]. Through the empirical data shown below, the convergent validity was also verified.

The correlation matrix was constructed, in order to observe the interconstruct correlations. It indicated that these variables were not mutually correlated with each other. The bivariate test variables were below the suggested threshold value (<0.7) [ 49 ]. Both attitude toward shopping and confidence on the web appeared to be highly positive ( r = 0.67 and r = 0.63, respectively), which provided reasonable relationships with E-shopping activity ( r = 0.54 and r = 0.49, respectively). Contrary to the other constructs, subjective norms had weak correlations with E-shopping intentions and E-shopping behaviour ( r = 0.19 and r = 0.23, respectively) mentioned in Table 7 .

Pearson correlations matrix.

Note. ∗∗ Correlation is significant at the 0.01 level (2-tailed).

Multicollinearity was measured by examining the tolerance and Variance Inflation Factor (VIF). The VIF statistics in Table 8 show the predictor variables were moderately correlated. All research constructs had VIF values less than the threshold value (<3) and higher than 1.

Multicollinearity statistics.

Dependent variable: E-shopping intentions.

Kaiser–Meyer–Olkin (KMO) and Bartlett's tests mentioned in Table 9 confirmed the adequacy and suitability of the data. Taken together, these tests satisfied the minimum standard which should be passed before conducting CFA on data. The KMO values of all five study constructs were greater than the recommended range (>0.6) and closer to 1, showing the adequacy of percentage of variance in data. Thus, we confirmed that the sampling was adequate, and the data for all study constructs was suitable for conducting confirmatory factor analysis (CFA).

KMO and Bartlett's tests.

Note. Value (KMO of >0.5 or ideally >0.7) for adequacy of percentage of variance [ 50 ].

4.4. Model Measurement

A five-step process, consisting of model definition, description, estimation, evaluation, and amendment, was carried out for the study model. First of all, the latent variables with their indicators were listed, and error terms were also described in the model specification. The model established that it had enough information on the equation to generate unknown parameter estimates. The estimation of various model fit indices, such as GFI, AGFI, CFI, TLI, and RMSEA, was used to estimate the hypothesised model parameters. Chi-square ( χ 2 ) and some other signs were added, in order to assess the model's degree of accuracy, as the chi-square value ( χ 2 ) is sensitive to a large sample ( n > 200). As a rule of thumb, some other indices, such as GFI, CFI, NFI, and RMSEA, may clarify the fit pattern if the value of chi-square/Df. is less than 3. Finally, the model was respecified by codefault terms, and some restrictions in path coefficients were enforced. The importance of fit indices, which otherwise showed a bad fit, was achieved, and the fit was improved. Therefore, other assumptions were made, namely, that there were no parity restrictions on the factor loadings for these measures.

The chi-square statistic ( χ 2 ) was below the minimum value (i.e., CMIN/Df.<3), which verified the latent construct's distributions to be substantially different. The values of GFI, AGFI, CFI, and TLI, which showed the overall fitness of this model, were higher than 0.9. The RMSEA analysis of a population involves the root mean square error approximation; when the RMSEA value is below 0.07, a model can be considered appropriate.

The fit indices of the model were respecified by showing that the model hypothesised had a good fit to the data ( N = 439, p < 0.001, GFI = 0.908, AGFI = 0.924, CFI = 0.910, TLI = 0.929, and RMSEA = 0.060). With the overall fit statistics, due to important and practical indications, the hypothesised model was presumed to be very strong for the current data. All elements that contribute significantly to their constructs were assisted by the predicted relationships between the constructs and their indicators.

Table 10 shows overall model fit summaries for the original and revised models. Some assumptions were taken into account for these indicators; for example, no equality constraints were set on the factor loadings. As per the overall measurement results of the actual model of the study (where N = 439, p < 0.001, GFI = 0.864, AGFI = 0.838, CFI = 0.899, TLI = 0.886, and RMSEA = 0.055) demonstrated an average fit of the model overall. So, this average fit led to the need for model respecification.

Model fit summary (original and revised model indices).

Note. GFI: Goodness of Fit Index; AGFI: Adjusted Goodness of Fit Index; CFI: Comparative Fit Index; TLI: Tucker–Lewis Index; RMSEA: The Root Mean Square Error of Approximation.

4.5. Structural Equation Modelling (SEM)

4.5.1. structural model assessment.

For testing the study hypotheses, a bootstra p value of 2000 resamples was calculated using standardised route coefficients. To obtain the same number of estimates, due to longer alignment, a large number of replicates were needed. The mean was less than the target value in the current analysis, so the test statistics may have also fell into one critically significant area. Thus, due to the expectation of both forms of interactions (i.e., positive or negative), two-tailed values and 95% confidence intervals were taken into account. The findings of the data analysis indicated that the path structure for the study variables (direct and indirect) was accurate and adequate. The findings of the hypothesis tests are summarised in Table 11 , where the path coefficients and p values of the study variables describe the direct, indirect, and complete influences.

Hypothesis testing result summary.

Note. ∗ Significant at level p < 0.05 and ∗∗ significant at level p < 0.01. Note. SN: subjective norms; ESA: E-shopping attitude; WT: website trust; ESI: E-shopping intention; ESB: E-shopping behaviour; CI: confidence interval.

The structural model analysis found that, aside from arbitrary criteria, there were two other structures—E-shopping attitudes and confidence in the website—that had significant explicitly positive effects on E-shopping behaviour. Eight out of ten findings of the analysis were supported by the final statistical tests. Interest indicates that the E-shopping satisfaction is a key factor in actual online purchasing actions. E-shopping intentions often effectively clarify and mediate the relation between the independent variables (i.e., attitude to E-shopping and confidence on the website) and the dependent variable of the analysis.

4.5.2. Hypothesis Discussion

Hypothesis H1a indicated the positive relation of subjective criteria to E-commercial behaviour. The SEM findings showed good support for the importance ( β = 0.091, p < 0.05) of hypothesis (H1a) and indicated that subjective regulations have a significant positive connection to E-shopping behaviour. Hypothesis H1b concluded that this relationship did not support ( β = 0.012, p < 0.05) the connection between social norms and E-shopping intentions early in the analysis. Therefore, it was not endorsed, as no significant correlation between subjective norms and E-shopping intentions existed. H1c, therefore, did not endorse the findings, as no relevant indirect relationship existed ( β = 0.003, p < 0.05) between E-shopping and subjective expectations through the mediator.

We verified successful direct (H2a) and indirect (H2c) E-shopping–attitude relationships, wherein positive relationships were formed ( β = 0.233, p < 0.01 and β = 0.128, p < 0.01, respectively). Hypothesis H2b suggested the relationship between attitude and E-shopping intentions. Our findings supported this substantially positive relationship ( β = 0.452, p < 0.01) and demonstrated that E-shopping is a vital predictor of online buying intentions.

Finally, the hypotheses H3a and H3c postulated that there exist direct and indirect relationships between website trust and E-shopping actions. Such favourable relationships ( β = 0.154, p < 0.01 and β = 0.098, p < 0.01, respectively) were verified by our findings, in that trust in a website is a successful predictor of E-shopping activity. A correlation between confidence and a mediator (E-shopping intentions) was suggested by hypothesis H3b. This substantially favourable relationship was confirmed by our findings ( β = 0.347, p < 0.01).

5. Conclusions and Findings

Our findings demonstrated the strong influence of E-shopping intentions on actions and indicated that E-shopping intentions effectively clarify and serve as mediators between E-shopping conduct and its context. Therefore, those aimed at developing E-shopping actions of working adults should, in particular, focus on E-shopping intentions. These results are compatible with those of previous similar studies (Hsu & Bayarsaikhan, 2012; Lim et al., 2015; Orapin, 2009; Pavlou & Fygense, 2006; Roca, Garcia, & Jose, 2009). However, E-shopping intention did not act as a mediator between subjective standards and E-shopping conduct, as no significant direct relationship between subjective standards and E-shopping intentions was observed, at least, for the working sphere of E-shoppers.

Therefore, as was originally assumed in hypothesis H1c, no partially mediating or indirect connection between subjective norms and E-shopping behaviour was observed. All proposed hypotheses except for H1b and H1c were endorsed, as no significant connection with the DV through mediating between subjective standards and E-shopping intentions was created.

