Consumer adoption of augmented reality products

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Consumer Adoption of Augmented

Reality Products

MSc Entrepreneurship (joint degree VU and UvA)

Name: Isabelle Verhagen

Student number: VU: 2507610 Uva: 11668113

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Preface

This document is written by Student Isabelle Verhagen 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.

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Abstract

Augmented reality has emerged as a new interactive technology and its market size keeps growing worldwide. However, little is known about how consumers respond to its features. The aim of this thesis is to examine the effect of an AR product on the consumer adoption intention, testing for a possible moderation effect of the consumer’s level of independence/interdependence.

To investigate the conceptual model, two hypotheses were formulated, namely: H1: Augmented Reality products compared to Non-Augmented Reality products will result in increased consumer adoption intention and H2: The positive effect of an AR product (versus non AR) on consumer adoption intention is strengthened by consumer independency.

Data was collected by means of a survey, in which a total of 304 people participated. In the survey, the Ikea “Home Planner” programme was used as an example of a non-AR product and the Ikea app “Place” was used as an example of an AR product. Also the moderating variable consumer independence was tested and the control variables were gender, age, education and consumer innovativeness.

Later on, a statistical analysis was done using ANOVA. The results show that, as hypothesised, people are indeed more willing to adopt AR products compared to non-AR products. However, for the moderating effect of consumer independence on the relation between AR product and consumer adoption intention no prove was found. The control variable consumer innovativeness shows indeed a positive effect on the adoption intention of both non-AR and AR products. The outcomes also show that older people have a lower intention to adopt an AR product. Finally, limitations, future research directions and theoretical

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1. Introduction

1.1 Problem background

Many people became familiar with Augmented Reality (AR) when they were introduced to Pokémon Go; the most downloaded app in history (Clark & Clark, 2016). Pokémon Go was launched in July 2016 and was in August 2016 already downloaded by almost 45 million users worldwide (Javornik, 2016b). This game lets people collect virtual characters on their phone by virtually catching the Pokémon that have been placed in real public locations, such as parks and streets. By walking to these locations people could catch the Pokémon and battle with other players (Clark & Clark, 2016).

The popularity of the game illustrated AR’s potential to be adopted as a technology in our culture (Javornik, 2016b). AR aims to create the illusion that virtual images could be seamlessly blended with the real world (Billinghurst, Clark, & Lee, 2015). When AR is applied to consumer products, it could offer people a variety of values and benefits such as entertainment value, and ease of use, and it may speed up the purchase decision-making process when people want to buy a product online and are uncertain if it matches their needs (Huang & Liao, 2015).

AR could be applied in different ways to different industries such as retail, marketing, travel, tourism, education and training (Carmigniani & Furht, 2011; Martínez, Skournetou, Hyppölä, Laukkanen, & Heikkilä, 2014; van Krevelen & Poelman, 2010). This creates new opportunities for new businesses. At the moment the market size of the AR and VR industry equals 17.8 billion dollars worldwide, and this is expected to grow to 215 billion dollars in 2021 (Statista, 2018). This shows that the market is growing rapidly and that it is important for entrepreneurs and business managers to take this technology trend into account.

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1.2 Introduction to research question

Before companies bring innovative products to the market, they should pay attention to how consumers will respond to these innovations. Sometimes this does not go as expected. For example, Google launched the Google Glass in 2014 to the public. The glasses function as a computer with a transparent screen that allows users to view data augmented in their real-time environment. However, due to fashion and privacy reasons, the product never survived among consumers and was withdrawn in January 2015 (Naughton, 2017). So there is a risk of failure involved by including AR in a business. This study could help businesses reduce certain risks by making a specific contribution to the literature of AR adoption. The effect of an AR product on consumer adoption intention will therefore be investigated in this research.

Also, different people could respond differently to product innovations. It is possible that a product innovation would not survive because the product was placed in a wrong customer segment. It is therefore important to be aware of different type of customers, when launching a product to the market. In this study, the type of customer will be divided over two kinds of cultures, namely independency and interdependency, in which people relate themselves to their self-construal. Self-construal refers to the perceptions that people have about their thoughts, feelings, and actions in relation to others (Markus & Kitayama, 1991). More details about self-construal and how a consumer’s self-construal would affect the relation between an AR product and consumer adoption intention will be explained in paragraph 2.5.

1.3 Research question and sub-questions

Based on the previous paragraph, the following research question can be formulated:

“What is the impact of Augmented Reality products on consumer adoption intention moderated by consumer self-construal?”

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The above-mentioned research question is divided into the following five sub-questions. First, the definitions of the theoretical constructs and the relationship between them are looked into. Finally, a moderating variable is added to investigate a possible strengthening/weakening effect on this relationship.

1) What are augmented reality products? 2) What is consumer adoption intention?

3) What is the impact of augmented reality products on consumer adoption intention? 4) What is consumer self-construal?

5) What is the impact of consumer self-construal on the relation between augmented reality products and consumer adoption intention?

