Graduate School of Communication
Track: Persuasive Communication
Master Thesis
Reallusion
The effect of augmented reality on purchase intention and
the moderating effect of engagement
Student: Nikiforos Aslanoglou Student ID: 11571500
Supervisor: Dr. Guda van Noort Date: 02-02-2018
Abstract ... 1
Introduction ... 2
Theoretical Framework ... 4
Augmented Reality (AR) developments in Marketing Communication ... 4
Augmented Reality and Purchase Intention ... 7
Engagement as a moderating factor of AR and purchase intention ... 9
Methodology ... 11
Design ... 11
Materials and Stimuli ... 11
Procedure ... 12 Measures ... 12 Sample ... 14 Results ... 14 Confound check ... 15 Hypotheses Testing ... 15 Discussion... 16
Implications of the findings ... 17
Limitations and future research ... 19
Conclusion ... 20
Abstract
During the last decade the conceptual as well as the practical developments in the field of augmented reality (AR) paved the way to marketers with unimaginable
possibilities for understanding, engaging and selling products to customers. Due to the fact that AR technology incorporates unique characteristics such as: telepresence, increased enjoyment and increased interactivity that could potentially have an effect on customers towards a purchase, many companies have launched AR related applications in the market. Although the market of AR is expected to grow significantly in the next three years, little is known regarding the effect of AR on purchase intention. The present study examines the effect of an AR versus a non- AR applications on purchase intention while taking into account the moderating effect of engagement. Moreover due to the fact that recent market attempts of AR applications prove to be successful among the members of Generation Y or Millenials, the sample of the study belonged to this age group category. The results of the study prompt that there is no significant effect of AR versus non- AR applications on purchase intention while there was no interaction effect of engagement. However, the results indicated that there is a main effect of engagement on purchase intention.
Introduction
In The Illusionist (2006) the main protagonist dazzles his audience with his magnificent gift of showmanship in 19th Century Vienna. One of his most striking yet feared tricks was calling people from the dead who responded to his requests while appearing on stage as ghosts. They could not be touched but were able to engage with both the magician and his audience. These magical phenomena seem impossible to explain in the 19th Century, a time where both the occult and realism were embraced. While nowadays we still don’t have the ability to call people from the dead, we are able to simulate objects and people in holograms through the use of screens,
projectors and eyeglasses. The technological concept that allows us to virtually create a real-looking object or person able to interact with the real world in real time and space is called Augmented Reality (AR).
Over the last decade, the introduction of new concepts such as augmented reality has transformed the way that individuals interact with technology (Carmigniani et al. 2011). The majority of user-desktop interactions have shifted towards user - mobile and wearable interactions where things are happening anytime, anywhere. Our personal devices have been converted into artificial external eyes capable of sensing inserted information in the surrounding environment. The blending of the real and virtual world into a single interface enhances the users’ interaction with the physical environment while creating space for the creation of new applications as well as complementary services (Olsson et al. 2011).
The starting point for defining the concept of AR, as it is known today, came during the early 1990s when two scientists working for Boeing Company invented the term “augmented reality”. Technological advancements such as the increased
maneuverability and portability of technology, the introduction of the Global Positioning System (GPS), the decrease of the cost of related technologies, and the availability of the Internet worldwide are some of the factors that have escalated both the utility and subsequent relevance of the possible AR applications (Javornik, 2016). The current trend of AR is supported by the Google trend data that depicts a 400 percent increase in interest in the concept during the last decade (Google Trends, 2016). Moreover, the biggest technological players: Google, Microsoft, Snapchat and
just while ago Facebook began developing AR related technologies (Constine, 2017; Robertson, 2017; Schroeder, 2015; Spence, 2017).
The concept of AR is focused on expanding the real world with add-on visual and audio capabilities, instead of embedding real world information into virtual worlds (Azuma, 1997). Thus, the main characteristic of AR aims to enhance users’ interactive capabilities with the real world, rather than to create an entirely artificial environment (Olsson et al., 2011) in which users lose the sense of time and space.
As a result of these technological shifts and developments, individuals are now able to interact with brands where physical and artificial objects counterbalance, support and interplay with each other (Ohta and Tamura, 2014) For example, users are now able to virtually modify the interior of their houses, test furniture and
decorative items and share their experiences with their friends (Javornik, 2016). Thus, companies now have the ability to reach, interact and understand their customers with the use of digital data while evolving their products and services through their
consumers’ input. For instance, marketers are now able to see which products and product features attract consumer attention, instantly address consumer inquires while creating stronger relationships with them by utilizing customer’s digital data (Chiu et al. 2014). However, given that the concepts of augmented and virtual reality are new both in academia and the market, there is little to no information regarding whether or not these concepts are adding value to consumers or companies, while enriching consumer-brand related experiences and thus increasing profitability (Javornik, 2014). Therefore the aim of this study is to examine the relationship between AR and
purchase intention.
