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International Journal of Human–Computer Interaction
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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.
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Trang 2The Impact of Trust-Building Mechanisms on Purchase Intention towards
Metaverse Shopping: The Moderating Role of Age
Lin Zhanga, Muhammad Adeel Anjumb, and Yanqing Wanga
a
School 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 findmodel-ings 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.
1 Introduction
Nowadays, the rapid technological development of artificial
intelligence (AI), virtual reality (VR), augmented reality (AR),
etc., has spawned a plethora of online shopping platforms
that have evolved from a static webpage into a more dynamic
three-dimensional (3D) space (Darbinyan, 2022) A new era,
termed“metaverse shopping,” has emerged in which
consum-ers experience immconsum-ersive shopping with virtual avatars and
engage in interactive shopping activities by guiding their
ava-tars through 3D stores According to an Accenture report,
94% of retail executives believe that the future digital
econ-omy needs to offer metaverse initiatives (Standish,2022) For
instance, enterprises such as Facebook recognize the potential
of the metaverse and are starting to activate
metaverse-enabled social commerce on their platforms (Nix, 2022) A
Chinese leading electronic commerce (e-commerce) platform,
Taobao, has also invested significant marketing efforts in
metaverse shopping (Ryder, 2022) Virtual shopping in the
metaverse is expected to have an US$800 billion market
opportunity by 2024 (Darbinyan,2022)
Despite the fact that metaverse shopping has become a
significant trend, the interconnected nature of the metaverse
heightens the related risks for security and privacy, which
potentially leads to distrust issues among consumers
regard-ing makregard-ing purchase decisions in the metaverse shoppregard-ing
context (Di Pietro & Cresci, 2021) According to a report
from the consumer insights data platform, Zappi, 80% of
respondents distrust virtual shopping activities in the meta-verse (Berthiaume, 2022) Therefore, trust in the metaverse
is identified as a salient factor for alleviating uncertainty when consumers make purchase decisions in the metaverse shopping context There is a call for more empirical studies
to uncover individuals’ trust formation process and the con-text-specific antecedents in this emerging research area The majority of the existing literature has concentrated
on trust in different contexts, such as online commerce (Cheng et al., 2017; N Wang et al., 2013; W Wang et al., 2016) and the sharing economy (Shao, Zhang, Li, et al., 2022; Shao & Yin, 2019) From a theoretical perspective, previous research has commonly emphasized two main research foci regarding trust-related behaviors: the determi-nants of trust (e.g., Cheng et al., 2021; Shao et al., 2019); and the dimensions of trust (e.g., Chi et al., 2021; Shao, Zhang, Brown, et al., 2022) Regarding the determinants of trust, most studies have focused on exploring trust antece-dents that lead people to have different tendencies toward trust-related behaviors (Cheng et al., 2021) For example, Cheng et al (2017) investigated the joint knowledge-based, institution-based, calculative-based, cognition-based, and personality-based trust antecedents in influencing social media communication behaviors Shao and Yin (2019) found that context-specific platform institutional mecha-nisms have positive effects on trust in the ridesharing plat-form, which in turn affects continuance intention Regarding CONTACT Yanqing Wang yanqing@hit.edu.cn School of Management, Harbin Institute of Technology, Harbin, China
ß 2023 Taylor & Francis Group, LLC
https://doi.org/10.1080/10447318.2023.2184594
Trang 3the dimensions of trust, most studies have investigated
multi-faceted trust concepts, such as competence, benevolence, and
integrity (McKnight et al., 2002) For instance, Pavlou (2002)
found that institution-based antecedents help engender two
specific trust dimensions (cognition-based credibility and
ben-evolence) and indirectly influence transaction success in
online marketplaces Some scholars have also defined trust as
a multidimensional construct comprising cognitive and
affect-ive components (Cummings & Bromiley,1996) and examined
the two dimensions of cognitive and affective trust in
e-com-merce (Leong et al.,2021)
We identify several knowledge gaps in the extant
litera-ture regarding trust literalitera-ture First, most studies have
exam-ined personality and institutional factors as the determinants
of trust (J Liang et al., 2022; Shao et al., 2019), where the
trustee is either a real human or an entity (e.g., a platform)
