Can augmented reality enhance brand storytelling?
The effects of AR on brand attitudes and brand associations, and the mediating role of perceived flow
Dimitrios Stikos 13267485 Master’s Thesis
Graduate School of Communication Master’s programme Communication Science
Supervisor: Dr. Z.M.C. (Zeph) van Berlo Word count: 7500
Can brands narrate compelling stories in a cluttered digital world? Stories have been traditionally employed by organizations and brands to communicate and resonate with their audiences. Nowadays, technological developments allow the use of new interactive media for such brand-consumer interactions. Augmented reality is one of these media, holding the potential to offer immersive and exploratory experiences, and with the introduction of the metaverse it is projected to be vastly utilized in our digital social interactions. The aim of this study was to examine the effectiveness of brand storytelling in combination with AR on brand responses, such as brand attitudes and brand associations, and the extent to which these effects can be explained by the flow users perceive while exploring an AR app. In order to investigate this research question, a between-subjects experimental design was employed, and 84 participants were randomly assigned into one of the two conditions (AR app vs. non-AR app). The results indicated that using an AR app led to a higher perceived flow. Even though AR had a direct effect on brand attitudes, no indirect effect through flow was revealed. The opposite was shown for brand associations, as AR did not have a direct effect on brand associations, only an indirect effect through flow. Additionally, different dimensions of flow are discussed and separately examined. Overall, the findings of the present study are of high importance, complementing the existing literature on AR and storytelling, and offering valuable insights for marketers and practitioners interested in using AR to enhance their storytelling efforts.
Storytelling is an ancient art form people have been employing to communicate and transmit their knowledge. In a world that brands take anthropomorphic substance (Aggarwal
& McGill, 2012), narratives have been evolved to communicate brand-related messages. In support, consumer psychology suggests that people think with narratives rather than with arguments (Ryu et al., 2019; Weick, 1995). By employing these narratives brands can emotionally connect and resonate with their customers, and eventually gain a competitive advantage (Chiu et al., 2012). From a branding perspective, storytelling is considered an integral part of a brand’s management strategy (Park et al., 2021).
The ways, through which brands can communicate with consumers, are constantly enriched with the evolution of technology. Today, with their digital presence, brands promote their products online, connect with their audiences, build communities, and tell their stories.
Combined with these digital and social media, one of the most promising technologies for our future online social interactions is augmented reality. Platforms are also moving towards this direction, and in 2021 Facebook introduced their new capabilities for social interactions, mentioning that “Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology”
Augmented reality (AR) is a technology that integrates virtual information into real- life settings (Faust et al., 2012; Javornik, 2016a) and provides the users with the illusion that the virtually depicted objects are present in the actual environment (Verhagen et al., 2014). In the last decades, technological developments enabled greater computational power on mobile devices, which made it feasible to integrate AR features on mobile smartphones. Apps
utilizing those features use the device’s geolocation, compass, accelerometer and camera capabilities (Carmigniani et al., 2011). Making use of the AR technology for marketing
purposes, more impactful brand-consumer relationships can be established (Scholz & Duffy, 2018). As a vehicle of communication among the two parties in these relationships,
storytelling could potentially be enhanced with AR.
To date, research has mainly focused on uses of AR for retail purposes and little empirical evidence exists examining storytelling in combination with AR. Although not within the marketing context, Pokémon GO is an AR storytelling game, and the study from Scholz and Smith (2017) explains how AR and narrative emplacement can be combined to provide an immersive experience to the user. They argue that this kind of geo-media storytelling can have an impact on narrative transportation in a sense of duality; users feel that story elements are being transported to their actual environment, but they also feel a transportation into a different world. Concluding, they suggest that brands trying to transport people into their stories, can benefit from the combination of AR and the actual environment, as it is a realistic and the least distractive way to transport. Building on the storytelling and AR literature, this study aims to further investigate the effectiveness of storytelling in combination with the feature of augmented reality.
More specifically, it aims to provide more insights on the effectiveness of AR storytelling, by using a real AR app, for a real brand, at a real location. First, the present study will complement the literature of AR by examining its effects on brand responses, and, more precisely, how AR storytelling influences brand attitudes and associations. It examines these two important brand responses, since consumers’ behavioral intentions are determined by their attitudes and thoughts linked to a brand (Koll et al., 2010). Second, it will
complement the literature of narratives and storytelling, by further exploring how flow, as a psychological mechanism similar to narrative transportation, in AR can enhance those brand responses.
Since AR is a technology that is projected to be implemented by many brands in the future, practitioners and marketers can also benefit from this study. In 2022, standalone AR apps are projected to exceed 1.30 billion downloads worldwide (Statista, 2017). Facebook has already launched an early version of AR glasses (Facebook Connect, 2020), has announced its rebranding to Metaverse (Facebook, 2021), Apple offers the ARKit for developers (Apple, n.d.) and LiDAR scanner (Apple, 2020) to help more users engage with this interactive technology. Given that brand-consumer interactions are already taking place digitally and that the future is promising in the field of augmented reality, empirical evidence is highly valuable to aid brands and platforms in their efforts to develop effective AR
experiences for storytelling purposes. Therefore, practical implications and insights on the topic are of high importance. Concluding, the present study will aim to answer the following research question:
RQ: What is the impact of using an AR app (vs. a non-AR app) for brand storytelling on brand attitudes and brand associations, and to what extent can the perceived flow explain these effects?
Uses of AR and the respective research can be found mainly in retail and e-commerce (Javornik, 2016b; Shankar et al., 2016). Users can make use of AR apps to augment
themselves, the environment, or products. Following the dominant application of AR in e- commerce, research has focused on branded AR apps in retail marketing. The branded apps predominantly used in previous studies are the Ray-Ban sunglasses app (Baek et al., 2018;
Pantano et al., 2017; Poushneh & Vasquez-Parraga, 2017) and Sephora virtual try-on (Smink et al., 2020) as virtual mirrors, and IKEA Place (Javornik, 2016b; Kowalczuk et al., 2021;
Rauschnabel et al., 2019; Rese et al., 2014, 2017) as an AR furniture planner. The present study, however, goes beyond retail and focuses on the effectiveness of using AR for brand storytelling.
Effects of AR on Flow
It is believed that AR will have a positive impact on the flow users perceive while exploring the app. Perceived flow is defined as the immersion and absorption into a specific activity (Csikszentmihalyi, 1997) and is often used to describe the perception of the user that the medium is playful and exploratory (Nel et al., 1999). Flow is an important construct for explaining the user’s experience with an interactive medium. More specifically, it has been suggested that a website, that elicits higher levels of flow, is expected to lead to more frequent and longer future visits to the website (Berthon et al., 1996).
To explain the underlying concepts that immerse the app’s users into the augmented world and mediate the effects of AR, previous studies have tried to explore to what extent those can be explained through flow (Javornik, 2016b), (spatial) presence (Hilken et al., 2017; Huang & Liao, 2017; Verhagen et al., 2014) or immersion (Yim et al., 2017). Although different concepts have been used, the notion of immersion or absorption is defining those constructs, while realism and vividness is a commonality among them (Smink et al., 2020).
