The impact of trust building mechanisms on purchase intention towards metaverse shopping the moderating role of age

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International Journal of Human–Computer Interaction ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/hihc20 The Impact of Trust-Building Mechanisms on Purchase Intention towards Metaverse Shopping: The Moderating Role of Age Lin Zhang, Muhammad Adeel Anjum & Yanqing Wang To cite this article: Lin Zhang, Muhammad Adeel Anjum & Yanqing Wang (10 Mar 2023): The Impact of Trust-Building Mechanisms on Purchase Intention towards Metaverse Shopping: The Moderating Role of Age, International Journal of Human–Computer Interaction, DOI: 10.1080/10447318.2023.2184594 To link to this article: https://doi.org/10.1080/10447318.2023.2184594 Published online: 10 Mar 2023 Submit your article to this journal Article views: 1488 View related articles View Crossmark data Citing articles: View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=hihc20 INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION https://doi.org/10.1080/10447318.2023.2184594 The Impact of Trust-Building Mechanisms on Purchase Intention towards Metaverse Shopping: The Moderating Role of Age Lin Zhanga, Muhammad Adeel Anjumb, and Yanqing Wanga aSchool of Management, Harbin Institute of Technology, Harbin, China; bBalochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Quetta, Pakistan ABSTRACT Given the uncertainty of online transactions in metaverse shopping, the digital economy encour- ages building a trustworthy virtual environment Based on media richness theory, this article examines how the perceived media richness of the metaverse helps engender multidimensional trust (i.e., cognitive trust and affective trust) and leads to purchase intention in the context of metaverse shopping The proposed model is tested based on survey data from 332 consumers on an online scenario-based platform pertaining to metaverse initiatives Structural equation model- ing is used to examine the proposed research model The empirical research findings show that the perceived media richness of the metaverse builds cognitive trust and affective trust, which in turn affects purchase intention towards metaverse shopping Furthermore, we classify consumers into digital natives (DNs) and digital immigrants (DIs) based on chronological age and examine the different influences of the two dimensions of trust on purchase intention towards metaverse shopping between the two groups We identify and address several knowledge gaps in the extant trust literature We also discuss the theoretical and managerial implications and propose several suggestions for future research Introduction respondents distrust virtual shopping activities in the meta- verse (Berthiaume, 2022) Therefore, trust in the metaverse Nowadays, the rapid technological development of artificial is identified as a salient factor for alleviating uncertainty intelligence (AI), virtual reality (VR), augmented reality (AR), when consumers make purchase decisions in the metaverse etc., has spawned a plethora of online shopping platforms shopping context There is a call for more empirical studies that have evolved from a static webpage into a more dynamic to uncover individuals’ trust formation process and the con- three-dimensional (3D) space (Darbinyan, 2022) A new era, text-specific antecedents in this emerging research area termed “metaverse shopping,” has emerged in which consum- ers experience immersive shopping with virtual avatars and The majority of the existing literature has concentrated engage in interactive shopping activities by guiding their ava- on trust in different contexts, such as online commerce tars through 3D stores According to an Accenture report, (Cheng et al., 2017; N Wang et al., 2013; W Wang et al., 94% of retail executives believe that the future digital econ- 2016) and the sharing economy (Shao, Zhang, Li, et al., omy needs to offer metaverse initiatives (Standish, 2022) For 2022; Shao & Yin, 2019) From a theoretical perspective, instance, enterprises such as Facebook recognize the potential previous research has commonly emphasized two main of the metaverse and are starting to activate metaverse- research foci regarding trust-related behaviors: the determi- enabled social commerce on their platforms (Nix, 2022) A nants of trust (e.g., Cheng et al., 2021; Shao et al., 2019); Chinese leading electronic commerce (e-commerce) platform, and the dimensions of trust (e.g., Chi et al., 2021; Shao, Taobao, has also invested significant marketing efforts in Zhang, Brown, et al., 2022) Regarding the determinants of metaverse shopping (Ryder, 2022) Virtual shopping in the trust, most studies have focused on exploring trust antece- metaverse is expected to have an US$800 billion market dents that lead people to have different tendencies toward opportunity by 2024 (Darbinyan, 2022) trust-related behaviors (Cheng et al., 2021) For example, Cheng et al (2017) investigated the joint knowledge-based, Despite the fact that metaverse shopping has become a institution-based, calculative-based, cognition-based, and significant trend, the interconnected nature of the metaverse personality-based trust antecedents in influencing social heightens the related risks for security and privacy, which media communication behaviors Shao and Yin (2019) potentially leads to distrust issues among consumers regard- found that context-specific platform institutional mecha- ing making purchase decisions in the metaverse shopping nisms have positive effects on trust in the ridesharing plat- context (Di Pietro & Cresci, 2021) According to a report form, which in turn affects continuance intention Regarding from the consumer insights data platform, Zappi, 80% of CONTACT Yanqing Wang yanqing@hit.edu.cn School of Management, Harbin Institute of Technology, Harbin, China ß 2023 Taylor & Francis Group, LLC L ZHANG ET AL the dimensions of trust, most studies have investigated multi- natives (DNs) and digital immigrants (DIs), which is applied faceted trust concepts, such as competence, benevolence, and to explain individuals’ differentiated attitudes and technol- integrity (McKnight et al., 2002) For instance, Pavlou (2002) ogy adoptions (Shao, Benitez, et al., 2022) DIs are used to found that institution-based antecedents help engender two traditional communication mediums (e.g., email or social specific trust dimensions (cognition-based credibility and ben- media) as the preferred social tools for the workplace or evolence) and indirectly influence transaction success in daily life, whereas DNs prefer to interact with one another online marketplaces Some scholars have also defined trust as via the interactive virtual world, such as Second Life (Hong a multidimensional construct comprising cognitive and affect- et al., 2013; L Zhang et al., 2021) Moreover, DNs grew up ive components (Cummings & Bromiley, 1996) and examined with digital technologies in a networked world and are more the two dimensions of cognitive and affective trust in e-com- comfortable adopting innovative technologies than their DI merce (Leong et al., 2021) counterparts (Kesharwani, 2020) Hence, attitudes toward technology may vary largely depending on one’s age While We identify several knowledge gaps in the extant litera- the role of age (DNs vs DIs) has been recognized in various ture regarding trust literature First, most studies have exam- contexts (Niehaves & Plattfaut, 2014; Ollier-Malaterre & ined personality and institutional factors as the determinants Foucreault, 2021; Shao, Benitez, et al., 2022), there is scant of trust (J Liang et al., 2022; Shao et al., 2019), where the literature that theorizes age-difference issues regarding the trustee is either a real human or an entity (e.g., a platform) relationships between multidimensional trust and purchase More recent IS research has called for the characteristics of intention in the context of metaverse shopping human–system interactions to be examined where the trustee is a technological environment, such as an AI chat To fill these research gaps, this study aims to develop a channel or a blockchain-enabled mutual aid environment theoretical model to comprehensively understand how the (Bao et al., 2021; Chi et al., 2021; Choung et al., 2022; Shao, perceived media richness of the metaverse helps engender Zhang, Brown, et al., 2022) Specifically, many features of multidimensional trust (i.e., cognitive trust and affective the metaverse work towards visualization and enhancing trust) and indirectly influences purchase intentions in the natural communication through a virtual technological metaverse shopping context Current literature has used environment Considering that the rich media-enabled vir- media richness theory (MRT) to explain how digital media’s tual world is created to interact with products, brands, and ability to convey channel richness can affect purchase deci- communities in metaverse shopping service delivery (Kim, sions and outcomes (Tseng & Wei, 2020; Zhu et al., 2010), 2021), consumers may have a high perception of media rich- which is considered an appropriate theoretical framework ness, which is beneficial to triggering trust in the metaverse for our study Meanwhile, we further incorporate age as a However, to the best of our knowledge, few studies have so salient contingency factor in the theoretical model to explore far investigated the role of the media richness of the meta- the specific differences between DNs and DIs in terms of verse in building trust beliefs and subsequently facilitating trust-related behaviors To this end, this study aims to shed purchase intention light on the role and nature of multidimensional trust in the metaverse shopping context by providing theoretical insights Second, prior studies have mainly focused on the dimen- and empirical findings for the following research questions: sion of cognitive trust