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Journal of Retailing and Consumer Services 63 (2021) 102720

Available online 10 August 2021 0969-6989/© 2021 Elsevier Ltd. All rights reserved.

Augmented reality shopping application usage: The influence of attitude, value, and characteristics of innovation

Yi Jiang a, Xueqin Wang b, Kum Fai Yuen c,*

a Department of Economics, Chung-Ang University, Seoul, South Korea b Department of International Logistics, Chung-Ang University, Seoul, South Korea c School of Civil and Environmental Engineering, Nanyang Technological University, Singapore

A R T I C L E I N F O

Keywords: Augmented reality shopping Intention to use Innovation diffusion Value

A B S T R A C T

Consumers’ intentions are crucial to the wide usage of augmented reality shopping applications (ARSAs). Combining innovation diffusion, perceived value, and attitude theories, this study proposes a theoretical model that identifies the antecedents of consumers’ innovation to use ARSAs and specifies their interrelationships. A total of 379 consumers were surveyed using questionnaires, and the data were analyzed through confirmatory factor analysis and structural equation modeling. Results show that the effects of the perceived relative advantage, perceived compatibility, and perceived observability on consumers’ intentions to use ARSAs are mediated by consumers’ attitudes toward ARSAs. In addition, attitudes have an indirect impact on consumers’ intentions to use ARSAs through perceived value. Theoretically, this study synthesizes behavioral theories anchored on innovation and marketing to explain consumer’s use intention. Managerially, this study provides strategic recommendations for technology companies developing ARSAs and retailers wishing to adopt ARSAs.

1. Introduction

The rapid growth of online retail has provided consumers with more conveniences. However, the service experience is limited because con- sumers can neither interact with nor form realistic expectations of the products. Therefore, simulating the shopping experience of real prod- ucts or environments is particularly important (Fan et al., 2020).

Augmented reality (AR) is a new interactive technology (Poushneh, 2018). It can superimpose virtual 3D models of real products into the real world such as human bodies or objects, and people can manipulate these virtual 3D models by rotating, shifting, and enlarging them (Poushneh and Vasquez-Parraga, 2017). Consequently, many e-com- merce platforms have begun to invest in the development of various forms of AR shopping applications (ARSAs) using AR technology (Fan et al., 2020).

Due to the current COVID-19 pandemic, countries all over the world have adopted necessary anti-epidemic measures and recommendations such as “closed isolation” and “maintaining social distance”, and con- sumer behavior is undergoing tremendous changes (Donthu and Gus- tafsson, 2020). Therefore, the academic community has conducted research on consumer buying behavior. Most scholars have made cor- responding research contributions mainly to consumers’ panic buying

behavior (Laato, 2020; Prentice et al., 2020, 2021; Naeem, 2021; Islam et al., 2021; Omar et al., 2021). At the same time, because of the pandemic, consumers’ consumption in physical stores has been severely affected. Thus, more consumers are switching from brick-and-mortar stores to online consumption (Pantelimon et al., 2020). As a result, some researchers have conducted related research in the field of online consumption such as e-retail and e-commerce, and mobile meal ordering programs (Jafarzadeh et al., 2021; Tran, 2021; Guthrie et al., 2021; Dirsehan and Cankat, 2021). Consequently, ARSAs are showing extremely important value (Kirk and Rifkin, 2020). They can confer convenience, functionality, sociality, and other benefits to consumers, which are becoming more prominent considering the COVID-19 pandemic.

Although ARSAs can provide various benefits to the online shopping market, the actual adoption of ARSAs has not yet reached expectations and not been widely used (Yim and Park, 2019). One reason is that ARSA is an application tool for innovative technologies, and potential adopters still have concerns about its use. Other researchers also highlighted that problems such as digital fatigue, installation difficulties, slow response speed, and privacy security caused the slow application of ARSAs (Feng and Xie, 2019; Yim and Park, 2019). More importantly, these benefits can only be achieved if consumers are willing to adopt ARSAs. This view

* Corresponding author. E-mail address: [email protected] (K.F. Yuen).

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https://doi.org/10.1016/j.jretconser.2021.102720 Received 28 January 2021; Received in revised form 3 August 2021; Accepted 4 August 2021

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agrees with the “critical mass” theory proposed by Markus (1994); that is, individual choices must be considered in the context of their com- munity or organization. As an increasing number of individuals adopt innovative technologies (tools) in the system (organization), such innovation will be considered more and more beneficial to adopters and potential buyers (Van Slyke et al., 2007).

Recently, to understand consumers’ acceptance intention of AR technology and ARSAs, much academic research has been conducted. Some researchers have adopted the technology acceptance model and its extended theory to understand. Moreover, they have examined the following factors influencing consumers’ adoption of AR technology and ARSAs: perceived ease of use, perceived usefulness (Huang and Liao, 2015; Pantano et al., 2017; Plotkina and Saurel, 2019; Rese et al., 2017; Holdack et al., 2020; Hinsch et al., 2020; Qin et al., 2021), expanded entertainment, fun, aesthetics, visual imagery, and multifaceted quality attributes (Li and Fang, 2020; Park and Yoo, 2020; Chiu et al., 2021; Jung et al., 2021), technical anxiety, privacy security and perceived risk (Kim and Forsythe, 2009; Zhang et al., 2019; Yoo, 2020; Bonnin, 2020). In addition, some scholars have studied the influence of factors related to hedonic and utilitarian attributes on consumer participation in AR applications and AR technology application products, brand attitudes, use intentions or decision-making behaviors (Rauschnabela et al., 2019; Hinsch et al., 2020; Bonnin, 2020; Qin et al., 2021; Nikhashemi, 2021). Other researchers based their studies on the situated cognition theory to explain consumers’ attitudes, intentions, and loyalty to AR technology and its applications (Chylinski et al., 2020; Fan et al., 2020; Hilken et al., 2020; Sung, 2021).

This study extends the current understanding of consumers’ in- tentions of using ARSAs by introducing innovation diffusion, attitude, and perceived value theories. These theories are selected because they address the key concerns of consumers when adopting new technology. First, innovation diffusion theory focuses on explaining how innovative technologies are accepted and spread among consumers (Plotkina and Saurel, 2019; Yuen et al., 2018). The spread of innovation is influenced by the following features or characteristics of ARSAs: (1) relative ad- vantages compared with existing alternatives, (2) compatibility with users, (3) complexity in users’ adoption, (4) trialability, and (5) observability, which reflects the ease of identifying the benefits and learning how to use ARSAs from others. Second, perceived value is a predictor of customer adoption behavior (Yang et al., 2016). This theory explains a consumer’s intention to use ARSAs from a utility perspective. If ARSAs can provide consumers with good value (i.e., economic, functional, social, and hedonic utility) compared with existing channels, then the consumer intention to adopt ARSAs will be stronger. Finally, we explore the attitude theory. Attitude is a determinant of individual behavioral intentions (Van Slyke et al., 2007). The theory of reasoned action (Fishbein and Ajzen, 1975) states that one’s behavioral intentions are largely affected by one’s attitude.

This study enriches existing scholarship by synthesizing and applying innovation diffusion theory, perceived value theory, and atti- tude theory, and introduced various paradigm theories (such as customer utility, social psychology and innovation acceptance). At the same time, this study not only includes the similar attributes of some influencing factors investigated by previous studies, such as complexity (ease of use), relative advantages (usefulness), and attitude to use, but also introduces new variables such as compatibility, observability and trialability, and value attributes. Therefore, this research enriches the existing academic research and explores the key issues when consumers adopt AR shopping application technology. In addition, this study pro- vides a logical causal structure, i.e., based on the “belief-attitude- intention” relationship and further introduces perceived value, thereby further expanding this causal relationship. In this regard, this study can better explain how innovative technologies are accepted and spread among consumers as compared to previous studies. Especially when the COVID19 epidemic is still on-going and escalating in many countries, the application value of ARSAs is more prominent, and further

exploration of the factors and methods that can more effectively pro- mote the use and spread of ARSAs by consumers confers more academic significance and practical value.

