["40 L. Xue et al. should aim to help consumers \ufb01nd what they want quickly and achieve the combination of safety and enjoyment. In this way, developing AR could offer consumers virtual try- on of the garment without queueing for changing rooms while providing convenient navigation, especially given the risk of purchasing an unsuitable garment due to the \ufb01tting room being closed. Existing research focused on consumer value to design an app (Javornik et al. 2016; Nikhashemi et al. 2021) However, little is known regarding scenario planning post COVID-19. Therefore, this study aims to reveal what category of AR solution is most useful for different fashion retail environments. This study needs to understand: 1. To what degree is the fashion retail market ready to use AR to develop the most effective AR platform in this speci\ufb01c situation? 2. What is the purpose and requirement to invest AR in different categories for fashion retailers to meet the signi\ufb01cant consumer behaviour changes post-COVID-19? 3. How can AR help retailers achieve their target to persuade retailers to invest in AR and help them support their brand strategy? While this study focuses on AR, we anticipate our research is generalisable to Mixed Reality (MR) technologies. However, as MR is yet to exist within everyday retail settings \u2013 and nearly every customer has an AR-capable smartphone \u2013 we focus on AR. 2 Literature Review 2.1 COVID-2019 and Consumer Demand Retailers were already asking how stores should look and function in a digital age (Soysal et al. 2019). However, with COVID-19 changing all the existing physical retailing rules, we must understand the new considerations for tomorrow\u2019s store. The COVID- 19 pandemic has created an unprecedented challenge for the retail community and its suppliers. Typical store operating principles have, in a short period, been replaced by new procedures. In contrast, new consumer habits borne out of necessity shall drive a lasting impact across all channels (Pantano et al. 2020). McGarrigle (2020) shows how COVID-19 is changing all the existing rules of physi- cal retailing. In particular, the retailers are unsure what experience to deliver given health restrictions such as social distancing and limited space within stores. Experiential has undoubtedly taken a back seat in the short term as store operations focused on providing essential categories and reducing dwell time and interaction. However, the consumer will still desire connection and engagement, so we must understand how retailers evolve to offer this in a digitally driven manner.","AR In-Store Solutions for Different Fashion Retail Environments 41 2.2 Different Retail Environments 2.2.1 High-End (Luxury) The signi\ufb01cant difference between luxury and high-street fashion goods is the impor- tance of the psychological bene\ufb01ts consumers get from luxury goods (Hagtvedt and Patrick 2009). In the luxury sector, status symbols and hedonistic value are vital fac- tors that determine consumer experience (Kastanakis and Balabanis 2012). In addition to functional practicality, luxury shoppers expect preferential treatment, more socially acceptable elements, and unique experiences in their shopping process (Stathopoulou and Balabanis 2016). As a result, luxury retailers are more likely to improve the consumer experience instead of offering discounts and price promotions. Given the difference between luxury and high-street consumption, hedonistic and symbolic bene\ufb01ts are critical to luxury customers. Consumers of high-end fashion retail- ers may prefer hedonistic and symbolic bene\ufb01ts because experiential and social values are more important in a luxury context (Wiedmann et al. 2009). 2.2.2 High-Street Strategies including discounts and promotions are more applicable for high-street brands, where price incentives can entice consumers to buy again. This is because loyalty pro- grammes are primarily rooted in low-end\/mid-end retailing and are often linked to prac- tical bene\ufb01ts such as saving money (Evanschitzky et al. 2012). Some luxury retailers have rejected such plans. In the low-end\/mid-end retail environment, by contrast, con- sumers are more driven by discounts and bene\ufb01ts (Meyer-Waarden and Benavent 2009) - utilitarian bene\ufb01ts are signi\ufb01cant. 3 Methodology Gaskin et al. (2010) recommended that 10\u201330 interviews produce 75\u2013150 statements from participants. The study conducted qualitative research through 13 interviews. Eight interviews focused on UK fashion retailers (head of\ufb01ce and store operations staff). Five interviews focused on AR\/UX designers to obtain an industry perspective. Purposive sampling was used to ensure a suitable spread of participants across the sampling frame (see Table 1). The interviews followed a semi-structured format. Each interview lasted around 30 min and was held through video call service Microsoft Teams. The interview questions were derived from the literature on the retailers\u2019 and designers\u2019 understanding of con- sumer value and their insight into how AR can enhance consumer engagement regarding each different market level. For example, to understand whether they are ready to adopt AR, participants were asked, \u201cAssuming the technology was ready for the market, what do you think are the barriers to adopt it?\u201d. To understand how AR can create value for customers within different market levels, participants were asked, \u201cWhat shopping experience do you want to create for your customers?\u201d. Participants were shown two demo videos of the AR concept apps (created based on our previous research (Xue et al. 2021) before starting the interview to conceptualise AR shopping better.","42 L. Xue et al. Table 1. Purposive sampling Industry sector High-street retailer Brand\/Organisation Position Boden Head-Of\ufb01ce High-end retailer Primark Head-Of\ufb01ce Urban Out\ufb01tter Store Operations Designer New Look Store Operations Alexander McQueen Head-Of\ufb01ce Ermenegildo Zegna Head-Of\ufb01ce Celine Store Operations Burberry Store Operations Impero AR Designer Facebook AR\/UX Designer PwC AR Designer Store Studio AR Designer Mesmerise Global AR\/UX Designer AR Branded App concept app: https:\/\/youtu.be\/dTIOOjCY5Lc Magic Mirror concept app: https:\/\/youtu.be\/dTIOOjCY5Lc The interviews show how retailers react to adopting AR in-store, exploring their desires and requirement toward the AR concept app. The study analysed transcripts with NVivo 12. Using the grounded theory approach, data were coded according to the study objectives and categorised to re\ufb02ect emerging themes. Thematic analysis of interviews was performed according to the industry sector group (high-street retailer, high-end retailer and AR\/UX Designer) to identify any opinion differences. 4 Results 4.1 Attitude 4.1.1 Usefulness High-Street AR can offer signi\ufb01cant value to high-street fashion retailers with an expansive product range. Since many consumers can get overwhelmed by vast options within-stores, AR can provide in-store navigation to overcome feelings of overpowering choice. Therefore, offering style suggestions would be bene\ufb01cial in this way. \u201cWe obviously put out style guides, anyway for how mannequins and everything looks, but it could be an expanded piece to try things (Participant 1). AR can help customers navigate aisles to a speci\ufb01c store location. Consumers will \ufb01nd it easy to explore detailed product information and check stock availability (including size and colour) when shopping in high-street stores.","AR In-Store Solutions for Different Fashion Retail Environments 43 Luxury In the luxury sector, consumers desire service and product quality. Consumers expect the sales associate to look after them to reduce the shopping effort. AR can help reduce cus- tomers\u2019 uncertainty making purchase decisions. While younger consumers - i.e. Gener- ation Z (born in 1997\u20132012) - would come in and browse around. In this case, AR could offer an opportunity to increase customer-brand engagement, which may encourage purchase intention. \u201cI think having that \ufb02exibility, especially in these current times where you can\u2019t go to the \ufb01tting room and try anything on, it could be really useful to scan something and see how you\u2019d look in it.\u201d (Participant 3). As a result, AR can be valuable in both settings but possibly applied in different ways. 4.1.2 Ease of Use Ten participants perceived our prototypes are very easy to use for their customers, as almost all consumers know how to use an app or shop online. \u201cIt looks very similar to the shopping apps like ASOS, that kind of mirror, also I think, once you used it once or twice, and very easy to use.\u201d (Participant 1). However, many consumers still \ufb01nd AR more of a gimmick than a useful tool. AR will be challenging for older adults as they rely more on human contact than digital interfaces. 4.1.3 Enjoyment High-Street Retailers are looking into engaging customers and keeping them in the store for longer through personalised experience and store entertainment. \u201cWe\u2019ve actually just launched a Spotify playlist. Customers can listen to in-store because that\u2019s not something.. we don\u2019t play music in store.\u201d (Participant 1). Retailers could have a slightly different type of music that gives consumers a tailored retail experience through AR. However, without it, stores are just providing a very generic retail experience instead. Luxury AR can attract more customers by making the shopping process more fun, intuitive, and personal. There will be a novelty effect for consumers who may \ufb01nd AR engaging and playful when they virtually try different products. \u201cEveryone loves being on their phone. If you snap away [to] see [if] it\u2019s got your size, or how are you looking at, people would be quite entertained by it.\u201d (Participant 3).","44 L. Xue et al. AR enables consumers to interact more with the product and have a deeper engage- ment in the brand\u2019s culture. Therefore, retailers can offer more innovative shopping experiences for their consumers. Spending time in store can become a delight. 4.2 Technology Acceptance 4.2.1 Value to Retailers High-Street AR could help consumers to \ufb01nd items quickly, given the current circumstances. This is because consumers may not wish to spend a long time in a store if they are speci\ufb01cally looking for something they have seen on social media (e.g. Instagram). Here they can look it up in the AR app and know exactly where to go to get it. However, retailers rely on consumers browsing in a store to make their choices. AR navigation may reduce consumers\u2019 browsing because it takes consumers directly to products. In contrast, participant 1 thought AR might reduce spontaneous browsing but will encourage more consumers to go into the store: \u201cI don\u2019t think it would change but will just enhance. People who think, I\u2019m too busy, I don\u2019t know what I\u2019m looking for\u2026 and that reassurance that, this is the product, it\u2019s here, it\u2019s in your size, \ufb01nd it on the ground \ufb02oor.\u201d However, if consumers want to browse, they will. AR can only offer support. Luxury AR will be a more instant reward for consumers to seek more product information and bring a positive interaction to consumers by scanning for information without \ufb01nding it for themselves or talking to staff. Accordingly, the human interactions between staff and customers will be reduced, as consumers will have more accessibility to the products through AR for self-shopping. Thus, luxury retailers have a low perception of AR value. It would depend on the incentive for the retailer in the end. \u201cIf they\u2019re investing all this money into an app, they\u2019d want to see that it was, came out the other end, and they can see it in monetary value. So they could see that there was a return on their investment.\u201d (Participant 3). Therefore, luxury retailers must be piloted before implementation to discover the engagement level and return on investment.","AR In-Store Solutions for Different Fashion Retail Environments 45 4.2.2 Adoption Barriers 4.2.2.1 High-Street Expensive\/Retail Margin\/Return on Investment Digital experience within high-street retail would be costly to implement. This will further in\ufb02uence retail margin: \u201cLarge digital screens, the setup of the software, and then the hardware is very expensive. And then back to a centralised position where it\u2019s managed from in terms of pushing content out to screens, is quite dif\ufb01cult to do.\u201d (Participant 4). Even though many companies have already started using this technology for a better customer experience, their ROI is not satisfactory. This will become the most signi\ufb01cant barrier to entry into this technology in small and medium-sized brands. Dif\ufb01cult to Input a Colossal Amount of Data Adopting AR for high-street retailers requires extensive efforts to input different products and product information due to the rich product lines. Even though the implementation helps in the long run, retailers need to spend a vast sum implementing AR. Most retailers will frequently change their visual merchandising and move around products on the shop \ufb02oor. Using AR service as a navigational device in the store will be very dif\ufb01cult to track a speci\ufb01c item unless retailers have a dynamic way of tracking where stock is on the sales \ufb02oor, which is different from the supermarket. Intrusive to Retail AR may be very intrusive into the retail environment. Despite digital technologies hav- ing excellent performance in improving the shopping experience, there is no evidence suggesting that these technologies can improve in-store consumers\u2019 conversion rates. This is because: \u201cConsumers don\u2019t want all of that \ufb02ashing, let\u2019s call it noise in the store when they\u2019re shopping quite often.