The statistical analysis of the data showed that social expectations, E-shopping location, and trust in websites are all significant factors that influence the E-shopping behaviour of consumers, which ultimately leads to an Online Shopping Purchase. Therefore, the situation is very different from that of other parts of society, in the event of apparel E-shopping plans for working adults. Subjective expectations did not create substantial positive or negative relationships with intentions, unlike E-shopping attitude and website confidence. Therefore, along with subjective criteria, these predictors contribute to compliance. The findings of the analysis, therefore, did not support the hypotheses H1b and H1c. Many previous studies (see, e.g., Chua et al., 2006; Jamil & Mat, 2011; Tseng et al., 2011) have predicted these relationships to be lacking. More specifically, the inconsistent relationship between subjective standards and expectations is the most important and frequently discussed weak point linked to the TPB. The founder of the theory (Ajzen, 1991) also explained this deficiency, by suggesting that motives are primarily influenced by behaviours and behavioural regulation of an individual's traits. The results of this study are, therefore, also related to previous research, in that the subjective expectations did not influence the actions of adults working for the purchase of their online equipment. While other social groups, such as students or housewives, may be effectively assisted or affected by their significant peers when deciding whether to participate in or not participate in such behaviour, it has been indicated that, once the customer has agreed to shop online, no more input is considered through other paths (e.g., from their social circle or peer group).

There were important direct and indirect relations between E-shopping actions and the mediator and, subsequently, the dependent variable. In terms of E-shopping mindset, its effects on mediators (E-shopping intentions) were positive both directly and indirectly (H2a and H2c), being substantially positive for E-shopping conduct. The relationship between E-shopping and E-shopping expectations showed a good relationship. The results demonstrate that E-shopping reflects the E-shopping activity of working adults, in order to pursue E-shopping as a way to purchase their clothes. E-shopping mindset is a key determinant of the goals and actions of E-shopping. The interviewees generally had a favourable evaluation and usually promoted their conduct.

The findings of the study were finally confirmed through hypotheses H3a, H3b, and H3c, all of which were significantly positive, both explicitly and indirectly, focused on DV (E-shopping behaviour) and explicitly for to the intermediary (E-shopping intentions). These results indicated that confidence in a website is an expanded construction which is ideally relevant to recognise, in the sense of E-shopping. The fact that consumers can shop or give up their shopping cart is an important factor. E-shopping consumers become more relaxed as their confidence in E-shopping media (e.g., a website) increases.

Trust plays a key role in defining the conduct of E-shopping, as it transforms the good expectations and actions of consumers, in order to create E-shopping requests for online shopping. Some previous studies have confirmed confidence to be a basic demand for growth in E-commerce (Mukherjee & Nath, 2007; Sutanonpaiboon & Hamimah, 2010); in particular, in 2012 (Hsu and Bayarsaikhan, 2012; Jiang, Chen & Wang, 2008). Around the same time, the literature noted that a confidence deficit is the key reason why E-shopping is not chosen as a shopping medium or why requests are abandoned. The dynamic disposal of cyberspace is very high, due to the insecurity of users (Whyte, 2016).

Eventually, when a customer visits a shopping website to check for the correct items, the E-shopping cycle begins. This quest either transforms into a real buying transaction or not, which is a secondary problem. He et al. (2008) concluded that the biggest obstacle for the growth of E-commerce is the absence of online purchasing intentions. Generally, several attempts are made to perform a certain activity, which is probably the main reason for the leaving of carts. Expectations are, therefore, possibly the best indicators to show how ready consumers are to purchase online. As Dolatabadi et al. [ 42 ] reported, the E-shopping intentions of consumers have major effects on their own purchasing decisions. Three key antecedents—social standards, attitude, and perceived power and expectations—influence the actions of TPB.

5.1. Contributions of the Study

Our theoretical contribution expands the literature by assessing the effects of website trust on the intentions and behaviours of consumers related to E-shopping, which has never been tested before in such a context and setting. In replacing PBC with website trust, we have extended the literature and, hence, proved the significant determinant of choosing E-shopping in the TPB setting. We have shown that “website trust” is worth consideration as a contributing factor that builds favourable intentions and behaviours toward E-shopping, rather than the opinions of significant peers (in the case of working adults).

In practical terms, this research provides valuable insight into the E-shopping preferences of E-shoppers (adult workers), for the advancement of relevant marketing strategies. We recommend that E-vendors design viable systems which support and attract customers toward E-shopping, through persuading them to believe that the E-vendor is honest and concerned about their customers. This is essential, as most customers question the integrity and trustworthiness of the E-vendor while performing an E-transaction. Thus, E-vendors must convince users to have trust in their shopping websites, as it is obvious that trust significantly influences the intent and behaviour related to E-shopping. Our findings suggest that E-shopping websites need to develop more trust in transactions for their clientele, in order to attract and motivate them more to build positive E-shopping behaviours.

This paper is intended as a guide for the transformation of E-commerce for companies attempting to project their businesses online. Such businesses should focus even more on the prestige of their E-shopping newspapers, which they would otherwise have overlooked.

5.2. Limitations of Study

There were several limitations to this study. The research sample was restricted to an explicit section of the population (working adults) and a particular geographical region; therefore, it may not have attracted students, young people, or housewives, and it may have produced some specific results, such that expanding these findings to other segments in the business is suggested. Second, we focused primarily on the E-shopping conduct related to the apparel industry. Ultimately, it is not necessary to draw or extend these findings to certain segments of consumer markets, such as electronics, beverages, cosmetics, books, or foodstuffs. Finally, in the constructs used to illustrate the decision to shop online, there were no additional variables, such as the fear of potential scarcity or the peculiar emotional state of individuals, such as enjoyment, disgust, or disdain with respect to their actions of interest. Such variables may lead to certain behaviours or not to conduct the interesting behaviour.

In relation to other online consumer goods, the proposed conceptual model should also be tested. Culture affects behaviours, thus driving demographic change interdependently and complementally (Pollak & Watkins 1993). For that reason, the potential E-commerce trends that have a cross-cultural influence or a particular demographic trend should be considered, which may be focused on specific sex or low-income groups, as well as students. The next assessed demographic may be housewives and students, as they tend to rely on their family heads. Likewise, it may be beneficial to increase the sample size and to adjust the geographical location.

5.3. Research Implications

The results of this study offer a better understanding of E-trust and recommend E-vendors to design a viable online selling system that supports and encourages positive and secure feelings toward E-shopping. Firstly, E-vendors should focus on strengthening these feelings by increasing the trustworthiness of their E-shopping medium (e.g., website). Secondly, the E-vendors must assure their customers that they will not behave opportunistically and will deliver the promised products and services to them. Such commitments and promises will reduce the uncertainty and add additional value in E-shopping. However, these results may not generalize to other geographical areas or social classes as a whole. Finally, this study opens up some new frontiers in support of future research relating to behavioural intent in the online shopping context.

In comparison to the former studies, this study has portrayed an improved explanatory power of two of the main components of behavioural sciences like intention and online shopping behaviour in a specific context. This study theoretical contributes further illustrative strength in explaining the reasons of variation in consumers' argumentative purchase intentions. The study also expands the theory by applying the effects of website trust on consumers' intentions and behaviours to shop their products and services through some online shopping medium. This study can also assist managers in recognizing and eliminating the potential key behavioural obstacles and allows them to deliver highly customer oriented online customized services and as well as to enlarge their loyal customer base by increasing trustworthiness of their shopping websites. Further, it also delivers guidance for future research, for focusing on the strengths and eliminating the weaknesses. Similar to others, this study also has some weak points which need to cater through further examination in this sphere. So, the results may not generalize to other geographical areas or social classes as a whole. Eventually, this study opened up some new frontiers in support of future research for knowing behavioural intent in online shopping context.

Acknowledgments

This study is supported by the National Statistical Science Research project (2021LY059) and the Pre-Study of Humanities and Social Science of Huzhou University (2020SKYY15).

Data Availability

The paper is extracted from the student's work with his/her consent.

Conflicts of Interest

The authors declare no conflict of interest.