2. Literature review

2.1. Theories and model

This study uses innovation diffusion, attitude, and perceived value theories to explore the determinants of consumers’ intention to use ARSAs, propose a research model, and determine the relationship be- tween structural factors in each theory and their relationships. Table 1 summarizes the applied theories.

We designed a model through the explanation of the three theories (Fig. 1). The model depicts the determinants of consumers’ adoption of ARSAs and explains their relationship.

This study proposes that if consumers perceived ARSAs to possess positive characteristics, such as relative advantages, compatibility, lower complexity, trialability and observability, they will form positive attitudes toward ARSAs and thus accept and use ARSAs. Further, con- sumers’ positive attitude toward ARSAs will enhance their perceived value of ARSAs, thereby promoting consumers’ use of ARSAs.

First, rooted in the theory of reasoned action, this study proposes that the innovation diffusion theory’s five innovative characteristics influ- ence the adoption intention of ARSAs through consumers’ attitude to- ward ARSAs (i.e., H1–H6). The theory holds that people’s behavioral intention is affected by their attitude, and that the behavioral intention is influenced by beliefs (Fishbein and Ajzen, 1975). Thus, the theory suggests a causal chain of "belief–attitude–intention". This causality relationship has been well demonstrated in consumer adoption studies. For example, the technology acceptance model, which extends the theory of reasoned action, follows this causal relationship (Wang et al., 2018; Rese et al., 2017; Kim and Forsythe, 2009; Van Slyke et al., 2007). This study operationalized beliefs using the innovation diffusion theory. Consumers’ positive perceptions or beliefs about ARSAs, including relative advantage, compatibility, complexity, trialability, and observ- ability, could improve their attitude toward ARSAs (i.e., cognitive and affective evaluation of ARSAs). Consequently, this leads to adoption behavior that is a manifestation of consumers’ positive attitude.

Second, this study proposes that consumers’ positive attitude toward ARSAs will encourage consumer intention to use ARSAs through perceived value (i.e., H7–H8). Attitude is the consumer’s feeling and evaluation of the product (Van Slyke et al., 2007), which can be either positive or negative. Perceived value refers to consumers’ overall eval- uation of the utility and value of the product based on its perceived benefits and costs (Lin et al., 2005; Ravald and Grönroos, 1996). Ac- cording to previous studies, consumers’ positive or negative feelings or evaluations of a product affect their overall evaluation of the product’s utility (Shiu et al., 2015). For instance, Salehzadeh and Pool (2017) found that positive attitudes positively influence perceived value, which is an agglomeration of economic, social, personal (hedonic), and func- tional dimensions (i.e., H7). In addition, perceived value is an important antecedent of behavioral intentions because consumers tend to use and continue to use products or services with higher perceived value (Pham et al., 2018; Ponte et al., 2015). When consumers perceive high value from using ARSA, it will prompt them to adopt ARSAs (i.e., H8).

2.2. Determinants of consumers’ attitude toward ARSAs

2.2.1. Effects of perceived relative advantages Perceived relative advantage is the degree whereby an innovative

technology (e.g., ARSAs) performs better than existing technologies (e. g., online shopping platforms without ARSAs) (Rogers, 1995). Davis (1989) and Davis et al. (1989) proposed that perceived relative advan- tage and perceived usefulness are similar in attribute characteristics, and perceived usefulness is considered to be the main determinant of

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users’ attitudes. Subsequent research has further demonstrated that perceived usefulness affects consumers’ attitudes towards the use of innovations (Agarwal and Prasad, 2000; Kim and Forsythe, 2009; Van Slyke et al., 2007; Rese et al., 2017; Zhang et al., 2019). Kim and For- sythe (2009) further illustrated that perceived usefulness is a powerful predictor of consumers’ attitudes towards using virtual try-on technol- ogy. Rese et al. (2017) showed that the perceived usefulness of AR apps in retail environments directly and positively influences users’ attitudes towards using them. Zhang et al. (2019) found that online consumers’ attitudes towards virtual reality technology are affected by perceived usefulness. In addition, the study of Van Slyke et al. (2007) demon- strated that the perceived relative advantage in the innovation diffusion theory has a significant direct impact on users’ attitudes toward communication technology.

This study proposes that compared with traditional online shopping platforms, ARSAs are positively perceived by some consumers to provide an enhanced shopping engagement and experience through pre- purchased product simulation or an actual shopping environment. It can also assist them in assessing the suitability of products and making more accurate and informed behavioral judgments and decisions. Therefore, it can reduce the problem of replacement or return caused by product suitability (such as: size, color, style, clothing and accessories, makeup matching, suit selection, etc.) (Kim, 2019; Zhang et al., 2019). Consequently, these advantages can lead to a positive attitude toward ARSAs.

H1. Consumers’ perceived relative advantage of ARSAs positively af- fects their attitude toward ARSAs.

2.2.2. Effects of perceived compatibility Perceived compatibility refers to the degree whereby ARSAs are

considered in line with consumers’ current lifestyles, values, shopping styles, and personal needs (Rogers, 1995; Yuen et al., 2018). When consumers are exposed to a new application technology, they will form an attitude of whether to use it or not in the basic framework of their own living habits, behavior, thinking and value system, and specific needs (Agag and El-Masry, 2016). Therefore, consumer perceived compatibility of new technologies is very important. Existing research provides support for the positive and effective impact of perceived compatibility on consumers’ attitudes towards using new technologies (Agarwal and Prasad, 2000; Van Slyke et al., 2007; Amaro and Duarte, 2015; Agag and El-Masry, 2016; Wang et al., 2018). Agarwal and Prasad (2000) demonstrated in a survey that experienced programmers perceived compatibility to significantly influence their attitudes on using new programming language. Chen et al. (2002) proposed that the compatibility of using online virtual stores with consumers’ existing lifestyles, behavior habits and specific needs has a positive, significant impact on their attitudes towards using online virtual stores. Amaro and Duarte (2015) and Agag and El-Masry (2016) demonstrated that the perceived compatibility of Internet consumers is a powerful factor affecting consumers’ attitudes towards online travel consumption and shopping.

In terms of the adoption of ARSAs, the perceived compatibility of ARSAs may vary among consumers, resulting in differences in their perception or evaluation of ARSAs. For example, busy professionals do not have extra time to shop in physical stores, and shopping through

Table 1 Theories and associated factors influencing ARSA usage.

Theory Innovation diffusion theory Attitude theory Perceived value theory

Paradigm Innovation acceptance Psychology Consumer utility Theory

description The speed at which an innovation is propagated and adopted in a society or among consumers is determined by their perception of the innovation’s characteristics.

Positive psychological tendencies to evaluate an entity with positive valence and intensity could lead to the adoption of an entity.

Consumers choose or use products that provide the best utility in the market.

Associated factors

Relative advantage, Compatibility, Complexity, Trialability, and Observability

Attitude Perceived value

Proposition This theory could justify how the five innovative characteristics of ARSAs could lead to ARSA usage.

This theory can explain how the characteristic beliefs adopted by the five innovations can lead to the formation of positive consumers’ attitudes toward ARSAs, thus improving their usage.

This theory can explain how positive attitudes toward ARSAs can increase perceived value and thereby increase ARSA usage.

Fig. 1. The theoretical model.

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ARSAs will make them perceive ARSA’s high compatibility with their own lifestyle and needs. Some consumers are keen to use innovative technologies or have limited mobility (e.g., people with disabilities or those placed in family isolation), and others are more likely to perceive a high degree of alignment between ARSAs and their values and personal needs. As a result, consumers will have a positive perception and belief in the compatibility of ARSAs, thereby eliciting consumer’s positive attitude toward ARSAs. Irrefutably, there is a large base of consumers who prefer to experience products on-site. They regard shopping in large department stores and brick-and-mortar brand stores as a hobby, an entertainment, and a form of social behavior. However, due to the COVID19 epidemic, a growing number of the consumers have changed their shopping behaviors and methods, thus perceiving the use of ARSA being highly consistent with their current personal needs and lifestyles (Donthu and Gustafsson, 2020; Kirk and Rifkin, 2020; Singh, 2020). Therefore, a high degree of perceived compatibility is also generated for ARSA, and thus a positive attitude toward the use of ARSA will be generated.

