Approach to Literature review
To write a thorough literature review about the topic, the authors first needed to conduct a sophisticated literature search. This search was carried out on three platforms (Primo, Scopus and Google Scholar) by using different key words such as for example “smartwatches”, “smartwatches influencing factors” and “smartwatches purchase intention” (for a detailed list of keywords used for article search see Appendix 1). For each of those keywords on each platform the first 50 articles resulting from the search were examined on their usefulness for the underlying research. If an article was considered to be useful it was downloaded and investigated further by the authors. More precisely speaking the authors reviewed these articles specifically based on their research framework, on the theory applied, on the methodology used – here in particular on where the study took place –, on the results obtained as well as on the recommendations for further research that were given. From reading all the downloaded articles the authors were able to identify a research gap which was addressed in this thesis.
Overview of Wearable Technologies
Technology can only be considered as ‘wearable technology’ if it has the capability of incorporating information technology to be able to interact autonomously and process information on the go (Park, Chung, & Jayaraman, 2014). This capability is what makes technology ‘smart’.
Wearables include a variety of devices, such as smartwatches, smart glasses, fitness trackers, contact lenses, smart garments, smart jewelries (e.g. smart rings), headbands or bracelets (Wright & Keith, 2014). Examples of manufacturers are Google Glass, Microsoft HoloLens, Apple Watch, Pebble Smartwatch, Fitbit fitness trackers, Oculus Rift and many more (Wright & Keith, 2014). There is a wide range of applications for both individuals and enterprises. Wearables usage includes communication, information, entertainment, fitness and health tracking, education, navigation and assisting services (Kalantari, 2017). On top of that, another important application of wearables is in marketing. Wearables can be used to observe information about consumers or users and their environment. With that, they can aggregate consumer’s purchase behavior, activities and location. This information is highly valuable for companies since it reveals consumer insights that can be used to improve customer experience (Kalantari, 2017). Researchers and industry representatives have proposed different classifications for wearable devices. In a market study published by Cognizant Solutions Corp they have been classified into five different segments based on functionality: fitness, medical, lifestyle, gaming and infotainment (Bhat & Reddi, 2014). Wearable devices are closer to people’s bodies than mobile phones. These devices have undergone experimentation and recently begun to be diffused (Jung, Kim, & Choi, 2016). They are distinctive from conventional mobile phones or portable computers, work without interruption and more inextricably intertwine with the body than other personal devices (Mann, 2014).
Benefits for Consumers
Wearable devices are supposed to assist consumers achieve a state of connected-self by using both sensors and software that facilitate data-exchange, communication and information in real-time (Kalantari, 2017). That is why, wearable devices are a big part of the internet of things (Kalantari, 2017; Swan 2012; Wang, 2015). In comparison to mobile phones, smartphones and computers, wearable devices are more convenient for consumers. This convenience can be attributed to their accessibility, lightweight, possibility to use while the user is in motion and the opportunity to make use of non-keyboard commands such as voice and hand gestures that give the user control (Kalantari, 2017). Hein and Rauschnabel (2016) state that these devices are generally not only perceived as “technology” but also as “fashion” and are therefore a “fashionology”. Since wearables have the opportunity to surpass smartphones and computers in performance, they can potentially also replace these technologies in the future. Accordingly, there has been an increase in awareness as well as knowledge of consumers and developers are also eager to release new wearable devices (Park et al., 2014).
Benefits for Society
Wearable technologies possess benefits that can change the landscape of societies and businesses – they can improve people’s wellbeing and help them make better and more informed decisions (Kalantari, 2017). They can support medical centers and hospitals by enhancing the accuracy of health information acquired and thereby improve the treatment of patients. Wearable devices’ ability to track health and fitness can thus lead to healthier behavior and therefore decreases healthcare costs (Park et al., 2014).
Within sports, wearable devices can be linked with data analysis – which is called physiolotics. By doing so, it is possible to monitor and improve performance by providing quantitative feedback (Wilson, 2013).