Authors' Contributions

This article is the result of the joint work by all authors who equally contributed to conceive, design, and carry out the research. All authors collaborated in analysing the data, preparing the data visualization, and writing the paper. All authors have read and agreed to the published version of the manuscript.

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Open Access

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Research Article

The impact of AR online shopping experience on customer purchase intention: An empirical study based on the TAM model

Contributed equally to this work with: Chunrong Guo, Xiaodong Zhang

Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Writing – original draft, Writing – review & editing

Affiliation School of Economics and Management, Ningbo University of Technology, Ningbo, Zhejiang, China

Roles Data curation, Formal analysis, Funding acquisition, Methodology, Resources, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Economics and Management, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China

• Chunrong Guo, 
• Xiaodong Zhang

• Published: August 26, 2024
• https://doi.org/10.1371/journal.pone.0309468
• Reader Comments

Augmented Reality (AR) offers a rich business format, convenient applications, great industrial potential, and strong commercial benefits. The integration of AR technology with online shopping has brought tremendous changes to e-commerce. The Technology Acceptance Model (TAM) is a mature model for assessing consumer acceptance of new technologies, and applying it to evaluate the impact of AR online shopping experiences on consumer purchase intention is an urgently needed area of research. Firstly, the typical applications of AR in online shopping were reviewed, and the connotations and experiences of AR online shopping were summarized. Secondly, using the five types of AR online shopping experiences as antecedent variables, and perceived ease of use and perceived usefulness as intermediate variables, a theoretical model was constructed to explore the impact of AR online shopping experiences on customer purchase intentions, followed by an empirical study. Finally, suggestions were proposed for optimizing the online shopping experience to enhance purchase intentions. The article expands the application scenarios of the Technology Acceptance Model and enriches the theory of consumer behavior in Metaverse e-commerce.

Citation: Guo C, Zhang X (2024) The impact of AR online shopping experience on customer purchase intention: An empirical study based on the TAM model. PLoS ONE 19(8): e0309468. https://doi.org/10.1371/journal.pone.0309468

Editor: Ricardo Limongi, Federal University of Goias: Universidade Federal de Goias, BRAZIL

Received: May 3, 2024; Accepted: August 8, 2024; Published: August 26, 2024

Copyright: © 2024 Guo, Zhang. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: This work was supported by the Interdisciplinary Research Fund of Inner Mongolia Agricultural University, “Research on Open Innovation Intelligent Decision-Making in E-commerce Based on Federated Learning” (Project Number: BR231518); Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region, “Research on E-commerce Intelligent Marketing Based on Multimodal Learning” (Project Number: NJYT24014); National Key R&D Program of China, “Intergovernmental International Science and Technology Innovation Cooperation” Key Special Project, “Research on Sino-Mongolian Agricultural and Pastoral Supply Chain” (Project Number: 2021YFE0190200); National Social Science Fund of China Post-funding Project, “Research on the Internationalization Development of Chinese Cross-border E-commerce Brands” (Project Number: 20FGLB033); Inner Mongolia Autonomous Region Graduate Education Teaching Reform Project, “Research on the Training Model for New Business Graduates in Inner Mongolia under the Background of Digital Economy” (Project Number: JGCG2022059). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

With the advent of the digital age, augmented reality (AR) technology has shown transformative potential across multiple industries, particularly in the realm of e-commerce [ 1 ]. Major retailers and brand corporations such as Google, Apple, Alibaba, Amazon, and Facebook have begun to employ AR technology to attract customers and boost sales. They actively integrate AR services into their business spheres to enhance customer awareness, brand engagement, and brand loyalty [ 2 ]. Surveys indicate that approximately 75% of consumers expect to experience AR services when shopping online, 71% state that they would shop more frequently if retailers utilized AR, and 40% are willing to pay more for products offered through AR. The AR market is projected to reach $50 billion by 2024 [ 3 – 6 ]. Due to their direct relationship with sales conversion rates and customer satisfaction, consumer shopping experiences have become one of the primary focuses of marketing management [ 7 ]. The AR strategy is crucial for merchants, especially in the highly competitive e-commerce market. A deep understanding and leveraging of AR technology’s potential can provide significant competitive advantages for businesses. Firstly, if studies find that AR experiences significantly enhance consumer purchase intentions, e-commerce platforms will be more inclined to invest in AR technology. Secondly, by identifying specific pain points in the user experience within AR applications through research, e-commerce businesses can optimize their AR applications, enhancing user satisfaction and loyalty. However, current research primarily explores the application of AR technology in e-commerce and its impact on consumer perceptions and behaviors [ 3 – 5 , 8 – 10 ], aiming to understand the psychological and behavioral changes consumers undergo during AR experiences. Whang et al. (2021) adopted the concept of consumer control to investigate the mediating and moderating effects of AR experiences on purchase intention within the shopping environment for beauty products, with a focus on cognitive control and behavioral control [ 11 ]. However, comprehensive studies that specifically investigate the impact of AR online shopping experiences on consumer purchase intention and analyze the intrinsic mechanisms behind consumer acceptance of this new technology remain rare. Unlike previous studies, this research applies the Technology Acceptance Model (TAM) to explore how AR online shopping experiences affect consumer purchase intentions. It evaluates consumers’ attitudes towards AR online shopping experiences, how these experiences influence perceived usefulness and perceived ease of use, and how these factors translate into purchasing behavior. Although the TAM model has been widely used to assess consumer acceptance of new technologies, applying it to evaluate AR use in online shopping remains a largely unexplored area of research. This study introduces five types of AR online shopping experiences as antecedent variables to comprehensively analyze their impact on customer purchase intention. It aims to help e-commerce companies understand how different types of AR experiences influence consumer behavior, thereby enabling them to optimize user experiences in a targeted manner. The study explores the role of perceived ease of use and perceived usefulness as mediating variables between AR online shopping experiences and purchase intention, revealing the intrinsic mechanisms through which AR experiences affect consumer purchase intention and providing a theoretical basis for optimizing AR applications. Therefore, this study extends the application scenarios of the Technology Acceptance Model. Based on the empirical research results, specific optimization suggestions are proposed to enhance customer purchase intention. These suggestions offer actionable strategies for e-commerce marketers and service providers, improving the market competitiveness of e-commerce platforms. By analyzing AR online shopping experiences, this study enriches the theory of consumer behavior in Metaverse e-commerce and provides new perspectives and methods for future e-commerce research in the Metaverse environment.

AR (Augmented Reality) and VR (Virtual Reality) are key gateways into the metaverse, serving as the intersection and overlay of virtual and real worlds. These two technologies differ in their technical aspects, devices used, application fields, advantages, and potential, as shown in Table 1 . AR, with its superior interactivity, real-time capabilities, visual effects, high portability, and ease of connectivity, demonstrates strong application value and development trends in areas such as e-commerce, shopping, marketing, advertising, social interaction, and entertainment.

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https://doi.org/10.1371/journal.pone.0309468.t001

2 Basic theories

2.1 ar online shopping.

Augmented Reality technology originated in the 1990s, but it was not until the early 21st century, with the widespread adoption of smartphones and high-speed internet, that this technology began to be applied in the online shopping sector. Retailers and tech companies invested in image recognition improvements and 3D modeling technologies to enable a more realistic product experience for consumers. One of the earliest applications was a virtual fitting room that allowed users to try on clothes via a web camera. In 2017, IKEA launched an AR app named “IKEA Place” that allowed users to virtually place furniture in their homes to see how it would look in a real environment. After 2020, the use of AR technology in online shopping became more widespread. Besides virtual try-ons, it was also used for home decor, cosmetics selection, and even in some high-tech stores for AR virtual shopping assistants (see Table 2 ). E-commerce giants like Amazon and Alibaba integrated AR technology into their shopping platforms, providing a richer and more interactive online shopping experience. For instance, Amazon’s AR View feature allows users to virtually view products in their own living spaces. In the future, augmented reality will further merge with technologies such as virtual reality and blockchain to create a comprehensive metaverse digital shopping environment, offering users a fully immersive and interactive shopping experience.

https://doi.org/10.1371/journal.pone.0309468.t002

Despite the rapid development of AR online shopping in practice, there is currently no unified definition. Summarizing existing theoretical research and practical developments, AR online shopping is an innovative shopping method that combines the convenience of online shopping with the experiential aspect of physical shopping. It overlays virtual information and images in the consumer’s actual environment and displays them in 3D. This allows consumers to perceive and interact with virtual elements in a more realistic and three-dimensional way in real-time, providing a richer and more immersive shopping experience. Consequently, consumers can more accurately understand the appearance and functions of products before purchasing, thereby enhancing shopping efficiency and satisfaction [ 13 , 14 ]. AR online shopping displays product elements in three-dimensional (3D) form and assimilates virtual objects into the physical reality, allowing users to experience the coexistence of real and virtual elements in the same space and interact with products in an enhanced manner [ 15 , 16 ]. AR interaction technology enables users to virtually try, verify, and inspect products from various angles and size [ 17 ], It responds instantly to user actions such as rotating, zooming, or altering products, and any changes made during user interactions are immediately reflected in the AR interface. This instant interaction enhances the dynamism and enjoyment of shopping [ 18 , 19 ], increases consumer engagement, and promotes sales in a heuristic and effective manner [ 20 ]. AR technology allows users to make personalized adjustments and trials according to their preferences and needs, turning customers into co-designers of the products they wish to purchase, thereby creating personalized products or customizing them in a personalized way [ 21 ].

