2. consumer outcomes within AR shopping experiences. Therefore, while
the potential benefits of flow have been espoused in the literature, it is
not clear whether AR has the capability to induce a state of flow, and
whether a state of flow in an AR shopping experience can more posi-
tively impact consumer outcomes compared to states of flow achieved
without the use of AR.
This study examines the “sweet spot” of augmented reality through
the lens of flow theory. We suggest that optimal AR shopping experi-
ences can be achieved by implementing design aspects that facilitate
ideal states of flow, which, in turn, have the propensity to enhance
other consumer experience outcomes. Considering its infancy in de-
velopment and adoption, AR presents simultaneous design challenges
for marketers to facilitate consumers’ level of flow with the AR ex-
perience. Therefore, an investigation into AR’s ability to optimally
achieve a state of flow among consumers is necessary for marketers who
are skeptical of AR’s marketing potential.
With the aim of supporting marketers in their decision making and
investments in AR, this study has two primary objectives. First, we seek
to determine whether the experience of flow with AR technologies
differs from the experience of consumer flow in ordinary shopping
experiences. Thus, this initial objective aims to determine whether an
investment in AR technologies is warranted by marketers, or if effort
might be better served by investing in everyday flow experiences.
Second, this study aims to help marketers to reap the full benefits of
flow in AR shopping experiences. We do so by examining the unique
capabilities of AR to facilitate an enhanced state of flow and its influ-
ence across several consumer outcomes, including learning, informa-
tion utility, enjoyment, and satisfaction. Hence, this second objective
will help marketers to understand the components of AR shopping ex-
periences that warrant consideration when there are investment and
design considerations involved.
To achieve our objectives, a commercially available AR app was
utilized to conduct an online, between-subjects experiment with 500
participants. Two short films were developed depicting identical
shopping experiences with or without AR. Following exposure to the
film, participants were asked to complete a series of questions to test
our conceptual model. Quantitative analysis in the form of partial least
squares structural equation modeling was used to analyze the predictor
variables of consumer flow, as well as the impact of flow on consumer
outcomes. Findings from this study provide key insights into under-
standing the most salient AR factors influencing the immersive state of
flow and suggest that AR experiences can enhance consumer outcomes
such as information utility, learning, enjoyment, and satisfaction by
leveraging flow. We draw managerial and practical conclusions for
marketers and designers alike in the creation and execution of AR
technology within consumer contexts.
2. Literature review
2.1. Experiences
Premised on the belief that consumers want satisfying experiences
rather than just products (Abbott, 1956), an entire stream of marketing
research concerned with how consumers experience products
(Holbrook & Hirschman, 1982), shopping (Hui & Bateson, 1991; Kerin,
Jain, & Howard, 1992), consumption (Holbrook & Hirschman, 1982),
brands (Brakus, Schmitt, & Zarantonello, 2009; Schmitt, 1999), and
environments (Chang & Chieng, 2006; Esbjerg et al., 2012; Tsaur, Chiu,
& Wang, 2007) has developed over the past few decades. Experiences
have been widely acknowledged as a key component to competitive
brand positioning in the minds of consumers due to their ability to
evoke connections with brands through sensory, affective, intellectual,
and physical stimulation (Brakus et al., 2009; Schmitt, 1999;
Zarantonello & Schmitt, 2010). For example, brands such as Lush in-
clude sensory experiences as a core component of their business model–
e.g., one is not just buying a bar of soap at Lush, but also the experience
of fragrant aromas, carefully curated music, and exciting colors of
soaps, bath bombs, and facial masks. Experiences thus occur as a result
of some form of stimuli and can happen through direct or indirect ob-
servation or participation in events, whether they are virtual, real, or
dreamlike (Brakus et al., 2009; Schmitt, 1999).
Experiences today, however, can be a blend of real, virtual, and
fantasy. Technological advances have enabled brands to transform
shopping experiences by using computer-generated objects that appear
to co-exist in the same space as the real world in order to provide ad-
ditional benefits to consumers. AR, for example, has been adopted by
L’Oréal to provide an opportunity for consumers to virtually try on
make-up and hair colors before they purchase (Pearl, 2019), and the
U.S. retailer Lowe’s uses AR to help consumers see what certain pro-
ducts will look like in their homes (Ruff, 2018). Although technological
advances such as these have transformed the shopping experience for
consumers, there remains a dearth of research on AR’s ability to induce
a state of flow in shopping experiences and whether a state of flow
induced through AR has the ability to enhance consumer outcomes such
as increased learning, information utility, enjoyment, and satisfaction.
Research that examines these consumer outcomes in an AR context is of
strategic importance to marketers. For example, satisfaction with ex-
periences has been associated with repeat purchase (Dick & Basu,
1994), customer loyalty, positive word of mouth (Bearden & Teel, 1983;
Fornell, 1992; Fornell, Johnson, Anderson, Cha, & Bryant, 1996), and
the continued success of firms (Schmitt, 1999). Further, enjoyment
(Holbrook & Hirschman, 1982), information utility (Tynan &
McKetchnie), and learning (Poulsson & Kale, 2004) have all been as-
sociated with optimal experiences and positive consumer outcomes in
the marketing literature. We thus move next to a literature review of AR
before exploring the role of flow and experiences in the AR context.
2.2. Augmented reality
The recent advancements in technology enable the possibility to
develop new enriched environments in order to extend the physical
world, blending real-world objects with virtual-world objects (Pantano
& Servidio, 2012), resulting in an augmented reality (AR). Although
many definitions of AR exist, most share a common theme in that its
features are interactive, simultaneous, vivid, and unique to the en-
vironment in which it is used. Azuma (1997) defines AR as a real-time
view of the physical world while overlaid (augmented) with virtual
computer-generated information such as text, images, video, or any
other interactive computer-generated media. Affirming this definition,
Faust et al. (2012) define AR as the superposition of virtual objects
(computer-generated images, texts, sounds, etc.) on the real environ-
ment of the user. AR provides the user with an enriched and immersive
experience as the technology provides high levels of interactivity and
vividness in comparison to traditional media (Yim & Park, 2019).
While AR has been in existence for quite some time, the use of AR in
consumer markets has been hindered by large and cumbersome devices
(Rese et al., 2017). However, given the continually growing adoption of
the ubiquitous smartphone, brands are able to offer AR services to
consumer markets through smartphone applications (Dacko, 2017).
Firms such as IKEA, Nike, ASOS and Amazon have implemented AR in
an attempt to enrich the realistic experience of their products (McLean
& Wilson, 2019) and assist consumers during decision making (Heller,
Chylinski, de Ruyter, Mahr, & Keeling, 2019). Javornik (2016) con-
ceptualizes the potential of AR in developing an immersive flow ex-
perience, whilst Rauschnabel, He and Ro (2018) outline the potential
utilitarian and hedonic benefits of AR from a Uses & Gratifications
theory perspective. AR’s ability to overlay the physical environment
with virtual elements, including text-based information, rich media
images, and video, which can interact with the physical environment
during real-time, offers firms new possibilities in delivering a unique
experience to consumers. During decision making consumers often use
mental imagery to develop a mental picture that reflects products or
J. Brannon Barhorst, et al. Journal of Business Research 122 (2021) 423–436
424
3. experiences under consideration (Pearson, Naselaris, Holmes, &
Kosslyn, 2015); however, the key benefit of AR is that consumers no
longer need to imagine. Instead, they are presented with a life-like
computer-generated and real-world representation of the product or
experience (McLean & Wilson, 2019). In turn, the functions available
through AR have the potential to change a number of experiential ac-
tivities such as product trials, information search, acquisition, and
product try-ons (Javornik, 2016). Thus, unlike virtual reality (VR), AR
does not alter or replace the individual’s real-world but instead en-
hances it by combining additional information (text, images, video)
into the individual’s current real-world experience (Yim & Park, 2019).