\u201d (Participant 4). When AR is commonly used, this will cause store traf\ufb01c, so it will be another challenge for retailers to overcome this issue. Technical Support (Lack of Awareness, the Challenge for Elders) As consumer AR is in the initial stages of development, having suf\ufb01cient expertise in AR design is a challenge, which also involves high costs. AR is straightforward to use for most consumers. However, AR requires technical in-store staff to assist consumers in using this, especially for those less digitally savvy customers. This may, however, be offset by a reduction in staff requirements due to the AR system taking over many of the traditional in-store staff roles. 4.2.2.2 Luxury Less Interaction with Consumers AR will be more ef\ufb01cient for brands to communicate with consumers through virtually trying on the products, making the products more accessible. However, this might reduce the human interactions between staff and customers.","46 L. Xue et al. \u201cUsing the AR enables consumers to self-oriented shopping mode\u2026 we cannot guarantee every customer could be well served in this way.\u201d (Participant 8). Uncertain Value High-end retailers are hesitant to invest in AR because they are unsure about the Return- on-Capital (ROC). While many luxury brands already own technology systems, it would be less input and more acceptable toward AR. Furthermore, high-end retailers are aiming to provide top service to their customers. They must, therefore, take consumers\u2019 privacy very carefully. Since adopting AR requires access to consumers\u2019 pro\ufb01les, it must be considered when developing AR apps. 4.3 Shopping Experience Offer to Consumers High-Street Consumers are buying into the brands as much as the product. Therefore, consumers purchase speci\ufb01c products (e.g. a pair of Adidas) instead of buying a product category (e.g. a pair of trainers). \u201cConsumers want to be af\ufb01liated with that brand, so I want to offer them a fully integrated omnichannel retail experience.\u201d (Participant 4). High-street consumers have complete accessibility to the product, whether online or in-store. However, consumers can get the same brand experience and the products they want, irrespective of the retail channel. Luxury Luxury retailers wish to offer consumers an immersive and seamless experience. \u201cWe want it to be fun and engaging through a retail space, different to [the] online experience, and come back as often as possible.\u201d (Participant 2). The high-end market\u2019s shopping experience must be as engaging as possible, creating a pleasant visual environment for consumers to be inspired by and return. AR is more potent in the entertainment realm in AR\u2019s infancy than AR has been developed. Once AR is widely used in the retail industry, it will lose its appeal, and then consumers may stop using it because it is less novel. Therefore, designers must focus on more valuable elements (e.g. personalisation and education) to encourage long-term AR engagement. 5 Discussion 5.1 Attitude Participants believe AR has the power to encourage consumers to shop in-store. This is especially true under COVID-19 where shopping shifted to the digital realm when customers did not wish to enter stores. Furthermore, this paper demonstrates that AR can be valuable in both settings but possibly applied in different ways, as luxury consumers seek human interaction and high-quality service.","AR In-Store Solutions for Different Fashion Retail Environments 47 Luxury consumers could use AR for virtual try-on. However, \ufb01tting room pressure will lower than high-street stores because of fewer customer \ufb02ows. Therefore, the luxury consumer may not use AR to save their time \u2013 for a utilitarian purpose. Alternatively, AR will offer entertainment value for high-end brands, as consumers can play around with it. For high-street consumers, AR capitalises on comfort (safety) and convenience through less interaction with products and consumers. AR offers consumers more rele- vant products and services to help them make decisions, allows customers to experience items they usually will not try and feel con\ufb01dent with how an item looks on their body before they place an order. This aligns with Brengman et al. (2019), that AR can lead to increased product perception and ownership. The results also show that high street participants perceive AR as relatively easy for consumers. Luxury participants still \ufb01nd AR a gimmick instead of a bene\ufb01t (Boardman et al. 2020). This is especially true for older adults, who may still prefer the traditional in-person interaction with the sales assistant (Piotrowicz & Cuthbertson, 2014; Xue et al. 2019). However, AR designers remove this concern. Older, less digitally savvy consumers are discovering and enjoying shopping online, welcoming the safety concern that post-COVID-19 technology brings. Consumers may spend considerable time with AR during their \ufb01rst interaction, but it would be straightforward to handle. Hence, if consumers are unfamiliar with innovative technology, it will not be a problem for the high-street brand. It is much less dif\ufb01cult for luxury consumers because there will always be someone to help in the luxury store. 5.2 Technology Acceptance This study uncovers the technology acceptance toward AR App and Magic Mirror from the retailer perspective, which \ufb01lls the research gap. Participants stated it would be well-timed to implement AR into their high-street store as the pandemic could last until 2024 before returning to \u2018normal\u2019 life where consumers feel safe without any restriction (Stokel-Walker, 2020). However, for a high-end fashion boutique or a traditional tailoring shop, AR would be much less likely within their retail experience because of the less human interaction between consumers and staff. This paper suggests high-end retailers could focus on AR\u2019s hedonic value to enhance consumer engagement. Therefore, AR has different solutions for different retail levels. Retailers are unsure about investing AR in the store, especially for Magic Mirror (Boardman et al. 2020). Magic Mirrors are also more expensive than Apps to develop, since retailers must purchase Magic Mirror\u2019s hardware while consumers already own an AR-capable smartphone. Similarly, an earlier study showed that consumers have limited con\ufb01dence in using Magic Mirror (Xue et al. 2020). Low con\ufb01dence using Magic Mirrors is because they can present the garments in a very arti\ufb01cial manner that still omit wearing an actual garment (Boardman et al. 2020). Therefore, designers suggest that the brand can start with the software development to begin the experience, so the consumers can bene\ufb01t from using the function of mobile AR App, whether they are in the store or at home, enhancing omnichannel development. On the other hand, brands will bene\ufb01t from installing Magic Mirror throughout stores, to create signi\ufb01cant media attention. Hence, retailers could partner with an AR company to trial the technology in their stores in different parts of the UK to understand","48 L. Xue et al. how AR is being perceived by the customers and the sentiment towards it. In this way, retailers will not overspend as implementing throughout the entire store portfolio. They will, instead, get all the data needed to justify the expansion rollout much more widely in the organisation. Once retailers get their brands on board, AR will become commonplace and raise consumers\u2019 awareness toward AR in retail. Furthermore, AR will be attractive as an increased basket spend because the longer someone stays in the store, the more they are likely to buy. If the technology is user-friendly, retailers can achieve implementation. 5.3 Effective Shopping Experience Participants wish to offer a seamless shopping experience for their consumers, where consumers can have full access to all channels\u2019 products and personalised experiences. Participants from an AR design background state that AR can be more fun on the practical side, but it depends on the market level. This aligns with Nikhashemi et al. (2021), the components of AR shopping Apps should be designed from the perspective of driving stimulation and entertainment, not pure functionality. High-street retailers should focus on utilitarian value as a priority yet add some entertainment elements. As Parker and Wang (2016) and Parker and Wenyu (2019) demonstrate, \u2018grati\ufb01cation\u2019 and \u2018social shopping\u2019 are critical determinants of m-Commerce retail apps, while greater salience can be attributed to utilitarian motivations. This is because utilitarian bene\ufb01ts are related to a product\u2019s functional and practical attributes that customers seek (Picot- Coupey et al. 2020). Entertainment elements may include incorporating advertisements but animating them or bringing them to life. As a result, consumers can scan an icon or a poster to get an immersive experience if needed. This study proposes AR is a worthwhile investment when consumers often encounter real issues in the shop (for example, the store is enormous so consumers must walk a great distance to \ufb01nd another item). AR design also depends on the store size (Cruz et al. 2019). Applying AR in a \ufb02agship store would be a colossal practical use case to help consumers avoid getting lost. Alternatively, implementing AR in a small store with many small intricate things allows consumers to locate items accurately. Within a Zara store, hundreds of items might be more ef\ufb01cient to identify an exact item quickly. High-end participants aim to offer luxurious service\/experience that enables con- sumers to feel unique, experiencing a salespersons\u2019 undivided attention. Therefore, AR can help luxury brands tell a story from a hedonic perspective, enabling consumers to engage with the story and build and create the story \ufb02ow physically. As Parker and Doyle (2018) suggest, luxury brands can show their history through storytelling and content to educate their consumers. After consumers scan the speci\ufb01c position from their devices, the AR\u2019s virtual layer can offer the opportunities to tell a story for those unusual ideas and scenes that are dif\ufb01cult to achieve. With a smartphone presenting an AR experience, consumers can personalise settings to meet different digital experience requirements that they want to engage with within a store (to get tailored information). This aligns with Piotrowicz and Cuthbertson (2014) who outlined how consumers should choose the channels and ways to interact with retailers. Here, the interactive choice is crucial. Any in-store technology should extend the customer experience, not become a completely new system that people unfamiliar","AR In-Store Solutions for Different Fashion Retail Environments 49 with. This could digitally combine the physical touchpoint such as sales assistant, product (information) to create an intelligent seamless shopping experience. 6 Conclusion This paper investigated the most useful AR solution for high-street and high-end fashion retailers. In answering study question one, we demonstrate that the fashion retail market is ill-prepared to use AR. This is especially true for Magic Mirror that includes physical hardware costs, despite all participants believing AR will transform the future retail. This is mainly because of the uncertain value, traf\ufb01c generated in high-street stores, and luxury brands\u2019 service quality. Therefore, fashion brands can develop an AR App to begin the experience with low risks. When the AR ecosystem becomes well developed, they can duplicate the AR app\u2019s bene\ufb01ts within a Magic Mirror. However, different retail types will result in different AR solutions. Furthermore, this paper answered study questions two and three via Table 2 - outlining the purpose, requirements, and solutions for in-store AR. Table 2. The purpose, requirement and solution of retailers to use In-Store AR Objective High-street High-end AR purpose (Study question 2) Seamless shopping experience Supreme service AR requirement (Study question 2) Help consumer make decision Engage with brand AR solution Comfort (safety) Enjoyable (Study question 3) Convenient Human interaction Personalised experience Personalised experience User-friendly User-friendly Prioritise utilitarian value (e.g. Prioritise hedonic value (e.g. navigation, virtual try-on, matching match with a different style, take suggestion, scan for information) pictures, product interaction) Adding entertainment elements as a Storytelling from a hedonic supplement (e.g. incorporating perspective enables consumers to advertisements but animating them scan the speci\ufb01c position for or bringing them to life) obtaining incredible ideas Digitally extend consumer experience with touchpoints (sales assistant, product, information) Choose interaction level (to get tailored information) Design a qualitative interface rather than a quantitative interface to avoid overloading information The purpose and requirement to invest in AR for the high-street brand are capital- ising on comfort (safety) and convenience through less interaction with products and consumers in the short term post-COVID. In the long term, AR aims to offer a seamless","50 L. Xue et al. shopping experience for consumers. Consumers can obtain more relevant products and services to help them make decisions through all channels and personalised experiences. Considering luxury brands seek excellent service quality for their consumers, designing AR retail environments that focus more on hedonic value enables consumers to have human interaction to ensure a superior service. AR can help retailers achieve their targets by prioritising the functional purpose but animate AR in an exciting way in a high-street store, enabling consumers to obtain an ef\ufb01cient and enjoyable shopping experience. AR can help luxury brands tell a story from a hedonic perspective, enabling consumers to engage with the story and save cost to build and create the story \ufb02ow physically. Consumers can also choose the level of digital experience they want to engage with within a store as many will not wish to have a digital experience. To reduce the barriers for consumers using AR, the digital experience could combine with the physical touchpoint - such as sales assistant, product (information) to create an innovative, seamless shopping experience. References Boardman, R., Henninger, C.E., Zhu, A.: Augmented reality and virtual reality: new drivers for fashion retail? In: Vignali, G., Reid, L.F., Ryding, D., Henninger, C.E. (eds.) Technology- Driven Sustainability, pp. 155\u2013172. