MASTER'S THESIS
Factors Influencing Consumer Acceptance of New Technology
A Case Study of Smartwatches
Robin Enér Linus Knutsbo
2015
Master of Science in Business and Economics (Civilekonom) Business and Economics
Luleå University of Technology
Department of Business Administration, Technology and Social Sciences
Factors influencing consumer acceptance of new technology
- A case study of smartwatches
Abstract
Wearables are becoming more commercially used among consumers around the world.
Fernandez (2014) defined wearables to “…include all forms of computational or sensory electronic devices that can be worn with clothing or on the body…” Smartwatches are one of the wearables that are becoming a current trend. The smartwatch can ease people’s everyday life with improved information- and communicative tools. A report by Risen (2014) revealed American citizens had a negative attitude towards the future of wearables.
The purpose of this study is to find out how smartwatches could gain acceptance by millennials on the Swedish market. The theory of reasoned action laid the foundation of the theoretical frame of reference, which then culminated into the TRAM model and external variables, perceived playfulness and perceived visual attractiveness. A qualitative research approach was chosen, where focus groups was conducted in order to collect millennials’
opinions regarding smartwatches. The results suggest that a there is a potential future for smartwatches, if they provide useful features along with a descent design.
Key words: Acceptance behaviour, technology acceptance, smartwatch, millennials, intention to use smartwatches
Acknowledgements
This final thesis was conducted during the spring semester of 2015, and marks an end to our time as students of Luleå University of Technology. As this is the final work of our education in Master of Science in Business and Economics, we consider it a privilege to credit the people that have made it possible to accomplish this achievement.
We would like to give our most sincere gratitude towards our friends and family that has supported and encouraged us all along during these four years at LTU. We would like to express a special gratefulness to our supervisor, Mana Farshid, as she has been helpful in guiding and giving constructive input to this project.
Finally, we would like to thank our colleagues and especially respondents for essential input and valuable opinions.
Luleå, June 2015
________________________ ________________________
Robin Enér Linus Knutsbo
Table of content
1. Introduction ... 1
1.1Background ... 1
1.2Problem discussion ... 2
1.3Research purpose ... 3
1.3.1Research questions ... 3
1.4Delimitation ... 3
2. Literature overview ... 4
2.1 Theory of reasoned action ... 4
2.2 The diffusion of innovations theory ... 5
2.3 The theory of planned behaviour ... 7
2.4 Technology acceptance model... 9
2.4.1 Extension of the technology acceptance model ... 10
2.5 Perceived playfulness ... 11
2.6 Perceived visual attractiveness ... 12
2.7 Technology readiness acceptance model ... 13
2.8 Frame of reference ... 14
2.9 Proposed research model ... 18
3. Methodology ... 19
3.3 Research design ... 20
3.4 Sampling ... 20
3.4 Data collection ... 20
3.5 Data analysis ... 22
3.6 Quality of research... 23
3.6.1 Construct validity ... 23
3.6.2 Internal validity ... 23
3.6.3 External validity ... 23
3.6.4 Reliability ... 24
3.7 Summary of methodology ... 25
4. Data presentation ... 26
5. Data analysis ... 35
6. Conclusions and implications ... 41
6.1 Research question 1 ... 41
6.2 Research question 2 ... 44
6.3 Implications of the study ... 46
6.3.1 Theoretical implications ... 46
6.3.2 Managerial implications ... 46
6.4 Limitations ... 47
6.5 Future research ... 47
References... 49
Appendices ... 52
Appendix A: Interview guide... 52
1
1. Introduction
1.1 Background
The accelerating rate of new technology products being released on the market has changed how people perform tasks in their working and everyday life. When new technology is introduced a challenge appears for companies whether or not this new product will gain social acceptance from consumers (Leyton, Pino & Ochoa, 2014).
New trends come and go and in the meantime, wearables are a hot topic on the market (Martini, 2014). Fernandez (2014) defined wearables to “… include all forms of computational or sensory electronic devices that can be worn with clothing or on the body.
In the broadest sense, any computer device that is carried with a person to assist them could conceivably be called a wearable.” Wearable technology is providing individuals with more efficient ways of performing tasks and communicating, making humans’ everyday life more convenient (Employee Benefit News, 2014).
Wearable computing is a concept that has been around for a while, but a certain pioneer in the field called Steve Mann, also known as “the world’s first cyborg” (Fernandez, 2014), has been experimenting with this since the 1960s. Mann (1999) developed a wearable computer series during the 1970s called WearComp0, which was further developed and enhanced later on.
The use of wearable technology has been explored in several fields, including entertainment, education, finance, gaming, and music (Wright & Keith, 2014). During the beginning of the twenty-first century, healthcare was the more prominent area of wearables which provided beneficial products for the health care providers. The design of wearables has gone from large and hefty devices to become easily ubiquitous in form of being lighter, leaner, and more fashionable (Wright & Keith, 2014). By this constant improvement of the design, products has become more appealing to the consumer market, and as a result, opened up new opportunities of development. Markets such as fitness and technological appliances for everyday life has been explored and progressed in the area of wearable technology.
