Improving service brand personality with augmented reality marketing

Abstract

Purpose Augmented reality (AR) apps offer a great opportunity for brands to provide better service to customers by creating augmented customer service. However, not every AR app is equally effective in improving customer experience. Investigation of underlying processes and brand-related outcomes of AR marketing remains scarce and it is unclear how different types of AR apps influence brand perceptions, such as brand personality. This paper aims to fill in this knowledge gap and provide practical insights on how different AR apps can improve service brand personality. Design/methodology/approach Using an experimental plan, the authors investigate how attitudes towards AR apps contribute to customer perceptions of brand personality (i.e. excitement, sincerity, competence and sophistication) according to two different variables, namely, the location of the AR app (location-specific vs non-location-specific) and its orientation (augmenting the product, brand or store experience). The authors also examine the effect of expected customer experience with the AR app (i.e. playfulness and pleasure) and customer technological innovativeness and shopping orientation as predictors of attitudes towards the AR app. Findings The findings show that non-location-specific and product-oriented AR apps (i.e. virtual try-on apps) receive more positive evaluations and lead consumers to perceive the brand as more exciting, sincere, competent and sophisticated. Moreover, the playfulness and pleasure experienced with the AR app determine consumers’ attitudes towards the app. Additionally, AR apps improve brand personality perceptions amongst more innovative and adventure-focussed shoppers. Originality/value The authors show that brand announcements on high-technology, customer-oriented service offerings are an effective branding tool. Thus, AR apps perceived as pleasant and playful can signal and improve brand personality.

Augmented Reality Marketing: A Systematic Literature Review and an Agenda for Future Inquiry

“…First, AR engages consumers in online settings by providing real-time direct product/service experiences in various aspects of marketing ( Chung et al, 2018 ). Specifically, it overcomes the limitations of online shopping by allowing prospects to try on products, such as makeup ( Smink et al, 2019 ; Hsu et al, 2021 ; Javornik et al, 2021 ), eyewear ( Pantano et al, 2017 ; Yim et al, 2017 ; Yim and Park, 2019 ), clothing ( Huang and Liu, 2014 ; Huang and Liao, 2017 ; Plotkina and Saurel, 2019 ), shoes ( Hilken et al, 2018 ; Plotkina et al, 2021 ), and furniture ( Rauschnabel et al, 2019 ; Kowalczuk et al, 2021 ; Qin et al, 2021b ) virtually without having to interact physically with them. Major online retailing platforms, such as Amazon ( McLean and Wilson, 2019 ), JingDong ( Fan et al, 2020 ), Alibaba ( Fan et al, 2020 ), and eBay ( Banerjee and Longstreet, 2016 ), as well as leading brands, such as Tiffany & Co. ( Whang et al, 2021 ), L’Oréal ( Hilken et al, 2017 ), Sephora ( Smink et al, 2019 ), Nike ( Hilken et al, 2018 ), Converse ( Whang et al, 2021 ), Zara ( Yuan et al, 2021 ), IKEA ( McLean and Wilson, 2019 ; Qin et al, 2021b ), Mini ( Carmigniani et al, 2011 ), and Lego ( Hinsch et al, 2020 ), have devoted lots of efforts to introduce various forms of AR.…”

“…Consumers’ brand attitude refers to their feelings about a brand ( Rauschnabel et al, 2019 ; Smink et al, 2019 , 2020 ; van Esch et al, 2019 ). Consumers’ perceived brand personality describes their systematic and enduring perception of a set of human traits that serve as the foundation of brand relational consequences and brand equity ( Plotkina et al, 2021 ). Consumers’ brand purchase intention refers to their willingness to buy the products of a specific brand ( Smink et al, 2020 ).…”

“…Furthermore, AR types such as goal and location affect consumers’ perceived brand personality. The impact is mediated by consumers’ perceived AR app experience and attitudes toward the AR app and moderated by consumer characteristics such as IT innovativeness and shopping orientation ( Plotkina et al, 2021 ).…”

“…With the fast-growing the application of AR in marketing, consumers using these applications, and products/services offered through these application, future research can delve deeper into the heterogenous effects of AR on consumers’ responses and behavior across consumer characteristics such as gender ( tom Dieck et al, 2018a ; Smink et al, 2019 ; Batat, 2021 ; Chen et al, 2021 ; Daassi and Debbabi, 2021 ; Javornik et al, 2021 ; Yuan et al, 2021 ), age groups (e.g., children, middle-aged people, and elder) ( Jung et al, 2015 ; Pantano et al, 2017 ; Plotkina and Saurel, 2019 ; Smink et al, 2019 ; Batat, 2021 ; Chiu et al, 2021 ; Daassi and Debbabi, 2021 ; Kowalczuk et al, 2021 ; Plotkina et al, 2021 ; Qin et al, 2021b ; Yuan et al, 2021 ), educational level ( He et al, 2018 ; Yuan et al, 2021 ), occupation ( Song et al, 2019 ), culture background ( Jung et al, 2015 , 2021 ; Rese et al, 2017 ; Rauschnabel, 2018 ; Jiang et al, 2019 ; Plotkina and Saurel, 2019 ; Jessen et al, 2020 ; Chen et al, 2021 ; Chiu et al, 2021 ; Javornik et al, 2021 ; Lim et al, 2021 ; Plotkina et al, 2021 ; Qin et al, 2021a ; Yuan et al, 2021 ), personality traits ( Tussyadiah et al, 2018 ; Park and Stangl, 2020 ; Uribe et al, 2021 ), cognitive style ( Fan et al, 2020 ), processing style ( Heller et al, 2019b ), innovativeness ( Huang and Liao, 2015 ; Huang, 2019 ; Smink et al, 2019 ; Yim and Park, 2019 ; Daassi and Debbabi, 2021 ), expertise regarding the products/services (e.g., novice vs. experienced) ( He et al, 2018 ; Jung et al, 2021 ), need for touch (e.g., high vs. low) ( Hilken et al, 2017 ; ...…”

Deep-Learning-Based Adaptive Advertising with Augmented Reality

“…A brief comparison of our work with some published works and industry applications [ 36 ] is shown in Table 1 . Academic papers focus on facial and gender recognition using various algorithms but do not incorporate other aspects such as Big Five personality analysis, generation of a personalized recommendation, and AR.…”