Although the add value qualities of Augmented Reality Marketing (ARM) have the ability to provide enjoyment, telepresence and experimental values to users (Bulearca and Tamarjan, 2010; Chen & Hsieh, 2010), the effect on customer behavior and purchase intention is still uncertain for corporations. Research in the field of AR in the domain of marketing communication examined the effects of “magic mirror” features to virtually testing apparel and make-up (Javornik et al. 2017; Schwartz, 2011) the intention to use AR apps and users’ satisfaction of AR applications
(Bulearca andTamarjan, 2010; Huang & Liu, 2014; Kang, 2014; Olsson &Salo, 2011; Rese, Schreiber, &Baier, 2014). However it is not yet clear whether AR can positively
affect consumer purchase intentions, given the fact that research findings are
contradictory (Kang, 2014; Owyang, 2010). The effect of AR on purchase intention is still unclear, marketers are dubious on whether or not to invest on AR since it might prove to be another entertaining gadget (Owyang, 2010).Moreover, academics as well as marketers have focused on the medium to the point that they forgot about the core of the message they are aiming to convey or the commercial outcomes that AR can bring (Leslie, 2016). Therefore, the aim of the current study is to examine whether AR has an impact on purchase intention.
During the last seven years several AR applications were developed for e-commerce purposes, but many of them failed to meet commercial goals. For instance some virtual try-on applications such as the ones of: Tobi Fashion, JC Penney or Converse, draw the attention of academics and marketers but they are no longer available to customers (Accenture, 2014; Kang, 2014). The reasons behind the
failures of these apps can be poor acceptance of technology by customers, poor design and failing to optimize customer base demands in regards to AR applications. In order for corporations to take the advantage of the rapidly growing market of AR they should first specify to what extend AR systems can compliment while enriching shopping behavior of their customers’ base so they can expand their marketing efforts via AR technologies (Digi-Capital, 2016). Therefore, this study aims to provide insights on whether AR adds value to firms’ marketing efforts and also to explore what the new opportunities and challenges of this new technology are.
Theoretical Framework
Augmented Reality (AR) developments in Marketing Communication
The first practical AR technology was first developed in the 1960’s by Ivan Sutherland, a computer scientist at Harvard, alongside with his student Bob Sproull. They invented the first head mounted display (HMD) system. Sutherland’s “The Sword of Damocles” was the maiden device that enabled the viewing of 3-D graphics in the real world using a holographic projection. During the 70’s and the 80’s, many research institutes such as NASA, as well as industry research, continued to explore the potential of possibilities of AR applications. In 1992 two Boeing scientists, Caudell and Mizell, coined the term Augmented Reality. Caudell and Mizell (1993)
created an assistance AR system where they blended virtual graphics in the real world aiming to reduce workplace accidents of people working with wiring harnesses (Fedorova, 2014).
The concept of AR represents the interactive technology which enables virtual graphic elements to overlay the physical surroundings. The visual graphic layers that are incorporated into AR systems and applications can be superimposed through the use of devices such as tablets, projectors, smartphones and wearable devices (head- mounted displays). According to Carmiagniani et al. (2011), the AR systems and applications vary in the elements that they augment. Specifically, AR is capable of superimposing virtual elements on three different levels: surrounding space, people, and products.
The concept of augmented reality (AR) is sometimes wrongly related with the concept of virtual reality (VR). Both these concepts are part of the current trend in digital technology and belong to the family of mixed reality technologies (Javornik, 2014). As defined by Ohta& Tamura (2014) mixed reality technologies are the ones which integrate and/or merge the real and virtual worlds where virtual as well as physical objects supplement, assist and/or interact with each other. Although AR is being wrongly related to VR there is a key difference between the two technologies. AR enhances the interactions of users with the real world through the use of a computer/screen/projector while VR submerge users in a virtual synthetic environment where users cannot interact with the physical world (Fuhrt, 2014). Moreover, AR enables users to sense the physical surrounding world through sights and sounds while providing additional stimuli such as sights (eg holograms) and sounds that are synchronized to the specific location relative to users’ three dimensional (3D) orientation to a geographic locale (Pavlik& Bridges, 2013).