1.4 Theoretical and practical relevance

As mentioned before, the market for AR is rapidly growing. However, the amount of research that has been done on how consumers respond to this technology is limited (Huang & Liao, 2015; Javornik, 2016), especially how different kind of consumers react to this. Kim & Forsythe (2008) suggested that future research within the field of consumer adoption and AR should focus on consumer personality variables. Therefore, this study will include consumer self-construal as a moderating variable, which has not been studied yet in relation with the consumer adoption intention of AR products. The study of Ma, Yang, & Mourali (2014) is the only research that included the consumer self-construal in their research when measuring the adoption of an innovative product. In their study they measured the effect of innovative products while in this study a type of innovation will be researched, namely: AR. The research takes place in the Netherlands, where no research about consumer adoption of AR has been

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conducted yet. Due to these reasons, this research will make a specific contribution to the innovation literature.

This thesis can be used as a starting-point for other researchers, but it can also be helpful for businesses in practice. The availability of this literature is important for entrepreneurs and business managers who want to launch an innovation. This thesis will help them make better decisions regarding the launch of an AR product. By including customer culture as a variable, the results of this study can help entrepreneurs or business managers make decisions about entering international markets. An explanation of how managers can implement AR in practice will follow in section 5.3.

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2. Theoretical framework

This section will explain the theoretical constructs, answering sub-questions 1, 2 and 4. Furthermore, based on the theories found, possible relations between the constructs will be discussed, making a prediction for sub-questions 3 and 5. Based on these predictions two hypotheses will be formulated and, based on these, the conceptual framework will be presented.

2.1 Augmented reality

2.1.1 Definition

Augmented Reality (AR) refers to a live view of the physical real-world environment whose elements are merged with augmented computer-generated images creating a mixed reality. The augmentation is done in real time and in semantic context with environmental elements. By using the latest AR techniques and technologies, the information about the surrounding real world becomes interactive and digitally usable (Carmigniani & Furht, 2011). The virtual layer, placed between the physical environments and the user, can add textual information, images, videos or other virtual items to the person’s perception of the physical environment. The devices that can be used for AR are smartphones, tablets, wearables, fixed interactive screens or projectors (Carmigniani, et al., 2011). AR could not only be used to augment the sense of sight, it could be applied to all senses, including hearing, touch, and smell (Carmigniani & Furht, 2011).

Although AR is frequently being discussed together with Virtual Reality (VR), the two technologies are quite different from one another. In contrast to VR, AR provides local virtuality (van Krevelen & Poelman, 2010). AR enhances the user’s perception of and

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2.1.3 AR types and application

AR has different types and can thus be applied in different ways. It can be used to access additional digital content by scanning a product’s logo, a barcode or QR code. An example of this is Pocket Bargain Finder, a handheld device for augmented commerce that allows customers to physically inspect products while simultaneously comparing the prices of the product online (Martínez et al., 2014). AR can also be used to add virtual layers in a person’s real-time environment (Javornik, 2016). These apps could enable people to view an object in 3-D from every angle and see how the object looks like in a certain space or can get additional information about the surrounding space (Javornik, 2016). This type of AR could increase convenience and perceived flexibility to the online shopping experience. An example of this type is Ikea, who implemented AR for visualizing products and providing a virtual preview of furniture in a person’s room (Martínez et al., 2014). In this thesis, the primary focus will be on this type of AR.

2.2 Consumer adoption

2.2.1 Definition

Previous literature describes that AR has certain effects on consumers: it can create immersive brand experiences, create more interactive advertising, and enable consumers to experience products and spaces in novel ways (Scholz & Smith, 2016). In this thesis, it will be examined what kind of influence products with an AR feature have on consumer innovation adoption. Adoption will be defined as “a decision to make full use of an innovation as the best course of action available”. When the decision is made to not adopt an innovation, it is called rejection (Rogers, 2010, p. 21).

To get a deeper understanding about the innovation adoption of a product, the Innovation-Decision Process model of Rogers (2010) will be explained. It is the process that

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an individual goes through before adoption arises. This process consists of five steps: knowledge, persuasion, decision, implementation, and confirmation. The process starts with being aware that the innovation exists. It continues to the persuasion stage, where an attitude about the innovation is formed. Thirdly, a decision will be made to adopt or reject the innovation. If the innovation is adopted, the implementation stage occurs, where the innovation will be put into use. Finally, the confirmation stage takes place, here the individual seeks reinforcement of the innovation-decision already made. During this stage, an already made decision could be reversed if there are conflicting messages found about the innovation or the individual could decide to adopt the innovation.

2.2.2 Determinants of consumer decision

How is the decision to adopt a product made? Engel, Kollat and Blackwell (1969) explained that both individual and situational factors must be considered in order to explain consumer choices. The individual factors consist of the motives a person has, and the response traits. Motives can be defined as stable predispositions to behave in a way that will attain a given goal or end such as an achievement or affiliation. Furthermore, each person acquires characteristic modes of reacting and behaving in a certain way. These are called “response traits”. One individual can for example be aggressive, while another can be passive.

In addition, there are factors not related to the individual that can trigger certain behaviour. Belk (1975) explains these factors as situational factors that are related to a time and place of observation. He explained that these situational factors consist of 5 types, namely: physical surroundings, social surroundings, temporal perspective, task definition and antecedent states. The physical surroundings could be, for example, the geographical location, the sounds, the lightening and the weather. Social surroundings could be the other persons that

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which may be specified in units ranging from time of day, to season of the year. Examples of these factors are the “time since last purchase”, “time since last meal” or “payday”. Task definition is a situation an individual is in to select, shop for, or obtain information about a general or specific purchase. For example, the situation would be different if someone would buy something as a gift for someone else than when that person buys something for him or herself. Finally, antecedent states are momentary moods such as acute anxiety, pleasantness, hostility, and excitation. They could also be momentary conditions such as cash on hand, fatigue or illness. How these triggers are related to the adoption of an AR product will be discussed in the next paragraph.