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030- 15483-7_9 Brengman, M., Willems, K., Van Kerrebroeck, H.: Can\u2019t touch this: the impact of augmented reality versus touch and non-touch interfaces on perceived ownership. Virtual Reality 23(3), 269\u2013280 (2018). https:\/\/doi.org\/10.1007\/s10055-018-0335-6 Calugar-Pop, C., Lee, P.: Post lockdown: a new digital consumer? 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Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68086-2_1","How Cognitive Flexibility Affects Sense of Power in a Coffee Virtual Setting: The Moderating Role of Personality Traits Sandra Maria Correia Loureiro1, Jo\u00e3o Guerreiro2(B), and Joana Villar2 1 Iscte-Instituto Universit\u00e1rio de Lisboa, Business Research Unit (BRU-IUL) and SOCIUS, Lisbon, Portugal [email protected] 2 Iscte-Instituto Universit\u00e1rio de Lisboa, Business Research Unit (BRU-IUL), Lisbon, Portugal {joao.guerreiro,joana_villar}@iscte-iul.pt Abstract. In this study, we cross two sensory experiences, one with the senses of sight and sound and the other with the senses of sight, sound, and smell. The experience takes place in a Virtual Coffee and measures the impact it has on the sense of power. We analyse the concepts of Sense of Power and Cognitive Flexi- bility. Personality traits is analysed as a moderator in the relation between Sense of Power and Cognitive Flexibility. A sample of 125 individuals participated in the experiment. Results show that Cognitive Flexibility signi\ufb01cantly and positively explains Sense of Power. Personality Traits moderates the relationship between Cognitive Flexibility and Sense of Power. Keywords: Sense of power \u00b7 Cognitive \ufb02exibility \u00b7 Personality traits \u00b7 Sight \u00b7 Sound \u00b7 Smell 1 Introduction A brand should always be improved and maximized in to offer a full emotional and sensorial experience (e.g., Bilro et al. 2021; Prentice et al. 2019; Roschk et al. 2017). It is not enough to visually present a product in an advertisement. It is important to associate a sound, music for example, or words and powerful symbols. The combination of visual and audible stimulus has a much bigger impact but to keep a strong brand, it is necessary to activate consumer\u2019s \ufb01ve senses (Loureiro et al. 2022). The touch, smell and \ufb02avours are crucial in the construction of a truly relevant brand (Loureiro et al. 2017). The usage of the \ufb01ve senses and a multisensory atmospheric retail is also very important to and have an in\ufb02uence on shopping behaviour in a cognitive and affective manner (Kaufmann et al. 2016; Loureiro and Ribeiro 2014; Rodrigues and Loureiro 2022). Virtual reality (VR) has several potential bene\ufb01ts for businesses and customers in a retail scenario (Saren et al. 2013; Bradford et al. 2017; M\u00fcller-Lankenau and Wehmeyer 2005; Loureiro et al. 2019). For instance, the use of virtual reality in retail may blur the barrier between in-person and online purchases, owing to its qualities such as interaction, \u00a9 The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Jung et al. (Eds.): XR 2022, SPBE, pp. 52\u201358, 2023. https:\/\/doi.org\/10.1007\/978-3-031-25390-4_4","How Cognitive Flexibility Affects Sense of Power 53 immersion, and sensory feedback (Smolentsev et al. 2017). Prior studies on VR tend to focus on engagement and interactivity (Mollen and Wilson 2010; Loureiro; et al. 2020), telepresence (Steuer 1992; Angelino et al. 2021), purchase behaviour (Waterlander et al. 2015; Loureiroet al. 2021) and customer experience (Novak et al. 2000). Yet, the litera- ture is scarce on how consumers cognitive abilities and personality traits affect the VR experience. Our study gives a contribution to the literature by exploring not only how the cognitive \ufb02exibility can affect the sense of power in a virtual environment, but also by exploring the different degrees of multisensory experience in a virtual environment and how it is affected by personality traits. Thus, the aims are: (i) explore how cogni- tive \ufb02exibility in\ufb02uences sense of power in a context of a VR coffee shop; (ii) analyse personality traits as moderator of cognitive \ufb02exibility and sense of power. 2 Literature Review and Hypotheses Development Cognitive \ufb02exibility is \u201cthe ability to switch cognitive sets to adapt to a changing envi- ronmental stimulus\u201d (Dennis and Vander Wal 2010, p. 242). It is characterized by the ability to adapt the way of thinking according to new facts and new circumstances in one\u2019s environment (Deak 2003). In the case of a VR environment, people of higher cog- nitive \ufb02exibility are expected to adapt better to the immersive stimuli (Loureiro 2020; Loureiro et al. 2020). And thus, we expect, tend to exhibit a higher sense of control in such setting. Sense of power has been de\ufb01ned as an \u201casymmetric control over valued resources in social relations\u201d (Rucker et al. 2012; Keltner et al. 2003; Thibaut and Kelley 1959). Nevertheless, power is not merely the control over resources or composed just by an individual\u2019s social position. Power is a psychological state\u2014a perception of a per- son\u2019s capacity to in\ufb02uence others (Galinsky et al. 2003). Feeling powerless is a typical uncomfortable sensation that customers want to alleviate. According to Rucker et al. (2012), when people perceive themselves to be powerless, they put a larger premium on products and particular characteristics that aid in their power restoration efforts. This is because \u2013 when it comes to consumption \u2013 power-compensatory behaviour is exempli- \ufb01ed by a preference for status-type things. Rucker et al. (2012) support that consumer spending (whether it is for their own bene\ufb01t or the bene\ufb01t of others) can be consider- ably affected by current psychological states of power. Given that cognitive \ufb02exibility is de\ufb01ned as the capacity to adapt one\u2019s style of thinking to changing circumstances, we claim that cognitive \ufb02exibility has a bene\ufb01cial in\ufb02uence on one\u2019s feeling of power, generating the following hypothesis: H1: Cognitive Flexibility has a positive and signi\ufb01cant effect on Sense of Power in a VR environment Speci\ufb01c personality traits can bene\ufb01t individuals to obtain higher levels of in\ufb02uence and control in their relationships, contributing to each personal sense of power (Buss & Craik, 1980). For example, dominating persons have a better capacity to in\ufb02uence others than introverted, shy, or submissive ones. Personality characteristics are uncontrollable elements, yet they are \ufb02exible and may be altered over time. These traits vary from higher or lower openness to experience, neuroticism, con- sciousness, agreeableness, and extroversion. These variations de\ufb01ne not only a person\u2019s","54 S. M. C. Loureiro et al. personality, but they will have impact on their sense of power. For example, an individual that scores higher in consciousness, is likely to have a greater sense of power than one low in consciousness. Therefore, we suggest that the same effect can occur in a VR environment. Hence: H2: Personality traits will moderate the relationship between cognitive \ufb02exibility and sense of power in a VR environment 3 Methodology A virtual coffee was created using silent avatars as customers to create a more realistic experience (using unity programming language) (see Fig. 1). Background music was playing, to set an environment. The participant sat in a chair, inserted the VR Goggles, and found him\/herself sitting in a caf\u00e9 table. In one condition, there was only the presence of vision and hearing. On the second condition an ambient scent of coffee was inserted, adding to the existing senses of vision and hearing. The two conditions were shown to different groups of people. The participants were \ufb01nally asked to \ufb01ll in a questionnaire. Participant\u2019s sense of power (Anderson et al. 2012); an evaluation of their cognitive \ufb02exibility (Martin and Rubin 1995); and an analysis of their personality traits (Guido et al. 2015) were measured using tested scales. There were 125 participants in the study (78 females and 47 males). 63 participants completed the sight and sound condition, while 62 participants explored the VR experience with sight, sound, and scent. Fig. 1. Virtual coffee","How Cognitive Flexibility Affects Sense of Power 55 4 Results A multi-regression analysis was conducted to test H1 and H2. Globally, the results show that cognitive \ufb02exibility signi\ufb01cantly predicts sense of power. Therefore, the H1 is supported. The \ufb01ndings on Table 1 also show that H2 was supported. Table 1. Moderation analysis total results \u2013 Sense of power as dependent variable and personality traits and cognitive \ufb02exibility as predictors Model B Std. Error \u00df t Sig R2 1. (Constant) (Sensory experience Sight 3.137 0.492 6.377 0.000 + Sound + Smell) Cognitive \ufb02exibility 0.417 0.109 0.325 3.809 0.000 0.105 a. Dependent variable: sense of power b. Predictors: (Constant). Cognitive Flexibility 2. (Constant) 2.703 0.640 4.223 0.000 Cognitive \ufb02exibility 0.379 0.115 0.296 3.301 0.001 Personality traits 0.180 0.169 0.095 1.062 0.291 0.114 3.(Constant) 2.826 0.631 4.479 0.000 Cognitive \ufb02exibility 0.356 0.113 0.278 3.143 0.002 Personality traits 0.181 0.166 0.096 1.088 0.279 CF_\u00d7_PT \u22120.134 0.058 \u22120.195 \u22122.323 0.022 0.152 a. Dependent variable: sense of power b. Predictors: (Constant). Personality Traits, Cognitive Flexibility 5 Conclusion and Implication The positive effect of cognitive \ufb02exibility on sense of power can be partially supported by the study of Anderson et al. (2012). Personal sense of power is organized in several levels of abstraction and may continuously change depending on the situation and one\u2019s relationship with others. This means that people with higher sense of power easily adjust that power according to the group, the situation, the type of relationship. Essentially, these individuals adjust easily and so they possess a higher cognitive \ufb02exibility. The \ufb01ndings show that the cognitive \ufb02exibility in\ufb02uences the sense of power in a VR environment. However, participants\u2019 responses were more positive when in a sensory experience with sigh, sound and smell then in an experience with just the senses of sight and sound. This \ufb01nding goes in line with studies by Lindtrom (2008) that concluded that the more senses associated with a brand the more relevant a brand becomes. Additionally, individuals that that have a high extroversion tend to be more sociable, energetic, outgoing, and enthusiastic (Costa and McCrae 1992; Thomas et al. 1996).","56 S. M. C. Loureiro et al. Individuals with high and low extroversion, are both signi\ufb01cant in explaining the rela- tionship between cognitive \ufb02exibility and sense of power. However, participants who are less agreeable play a more signi\ufb01cant role in explaining the relationship between cogni- tive \ufb02exibility and sense of power than those who are more agreeable. Participants with lower consciousness are better in explaining the relation between cognitive \ufb02exibility and sense of power in the model. Both, individuals with high and low openness, were found to be signi\ufb01cant in explaining the relationship between the variable\u2019s cognitive \ufb02exibility and sense of power. Brand managers should be aware about the importance of the customer\u2019s cognitive \ufb02exibility and how they adapt to the environment, this can be very important when designing a VR store, for example. References Anderson, C., John, O.P., Keltner, D.: The personal sense of power. J. 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Retail. 93(2), 228\u2013240 (2017). https:\/\/doi.org\/10.1016\/j.jretai.2016.10.001 Rucker, D.D., Galinsky, A.D., Dubois, D.: Power and consumer behavior: how power shapes who and what consumers value. J. Consum. Psychol. 22(3), 352\u2013368 (2012) Saren, M., Harwood, T., Ward, J., Venkatesh, A.: Marketing beyond the frontier? Researching the new marketing landscape of virtual worlds. J. Mark. Manag. 29(13\u201314), 1435\u20131442 (2013) Smolentsev, A., Cornick, J.E., Blascovich, J.: Using a preamble to increase presence in digital virtual environments. Virtual Reality 21(3), 153\u2013164 (2017). https:\/\/doi.org\/10.1007\/s10055- 017-0305-4 Steuer, J.: De\ufb01ning virtual reality: dimensions determining telepresence. J. Commun. 