Additionally, in comparison to other technologies (e.g., VR), in a re-
tailing context, AR is more easily incorporated into consumers' daily
lives, given that it easily integrates with ubiquitous technology such as
the smartphone (Heller et al., 2019). Accordingly, AR has been con-
ceptualized as generating more interesting and greater shopping ex-
periences in the e-commerce environment by developing highly inter-
active and immersive experiences (Hilken et al., 2018; Javornik, 2016).
The effects of this more realistic product experience via the individual’s
now enhanced real-world could help consumers to form closer re-
lationships with brands, when compared with traditional forms of
media, and influence overall consumer satisfaction (Dacko, 2017; Yim
& Park, 2019).
Azuma’s (1997) early research on AR outlines key characteristics of
the technology. According to Azuma (1997), AR combines the real
world and the virtual world to provide a novel experience. Secondly,
AR is interactive in real-time. Thirdly, AR is presented in 3D, offering a
clear and vivid representation of objects. Drawing on Azuma’s AR
characteristics, McLean and Wilson (2019) outline three AR attributes,
namely AR interactivity, AR vividness, and AR novelty. Accordingly, in
relation to AR interactivity, AR enables the user to interact with their
environment by controlling what they see, combining the consumer’s
physical environment with digitally-enhanced sensory information in-
cluding interactive visual, auditory and tactile information (Dwivedi
et al., 2020; Carrozzi et al., 2019; Hilken et al., 2018; Javornik, 2016;
Yim et al., 2017). In relation to AR vividness, AR provides a clear, vivid,
and detailed representation of an image combining the real world and
the virtual world (Hilken et al., 2018; van Esch et al., 2016). Lastly, in
relation to AR novelty, AR is novel in that it provides unique user-
specific sensory information to each user based on their current cir-
cumstances or situation (Petit, Velasco, & Spence, 2019). Table 1 dis-
tinguishes the characteristics of AR across ten studies and finds support
for Azuma’s (1997) and McLean and Wilson’s (2019) categorization of
AR characteristics with numerous conceptually overlapping variables.
For example, Heller et al. (2019) and Carrozzi et al. (2019) use the
terms personalization and customization in reference to the novelty of
the content displayed through AR apps. More so, Hilken et al. (2019)
refer to embedding, which is conceptually related to vividness, and
embodiment, which is conceptually related to interactivity. While al-
ternative terms may appear in the literature, McLean and Wilson’s
(2019) categorization of AR characteristics based on Azuma’s (1997)
early research provides a useful understanding of the key attributes of
AR. For example, in encompassing each attribute as distinguished by
McLean and Wilson (2019), a consumer may try on a virtual pair of
shoes on their feet. During the experience, the consumer is able to in-
teract by pinching and swiping to control the point of view. These vivid
interactions create sensory feedback, which develops a mental re-
presentation of the product in use, providing a novel experience for the
consumer (Heller et al., 2019). Such characteristics of AR enable in-
dividuals to offload the development of mental imagery during decision
making as they are able to draw out a visual representation of a par-
ticular product or service from the technology through the consumption
of richer media information. Although these characteristics of AR have
been identified in the literature as salient to optimal experiences, it is
not understood what role these constructs play in facilitating a heigh-
tened state of flow in a shopping context (Hilken et al., 2018; Javornik,
2016; Parise, Guinan, & Kafka, 2016; Yim, Chu, & Sauer, 2017), if any.
We, therefore, proceed next to a discussion of flow and its importance
to optimal experiences.
2.3. Flow theory
When experiencing the notion of flow, individuals often enter into a
state where they are completely switched off to the outside (real) world,
and become so engrossed in an activity that they feel like they are
encountering a natural, and highly enjoyable, out of body experience.
Csikszentmihalyi (1997) described flow as a situation in which an in-
dividual has completely focused motivation, and where the individual
is fully immersed, absorbed and engaged in the task at hand, with a loss
of self-consciousness and experiencing enjoyment in the process.
Seligman and Csikszentmihalyi (2014) explain that flow is a relatively
rare occurrence in everyday life, yet almost every activity (e.g., work,
study, or religious ritual) can produce it. Csikszentmihalyi (1997) il-
lustrated how flow exists during games such as chess and other activ-
ities such as rock climbing and dancing. More recent research has ex-
plored the existence of flow in sports, shopping (online and offline), and
in virtual games. Hoffman and Novak (2009) suggest that the critical
aspect of the concept of flow is full concentration and immersion in an
activity. Chen, Wigand, and Nilan (1999) outline that those that have
experienced flow normally report feelings of immersed pleasure, ab-
sorbed interest, focused attention, and a perceived acceleration of time.
The concept of flow and its application to technology has evolved
through the works of Hoffman and Novak (1996), Novak et al. (2000),
Novak, Hoffman, and Duhachek (2003) and Hoffman and Novak
(2009). Hoffman and Novak (2009, p.57) indicate that the flow ex-
perience with technology is ‘characterized by a seamless sequence of
Table 1
AR Characteristics.
Studies AR characteristics
Azuma (1997) Interactivity, Vividness, and Novelty
McLean and Wilson Interactivity, Vividness, and Novelty
Hilken et al. (2020) Interactivity
Petit et al. (2019) Sensory Interaction
Yim et al. (2017) Interactivity and Vividness
Heller et al. (2019) Sensory Richness, Vividness, Interactivity, Control, Personalisation, Informativeness
Yim and Park (2019) Interactivity and Vividness
Javornik (2016) Interactivity, Media Richness
Hilken et al. (2018) Embedding (richness and vividness), Embodiment (Interactivity and Physical Control) and Extension (co-creation)
Dieck et al. (2015) Informativness, Unique information
Flavian et al. (2019) Real-time Interactivity
Carrozzi et al. (2019) Customization
Rese et al. (2019) Interactivity
Dacko (2017) Unique and Interactive
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425
4. responses facilitated by machine interactivity, intrinsically enjoyable,
accompanied by a loss of self-consciousness.’ Kim et al. (2012) further
highlight that immersive tendency is a predictor of ‘human–computer
interaction’ as it influences the psychological state an individual is in
while they interact with stimuli in a digital environment.
However, the conceptualization of flow remains somewhat murky.
