Master Thesis
Gamification and Augmented Reality
The efficacy of gamification in
the diffusion of augmented reality technology
MSc Business Administration – Marketing track
Student:
LokCheng Tong
Student number:
11412437
Supervisor:
Andrea Weihrauch
Date:
24-01-2018
Statement of Originality
This document is written by LokCheng Tong, who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its
references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
Augmented reality technology has the power to revolutionize the way individuals communicate and to increase productivity. Also, gamification appears to have the potential to accelerate technology acceptance and diffusion. However, augmented reality still in its nascent stage of technology diffusion and little is known about the impact of gamification an advertising strategy affecting consumer behaviour. Hence, further research is needed to understand consumers’ willingness to adopt augmented reality technology. Therefore, this study examined the potential of gamification as a possible strategy to influence consumers’ willingness to adopt augmented reality smart glasses.
The research took a quantitative approach and employed an online experiment including a survey to investigate the causal relationship between gamification and the intention to adopt augmented reality technology. Based on the findings, there were no significant effects indicating the efficacy of gamified advertisements of augmented reality smart glasses and that this led to a higher willingness to purchase the new product innovation. Moreover, there was no evidence of the role of consumers’ perceived value supposedly mediating the relationship between gamified advertisements and consumers’ intention to adopt augmented reality technology. Notably, consumer innovativeness appeared to have a significant effect on consumers’ behaviour towards technology acceptance.
Although this study did not show results in favour of the efficacy of gamification on consumer behaviour, the potential of gamification remains debatable due to the research limitations of this study. Existing studies show that gamification is increasingly popular as it delivers promising outcomes, particularly in marketing initiatives. Practitioners are advised to consider specific themes that could be implemented in gamification advertisements to trigger a feeling of nostalgia in their target audience, specifically consumers who are the first to adopt new technology. Also, utilitarian and hedonic aspects of consumers’ experience are equally as important when promoting new product innovations. As for academic implications, this study sheds light on the Technology Acceptance Model that needs to be refined in the context of augmented reality technology. In addition, this study contributes to marketing literature, specifically advertising techniques to accelerate the diffusion of innovation. There is a pressing need for further research in the area of gamification and its efficacy in the marketing field.
Table of Contents
Abstract ... 1
1 Introduction ... 3
1.1 Problem Background ... 31.2 Academic and Managerial Relevance ... 4
1.3 Thesis Overview ... 5
2 Literature review ... 6
2.1 Augmented Reality ... 62.2 Consumer Behaviour ... 7
2.3 Gamification ... 9
3 Research Question ... 11
3.1 Hypotheses ... 113.2 Conceptual Model ... 12
4 Data and Method ... 13
4.1 Setting and Data Collection ... 13
4.2 Analytical Approach ... 16
4.3 Research Ethics ... 17
5 Results ... 17
5.1 Descriptive Statistics ... 17
5.2 Reliability Analysis for Scales ... 18
5.3 ANOVA ... 19
5.4 Regression Analysis ... 20
6 Discussion and Conclusion ... 22
6.1 Managerial Implications ... 23
6.2 Academic Implications ... 24
References ... 26
Appendices ... 30
Appendix I
Questionnaire ... 30
Appendix II
Research Ethics ... 33
Appendix III
Regression Standardized Residual ... 33
1 Introduction
1.1 Problem Background
Marketing theorists emphasize on ‘creating value for customers in order to capture customer value in return’ (Kotler & Armstrong, 2012), however, it does not always appear to be robust advice (Von Hippel, 1988). In instances of technological change, listening to customers might even mislead organizations, driving their business further away from achieving a leadership position (Henderson, 1993). Disruptive innovations appear to have a paralyzing effect on industry leaders resulting in firms unable to respond to emerging market opportunities (Christensen & Raynor, 2013). Firms underestimate the potential value for new markets and tend to reject innovative technologies rather than investing in them (Reinhardt & Gurtner, 2015). Even when firms invest in new technologies, 50-90% of new product innovations are prone to failure (Talke & Heidenreich, 2014) due to the double-edged mental bias illustrating the mismatch between presumptions of practitioners and consumers (Gourville, 2006). Consumers appear to underestimate the value of new product innovations due to their sceptical view about the potential of these new product innovations causing consumer resistance. Practitioners, on the other hand, overestimate the value of new product innovations due to their firm belief in the potential of these innovations. Furthermore, firms often delay the introduction of new technologies that have the potential to disrupt industries due to the fear of cannibalizing revenue streams from existing business units (Christensen, 2011). However, this fear of cannibalization can become a self-fulfilling prophecy when firms decide to wait until these innovations have reached its maturity stage. By the time these firms catch the wave, new entrants are already disrupting the market (Kim & Mauborgne, 2004). An example of such an innovation is Augmented Reality (AR), which is an increasingly popular technology (Martínez et al, 2014) that has the ability to project virtual elements to the perceived environment in real time (Rauschnabel et al, 2015, 2016; Javornik, 2016). In other words, AR technology has the ability to add information to the real world. Some AR projects failed, while others succeeded. For instance, Google Glass failed (Due, 2015; Rauschnabel et al, 2015, 2016), whereas a gamified version of an AR product such as Pokémon GO was successfully launched (Azuma, 2016).
Gamification has become an increasingly popular tool (Goh & Ping, 2014; Seaborn & Fels, 2015) that is incorporated in marketing strategies increases consumer engagement (Terlutter & Capella, 2013; Lucassen & Jansen, 2014; Harwood & Garry, 2015; Baptista & Oliveira, 2017) and facilitates the adoption of new product innovations (Rodrigues et al, 2016). More specifically, gamification has been implemented as a unique advertising strategy (Terlutter & Capella, 2013) as it appears to be an effective promotional instrument encouraging new product trial behaviour (Kinard & Hartman, 2013). Framing AR smart glasses in a game context holds a unique psychological power to shape consumers’ behaviours into adopting AR technology (Sigala, 2015; Yang et al, 2017). Gamified marketing applications have the potential to positively influence intentional behaviour (McCarthy et al, 2014; Robson et al, 2016). It enables a hedonic approach that positively affects product evaluation when consumers are considering the product (Rese et al, 2016; Vera et al, 2016). Thus, gamification provides a fun aspect of using new product innovations and has the potential to play a critical role in technology acceptance as it could positively influence consumers’ beliefs
(Van Der Heijden, 2004; Kim & Shin, 2015) and reduce the perceived risks and consumer resistance, which leads to purchasing behaviour and the diffusion of AR technology (Cui et al, 2009; Kim et al, 2016).
So far, little is known about how to successfully launch new product innovations of AR involving consumer acceptance and the adoption of AR technology (Dunleavy, 2014; Billinghurst et al, 2015; Kim & Shin, 2015; Rauschnabel et al, 2015, 2016; Javornik, 2016). Also, there is little empirical evidence about the effects of gamification (Terlutter & Capella, 2013; Lucassen & Jansen, 2014; Bittner & Schipper, 2014; Goh & Ping, 2014; Seaborn & Fels, 2015). Considering the successful launch of Pokémon GO, it is an interesting topic for further research. Therefore, the main research question of this study is as follows:
What is the effect of using gamification (versus non-gamification) advertisements on consumer behaviour when introducing new product innovations involving augmented reality technology, particularly consumers’ willingness to adopt augmented reality smart glasses?
