2018
Caroline Doove
(11418974) 12-08-18 MSc in Business Administration (Marketing track)Supervisor: Drs. Frank Slisser
Mobile augmented reality in a retail context
“Factors affecting the intention to use a mobile augmented
reality application”.
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Statement of originality
This document is written by Student Caroline Doove, who declares to take full
responsibility for the contents of this document.
I declare that the text and the work presented in this document is 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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AbstractMany companies have shown interest in mobile augmented reality applications for retail
purposes. Therefore, it is expected that in the coming years a lot of money will be
invested in this relatively new technology. However, a lot of those applications that were
already available have failed to succeed. Not much research has been done on the factors
that affect the use intention for those apps. The purpose of this study is to conduct more
research on the most important factors affecting the variables from the technology
acceptance model (TAM), which are; the perceived usefulness (PU), perceived ease of
use (PEOU) and the perceived enjoyment (PE) of users of a mobile augmented reality
application. Furthermore, the effect of PU, PEOU and PE on attitude and on the use
intention will be examined. An experimental method has been used, by which 99
participants took part that all belong to generation Y. All the participants had to use the
mobile augmented reality application, IKEA PLACE, after which they had to fill out a
questionnaire. The results of this study show that virtual product experience has a
significant positive effect on PU. Furthermore, authenticity and virtual product
experience turned out to have a significant positive effect on PE. Additionally, this study
confirms findings of earlier research in which it was already mentioned that perceived
usefulness is the most important factor in affecting the attitude and the use intention of
information systems.
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Table of Contents
1. Introduction ... 5 Background ... 5 Problem discussion ... 6 Purpose ... 9 2. Literature review ... 12 Beginning of AR ... 12Difference between AR and VR ... 12
AR in different contexts ... 14
AR mobile shopping applications ... 15
3. Theoretical foundation and research model ... 16
4. Conceptual model ... 25
5. Methodology & Data collection ... 25
Introduction ... 25 Research philosophy ... 26 Research approach... 26 Research design ... 27 Data Collection ... 27 Experimental setup ... 28 Questionnaire design ... 29 Measures ... 29 Sampling ... 30 6. Research results... 31 Descriptive statistics ... 31 Reliability tests ... 31 Correlations ... 32 7. Discussion ... 36 8. Conclusion ... 38
9. Limitations & future research... 38
References ... 40
Appendices ... 46
Appendix A - Questionnaire ... 46
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1.
Introduction
“One day, we believe this kind of immersive, augmented reality will become a part of daily life for billions of people”
Mark Zuckerberg, CEO Facebook (Facebook, 2014) Background
A few years ago, many people expected that augmented reality (AR) was one of the most
promising technologies. The website www.Gartner.com showed and explained it in 2010 already by using their “hype cycle”. With this cycle they map their predictions about information
technology trends and investments prospects. According to them different fields are going to use augmented reality technologies, such as the medical, educational, entertainment and the robotic. Especially augmented reality integrated in mobile phone apps was expected to be created fast (Gartner, 2010).
In 1950’s augmented reality has been introduced for the first time and since then the quality of augmented reality has increased tremendously (Carmigniani, Furht, Anisetti, Ceravolo, Damiani, & Ivkovic, 2011). There are many different definitions for augmented reality. Faust et al (2012) define it as “the superposition of virtual objects (computer generated images, texts, sounds etc.) on the real environment of the user”. Augmented reality contains the possibility to overlay the physical environment with images, objects and information in real time (Javornik, 2016). The theory on augmented reality mentions that it is the combination of real and virtual imagery, it is interactive and in real time and it registers virtual imagery with the real world (Azuma, 1997). With the launch of the Pokémon GO app in 2016 it was the first time that augmented reality (AR) was used worldwide on such a big scale. This showed that mobile augmented reality is possible to be adopted and used by the mainstream (Rauschnabel, Rossmann, & Dieck, 2017). AR is a medium with which it is possible to show users a combination of the physical world and a digital world by integrating these two forms in one new platform (Javornik, 2016). In a report about virtual and augmented reality of Goldman Sachs (2016) it is mentioned that; “AR can become the next big computing platform, and as we’ve seen with the PC and smartphone, we expect new markets to be created and existing markets to be disrupted”. Due to the improvement of AR technology and the success of the Pokémon Go app, other industries gained an increasing interest in developing and using AR, such as the retail industry. E-commerce retailers noticed
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the new technology and are experimenting with how they can integrate it in their shopping channels (Li & Meshkova, 2013).
Problem discussion
Many decisions of companies drive on the technological possibilities that they have at that moment in time. This also counts for decisions about marketing and advertising that those companies have to deal with. The marketing and advertising tools that companies can use have increased over the past years. However, there is not a lot of information available on the effects of those new tools. This means that marketers need to base their decisions about these new technologies on their instinct instead of evidence-driven theory. Augmented reality is one of those new technologies that companies can use. As mentioned above e-commerce retailers have gained an increasing interest in augmented reality, since it has the potential to improve the shopping experience (Yaoyuneyong, Foster, Johnson, & Johnson, 2016).
Research has shown that augmented reality applications are able to provide entertainment and experiential value, however the effects of those apps on consumer behaviour are still unclear. Therefore, marketers do still not know whether augmented reality is an effective marketing tool or if it is just a cool “gimmick”. E-commerce retailers face a lot of competition. Therefore, it is important that companies differentiate themselves in order to gain competitive advantages. One way of doing this is adapting to new technologies, such as integrating augmented reality in shopping channels, in order to improve the consumer experience (Li & Meshkova, 2013).
Many big companies, such as L’Oreal have introduced an augmented reality app that people could download via the app store. Users of these apps could virtually try-on products. In most cases the apps created a so-called ‘wow-effect’ when people used them for the first time, since the users were new to AR and they were enthusiastic about the technological possibilities. However, many of these apps have failed to succeed and were removed from the Apple store after a few months (Rauschnabel, Rossmann, & Dieck, 2017).
While many AR mobile apps fail, a lot of money is and will be invested in the AR industry the coming years. It is expected that the AR industry will reach 56,8 billion US Dollars (MarketsandMarkets, 2015) by 2020 and the estimated revenue is 120 billion US Dollars (Gaudiosi, 2015). Furthermore, some of the big technological companies, such as Facebook, Snapchat, Microsoft and Google are starting to create augmented reality experiences, which
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shows the serious interest in AR by the big players in this field (Kharpal, 2017) (Robertson, 2017) (Kirckpatrick, 2017) (Warren, 2017).