Over the years, both academic and practitioners research stressed that AR can benefit many different areas of interest. As noted above, the first AR application was initially focused on preventing incidents in the airline manufacturing industry
(Fedova, 2014). Nowadays, AR related applications have been developed and used by many different sectors such as the military, journalism, sports, while in the last few years AR has gained popularity in the field of marketing communications and entertainment (Javornik, 2014).
New technological developments in the field of AR created new opportunities for marketers to further develop new applications in the market while showcasing their products to their customers more effectively. At the same time, the adoption pace of new AR technological improvements from consumers seems prominent. The most recent AR application that gained popularity among individuals worldwide was the introduction of PokemonGO in 2016. At the same time, over the last 2 years individuals that use Snapchat have confirmed the fascinating trend around potential monetization possibilities of AR (Newberry, 2016).
The Digi-Capital report of 2016 highlighted the current predictions for augmented reality and its marketability, while stating that the market of augmented reality will reach 87 billion dollars in 2021. Additionally, the same report takes into account not only companies such as Apple, Microsoft, and Google that aim to launch hardware devices like Google glasses but also e-commerce sales that would be derived from AR applications and devices, AR advertising and in AR app purchases (Digi- Capital, 2016). According to the latest market study released by Technavio, the global augmented reality of the advertising market is expected to grow at a compound annual growth rate (CAGR) of almost 31% during the predicted period (2017-2022). It is estimated that by 2020, 100 million consumers will use AR for shopping. It is also expected that 20% of the global brands will use AR related applications for promoting their products by the end of 2017 (Earnest, 2016). Moreover, Michael Valdsgraard, the leader of the digital transformation of IKEA, recently described the ‘dream scenario’ of IKEA which is that in 2020 the online sales of the company would reach 5.9 billion dollars while adopting new approaches through the creation of smaller stores (Petroff, 2017).
Although AR applications in the field of marketing communications are new, recent research showed the specific characteristics that differentiate AR from non-AR applications and potentially can result in more positive attitudes regarding a brand as well as increased intention to conduct a purchase (Javornik et al 2017, Javornik, 2016, Papagiannidis et al. 2014; Scholz and Smith, 2016 ). Specifically, empirical studies have shown that AR can result in increased entertainment and hedonic value
compared to non-AR applications. For instance, due to the fact that AR applications can provide virtual try ons, users are able to try furniture, clothing and sunglasses from the comfort of their homes (Dacko, 2017). The effect is greater when comparing
AR with non-AR applications given that individuals cannot interact with the external environment (Stoyanova, Georgieva, Brito &Milanova, 2015). At the same time, as research has shown interactivity is a distinct characteristic of AR since it can result in an increase of numerous affective (joyfulness, entertainment experiences) as well as cognitive responses (advanced knowledge of product features) (Schwartz, 2011; Lu and Smith, 2007). The responses that are triggered by AR can enhance product experience while creating immersive customer experiences.
Augmented Reality and Purchase Intention
The foundation for the impact of AR in marketing communication on consumer behavior can be found in multiple different theoretical domains (Ajzen, 1985; Smith and Swinyard, 1983). Despite the fact that recent empirical research regarding the positive effect of AR on purchase intention is ambiguous; AR impact on consumer behavior examined under the prism of conceptual frameworks such as the theory of planned behavior (TPB) as well as the attitude-behavior consistency (ABC) provide some theoretical explanations regarding the underlying reasons that AR might positively affect purchase intention. Subsequently, according to the theory of planned behavior (TPB), one of the factors that influence a behavior is the attitude towards a behavior that can result in increased intention of actually performing the behavior. Thus, the more positive an attitude regarding a product or a brand is, the higher the purchase intention (Ajzen, 1985). Moreover, according to the theory of attitude-behavior consistency (ABC), immediate product experiences compared to indirect product experiences result in more favorable product attitudes towards a product (Smith and Swinyard, 1983). For example, users that are able to test products through AR applications are more inclined to remember product features and attributes which could potentially results in increased purchase intention, while experiencing high levels of enjoyment (Scholz and Smith, 2016). Concurrently, AR applications have the ability to provide telepresence attributes to users, meaning users can actually test products in real- time. Hence the unique characteristics of AR, combined with the cognitive and affective responses that can be evoked, could result in increased intention towards buying a product compared to non-AR applications while generating more favorable responses towards products (Huang and Liao, 2015).