related to competence and reliability (Shao et al., 2019; Shao, Zhang, Li, et al., 2022), while affect- How does the perceived media richness of the metaverse ive trust, in the context of the new generation of technolo- affect consumers’ purchase intention through the medi- gies, is yet to be advanced in the online shopping field In ation effects of multidimensional trust (cognitive and contrast to cognitive trust, affective trust is rooted in emo- affective trust)? tional bonds and connections (Cummings & Bromiley, 1996) Considering the uncertainty and potential risks of the How does age moderate the relationships between multi- metaverse (Kim, 2021), consumers may make a rational dimensional trust and purchase intention in the meta- assessment regarding the security and reliability of the meta- verse shopping context? verse and form the cognitive trust belief when making pur- chase decisions On the other hand, consumers may also The remainder of this study is structured as follows generate the affective trust belief towards the metaverse, Section reviews the related literature and presents the the- resulting from the virtual world’s immersive, entertaining, oretical foundation Section develops the research model and interactive shopping experience As a result, it remains and formulates the hypotheses Section describes the unclear whether or not the perceived media richness of the research method and data analysis Section concludes the metaverse will exert different effects on cognition-based and article, presenting the key findings, implications, limitations, affect-based trust in the metaverse shopping context and future research directions Third, we consider individual characteristics contingent Research background and theoretical foundations on trust-related behaviors (Shao et al., 2019; Shao, Zhang, Li, et al., 2022) Prior literature has indicated that human 2.1 Literature review in the field of the metaverse perceptions and behavioral outcomes in technology imple- mentations are contingent on age (e.g., Ghobadi & The term metaverse originated in the 1992 science fiction Mathiassen, 2020; Hong et al., 2013) In particular, age is novel Snow Crash and gained global popularity in recent one of the criteria used to differentiate between digital INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION years (Oh et al., 2023) Based on the recent research work to walk and shop in the 3D shopping environment and to (Hennig-Thurau et al., 2022; Oh et al., 2023), metaverse can complete specific interactive actions Despite the more uni- be conceptualized as a new computer-mediated environment fied experience (i.e., a blended virtual and physical experi- that consists of virtual worlds in which people can act and ence) that metaverse shopping offers, to the best of our communicate with each other via avatars In past studies, knowledge, few studies have focused on the impact of media the impacts of the metaverse have been examined from dif- richness created by the metaverse environment on consum- ferent perspectives For example, some studies have focused ers’ trust-building process and related purchase outcomes on the metaverse’s technological infrastructures, finding that In order to address this research gap, our study aims to a heterogeneous crowd of technological capabilities and uncover trust-building mechanisms and subsequent purchase tools can afford or constrain the potential action spaces decision-making behaviors towards metaverse shopping available within the metaverse environments (Grupac et al., from the theoretical perspective of MRT, which will be 2022; Grupac & Lazaroiu, 2022; Hudson, 2022; Jenkins, described in the next section 2022; Kliestik et al., 2022; Zvarikova et al., 2022) Another study indicated that the implementation of 3D space com- 2.3 Media richness theory (MRT) puter-generated simulations, data-driven artificial intelli- gence, and text-to-image synthesis models is beneficial for Originating from computer science, MRT is defined as the meeting customer dynamic demands in the metaverse envir- ability of a computer-mediated communication channel to onment (Nica et al., 2022) Apart from examining the meta- deliver rich information and messages (Cummings & verse’s technological infrastructures, attention has also been Bromiley, 1996) According to Daft and Lengel (1986), the paid to the impacts of user perceptions and experiences (Oh theoretical concept of media richness can be conceptualized et al., 2023; Shin, 2022; Wongkitrungrueng & Suprawan, as a set of objective features, including multiple information 2023; Xi et al., 2022) Further, Hennig-Thurau et al (2022) cues, language variety, immediate feedback, and personal found the metaverse-enabled environment to be positively focus Specifically, multiple information cues mean that indi- related to interaction performance outcomes through the viduals can gather more information in a variety of ways mediation effects of psycho-physiological mechanisms To (e.g., texts, images, videos, etc.) with the support of digital extend the existing research, recent studies have emphasized technology Language variety refers to the ability of digital to comprehend how trust can be developed among users in technology to support individuals in communicating with the metaverse context (Tan & Saraniemi, 2022) because the natural languages (e.g., language symbols, emoticons, etc.) metaverse may expose user identifications to the service pro- Immediate feedback refers to the ability of digital technology viders and cause privacy concerns (Dwivedi et al., 2022) to receive and send feedback quickly Personal focus is the However, scant attention has been paid to the antecedents degree to which individuals can customize messages or per- of trust-building in facilitating users’ behavioral intention in sonal profiles according to their personal needs and the sur- the metaverse shopping context rounding environment 2.2 Metaverse shopping As presented in Table 1, the earliest application of MRT in enterprise organizations (Dennis & Kinney, 1998; Kishi, The metaverse has become a new economic paradigm that 2008; Suh, 1999; Yoo & Alavi, 2001) explains how organiza- builds on sharing an interactive and immersive virtual world tions can meet individual demands by reducing information environment (Kim, 2021) Multifarious applications for the complexity and fuzziness In recent years, MRT has been metaverse have gained considerable attention in different introduced and widely applied in e-commerce research (D fields, such as improving work productivity (Xi et al., 2022), K L Lee & Borah, 2020; Li et al., 2022; Mirzaei & social media value creation (Kraus et al., 2022), interactive Esmaeilzadeh, 2021; Shen et al., 2021) For example, Tseng learning environments (Rospigliosi, 2022), and advertising and Wei (2020) showed the impact of mobile advertising strategy (Kim, 2021; Taylor, 2022) Recently, the metaverse with various degrees of media richness on consumer deci- has been introduced as a new business model to facilitate sion-making behavior Zhu et al (2010) found the positive the immersive shopping experience among peers online influence of navigation support and communication support (Grupac et al., 2022; Hudson, 2022; Jenkins, 2022; Zvarikova on collaborative online shopping behaviors Despite great et al., 2022), and many famous platforms have rapidly attention having been paid to online marketplaces, MRT invested in and developed metaverse shopping initiatives was originally used only to examine traditional media tech- (Nix, 2022; Ryder, 2022) nologies (e.g., telephone, electronic documents, email, etc.); however, a few attempts have expanded its use to the meta- Compared with traditional layouts of shopping platforms verse context Boughzala et al (2012) argued that the meta- (e.g., plain text, images, or video), a key feature of the meta- verse is a special case of a socially interactive media channel; verse is that it allows customers to immerse themselves sur- thus, MRT is suitable for gaining a rich understanding of rounding a virtual shopping world, with the support of rich context-specific purchase behaviors in the emerging meta- digital content (Kim, 2021) Specifically, in the metaverse verse realm shopping world, consumers are immersed in a media-rich- ness-enabled virtual world where they can control their In particular, drawing upon MRT literature, there are two movement through smartphones, guiding virtual characters key approaches for operationalizing media richness (see Table 1) The first focuses on the category match approach L ZHANG ET AL Table A literature review of MST adoption in studies Operationalization of Research data and context Main findings Research method Author perceived media richness Experiment (Dennis & Kinney, 1998) The use of richer media rather than Survey (Kishi, 2008) Category match approach 132 Team members from leaner media did not lead to better Survey (D K L Lee & Borah, 2020) predesigned media- performance on the higher enabled tasks equivocality task Econometrics (Li et al., 2022) Survey (Mirzaei & Esmaeilzadeh, 2021) Perceptual approach 1062 Managers were studied Organizational interpretation of the Survey (Shen et al., 2021) Perceptual approach from social media use in environment substantially affects Experiment (Suh, 1999) the workplace the use of rich media Experiment (Otondo et al., 2008) Experiment (Tseng & Wei, 2020) 671 Participants were Perceived media richness and self- recruited using a social presentation can affect friendship Experiment (Yoo & Alavi, 2001) media tool development through the mediating Experiment (Zhu et al., 2010) effect of perceived functionality and Category match approach A panel data of 87,540 posts the moderating effect of personality Perceptual approach was collected from the trait Sina Weibo platform Perceptual approach The relationship between information Category match approach 348 Users from online health