Apple developed a software package called “ARKit” and a while later Google also came up with “ARCore”. These are both software packages with which it is possible to develop mobile augmented reality applications. Before the launch of these software packages it was already possible to develop apps with AR, but these software packages make it much easier for developers to create them. In this way companies can easier develop and integrate this new technology now (ICulture, 2017).
IKEA already launched an app called “IKEA Place” in September, which makes use of the ARkit software. With this app you can virtually put furniture in your own house to see how it looks before buying the product (IKEA, 2017). A few months earlier, Amikasa and Bol.com launched an identical app (ICulture, 2017).
Since the rise of the Internet, the culture of retail changed from only offline sales to online sales as well. Online sales have grown steadily over the last few years. Due to emerging technologies, the interest in mobile shopping has increased as well. Nowadays, consumers can buy products online via their mobile devices either on a website optimized for a mobile device or via mobile applications. In this way it is possible for mobile users to buy products wherever and whenever they want. With a shorter transaction time, a different interface and a smaller screen, the shopping experience between an offline and an online purchase differs. One of the biggest differences between physically shopping in a shop and ordering online is that a consumer cannot see, feel or try the product before purchasing it (Pantano & Priporas, 2016) (Li & Meshkova, 2013). This causes uncertainty about how the product will look in reality. Already conducted research has concluded that this leads to consumers experiencing difficulties when evaluating products. These difficulties seem to be the biggest for products such as clothing and furniture (Smith, Johnston, & Howard, 2011).
Augmented reality is one of the many examples of new technologies that are available for marketers and retailers and it could solve the product evaluation problem for consumers by increasing the product knowledge, which will decrease uncertainty, lower the perceived risk of an online purchase and therefore increase the purchase intention of consumers (Li & Meshkova, 2013). Augmented reality makes it possible to virtually put the product in the hand of a consumer. In this way brands can engage with their consumers (Yaoyuneyoung, Foster, Johnson, & Johnson, 2016). In a research on 3-D advertising in 2008, they found already that when showing a compelling online virtual experience with products, it is possible for advertisers to provide consumers with more valuable product information. Furthermore, they mention that
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d product advertising exceeded the performance of 2-d product advertising (Daugherty, Li, & Biocca, 2008).
In a research by Dacko in 2017 about mobile augmented reality shopping apps (MARSA), users were asked about their experience with those apps. The survey included questions on where the users used such apps, what they find the benefits in using them, and which of those benefits they valued the most. The answers revealed that the respondents found that MARSA have benefits that they would normally not get in a shopping experience. 56,6% of the respondents believed that it could provide them with more complete information on products and 42,2% mentioned that MARSA has the benefit that you are more certain that you are buying what you want. Furthermore, 27,3% of the respondents answered that ‘trying’ out a product before buying it was a benefit that they would normally not get in an online shopping experience. The respondents were also asked about what they find the drawbacks or negative aspects of (MARSA). Only 9% answered that they find the apps difficult to use. Since the perceived ease of use of a new technology influences the intention of someone to use a technology, this is an important given (Dacko, 2017).
Nowadays, there are so many mobile applications available. Many investors have gained interest in developing and investing in those apps, since it is quite easy to enter this market. Therefore, so many apps have been created and there is an app for almost everything. This leads to a very competitive market, where consumers easily switch between different apps and easily remove applications and download new ones. According to Rao (2011), the percentage of people that still use a mobile application after one month is approximately 33% and after a year it is not more than 4%. Therefore, in order to be able to say whether an application is successful it is less important to look at how many times an app has been downloaded, than looking at whether people still intent to use it after their initial usage. Since, if an information system won’t be used anymore after the initial adoption, it will not be successful (Ding & Chai, 2015).
From a theoretical point of view this means that in this case it is important to look at the intention of consumers to use an information system and the factors affecting this intention. Since, the intention to use such a system after initial adoption will eventually influence the continuous intention. The continuous intention can be measured when people have used an information system for a while, whereas the acceptance of a technology and the use intention (UI) can also be measured when people have no previous experience with a system (Hu et al, 2009). Lin (2011) defines continuance behaviour as; “the continued usage of an information system by adopters, where a continuance decision follows an initial acceptance decision”. Since
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MARSA is quite a new technology and not many people have used it yet, this study will focus on the use intention.
Purpose
The purpose of this research is to close the gap within the field of augmented reality. The focus of this study is on the commercial potential of this new technology, which Javornik (2016) and Yadav and Pavlou (2014) suggested in their recent research. The technology of augmented reality has been there for a few years, but we still don’t know what the influence is of this technology in the retail context on the behaviour of consumers. As Javornik (2016) mentioned in her article on the research agenda for augmented reality: “How exactly users are drawn into this new form of reality and what effects it has on them has not been exploited in consumer behaviour literature.” More knowledge on the effects of these new interactive technologies will contribute to the existing knowledge on consumer behaviour.
Academical relevance
The outcomes of this research will contribute to the already existing theory on the effects of consumer behaviour in the context of interactive technologies and it will look at the technology acceptance and the behavioural intention of consumers to use of mobile augmented reality in a retail context. This draws on earlier research of Kim et al. (2016) that used the technology acceptance (TAM) model to conduct research on the use intention of augmented reality apps in general. They used three variables that are supposed to be most important for the evaluation of augmented reality apps, which are visual quality (VQ), information quality (IQ) and interactivity (INT). In addition to these variables, the effect of product knowledge (PK), Virtual product experience (VPE) and Authenticity (AUT) on the intention to use will be tested in this research, which has not been done before.
The theoretical contribution of this research will be discovering the effect of augmented reality on the behaviour of consumers, and thus on the variables that are seen as determinants of this behaviour. According to Kim. Et al (2016), research on augmented reality in an e-commerce context is scarce. Furthermore, the research will be conducted by using a quantitative method in an experimental setting, which has not been done before in this context. This makes it possible to investigate the cause and effect relationship of augmented reality shopping applications and the use intention. There have been some studies that have conducted research on the attributes of mobile AR applications and on the technological aspects, but not many on factors influencing the behavioural intention of the users of those mobile apps (Kim, Kim, Choi, & Trivedi, 2017).
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Managerial relevance
From a managerial perspective, this research will contribute in several ways as well. Due to the increasing interest in augmented reality by companies, the continuous improvement of the AR technology and the increasing investments in combination with many failing mobile augmented reality e-commerce applications, research needs to be done on the factors affecting the behavioural intention to use these apps after initial usage. With the results, suggestions can be made for app developers and e-commerce retailers will have more knowledge for deciding whether they want to invest money in this new technology. Furthermore, they will gain insight in which factors influence the behavioural intention to use, so that they can keep these factors in mind while designing and developing those apps. According to Yadav and Pavlou (2014) more in-depth investigation of this new form of human-computer-interaction is very necessary. If more knowledge will be gained on consumers’ behavioural intentions, practitioners can use this to create effective AR campaigns instead of failing apps (Javornik, 2016).