More recent IS research has called for the characteristics of
human–system interactions to be examined where the
trustee is a technological environment, such as an AI chat
channel or a blockchain-enabled mutual aid environment
(Bao et al.,2021; Chi et al.,2021; Choung et al.,2022; Shao,
Zhang, Brown, et al., 2022) Specifically, many features of
the metaverse work towards visualization and enhancing
natural communication through a virtual technological
environment Considering that the rich media-enabled
vir-tual world is created to interact with products, brands, and
communities in metaverse shopping service delivery (Kim,
2021), consumers may have a high perception of media
rich-ness, which is beneficial to triggering trust in the metaverse
However, to the best of our knowledge, few studies have so
far investigated the role of the media richness of the
meta-verse in building trust beliefs and subsequently facilitating
purchase intention
Second, prior studies have mainly focused on the
dimen-sion of cognitive trust related to competence and reliability
(Shao et al.,2019; Shao, Zhang, Li, et al.,2022), while
affect-ive trust, in the context of the new generation of
technolo-gies, is yet to be advanced in the online shopping field In
contrast to cognitive trust, affective trust is rooted in
emo-tional bonds and connections (Cummings & Bromiley,
1996) Considering the uncertainty and potential risks of the
metaverse (Kim, 2021), consumers may make a rational
assessment regarding the security and reliability of the
meta-verse and form the cognitive trust belief when making
pur-chase decisions On the other hand, consumers may also
generate the affective trust belief towards the metaverse,
resulting from the virtual world’s immersive, entertaining,
and interactive shopping experience As a result, it remains
unclear whether or not the perceived media richness of the
metaverse will exert different effects on cognition-based and
affect-based trust in the metaverse shopping context
Third, we consider individual characteristics contingent
on trust-related behaviors (Shao et al., 2019; Shao, Zhang,
Li, et al., 2022) Prior literature has indicated that human
perceptions and behavioral outcomes in technology
imple-mentations are contingent on age (e.g., Ghobadi &
Mathiassen, 2020; Hong et al., 2013) In particular, age is
one of the criteria used to differentiate between digital
natives (DNs) and digital immigrants (DIs), which is applied
to explain individuals’ differentiated attitudes and technol-ogy adoptions (Shao, Benitez, et al., 2022) DIs are used to traditional communication mediums (e.g., email or social media) as the preferred social tools for the workplace or daily life, whereas DNs prefer to interact with one another via the interactive virtual world, such as Second Life (Hong
et al., 2013; L Zhang et al., 2021) Moreover, DNs grew up with digital technologies in a networked world and are more comfortable adopting innovative technologies than their DI counterparts (Kesharwani, 2020) Hence, attitudes toward technology may vary largely depending on one’s age While the role of age (DNs vs DIs) has been recognized in various contexts (Niehaves & Plattfaut, 2014; Ollier-Malaterre & Foucreault, 2021; Shao, Benitez, et al., 2022), there is scant literature that theorizes age-difference issues regarding the relationships between multidimensional trust and purchase intention in the context of metaverse shopping
To fill these research gaps, this study aims to develop a theoretical model to comprehensively understand how the perceived media richness of the metaverse helps engender multidimensional trust (i.e., cognitive trust and affective trust) and indirectly influences purchase intentions in the metaverse shopping context Current literature has used media richness theory (MRT) to explain how digital media’s ability to convey channel richness can affect purchase deci-sions and outcomes (Tseng & Wei, 2020; Zhu et al., 2010), which is considered an appropriate theoretical framework for our study Meanwhile, we further incorporate age as a salient contingency factor in the theoretical model to explore the specific differences between DNs and DIs in terms of trust-related behaviors To this end, this study aims to shed light on the role and nature of multidimensional trust in the metaverse shopping context by providing theoretical insights and empirical findings for the following research questions:
1 How does the perceived media richness of the metaverse affect consumers’ purchase intention through the medi-ation effects of multidimensional trust (cognitive and affective trust)?
2 How does age moderate the relationships between multi-dimensional trust and purchase intention in the meta-verse shopping context?