Research regarding the effects of AR on individuals has shown that AR has the potential to create positive cognitive and affective responses in its users which may result in increased purchase intention. Initially, augmented reality applications could result in a variety of positive cognitive and affective responses for its users: from increased entertainment value to enhanced product knowledge (Javornik, 2016). Research regarding the affective users’ responses of AR stressed the critical
dimension of enjoyment that affects users’ purchase intentions. Specifically, it has depicted that the enjoyment of AR is an essential dimension of both AR and non-AR applications aiming to increase purchase intention (Hsu and Lu, 2009). For example, studies examining the underlying reasons that AR might positively influence purchase intention have found that users who believed that AR apps provide them with
enjoyment value were more inclined to conduct a purchase (Javornik, 2016; Kim and Forsythe 2010). Moreover, Davis et al. (1992) stressed that both intrinsic and extrinsic motivation could influence customers motivation to conduct a purchase either
positively or negatively. In the context of online purchasing behavior it has been also noted that intrinsic motivation is connected to the direct enjoyment that derives from an application (Huang and Liao, 2015). For example, Chiu et al. (2014) suggested that customers who perceived a music website as playful were more inclined to purchase music online. On the other hand, external awards that are derived from AR and non AR apps are considered factors positively influencing extrinsic motivation of users. For example, product discounts or promotions in the application are considered to be factors influencing extrinsic motivation. Although, promotions and discounts are considered as significant factors positively affecting consumer responses towards a purchase, research has shown that has no effect on AR and purchase intention.
Although, enhanced AR shopping experience that AR is capable of providing to its users, has been shown through previous research to depict the reasons why AR applications can facilitate purchase (Javornik et al. 2016; Javornik, 2016;
Papagiannidis, 2014; Scholtz and Smith, 2016). Javornik (2014) asserted that people tend to use an application to the extent they believe it will help them decide accurately whether to purchase a product or not. For example, users of AR applications that had access to product information reported that they were more willing to purchase the product in the future (Javornik, 2016). The enhanced experience of AR can provide increased utilitarian and hedonic value to users while being capable of positively
influencing cognitive responses of users regarding the probability of a purchase. Moreover, recent research shows that richer product related information can potentially lead to more positive responses regarding a product purchase. Previous research depicted contradictory findings with regards to AR and purchase intention. Schwartz (2011) examined the influence of AR in apparel shopping but failed to a significantly higher purchase intention. Therefore, given the fact that AR applications can potentially result in increased purchase intention, it is hypothesized that
augmented reality can positively influence purchase intention.
H1: Augmented reality application compared to Non-Augmented application
will result in increased purchase intention
Engagement as a moderating factor of AR and purchase intention
Increased product involvement, enjoyment, and enhanced shopping
experiences are some of the factors that can positively affect engagement which in turn can further affect the relationship of AR and non AR applications with purchase intention positively. Papagiannidis et al. (2014) argued that the higher the degree of customers’ engagement with an application or a product the higher the positive effect on the relationship among a medium and product purchasing likelihood. Specifically, it was depicted that the higher the engagement with a product through a medium before individuals were exposed to an advertisement of a product the higher the likelihood to purchase the product in the future. Moreover, product related
engagement as well as medium engagement (e.g. engagement with an application, website etc.) can positively influence consumers to search for more information regarding a product and therefore to conduct a purchase (Dacko, 2017). It has been found that engagement is higher when applications provide product related
information, due to the fact that it can potentially enhance the shopping experience while easing related product purchasing decisions (Chiu et al. 2014, Javornik, 2016). Moreover, consumers’ engagement with a brand or products of a brand could
positively influence the relationship of AR and non AR applications with purchase intention because it can enhance users’ social interactions. For example users might want to share their fervor regarding their brand related experiences with their social circle on social media thus individuals that share their product related experiences are more inclined towards a purchase (Javornik, 2016).
Engagement in the context of AR can be defined as the process of consumer involvement in interactive experiences or interactions with the brand that can result in increased purchase intention while enhancing brand to consumers’ relationships (Scholtz and Smith 2016). Moreover, they argued that in the context of AR
applications engagement can be assorted in three basic types: user to brand, user to user, user to bystander. Initially, user to brand engagement occurs when consumers explore product related information, features, price, and attributes. User to user engagement occurs when consumers share their experience with their social surroundings in any social interaction varying from social media to daily
conversations. Finally, the user to bystander engagement refers to the interaction of the user and the possible AR capabilities, for example, users that virtually experience a new sofa in their houses (Scholtz and Smith, 2016). In line with Scholtz and Smith (2016), Papagiannidis et al. (2014) found out that engagement as a moderating factor of the AR and purchase intention can result in a stronger relationship between the user and the simulated product and thus positively influence purchase intention.
Specifically, participants that virtually experienced a MINI car were more likely to purchase the car in the future. The increased intention of the participants to purchase the car was influenced by the increased hedonic and utilitarian values deriving from the increased engagement customers’ experience (Papagiannidis et al. 2014).