Hence, in summary, since the rise of the Internet a lot of new technologies have been developed. One of these technologies are mobile applications. They have been developed for many different purposes and one of the industries that are using them is the retail industry. Via these apps they can offer services and products in order to target consumers (Lee, Cha, & Cho, 2012) (Strom, Vendel, & Bredican, 2014) (Nagar, 2016). Mobile apps can arouse interest by consumers, since they are able to provide them with useful information about products and services. Retailers try to implement this new form of technology strategically in their retail channels, but many of them are still struggling how to use it in the most effective way (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011) (Oliveira, Thomas, & Espadanal, 2014). Since there is a lot of competition in the retail industry it is important for many of them to keep innovating in order to keep competitive advantages. One way of innovation is the creation of a mobile augmented reality shopping application. Via these apps consumers have access to more product information before buying a product and without actually trying a product. Many of those MARSA have failed. Research has been done on the adoption of MARSA in general, but there is a scarce amount of research on the technology acceptance and the intention to use those apps in a mobile shopping context
(Javornik, 2016).Therefore, the use intention and factors affecting this will be tested by using an experimental method, in which users can actually interact with the technology before filling out the questionnaire.
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The introduction and the problem discussion led to the following research questions; Research questions:
1. Which important antecedents have an influence on the perceived usefulness (PU), perceived ease of use (PEOU) and perceived enjoyment (PE) of mobile AR shopping applications? 2. How do these (coherently) influence the attitude (ATT) and the use intention (UI) of users
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2.
Literature review
In the following paragraphs the already existing literature on augmented reality, mobile
shopping, the technology acceptance model, behavioural intention and external factors that will possibly have an influence on the technology acceptance model and the use intention will be discussed. Additionally, the frame of reference will be reviewed, which will eventually lead to the conceptual model.
Beginning of AR
There are many different definitions for augmented reality. Faust et al (2012) define it as “the superposition of virtual objects (computer generated images, texts, sounds etc.) on the real environment of the user”. The theory on augmented reality mentions that it is the combination of real and virtual imagery, it is interactive and in real time and it registers virtual imagery with the real world (Azuma, 1997). Augmented reality shows users “information that is directly registered to the physical environment”. This information will be shown in some sort of layer over the real world so that the user will perceive the combination of both layers as the “real” world (Schmalstieg & Holerer, 2017). The idea of combining the real and the virtual world in order to bring them closer together led to the initial idea of creating augmented reality systems (Olsson, Lagerstam, Karkkainen, & Vaananen-Vainio-Matilla, 2011).
In 1950’s augmented reality has been introduced for the first time when the cinematographer Morton Heilig thought of the ability to draw people in cinemas in what they saw on the screen. In 1962 he built a prototype of this idea. In 1968 the first head mounted display with AR was invented. A few years later, in 1975, it was the first time that it was possible to interact with virtual objects in a room called “the Videoplace”, created by Myron Krueger (Carmigiani, Fuhrt, Anisetti, Ceravolo, Damiani, & Ivkovic, 2011).
In the 1990’s augmented reality became bigger and more relevant due to the rise of the Internet, invention of GPS, increased mobility and portability of technology. It became easier to use AR and therefore it gained a higher relevance. From this moment people really started to use the term “augmented reality” (Javornik, 2016).
Difference between AR and VR
As discussed above, there are many different definitions of augmented reality. The reality-virtuality continuum developed by Milgram and Koshino (1994), makes a distinction between different types of digital technologies that integrate and merge virtual worlds and the real world into each other where physical and virtual objects complement, support and interact with each
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other (Ohta & Tamura, 2014). AR and virtual reality (VR) can both be found on this continuum of mixed reality. The level of reality and virtuality can define where a technology can be placed on this continuum. Four types of different environments with different levels of reality and virtuality are displayed on the image of the mixed reality continuum below. Milgram and Koshino suggest that mixed reality environments lay between the real and the virtual environment and that between a real and a virtual environment there is augmented reality and augmented virtuality. These four environments all have different characteristics. A real environment is defined as one where there are only real objects, whereas a virtual environment is made of only virtual objects. Augmented reality is placed on the left side of the middle on the mixed reality continuum. This implies that AR is a technology in which an environment is created that consists of reality and some virtual objects (Kim, Hwang, & Zo, 2016).
Figure 1. Mixed reality continuum adapted from (Milgram & Kishino, 1994)
The difference between AR and VR is that users of AR can still see their surrounding and hear the sounds around them, but with a layer of additional elements such as sounds and sights. It contains the possibility to overlay the physical environment with images, objects and information in real time (Javornik, 2016). These additional elements are synchronized to the exact location relative to a user’s three-dimensional (3D) orientation to a geographic locale (Pavlik & Bridges, 2013). In contrast, users of VR are completely immersed in a computer generated and synthetic environment. In VR a user is completely locked-out from his “real” environment and will only experience everything within the artificial environment. This can cause that users won’t have any idea about the time and space anymore. Therefore, AR can be seen as a tool to enhance and complement a user’s interaction with reality, whereas this is not possible with VR (Fuhrt, 2014).
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AR in different contextsNowadays, people are using their mobile devices more and more. They use their mobile devices not only to communicate with others, but also for many other tasks for which they would use a desktop computer back in the days. This leads to the shift from desktop interaction, to
interaction with mobile and wearable computers. Where people can interact with these devices anytime and anywhere (Satyanarayanan, 2001) (Ware & Balakrishnan, 1994).
As mentioned before, AR has been used in many different contexts. First it was more used for military and medical purposes, but nowadays there is an increasing interest in AR for commercial use, use in journalism, sports, marketing or the entertainment industry. Over the past years, more contexts have been found in which it can benefit. AR on smart devices, such as a mobile phone, or on big screens are the most common way of how AR has been used. Smart devices possess an operating system, GPS and a camera, which is needed for using AR on it. (Krevelen & Poelman, 2010) The fact that the real and the digital world can be combined into one single interface led to the possibility to develop new apps and services that make use of this new technology (Olsson, Lagerstam, Karkkainen, & Vaananen-Vainio-Matilla, 2011).