The remainder of this study is structured as follows Section 2 reviews the related literature and presents the the-oretical foundation Section 3 develops the research model and formulates the hypotheses Section 4 describes the research method and data analysis Section 5 concludes the article, presenting the key findings, implications, limitations, and future research directions
2 Research background and theoretical foundations 2.1 Literature review in the field of the metaverse
The term metaverse originated in the 1992 science fiction novel Snow Crash and gained global popularity in recent
Trang 4years (Oh et al., 2023) Based on the recent research work
(Hennig-Thurau et al., 2022; Oh et al.,2023), metaverse can
be conceptualized as a new computer-mediated environment
that consists of virtual worlds in which people can act and
communicate with each other via avatars In past studies,
the impacts of the metaverse have been examined from
dif-ferent perspectives For example, some studies have focused
on the metaverse’s technological infrastructures, finding that
a heterogeneous crowd of technological capabilities and
tools can afford or constrain the potential action spaces
available within the metaverse environments (Grupac et al.,
2022; Grupac & Lazaroiu, 2022; Hudson, 2022; Jenkins,
2022; Kliestik et al., 2022; Zvarikova et al., 2022) Another
study indicated that the implementation of 3D space
com-puter-generated simulations, data-driven artificial
intelli-gence, and text-to-image synthesis models is beneficial for
meeting customer dynamic demands in the metaverse
envir-onment (Nica et al.,2022) Apart from examining the
meta-verse’s technological infrastructures, attention has also been
paid to the impacts of user perceptions and experiences (Oh
et al., 2023; Shin, 2022; Wongkitrungrueng & Suprawan,
2023; Xi et al., 2022) Further, Hennig-Thurau et al (2022)
found the metaverse-enabled environment to be positively
related to interaction performance outcomes through the
mediation effects of psycho-physiological mechanisms To
extend the existing research, recent studies have emphasized
to comprehend how trust can be developed among users in
the metaverse context (Tan & Saraniemi, 2022) because the
metaverse may expose user identifications to the service
pro-viders and cause privacy concerns (Dwivedi et al., 2022)
However, scant attention has been paid to the antecedents
of trust-building in facilitating users’ behavioral intention in
the metaverse shopping context
2.2 Metaverse shopping
The metaverse has become a new economic paradigm that
builds on sharing an interactive and immersive virtual world
environment (Kim, 2021) Multifarious applications for the
metaverse have gained considerable attention in different
fields, such as improving work productivity (Xi et al.,2022),
social media value creation (Kraus et al., 2022), interactive
learning environments (Rospigliosi, 2022), and advertising
strategy (Kim, 2021; Taylor, 2022) Recently, the metaverse
has been introduced as a new business model to facilitate
the immersive shopping experience among peers online
(Grupac et al.,2022; Hudson,2022; Jenkins,2022; Zvarikova
et al., 2022), and many famous platforms have rapidly
invested in and developed metaverse shopping initiatives
(Nix,2022; Ryder,2022)
Compared with traditional layouts of shopping platforms
(e.g., plain text, images, or video), a key feature of the
meta-verse is that it allows customers to immerse themselves
sur-rounding a virtual shopping world, with the support of rich
digital content (Kim, 2021) Specifically, in the metaverse
shopping world, consumers are immersed in a
media-rich-ness-enabled virtual world where they can control their
movement through smartphones, guiding virtual characters
to walk and shop in the 3D shopping environment and to complete specific interactive actions Despite the more uni-fied experience (i.e., a blended virtual and physical experi-ence) that metaverse shopping offers, to the best of our knowledge, few studies have focused on the impact of media richness created by the metaverse environment on consum-ers’ trust-building process and related purchase outcomes
In order to address this research gap, our study aims to uncover trust-building mechanisms and subsequent purchase decision-making behaviors towards metaverse shopping from the theoretical perspective of MRT, which will be described in the next section
2.3 Media richness theory (MRT)
Originating from computer science, MRT is defined as the ability of a computer-mediated communication channel to deliver rich information and messages (Cummings & Bromiley, 1996) According to Daft and Lengel (1986), the theoretical concept of media richness can be conceptualized
as a set of objective features, including multiple information cues, language variety, immediate feedback, and personal focus Specifically, multiple information cues mean that indi-viduals can gather more information in a variety of ways (e.g., texts, images, videos, etc.) with the support of digital technology Language variety refers to the ability of digital technology to support individuals in communicating with natural languages (e.g., language symbols, emoticons, etc.) Immediate feedback refers to the ability of digital technology
to receive and send feedback quickly Personal focus is the degree to which individuals can customize messages or per-sonal profiles according to their perper-sonal needs and the sur-rounding environment
As presented in Table 1, the earliest application of MRT
in enterprise organizations (Dennis & Kinney, 1998; Kishi, 2008; Suh, 1999; Yoo & Alavi, 2001) explains how organiza-tions can meet individual demands by reducing information complexity and fuzziness In recent years, MRT has been introduced and widely applied in e-commerce research (D