At the same time it has been noted that engagement with a brand or a product can vary from informational engagement where individuals are searching product or brand related information to emotional engagement where users engage with a product or a brand due to the fact that they receive multiple benefits such as
entertainment value. Moreover, as research has shown the way that engagement can leverage purchase intention and even increase loyalty is through connecting
emotionally consumers with the brand (Sashi, 2012). Additionally, brands that offer product experiences that incorporate entertainment elements while offering enhanced cognitive consumer experiences can result in increased purchase intention while they can expect increased product/brand engagement (Sashi, 2012; Scholtz and Smith 2016). Subsequently, the higher the degree of engagement with the brand, the higher the potential effect of the relationship between AR and purchase intention Therefore it is hypothesized that the higher the levels of engagement the higher the purchase intention of the participants.
H2a: Increased brand engagement will positively influence the effect of
Augmented Reality (versus non AR) application on purchase intention
H2b: Increased product engagement in the application will positively
influence the effect of Augmented Reality (versus non AR) application on purchase intention
Figure 1: Conceptual Model of the present study
Methodology Design
In order to test the hypotheses, a 2 (Augmented Reality; non- Augmented Reality) randomized between subjects factorial design was employed with purchase intention as dependent variable and engagement as moderating variable.
Materials and Stimuli
Many experimental studies have used videos as well as real feel experiences of AR applications and websites in order to measure the effect of AR on purchase
intention (Beck and Crie, 2016; Javornik, 2016 Papagiannidis et al. 2014; ). The method used to test the effect of AR on purchase intention was the use of two videos. The augmented reality application was presented through a 1 minute video depicting the characteristics of the AR application as well as its current possibilities. The non-augmented reality application was also presented through a 1 minute video depicting what would be the attributes of the non-app when using the application. Although the method used is the most inexpensive and time efficient the results lack of
understanding of the real impact of AR on purchase intention when participants would actual test in a lab set the AR and non- AR effect of purchase intention (Hulten, 2012; Javornik, 2016; Raska and Richer, 2017; Seo and Lee, 2016; Stoyanova et al. 2015; ). Finally, the wording of the questionnaire was entirely in English
Procedure
The online experiment was set up on Qualtrics, and remained open for participants for 14 days. Before starting the experiment, the participants recruited to complete the survey received an informed consent. After agreeing with the informed consent, participants were assigned to provide information regarding their previous experience with IKEA products and IKEA applications which acted as the control variables of the study. Afterwards, participants also received questions aimed at determining the degree of participants’ engagement regarding IKEA products and applications. Further, after the participants were exposed to the IKEA app, they received the first set of questions aimed to determine the likelihood of conducting a purchase in the future. The second set of questions aimed to examine the reasons that the app influenced purchase intention. The third set of questions aimed to examine the characteristics of the applications that influenced purchase intention while the last set of questions aimed to check the likelihood of participants to download the IKEA applications. Finally, after the completion of the questions, participants were asked to provide demographic information including age, gender and nationality and
education.
Measures
In order to measure engagement and purchase intention itemized rating scale questions were developed (Malhorta and Brinks. 2007). To examine the engagement as well as the purchase intention it was presumed that the itemized rating scale questions were the appropriate (Saunders et al., 2007). At the same time, in order to ensure the validity of the construct the items selected to measure engagement and purchase intention as well as the underline reasons that might influence purchase intention were selected on the base of prior studies (Papagiannidis et al,. 2014; Javornik, 2016; Fiora et al. 2005; Scwartz 2011; Li & Biocca 2005; Davis 1989; Davis 1996; Mao et al,. 2005).
Engagement
Engagement with the brand as well as engagement with the products in the application were assessed with a 5- point Likert scale items ranging from None (0) to
A Ton (5) were used. Specifically four items measured the engagement of participants
with the brands as well as the products in the application. For instance, participants were asked “how much information you have about IKEA products” (SD= 7.94, M= 3.35 a= .51), “I have searched about IKEA products in the IKEA Catalog” (SD= 2.34,
M= 2.34, a= .81) deriving from Schwartz (2011), Li& Biocca (2002) and Javornik
(2016).
Purchase Intention
In order to measure purchase intention participants received a 7-point Likert scale constituted from six item questions that could be answered with a likability scale ranging from Not at all likely (0) to Very Likely (7). The set of items aimed to measure the likelihood of participants to conduct a purchase after viewing the two videos after being exposed to the videos of AR and non AR applications. For instance “I would consider buying the products tested in the app” (SD= .892 , M= 3.47), “I would be interested to check price of the products tested in the IKEA place app” (SD= 1.09, M= 4.15) (Davis et al. (1992); Olso & Salo (2011); Schwartz (2011); Javornik (2016); Papagiannidis et al.; (2014); Javornik (2016).