As mentioned in the introduction, the mobile application, “Pokémon GO”, showed that it is possible for an AR app to go viral and to be used by the mainstream. This was great evidence for the gaming industry, that in some cases it is worth it to integrate AR in games (Rauschnabel, Rossmann, & Dieck, 2017). Another success story of a context in which AR has been successful is by looking at the application Snapchat. This is an application, which is mainly popular because of the possibility to use so-called “lenses”. Users can overlay their faces with a “filter”, take a picture and send this picture to people in their network (Kar, 2016).
In a retail context, augmented reality on a smart device allows a user to see virtual products in a real environment or to try products virtually with a virtual try-on mirror. In this way it is for example possible to place furniture in a room virtually or to try on make-up, clothes or sunglasses on oneself with a virtual try-on. This makes it possible to see how a product looks before really buying it. It was already possible to see how products looked with websites where you could upload a picture of yourself to see how something would look on you. This is much more static and less interactive than AR in a mobile app, where it is possible to move and try it in real-time (Javornik, 2016).
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AR mobile shopping applicationsMobile augmented reality (MAR) is AR on a mobile wireless device, such as a smartphone or a tablet. It is defined as “AR created and accessed with mobile devices in mobile contexts of use” (Olsson, Lagerstam, Karkkainen, & Vaananen-Vainio-Matilla, 2011). Since a few years many developers have created apps that are available worldwide with integrated AR. In comparison to other more traditional devices, the development of AR is the biggest for mobile devices. The AR on mobile devices is integrated in mobile apps (Kim, Hwang, & Zo, 2016). After downloading such an app on a mobile device one can use the augmented reality system that is integrated in such an app by pointing the camera of the device towards a point in his or her environment. In this way the additional information that is provided by the augmented layer can be found on the display of the mobile device (Linaza, et al., 2012).
There are two types of mobile AR apps; marker-less apps and marker-based apps. Marker-based mobile apps need a camera in order to scan a code. This code can be a QR code, a barcode or another image. The camera of a mobile device needs to scan the code so that the device can convert the code into a grayscale image to accelerate the image-processing algorithm. This algorithm will use the code or the image that the camera has scanned to create the augmented virtual object and to place this “virtual layer” onto the physical world. Marker-based apps cannot be used anytime and anywhere without a “marker”, whereas with marker-less apps it is possible to use them anytime and anywhere. Marker-less apps use the data of your mobile device to find out the location of the user in order to add the virtual layer on that environment (Olsson, Lagerstam, Karkkainen, & Vaananen-Vainio-Matilla, 2011) (Kim, Hwang, & Zo, 2016).
As mentioned before, AR is more useful for e-commerce than VR, since with AR it is still possible to see the “real” world. Examples of in which case AR can be used are; virtually trying on clothes or accessories or for virtually furnishing a room. As mentioned in the introduction, the biggest challenge for e-commerce retailers is overcoming the problem for customers of not being able to feel, touch or smell a product when shopping online, this problem seems to be the biggest for the evaluation of clothing and furniture. Augmented reality could solve a part of this problem, because of the possibility of trying and seeing products virtually (Jung & Cho, 2014).
The virtual layer that is added onto the real environment can give additional information. In this way consumers can see how products look on themselves or in a room before buying the product. It can improve the way consumers can evaluate a product. Nowadays it is possible to place furniture virtually in your environment, in order to see whether you are satisfied with the
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product before buying it. This can reduce the uncertainty of consumers about a product before buying it and it give more complete information on products. These are also two of the most mentioned unique benefits of augmented reality shopping applications in the article of Dacko (2017). Traditional e-commerce did not provide enough product information about products and this caused that people often return products that they bought online or that they do not buy products online anymore (Lu & Smith, 2007). Likely, there is an increasing interest under commercial retailers to use AR for commercial purposes in their shopping channels, such as mobile shopping (Yim, Chu, & Sauer, 2017).
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Theoretical foundation and research model
In the following paragraphs the variables that will be tested are explained and discussed. First the theories on information system usage and the TAM-model will be discussed and then the variables information quality, visual quality, interactivity, product knowledge, authenticity and virtual product experience.
The research that has been done on information systems is mostly based on cognition-based behavioural models. These models include; the technology acceptance model (Davis, 1989), the theory of planned behaviour (Azjen, 1991) and the information system continuance model. (Battarchjee, 2001). These cognition-based models take the mental process of knowing, including aspects such as perception, reasoning and judgement into account. It is also based on beliefs. The beliefs result from perception and reasoning (Kim, Chan, & Chan, 2007). A belief can be defined as the individual’s subjective probability that performing the target behaviour will result in a specified outcome (Fishbein & Azjen, 1975).
A theory that is often used for the evaluation of innovations, that is applicable to AR, is the theory of diffusion of innovation of Rogers. The initial theory was developed by Gabriel Tarde, but Everett Rogers became well known for it. Rogers describes five stages that an innovation goes through divided into five different groups. These groups are based on types of people and on how they accept a new idea or innovation (Rogers, 2003).
The technology acceptance model (TAM) is a well-known theory that Davis developed in 1989. The model can be used for studying how users accept and use a technology. It is based on the theory of reasoned action of Fishzbein and Azjen (1975), which states that someone’s attitude can be used to predict his or her behavioural intention. According to Davis in 1989, perceived
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ease of use (PEOU) and perceived usefulness (PU) are major predictors of the technology adoption. Perceived usefulness is defined as: “the degree to which a person believes that using a particular system would enhance his or her job performance”, (Davis, 1989) and perceived ease of use (PEOU) is defined by Davis (1989) as: “the degree to which a person believes that using a particular system is free of effort”. These two elements influence the attitude of someone towards adopting the new technology (Davis, 1989).
There are many studies that have shown support for adding other variables to the explanatory power of a TAM-based model. One of these variables that have been discussed often is ‘perceived enjoyment’ (PE). Information system scholars have tested the effects of PE on the technology acceptance and the results supported the effects of it on the adoption of several information systems (Childers et a., 2001) (Cyr & Head, 2008).
The technology acceptance model will be the theoretical framework for this research. However, the experience and interaction that humans have with a computer or information system depends very much on the context (Coursaris & Kim, pr, 2007). Davis (1989) mentions that there are a lot of possible factors that can influence the usage and behavioural intentions for technologies.
Perceived usefulness (PU)
Perceived usefulness is seen as an important factor that influences the acceptance of a
technology. In the case of mobile shopping apps, PU can be seen as the perception that using the app will cause a higher efficiency and productivity and that it will be helpful for making
shopping decisions as well as for the whole task in general (Rezaei, Shahijan, Amin, & Ismail, 2016). According to Davis (1989), the PU will influence the attitude of a user towards the application and it will also influence the use intention.