Immersion refers to the degree to which an individual’s senses are cut off from the real world and replaced by a virtual simulation [ 16 ]. Initially a way for gamers to interact with their physical environment, immersive AR technology is now used to enhance e-commerce platforms through richer media experiences, simpler navigation, and the multidimensional and multisensory presentation of products [ 8 ], placing customers in a new immersive space. This allows users to navigate spatial locations via web browsers in an interactive simulated manner, experiencing the sensation of shopping in person at actual locations, thus creating a retail store shopping feel accessible from anywhere. This can evoke emotional, cognitive, and behavioral responses [ 9 ], enhancing enjoyment, perceived usefulness, and purchase intent. With rapid advancements in augmented reality technology, along with the swift development of VR, 5G/6G, Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), Artificial Intelligence (AI), and blockchain technology, immersive 3D experiences and multisensory communications blur the lines between virtual and physical worlds to form a metaverse of mixed reality [ 22 ]. Within the context of the metaverse, AR, VR, and XR (Extended Reality) have all made significant progress, considered the next generation of the internet or social media, poised to revolutionize shopping and marketing [ 23 ].

2.2 Experiential marketing

Intense competition has made the functional attributes of products and services increasingly similar, making experience a key differentiator among businesses, especially in the retail environment. A core goal for businesses is to create outstanding customer experiences. Experiential marketing has become a standard practice for many merchants [ 24 ]. It goes beyond the transactional level of traditional marketing of products and services, focusing instead on creating emotional and sensory connections with consumers. The core of experiential marketing is to create a comprehensive consumption experience. Experiential marketing includes five main dimensions. Sensory Experience (SENSE): Sensory experience focuses on stimulating the consumer’s senses—sight, hearing, smell, touch, and taste. By creating an appealing visual environment, playing pleasing music, offering unique tastes and scents, or providing tactile experiences, businesses can enhance consumers’ product perceptions and memories. Emotional Experience (FEEL): Emotional experience aims to evoke consumers’ emotional responses and emotional connections. For example, businesses can touch consumers’ hearts through emotionally resonant advertising, storytelling, or user experiences. This type of experience might be based on joy, surprise, nostalgia, or other emotions, with the goal of establishing a deeper emotional connection with consumers. Creative Cognitive Experience (THINK): The creative cognitive experience encourages consumers to actively think, explore, and innovate. This type of experience often stimulates consumers’ curiosity and imagination by solving problems, offering novel perspectives, or introducing unfamiliar concepts. For example, consumers’ thinking and engagement may be stimulated through interactive exhibitions, educational workshops, or innovative product demonstrations. Physical Experience, Actions, and Lifestyle (ACT): Physical experience involves consumer behaviors and direct interactions with products or services, including using products, participating in activities, or adopting specific lifestyles. For example, experiential retail stores or interactive exhibitions encourage consumers to engage with and experience the brand’s lifestyle. Social Identity Experience (RELATE): The social identity experience emphasizes the relationships between consumers and others, and how they define their social identities through brands. This can be achieved through interactions on social media, community events, or associations with certain cultures or groups. For example, some brands incorporate specific cultural values or social movements, making consumers feel like part of a larger group [ 25 , 26 ]. In summary, experiential marketing creates a comprehensive and immersive consumer experience through these five dimensions, aiming to establish a deeper emotional connection between the brand and consumers [ 27 ].

In the field of online shopping, creating a unique online shopping experience has become key to attracting customers and maintaining customer loyalty. Experiential marketing in online shopping has now become a focus of attention for both academic researchers and practitioners. Key factors in building positive online experiences include vividness, interactivity, and uniqueness. However, achieving these objectives faces several challenges. On one hand, due to the complex cognitive structures of consumers, exploring the mechanisms behind consumer online buying behaviors is difficult. On the other hand, virtual experiences have certain limitations that directly impact customer purchasing behavior. Marketers should seek innovative methods to overcome these challenges, including the use of metaverse technologies such as augmented reality and virtual reality, enabling consumers to interact with virtual content in the real world and experience it in a holistic manner.

2.3 Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM), proposed by Davis in 1989 [ 28 ], has been a key theoretical framework widely used in the field of information systems since the late 1980s [ 29 ]. TAM aims to explain and predict user behavior in accepting and using new technologies [ 30 ]. The model suggests that an individual’s intention to use a technology is primarily determined by two main factors: Perceived Ease of Use (PEOU) and Perceived Usefulness (PU). Perceived Usefulness refers to the user’s belief that using a particular technology will enhance their job performance, meaning that a practical technology is more likely to be accepted and used by users [ 31 ]. Perceived Ease of Use refers to the user’s perception of how easy or difficult a technology is to use; if users believe a technology is easy to use, they are more likely to adopt it [ 32 ]. Perceived Usefulness is influenced by Perceived Ease of Use because, all other conditions being equal, a technology that is easier to use is more likely to be accepted.

Since its inception in 1991, the Technology Acceptance Model has generated over 1,000 related publications in the field of management, making it one of the most popular theoretical models [ 7 ]. TAM has also become an appropriate hypothesis model for studying the acceptance of AI technology in e-commerce [ 33 ]. Magsamen-Conrad et al. [ 34 ] have used Perceived Ease of Use to define the comfort level when using social networking platforms. Jacob and Pattusamy [ 35 ] have described how Perceived Usefulness indicates the extent to which using social networks can aid in sustaining people’s learning activities. If users believe that the technology behind an online shopping experience is useful and easy to use, they are more inclined to use that technology. However, the TAM model focuses only on the extrinsic motivations for technology use [ 23 ]. To enhance the explanatory power of TAM, researchers have expanded it by incorporating various external variables when using the model. This expansion allows studies to address the intrinsic motivations of users for using a particular technology. One such variable is Perceived Enjoyment, meaning if consumers enjoy the online shopping experience, they are likely to have a positive attitude towards the specific technology [ 36 ]. Another expansion of TAM involves the introduction of the trust factor, especially in e-commerce and online environments, where perceived trust is considered a key factor influencing user acceptance and use of new technologies. The inclusion of trust has enhanced the model’s accuracy in predicting user behavior [ 37 ]. Some scholars have also introduced subjective norms and external regulations, Research by Wang et al. [ 33 ] found that in the use of AI technology in e-commerce, subjective norms positively influence perceived usefulness and perceived ease of use, and trust has a positive effect on perceived usefulness. Pan et al. [ 23 ] studied how TAM-related factors influence two types of usage behaviors on current metaverse platforms. The driving forces for using popular metaverses are perceived usefulness and subjective norms, while the adoption of emerging metaverses is significantly influenced by perceived enjoyment and external regulations. In 2003, Venkatesh et al. [ 38 ] proposed the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which adds “facilitating conditions” that influence users’ intentions to accept and use technology as well as actual usage behavior, helping researchers and practitioners better understand the process of technology acceptance.

3 Research hypotheses and model construction

3.1 research hypotheses, 3.1.1 antecedent variables..