42, 73\u201393 (1992)","58 S. M. C. Loureiro et al. Thibaut, J.W., Kelley, H.H.: The Social Psychology of Groups. Transaction Publishers, New Brunswick (1959) Waterlander, W.E., Jiang, Y.N., Steenhuis, I.H.M., Mhurchu, C.N.: Using a 3D virtual supermarket to measure food purchase behavior: a validation study. J. Med. Internet Res. 17(4), e-107\u2013e-111 (2005)","The Great Unknown: How Brand Familiarity Affects the Relationship Between Augmented Reality and Brand Attitude in the Retail Industry E. J. Morren1(B), P. E. Ketelaar1, and A. R. Smink2 1 Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands [email protected], [email protected] 2 Amsterdam School of Communication Research\/ASCoR, University of Amsterdam, Amsterdam, The Netherlands [email protected] Abstract. Augmented Reality (AR) is increasingly being used in the retail indus- try in order to provide consumers the opportunity to virtually try on products. Particularly well-known retail brands invest in AR, but AR might be especially bene\ufb01cial for lesser-known brands. Based on the Association Theory and Informa- tion Integration Theory, this study compared the effects of the use of AR on brand attitude between well-known brands, and unknown brands. An online experiment showed, as expected, that the use of an AR app had a more positive in\ufb02uence on brand attitude for an unknown than for a well-known brand. Not in line with expectations, both the enjoyment of using AR (perceived enjoyment) and the com- fortable feeling when deciding for a product after using AR (decision comfort) did not explain this difference in brand attitude. To our knowledge, this is the \ufb01rst AR study that includes brand familiarity and shows that investing in AR pays off, especially for unknown brands. Keywords: Augmented Reality (AR) \u00b7 Brand familiarity \u00b7 Brand attitude 1 Introduction Augmented Reality bridges the gap between of\ufb02ine and online shopping experience due to the \u201ctry before you buy\u201d principle. The technology adds a new dimension to the online shopping experience by overlapping the consumer environment with virtual products in real-time (Ketelaar et al. 2019). Using the camera, consumers are able to try on products on their own body or face (e.g. make-up; Smink et al. 2020), or in the space around them (e.g. furniture; Javornik 2016, Smink et al. 2020). The possibility for consumers to interact with products via AR provides additional knowledge about the products, especially when it comes to brands that the consumer has little associations with. Research shows that AR apps are becoming more widely adopted, and especially try-on apps are preferred and valued by consumers (Smink et al. 2021). \u00a9 The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Jung et al. (Eds.): XR 2022, SPBE, pp. 59\u201371, 2023. https:\/\/doi.org\/10.1007\/978-3-031-25390-4_5","60 E. J. Morren et al. Previous research on what AR can contribute to a brand, for example in the form of a more positive brand attitude, yielded mixed results (Smink et al. 2020; Javornik 2016; Rauschnabel et al. 2019). Strikingly, these studies on the effects of AR on brand attitude have not focused on the effects AR can have for unknown brands. This can be interesting, as an AR app may serve to perpetuate an already positive association that consumers have with a well-known brand, while an AR app may contribute to \u2018getting to know\u2019 an unknown brand, causing a more positive effect on brand attitude. As the mixed results from previous research may be caused by the fact that these studies neglected to include unknown brands in their research, it is worth looking into what underlying mechanisms may cause these possible differences in effect on brand attitude. Firstly, as consumers can already make an estimate of a product from a well-known brand through previous experiences with the brand, AR may have less added value. Therefore, it is likely that an AR app of an unknown brand is more enjoyable for con- sumers, given the greater increase in knowledge and associations about the products of the unknown brand. This in turn may have a more positive effect on the consumer\u2019s attitude towards the unknown brand, compared to the well-known brand. A second possible explanation for the expected difference in effect on brand atti- tude between well-known and unknown brands could be the comfortable feeling after choosing a product (decision comfort), as AR reduces the chances of a wrong purchase. Well-known brands are already expected to provide consumers with a feeling of high comfort about their decision to buy these brands, because existing associations and pre- vious experiences with those brands reduce uncertainty. For unknown brands however, AR can boost knowledge, which in turn can signi\ufb01cantly increase the comfort in decid- ing to buy these unknown brands. This effect may spill over to the consumer\u2019s attitude towards the brand. The following research question is answered in this study: To what extent does the effect of Augmented Reality on brand attitude differ for well-known and unknown brands and to what extent can this difference be explained by perceived enjoyment and decision comfort? This study adds to existing scienti\ufb01c knowledge about the relevance of AR for the promotion of brands because, to our knowledge, it is the \ufb01rst AR research that examines the effect of AR on brand attitude not only for well-known, but also for unknown brands. For marketers, this means that regardless of whether they promote well- known or unknown brands, they are able to make a responsible choice about whether or not to invest in AR. 2 Theory and Hypotheses Previous studies into the effects of AR on brands have produced mixed results. While Rauschnabel et al. (2019) found positive effects on brand attitude after using an AR app by means of a pre- and post-test, Javornik (2016) and Smink et al. (2020) found no effects of AR on brand responses at all. Therefore, the question to what extent AR actually has an effect on brand attitude remains unanswered. However, it is possible that these mixed results from previous studies can be explained by the fact that brand familiarity was not previously included as a variable.","The Great Unknown 61 In this study brand familiarity will be de\ufb01ned as a consumer\u2019s previous experience, both direct and indirect, with a brand (Campbell and Keller 2003). Consumer perceptions of unknown brands are unstable and easy to change, for example by using an AR app. New and relevant information is easily associated with existing associations. Conversely, consumer knowledge about well-known brands is usually stable and dif\ufb01cult to change (Sheinin 2000). Unknown brands are thus like a blank slate for the consumer, where \ufb01rst impressions can lead to the creation of \ufb01rst associations towards the brand, while it is more dif\ufb01cult for well-known brands to change existing associations. The Association Theory (Keller 2003) states that the associations one has about related people (staff and supporters), brands (collaborations, expansions, etc.), things (events, goals, third-party endorsement), and places (country of origin, channels) can spill over to the brand. In this case, associations that one has with an AR app can spill over to the brand. In addition to this, the Information Integration Theory (Anderson 1962) states that existing associations can be changed once new related information has been processed and integrated into existing knowledge. For brands, this means that brand attitudes are in\ufb02uenced when consumers receive, interpret and evaluate new information (Simonin and Ruth 1998), for example after using an AR app. Previous research by Rauschnabel et al. (2019) and Smink et al. (2019) show us that AR apps add more positive than negative associations to a brand, since they found positive effects on brand attitude through inspiration gained by AR and perceived enjoyment. Since people often already have positive associations with well-known brands and this knowledge is stable and dif\ufb01cult to change (Sheinin 2000), a logical expectation is that an AR app will have a slight, but less strong positive effect on the brand attitude towards a well-known brand than towards an unknown brand. For unknown brands, on the other hand, even one-time use of an AR app can lead to new positive associations with the app and the displayed products in the app, which in turn may spill over to the brand. Building on the Association Theory (Keller 2003), the Information Integration Theory (Anderson 1962) and the mixed empirical results from previous research, we examined the following hypothesis: H1: The positive effect of AR apps on brand attitude is stronger for unknown brands than for well-known brands Perceived enjoyment of an AR app could explain the difference in effects of an AR app on brand attitude for well-known and unknown brands. Perceived enjoyment is de\ufb01ned as the extent to which the activity of using the computer (here: app) is perceived as enjoyable in itself, regardless of any performance consequences that might be expected (Davis et al. 1992; Venkatesh 2000). AR apps can overall be experienced as enjoyable, as they contain a high degree of interactivity. Multiple studies have shown that enjoyment during online shopping can be generated by increased interactivity and liveliness of the experience (Fiore et al. 2005; Yim et al. 2017; Yim and Park, 2019). Online shopping with an AR app has also been found to be more enjoyable than shopping on a conventional website (Yim et al. 2017). It is however still unknown whether this perceived enjoyment differs for AR apps of well- known versus unknown brands. Since AR apps for unknown brands provide a higher increase in knowledge and associations about a brand, and since unfamiliarity with a","62 E. J. Morren et al. brand can bring about a feeling of freshness and change (Karpinska-Krakowiak 2021), the following hypothesis has been examined: H2a: The use of AR apps for unknown brands leads to more perceived enjoyment compared to using AR apps for well-known brands The positive associations expected to arise from interacting with products in an AR app can be transferred to the brand, following the Association Theory (Keller 2003) and the Information Integration Theory (Anderson 1962). Previous research on shopping with AR also indicates that there are hedonistic effects on brand attitude. Both Smink et al. (2019) and Rauschnabel et al. (2019) showed that affective brand responses are enhanced by enjoyment of an AR app. This effect will be examined to discover a relationship between brand familiarity, perceived enjoyment, and brand attitude. This leads to the following hypotheses: H2b: Perceived enjoyment has a positive effect on brand attitude H2c: The relationship between brand familiarity and brand attitude is medi- ated, at least in part, by perceived enjoyment Another possible explanation for the difference in effect of AR on brand attitude for well-known versus unknown brands is decision comfort. Decision comfort is the extent to which the consumer feels psychologically and physiologically comfortable or satis\ufb01ed with a decision made, despite being uncertain about the optimality or consequences of that decision (Parker et al. 2016). Research by Parker et al. (2016) has shown that affective cues, such as feelings of worry or excitement, in\ufb02uence a person\u2019s decision comfort. AR apps may provide consumers with several emotional signs, such as a sense of excitement due to the high level of interaction with the products; a sense of calmness, as it can remove potential doubts of consumers due to the additional knowledge they receive about the products; and a feeling of astonishment due to the high level of spatial presence that users are able to experience in their own private sphere (Smink et al. 2020). The relevance of decision comfort in AR research is previously shown by Hilken et al. (2017), who found that decision comfort increases once an AR app is able to make consumers feel like the online service experience is realistic. Decision comfort could differ for well-known and unknown brands. Based on what the consumer already knows about a familiar brand - its quality, product features, and so on - they can make assumptions and form reasonable expectations about what they may not know about the brand (Keller and Swaminathan 2019). The use of AR is therefore expected to increase decision comfort for well-known brands only slightly. Conversely, for unknown brands no assumptions or reasonable expectations can be formed. There- fore, it is expected that decision comfort signi\ufb01cantly increases after using AR. This presumption has been tested, using the following hypothesis: H3a: The use of AR apps for unknown brands leads to more decision comfort compared to using AR apps for well-known brands Parker et al. (2016) found that the feeling of comfort after a decision to purchase a product has a positive effect on re-enactment (the likelihood that an individual will exhibit the same behaviour in the future) and recommendation intentions (the probability that an individual will recommend the same action to others). Hilken et al. (2017) also found that the realistic experience that AR apps offer by placing products in their own space","The Great Unknown 63 is bene\ufb01cial for the idea of using these products in the everyday practice of consumers, which contributes to positive affective feelings towards these products. Building on the Association Theory (Keller 2003) and Information Integration The- ory (Anderson 1962), this could mean that increased decision comfort, and the positive associations with an AR app that follow from this experience, improve consumers\u2019 brand attitude. The relationship between brand familiarity, decision comfort and brand attitude has been examined by testing the following hypotheses: H3b: Decision comfort has a positive effect on brand attitude H3c: The relationship between brand familiarity and brand attitude is medi- ated, at least in part, by decision comfort 3 Methodology 3.1 Study Design An online experiment was conducted, embedded in an online survey, with a one-factor between-subjects design (unknown versus well-known brand) (Field and Hole 2003). The participants were randomly assigned to one of the two conditions. Within those con- ditions pre- and post-measurements were performed on the variables decision comfort and brand attitude to measure the difference before, and after the use of the AR app. The participants in condition 1 (n = 43) were shown product photos and an AR product presentation for the unknown brand Sergio Rossi. The participants in condition 2 (n = 45) were shown product photos and an AR product presentation for the well-known brand Nike. Due to the online nature of the experiment, the experiment took place in the private setting of the participants, which bene\ufb01ts the ecological validity (Beentjes et al. 2006). This way, \ufb01ndings can be generalized to the actual purchase situation, since AR in online shopping also takes place in the private sphere of individuals. 