An example of a wearable initiative can be seen from Disney’s MagicBand. This wearable device allows customers of Disney to make their experience more enjoyable by integrating the several systems in the theme park, by the use of Bluetooth technology and radio frequency. The MagicBand enable Disney to collect and transmit data to extract customer preferences while the customers are able to perform certain self-services, such as reservation for a ride and to charge meals on their hotel rooms. The implementation by
2 Disney displays how successful it can be to create a more pleasant experience for customers while also gaining useful benefits to themselves (Fernandez, 2014).
The fitness tracker is another device that has become increasingly more popular as result of a more customer-oriented direction of wearables. The fitness tracker allows consumers to track their movements and realization throughout the day based on the goals of the individual (Fernandez, 2014). This feature of tracking movements is one of the more popular traits consumers currently desire in wearable products (Employee Benefit News, 2014). As tracking of people’s movements and the collection of data overall is increasing, issues with privacy have been disputed. Consumers are worried about the data collected and how it is being used and the level of security in how it is being stored. Even though the use of Bluetooth technology provides consumers with certain advantages in wearables, it might also cause problems as it makes it easy to transfer data between devices. It is also easy to hack information by the use of this technology (Fernandez, 2014).
When new wearables are introduced to the market other questions remained to be answered. Since wearables are considered as a new technology in the mind of consumers, the social acceptance of new technology is important to explore (Venkatesh, Morris, Davis,
& Davis, 2003).
1.2 Problem discussion
From the introductory chapter it is safe to say that wearables have been around for a while, both as a concept and in a more industrialized context. Although, it is not until more recently it has become a familiar term in consumers markets, as new technology devices are introduced to the market. Wearables will provide advantages in people’s everyday life, simplifying their information- and communicative resources. Disadvantages with new technologies are existing as well, where privacy issues has been identified, but also the concerns of what new technological innovations will bring to the future and how it will be accepted. Risen (2014, April 21) wrote about a survey conducted by Pew Research Center and Smithsonian magazine. Approximately a thousand Americans had responded to questions concerning the future of technology. In the study, nearly 53 % of the respondents in various age groups believed the future will be worse off by the use of wearable electronic devices.
Previous research within the field of wearables is slim, yet research has been conducted in the area of smart clothing for different purposes, whether it be fashion apparel (Fox, 2014) or healthcare (Park & Jayaraman, 2003). Previous research exists within smart clothing but Hong et al. (2007) believe further research is needed to further elaborate on the frames of acceptance of new technology. This is why further exploration regarding the social acceptance of new technology is interesting. In this study it is concentrated to
3 smartwatches. Smartwatches is an ongoing new trend that is predicted to take a large portion within the wearable technology market in the consumer market (Sheehy, 2015, January 16).
Acceptance theory has been extensively studied and from a technology perspective it has been primarily explored in a web context and its impacts on e-commerce. As wearable technology is becoming more commercialized, it is necessary to investigate how previous research regarding the factors of acceptance theory can be used in the context of wearable technology. Consequently, it is vital to examine the factors influencing consumers’
acceptance of new technology. This has been studied in other areas of the literature but has not yet been answered with a specialization on wearables, such as smartwatches.
1.3 Research purpose
The purpose of this study is to gain a deeper understanding of the topic wearables. It is interesting to go deeper into a specific product within this technology. The aim is to identify the factors that influence consumers’ acceptance of smartwatches, and how large the intentions are to use a smartwatch. Ultimately, this thesis is going to explore, from a perspective of millennials, the current possibilities of how a smartwatch could gain acceptance on the Swedish market.
1.3.1 Research questions
Based on the background, problem discussion, and purpose of the study, the following research questions have been formulated to guide the objectives of this thesis.
RQ1: Which factors influence consumers’ acceptance of smartwatches?
This research question aims to explore which factors are important to consumers’
acceptance of smartwatches.
RQ2: How do these factors influence consumers’ intention of using smartwatches?
The second research question seeks to determine how the factors of acceptance influence consumers’ intention to use smartwatches.
1.4 Delimitation
Due to a limited time frame and resources this study has had access to, the scope of the study has been influenced. This thesis is delimited to only focus on the topic of smartwatches where millennials has been the examined target group.
4
2. Literature overview
This chapter provides an overview of relevant theories and models to give an enhanced understanding and knowledge of the problem area. Established theories related to acceptance behaviour such as the TRA, TPB, TAM, and the TRAM model will be presented.
Perceived playfulness and perceived visual attractiveness are other factors that will be discussed in a technology context. Finally, the most relevant theories for each research question will be put into a frame of reference with a proposed research model in the end.
2.1 Theory of reasoned action
When studying adoption of new technology, one of the most featured models in literature is the theory of reasoned action, TRA (Venkatesh et al., 2003). It was developed by Martin Fishbein and Icek Ajzen (1975). This model is a generally accepted model for predicting human behaviour in a broad context (Ajzen & Fishbein, 1980). In their book they start off from the assumption that “...human beings are usually quite rational and make systematic use of the information available to them” (Ajzen & Fishbein, 1980, p.5). From there they continue with this statement in mind and argue for when humans engage in an action, a process of pondering the implications of the action takes place before engagement is preceded to (Ajzen & Fishbein, 1980). The authors suggest that to understand behaviour one must first identify intentions of performing a behaviour. They argue that in order to predict intention, the underlying factors of intention must be understood. From here, the conceptual framework is being shaped. The authors report the first determinant factor of intention as attitude (Ajzen & Fishbein, 1980).