2.2.3 Augmented reality and consumer adoption

Products are made to meet the demands and needs of the customer. However, developing commercially successful products can be a challenge (Sandberg, 2002). Understanding the elements that trigger a customer’s choice can help to define marketing strategies for launching innovations. Companies can leverage these triggers by using AR. In fact, Kourouthanassis, Boletsis, Bardaki and Chasanidou (2015) proves that, within the tourist industry, AR has a positive impact on user emotion, which eventually affects adoption behaviour. Their study investigated how users respond to a mobile AR travel guide named CorfuAR. This is a personalized app that displays information about the points of interest, and gives navigation directions to these places. The study showed that AR can evoke feelings of pleasure and arousal, which in turn influences behavioural intention of using it.

Moreover, Kim & Forsythe (2008) investigated the effect of “virtual try-ons” on the online shopping experience. They found that the virtual try-on technology has a moderating effect on the relationship between attitude and the use of the virtual try-on technology. According to the researchers, virtual try-ons do not only simulate the functionality and

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appearance of a product, but the interactivity also creates involvement, and enhances the entertainment value of the online shopping experience. This will encourage customers to spend more time using the feature and will potentially increase sales.

In addition, Huang & Liao (2015) studied what kind of effect AR has on the consumer’s technology acceptance in the online shopping industry. They found that AR has not only a functional value, it also creates an experiential value. Usefulness, ease of use, service excellence, aesthetics, and playfulness are important aspects of AR that stimulate consumers’ intentions to buy the brand.

The application of AR in mobile games has implications on consumers. Rauschnabel, Rossmann and Tom-Dieck (2017) investigated the mechanisms that explain the popularity of mobile AR-games. They showed that the consumer attitudes toward playing mobile AR games are mostly driven by the level of enjoyment they receive, and the image they get from other people by playing the game. Also nostalgia, the flow experience, and the psychical activity from playing seem to contribute to a positive association, while the risk of being injured or hurt while playing decreases the user’s positive attitude.

Furthermore, Wojchiechowski & Cellary (2013) studied the effect of AR in learning environments. They found that both perceived usefulness and perceived enjoyment had a comparable effect on the attitude toward using AR e-learning applications, while perceived enjoyment played a dominant role in determining the actual intention to use them. According to the researchers, AR could provide students extra motivation to learn.

These studies show that AR can trigger an individual’s antecedent states. Business managers can thus use AR in their strategies because it can shape the individual’s choice and thus influence their adoption intention (Belk, 1975). Based on these theories, the following hypothesis can be formulated:

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H1: Augmented reality products compared to Non-Augmented reality products will result in increased consumer adoption intention.

2.3 Consumer self-construal

2.3.1 Definition

People from different cultures may experience innovative products differently. Culture is, according to Hofstede (1984), the collective programming of the mind which distinguishes the members of one group or society from those of another. An important dimension of culture is an individual’s self-construal: the “I” or “We” (Hofstede, 1984). As explained in section 1.2, self-construal can be defined as the perceptions that people have about their thoughts, feelings, and actions in relation to others. Self-construal theory recognizes two perspectives or concepts about the self: the independent self and the interdependent self (Hofstede, 1984; Markus & Kitayama, 1991).

People from cultures such as those of Western Europe and North America can be characterized as having an independent self-construal. In these societies people seek to maintain individuality and separateness from others (Markus & Kitayama, 1991). They are autonomous and independent from their in-groups and they give priority to their personal goals. They also behave primarily according to their own attitudes and opinions, rather than the norms of their in-groups, which translates into their social behaviour (Triandis, 2001).

People from other cultures, such as those of Asia, Africa and South America, can be characterized by the interdependent view (Markus & Kitayama, 1991). People within these societies will tend to think and behave in ways that emphasize their connectedness to others and strengthens existing relationships (Cross, Bacon, & Morris, 2000). They give priority to the goals of their in-groups, shape their behaviour on the basis of their in-group’s norms and behave in a communal way (Triandis, 2001).

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2.3.2 Consumer self-construal and adoption of augmented reality products

Independent and interdependent self-concepts have different goal orientations. Independents could be seen as more promotion-focussed while interdependent people could be seen as more prevention-focused (Aaker & Lee, 2001). Herzenstein, Posavac and Brakus (2007) showed in their research that promotion focussed people prefer innovative high technology products and newly launched repeat-purchase products, compared to prevention focussed people. It can therefore be assumed that independent people are more likely to adopt an innovative product like AR compared to interdependent people.

Also, according to Ma et al. (2014), how consumers view themselves in relation to others has important implications for new product adoption. They found that consumers with an independent self-construal were more willing to adopt Really New Products (RNPs), while interdependent consumers were more willing to adopt Incrementally New Products (INPs). AR could be seen as an RNP, as it could provide new product categories or subcategories for companies. It can therefore be assumed that independent people are more willing to adopt AR. Based on the above-mentioned theories the following hypothesis can be formulated:

H2: The positive effect of an AR product (versus Non-AR) on consumer adoption intention is strengthened by consumer independency

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2.4 Conceptual framework

The theories and hypotheses that are stated above lead to the conceptual framework as shown in Figure 1. Figure 1 Conceptual framework Type of product (AR vs Non-AR) Consumer Adoption Intention Consumer Independence +H1 +H2

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3. Methodology

This section will describe the research methodology and how the data has been collected. Furthermore, it will describe how the statistical data analysis method has been used.