H2. The perceived compatibility of ARSAs with consumers’ existing lifestyle, shopping style, actual demand, and situation positively affects consumers’ attitudes toward ARSAs.

2.2.3. Effects of perceived complexity Perceived complexity refers to the perceived difficulty of under-

standing or using ARSAs (Rogers, 1995). Some scholars demonstrated that perceived ease of use (or complexity) is the main factor influencing technology adoption attitudes (Chen et al., 2002; Vijayasarathy, 2004; Van Slyke et al., 2007; Amaro and Duarte, 2015; Agag and El-Masry, 2016). In particular, the research of Amaro and Duarte (2015) demon- strated that there is a negative relationship between perceived complexity and consumers’ adoption attitudes.

People have different perceptions of the complexity of ARSAs. For example, some consumers (e.g., people who are receptive and enthusi- astic about innovative technologies, usually young people.) think that making extra efforts in the process of getting acquainted with ARSAs is trivial. However, some consumers (e.g., people who are unreceptive of or dislike innovative technologies such as the elderly) believe that the innovative technologies possessed by ARSAs are inherently complicated, which will make them naturally feel a sense of burden, thus forming a negative attitude toward innovative technology. Therefore, this study believes that consumers’ perceived complexity of ARSAs will lead to consumers’ negative attitude toward the use of ARSAs. Based on this, the following hypotheses are proposed:

H3. The perceived complexity of ARSAs negatively affects consumers’ attitudes toward ARSAs.

2.2.4. Effects of perceived trialability Perceived trialability refers to the degree to which consumers are

considered needing to try ARSAs first before formally adopting them (Rogers, 1995). The various characteristic attributes (relative advan- tage, compatibility, complexity, trialability, observability) in the inno- vation diffusion theory are the main influencing factors of a user’s attitude towards adoption (Van Slyke et al., 2007). Prior to this, Kar- ahanna et al. (1999) found that the perceived trialability of potential adopters had a positive effect on attitudes toward the use of online electronic operating systems. This view was further supported in sub- sequent studies (Wang et al., 2018; Al-Rahmi et al., 2019). At the same time, researches conducted for different groups of people also found that users’ attitudes towards innovative technology systems are greatly affected by their trialability (Al-Rahmi et al., 2019).

On the other hand, from the perspective of prospect theory, people considering pre-testing ARSAs tend to avoid uncertainty, that is, risk aversion (Strömberg et al., 2016). The trialability characteristics of ARSAs can precisely enable risk-averse consumers to perform “risk-free exploration” on ARSAs before they formally adopt them. The trialability

of ARSAs provides a safe environment for trying new behaviors; hence, consumers can safely try various new behaviors and eventually gain confidence. Moreover, such an experimental experience not only is a process of learning for ARSA familiarity but also gives consumers sur- prises. Therefore, consumers have positive perceptions and beliefs about the trialability characteristics of ARSAs, which will help form a positive attitude toward ARSAs.

H4. The consumers’ perceived trialability positively affects their atti- tudes toward ARSAs.

2.2.5. Effects of perceived observability Perceived observability refers to the extent to which the process and

results of the use of ARSAs are considered observable by others (Rogers, 1995), that is, consumers’ perception or beliefs of the degree of visual- ization of the actual use process and results of ARSAs (Van Slyke et al., 2007). Moore and Benbasat (1991) proposed that the perceived observability includes the two belief attributes of perceived result provability and visibility. Later, Karahanna et al. (1999) showed that both perceived observability and perceived trialability are the main influencing factors of user attitudes. In fact, perceived observability and perceived trialability are different characteristic attributes, but both affect the formation of consumer attitudes (Van Slyke et al., 2007). In other words, before the actual use, both will provide consumers with a kind of confidence (Wang et al., 2018). However, although they are similar in that respect, they are different especially at different stages of occurrence. Perceived trialability occurs in the initial use stage, and does not apply to subsequent uses after the initial trial, whereas perceived observability can exist in both stages (Agarwal and Prasad, 1997). Therefore, in this study, the two characteristic attributes are indepen- dent and not identical.

The perceived observability of ARSAs can make consumers believe that they can easily learn and familiarize themselves with the process of using ARSAs by observing others. In addition, the perceived observ- ability of ARSAs can make consumers feel that the entire purchase transaction process is more open and transparent, and that the entire shopping environment of ARSAs is safe and stable. Therefore, the perceived observability of ARSAs will provide consumers with sufficient confidence before the actual use. Furthermore, the perceptual observ- ability of ARSAs allows consumers to gain social recognition and acceptance with significant referents who are also using ARSAs.

H5. The consumers’ perceived observability of ARSAs positively af- fects their attitudes toward ARSAs.

2.3. Direct effect of consumers’ attitude toward ARSAs on consumers’ use intention

A positive attitude will increase people’s motivation to engage in an activity (Reardon et al., 2006). The cognitive consistency theory be- lieves that people usually seek consistency among attitudes, behavioral intentions, and behaviors (Heider, 1946). In addition, rational action theory and technical acceptance models explain the significant rela- tionship between attitude and behavioral intention (Davis, 1989; Fish- bein and Ajzen, 1975). However, this relationship has not only been explained at the theoretical level, but has also been empirically tested. For example, in the context of Internet retail, consumers’ attitude to- ward products or the application of a certain technology has a significant effect on the purchase intention of the product or the adoption intention of technology application (Fan et al., 2020; Pantano et al., 2017; Plot- kina and Saurel, 2019; Rese et al., 2017; Zhang et al., 2019). Further- more, consumers’ positive attitudes toward automated parcel station self-service collection services and communication technology will lead to stronger adoption intentions (Van Slyke et al., 2007; Wang et al., 2018).

H6. The consumers’ attitudes toward ARSAs positively affects their

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use intention.

2.4. Indirect effect of consumers’ attitude toward ARSAs on consumers’ use intention

Perceived value is defined as an overall evaluation of consumers’ perception of the utility of a service or product by comparing perceived benefits with the corresponding costs (Lin et al., 2005; Ravald and Grönroos, 1996). Consumers’ positive or negative evaluations and feelings of products will affect their overall evaluation of the product’s utility and value (Shiu et al., 2015). In addition, Salehzadeh and Pool (2017) found that attitudes positively affect the perceived value and its multiple dimensions (social, personal (hedonic), and functional value). Similar findings are found in other related contexts such as travel companies, luxury retail, Internet information websites, and regional brands (Charton-Vachet et al., 2020; Moliner-Velazquez et al., 2014; Salehzadeh and Pool, 2017; Shiu et al., 2015). Therefore, we posit the following:

H7. The consumers’ attitudes toward ARSAs positively affects the consumer’s perceived value of ARSAs.

Consumers’ intention to use ARSAs can be explained using the theory of perceived value. Consumers are rational and will choose products or services that can provide them with the greatest value, which leads to positive behavioral intentions such as loyalty or continuous purchase intentions and behaviors (Zauner et al., 2015). In this regard, consumers are expected to use ARSAs for shopping if using them confers the most economical, functional (i.e., convenience, security, and safety), hedonic (i.e., interesting and fun), and social utility (i.e., reduced traffic congestion and environmental pollution) among other alternatives (Pham et al., 2018; Ponte et al., 2015). Customer value is the main influencing factor of behavioral intention (Parasuraman and Grewal, 2000). This relationship has also been confirmed by existing studies (Zauner et al., 2015; Ponte et al., 2015; Yang et al., 2016; Pham et al., 2018; Yuen et al., 2019; Vishwakarma et al., 2020). Among them, Yang et al. (2016) and Vishwakarma et al. (2020) demonstrated that con- sumers’ perceived value of using virtual reality technology has a positive and significant impact on customers’ use intention. Therefore, we posit the following:

H8. The perceived value of ARSAs positively affects consumers’ intention to use ARSAs.

3. Methodology

3.1. Measurement items

Table 2 shows the measurement items using a 5-point Likert scale and related references used to develop and operationalize the constructs.