1.2 Academic and Managerial Relevance
Previous studies have shown that the Technology Acceptance Model (TAM) and the Diffusion of Innovation Theory (DIT) consumer behaviour in terms of consumers’ willingness to adopt technology (Kim & Shin, 2015; Kim et al, 2016; Rodrigues et al, 2016). However, it is still unclear how potential users perceive augmented reality technology (AR) and what the underlying mechanisms are, which might influence the adoption of smart glasses (Billinghurst et al, 2015; Azuma, 2016; Rauschnabel & Ro, 2016). Therefore, as it is still in its nascent stage of technology diffusion, further research is needed to understand consumers’ willingness to adopt AR technology (Billinghurst et al, 2015) and to determine how firms can provide compelling AR products and services (Azuma, 2016). This study focuses on consumers’ intention to adopt AR smart glasses in order to determine a clear path for practitioners in the creation of an effective marketing strategy for the commercialization of new product innovations. Moreover, it examines consumer behaviour in terms of individuals’ perceived value and to what extent this could influence their intention to adopt AR smart glasses. Ultimately, the findings of this study give some direction to what circumstances practitioners and scholars should focus on in terms of marketing initiatives as well as consumer behaviour when commercializing new product innovations and technology that still is at a nascent stage.
Furthermore, in order to minimize consumer resistance (Ram, 1989; Kleijnen et al, 2009), it is important to determine the process of technology acceptance since there is little empirical evidence that identifies the drivers of AR adoption (Van Der Heijden, 2004; Kim & Shin, 2015; Javornik, 2016; Rauschnabel & Ro, 2016) and under what conditions consumers’ beliefs lead to their intention to adopt AR technology (Kim et al, 2016). Gamification is considered a promising technique that could accelerate the diffusion of new technology (Terlutter & Capella, 2013; Dunleavy, 2014; Robson et al, 2014; Lieberoth, 2015; Seaborn & Fels, 2015; Sigala, 2015; Lunney et al, 2016; Vera et al, 2016; Muller-Stewens et al, 2017; Yang et al, 2017). However, there is a need for empirical evidence to understand its potential (Terlutter & Capella, 2013; Lucassen & Jansen, 2014; Bittner & Schipper, 2014; Goh & Ping, 2014; Seaborn & Fels, 2015). This study investigates the efficacy of gamification, a field of marketing literature and business practice that has yet to be fully explored.
All in all, existing literature has not yet explored the role of strategies influencing the technology adoption process, specifically, consumers’ product beliefs and purchase behaviour (Cui et al, 2009). Therefore, this study examines the potential of gamification as a possible strategy to influence consumers’ willingness to adopt new product innovations around AR technology, particularly AR smart glasses. It opens a door encouraging scholars and practitioners to investigate the impact of gamification, which is a fairly new field. The findings of this study investigate the efficacy of gamification and contribute to marketing literature, particularly advertising literature. Gamification is a persuasive advertising strategy that has the potential to accelerate AR adoption. Also, the results of this study add value to existing literature based on technology acceptance and the diffusion of new product innovations in the context of AR. In addition, practitioners benefit from this study as these provide insights on how to successfully launch new product innovations through the use of gamification in their marketing communication strategies. The findings of this study shed light on specific aspects when implementing gamified advertisements in order to influence consumer behaviour in favour of the use of AR smart glasses and enhance the diffusion of AR technology.
1.3 Thesis Overview
The aim of this study is to examine the effectiveness of gamification in persuading consumers to adopt AR products towards the diffusion of the technology. First of all, an integrative overview of existing literature regarding AR, technology adoption, and gamification is presented in chapter two. Secondly, the research question and hypotheses are explained in chapter three, followed by the research design explained in chapter four. Finally, the results are shown in chapter five, followed by the discussion and conclusion in chapter six.
2 Literature review
2.1 Augmented Reality
‘Augment’ derives from the Latin word augere, which means ‘to increase’. Augmented Reality (AR) technology is a non-immersive type of virtual reality technology that bridges the real physical world with a virtual world (Burdea & Coiffet, 2003). AR is evolving as a workplace tool to complement and enhance business processes, which enables decision-making by providing additional layers of information on top of what a user experiences with the naked eye (Nguyen & Blau, 2014; Barfield, 2015). In other words, it can be used as a means to gain access to additional information on top of what is visible with bare eyes and to improve cognitive information processing. Also, AR has the potential to enhance consumption experience (Dunleavy, 2014; Huang & Liu, 2014; Huang & Liao, 2015) by augmenting the real world with a built-in camera projecting virtual elements through a screen in real time (Rauschnabel et al, 2015, 2016; Javornik, 2016). AR technology, particularly AR smart glasses, has been increasingly popular in various domains, particularly in the military, healthcare, education, tourism, and marketing applications (Martínez et al, 2014).
2.1.1 AR Smart Glasses
AR smart glasses (e.g. Google Glass and Microsoft HoloLens) have the potential to substantially influence media usage (Rauschnabel & Ro, 2016). AR smart glasses are wearable devices in the form of spectacles with the ability to merge the physical environment with virtual information (Rauschnabel et al, 2015). Sceptics raised concerns given its ramifications (Rauschnabel et al, 2015, 2016). For instance, Google Glass users were frowned upon as they could secretly record a movie in the cinema. Another example is that consumers were afraid that these so-called ‘Glassholes’ could record individuals’ every movement without noticing nor their consent (Due, 2015). Others see potential in Google Glass and have a positive attitude toward this technology as they consider it to be the ‘next big thing’ (Rauschnabel et al, 2015, 2016) such that smart glasses enhance consumer learning, interaction, and collaboration (FitzGerald et al, 2013; Bacca et al, 2014; Bower et al, 2014; Billinghurst et al, 2015). Although Google Glass received a tremendous amount of media attention, its success was rather limited. Microsoft HoloLens, however, is expected to have a more promising future (Kalantari & Rauschnabel, 2017). Interestingly, Microsoft HoloLens is a similar device as compared to Google Glass, but it is even more obtrusive in that the device is bulkier. It displays additional information as an overlay of environments viewed in real time transforming the way people communicate, enabling them to work with digital content in relation to the real world (Microsoft, 2017). Microsoft advertises this by emphasizing on both hedonic benefits (e.g. the ability to play games such as Minecraft) and utilitarian benefits (e.g. conference calls while showing a holographic presentation).
Product evaluations differ per AR product (Rese et al, 2016). It is unclear how potential users perceive this technology and what underlying mechanisms drive consumer behaviour enabling the adoption of smart glasses (Billinghurst et al, 2015; Azuma, 2016; Rauschnabel & Ro, 2016). As AR smart glasses are still in its nascent stage, it is unclear what consumers’ willingness to adopt AR technology (Billinghurst et al, 2015). Hence, empirical evidence is needed to understand consumer behaviour and to identify what compels consumers to adopt AR technology, particularly AR smart glasses (Azuma, 2016).