3.2 Participants A total of 197 participants took part in the survey. Of these participants, 109 were excluded from the analysis because they did not meet the requirements. They had not completed the questionnaire (n = 76), had indicated that they did not have an iOS device (n = 8), or they were unable to download the Wanna Kicks app (n = 5). Within the experiment, participants were excluded because they indicated that the virtual \ufb01tting of the shoes was unsuccessful (n = 1), they were familiar instead of unfamiliar with Sergio Rossi (n = 3), they only had seen the sneakers through product photos and not AR (n = 5), it took them less than half a minute to try on the virtual sneakers (n = 10), or that they tried on a different shoe in the app than the one they had chosen after seeing the product photos (n = 1). Ultimately, the analysis was performed on 88 participants. A total of 43 participants has been assigned to the unknown brand condition of Sergio Rossi, and 45 participants have been assigned to the well-known brand condition of Nike. Between these conditions there were no signi\ufb01cant differences with regard to age, gender and educational level.","64 E. J. Morren et al. 3.3 Stimulus Material The Wanna Kicks app was used to conduct the experiment, enabling participants to virtually try on sneakers on their feet. This AR app was chosen because the app contains both well-known brands and unknown brands. This made it possible to distinguish between the two conditions, while the layout remained the same. In addition, the app is user-friendly, and sneakers are a relevant product for the research population of 18\u2013 35 year olds, since young people spend on average a quarter of their clothing budget on sneakers (De Moor 2019). Before the participants could virtually try on the sneakers, they were shown three product photos of the sneakers that could be chosen. The choice for these three sneakers is based on how easy they were to \ufb01nd in the app and on their unisex appearance. The reason for showing product photos prior to the AR try on is that the pre-measurements of the variables decision comfort and brand attitude could be performed in order to subsequently be able to measure the effect of AR on these variables, whereby the conditions remained the same by using the same pair of sneakers (Figs. 1 and 2). Fig. 1. Product photos Sergio Rossi and Nike Fig. 2. AR app Nike or Sergio Rossi in which participants virtually try on the sneakers chosen from the pictures of the products","The Great Unknown 65 3.4 Procedure Participants were recruited using a snowball sampling procedure using multiple social media channels. Informed consent has been applied in all cases. After successfully installing the app, they could start \ufb01lling in the questionnaire. They were automatically assigned to either the well-known condition (Nike), or the unknown condition (Sergio Rossi). During the survey they were told to shortly leave the survey to try on the sneakers that they had chosen earlier. Variables were measured using an online questionnaire. Participants were \ufb01rst assigned to one of the conditions, after which they had to choose one pair of sneakers from that brand. Then the pre-measures for the mediator decision comfort and dependent variable brand attitude were carried out. After completing these questions, participants had to try on the previously chosen pair of sneakers in the Wanna Kicks app. This experiment was followed by the post-measures for decision comfort and brand attitude and the second mediator, perceived enjoyment. Seven control variables followed. The questionnaire concluded with the participant\u2019s demographics. 3.5 Variables and Measurement Instrument Brand Familiarity. Brand familiarity was measured using a 7-point semantic scale, orig- inating from Sheinin (2000). The following statement was presented to the participants: \u2018Brand X is to me\u2026\u2019, with answering possibilities varying between the two opponents \u2018unfamiliar\u2019 and \u2018familiar\u2019. A pre-test showed that it could be assumed that participants consider Nike a well-known brand and Sergio Rossi an unknown brand. The measure was also used during the actual experiment as a manipulation check to check whether participants perceived the brand as well-known or unknown. Brand Attitude. The scale for measuring the dependent variable brand attitude was adopted from Smink et al. (2020) who modi\ufb01ed the original scale by Li et al. (2002) to include only AR-relevant items. Brand attitude is measured twice (pre- and post- measurement) on a semantic differential. Participants were presented with the following statement: I think brand X is\u2026\u2019, with answer options between the following extremes: bad-good; unattractive-attractive; negative-positive. The scale showed a high reliability both for the pre-measurement (\u03b1 = .91) and the post-measurement (\u03b1 = .93) (Field 2013). Enjoyment. The scale used to measure the mediator enjoyment consists of six items from Huang and Liao (2015) and Kim and Forsythe (2008) and is used in AR wearables research by Holdack et al. (2020). Response was measured on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Participants were presented the following statements: \u2018Shopping with the Wanna Kicks app is enjoyable in itself\u2019, \u2018Shopping with the Wanna Kicks app makes me feel good\u2019, \u2018Shopping with the Wanna Kicks app is boring\u2019, \u2018Shopping with the Wanna Kicks app is exciting\u2019, \u2018Shopping with the Wanna Kicks app is fun\u2019, \u2018Shopping with the Wanna Kicks app is interesting\u2019. The scale showed a high internal consistency (\u03b1 = .86) and can therefore be assumed reliable (Field 2013).","66 E. J. Morren et al. Decision Comfort. The scale for measuring decision comfort consists of \ufb01ve items orig- inating from Parker et al. (2016). It was also used in the AR study by Hilken et al. (2017). Decision comfort was measured twice (pre- and post-measurement) on a 7-point Lik- ert scale (1 = strongly disagree, 7 = strongly agree). The following statements were presented to the participants: \u2018I feel comfortable with my choice for this product\u2019, \u2018I feel good about my choice for this product\u2019, \u2018I\u2019m experiencing negative emotions about my choice for this product\u2019, \u2018Whether it\u2019s the right choice or not, I feel okay with my choice for this product\u2019, \u2018Despite not knowing whether this product is the best option, I feel very comfortable with my choice\u2019. Reliability of the scale is high for both the pre-measurement (\u03b1 = .89) and the post-measurement (\u03b1 = .93). Product Involvement. Product involvement has been included as a control variable in this research as it can in\ufb02uence the evaluation of the app by consumers (Smink et al. 2020). The variable was measured by \ufb01ve items originating from Smink et al. (2020) and from Zaichkowsky (1985). The variable was measured on a semantic dif- ferential. The scale has been adjusted to the product used in this study. Participants were presented with the following statement: \u2018To me, sneakers are\u2026:\u2019 with the fol- lowing extremes: irrelevant-relevant, unnecessary-necessary, not essential-essential, not important-important, meaningless-meaningful. The reliability of the scale is high (\u03b1 = .94). Execution. Once the participants came back in the survey after trying on the sneakers, they were asked whether they had succeeded in virtually trying on the pair of shoes. If not, these participants were excluded from the analysis. At the end of the survey, participants were asked again how they had viewed the sneaker pair in the Wanna Kicks app: \u2018Only by looking at photos of the product\u2019, or \u2018By virtually trying on the sneakers using my camera\u2019. This way, it was ensured that only participants that had correctly executed the experiment were used for analysis. Sneaker Choice. Participants were asked twice which pair of sneakers they had chosen. Once after seeing the product photos, and once after trying on the previously chosen pair of sneakers, in order to check whether they chose the same pair of shoes. Previous Use. Using a single-item, participants were asked whether they had used the Wanna Kicks app before. If they had, this could in\ufb02uence the results and the data of these participants were excluded from the analysis. Understanding of Instructions. Participants were asked whether they understood the instructions. This way the instructions could be adjusted, if necessary, based on any feedback that they could \ufb01ll in. Demographics. The questionnaire concluded with demographics, age, gender and edu- cation. The scale for educational levels comes from the MOA (2018) and contains eight options from \u2018No or primary education\u2019 to \u2018Master\/doctoral\/postgraduate\u2019.","The Great Unknown 67 4 Results In this study, a parallel mediation model was tested using a multiple regression analysis in PROCESS v3.5 by Hayes. PROCESS model 4 tests all total, direct and indirect effects in one analysis, meaning it is not necessary to perform separate regression analyses. In order to make accurate and generalizable statements about the hypotheses, the assumptions of a regression analysis have been checked: linearity, homoscedasticity, normality and collinearity (Field 2013). Only the assumption of normality has not been met, since in the second condition (well-known brand, Nike) the distribution on \u2018brand attitude\u2019 (D(45) = .74, p < .001) and \u2018decision comfort\u2019 (D(45) = .89, p = .001) differed signi\ufb01cantly from normality. To correct for this violated assumption of normality, bootstrapping of 5000 samples with a two-tailed con\ufb01dence interval of 95% has been used in the analysis, (Hayes 2009). To make sure there were no differences between the two conditions with regard to sex, age and educational level, a randomization check has been performed. A chi-square test showed that there is no signi\ufb01cant difference between the conditions based on sex (\u03c72 (1, N = 88) = 2.60, p = .107). A t-test showed that there is no signi\ufb01cant difference between the conditions in the age distribution (F = 1.25, t = 0.32, p = .267). Finally, a Chi-square test was performed which showed that there is also no signi\ufb01cant difference between the conditions based on educational level (\u03c72 (5, N = 88) = 4.12, p = .532). Hypothesis 1 predicted that the effect of AR apps on brand attitude is positive and stronger for unknown brands than for well-known brands. The results show that this overall effect is present and signi\ufb01cant (B = .37, t = 2.92, p = .005, CI[0.12, 0.62]). This means that AR apps, as expected, have a stronger positive effect on the brand attitude of unknown brands than of well-known brands. Hypothesis 1 can thus be accepted. On average, brand attitude of the unknown brand changes signi\ufb01cantly after using AR, in the sense that it becomes signi\ufb01cantly more positive after using AR (M = .33, SD = .79, p = .008), in contrast to brand attitude of the well-known brand, which does not increase signi\ufb01cantly (M = \u2212.04, SD = .31, p = .43). Hypothesis 2 predicted that (a) AR apps for unknown brands lead to more experi- enced enjoyment than AR apps for well-known brands (B = .09, t = 0.56, p = .575, CI[\u22120.22, 0.40]); (b) that enjoyment of AR has a positive effect on brand attitude (B = .09, t = 1.11, p = .273, CI[\u22120.07, 0.25]); and (c) that the relationship between brand familiarity and brand attitude is at least partly mediated by perceived enjoyment while using AR apps (B = .01, BootCI[\u22120.03, 0.05]). All three effects were non-signi\ufb01cant, which means that an AR app from an unknown brand is not more enjoyable than an AR app from a well-known brand; perceived enjoyment does not lead to a more positive brand attitude and \ufb01nally, enjoyment of an AR app does not mediate the effect of brand familiarity on brand attitude. The lack of a signi\ufb01cant effect leads to the rejection of hypotheses 2a, 2b and 2c. Hypothesis 3 predicted that (a) decision comfort after using an AR app increases more for unknown brands than for well-known brands (B = \u2212.13, t = \u22120.45, p = .655, CI[\u22120.73, 0.46]); (b) that there is a positive effect of decision comfort after using AR on brand attitude (B = .16, t = 3.70, p = .001, CI[0.07, 0.24]); and (c) that the relationship between brand familiarity and brand attitude is at least partly mediated by perceived decision comfort (B = \u2212.02, BootCI [\u22120.15, 0.07]. First of all, it can be","68 E. J. Morren et al. concluded from the analysis that an AR app from an unknown brand does not provide more decision comfort for consumers than an AR app from a well-known brand. In both conditions, decision comfort did not increase signi\ufb01cantly after AR use. There was however a signi\ufb01cant effect of decision comfort after using an AR app on brand attitude. Since the indirect effect is also not signi\ufb01cant, we cannot speak of a mediation effect. Hypothesis 3a and 3c are rejected and hypothesis 3b is accepted. 