The most prominent issue people face when shopping online is still the lack of physical contact with products and insufficient information about them. Online shopping cannot provide the immediate experience and trial opportunities that physical stores offer. The product images, descriptions, and even videos in online shopping may significantly differ from the actual goods received [ 39 ], leading to consumer disappointment and the choice to leave. The ideal solution to this problem is to provide a virtual product experience on consumers’ own shopping devices. AR technology overlays digital information on real-world visual elements, seamlessly integrating virtual products into consumers’ real environments. This not only allows consumers to browse products in entirely new ways but also offers a more personalized and interactive shopping experience, enabling people to have an “immersive” shopping experience without actual contact with the product [ 14 ]. With the widespread use of mobile devices such as smartphones and tablets, the application of AR technology in online shopping has become increasingly convenient and popular, changing the way people shop [ 40 ]. For instance, virtual try-ons or trials, which are very popular in the fashion and retail industries, allow consumers to virtually try on clothes or shoes, or test various cosmetics on their faces to preview effects before purchasing. They can even try out furniture and decorative items at home, to better understand how these products would look in actual use [ 11 , 41 ]. AR technology enables interactive product displays, allowing consumers to view 3D models of products through AR apps on their smartphones or tablets, understand products from different angles, zoom in on details, and even observe different configurations and colors of the product [ 42 ]. Moreover, AR can create an exciting, enjoyable, and fun atmosphere, providing users with a gamified shopping experience. For example, customers can participate in virtual treasure hunts, searching for specific virtual items in the store to receive discounts or rewards [ 43 ]. With these innovative features, AR enhances consumer engagement in online shopping. The AR experience significantly impacts perceived ease of use, leading us to propose the hypothesis:

• H1: Sensory experience has a significant impact on perceived ease of use
• H2: Emotional experience has a significant impact on perceived ease of use
• H3: Cognitive experience has a significant impact on perceived ease of use
• H4: Action experience has a significant impact on perceived ease of use
• H5: Relational experience has a significant impact on perceived ease of use

Although existing literature has covered various aspects of AR technology, including its applications in fields such as education, healthcare, and entertainment, there has been limited in-depth discussion on its impact in the e-commerce sector, especially in terms of how AR technology influences consumer purchase intentions. Compared to traditional online product displays, AR offers better immersion, novelty, and enjoyment [ 43 ], and it has a significantly positive impact on consumers’ online purchase intentions by enhancing user experience [ 44 ]. Uhm et al. [ 10 ] have further confirmed that augmented reality will improve consumers’ diagnostic perceptions, psychological distance, risk perception, and purchase intentions in e-commerce products, but to varying degrees, with greater impacts on diagnostic perceptions and purchase intentions. Xu et al. [ 3 ] identified key AR features in the e-commerce environment and analyzed their effectiveness in helping consumers understand products deeply and creating an engaging atmosphere for customers. Immersive overlays, creative scenarios, and digital twins are important developmental pathways for the e-commerce metaverse [ 45 ]. We propose the hypothesis that the AR online shopping experience has a significant impact on perceived usefulness:

• H6: Sensory experience has a significant impact on perceived usefulness
• H7: Emotional experience has a significant impact on perceived usefulness
• H8: Cognitive experience has a significant impact on perceived usefulness
• H9: Behavioral experience has a significant impact on perceived usefulness
• H10: Relational experience has a significant impact on perceived usefulness

3.1.2 Mediating variables.

By integrating AR-based product displays into e-commerce channels, a key goal in the evolution of AR applications in e-commerce is to define and create platforms that merge the physical world of reality with the virtual world of products or services, forming an augmented reality environment. This allows users to overlay and interact with virtual objects within their real-life surroundings, obtain relevant information, engage in creating personalized products, and enhance the shopping experience [ 13 ]. Therefore, the characteristics of AR online shopping are reflected in three aspects: vividness, interactivity, and immersion. In the context of e-commerce, vividness is often interpreted as the quality of product presentation [ 46 ]. Wang [ 47 ] studied the impact of information-oriented and entertainment-oriented smart shopping experiences on consumer purchase intentions. AR technology integrates sensory virtual digital content such as sound, video, graphics, and images, projecting holographic three-dimensional images of products into the surrounding real-world environment in a vivid and novel way [ 48 , 49 ]. It displays multi-dimensional elements of products, delivering higher quality visual, auditory, and tactile stimuli to media users. This enhances the perceived information quality, expands the number of sensory dimensions a user can experience, and allows users to perceive and interact with virtual elements in a more realistic and three-dimensional manner [ 50 ]. Consequently, users can psychologically pre-experience product experiences in future consumption environments, assess the suitability of the products, enhance confidence in their purchasing decisions, and form more enduring memories of the information [ 45 ]. Therefore, the following hypotheses are proposed:

• H11: Perceived ease of use has a significant impact on purchase intentions
• H12: Perceived usefulness has a significant impact on purchase intentions

3.2 Model construction

Combining the Technology Acceptance Model (TAM), the AR shopping experience incorporates sensory experience, emotional experience, cognitive experience, behavioral experience, and relational experience as antecedent variables. Perceived ease of use and perceived usefulness are treated as mediating variables, and purchase intention as the dependent variable. We construct a theoretical model on the impact of AR online shopping experience on customer purchase intentions, as shown in Fig 1 .

https://doi.org/10.1371/journal.pone.0309468.g001

3.3 Variable measurement

Based on a thorough consideration of related research and practical developments in AR online shopping, a model has been developed to examine the impact of the AR shopping experience on customer purchase intentions. This model consists of eight latent variables (sensory experience, emotional experience, cognitive experience, behavioral experience, relational experience, perceived ease of use, perceived usefulness, and purchase intention) and 30 measurement variables, as seen in Table 3 . Each item is measured using a 5-point Likert scale, where 1, 2, 3, 4, and 5 represent “strongly disagree,” “disagree,” “neutral,” “agree,” and “strongly agree,” respectively, allowing respondents to make effective perceptual judgments. The development of the items referred to established scales used in expert and scholarly research and was adjusted according to the characteristics of AR e-commerce shopping, ensuring the accuracy and reliability of the scales.

https://doi.org/10.1371/journal.pone.0309468.t003

4 Empirical study

4.1 reliability and validity analysis.

Based on the measurement items for the relevant variables, a survey questionnaire was developed. The questionnaire focuses on the experiences and evaluations of consumers who have shopped using AR online. The survey was conducted online, targeting AR online shopping consumers, and 279 questionnaires were collected. After discarding invalid questionnaires, 202 valid responses were retained. The standardized Cronbach’s alpha coefficients of the samples are all greater than 0.8, indicating a high level of reliability for the entire survey questionnaire. This suggests that the survey questionnaire is both reliable and stable. Therefore, it is necessary to maintain the measurement items for sensory experience, emotional experience, cognitive experience, behavioral experience, relational experience, perceived ease of use, perceived usefulness, and purchase intention. The Kaiser-Meyer-Olkin (KMO) test statistic is primarily used to compare the simple correlations and partial correlations among variables. When the sum of squares of all simple correlations among variables is significantly greater than the sum of squares of partial correlations, the KMO value approaches 1. The closer the KMO value is to 1, the stronger the correlation among the variables, and the more suitable they are for factor analysis. The KMO values for all variables are not less than 0.7, indicating that factor analysis can be conducted. The Average Variance Extracted (AVE) can test the internal consistency within structural variables. When the AVE value is greater than 0.50, it indicates that the latent variable has good measurement validity. The AVE values for all variables in the table are greater than 0.7, indicating that the validity of the survey questionnaire meets the requirements, as shown in Table 4 .

https://doi.org/10.1371/journal.pone.0309468.t004

The square roots of the AVE for each variable are greater than their correlation coefficients with other variables in the same column, indicating that the measurement scale has good discriminant validity, as shown in Table 5 .

https://doi.org/10.1371/journal.pone.0309468.t005

4.2 Estimation of structural equation model

Using AMOS 22 software to fit the structural equation model, the initial structural equation model yielded T-values of -4.779 for “TE→PEOU” and -4.526 for “RE→PEOU,” which do not meet the standard of T-values > 1.96. After removing the two non-significant paths “TE→PEOU” and “RE→PEOU,” the model was refitted, resulting in the revised structural equation model and path coefficients as shown in Fig 2 .