K L Lee & Borah, 2020; Li et al., 2022; Mirzaei & Esmaeilzadeh, 2021; Shen et al., 2021) For example, Tseng and Wei (2020) showed the impact of mobile advertising with various degrees of media richness on consumer deci-sion-making behavior Zhu et al (2010) found the positive influence of navigation support and communication support
on collaborative online shopping behaviors Despite great attention having been paid to online marketplaces, MRT was originally used only to examine traditional media tech-nologies (e.g., telephone, electronic documents, email, etc.); however, a few attempts have expanded its use to the verse context Boughzala et al (2012) argued that the meta-verse is a special case of a socially interactive media channel; thus, MRT is suitable for gaining a rich understanding of context-specific purchase behaviors in the emerging meta-verse realm
In particular, drawing upon MRT literature, there are two key approaches for operationalizing media richness (see Table 1) The first focuses on the category match approach
Trang 5by designing different levels of communication media
tools, such as using computer-mediated text, audio, video,
and face-to-face to present lowest to highest richness (e.g.,
Otondo et al., 2008; Suh, 1999; Tseng & Wei, 2020; Yoo
& Alavi, 2001; Zhu et al., 2010) In contrast, using a
sur-vey approach, the second approach focuses on the
percep-tion of media richness (Kishi, 2008; D K L Lee & Borah,
2020; Mirzaei & Esmaeilzadeh, 2021) The perceptual
sur-vey approach synthesizes the influence of perceived media
richness through general psychological multidimensional
measurement (i.e., multiple information cues, language
var-iety, immediate feedback, and personal focus) Given that
we are interested in consumers’ perceived media richness
of the metaverse, we adopt the second approach and
intro-duce four significant dimensions, namely the extent to
which a user perceives: (1) the use of various cues, e.g.,
texts, images, graphical symbols, physical presence,
ges-tures, etc.; (2) communication with natural languages; (3)
immediate interaction; and (4) personalized virtual images
(avatars) Accordingly, this study identifies the construct of
the perceived media richness of the metaverse as a
significant trust-building antecedent in the research
framework
2.4 Multidimensional trust: cognitive trust vs affect trust
Originating from social psychology science, trust is defined
as a general belief in a person who will act in line with favorable expectations towards the trustee (Gefen, 2000) Two distinct dimensions of trust (i.e., cognitive trust and affective trust) were identified by McAllister (1995), and they have been widely applied in recent studies (Chih et al., 2017; Goles et al., 2009; J Lee et al., 2015; K Z K Zhang
et al., 2014) In particular, cognitive trust (generated by rational assessment regarding the trustees’ ability and reli-ability) is important in online exchange relations where uncertainty is present (Pavlou, 2002; Shao et al.,2019; Shao
& Yin, 2019) Affective trust (generated by perceived strength and the level of emotional attachment, caring, and social reciprocity between a trustor and a trustee) also plays
an important role in the context of online marketplaces (Chih et al.,2017; Goles et al.,2009; J Lee et al.,2015; K Z
K Zhang et al.,2014)
This study extends the two dimensions of trust from the 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
Table 1 A literature review of MST adoption in studies.
Operationalization of
Category match approach 132 Team members from
predesigned media-enabled tasks
The use of richer media rather than leaner media did not lead to better performance on the higher equivocality task.
Experiment (Dennis & Kinney, 1998 )
Perceptual approach 1062 Managers were studied
from social media use in the workplace.
Organizational interpretation of the environment substantially affects the use of rich media.
Survey (Kishi, 2008 )
Perceptual approach 671 Participants were
recruited using a social media tool
Perceived media richness and self-presentation can affect friendship development through the mediating effect of perceived functionality and the moderating effect of personality trait.
Survey (D K L Lee & Borah, 2020 )
Category match approach A panel data of 87,540 posts
was collected from the Sina Weibo platform
The relationship between information timeliness and public engagement is moderated by information richness.
Econometrics (Li et al., 2022 )
Perceptual approach 348 Users from online health
communities were surveyed.
Perceived channel richness affects perceived social support, willingness
to exchange information, and engagement outcomes.
Survey (Mirzaei & Esmaeilzadeh, 2021 )
Perceptual approach 3309 Users from the blogging
service were surveyed.
Immediate feedback and personal focus positively affect social identity, which in turn, leads to we-intention.
Survey (Shen et al., 2021 )
Category match approach Data were gathered from 316
participants using a computer-mediated mode.
Communication media richness can positively influence task performance and satisfaction.
Experiment (Suh, 1999 )
Category match approach 688 Participants were
surveyed using a predesigned web interface.
Media richness features can influence communication outcomes, i.e.
effectiveness and satisfaction.
Experiment (Otondo et al., 2008 )
Category match approach 259 Consumers of mobile
advertising contexts
The influence of media richness on consumer decision-making behavior
is greater in the early stage than in the later AS stage, and the relationship is moderated by product type.
Experiment (Tseng & Wei, 2020 )
Category match approach 135 Participants from a
decision-making task
Different media conditions can influence task participation
Experiment (Yoo & Alavi, 2001 ) Category match approach 128 Participants were studied
using a Web collaboration tool
Navigation and communication support can positively influence collaborative online shopping.