5 Discussion This study shows that brand attitude changes signi\ufb01cantly after the use of an AR app for unknown brands, in the sense that it becomes more positive, but for well-known brands brand attitude does not change. However, this difference in effect is not explained by perceived enjoyment and decision comfort when using the AR app. Since brand attitude signi\ufb01cantly changes for unknown brands \u2013 it becomes more positive \u2013 and does not change signi\ufb01cantly for well-known brands after AR use, brand familiarity could serve as an explanation for the lack of effects in the studies by Smink et al. (2020) and Javornik (2016), both of which only included well-known brands. A possible explanation for the non-signi\ufb01cant effect of using an AR app for a well- known brand on brand attitude could be that people have already made an assessment of the product through previous experiences with the brand. This may explain why AR did not add anything to, or even detract from, the attitude of the consumer towards the brand. Moreover, since people already have strong existing associations with well- known brands, their attitudes may be dif\ufb01cult to change through one-time AR app use (Campbell and Keller 2003). However, for unknown brands, little associations exist, enabling the AR app to create positive associations with the brand and therefore change brand attitude positively. The difference in effect between unknown and well-known brands on brand attitude could not be explained by the perceived enjoyment of the AR app. Both in the well-known and the unknown brand condition, perceived enjoyment amongst participants was high. A possible explanation could be that the advantages of new or unfamiliar brands, such as freshness and change, have little to do with enjoying an AR app featuring an unfamiliar brand. The expectation that perceived enjoyment would spill over into brand attitude, based on the Association Theory (Keller 2003) and Information Integration Theory (Anderson 1962), also failed to materialize, which may simply be explained by the idea that consumers can experience a brand\u2019s AR app as enjoyable, without actually liking the brand itself. Future research could examine other processes that may explain the positive effect of AR on brand attitude for unknown brands. For example, Rauschnabel et al. (2019) examined feelings of inspiration provided by an AR app as a predictor for brand attitude. Since inspiration is considered a motivational state in which new possibilities are revealed that can lead to the realization of new ideas (Rauschnabel et al. 2019), this may be especially the case for new unknown brands that can inspire consumers to try something new. Contrasting the study of Hilken et al. (2017), this study has shown that decision comfort is not enhanced after using an AR app for the well-known as well as the unknown brand. Even though this feeling of comfort does not change after using AR, decision comfort did have a signi\ufb01cant effect on brand attitude.","The Great Unknown 69 A possible explanation for this \ufb01nding is that the range of sneaker options did not suit the taste of the participants, which resulted in the fact that AR only con\ufb01rmed their idea that they did not like the chosen pair of sneakers. Second, other needs that AR cannot meet, such as \ufb01t and comfort, may be needed to make a more informed purchase decision and thus to increase the decision comfort. Therefore, the positive effect of AR on decision comfort may be exclusively limited to certain product categories. Further research could establish for which types of products AR can help in increasing decision comfort, and whether this effect differently applies to known and unknown brands. This research was the \ufb01rst to succeed in giving participants the experience that they were dealing with either a well-known or an unknown brand, with an AR experience offered within one and the same app. This study offers relevant practical insights to retailers and marketers, as it shows that investing in AR may be especially interesting for new and unknown brands in order to induce positive associations with these brands through AR. Now that AR is becoming much more easily integrated and used in existing apps (e.g. through social media; Smink et al. 2021), AR gives many possibilities for new brands to familiarise consumers with their brand in a fun and interactive way. 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Res. 100, 581\u2013589 (2019). https:\/\/doi.org\/10.1016\/j.jbusres.2018.10.041 Zaichkowsky, J.L.: Measuring the involvement construct. J. Consum. Res. 12(3), 341\u2013352 (1985). https:\/\/doi.org\/10.1086\/208520","Augmented Reality (AR) Brand Storytelling: The Role of Flow in Attitude Formation and Associative Learning Zeph M. C. van Berlo1(B) and Dimitrios Stikos2 1 Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, the Netherlands [email protected] 2 Graduate School of Communication, University of Amsterdam, Amsterdam, the Netherlands Abstract. Organizations and brands have long since used stories to communicate and resonate with their audiences. Nowadays, novel interactive media formats are used to enhance these brand-consumer interactions. Augmented reality (AR) holds the potential to aid brands in having immersive and exploratory interactions with consumers. The aim of this study is to examine the effects of AR in brand storytelling on brand attitude and brand associations, and to explore to what extent (the dimensions of) \ufb02ow can explain these effects. A single factorial (Type of brand storytelling: AR vs. non-AR) between-subjects \ufb01eld experiment is conducted (N = 83). The results show that AR brand storytelling leads to a higher perceived \ufb02ow than regular brand storytelling. Furthermore, \ufb02ow mediates the effects of AR brand storytelling on both brand attitude and brand associations. Notably, the \ufb02ow dimensions control and attention focus are found to be particularly important for explaining the effect on brand associations. Keywords: Augmented Reality (AR) \u00b7 Brand storytelling \u00b7 Flow \u00b7 Brand attitude \u00b7 Brand associations 1 Introduction Many brands use storytelling to strengthen their image and to communicate their branded messages to consumers. From a branding perspective, storytelling is considered an inte- gral part of a brand\u2019s management strategy (Park et al. 2021). Also, it can offer brands a competitive advantage because it allows them to resonate with consumers (Chiu et al. 2012). Nowadays, new technologies are used to enhance brand storytelling. One of the most promising of these is augmented reality (AR). Augmented reality is a technology that integrates virtual information into real-life settings (Faust et al. 2012; Javornik 2016a; Rauschnabel et al. 2019) and provides users the illusion that virtually depicted objects are present in their actual real-world environ- ment (Verhagen et al. 2014). Many contemporary smartphones offer AR features, using the phone\u2019s geolocation, compass, accelerometer, and camera capabilities (Carmigniani \u00a9 The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Jung et al. (Eds.): XR 2022, SPBE, pp. 72\u201384, 2023. https:\/\/doi.org\/10.1007\/978-3-031-25390-4_6","Augmented Reality (AR) Brand Storytelling 73 et al. 2011). By leveraging AR technology, brands are believed to be able to establish more impactful brand-consumer relationships (Smink et al. 2019; 2021; Scholz and Duffy 2018). In a non-commercial context, Scholz and Smith (2017) demonstrated that AR sto- rytelling can offer immersive experiences to users. For example, they showed that geo- based storytelling positively affects users\u2019 narrative transportation\u2014for the story (world) is transported to the user while at the same time the user is transported into the story world. Furthermore, the interactive nature of AR is believed to have a positive impact on user\u2019s perceived \ufb02ow when using an AR application. Flow is a psychological state, char- acterised by immersion and absorption into a speci\ufb01c activity (Cs\u00edkszentmih\u00e1lyi 1990). In a commercial context, \ufb02ow has been associated with both affective and cognitive brand responses (Javornik 2016b). The aim of this study is to examine to what extent perceived \ufb02ow can explain the effects of AR on brand attitude and brand associations, in the context of brand story- telling. Furthermore, the study also explores the potential roles of four dimensions of \ufb02ow (i.e., control, attention focus, curiosity, and intrinsic interest). Insights from \ufb02ow theory (Cs\u00edkszentmih\u00e1lyi 1990), the affect transfer hypothesis (Mackenzie et al. 1986), and associative learning theories (Van Osselaer and Janiszewski 2001) are used to explain these effects. Exploring the role of \ufb02ow in explaining the effects AR on brand responses is rel- evant, because research into these effects showed mixed results. Some studies (e.g., Rauschnabel et al. 2019), for example, found that branded AR apps can lead to more positive brand responses, whereas other studies reported mixed results or no effects (e.g., Javornik 2016b; Smink et al. 2020). Furthermore, for marketing professionals, studying the effects of extended reality (XR) features on brand attitude and brand associations is relevant, because these are important indicators of successful marketing and\/or advertising behaviour and indicative of strong consumer-brand relationships (Hess and Story 2005; Wedel et al. 2020). 2 Theoretical Framework 2.1 Four Dimensions of Flow Flow theory (Cs\u00edkszentmih\u00e1lyi 1990) suggests that playful interactive experiences with media can be self-motivating, because they can lead users to experience a state of \ufb02ow. Flow is a multidimensional construct (Webster et al. 1993; Nel et al. 1999) describ- ing a psychological state in which users experience: (a) high levels of control of their interactions with a medium, (b) a narrowed attention focus on that which they interact with, (c) elevated levels of curiosity through cognitive and\/or sensory stimulation while interacting, and (d) intrinsic interest and satisfaction with the interaction. 2.2 Flow and AR Brand Storytelling For brand storytelling, incorporating AR features is expected to lead to higher levels of perceived \ufb02ow among users\u2014on each of its four dimensions:","74 Z. M. C. van Berlo and D. Stikos First, an integral part of AR technology is that it allows users to place virtual objects in their direct environments. Users can interact with these objects, leading them to expe- rience high levels of control over their interactions. In the context of branded storytelling, AR allows users to interact with objects relevant to the branded story. Second, in addition to its distinct feature of virtual emplacement and augmentation, AR is characterized by the interaction between the user and the interface (Azuma et al. 2001). This is believed to increase attention focus and lead to higher levels of absorption in the interaction with the branded story. By design, AR apps redirect the attention of users away from their actual immediate environments, toward the augmented reality displayed on a screen (e.g., smartphone, tablet, AR headset). This attention focus, subsequently, is expected to lead to absorption. Third, AR is believed to drive curiosity due to its cognitively and sensory stimulating and immersive nature (Xue et al. 2021). Like other XR technologies, AR allows for sensory immersion into the mediated environment and can offer users the illusion of \u2018being there\u2019 in (or interacting with) the virtual world (Sundar et al. 2015; Abels et al. 2021). This sensory illusion is in the XR literature generally referred to as presence (Hartmann et al. 2015; Van Berlo et al. 2020). And fourth, AR is believed to lead to higher levels of intrinsic interest. The playful interactions AR enables are generally believed to be a fun and pleasurable experience, which subsequently result in satisfaction with the interaction (Zheleva et al. 2021). All in all, it is hypothesized that AR brand storytelling (compared to non-AR brand storytelling) leads to higher levels of perceived \ufb02ow, on all four dimensions: H1: AR (vs. non-AR) brand storytelling has a positive direct effect on users\u2019 per- ceptions on all four \ufb02ow dimensions: (a) control, (b) attention focus, (c) curiosity, and (d) intrinsic interest. 