Similarly, telepresence follows the concept of flow as its component or antecedent (Nah et al., 2011). In the literature of narratives, the term narrative transportation is used, which also refers to the absorption into a story, and reflects the concept that the reader loses access to some facts of the real world in order to accept the world in the narrative (Green & Brock, 2000). Since there is not one single concept explaining how users are immersed or ‘lost’ into an AR experience or a storytelling narrative, the concept of flow will be used for the purposes
of this study. Flow is nevertheless more general than the other relevant constructs and can be experienced in a wide variety of activities (van Laer et al., 2014).
Nel et al. (1999) suggest that flow is a multidimensional construct, composed of the user’s attention focus, the perceived control, and the curiosity or intrinsic interest. Since the common feature among storytelling and AR is their ability to transport and absorb the users or readers, and this study investigates to what extent brand storytelling can be enhanced with AR, attention focus is the most relevant dimension to examine. It is the part that better
explains the absorption or immersion into the activity. Furthermore, it has been found that the more immersed the users, who experience flow, are, the higher their expectations regarding the use and the experience with an interactive medium (Hoffman & Novak, 1996).
Previous studies have explored the effects of interactive environments and AR on flow and the perception of ‘being there’, that is the sensory immersion into the mediated environment (Sundar et al., 2015). It has been found that users of AR gamified environments experience transportation into the story and perceive a feeling of reality (Scholz & Smith, 2017). Moreover, visitors that find a website more interactive show a higher perceived flow (van Noort et al., 2012), while the same effect was confirmed by a study using an AR furniture app (Javornik, 2016b), and by another using an AR apparel app (Huang & Liao, 2017). Considering the above, it is hypothesized that the perceived flow will be higher for the users of the AR app and, thus, this study hypothesizes:
H1: Using an AR app will lead to higher perceived flow than using a non-AR app.
Besides this expected positive effect of AR on flow, it is proposed in the following sections that app type (AR vs. non-AR) will influence brand attitudes and brand associations,
and that flow plays an important role in explaining the effects of app type on these brand responses.
Effects of AR on Brand Attitudes
Augmented reality can elicit positive affective responses, like brand attitudes, among its users when they explore this mediated environment. Brand attitudes are defined as the internal evaluation of an individual on the brand (Mitchell & Olson, 1981). These evaluations are of paramount importance, since when consumers have to make a choice, they tend to choose based on these attitudes they have towards a brand (Hess & Story, 2005).
Augmented reality, in addition to its distinct feature of virtual emplacement and augmentation, is characterized by the interactivity between the user and the interface (Azuma et al., 2001). This interactivity modality, or medium-based interactivity, refers to the
interaction with the information, as offered on the interface of the medium, and is considered to be a peripheral cue (Oh & Sundar, 2015). Peripheral cues can influence the persuasive outcomes of a communicational attempt (Petty & Cacioppo, 1986), and the additivity
hypothesis suggests that, when these peripheral cues are compatible with the arguments (i.e., the storytelling), the outcomes are expected to be bolstered (Chaiken & Maheswaran, 1994).
Hence, with the presence of AR, as a peripheral cue complementing the central messaging in the branded story, it is expected that brand attitudes will be enhanced.
Even though previous studies have tried to explore the effects of AR on brand attitudes, they show mixed results. One study found that branded AR apps can lead to more favorable attitudes towards the brand (Rauschnabel et al., 2019), whereas other studies did not reveal an effect of the AR app on brand attitudes (Javornik, 2016b; Smink et al., 2020).
In sum, even though a consensus was not reached in previous studies, I propose that the promising use of AR as an interactive feature and a peripheral cue next to a brand
narrative, will elicit more favorable attitudes towards the brand. Hence, the following hypothesis is postulated:
H2: Using an AR app will lead to more positive brand attitudes than using a non-AR app.
Simple Mediation Effects of AR on Brand Attitudes Through Flow
Furthermore, it is expected that AR will have an indirect effect on brand attitudes through flow. Augmented reality, holding capabilities to transport its users into the stories, can be utilized as a persuasive tool (Li et al., 2013). The effects on brand attitudes can, thus, be explained by the concept that users immerse themselves and perceive a higher flow.
Users that experience immersion into the story world show more positive brand attitudes and behavioral intentions (Escalas, 2004). Following the literature on narratives, transportation can positively influence the brand image (Ryu et al., 2019), because, when consumers are absorbed, they become less analytical and critical (Green & Brock, 2000).
Besides, telepresence, as a construct describing perceived flow, in 3D virtual worlds has been found to positively influence brand attitudes (Nah et al., 2011).
Augmented reality can lead to higher affective responses, and this can be explained by the flow users experience. In interactive environments, absorption into the task has shown to lead to more favorable attitudes towards the website (Oh & Sundar, 2015). Comparing AR and non-AR apps, Javornik (2016b) confirmed that flow mediated the effect of AR modality on affective responses, and, more specifically, on app attitudes. Since the attitudes towards a branded AR app are also brand-related affective responses, it is expected that also the
attitudes towards the brand will be more favorable because of the higher perceived flow, and the following hypothesis is formulated:
H3: Using an AR app will lead to more positive brand attitudes than using a non-AR app, which is positively mediated by the perceived flow.
Effects of AR on Brand Associations
Even though attitudes constitute an important aspect for the brand, brand associations are also principal for the brand equity. Aaker (1991, p. 33) defines brand equity as the
aggregate of all brand assets or liabilities, including the brand image and the associations consumers have about the brand. Therefore, a direct and an indirect effect, through flow, of app type on brand associations are also proposed.
Users exploring an AR app will develop stronger associations for the brand, compared to users of a non-AR app. Brand associations refer to the aggregation of assets and liabilities of a brand and their connections in memory (Aaker, 1991), and contain the meaning of the brand for the consumers as ‘informational nodes’ (Keller, 2003).
Brand associations is a concept that is by definition a learning process, since nodes are linked to build the connections with the brand. Van Osselaer and Janiszewski (2001) explain that the processes, through these associations are established, are the human associative memory (HAM) models (Anderson & Bower, 1973), or the adaptive learning model as explained by Janiszewski & Van Osselaer (2000). These two models mainly differ from each other in terms of cue learning and interactivity; the former proposes that cues are learned independently, whereas the latter suggests that these cues interact. In other words, HAM models suggest that multiple brand associations can be promoted simultaneously, whereas the adaptive learning model proposes that the promotion of an association may be less effective when it is ‘trained’ with another association of greater salience. Even though the interaction differs, the above indicate that brand associations in both cases are being established or strengthened through a learning process.