Thomas and Znaniecki (1918-1920) regarded attitude as individual mental processes that define a person’s actual and potential responses. Ajzen and Fishbein (1980) put attitude in a behavioural context called attitude towards behaviour. This term aims to explain an individual’s assessment of taking action on this behaviour, which is observed from an individuals’ standpoint, to be good or bad. The second factor determining intention is called subjective norm. It implies that social pressure is involved in humans’ perception of behaviour. When people behave in a certain way it is very likely social influences will affect a person’s intention to perform the behaviour (Ajzen & Fishbein, 1980). To get a better understanding of intentions it is necessary to take a step back in the TRA model. The authors speak for the need of studying why individuals consume certain attitudes and subjective norms. It originates from where beliefs are the foundation to attitudes and subjective norms. Hence, attitudes stem from what is called behavioural beliefs, while subjective norms are derived from normative beliefs. Behavioural beliefs could be described as if an individual is positive towards a behaviour, it will accelerate the process of performing the behaviour, while the opposite happens if anything negative concerns the
5 behaviour. Normative beliefs refer to the motivation from others that would be supporting of a particular behaviour, and the opposite reaction if they would disapprove (Ajzen &
Fishbein, 1980).
All those factors results in a chain, starting from the foundations of beliefs into an actual behaviour being the outcome. This is illustrated in figure 1 below.
Figure 1: Theory of Reasoned Action
Source: Adapted from Ajzen & Fishbein (1980, p. 8)
In a study written by Lee, Ham, and Kim (2013) they used the TRA model to predict and understand consumers’ pass-along behaviour of online video ads. The authors were able to examine how the attitude towards pass-along behaviour affected this particular behaviour.
Their findings concluded, as participants had more positive attitudes toward passing along online video ads, the subjective norm influenced their intention to pass on the ads.
Additionally, participants with more positives attitudes and social pressure from essential sources resulted in larger intentions of passing-along ads.
2.2 The diffusion of innovations theory
To enrich the perspective of user adoption Rogers (1995) developed the diffusion of innovations framework. His intention with the model was to find out “how properties of innovations affect their rate adoption” (Rogers, 1995, p. 204). Adoption was defined as “a decision to make full use of an innovation as the best course of action available” (Rogers, 1995, p. 21). The diffusion of innovations is an acknowledged model in the literature of information systems.
The framework consists of a variety of different variables, all connecting to the rate of adoption of innovations. Rogers (1995) explains that the rate of adoption can be viewed as how fast an innovation is adopted by individuals in a social construct. Rogers argues that
“49 to 87 percent of the variance in rate of adoption can be explained by five attributes”
(Rogers, 1995, p. 206). The main attributes are relative advantage, compatibility,
6 complexity, trialability, and observability. Other factors affecting the rate of adoption are the type of innovation-decision, communication channels, the existing social system, and the extent of change agents’ promotion efforts.
Figure 2: The diffusion of innovations framework Source: Adapted from Rogers (1995, p. 207)
To get a more profound understanding of this model, the perceived attributes of innovations has to be defined. The first attribute is relative advantage, which explains how the perception of an innovation is exceeding the previous idea it builds on. In this context, relative advantage can be shown through “economic profitability, social prestige, or other benefits” (Rogers, 1995, p. 212). The second attribute is compatibility, which Rogers define as “the degree to which an innovation is perceived as consistent with existing values, past experiences and needs of potential adopters” (Rogers, 1995, p. 224). Compatibility examines how an innovation fits into an individual’s certain lifestyle. The more compatible an innovation is, the more likely it is for an innovation to be adopted in a higher rate. The third attribute is complexity, described as “the degree to which an innovation is perceived as relatively difficult to understand and use” (Rogers, 1995, p. 242). The complexity of a product has been stated to affect the adoption negatively or positively, which has been illustrated by Rogers, Daley, and Wu (1980). In their research, the effect of complexity of home computers was observed during the early 1980s. Results indicated that the perceived
7 complexity of the product had a negative impact on adoption, and it took up to six to eight weeks of severe frustration for a new adopter to adopt the computer.
Trialability has proved to be an interesting attribute in this model. Rogers explains it as “the degree to which an innovation may be experimented with on a limited basis” (Rogers, 1995, p. 243). By allowing an individual to try out an innovation on a limited basis, it enables them to explore the personal meaning with the innovation and how it would work on their own terms. Rogers (1995) concluded this factor had a positive effect on adoption. The last attribute of this model is observability, which is defined as “the degree to which results of an innovation are visible to others” (Rogers, 1995, p. 244).
Observability is also claimed to have a positive influence on rate of adoption.
Communication channels are an important variable in determining rate of adoption. It allows an innovation to be diffused, but it also impacts the rate of adoption. For example, mass media channels may speed the rate of adoption for new adopters, while interpersonal channels may slow down the rate of adoption for late adopters as it creates knowledge awareness (Rogers, 1995).
Furthermore, the innovation decision process specifies that “the more people involved in making an innovation decision, the slower the rate of adoption” (Rogers, 1995, pp. 206- 207). In accordance with Rogers’ model, the construct of our social system, the norms of which we abide to and how our communication network is interconnected, affects an innovations adoption rate.