timeliness and public engagement is Category match approach communities were moderated by information richness Category match approach surveyed Perceived channel richness affects 3309 Users from the blogging perceived social support, willingness service were surveyed to exchange information, and engagement outcomes Data were gathered from 316 participants using a Immediate feedback and personal computer-mediated mode focus positively affect social identity, which in turn, leads to we-intention 688 Participants were surveyed using a Communication media richness can predesigned web interface positively influence task performance and satisfaction 259 Consumers of mobile advertising contexts Media richness features can influence communication outcomes, i.e Category match approach 135 Participants from a effectiveness and satisfaction Category match approach decision-making task The influence of media richness on 128 Participants were studied consumer decision-making behavior using a Web collaboration is greater in the early stage than in tool the later AS stage, and the relationship is moderated by product type Different media conditions can influence task participation Navigation and communication support can positively influence collaborative online shopping by designing different levels of communication media 2.4 Multidimensional trust: cognitive trust vs affect tools, such as using computer-mediated text, audio, video, trust and face-to-face to present lowest to highest richness (e.g., Otondo et al., 2008; Suh, 1999; Tseng & Wei, 2020; Yoo Originating from social psychology science, trust is defined & Alavi, 2001; Zhu et al., 2010) In contrast, using a sur- as a general belief in a person who will act in line with vey approach, the second approach focuses on the percep- favorable expectations towards the trustee (Gefen, 2000) tion of media richness (Kishi, 2008; D K L Lee & Borah, Two distinct dimensions of trust (i.e., cognitive trust and 2020; Mirzaei & Esmaeilzadeh, 2021) The perceptual sur- affective trust) were identified by McAllister (1995), and vey approach synthesizes the influence of perceived media they have been widely applied in recent studies (Chih et al., richness through general psychological multidimensional 2017; Goles et al., 2009; J Lee et al., 2015; K Z K Zhang measurement (i.e., multiple information cues, language var- et al., 2014) In particular, cognitive trust (generated by iety, immediate feedback, and personal focus) Given that rational assessment regarding the trustees’ ability and reli- we are interested in consumers’ perceived media richness ability) is important in online exchange relations where of the metaverse, we adopt the second approach and intro- uncertainty is present (Pavlou, 2002; Shao et al., 2019; Shao duce four significant dimensions, namely the extent to & Yin, 2019) Affective trust (generated by perceived which a user perceives: (1) the use of various cues, e.g., strength and the level of emotional attachment, caring, and texts, images, graphical symbols, physical presence, ges- social reciprocity between a trustor and a trustee) also plays tures, etc.; (2) communication with natural languages; (3) an important role in the context of online marketplaces immediate interaction; and (4) personalized virtual images (Chih et al., 2017; Goles et al., 2009; J Lee et al., 2015; K Z (avatars) Accordingly, this study identifies the construct of K Zhang et al., 2014) the perceived media richness of the metaverse as a significant trust-building antecedent in the research This study extends the two dimensions of trust from the framework traditional communication context (i.e., buyer–seller inter- personal relationships) to the metaverse shopping environ- ment This extension can be justified by the fact that INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION The metaverse-enabled shopping environment Age Digital natives (DNs) vs Digital immigrants (DIs) Multiple information cues Affective trust H6b (DNs > DIs) Language Perceived media H5 (+) H6a (DIs > DNs) Purchase intention variety richness of the towards the Immediate metaverse metaverse shopping feedback Cognitive trust Control variables Gender, Education, Shopping experience Personal focus Figure The research model consumers may tend to rationally evaluate the security and interact with brands, stores, or other consumers to obtain reliability of the virtual space created by the metaverse, and more realistic, unambiguous product information (Kishi, to emotionally assess the enjoyable and interactive experi- 2008) Thus, the perceived media richness of the metaverse ence created by the metaverse environment (Chih et al., has the potential to improve consumers’ cognitive trust In 2017; W Wang et al., 2016) Therefore, this study focuses addition, the metaverse (in a 3D virtual world) is more vivid on trust in the metaverse (the trustee is a technological and interesting than traditional static web page-based shop- environment) and divides it into two dimensions (i.e., cogni- ping (Kim, 2021), which is beneficial to facilitating affective tive trust vs affective trust) to examine their separate influ- trust Therefore, we hypothesize the following: ence on purchase intention H1: There is a positive relationship between the perceived Research model and hypotheses media richness of the metaverse and cognitive trust By integrating a trust-building framework with MRT, we H2: There is a positive relationship between the perceived aim to explore the impact of the perceived media richness media richness of the metaverse and affective trust of the metaverse on purchase intention through the medi- ation effects of cognitive trust and affective trust 3.2 Cognitive trust, affective trust, and purchase Meanwhile, we incorporate age as a moderator between intention towards metaverse shopping multidimensional trust and purchase intention Additionally, gender, education, and shopping experience are controlled Trust is considered to be a key factor in determining behav- in the structural model Figure shows the proposed ioral intention (Cheng et al., 2021; Shao, Zhang, Brown, research model et al., 2022) McAllister (1995) proposed that trust is a multidimensional construct, including cognitive trust and 3.1 Perceived media richness of the metaverse, affective trust On the one hand, cognitive trust depends on cognitive trust, and affective trust the trustor’s rational evaluation, which can mitigate risk per- ceptions and facilitate behavioral outcomes (Chih et al., Based on MRT, we propose that perceived media richness is 2017; Shao et al., 2019) On the other hand, affective trust a second-order construct expressed by multiple information originates from the emotional attachment between the cues, language variety, immediate feedback, and personal trustor and the trust target, which can generate comfortable focus (Daft & Lengel, 1986; Shen et al., 2021) Prior research and positive attitudes (W Wang et al., 2016) Following the has argued that the level of intuitionistic and abundant theory of reasoned action, affective trust as a form of trust- information content, vividness, and social cues provided by ing attitude will predict individuals’ behavioral intentions to multimedia technologies is highly related to message persua- perform an action (Komiak & Benbasat, 2006) In the con- sion (Kishi, 2008; D K L Lee & Borah, 2020; Shen et al., text of online shopping, both cognitive trust and affective 2021), which can induce effective communication perform- trust play significant roles in affecting consumers’ purchase ance (Dennis & Kinney, 1998; Zhu et al., 2010) Considering intention (Kimiagari & Malafe, 2021; Wu et al., 2023) that effective interactions help build trust (Bao et al., 2021; Pavlou, 2002; Shao et al., 2022b; Shao & Yin, 2019), it is In the metaverse shopping context, consumers with high plausible to expect that trust will be affected by the modality cognitive trust in the metaverse will perceive a lower level of type or level of perceived media richness uncertainty and potential risks (Chih et al., 2017), which is likely to enhance their willingness to buy recommended In the metaverse shopping context, consumers use virtual goods in the metaverse In addition, consumers who have avatars to move freely in a 3D shopping world in a more affective trust in the metaverse will form emotional and interactive way (Kim, 2021), which enables rich media to pleasant feelings (Kimiagari & Malafe, 2021; W Wang et al., L ZHANG ET AL 2016) and may be motivated to buy the products recom- be more comfortable with digital technologies (Gurtner mended by the metaverse Therefore, we formulate the fol- et al., 2014) Specifically, DNs have more experience lowing hypotheses: with the virtual world (such as Second Life or 3D gam- ing) and may show positive and hedonic attitudes H3: There is a positive relationship between cognitive trust towards emerging technologies (e.g., AR, VR, etc.) to and purchase intention towards metaverse shopping complete virtual shopping processes (L Zhang et al., 2021) As such, in the metaverse shopping context, DNs H4: There is a positive relationship between affective trust are more likely to form comfortable and emotional per- and purchase intention towards metaverse shopping ceptions; thus, they may be more affected by affective trustworthiness when making purchase decisions Based Furthermore, (McAllister, 1995) proposed that cognitive on this logic, we argue that affective trust plays a more trust is a prerequisite for affective trust When a consumer significant role in enabling DNs to purchase in meta- makes a rational assessment of purchase behavior, he/she is verse shopping The above analysis leads to the follow- more likely to respond emotionally (Chen et al., 2019; Legood ing hypothesis: et al., 2023) Therefore, in the metaverse shopping context, cognitive trust may be necessary for affective trust to develop; H6b: Affective trust