3.2. Survey administration

The subjects of this study are shoppers in China. The measurement items in survey questionnaire were developed with reference to the literature. The study first translated the English questionnaire into Chinese, and then translated it back into English. After that, this study compared the two English versions and checked for differences. This is to ensure the accuracy of the expression of meaning. This study also conducted a pre-test on 20 participants before the formal survey administration to verify the readability of the questionnaire. Finally, the questionnaire was revised based on the comparison results and the re- spondents’ feedback, before administering the Chinese questionnaire. A survey is created on an online platform “WenJuanXing”, and a survey company was engaged to administer the survey questionnaires for the targeted group based on random sampling. The questionnaire consists of

Table 2 Survey questions.

Construct Item Source

Perceived relative advantage (PRA)

ARSAs would be better than traditional product display mode because:

Moore and Benbasat (1991); Yuen et al. (2018)

PRA1: They would improve my online shopping experience. PRA2: They would make it easier for me to make a purchase decision. PRA3: They would be allowed to complete the process of online shopping more efficiently. PRA4: They would be more beneficial to me. PRA5: They would be the best way for me to experience online shopping.

Perceived compatibility (PCA)

Online shopping using ARSAs would be compatible with: PCA1: My lifestyle. PCA2: My actual needs. PCA3: the way I like online shopping. PCA4: My current situation.

Perceived complexity (PCL)

I feel ARSAs: PCL1: are easy to use.* PCL2: are difficult to use. PCL3: are difficult to learn how to use. PCL4: are frustrating to use. PCL5: are cumbersome to use. PCL6: requires a lot of effort to use.

Perceived trialability (PTB)

PTB1: ARSAs are easy to try. PTB2: I know where I can try ARSAs out. PTB3: I am permitted to try ARSAs for a long enough period. PTB4: I can make a tentative use of ARSAs first if necessary. PTB5: I can access ARSAs adequately.

Perceived observability (POB)

By observing how others use ARSAs, I feel POB1: I can learn how to use them. POB2: I can explain to others how to use them. POB3: I can judge whether they are beneficial. POB4: The whole process is very clear to me.

Attitude (ATT) PTR1: I think I will be filled with affection and satisfaction for ARSAs.

Rese et al. (2017); Plotkina and Saurel (2019)

PTR2: I think ARSAs are so interesting that it makes you want to know more PTR3: I think using ARSAs will make a lot of sense PTR4: I think using ARSAs is a good idea PTR5: I think other people should also use ARSAs PTR6: I think ARSAs is a good experiential online shopping technology

Perceived value (PVA)

Concerning ARSAs, Yuen et al. (2018) PVA1: Their products and services are reasonably priced. PVA2: using them would be efficient. PVA3: using them would be pleasant. PVA4: using them would have positive effects on the environment and society.

Yuen et al. (2018)

(continued on next page)

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four sections: (i) background information and purpose of survey, (ii) introduction of ARSAs with questions on whether respondents have used or heard about ARSAs, (iii) respondents’ demographic information, and (iv) measurement items using a Likert scale. In addition, the question- naire in this study uses the 5-point Likert scale, with 1 being ‘strongly disagree’, 3 being neutral and 5 being ‘strongly agree’.

The survey was conducted from October 10, 2020 to October 26, 2020, and a total of 477 questionnaires were collected. After the quality screening, the unqualified samples were rejected (e.g., when a respon- dent rates PCL1 and PCL2 both positively or negatively; all answers are rated equally; or there are obvious “dishonest answers” where re- spondents successive assign equal numbers to all survey questions), and 379 valid samples were finally used. According to the recommendations of Kline (1998), the use of more than 200 samples meets the data re- quirements of structural equation analysis. Moreover, when the sample size is greater than 200, the stability of SEM analysis will not be affected (Wang et al., 2018). This study uses China’s 506 million mobile e-commerce users as the target population (CNNIC, 2018), and calcu- lates the sample size using the margin of error approach with the given parameter of 95% confidence level. The result shows that a minimum sample size of 320 is required to maintain a margin of error of 5%. The study’s sample size of 379 meets the minimum requirement. Table 3 presents the respondents’ socio-demographic.

The questionnaire method used in this study may cause non-response bias. The method used in this study is to compare the responses of early and late respondents, that is, late respondents are not likely to respond, so they tend to exhibit non-respondent characteristics. According to the total time from the respondent receiving the invitation to completing the questionnaire, the data set is divided into two groups (i.e., early and late respondents). Subsequently, the mean value of each construct was calculated, and an independent t-test was used to compare the both groups. The results were not significant (p > 0.05). Hence, non-response bias is not a key problem.

3.3. Analytical method

In this study, a confirmatory factor analysis (CFA) was carried out by using the AMOS 24.0 program to examine reliability and discriminant validity. Thereafter, this study followed the recommendations of Anderson and Gerbing (1988), Alzahrani et al. (2012) and Hair et al. (2012), using structural equation modelling (SEM) to test the hypothesis.

SEM is useful for analyzing data collected by methods such as surveys and experiments, as well as evaluating the validity, reliability and pre- dictability of structural measurement scales (Jiang et al., 2021). There are two types of SEM, one is Covariance-based technique (CB-SEM), and the other is Partial Least Square (PLS-SEM). CB-SEM is suitable for confirmatory research when a model is grounded on theory and needs to be explained using data (Astrachan et al., 2014). PLS-SEM is more suitable for exploratory research, that is, to find and evaluate causality at the early stage of theory development (Hair et al., 2014). However, since PLS does not explicitly model the measurement error

variance/covariance structure unlike CB-SEM, PLS may produce biased parameter estimates (David et al., 2011). In addition, CB-SEM de- termines the differences between the observed and implied covariance matrices using chi-square, and its various analytical requirements are rigorous, providing a variety of Goodness-of-Fit indices. In addition, this study fulfilled the minimum sample size requirements of 200 needed to use CB-SEM (Astrachan et al., 2014). Therefore, this study uses CB-SEM for measurement examination and hypotheses verification.

4. Results and discussion

4.1. Confirmatory factor analysis

Confirmatory factor analysis is conducted to ascertain the model’s goodness-of-fit, reliability, and validity. The fit indices at the bottom of Table 4 demonstrate a good model fit.

The factor loadings (λ) and the composite reliabilities (CRs) of the constructs demonstrate reliability because they are higher than the recommended critical values of 0.70 and 0.80, respectively (Hair et al., 2009). This research also performed reliability analysis. The Cronbach’s α values are all higher than the threshold requirement of 0.70 (Bagozzi et al., 1981). This result further shows that the measurement items have high reliability or internal consistency.

The validity of the measurement tool was assessed using convergent validity and discriminant validity. First, the average variance extracted (AVE) values are above 0.50, which is above the recommended criteria, indicating construct validity (Fornell and Larcker, 1981). Second, the AVE value of each construct is greater than its squared correlations with other constructs (not shown in this study), which supports discriminant validity.

Table 2 (continued )

Construct Item Source

Intention to use ARSAs (INT)

INT1: The next time I buy online, I plan to use AR-trop for shopping. INT2: Using ARSAs is my first choice when shopping online.

INT3: I would recommend ARSAs to my friends.

INT4: I have positive things to say about ARSAs to my friends.

Note: *PCL1 is a reverse-scaled item for screening purpose.

Table 3 Respondents’ information (n = 379).

Characteristics Observations Frequency Percentage (%)

Gender Male 152 40.11 Female 227 59.89

Age ≤19 years 43 11.35 20–29 years 159 41.95 30–39 years 135 35.62 40–49 years 31 8.18 ≥50 years 11 2.90

Educational background

Junior high school 7 1.85 High school 26 6.86 Bachelor 294 77.57 Master 36 9.50 PhD 9 2.37 Others 7 1.85

Occupation Civil servants and academic institutions

66 17.41

Enterprise management or general staff

141 37.20

Skilled workers, farmers and homemakers

75 19.79

Students 84 22.16 Self-employed and others 13 3.43

Monthly income (CNY)

≤3000 101 26.65 3000 to 6000 123 32.45 6001 to 10,000 93 24.54 10,001 to 15,000 40 10.55 15,001 and above 22 5.81

Place of residence Municipality (i.e., Beijing, Shanghai, Tianjin, and Chongqing)

65 17.15

Provincial capital (Including Shenzhen, Qingdao, Dalian, Suzhou)

148 39.05

Prefecture level city 95 25.07 County-level city 71 18.73

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4.2. Structural model analysis

Fig. 2 shows the estimated theoretical model. Education level, in- come, and experience (i.e., prior experience with using ARSAs) were used to the model to control for their impact on consumers’ intentions to

use ARSAs (Marinković et al., 2020). Fig. 2 shows that the theoretical model has good model fit (x2/df =

1.623 (p < 0.05, df = 576); CFI = 0.96; TLI = 0.96; RMSEA = 0.041; SRMR = 0.063). The squared multiple correlations (R2) of endogenous variable attitudes, perceived value, and consumers’ intention to use ARSAs are 0.82, 0.64, and 0.78, respectively. They are more than 0.5, highlighting the model’s explanatory power.