Weigel, Hazen, Cegielski, & Hall (2014) used the common characteristics of the diffusion of innovations theory, and the framework of planned behaviour in a meta-analytic study in an attempt to provide a model of innovation adoption-behaviour. By reviewing previous research within the field of adoption behaviour they were able to examine and validate the hypotheses of the two models. The authors evaluated the past thirty years of information systems research that empirically had studied the effects of the variables of innovation adoption. The results suggested and validated that all five of Roger’s attributes of innovation were positively correlated to adoption. Besides, they draw the conclusion that the two models are relevant even today when analysing adoption behaviour. However, complexity was the one attribute that had the least significant correlation with adoption behaviour (Weigel et al., 2014).
2.3 The theory of planned behaviour
The theory of planned behaviour (TPB) was conceived by Ajzen (1991) and became an extension of the TRA model, trying to explain specific individual behaviour. It can be distinguished from the TRA model by the implementation of the perceived behavioural
8 control variable affecting intention to use. The major focus in this model is the intention that leads to behaviour, but within this model Ajzen (1991) added the variable called perceived behavioural control. He argues for that “behavioural intention can find expression in behaviour only if the behaviour in question is under volitional control”
(Ajzen, 1991, p. 181). By volitional control he implies that an individual at will can decide to perform this behaviour or not. The decision is determined by requisites as if an individual has resources to perform this behaviour. Resources in this context are defined as
“time, money, skills, cooperation of others” (Ajzen, 1991, p. 182). These fundamentals can be correlated to the perceived risk of performing behaviour. Ajzen (1991) provides an example to strengthen the correlation between perceived behavioural control and behavioural intention. If two individuals try to master the art of skiing, and both individuals having the intention of doing so, the individual with most belief in that he will control the skill of skiing, will be the one who is more plausible to master this activity.
Figure 3: Theory of planned behaviour Source: Adapted from Ajzen (1991, p. 182)
The TPB model has served as the theoretical basis in a previous research by Pavlou and Fygenson (2006), where they investigated e-commerce adoption of consumers by the use of the TPB model to explain and predict the process. The authors concluded that the power of the TPB model in predicting behaviour was valid. The researchers also proposed an extended model of TPB by applying technology variables that are familiar from the TAM model, such as perceived ease of use and usefulness. Other variables such as technological characteristics, PBC resources (time, skills etc.), and product characteristics were used to enhance the predictive and explanatory power of this framework.
9 A recently published study from Kroll (2015) examined how public managers made use of performance information. The researcher conducted a survey addressed to middle managers in Germany where the basis of the study was to utilize the theory of planned behaviour. The results of this study indicated an approval of the TPB model by explaining 76% of the variation in performance information use.
2.4 Technology acceptance model
In 1986, Fred D. Davis proposed a new alternative based on the TRA model. The model is called the technology acceptance model (TAM). It was developed with the intention of explaining and predicting individuals’ acceptance behaviour of a new technological innovation. Davis (1986) restructured the model to be applicable from a more technological view. He stated that the social influences of TRA, the subjective norms mentioned earlier, does not fit into a technological context of acceptance and adoption. This is what separates this model from the TRA model. Instead of using the subjective norm, Davis (1986) uses the concept of external variables and breaks it down into two concepts. These two concepts are perceived usefulness and perceived ease of use, which are intended to explain the technological adoption of a new IT system. Davis defines perceived usefulness as “the degree to which an individual believes that using a particular system would enhance his or her job performance” (Davis, 1986, p. 26). Perceived ease of use was defined as “the degree to which an individual believes that using a particular system would be free of physical or mental effort” (Davis, 1986, p. 26). Furthermore, he claimed that when a system is easier to use, the overall job performance will be improved. This is a statement that legitimates the idea of that perceived ease of use has a direct effect on the perceived usefulness. When comparing both determinants, perceived usefulness has been shown by previous research (Davis, 1989, p. 333) to be the leading factor in determining intention to use. Both of these concepts relate to attitude towards use by examining the model, but perceived usefulness has a direct correlation to intention to use.
Figure 4: Technology acceptance model Source: Adapted from Davis (1986, p. 24)
10 Previous research from Gentry and Calantone (2002) focused on a comparison of TRA, TPB, and TAM to examine behavioural intention to use shopbots on the web. The results indicated that the TAM model exceeded the other two models by displaying a variance of 81.2 % of the explanation in behavioural intention, while TRA explained 43.2 %, and TPB were in between the other two. This study confirms the statement of TAM explaining behavioural intent more accurately in a technology environment.
Another study made by Ashraf, Thongpapanl, and Auh (2014) explains the adoption of e- commerce across cultures with the utilization of the TAM framework. Perceived ease of use and perceived usefulness was the most critical factors when examining consumers’
intention to shop online.
2.4.1 Extension of the technology acceptance model
The distinguishing factor between the TRA and TAM model is the incorporation of perceived usefulness and perceived ease of use, and the lack of social norms within TAM.
In 2000 Venkatesh and Davis developed an extension of the original TAM model called the technology acceptance model 2, TAM2. The model was extended by the use of social influences and cognitive instrumental variables. The social influences are subjective norm, image, and voluntariness. These are illustrated in figure 5 below.