2.2 Consumer Behaviour
Consumer behaviour refers to the reciprocal relationships among consumer affect, cognition, behaviour, and the environment that influence decision-making in consumption processes (Peter & Olson, 2010). Consumers tend to irrationally reject new product innovations without considering its potential (Talke & Heidenreich, 2014). So far, researchers have been studying consumer behaviour in terms of their willingness to adopt new technologies. This study takes a closer look at existing literature regarding consumer resistance, technology acceptance, and gamification.
2.2.1 Consumer Resistance towards New Product Innovations
Often, consumers reject new product innovations prior to product evaluation leading to failure rates of new products between 50 and 90% (Talke & Heidenreich, 2014). Without considering the AR’s potential, consumers tend to have no interest in adopting this new technology. According to Gourville (2006), the primary reasons for consumers’ mental bias are as follows: (1) a sceptical view on new product innovation’s performance, (2) inability to see the need for it, (3) a status quo bias describing the feeling of loss when consumers have to give up their possession even if the alternative product is better, and (4) an endowment effect illustrating consumers valuing existing products more than those they do not possess.
Marketing communication strategies can minimize consumer resistance caused by this mental bias (Ram, 1989; Kleijnen et al, 2009). More specifically, advertisement strategies aligning new product innovations with familiar practices are highly effective in getting consumers accustomed to radical changes in using these new products (Talke & Heidenreich, 2014; Heidenreich & Kraemer, 2015). However, before looking into strategies to minimize consumer resistance, it is important to determine the process of technology acceptance as there is little empirical evidence to determine AR acceptance (Yang et al, 2016).
2.2.2 Technology Acceptance Model
Consumers’ beliefs, attitude, and behaviour can be influenced by environmental factors (e.g. advertisements, product appearance, pricing) under the condition that consumption processes are well understood (Peter & Olson, 2010). Therefore, in order to increase the probability of consumers’ willingness to adopt new product innovations, one must understand how these environmental stimuli are being processed in the mind of the consumer. The Theory of Reasoned Action (TRA) is an effective predictor of behaviour and indicates that behavioural intentions are determined by attitudes and beliefs (Armitage & Christian, 2003).
The Technology Acceptance Model (TAM) further extends TRA and explains consumers’ willingness to adopt technology (Kim et al, 2016; Rodrigues et al, 2016). It is considered to be a robust model to examine AR adoption (Kim & Shin, 2015; Rese et al, 2016) as it explains that beliefs and behavioural intention are mediated by attitude (Van Der Heijden, 2004; Balog & Pribeanu, 2010; Kim & Shin, 2015; Lunney et al, 2016). However, existing studies on TAM appear to have contradicting results (Kim et al, 2009; Teo, 2009; Marangunic & Granic, 2015; Kim et al, 2016). For instance, according to Teo (2009), attitude does not appear to have a mediating role when predicting technology acceptance behaviour and concludes that this variable should not be concluded in the TAM framework.
Kim et al (2008) further explain that attitude in TAM does not play a significant role in the beliefs-intention linkage. Instead, consumers’ beliefs, particularly their perceived value of a product or service, play a crucial role in their decision to adopt AR technology (Yang et al, 2016). Some researchers argue that perceived value is identified by their perceived ease of use and perceived usefulness (Rodrigues et al, 2016). Others argue that perceived enjoyment should be included as it plays a pivotal role in the TAM framework (Van Der Heijden, 2004; Balog & Pribeanu, 2010; Kim & Shin, 2015). More recent studies stipulate that perceived value is determined by perceived ease of use, perceived enjoyment, and perceived usefulness (Lunney et al, 2016; Rese et al, 2016; Rodrigues et al, 2016; Yang et al, 2016). However, there is limited empirical evidence that identifies the drivers of AR adoption (Kim & Shin, 2015; Javornik, 2016; Rauschnabel & Ro, 2016), particularly under what conditions consumers’ beliefs influence their intention to adopt AR products and services leading to the diffusion of those new product innovations (Maragunic & Granic, 2015; Kim et al, 2016).
2.2.3 Diffusion of Innovation Theory
The Diffusion of Innovation (DOI) theory explains that consumers’ beliefs are driven by specific attributes of an innovation that ultimately lead to the adoption of that innovation (Rogers, 1995; Agarwal, 2000). It is considered to be one of the fundamental frameworks for studying the adoption and diffusion of new technologies (Kim & Shin, 2015). The DOI describes five characteristics that determine technology acceptance: (1) relative advantage (refers to the extent to which the benefits of AR outweigh its substitutes), (2) complexity (refers to AR’s ease of use), (3) trialability (refers to the opportunity of test the technology), (4) compatibility (refers to AR’s consistency with social practices and norms), and (5) observability (refers to the extent to which consumers are aware of the benefits that AR could bring) (Rogers, 1995; Martínez et al, 2014). According to Martínez et al (2014), the first three characteristics (relative advantage, complexity, and trialability) were already fulfilled by the current state of AR technology. However, the authors also note that some AR devices are not socially accepted yet as consumers do not see the benefits of AR technology.
Consumers often reject new product innovations even though these offerings are objectively superior compared to existing products as they overvalue existing products and experience discomfort in changing their behaviour to adopt new technology (Gourville, 2006). Thus, AR developers should focus on the compatibility and observability of AR in order to enhance the adoption of this technology (Martínez et al, 2014). The diffusion of AR technology will accelerate once consumers can perceive the added value of AR in order to accept and adopt this technology (Billinghurst et al, 2015). Furthermore, for AR to become ubiquitous in consumer usage, practitioners must develop a compelling AR experience (Azuma, 2016). However, even though consumers may perceive the benefits of adopting a new technology, it does not necessarily mean that they will develop preferences (Ram, 1989; Kim et al, 2016). Therefore, marketing communication strategies must be created in order to tweak consumers’ resistance to innovation (Keijnen et al, 2009) by changing consumers’ less desirable affect, cognition, and behaviour into a more favourable one (Nord & Peter, 1980; Ram, 1989; Kleijnen et al, 2009).
Studies show that utilitarian and hedonic aspects should be balanced when creating a comprehensive approach to encourage the adoption of AR technology (Voss et al, 2003; Van Der Heijden, 2004; Kim & Shin, 2015; Kim, 2016; Rodrigues et al, 2016; Yang et al, 2016; Baptista & Oliveira, 2017). Gamification is considered as an instrument with the power to diffuse new technology (Dunleavy, 2014; Lieberoth, 2015; Seaborn & Fels, 2015; Lunney et al, 2016; Vera et al, 2016; Muller-Stewens et al, 2017; Yang et al, 2017) through the presentation of the utilitarian aspects of AR adoption in an advertisement as well as hedonic aspects in a game context (Van Der Heijden, 2004; Balog & Pribeanu, 2010; Cheng & Tsai, 2013; Rauschnabel et al, 2015, 2016; Kim, 2016; Rese et al, 2016; Baptista & Oliveira, 2017).