Researchers agree that flow is something that most individuals will
have experienced to some extent, either through games, reading, dan-
cing, shopping, or sports, and thus researchers, along with research
respondents, have an understanding of the concept of flow. However,
despite the conceptual and empirical advancement of flow theory over
recent years, the concept still encourages debate. A unified measure-
ment of flow has not been established within the extant literature (see:
Choi, Kim & Kim, 2007; Ghani & Deshpande, 1994; Hoffman & Novak,
1996; Hoffman & Novak, 2009). However, consensus illustrates com-
munality that flow encompasses immersion in an activity. In parallel,
the literature highlights conflicting findings regarding antecedents and
consequences of flow where variables have been assessed inter-
changeably (Lee et al., 2019).
3. Conceptual model
In this section, we build our conceptual model by first conducting a
literature review of the design aspects that have been referenced in the
literature as important to inducing a state of flow. Next, we discuss the
literature related to flow’s ability to enhance the elaboration of in-
formation. We then complete our model with a review of the literature
on aspects of consumer experiences that have been associated with
positive consumer outcomes. We complete this section with a for-
malized conceptual model to take the study forward.
3.1. AR interactivity and flow
Although there are ongoing debates regarding the concept of flow,
one of the most consistently argued drivers of flow is interactivity (Lee
et al., 2019; van Noort, Voorveld, & van Reijmersdal, 2012). Hoffman
and Novak (2009) illustrate that interactivity is a technological system’s
capability to enable individuals to interact easily, control, manipulate,
and be involved with content. Interactivity can be considered from two
complementary positions, (1) the features of the technology and (2) the
user’s perception (Yim, Chu, & Sauer, 2017). Accordingly, such a hol-
istic description of interactivity provides an understanding of the role of
interactivity in AR.
Steuer (1992) outlines the importance of technology features in
defining interactivity from the technology used. Thus, consumers’
perceptions of interactivity may be swayed by the subcomponents of
the technology including its speed, how fast individuals are able to
manipulate the content; mapping, the similarity of the control in the
computer-generated world to the real world; and range, how much the
content can be manipulated by the individual. From a user perception
perspective, interactivity is an individual’s subjective perception of
interactivity, whether they are real or virtual in nature (Petit et al.,
2019). Importantly, inherent to AR technology is participation in ma-
nipulating computer-generated objects combining the real world with
the virtual world.
AR technology is arguably one of the most interactive types of
technology, consisting of the aforementioned high levels of user parti-
cipation. Given that the interactivity involved with AR involves ma-
nipulating both the real world and the virtual world and extends be-
yond the screen (Javornik, 2016), we posit that such user participation
and control will lead to an absorbing state of mind while using the
interactive features (Yim et al., 2017), immersing the individual in the
activity and positively influencing the state of flow (Parise et al., 2016).
Thus we hypothesize:
H1: The interactivity of the AR technology will more positively
influence the state of flow than a traditional shopping experience
3.2. AR vividness and flow
Vividness is defined as ‘the ability of a technology to produce a
sensorially rich mediated environment’ (Steuer, 1992). It refers to the
process of combining the sensory experience of real objects that can be
seen with the non-sensory imaginary objects created in an individual’s
mind to create a clear image of a product or experience (Lee, 2004).
Flavián, Gurrea, and Orús (2017) posit that vivid information can come
in many forms, including images, audio, and visual content that evokes
the physical and experiential aspects of a purchase. In the digital en-
vironment, vividness is often associated with the aesthetic appeal of the
product display on a website or mobile app (Flavián et al., 2017). A
more vivid display of products is more likely to influence a consumer’s
cognitive processing (Keller & Block, 1997) due to its more interesting
appeal, resulting in an increased evaluation of the product’s informa-
tion than what pallid information would induce (Jiang & Benbasat,
2007).
Accordingly, the vividness of the information can heighten the
perception of information quality through increasing the number of
sensory dimensions, which in turn may increase cognitive processing.
Similar to interactivity, vividness helps consumers to mentally visualize
products and upcoming experiences (Phillips, Olson, & Baumgartner,
1995). Thus, enhancing the vividness of product depictions can result in
increased product-related thoughts and cognitive absorption. AR en-
ables individuals to develop a clear and detailed view of the real world
and the virtual world (McLean & Wilson, 2019). Such an enriched en-
vironment provides multiple sensory objects, offloading the need to
imagine how products may look, or the need to seek further informa-
tion. Therefore, the detail, clarity, and well-defined representation of
products combining both the real world and virtual world is likely to
influence an individual’s absorption in their activity, leading to an
immersive flow experience (Hilken et al., 2018). Thus we hypothesize:
H2: The vividness of the AR technology will more positively influ-
ence the state of flow than the vividness of a traditional shopping ex-
perience
3.3. AR novelty and flow
McLean and Wilson (2019) outline that the augmented combination
of the real world and the virtual world results in a continually unique
experience. Thus, each time an individual utilizes AR, they often en-
counter unique stimuli due to the range and scope of manipulation
between the virtual world and the real world. Therefore, it is important
to note that novelty in the context of this study does not refer to the
newness of the technology. Instead, novelty refers to the unique, per-
sonalized, novel information (stimuli) individuals are presented each
time they use the AR technology. AR content can be delivered in the
form of text, images, audio, and video (Javornik, 2016). Recent AR apps
have enabled brands to extend storytelling through audio and video
with AR (e.g., 19 Crimes wine) and through the use of image placement
(e.g., IKEA). The ability to place virtual objects such as furniture in
one’s own room, or to view a video overlaid on one’s current real-world
environment, provides highly personalized, novel information (McLean
& Wilson, 2019; Preece, Sharp, & Rogers, 2015). As a result, AR enables
consumers to personalize information to their own needs and pre-
ferences.
Moreover, a distinguishing feature of novelty is encountered during
information processing, as novel information has the capacity to draw
the attention of consumers leading to curiosity and the propensity to
become deeply engrossed (Hoffman & Novak, 2009; Kover & James,
1993). Drawing on Cue Utilization theory (see: Easterbrook, 1959), the
unusual characteristic of novel stimuli appears to encourage cognitive
processing. Conversely, familiar stimuli do not provide the same cues
required to ignite cognitive processing, resulting in less arousal and
immersion in the activity (Yim et al., 2017). Given that the novel sti-
muli presented through AR elicit cognitive processing, we suggest that
J. Brannon Barhorst, et al. Journal of Business Research 122 (2021) 423–436
426
5. AR novelty will spark an individual’s cognitive flow leading to higher
arousal. Thus we hypothesize:
H3: The novel information presented through AR technology will
more positively influence the state of flow than the novel information of
a traditional shopping experience
3.4. AR, flow theory, and the elaboration of information
As discussed previously, AR may be uniquely positioned to facilitate
an enhanced state of flow due to the distinctive aspects of interactivity,
vividness, and novelty. Notably, research associated with the
Elaboration Likelihood Model (ELM) indicates that an enhanced state of
flow may also facilitate an enhanced state of elaboration of information
or cognitive processing.
The ELM is a dual-process theory of cognitive processing that has
garnered widespread attention and acceptance in the literature of at-
titude change, persuasion, and information processing (Cacioppo &
Petty 1984; Petty & Cacioppo, 1986a; 1986b). The basic tenets of ELM
argue that consumers process information on a continuum of low to
high effort, where low effort results in low elaboration of information,
and higher amounts of effort lead to high elaboration of information.