Augmented reality, by serving its purpose as a cue, is expected to enhance this
learning process and lead to stronger brand associations. Consumer learning can be enhanced with the aid of interactive and rich presentation of the information (Kim & Biocca, 1997), and previous studies investigating the effects of virtual reality, as a similarly rich and interactive medium, also confirmed the enhancement of consumer learning (Suh & Lee, 2005; van Berlo et al., 2021). In an educational context, a study with interactive storytelling AR app (Lu & Liu, 2015) , a study with mixed reality simulation (Lindgren et al., 2016), and a study with location-based gamified AR (Li et al., 2013) showed that the AR enhanced learning in young students. However, I am not aware of any studies to date investigating specifically the effects of AR on brand associations.
Even though no studies have examined this specific relationship, I propose that AR will lead to stronger brand associations, as it has been shown that associations are established through learning processes and previous studies have affirmed the learning capabilities of AR. Hence, this study hypothesizes:
H4: Using an AR app will lead to stronger brand associations than using a non-AR app.
Simple Mediation Effects of AR on Brand Associations Through Flow
Moreover, an indirect effect of AR on brand associations through flow is also expected. In human-computer interactions, perceptual interfaces, and more specifically, media that provide more sensory experiences, increase engagement and the respective memory about these experiences (Reeves & Nass, 2000). The limited capacity model by Lang (2000) could explain how users encode and store information while interacting with an AR app. Based on this model, Li et al. (2013) explain that, even though the modality of AR
does not seem to promote storage of new information over retrieval of existing information, the unique feature of narrative transportation, or the sense of presence, can enhance cognitive processing and the subsequent learning.
Previous studies, albeit not using an AR app, have tried to explore the effects of interactive and immersive media on brand equity and learning through the perceived
immersion into the story world. In the context of 3D virtual environments, Nah et al. (2011) affirmed the effect on learning, while showing that flow was the most relevant concept explaining this effect. Another study by Bae et al. (2020), employing mixed reality (virtual hologram portrayals and projection mapping on a physical display wall), showed that the immersion, which is a dimension of flow, mediated the effects of mixed reality’s interactivity on brand associations. Given the similarity among those environments and AR, in terms of their interactivity capabilities, as AR is often described as a type of virtual reality (Guttentag, 2010; Vince, 2004), I expect the AR app used in this study to elicit stronger brand
associations, and, thus, it is proposed that:
H5: Using an AR app will lead to stronger brand associations than using a non-AR app, which is positively mediated by the perceived flow.
The conceptual model of the study is depicted in Figure 1.
Figure 1. Conceptual model
Brand attitudes H2a
AR app versus non-
Note. Hypothesized direct and indirect effects.
bIndirect effects through flow.
The current study adopted a one-factor (app type: AR vs non-AR) between-subjects experimental design, with brand attitudes and brand associations as dependent variables and flow as a mediator variable. Between-subjects, instead of within-subjects, was chosen to reduce threats to the internal validity and potential carryover effects, since the presence or absence of the feature of AR was distinct among the two conditions. The AR app condition serves as the experimental condition, whereas the non-AR serves as the control condition.
Participants were randomly assigned into one of these conditions.
In total, 84 participants participated in the study, from a 100 people initially approached. They were recruited through convenience sampling at the University of
Amsterdam Roeterseiland campus during the days from November 30, 2021 to December 3, 2021. The recruitment happened on campus, since the AR feature of the app had a location- based nature. The AR experience included the augmentation of the user’s surroundings with the depiction of the brand in the stimulus material. One participant that gave the exact same answer on fifteen consecutive 7-point scale items measuring the variables perceived
augmentation and flow was identified as straightliner and was, thus, excluded from further
analyses. The remaining participants (N = 83) had a mean age of 21.27 years (SD = 2.25), of whom 44 (53%) were assigned in the AR condition and 39 (47%) in the non-AR condition.
Thirty-nine participants (47%) were males, 41 (29.4%) females, 2 (2.4%) identified as non- binary, and 1 preferred not to say (1.2%). Also, the majority represented by 39 participants (47%) were high-school graduates, 37 (44.6%) had a Bachelor’s degree, and 7 participants (8.4%) were Master’s graduates.
Participants were approached in the public space of buildings ABC of University of Amsterdam. I, as the researcher, approached them and asked whether they wanted to
participate in a study concerning brand storytelling, conducted for the purposes of my thesis.
They were presented with the informed consent, which they signed. They agreed that they had an adult age (18+) and their participation was voluntary. They were randomly assigned into one of the two conditions. Participants in the AR condition were instructed to read a short story on the app and were guided on how to use the AR feature after reading the story, whereas those in the non-AR condition were instructed to only read the same story using the app. The former were also asked to try to explore their environment, using the AR app, and experiment with the feature. After the exposure to the stimulus material, all participants filled out a questionnaire measuring demographics, perceived flow, brand attitudes, and brand associations. Finally, they were debriefed and thanked for their participation. No incentive for participating in the study was given.
The brand used in the stimulus material was University of Amsterdam, and the respective brand attitudes and brand associations are therefore referred to this brand. This
experiment was a field study and not a scenario-based experiment, thus, the app used was an existing mobile AR app, named Artelot. The app is used for storytelling with the use of AR.
A short story was developed depicting the professor of the university and Nobel prize winner, Johannes Diderik van der Waals, and a lady discussing back in 1910. The topic of their discussion was on the accomplishments of the professor, and the magnitude of the university.
Elements highlighting the historical, prestigious and inclusive aspect of the brand ‘University of Amsterdam’ were incorporated into the story. The goal was to promote these three brand associations, which were afterwards measured in the questionnaire. Both conditions had the exact same story in written format, accompanied with an image featuring the two characters of the story and the university’s logo on a solid-color background. The difference between the two conditions was the use of AR, for which there was a button in the AR condition instructing “View in AR”. After clicking the button, the two characters in their full height were appearing in an AR app’s interface offering the capability to the participants to move and place them within their actual environment or capture a photo. See Appendix A for screenshots of the stimulus material and the app’s interface.
Since the manipulation of the factor relies on the presence of the unique feature of AR, the manipulation check examines whether the participants in the experimental condition perceived a significantly higher level of augmentation than those in the control condition.
Hence, three single-item measures (“I felt like the objects were actually there in the real world”, “It seemed as if the objects had shifted from the phone into the room”, “It seemed that everything I saw on the display was real”) on a seven-point Likert scale, ranging from 1
(Strongly disagree) to 7 (Strongly agree), from the study of Rauschnabel et al. (2019) were used to examine for the aforementioned difference. A factor analysis with principal axis factoring and direct oblimin rotation on 3 items was conducted. One extracted factor was retained with all items explaining 74.56% of the total variance and had an eigenvalue of 2.24. A reliability analysis of the factor was also good, Cronbach’s alpha = .83. Finally, a new variable was created, named perceived augmentation (M = 4.18, SD = 1.58).