2.3 Gamification
Gamification refers to “the use of game design elements in non-game contexts” (Deterding et al, 2011). It can influence consumer behaviour by incorporating game elements in the presentation of non-game products and services (Conejo, 2014; Morford et al, 2014) inducing voluntary change, which can lead to technology acceptance (McCarthy et al, 2014; Rodrigues et al, 2016; Baptista & Oliveira, 2017). However, more research is required to determine how consumers’ beliefs are influenced by gamification, in particular, how gamified advertisements impact consumers’ behaviour (Terlutter & Capella, 2013; Yang et al, 2017).
2.3.1 Gamification in a Marketing Context
Gamification has become an increasingly popular tool (Goh & Ping, 2014; Seaborn & Fels, 2015) incorporated in marketing strategies (Terlutter & Capella, 2013; Lucassen & Jansen, 2014; Harwood & Garry, 2015; Baptista & Oliveira, 2017). Lucassen & Jansen (2014) argue that marketing executives believe that the impact of gamification is promising and expect that adoption rate of gamification in marketing initiatives will accelerate in the near future. Rodrigues et al (2016) further explain that gamification could facilitate the adoption of new product innovations. More specifically, gamification has been implemented as a unique advertising strategy (Terlutter & Capella, 2013) as it appears to be an effective promotional instrument encouraging new product trial behaviour (Kinard & Hartman, 2013). The integration of gamification in contemporary marketing strategies has the potential to motivate consumers to use augmented reality technology as a novel way to improve customer journeys (Vera et al, 2016) and boosting the diffusion of new product innovations (Muller-Stewens et al, 2017). It can lead to a number of outcomes in the consumer behaviour realm such as attracting potential customers (Muller-Stewens et al, 2017), enhanced customer engagement and loyalty (Rodrigues et al, 2016), as well as increasing purchase intentions (McCarthy et al, 2014; Robson et al, 2016). In particular, framing AR smart glasses in a game context holds a unique psychological power to shape consumers’ behaviours into adopting AR technology (Sigala, 2015; Yang et al, 2017). Despite the practical importance of gamification, current literature on its efficacy lacks academic rigour (Yang et al, 2017). Moreover, Poncin et al (2017) argue that gamification is not always as effective as the aforementioned studies suggest. Existing studies lack empirical support explaining gamification in a marketing context (Yang et al, 2017), particularly consumers’ acceptance towards new technology as a result of gamified marketing applications (Sigala, 2015).
2.3.2 Technology Acceptance through Gamification
Although it is important to put an emphasis on the utilitarian value of AR smart glasses are (Kalantari & Rauschnabel, 2017), the hedonic value of this new product innovation also plays a pivotal role in consumers’ acceptance of AR technology (Van Der Heijden, 2004). Gamified marketing applications have the potential to positively influence intentional behaviour (McCarthy et al, 2014; Robson et al, 2016). It enables a hedonic approach that positively affects product evaluation (Rese et al, 2016; Vera et al, 2016). Thus, gamification provides a fun aspect of using new product innovations and plays a critical role in technology acceptance as it influences consumers’ beliefs (Van Der Heijden, 2004; Kim & Shin, 2015) leading to purchasing behaviour (Cui et al, 2009; Kim et al, 2016).
The gamified presentation of a product innovation increases consumers’ inclination to accept new technology in two ways: (1) it stimulates consumers’ playfulness increasing their curiosity, and (2) consumers perceive gamified presentations of new product innovations as more vivid illuminating the advantages that AR technology brings (Muller-Stewens et al, 2017). This way a balance is found between utilitarian and hedonic benefits of AR smart glasses with the aim to minimize consumer resistance towards AR technology (Kleijnen et al, 2009; Talke & Heidenreich, 2014; Heidenreich & Kraemer, 2015). In other words, gamification is considered to have a persuasive effect on advertisements as it enhances perceived enjoyment and reduces their risk perception, thereby, positively affecting technology acceptance (Bittner & Schipper, 2014). More specifically, it appears to be highly effective for conveying information about utilitarian products to consumers, consequently attracting consumers, promoting the adoption of new product innovations (Muller-Stewens et al, 2017), and reducing risk averse behaviour (Kleijnen et al, 2009; Talke & Heidenreich, 2014; Heidenreich & Kraemer, 2015). For instance, Pokémon GO is an AR application in the form of a game used through smartphones that opened consumers’ eyes to the compelling potential of AR. This stimulated consumers to use Pokémon GO, evidently accelerating the acceptance of AR technology (Azuma, 2016). Furthermore, Sigala (2015) suggests that the TAM framework is a valid theory that could be reinforced with the integration of gamification when it comes to product innovations in non-game contexts.
All in all, gamification techniques show a positive impact on consumer experiences with smart technologies (Poncin et al, 2017). The integration of gamification with AR-related products and services encourages consumers to accept and adopt AR technology (Vera et al, 2016). Particularly, gamified advertisements of AR innovations can decrease risk perceptions and boost the acceptance of AR products and services, which may lead to the diffusion of AR technology (Bittner & Schipper, 2014). However, there is a need for more empirical evidence to understand the impact of gamification (Terlutter & Capella, 2013; Lucassen & Jansen, 2014; Bittner & Schipper, 2014; Goh & Ping, 2014; Seaborn & Fels, 2015). Existing literature has not yet explored the role of gamification influencing the technology adoption process, specifically, consumers’ product beliefs and purchase behaviour (Cui et al, 2009). Therefore, this study examines the potential of gamification as a possible strategy to influence consumers’ willingness to adopt new product innovations involving AR technology, specifically AR smart glasses.
3 Research Question
As mentioned before, the TAM framework contributes to explaining consumers’ willingness to adopt new product innovations (Kim & Shin, 2015; Kim et al, 2016; Rese et al, 2016; Rodrigues et al, 2016) by describing the process of consumers’ decision making in purchasing new product innovations. Furthermore, gamification is one of the most prominent methods of communicating new product innovations (Goh & Ping, 2014; Seaborn & Fels, 2015) with the persuasive power to influence consumers’ decision-making processes in terms of technology acceptance (Rodrigues et al, 2016) by increasing the perceived value (Bittner & Schipper, 2014) and minimizing consumer resistance (Kleijnen et al, 2009). The failure of Google Glass and the success of Pokémon GO (Godwin-Jones, 2016) make an interesting case to conduct further research in consumers’ technology acceptance toward AR. Google Glass presents the added value of AR technology in a non-game context, whereas Pokémon GO presents AR technology in a game-context. With this observation, scholars argue that there is a pressing need for more experiments on the effects of gamification in non-gaming environments (Terlutter & Capella, 2013; Lucassen & Jansen, 2014; Bittner & Schipper, 2014; Goh & Ping, 2014; Seaborn & Fels, 2015). Furthermore, this study was narrowed down to AR smart glasses since the inclusion of AR smart applications would be too broad. Therefore, this study examines the TAM framework in a gamified context. Hence, the main research question of this study is as follows:
What is the potential of using gamification (versus non-gamification) advertisements on consumer behaviour when introducing new product innovations involving augmented reality technology, particularly consumers’ willingness to adopt augmented reality smart glasses?