3.1 Research design

The objective of this study is to examine whether consumers are more likely to adopt an AR product compared to a non-AR product. According to the literature, independent consumers are more likely to adopt an innovation so this variable is considered to have a positive moderating effect on the intention to adopt an AR product.

Other factors might also have an impact on this model. When these factors are left out, it could lead to biased results. Therefore, control variables are added in this study. The first one is Consumer Innovativeness. Consumer Innovativeness can be defined as the tendency to buy new products more often and more quickly than other people (Midgley & Dowling, 1978). As was already explained in section 2.2.3, Kim & Forsythe (2008) found that a consumer’s level of innovativeness affects the consumer’s acceptance of AR. Gender, age and education will also be included as control variables because, as previous research indicates, they might influence the dependent variable as well (Kim & Forsythe, 2008; Rauschnabel et al., 2017; Venkatesh & Morris, 2000)

The research will be considered to have an explanatory nature, as it aims to find causal relationships between variables. In order to test these relationships, a quantitative research strategy is used. By means of an online survey, it was possible to collect a lot of data in a short amount of time. This allowed the researcher to collect width data rather than depth, which is necessary when finding out how consumers respond to AR on a large scale.

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Because the population in this research are consumers, it was easy to reach many people. In order to reach many respondents in a cost-effective way and a short amount of time, convenience- and snowball sampling was used as a way to collect respondents. Convenience sampling was done using social media and email. These respondents were mostly friends, family and colleagues of the researcher. To increase the amount of participants even more, the researcher used snowball sampling by asking the researcher’s relatives to share the online survey with their network. Through these sampling methods it was possible to reach people with different backgrounds, which will be useful when determining the effect of the control variables. The research was conducted in the Netherlands, therefore most of the people who participated were Dutch. However, also international connections of the researcher participated. The survey was only sent out at one point of time, meaning the research has a cross-sectional nature.

Before the survey was spread out, a quota of 200 respondents was chosen. Because consumers will be investigated in this research, it is not easy to make a prediction about the amount of people that can be reached. The quota was therefore determined on the basis of the researcher’s network. It was predicted that 100 people from the researcher’s direct network and 100 people from second connections would fill out the survey. This will be a sufficient amount to get an impression about the results and positively influences the external validity. The pre-established quota was reached after four days. In total 304 people filled out the questionnaire.

3.2 Description of procedure

The survey consisted of 19 questions divided among 5 parts and it took around 3 to 4 minutes to answer them (See Appendix 1). The questionnaire started with an introduction where the anonymity of the respondent was assured. This could have a positive impact on the reliability of the study, as respondents are more likely to fill in true answers. In the first part of the survey

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the questions belonging to the control variable Consumer Innovativeness where asked. It was chosen to start with this question to reduce bias. This has been done because participants could otherwise guess the purpose of the study and try to answer in a desired way. For this variable, it has been chosen to use 3 items of the scale of Manning, Bearden and Madden (1995). An example of an item was “I often seek out information about new products and brands”.

Secondly, the questions belonging to the moderator Consumer Independence were asked, where the scale of Chan, Yim and Lam (2010) was used. One of the items was for example “Group loyalty should be encouraged even if individual’s goals suffer”.

In order to measure the Consumer Adoption Intention of AR products compared to non-AR products, the example of Ikea was used. This example has been used before in the literature to examine the consumer response to AR (Javornik, 2016; Rese, Schreiber, & Baier, 2014). To measure the effect of a non-AR product, the Ikea “Home Planner” programme was presented including an image of what the programme looks like. The brand name of Ikea was not mentioned in the example, because this may influence the results. This was done by using a vignette, where the respondent was asked to imagine him/herself in the particular situation (Schoenberg & Ravdal, 2000). To measure the effect of the non-AR product, the consumer adoption intention scale of Ram (1989) was used. An example of an adoption scale item that was used was “I will continue using this programme/app”.

To measure the effect of the AR product, the Ikea app “Place” was presented with 2 images of what the app looks like. The brand name was again not mentioned in the example. The same vignette technique was used with the same consumer adoption intention scale of Ram (1989) as in the previous question (Schoenberg & Ravdal, 2000). For all the previously described items an ordinal 5-point Likert-scale was used where 1 stands for Strongly Disagree and 5 stands for Strongly Agree.

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The demographic questions were saved for last, because these were considered less important. This has been done as a precaution, so that when people would not fill in the last questions, there would still be data collected for the first part of the survey. These demographical items include gender, age, highest education and country of origin. Except for country of origin the answers were all made multiple choice to make the statistical analysis easier later on.

3.3 Reliability

The collected data consisted of words that needed to be recoded in order to analyse it. After the data has been recoded, the first step was to determine whether the scales are reliable. The reliability in this thesis has been determined for the 4 scales using Cronbach’s Alpha in SPSS. The Alpha has to be greater or equal to 0.7 to be reliable. The higher the Alpha, the higher the correlation is. This needs to be done to test whether there is a certain internal consistency in the answers of the respondents. When taking a look at the data, it can be decided to delete certain items with a low reliability or to continue to the validity analysis. The results of the reliability analysis will be discussed in section 4.1.