In order to measure the attitudes towards the brand, a seven-point semantic differential scale by Spears and Singh (2004) was adopted, with five items
(“Unappealing/appealing”, “Bad/good”, “Unpleasant/pleasant”, “Unfavorable/favorable”,
“Unlikable/likable”). A factor analysis with principal axis factoring and direct oblimin rotation on these 5 items was conducted. One extracted factor was retained with all items explaining 68.48% of the total variance and had an eigenvalue of 3.42. A reliability analysis of the factor was also good, Cronbach’s alpha = .88. Finally, a new variable was created, named brand attitudes (M = 5.65, SD = 0.92).
Three different associations with the University of Amsterdam were measured. More specifically, a three-item 7-point Likert scale was used, measuring how well the University could be described as “historical”, “prestigious”, and “inclusive”. The scale was developed following the conceptualization of other studies measuring brand associations (Dahlén, 2005;
Sasmita & Suki, 2015). However, not with the exact same items, since a universal scale is not easily applicable, because previously developed scales measuring brand associations are
either too specific and exclusive to product categories, or too long to use them (Low & Lamb, 2000). A factor analysis with principal axis factoring and direct oblimin rotation on 3 items was conducted, measuring brand associations. One extracted factor was retained with all items explaining 63.21% of the total variance and had an eigenvalue of 1.90. A reliability analysis of the factor was also good, Cronbach’s alpha = .70. Finally, a new variable was created, named brand associations (M = 5.35, SD = 1.00).
In order to measure the perceived flow, a multidimensional scale by Nel et al. (1999) was used and adapted to fit the AR app measurements instead of the initial ones intended for website use. Different items were referring to the perceived control, attention focus, curiosity and intrinsic interest, composing the flow construct. Twelve items were used, including, among others, “When I used the app I felt in control”, “When I used the app I was totally absorbed in what I was doing”, “Visiting the app excited my curiosity”, on seven-point, Likert-type scales, ranging from 1 (Strongly disagree) to 7 (Strongly agree). Three items were recoded to fit the scale’s direction (see Table B2, Appendix B).
A factor analysis with principal axis factoring and direct oblimin rotation on the 12 items was conducted. Three extracted factors were retained. Six items loaded on the first factor explaining 51.18% of the total variance with an eigenvalue of 6.14, three items loaded on the second factor explaining 11.64% of the total variance with an eigenvalue of 1.40, and three items loaded on the third factor explaining 8.70% of the total variance with an
eigenvalue of 1.04. The first factor was reliable with a Cronbach’s alpha = .90 and referred to the curiosity and intrinsic interest, from which a new variable was created, named flow interest (M = 4.96, SD = 1.28). The second factor was reliable with a Cronbach’s alpha = .69 and referred to the attention focus. However, by omitting one item, the scale was more
reliable with a Cronbach’s alpha = .76, and, thus, the item was excluded. Then, a new variable was created, named flow focus (M = 4.54, SD = 1.46). For the third factor, the reliability was good with a Cronbach’s alpha = .80, and referred to the perceived control, from which a new variable was created, named flow control (M = 4.85, SD = 1.44).
For the main analyses, exploring the effect to and of flow, only the second factor (flow focus) will be used since it better explains the notion of absorption and focus into the activity of exploring the story and the app (Nel et al., 1999). See Appendix B for all
constructs, (omitted) items and loadings.
Results Randomization Check
In order to investigate whether there were any differences among the participants in the two conditions in terms of their gender, age and educational level, several tests were performed. More specifically, a chi-square test was conducted to examine whether gender was comparable across conditions, with app type as independent variable, and gender (male vs. female) as dependent variable. The test showed no differences in terms of gender, χ2 (1, 80) = 0.06, p = .814, all expected counts > 5, which means that all assumptions are met.
To explore any differences between the groups in terms of participants’ age, an independent samples t-test was conducted with app type as independent variable, and age as dependent variable. The test showed no significant differences in terms of age, t(81) = 0.32, p = .747, 95% CI [-0.83, 1.15], d = 0.07. Finally, a chi-square test was conducted to investigate whether educational level was different among conditions, with app type as independent variable, and educational level (high school vs. higher education) as dependent variable. The test showed no differences in terms of educational level, χ2 (1, 83) = 0.088, p = .766, all expected counts > 5.
In sum, no significant differences were found among the two groups, comparing their age, educational level and gender, and the randomization was, therefore, successful.
To examine whether the AR environment was perceived as intended, an independent samples t-test was conducted with app type as independent variable and perceived
augmentation as dependent variable. The Levene’s test for equality of variances was not significant, F = 0.96, p = .331. In the AR condition, participants reported a significantly higher augmentation (M = 4.78, SD = 1.57), compared to the non-AR condition (M = 3.51, SD = 1.33); t(81) = 3.95, p < .001, 95% CI [0.63, 1.91], d = 0.88. The above indicate that participants perceived the augmentation as intended in the AR condition compared to the non-AR condition and, thus, the manipulation was successful.
Effects of App Type on Flow
To examine the extent to which participants in the AR condition, compared to the non-AR condition, experienced a higher perceived flow, as proposed by H1, an independent samples t-test was conducted with app type as independent variable and flow as dependent variable. The Levene’s test for equality of variances was not significant, F = 0.84, p = .361.
In the AR condition, participants reported a significantly higher perceived flow (M = 5.20, SD = 1.21) compared to the non-AR condition (M = 3.79, SD = 1.35); t(81) = -5.01, p <
.001, 95% CI [-1.97, -0.85], d = 1.11. Therefore, Hypothesis 1 was confirmed.
To test Hypotheses 2-5, two separate mediation models were estimated with the Model 4 of PROCESS macro (Hayes, 2017), with 95% confidence intervals (10,000
bootstrap samples), including app type as independent variable and flow as the mediator. As for the dependent variables, brand attitudes were included in the first model, and brand associations in the second model. All results can be found in Table 1.
Effects of App Type on Brand Attitudes With Flow as Mediator
The first mediation analysis was significant, F(2, 80) = 6.53, p = .002, 𝑅2 = .140. As shown in Table 1, app type had a significant direct effect on brand attitudes (p = .034). This means that the AR app, compared to the non-AR app, led to more positive brand attitudes.
Therefore, Hypothesis 2 is confirmed. However, examining the effects of app type on brand attitudes as mediated by flow, no significant indirect mediation effect through flow was shown. In other words, flow did not explain the effect of app type on brand attitudes. Hence, Hypothesis 3 is rejected.
Effects of App Type on Brand Associations With Flow as Mediator
The second mediation analysis was proven to be significant, F(2, 80) = 19.33, p <
.001, 𝑅2 = .571. As shown in Table 1, app type did not have a significant direct effect on brand associations (p = .667). Therefore, Hypothesis 4 is rejected. In contrast, examining the effects of app type on brand associations as mediated by flow, a significant indirect mediation effect through flow was shown. Hence, Hypothesis 5 is confirmed.
Table 1. Results showing the direct and indirect effects of app type on brand attitudes and brand associations.