Note: *** indicates that the significance (sig) value is less than 0.001.

https://doi.org/10.1371/journal.pone.0309468.g002

In the revised model’s path coefficient test results, all path T-values exceeded the minimum standard of 1.96, and all p-values were significant at the 0.001 level. Overall, the path coefficients in the revised model are quite significant. From the perspective of various fit indices, the structural equation model has a χ2/df value of 4.601, which is less than 10; GFI value of 0.810, close to 1; AGFI value of 0.742, close to 1; RMSEA value of 0.034, less than 0.05; and NFI, CFI, and IFI values are 0.708, 0.754, and 0.756, respectively, all close to 1, as shown in Table 6 . These results indicate that the model fits well and has good adaptability, and the model should be accepted.

https://doi.org/10.1371/journal.pone.0309468.t006

Using the Bootstrap method to test for mediating effects, the sample was bootstrapped 5000 times with replacement at a 95% confidence level, and the results indicate the presence of mediating effects, as shown in Table 7 .

https://doi.org/10.1371/journal.pone.0309468.t007

4.3 Hypothesis testing

The study demonstrates significant effects of sensory experience (SE) on perceived ease of use (PEOU), emotional experience (EE) on PEOU, behavioral experience (AE) on PEOU, sensory experience on perceived usefulness (PU), emotional experience on PU, cognitive experience (TE) on PU, behavioral experience on PU, and relational experience (RE) on PU. Additionally, PEOU on purchase intention (PI) and PU on PI are significantly impacted. However, the hypotheses that relational experience significantly affects PEOU and that cognitive experience significantly affects PEOU are not supported, as shown in Table 8 .

https://doi.org/10.1371/journal.pone.0309468.t008

Among all the effects, the impact of PU on PI is the greatest, with a coefficient of 1.010; followed by the impact of RE on PI, with a coefficient of 0.611; the third highest is the impact of AE on PEOU, with a coefficient of 0.598; the fourth is the impact of AE on PI, with a coefficient of 0.563; the smallest impact is from PEOU on PI, with a coefficient of 0.020, as detailed in Fig 3 .

https://doi.org/10.1371/journal.pone.0309468.g003

5 Discussion

5.1 conclusions.

This study extends the Technology Acceptance Model (TAM) by incorporating five types of AR online shopping experiences (sensory experience, emotional experience, cognitive experience, action experience, and relational experience) as antecedent variables, with perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. A structural equation model was constructed and empirically tested to explore the impact mechanisms of AR online shopping experiences on customer purchase intention. The main findings are as follows:

• Positive Impact of Sensory Experience: The vividness, interactivity, and immersive sensory experience of AR enhance the perceived ease of use and perceived usefulness of online shopping for consumers.
• Role of Emotional Experience: The positive emotions triggered by AR improve consumers’ perceptions of the usability and usefulness of online platforms, increasing their shopping pleasure and utility. This supports the findings of studies [ 3 , 8 , 41 ].
• Impact of Cognitive Experience: Cognitive experience significantly influences perceived usefulness but does not affect perceived ease of use. This indicates that the comprehensive and detailed understanding of product information, the vivid presentation of matching effects, the display of post-purchase usage scenarios, interaction with products, matching of related products and scenes, and enhancement of guidance functions provided by AR are very valuable and practical for providing information and aiding decision-making. However, in actual operation, consumers may still find using AR technology somewhat complex, and thus it does not significantly simplify the shopping process or improve shopping efficiency.
• Contribution of Action and Relational Experiences: Action and relational experiences enhance shopping experiences and social interactions, leading to stronger purchase intentions among consumers. Although relational experience does not significantly affect perceived ease of use, it enhances consumers’ sense of social recognition, consistent with the conclusion in the literature [ 11 ] that AR stimulates purchase intentions in shopping environments.
• Mediating Role of Perceived Ease of Use and Perceived Usefulness: Both perceived ease of use and perceived usefulness significantly influence purchase intention, with the impact of perceived usefulness being the greatest. This indicates that although ease of use contributes to an improved shopping experience, it does not significantly drive purchase decisions unless it provides substantial practical utility. This finding aligns with the conclusion in the literature [ 5 ] about the mediating role of perceived value in AR usage motivation and purchase intention.

In summary, this study expands the application scope of the Technology Acceptance Model (TAM), providing new insights into how different types of AR experiences influence consumer behavior. It reveals the multiple impact mechanisms of AR online shopping experiences on customer purchase intention, enriching the theory of consumer behavior in Metaverse e-commerce.

5.2 Recommendations

5.2.1 enhancing scenario construction to empower ar online shopping experience..

The creation of AR scenarios is a crucial step in enhancing the online shopping experience and boosting purchasing intentions. Empowering AR scenarios includes two major aspects. First is the diversification of scenario construction. Currently, AR online shopping scenarios are mainly focused on product demonstrations. Further development needs to create more diversified scenarios, including the integration of AR technology in the production of raw materials, product manufacturing, warehousing and transportation, customer service, and live commerce, allowing consumers to have a more direct and enhanced experience of the entire supply chain. Second is the enrichment of interactive development. Current interactions focus on gesture recognition, but there is a need to further develop technologies such as spatial positioning, eye-tracking, facial recognition, full-body tracking, and random interaction in AR shopping to more accurately determine the shopping space, analyze consumer emotions, display full-body effects and overall environmental effects, and enhance the level of interaction. AR scenario empowerment can simultaneously enhance the five major experiences of AR shopping and positively impact consumers’ online shopping intentions in terms of product discovery, leisure and entertainment, enhanced immersion, improved usefulness and ease of use, promotion of communication, development of word-of-mouth, strengthening brand consolidation, and facilitating commercial conversion, as shown in Fig 4 .

https://doi.org/10.1371/journal.pone.0309468.g004

5.2.2 Enhancing sensory experience in AR online shopping.

Utilize high-definition images and advanced rendering technologies to create realistic 3D models, enhancing the detail and authenticity of products displayed in an AR environment. Incorporate unique visual effects, such as dynamic lighting and shadows or adding interactive elements, to make the user experience more engaging and memorable. Develop a variety of AR application scenarios, allowing users to experience the effects of products in their own environment. Guide users through AR games or interactive tutorials to learn about products, providing an educational and entertaining shopping experience. Design personalized shopping paths that allow users to explore actively within the AR environment based on their interests and shopping habits. Offer unique AR product trial experiences that include multisensory elements, such as simulating the texture and color changes of products, and even providing olfactory and gustatory stimuli, enabling users to feel the products more genuinely and ensuring that the AR trial features align closely with the actual quality and characteristics of the products. Provide highly customized experiences, allowing users to adjust the product’s color, size, or design to suit their personal preferences.

5.2.3 Enhancing the emotional experience in AR online shopping.

Design beautiful, vivid, and attractive AR interfaces to create compelling immersive effects. Analyze user preferences scientifically based on their shopping history data and recommend customized AR product displays. Provide diverse AR display options, such as 3D views and 360-degree rotations, allowing users to explore products from multiple angles and details. Integrate emotional elements into the products, such as using AR to display the product’s story or origins and employing narrative techniques to present products, allowing users to enjoy the storyline while exploring the product, enhancing the emotional connection between users and products. Merge metaverse and virtual reality technologies to create a new shopping environment that transcends traditional online shopping, enabling users to experience products in a novel and fully immersive way. Offer lively and interactive experiences, such as incorporating gamified elements, where users can earn discounts or points by completing small tasks within the AR experience; allow users to customize or experiment with products using AR technology.

5.2.4 Enhancing the cognitive experience in AR online shopping.

Utilize AR technology to provide detailed and comprehensive product information, including 3D models that show every angle and detail of the product; incorporate enhanced description features that automatically display related product specifications, materials, or usage methods when users view specific parts. Offer additional information related to the product, such as customer reviews, production background, and usage scenarios, enabling users to fully understand the product information. Visually demonstrate pairing effects, using AR technology for virtual try-ons or home setups, allowing users to see the product pairing effects intuitively. Provide diverse pairing options and suggestions to help users explore different styles or design proposals. Allow users to freely mix and match different products in a virtual environment, increasing space for experimentation and innovation, and better imagine scenarios post-purchase. Create realistic post-purchase usage scenario simulations, such as allowing users to see the product’s effect in their own home or anticipated usage environment. Integrate emotional elements, such as simulating users’ feelings or life improvement effects after using the product, enhancing emotional resonance. Transform perceptions of traditional e-commerce, emphasizing the unique value provided by AR shopping, such as higher interactivity and more accurate product experiences. Educate and guide users to understand the advantages of AR shopping, such as accuracy, convenience, and personalized experiences. Analyze new insights and feelings gained by users through AR shopping, and how this influences their shopping decision process.