The amount of effort directed towards processing information depends
on an individual’s motivation as well as cognitive ability. As mentioned
previously, research has outlined that flow has been described as a si-
tuation in which an individual has focused motivation, and where the
individual is fully immersed and engaged in the task at hand (Csiks-
zentmihalyi, 1997). As AR is uniquely positioned to induce an enhanced
state of flow, it would be pertinent to understand what influence, if any,
an enhanced state of flow facilitated through AR could have on the
elaboration of information in a shopping context.
Consumer elaboration of marketing messages is highly relevant to
marketers as higher levels of elaboration have been shown to lead to
more enduring outcomes related to attitude change, learning, in-
formation recall, and increased persuasion when accompanied by
strong arguments (Cyr, Head, Lim & Stibe, 2018; Petty, Brinol, &
Priester 2009; Petty & Cacioppo, 1986a, 1986b). When consumers are
motivated to engage in high elaboration, they are more likely to process
the arguments in a message and make evaluations based on the content
of the message and argument strength, leading to long-lasting attitudes
and evaluations. By comparison, consumers who engage in low ela-
boration are more persuaded by surface heuristics, such as spokes-
person attractiveness or colors (Hennessey & Anderson, 1990). These
attitudes are less enduring and less persuasive long term and are as-
sociated with lower recall of message information (Heath and Nairn,
2005). As mentioned previously, AR has the capability of presenting
vivid displays of novel information in consumers’ real worlds. Com-
pared with a traditional shopping experience, the presentation of vivid
and novel information through AR may, therefore, garner the attention
of consumers and lead to higher levels of motivation to process in-
formation.
A comparison of ELM with flow theory suggests that the experience
of flow may affect levels of elaboration. This relationship has been
studied in the context of other new technological advancements, such
as in website design. van Noort et al. (2012) studied consumer adoption
of new technologies and conducted research that directly related the
experience of online flow, or consumers’ full immersion in a website
experience, to higher levels of elaboration and enhanced cognitive
outcomes as predicted by ELM. The authors argue that “the more web
users are immersed in an online activity, the more likely that they are
motivated to process information…higher levels of motivation to pro-
cess should result in increased elaboration levels, affecting the magni-
tude of cognitive responses generated.” The authors found empirical
evidence for this relationship, concluding that online flow increased the
level of elaboration of the website content resulting in more thoughts
generated by visiting a brand website. Other research similarly suggests
that the experience of flow may enhance consumer elaboration, such as
through website content recall and learning (Skadberg & Kimmel,
2004) and in e-learning in an online training context (Choi et al., 2007).
Based on this literature, it should follow that flow experienced with AR
should lead to increased immersion and motivation, and therefore
higher levels of elaboration with AR message content. Increased ela-
boration with AR message content should affect consumers’ cognitive
responses to the information such as through learning and perceived
usefulness of content or information. We, therefore, continue to build
our model with the following hypotheses:
H4: Flow more positively affects information utility when the use of
AR technology is part of the experience
H5: Flow more positively affects learning when the use of AR
technology is part of the experience
3.5. Flow and enjoyment
As stated previously, a state of flow has been associated with a deep
sense of enjoyment due to a focused state and loss of self-consciousness
(Chen et al., 1999; Csikszentmihalyi, 1997). In addition, and with re-
gard to technology, a state of flow has been associated with intrinsic
enjoyment (Hoffman & Novak, 2009), with media and video game
enjoyment directly associated with a state of flow (Weibel et al., 2008).
However, it is not empirically clear within the literature, whether a
state of flow will more positively enhance enjoyment when AR is a
component of a shopping experience. Given its emphasis in the litera-
ture and the unique aspects associated with AR (interactivity, novelty,
and vividness), it is plausible that an enhanced state of flow provided by
AR could influence a greater sense of enjoyment. We, therefore, con-
tinue to build our model with the following hypothesis:
H6: Flow more positively affects enjoyment when the use of AR
technology is part of the experience
3.6. What consumers want from experiences and AR’s influence
The marketing literature has long espoused that consumers seek
entertaining experiences (Holbrook & Hirschman, 1982) that provide
useful information (Tynan & McKechnie, 2009), and engender learning
(Poulsson & Kale, 2004). Experiences, therefore, serve hedonic and
utilitarian purposes for the consumer facilitating, not only entertain-
ment and pleasure (Holbrook, 2000) but also value to the consumer
with the provision of useful information and learning (Tynan &
McKechnie, 2009). Experiences must also be engaging as they are a
distinct component of the consumption journey that should foster in-
teraction between the consumer and the provider of the experience
(Lusch et al., 2007; Poulsson & Kale, 2004; Tynan & McKechnie, 2009).
As such, experiences happen as a result of engaging consumers in a co-
created activity between the consumer and experience provider. Un-
surprisingly, consumer engagement through experiences has been re-
ferred to as flow within the experiential marketing literature (Poulsson
& Kale, 2004; Tynan & McKechnie, 2009) due to the value that ex-
periences must be able to offer to the consumer, the interaction be-
tween the experience provider and the consumer, and the sense of
enjoyment that should be a part of the experience.
What is not empirically understood in the literature, however, is
whether these aspects of satisfaction with experiences (flow, informa-
tion utility, learning, and enjoyment) are strengthened when AR is a
part of the experience. As stated previously, AR technology brings new
facets to experiences including the ability of users to interact with the
technology (Javornik, 2016), experience a virtual world that is overlaid
on their real-world (Rauschnabel et al., 2017), and experience a sense
of novelty and richness of experience due to the vividness of the ex-
perience (McLean & Wilson, 2019). AR, therefore, provides a unique
context to consumers with the propensity to facilitate a heightened
state of message elaboration and enjoyment due to an enhanced state of
flow. In turn, the use of AR technology could enhance the known uti-
litarian and hedonic aspects of experiences that influence satisfaction
J. Brannon Barhorst, et al. Journal of Business Research 122 (2021) 423–436
427
6. with an experience. We, therefore, complete our model with the fol-
lowing hypotheses:
H7: Information utility will have a more positive effect on sa-
tisfaction with the experience when the use of AR technology is part of
the experience
H8: Learning will have a more positive effect on satisfaction with
the experience when the use of AR technology is part of the experience
H9: Enjoyment will have a more positive effect on satisfaction with
the experience when the use of AR technology is part of the experience
Fig. 1 provides a graphical representation of our hypotheses. Next,
we discuss the methodological approach of our study.
4. Overview of the present research methodology
In order to construct a context that allows for AR technology to be
readily experienced in an online survey collection platform, two videos
were created (with and without the use of AR) utilizing a first-person
perspective of a shopping experience for a bottle of wine. In the AR
condition, the first-person perspective enters a wine shop and picks up a
bottle of wine, turning the wine left and right to read the label. The
video then shows a hand pulling out a cell phone, opening the AR
phone application, and initiating an AR experience in which the wine
label begins to interact with the viewer through narrative storytelling.
