Journal of Global Sport Management
2024, VOL. 9, NO. 3, 545–574
https://doi.org/10.1080/24704067.2022.2155210
A Multi-Method Analysis of Sport Spectator Resistance
to Augmented Reality Technology in the Stadium
Kim Uhlendorf
and Sebastian Uhrich
Department of Sport Business Administration, Institute of Sport Economics and Sport Management,
German Sport University Cologne, Cologne, Germany
ABSTRACT
Past research in sports marketing examining technological innovations has predominantly focused on their acceptance by sport
spectators. However, theoretical and empirical arguments suggest
that investigating innovation resistance provides a distinct and
complementary perspective to understanding spectators’ responses
to technological innovations. Therefore, the present study examines
spectator resistance toward augmented reality (AR) technology
used within the stadium. We explore adoption barriers to in-stadium
AR, and empirically test their influence on three forms of resistance
(postponement, rejection, and opposition). The study used a
mixed-method approach comprised of qualitative in-depth interviews (N = 22) and a cross-national online survey (N = 1,206) targeting spectators in Germany and the UK. The study identified
seven adoption barriers to in-stadium AR: distraction from the live
experience, interference with fan rituals supporting the team,
reduced social interactions, reduced emotionality in discussions,
risk of personal image damage, fan identity incongruence, and
loss of stadium atmosphere. These barriers mainly influenced the
resistance forms of rejection and opposition, while the relation to
postponement was weaker. Beyond AR technology, this research
is the first to examine innovation resistance among sport spectators highlighting the importance to consider downsides of technological innovations and their consequences as well.
ARTICLE HISTORY
Received 30 March 2022
Revised 1 November 2022
Accepted 15 November 2022
KEYWORDS
Innovation resistance;
adoption barriers;
augmented reality; sport
fans; technology
1. Introduction
Augmented reality (AR) is an emergent technology that attracts increasing attention
from both practitioners and researchers (Wedel et al., 2020). A central promise of
AR technology is its ability to enhance consumption experiences by blending the
real-world environment with computer-generated elements (Berryman, 2012). Industry
reports forecast that the (mobile) AR market will grow to US$13 billion by 2022
and over US$26 by 2025 (Artillery, 2022). Service settings, like museums or art
galleries, use AR to create unique experiences for their audience, by integrating the
exhibits with digital illustrations or background information.
CONTACT Kim Uhlendorf
k.uhlendorf@dshs-koeln.de
Department of Sport Business Administration, Institute
of Sport Economics and Sport Management, German Sport University Cologne, Cologne, Germany.
© 2022 Global Alliance of Marketing & Management Associations (GAMMA)
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AR technology is also increasingly employed in sport consumption settings
(Goebert & Greenhalgh, 2020). The prospect of stadia equipped with 5G networks,
which enable advanced connectivity and low latency needed for real-time applications
like AR, has prompted several professional sport leagues and clubs to incorporate
AR directly at the venue to enhance the in-stadium experience of spectators. In
stadia, AR features are mostly integrated into existing or new applications for mobile
devices, and provide spectators with a virtual extension of the stadium environment
by adding digital elements (e.g. live statistics, objects, and graphics). For example,
Germany’s premier football league, the Bundesliga, is currently introducing a league
app that includes AR-generated real-time statistics to enhance the live game experience (DFL, 2019). Moreover, the US football team Dallas Cowboys recently utilized
AR features for in-stadium activations, such as the presentation of a virtual mascot
and the opportunity to take pictures with a virtual integration of the team’s players
(Forbes, 2019).
Given the increasing popularity of this technology in the sport industry, the
question arises: how do sport spectators evaluate technological innovations such as
AR? Anecdotal evidence suggests ambivalent responses. Market research indicates
that spectators are generally inclined to use new technologies in the stadium to
increase their enjoyment (Capgemini Research Institute, 2020). However, skepticism
has also often accompanied the introduction of new technologies. For instance,
market research found that the majority of spectators would refuse the opportunity
to use AR applications in the stadium (Facit Digital, 2018). Spectators fear that the
technology could harm the traditional stadium atmosphere, and perceive it as part
of sports’ undesired commercialization (Forbes, 2020). In the past, spectator resistance even led to innovations being shut off. One example is Fox and the NHL’s
innovation FoxTrax, a puck tracking system that highlights the puck, enabling better
visibility of it. The innovation was heavily criticized by spectators for being distracting and inauthentic (The Wall Street Journal, 2016). To anticipate and avoid
such undesired responses to technological innovations, sport properties need to have
a detailed understanding of the antecedents of spectator resistance and the manifestations of this behavior.
However, despite the frequent problems with introducing technological innovations,
past research has largely overlooked spectator resistance and its drivers. The extant
work focused on positive innovation characteristics and analyzed the drivers of
adoption decisions, mainly utilizing the technology acceptance model (e.g. Kang,
2014; Kim et al., 2017). One exception is Uhrich’s (2022) work on fan experience
apps. This study distinguished between reasons for and reasons against adopting
innovations and showed that both factors independently influence adoption behavior.
While the study makes an important contribution in differentiating drivers and
barriers of adoption, it does not consider spectator resistance as a unique outcome
variable. However, doing so is important as research shows that resistance and
non-adoption should be considered as distinct concepts (Kleijnen et al., 2009). That
is because resistance refers to different behavioral facets that go beyond simply not
using an innovation. For example, non-adopters may generally like an innovation,
but still not use it at this point because they perceive it as not sufficiently developed.
However, other non-adopters may be strongly opposed to the innovation and show
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547
adversarial behaviors like spreading negative word-of-mouth or engaging in protests
(Szmigin & Foxall, 1998). Seeing such different behavioral reactions simply as the
opposite of adoption with not further differentiation impedes a nuanced understanding of negative spectator responses to technological innovations.
Against this background, this study makes the following contributions: First, we
explore adoption barriers related to AR technology in the stadium using in-depth
interviews with spectators from different sports. This qualitative exploration reveals
key factors leading to AR technology resistance and, hence, extends previous literature that investigates factors for AR adoption in the sport spectator setting (Goebert
& Greenhalgh, 2020; Rogers et al., 2017). The exploration of such barriers is important, because besides pro-adoption factors, innovation-specific barriers may exist
which drive spectator resistance (Uhrich, 2022; Winand et al., 2021). Such barriers
must be analyzed in addition to adoption drivers, because they are often not simply
the opposite of those factors that cause acceptance or non-acceptance of an innovation (Heidenreich & Spieth, 2013; Kleijnen et al., 2009). For example, people may
appreciate the advantages of an innovation, but still not adopt it because of cost or
image barriers.
Second, we use quantitative data to validate and empirically link the adoption
barriers to spectator resistance. The quantitative study confirms that all factors
identified in the qualitative phase are significant reflections of why spectators resist
AR. Further, we test and confirm the generalizability of the results across several
important fan segmentation variables (i.e. fan identification, season ticket ownership,
favorite sport) as well as two different nations (UK and Germany).
Third, our study is the first to address the concept of innovation resistance as a
dependent variable in the sport marketing literature, hence, identifying a novel
category of sport spectator responses to technological innovations. Drawing on
existing innovation resistance theory (Kleijnen et al., 2009), we conceptualize sport
spectator resistance in terms of three dimensions: postponement, rejection, and
opposition. This is important because sport spectators often publicly voice their
concerns, and engage in active rebellion when they are dissatisfied with managerial
decisions (Merkel, 2012). Thus, an in-depth understanding of spectator resistance
is particularly important for sport properties. Knowledge of the drivers of spectator
resistance is a prerequisite for sport managers to take effective countermeasures
before introducing the innovation.
2. Theoretical Background and Literature Review
2.1. Defining and Conceptualizing Innovation Resistance
The innovation management literature broadly defines consumer resistance to innovations as a form of negative attitude toward new products and services that trigger
change or conflict within the status quo (Ram & Sheth, 1989; Szmigin & Foxall,
1998). A growing stream of literature in this field (e.g. Gatignon & Robertson, 1989;
Kleijnen et al., 2009; Szmigin & Foxall, 1998) stresses the importance of distinguishing between innovation adoption and innovation resistance as these are distinct
concepts and innovation resistance ‘is not the mirror image of adoption, but a
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different form of behavior’ (Gatignon & Robertson, 1989, p. 47). Thus, innovation
resistance must be examined separately from, or in addition to, innovation adoption
to account for these distinct underlying behavioral patterns (e.g. Kleijnen et al.,
2009). Thus, the rationale for this distinction is twofold. First, there are often unique
factors driving consumers’ decisions to reject an innovation (Ram & Sheth, 1989).