3.4 Validity

The final element in the data that has to be checked prior to testing the hypotheses, is whether the data has an appropriate validity. The validity of the study determines if the measurement instrument measures what it is intended to measure. When this analysis is done, it can be decided to delete items or to continue to testing the hypotheses. To determine the validity, a Factor Analysis has been done. When looking at the statistical output to the “total variance explained” table, only the factors higher than 1 need to be taken into account. Furthermore, it has been reviewed how the factors are related to the items. When the amount is higher than

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0.4, the items are considered to be relevant to the factor. The results of the validity analysis will be discussed in section 4.2.

3.5 Steps in hypothesis testing

After the reliability and the validity have been determined, a first impression of the data can be obtained. A descriptive statistics analysis has been done to get an idea about the people that participated in the research. The overview of the participants’ demographics and their distribution is explained in section 4.3.

Before starting the analyses, the mean of the items of each variable needed to be calculated and transformed into a new variable. The items belonging to the variable Consumer Independence measure the likelihood whether a consumer is interdependent. For this reason, the mean of these items have been reversed. When the new variables were made, the analysis could be started.

The first part of the analysis is to check the descriptive information of the scale variables. This has been done to see what kind of answers were given; where the mean is, whether there is a normal distribution and if there are any outliers.

Furthermore, to get a first impression about the relations between the variables, a correlation analysis has been done. This does not yet test the hypotheses, but provides an indication of how the variables are related.

The next step is the actual hypotheses testing. It has been chosen to use an ANOVA analysis, in order to take all the variables including the control variables in one model. By means of ANOVA, it can be determined whether there are any statistically significant differences between the means of two or more groups (Doane & Seward, 2009). So to test the effect of the moderator Consumer Independence on repeated measures, the moderator has to

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consumer self-construal into 2 groups: independent consumers and interdependent consumers. In Appendix 7, it can be seen that 74.3% has a score of 2.89 or lower. This point will be used to divide both groups. When transformed, it can be seen that 225 respondents score low on Consumer Independence, while 78 respondents score high on Consumer Independence. There is now a model for 2 repeated measures: Consumer Adoption Intention Non-AR and Consumer Adoption Intention AR. After the control variables are added, the ANOVA assumptions need to be checked. This can be done by performing a Levene’s Test and a Box’s Test. Both need to be non-significant in order to continue to testing the hypotheses. When this is the case, both hypotheses 1 and 2 can be tested in this model. The results of the hypotheses tests are explained in section 4.4.

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4. Results

This section will analyse the collected data. First, the reliability and the validity will be determined, followed by a description of the participants. Finally, the results of the model will be provided.

4.1 Reliability

As explained before, the Cronbach’s Alpha of the variables needs to be 0.7 or higher to be reliable. This is the case for all the variables: Consumer Innovativeness (α = 0.81), Consumer Adoption Intention Non-AR (α = 0.83), Consumer Adoption Intention AR (α = 0.84) and Consumer Independence (α = 0.81) (See Appendix 2). It has also been checked whether the measurements could be improved. In Table 1, it can be seen that the Cronbach’s Alpha will be lower when an item is deleted for the variables Consumer Innovativeness and Consumer Independence. This means that all the items belong to the scale, because when an item will be deleted the scale will be less reliable. For the other two variables the reliability could be slightly improved. In the case of Consumer Adoption Intention Non-AR, the Cronbach’s Alpha can increase from 0.83 to 0.87 and for Consumer Adoption Intention AR the Cronbach’s Alpha can increase from 0.84 to 0.86 when the second item will be deleted. However, the Cronbach’s Alpha of all the items was high enough for both variables, and when items would be deleted that are already sufficient, it could harm the content validity. Therefore, it has been chosen to not delete any items.

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Variable N of Items Cronbach’s Alpha

Highest Cronbach’s Alpha if item deleted

Items deleted

Consumer Innovativeness 3 0.806 0.781 None

Consumer Adoption Intention Non-AR 3 0.830 0.866 None Consumer Adoption Intention AR 3 0.839 0.857 None

Consumer Independence 6 0.806 0.787 None

Table 1 Cronbach’s Alpha

4.2 Validity

In the validity analysis the “Initial Eigenvalues” need to be taken into account and need to be higher than one. When the validity analysis has been conducted, 4 factors were found (See Appendix 3). The next step was to see how these four factors are related with the items. When the amount is higher than 0.4, the items are considered to be relevant to the factor. This shows that the 4 factors found belong to the 4 variables in this study, namely: Consumer Innovativeness, Consumer Adoption Intention Non-AR, Consumer Adoption Intention AR, Consumer Independence. Yet the second item of Consumer Adoption Intention Non-AR seems to have a loading with both factor 2 and 3. However, according to the literature, when the difference between two loadings is at least 0.25, the lowest loading can be ignored (Costello & Osborne, 2005). In this case the difference is 0.29, so the loading of Consumer Adoption Intention Non-AR on Factor 2 can be ignored. To conclude, the items measure what they were supposed to measure which means that the convergent and discriminant validity are appropriate and no items need to be deleted.