In the control condition, the first-person perspective enters a wine shop
and picks up a bottle of wine and evaluates the label, but does not pull
out a mobile phone and engage the AR experience. Both videos are
identical, with the exception of the phone and AR experience. In order
to ensure both experiences were as true to life as possible, a sound
technician was employed to add ambient background noise recorded in
the wine shop using a ZOOM H4n Portable Audio Recorder to both
videos. The first-person perspective in the video was achieved using a
GoPro Hero 5 Black mounted on the researcher’s head with a head
strap. Head and arm movements were kept slow and controlled during
recording so as not to disorient the viewer. The footage was edited with
Final Cut Pro X on an iMac Pro to create the finished video content.
Wine shopping was chosen as an attractive test scenario as wine
decisions are often based on limited knowledge and are heavily influ-
enced by heuristic cues such as those provided by various marketing
tactics (e.g., labels, brand names, and shopping experiences; Danner
et al., 2016). Additionally, we chose to make use of a commercially
available AR experience developed by the 19 Crimes wine brand to
increase the realism of the experience for our participants.
As prior brand knowledge was identified as a potential confound,
we conducted our study in the UK to allow for the most effective pre-
screening of participants to produce a sample of consumers who did not
recognize the brand. Age, location, wine consumption frequency, and
brand recognition were measured and used to screen participants in
both our pilot and experiment. We determined our target sample size
and manipulations in advance and reported all data exclusions and
analyses conducted on the data in this report.
4.1. Experimental design
Five hundred UK data panel participants were randomly assigned to
a between-groups research design (AR vs. no AR). The treatment group
(249 participants) were exposed to a shopping experience with AR,
whilst the control group (251 participants) were exposed to the same
shopping experience without AR. The panel was comprised of 356 fe-
male and 144 male college-educated participants aged 18 + with
12.4% between the ages of 18–24, 39.4% between the ages of 25–34,
26.1% between the ages of 35–44, 15.3% between the ages of 45–54,
5.2% between the ages of 55–64, and 1.6% over the age of 65. This
sample is representative of wine consumption by gender in the UK
(Statista, 2013).
Participants were recruited to participate in a 10–15-minute ex-
periment on wine shopping. Participants were screened for age (above
18), location (UK), frequency of wine consumption (more than ‘never,’
and recognition of the wine brand (no recognition)). Upon passing the
pre-screen assessment, participants read about a hypothetical shopping
scenario and were asked to do their best to imagine themselves in the
role described while watching a video in the first-person. Participants
were instructed to imagine themselves with the need to purchase a
bottle of wine. Subsequently, participants were randomly presented to
one of two first-person perspective videos simulating shopping in a
wine store and assessing a particular bottle of wine with and without
the experience of AR. After watching the video, participants completed
several scales presented in randomized order.
4.2. Measures
To test our conceptual model, several survey instruments were
identified from prior literature and adapted to relate the scales to the
AR experience. Detailed descriptions of survey items relating to each
variable are provided in Table 2.
4.3. Data analysis
We conducted two forms of data analysis to meet our objectives.
First, we conducted a descriptive statistics analysis to meet the first
objective of our study. Second, to examine flow’s influence across the
consumer outcomes of learning, information utility, enjoyment, and
satisfaction in an ordinary and AR shopping context, we employed
partial least squares structural equation modeling (PLS-SEM).
According to Hoyle (1995, p. 1), SEM ‘is a comprehensive statistical
approach to testing hypotheses about relations among observed and
latent variables.’ This method has also become ‘quasi-standard in
marketing and management research when it comes to analyzing the
cause-effect relations between latent constructs’ (Hair, Ringle, &
Sarstedt, 2011, p. 139). PLS-SEM was suitable for our study because the
theoretical model includes a mix of reflective and formative indicators
(Lowry & Gaskin, 2014) and the models being tested are exploratory in
nature (Hair, Hult, Ringle, Sarstedt, & Thiele, 2017; Lowry & Gaskin,
2014; Sarstedt, Hair, Ringle, Thiele, & Gudergan, 2016).
5. Results
5.1. Descriptive statistics
To meet our first objective, it was necessary to determine whether
the experience of flow with AR technology (treatment group) differed
Fig. 1. Factors Influencing Satisfaction with an AR Experience.
J. Brannon Barhorst, et al. Journal of Business Research 122 (2021) 423–436
428
7. from the experience of consumer flow in an ordinary shopping ex-
perience (control group). As the descriptive statistics in Table 3 de-
monstrate, flow had a mean score difference of 1.50, demonstrating the
additional amount of flow experienced by those in the treatment group
(AR) versus the control group (no AR). Additionally, the antecedents to
flow (vividness, novelty, and interactivity) saw higher mean score differ-
ences with novelty garnering the largest difference (6.99), followed by
vividness (3.10) and interactivity (2.29). With regard to the elaboration
of information and enjoyment constructs included in our model, en-
joyment had the greatest mean score difference when comparing the
treatment group versus control (5.91), followed by learning (1.26) and
information utility (0.52). Finally, overall satisfaction with the shopping
experience was greater in the treatment group (AR) versus the control
group (no AR) with a mean score difference of 2.22.
5.2. Evaluation of the structural model
Two PLS-SEM models were created to test the variables in our
conceptual model. Seven different independent variables, demonstrated
in Table 2, were tested in the models based on the literature review. In
order to assess the validity of the measurement models, the methods
detailed by Wong (2013) and Hair, Hult, Ringle, and Sarstedt (2016)
were utilized. Discriminant validity was established when the factor
loading coefficients for the items that constituted each latent variable
were greater than their cross-loadings on alternative latent variables.
The cross-loadings for the models were assessed, and both models fit
the criteria. These are demonstrated in Tables A.1 and A.2. Convergent
validity was established as the average variance explained (AVE) by the
multiple indicators of each latent variable was > than 0.5. Internal
consistency reliability was established, as all of the composite reliability
coefficients for the latent variables were > 0.6. These are demon-
strated in Tables A.3 and A.4 in the Appendix. We estimated the sta-
tistical significance of each path coefficient (β) through bootstrapping.
We randomly sampled the raw data 5,000 times and computed the
mean of each β coefficient. To confirm the validity of our models, we
use Cronbach’s alphas and the composite reliability scores. These are
demonstrated in Tables A.3 and A.4 in the Appendix. Tests to see if the
data met the assumption of collinearity indicated that multicollinearity
was not an issue as the Variance Inflation Factor (VIF) measures of the
independent variables were all < 0.5 (Hair et al., 2016). These are
Table 2
Augmented Reality Modified Scale Measures.
Measure Authors Scale Items
Vividness Adapted from Yim et al., 2017 Thinking about the wine experience you just saw, please indicate the extent you agree or
disagree with the following statements.
• It was clear
• It was detailed
• It was vivid
• It was sharp
• It was well defined
Interactivity Adapted from Yim et al., 2017 Thinking about the wine experience you just saw, please indicate the extent you agree or
disagree with the following statements.
• The user appeared in control of the augmented reality technology/the user appeared in
control (control group)
• The user appeared to have some control over what they wanted to see
• The user appeared to have control over the pace of the interaction
• The technology appeared to respond to the user’s specific actions quickly and efficiently
Novelty Adapted from Yim et al., 2017 Thinking about the wine experience you just saw, please indicate the extent you agree or
disagree with the following statements.