has a stronger influence on purchase accordingly, we propose the following hypothesis: intention towards metaverse shopping for digital natives compared with digital immigrants H5: There is a positive relationship between cognitive trust and affective trust Research design and execution 3.3 The moderating role of age: digital natives (DNs) 4.1 Research context and data collection vs digital immigrants (DIs) This study employed the scenario-based survey method to Age has a significant functional meaning in understanding collect data since it is an effective way to promote partici- individuals’ attitudes with regard to technology adoption pants’ contextualized understanding of metaverse shopping (Hong et al., 2013; Shao, Benitez, et al., 2022; L Zhang in a hypothetical situation (Chang et al., 2013; Kwak et al., et al., 2021) According to Hong et al (2013), chronological 2021; X Wang et al., 2020), especially when research on the age plays an important role in distinguishing between DNs role of the metaverse is still in its infancy (Hua et al., 2018) (who were born after the 1980s) and DIs (who were born Using insights from the past literature, vignettes were used before the 1980s) (Shao, Benitez, et al., 2022) Previous lit- to present subjects with written descriptions of realistic sit- erature has suggested significant age-related generational dif- uations (Trevino, 1992) The use of vignettes can provide ferences between DNs and DIs in predicting consumption control by placing all subjects in the same scenario with the orientation and purchasing behaviors For example, Gurtner same fictitious metaverse shopping context The vignette- et al (2014) found that DIs are more likely to adopt new based approach has been widely adopted in past studies due technologies based on cognitive evaluation, while DNs value to numerous benefits such as the relevance of scenarios to more the hedonic experience of using new technologies L realistic situations, lesser social desirability and memory Zhang et al (2021) explained that DNs’ purchase behaviors lapse biases, and ease of data collection from large samples are driven by enjoyment perception, while DIs are more cir- (Tong et al., 2013; Vance et al., 2012; S Zhang & Leidner, cumspect or judicious and tend to perform purchase behav- 2018) In summary, respondents were required to make iors via the cognitive evaluation process behavioral decisions based on true-to-life vignettes embedded in the hypothetical metaverse shopping scenario From the perspective of age (DNs vs DIs) (Hong et al., (see Appendix A) 2013; Tams et al., 2018), there will be high transaction risk and vulnerability perceptions for DIs because they are not In the scenario-based investigation, we entrusted the familiar with digital technologies Therefore, DIs always third-party questionnaire website (www.sojump.com) to require conscious calculation through a certain degree of randomly invite 500 consumers1 from its database to information search, rational thinking, and risk assessment complete an online questionnaire from June 01 to June when making decisions (Gurtner et al., 2014; L Zhang et al., 10, 2022 (L Zhang et al., 2021) The questionnaire 2021) As such, in the metaverse shopping context, DIs focus included three sections The first section comprised a sim- more on making rational assessments to eliminate uncertainty ple explanation of the concept of the metaverse and our and perceived risk towards the surrounding purchase envir- hypothetical shopping scenario and contained one screen- onment; thus, cognitive trust may exhibit a stronger influence ing item (Appendix A presents the scenario used in the on DIs’ purchase intention towards metaverse shopping The current study, and please see the endnote for more discus- following hypothesis is therefore proposed: sion about the screening item).2 We stated that the ano- nymity of the participants would be ensured, and that the H6a: Cognitive trust has a stronger influence on purchase collected data would only be used for academic research intention towards metaverse shopping for digital immigrants The second section contained the measurements for each compared with digital natives variable included in the research model The third section comprised demographic information, including gender, Compared with DIs, DNs are more exposed to new technologies and digital media; therefore, they tend to INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION age, etc A monetary reward was given to any respondent cause the indicators, and some indicators can be inter- completing the questionnaire online, and 479 responses changed without altering the meaning of the latent vari- were received, with a response rate of 95.8% After remov- ables (Benitez et al., 2020) In summary, the proposed ing 103 respondents who chose items or for the research model comprises formative and reflective screening item and 44 invalid responses (e.g., an extreme constructs answer to all questions), a total of 332 questionnaires were used for data analysis In order to assess the nonres- Perceived media richness of the metaverse ponse bias, we compared the demographic variables Conistent with Shen et al (2021), the construct was opera- between the first 50 respondents and the last 50 respond- tionalized in terms of multiple information cues, language ents using t-tests (Armstrong & Overton, 1977) The stat- variety, immediate feedback, and personal focus That is, we istical analysis results suggested that there were no operationalized perceived media richness of the metaverse as significant differences between the two groups in terms of a broader concept having four dimensions: (1) the use of gender, age, and shopping experience (p > 0.1); thus, non- various cues (e.g., texts, images, graphical symbols, physical response bias does not exist in our study (H Liang et al., presence, gestures, etc.); (2) communication with natural 2019) languages (e.g., language symbols, and emoticons); (3) immediate interaction (e.g., receive and send feedback The statistical characteristics of the sample are presented quickly); and (4) personalized virtual images (e.g., avatars) in Table Male and female consumers were almost evenly The dimensions were measured using reflective scales with distributed In terms of age, 6.0% were below 18 years old, three items each 24.5% were 18–29 years old, 29.5% were 30–39 years old, 32.8% were 40–49 years old, and 7.2% were over 50 years Multidimensional trust old Most of the participants had a Bachelor’s degree and The scales by McAllister (1995) and W Wang et al over three years’ online shopping experience The results (2016) were used to measure cognitive trust and affective suggest that no significant differences exist between our trust Specifically, cognitive trust reflects consumers’ sample and the actual online consumers in China (CNNIC, rational expectations that the metaverse has the necessary 2021), demonstrating that our sample is representative of attributes to ensure a proficient and reliable virtual shop- the actual online consumers in China ping space while affective trust refers to consumers’ com- fort and emotional bonds regarding the virtual shopping 4.2 Measurements space created by the metaverse Both of the constructs were operationalized as reflective variables with three A seven-point Likert scale with the categories from items each “Strongly agree” (7) to “Strongly disagree” (1) was used to measure constructs Constructs can be operationalized Purchase intention as reflective or formative (Benitez et al., 2020; L Zhang Following K Z K Zhang et al (2014), purchase intention et al., 2022) Multiple information cues, language variety, was conceptualized as consumers’ willingness to purchase immediate feedback, personal focus, cognitive trust, goods or services in the metaverse shopping space It was affective trust, and purchase intention were specified as specified as a reflective construct and measured with five reflective constructs, while perceived media richness of items the metaverse was specified as a second-order formative construct, which emerges from the first-order constructs A few revisions were made to adapt our measurement to of multiple information cues, language variety, immedi- the metaverse shopping context A pilot study was con- ate feedback, and personal focus (Benitez et al., 2020) ducted using 54 college students with online shopping Other constructs were operationalized as reflective meas- experience Several items with factor loadings lower than 0.4 urement models because they (i.e., latent variable) can were adjusted based on the respondents’ feedback (L Zhang et al., 2022) Table describes the definition and measure- Table Descriptive statistics ment items of the constructs Attributes Options Frequency Percentage (%) 4.3 The estimation strategy Gender Male 164 49.4 We use partial least squares (PLS) path modeling to test the Age 168 50.6 proposed research model, which is recognized as an appro- Female 20 6.0 priate statistical tool for structural equation modeling (SEM) Education 60 24.3 81 63.0 High school and below 209 12.7 Bachelor’s degree 42 1.2 13.6 Master’s degree and above 45 42.8 4 years L ZHANG ET AL Table Constructs and items Multiple information cues Definitions Items References MIC1-MIC3 (Shen et al., 2021) Constructs A consumer’s perception that the Perceived media richness of metaverse supports multiple information LV1-LV3 (McAllister, 1995; W Wang the metaverse through a variety of ways (e.g., texts, et al., 2016) images, and videoes) IF1-IF3 Language variety PF1-PF3 (K Z K Zhang et al., 2014) A consumer’s perception that the Immediate feedback metaverse enables them to CT1-CT3 Personal focus communicate with natural languages (e.g., language symbols, and emoticons) AT1-AT3 Cognitive trust PI1-PI3 A consumer’s perception that the Affective trust metaverse helps them to receive and Purchase intention send feedback quickly A consumer’s perception