This means that in addition to adoption drivers, such as perceived usefulness or
entertainment (Davis, 1989), distinct factors may exist that cause resistance to innovations (Claudy et al., 2015; Ram & Sheth, 1989). These factors are referred to as
adoption barriers, namely, they are factors that ‘paralyze the desire to adopt innovations’ (Ram & Sheth, 1989, p. 7). Knowledge of the adoption barriers relating to
a specific innovation is important because consideration of them alone is often
insufficient to explain consumers’ responses to innovations.
The second reason why resistance must be distinguished from adoption is that
resistance is more than simply non-adoption. Innovation resistance is a broad concept that embraces different attitudinal and behavioral patterns different from acceptance behaviors (Kleijnen et al., 2009; Szmigin & Foxall, 1998). Thus, adoption
barriers can influence consumers’ decisions in different ways than adoption drivers
(Gatignon & Robertson, 1989). As a result, resistance cannot simply be captured by
non-adoption behavior or not-trying an innovation (e.g. Gatignon & Robertson,
1989; Kleijnen et al., 2009). It is a multi-faceted phenomenon that can embrace
different and very distinct behaviors (Kleijnen et al., 2009). For example, opposing
an innovation by spreading negative word-of-mouth is not the same as non-adoption,
in the sense of not-trying it. There are behavioral patterns underlying resistance,
which cannot be captured when considering it as the opposite of adoption. Based
this notion, Kleijnen et al. (2009) define three specific dimensions of innovation
resistance embracing different behavioral patterns, namely postponement, rejection
and opposition.
Postponement is a temporal form of resistance where consumers consider an
innovation acceptable in principle, but decide not to adopt it until the circumstances are more suitable. For example, sport spectators can be initially reluctant
to adopt AR in the stadium because they may be uncertain as to whether using
the technology is compatible with the group consumption norms. This barrier
can be overcome at a later stage if others’ use of AR signals acceptance of the
social environment. However, spectators can also exhibit a lasting negative attitude toward AR, resulting in the deliberate decision to refuse it, regardless of
its further development. This is a stronger form of resistance, which is referred
to as rejection.
Rejection implies an active evaluation on the part of the consumer, resulting in
a strong reluctance to accept the innovation (Kleijnen et al., 2009). The main
difference to postponement is that spectators rejecting AR are unlikely to be convinced of its use, by changes regarding its features or other circumstance, as their
attitude toward the technology is relatively persistent over time. In addition, since
many sport spectators are highly identified with their role as fans and emotionally
connected with their favorite club, they tend to be actively opposed to what they
perceive as undesired interventions in their consumption habits (Merkel, 2012).
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Thus, another relevant manifestation of resistance includes active rebellion and
boycotts of AR.
This is the strongest form of resistance and is labeled opposition (Kleijnen et al.,
2009). In the case of opposition, consumers are convinced that the innovation is
unsuitable, and decide to take action against its introduction or presence on the
market. In contrast to rejection, which refers to passive refusal, opposition means
that consumers actively express their dissatisfaction with the respective innovation.
Although this three-dimensional conceptualization allows a fine-grained analysis
of innovation resistance and highlights its distinct characteristics compared to acceptance, the majority of empirical research does not distinguish between different
manifestations of the construct (e.g. Antioco & Kleijnen, 2010; Kuisma et al., 2007;
Laukkanen et al., 2007). This is a shortcoming because it is unlikely that adoption
barriers influence all facets of resistance to the same extent.
2.2. Adoption Barriers
Research in the field of innovation resistance has proposed several models defining
adoption barriers for technological innovations. The majority of studies draw on
the established model by Ram and Sheth (1989), which suggests five general adoption barriers relating to technological innovations (i.e. usage, value, risk, tradition,
and image barrier). These are further divided into functional and psychological
barriers. Functional barriers relate to usage patterns, the perceived value and risks
associated with using a new product or service. These barriers emerge when consumers perceive that the adoption of an innovation leads to significant changes in
these areas. Psychological barriers arise when the innovation is in conflict with
consumers’ prior beliefs. These beliefs relate to traditions and norms, as well as to
the image of an innovation. As a result of its broad applicability, Ram and Sheth’s
(1989) conceptualization is widely used to explain resistance toward technological
innovations across various contexts (e.g. Antioco & Kleijnen, 2010; Kuisma et al.,
2007; Laukkanen, 2016; Laukkanen et al., 2007). However, as with any higher-level
theory, this broad conceptualization is somewhat limited in explaining consumer
resistance to specific innovations like AR technology. Simply transferring these
general adoption barriers to AR might result in neglecting the innovations’ unique
characteristics, and leave specific factors driving resistance undisclosed.
With this limitation in mind, several studies explore innovation-specific barriers,
for instance, in relation to Internet of Things devices (Mani & Chouk, 2018) or
autonomous vehicles (Casidy et al., 2021). This research highlights the importance
of studying specific innovations because consumer resistance is often driven by
unique barriers. For example, Claudy et al. (2015) find that availability is an important barrier in the context of car-sharing services; De Bellis and Johar (2020) identify
the barrier of reduced social connectedness for autonomous shopping systems; and
Mani and Chouk (2018) show that resistance to Internet of Things devices is driven
by self-image incongruence.
However, research on resistance to in-stadium innovations is almost non-existent.
An exception is Uhrich’s (2022) exploration of drivers and barriers of fan experience
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app adoption. The study identifies several reasons against using such apps in the
stadium, including distraction from the game, negative impact on the atmosphere,
security concerns, and social risk. While this study provides an initial insight into
innovation adoption barriers that may also be relevant for AR technology, it does
not examine how such barriers influence spectator resistance. In addition, since
adoption barriers are context-specific, different barriers might exist for the distinct
technology of AR compared to fan experience apps in general. Further, studies have
found that reduced opportunities for emotional debates are a reason for spectators
to reject video assistant referee technology in stadia (Winand et al., 2021; Winand
& Fergusson, 2018). Forslund (2017) notes that technological innovations conflicting
with long-established traditions and social consumption norms of the sport consumption context are likely to face resistance from spectators. For in-stadium AR
technology, only adoption drivers, such as perceived usefulness, have been examined
(e.g. Goebert & Greenhalgh, 2020). In general, consumer evaluations relating to AR
are still largely unexplored (Wedel et al., 2020). In the broader marketing context,
AR is mostly studied in retail settings (e.g. Tan et al., 2022), or the e-commerce
sector and here especially for product experiences (e.g. Hilken et al., 2017). This
research centers on pro-adoption factors of this technology (e.g. Castillo & Bigne,
2021), a shortcoming that has recently been pointed out as consumers can encounter
several distinct challenges when using AR, for instance, privacy concerns (Cowan
et al., 2021; Wedel et al., 2020). Thus, adoption barriers inhibiting the introduction
of AR technology, in particular in the sport consumption context, are yet to be
examined.
In view of these research gaps, we set out to explore adoption barriers to AR
technology in the stadium. To achieve this, we conducted a qualitative study that
is presented in the next section.
3. Study 1: Exploration of AR Adoption Barriers
3.1. Method
As the paper is the first to focus on spectator resistance, a qualitative study intended
to explore barriers driving resistance toward AR technology in the stadium from
the perspective of sport spectators. To this end, we conducted semi-structured
in-depth interviews with team sport spectators (N = 22).
3.1.1. Study Context, Participants, and Procedure
Professional team sport leagues in Germany served as the empirical setting for the
study. AR technology is currently being widely discussed among German clubs and
leagues. The football league Bundesliga recently initiated tests to introduce the
technology in stadia (DFL, 2019). Given that AR is predominantly integrated into
mobile apps (eMarketer 2020), we focus on smartphone-based AR in our analyses.
The sampling procedure consisted of two steps. We initially defined general criteria
participants must meet to be eligible as interview partners. For example, they had
to be a fan of a professional sports team and regularly attend live games in the
stadium. Moreover, fans of one of Germany’s four most popular team sports in
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Table 1. Participants of Study 1 and their characteristics.