The positive effect of perceived usefulness on the use intention was confirmed in another research on mobile shopping via apps, where perceived usefulness and confirmed expectations were both found to have a strong influence on the intention of users to use those apps (Rezaei, Shahijan, Amin, & Ismail, 2016) (Shang & Wu, 2017). In the research on mobile augmented reality apps by Kim, et al (2016), PU influences the intention through satisfaction, but also directly. This was already proven in the research of Battarchjee (2001), who mentioned that perceived usefulness is a stronger predictor of the user satisfaction and intention to use than perceived enjoyment. Therefore, they mention that developers of mobile augmented reality apps should focus more on the usefulness of new apps than on the enjoyment factors. To test the
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effect of perceived usefulness on attitude and the use intention, the following hypotheses have been made:
H1: There is a positive relationship between PU and Attitude
H2: There is a positive relationship between PU and the Use Intention
Perceived ease of use
Just like the perceived usefulness, the perceived ease of use is also seen as an important factor in determining the use intention of users of an information system. When users believe a system is easy to use, their intention to use the application is likely to increase and this has been
supported in many studies (Coursaris & Sung, 2012 ). The research on the use intention of mobile augmented reality by Kim et al. (2016) also showed the positive relationship between the perceived ease of use and satisfation and the use intention. In order to test the influence of the perceived ease of use on the attitude and the use intention in the context of a mobile augmented reality shopping application , the following hypotheses have been made. H3: There is a positive relationship between Perceived ease of use and Attitude
H4: There is a positive relationship between Perceived ease of use and Use Intention
Perceived enjoyment (PE)
Through the years the theories on information system acceptance and usage have been
expanded with new concepts for the evaluation of new technologies (Kim, Hwang, & Zo, 2016). As mentioned before, according to many scholars including Van der Heijden (2004), there is another key factor that determines the intentional behaviour of an individual. This key factor that includes hedonic value is the perceived enjoyment of an individual. Van der Heijden mentions that the perceived enjoyment can play a big role in the validity of the technology acceptance model and that perceived usefulness loses their dominant role when users perceive an information system enjoyable and easy to use. Furthermore, Van der Heijden mentions that perceived enjoyment can have a direct effect on the intention to use and a positive influence on the attitude towards a mobile device or a website.
According to Chiou (2004), satisfaction is linked to a user’s perception and whether someone had an enjoyable experience is in relation with his or her intention to use the system again. A research conducted by Shang and Wu (2017) on mobile shoppers’ use intention mentions that satisfaction is one of the two most important factors that influence the intention of a user. Song
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et al (2014) mention that in the case of mobile applications, a user’s satisfaction after using the app, will lead to a “pleasurable fulfilment” of the system. When a person is satisfied with such a system, it is likely that he or she will use it again (Rezaei, Shahijan, Amin, & Ismail, 2016). In the context of online consumer behaviour, Koufaris (2002), mentions that the enjoyment that people gain from shopping, plays an important role in whether someone intents to return to an online store.
The research by Kim et al (2016) does not confirm the effects of perceived enjoyment in the context of mobile AR apps. They explain their results by discussing the fact that perceived enjoyment can cause curiosity and popularity when an app is launched, but that it will not guarantee the success of an app in the long-run (Kim, Hwang, & Zo, 2016). However, this
research was based on augmented reality applications in general and not in the mobile shopping context. To test the effect of perceived enjoyment in the context of augmented reality mobile shopping applications, the following hypotheses have been made.
H5: There is a positive relationship between Perceived enjoyment and Attitude
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Description of AntecedentsIn the following section the important antecedents that are not included in the technology acceptance model, but that potentially influence these factors, will be discussed. Per variable the hypotheses will be presented.
Authenticity (AUT)
Augmented reality can provide users of mobile shopping with additional product information. If the product on your mobile phone looks a lot like how the product would look like in reality, it can help someone with the evaluation of the product. In order to measure whether the product presentation looks like a product presentation as in reality, Algharabat and Dennis (2010) came up with the concept of 3D authenticity. According to them: “authenticity captures consumers’ psychological state in a way that consumers perceive the virtual objects comprised in the 3D over the virtual area as actual objects”.
Regarding online shopping experiences, it is important that consumers perceive the products that are shown in the online experience as if it is a real product. Since, a high level of authenticity in online shopping will make it easier for the consumers to evaluate a product and will decrease uncertainty, which will satisfy their needs and will make the application useful when shopping for products (Algharabat and Dennis, 2010). Therefore the following hypotheses have been made;
H7: There is a positive relationship between AUT and perceived usefulness H8: There is a positive relationship between AUT and perceived ease of use H9: There is a positive relationship between AUT and perceived enjoyment
Virtual product experience (VPE)
According to Agharabat, (2016) there is always a need to study consumer perceptions and experiences towards all the factors related to online shopping and especially virtual product experience. The virtual product experience has been studied before in research on online shopping on electronic websites. Li at all. (2001) defined virtual product experience (VPE) as: “the psychological and emotional states which consumers have once interact with products in a 3D environment”. When the quality of the virtual product experience is perceived as high, the consumer can have the feeling that he has the ability to feel, touch and try the products and experience the evaluation of the product the same as when evaluating a product in a store. The main purpose of a virtual product presentation is that a consumer understands more about the
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performance, the functionality and about the product features, which can lead to a more pleasurable experience (Algharabat, Rana, & Dwivedi, 2017).
Jiang and Benbasat (2005) mention that the VPE can be classified into two types; visual control and functional control. The functional and visual control can be perceived by consumers as high or low. If someone for example can’t rotate a product on an electronic website which causes that you can’t see all the sides and angles of a product, the functional and visual control can be perceived as low. Jiang and Benbasat (2005) have measured the impact of these two on the products that were presented in 3D. They found that a high level of visual and functional control leads to a higher level of perceived diagnosticity. By diagnosticity is meant, how much do
consumers believe that the specific product presentation was helpful for the evaluation of the product. Furthermore, higher levels of visual and functional control led to a higher level of “flow”. Flow refers to the affective responses of users towards using computer systems, by looking at the playfulness and the exploration (Algharabat, 2016).
According to the discussed literature the following hypotheses have been made: H10: There is a positive relationship between VPE and perceived usefulness H11: There is a positive relationship between VPE and perceived ease of use H12: There is a positive relationship between VPE and perceived enjoyment
Product knowledge (PK)
As mentioned in the introduction, in the research of Dacko in (2017) about mobile augmented reality shopping apps (MARSA), the answers of the users revealed that MARSA can provide users of those apps with more product knowledge and that they find this a unique benefit that they would not have in a normal experience.