that the metaverse enables them to customize avatars or personal profiles according to their personal needs and preferences Consumers’ rational expectations that the metaverse has the necessary attributes to ensure a proficient and reliable virtual shopping space Consumers’ comfort and emotional bonds regarding the virtual shopping space created by the metaverse Consumers’ willingness to purchase goods or services in the metaverse shopping space Table Overall model fit of the saturated model analysis Second-order level First-order level Discrepancy Value HI99 Conclusion Value HI95 Conclusion SRMR 0.048 0.049 Supported 0.023 0.025 Supported dULS 0.825 0.854 Supported 0.056 0.066 Supported dG 0.567 0.602 Supported 0.086 0.094 Supported Notes: Perceived media richness of the metaverse is a second-order formative construct, whilst its first-order dimensions are operationalized with reflective meas- urement Formative constructs were estimated with mode B, and the reflective construct was estimated with mode A consistent (PLSc) (for a discussion, see Benitez et al., 2020) formative construct, which used a regression weighting 4.3.2 Evaluation of the measurement model scheme represented by arrows pointing from indicators to We assessed the reflective constructs through the indicators their corresponding constructs (see details in Appendix B) of factor loadings, rho_A, and average variance extracted (Benitez et al., 2020, 2022; Castillo et al., 2021) We (AVE), and we tested for multicollinearity, weights, and the employed the statistical software package ADANCO 2.3 significance level of the formative construct (i.e., the per- Professional for Windows (http://www.composite-modeling ceived media richness of the metaverse) As noted in Table com/) to analyze the structural model 5, all indicators (i.e., factor loadings, rho_A, and AVE) for the reflective constructs met the threshold criteria (Benitez 4.3.1 Evaluation of the overall fit of the saturated model et al., 2020) For the second-order formative construct (i.e., Following Benitez et al (2020), we evaluated the overall the perceived media richness of the metaverse), the results model fit of the saturated model to assess the validity of the showed that the variance inflation factor (VIF) values ranged formative and reflective measurement models The goodness from 1.529 to 2.175, which are below the threshold value of of fit of the saturated model was evaluated by the discrep- 10, suggesting that multicollinearity is not a problem in our ancy between the empirical correlation matrix and the study (Benitez et al., 2020) All indicator weights (from model-implied correlation matrix through the indicators for 0.311 to 0.464) and dimension weights (from 0.081 to 0.409) SRMR, dULS, and dG (Benitez et al., 2020, 2022; Benitez were significant except for the dimension weight of language et al., 2022; Castillo et al., 2021) In order to guarantee an variety (0.081) Following the guidelines of Benitez et al adequate model fit, SRMR should be less than 0.080, and (2020) for formative measurement, although the dimension SRMR, dULS, and dG should be below the 95% quantile weight of language variety was small, the factor loading for (HI95) of the bootstrapping discrepancies or at least below language variety (0.755) was significant on an alpha level of the 99% quantile (HI99) Table summarizes the results of 0.001 Thus, we retained language variety in our model The the goodness of fit for the saturated model Overall, the fit results suggested that our variables possess very good meas- of the saturated model was satisfactory to proceed with the urement properties (Benitez et al., 2020; Benitez et al., evaluation of the measurement model (Cheng et al., 2022) 2022), and that we could proceed with the hypothesis testing (see details in Appendix B) INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION Table Validity and reliability of the scales Constructs rho_A AVE Loading VIF Weights Multiple information cues (MIC) 0.876 0.655 0.828ÃÃÃ 1.821 0.362ÃÃ 0.973ÃÃÃ 1.833 0.464ÃÃÃ MIC1 The metaverse shopping virtual world transmits a variety of different cues beyond the explicit text-based product information 0.740ÃÃÃ 0.353ÃÃÃ MIC2 The metaverse shopping virtual world conveys 2.501 multiple types of product information (verbal and nonverbal) 0.685ÃÃÃ 0.326ÃÃÃ MIC3 The metaverse shopping virtual world presents 2.141 vivid product information through facial expressions and body language 0.755ÃÃÃ 0.855ÃÃÃ Language variety (LV) 0.840 0.609 2.175 0.081 2.16 0.429ÃÃÃ LV1 The metaverse shopping virtual world transmits varied symbols (e.g., texts, photos, videos, audios, links, and so on) 0.620ÃÃÃ 0.311ÃÃÃ LV2 The metaverse shopping virtual world 1.725 communicates rich meanings about products using a large pool of language symbols 0.844ÃÃÃ 0.423ÃÃÃ LV3 The metaverse shopping virtual world uses rich 1.790 and varied language 0.799ÃÃÃ 0.366ÃÃ 0.669ÃÃÃ 0.331ÃÃÃ Immediate feedback (IF) 0.847 0.619 1.529 1.955 IF1 The metaverse shopping virtual world has the ability to give and receive timely feedback 0.751ÃÃÃ 0.372ÃÃÃ IF2 The metaverse shopping virtual world can 2.560 provide immediate feedback 0.920ÃÃÃ 0.456ÃÃÃ IF3 The metaverse shopping virtual world enables 1.755 consumers to send/receive information quickly 0.848ÃÃÃ 0.409ÃÃ 0.929ÃÃÃ 0.402ÃÃÃ Personal focus (PF) 0.902 0.731 2.039 2.032 PF1 The metaverse shopping virtual world enables consumers to personalize their virtual avatars 0.727ÃÃÃ 0.315ÃÃÃ PF2 The metaverse shopping virtual world enables 3.488 consumers to edit personal profiles or decorate virtual avatars 0.896ÃÃÃ 0.388ÃÃÃ PF3 The metaverse shopping virtual world enables 3.995 consumers to tailor personal avatars and share their virtual images in the interactive shopping community Cognitive trust (CT) 0.891 0.731 0.864ÃÃÃ 0.372ÃÃÃ CT1 When I browse products and participate in tasks 2.598 in the metaverse shopping scenario, I feel it is capable and proficient 0.829ÃÃÃ 0.357ÃÃÃ CT2 When I browse products and participate in tasks 2.979 in the metaverse shopping scenario, I feel it is a reliable and secure virtual shopping world 0.872ÃÃÃ 0.375ÃÃÃ CT3 When I browse products and participate in tasks 2.442 in the metaverse shopping scenario, I feel it is competent and effective in presenting product contents Affective trust (AT) 0.916 0.780 0.851ÃÃÃ 0.348ÃÃÃ AT1 When I browse products and participate in tasks 2.549 in the metaverse shopping scenario, I feel it responded caringly 0.928ÃÃÃ 0.379ÃÃÃ AT2 When I browse products and participate in tasks 4.062 in the metaverse shopping scenario, I feel it displays a warm and caring attitude towards me 0.868ÃÃÃ 0.355ÃÃÃ AT3 When I browse products and participate in tasks 3.663 in the metaverse shopping scenario, I feel comfortable and enjoyable Purchase intention towards the metaverse shopping (PI) 0.954 0.869 0.878ÃÃÃ 0.329ÃÃÃ PI1 Given a chance, I predict that I should spend 4.592 money on the products recommended by the metaverse shopping scenario in the future 0.945ÃÃÃ 0.354ÃÃÃ PI2 Given a chance, I intend to buy products from 7.433 the metaverse shopping scenario 0.972ÃÃÃ 0.364ÃÃÃ PI3 If I could, I am very likely to buy products from 5.282 the metaverse shopping scenario Notes: ÃÃp < 0.01; ÃÃÃp < 0.001 Formative constructs were estimated with mode B and the reflective construct was estimated with mode A consistent (PLSc) (for a discussion, see Benitez et al., 2020) 10 L ZHANG ET AL Table HTMT analysis 10 MIC 0.717 0.589 0.585 0.464 0.778 0.638 0.227 0.094 0.208 LV 0.609 0.783 0.456 0.455 0.638 0.067 0.135 0.007 IF 0.627 0.447 0.434 0.397 0.076 0.162 0.095 PF 0.496 0.421 0.369 0.021 0.191 0.099 CT 0.421 0.366 0.081 0.033 0.115 AT 0.374 0.043 0.120 0.044 PI 0.088 0.136 0.031 Gender 0.150 0.083 Education 0.095 10 Experience Discriminant validity was analyzed using the heterotrait- Table Estimated model fit evaluation monotrait ratio of correlations (also called HTMT) (Henseler et al., 2015) Based on the multitrait-multimethod Discrepancy Value HI95 Conclusion matrix, HTMT demonstrates a superior performance by means of a Monte Carlo simulation test compared to the SRMR 0.023 0.025 Supported Fornell–Larcker criterion when conducting PLS-SEM dULS 0.056 0.066 Supported (Benitez et al., 2020) Thus, this study chose HTMT to dG 0.086 0.094 Supported evaluate discriminant validity As noted in Table 6, HTMT values were below 0.8, indicating adequate discriminant val- 4.3.5 Hypotheses testing idity for our measurement model The results showed that the perceived media richness of the metaverse positively affected cognitive trust (b ¼ 0.338, 4.3.3 Common method bias (CMB) test p < 0.001) and affective trust (b ¼ 0.109, p < 0.05), thus sup- Given that a self-reported survey was conducted, there is a porting H1 and H2, respectively Both cognitive trust potential for common method bias (CMB) resulting from (b ¼ 0.218, p < 0.05) and affective trust (b ¼ 0.255, p < 0.05) the consistency motif, social desirability, and common scale had strong influences on purchase intention towards meta- formats (Podsakoff et al., 2003) Therefore, we employed a verse shopping Meanwhile, cognitive trust was positively marker variable approach to statistically assess CMB associated with affective trust (b ¼ 0.569, p < 0.001), thus (Ronkko & Ylitalo, 2011) Following (Miller & Simmering, supporting H3–H5 Regarding the demographic variables, 2022), we selected the blue color attitude with three-item only gender (b ¼ 0.190, p < 0.001) had a significant influence scales (i.e., “I like the color blue”) as a marker variable, on purchase intention which is theoretically unrelated to our research model As shown in Appendix C, the results showed that the color 4.3.6 Multi-group analysis results: digital natives (DNs) blue attitude (marker variable) had no significant influence vs digital immigrants (DIs) on cognitive trust (b ¼ À0.033, n.s.), affective trust (b ¼ To further explore the moderating effect