5.2.5 Optimizing the behavioral experience in AR online shopping.

Enhance shopping convenience by developing intuitive and user-friendly AR application interfaces, ensuring that users of all ages and technical levels can easily utilize them. Simplify the shopping process through AR technology, such as implementing one-click shopping, allowing users to directly select and purchase products within the AR experience. Provide efficient product search and filtering tools, enabling users to quickly find the AR experience products they need. Offer dynamic pairing suggestions to help users choose the right product combinations based on their personal style and occasion needs. Enable users to experience the effects of different product combinations at home through virtual try-on and pairing features, reducing the hassles of purchase errors and returns. Shift traditional shopping habits by emphasizing the advantages of AR shopping over traditional flat webpage shopping, such as more realistic product previews and higher interactivity. Educate users on how to effectively use AR technology for shopping, helping them adapt to this new mode of shopping through case demonstrations or tutorials. Encourage merchants to incorporate AR experiences into product displays, enhancing users’ affinity for AR-capable products by providing richer and more in-depth product information, and attracting users with more vivid and immersive shopping experiences. Collect user feedback and continuously improve the AR shopping experience to ensure it meets user needs and exceeds expectations.

5.2.6 Enhancing the relational experience in AR online shopping.

Utilize AR technology to provide direct interaction with products, such as allowing users to rotate, zoom in, and zoom out on product models via gestures or touch, and even try on or test products. Create an interactive virtual environment, for example, by simulating actual usage scenarios, allowing users to experience products in novel ways. Develop AR tools that enable users to virtually place products in their own environments to assess their adaptability and aesthetic fit. Offer virtual pairing suggestions, such as automatically displaying other items that complement the selected product or suggested pairing methods. Facilitate user interaction with the pairing scenario, such as adjusting the lighting or background in the scene to better display the product effects. Act as a shopping guide by using AR technology to provide personalized shopping suggestions, such as recommending products based on a user’s shopping history and preferences. Integrate chatbots or virtual shopping assistants to provide real-time answers and advice, enhancing the interactivity and helpfulness of the shopping experience.

6 Limitations and future directions

The limitations of this study are primarily reflected in the data collection process. The quantitative data used for the structural equation modeling (SEM) analysis were obtained through a cross-sectional survey conducted in China. The sample was concentrated on specific demographic characteristics or geographic regions. The cross-sectional study design only captures data at a single point in time, failing to reveal the long-term impact of AR online shopping experiences on purchase intention, thus limiting the generalizability of the study results. Future research should consider more diverse samples to validate the applicability of the findings across different populations and regions. Additionally, studies should adopt longitudinal designs to track changes in consumer behavior before and after using AR technology, to understand the long-term impact of AR shopping experiences on purchase intention and potential behavior changes. Secondly, this study primarily focused on the impact of AR technology on online shopping experiences and customer purchase intention. Future research could further explore the impact of combining AR and artificial intelligence technologies on e-commerce customer online shopping, as well as investigate the patterns of consumer behavior in the metaverse e-commerce environment.

Supporting information

S1 data. raw data and the means, standard deviations, variances, minimum, and maximum values of the raw data..

https://doi.org/10.1371/journal.pone.0309468.s001

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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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Money blog: Oasis resale U-turn as official reseller lowers fee amid criticism

The Money blog is your place for consumer and personal finance news and tips. Today's posts include Twickets lowering fees for Oasis tickets, the extension of the Household Support Fund and O2 Priority axing free Greggs. Listen to a Daily podcast on the Oasis ticket troubles as you scroll.

Monday 2 September 2024 20:11, UK

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Twickets has announced it is lowering its charges after some Oasis fans had to pay more than £100 in extra fees to buy official resale tickets.

The site is where the band themselves is directing people to buy second-hand tickets for face value - having warned people against unofficial third party sellers like StubHub and Viagogo.

One person branded the extra fees "ridiculous" (see more in 10.10 post), after many people had already been left disappointed at the weekend when Ticketmaster's dynamic pricing pushed tickets up by three times the original advertised fee.

Twickets said earlier that it typically charged a fee of 10-15% of the face value of the tickets.

But it has since said it will lower the charge due to "exceptional demand" from Oasis fans - taking ownership of an issue in a way fans will hope others follow. 

Richard Davies, Twickets founder, told the Money blog: "Due to the exceptional demand for the Oasis tour in 2025, Twickets have taken the decision to lower our booking fee to 10% and a 1% transactional fee (to cover bank charges) for all buyers of their tickets on our platform. In addition we have introduced a fee cap of £25 per ticket for these shows. Sellers of tickets already sell free of any Twickets charge.

"This ensures that Twickets remains hugely competitive against the secondary market, including sites such as Viagogo, Gigsberg and StubHub.

"Not only do these platforms inflate ticket prices way beyond their original face value but they also charge excessive booking fees, usually in the region of 30-40%. Twickets by comparison charges an average fee of around 12.5%"

The fee cap, which the Money blog understands is being implemented today, will apply to anyone who has already bought resale tickets through the site.

Mr Davies said Twickets was a "fan first" resale site and a "safe and affordable place" for people to trade unwanted tickets.

"The face value of a ticket is the total amount it was first purchased for, including any booking fee. Twickets does not set the face value price, that is determined by the event and the original ticketing company. The price listed on our platform is set by the seller, however no one is permitted to sell above the face-value on Twickets, and every ticket is checked before listing that it complies with this policy," he said.

Meanwhile, hundreds of people have complained to the regulator about how Oasis tickets were advertised ahead of going on sale. 

The Advertising Standards Authority said it had received 450 complaints about Ticketmaster adverts for the gigs.

Some  expressed their anger on social media , as tickets worth £148 were being sold for £355 on the site within hours of release, due to the "dynamic pricing" systems.

A spokesperson from ASA said the complainants argue that the adverts made "misleading claims about availability and pricing".

They added: "We're carefully assessing these complaints and, as such, can't comment any further at this time.

"To emphasise, we are not currently investigating these ads."

Ticketmaster said it does not set prices and its website says this is down to the "event organiser" who "has priced these tickets according to their market value".

Despite traditionally being an affordable staple of British cuisine, the average price for a portion of fish and chips has risen by more than 50% in the past five years to nearly £10, according to the Office for National Statistics.

Sonny and Shane "the codfather" Lee told Sky News of the challenges that owning J-Henry's Fish and Chip Shop brings and why prices have skyrocketed. 

"Potatoes, fish, utilities, cooking oil - so many things [are going up]," he said. 

Shane also said that he is used to one thing at a time increasing in price, but the outlook today sees multiple costs going up all at once.  

"Potatoes [were] priced right up to about £25 a bag - the previous year it was about £10 a bag," Sonny said, noting a bad harvest last year. 

He said the business had tried hake as a cheaper fish option, but that consumers continued to prefer the more traditional, but expensive, cod and haddock. 

"It's hard and we can we can absorb the cost to a certain extent, but some of it has to be passed on," Shane added. 

After a long Saturday for millions of Oasis fans in online queues, the culture secretary says surge pricing - which pushed the price of some tickets up by three times their original advertised value to nearly £400 - will be part of the government's review of the ticket market. 

On today's episode of the Daily podcast, host Niall Paterson speaks to secondary ticketing site Viagogo. While it wasn’t part of dynamic pricing, it has offered resale tickets for thousands of pounds since Saturday. 

Matt Drew from the company accepts the industry needs a full review, while Adam Webb, from the campaign group FanFair Alliance, explains the changes it would like to see.

We've covered the fallout of the Oasis sale extensively in the Money blog today - see the culture secretary's comments on the "utterly depressing" inflated pricing in our post at 6.37am, and Twickets, the official Oasis resale site, slammed by angry fans for its "ridiculous" added fees at 10.10am.