Subjective norm suggests that an individual is influenced by the people that are considered to be important in one’s social environment, and if they are approving of you as an individual performing a certain behaviour. This variable is gathered from previous research of the TRA model (Fishbein & Ajzen, 1975), and the TPB model from Ajzen (1991).
Voluntariness can be defined as the “degree to which the use of innovation is perceived to be voluntary or by free will” (Žvanut et al., 2011). The last construct in social influences is the incorporation of image. The variable image found in the model of Venkatesh and Davis (2000) can be defined as “the degree to which use of an innovation is perceived to enhance one’s image or status in one’s social system.” (Moore & Benbasat, 1991, p. 195).
Venkatesh and Davis (2000) also added cognitive instrumental constructs into their model.
Those were job relevance, output quality, and result demonstrability. These constructs have been drawn from different areas to fit into their context. Job relevance is defined as “an individual’s perception regarding the degree to which the target system is applicable to his or her job.” (Venkatesh & Davis, 2000, p. 191). Job relevance is considered to have a direct effect on perceived usefulness. This is based on the assumption that an individual knows what tasks are needed for an innovation or system to perform, to connect with their job situation. The second cognitive variable is output quality. It can be explained by an individual’s views on what a system is capable of doing, their job situation, and by
11 overviewing how good the system performs certain tasks (Venkatesh and Davis, 2000). The third variable is result demonstrability. It is directly correlated to perceived usefulness and is defined as “tangibility of the results of using the innovation.” (Moore and Benbasat, 1991, p. 203). If the results of an innovation are more tangible, individuals are more likely to positively perceive the usefulness of the innovation. Finally, the authors chose to use a moderator variable, which is the experience factor.
Figure 5: TAM 2-model
Source: Adapted from Venkatesh & Davis (2000, p. 188)
The results of this study with the utilization of this model were descent, and were able to explain 37% and 52% of the variance in usage intentions. It also explained the variance in perceived usefulness up to 60 %.
In a more recent study, user acceptance of procedural learning by the use of YouTube was investigated. Lee and Lehto (2013) used the extended TAM model (TAM2) and integrated other variables to find out how acceptance is influenced in a YouTube context. The results of their study validate that the TAM2 model still is in current use, and is reliable when investigating acceptance and intention to use (Lee & Lehto, 2013).
2.5 Perceived playfulness
Perceived playfulness is a concept that has been broadly used in previous research, studying user acceptance of innovations. It has been used in different settings, but was described by Moon and Kim (2001) as an intrinsic motivation. Intrinsic motivation is defined as “the performance of an activity for no apparent reason other than the process of performing it”
(Moon and Kim, 2001, p. 218). The authors split the concept into three components. The first level is concentration. In this state, the individual becomes absorbed with the
12 performance of the activity. He or she put great focus on the interaction and shuts out eventual irrelevant perceptions. Curiosity is the second component within the concept. It implies that an individual could be affected by certain tools that can arouse sensory curiosity or cognitive curiosity. If that is the case, the individual can be motivated to gain more competence within the technology and explore it further by the use of simple tools, such as multimedia effects and bookmarks. The last piece of the concept is enjoyment.
Basically, it can be concluded that individuals “are involved in the activity for pleasure and enjoyment rather than for extrinsic rewards.” (Moon and Kim, 2001, p. 220)
The authors found out that perceived playfulness had a significant relevance to users’
acceptance behaviour of a website. Previous research by Ahn, Ryu, and Han (2007) confirmed the significance of perceived playfulness. They scrutinized web quality and playfulness as variables affecting user acceptance. Their results revealed that playfulness had a major effect on users’ acceptance in an online retailing context. Consumers were more likely to use an online retailer if they perceived the experience to be more playful (Ahn et al., 2007).
Figure 6: Perceived playfulness
Source: Adapted Moon & Kim (2007, p. 220)
In a study about in-game purchase intentions by Han and Windsor (2013), it was investigated how perceived playfulness had an effect on purchase intentions. Their results discovered that perceived playfulness has a positive effect on intention to purchase, and it strongly works as a motivational factor (Han & Windsor, 2013).
2.6 Perceived visual attractiveness
In 2003 Van der Heijden (2003) proposed a different direction of the original TAM model.
In the study the author wanted to explore how aesthetics affect consumers’ intention to use a website. To modify the TAM model the researcher included a new variable called perceived visual attractiveness (Van der Heijden, 2003). The author described the new concept as “the degree to which a person believes that the website is aesthetically pleasing
13 to the eye” (Van der Heijden, 2003, p. 544.). The author constructed three hypothesises regarding the visual attractiveness. It was hypothesised that perceived visual attractiveness had a positive connection to perceived usefulness, perceived ease of use, and perceived enjoyment. All of these were confirmed, in a consumer context, to have a positive impact on the user acceptance of a web portal (Van der Heiden, 2003).
Figure 7: Perceived visual attractiveness
Source: Adapted from Van der Heijden (2003, p. 542)
The effects of attractiveness on a product have been previously studied. In 2010, Sonderegger and Sauer (2010) examined how perceived visual attractiveness among other variables affected the usability of mobile phones. Their results suggested that perceived visual attractiveness of a mobile phone, versus a visually unattractive mobile phone, had a positive impact on usability and overall perceived performance.