3.1 Hypotheses
According to Seaborn & Fels (2015), gamification encourages consumer motivation, engagement, and enjoyment. Gamification is a highly effective tool to convey information about new product innovations, particularly utilitarian products (Muller-Stewens et al, 2017). Moreover, gamification induces voluntary change and drives behaviour, which leads to technology acceptance (Baptista & Oliveira, 2017). The fun aspect of gamified marketing applications may lead to technology acceptance (Van Der Heijden, 2004; Kim & Shin, 2015) and adoption (Cui et al, 2009; Kim et al, 2016). Gamified advertisements are increasingly popular when it comes to minimizing consumer resistance (Kleijnen et al, 2009) since it has the pervasive power to increase enjoyment and reduce risk perception (Talke & Heidenreich, 2014; Heidenreich & Kraemer, 2015). Consequently, it increases the probability of positive beliefs towards the new product innovation (Bittner & Schipper, 2014). Thus, gamification is considered as vehicles of communication for new innovations increasing the likelihood of purchase behaviour (Baptista & Oliveira, 2017; Müller-Stewens et al, 2017). Framing AR smart glasses in gamified marketing applications facilitates the acceptance and adoption of AR technology (McCarthy, 2014; Sigala, 2015; Robson et al, 2016; Yang et al, 2017). However, there is little empirical evidence regarding the efficacy of gamification (Terlutter & Capella, 2013; Lucassen & Jansen, 2014; Bittner & Schipper, 2014; Goh & Ping, 2014). Hence, the following hypothesis:
Table 1 – Constructs Defined
H1: Gamified (versus non-gamified) advertisements of AR smart glasses leads to higher consumers’ perceived value of AR smart glasses.
Kalantari & Rauschnabel (2017) argue that more empirical evidence is needed to determine what influences the intention to adopt AR smart glasses, particularly Google Glass and Microsoft HoloLens. Consumers’ beliefs play a pivotal role in the TAM framework (Teo, 2009; Yang et al, 2016), particularly new product innovations they are not familiar with (Kim et al, 2009). However, others question the validity of this approach to the TAM framework, specifically the direct link between consumers’ beliefs and their intention to adopt new technology (Ursavas, 2013; Kaushal & Kumar, 2016). Nevertheless, Teo (2009) argues that attitude does not appear to play a significant role in the beliefs-intention linkage of the TAM framework, especially when the technology is at a nascent stage (Kim et al, 2008). Instead, consumers’ beliefs constitute the perceived value of the new product innovation and have a determining role in the intention to adopt it (Billinghurst et al, 2015; Yang et al, 2016). Thus, consumers’ perceived value of AR smart glasses directly leads to their intention to adopt AR smart glasses. Therefore, the next hypothesis is as follows:
H2: Consumers’ perceived value of AR smart glasses mediates the relationship between gamified (versus non-gamified) advertisements of AR smart glasses and consumers’ intention to adopt AR smart glasses.
3.2 Conceptual Model
All hypotheses are depicted in the conceptual model illustrated in figure 1. The conceptual model includes an independent variable (gamified advertisement), and a dependent variable (behavioural intention) that describe technology acceptance by consumers, particularly AR smart glasses. Also, this causal relationship includes a mediator (consumers’ perceived value). Furthermore, it incorporates the TAM framework in terms of the relationship between consumers’ beliefs and their intention to adopt AR technology. The definition per construct used within the conceptual model is described in table 1.
Construct Conceptual Definition Source
AR smart glasses
Wearable devices in the form of regular glasses that have the ability to merge physical information with virtual information in an environment. Examples: Google Glass, Microsoft HoloLens.
Rauschnabel et al (2015), Kalantari & Rauschnabel (2017) Consumers’
perceived value
Consumers’ evaluation of AR technology, which is divided into three dimensions: perceived enjoyment, perceived usefulness, and perceived ease of use.
Lunney et al (2016), Rese et al (2016), Rodrigues et al (2016), Yang et al (2016)
Behavioural
intention Consumers’ willingness to purchase and/or recommend the technology, in this case, AR smart glasses. Rese et al (2016), Kim et al (2016) Gamified
advertisement
Advertisement with game elements as a marketing strategy, promoting new product trial behaviour, and facilitating technology acceptance.
Deterding et al (2011), Terlutter & Capella (2013), Kinard & Hartman (2013), Rodrigues et al (2016)
4 Data and Method
4.1 Setting and Data Collection
This thesis is an explanatory study that examined why an AR products failed (i.e. Google Glass), while other AR products succeeded (i.e. Pokémon GO). It focused on existing literature and further built on it with a deductive approach using both primary and secondary data to answer the research question. A combined approach to data collection makes this study more reliable (Saunders et al, 2016). First of all, secondary data was collected to explore existing findings concerning technology acceptance, gamification, and augmented reality. Based on this, a literature review (chapter 2) and conceptual model (chapter 3) was created for this research. Secondly, in order to test the hypotheses and to answer the research question, primary data had to be collected. In order to examine the causal relationship between the independent variable (gamified advertisement) and the dependent variable (consumers’ intention to adopt AR technology), this study incorporated quantitative research design (Kothari, 2004; Saunders et al, 2016). More specifically, this research employed an online experiment with a questionnaire in order to study the causal link between the aforementioned variables (Saunders et al, 2016). This section discusses the experimental setup, survey design, operationalization of the concepts, and the sample.
4.1.1 Experimental Setup
Due to budgetary and time constraints, this study implemented an online experiment with a simple, between-subjects approach design. Participants were randomly assigned to either an experimental group (first condition: gamified advertisement) or control group (second condition: non-gamified advertisement) (Kothari, 2004). They were given the link to the website where the experiment and survey could be found. The participants were first given a short description of augmented reality technology and smart glasses, followed by two images of what it is like to use AR smart glasses in the eyes of a user. In addition, a timer of 10 seconds was set before the button to the next page was visible to ensure that participants took their time to observe those images. They were then asked to answer 25 questions (see Appendix I).
The artificial setting of the experiment risks the external validity of this research. Furthermore, a between-subjects design requires independent groups of participants for each condition, which means that such an experiments would require much more participants than in a within-subjects design in order to generate reliable data (Saunders et al, 2016). Nonetheless, experiments are considered valid methods for investigating causal relationships (Field, 2013). Furthermore, the advantage of the between-subjects approach is that the behaviour of the participant could not be contaminated when being exposed to the independent variable due to the fact that each participant was exposed to only one level of that independent variable (Martin, 2007). In addition, participants in a within-subjects approach could have lost interest quicker when participating in multiple sessions. They are more likely to complete a single experimental session that is incorporated in a between-subjects design, which offers more data to be collected in a shorter amount of time (Martin, 2007).
4.1.2 Survey Design
A survey strategy answers questions regarding ‘who (i.e. age and gender), what (i.e. adopt AR or not), how much (i.e. purchasing intention)’ (Saunders et al, 2016). In order to quantitatively analyse data from the experiment and measure the dependent variable, a standardized survey was designed for collecting data from the experimental group (gamified advertisement) as well as the control group (non-gamified advertisement). After looking at two advertisements of AR smart glasses, participants were asked to answer questions to determine their perceived value towards AR and their willingness to adopt this technology. All questions in the survey were extracted from existing studies (see Appendix I). The survey was administered through an online survey tool called Qualtrics. It is deemed an appropriate instrument due to its comprehensive online documentation, its ability to program experimental studies, and it enables users to configure surveys in accordance with their own preferences. When creating an online questionnaire, options such as survey flow and randomizers are important in order to effectively collect data (Qualtrics, 2017).