• It was a new experience for me
• It was a unique experience
• It was a different experience
• It was an unusual experience
Flow Adapted from Yim et al., 2017 Thinking about the wine experience you just saw, please indicate the extent you agree or
disagree with the following statements.
• I was deeply engrossed
• I was absorbed in the experience
• My attention was not focused on the experience (reverse scored)
Information Usefulness Adapted from Bhattacherjee & Sanford,
2006
Thinking about the wine experience you just saw, please indicate the extent you agree or
disagree with the following statements.
• The information provided was informative
• The information provided was helpful
• The information provided was valuable
• The information provided during the video was persuasive
Learning Adapted from Schlinger, 1979 Thinking about the wine experience you just saw, please indicate the extent you agree or
disagree with the following statements.
• Watching it reminded me that I am dissatisfied with the wine that I purchase now
• I learned something from the experience that I did not know before
• The experience told me about a new product that I think I’d like to try.
• During the experience, I thought how that wine might be useful to me
Enjoyment Adapted from Schlinger, 1979 Thinking about the wine experience you just saw, please indicate the extent you agree or
disagree with the following statements.
• It was lots of fun to watch and listen to
• Watching it was fun and entertaining
• The experience I just watched was not just selling the wine, it was entertaining and I
appreciate that.
• The characters captured my attention.
Satisfaction with the AR Experience Adapted from Song and Zinkhan, 2008 Thinking about the wine experience you just saw, please indicate the extent you agree or
disagree with the following statements
• I am satisfied with the experience
• This experience is exactly what I needed.
• This experience hasn’t worked out as well as I thought it would (reversed scored)
J. Brannon Barhorst, et al. Journal of Business Research 122 (2021) 423–436
429
8. demonstrated in Table A.5.
5.3. PLS-SEM results
Table 4 displays the significant predictors and hypotheses results of
flow’s influence across the consumer outcomes of learning, information
utility, enjoyment, and satisfaction for both the treatment (AR) and
control group (no AR) PLS-SEM structural models. As demonstrated in
Table 4, a comparison of the R2
values of the flow constructs between
the shopping experience with AR (Panel A) and without AR (Panel B)
further demonstrates the difference in flow between the two groups
with R2
s of 0.49 and 0.27 respectively.
Additionally, Panel A (treatment group, with AR experience) de-
monstrates that most of the hypotheses were supported with the fol-
lowing results: vividness (H1), interactivity (H2) and novelty (H3) all
positively affect flow (R2 0.49); flow (H4-H6) more positively affects
information utility (R2
0.27), learning (R2
0.31), and enjoyment (R2 0.68).
Notably, the two paths with the highest effect are flow (H6) affecting
enjoyment (t-value 42.61, R2
0.77) and flow (H5) affecting learning (t-
value 13.52, R2
0.31).
Table 4, Panel B demonstrates the results of the Control Group (no
AR experience). As demonstrated in the table, all of the factors were
significant, with the exception of learning affecting satisfaction with the
shopping experience. Table 4, Panel B, also demonstrates several key
differences when compared to the treatment group (Panel A). Notably,
the standardized coefficients for a majority of the variables in the
treatment group (Panel A) are greater than the control group (Panel B) -
the exceptions being interactivity affecting flow (0.151 versus 0.194),
and information utility affecting AR experience satisfaction (0.343 versus
0.433). It is worth noting that although these two individual standar-
dized coefficients were slightly less than the control group, they were
significant, and their effect sizes (R2
) were also greater than the control
Table 3
Descriptive Statistics of Key Constructs. The table below shows the descriptive statistics for each composite variable. The Likert scale items to build the composite
variables can be found in Table 2. The Likert scale ranges used to build the composite variables below were as follows: FLOW/CFOW, ARSAT/CSAT: 1 (strongly
disagree) to 7 (strongly agree) for a composite range of 3 (minimum) to 21 (maximum); LEARNING/CLEARNING, ENJOYMENT/CENJOYMENT, INFOU, CINFOU,
NOVELTY/CNOVELTY: 1 (strongly disagree) to 7 (strongly agree) for a composite range of 4 (minimum) to 28 (maximum); INTERACTIVITY/CINTERACTIVITY: 1
(strongly disagree) to 6 (strongly agree) for a composite range of 4 (minimum) to 24 (maximum).
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation Mean Difference
FLOW 249 3.00 21.00 14.9639 4.21148 1.50
CFLOW 251 3.00 21.00 13.4603 3.86145
LEARNING 249 4.00 27.00 14.4297 4.88126 1.26
CLEARNING 251 4.00 24.00 13.1746 5.01008
ARSAT* 248 4.00 21.00 12.5887 3.93471 2.22
CSAT 251 3.00 21.00 10.3705 4.03313
ENJOYMENT 249 4.00 28.00 18.8233 6.35516 5.91
CENJOYMENT 251 4.00 28.00 12.9163 6.03962
INFOU** 248 4.00 28.00 15.5444 6.03549 0.52
CINFOU 251 4.00 28.00 15.0239 5.54395
VIVIDNESS 249 10.00 35.00 26.0241 5.05209 3.10
CVIVIDNESS 251 6.00 35.00 22.9286 6.01483
INTERACTIVITY 249 6.00 24.00 17.2048 3.68221 2.29
CINTERACTIVITY 251 4.00 24.00 14.9163 5.31836
NOVELTY 249 9.00 28.00 24.3333 3.59958 6.99
CNOVELTY 251 4.00 28.00 17.3426 5.93179
C denotes ‘control group’, *ARSAT = AR Satisfaction, **INFOU = Information Utility.
Table 4
SEM Model Results.
Panel A: Treatment Group
Hypotheses Result Standardized Estimate β t-value R2
H1 Vividness → Flow Supported 0.346***
5.45 0.49
H2 Interactivity → Flow Not supported 0.151**
2.40 0.49
H3 Novelty → Flow Supported 0.384***
6.50 0.49
H4 Flow → Info Utility Supported 0.517***
11.66 0.27
H5 Flow → Learning Supported 0.559***
13.52 0.31
H6 Flow → Enjoyment Supported 0.825***
42.61 0.68
H7 Info Utility → ARES Not supported 0.343***
6.51 0.77
H8 Learning → ARES Supported 0.240***
4.40 0.77
H9 Enjoyment → ARES Supported 0.412***
9.16 0.77
Panel B: Control Group
Standardized Estimate β t-value R2
Vividness → Flow 0.345***
5.04 0.27
Interactivity → Flow 0.194***
2.72 0.27
Novelty → Flow 0.151***
2.59 0.27
Flow → Info Utility 0.484***
9.02 0.23
Flow → Learning 0.387 ***
6.48 0.15
Flow → Enjoyment 0.652***
15.83 0.43
Info Utility → ARES 0.433***
8.28 0.67
Learning → ARES .048NS
0.82 0.67
Enjoyment → ARES 0.420***
7.10 0.67
***ρ < 0.001, **ρ < 0.05, ns = not significant *Info Utility = Information Utility, ARES = Augmented Reality Experience Satisfaction.