Participant
Age
Gender
Sport
Fan category
Attendance frequency Season
Preferred
(homes games)
ticket ticket category
Jonathan
23
m
Soccer
Active fan
25-49%
Yes
Standing area
Mario
24
m
Soccer
Active fan
50-74%
No
Standing area
Sophie
24
f
Soccer
Fan
25-49%
No
Seating area
Henning
67
m
Soccer
Casual supporter
75-100%
Yes
Seating area
Lennart
23
m
Soccer
Active fan
50-74%
No
Seating area
Joscha
22
m
Soccer
Active fan
75-100%
Yes
Seating area
Achim
66
m
Soccer
Active fan
0-24%
Yes
Seating area
Conny
57
f
Soccer
Active fan
0-24%
Yes
Seating area
Lara
25
f
Soccer
Casual supporter
0-24%
No
Standing area
Peter
58
m
Soccer
Fan
0-24%
No
Seating area
Josi
24
f
Handball
Fan
0-24%
No
Seating area
Mirco
38
m
Soccer
Fan
75-100%
Yes
Standing area
Volker
69
m
Ice Hockey Fan
75-100%
Yes
Both
Helena
23
f
Handball
Fan
0-24%
No
Seating area
Angelika
61
f
Handball
Fan
75-100%
Yes
Seating area
Basti
27
m
Ice Hockey Fan
0-24%
No
Seating area
Bene
32
m
Basketball Casual supporter
0-24%
No
Both
Anton
23
m
Ice Hockey Active fan
50-74%
No
Both
Bjarne
24
m
Basketball Fan
25-49%
No
Both
Florian
25
m
Ice Hockey Fan
75-100%
Yes
Seating area
Uwe
56
m
Basketball Fan
25-49%
No
Standing area
Florian R.
32
m
Ice Hockey Fan
25-49%
Yes
Seating area
Notes: Fan categories are defined as follows: Casual supporter = sympathizes with a team; Fan = regular fan, but not
a member of a supporters’ club; Active Fan = engaged fan who is a member of a supporters’ club.
terms of attendance (i.e. soccer, ice hockey, handball, basketball) were eligible to
participate in the study (Presseportal, 2017). As the data collection progressed, we
applied theoretical sampling because the selection of participants became more
purposive (Charmaz, 2006; Fischer & Guzel, 2022). We targeted spectators who
varied in attendance frequency, season ticket ownership, preferred ticket category,
fan category, and gender and age to represent different spectator segments. The
consideration of these potentially conceptually relevant variables served the purpose
to develop a comprehensive classification of drivers of in-stadium AR technology
resistance. Participants were recruited via personal contacts. A short questionnaire
covered the demographics of the participants and assessed the selection criteria
before the interviews. All participants had at least basic knowledge of AR technology
and were given detailed explanations about its application in the stadium context
by the interviewer. Table 1 displays the characteristics of the informants.
We developed an interview guide based on the recommendations given by Creswell
(2013) that addressed five major topics (see Appendix A). The first section included
an introduction to the topic, a detailed definition of AR technology and its applications in the stadium context as well as explanations concerning the interview
process. Second, photo-elicitation was used to familiarize participants with the
technology again and further assess participants’ general thoughts about the technology. Afterwards, they were asked about potential problems regarding this technology. Specifically, respondents replied to questions such as what factors would
prevent them from using AR technology, and what difficulties they would encounter
when using it in the stadium. Next, the informants were asked to report situations
in the stadium and during the game in which they would experience AR as disturbing. Fourth, a set of questions dealt with the social component of the stadium
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visit. For example, informants were asked to what extent friends and other fans
might play a role in their decision to use AR. Finally, the perceived image of the
technology was addressed by asking respondents what kind of people they imagined
would use AR technology and what might differentiate these individuals from
themselves. To conclude the interviews, the interviewer provided a summary of the
answers and respondents were asked to confirm that their view had not been
misinterpreted.
Initially, test interviews were conducted to refine the interview guide and check
the technical requirements. The interviews lasted between 25 and 45 minutes and
were conducted either as video calls or face to face. Participants filled out a consent
form to approve the recording of their interviews. When theoretical saturation was
reached no further interviews were conducted (Charmaz, 2006). All interviews were
transcribed verbatim, resulting in 212 pages of text with 1.5 line spacing. The average
number of words per interviews was 4,217 (ranging from 2,828 to 6,900 words).
3.1.2. Analysis
The qualitative analysis followed the steps suggested by Creswell (2013), and used
the software package MAXQDA to process the text material. We read through all
transcripts to get an overview of the data, and a general sense of the information
contained in them. Next, a mix of deductive and inductive coding was used to
identify categories of possible barriers. Existing conceptualizations of technology
resistance barriers (e.g. Ram & Sheth, 1989) provided initial guidance for broad
categories, such as usage or image barriers. Inductive coding was then predominant
when establishing specific barriers of in-stadium AR technology resistance. For this
purpose, we developed a codebook with coding rules to establish the respective
categories. These categories were then described in more detail, and distinct barriers
toward AR were generated. Finally, the findings’ meaning was interpreted and put
into the context of previous literature. We followed previous work (e.g. Mani &
Chouk, 2018), and juxtaposed the identified barriers of adopting AR technology in
the stadium with barriers included in Ram and Sheth’s (1989) conceptualization.
This was done to explore whether our findings indicated specifics and notable differences (e.g. new categories of adoption barriers) compared to the Ram and Sheth
(1989) model. Thus, the theory was tested and refined regarding its application to
the context of in-stadium AR. To establish validity, the findings were constantly
compared and discussed among the authors.
3.2. Results
The data analysis yielded seven barriers driving resistance toward smartphone-based
AR in the stadium. Figure 1 displays the seven barriers and their categorization
according to the Ram and Sheth (1989) model. Further, Table B1 in Appendix B
presents a full overview of the barriers’ definitions, exemplary quotes, and their
relation to Ram and Sheth’s (1989) barriers.
In line with Uhrich’s (2022) finding for app usage, the first barrier to using AR
refers to concerns about being distracted from the game and the live experience,
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Figure 1. Model of in-stadium AR technology resistance juxtaposed against Ram and Sheth’s
(1989) classification of adoption barriers.
labeled distraction from the live experience. AR causes sport spectators to move their
focus away from the game and toward the technology. However, fully focusing on
the live action on and off the pitch is considered a core activity by most spectators.
Using AR technology strongly conflicts with this activity and, therefore, this aspect
represents a key barrier driving resistance.
Using AR also interferes with established fan rituals performed during the game
in support of the fans’ own team (e.g. singing, clapping, shouting). Thus, the second
barrier is called interference with fan rituals supporting the team. AR negatively
affects—or even completely prevents—fans’ engagement in such rituals. Since fan
rituals can be a crucial element of the stadium experience, spectators are reluctant
to change or even give up such rituals to use AR.
The barrier of reduced social interactions reflects the fear of having reduced
interactions with other fans in the stadium through the use of AR technology. Since
using the innovation requires spectators’ full attention, they would not be able to
interact with others during this time. Interacting with the people around them is
important to spectators and, hence, they are not willing to reduce such behavior.
Next, the barrier reduced emotionality in discussions refers to the fear that the
information provided by AR technology, specifically live statistics and data about
the match, reduces heated discussions among spectators. Engaging in emotionally
charged conversations with others is a desirable activity in stadia. Spectators are
concerned that the character of such discussions would shift from being emotional
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and speculative to data-based objectivity. This would decrease spectators’ enjoyment
of the stadium visit. This is in line with Winand et al.’s (2021) finding that sport
spectators are dissatisfied with video assistant refereeing technology because it inhibits
controversial discussions.
The barrier risk of personal image damage is related to the social component of
the stadium setting. Fans were concerned that they would be perceived as rude or
disrespectful when they disturbed others in their experience of the game through
AR usage. Additionally, they feared direct criticism like unfriendly comments from
friends and other relevant people around them.
Next, the barrier loss of stadium atmosphere refers to spectators’ fear of losing
the traditional atmosphere in the stadium. This could be the case if fans were to
replace cheering for their team by excessive use of AR technology in the stands.
This barrier is consistent with Uhrich’s (2022) finding that concerns about a
decline in stadium atmosphere is a reason against using in-stadium apps
during games.
Lastly, fan identity incongruence describes spectators’ perception that their own
values and beliefs about sport fandom are inconsistent with the image of using AR.