of age, we con- À0.022, n.s.), and purchase intention towards metaverse ducted a multi-group analysis (MGA) following Benitez shopping (b ¼ À0.052, n.s.) Therefore, we conclude that et al.’s (2020) guidelines Age was operationalized as a CMB is not a major concern in this study dummy variable to represent DNs and DIs with an age threshold of 40 years old (Shao, Benitez, et al., 2022) 4.3.4 Hypotheses testing Following previous studies (Shao, Benitez, et al., 2022), we We examined the structural model using the bootstrapping adopted the MGA method to compare the path coefficients resampling procedure to estimate the significance of the of the two groups (DNs vs DIs) As illustrated in Table 8, path coefficients For reference, we assessed the estimated the relationship between cognitive trust and purchase inten- model fit by calculating the SRMR, dULS, and dG (Benitez tion towards metaverse shopping was higher for DIs et al., 2020) As noted in Table 7, the SRMR was 0.023 for (b ¼ 0.442, p < 0.001) than for DNs (b ¼ 0.113, n.s.), sup- the estimated model, which is lower than the threshold of porting H6a In contrast, the relationship between affective 0.080 The SRMR, dULS, and dG were also below the 95% trust and purchase intention towards metaverse shopping quantile of the bootstrap discrepancies, suggesting a high was stronger for the DN group (b ¼ 0.327, p < 0.001) than level of model fit Moreover, the explanatory power of the for the DI group (b ¼ 0.072, n.s.), supporting H6b structural model was assessed by calculating the amount of variance (R2) explained in the endogenous variable As illus- 4.3.7 Post-hoc mediation test trated in Figure 2, our theoretical model explicated 54.4% In order to examine whether multidimensional trust in the variance in cognitive trust, 64.2% variance in affective trust, metaverse mediated the relationship between the perceived and 54.2% variance in purchase intention towards metaverse media richness of the metaverse and purchase intention, our shopping, demonstrating the good explanatory power of the study followed Hair et al.’s (2021) criteria to conduct a proposed research model mediation test using the bootstrapping method First, we linked the direct relationship between the perceived media richness of the metaverse and purchase intention and tested INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 11 The metaverse-enabled shopping environment Multiple 0.3 62 ** * 0.109* Affective trust 0.255* Purchase intention 0.1 90 ** * Gender information 0.081 R2=64.2% 0.218* 0.033 0.3 66 ** * Perceived media towards the metaverse 0.008 Education cues richness of the 0.5 69 ** * 0.4 09 ** * shopping Shopping Language metaverse R2=54.2% experience variety 0.3 38 ** * Immediate feedback Personal Cognitive trust focus R2=54.4% Figure Structural model results Note: Ãp < 0.05, ÃÃÃp < 0.001 (two-tailed test) Table Results of PLS-MGA Path coefficient Paths DNs DIs Coefficient diff (p-value) Hypotheses À0.329Ã (0.018) H6a: Cognitive trust in the metaverse ! Purchase intention towards the metaverse shopping 0.113 ns 0.442ÃÃÃ 0.255Ã (0.045) Supported H6b: Affective trust in the metaverse ! Purchase intention towards the metaverse shopping 0.327ÃÃÃ 0.072 ns Supported Note: Ãp < 0.05, ÃÃÃp < 0.001, ns: Non-significant, sample size for DNs ¼ 199, sample size for Dis ¼ 133 Table Mediation test results Indirect effect [95% CI] Conclusion 0.054ÃÃ [0.018, 0.087] Full mediation Relationship Full mediation 0.032Ã [0.007, 0.063] Full mediation Perceived media richness of the metaverse!Cognitive trust! Purchase intention towards the metaverse shopping 0.032ÃÃ [0.011, 0.057] Perceived media richness of the metaverse!Affective trust! Purchase intention towards the metaverse shopping Perceived media richness of the metaverse!Cognitive trust! Affective trust!Purchase intention towards the metaverse shopping Note: Ãp < 0.05; ÃÃp < 0.01 the significance level of the indirect relationships Second, 5.1 Discussion of results partial or full mediation effects were assessed depending on whether the independent variable directly impacted the 5.1.1 Reconsidering the role of trust in metaverse dependent variable after the mediator variable was included shopping Specifically, the direct relationship between the perceived The literature has indicated that the metaverse can shape media richness of the metaverse and purchase intention was consumers’ sentiment and purchase behaviors through the insignificant (b ¼ 0.107, p > 0.05) Thus, the bootstrapping synergetic integration of technological features in the infra- test results demonstrate full mediation effects for our structure and application layers such as the implementations research model (see Table 9) of data mining techniques, simulation modeling tools, and cognitive enhancement technologies (Grupac et al., 2022; Discussion and core conclusions Hudson, 2022; Jenkins, 2022; Zvarikova et al., 2022), which can be categorized within the general discussion of bounda- In recent years, trust issues in the metaverse have gained ries and scope of potential technology in actualized opera- much attention across disciplines However, little has been tions However, there has been a dearth of research done to explore trust-building mechanisms in the meta- exploring the underlying psycho-physiological mechanisms verse context This study proposes and empirically vali- that may affect users’ purchase behaviors towards metaverse dates a theoretical model that explicates how the shopping (Hennig-Thurau et al., 2022) In particular, con- metaverse-enabled shopping environment affects users’ sumers’ trust issues have been a major challenge in the trusting beliefs and purchase intention toward the meta- metaverse environment (Tan & Saraniemi, 2022) It is also verse shopping The critical assessment and discussion of challenging to convince users that the online shopping is the major findings and their implications are summarized safe within the metaverse (Dwivedi et al., 2022) This study in the following subsections demonstrates the importance of trust in the metaverse con- text, i.e., the study highlights that trust plays an important 12 L ZHANG ET AL role in shaping users’ purchase intention towards metaverse 5.1.3 Uncovering contingency effect of trust belief—Trust shopping This finding provides empirical support to the outcome relationship labeled by the “digital divide” in theoretical work of McAllister (1995) and Chen et al (2019) metaverse shopping in proposing, operationalizing, and validating the multidi- It has been argued that consumers’ perceptions and pur- mensional trust variables, and extends previous research chase behaviors are contingent upon the generation gap (Chih et al., 2017; W Wang et al., 2016; Wu et al., 2023) labeled by the digital age divide (Chan et al., 2017; L Zhang from traditional shopping to the metaverse shopping con- et al., 2021) However, little effort has been made to evaluate text More specifically, our study found that multidimen- age-related generational differences in the context of meta- sional trust in the metaverse significantly influences verse shopping To address this research gap, we incorporate purchase intention towards metaverse shopping, highlighting age as a salient contingency factor in the theoretical model the predictive power of cognitive and affective trust in trig- to explore the differences between DNs and DIs in the trust gering the willingness to purchase in the metaverse shopping belief—trust outcome relationship regarding metaverse shop- setting With this construction of multidimensional trust- ping Specifically, our research findings support the moder- purchase intention relationships, a better proportion of the ating effect of age on the relationship between variance in purchase intention towards metaverse shopping multidimensional trust and purchase intention towards was captured compared to existing works (e.g., Hwang & metaverse shopping, implying that cognitive trust as a Lee, 2022; Patil & Pramod, 2022) Moreover, previous rational assessment process plays a more salient role in fos- research has shown that positive cognitive trust is strongly tering DIs’ purchase intention, while DNs’ purchase inten- associated with positive affective trust (Chen et al., 2019; tion is more likely to be triggered when they perceive higher Chih et al., 2017; W Wang et al., 2016) Our research find- levels of affective trust Consistent with the age-based stereo- ings support the notion that cognitive trust directly leads to type’s predictions (Hong et al., 2013; Shao, Benitez, et al., affective trust and indirectly affects trust outcomes (i.e., pur- 2022; Tams et al., 2018; L Zhang et al., 2021), age affects chase intention), similar to previous empirical findings individual attitudes, technology acceptance, and usage Therefore, we believe that McAllister’s (1995) classification behaviors We introduced age as a salient digital-divide fac- of trust remains important; however, future studies should tor and showed the contingency influences of multidimen- justify the interplay between cognitive and affective trust in sional trust on consumers’ purchase intention towards a metaverse shopping context metaverse shopping across DNs and DIs Overall, the find- ings suggest that DNs and DIs exhibit distinct cognitive and 5.1.2 Developing trust-building antecedents in metaverse behavioral patterns, i.e., the impacts of multidimensional shopping trust on purchase behaviors in the metaverse shopping Prior literature has conceptualized metaverse as a new com- context puter-mediated environment and shown that metaverse shopping consists of virtual worlds in which users can com- 5.2 Key contributions to theoretical research municate with each other and make shopping experiences via avatars (Hennig-Thurau et al., 2022; Oh et al., 2023) Our study makes three major contributions First, our study However, the context-specific computer-mediated media brings MRT into the context