Flow Brand attitudes Brand associations
b SE 95% CI b SE 95% CI b SE 95% CI
App type 1.41 0.28 [-1.97, -0.85] 0.47 0.22 [0.35, 0.90] 0.90 0.21 [-0.33, 0.51]
Flow - - - 0.11 0.75 [-0.04, 0.26] 0.38 0.72 [0.23, 0.52]
> Flow - - - 0.16 0.12 [-0.09, 0.41] 0.53 0.14 [0.29, 0.83]
Note. Regression coefficients in bold are significant.
As discussed in previous section, this study focuses on the immersive dimension of flow, that is the attention focus. However, in order to provide more insights for all
dimensions of flow, separate analyses were performed, the results of which can be found in Appendix C, with the other two components of flow (i.e., flow interest and flow control).
More specifically, two independent samples t-tests and two separate mediation model estimations including the other two flow components were conducted.1
1 Beside the main analyses that included only the focus component of flow, additional analyses were also performed including all components of flow (i.e., control, focus, interest). In sum, the additional t-tests showed that participants also reported significantly higher interest (p < .001) and control (p < .001) in the AR compared to the non-AR condition. The additional mediation model estimations did not reveal significant direct effects of app type on brand attitudes (p = .300) and brand associations (p = .302).
However, significant indirect total mediation effects through all components of flow on brand attitudes (b
= 0.37, 95% CI [0.33, 0.75]) and on brand associations (b = 0.85, 95% CI [0.50, 1.31]) were revealed.
Therefore, hypotheses 2 and 5 were confirmed, and hypotheses 3 and 4 were rejected when the analyses included only the attention focus component of flow. On the other hand, when all components of flow were included in the models, hypotheses 3 and 5 were confirmed, and hypotheses 2 and 4 were rejected.
Conclusion and Discussion
The aim of the study was to examine the effects of brand storytelling with the aid of AR on perceived flow, brand attitudes and brand associations, and to investigate the extent to which flow explains the latter two effects. Three main conclusions are drawn upon the results. First, AR was shown to positively influence perceived flow. Second, regarding the effects of app type on brand attitudes, AR led to more positive brand attitudes directly, but not indirectly though flow. Third, for the brand associations, the exact opposite pattern of findings was revealed. Even though AR did not influence brand associations directly, the results indicate that flow was found to explain the effect of AR on brand associations.
Effects of AR on Flow
The AR feature positively influenced the flow users perceived while exploring the app. In other words, AR elicited a feeling of ‘being there’ and an immersion into the augmented experience. The finding is also in line with previous studies suggesting that AR holds the potential to immerse and absorb its users into the story world (Huang & Liao, 2017;
Javornik, 2016b; Scholz & Smith, 2017; Sundar et al., 2015). Moreover, the interactivity modality, that is inherent to AR, by itself is known to lead to higher flow (Skadberg &
Kimmel, 2004), and previous studies have affirmed a positive effect from websites as interactive media on flow (Hoffman & Novak, 2009; van Noort et al., 2012). Therefore, the present experiment extents the findings of previous studies, which showed positive effects of different interactive media on flow.
Effects of AR on Brand Attitudes
The first finding, regarding the effects of app type on brand responses, shows that AR directly influences brand attitudes. The results of this study indicate that apps, that have AR as a feature to augment their stories, can be peripherally processed with the AR feature being encountered as a cue, and ultimately elicit more positive brand attitudes. In other words, AR leads to more favorable brand attitudes, in line with previous research (Rauschnabel et al., 2019). Moreover, in the present study, the AR feature was explored by the participants after reading a story in a written text format. Research has shown that exposing users to a virtual experience, after an indirect experience, elicits more favorable brand attitudes and affective responses (Daugherty et al., 2008). Hence, it is affirmed that the use of AR in a mobile app with storytelling has a positive impact on brand attitudes.
However, this study found no support for the indirect relation of AR with brand attitudes through flow. In other words, flow was not found to be explaining the effect of AR on brand attitudes. Previous studies have only examined flow and its mediating role between interactive media (Javornik, 2016b; Oh & Sundar, 2015) and attitudes towards the medium, but not brand attitudes. Even though attitudes towards the medium and the brand itself are both brand-related affective responses, Rauschnabel et al. (2019) suggest that the former come before the latter. More precisely, they argue that brand attitudes in such interactions are formed by means of spillover from the attitudes users formulate towards the medium itself.
Therefore, an explanation is that flow may have influenced app attitudes, which were not measured in the present study, but this impact was not that powerful to spill over to the brand attitudes.
In sum, AR leads to more positive brand attitudes, and this happens because the AR is perceived as a peripheral cue next to the branded storytelling, rather than as a feature
promoting immersion and absorption into the narrative.
Effects of AR on Brand Associations
The results also suggest that AR apps do not have a direct effect on brand
associations. By examining to what extent students perceived the brand to be prestigious, historical and inclusive, AR as an interactive feature did not seem to directly strengthen these associations by enabling a learning process, as proposed by theory and shown in previous research on interactive media and consumer learning (Kim & Biocca, 1997; Suh & Lee, 2005; van Berlo et al., 2021). Li et al. (2013) argue that the modality of AR by itself does not promote information storage over retrieval, and this study did not investigate whether the examined brand associations were pre-existing or novel ones. An interpretation of this finding is that the expected effect was not shown because these associations were non- existent to the consumers prior to the experiment. Hence, promoting new associations over existing ones became infeasible only with the presence of AR as an interactive medium.
However, it has also been suggested that flow is the most important factor to explain the effect of interactive media on the subsequent learning (Skadberg & Kimmel, 2004). The mediation analyses of the present study revealed that, when flow was included as a mediator, flow was shown to explain the effect of AR on brand associations. This finding agrees with the argument of Li et al. (2013), that when AR has the notion of transportation and the sense of ‘being there’, then it promotes cognitive processing and enhances learning.
The additional analyses showed that AR leads to higher perceived flow, and not only for the focus dimension, but for all its dimensions (the perceived control, interest and focus;
see Appendix C for the additional analyses).
Moreover, based on the additional mediation models that were estimated, a
conclusion could be drawn that the effects of AR on brand attitudes can be explained by flow
only when all dimensions of flow are considered altogether, and that an effect on brand associations is mediated only by the focus and control dimensions of flow. In other words, an AR app that holds the capability to make its users focused is not enough to promote brand attitudes, since it must also be interesting and controllable. Regarding the associations, this study suggests that focus and control are enough to explain the effect of the AR app on brand associations.
Limitations and Future Research
The study took place at a university campus, using a real brand, to replicate a real-life setting. By doing so, a higher external validity was achievable, but this could lead to several drawbacks. Using a real brand may have interfered with the participants’ pre-existing attitudes, since the participants were mostly students at the University of Amsterdam, the brand that was used in the stimulus material. This limitation may have affected the results of the study, because of a potential existence of ceiling effects, suppressing the sizes of the effects on brand attitudes and associations. Hence, future research should examine the effects of AR by using a less known or a fictitious brand to avoid such potential ceiling effects.