Experiment (Zhu et al., 2010 )
Trang 6consumers may tend to rationally evaluate the security and
reliability of the virtual space created by the metaverse, and
to emotionally assess the enjoyable and interactive
experi-ence created by the metaverse environment (Chih et al.,
2017; W Wang et al., 2016) Therefore, this study focuses
on trust in the metaverse (the trustee is a technological
environment) and divides it into two dimensions (i.e.,
cogni-tive trust vs affeccogni-tive trust) to examine their separate
influ-ence on purchase intention
3 Research model and hypotheses
By integrating a trust-building framework with MRT, we
aim to explore the impact of the perceived media richness
of the metaverse on purchase intention through the
medi-ation effects of cognitive trust and affective trust
Meanwhile, we incorporate age as a moderator between
multidimensional trust and purchase intention Additionally,
gender, education, and shopping experience are controlled
in the structural model Figure 1 shows the proposed
research model
3.1 Perceived media richness of the metaverse,
cognitive trust, and affective trust
Based on MRT, we propose that perceived media richness is
a second-order construct expressed by multiple information
cues, language variety, immediate feedback, and personal
focus (Daft & Lengel,1986; Shen et al.,2021) Prior research
has argued that the level of intuitionistic and abundant
information content, vividness, and social cues provided by
multimedia technologies is highly related to message
persua-sion (Kishi, 2008; D K L Lee & Borah, 2020; Shen et al.,
2021), which can induce effective communication
perform-ance (Dennis & Kinney,1998; Zhu et al.,2010) Considering
that effective interactions help build trust (Bao et al., 2021;
Pavlou, 2002; Shao et al., 2022b; Shao & Yin, 2019), it is
plausible to expect that trust will be affected by the modality
type or level of perceived media richness
In the metaverse shopping context, consumers use virtual
avatars to move freely in a 3D shopping world in a more
interactive way (Kim, 2021), which enables rich media to
interact with brands, stores, or other consumers to obtain more realistic, unambiguous product information (Kishi, 2008) Thus, the perceived media richness of the metaverse has the potential to improve consumers’ cognitive trust In addition, the metaverse (in a 3D virtual world) is more vivid and interesting than traditional static web page-based shop-ping (Kim, 2021), which is beneficial to facilitating affective trust Therefore, we hypothesize the following:
H1: There is a positive relationship between the perceived media richness of the metaverse and cognitive trust
H2: There is a positive relationship between the perceived media richness of the metaverse and affective trust
3.2 Cognitive trust, affective trust, and purchase intention towards metaverse shopping
Trust is considered to be a key factor in determining behav-ioral intention (Cheng et al., 2021; Shao, Zhang, Brown,
et al., 2022) McAllister (1995) proposed that trust is a multidimensional construct, including cognitive trust and affective trust On the one hand, cognitive trust depends on the trustor’s rational evaluation, which can mitigate risk per-ceptions and facilitate behavioral outcomes (Chih et al., 2017; Shao et al., 2019) On the other hand, affective trust originates from the emotional attachment between the trustor and the trust target, which can generate comfortable and positive attitudes (W Wang et al., 2016) Following the theory of reasoned action, affective trust as a form of trust-ing attitude will predict individuals’ behavioral intentions to perform an action (Komiak & Benbasat, 2006) In the con-text of online shopping, both cognitive trust and affective trust play significant roles in affecting consumers’ purchase intention (Kimiagari & Malafe, 2021; Wu et al., 2023)
In the metaverse shopping context, consumers with high cognitive trust in the metaverse will perceive a lower level of uncertainty and potential risks (Chih et al., 2017), which is likely to enhance their willingness to buy recommended goods in the metaverse In addition, consumers who have affective trust in the metaverse will form emotional and pleasant feelings (Kimiagari & Malafe,2021; W Wang et al.,
Affective trust
Perceived media richness of the metaverse
Purchase intention towards the metaverse shopping
Gender, Education, Shopping experience
Multiple
information cues
Language
variety
Immediate
feedback
Personal focus
The metaverse-enabled shopping environment Age
Digital natives (DNs) vs Digital immigrants (DIs)
H6a (DIs > DNs)
H6b (DNs > DIs)
Figure 1 The research model.