In the literature, scholars talk about two kinds of experiences people can have with a product, either direct or indirect. A direct experience is related to when someone sees a product in real life, for example in a shop. In such an experience, the person can touch, feel and try the product. An indirect experience means that someone sees an image, an ad or a picture of the product on a website for example. Telepresence is seen as something in between a direct and an indirect experience. According to Steuer (1992), telepresence can be described as “the extent to which users can participate in modifying the format and content of a mediated environment in real time”. Telepresence is also known as a virtual experience.
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Li, Biocca and Daugherty (2001) describe a virtual experience as a simulation of a real or physical experience, which occurs within a computer-mediated environment. Such an experience can be gained from an advanced e-commerce tool like an augmented reality tool or when seeing an interactive video for example. In their study in 2008 they looked at the levels of three variables after having a direct experience, an indirect experience and a virtual experience with a video camera. One of those three variables was product knowledge. The remarkable finding of this research was that the participants who underwent a virtual experience reported a significantly higher level of product knowledge than the participants who underwent an indirect experience, but also higher than the ones that underwent a direct experience. In their research article they mention that this higher level of product knowledge has a positive effect on attitude and purchase intent (Daugherty, Li, & Biocca, 2008).
In this research the effect of product knowledge on the perceived usefulness, the perceived ease of use and perceived enjoyment will be tested, to find out whether the product knowledge gained via a virtual experience will have a positive influence on the intention to use augmented reality mobile shopping apps. Due to the fact that increased product knowledge has a positive effect on attitude and purchase intention (Daugherty, Li, & Biocca, 2008), and that users of mobile AR shopping apps in the research of Dacko (2017) have mentioned that they see the higher level of product knowledge as a unique benefit of mobile AR shopping apps since it provides more complete information and it will give you more certainty that you are buying what you want, the following hypotheses will be tested:
H13: There is a positive relationship between PK and the perceived usefulness H14: There is a positive relationship between PK and perceived ease of use H15: There is a positive relationship between PK and perceived enjoyment
Information quality (IQ)
When consumers look for products they will seek for information about these products. The quality of this information depends on how users perceive the quality of outputs that the information system presents to users (DeLone & McLean, 1992). According to Algharabat and Abu-ElSamen (2013), the level of the perceived 3D information quality is based on how accurate, relevant, complete and precise the content of the presented product is that the consumers seek for. The provided information should make it easier for consumers to evaluate and understand the quality and the performance of the product that they are looking for.
Many researches have concluded that when looking at information systems, the quality of the information influences the perceived usefulness. In the context of mobile applications with integrated AR, the virtual objects need to be presented very precisely and need to be tracked and
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registered in a way that the user perceives the presented information as of high quality. In a research of Kim, Hwang and Zo (2016) about the continuance intention of smartphone augmented reality applications, they found that information quality had a strong influence on the perceived usefulness.
As mentioned above, information quality depends on whether the information is complete, accurate, relevant and precise. When this is the case, users could have the feeling that they can easily access the information and extract it from the system in order to find what they need. This could lead to an increased perceived ease of use (Wixom & Peter, 2005).
When the quality of the information that consumers seek for is high, it will satisfy the expectations of the user. According to Shin (2007), the factors that determine the perceived information quality are equally import and they have an impact on the perceived enjoyment of an information system. When users of an information system find the information quality not high enough, they may have the feeling that using the system is not an enjoyable experience. H16: There is a positive relationship between information quality and perceived usefulness H17: There is a positive relationship between information quality and perceived ease of use H18: There is a positive relationship between the information quality and the perceived enjoyment
Interactivity (INT)
Augmented reality can be seen as a very interactive technology. Interactivity is defined by Steuer (1992) as; the extent to which users can participate in modifying the format and content of a mediated environment in real time. When users perceive this environment as highly interactive, it can give them the feeling that they are in control and give them a sense of a feeling of
autonomy (Kim, Hwang, & Zo, 2016). In the context of augmented reality, this can be linked to the possibility to manipulate virtual objects.
According to Wojciechowski and Cellary (2013), the possibility to manipulate virtual objects in an AR context, influences the perceived usefulness. When an AR application responds fast and it is possible to manipulate the virtual product, the user will perceive the virtual products more as part of the real world. Regarding the product evaluation in the mobile shopping context, a higher level of interactivity could lead to being able to better evaluate the product, which could lead to a higher level of perceived usefulness (Kim & Forsythe, 2008).
Additionally, a high level of interactivity could lead to an increased level of perceived ease of use. The study by Constantinos and Sung (2012) in the context of mobile websites, confirmed this relationship. They mention that the greater level of interactivity, which includes being able to better control the website and access content, the more easy it is to use.
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According to Yoo et al. (2010) interactivity has an influence on user perceptions and in the e-commerce context it leads to pleasant feelings. Furthermore, a high level of interactivity in a virtual context can give people a feeling of autonomy and control, which leads to a higher level of perceived enjoyment (Animesh, Pinsonneault, & Yang, 2011).
H19: There is a positive relationship between interactivity and perceived usefulness H20: There is a positive relationship between interactivity and perceived ease of use H21: There is a positive relationship between interactivity and perceived enjoyment
Visual Quality (VQ)
The visual quality of the images in a mobile application relies on whether the provided
information is perceived as clear by a user. A high visual quality has a positive influence on the perception of the user of such an app (Kim, Hwang, & Zo, 2016). In a study on the visual quality and the representational richness of information in a “YouTube” context, Lee and Lehto (2013) mentioned that a higher level of visual quality had a positive effect on the perceived usefulness. The research of Kim et al. (2016) about the use intention of augmented reality apps in general, confirmed these findings. They mention that when someone uses a mobile augmented reality application, they find it important that the virtual image is well-represented and that the quality of the images is high. When the users of a mobile application in an e-commerce context can perceive the information in a clear way, the visual quality can have a positive influence on e-commerce, since it is much easier when the information is clear. According to Jiang and Benbasat (2007), visual quality is a strong predictor of the perceived enjoyment. Therefore, we
hypothesize;
H24: There is a positive relationship between visual quality and perceived enjoyment H22: There is a positive relationship between visual quality and perceived usefulness H23 There is a positive relationship between visual quality and the perceived ease of use
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4.
Conceptual model
5.
Methodology & Data collection
The following paragraphs describe how the research on the user intention of mobile augmented reality shopping applications in a furniture context was conducted and which method was used for this research. The chapter includes a description of the research philosophy, the research design, the research approach, the collection of the data and finally the analysis of the
experiment will be discussed.
Introduction
The aim of this study is to expand the literature on augmented reality in a mobile shopping context. Specifically, focusing on the intention to use of a mobile shopping application with AR. An integrated model consisting of elements of the technology acceptance model will be used to examine the factors influencing the intention to use of users of such apps.