of metaverse shopping and richness that triggers users’ trust formation and purchase identifies the perceived media richness of the metaverse as a intention towards metaverse shopping remains relatively significant trust-building determinant Previous literature underexplored To address this research gap, this study vali- has mostly focused on personality and institutional factors dated the relationships between the perceived media richness (such as structural assurances) as trust-building antecedents of the metaverse and multidimensional trust, suggesting that in e-commerce contexts (Shao et al., 2019; Shao, Zhang, consumers’ cognitive trust and affective trust formation Brown, et al., 2022; Shao, Zhang, Li, et al., 2022), whereas depend on rich information gathered from the metaverse the influence of technological virtual space on trust-building environment Although the relationship between media rich- behaviors has been largely overlooked in the context of ness and multidimensional trust (cognitive trust vs affective metaverse shopping (Dwivedi et al., 2022; Tan & Saraniemi, trust) has not been studied before, a positive association 2022) To fill this gap, grounded in the media richness the- between these constructs is consistent with previous empir- ory, we unearth perceived media richness as a broader con- ical evidence that indicates utilitarian and hedonic values of cept constituting four dimensions: multiple information rich media features (e.g., D K L Lee & Borah, 2020; cues, language variety, immediate feedback, and personal Mirzaei & Esmaeilzadeh, 2021; Shen et al., 2021) This find- focus, which would strengthen consumers’ trustworthy ing is also in line with previous literature in that virtual ava- beliefs in the metaverse environment and possibly contribute tar-oriented interface design can increase cognitive trust and to purchase intentions The research findings can enrich our facilitate affective trust in AI agent contexts (Bao et al., knowledge of trust-building antecedents and related out- 2021; W Wang et al., 2016) Therefore, it would be mean- comes in the context of metaverse shopping through the ingful to develop trust-building mechanisms by focusing on lens of MRT rich media features of the metaverse that would send out perceptual cues regarding multiple information cues, lan- Second, our study integrated McAllister’s (1995) multidi- guage variety, immediate feedback, and personal focus mensional trust framework with MRT, and uncovered the mediation mechanism of cognitive vs affective trust in the INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 13 relationship between the perceived media richness of the Despite the potential benefits of the metaverse, difficulties metaverse and purchase intention Prior literature has arise from consumers’ lack of faith in taking the potential mostly focused on the role of multidimensional trust in risks Our study shows that visualization and natural com- affecting consumers’ purchase intention in traditional e- munication features are beneficial to facilitating trusting commerce contexts (Chih et al., 2017; Kimiagari & Malafe, beliefs in the metaverse Therefore, online retailers and web- 2021; W Wang et al., 2016; Wu et al., 2023), whereas less site designers should utilize the findings of this study to attention has been paid to the metaverse shopping context improve the design of their media richness mechanisms to In particular, this study makes a theoretical and empirical institute a trustworthy virtual shopping environment contribution to the interpersonal or institutional trust litera- Specifically, providing multiple information cues, language ture by specifying the trust target as the virtual metaverse variety, immediate feedback, and personal focus in the meta- environment Interestingly, our results suggest that cognitive verse shopping environment will increase consumers’ trust trust and affective trust significantly mediate the relationship formation This suggests that the management of a meta- between the perceived media richness of the metaverse and verse marketplace needs to take steps to increase the effect- purchase intention The empirical research findings can iveness of media communication mechanisms to enhance enrich our understanding of consumers’ cognitive beliefs trust in the metaverse and emotional reactions aroused by the rich media of the metaverse when making purchase decisions in the virtual Second, our study proposes that multidimensional trust 3D shopping world (Hennig-Thurau et al., 2022) Moreover, signals to consumers that it is safe and reliable both to act the bootstrapping analysis results showed that the indirect and to make purchases in the virtual shopping environment effect of the perceived media richness of the metaverse on provided by the metaverse Thus, online retailers should pay purchase intention is fully and serially mediated by cognitive attention both to cognitive and affective trust Furthermore, trust and affective trust The empirical findings contribute to online retailers and website designers should recognize that the extant trust literature by uncovering the nomological the effects of cognitive trust vs affective trust on purchase network among cognitive vs affective trust and their antece- intention towards metaverse shopping exhibit significant dif- dents in the emerging context of the digital economy ferences between DNs and DIs Given that DNs are more affected by affective trust when making purchase decisions Third, we explicated the boundary condition of trust- in the metaverse shopping context, online retailers and web- related purchase outcomes in the metaverse shopping con- site designers must invest more in the development of the text by incorporating the moderating role of age pleasurable and entertaining experience and a sense of Specifically, we divided the overall sample into two groups belonging On the contrary, DIs focus more on the quality, (DNs vs DIs) based on their age, categorized by the digital authenticity, and credibility of the content presented in the divide (L Zhang et al., 2021), and uncovered the path rela- virtual space and are more cautious in making purchase tionship differences between the two dimensions of trust decisions in the metaverse shopping context Therefore, and purchase intention towards metaverse shopping online retailers and website designers should attend to build- Although previous studies have examined the individual atti- ing DIs’ cognitive trust by providing a safe and reliable tudes and behavioral differences between DNs and DIs environment through the metaverse purchase process (Hong et al., 2013; Kesharwani, 2020; Shao, Benitez, et al., Overall, such efforts to understand the differentiated pur- 2022; L Zhang et al., 2021), few studies have incorporated chase behaviors of DNs vs DIs are important when design- age as a key boundary condition of the multidimensional ing marketing strategies to bring greater efficiency and trust–purchase intention relationships To the best of our impact to product campaigns knowledge, this is one of the first studies to reveal the con- tingency influence of multidimensional trust on purchase 5.4 Conclusions, limitations, and future research intention in the emerging context of metaverse shopping avenues Specifically, we found that cognitive trust exhibits a stronger influence on purchase intention towards metaverse shopping Our study has delineated the significance of multidimen- for DIs than DNs, while affective trust demonstrates a stron- sional trust in affecting consumers’ purchase intention in ger influence on purchase intention towards metaverse shop- the domain of metaverse shopping Drawing on MRT, this ping for DNs than DIs Overall, this research’s findings study has developed a research model to examine the ante- advance research on IS behavioral phenomena by highlight- cedents and impact outcomes of multidimensional trust in ing the digital-divide difference in explaining trust-related the metaverse A scenario-based survey was conducted in behaviors in the metaverse field (Dwivedi et al., 2022) China in which 332 valid responses were collected from users, and empirical results suggested that the perceived 5.3 Implications for practice media richness of the metaverse positively influences con- sumers’ purchase intention through the joint mediating Our study can be used to provide guidelines for online effects of cognitive and affective trust Furthermore, we per- retailers and website designers to enhance profitability in the formed a MGA and found that cognitive trust exhibits a digital economy First, online retailers need to recognize that stronger influence on purchase intention towards metaverse building trust is critical to induce consumers to make risky shopping for DIs, while affective trust has a greater effect on purchase decisions in the metaverse shopping context purchase intention towards metaverse shopping for DNs 14 L ZHANG ET AL Our research provides fresh insights into the effective use of Disclosure statement media richness characteristics, which reinterpret the trust- building mechanisms in the emerging metaverse shopping No potential conflict of interest was reported by the author(s) context and opens a promising avenue for future research in considering the generation gap labeled by the digital divide Funding This study has several limitations that highlight future This research study was funded by the Humanities and Social Sciences research directions First, this study measured the media project, Ministry of Education in China (21YJA880065) richness of the metaverse using a survey approach, which may limit the generalizability of the results Future studies References can focus on objective media features of the metaverse using experiments Additionally, metaverse-enabled technology Armstrong, J S., & Overton, T S (1977) Estimating nonresponse bias affordances and multisensory customer experience during in mail surveys Journal of Marketing Research, 14(3), 396–402 the purchase process may also influence the trust-building https://doi.org/10.2307/3150783 process While this limitation was inevitable given the study’s exploratory nature, further research should include Bao, Y., Cheng, X., De Vreede, T., & De Vreede, G.