While exploring the app, the participants were presented with a story in written format, and they subsequently experienced the AR feature depicting a static image. Even though previous literature suggests that this sequence can enhance brand attitudes (Daugherty et al., 2008), it makes the content consumption more difficult. More specifically, the modality effect in multimedia learning (Moreno & Mayer, 1999) suggests that spoken text further enhances the learning process compared to written text. Therefore, it is suggested that a simultaneous presentation of the characters in the story with the text in spoken format would better deliver the AR experience to the app’s users and enhance the subsequent learning process.
Finally, there is one more limitation of the present study that should be addressed. In order to investigate the extent to which users of the app felt immersion and absorption, this study used flow as a construct more general and commonly used in studies with AR apps (Huang & Liao, 2017; Javornik, 2016b) and interactive media (van Noort et al., 2012).
However, other concepts have also been previously used throughout the literature of storytelling (e.g., narrative transportation) or the augmented reality (e.g., spatial presence, telepresence), that describe the notion of making the user absorbed into the activity. By making the choice to include flow and examine its mediating role, this study may have missed the opportunity to explain the effects from a different, but similar, perspective.
Therefore, future studies should include other relevant constructs, related to the perceived immersion, to explain the effectiveness of storytelling with AR on brand responses.
This study contributes to the literature of narratives and storytelling, but also of augmented reality. Regarding the former, the findings indicate that researching brand
storytelling can benefit from the use of this novel interactive technology and suggest that AR can also be employed as a medium for researching storytelling and brand-related affective responses. On the other side, an implication for the augmented reality literature, and its researchers, is that AR can be researched in uses beyond retail, e-commerce and gaming, and be extended to brand-consumer social interactions by means of storytelling.
One more interesting finding concerns brand associations, perceived flow, and their relationship with AR. First, this study showed that the construct of flow is applicable to augmented reality and, even more, plays an important role in explaining the effects of AR on brand associations. Second, this experiment showed that flow is not a unidimensional, but rather a multidimensional construct, as described by Nel et al. (1999), with its dimensions
explaining the effects on brand associations and attitudes, separately and collectively, in different ways. Finally, no other previous study to my knowledge has ever examined how AR influences brand associations and, moreover, whether flow can explain this effect. This constitutes an addition to the current scientific landscape, since to date other studies have only focused on consumer learning outcomes (Li et al., 2013; Lu & Liu, 2015) or examined brand associations in mixed reality environments (Bae et al., 2020). With the present study, a new relationship between AR and brand associations through flow is established. This underlines that flow is an important mechanism for learning, and offers insights for researchers interested in associations, that AR can be utilized to enhance learning and promote brand associations, only when the experience facilitates a flow state.
In sum, the main contribution of this study is the extension of the AR literature, going beyond retail marketing and utilizing the technology for brand storytelling purposes. Since online social interactions are moving towards 3D and immersive environments, this study comes one step closer and becomes more relevant to our future everyday interactions with technology and brands, and offers inspiration for research to further investigate the effects derived from this combination.
The present study provides insights that go beyond the theoretical field and have practical implications for marketers, brands, or software developers. Based on the findings of this study, it is shown that AR can contribute to the brands’ efforts to tell their stories and promote more positive brand attitudes and establish brand associations, with its unique
capability to absorb its users into the narrative. Moreover, regarding developers and platforms hosting AR, they should consider that flow is an important psychological correlate, which can enhance the augmented experience. Based on this study’s additional findings, they need to
develop more interesting, immersive and controllable user experiences, and allow brands to build efficient storytelling. More explicitly, to promote brand associations, brands should use apps that are immersive, whereas to promote brand attitudes, immersion is not enough and apps should also be controllable and interesting.
Augmented reality, albeit already used in several instances, holds the potential to be pervasively implemented across many applications in the future. The technology of 5G networks and the upcoming rapid performance improvements of mobile devices offer a promising future for AR (Qiao et al., 2019). In today’s competitive environment, brands urge the need to break through the ‘clutter’ and differentiate from their competitors (Srivastava &
Dorsch, 2020), and it seems that they already rush to harness the potential of this new technology (Ellwood, 2021). In this near future, all interested parties will benefit from more informed evidence-based decisions and developments, and the findings of this study
contribute to this direction.
Aaker, D. A. (1991). Managing brand equity: Capitalizing on the value of a brand name. The Free Press.
Aggarwal, P., & McGill, A. L. (2012). When brands seem human, do humans act like
brands? Automatic behavioral priming effects of brand anthropomorphism. Journal of Consumer Research, 39(2), 307–323. https://doi.org/10.1086/662614
Anderson, J. R., & Bower, G. H. (1973). Human associative memory. Psychology press.
Apple. (n.d.). ARKit—Augmented reality. Apple Developer. Retrieved November 9, 2021, from https://developer.apple.com/augmented-reality/arkit/
Apple. (2020, March 18). Apple unveils new iPad Pro with LiDAR Scanner and trackpad support in iPadOS. Apple Newsroom.
Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., & MacIntyre, B. (2001). Recent advances in augmented reality. IEEE Computer Graphics and Applications, 21(6), 34–47. https://doi.org/10.1109/38.963459
Bae, S., Jung, T. H., Moorhouse, N., Suh, M., & Kwon, O. (2020). The influence of mixed reality on satisfaction and brand loyalty in cultural heritage attractions: A brand equity perspective. Sustainability, 12(7), 2956. https://doi.org/10.3390/su12072956
Baek, T. H., Yoo, C. Y., & Yoon, S. (2018). Augment yourself through virtual mirror: The impact of self-viewing and narcissism on consumer responses. International Journal of Advertising, 37(3), 421–439. https://doi.org/10.1080/02650487.2016.1244887 Berthon, P., Pitt, L. F., & Watson, R. T. (1996). The World Wide Web as an advertising
medium. Journal of Advertising Research, 36(1), 43–54.
Carmigniani, J., Furht, B., Anisetti, M., Ceravolo, P., Damiani, E., & Ivkovic, M. (2011).
Augmented reality technologies, systems and applications. Multimedia Tools and Applications, 51(1), 341–377. https://doi.org/10.1007/s11042-010-0660-6
Chaiken, S., & Maheswaran, D. (1994). Heuristic processing can bias systematic processing:
Effects of source credibility, argument ambiguity, and task importance on attitude judgment. Journal of Personality and Social Psychology, 66(3), 460–473.
Chiu, H.-C., Hsieh, Y.-C., & Kuo, Y.-C. (2012). How to align your brand stories with your products. Journal of Retailing, 88(2), 262–275.
Csikszentmihalyi, M. (1997). Flow and the psychology of discovery and invention (Vol. 39).
Dahlén, M. (2005). The medium as a contextual cue: Effects of creative media choice.
Journal of Advertising, 34(3), 89–98.