Fig. 2 shows that innovation diffusion theory’s perceived relative advantage, perceived compatibility, and perceived observability have a significant positive impact on consumers’ attitudes toward ARSAs, and their standardized estimation coefficients are 0.28, 0.54, and 0.21 (p < 0.05), respectively. Therefore, only H1, H2, and H5 are accepted. However, the impact of perceived trialability and perceived complexity on consumers’ attitudes toward ARSAs was not significant (p > 0.05). Therefore, H3 and H4 are rejected. Simultaneously, these variables together explain the variance of 82% of consumers’ attitudes toward ARSAs (R2 = 0.82). The analysis result does not fully agree with the existing research based on innovation diffusion and attitude theories. In other words, in the context of ARSAs, not all innovation diffusion vari- ables contribute to consumers’ positive attitudes toward ARSAs.

Fig. 2 also shows that attitudes have a significant direct impact on consumers’ intention to use ARSAs (b = 0.34, p < 0.05). Consequently, H6 is accepted. In addition, attitudes have a positive and significant impact on consumers’ perceived value of ARSAs (b = 0.80, p < 0.05). Consumers’ perceived value of ARSAs significantly affects consumers’ use intention (b = 0.58, p < 0.05). As a result, H7 and H8 are accepted. This indicates that perceived value is a mediating variable in the rela- tionship between attitude and behavioral intention, and consumer attitude toward ARSAs will have a positive indirect effect his/her use intention through perceived value.

4.3. Discussion

According to the results shown in Fig. 2, the innovation diffusion theory’s perceived relative advantage, perceived compatibility, and perceived observability have a significant positive impact on consumers’ attitudes toward ARSAs. Therefore, hypothesis H1, H2, and H5 are accepted.

First, on the relationship between consumers’ perceived relative advantage and consumers’ attitudes toward ARSAs, the results show

Table 4 Confirmatory factor analysis.

Construct Item λ AVE CR Cronbach’s α

Perceived relative advantage (PRA)

PRA1 0.82 0.642 0.899 0.900 PRA2 0.81 PRA3 0.78 PRA4 0.78 PRA5 0.82

Perceived compatibility (PCA) PCA1 0.84 0.666 0.889 0.889 PCA2 0.81 PCA3 0.83 PCA4 0.79

Perceived complexity (PCL) PCL2 0.87 0.671 0.911 0.910 PCL3 0.84 PCL4 0.82 PCL5 0.81 PCL6 0.75

Perceived trialability (PTB) PTB1 0.83 0.614 0.864 0.863 PTB3 0.74 PTB4 0.80 PTB5 0.76

Perceived observability (POB) POB1 0.82 0.585 0.808 0.807 POB2 0.75 POB3 0.73

Perceived value (PVA) PVA1 0.75 0.665 0.888 0.888 PVA2 0.84 PVA3 0.86 PVA4 0.81

Attitude (ATT) ATT1 0.84 0.639 0.876 0.877 ATT2 0.80 ATT3 0.78 ATT4 0.79

Intention to use ARSAs (INT) INT1 0.84 0.713 0.909 0.908 INT2 0.83 INT3 0.86 INT4 0.85

Note: Model fit indices: χ2 = 744.811; χ2/df = 1.595, (p < 0.05, df = 467); CFI = 0.97; TLI = 0.96; RMSEA = 0.04; SRMR = 0.039.

Fig. 2. The structural equation model.

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that when consumers have positive perceptions or beliefs on the ad- vantages of ARSAs, they will form a positive attitude toward ARSAs. This result is consistent with Van Slyke et al. (2007). Some consumers will realize that ARSAs can improve their engagement and shopping expe- rience, and help consumers solve fitting concerns, regarding clothes (eg., product attributes such as size, color, style, etc.), the matching of clothing accessories and makeup (eg., lipstick color and foundation, sunglasses, jewelry and clothing, shoes and clothing, etc.), and the se- lection of suits and dresses (eg., attending dinners, participating in outdoor sports, various celebrations, etc.) (Zhang et al., 2019). In addition, it can reduce the replacement or return due to suitability problems, thereby saving financial expenses and reducing delivery and return costs (Kim, 2019). Simultaneously, consumers will also perceive ARSAs’ characteristics to assist them in evaluating the suitability of products and making behavioral judgments and decisions more effi- ciently (Fan et al., 2020). Therefore, consumers who actively perceive the relative advantages of ARSAs will have positive opinions and eval- uations, thereby forming a positive attitude toward ARSAs.

Second, consumers’ perceived compatibility with ARSAs has a pos- itive effect on consumers’ attitudes toward ARSAs. The results support previous studies (Such as: Van Slyke et al., 2007; Amaro and Duarte, 2015; Agag and El-Masry, 2016). People who are busy at work, keen to use innovative technologies, and have limited mobility will positively perceive the compatibility of ARSAs’ innovative characteristics with their actual needs, values, and life and shopping styles. However, due to the COVID19 epidemic, consumers who once liked to experience prod- ucts on-site and view physical store shopping as an enjoyment and social activity may also perceive ARSAs to be compatible with their own life- style and shopping needs. Therefore, such consumers will also have positive views and beliefs about the compatibility of ARSAs, which positively influence their attitude towards ARSAs.

Third, we look at the relationship between consumers’ perceived observability of ARSAs and consumer attitudes toward ARSAs. This is in line with the views put forward by Karahanna et al. (1999) and Van Slyke et al. (2007). When consumers perceive ARSAs’ observability characteristics, they will think that by observing other people’s opera- tion demonstrations, they can easily learn and become familiar with the process of using ARSAs, enabling them to gain confidence. Wang et al. (2018) made the same statement in the study, that is, the observability of a technology will provide consumers with a confidence before actual use, thereby allowing consumers to form a positive attitude towards using the technology. In addition, according to prospect theory, people tend to avoid uncertainty and potential risks (Strömberg et al., 2016). The observability of ARSAs could improve consumers’ perception of the purchase transaction process being more transparent, and the entire shopping environment is safe, stable and reliable, which enhances their attitude towards ARSAs adoption. On the contrary, if consumers do not perceive the observability of ARSAs, they will develop concerns and worries about their safety and reliability, and thus develop negative attitudes on ARSA adoption (Zhang et al., 2019). Therefore, consumers’ perceived observability characteristics of ARSAs will prompt them to form positive cognitive and emotional evaluations of ARSAs and thus positive attitude.

In addition, the analysis results also show that the effect of trial- ability and complexity on consumers’ attitudes towards ARSAs is not statistically significant, and the research hypotheses H3 and H4 are rejected. The analysis results are not consistent with existing research based on innovation diffusion and attitude theories. In other words, in the context of ARSA, not all innovation diffusion variables contribute positively to consumer attitudes towards ARSA. First, the trialability of ARSAs might not be the main concern for consumers in using ARSA. Similar findings are noted in Agarwal and Prasad (1997) who found similar results on Web usage. ARSAs are an extension of the traditional Internet shopping platform, and its stability and security are built from the latter. Moreover, ARSAs is an improvement to the core shopping process, and its innovation is mainly in its AR function. Therefore, some

consumers might think that ARSAs do not require additional or exten- sive trials to learn.