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4.3 Descriptive statistics

As it can be seen in Table 2, the 304 respondents consisted of 67% women and 33% men (See Appendix 4). The respondents were divided among age categories: 4% is between 12 to 18 years, 34% between 19 to 25 years, 32% between 26 to 35 years, 11% between 36 to 50 years and 18% is 50 years and older. Of the participants, 62% is highly educated, 22% is lower educated and the other 15.5% has/has only primary education, HAVO or VWO. Later on, the control variable country of origin was added, because it was expected that culture differences might show an impact on the results. For this reason, 144 of the answers for this item are missing. 161 participants filled in this question, and the answers where later on divided between Dutch and non-Dutch, because the majority was Dutch and the rest of the amount of countries was too diverse. 54.7% of these people were Dutch, while 45.3% were non-Dutch. The overview of the participants’ demographics is shown in the table below.

Gender Male 33.3% Female 66.7% Age 12 – 18 4.0% 19 – 25 34.3% 26 – 35 32.3% 36 – 50 11.2% over 50 18.2%

Education Primary school 2.6%

Havo 6.6%

VWO 6.3%

MBO / high school 22.4%

WO (Bachelor / Master) 62.0%

Nationality Dutch 54.7%

Non-Dutch 45.3%

Table 2 Participants’ demographics

4.4 Results of hypothesis testing

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with no outliers. For the variable Consumer Innovativeness (M = 3.25, SD = 0.83), the score was slightly above average. Consumer Adoption Intention AR (M = 3.89, SD = 0.81) has a higher mean compared to the mean value of Consumer Adoption Intention Non-AR (M = 3.42, SD = 0.86). This is a difference of 0.47, meaning people responded on average more positive towards the AR product compared to the non-AR product. This provides a first impression about hypothesis 1 before the actual hypothesis test takes place. The data of Consumer independence (M = 2.89, SD = 0.52) shows that people who participated are on average more the interdependent type. This is a surprising result, as most participants are Dutch, and, as explained in paragraph 2.3.1, Western European countries tend to have more independent people. This variable has a wider spread, while most respondents are neutral in this case.

After the distribution of the variables have been evaluated, the next step is a correlation analysis. The correlation between Consumer Adoption Intention Non-AR and Consumer Adoption Intention AR is strongly positive (β = 0.57), meaning the people who score high on Consumer Adoption Intention Non-AR also have a high score on Consumer Adoption Intention AR (See Appendix 6). When Consumer Independence is high, it seems that the respondents want to adopt less. This means that when the respondents are more independent, their adoption intention for non-AR products (β = -0.15) and AR products (β = -0.12) decreases. This is a surprising result, as it was predicted in section 2.3.2 that consumers with a high level of independence are more likely to adopt AR. Possible reasons for this result will explained in section 5.2. Whether this result is significant will be explained later on. For Consumer Innovativeness it is the other way around. When the Consumer Innovativeness is higher, the adoption intention for non-AR products (β = 0.24) and AR products (β = 0.30) is higher. The female respondents are more independent (β = 0.12) compared to the male respondents and older people have a lower adoption intention for AR (β = -0.22). Education has no significant effect and has therefore no influence on the adoption intention for both non-AR and AR. The

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Dutch respondents want to adopt less non-AR (β = -0.20) and AR (β = -0.26), compared to non-Dutch respondents. However, this variable has missing values and is therefore hard to include in the model. Therefore, it has been chosen not to include this variable in the further analysis.

The final step was to use an ANOVA analysis to test the hypotheses (See Appendix 7). All the variables were included in one model, so that the two hypotheses could be tested at once. Prior to testing the hypotheses, the ANOVA assumptions need to be checked. In this case both Levene’s Test and Box’s Test are non-significant, which means that the testing of the hypotheses can be performed. As it can be seen in Appendix 7, the Consumer Adoption Intention AR (M = 3.82) and Consumer Adoption Intention Non-AR (M = 3.37) have a mean difference of 0.45. This difference is significant [F(1, 297) = 78.71, p < 0.05]. These results show that the participants have a higher intention to adopt an AR product compared to the non-AR product. Therefore, hypothesis 1 can be accepted.

When looking at the data for the second hypothesis, it can be seen that the difference between Consumer Adoption Intention Non-AR and Consumer Adoption Intention AR is approximately the same for people with a low independence as for people with a high independence. The results show that there is no significant interaction effect between Consumer Adoption Intention AR and Consumer Independence, meaning that there is no prove that hypothesis 2 can be accepted [F(1, 297) = 0.792, p > 0.05].

The control variables were also included in this model. As expected, Consumer Innovativeness and age show to have has an influence on the Consumer Adoption Intention. Consumer Innovativeness seems to have a significant effect on both Consumer Adoption Intention Non-AR as for Consumer Adoption Intention AR. This means that consumers who score high on innovativeness, have a higher intention to adopt the AR product but also have a

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Intention AR, meaning that older people have a lower intention to adopt an AR product. As mentioned in section 2.5, the control variables gender and education were also included in the model. However, these variables do not have a significant effect on both Consumer Adoption Intention Non-AR and Consumer Adoption Intention AR.

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5. Conclusion and discussion

This section will present a summary of the research and its findings, the limitations and the possibilities for future research. The theoretical and practical contributions will also be discussed.