J. Brannon Barhorst, et al. Journal of Business Research 122 (2021) 423–436
430
9. group. Enjoyment had a marginal difference in standardized coefficients
(0.412 versus 0.420), with the treatment group t-value higher at 9.16
versus 7.10 for the control group. Finally, Table 4, Panel A, also de-
monstrates that the overall effect sizes (R2
) of all of the factors of the
treatment group (AR) are greater versus the control group (Panel B, no
AR), further demonstrating the impact of the AR experience versus the
shopping experience without AR.
Graphical representations of our conceptual model results can be
seen in Figs. 2 and 3.
6. Discussion
Given the significant investment in AR by brands and its potential to
enhance the shopping experience, this research advances our theore-
tical knowledge and practical application of AR. Firstly, the research
confirms AR’s ability to induce a heightened state of flow when com-
pared to a normal shopping experience. Secondly, the research outlines
the role of unique AR characteristics (AR interactivity, AR vividness,
and AR novelty) in inducing the state of flow. Finally, the research
outlines the important role of AR in inducing a heightened state of flow
and this heightened state of flow’s effect on several consumer outcomes.
The results of our experiment provide supporting evidence for our
conceptual model with significant casual relationships determined for
vividness (H1), interactivity (H2), novelty (H3), flow (H4-H6), information
utility (H7), learning (H8), and enjoyment (H9). Our conceptual model,
therefore, adds to the extant literature on AR by identifying and map-
ping out the key variables for inducing a state of flow in an AR shopping
experience, and other variables that are important to designing sa-
tisfactory AR shopping experiences. These findings are discussed in
detail in the following sections.
6.1. AR’s propensity to induce a heightened state of flow
With regard to inducing a state of flow, this study confirms AR’s
ability to induce a heightened state of flow when compared to a normal
shopping experience. Although the literature discussed the benefits of
flow, there was a dearth of research that examined AR’s ability to in-
duce a heightened state of flow, and the factors that were most salient
to doing so. Findings from this study, therefore, suggest that shopping
experiences that include AR as a component of the experience present
unique opportunities for marketers to capitalize on the benefits asso-
ciated with a state of flow – including enhanced cognitive processing,
enjoyment, and overall satisfaction with a shopping experience.
Further, there are unique design characteristics that are integral to
producing an optimal state of flow. These are discussed next.
6.2. Important design aspects of flow
The findings from this study affirm that the AR characteristics,
namely AR vividness, AR interactivity, and AR novelty, are all key con-
tributors to the immersive state of flow. While previous research has
outlined the role of interactivity in influencing the state of flow
(Hoffman & Novak, 2009), AR’s mode of operation goes beyond the
screen and interacts with the real-world space. The results of this re-
search indicate a more significant state of flow with AR in comparison
to a regular shopping experience. Thus, our research adds support to
the previous conceptualizations (Flavián et al., 2017; Javornik, 2016)
that AR may differ in relation to its impact on flow, which we find can
create a more immersive environment, resulting in consumers’ be-
coming more engrossed in their activity.
Moreover, the vividness of the AR technology enables brands to
provide consumers with a sensorially-rich mediated environment. AR
enables consumers the control to combine the sensory experience of
real objects with the added sensory experience of computer-generated
objects. Conversely, traditional real-world environments or other di-
gital environments (e.g., the web) requires individuals to use the sen-
sory experience of real-world objects along with their imagination to
create a clear picture of a product, the brand’s story or experience (Lee,
2004). Such heightened computer/real-world mediated vividness,
which can be presented in multiple formats (images, text, video, audio),
Fig. 2. Factors Influencing Satisfaction with an AR Experience (treatment group).
Fig. 3. Factors Influencing Satisfaction with a Normal Shopping Experience (control group).
J. Brannon Barhorst, et al. Journal of Business Research 122 (2021) 423–436
431
10. evokes a deeper immersive flow state. Thus, such an enriched en-
vironment provides multiple sensory objects, offloading the need to
imagine how products may look, or the need to seek further informa-
tion, enabling consumers to focus on their activity. Therefore, in line
with Keller and Block (1997), a more vivid display of products, in this
case through AR, is more likely to influence a consumer’s cognitive
processing resulting in the flow experience due to its more interesting
appeal. This results in an increased evaluation of the product and its
information than what pallid information would involve.
Furthermore, the augmented combination of the real world and the
virtual world continually creates a unique experience, personal to each
consumer’s environment. This research finds that the novel (unique)
information encountered from AR technology during information pro-
cessing draws the attention of consumers and leading them to become
deeply engrossed in the activity. Our findings are in line with Cue
Utilisation theory (Easterbrook, 1959) in that novel stimuli from the AR
encourages cognitive processing, while usual pallid stimuli do not
provide the same cues resulting in less immersion in the activity. Thus,
the novel stimuli presented through AR has a greater influence in in-
ducing the state of flow than non-AR presented information.
6.3. AR induced flow and shopping experience outcomes
We further find evidence that the state of flow more positively in-
fluences consumer perceptions of information utility, learning and en-
joyment, and that these perceptions, in turn, are significant predictors of
overall satisfaction with the experience. Two of the most compelling
findings from the study were that learning was a significant predictor of
satisfaction with the AR experience in the treatment group, versus no
significance in the control group, and that flow was a significant pre-
dictor of learning in the treatment group versus no significance in the
control group. Further, flow more positively influenced information
utility and enjoyment when comparing the shopping experience with AR
versus the shopping experience without AR. Thus, the AR experience
not only induces an intensified state of flow, but also enables a heigh-
tened state of elaboration of information and overall enjoyment. The
perception of useful information, learning, and enjoyment experienced
through AR, in turn, influences a consumer’s satisfaction with their
experience. These findings can be explained by revisiting the concepts
of flow, the elaboration likelihood model, and the experiential mar-
keting literature. A state of flow is characterized by a sense of serenity,
losing the worries of everyday life, immersion, enjoyment, and focused
attention. A state of flow, therefore, supports cognitive information
processing as attention is focused and free of distraction
(Csikszentmihalyi, 2014, van Noort et al., 2012). Based on his research
of flow, Csikszentmihalyi (2014) reports that 15% of the best everyday
experiences occur in the context of learning – and that learning is di-
rectly associated with happiness due to personal growth. He explains
that humans inherently seek challenges and growth through learning
and find ways to ‘get deeply involved with the world around’ by
learning (Csikszentmihalyi, 2014, p. 163). Further, the experiential
marketing literature has espoused the importance of enjoyable experi-
ences (Holbrook & Hirschman, 1982) that provide useful information
(Tynan & McKechnie, 2009), and engender learning (Poulsson & Kale,
2004). In the context of AR, the results of our study suggest that AR
presents a powerful way to engender an enhanced state of flow and,
subsequently, learning, enjoyment, and satisfaction in the shopping
environment. It does so by facilitating a heightened state of flow
through the components that are unique to AR (interactivity, novelty, and
vividness) and the enhanced cognitive processing of useful information,
learning, and enjoyment that takes place via the combination of the real
world and the virtual world. The findings from our study, therefore, add
to the extant literature on flow and experiential marketing with em-
pirical evidence of the value that AR adds to inducing satisfactory
shopping experiences by facilitating a heightened state of flow, en-
hanced cognitive processing, and enjoyment.