Using this technology in the stadium is seen as a component of undesired commercialization processes, which change the way spectators consume sport. This stands
in contrast to many spectators’ identity. Thus, respondents feel a distance between
themselves and the prototypical user of in-stadium AR technology, which is seen
as inauthentic fan behavior. This barrier is similar to Mani and Chouk’s (2018)
self-image incongruence barrier, which refers to the perceived incompatibility between
the consumer’s self-image and the image of the innovation and/or the image of the
innovation’s typical users.
3.3. Discussion of Study 1
Study 1 identifies seven barriers that can cause sport spectators to resist using
AR in the stadium. The findings confirm the theoretical notion that adoption
barriers must be distinguished from adoption drivers because the identified barriers represent distinct aspects that are not simply the opposite of adoption
drivers. Thus, the barriers can co-occur with factors that drive AR adoption (e.g.
perceived fun). While the seven barriers are unique aspects leading to resistance
to in-stadium AR adoption, they can be assigned to the higher-level barriers
included in Ram and Sheth’s (1989) general classification, as displayed in Figure
1. Thus, our study offers a context-specific version of this broader theory. However,
it is still unclear if, and to what extent, these barriers influence spectator resistance. Studies 2 and 3 address this issue by analyzing the relationships between
the barriers and the three suggested manifestations of resistance based on quantitative data.
4. Study 2: Assessment of Measurement Properties
Study 2 aimed to evaluate the measurement properties of the self-developed scales
for the seven adoption barriers. The study employed two consecutive surveys as
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pretests targeting team sport spectators who regularly attend the stadium. We chose
to conduct two surveys in order to increase the generalizability of the measurement
properties using two different samples.
4.1. Method
4.1.1. Item Development
Measures for the adoption barriers were developed based on the text material of
the exploratory study. To achieve content validity, the item generation was closely
linked to the definitions of the constructs, as established in Study 1. In addition,
expressions and statements from the interview participants were considered to develop
items that match the wording of the targeted population. A few participants of the
qualitative study were contacted again, and the measures of all constructs, as well
as their overall definitions, were presented to them. The participants were asked to
state if the items represented the respective constructs clearly and unambiguously.
The majority of respondents confirmed the items as good reflections of the constructs and only small adjustment were made. Further refinements were made to
the item formulations based on the results of Study 2. Table 4 presents the final
items used in the main study.
4.1.2. Data Collection and Analysis
As in the qualitative study, we used the four most popular team sport leagues in
Germany as our empirical setting. Data were collected via an online survey using a
convenience sampling approach for both samples. The links to the respective surveys
were spread via social media channels of team sport clubs and leagues, as well as
by online discussion boards and supporters’ clubs. Regular stadium visitors were
identified using a pre-screening question asking respondents to indicate whether they
attended live games in the stadium from time to time. To ensure that participants
were familiar with AR technology and its usage in the stadium, the first part of the
survey included an explanation of the technology, its characteristics and specific
features for in-stadium use. Pictures of sport spectators using the technology complemented the text-based explanations. Respondents were excluded if they did not
pass the attention checks included in the study (e.g. tick ‘2′ and go to the next
question) and/or finished the questionnaire after a break. Survey 1 was run in March
2021, resulting in 280 valid responses, and Survey 2 in April 2021, resulting in 286
valid responses. Both samples included predominantly male respondents (Survey 1:
74.6% male; Survey 2: 85.0% male) with an average age of 37.1 years (SD = 14.53)
and 29.5 years (SD = 10.85), respectively. The respondents were mainly fans of soccer
teams (Survey 1: 72.9%; Survey 2: 92.7%). Confirmatory factor analyses using SPSS
AMOS (version 27) tested the measurement properties of the seven adoption barriers.
4.2. Results and Discussion of Study 2
The scales for all adoption barriers were tested regarding their internal consistency
by assessing Cronbach’s alpha. In both surveys, the values for Cronbach’s alpha
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K. UHLENDORF AND S. UHRICH
Table 2. CFA results and construct descriptives for Study 2 (both surveys).
Ma
SD
#Items CR AVE
1
2
3
4
5
6
7
1. Interference with fan rituals
supporting the team
Survey 1
4.58 1.83 .88
2
0.88 0.79 0.89
Survey 2
3.63 1.29 .90
2
0.90 0.82 0.91
2. Distraction from the live experience
Survey 1
5.23 1.62 .94
3
0.95 0.85 0.77 0.92
Survey 2
3.96 0.97 .90
3
0.90 0.75 0.75 0.86
3. Reduced social interactions
Survey 1
4.77 1.87 .93
3
0.93 0.82 0.83 0.79 0.90
Survey 2
3.81 1.10 .90
3
0.90 0.75 0.80 0.80 0.86
4. Reduced emotionality in discussions
Survey 1
4.42 1.46 .81
3
0.80 0.58 0.70 0.59 0.68 0.76
Survey 2
3.40 1.02 .79
3
0.78 0.55 0.56 0.59 0.66 0.74
5. Risk of personal image damage
Survey 1
3.88 1.56 .92
6
0.92 0.67 0.70 0.57 0.67 0.48 0.82
Survey 2
3.23 1.08 .90
5
0.90 0.64 0.73 0.60 0.66 0.51 0.80
6. Fan Identity Incongruence
Survey 1
4.51 1.57 .90
5
0.90 0.63 0.76 0.70 0.79 0.65 0.68 0.80
Survey 2
3.36 1.09 .85
4
0.86 0.61 0.80 0.78 0.82 0.64 0.75 0.78
7. Loss of stadium atmosphere
Survey 1
4.70 1.80 .94
3
0.94 0.83 0.81 0.76 0.83 0.68 0.66 0.87 0.91
Survey 2
3.64 1.14 .92
3
0.92 0.79 0.81 0.82 0.84 0.63 0.70 0.90 0.89
Note: all correlations were significant (p < .001).
a
the seven barriers were measured on a seven-point rating scale in Survey 1 and a five-point rating scale in Survey 2.
indicated good reliability for all barriers, ranging from .79 to .94. Table 2 displays
the mean values, standard deviations, number of items, and Cronbach’s alpha of all
constructs for both surveys.
Regarding the remaining measurement properties, results indicate a good overall
fit for both the model of Survey 1 (χ2 = 533.982, df = 254, p < .001; χ2/df = 2.18;
RMSEA = 0.065; CFI = 0.95; SRMR = 0.036; TLI = 0.95) and the model of Survey
2 (χ2 = 410.321, df = 209, p < .001; χ2/df = 1.96; RMSEA = 0.058; CFI = 0.96; SRMR
= 0.039; TLI = 0.95). All factor loadings were highly significant (p < .001) and
above the cut-off criterion of 0.5. Further, all seven barriers indicated high reliability
and convergent validity as the values for construct reliability and average variance
extracted (AVE) exceed the recommended thresholds of 0.7 and 0.5, respectively
(Bagozzi & Yi, 2012). Moreover, for all but one scale, the square root of the AVE
was higher than the highest correlation with any other construct, supporting discriminant validity (Fornell & Larcker, 1981). The exception is the correlation between
loss of stadium atmosphere and fan identity incongruence, which was slightly higher
than the square root of the AVE for fan identity incongruence in both surveys.
However, as all other measurement properties were satisfactory for these constructs,
they remain in the subsequent analyses. Table 2 displays the results of the respective
CFAs in more detail.
5. Study 3: Test of Structural Model
The goal of Study 3 was to test the relationships between the adoption barriers and
the three manifestations of resistance (postponement, rejection, opposition).
Journal of Global Sport Management
557
5.1. Method
5.1.1. Study Context, Data Collection, and Participants
For Study 3, we recruited team sport fans from two countries, namely Germany
and the UK. The cross-national sample aimed to increase the generalizability of our
findings. Like the German team sport industry, AR also proliferates in the UK, and
has been introduced by several professional clubs (e.g. Arsenal FC, Manchester City,
Tottenham Hotspur). Further, the total attendance-per-capita of professional live
sports is among the highest worldwide (Two Circles, 2019). Thus, the UK market
provides an appropriate setting for our study. The data were collected from 10 to
30 June, 2021, using online surveys. We applied quota sampling (age, gender, favorite
team sport) to match our sample with the characteristics of team sport spectators
in Germany and the UK, respectively. UK respondents were recruited via the online
panel provider Talk Online Panel, while German participants were recruited by
research assistants who distributed the link to the survey among potential participants using social media channels, as well as emails. In order to meet the quotas
in Germany, we regularly checked the data, and implemented pre-screening questions
that ensured fulfillment of the criteria. We included soccer, ice hockey, basketball,
and handball spectators from Germany as well as soccer, rugby, cricket, and ice
hockey spectators from the UK because these sports are the most popular live sports
in terms of attendance figures in their respective countries (Presseportal, 2017; Two
Circles, 2018). The quotas for age and gender were selected based on several sources,
including previous surveys and statistics on sport spectators in the two countries
(e.g. EFL Report, 2019).