One point that this research also illustrates is that a key factor in the trialability of ARSAs hinges on the capability and availability of the supporting devices that enable the usage of ARSAs. However, nowadays, the use of ARSA is convenient, which only requires a smart phone, tablets and other mobile electronic devices (Rauschnabela et al., 2019; Qin et al., 2021). Moreover, all operating systems used on current smart electronic devices are compatible with the installation of ARSA software (APP). Therefore, this study believes that the equipment concerns are negligible.

Second, the complexity of ARSAs does not significantly affect con- sumers’ attitudes. Some existing studies found similar results albeit on different context (Karahanna et al., 1999; Agarwal and Prasad, 2000; Brown et al., 2002; Chau and Hu, 2002). Another possible explanation is that the target groups of this study are shopping consumers who have a strong awareness and acceptance of innovative technologies and main- tain an open state to innovative technologies. Therefore, people may not perceive the complexity of ARSAs, and its effect on consumer attitudes will not be significant.

Fig. 2 also shows that attitudes have a significant direct impact on consumers’ intention to use ARSAs. Hence, hypothesis H6 is accepted. This finding is consistent with the literature on cognitive consistency and attitude theories. That is, one’s behavioral intention is affected by one’s attitude (Fishbein and Ajzen, 1975), and people usually seek consistency between attitudes and behavioral intentions and behaviors (Heider, 1946). Overall, the acceptance of hypothesis H1, H2, H5, and H6 supports the rational action theory which proposed the causal chain of “belief-attitude-intention” (Fishbein and Ajzen, 1975).

In addition, there are positive, significant relationships between consumers’ attitude towards ARSAs and their perceived value, and the latter significantly influences intention to use them. Therefore, hy- pothesis H7 and H8 are accepted. This indicates that perceived value is a mediating variable in the relationship between attitude and behavioral intention, and consumer attitude toward ARSAs has a positive indirect effect his/her use intention through perceived value.

First, attitude has a positive impact on consumers’ perceived value of ARSAs. This is consistent with Salehzadeh and Pool (2017). Attitude is originally a positive or negative (good or bad) feeling or evaluation that people have about a certain behavior (Van Slyke et al., 2007). Con- sumers have a positive and beneficial feeling of ARSAs, which will form a positive and advantageous attitude. Moreover, people holding a pos- itive attitude toward ARSAs will increase their attention to ARSAs, and thus, they will form positive perception and evaluation of the value of ARSAs.

Second, perceived value has a significant positive impact on con- sumers’ use intention to ARSAs. This is consistent with the existing literature on perceived value theory (Parasuraman and Grewal, 2000; Yang et al., 2016; Vishwakarma et al., 2020). This study believes that products or services with higher perceived value could encourage people to use or continue to use them. That is, when the use of ARSAs is considered to have a higher value than traditional shopping platforms, consumers may have a greater intention to use ARSAs.

5. Conclusion, limitations and future research

This study enriches existing scholarship by synthesizing and applying innovation diffusion theory, perceived value theory, and atti- tude theory, and by introducing various paradigm theories to expand existing research. In addition, this study introduces perceived value on the basis of the “belief-attitude-intention” relationship, thereby further expanding this causal relationship. The academic contribution and sig- nificance of this research are as follows.

First, by synthesizing and applying theories of innovation diffusion, perceived value, and attitude, we understand the factors that affect consumers’ intention to use ARSAs. At present, most research on the

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factors influencing consumers’ intention to use ARSAs applies the technology acceptance model or its extended model (Kim and Forsythe, 2009; Pantano et al., 2017; Plotkina and Saurel, 2019). This study ex- pands existing research by introducing theories from various paradigms (e.g., customer utility, social psychology, and innovation acceptance), thereby enriching existing academic research.

Next, this study summarizes several factors that affect the use of ARSAs. Collectively, innovation diffusion theory explained 82% of the variance in consumer attitudes toward ARSAs, and the entire model explained 78% of the variance in consumer intentions to use ARSAs. This demonstrates the complementarity of theories. As a whole, the theories are effective and provide multiple perspectives for explaining con- sumers’ use intention.

In addition, this study provides a logical nomological structure that explains the relationship among the factors that influence the use of ARSAs. The network of hypotheses proposed in this study generally agrees with the core principles of consumer behavior; that is, human behavior is affected by their behavioral intentions, which in turn are affected by attitudes, and beliefs are the determinants of attitudes, thus forming a “belief–attitude–intention” relationships. Our study further extends this causal relationship with the introduction of perceived value, which can be viewed as a consequence of attitude and antecedent of intention.

This research also has practical implications. This study provides empirical support for technology companies to develop ARSAs and on- line or offline retailers using ARSAs. First, developers and retailers should pay attention to improving the compatibility of ARSAs. More- over, ARSAs should be designed and marketed to be aligned with the existing lifestyles, values, shopping styles, and individual needs of consumers. For example, for busy “office workers” or people with higher incomes but limited leisure time, they would not be able to shop at physical stores to assess the size, performance and suitability of the products. Hence, ARSA should be targeted at these individuals to meet their needs to try-on these products virtually. ARSAs should also be advertised as a greener option because it reduces the possibility of a product return or exchange. They can also be targeted at consumers with limited mobility (e.g., the disabled) who could still enjoy the fun and excitement of shopping without visiting the physical stores.

Second, technology companies should focus on enhancing the rela- tive advantages of ARSAs. They can enhance the virtual interactions with products, highlight the benefits of virtual product simulation, and reduce consumers’ anxiety and uncertainty about purchasing products. Consequently, the recycling or exchange of products is reduced, thereby improving the shopping experience and providing consumers with greater convenience and cost savings. Simultaneously, R&D companies should continue to improve ARSAs’ reliability, interactivity, and accu- racy, to simulate an environment similar to offline shopping and saving time and energy.

Finally, the observability of ARSAs should be enhanced overall. R&D companies must continuously simplify the entire operation process, and retailers must increase the transparency of shopping transactions. Con- sumers can obtain information and experience shared by other users through forums, blogs, or live broadcast platforms, to easily and conveniently learn the application operation of ARSAs. Moreover, con- sumers can experience a safe shopping environment in a highly trans- parent shopping transaction process. More importantly, these methods can not only maintain existing consumers but also attract more potential consumers.

In addition, the results of this study can provide reference and empirical support for overseas technology companies investing in the e- commerce industry in China. It also provides a practical reference for the development and innovation of the e-commerce industry as a whole.

This study has some limitations. First of all, this survey was con- ducted on Chinese shoppers. However, the findings may not be appli- cable to other countries with their own unique shopping preferences and demographics. Therefore, in the future, we recommend expanding the

research to other regions or countries and expand the overall amount of data collected. In this way, the representativeness of survey data can be improved.

Another limitation of this research is that it only introduces three theoretical perspectives to understand and explain the factors that in- fluence consumers’ intention to use ARSAs. Future research can focus on introducing and integrating other theories or models to study other factors that affect consumers’ intention to use ARSAs. In addition, further comparative studies on the effects of the combination of various theories and models can be considered.

Finally, this study uses a stated preference survey, and the data is cross-sectional. Moreover, the questionnaire survey was conducted during the COVID19 epidemic. As a result, the behaviors and decisions of consumers participating in the survey in this study were more or less affected by the epidemic environment. Therefore, the general applica- bility of the research results needs further confirmation. The future research can conduct separate questionnaire surveys in the post- epidemic era. An analysis of the market situation of ARSA in the con- sumer society can be conducted by comparing data obtained from pre- and post-epidemic era.

References

Agag, G., El-Masry, A.A., 2016. Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: an integration of innovation diffusion theory and TAM with trust. Comput. Hum. Behav. 60, 97–111.

Agarwal, R., Prasad, J., 1997. The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decis. Sci. J. 28 (3), 557–582.

Agarwal, R., Prasad, J., 2000. A field study of the adoption of software process innovations by information systems professionals. IEEE Trans. Eng. Manag. 47 (3), 295–308.

Al-Rahmi, W.M., Yahaya, N., Aldraiweesh, A.A., Alamri, M.M., Aljarboa, N.A., Alturki, U., Aljeraiwi, A.A., 2019. Integrating technology acceptance model with innovation diffusion theory: an empirical investigation on students’ intention to use e-learning systems. IEEE Access 7, 26797–26809.