2.7 Technology readiness acceptance model
Lin, Shih, and Sher (2007) composed the technology readiness acceptance model (TRAM) model, which builds on the TAM model, with an additional component taken into consideration. Instead of exploring the prediction of technology-adopting behaviour in a work setting, the authors wanted to test its applicability in a consumer environment. To rework the TAM model more suitable to consumers, the authors added the concept of technology readiness (TR) (Lin et al., 2007). This term was described as “people’s propensity to embrace and use new technologies for accomplishing goals in home life and at work” (Parasuraman, 2000, p. 308). It can be divided into four sub-levels, affecting the overall technology readiness. The first sub-process is optimism, which relates to a general positive overview of technology adoption and the benefits in form of flexibility and control it brings to consumers. The second dimension refers to innovativeness. It summarizes how consumers like to be thought of as a pioneer in adoption of technology. The third sub-level is discomfort. It illustrates how consumers do not feel in control when adopting a new technological innovation. The last dimension is insecurity. It corresponds to how consumers are insecure about how technological innovations actually will function
14 properly, illustrating a general negative view of technology adoption overall. The first two sub-levels are positively related to consumers’ technology adoption, while the last two are negatively associated (Lin et al., 2007).
Figure 8: TRAM-model
Source: Adapted from Lin et al. (2007, p. 646)
Lin et al. (2007) discuss the correlation between technology readiness, perceived ease of use, and perceived usefulness in their research. They validate the connection between the concepts, where perceived usefulness and perceived ease of use were mediating effects between TR and usage intention. Technology readiness verified a more clear understanding of individual technology acceptance behaviour.
Jin (2013) examined factors that affect consumers’ acceptance of Facebook, with the bases of the theory in the TRAM model. The results concluded significant information, validating both positive and negative TR are critical in the formation of perceived ease of use, perceived usefulness, and perceived playfulness. Nevertheless, negative TR had no impact on perceived playfulness.
2.8 Frame of reference
From the literature overview, a theoretical model has been selected that relates to acceptance theory and behaviour. In this section, a frame of reference has been conducted, to summarize the literature that is intended to provide answers to the stated research questions. To end the literature chapter, a proposed research model will be presented to give a rough picture of how the research problem will be addressed.
RQ1: Which factors influence consumers’ acceptance of smartwatches?
The first research question aims to explore which factors are central to consumers’
acceptance of smartwatches. The theoretical model that will be used the TRAM model. It
15 has been selected due to its applicability in a consumer context, which is suitable to the purpose of this study. The added concept of technology readiness in the TRAM model illustrates consumers’ tendency to adopt new technologies that intends to improve their work and everyday life. This is why the authors think the TRAM model is the most suitable option, since smartwatches also intends to provide people with more convenient solutions.
Besides from the TRAM model, two external variables will be used to further explain the first research question. These variables have been identified to have a connection to technology acceptance and adoption behaviour. The first external factor is perceived playfulness, which has been studied in different areas, exploring user acceptance of innovations. Since smartwatches is a fairly new product category, it might be considered as an innovation. Furthermore, earlier research has shown that perceived playfulness has played a major role when accepting new technology. The second factor is perceived visual attractiveness, which importance has been displayed to have a positive correlation to user acceptance. In the study from Sonderegger and Sauer (2010), the authors came to the conclusion that in mobile phone context, a visually attractive mobile phone would be perceived as more useful than an unattractive mobile phone. As smartwatches are a closely related technology, the authors believe the design of a smartwatch could have an impact on the acceptance of it.
Table 1: Conceptualization of the factors central to consumers’ acceptance of smartwatches
Concept Sources Operational definition References (concept)
Optimism Jin (2013), Lin et al. (2007)
General positive
overview of technology adoption
Technology gives control
Prefer to use the most advanced technology
Confidence in that technology will perform as instructed
16 Innovativeness Jin (2013), Lin et
al. (2007)
Degree of how to be thought of as a pioneer in technology adoption
Early adopter of new technology
Keep up with technology development
Enjoyment of figuring out high- tech gadgets Discomfort Jin (2013), Lin et
al. (2007)
Degree of how
consumers do not feel in control when adopting a new technological innovation
New technology is difficult to understand
Extraction of personal data Insecurity Jin (2013), Lin et
al. (2007)
Level of how consumers are insecure of how a technological innovation will have the ability to function properly
Privacy issues with new technology
It is easy use explain how to use new technology Perceived ease
of use
Davis (1986) Lai & Li (2005), Lin et al. (2007)
Degree to which an individual believes that using a particular system would be free of physical or mental effort
How to use new technology is clear
It is easy to explain how to use new technology Perceived
usefulness
Davis (1986) Lai & Li (2005), Lin et al. (2007)
Degree to which an individual believes that using a particular system would enhance his or her job performance
Acquiring useful information from new technology
Using acquired information with satisfying results Enjoyment Moon and Kim,
(2001)
Degree of involvement in an activity for pleasure and enjoyment
Enjoyment of using new technology
Curiosity Moon and Kim (2001)
Degree of curiosity to gain more competence within the technology
Stimulation of curiosity in using new technology
17 Perceived
visual
attractiveness
Van der Heijden (2003)
The degree to which a person believes that the website is aesthetically pleasing to the eye
Attractiveness of new technology
RQ2: How do these factors influence consumers’ intention of using smartwatches?