2.3 Explaining Effects on Brand Attitude via Flow In a marketing context, \ufb02ow, as a psychological state, has been associated with both affective and cognitive responses to interactive media formats (Van Noort et al. 2012; Javornik 2016b). The affective responses to \ufb02ow are often explained by considering insights directly from \ufb02ow theory (Cs\u00edkszentmih\u00e1lyi 1990). This theory characterises the state of \ufb02ow as an overall pleasurable experience rooted in the control of, absorption in, stimulation by, and satisfaction with the interaction with a medium (Webster et al. 1993; Nel et al. 1999). Users are subsequently expected to attribute the pleasure experienced while in a state of \ufb02ow, \ufb01rst of all, to the medium they interact with\u2014leading, in an AR context for example, to higher levels of app attitude mediated through \ufb02ow (Javornik 2016b). In addition, in line with the affect transfer hypothesis (Mackenzie et al. 1986), one could argue that this positive affective state could also transfer over to other consumer responses, like brand attitude. Similar effects have been found in other XR contexts (Van Berlo et al. 2021). Even though several studies showed that branded AR apps can lead to more favourable attitudes towards the brand (e.g., Rauschnabel et al. 2019), the empirical evidence for the role of \ufb02ow in explaining the effects of interactive media formats on","Augmented Reality (AR) Brand Storytelling 75 brand attitude is still inconclusive. Therefore, in line with \ufb02ow theory (Cs\u00edkszentmih\u00e1lyi 1990), the follow hypothesis is proposed: H2: Users\u2019 perceived \ufb02ow positively mediates the effect of AR (vs non-AR) brand storytelling on brand attitude. 2.4 Explaining Effects on Brand Associations via Flow In addition to explaining affective responses to interactive media formats, \ufb02ow is also expected to explain cognitive responses, like brand associations. 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 \u2018informational nodes\u2019 (Keller 2003). The construction of brand associa- tions is a learning process, with these informational nodes being linked in consumers\u2019 minds to build an associative network of connections with the brand. Van Osselaer and Janiszewski (2001) showed that the processes through which these associations are established are the human associative memory (HAM) models (Ander- son and Bower 1973) and the adaptive learning model (Janiszewski and Van Osselaer 2000). These two models mainly differ from each other in terms of cue learning and interactivity\u2014the former proposes that cues are learned independently, whereas the latter suggests that these cues interact. In other words, HAM models suggest that mul- tiple 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 \u2018trained\u2019 with another association of greater salience. Even though the interaction differs, the above indicates that brand associations in both cases are being established (and\/or strengthened) through a learning process. Flow, experienced while interacting with AR content, is expected to facilitate this learning process\u2014primarily because a \ufb02ow state is characterized by a narrower focus of awareness on the content that one is interacting with (cq. The branded content) and irrel- evant perceptions are \ufb01ltered out (Cs\u00edkszentmih\u00e1lyi 1975). But also, because, in human- computer interactions, perceptual interfaces increase engagement and the respective memory about these experiences (Reeves and Nass 2000). Previous studies, albeit not using an AR apps, have tried to explore the effects of interactive and immersive media on brand associations and learning through the perceived immersion into the story world. In the context of 3D virtual environments, Nah et al. (2011) af\ufb01rmed the effect on learning, while showing that \ufb02ow was the most relevant concept explaining this effect. Similarly, Bae et al. (2020) found in a mixed reality context (i.e., virtual hologram portrayals and projection mapping on a physical display wall) that feelings of immersion mediated the effects of mixed reality\u2019s interactivity on brand associations. In sum, \ufb02ow is expected to mediate the effect of AR on brand associations. The following hypothesis is proposed: H3: Users\u2019 perceived \ufb02ow positively mediates the effect of AR (vs non-AR) brand storytelling on brand attitude.","76 Z. M. C. van Berlo and D. Stikos 2.5 Relative Effects of Flow Dimensions Notably, previous studies (e.g., Javornik 2016b) into the role of \ufb02ow in the context of AR effects have exclusively operationalised \ufb02ow as a unidimensional construct. It is however conceivable that some \ufb02ow dimensions play a (relatively) more important role than others in explaining AR effects like attitude formation and associative learning. For attitude formation for example, the dimension \u2018intrinsic interest\u2019 seems particularly important, because this dimension is directly related to feelings of satisfaction and plea- sure; whereas for associative learning the dimensions \u2018control\u2019 and \u2018attention focus\u2019 seem more relevant, because these dimensions can be linked to enhanced processing of information. However, due to the lack of existing empirical evidence, a research question is proposed instead of a hypothesis: RQ1: What is the relative effect of each of the four dimensions of \ufb02ow in explaining the effect of AR (vs non-AR) brand storytelling on users\u2019 (a) brand attitude and (b) brand associations? 3 Methods 3.1 Participants and Procedure To test the hypotheses, a \ufb01eld experiment was conducted with a single-factor (Type of brand storytelling: AR vs non-AR) experimental design. Flow was measured as a mediator variable. The sample consisted of 83 young adults (41 identi\ufb01ed as women, 39 as men, 2 as non-binary, and 1 preferred not to say) with an average age of 21.27 years old (SDage = 2.25). Data was collected in late 2021. Participants were approached on a university campus and asked to participate in a study. After giving informed consent, participants were randomly assigned to either the experimental (AR brand storytelling) or control (non- AR brand storytelling) condition. Participants in both conditions were instructed to read a short interactive brand story developed for this experiment on an app used for AR brand storytelling. In the experimental condition, the participants could, in addition to reading the story, also explore their environment using an AR feature in the app. In the control condition, participants were only able to read the story (meaning that the AR feature was disabled). Afterwards, all participants \ufb01lled out a questionnaire measuring several demographic variables, perceived \ufb02ow, brand attitude, and brand associations. Finally, everyone was debriefed and thanked for their participation. 3.2 Stimulus Materials The stimulus material for this study was created using the AR storytelling app Artelot. This app allows users to read a (branded) story, which can be augmented using geo- located AR animations. For this study, a geo-located historical short story was created based around a Nobel prize-winning professor from the university at which the data was collected. The host university served as the target brand of this study. In the story, the professor has a conversation with another person. Throughout the conversation, the rich","Augmented Reality (AR) Brand Storytelling 77 history, prestigiousness, and inclusiveness of the host university were highlighted. The words \u2018historical\u2019, \u2018prestigious\u2019, and \u2018inclusive\u2019 served as the three target associations that the narrative aimed to communicate. Both conditions showed the exact same story in written format, accompanied by an image featuring the two characters of the story and the university\u2019s logo on a solid-colour background. The only difference between the two conditions was the AR feature. In the experimental (AR) condition version of the story, users could use the AR interface to place (and interact with) the story\u2019s characters in the real-world. A still image of the stimulus materials can be found as Fig. 1. Fig. 1. Stimulus materials. 3.3 Measures Perceived Flow. To measure perceived \ufb02ow, a twelve-item (e.g., \u201cWhen I used the app I felt in control\u201d, \u201cVisiting the app excited my curiosity\u201d, \u201cThe app was interesting\u201d) 7-point Likert scale (1 = strongly disagree; 7 = strongly agree), was used. This scale, adapted from a validated scale by Nel et al. (1999), measured four dimensions of \ufb02ow: Control (M = 4.85, SD = 1.44, Cronbach\u2019s alpha = .80), attention focus (M = 4.26, SD = 1.28, Cronbach\u2019s alpha = .69), curiosity (M = 4.98, SD = 1.43, Cronbach\u2019s alpha = .84), and intrinsic interest (M = 4.94, SD = 1.26, Cronbach\u2019s alpha = .83). Brand Attitude. Brand attitude was measured on a \ufb01ve-item (e.g., \u2018Bad\/good\u2019, \u2018Unpleasant\/pleasant\u2019, \u2018unlikable\/likable\u2019) 7-point semantic differential scale (Spears and Singh 2004). The scale (M = 5.65, SD = 0.92) proved valid (EV = 3.42, R2 = 0.68) and reliable (Cronbach\u2019s alpha = 0.88). Brand Associations. Following a procedure outlined by Dahl\u00e8n (2005), brand associ- ations were measured by asking participants to indicate, on a three-item 7-point Likert","78 Z. M. C. van Berlo and D. Stikos scale (1 = strongly disagree, 7 = strongly agree), to what extent they felt the target brand could be described as \u2018historical\u2019, \u2018prestigious\u2019, and \u2018inclusive\u2019. The scale (M = 5.35, SD = 1.00) proved valid (EV = 1.90, R2 = 0.63) and reliable (Cronbach\u2019s alpha = 0.70). 4 Results 4.1 Direct Effects of AR Brand Storytelling To test the effects of AR in brand storytelling on \ufb02ow, four independent samples t tests were conducted. As shown in Table 1, all four tests were signi\ufb01cant, meaning that people in the AR storytelling condition showed higher levels of control, attention focus, curiosity, and intrinsic interest, when compared to people in the non-AR storytelling condition. These \ufb01ndings support H1. Table 1. Direct effects of AR brand storytelling on \ufb02ow. Measure Brand storytelling t p Cohen\u2019s d AR Non-AR <.001 1.18 (n = 44) (n = 39) <.001 0.82 <.001 1.29 M SD M SD <.001 1.26 Control 5.55 0.90 4.06 1.54 5.23 Attention focus 4.72 1.14 3.74 1.24 3.74 Curiosity 5.71 1.20 4.15 1.22 5.78 Intrinsic interest 5.57 1.00 4.22 1.14 5.62 Note. The tests described in this table have 81 degrees of freedom 4.2 Indirect Effects of AR Brand Storytelling To test hypotheses 2 and 3, two mediation models (Model 4) were estimated using the PROCESS macro by Hayes (2013). For the \ufb01rst model, type of storytelling served as independent variable, brand attitude as dependent variable, and the four dimensions of perceived \ufb02ow as mediator variables. The second model differed from the \ufb01rst model in that brand associations were measured as dependent variable. Brand Attitude. The second model was estimated to test the indirect effect of AR brand storytelling, via \ufb02ow, on brand attitude. As shown in Table 2, the total indirect effect of AR brand storytelling on brand attitude was signi\ufb01cant (b* = 0.40, SE = 0.17, 95%CI [0.05, 0.71]). This means that the data support H2. Notably, non-signi\ufb01cant indirect effects were found for the individual \ufb02ow dimen- sions control (b* = 0.13), attention focus (b* = 0.03), curiosity (b* = 0.05), and intrinsic interest (b* = 0.18).","Augmented Reality (AR) Brand Storytelling 79 Brand Associations. The second model was estimated to test the indirect effects of AR brand storytelling on brand associations. As shown in Table 3, the total indirect effect of AR brand storytelling on brand associations via \ufb02ow was signi\ufb01cant (b* = 0.79, SE = 0.18, 95%CI [0.46, 1.16]). This means that the data support H3. Furthermore, signi\ufb01cant indirect effects were found for the individual \ufb02ow dimen- sions control (b* = 0.31) and attention focus (b* = 0.34). The indirect effects of the \ufb02ow dimension curiosity (b* = 0.25) and intrinsic interest (b* = \u22120.11), however, were non-signi\ufb01cant. Table 2. Direct and indirect effects of AR brand storytelling on brand attitude. Measures Brand attitude Direct effects Indirect effects b SE 95% CI b SE 95% CI AR brand storytelling 0.26 0.24 [\u22120.22, 0.74] \u2013 \u2013 \u2013 [0.05, 0.71] Flow \u2013\u2013\u2013 0.36 0.17 [\u22120.17, 0.41] [\u22120.18, 0.21] Control 0.08 0.09 [\u22120.11, 0.26] 0.12 0.14 [\u22120.30, 0.43] [\u22120.28, 0.61] Attention focus 0.03 0.10 [\u22120.17, 0.23] 0.03 0.10 Curiosity 0.03 0.12 [\u22120.21, 0.27] 0.04 0.18 Intrinsic interest 0.13 0.17 [\u22120.21, 0.46] \u22120.17 0.22 Note. Regression coef\ufb01cients presented in bold are signi\ufb01cant at the .05 level Table 3. Direct and indirect effects of AR brand storytelling on brand associations. Measures Brand associations Direct effects Indirect effects b SE 95% CI b SE 95% CI \u2013 AR brand storytelling \u22120.17 0.22 [\u22120.61, 0.27] \u2013 \u2013 [0.42, 1.23] [0.06, 0.60] Flow \u2013 \u2013\u2013 0.79 0.21 [0.13, 0.60] [\u22120.03, 0.61] Control 0.21 0.08 [0.05, 0.36] 0.31 0.14 [\u22120.49, 0.25] Attention focus 0.34 0.09 [0.16, 0.52] 0.34 0.12 Curiosity 0.16 0.10 [\u22120.03, 0.36] 0.25 0.16 Intrinsic interest \u22120.08 0.14 [\u22120.37, 0.20] \u22120.11 0.19 Note. Regression coef\ufb01cients presented in bold are signi\ufb01cant at the .05 level","80 Z. M. C. van Berlo and D. Stikos 5 Discussion The aim of this study was to examine how AR can be used to enhance the effect of brand storytelling on brand attitude and brand associations, and to what extent \ufb02ow can explain these effects. Overall, AR in brand storytelling was found to have a positive effect on \ufb02ow, which in turn explained the effects on brand attitude and brand associations. From the results three main conclusions can be drawn. 