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For answering the proposed research question an explanatory research was used. To test the hypotheses a quantitative study was used in the form of an experiment. For collection of the data a questionnaire was used to gather cross-sectional data. The questionnaires were administered at the University of Amsterdam. The subjects had to use the mobile application after which they had to fill out the questionnaire.
Research philosophy
When establishing the design of this research a research philosophy was chosen. This is
important since it shows on which philosophy the assumptions of this research are based on and how the author looks at the world. Furthermore, it influenced the research design and the way in which the research was conducted (Malhotra & Birks, 2007). According to Saunders and Lewis (2012) there are four possible research philosophies; positivism, realism, interpretivism and pragmatism. Since this research was based on measurable variables in controlled conditions in order to test the cause and effect relationships of mobile augmented reality applications and the intention to use, the philosophy of this research can be called positivistic. Within this philosophy a scientific method was used, which suggests that a cause and effect can be tested in order to determine whether a proposed theory can be confirmed (Saunders & Lewis, 2012).
The reason why a positivistic approach has been used is that hypotheses could be made drawn upon existing literature and tested in a laboratory setting. In this way the hypotheses could be confirmed or rejected. The choice of selecting a scientific approach for this marketing research was to test a cause and effect relation in an experimental setup in order to predict and explain intentional behaviour in an augmented reality and mobile shopping context (Malhotra & Birks, 2007).
Research approach
There are two different types of research approaches possible; deduction and induction. For this research a deductive approach has been used. When using a deductive approach a theory have been developed on which the hypotheses will be based. Thereafter a research strategy will be developed in order to test the hypotheses (Saunders & Lewis, 2012).
Deduction can be recognized by its four key characteristics. Firstly, deduction explains a cause and effect relationship of variables. Secondly, the relevant concepts need to be operationalized, meaning that the concepts need to be explained so that facts can be derived from them. Thirdly,
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deduction is characterized by the need to collect and analyse data in order to answer the
research question. Finally, a clear and structured methodology is used, to replicate it easily. This master thesis will work accordingly to this deductive concept in order to answer the main research question. Furthermore, this approach is in line with the above-mentioned positivistic perspective (Saunders & Lewis, 2012).
For the administration of the data, a quantitative method was used which included questionnaires (Malhotra & Birks, 2007). According to Saunders and Lewis (2012), a
questionnaire is a suitable approach when the goal is to expand the knowledge in this area of research. Furthermore, a quantitative research method, such as a questionnaire is able to gather data of a bigger sample size than that of qualitative research in most cases (Curwin & Slater, 2007).
Research design
A research design was created in order to have some sort of framework for conducting this marketing research. There are different available types of research designs. For this study an explanatory research method was chosen. According to Saunders and Lewis (2012) this means that a study looks at the reasons behind a particular occurrence through the discovery of causal relationships between key variables. In this case the particular occurrence is the failure of many augmented reality apps and the variables are those discussed in the literature review. As
mentioned above, this research method looks at cause and effect relationships. This was
necessary to determine the relationship between the causal variable (the experience of a mobile augmented reality shopping app) and the intention to use of those users. Furthermore, Malhotra and Birks (2007) mention that this is an appropriate design to use for hypothesis testing. Given the time constraints of this research and the fact that the data of the sample was only needed once, a cross-sectional study was the most suitable form for this research (Saunders & Lewis, 2012).
Data Collection
In order to answer the research question, primary and secondary data have been used. Saunders and Lewis (2012) mention that the combination of these two forms of data collection enables researchers to establish a more solid foundation for research. The secondary data was used to explore relevant topics and to find theories and conceptual models that could be integrated in this research. In this way the research gap could be identified and the literature review created
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in order to come up with a theoretical framework. The literature was obtained via Google scholar and the digital library of the University of Amsterdam.
Since not all the data was available yet to answer the research question, primary data needed to be collected. Primary data is described as original data obtained by researchers that can provide them with relevant information for specific studies (Saunders & Lewis, 2012). The primary data that was collected provides insight into the cause and effect relationship of augmented reality on consumers’ intention to use.
Experimental setup
In order to collect the primary data, an experimental setup has been used. An experimental setup has been described as a suitable way to examine a cause and effect relationship and to test whether a change in one variable (the independent variable) causes a change in the dependent variable. In this case, with an experimental setup it was possible to test the effect of an
augmented reality experience of a consumer on their intention to use the application.
The subjects had to use the mobile augmented reality application of IKEA called the IKEA PLACE app. With this application it is possible to scroll through the furniture collection of IKEA and place them in your home or any other space that this app can scan virtually. This app is available in The Netherlands on Apple devices since 2017 and can be downloaded for free in the Appstore. The application can assist people by showing how furniture will look in their home virtually before buying the product and thus help them in their decision-making processes (IKEA, 2017). The experiment was set up in a classroom at the University of Amsterdam Roeterseiland
campus. In an empty corner of the UvA a setting was created where a subject’s home could be simulated. Students walking in the hallway of the university were asked to participate in the experiment. The students that were willing to take part in the experiment were given a mobile device (Iphone 6 plus) on which the IKEA PLACE app and the IKEA STORE app were
downloaded. The subject received the mobile device and had to open the mobile application. The subject had to use the mobile device’s camera to scan the floor. Hereafter, the subject should be able to browse through pieces of furniture and place them in the room virtually. A corner in a classroom at the university was made empty. The subject was given the instructions to select the table Norraker, to furniture the empty corner in the room. In this way the subject really had to make use of the app for a while, so that he or she could get a feeling of how the app works in
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reality. After selecting the piece of furniture, the subject was asked to fill out the questionnaire that had to be made on the laptop available in the laboratory room.
Since Malhotra and Birks (2007) mention the importance of eliminating factors that could have an influence on the experiment, the data collection was performed in the same laboratory setting. Furthermore, the subjects were asked to select and evaluate the same piece of furniture, so that factors, such as price, could be eliminated.
Questionnaire design
Since this research is based on a quantitative research method, a questionnaire was used to collect all the primary data. The questionnaire was created into the online program “Qualtrics”, since this tool gathers data accurately and it transfers the data easily into SPSS, which is the program that was used to process and analyse all the data.