The content of the survey included 25 questions and was divided into three parts (see Appendix I). The first part consisted of 12 questions regarding their perceived value of the smart glasses. The second part consisted of an image of the Google Glass with the price of the product ($1500), including two questions consumers’ intention to adopt AR smart glasses. This image was extracted from the company’s website with the exact same price to avoid too much deviation from the real world. The final part consisted of six questions related to control variables to control possible effects on the model constructs. Another two questions were included with regards to consumers’ intention to adopt augmented reality technology in general (e.g. mobile applications such as Pokémon GO and safety helmets such as Daqri). These were included to see whether or not participants had a different sentiment towards other AR products and services than AR smart glasses. Furthermore, the participants were encountered with a manipulation check to test their diligence for a higher statistical power and reliability of the dataset (Oppenheimer et al, 2009). More specifically, respondents had to answer two multiple-choice questions about the advertisements. Those who failed the manipulation check were immediately removed from the data set. Moreover, there were no missing values since the data can only be recorded once all questions were answered.
4.1.3 Operationalization
The Independent Variable, gamified advertisement, has two levels, whereby participants were randomly allocated to one of the two treatment conditions:
1) Experimental group: gamified advertisement of AR smart glasses 2) Control group: non-gamified advertisement of AR smart glasses
The Dependent Variable, consumers’ intention to adopt AR technology refers to consumers’ willingness to purchase and recommend AR smart glasses. This variable was anchored on a 7-point Likert scale (from 1=‘strongly agree’ to 7=‘strongly disagree’) (Rese et al, 2016; Kim et al, 2016).
The Mediator Variable, consumers’ perceived value, is the causal link between the independent variable (gamified advertisement) and the dependent variable (consumers’ intention to adopt AR technology). Consumers’ perceived value was divided into three dimensions: perceived ease of use, perceived enjoyment, and perceived usefulness (Lunney et al, 2016; Rese et al, 2016; Rodrigues et al, 2016; Yang et al, 2016). Each dimension included four items and was measured on a 7-point Likert scale descriptors (from 1=‘strongly agree’ to 7=‘strongly disagree’) (Rese et al, 2016; Rodrigues et al, 2016; Yang et al, 2016). The Control Variables were age, gender, gaming experience, and consumer innovativeness. Consumers in a younger age group (i.e. between 5 and 30 years) who are exposed to gamified product advertisement are ultimately more likely to have higher purchase intentions (Bittner & Schipper, 2014; Baptista & Oliveira, 2017). Moreover, gender also appears to be a strong factor influencing the relationship between gamification and behavioural intention such that the impact of hedonic motivation on behavioural intention appears to be a stronger inhibitor for men (Baptista & Oliveira, 2017). However, other studies show the contrary (Venkatesh et al, 2012). Also, gaming experts process information differently as compared to non-gaming experts (Lee & Heeter, 2017). Consumers who have gaming experience prior to the presentation of the gamified product advertisement are ultimately more likely to have higher willingness to adopt gamified products (Godwin-Jones, 2016). The measurement scale for gaming experience was borrowed from Soylu & Bruning (2016), whereby participants were asked to rate their own gaming expertise on a scale from 0 (not expert at all) to 100 (highly expert). Finally, existing studies suggest that the consumer innovativeness is universally applicable (Truong, 2013). Highly innovative consumers, also called ‘early adopters’, are considered risk-takers (Truong, 2013) and are more inclined to adopt innovative, high technology products (Hirunyawipada & Paswan, 2006; Truong et al, 2017). Consumer innovativeness was measured through the use of a 7-point Likert scale (from 1=‘strongly agree’ to 7=‘strongly disagree’) borrowed from existing studies (Truong et al, 2017).
4.1.4 Sample
The sample was selected at random to avoid bias such that the sample could be representative of the target population (Saunders et al, 2016). Moreover, volunteer sampling techniques were used for this study, particularly snowball and self-selection sampling, whereby individuals were asked to take part in the research as volunteers. Also, Amazon’s Mechanical Turk was used to recruit respondents. The reason for this is that it was difficult to identify individuals of the desired population and due to the fact that this study had a low budget and was time constrained. In order to have a representative sample, this between-subjects study needed to reach a confidence level of 95%, meaning that the sample size had to include at least 50 respondents per condition (Saunders et al, 2016). Thus, this study had to consist of a minimum of 100 respondents.
4.2 Analytical Approach
Once the data was collected, it was analysed in four phases: (1) ANOVA, (2) reliability analysis for scales, and (3) regression analysis.
4.2.1 Descriptive and Frequencies Statistics
Before the data analysis can be done, descriptive and frequency statistics had to be prepared in order to have a snapshot of the data and to determine whether these groups were equally distributed (Field, 2013). In this case, it gave the description of respondents of both conditions (respondents exposed by gamified vs respondents non-gamified advertisements).
4.2.2 Reliability Analysis for Scales
First of all, a factor analysis was done to scan clusters of variables that might relate to each other and to determine whether the items loaded onto factors are reliable measures (Field, 2013). This was to reduce a large data set into a smaller, more concise, subset of measurement variables. According to Field, factor selection is based on factors with eigenvalues greater than 1. Also, a scree plot was included as an additional criterion for factor selection (Stevens, 2002). This graph depicts the inflection point, whereby factors to the left of that point represent the factors to be retained. Furthermore, the reliability analysis was conducted to measure the consistency of the items in the questionnaire (Field, 2013). According to Field (2013), this is indicated by Cronbach’s α, which shows whether removing an item will improve the overall reliability. Tavakol & Dennick (2011) explain that items with Cronbach’s α ranging from 0.70 to 0.90 are considered acceptable. Values lower than 0.70 indicate poor inter-relatedness between items or heterogeneous constructs and values higher than 0.90 may suggest that some items are redundant (Tavakol & Dennick, 2011).
4.2.3 Analysis of Variance (ANOVA)
An independent t-test was run to since the participants were randomly divided into two experimental groups. More specifically, the Levene’s Test for Equality of Variance was run to determine whether or not the groups are equally divided. Equal variances can be assumed if the p-value is higher than 0.05 (Field, 2013). This test was executed to analyse whether or not the data support the hypotheses and to plot the marginal means of the dependent variable (intention to adopt AR smart glasses) for the independent variable (gamified vs non-gamified ads) (Field, 2013).
4.2.4 Regression Analysis
A hierarchical regression analysis was conducted to test model significance and causal relationships among the variables (Field, 2013), which resulted in three models: model 1 consists of control variables, model 2 includes the independent variable, and model 3 involves the mediation. In order to determine to what extent the model could be explained, the overall fit of the regression models was tested. The model significance was indicated by 𝑅!, adjusted 𝑅!, F-ratio, and p-value. Three key assumptions of the regression analysis were tested: (1) normality, (2) homoscedasticity, and (3) multicollinearity. This is to determine whether or run the tests were accurate (Field, 2013).