5.1 Findings

In chapter 2 it is clarified what Augmented Reality is, what Consumer Adoption Intention is, and what consumer self-construal is. Based on previous research, it is predicted how these variables are related. Two hypotheses were formulated in order to test these predictions. These were: H1: Augmented reality products compared to Non-Augmented reality products will result in increased consumer adoption intention, and H2: The positive effect of an AR product (versus non-AR) on consumer adoption intention is strengthened by consumer independency. The variables gender, age, education and consumer innovativeness were also taken into account to control for effects based on previous research.

Data needed to be collected to test the hypotheses. As explained in chapter 3, this was done by means of a survey, in which a total of 304 people participated. In the survey, the Ikea “Home Planner” programme was used as an example of a non-AR product and the Ikea app “Place” was used as an example of an AR product. To test the effect of the moderating variable, the respondents had to answer the items belonging to the Consumer Independence scale. The items belonging to the control variables gender, age, education and consumer innovativeness were also included in the survey.

As explained in chapter 4, the data was analysed using ANOVA. The results show that, as hypothesized, people indeed are more willing to adopt AR products compared to a non-AR

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that this study, within its own context, is an extension to the literature. However, for the moderating effect of consumer independency on the relationship between an AR product and the consumer adoption intention was no prove found, so this hypothesis could not be accepted. Possible reasons for this will be explained in the next paragraph. Based on these outcomes, the research question has been answered. Besides this, the control variable Consumer Innovativeness shows a positive effect on the adoption intention of both non-AR as AR products, which was also found in previous research. As expected, the outcomes also show that older people have a lower intention to adopt an AR product.

5.2 Limitations

This research has some limitations due to time- and resource constraints. A first limitation of this research is that the people who participated could have been more equally divided. 67% of the participants were women, while 33% were men. Also, most of the participants were high educated while a small part was low educated. These people are not representative for the whole population so generalization is not very accurate in this case. The age groups could have been more diverse, but from each age group was a sufficient amount of people gathered. This allowed the researcher to see the effect of this control variable.

For the moderating effect of the variable Consumer Independence on the relationship between an AR product and the consumer adoption intention was no prove found. There could have been several reasons for this. First, as explained in section 2.3.1, the consumer self-construal could be determined by someone’s culture. North-Americans and West-Europeans are more likely to be independent while Asian or African people are more likely to be interdependent. In this research, most participants were from the Netherlands, which again may not be representative for the whole population of the study. If there would have been made an equal distinction between cultures in this research, the answers for measuring the consumer

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self-construal may have been more diverse. Another possible explanation for the lack of prove for this hypothesis, is that the questions were in English. The questions of this scale were not perceived as easy and the Dutch respondents may not have understood the questions correctly. In this case it is possible that they filled in “neutral” for most items of this scale. This was also shown in the results, where most participants were placed around the median: they were not independent nor interdependent.

Another limitation of this research could be the example of AR that was chosen. While the brand name Ikea was removed from the examples, and the store was referred to as “a furniture store”, people could still recognize that Ikea was used as an example. People may have certain perceptions about Ikea, which could unintentionally influence the results. Moreover, the price range of the furniture was not provided. For example, when the furniture is expensive people may be less willing to buy the product online by means of AR. In this case they may prefer to check the quality in the store either way. Because “I will buy this couch” is an item of the adoption intention scale, this could have influenced the results.

5.3 Future research

Based on the previous mentioned limitations of this thesis, some recommendations for future research can be made. When this research would be used as a starting-point for future researchers, they should consider using quota sampling as a way to collect participants. The groups in the sample should be proportional to the groups in the population. This is especially important when studying the effect of consumer self-construal. Country of origin should then be included as a variable. The nationalities of the participants should be approximately equally divided in order to make generalizations. This may provide significant results that could prove hypothesis 2. Moreover, it is suggested to provide better results by translating the items to the

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While this research provides a general indication of what the effect is of AR on consumers, future research could focus on testing it in more detail. Beside the furniture industry, future research could focus on other industries. Travel and tourism is for example a market that is growing and has huge opportunities for AR (Martínez et al., 2014). In this field AR is often used as a way to scan a barcode, QR code or a physical object that exists in the world. By scanning the item people could visualize an enhanced view of the object (Edwards-Stewart, Hoyt, & Reger, 2016; Javornik, 2016). This is a different kind of application and the consumer response could be different. For this reason, this type of AR should be further investigated.

Moreover, this research is one of the first that investigates the effect of a consumer variable on the adoption of AR. The research of how different kind of consumers respond to AR can be extended. Next to the consumer self-construal there are other consumer variables that can be included in future research. Future research could focus on Hofstede’s other cultural dimensions. An example of another cultural dimension is “uncertainty avoidance”. People who can be placed within this culture are more likely to become nervous by situations that they consider to be unstructured, unclear, or unpredictable, and the extent to which they try to avoid such situations (Hofstede, 1984). As explained in paragraph 2.3.2, prevention-focussed people are less likely to adopt an innovation compared to promotion-focussed people (Herzenstein et al., 2007). Whether this can also be applied to the cultural dimension “uncertainty avoidance” and the adoption of AR, may be interesting to investigate in future research.

5.3 Managerial implications

The proposed results of this research could be useful for business managers and for entrepreneurs who are starting their own business. This research proves that consumers respond positively towards an AR product versus a non-AR product. Thus including AR features in a

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business, or building an AR core product may be a successful strategy and can maximize performance in the long run. However, when the targeted customers are older people or customers who do not seem to be very innovative, including AR in the business may not be the right strategy. In addition, according to the theoretical findings in section 2.3.2, implementing a successful AR strategy in countries with a high level of interdependence may be harder than implementing it in countries with a high level of independence. However, for this prediction future research is necessary.