-J (2021) these excluded factors Second, this study focused on con- Investigating the relationship between AI and trust in human-AI col- sumers’ trust in the metaverse where the trustee is a techno- laboration [Paper presentation] Proceedings of the Hawaii logical environment, while we did not control the International Conference on System Sciences, 54th (pp 607–616) interpersonal or institutional trust This constrains our https://doi.org/10.24251/HICSS.2021.074 opportunity to observe the differential influences of trust antecedents on different trust targets Future studies can Benitez, J., Arenas, A., Castillo, A., & Esteves, J (2022) Impact of consider other trust targets (e.g., trust in the platform, trust digital leadership capability on innovation performance: The role of in the product provider) and examine the trust transfer platform digitization capability Information & Management, 59(2), mechanism Third, we explored the contingency influences 103590 https://doi.org/10.1016/j.im.2022.103590 of cognitive trust vs affective trust on purchase intention towards the metaverse across DNs and DIs The moderating Benitez, J., Henseler, J., Castillo, A., & Schuberth, F (2020) How to effects of other individual characteristics on purchase inten- perform and report an impactful analysis using partial least squares: tion towards the metaverse can be examined in future Guidelines for confirmatory and explanatory IS research research For instance, future studies can incorporate gender Information & Management, 57(2), 103168 https://doi.org/10.1016/j as a contingency factor in the research model to examine its im.2019.05.003 influence on the trust belief—trust outcome relationship Fourth, metaverse shopping platforms may be able to per- Benitez, J., Ruiz, L., & Popovic, A (2022) Impact of mobile technol- form better in terms of both instrumental outcomes and ogy-enabled HR gamification on employee performance: An empir- experiential outcomes, which may limit the explanatory ical investigation Information & Management, 59(4), 103647 power of our research model to a certain extent https://doi.org/10.1016/j.im.2022.103647 Accordingly, future studies can test many other trust-related outcomes (i.e., satisfaction) in the metaverse shopping Berthiaume, D (2022) Survey: Are consumers ready for metaverse shop- context ping? 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An inquiry of their psychology mechanism tomer identification technology in the metaverse commerce Review Internet Research, 30(4), 1203–1228 https://doi.org/10.1108/INTR- of Contemporary Philosophy, 2022(21), 171–187 https://doi.org/10 05-2019-0183 22381/RCP21202211 Wongkitrungrueng, A., & Suprawan, L (2023) Metaverse Meets Branding: Examining Consumer Responses to Immersive Brand About the authors Experiences International Journal of Human–Computer Interaction Advance online publication https://doi.org/10.1080/10447318.2023 Lin Zhang is a PhD Candidate at the School of Management, Harbin 2175162 Institute of Technology, and a visiting student at EDHEC Business Wu, W., Wang, S., Ding, G., & Mo, J (2023) Elucidating trust-build- School His research focuses on trust, and IT adoption, and published ing sources in social shopping: A consumer cognitive and emotional articles in journals such as Decision Support Systems, Internet trust perspective Journal of Retailing and Consumer Services, Research, International Journal of Information Management, etc 71(March), 103217 https://doi.org/10.1016/j.jretconser.2022.103217 Xi, N., Chen, J., Gama, F., Riar, M., & Hamari, J (2022) The chal- Muhammad Adeel Anjum is an Assistant Professor of Management lenges of entering the metaverse: An experiment on the effect of Sciences at Balochistan University of Information Technology, extended reality on workload Information Systems Frontiers Enineering and Management Sciences (BUITEMS), Quetta, Pakistan Advance online publication https://doi.org/10.1007/s10796-022- His area of interest is Organizational Behavior and HRM His research 10244-x has been published in journals including Internet Research, Journal of Management and Organization, etc Yanqing Wang is an Associate Professor of Management Science and Engineering at the Harbin Institute of Technology His research pri- marily focuses on E-learning, computer-supported communication, implementation, adoption, and diffusion of information technology His work has been published in academic journals, including Computers and Education, Computers in Human Behavior, etc 18 L ZHANG ET AL Appendix A Hypothetical scenario The corresponding scenario The term “Metaverse” originated in the 1992 science fiction novel Snow Crash I n the novel, i t reflects a virtual world that can be linked with the physical world, which creates a digital virtual space with a new social system With the development of Web 3.0 Technology, many enterprises try to launch their own Metaverse solutions as a preemptive digital economy initiative, such as Metaverse shopping Assume that you are using a Metaverse shopping application called “Metastore” In the Metaverse environment, you will keep a virtual avatar-centered perspective and act naturally and intuitively in the 3D environment through navigation Meanwhile, you will be able to interact with other shoppers, real store attendants, and their brand in a unique immersive environment that tells a brand’s story, the dimensions of your desired products, and multiple rich product information Clicking through a specific store icon, you can turn into a more detailed product store and purchase immediately Section one: Based on the screenshot, please look at the described Metaverse shopping scenario and imagine it in mind, and please choose which of the following statement is in line with your current perception of the aforementioned hypothetical scenario: Key: = After reading the corresponding scenario, I still have no idea about Metaverse shopping = After reading the corresponding scenario, I feel a little difficult to imagine Metaverse shopping = After reading the corresponding scenario, I can imagine the Metaverse shopping environment to some extent = After reading the corresponding scenario, I can easily imagine the Metaverse shopping environment Section two: Based on the screenshot, please look at the described Metaverse shopping scenario and imagine it in mind, and indicate the extent to which you agree or disagree with each of the following statements Use the key below to determine your response: Key: = Strongly disagree = Disagree = Somewhat disagree = Neutral = Somewhat agree = Agree = Strongly agree Measurements of each variable included in our study 1234567 Section three: demographic information, including gender, age, etc INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 19 Appendix B PLS analysis process based on Benitez et al (2020) Two-step estimation strategy Focus Operationalization procedural Function description Step First-order constructs Create first-order constructs (including Collect the standardized latent Step Second-order constructs all reflective constructs) variable scores (LVS) of multiple information cues, language variety, Freely link all the constructs, keep the immediate feedback, and personal algorithm settings by default and run focus, respectively it on the ADANCO 2.2 application Assess the first-order-goodness of Create the second-order constructs as model fit (saturated model) follows: (a) collect the LVS from Step 1; (b) copy the LVS and paste it into Build the second-order construct of the original dataset; (c) import the perceived media richness of the new dataset (with the LVS) metaverse Create the second-order constructs Assess the second-order-goodness using the LVS of model fit (saturated model) Freely link the second-order construct Assess the second-order-goodness with all reflective constructs (saturated of model fit (estimated model) model) Analyze the structural model to Create our conceptual model, examine the path relationship and including the key variables, and link explanatory power of the research them in the proposed way (estimated model (estimated model) model) Notes: Perceived media richness of the metaverse was specified as a second-order formative construct determined by multiple information cues, language variety, immediate feedback, and personal focus These four constructs were specified as reflective at the first-order level (Benitez, Ruiz, et al., 2022; Braojos et al., 2019) We followed a two-step estimation strategy to describe the operationalization process: (1) the first step was to collect the standardized latent variable scores (LVS) of the first-order constructs (i.e., multiple information cues, language variety, immediate feedback, and personal focus), and (2) the second step is to create the second-order construct (i.e., perceived media richness of the metaverse) using LVS of the first-order constructs (Benitez et al., 2020) Appendix C Common method bias analysis Relationship Baseline model without the marker CMB test model with the marker Perceived media richness!Cognitive trust 0.338ÃÃÃ 0.337ÃÃÃ Perceived media richness!Affective trust 0.109Ã 0.109Ã Cognitive trust!Affective trust 0.569ÃÃÃ 0.573ÃÃÃ Cognitive trust!Purchase intention 0.218Ã 0.216Ã Affective trust!Purchase intention 0.255Ã 0.252Ã Gender!Purchase intention 0.190ÃÃÃ 0.195ÃÃÃ Education!Purchase intention 0.033 ns 0.0366 ns Online shopping experience!Purchase intention 0.008 ns 0.006 ns Marker variable!Cognitive trust –0.033 ns Marker variable !Affective trust N/A –0.022 ns Marker variable!Purchase intention –0.052 ns N/A Notes: ns not significant; Ãp < 0.05; ÃÃÃp < 0.001 N/A