The subjects were asked to answer most of the questions on a 7-point likert scale. The items for the measurement of product knowledge were on a 5-point likert scale. Only the questions that served to gather data on the demographics of the subjects were of another type. These could be answered via choosing the right category, fill out their age in numbers, or select male or female. The questionnaires were written in English. In order to test whether the subjects really selected their answers carefully and really read the questions, the questionnaire contained questions where the answers were based on a negative construct and questions that were based on a positive construct. The questionnaire contained a clear introduction that explained why this topic is important and what was expected of the subjects. At the end they were thanked for their participation and it was clear that they were at the end of the experiment (Saunders & Lewis, 2012).
Measures
For this research existing validated scales and empirical procedures were used. The items in the questionnaire are a combination of items used in research on the intention to use of mobile shopping apps, on research about augmented reality and on the technology acceptance model. All the measurement items were adapted to the context of this research; mobile augmented reality shopping apps. For the measurement of the variables “intention to use”, “attitude” and “perceived ease of use, the scale guidelines of Ahn et al (2007) were used. In order to test the
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variable perceived usefulness and perceived enjoyment, the items from the scale of Lee, Fiore and Kim (2006) were used.
For the variable information quality, the measurement items of Ahn et al. (2004) were used. The variable interactivity was measured by the scale of Animesh et al. (2011). Visual quality was measured by the scale of Jiang and Benbasat (2007). Product knowledge was measured by the scale of Schwartz (2011). Lastly, the variables virtual product experience, information quality and authenticity have been measured by the scales used in the research of Algharabat, Rana and Dwivedi (2017).
Sampling
For the collection of primary data, subjects were needed. Therefore a target population was determined. According to Malhotra and Birks (2007) a target population is the group that all share the same set of attributes. For this research, the target population consisted of people belonging to generation Y. This generation is between the age of 18 and 35, they account for more than 25% of the world population (Nusair, Bilgihan, Okumus, & Cobanoglu, 2013).
According to Farris, Chong and Danning (2002) and Nusair et al (2013), this generation is tech-savvy, tech literate and highly sociable. Furthermore, these people are seen as early adopters of new technologies. They are the generation that are involved in online activities most often, such as e-commerce and m-commerce (Lester, Forman, & Loyd, 2006).
Since it was not possible to involve the whole target group, due to time and money constraints, a sample was used (Saunders & Lewis, 2012) . The sampling technique that was used is
non-probability sampling, since it was not possible to get a list of all the people belonging to
generation Y. The type of non-probability sampling used was convenience sampling. In that way a sample of the group that represents generation Y could be created. Regarding the size of the sample, the guidelines of Malhotra and Birks (2007) were used. They mention that a sample size that was used in similar researches can be used to establish a sample size for a new research.
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6.
Research results
Descriptive statistics
The number of subjects that took part in this study was 118, but only 99 of them completed all the questions in the questionnaire. 71 of the respondents were female and 28 were male. Most of the respondents have a bachelor or a master’s degree. The average age is 24.
Table 1.
Descriptive statistics
Mean DeviaStd. tion NUI
4,53 1,04 99
ATT
5,85 0,71 99
PU
5,48 0,83 99
PEOU 5,53 0,79 99
PE
5,65 0,81 99
AUT
4,03 1,17 99
VPE
4,65 1,03 99
PK
3,23 0,58 99
IQ
5,101,03 99
INT
4,97 0,84 99
VQ
5,31 0,87 99
Reliability testsThe reliability statistics, including the cronbach’s alpha’s of all the scales can be found in table 2 below. The intention to use (IU), attitude (ATT), perceived usefulness (PU), perceived enjoyment (PE), authenticity (AUT), virtual product experience (VPE), information quality (IQ) and
interactivity (INT) all have a relatively high level of reliability, with Cronbach’s alpha scales of .711 or higher. The Cronbach’s alpha of product knowledge (PK) and visual quality (VQ) were both sufficient enough. Furthermore, dropping an item from the scales of one of these two variables would not have caused a much higher reliability of the scales. The variable perceived ease of use (PEOU) had a Cronbach’s alpha of .696. However, when deleting the item PEOU2REC, the reliability increased from .696 to .815. Therefore, it was decided to delete this item in order to make the scale more reliable.
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Table 2.
Reliability statistics
Scale Cronbach's alpha
N of items
UI
.850
5
ATT
.788
5
PU
.711
5
PEOU
.815*
7
PE
.832
6
AUT
.799
4
VPE
.747
4
PK
.697
6
IQ
.848
4
INT
.737
5
VQ
.689
3
*
Note: Cronbach’s alpha after deleting 1 item.Correlations
In order to test the relationship amongst all variables, a correlation matrix has been made. The results can be found in the correlation matrix in table 3 below. There is a strong correlation between attitude and use intention. Furthermore, all the variables, except for age and education, are positively related to the use intention. Regarding the variables PU, PEOU and PE, PU has the highest positive correlation with use intention. When looking at the variable attitude, all the variables are positively correlated as well, except for age and education. These last two variables do not seem to have any significant relationship with any variable, except for the relationship between age and education.
The variables AUT, VPE, PK, IQ, INT and are all positively related to PU and PEOU. Regarding the variable PE, the results show that there is no significant positive relationship between PK, IQ, and PE.
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Table 3. Correlation matrix 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 UI 2 ATT .652** 3 PU .677** .766** 4 PEOU .396** .296** .317** 5 PE .503** .419** .409** .403** 6 AUT .539** .492** .498** .262** .333** 7 VPE .591** .599** .599** .246* .330** .589** 8 PK .294** .307** .266** .209* 0,037 .564** .338** 9 IQ .293** .272** .334** .202* 0,136 .528** .364** .470** 10 INT .492** .507** .464** .237* 0,174 .609** .486** .498** .511** 11 VQ .404** .364** .370** .266** .301** .475** .271** .469** .356** .509** 12 Age 0,024 0,049 -0,162 0,036 0,099 -0,089 0,011 0,017 -0,138 -0,132 -0,002 13 Edu 0,020 0,068 -0,002 0,061 0,132 -0,150 -0,029 -0,108 -0,115 -0,140 -0,002 .321** 14 Fem ale . 278** .216* .239* 0,004 .207* .323** .316** 0,085 .263** 0,144 0,170 0,067 0,100**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). c. Listwise N=99
Regression analyses
The variables that were taken into account in this research can be found in table 1. For the analyses of all the hypotheses, several multiple regression analyses have been done. First the effect of PU, PEOU and PE on attitude and then the effect of PU, PEOU and PE on the intention to use have been tested. Secondly, the effects of the variables AUT, VPE, PL, IQ, INT and VQ on PU, PEOU and PE were taken into account. The results of the regression analyses on the attitude and the intention to use are shown in table 4 and the results of the regression analyses of AUT, VPE, PL, IQ, INT and VQ on PU, PEOU and PE can be found in table5.