Furthermore, Hayes’ mediation analysis was conducted to test the causal process that included a mediation component and to interpret its effects. The regression-based mediation analysis was done through PROCESS, which is a modelling tool for SPSS to provide statistical inferences and to determine mediation effects (Hayes, 2017). In this case, it helped determine the direct effect of the independent variable (gamified advertisements) on the dependent variable (behavioural intention) as well as the indirect effect of the independent variable (gamified advertisements) on the dependent variable (behavioural intention) through the mediator (perceived value).
4.3 Research Ethics
Research ethics are critical aspects and occur at all stages of the research project (Saunders et al, 2016). This involves the appropriateness of the researcher’s behaviour in relation to the rights of participants who become subject of their work. Therefore, each participant was given a consent form guaranteeing their anonymity and informing them that participation is completely voluntary and that they have the freedom to withdraw at any time. Furthermore, a debriefing was included in order to thank the participant and to inform them about the purpose of this study and a short description of the research procedure. Both the consent form and debriefing can be found in Appendix II.
5 Results
5.1 Descriptive Statistics
The following variables were used as covariates: age, gender, and intention to adopt AR smart glasses. The two groups described the intention to purchase AR smart glasses (table 2) including participants exposed to non-gamified advertisements in one group (n = 161) and others exposed to gamified advertisements in the second group (n = 143). Table 2 shows that the mean of the group exposed to gamified ads (M = 3.16) is higher than the mean of the group exposed to non-gamified ads (M = 2.96). These values were in line with the hypotheses and directionality could be argued in this case. The age of the respondents ranged between 18 and 68 years old with the mean age equal to 34 years (table 3). Furthermore, 54.6% of the respondents were male and 45.4% of the respondents were female. In total, 166 male and 138 female responses were recorded. Table 2 - Descriptive Statistics
Experimental Groups M SD N
Non-Gamified 2.96 1.52 161
Gamified 3.16 1.50 143
Total 3.05 1.51 304
5.2 Reliability Analysis for Scales
The factor analysis showed two factors with eigenvalues greater than 1 and the scree plot depicted that the inflection point occurs at the third data point. This means that two factors were to be retained. However, according to Field (2013), eigenvalues greater than 1 may be too strict. He argues that, with a sample size that exceeds 250, average communality is greater than or equal to 0.60. The results showed that the third factor had an eigenvalue of 0.62, which begs the question whether or not a dimension should be eliminated or merged with another dimension. This was further analysed in the reliability analysis for scales.
Moreover, the reliability analysis confirmed that there could be something going on with the aforementioned issue. More specifically, perceived ease of use indicated a homogenous construct (α = 0.88), whereas perceived usefulness (α = 0.92) and perceived enjoyment (α = 0.94) suggested that items in the dimensions might be redundant (table 3). This was also the case with the intention to adopt AR smart glasses (α = 0.91) and AR technology in general (α = 0.92). Consumer innovativeness showed an acceptable value (α = 0.87), which suggested that the items were interrelated. As Tavakol & Dennick (2011) argued, a Cronbach’s α greater than 0.90 may question the reliability of the scales. Perceived usefulness, perceived enjoyment, intention to adopt AR smart glasses, and intention to adopt AR technology all indicated a slightly higher value. These findings were taken into account in the next regression analysis, particularly, in testing the model significance in the regression analysis (section 5.4).
Table 3 - Means (M), Standard Deviations (SD), Pearson correlations for all Variables, Cronbach’s Alpha (x)
Variables M SD 1 2 3 4 5 6 7 8 9 1. Gender 1.45 .50 (-) 2. Age 33.67 10.77 .102 (-) 3. Gaming Experience 55.76 28.29 -.336** -.367** (-) 4. Consumer Innovativeness 2.90 1.28 .192** .246** -.550** (.87) 5. Perceived Value 1: ease of use 2.54 1.05 .108 .192** -.356** .597** (.88) 6. Perceived Value 2: usefulness 2.89 1.37 .151** .172** -.362** .617** .826** (.92) 7. Perceived Value 3: enjoyment 2.56 1.32 .000 -.146* -.145* .357** .452** .513** (.94) 9. Experimental Groups .47 .50 .054 -.036 .042 .082 -.014 .063 .022 (-) 8. Intention to adopt AR Smart Glasses 3.30 1.76 .127* .188** -.397** .632** .723** .783** .434** .055 (.91)
Note. N = 304. Gender was coded as 0 = male and 1 = female. Age was measured in years. Gaming Experience was measured on a scale from 1 = not expert at all to 100 = highly expert. Consumer Innovativeness, Perceived Value 1-3, Intention to adopt AR Smart Glasses and AR Technology were measured on a 7-point Likert scale with 1 = strongly agree to 7 = strongly disagree. * p < .05 ** p < .01 *** p < .001 (two-tailed)
5.3 ANOVA
The Levene’s test showed that the groups were equally divided (p-value > 0.05). However, there was a difference between the means of the two conditions (p-value > 0.05) and this was likely due to chance rather than the manipulation of the independent variable.
An independent t-test was then executed to compare the intention to adopt AR smart glasses in the two experimental groups (exposure to gamified ads vs non-gamified ads) at 95% confidence interval. There was no significant difference in the scores for gamified (M = 3.16, SD = 1.50) versus non-gamified (M = 2.96, SD = 1.52) conditions with t (302) = 0.628, p-value = 0.530. These results suggest that there was not enough statistical evidence to support that gamified advertisements have an effect on the intention to adopt AR smart glasses. Moreover, there was no significant difference in the scores for gaming experience in the gamified (M = 57.01, SD = 28,79) and non-gamified (M = 54.65, SD = 27.88) conditions with t (302) = -0.726, p-value = 0.468. This was also the case for consumer innovativeness in the gamified and non-gamified conditions with t (302) = -1.434, p-value = 0.153. These results indicate that there was not enough statistical evidence to support the effect of gaming experience, consumer innovativeness, and gamified advertisements on the intention to adopt AR smart glasses. In other words, regardless of their gaming experience and consumer innovativeness, there was no increase in the intention of adopting AR smart glasses when consumers were exposed to gamified advertisements.
Moreover, the low score of the coefficient of determination (𝑅! = 0.051) indicates that the variation in the dependent variable explained by the independent variable was rather small. Figure 2 plots the marginal means of the intention to adopt AR smart glasses for the gamified and non-gamified conditions. The graph depicts that the direction of the effect of gamified advertisement on the intention to adopt AR smart glasses shows the opposite of what was hypothesized. Specifically, respondents assigned to the gamified condition were less likely to adopt AR smart glasses than those assigned to the non-gamified condition. Thus, the data did not support the hypotheses.
5.4 Regression Analysis
As shown in table 4, model 3 indicates a model fit of 66%. Thus, 66% of that model explained covariance. Gender and age did not have a significant effect (p > 0.05). Interestingly, consumer innovativeness did appear to have a significant effect (p-value < 0.05). Gaming experience, on the other hand, did not have an effect (p-value > 0.05), which begs the question whether the experience of gamified advertisements was strong enough. Furthermore, perceived usefulness and enjoyment appeared to have a significant effect (p-value < 0.05), whilst perceived ease of use did not (p-value > 0.05). This indicates a possible consistency between the dimensions of perceived value (perceived usefulness and enjoyment) as discussed in the reliability analysis for scales. Nonetheless, there appeared to be no evidence of collinearity (all values showed Tolerance > 0.5 and VIF < 2).