Daugherty, T., Li, H., & Biocca, F. (2008). Consumer learning and the effects of virtual experience relative to indirect and direct product experience. Psychology and Marketing, 25(7), 568–586. https://doi.org/10.1002/mar.20225
Ellwood, M. (2021, December 9). Luxury fashion brands are already making millions in the metaverse. Bloomberg. https://www.bloomberg.com/news/articles/2021-12-
09/luxury-fashion-brands-are-already-making-millions-in-the-metaverse Escalas, J. E. (2004). Imagine yourself in the product: Mental simulation, narrative
transportation, and persuasion. Journal of Advertising, 33(2), 37–48.
Facebook. (2020, September 16). Facebook Connect: The road to AR glasses. Facebook Technology. https://tech.fb.com/facebook-connect-the-road-to-ar-glasses/
Facebook. (2021, October 28). The Facebook company is now Meta. Meta.
Faust, F., Roepke, G., Catecati, T., Araujo, F., Ferreira, M. G. G., & Albertazzi, D. (2012).
Use of augmented reality in the usability evaluation of products. Work,
41(Supplement 1), 1164–1167. https://doi.org/10.3233/WOR-2012-0298-1164
Green, M. C., & Brock, T. C. (2000). The role of transportation in the persuasiveness of public narratives. Journal of Personality and Social Psychology, 79(5), 701–721.
Guttentag, D. A. (2010). Virtual reality: Applications and implications for tourism. Tourism Management, 31(5), 637–651. https://doi.org/10.1016/j.tourman.2009.07.003
Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications.
Hess, J., & Story, J. (2005). Trust‐based commitment: Multidimensional consumer‐brand relationships. Journal of Consumer Marketing, 22(6), 313–322.
Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., & Keeling, D. I. (2017). Augmenting the eye of the beholder: Exploring the strategic potential of augmented reality to enhance online service experiences. Journal of the Academy of Marketing Science, 45(6), 884–
Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50–68.
Hoffman, D. L., & Novak, T. P. (2009). Flow online: Lessons learned and future prospects.
Journal of Interactive Marketing, 23(1), 23–34.
Huang, T.-L., & Liao, S.-L. (2017). Creating e-shopping multisensory flow experience through augmented-reality interactive technology. Internet Research, 27(2), 449–475.
Janiszewski, C., & Van Osselaer, S. M. J. (2000). A connectionist model of brand–quality associations. Journal of Marketing Research, 37(3), 331–350.
Javornik, A. (2016a). Augmented reality: Research agenda for studying the impact of its media characteristics on consumer behaviour. Journal of Retailing and Consumer Services, 30, 252–261. https://doi.org/10.1016/j.jretconser.2016.02.004
Javornik, A. (2016b). ‘It’s an illusion, but it looks real!’ Consumer affective, cognitive and behavioural responses to augmented reality applications. Journal of Marketing Management, 32(9–10), 987–1011. https://doi.org/10.1080/0267257X.2016.1174726 Keller, K. L. (2003). Brand synthesis: The multidimensionality of brand knowledge. Journal
of Consumer Research, 29(4), 595–600. https://doi.org/10.1086/346254
Kim, T., & Biocca, F. (1997). Telepresence via television: Two dimensions of telepresence may have different connections to memory and persuasion. Journal of Computer- Mediated Communication, 3(2). https://doi.org/10.1111/j.1083-6101.1997.tb00073.x Koll, O., von Wallpach, S., & Kreuzer, M. (2010). Multi-method research on consumer–
brand associations: Comparing free associations, storytelling, and collages.
Psychology & Marketing, 27(6), 584–602. https://doi.org/10.1002/mar.20346 Kowalczuk, P., Siepmann (née Scheiben), C., & Adler, J. (2021). Cognitive, affective, and
behavioral consumer responses to augmented reality in e-commerce: A comparative study. Journal of Business Research, 124, 357–373.
Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46–70. https://doi.org/10.1111/j.1460-2466.2000.tb02833.x Li, R., Zhang, B., Sundar, S. S., & Duh, H. B.-L. (2013). Interacting with augmented reality:
How does location-based AR enhance learning? In P. Kotzé, G. Marsden, G.
Lindgaard, J. Wesson, & M. Winckler (Eds.), Human-computer interaction – INTERACT 2013 (Vol. 8118, pp. 616–623). Springer Berlin Heidelberg.
Lindgren, R., Tscholl, M., Wang, S., & Johnson, E. (2016). Enhancing learning and engagement through embodied interaction within a mixed reality simulation.
Computers & Education, 95, 174–187.
Low, G. S., & Lamb, C. W. (2000). The measurement and dimensionality of brand associations. Journal of Product & Brand Management, 9(6), 350–370.
Lu, S.-J., & Liu, Y.-C. (2015). Integrating augmented reality technology to enhance
children’s learning in marine education. Environmental Education Research, 21(4), 525–541. https://doi.org/10.1080/13504622.2014.911247
Mitchell, A. A., & Olson, J. C. (1981). Are product attribute beliefs the only mediator of advertising effects on brand attitude? Journal of Marketing Research, 18(3), 318–332.
Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91(2), 358–368.
Nah, F. F.-H., Eschenbrenner, B., & DeWester, D. (2011). Enhancing brand equity through flow and telepresence: A comparison of 2D and 3D virtual worlds. MIS Quarterly, 35(3), 731–747. https://doi.org/10.2307/23042806
Nel, D., van Niekerk, R., Berthon, J., & Davies, T. (1999). Going with the flow: Web sites and customer involvement. Internet Research, 9(2), 109–116.
Oh, J., & Sundar, S. S. (2015). How does interactivity persuade? An experimental test of interactivity on cognitive absorption, elaboration, and attitudes: Persuasive effects of interactivity. Journal of Communication, 65(2), 213–236.
Pantano, E., Rese, A., & Baier, D. (2017). Enhancing the online decision-making process by using augmented reality: A two country comparison of youth markets. Journal of Retailing and Consumer Services, 38, 81–95.
Park, A., Treen, E., Pitt, L., & Chan, A. (2021). Brand stories in marketing: A bibliographic perspective. Journal of Strategic Marketing, 1–20.
Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In R.
E. Petty & J. T. Cacioppo (Eds.), Communication and persuasion: Central and peripheral routes to attitude change (pp. 1–24). Springer. https://doi.org/10.1007/978- 1-4612-4964-1_1
Poushneh, A., & Vasquez-Parraga, A. Z. (2017). Discernible impact of augmented reality on retail customer’s experience, satisfaction and willingness to buy. Journal of Retailing and Consumer Services, 34, 229–234. https://doi.org/10.1016/j.jretconser.2016.10.005 Qiao, X., Ren, P., Dustdar, S., Liu, L., Ma, H., & Chen, J. (2019). Web AR: A promising
future for mobile augmented reality—State of the art, challenges, and insights.
Proceedings of the IEEE, 107(4), 651–666.
Rauschnabel, P. A., Felix, R., & Hinsch, C. (2019). Augmented reality marketing: How mobile AR-apps can improve brands through inspiration. Journal of Retailing and Consumer Services, 49, 43–53. https://doi.org/10.1016/j.jretconser.2019.03.004
Reeves, B., & Nass, C. (2000). Perceptual user interfaces: Perceptual bandwidth.