Trang 72016) and may be motivated to buy the products
recom-mended by the metaverse Therefore, we formulate the
fol-lowing hypotheses:
H3: There is a positive relationship between cognitive trust
and purchase intention towards metaverse shopping
H4: There is a positive relationship between affective trust
and purchase intention towards metaverse shopping
Furthermore, (McAllister, 1995) proposed that cognitive
trust is a prerequisite for affective trust When a consumer
makes a rational assessment of purchase behavior, he/she is
more likely to respond emotionally (Chen et al.,2019; Legood
et al., 2023) Therefore, in the metaverse shopping context,
cognitive trust may be necessary for affective trust to develop;
accordingly, we propose the following hypothesis:
H5: There is a positive relationship between cognitive trust
and affective trust
3.3 The moderating role of age: digital natives (DNs)
vs digital immigrants (DIs)
Age has a significant functional meaning in understanding
individuals’ attitudes with regard to technology adoption
(Hong et al., 2013; Shao, Benitez, et al., 2022; L Zhang
et al., 2021) According to Hong et al (2013), chronological
age plays an important role in distinguishing between DNs
(who were born after the 1980s) and DIs (who were born
before the 1980s) (Shao, Benitez, et al., 2022) Previous
lit-erature has suggested significant age-related generational
dif-ferences between DNs and DIs in predicting consumption
orientation and purchasing behaviors For example, Gurtner
et al (2014) found that DIs are more likely to adopt new
technologies based on cognitive evaluation, while DNs value
more the hedonic experience of using new technologies L
Zhang et al (2021) explained that DNs’ purchase behaviors
are driven by enjoyment perception, while DIs are more
cir-cumspect or judicious and tend to perform purchase
behav-iors via the cognitive evaluation process
From the perspective of age (DNs vs DIs) (Hong et al.,
2013; Tams et al., 2018), there will be high transaction risk
and vulnerability perceptions for DIs because they are not
familiar with digital technologies Therefore, DIs always
require conscious calculation through a certain degree of
information search, rational thinking, and risk assessment
when making decisions (Gurtner et al.,2014; L Zhang et al.,
2021) As such, in the metaverse shopping context, DIs focus
more on making rational assessments to eliminate uncertainty
and perceived risk towards the surrounding purchase
envir-onment; thus, cognitive trust may exhibit a stronger influence
on DIs’ purchase intention towards metaverse shopping The
following hypothesis is therefore proposed:
H6a: Cognitive trust has a stronger influence on purchase
intention towards metaverse shopping for digital immigrants
compared with digital natives
Compared with DIs, DNs are more exposed to new
technologies and digital media; therefore, they tend to
be more comfortable with digital technologies (Gurtner
et al., 2014) Specifically, DNs have more experience with the virtual world (such as Second Life or 3D gam-ing) and may show positive and hedonic attitudes towards emerging technologies (e.g., AR, VR, etc.) to complete virtual shopping processes (L Zhang et al., 2021) As such, in the metaverse shopping context, DNs are more likely to form comfortable and emotional per-ceptions; thus, they may be more affected by affective trustworthiness when making purchase decisions Based
on this logic, we argue that affective trust plays a more significant role in enabling DNs to purchase in meta-verse shopping The above analysis leads to the follow-ing hypothesis:
H6b: Affective trust has a stronger influence on purchase intention towards metaverse shopping for digital natives compared with digital immigrants
4 Research design and execution 4.1 Research context and data collection
This study employed the scenario-based survey method to collect data since it is an effective way to promote partici-pants’ contextualized understanding of metaverse shopping
in a hypothetical situation (Chang et al., 2013; Kwak et al., 2021; X Wang et al.,2020), especially when research on the role of the metaverse is still in its infancy (Hua et al.,2018) Using insights from the past literature, vignettes were used
to present subjects with written descriptions of realistic sit-uations (Trevino, 1992) The use of vignettes can provide control by placing all subjects in the same scenario with the same fictitious metaverse shopping context The vignette-based approach has been widely adopted in past studies due
to numerous benefits such as the relevance of scenarios to realistic situations, lesser social desirability and memory lapse biases, and ease of data collection from large samples (Tong et al., 2013; Vance et al., 2012; S Zhang & Leidner, 2018) In summary, respondents were required to make behavioral decisions based on true-to-life vignettes embedded in the hypothetical metaverse shopping scenario (see Appendix A)
In the scenario-based investigation, we entrusted the third-party questionnaire website (www.sojump.com) to randomly invite 500 consumers1 from its database to complete an online questionnaire from June 01 to June