The data collection yielded 503 valid responses from the UK and 703 valid
responses from Germany with both samples fulfilling the targeted quotas (see
Table 3). As detailed in the preregistration,1 two initial screening questions filtered
out respondents who never attended live games at the stadium or who did not have
German or British citizenship, respectively. Further, participants were eliminated if
they did not pass the three attention checks (e.g. tick ‘6′ and go to the next question) and/or finished the questionnaire after a break. The preregistration included
all details regarding the research questions, methods, measures, and exclusion criteria.
5.1.2. Measures and Analysis
The dependent variables postponement (3 items [Kleijnen et al., 2009]; α = .61),
rejection (3 items [Mani & Chouk, 2018]; α = .93), and opposition (3 items [Kleijnen
et al., 2009]; α = .92) were measured using a seven-point rating scale (1 = strongly
disagree to 7 = strongly agree). Further, we included innovativeness, age, gender, and
knowledge about AR as control variables, as previous studies show that these constructs predict innovation resistance (e.g. Laukkanen et al., 2007; Szmigin & Foxall,
1998). The scale for innovativeness (3 items, α = .88) stems from Kim et al. (2017),
and the measures of knowledge about AR (3 items, α = .91) were adapted from
Bang et al. (2000), both using the above mentioned seven-point rating scale. Table 4
displays the items, Cronbach’s alpha, means, and standard deviations for all constructs
used in the main study.
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K. UHLENDORF AND S. UHRICH
Table 3. Sample characteristics of Study 3.
UK (n = 503)
n
%
Germany (n = 703)
Quotas
1
n
Gender
Male
424
84.3%
85%
486
Female
79
15.7%
15%
211
Othera
–
–
6
Age
16-24
53
10.5%
10%
253
25-34
105
20.9%
20%
198
35-44
104
20.7%
20%
88
45-54
131
26.0%
25%
88
55-64
78
15.5%
15%
68
> 65
32
6.4%
10%
8
Education
No degree
96
19.1%
2
Certificate of secondary educationa
–
–
108
A-Level
170
33.8%
282
University degree
208
41.1%
293
Doctoral degree
29
5.8%
18
Favorite team sport
Soccer
433
86.1%
85%
491
Rugby (Ice Hockey)b
42
8.3%
9%
121
Cricket (Handball)b
19
3.8%
4%
49
Ice hockey (Basketball)b
9
1.8%
2%
42
Fan group membership
Active fan (member in a
118
23.5%
91
supporter’s club)
Fan (not a member in a supporter’s
289
57.5%
444
club)
Casual fan/supporter
96
19.1%
168
Season ticket holder
Yes
115
22.9%
185
No
388
77.1%
518
Attendance frequency (home games)
0-24%
261
51.9%
353
25-49%
84
16.7%
104
50-74%
73
14.5%
61
75-100%
85
16.9%
185
Previous usage of AR applications
Yes
163
32.4%
280
No
340
67.6%
423
Frequency of previous used AR
applications
1 to 4 times
91
55.8%
130
5 to 9 times
31
19.0%
73
10 to 14 times
19
11.7%
33
15 to 19 times
11
6.7%
11
20 times and more
11
6.7%
33
a
only queried in German questionnaire.
b
Sports for the German sample are displayed in brackets.
1
References: Two Circles, 2018; EFL Report, 2019; FSA National Supporters Survey, 2017.
2
References: Grau et al., 2016; Ziesmann et al., 2017.
%
Quotas2
69.1%
30.0%
0.9%
75%
25%
36.0%
28.2%
12.5%
12.5%
9.7%
1.1%
35%
25%
15%
10%
10%
5%
0.3%
15.4%
40.1%
41.7%
2.6%
69.8%
17.2%
7.0%
6.0%
75%
10%
10%
5%
12.9%
63.2%
23.9%
26.3%
73.7%
50.2%
14.8%
8.7%
26.3%
39.8%
60.2%
46.4%
26.1%
11.8%
3.9%
11.8%
Regarding the analyses, we first tested for measurement invariance before collapsing the samples from the two different countries for further analyses, following
the steps suggested by Cheung and Rensvold (2002) and Chen (2007) for large
sample sizes. We also relied on their recommended thresholds for the goodness-offit indices (i.e. CFI, RMSEA, and SRMR). First, we conducted a CFA separately with
all focal measures of the study for both countries (Models 1 and 2 in Table 5) to
test configural invariance. Second, metric invariance was tested by running a CFA
Journal of Global Sport Management
559
Table 4. Constructs and items used in Study 3, mean values, standard deviation, and Cronbach’s
alpha.
Construct
Distraction from the
live experience
Interference with fan
rituals supporting
the team
Reduced emotionality
in discussions
Reduced social
interactions
Risk of personal image
damage
Fan identity
incongruence
Loss of stadium
atmosphere
Items
Mean
SD
Cronbach’s alpha
The use of AR technology during the game
distracts me from the game and the
stadium environment ( e.g. fan actions
within the stands).
The use of AR technology during the game
leads to missing something important from
the game.
The use of AR technology during the game
prevents me from focusing on the game
highlights and the stadium environment.
The usage of AR technology during the match
will hinder me from cheering for my team
within the stadium ( e.g. singing, clapping,
waving scarves).
The usage of AR technology during the match
will prevent me from supporting my team.
The facts, data, and statistics presented by AR
technology reduce emotional discussions
with other fans and friends.
The facts, data, and statistics presented by AR
technology lead to data-driven rather than
emotional discussions.
The facts, data, and statistics presented by AR
technology reduce joint debates about
controversial match decisions.
The use of AR technology during the game
means that I interact less with other fans/
friends in the stadium.
The use of AR technology during the game
means that I cheer less with other fans/
friends around me.
The use of AR technology during the game
means that visiting the stadium becomes
more impersonal.
If I used AR technology during the game other
fans would think badly of me because I
disturb their game experience.
If I used AR technology during the game other
fans would think of me as being rude/
disrespectful because I obstruct their view.
If I used AR technology during the game my
friends/other fans in the stadium would not
perceive it well.
If I used AR technology during the game, I
would fear negative feedback from other
fans.
Fans who use AR technology are different fans
than I am.
AR technology is something for casual
spectators, but not for me.
I do not identify with the typical user of AR
technology.
AR technology represent a part of the
commercialization which I reject.
When using AR technology during the game
the stadium atmosphere gets lost.
When using AR technology during the game
the stadium atmosphere changes for the
worse.
When using AR technology during the game
the stadium and fan culture suffers.
3.71
1.13
.93
3.38
1.23
.83
3.42
1.02
.84
3.62
1.11
.89
3.13
1.11
.89
3.15
1.10
.89
3.50
1.02
.93
(Continued)
560
K. UHLENDORF AND S. UHRICH
Table 4. Continued
Construct
Postponement
Rejection
Opposition
Items
Mean
SD
Cronbach’s alpha
At the moment, using AR technology is out of
the question for me; I would rather wait to
use it.
Currently, I cannot imagine using AR
technology, but maybe later I will.
Right now, I would not use AR technology, but
I can imagine using it in the future.
Generally, I reject AR technology.
I am against the use of AR technology.
AR technology is not for me.
I would take action to oppose the introduction
of AR technology.
I would participate in fan protests against the
introduction of AR technology.
I would support initiatives against AR
technology.
4.24
1.40
.61
3.52
1.97
.93
2.44
1.70
.92
Table 5. Results of measurement invariance testing for Study 3.
Model
CFI
ΔCFI
RMSEA
ΔRMSEA
SRMR
ΔSRMR
Model 1 (UK):
Model 2 (Germany):
Model 3 (combined baseline model of free
factor loadings & intercepts)
Configural invariance established
Model 4 (fixed factor loading against Model 3)
Metric invariance established
Model 5 (fixed factor loadings and intercepts
against Model 4)
Scalar invariance established
.972
.969
.970
–
–
–
.048
.048
.034
–
–
–
.032
.037
.032
–
–
–
.969
.001
.034
0.0
.032
0.0
.960
.009
.038
.004
.034
.002
with specifying equality constraints on the factor loadings across both groups (Model
4 in Table 5). Third, in addition to the factor loadings, the items’ intercepts were
fixed (Model 5 in Table 5) to test scalar invariance. If all thresholds are met across
the five models, measurement invariance is established. Afterwards, we conducted
a CFA using the full sample to test additional measurement properties of all constructs (i.e. seven barriers and three resistance dimensions).