6.4. Practical implications
Our research further provides practical contributions to the areas of
marketing strategy, advertising, consumer engagement, and design.
Importantly, our research suggests that AR can be an effective tool with
which to induce optimal states of flow and enhance satisfaction with
customer experiences in the shopping context. As consumers are pre-
sented with numerous brands while shopping, and any number of dis-
tractions, the use of AR to induce a state of flow and thus focused at-
tention, could help brands draw consumers’ attention and further
differentiate themselves from their competitors in the shopping en-
vironment. Further, by providing useful information and engendering a
sense of learning and enjoyment, bricks and mortar retailers could
potentially benefit from stocking products that use AR. Our research
suggests that AR provides additional value to the shopping experience
by inducing a heightened state of flow and enhancing cognitive pro-
cessing and enjoyment. In a world where online retailers continue to
take market share from bricks and mortar stores, the provision of well-
executed and designed AR experiences could potentially help managers
bring consumers into the store.
Given the positive influence of flow on cognitive processing, AR
offers managers a way to more optimally provide information and
educate customers on their products and services. Utilizing AR tech-
nology enables brands to go beyond the label or packaging of the
product to provide consumers with other relevant product or brand
information, joining up the physical world and the digital world.
Relatedly, brands can include interactive, vivid, and novel design ele-
ments within AR to facilitate cognitive processing.
Accordingly, the results of our research stress the importance of
practitioners to design AR experiences that balance the attributes of
vividness, interactivity, and novelty to better facilitate the consumer ex-
perience of flow. Thus, managers should develop AR technology that
provides a clear, detailed, and well-defined representation of products
that enable consumers to manipulate the real world and virtual world
and offer an experience unique to the consumer’s environment.
Lastly, managers should note that AR offers consumers an enriched
environment that provides multiple sensory objects, which in turn
offloads the need to imagine how products may look, or the need to
seek further information. Therefore, the detail, clarity, and well-defined
representation of products combining both the real world and virtual
world enhances an individual’s cognitive processing about the product
or brand. Additionally, managers should note that familiar stimuli do
not provide the same cues required to ignite cognitive processing, re-
sulting in less arousal and immersion in the activity. Given the novel
stimuli presented through AR eliciting cognitive processing, managers
are able to story-tell about the brand while sparking an individual’s
cognitive flow leading to higher arousal, increased learning about the
brand, and positive experiences.
7. Limitations and future research
Limitations associated with this study may pave the way for future
research. A key limitation is our focus on a commercially available AR
experience focused on the wine industry. We chose this AR experience
and industry due to the nature of wine shopping and to increase the
realism of the experience for the study’s participants. Although the use
of this industry and AR were practical for a sound execution of the
study, research could undertake a similar study with other forms of AR
and industries to determine whether similar outcomes would occur.
Moreover, it would be useful to assess different types of AR tech-
nology and their effects on flow. For example, examining AR apps with
differing levels of interactivity, vividness, and novelty to assess if there
are any differences in the flow experience could provide further in-
sights.
Additionally, this research assesses the influence of AR at one point
in time. It would be useful to assess the influence of AR over time to
J. Brannon Barhorst, et al. Journal of Business Research 122 (2021) 423–436
432
12. discern whether the passage of time has any influence on outcomes.
Another limitation of the study concerns the use of a video to ex-
amine flow in two shopping contexts – one with AR and one without.
Future research could undertake similar studies in retail and other
environments to determine whether similar outcomes would occur.
Finally, a further limitation is the location of the experiment (the
United Kingdom). Given that AR is being adopted by brands around the
world, researchers should undertake a similar analysis with receivers in
other countries.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influ-
ence the work reported in this paper.
Acknowledgements:
The authors would like to thank the reviewers and the special issue
editors for their invaluable suggestions. Additionally, the authors would
like to thank Mr. Jack Wolfe for his support with the filming and pro-
duction of the experiments used in this study. Finally, the authors
would like to thank Mr. Joshua Walker for the use of his premises at
Wine & Company to film the experiment.
Appendix
See Tables A1–A5.
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Table A3
Treatment Group – Cronbach’s Alpha, Composite Reliability, Average Variance Explained.
Variable Cronbach's
Alpha
Composite
Reliability
Average Variance
Extracted (AVE)
Vividness 0.85 0.894 0.629
Interactivity 0.78 0.857 0.601
Novelty 0.887 0.921 0.747
Enjoyment 0.952 0.965 0.874
Flow 0.886 0.929 0.815
Information Utility 0.931 0.951 0.829
Learning 0.736 0.835 0.569
AR Experience
Satisfaction
0.744 0.853 0.665
Table A4
Control Group - Cronbach’s Alpha, Composite Reliability, Average Variance Explained.
Variable Cronbach's
Alpha
Composite
Reliability
Average Variance
Extracted (AVE)
CVividness 0.886 0.916 0.687
CInteractivity 0.943 0.959 0.853
CNovelty 0.906 0.926 0.759
CEnjoyment 0.947 0.962 0.864
CFlow 0.804 0.886 0.728
CInformation Utility 0.934 0.953 0.834
CLearning 0.746 0.841 0.591
CAR Experience
Satisfaction
0.819 0.892 0.735
Table A5
Treatment and Control Group Collinearity Statistics (VIF).
Independent Variable Dependent Variable VIF (T) VIF (C)
Interactivity Flow 1.595 1.311
Vividness 1.819 1.33
Novelty 1.282 1.05
Enjoyment AR Experience Satisfaction 2.218 2.266
Information Utility 3.128 2.92
Learning 3.688 2.511
T denotes treatment group (AR), C denotes control group (No AR).
J. Brannon Barhorst, et al. Journal of Business Research 122 (2021) 423–436
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Jennifer Brannon Barhorst, Ph.D. is an Assistant Professor of Marketing at the College
of Charleston in Charleston, South Carolina. Prior to completing her Ph.D. in Marketing at
the University of Strathclyde Business School, Dr. Barhorst spent several years as a brand
management consultant working with multinational firms around the world. Her research
and teaching interests comprise brand management and digital marketing.
Graeme McLean, Ph.D. is a Senior Lecturer of Marketing at the University of Strathclyde
Business School, Glasgow, UK. He completed his Ph.D. in Marketing at the University of
Strathclyde and his research and teaching interests comprise services marketing, cus-
tomer experience and digital technologies. In addition to his teaching and research re-
sponsibilities, Dr. McLean is also the Director of the Msc in Digital Marketing
Management at Strathclyde Business School.
Esta Shah, Ph.D. is an Assistant Professor of Marketing at the College of Charleston in
Charleston, South Carolina. She completed her Ph.D. in Marketing at Northwestern
University. Her teaching and research interests are in the areas of consumer behavior,
judgement and decision making, and advertising strategy.
Rhonda Mack, Ph.D. is a Professor of Marketing at the College of Charleston. She
completed her Ph.D. in Marketing at the University of Georgia. Her teaching and research
interests are in the areas of services marketing, buyer behavior, sustainability and mar-
keting, and corporate social responsibility.
J. Brannon Barhorst, et al. Journal of Business Research 122 (2021) 423–436
436