10, 2022 (L Zhang et al., 2021) The questionnaire included three sections The first section comprised a sim-ple explanation of the concept of the metaverse and our hypothetical shopping scenario and contained one screen-ing item (Appendix A presents the scenario used in the current study, and please see the endnote for more discus-sion about the screening item).2 We stated that the ano-nymity of the participants would be ensured, and that the collected data would only be used for academic research The second section contained the measurements for each variable included in the research model The third section comprised demographic information, including gender,
Trang 8age, etc A monetary reward was given to any respondent
completing the questionnaire online, and 479 responses
were received, with a response rate of 95.8% After
remov-ing 103 respondents who chose items 1 or 2 for the
screening item and 44 invalid responses (e.g., an extreme
answer to all questions), a total of 332 questionnaires
were used for data analysis In order to assess the
nonres-ponse bias, we compared the demographic variables
between the first 50 respondents and the last 50
respond-ents using t-tests (Armstrong & Overton, 1977) The
stat-istical analysis results suggested that there were no
significant differences between the two groups in terms of
gender, age, and shopping experience (p> 0.1); thus,
non-response bias does not exist in our study (H Liang et al.,
2019)
The statistical characteristics of the sample are presented
in Table 2 Male and female consumers were almost evenly
distributed In terms of age, 6.0% were below 18 years old,
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
old Most of the participants had a Bachelor’s degree and
over three years’ online shopping experience The results
suggest that no significant differences exist between our
sample and the actual online consumers in China (CNNIC,
2021), demonstrating that our sample is representative of
the actual online consumers in China
4.2 Measurements
A seven-point Likert scale with the categories from
“Strongly agree” (7) to “Strongly disagree” (1) was used
to measure constructs Constructs can be operationalized
as reflective or formative (Benitez et al., 2020; L Zhang
et al., 2022) Multiple information cues, language variety,
immediate feedback, personal focus, cognitive trust,
affective trust, and purchase intention were specified as
reflective constructs, while perceived media richness of
the metaverse was specified as a second-order formative
construct, which emerges from the first-order constructs
of multiple information cues, language variety,
immedi-ate feedback, and personal focus (Benitez et al., 2020)
Other constructs were operationalized as reflective
meas-urement models because they (i.e., latent variable) can
cause the indicators, and some indicators can be inter-changed without altering the meaning of the latent vari-ables (Benitez et al., 2020) In summary, the proposed research model comprises formative and reflective constructs
Perceived media richness of the metaverse
Conistent with Shen et al (2021), the construct was opera-tionalized in terms of multiple information cues, language variety, immediate feedback, and personal focus That is, we operationalized perceived media richness of the metaverse as
a broader concept having four dimensions: (1) the use of various cues (e.g., texts, images, graphical symbols, physical presence, gestures, etc.); (2) communication with natural languages (e.g., language symbols, and emoticons); (3) immediate interaction (e.g., receive and send feedback quickly); and (4) personalized virtual images (e.g., avatars) The dimensions were measured using reflective scales with three items each
Multidimensional trust
The scales by McAllister (1995) and W Wang et al (2016) were used to measure cognitive trust and affective trust Specifically, cognitive trust reflects consumers’ rational expectations that the metaverse has the necessary attributes to ensure a proficient and reliable virtual shop-ping space while affective trust refers to consumers’ com-fort and emotional bonds regarding the virtual shopping space created by the metaverse Both of the constructs were operationalized as reflective variables with three items each
Purchase intention
Following K Z K Zhang et al (2014), purchase intention was conceptualized as consumers’ willingness to purchase goods or services in the metaverse shopping space It was specified as a reflective construct and measured with five items
A few revisions were made to adapt our measurement to the metaverse shopping context A pilot study was con-ducted using 54 college students with online shopping experience Several items with factor loadings lower than 0.4 were adjusted based on the respondents’ feedback (L Zhang
et al., 2022) Table 3 describes the definition and measure-ment items of the constructs
4.3 The estimation strategy
We use partial least squares (PLS) path modeling to test the proposed research model, which is recognized as an appro-priate statistical tool for structural equation modeling (SEM) analysis (Benitez et al., 2020) Compared with the covari-ance-based approach, PLS is more robust for concurrently handling formative and reflective constructs (Benitez et al.,
2020, 2022; Castillo et al.,2021) In our study, the perceived media richness of the metaverse was estimated by a
Table 2 Descriptive statistics.