We employed several measures to prevent and test for the occurrence of CMV
because the data for all variables stem from the same cross-sectional source (Podsakoff
et al., 2003). We followed the guidelines of the methodological literature (Hulland
et al., 2018; Williams et al., 2010). First, the dependent and independent variables
were separated in the questionnaire, and were measured using different scale formats
(five-point vs. seven-point rating scales). Further, filler variables interrupted the
response flow. For example, we included measures of adoption drivers, such as ease
of use, to have not only a physical, but also a content-based separation. Second,
three attention checks were integrated ensuring that respondents carefully read
through the items. Third, a marker variable was included capturing respondents’
global identity (3 items [Tu et al., 2012]; α = .68) to statistically test the occurrence
of CMV (Williams et al., 2010). To further account for CMV at the item level, an
unobserved latent method factor was integrated into a CFA with all items loading
on both their respective construct and the method factor (Podsakoff et al., 2003).
Journal of Global Sport Management
561
Finally, the second-order structure of the barriers was tested conducting a CFA.
Next, a structural equation model with barriers as the independent variable, and
the three dimensions of resistance as dependent variables, as well as innovativeness,
age, gender, and knowledge about AR as control variables, tested the relationships
among the constructs.
5.2. Results
5.2.1. Measurement Properties and Common Method Variance (CMV)
Table 5 presents the results of the measurement invariance tests in detail. Configural
invariance, metric invariance and scalar invariance were established and we collapsed
both samples for further analyses.
The results of the CFA with the full sample indicated a good overall model fit
(χ2 = 1167.69 df = 333, p < .001; χ2/df = 3.51; RMSEA = 0.046; CFI = 0.97; SRMR
= 0.033; TLI = 0.97) (Bagozzi & Yi). All factor loadings were statistically significant
(p < .001) and above 0.5. Construct reliability (ranging from 0.83 to 0.94) was
established for all measures as their values exceeded 0.70 (Bagozzi & Yi, 2012).
Further, the AVE values for all constructs (ranging from 0.63 to 0.83) exceeded 0.50,
indicating good convergent validity (Hair et al., 2010). Moreover, for most constructs
the square root of the AVE was higher than the highest correlation with any other
construct, hence supporting discriminant validity (Fornell & Larcker, 1981). Only
the correlation between loss of stadium atmosphere and fan identity incongruence
was marginally higher than the square root of the AVE for fan identity incongruence.
Regarding CMV, the marker variable showed several significant (p < .01) correlations with the study’s focal constructs (r ranging from 0.11 to 0.21), indicating the
occurrence of a small to medium amount of CMV. The model with the unobserved
latent method factor fitted the data very well (χ2 = 743.23, df = 304, p < .001; χ2/df
= 2.45; RMSEA = 0.035; CFI = 0.99; SRMR = 0.018; TLI = 0.98) and a χ2-difference
test indicated that adding the latent method factor significantly improves the model
fit (χ2 = 424.46, df = 29, p < .001). Thus, the method factor was integrated into the
structural model.
5.2.2. Second-Order Structure
We considered a second-order structure as an appropriate modeling approach for
the seven adoption barriers. Measurement theory recommends using second-order
models when lower-order dimensions are highly correlated, and when these dimensions can be interpreted as reflections of a higher-order construct (Bagozzi & Yi,
2012). There are substantial correlations among all barriers, indicating empirical
overlap. In addition, the barriers can be seen as belonging to a higher-level construct
because all barriers represent reasons to resist the adoption of AR. This is in line
with the conceptualizations of specific barriers in previous innovation resistance
studies (e.g. Claudy et al., 2015). Thus, the second-order construct of barriers comprises all seven specific barriers to AR as first-order constructs. The CFA indicated
a good model fit (χ2 = 754.036, df = 202, p < .001; χ2/df = 3.73; RMSEA = 0.048;
CFI = 0.98; SRMR = 0.033; TLI = 0.97), and all first-order and second-order loadings are statistically significant (p < .001).
562
K. UHLENDORF AND S. UHRICH
Table 6. Results for the structural model of Study 3.
Paths
Barriers → Interference with fan rituals supporting the team
Barriers → Distraction from the live experience
Barriers → Reduced social interactions
Barriers → Reduced emotionality in discussions
Barriers → Risk of personal image damage
Barriers → Fan identity incongruence
Barriers → Loss of stadium atmosphere
Barriers → Postponement
Barriers → Rejection
Barriers → Opposition
Control1: Innovativeness → Postponement
Control1: Innovativeness → Rejection
Control1: Innovativeness → Opposition
Control2: Age → Postponement
Control2: Age → Rejection
Control2: Age → Opposition
Control3: Knowledge about AR → Postponement
Control3: Knowledge about AR → Rejection
Control3: Knowledge about AR → Opposition
Control4: Gender → Postponement
Control4: Gender → Rejection
Control4: Gender → Opposition
R2
Postponement
Rejection
Opposition
Std. Estimates
p
0.86
0.87
0.88
0.61
0.76
0.91
0.92
0.53
0.85
0.84
−0.16
−0.17
−0.07
0.05
−0.01
−0.05
0.02
0.02
0.09
0.03
−0.02
0.01
< .001
< .001
< .001
< .001
< .001
< .001
< .001
< .001
< .001
< .001
< .001
< .001
.084
.069
.701
.043
.485
.442
.038
.162
.241
.861
0.35
0.81
0.71
5.2.3. Structural Model
The results of the structural model indicated a good model fit (χ 2 = 1641.08, df =
566, p < .001; χ2/df = 2.90; RMSEA = 0.04; CFI = 0.97; SRMR = 0.036; TLI =
0.97). Adoption barriers were positively and significantly related to the resistance
dimensions of postponement (β = .53, p < .001), rejection (β = .85, p < .001),
and opposition (β = .84, p < .001). All seven first-order adoption barriers are
significantly (p < .001) associated with the higher-order construct of barriers. As
indicated by the second-order loadings, the barriers loss of stadium atmosphere
(γ = .92), fan identity incongruence (γ = .91), reduced social interactions (γ =
.88), distraction from the live experience (γ = .87), and interference with fan
rituals supporting the team (γ = .86) exhibit very strong associations with the
higher-order construct. While also being significantly associated with the
higher-order construct, reduced emotionality in discussions (γ = .61) and risk of
personal image damage (γ = .76) are the weakest reflections of barriers. Table 6
displays the results in more detail.
Further, we tested the relationships between adoption barriers and the three
resistance forms across typical fan segmentation variables (i.e. fan identification,
season ticket ownership, favorite sport) and two countries (UK and Germany) using
multigroup analyses in AMOS. These additional analyses aimed to explore whether
our findings generalize across different spectator groups. The results indicate that
the influence of barriers on the resistance dimensions are consistent over different
levels of fan identification (high vs. low), different sports (soccer vs. other), and
types of ticket owners (season ticket ownership yes vs. no) and, thus, similar to the
overall sample. The findings are also widely robust across the two countries, although
Journal of Global Sport Management
563
the relationship between barriers and postponement was slightly stronger in the
German sample (β = .59) compared to the UK sample (β = .44).
5.3. Discussion of Study 3
The study reveals that AR adoption barriers are significantly related to the three
dimensions of spectator resistance. The finding that the strongest relationship
occurred between barriers and the dimensions rejection and opposition is plausible
because rejection appears to be the most straightforward representation of resistance,
and spectators tend to actively oppose what they perceive as undesirable changes
in their consumption habits (Merkel, 2012). Further, all specific adoption barriers
significantly reflect the higher-order barriers construct. Thus, these findings confirm
the result of the qualitative study, as the barriers identified represent distinct drivers
of spectator resistance to AR technology.
6. General Discussion
6.1. Theoretical Implications
Previous research on spectators’ evaluations of technological innovations in general,
and AR in particular, has taken a positive view and examined the influence of adoption drivers that facilitate technology acceptance (e.g. Goebert & Greenhalgh, 2020;
Rogers et al., 2017). While such research offers important insights, our focus on
spectator resistance provides a complementary perspective that is needed for a full
understanding of spectators’ responses to technological advancements such as AR.