The second research question seeks to examine how the different factors influence consumers’ intention to use a smartwatch. To answer this question, the TRAM model will be used to explore consumers’ intention to use. This thesis will rely heavily on this model since it has accumulated positive results from earlier research in explaining acceptance of new technological innovations. Nonetheless, the external variables perceived playfulness and perceived visual attractiveness will be used as well. In the study from Ahn et al. (2007) the authors concluded that consumers were more likely to use an online retailer if they perceived their experience to be more playful. This is why perceived playfulness will be examined in this research question. The study from Sonderegger & Sauer (2010) showed that consumers perceived an attractive product as more useful. When reviewing the TRAM model, it displays that perceived usefulness has a direct relation to intention to use.
Consequently, it would be interesting to examine this factor to determine if the design has an impact on perceived usefulness, and thus consumers’ intention to use the smartwatch.
Table 2: Conceptualization of the factors influencing individuals’ intention of using smartwatches
Concept Source Definition References
(questions) Usage intention Davis (1986)
Jin (2013)
Degree of intention to use
Tendency of using new technology
Tendency to recommend new
technology
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2.9 Proposed research model
Figure 9: The proposed research model
This proposed research model aims to visualize how to address both research questions.
The model is derived from the TRAM model, where perceived playfulness and perceived visual attractiveness are added. The TRAM model will be used since it is a relatively new acceptance model, more designed for technological innovations in a consumer context. As the theory suggests, both positive and negative technology readiness should have an impact on perceived ease of use, perceived usefulness, and intention to use of smartwatches.
Previous research suggests that perceived ease of use should have a connection to perceived usefulness, and both of these variables can have an impact on the intention to use.
Perceived playfulness has been shown to have an impact on intention to use new technology. The perceived attractiveness variable has been verified to have an impact on perceived ease of use and perceived usefulness. Since this is a new field of study where limited research exists, it is not possible to draw any conclusions about the connections and impact of those variables in the proposed research model. Hence, these have been left with no connection to later on be analysed how they influence consumers’ acceptance and intention to use smartwatches.
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3. Methodology
This chapter explains the research methodology used for this paper, including the research approach, research purpose, tools used for data collection and analysis. Finally, measures used for ensured validity and reliability of the study will be addressed.
3.1 Research purpose
A research purpose can be divided into three different categories; exploratory, descriptive, or an explanatory research purpose (McGivern, 2013). An exploratory purpose implies that the research seek to explore an issue or a specific subject. A descriptive purpose aims to identify, describe, and answer defined research questions. Finally, explanatory studies seek to answer the “why” questions and understand causal connections between different variables. This thesis is descriptive with the purpose of examining consumers’ acceptance of smartwatches, but it also contains elements of exploration in order to clarify how the factors influence consumers’ intention to use smartwatches.
3.2 Research approach
According to McGivern (2013) there are two types of research approaches, and these are quantitative, respectively qualitative research. A quantitative research approach involves gathering large data samples which can be collected through sample surveys or panels. The intention with a quantitative study is to examine conclusive research enquires which is descriptive or explanatory. With a qualitative research approach there is not a need of the same sample sizes. The data can be collected through the use of in-depth interviews, focus groups, and workshops. This method intends to give a clear description and understanding of the problem that has been stated (McGiven, 2013).
Additionally, a researcher has to decide whether to use an inductive or a deductive approach with the study. Inductive research originates from empirical data, while not intending to test hypotheses or existing theories. Meantime, a deductive approach serves to use existing theory as a foundation to answer a stated research problem (McGivern, 2013).
This study applied a deductive approach since the theoretical framework stems from previously known models in order to answer the research questions.
A qualitative research approach was used in this study, to test the conceptual framework and to gather in-depth knowledge about which factors are central to consumers’ acceptance of smartwatches and how these influence intentions to use this technology. The approach was applied due to its effectiveness of finding out about people’s experiences, attitudes, knowledge, and also because the authors thought it was the best way of approaching the research problem (McGivern, 2013).
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3.3 Research design
Researches have to structure a study in an appropriate manner in order to plan the best route of successfully answering a research problem. Ultimately, there are four main types of research designs; a cross-sectional study, a longitudinal study, an experiment, or a case study (McGivern, 2013). In this thesis, a case study of smartwatches was approached. The aim of the study was to get detailed understanding of individuals’ attitudes and behavioural patterns when it comes to the acceptance of smartwatches, which is why this research design was the most appropriate to apply. Another advantage with this design includes its ability to generate detailed answers to the research questions of this study, because it provides an understanding of how-, what-, and why- questions (Saunders, Lewis &
Thornhill, 2012).
3.4 Sampling
A sampling unit holds the elements of the sample, which are the people that are intended to be studied in a research (McGivern, 2013). In this study, so called millennials, was used as the sample unit. Gurau (2012, p. 103) defines millennials as “people born between 1980 and 2000”. Tyler (2007) on the other hand, categorizes millennials as individuals born between 1980 and 2002 and described them as “...technologically sophisticated multitaskers...” Since this generation has grown up with technology it was a suitable target group to study. The sample of the study was composed of three focus groups, with each group consisting of 5-6 participants. The survey population was determined by the authors and controlled by one single element; respondents to be categorized as millennials. The sample included students on the campus of Luleå University of Technology that was geographically residing in the county of Luleå. The authors reached out to respondents, regardless of gender, only to be defined as a millennial. The age range of the participants varied from twenty-three and twenty-nine.