Alzahrani, A., Stahl, B.C., Prior, M., 2012. Developing an instrument for e-public services’ acceptance using confirmatory factor analysis: Middle East context. J. Organ. End User Comput. 24 (3), 18–44.

Amaro, S., Duarte, P., 2015. An integrative model of consumers’ intentions to purchase travel online. Tourism Manag. 46, 64–79.

Anderson, J.C., Gerbing, D.W., 1988. Structural equation modeling in practice: a review and recommended two-step approach. Psychol. Bull. 103 (3), 411–423.

Astrachan, C.B., Patel, V.K., Wanzenrie, G., 2014. A comparative study of CB-SEM and PLS-SEM for theory development in family firm research. Journal of Family Business Strategy 5, 116–128.

Bagozzi, R.P., Fornell, C., Larcker, D.F., 1981. Canonical correlation analysis as a special case of a structural relations model. Multivariate Behav. Res. 16 (4), 437–454.

Bonnin, G., 2020. The roles of perceived risk, attractiveness of the online store and familiarity with AR in the influence of AR on patronage intention. J. Retailing Consum. Serv. 52, 101938.

Brown, S.A., Massey, A.P., Montoya-weiss, M.M., Burkman, J.R., 2002. Do Ireally have to? User acceptance of mandated technology. Eur. J. Inf. Syst. 11, 283–295.

Charton-Vachet, F., Lombart, C., Louis, D., 2020. Impact of attitude towards a region on purchase intention of regional products: the mediating effects of perceived value and preference. Int. J. Retail Distrib. Manag. 48 (7), 707–725.

Chau, P.Y.K., Hu, P.J., 2002. Examining a model of information technology acceptance by individual professionals: an exploratory study. J. Manag. Inf. Syst. 18 (4), 191–229.

Chen, L., Gillenson, M.L., Sherrell, D.L., 2002. Enticing online consumers: an extended technology acceptance perspective. Inf. Manag. 39 (8), 705–719.

Chiu, C.L., Ho, H.C., Yu, T., Liu, Y., Mo, Y., 2021. Exploring information technology success of Augmented Reality Retail Applications in retail food chain. J. Retailing Consum. Serv. 61, 102561.

Chylinski, M., Heller, J., Hilken, T., Keeling, D.I., Mahr, D., de Ruyter, K., 2020. Augmented reality marketing: a technology-enabled approach to situated customer experience. Australas. Market J. 28 (4), 374–384.

CNNIC, 2018. Statistic Report on Internet Development in China. China Internet Network Information Center, China. https://www.cnnic.com.cn/IDR/Report Down loads/201807/P020180711391069195909.pdf. (Accessed 1 October 2019).

David, G., Edward, E.R., Detmar, S., 2011. Editor’s comments: an update and extension to SEM guidelines for administrative and social science research. MIS Q. 35 (2), 3–14.

Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13 (3), 319–340.

Davis, F.D., Bagozzi, R.P., Warshaw, P.R., 1989. User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35 (8), 982–1003.

Y. Jiang et al.

Journal of Retailing and Consumer Services 63 (2021) 102720

10

Dirsehan, T., Cankat, E., 2021. Role of mobile food-ordering applications in developing restaurants’ brand satisfaction and loyalty in the pandemic period. J. Retailing Consum. Serv. 62, 102608.

Donthu, N., Gustafsson, A., 2020. Effects of COVID-19 on business and research. Journal of Bussiness Research 117, 284–289.

Fan, X., Chai, Z., Deng, N., Dong, X., 2020. Adoption of augmented reality in online retailing and consumers’ product attitude: a cognitive perspective. J. Retailing Consum. Serv. 53, 101986.

Feng, Y., Xie, Q., 2019. Privacy concerns, perceived intrusiveness, and privacy controls: an analysis of virtual try-on apps. J. Interact. Advert. 19 (1), 43–57.

Fishbein, M., Ajzen, I., 1975. Belief, attitude, intention and behavior: an introduction to theory and research. Philos. Rhetor. 10 (2), 130–132.

Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Market. Res. 18 (1), 39–50.

Guthrie, C., Fosso-Wamba, S., Arnaud, J.B., 2021. Online consumer resilience during a pandemic: an exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. J. Retailing Consum. Serv. 61, 102570.

Hair, J.F., Gabriel, M., Patel, V., 2014. AMOS covariance-based structural equation modeling (CB-SEM): guidelines on its application as a marketing research tool. Brazilian Journal of Marketing 13 (2), 44–55.

Hair, J.F., Sarstedt, M., Ringle, C.M., Mena, J.A., 2012. An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Market. Sci. 40 (3), 414–433.

Hair, J.F., Black, W., Babin, B., Anderson, R., 2009. Multivariate Data Analysis (Chapter 8). Upper Saddle River. Pearson Education, Inc, New Jersey.

Heider, F., 1946. Attitudes and cognitive organization. J. Psychol. 21 (1), 107–112. Hilken, T., Keeling, D.I., de Ruyter, K., Mahr, D., Chylinski, M., 2020. Seeing eye to eye:

social augmented reality and shared decision making in the marketplace. J. Acad. Market. Sci. 48 (2), 143–164.

Hinsch, C., Felix, R., Rauschnabel, P.A., 2020. Nostalgia beats the wow-effect: inspiration, awe and meaningful associations in augmented reality marketing. J. Retailing Consum. Serv. 53, 101987.

Holdack, E., Lurie-Stoyanov, K., Fromme, H.F., 2020. The role of perceived enjoyment and perceived informativeness in assessing the acceptance of AR wearables. J. Retailing Consum. Serv., 102259. Available online 3 September.

Huang, T.L., Liao, S., 2015. A model of acceptance of augmented-reality interactive technology: the moderating role of cognitive innovativeness. Electron. Commer. Res. 15 (2), 269–295.

Islam, T., Pitafi, A.H., Arya, V., Wang, Y., Akhtar, N., Mubarik, S., Liang, X., 2021. Panic buying in the COVID-19 pandemic: a multi-country examination. J. Retailing Consum. Serv. 59, 102357.

Jafarzadeh, H., Tafti, M., Intezari, A., Sohrabi, B., 2021. All’s well that ends well: effective recovery from failures during the delivery phase of e-retailing process. J. Retailing Consum. Serv. 62, 102602.

Jiang, Y., Lai, P., Chang, C.H., Yuen, K.F., Li, S., Wang, X., 2021. Sustainable management for fresh food E-commerce logistics services. Sustainability 13 (6), 3456.

Jung, T.H., Bae, S., Moorhouse, N., Kwon, O., 2021. The impact of user perceptions of AR on purchase intention of location-based AR navigation systems. J. Retailing Consum. Serv. 61, 102575.

Karahanna, E., Straub, D.W., Chervany, N.L., 1999. Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Q. 23 (2), 183–213.

Kim, J., Forsythe, S., 2009. Adoption of sensory enabling technology for online apparel shopping. Eur. J. Market. 43 (9/10), 1101–1120.

Kim, M., 2019. Digital product presentation, information processing, need for cognition and behavioral intent in digital commerce. J. Retailing Consum. Serv. 50, 362–370.

Kirk, C.P., Rifkin, L.S., 2020. I’ll trade you diamonds for toilet paper: consumer reacting, coping and adapting behaviors in the COVID-19 pandemic. Journal of Bussiness Research 117, 124–131.

Kline, R.B., 1998. Principles and Practice of Structural Equation Modeling. The Guilford Press, New York, NY.

Laato, S., Islam, A.K.M.N., Farooq, A., Dhir, A., 2020. Unusual purchasing behavior during the early stages of the COVID-19 pandemic: the stimulus-organism-response approach. J. Retailing Consum. Serv. 57, 102224.

Li, C.Y., Fang, Y.H., 2020. I searched, I collected, I experienced: exploring how mobile augmented reality makes the players go. J. Retailing Consum. Serv. 54, 102018.

Lin, C.H., Sher, P.J., Shih, H.Y., 2005. Past progress and future directions in conceptualizing customer perceived value. Int. J. Serv. Ind. Manag. 16 (4), 318–336.