Specifically, our work explores adoption barriers relating to smartphone-based
AR usage within the stadium, and examines how these barriers are linked to spectator resistance. In doing so, we make a contribution by complementing existing
work on the drivers of AR adoption (Goebert & Greenhalgh, 2020; Rogers et al.,
2017). Our study provides detailed insight into the factors that lead sport spectators
to resist AR technology in the stadium. The qualitative study identified seven barriers representing different facets of the stadium visit that are in conflict with AR
usage. While these seven dimensions can be subsumed under the general adoption
barriers included in Ram and Sheth’s (1989) classification, our exploration recontextualizes this model, and reveals some notable amendments and extensions. First,
we show the particular relevance of usage barriers for in-stadium AR technology,
because four of the seven identified barriers represent factors that contrast with
spectators’ existing consumption practices and routines. This is in line with previous
innovation-specific research showing that not all of Ram and Sheth’s (1989) general
adoption barriers are equally important for all innovations (De Bellis & Johar, 2020;
Mani & Chouk, 2018). Second, we extend the Ram and Sheth (1989) model by
relating the adoption barriers to three different forms of resistance. This model has
only considered the concept of resistance in general and subsequent applications
examining different forms of resistance are restricted to qualitative inquiries (Kleijnen
et al., 2009). Thus, we do not only show that AR adoption barriers relate to resistance, but also delineate specific effects on different manifestations of resistance,
hence providing more theoretical nuance.
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K. UHLENDORF AND S. UHRICH
The present research also makes a broader contribution beyond AR technology
by offering the first inquiry regarding innovation resistance in the sport consumer
behavior literature. Uhrich’s (2022) work on fan experience apps included adoption
barriers, but these were only linked to adoption attitudes, intentions, and behavior,
and showed negative relationships. By considering sport spectator resistance and its
three manifestations as the dependent variables, our study extends this previous
work because it shows that adoption barriers can have unique consequences beyond
simply negative attitudes and non-adoption, thus providing a more granular view
of the consequences of AR adoption barriers. These consequences range from temporal uncertainty about adopting the technology to negative word of mouth, or even
spectator protests. We show that barriers are strongly related to the resistance
dimension of rejection, which represents an enduring negative attitude, and which
appears to be conceptually close to the general concept of non-adoption. However,
importantly, we find that barriers are also strongly related to opposition, a resistance
dimension that reflects active engagement of the spectators in behaviors (e.g. negative
word-of-mouth) aimed at preventing the diffusion of AR. This provides support for
the notion that sport fans tend to actively express their opinions against undesired
changes, and may engage in boycotts and protests (Cocieru et al., 2019). Further,
we find that barriers are also related to postponement, although the magnitude of
this relationship is lower compared to rejection and opposition. This comparatively
weak link is not unexpected because it is likely that other factors also drive postponement in addition to adoption barriers. For example, Szmigin and Foxall (1998)
suggest that situational factors (e.g. temporary budget constraints on the consumers’
side) often influence postponement. We further show that these relationships are
robust across different levels of fan identification, different sports, and ticket categories, which speaks to the generalizability of the findings. Thus, the negative
consequences that come with adoption barriers manifest within sport spectators,
regardless of different fan characteristics.
6.2. Managerial Implications
Our research offers important managerial implications. Broadly, the study advises
marketers to not only focus on the benefits and advantages of AR, but also to
consider its downsides. Two aspects of our study’s results are particularly important
for marketers to incorporate in their marketing decisions regarding the introduction
of AR. First, sport spectators have technology-specific reasons against the use of
AR that marketers must address as early as possible in the technology’s introduction
process. Second, these barriers lead to distinct forms of spectator resistance associated with different behaviors on the spectator side. Here, marketers must take
effective countermeasures to mitigate these consequences. Regarding AR-specific
adoption barriers, our work highlights that technology features creating these barriers
should be addressed before introducing the technology. Thus, clubs and leagues
should already be taking into account these features when app developers or agencies
present their products and thoughts on introducing AR. This means they should
ask, in particular, to what extent the developers are able to evade possible barriers
Journal of Global Sport Management
565
and subsequently evaluate countermeasures. For example, the issue of distracting
supporters from the live experience might be tackled by restricting AR visualizations
to only some parts of the game (e.g. during a break for the video assistant referee)
and by showing individual statistics at a time (e.g. the speed of the players).
Considering such elements before introducing AR enables the development of a
technology design that minimizes barriers and fits the consumption context with
its traditions, routines, and rituals.
Our study further demonstrates that AR adoption barriers have a distinct influence on different manifestations of spectator resistance. For example, barriers have
a substantial influence on the resistance form of opposition, where responses include
spreading negative word of mouth or calling for boycotts. Such detrimental behaviors
may have consequences for clubs and leagues that go beyond AR technology. That
is, oppositional reactions might damage the image of the club or league, and may
even diminish the spectators’ identification with the sports property. This is because
adoption barriers do not only relate to consumption practices, but also to spectators’
identity as sport fans. This highlights the importance of considering technology
resistance and developing effective countermeasures.
6.3. Limitations and Future Research
First, our study is only the beginning of understanding spectator resistance to AR
and its drivers. While we tested the generalizability of our results across important
fan segmentation variables, we did not explore other potential boundary conditions.
For instance, fans who often take action against undesired changes initiated by the
club might also be more likely to show oppositional behavior compared to fans who
do not show such tendencies. Future studies can address this limitation by integrating
possible moderating factors into the model of spectator resistance that go beyond
the segmentation variables we used, thus further enriching understanding of this
complex construct.
Second, we developed measures for the identified AR adoption barriers based on
the qualitative data and tested the scales using two pretests that provide wide support for their discriminant validity. Despite taking care in the development of the
measures, it remains uncertain as to whether the need to model the barriers as a
second-order construct rests on the theoretical fact that a higher-order concept
accounts for the barriers, or if the measures were simply unable to capture the
unique aspects of each barrier. The fact that several previous innovation resistance
studies (e.g. Claudy et al., 2015; Tandon et al., 2020; Uhrich, 2022) found the
empirical necessity to model barriers as a second-order construct supports the notion
of barriers being a second-order construct. Moreover, both Ram and Sheth (1989)
in their original work and subsequent studies based on this work do not provide
empirical evidence for the independence of the adoption barriers (e.g. Joachim et al.,
2018; Laukkanen et al., 2007). Further, it might be possible that in the current early
stage of AR diffusion consumers are able to differentiate several barriers in in-depth
interviews, while this seems more difficult when responding to a quantitative survey.
Whatever might cause the empirical overlap, things may change as AR technology
566
K. UHLENDORF AND S. UHRICH
diffusion increases. This is because with more experience, spectators may be better
able to recognize the distinct features of specific barriers, and separate them from
other barriers. This offers opportunities for interesting future research. Studies could
examine whether adoption barriers are a higher-order construct in the early stages
of innovation diffusion, while clearly separable dimensions occur in later stages. It
would then be interesting to delineate individual influences of specific barriers on
the three manifestations of resistance.
Finally, we asked respondents to report their level of agreement with the three
manifestations of resistance. Since the majority of spectators have not had the chance
to use in-stadium AR technology, the answers represented intentions and opinions
regarding a very early stage innovation. Since to this day, in-stadium AR technology
is only available to a selected group of spectators for testing purposes in the examined countries, we were unable to capture actual behavior. Thus, we cannot be
confident that the intentions reported by the participants will turn into behavior.
Future studies conducted when in-stadium AR technology is more widely available
will be able to assess resistance in a way that better reflects spectators’ actual evaluations and behaviors, rather than their predictions.
Note
1.
The preregistration document is available at https://aspredicted.org/8qe7j.pdf
Disclosure Statement
No potential conflict of interest has to be reported.
Funding
This work was supported by the Internal Research Funds of the German Sport University
Cologne under Grant L-11-10011-235-061000.
Notes on contributors
Kim Uhlendorf (M.Sc.) is a PhD Student at the Institute of Sport Economics and Sport
Management at the German Sport University Cologne in Germany. Her research interest
focuses on the marketing of technological innovations from a consumer perspective. She has
published in the Journal of Global Sport Management.
Sebastian Uhrich (Ph.D.) is a Professor at the Institute of Sport Economics and Sport
Management at the German Sport University Cologne in Germany. His research centers on
consumer behavior in sport and has been published in various journals, including Journal
of Business Research, Psychology and Marketing, Journal of Sport Management, European
Sport Management Quarterly, Sport Management Review, among others.