A sampling technique illustrates the way a survey population is chosen (McGivern, 2013).
In this thesis, a non-probability sampling was used to choose participants. The technique used to fit this study is a quota sampling method. There are two different designs of a quota sample, independent and interlocking quota (McGivern, 2013). The independent quota sample design was chosen in this thesis. This design was most appropriate as the authors were able to freely select participants that fulfilled the criteria of the study.
3.4 Data collection
The sources for data collection can be gathered through either primary or secondary data, or both. The primary research does not exist prior to the data collection, meaning it has to be
21 collected for the specific problem (McGivern, 2013). Furthermore, the author refers primary data as to collecting data out on the field. Secondary data, on the other hand, involves gathering existing data and theories from sources such as books, journal articles, documents, reports, and etcetera.
Conducted focus groups were the pivotal source for the primary data collection of this study. McGivern (2013) describes a standard group discussion or focus group consisting of 8-10 people. The researcher, also called the moderator, guides the discussion between the respondents. Three focus groups of 5 to 6 respondents in each group, and sixteen respondents in total, were used in this study as it allowed the authors to collect more profound discussions from each individual of the groups (McGivern, 2013). Even though this study was conducted in English, the authors chose to conduct the focus group interviews in Swedish. It was determined since Swedish was the native language of the participants, and to reduce possible language barriers that might have occurred.
The researchers had different roles during the interviews. One was the moderator, with the intention of guiding the discussion, while the other researcher was sitting a bit further away from the table, taking notes while observing and carefully listening to what was being said.
The focus groups took place in a central location at Luleå University of Technology. Each focus group session lasted for approximately sixty to seventy-five minutes. The moderator started off the discussion by introducing the respondents to each other, and made sure a short small talk was held in order to make all respondents feel comfortable in the group.
Afterwards, the subject of smartwatches was presented by viewing a short compilation video of smartwatches to illustrate how the smartwatches look and performs. After viewing the video, an overall discussion of the topic was initiated by the respondents until the moderator guided the conversation from the designated interview guide.
The moderator led the conversation by the help of a designed questionnaire. The respondents were allowed to freely express their opinions that was not relevant to the discussion, to make sure all opinions and experiences were heard. To find out whether or not respondents agreed with each other, whenever a statement was made, the participants were asked if they agreed or thought differently. The purpose with this strategy was twofold. It was done to clarify all statements, since opinions might be influenced by others.
Moreover, it eliminates risks of incorrect generalizations of the focus groups.
When designing an interview guide there are several steps that must be followed to ensure that the collected data is reliable and suitable to the purpose of the study. First of all, the research problem was defined, to continuing with the type of evidence that was needed to address the research problem (McGivern, 2013). Since this study is descriptive and to some extent exploratory, the questions had to be formulated with this taken into consideration.
Later on, the authors had to decide on the variables that were to be measured. The variables
22 were derived from the frame of reference, allowing the questions to be designed from those concepts, in order to provide answers to the research questions. When designing the questions, interview guides from other researchers were examined to find studies with similar research problems, which eventually was adapted to the objectives of this study. The responses from the focus groups were captured by the use of audio recording to facilitate the data analysis and to make sure nothing was forgotten or left out. The total audio recording amount added up to 191 minutes. The last step according is to decide how the responses were to be analysed. The authors chose to use a manual technique of analysing the data as they possess larger experience within that particular technique, rather than using a computer based analysis. (McGivern, 2013)
Secondary data involved scientific articles and books in order to explore and explain the background to acceptance theory. These articles and books also laid the foundation to the conceptual framework developed to explore consumers’ acceptance of smartwatches.
Journal articles were used to help develop the interview guide for the focus groups. Data bases used when finding articles were PRIMO, Business Source Premier, Emerald, Google Scholar, and Web of Science. Different combinations of keywords was used; “wearable computing”, “wearable technology”, “technology acceptance model”, and “acceptance of new technology”.
3.5 Data analysis
When conducting a qualitative analysis it is important to choose a data analysis strategy (Yin, 2009). The author proposes four possible strategies that can be applied, but the one used in this study was relying on theoretical propositions. This strategy allowed the researchers to guide the study’s material to be reflected on theoretical propositions. The literature review enabled the ability to suggest a theoretical proposed model that allowed research questions and data collection process to follow a clear structure. It empowered the authors to focus on relevant data.
After settling on a qualitative data analysis approach, a specific data analysis technique was chosen. The technique chosen was pattern matching. In this way, the authors were able to match and compare the data with the theoretical framework that was constructed (Yin, 2009). To start the process of analysing, a transcription process was held immediately after the focus groups were completed. This strategy was utilized to ensure that no important data would be forgotten, and to also get a more detailed transcription process. After the transcription process was done, the data was translated to English.
When conducting a qualitative data analysis several difficulties may occur. To reduce the risks of these, the authors conducted a data reduction process, comparing theories and models to the collected data to determine the most relevant information to the study. The