Marinković, V., Đorđević, A., Kalinić, Z., 2020. The moderating effects of gender on customer satisfaction and continuance intention in mobile commerce: a UTAUT- based perspective. Technol. Anal. Strat. Manag. 32 (3), 306–318.

Markus, M.L., 1994. Electronic mail as the medium of managerial choice. Organ. Sci. 5 (4), 502–527.

Moliner-Velazquez, B., Fuentes-Blasco, M., Gil-Saura, I., 2014. Value antecedents in relationship between tourism companies. J. Bus. Ind. Market. 29 (3), 215–226.

Moore, G.C., Benbasat, I., 1991. Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2 (3), 192–221.

Naeem, M., 2021. Do social media platforms develop consumer panic buying during the fear of Covid-19 pandemic. J. Retailing Consum. Serv. 58, 102226.

Nikhashemi, S.R., Knight, H.H., Nusair, K., Liat, C.B., 2021. Augmented reality in smart retailing: a (n)(A) Symmetric Approach to continuous intention to use retail brands’ mobile AR apps. J. Retailing Consum. Serv. 60, 102464.

Omar, N.A., Nazri, M.A., Ali, M.H., Alam, S.S., 2021. The panic buying behavior of consumers during the COVID-19 pandemic: examining the influences of uncertainty,

perceptions of severity, perceptions of scarcity, and anxiety. J. Retailing Consum. Serv. 62, 102600.

Pantano, E., Rese, A., Baier, D., 2017. Enhancing the online decision-making process by using augmented reality: a two country comparison of youth markets. J. Retailing Consum. Serv. 38, 81–95.

Pantelimon, F.V., Georgescu, T.M., Posedaru, B.S., 2020. The impact of mobile e- commerce on GDP: a comparative analysis between Romania and Germany and how COVID-19 influences the e-commerce activity worldwide. Inf. Econ. 24 (2), 27–41.

Parasuraman, A., Grewal, D., 2000. The impact of technology on the quality-value- loyalty chain: a research agenda. Journal of Academic of Marketing Science 28, 168–174.

Park, M., Yoo, J., 2020. Effects of perceived interactivity of augmented reality on consumer responses: a mental imagery perspective. J. Retailing Consum. Serv. 52, 101912.

Pham, Q., Tran, X., Misra, S., Maskeliūnas, R., Damaševičius, R., 2018. Relationship between convenience, perceived value, and repurchase intention in online shopping in Vietnam. Sustainability 10 (2), 1–14.

Plotkina, D., Saurel, H., 2019. Me or just like me? The role of virtual try-on and physical appearance in apparel M-retailing. J. Retailing Consum. Serv. 51, 362–377.

Ponte, E.B., Carvajal-Trujillo, E., Escobar-Rodríguez, T., 2015. Influence of trust and perceived value on the intention to purchase travel online: integrating the effects of assurance on trust antecedents. Tourism Manag. 47, 286–302.

Poushneh, A., 2018. Augmented reality in retail: a trade-off between user’s control of access to personal information and augmentation quality. J. Retailing Consum. Serv. 41, 169–176.

Poushneh, A., Vasquez-Parraga, A.Z., 2017. Discernible impact of augmented reality on retail customer’s experience, satisfaction and willingness to buy. J. Retailing Consum. Serv. 34, 229–234.

Prentice, C., Chen, J., Stantic, B., 2020. Timed intervention in COVID-19 and panic buying. J. Retailing Consum. Serv. 57, 102203.

Prentice, C., Nguyen, M., Nandy, P., Winardi, M.A., Chen, Y., Monkhouse, L.L., Dominique-Ferreira, S., Stantic, B., 2021. Relevant, or irrelevant, external factors in panic buying. J. Retailing Consum. Serv. 61, 102587.

Qin, H., Peak, D.A., Prybutok, V., 2021. A virtual market in your pocket: how does mobile augmented reality (MAR) influence consumer decision making? J. Retailing Consum. Serv. 58, 102337.

Rauschnabela, P.A., Felixb, R., Hinsch, C., 2019. Augmented reality marketing: how mobile AR-apps can improve brands through inspiration. J. Retailing Consum. Serv. 49, 43–53.

Ravald, A., Grönroos, C., 1996. The value concept and relationship marketing. Eur. J. Market. 30 (2), 19–30.

Reardon, J., Miller, C., Foubert, B., Vida, I., Rybina, L., 2006. Antismoking messages for the international teenage segment: the effectiveness of message valence and intensity across different cultures. J. Int. Market. 14 (3), 115–138.

Rese, A., Baier, D., Geyer-Schulz, A., Schreiber, S., 2017. How augmented reality apps are accepted by consumers: a comparative analysis using scales and opinions. Technol. Forecast. Soc. Change 124, 306–319.

Rogers, E., 1995. Diffusion of Innovations. The Free Press, New York. Salehzadeh, R., Pool, J.K., 2017. Brand attitude and perceived value and purchase

intention toward global luxury brands. J. Int. Consum. Market. 29 (2), 74–82. Shiu, E., Walsh, G., Hassan, L.M., Parry, S., 2015. The direct and moderating influences

of individual-level cultural values within web engagement: a multi-country analysis of a public information website. J. Bus. Res. 68 (3), 534–541.

Singh, R.P., Javaid, M., Kataria, R., Tyagi, M., Haleem, A., Suman, R., 2020. Significant applications of virtual reality for COVID-19 pandemic. Diabetol. Metab. Syndrome 14 (4), 661–664.

Strömberg, H., Rexfelt, O., Karlsson, I.C.M., Sochor, J., 2016. Trying on change – trialability as a change moderator for sustainable travel behaviour. Travel Behaviour and Society 4, 60–68.

Sung, E., 2021. The effects of augmented reality mobile app advertising: viral marketing via shared social experience. J. Bus. Res. 122, 75–87.

Tran, L.T.T., 2021. Managing the effectiveness of e-commerce platforms in a pandemic. J. Retailing Consum. Serv. 58, 102287.

Van Slyke, C., Ilie, V., Lou, H., Stafford, T., 2007. Perceived critical mass and the adoption of a communication technology. Eur. J. Inf. Syst. 16 (3), 270–283.

Vijayasarathy, L.R., 2004. Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Inf. Manag. 41 (6), 747–762.

Vishwakarma, P., Mukherjee, S., Datta, B., 2020. Travelers’ intention to adopt virtual reality: a consumer value perspective. Journal of Destination Marketing & Management 17, 1–13.

Wang, X., Yuen, K.F., Wong, Y.D., Teo, C.C., 2018. An innovation diffusion perspective of e-consumers’ initial adoption of self-collection service via automated parcel station. Int. J. Logist. Manag. 29 (1), 237–260.

Yang, H., Yu, J., Zo, H., Choi, M., 2016. User acceptance of wearable devices: an extended perspective of perceived value. Telematics Inf. 33 (2), 256–269.

Yim, M.Y.C., Park, S.Y., 2019. I am not satisfied with my body, so I like augmented reality (AR). J. Bus. Res. 100, 581–589.

Yoo, J., 2020. The effects of perceived quality of augmented reality in mobile commerce—an application of the information systems success model. Informatics 7 (2), 14.

Yuen, K.F., Wang, X., Ma, F., Wong, Y.D., 2019. The determinants of customers’ intention to use smart lockers for last-mile deliveries. J. Retailing Consum. Serv. 49, 316–326.

Y. Jiang et al.

Journal of Retailing and Consumer Services 63 (2021) 102720

11

Yuen, K.F., Wang, X., Ng, L.T.W., Wong, Y.D., 2018. An investigation of customers’ intention to use self-collection services for last-mile delivery. Transport Pol. 66, 1–8.

Zauner, A., Koller, M., Hatak, I., 2015. Customer perceived value—conceptualization and avenues for future research. Cogent Psychology 2 (1), 1061782.

Zhang, T., Wang, W.Y.C., Cao, L., Wang, Y., 2019. The role of virtual try-on technology in online purchase decision from consumers’ aspect. Internet Res. 29 (3), 529–551.

Y. Jiang et al.