Control Variables
Research showed that participants who had a previous experience with products of a brand or an application of the brand seem to be either positively or negatively inclined towards purchasing from a brand depending on the experience (Papagiannidis et al. 2014). In the study, the control variables were: exposure to IKEA applications: Cataolog (M= 1.78 ,SD= .41), Place (M= 1.93, SD= .26) shopping at IKEA (M= 1.04, SD= .20) was measured with three different questions “have you ever shopped at IKEA”, “have you ever downloaded IKEA Catalog app” and “have you ever downloaded IKEA Place app” accordingly. In the same way age (M= 25.38, SD= 4, 37) and gender (58% were females and 42% were males) were calculated.
Manipulation Check
In this study, due to the fact that the only manipulated variable in the experiment was the type of the app, no manipulation check took place. Previous studies examining the effect two different versions of an advertisement did not check for manipulation (Tutaj and Reijmersdal, 2014). Thus, it is assumed that there was no reason to check for manipulation given the fact that the videos presenting the AR as well as the non AR applications were different.
Sample
A non-random sampling method was employed while participants were reached via social media and emails. Participants would be required to be in the age range of 18-34. This age group belongs to the Generation Y or Millenials and is considered to be the group that is the most likely to adopt new technological developments and applications (Newberry, 2016). Concurrently, the willingness of this group to engage in AR applications is apparent when taking into consideration the social network, Snapchat, that actively provides AR tools (e.g. allows people to apply on their faces various graphics and filters) to its users is the most popular in users under the age of 25 (Newberry, 2016). Moreover, despite the fact that this age group is the most willing to use both AR and non-AR applications, research has shown that people are less willing to purchase IKEA products as they grow older. Specifically, the group that conducts the most purchases from IKEA stores is considered to be people between the ages of 18-34 (Earnest, 2017). A total of 247 participants
recruited to participate as they agreed to participate in the experiment. Out of the 247 participants, 181 completed the experiment.
Results
Initially, the data gathered from the survey was cleaned; specifically
participants that failed to complete the questionnaire were removed. The number of participants that completed the survey was n= 181.After the cleaning of the data some descriptive statistics were constructed in order to find out the characteristics of the sample. The mean age of the sample was 25 years old (SD= 4.37) while the majority of the participants (60, 2%) was Greeks. Moreover, the majority of the participants (47, 5%) completed a bachelor degree.
Confound check
In order to check for possible confounding variables, a randomization check was conducted. Four ANOVA analyses and one independent t-test (for the age variable) for each of the control variables (i.e. engagement with IKEA, engagement with IKEA catalog, engagement with IKEA place, age and gender) as dependent variables and the two conditions as between-subjects factor conducted. The results depicted that conditions did not significantly differ with respect to: IKEA brand engagement F (1,177) = .006, p= .937; Catalog F (1,177) = 1.20, p= .247; Place, F (1,177) = 3.69, p= .06; age t (1,177) = .138, p= .710 and gender F (1,177) = 2.22, p= .937.
Hypotheses Testing
Before testing the hypotheses a factor analysis was conducted in order to test the reliability of the two engagement variables. The results for the reliability of engagement was not satisfactory since Crobach’s alpha was .51. However, reliability of engagement with IKEA products and the IKEA app was satisfactory due to
Crobach’s alpha which was .81. Therefore, the variable engagement with the IKEA brand was excluded from the testing of hypotheses.
In order to test the two hypotheses a moderation analysis was done using the PROCESS macro by Andrew Hayes. The regression model with Purchase Intention as dependent variable and the two different conditions (AR and non AR apps), engagement with the brand and the products in the app as independent variables was significant F(3,177)= 5.48, p= .003. The regression model can therefore be used to predict purchase intention, but the strength of the prediction is moderate: 5 per cent of the variation in purchase intention can be predicted on the basis of Augmented
Reality, Non- Augmented Reality and engagement with the products in the application (R2= .05).
Hypothesis 1 suggested that purchase intention will be higher for participants experiencing the AR application compared to the ones that experience the non- AR application. However the results of the model suggested that there is no significant effect between the two conditions b= .36, t (177) = 1.59, p > .05. Therefore the data do not support H1.
Hypothesis 2a suggested that engagement with the brand will positively affect the relationship of AR and non AR applications with purchase intention. Due to the fact that the reliability of the brand engagement variable was not satisfactory (.51), hypothesis 2a was not tested.