Javornik (2016) suggests how companies can implement AR in their strategies. AR differs from other interactive technologies because of its augmentation aspect. The interactivity aspect of AR is machine- and space-related and less associated with two-way communication. So AR interactivity could lead to different consumer responses than those of other applications. Furthermore, connectivity is less present in AR apps, which can cause an absence of responses that are associated with social-interactive engagement. On the other hand, location-specificity and mobility are aspects of AR that can increase customised or convenient customer service. Companies can use these aspects of AR to influence the consumer adoption (Javornik, 2016). They can create interactive advertising and packaging, enhance retail experiences, and developing engrossing games (Scholz & Smith, 2016). Hereby they have to think about the active ingredients of AR, which are: the AR content, the users, and objects that are augmented with digital information. They also have to think about the passive ingredients, which are: non-participant witnesses and nearby non-augmented objects and ambient conditions. In order to successfully launch AR in their business they should also consider the communication objectives, target audience characteristics, content management strategies, triggers, and the social-physical context of consumers’ lives (Scholz & Smith, 2016).

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Appendices

Appendix 1: Survey

Survey

Start of Block: Introduction Dear participant,

Thank you for agreeing to take part in this survey about augmented reality products. This survey should only take 3 to 4 minutes to complete. Be assured that your answers will be kept strictly confidential.

As a special thanks for your contribution, a gift card of 15 euro for bol.com is randomly given to one participant. If you are interested in winning this gift card, please write your email address on the last question of this survey.

Page Break End of Block: Introduction Start of Block: Measuring Consumer Innovativeness

Questions 1/5

For the following statement, please indicate how much they apply to you. There are no wrong answers.

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I often seek out information about new products and brands

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree

I like to go to places where I will be exposed to information about new products and brands

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree

I take advantage of the first available opportunity to find out about new and different products

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree

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Questions 2/5

For the following statement, please indicate how much they apply to you. There are no wrong answers.

Individuals should sacrifice self-interest for the group

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree

Individuals should stick with the group even through difficulties

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree

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Individuals should only pursue their goals after considering the welfare of the group

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree

Group welfare is more important than individual rewards

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree

Group success is more important than individual success

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

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Group loyalty should be encouraged even if individual goals suffer

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree End of Block: Measuring Consumer Independence Start of Block: Measuring Consumer Adoption Intention Non-AR

Questions 3/5

Please read the following text carefully before answering the questions.

You are looking for a new couch for in your house. You can go to the furniture store, where you can see how the couch looks in the showroom.

You can also stay at home and look online for the couch you want, while using a programme that the website of the furniture store offers. This programme allows you to try out your furniture choices from the store's assortment in your self-made true to scale floor plan. Below you can view how the programme looks like, while placing the couch you like in your room.

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Please rate the likelihood of each statement according to your own perception while keeping the scenario in mind.

I will try this programme

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree

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I will buy the couch after I used this programme (assuming the couch is in accordance with your wishes)

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree

I will continue using this programme whenever I’m looking for new furniture from this particular store

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree End of Block: Measuring Consumer Adoption Intention Non-AR Start of Block: Measuring Consumer Adoption Intention AR

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Questions 4/5

Please read the following text carefully before answering the questions.

You are looking for a new couch for in your house. You can go to the furniture store, where you can see how the couch looks in the showroom or you can use the programme that was explained in the previous question.

You can also stay at home and look online for the couch you want, while using the mobile application of the furniture store. The app allows you to virtually view furniture from the store's assortment in your own real-time environment. The furniture is in 3D and true to scale so you can see whether it fits in your room.

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Please rate the likelihood of each statement according to your own perception while keeping the scenario in mind.

I will try this app

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree

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I will buy this couch after I used this app (assuming the couch is in accordance with your wishes)

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree

I will continue using this app whenever I’m looking for new furniture from this particular store

o

Strongly agree

o

Agree

o

Neutral

o

Disagree

o

Strongly disagree End of Block: Measuring Consumer Adoption Intention AR Start of Block: Demographic variables

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Questions 5/5

This is the last part of the survey. There are no wrong answers.

What is your gender?

o

Male

o

Female

What is your age?

o

12 – 18

o

19 – 25

o

26 – 35

o

36 – 50

o

50 +

What is your highest education level?

o

Primary education

o

Havo

o

VWO

o

MBO/high school

o

WO/bachelor or master

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What is your country of origin? ________________________________________________________________ End of Block: Demographic variables Start of Block: Gift card

Thank you for participating.

Please write down your email address if you want to win the gift card.

________________________________________________________________

End of Block: Giftcard

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Appendix 2: Reliability output

Consumer Innovativeness:

Reliability Statistics

Cronbach's Alpha Cronbach's Alpha Based on Standardize d Items N of Items .806 .806 3

Consumer Adoption Intention Non-AR:

Reliability Statistics

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Consumer Adoption Intention AR:

Reliability Statistics

Cronbach's Alpha Cronbach's Alpha Based on Standardize d Items N of Items .839 .848 3 Consumer Independence:

Reliability Statistics

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Consumer adoption of augmented reality products