ORCID
Kim Uhlendorf
Sebastian Uhrich
http://orcid.org/0000-0002-7815-0768
http://orcid.org/0000-0003-1099-1795
Journal of Global Sport Management
567
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Appendix
Appendix A: Interview Guide for Study 1
Note: The interviews were conducted in German. What follows is a translation of the original interview guide.
Introduction:
•
Short description of the general topic discussed in the interview (including definition
of and explanations regarding AR technology)
•
Description of the interview process and its length
•
Privacy statement
•
Demographic questions regarding the respondents’ age and occupation
•
General questions about the respondents’ relationship toward team sports (e.g. Which
team do you support? How often do you attend home games of the team? Do you
have a season ticket?)
Photo-elicitation:
•
Respondents were shown pictures of smartphone-based AR applications (different
angles and contexts presented, e.g. live statistics, player figures) in the stadium
context with clear explanations and indications regarding its usage (e.g. hold smartphone towards pitch)
•
After viewing the pictures with enough time, participants were asked to state general
thoughts about the technology when seeing them.
Block I: Perceived problems with the innovation in general
•
What problems do you see when using such a technological innovation? Can you
specify these problems? Why would you not use AR in general? What reasons would
you have to not use AR technology?
Block II: Perceived problems during specific situations within the stadium
•
In which specific situations within the stadium and during the game would you
experience the most problems using the technology? Why does it disturb you in
these situations? What are concrete problems that you would encounter during such
a situation?
Block III: Influence of social components within the stadium
•
To what extent do friends and other fans around you in the stadium influence your
opinions about AR? To what extent do they play a role in your decision to use the
innovation during the game?
Block IV: Perceived problems concerning the image of the innovation
•
What kind of people do you expect would use such an innovation? What makes
these people different from you?
Conclusion
•
How exactly would you express possible criticism toward such an innovation?
•
Summary of the interview’s conversation topics
572
Appendix B
General Barrier
Usage Barrier
Definition
(Ram & Sheth, 1989)
Perception that the
innovation is in contrast
with existing practices
and routines consumers
regularly enact.
Specific Barrier
Definition
Distraction from the live
experience
The innovation prevents
supporters from watching
the game and other
actions happening within
the stadium
uninterruptedly.
Exemplary quotes
‘I'm just afraid that I'll miss some crucial scenes, and especially in ice
hockey there are many goals, a lot of offensive actions, shots, or
spectacular tackles, and that’s what you want to see, that’s why you
go to the arena, and that’s why I'd not use it [AR technology] during
the game.’ (Basti)
‘While using the innovation, things are going on [in the stadium]
that I might have been much more interested in, so for me that’s a
reason not to use it at all. I visit the stadium to watch the game […]
and this technology would simply distract me from it.’ (Achim)
‘I personally prefer to be very close to the playing field and all the
action, but I don’t want to be distracted by a technology. I want to
see everything directly, analog, so to say.’ (Uwe)
Interference with fan
The use of the innovation is
‘[…] clapping, singing, shouting, or doing a La Ola wave all these are
rituals supporting the
incompatible with
rituals I typically engage in during a match and I would find it
team
engaging in typical fan
annoying to use this technology while keeping on cheering for my
rituals related to cheering
team.’ (Conny)
for the team.
‘I mean, typically there is a lot of chanting, clapping and stuff going
on, and I just can’t participate if I have a smartphone in my hand to
use the innovation. I simply can’t hold it and clap at the same time.’
(Lara)
‘I also show a fan behavior during the 90 minutes, where I get upset
sometimes and where you just support the team almost continuously
and I wouldn’t do that with a smartphone in my hand which is why
such an innovation would only disturb me in this situation.’ (Peter)
Reduced social
The adoption of the
‘You don’t go to the stadium to just watch the game; you also want to
interactions
innovation causes a
chat and share the excitement with your friends. I could imagine by
reduction of social
using AR technology the whole time […] you would not talk with
interactions with other fans
your friends anymore and enjoy the experience together.’ (Anton)
in the stadium.
‘I mean, it is normal that you get to talk to everyone around you in
the stands, there are even a few fans that you see again and again
every game and this typical personal exchange would be completely
missing if they are all concentrated on using AR.’ (Mirco)
‘I think this technology would cause the stadium visit to become
very impersonal.’ (Peter)
(Continued)
K. UHLENDORF AND S. UHRICH
Table B1. Classification of barriers, their integration into Ram and Sheth’s (1989) model, definitions, and exemplary quotes resulting from the qualitative
study.
Table B1. Continued
General Barrier
Consumers’ fear that the
innovation is not
accepted by relevant
others.
Specific Barrier
Definition
Reduced emotionality in
discussions
The information provided by
the innovation alters
heated discussions about
critical match decisions
toward data-driven,
rational conversations.
Risk of personal image
damage
Exemplary quotes
‘I want to form my own opinion about match decisions and discuss it
among friends. But, if that is objectified by data, and by analyses,
then the possibility of this emotional, subjective discussion with
others about this scene is weakened or somewhat not possible
anymore.’ (Bene)
‘I’m afraid that conversations would probably go in a completely
different direction, it would be more like "did you see how fast he
just ran", so really related to the stats that the app gives you, but
not like currently where you’re having emotional discussions together
and know everything better about what happened on the field.’
(Bjarne)
‘It’s typical to have heated exchanges, like: "That was such a crappy
pass", and then you expect others to be like: "Yeah, really, he’s been
playing like crap all season". But if you have this innovation and then
say: “Wait a minute I'll have a look at his stats”, I don’t want to know
that in this moment as it would take away all these typical emotions.’
(Lennart)
Sport spectators’ fear of being ‘If other fans notice that I'm using this technology, I can imagine that I
perceived as rude and/or
could be a disruptive factor. The fans who are directly next to me
earning direct criticism
would certainly say something like “Don’t you want to watch the
from relevant people
game? You aren’t here for the team, but only for your innovation.”
within the stadium.
Such comments are common in my block and I’d not want to disturb
others.’ (Mirco)
‘I'm afraid that the spectators sitting around me would grumble very
quickly. Therefore I’d rather not use it.’ (Angelika)
‘I would certainly feel ashamed myself using it [the innovation] too
often if I knew that the person sitting behind me would see worse
as a result.’ (Florian R.)
(Continued)
Journal of Global Sport Management
Social Risk
Definition
(Ram & Sheth, 1989)
573
574
General Barrier
Definition
(Ram & Sheth, 1989)
Specific Barrier
Tradition
Barrier
Evaluation that the
innovation causes a
deviation from
established norms of a
social group relevant to
the consumer.
Loss of stadium
atmosphere
Image Barrier
The degree to which an
Fan identity
innovation is perceived
incongruence
as having an unfavorable
image.
Definition
Exemplary quotes
Sport spectators’ concern that ‘I think that it would develop toward a typical fan who sits, doesn’t
using the innovation would
participate in fan-actions, and deals with the technology rather than
cause the typical stadium
contributing to the atmosphere. I fear that at some point people will
atmosphere to be lost or
only have their smartphones in their hands using AR, and as a result,
changed for the worse.
the atmosphere will be lost.’ (Jonathan)
‘That’s exactly what I'm afraid of, if suddenly the spectators instead
of having the scarf in their hand, have their smartphones in their
hand, I think the whole stadium atmosphere would get lost this way.’
(Mario)
‘What I find most problematic about it is, if at some point everyone
stands there with their smartphone using AR technology and just
stares at it, then a lot of the typical atmosphere itself could get
totally lost.’ (Lennart)
Incongruency between the
‘I think it [the innovation] would mostly be used by people who aren’t
sport spectators’ fan
interested in the game. But fans, like me, who are there for the
identity (i.e. beliefs, values,
game, the team, and the whole stadium experience, would for sure
etc.) and the image of the
not use this.’ (Lara)
innovation as it stands for
‘For me, using a technology like this is not part of the fan behavior I
commercialization and
typically exhibit, nor is it part of what I see next to me.’ (Uwe)
inauthentic fan behavior.
‘People who don’t go to the stadium often and don’t know what’s
going on there would probably use it, but spectators like me who
are authentic fans, who follow the action, they wouldn’t accept it.’
(Mario)
K. UHLENDORF AND S. UHRICH
Table B1. Continued
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