Hypothesis 2b suggested that product engagement in the application will result in increased purchase intention for the AR compared to the non-AR applications. The results of the model indicated that there is no interaction among product engagement in the app b= 2.9, t (177) = -.50 p= >.05. Although there was no significant
interaction effect for AR versus non AR. It was depicted that engagement with the products and the two applications has a main effect on purchase intention, b= .22, t (177) = 1.59, p = .001. Therefore, the data do not support hypothesis 2b.
Discussion
This study focused on the effect of AR on purchase intention while taking into consideration the moderating effect of engagement with the products in the
application as well as with the brand itself. Two IKEA videos presented to the participants of the study, specifically, one video that demonstrated the potential benefits that customers receive when using the AR app of IKEA and one that depicted the benefits of the use of non AR app of IKEA.
The first finding of the study was that there is no significant difference between AR and non AR apps and purchase intention. This implies that augmented reality and non augmented reality applications have limited to no effect on purchase intention in regards to purchasing furniture (Kang, 2014; Owyang, 2010). Although there are few studies examining the direct effect of AR versus non AR apps on
purchase intention for the specific study there might be several underlying factors that affected the results varying from the perceived usefulness of the AR technology in buying furniture decisions to technological acceptance.
Although, based on previous research it was expected that in this study AR might have a positive effect on purchase intention, the results of the study could not confirm this view. However, one of the reasons explaining the fact that AR has no significant effect on purchase intentions could be the incorrect selection of AR application designing tools. Research done so far, depicted that the lack of a clear
definition of the target audience when designing AR applications as well as the lack of clear marketing goals that illustrate the expected outcomes (i.e. increased purchase intention, increased brand and product related engagement etc) when launching an AR application can result in unfavorable results such as failing to increase purchase intention (Javornik, 2016; Papagiannidis, 2014; Scholz and Smith, 2016; Seo and Lee, 2013). Moreover, according to previous research when designing AR applications in order to increase purchase intention media characteristics are highly important. Factors such as interactivity of the applications (i.e. ability of a two way communication among the brand and the app user), usefulness and enjoyment
considered as important variables when aiming to increase purchase intention (Cyr et a. 2009, Javornik, 2016; Lu and Smith, 2007; Song and Zinkham, 2008 ;).
Moreover, the results of the study indicated that there is an effect of product and app engagement for both the applications on purchase intention. It can be inferred therefore, that prior engagement with products in both of the applications has a
positive effect on purchase intention (Papagiannidis et al. 2014). Thus the higher the engagement with products in the applications the higher the likelihood of the
respondents to report increased purchasing intention rates. However, various aspects could have potentially triggered this effect including: prior positive online and offline purchasing experience on IKEA website and apps, the perceived usefulness of these technologies, positive experiences with searching for IKEA products (Javornik et al. 2017). Consequently, it can be concluded that the more customers are involved with products in the apps the more likely it is for them to contact a purchase (Sashi 2012; Scholtz and Smith, 2016). Therefore, due to the fact that engagement does not moderate the relationship of AR with purchase intention, further exploration of the effects of the engagement variable as mediator even as an outcome both in an
academic as well as in a practitioners level can leverage useful conclusions regarding the role of engagement in the marketing communication as well as in the consumer behavior field.
Implications of the findings
The statistical results of this study showed that the AR and non AR do not significantly differ in the effect that they have on purchase intention. The specific findings of the study, although there are few studies examining the effect of AR
compared to non AR applications on purchase intention, should be verified by future studies. Moreover, the study justifies potential future investments in the field of AR by examining and the effect of engagement on AR and non AR applications with purchase intention.
The results drawn from the study seem to gain importance due to the fact that in 2017 Mark Zuckerberg, the CEO of Facebook, introduced the first open AR platform where developers can work on to change modify and push on the new AR dimensions (Constine, 2017). Moreover, Zuckeberg (2017) stated that since everyone has a camera on their phones, the spread of new technological developments should not necessarily depend on the pace of the social adaptation of new devices such as lenses or smart glasses. Moreover, as it was depicted in the previous sections, the field of AR is one of the most prominent while quickly growing technologies (Digi Capital, 2016). Additionally, it should be noted that future AR developments in the field of furniture industry should first try to further understand the existing customer base that posses already a certain degree of engagement with the products of the brand as well as with the possible applications of the brand (Li and Zhao, 2016). Therefore, the customers’ base preferences and existed product experience should be further consolidated in the designing process of AR and non- AR commercial applications. Additionally, another conclusion that can be drawn from this study is that Generation Y or Millenials are more willing to conduct a purchase if they first have engaged with products of a furniture brand as well as with brand applications (Parment, 2013). Consequently, shopping with AR or non AR apps cannot fully replace online or offline purchases due to the fact that the real feel experience of a product such as furniture might be an important variable that influences the purchasing intention rates. However, as it has been noted product knowledge through the apps has also the ability to decrease the goods returns in the furniture industry (Schwartz, 2011, Lambrea, 2016; McDowell, Wilson, & Kile, 2016).