Communications of the ACM, 43(3), 65–70. https://doi.org/10.1145/330534.330542 Rese, A., Baier, D., Geyer-Schulz, A., & Schreiber, S. (2017). How augmented reality apps
are accepted by consumers: A comparative analysis using scales and opinions.
Technological Forecasting and Social Change, 124, 306–319.
Rese, A., Schreiber, S., & Baier, D. (2014). Technology acceptance modeling of augmented reality at the point of sale: Can surveys be replaced by an analysis of online reviews?
Journal of Retailing and Consumer Services, 21(5), 869–876.
Ryu, K., Lehto, X. Y., Gordon, S. E., & Fu, X. (2019). Effect of a brand story structure on narrative transportation and perceived brand image of luxury hotels. Tourism Management, 71, 348–363. https://doi.org/10.1016/j.tourman.2018.10.021
Sasmita, J., & Suki, N. M. (2015). Young consumers’ insights on brand equity: Effects of brand association, brand loyalty, brand awareness, and brand image. International Journal of Retail & Distribution Management, 43(3), 276–292.
Scholz, J., & Duffy, K. (2018). We ARe at home: How augmented reality reshapes mobile marketing and consumer-brand relationships. Journal of Retailing and Consumer Services, 44, 11–23. https://doi.org/10.1016/j.jretconser.2018.05.004
Scholz, J., & Smith, A. (2017). Monsters in our world: Narrative transportation in Pokémon Go’s mixed reality. In ACR North American Advances (Vol. 45, pp. 869–871).
Association for Consumer Research.
Shankar, V., Kleijnen, M., Ramanathan, S., Rizley, R., Holland, S., & Morrissey, S. (2016).
Mobile shopper marketing: Key issues, current insights, and future research avenues.
Journal of Interactive Marketing, 34, 37–48.
Skadberg, Y. X., & Kimmel, J. R. (2004). Visitors’ flow experience while browsing a web site: Its measurement, contributing factors and consequences. Computers in Human Behavior, 20(3), 403–422. https://doi.org/10.1016/S0747-5632(03)00050-5
Smink, A. R., van Reijmersdal, E. A., van Noort, G., & Neijens, P. C. (2020). Shopping in augmented reality: The effects of spatial presence, personalization and intrusiveness on app and brand responses. Journal of Business Research, 118, 474–485.
Srivastava, R. K., & Dorsch, M. J. (2020). Understanding the viability of three types of approach of advertising in emerging markets. Journal of Marketing Communications, 26(8), 799–812. https://doi.org/10.1080/13527266.2019.1586749
Statista. (2017). Consumer mobile AR applications worldwide 2016-2022. Statista.
Suh, K.-S., & Lee, Y. E. (2005). The effects of virtual reality on consumer learning: An empirical investigation. MIS Quarterly, 29(4), 673–697.
Sundar, S. S., Jia, H., Waddell, T. F., & Huang, Y. (2015). Toward a theory of interactive media effects (TIME): Four models for explaining how interface features affect user psychology. In The handbook of the psychology of communication technology (pp.
47–86). Wiley Blackwell. https://doi.org/10.1002/9781118426456.ch3
van Berlo, Z. M. C., van Reijmersdal, E. A., Smit, E. G., & van der Laan, L. N. (2021).
Brands in virtual reality games: Affective processes within computer-mediated
consumer experiences. Journal of Business Research, 122, 458–465.
van Laer, T., de Ruyter, K., Visconti, L. M., & Wetzels, M. (2014). The extended
transportation-imagery model: A meta-analysis of the antecedents and consequences of consumers’ narrative transportation. Journal of Consumer Research, 40(5), 797–
van Noort, G., Voorveld, H. A. M., & van Reijmersdal, E. A. (2012). Interactivity in brand web sites: Cognitive, affective, and behavioral responses explained by consumers’
online flow experience. Journal of Interactive Marketing, 26(4), 223–234.
van Osselaer, S. M. J., & Janiszewski, C. (2001). Two ways of learning brand associations.
Journal of Consumer Research, 28(2), 202–223. https://doi.org/10.1086/322898 Verhagen, T., Vonkeman, C., Feldberg, F., & Verhagen, P. (2014). Present it like it is here:
Creating local presence to improve online product experiences. Computers in Human Behavior, 39, 270–280. https://doi.org/10.1016/j.chb.2014.07.036
Vince, J. (2004). Introduction to virtual reality. Springer Science & Business Media.
Weick, K. (1995). Sensemaking in organizations. Sage Publications.
Yim, M. Y.-C., Chu, S.-C., & Sauer, P. L. (2017). Is augmented reality technology an effective tool for e-commerce? An interactivity and vividness perspective. Journal of Interactive Marketing, 39, 89–103. https://doi.org/10.1016/j.intmar.2017.04.001
Appendix A Stimulus Material
For the stimulus material, a picture and short story were included, depicting a professor and Francisca (a lady of the time), discussing about the professor’s achievements and the university’s magnitude. The picture and the written story are shown in Figure A1.
Figure A1. Image and written story included in stimulus material for both conditions
- Johannes Diderik van der Waals (left): I was not the first professor of the University of Amsterdam to win a Nobel prize. And I will not be the last! These walls are painted with history, of breakthroughs that happened and will happen in the future.
- Francisca (right): Thanks to great men like you!
- Johannes Diderik van der Waals: No. Thanks to those who made these walls a place of inclusion. So that great men and women, rich and poor can flourish without prejudice.
In its rich history, the University of Amsterdam produced 6 Nobel prize winners. One of them was Johannes Diderik van der Waals, a physicist professor at UvA. He won the Nobel prize in 1910. That was an unlikely success for the son of a working-class carpenter from Leiden who found in this university the opportunity to flourish despite his humble origins.
Participants in the non-AR condition were exposed only to the story and the
accompanied image, whereas participants in the AR condition were also presented with the button “View in AR”, and the AR interface was shown to allow them to interact with the app (Figure A2).
Figure A2. Screenshots of the app’s interface in the two conditions
Appendix B Measures
Table B1. Items in the questionnaire
Perceived flow (adapted from Nel et al., 1999)
(1) When I used the app I felt in control
(2) I felt I had no control while interacting with the app (3) The app allowed me to control the interaction (4) When I used the app I was aware of distractions (5) When I used the app I thought about other things
(6) When I used the app I was totally absorbed in what I was doing (7) Visiting the app excited my curiosity
(8) Interacting with the app made me curious (9) The app aroused my imagination
(10) The app interaction bored me (11) The app was interesting (12) It was fun to explore the app Brand
attitudes (adopted from Spears and Singh, 2004)
Please describe your overall feelings about the UvA (1-7) (1) Unappealing/ Appealing
(2) Bad/ Good
(3) Unpleasant/ Pleasant (4) Unfavorable/ Favorable (5) Unlikable/ Likable Brand
How well can the UvA be described by the following words? (1-7) (1) Historical
(2) Prestigious (3) Inclusive