The growing backlash culminated in action from Twickets - the company said it would lower its charges after some fans had to pay more than £100 in extra fees for resale tickets (see post at 15.47).

Tap here to follow the Daily podcast - 20 minutes on the biggest stories every day

Last week we reported that employers will have to offer flexible working hours - including a four-day week - to all workers under new government plans.

To receive their full pay, employees would still have to work their full hours but compressed into a shorter working week - something some workplaces already do.

Currently, employees can request flexible hours as soon as they start at a company but employers are not legally obliged to agree.

The Labour government now wants to make it so employers have to offer flexible hours from day one, except where it is "not reasonably feasible".

You can read more of the details in this report by our politics team:

But what does the public think about this? We asked our followers on LinkedIn to give their thoughts in an unofficial poll.

It revealed that the overwhelming majority of people support the idea to compress the normal week's hours into fewer days - some 83% of followers said they'd choose this option over a standard five-day week.

But despite the poll showing a clear preference for a compressed week, our followers appeared divided in the comments.

"There's going to be a huge brain-drain as people move away from companies who refuse to adapt with the times and implement a 4 working week. This will be a HUGE carrot for many orgs," said Paul Burrows, principal software solutions manager at Reality Capture.

Louise McCudden, head of external affairs at MSI Reproductive Choices, said she wasn't surprised at the amount of people choosing longer hours over fewer days as "a lot of people" are working extra hours on a regular basis anyway.

But illustrator and administrative professional Leslie McGregor noted the plan wouldn't be possible in "quite a few industries and quite a few roles, especially jobs that are customer centric and require 'round the clock service' and are heavily reliant upon people in trades, maintenance, supply and transport". 

"Very wishful thinking," she said.

Paul Williamson had a similar view. He said: "I'd love to know how any customer first service business is going to manage this."

We reported earlier that anyone with O2 Priority will have their free weekly Greggs treats replaced by £1 monthly Greggs treats - see 6.21am post.

But did you know there are loads of other ways to get food from the nation's most popular takeaway for free or at a discount?

Downloading the Greggs app is a good place to start - as the bakery lists freebies, discounts and special offers there regularly. 

New users also get rewards just for signing up, so it's worth checking out. 

And there's a digital loyalty card which you can add virtual "stamps" to with each purchase to unlock discounts or other freebies.  

Vodafone rewards

Seriously begrudged Virgin Media O2 customers may want to consider switching providers. 

The Vodafone Rewards app, VeryMe, sometimes gives away free Greggs coffees, sausage rolls, sweet treats and more to customers.

Monzo bank account holders can grab a sausage roll (regular or vegan), regular sized hot drink, doughnut or muffin every week. 

Birthday cake

Again, you'll need the Greggs award app for this one - which will allow you to claim one free cupcake, cream cake or doughnut for your birthday each year.

Octopus customers

Octopus Energy customers with smart meters can claim one free drink each week, in-store from Greggs (or Caffè Nero).

The Greggs freebie must be a regular size hot drink.

Make new friends

If you're outgoing (and hungry), it may be worth befriending a Greggs staff member.

The staff discount at Greggs is 50% on own-produced goods and 25% off branded products. 

If you aren't already aware, Iceland offers four Greggs sausage rolls in a multi-pack for £3. 

That means, if you're happy to bake it yourself, you'll only be paying 74p per sausage roll. 

Millions of Britons could receive extra cash to help with the cost of living this winter after the government extended the Household Support Fund.

A £421m pot will be given to local councils in England to distribute, while £79m will go to the devolved administrations.

The fund will now be available until April 2025 having been due to run out this autumn.

Councils decide how to dish out their share of the fund but it's often via cash grants or vouchers.

Many councils also use the cash to work with local charities and community groups to provide residents with key appliances, school uniforms, cookery classes and items to improve energy efficiency in the home.

Chancellor Rachel Reeves said: "The £22bn blackhole inherited from the previous governments means we have to take tough decisions to fix the foundations of our economy.

"But extending the Household Support Fund is the right thing to do - provide targeted support for those who need it most as we head into the winter months."

The government has been criticised for withdrawing universal winter fuel payments for pensioners of up to £300 this winter - with people now needing to be in receipt of certain means-tested benefits to qualify.

People should contact their local council for details on how to apply for the Household Support Fund - they can find their council  here .

Lloyds Bank app appears to have gone down for many, with users unable to see their transactions. 

Down Detector, which monitors site outages, has seen more than 600 reports this morning.

It appears to be affecting online banking as well as the app.

There have been some suggestions the apparent issue could be due to an update.

Another disgruntled user said: "Absolutely disgusting!! I have an important payment to make and my banking is down. There was no warning given prior to this? Is it a regular maintenance? Impossible to get hold of someone to find out."

A Lloyds Bank spokesperson told Sky News: "We know some of our customers are having issues viewing their recent transactions and our app may be running slower than usual.

"We're sorry about this and we're working to have everything back to normal soon."

We had anger of unofficial resale prices, then Ticketmaster's dynamic pricing - and now fees on the official resale website are causing consternation among Oasis fans.

The band has encouraged anyone wanting resale tickets to buy them at face value from Ticketmaster or Twickets - after some appeared for £6,000 or more on other sites.

"Tickets appearing on other secondary ticketing sites are either counterfeit or will be cancelled by the promoters," Oasis said.

With that in mind, fans flocked to buy resale tickets from the sites mentioned above - only to find further fees are being added on. 

Mainly Oasis, a fan page, shared one image showing a Twickets fee for two tickets as high as £138.74. 

"Selling the in demand tickets completely goes against the whole point of their company too… never mind adding a ridiculous fee on top of that," the page shared. 

Fan Brad Mains shared a photo showing two tickets priced at £337.50 each (face value of around £150, but increased due to dynamic pricing on Saturday) - supplemented by a £101.24 Twickets fee. 

That left him with a grand total of £776.24 to pay for two tickets.

"Actually ridiculous this," he  said on X .

"Ticketmaster inflated price then sold for 'face value' on Twickets with a £100 fee. 2 x £150 face value tickets for £776, [this] should be illegal," he added. 

Twickets typically charges between 10-15% of the ticket value as its own fee. 

We have approached the company for comment.

Separately, the government is now looking at the practice of dynamic pricing - and we've had a response to that from the Competition and Markets Authority this morning.

It said: "We want fans to get a fair deal when they go to buy tickets on the secondary market and have already taken action against major resale websites to ensure consumer law is being followed properly. 

"But we think more protections are needed for consumers here, so it is positive that the government wants to address this. We now look forward to working with them to get the best outcomes for fans and fair-playing businesses."

Consumer protection law does not ban dynamic pricing and it is a widely used practice. However, the law also states that businesses should not mislead consumers about the price they must pay for a product, either by providing false or deceptive information or by leaving out important information or providing it too late.

By James Sillars , business reporter

It's a false start to the end of the summer holidays in the City.

While London is mostly back at work, trading is fairly subdued due to the US Labor (that's labour, as in work) Day holiday.

US markets will not open again until Tuesday.

There's little direction across Europe with the FTSE 100 trading nine points down at 8,365.

Leading the gainers was Rightmove - up 24%. The property search website is the subject of a possible cash and shares takeover offer by Australian rival REA.

The company is a division of Rupert Murdoch's News Corp.

One other point to note is the continuing fluctuation in oil prices.

Brent crude is 0.7% down at the start of the week at $76.

Dragging the cost lower is further evidence of weaker demand in China.

Australia's REA Group is considering a takeover of Rightmove, in a deal which could be worth about £4.36bn.

REA Group said in a statement this morning there are "clear similarities" between the companies, which have "highly aligned cultural values".

Rightmove is the UK's largest online property portal, while REA is Australia's largest property website. 

It employs more than 2,800 people and is majority-owned by Rupert Murdoch's News Corp,.

REA Group said: "REA sees a transformational opportunity to apply its globally leading capabilities and expertise to enhance customer and consumer value across the combined portfolio, and to create a global and diversified digital property company, with number one positions in Australia and the UK.

"There can be no certainty that an offer will be made, nor as to the terms on which any offer may be made."

Rightmove has been approached for comment.

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