Finally, the nationality of the majority of the participants was Greeks, meaning that possibly the Greek market differs to other European markets in terms of cultural preferences, buying behavior and integration of new technologies (Papafotikas et al. 2014). Moreover, it can also be suggested that companies aiming to launch AR and non AR apps in the market or in the process of designing new ones should strive to find ways to further increase the product and apps engagement of their customers.
Lack of engagement might lead to lower purchasing rates and customer conversion rates (Lambrea, 2016; McDowell et al. 2016)
Limitations and future research
The sample of this study drawn from mainly people belonging to the Millenials or Generation Y age group while the method used was convenient
sampling. Studies on different age groups such as Generation X as well as the use of different sampling methods are required for further generalization of the results. Moreover, the majority of the participants were Greek, when taking into consideration factors such as the financial crisis that Greece is going through as well as the
reduction of the disposable GDP per capita of Greeks it can be understood that the purchasing power of the sample has been affected as well as the group’s purchasing intentions (CIA Factbook, 2017). Future research should focus on groups of people from other countries in order to further generalize the results
Moreover, as previous research suggests when examining purchase intention, the conceptual framework of Hoefstede’s cultural dimensions should be taken into consideration. Customers from different cultures have different attitudes, preferences and values when they choose to buy specific products (Moon et al. 2008). Due to the fact that Greece market during the last years has been experiencing an economic (fiscal) crisis, over the last years customers’ behavior and purchasing choices have been significantly transformed. Purchases of technological products have reduced around 30% since the beginning of the crisis (European Commision, 2017) .At the same time, in spite of the fact that online furniture purchasing rates are on the rise on a global scale, the Greek market seems to follow more traditional purchasing
decisions preferring not to shop online for furniture related products and services (Li and Zhao, 2016; Papafotikas et al. 2014). Therefore, future studies examining the effect of AR on purchase intention should take into consideration several cultural as well as purchasing preferences factors (Li et al. 2016; Papafotikas et al. 2014; Ponder, 2013).
Moreover, the study demonstrated the capabilities of AR and non AR apps through the use of two 1 minute videos. Several previous researches done in the past testing the effect of AR on consumers behaviors used other methodological
comparing AR experiences with the real feel of products. In this study, the real feel of the products as well as the real experience of using AR and non AR applications were absent and thus this is limiting the generalization of the study. The effectiveness of previous methods used was justified by previous research (Javornik et al. 2017; Javornik, 2016; Papagiannidis et al. 2014; Seo and Lee 2013). Future studies can further examine the effect of real AR and non AR experiences on purchase intention in lab settings. Moreover, prominent research techniques from consumer
neuroscience; such as the use of electroencephalograms and eye tracking technologies can further facilitate the understanding of AR and its applications on consumers (Khushaba et al. 2013). Moreover, the AR technology is still in an early- stage and still has many considerable windows of opportunities both in the devices and applications field. Various AR glasses related similar to Google Glasses, AR applications as well as breakthrough conceptual technologies such as AR lenses are continuously developing while many of them aim to meet and even exceed customers’ preferences and provide impressive augmented reality experiences (Javornik, et al. 2017; Schroeder 2015). Consequently, new AR devices and advanced app
characteristics will potentially alter some aspects of the current consumers’ experience. Therefore, future research could examine the relationship of AR and purchase intention through the use of other possible future devices and applications while taking into consideration possible side effects of AR devices and applications such as immersion with products and brands and how the context of use is modified according to the type of AR applications or devices.
Conclusion
This study focused on the effect of AR and non AR apps on purchase intention by taking into account the moderating effect of engagement with the brand as well as the products of the brand in the apps. The study underlines the fact that AR should not be considered as merely another interactive technology but rather a game changing technology that enables the modification of the visual representation of reality in real time. As consumer perceptions of media characteristics represent significant drivers of costumers’ responses, AR technologies can serve as both a theoretical as well as a practitioners’ framework for the better understanding of costumers purchasing behavior. The proposed conceptualization of AR in purchase intention can further facilitate a more in depth examination of the concept as well as the testing of it in
other contexts and with more complex manipulations. Given the fact that in this study AR and non AR apps prove to have no significant different impact on purchase intention, greater emphasis should be given on the engagement of app users in order to increase purchase intention.
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