Bachelor ’s degree 209 63.0 Master ’s degree and above 42 12.7
Trang 9formative construct, which used a regression weighting
scheme represented by arrows pointing from indicators to
their corresponding constructs (see details in Appendix B)
(Benitez et al., 2020, 2022; Castillo et al., 2021) We
employed the statistical software package ADANCO 2.3
Professional for Windows (http://www.composite-modeling
com/) to analyze the structural model
4.3.1 Evaluation of the overall fit of the saturated model
Following Benitez et al (2020), we evaluated the overall
model fit of the saturated model to assess the validity of the
formative and reflective measurement models The goodness
of fit of the saturated model was evaluated by the
discrep-ancy between the empirical correlation matrix and the
model-implied correlation matrix through the indicators for
SRMR, dULS, and dG (Benitez et al., 2020, 2022; Benitez
et al., 2022; Castillo et al., 2021) In order to guarantee an
adequate model fit, SRMR should be less than 0.080, and
SRMR, dULS, and dG should be below the 95% quantile
(HI95) of the bootstrapping discrepancies or at least below
the 99% quantile (HI99) Table 4 summarizes the results of
the goodness of fit for the saturated model Overall, the fit
of the saturated model was satisfactory to proceed with the
evaluation of the measurement model (Cheng et al.,2022)
4.3.2 Evaluation of the measurement model
We assessed the reflective constructs through the indicators
of factor loadings, rho_A, and average variance extracted (AVE), and we tested for multicollinearity, weights, and the significance level of the formative construct (i.e., the per-ceived media richness of the metaverse) As noted in Table
5, all indicators (i.e., factor loadings, rho_A, and AVE) for the reflective constructs met the threshold criteria (Benitez
et al., 2020) For the second-order formative construct (i.e., the perceived media richness of the metaverse), the results showed that the variance inflation factor (VIF) values ranged from 1.529 to 2.175, which are below the threshold value of
10, suggesting that multicollinearity is not a problem in our study (Benitez et al., 2020) All indicator weights (from 0.311 to 0.464) and dimension weights (from 0.081 to 0.409) were significant except for the dimension weight of language variety (0.081) Following the guidelines of Benitez et al (2020) for formative measurement, although the dimension weight of language variety was small, the factor loading for language variety (0.755) was significant on an alpha level of 0.001 Thus, we retained language variety in our model The results suggested that our variables possess very good meas-urement properties (Benitez et al., 2020; Benitez et al., 2022), and that we could proceed with the hypothesis testing (see details in Appendix B)
Table 3 Constructs and items.
Perceived media richness of
the metaverse
Multiple information cues A consumer ’s perception that the
metaverse supports multiple information through a variety of ways (e.g., texts, images, and videoes)
MIC1-MIC3 (Shen et al., 2021 )
Language variety A consumer ’s perception that the
metaverse enables them to communicate with natural languages (e.g., language symbols, and emoticons).
LV1-LV3
Immediate feedback A consumer ’s perception that the
metaverse helps them to receive and send feedback quickly.
IF1-IF3
Personal focus A consumer ’s perception that the
metaverse enables them to customize avatars or personal profiles according to their personal needs and preferences.
PF1-PF3
metaverse has the necessary attributes
to ensure a proficient and reliable virtual shopping space
CT1-CT3 (McAllister, 1995 ; W Wang
et al., 2016 )
regarding the virtual shopping space created by the metaverse
AT1-AT3
or services in the metaverse shopping space
PI1-PI3 (K Z K Zhang et al., 2014 )
Table 4 Overall model fit of the saturated model analysis.
Discrepancy
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 ).
Trang 10Table 5 Validity and reliability of the scales.
MIC1 The metaverse shopping virtual world transmits
a variety of different cues beyond the explicit text-based product information
MIC2 The metaverse shopping virtual world conveys
multiple types of product information (verbal and nonverbal)
MIC3 The metaverse shopping virtual world presents
vivid product information through facial expressions and body language
LV1 The metaverse shopping virtual world transmits
varied symbols (e.g., texts, photos, videos, audios, links, and so on)
LV2 The metaverse shopping virtual world
communicates rich meanings about products using a large pool of language symbols
LV3 The metaverse shopping virtual world uses rich
and varied language
IF1 The metaverse shopping virtual world has the
ability to give and receive timely feedback
IF2 The metaverse shopping virtual world can
provide immediate feedback
IF3 The metaverse shopping virtual world enables
consumers to send/receive information quickly
PF1 The metaverse shopping virtual world enables
consumers to personalize their virtual avatars
PF2 The metaverse shopping virtual world enables
consumers to edit personal profiles or decorate virtual avatars
PF3 The metaverse shopping virtual world enables
consumers to tailor personal avatars and share their virtual images in the interactive shopping community
CT1 When I browse products and participate in tasks
in the metaverse shopping scenario, I feel it
is capable and proficient
CT2 When I browse products and participate in tasks
in the metaverse shopping scenario, I feel it
is a reliable and secure virtual shopping world
CT3 When I browse products and participate in tasks
in the metaverse shopping scenario, I feel it
is competent and effective in presenting product contents
AT1 When I browse products and participate in tasks
in the metaverse shopping scenario, I feel it responded caringly
AT2 When I browse products and participate in tasks
in the metaverse shopping scenario, I feel it displays a warm and caring attitude towards me
AT3 When I browse products and participate in tasks
in the metaverse shopping scenario, I feel comfortable and enjoyable
Purchase intention towards the metaverse shopping (PI) 0.954 0.869
PI1 Given a chance, I predict that I should spend
money on the products recommended by the metaverse shopping scenario in the future
PI2 Given a chance, I intend to buy products from
the metaverse shopping scenario
PI3 If I could, I am very likely to buy products from
the metaverse shopping scenario
Notes: p < 0.01; p < 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 ).