Table 4 - OLS Regression Analysis with Intention to adopt AR Smart Glasses as Dependent Variable
Model 1 Model 2 Model 3
Estimates B SE β p B SE β p B SE β p Gender -.041 .167 -.012 .807 -.044 .168 -.012 .794 -.077 .128 -.022 .546 Age .003 .008 0.19 .698 .003 .008 .019 .693 .002 .006 .012 .744 Gaming Expertise -.004 .004 -.069 .237 -.004 .004 -.070 .000 -.003 .003 -.056 .211 Consumer Innovativeness .815 .074 .592 .000 .812 .074 .590 .230 .253 .068 .184 .000 Experimental groups .038 .159 .011 .810 .052 .121 .015 .667
Perceived Ease of Use .053 .070 .031 .450
Perceived Usefulness .623 .083 .488 .000 Perceived Enjoyment .234 .083 .176 .005 R2 .403 .403 .662 Adjusted R2 .395 .393 .652 F-value 50.524*** 40.304*** 72.083*** Note. N = 304. * p < .05 ** p < .01 *** p < .001 (two-tailed)
A mediation-based regression analysis was done through the PROCESS procedure in order to analyse the effect of the hypothesized mediation effect. A bootstrap sample equal to 5000 was used at a 95% confidence level. The hypothesis indicating that perceived value mediates the effect of gamified advertisements on the intention to adopt AR smart glasses was not supported since the effects of the independent variable on the mediation variables were non-significant (table 5). Neither was the effect of X on Y significant (i.e. total effect of X on Y shows p-value of 0.8101 and direct effect of X on Y shows a p-value of 0.6674). The 95% interval of perceived ease of use, perceived usefulness, and perceived enjoyment ranged from [-.028; .020], [-.132; .173], and [-.111, 0.020], respectively. Thus, the mediation effect was statistically non-significant. Interestingly, the direct effect of gamified advertisements on the intention to adopt AR smart glasses was significant (p-value = 0.0398), while the effect of gamified
Figure 3 - Hayes' PROCESS outcomes
advertisements on perceived enjoyment was not (p-value = 0.8865). Although the effect of gamified advertisements on the mediation variable and dependent variable was significant, there were partially significant effects of from the mediator to the dependent variable (figure 3). More specifically, perceived usefulness (p-value = 0.000) and perceived enjoyment (p-value = 0.005) appeared to have a significant effect on the intention to adopt AR smart glasses.
Furthermore, the necessary conditions for the analyses (i.e. observations drawn from a normally distributed population without large outliers and homoscedasticity) were approximately satisfied. More specifically, the regression standardized residual plotted a graph that is slightly skewed to the right and the normal P-Plot also depicted that there was a matter of a slight heteroscedasticity (see Appendix III), which could possibly explain that the p-values were inflated. However, one could argue that the inflation was not very significant since the normality and homoscedasticity were not extremely far off of the plotted graphs. Table 5 - Hayes' PROCESS outcomes
Independent variable (X) Gamified ads
Mediator (M)
Mediator dimension 1 (M1) Mediator dimension 2 (M2) Mediator dimension 3 (M3)
Perceived value Perceived ease of use Perceived usefulness Perceived enjoyment
Dependent variable (Y) Intention to adopt AR smart glasses
Mediation based regression analysis
Effect of X on M1 NOT SIGNIFICANT
Effect of X on M2 NOT SIGNIFICANT
Effect of X on M3 NOT SIGNIFICANT
Effect of M1 on Y NOT SIGNIFICANT
Effect of M2 on Y SIGNIFICANT
Effect on M3 on Y SIGNIFICANT
Effect of X on Y NOT SIGNIFICANT
Total, direct and indirect effect
Total Effect of X on Y coefficient t-statistic p-value
.0382 (.1590) .2405 .8101
Direct effect of X on Y coefficient t-statistic p-value
.0522 (.1214) .4302 .6674
Indirect effect of X on Y NOT SIGNIFICANT
6 Discussion and Conclusion
This study investigates the potential of using gamification advertisements on consumer behaviour when introducing new product innovations involving AR technology, particularly consumers’ willingness to adopt AR smart glasses. Based on the results, there was not enough statistical evidence to support that gamified advertisements of AR smart glasses lead to a higher perceived value of AR smart glasses. This confirms the empirical scepticism on the efficacy of gamification indicating that there is thin evidence that gamification actually works better than traditional marketing schemes (Lieberoth et al, 2015). There is a need for more controlled studies on the impact of gamification in different contexts per specific product innovation (Seaborn & Fels, 2015; Yang et al, 2017). More specifically, advertisements on display (e.g. posters) are considered as a low-level implementation of gamification (Muller-Stewens et al, 2017) and seem to have no positive effect on consumers’ behaviour (Chaney et al, 2004). Also, the effectiveness of gamification is dependent on the implementation (e.g. what devices are used, game context) and the experience (e.g. game complexity and performance) (Seaborn & Fels, 2015; Muller-Stewens et al, 2017).
Moreover, there was not enough evidence to support that consumers’ perceived value mediates the relationship between gamified advertisements and consumers’ intention to adopt AR smart glasses. As Ram (1989) argued, consumers do not necessarily develop preferences when they perceive differences (i.e. greater benefits of new products as compared to existing products). Martinez et al (2014) argue that the perceived value of the technology is needed as an antecedent to enhance its acceptance by consumers. Some scholars argue that creating AR experiences that are principally hedonic (i.e. perceived enjoyment) lead to stronger behavioural responses (Javornik, 2016), whereas others argue that the inclusion of utilitarian benefits (i.e. perceived usefulness) have a greater role in the adoption intention (Kalantari & Rauschnabel, 2017). Poncin et al (2017) further note that it is necessary to stress out the importance of perceived ease of use. On the contrary, this study showed that the combined effect of perceived usefulness and enjoyment are more significant. This was also evident in existing studies stating that hedonic aspects are equally influential determinants of adoption as utilitarian aspects (Kim & Shin, 2015; Rodrigues et al, 2016; Yang et al, 2017).
However, this study did confirm that consumers’ level of cognitive innovativeness plays an important role in the behaviour towards AR technology, which is consistent with the implications by Huang & Liao (2015). Scholars also argue that consumer innovativeness enhances the adoption of new product innovations (Truong, 2013; Hirunyawipada & Paswan, 2016). On the other hand, age, gender, and gaming experience did not appear to be strong influencing factors in the adoption of AR smart glasses, which is consistent with the implications given by Venkatesh et al (2012).
Nonetheless, there appeared to be some directionality that could be a possible indication of the potential of gamification techniques on consumer behaviour when introducing new product innovations. The caveats in this study could explain that the hypotheses were not supported, one of which is the effectiveness of the advertisements in the experiment. There are many forms of gamified advertisements (e.g. in-game advertising, advergames, advertising social network games) that may differ in delivering gamified marketing experiences triggering different psychological responses (Terlutter & Capella, 2013).