5.1 Flow and AR Brand Storytelling First, the results show that the use of AR features can enhance \ufb02ow. This is in line with previous research, suggesting that AR holds the potential to immerse and absorb its users into the story world and evoke a state of \ufb02ow (Huang and Liao 2017; Javornik, 2016b; Scholz and Smith, 2017; Sundar et al., 2015). Notably, AR was found to positively affect all four dimensions of \ufb02ow (e.g., control, attention focus, curiosity, and intrinsic interest). 5.2 Effects on Brand Attitude via Flow Second, \ufb02ow was found to mediate the effect of AR storytelling on brand attitude. This is in line with \ufb02ow theory (Cs\u00edkszentmih\u00e1lyi 1990) and suggests that users attribute the overall pleasurable state of mind, experienced when in a state of \ufb02ow, to the branded content they interact with. Moreover, given that positive valance, experienced during a state of \ufb02ow, seems to be transferred over to the brand, these \ufb01ndings also support the affect transfer hypothesis (Mackenzie et al. 1986). Notably, the results suggest that none of the \ufb02ow dimensions individually explain a signi\ufb01cant part of the effect of AR on brand attitude. 5.3 Effects on Brand Associations via Flow Third, \ufb02ow was found to mediate the effect of AR storytelling on brand associations. In line with previous research on the role of \ufb02ow in mediating the effects of interactive media (e.g., Skadberg and Kimmel 2004), \ufb02ow was found to play an important role in the creation of brand associations (Skadberg and Kimmel 2004). The current study shows that being in a state of \ufb02ow can enhance (associative) learning processes. Furthermore, the results also show that the \ufb02ow dimensions control and attention focus are most important in explaining the effect of AR on brand associations. Control allows users to determine their own pace in processing information they interact with. Previous research into learning has shown that incorporating a modest amount of interac- tivity can promote deeper learning (Mayer and Chandler 2001), which could potentially explain the mediating role of control. A potential explanation for the role of attention focus could be that when users become more absorbed into the activity, they are less distracted by other stimuli, which in turn could then improve the processing (encoding and retention) of information (Lang 2000). Notably, the results suggest that the \ufb02ow dimensions curiosity and intrinsic interest, albeit an integral part of the \ufb02ow experience, seem less important for facilitating associative learning.","Augmented Reality (AR) Brand Storytelling 81 5.4 Limitations and Future Research The current study offers novel insight into the workings of AR in brand storytelling and the role of \ufb02ow. However, it also has its limitations. Arguably, the most pressing limita- tion of the study is the choice of target brand. By collecting data on the campus of the university that was also the target brand of the experiment, it is conceivable that partic- ipants already had strong pre-existing attitudes and associations with the brand. Where this does not have to be an issue per se, strong pre-existing attitudes and associations could have potentially led to ceiling effects (and thus suppressed the effects that were found). In the future, researchers are advised to use less known (or \ufb01ctitious) brands in their stimulus materials, to avoid any potential confounds resulting from ceiling effects. 5.5 Implications for Theory and Practice For theory, the most important implication of the current study is that it demonstrates how integrating \ufb02ow as a multidimensional construct, rather than a unidimensional one, offers deeper insight in the understanding of the workings of \ufb02ow. In particular, the results indicate that there can be relative differences into what extent individual \ufb02ow dimensions contribute to the overall explanation of the effects of AR. For example, on the one hand, when explaining of the effects on brand attitude, only the cumulative effect of all four \ufb02ow dimensions was found to be signi\ufb01cant (and the individual effects of the \ufb02ow dimensions were not deemed meaningful). 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Tolba1(B), Taha Elarif1, Zaki Taha1, and Ramy Hammady2 1 Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt [email protected] 2 School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK [email protected] Abstract. The traditional methods used for learning phonetics (LP) are some- how overwhelming and lack interactivity. Therefore, researchers adapted popular technologies such as Mobile Augmented Reality (MAR). Until now, there isn\u2019t any review conducted about using MAR for LP. So, this review provides the basic knowledge needed for those interested in this \ufb01eld. MAR applications published between 2012 and 2022 are summarized, the technical requirements of making an LP MAR app are described and the bene\ufb01ts\/limitations of using MAR for LP are discussed. The review showed that using MAR technology increased learners\u2019 attention and made the learning process more interactive. Even though it still suf- fers in some areas, such as instability of marker tracking, in\ufb02exibility of updating AR content, and inability to correct learners\u2019 pronunciation as it happens in real life by language teachers. Keywords: Mobile Augmented Reality \u00b7 MAR \u00b7 Phonetics \u00b7 Learning phonetics 1 Introduction MAR is the most popular and most used Augmented Reality (AR) type (Nizam et al. 2018). It uses mobile devices - such as smartphones and tablets - to blend the information of the real world with computer-generated objects in a way that wasn\u2019t possible before (Chatzopoulos et al. 2017). Phonetics is a part of linguistics that studies the sounds of human speech (Bruhn 2018). The phonics method focuses on the sound of symbols (Mahayuddin and Mamat 2019). It enables the students to pronounce the words rather than memorize the word pronunciation. The phonics method is ideal for teaching reading (Engwall 2012). (Jumi 2018) proposed courseware for English vocabulary pronunciation using the phonic reading method in her thesis. Many researchers have conducted studies about using MAR as an educational tool for Language Learning (Majid and Salam 2021), (Fan et al. 2020), (Karacan 2021), (Yilmaz et al. 2022). But only few focused on using it for learning phonetics. (Booton et al. 2021) covered in their systematic review the part of language pronunciation and how it is enhanced by using MAR applications. (Munshi and Aljojo 2020) showed how \u00a9 The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Jung et al. (Eds.): XR 2022, SPBE, pp. 87\u201398, 2023. https:\/\/doi.org\/10.1007\/978-3-031-25390-4_7","88 R. M. Tolba et al. effective the MAR multimodal input applications are in assisting the problems of learn- ing vocabulary pronunciation\/spelling. (Poompimol 2017) explored to what extent AR materials implementation can help improve Prathom 1 students\u2019 English pronunciation pro\ufb01ciency. MAR has been used as an interactive tool for learning phonetics in the classroom. (Wu 2019) used the famous Pokemon Go game as a learning activity in classrooms to enable the students to write and pronounce the sound \u2018pi\u2019, \u2018ka\u2019, and \u2018chu\u2019. (Mei 2021) reviewed the use of the Clips app in language classrooms and how that provides instant feedback on students\u2019 pronunciation and makes language learning more engaging. (Chen 2018) assisted students\u2019 phonic learning and helped them to decode letters into their respective sounds, forming an essential skill to read unfamiliar words by themselves using MAR application. (Nugraha et al. 2019) described the steps and procedures of developing MAR English phonetic learning media. (Arunsirot 2020) examined how MAR enhanced the students\u2019 abilities to produce English consonant sounds. MAR apps are also used to help learners with disabilities and autism (Mahayuddin and Mamat 2019), (Zaman 2012), (Shaltout et al. 2020), (Antkowiak et al. 2016), (Bhatt et al. 2020). For example, (Wook et al. 2020) investigated the effect of using video modeling in the MAR app, on the phonics performance of \ufb01rst-grade students who are at risk for reading disabilities. (Anas and Mahayuddin 2017) proposed a system that helps Autistic children to learn the Arabic alphabet. (Sidi et al. 2017) presented a prototype of MAR interactive synthetic phonics courseware for kindergarten Consonant-Vowel- Consonant (CVC) word. The courseware started with learning phonics sounds and then blended the phonics sounds to read the CVC words. Most of the studies used a card to learn how a character\/word should be pronounced (Opu et al. 2021; Tsai 2020; Beder 2012; Fung and Wan 2019; Rozi et al. 2020; Khan et al. 2019; Ulfah et al. 2020; Wulan and Rahma 2020; Florentin 2016; K\u00fc\u00e7\u00fck et al. 2014; Sorrentino et al. 2015; Mart\u00ednez et al. 2017; Wen 2020; Zhang et al. 2020). As (Wen 2020) demonstrated in his Chinese character composition game with the paper interface designed and implemented in classrooms. Also, (Welbeck 2020) discussed how audio features in MAR apps could potentially enhance pronunciation of the vocabulary in the case of using native accents. (Jalaluddin et al. 2020) the experimental study aimed to explore the effectiveness of using MAR application in vocabulary learning among LINUS students and how that helped the students grasp the meaning and the concept of how to pronounce the words. Despite the importance of learning phonetics, only one review was found about using MAR technology in the \ufb01eld of Learning Phonetics. So, this review was conducted to provide a broad overview of user-based MAR research, to help researchers \ufb01nd example papers that contain related studies, to help identify areas where there have been few user studies conducted, and to highlight exemplary user studies that embody the use of MAR app in LP. 2 Methodology To help the AR community improve usability, this paper provides an overview of 10 years of MAR user studies, from 2012 to 2022. Four research questions (RQ) were","Mobile Augmented Reality for Learning Phonetics: A Review (2012\u20132022) 89 designed, as shown in Table 1. Then related data to these questions were collected from \ufb01ve interdisciplinary databases: Google Scholar, IEEE Xplore, ACMD Digital Library, Springer, and ResearchGate. The search strings are Augmented Reality for learning phonetics, Augmented reality for Phonics Learning, and Augmented Reality Language Pronunciation. Table 1. Research Questions. ID Research question RQ1 What are the existing MAR applications for learning phonetics? RQ2 Which languages are using MAR for learning phonetics? RQ3 What types of activities are covered in the MAR apps for LP? RQ4 What are the technical requirements to make MAR apps for LP? The title and abstract of each paper are considered, and the results are cross-checked to discard any repetition. Certain inclusion and exclusion criteria are applied. These criteria are shown in Table 2. The initial search was conducted from Dec. 2021 to Feb. 2022. Updates have been done during the review process in March 2022 to include the latest published studies. Table 2. The inclusion and exclusion criteria. Inclusion criteria \u2022 Containing search keywords in the title or the abstract or keywords \u2022 English research articles published from January 2012 till March 2022 \u2022 Review studies that have domain in using MAR in Language Learning \u2022 Papers that describe MAR applications for learning phonetics Exclusion criteria \u2022 Papers focusing only on Language Learning \u2022 Publications that didn\u2019t contain terms \u2018AR\u2019 and \u2018phonetics\u2019 \u2022 Any study published before 2012 \u2022 Redundant publications 3 Results 223 studies were found using search strings. After applying the inclusion and exclusion criteria, 65 topic-related studies were analyzed. The distribution of these studies over the last decade (2012\u20132022) has been illustrated in Fig. 1. The \ufb01gure gives an in-depth understanding of the current research state of the review topic.","90 R. M. Tolba et al. 20 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 15 10 5 0 2012 Fig. 1. Distribution of related studies over the last decade. There are only a few studies published between 2012 and 2015. This could imply that researchers were doubted of emerging new technology tools in the educational settings at this time. Nevertheless, studies on using MAR for LP have an upward trend from 2016, reaching a peak of 16 studies in 2020. 3.1 MAR Applications for LP Not all reviewed studies focused on making an application to be used. Only 32% of them made applications that are summarized in Table 3. The Table shows the study, application name, year of production, used for which language, and target audience. Table 3. MAR applications for LP that are published between 2012 and 2022. Study App name Year Language Audience (Sirat et al. 2021) ReModAR 2021 \u2013 Kindergarten with\/without 2021 disabilities (Daud et al. 2021) ARabic-Kafa 2021 Arabic Primary school students (Khatoony 2021) ARET 2021 English For teachers and English 2020 learners (Piatykop et al. 2021) Fox Alphabet 2020 Ukrainian Children (Daniel et al. 2020) AR 2019 2019 English Children whose native InglesAR language is Portuguese Buginese Elementary school (Hasbi et al. 2020) Lontara students Malay Children with Autism (Mahayuddin and Mamat \u2013 2019) English\\\\ School age children Indonesian (Nasution et al. 2019) Translation (continued) Agent"]
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