Another great benefit wearable technology provides, are assistive services for disabled individuals who have limited ability to use technological devices (Kalantari, 2017).
Within academic literature, there is no clear definition of smartwatch technology and no clear distinction of related technologies. For Kim and Shin (2015) for example, several wearables including Fitbit Flex or Samsung Galaxy Gear are smartwatches. Although these devices are wrist-worn there are differences that require a more detailed differentiation.
There often is a confusion between smart wristbands or fitness wristband trackers, which track a user’s physical functions (e.g. pulse) and provide limited information on small displays, and smartwatches (Chua, et al., 2016; Pal, Funikul, & Vanija, 2018). The primary purpose of the wristbands further is collecting data which can be analyzed on a different device (e.g. smartphone or computer). There is no possibility to install applications and the presentation of information is very limited (e.g. time or pulse). Some examples of such devices are Nike Fuelband or Fitbit Surge.
Smartwatches, on the contrary, are larger than smart wristbands or fitness wristband trackers. Usually, the face of a smartwatch is a touchscreen and users are able to install various apps. More than 10.000 apps are available for iOS (Apple) and more than 4.000 apps for Android (Chua, et al., 2016).
Additionally, compared to smart wristbands or fitness wristband trackers, smartwatches provide the most benefits when they are connected to the internet (Bluetooth, Wi-Fi or mobile internet). For smart wristbands and fitness wristband trackers, the main purpose is to collect data. For smartwatches on the other hand, a primary function is presenting relevant information (e.g. emails). After contemplating the uniqueness of smartwatches in comparison to smart wristbands and fitness wristband trackers, the authors define a smartwatch following Cecchinato, Cox and Brid’s (2015) notion who see them as “a wrist-worn device with advanced computational power that allows the installation and use of applications, that can connect to other devices via short-range wireless connectivity; provides alert notifications; collects personal data through a range of sensors, stores them; and has an integrated clock” (p. 2134).
Smartwatches are computing devices that are also regarded as fashionable products or accessories (Jung et al., 2016). Previous research has shown that in order to be recognized as independent computing devices, display size and standalone communication functions are important technological properties of smartwatches (Rawassizadeh et al., 2015).
Although there were computer-based wristwatches (e.g. Fossil wrist PDA, IBM/Citizen WatchPad, Microsoft’s STOP Watch) in the early 2000’s, their functional limitations prevented their success (Rawassizadeh at al., 2015). In 2012, the first widely adopted computer-based watch was released: The Pebble watch. In 2014, Apple released their first Apple watch and after that leading IT vendors – including Samsung, Google and Microsoft – did release their new models of smartwatches.
The small display size can be a disadvantage of smartwatches compared to smartphones. According to Cho, Jung and Im (2014), usability, including screen size, has a significant impact on consumers’ satisfaction with mobile devices. Typing is possible on most smartwatches; however, it is more convenient on smartphones. To mitigate this problem, some smartwatches (e.g. Google Android Phone, Apple Watch) included voice recording systems.
Another important technological issue is the fact that most smartwatches models are indirectly connected to smartphones by wireless networks. Short-distance communication systems like Bluetooth are often used. With this technological characteristic, smartwatches work as communication tools, nevertheless they also are dependent on smartphones. If there was a possibility for smartwatches to be capable of standalone communication, they would get more independent and maybe even replace smartphones (Quain, 2015).
Smartwatches on the German Market
According to Euromonitor International (2018), wearable technologies are popular on the German market. The reason for this was found to lie in the consumption patterns of Germans. More specifically it was found that in 2018, Germans’ consumption behavior was driven by ongoing health and fitness trends as well as an increasing demand for more sophisticated products (Euromonitor International, 2018). As described previously, wearable technologies serve those demands, for example by providing people with the opportunity to track their health and fitness data on the go. Therefore, those trends enabled wearable technologies, and also smartwatches in particular, to strongly gain in sales volume as well as in current value terms on the German market (Euromonitor International, 2018). More precisely, compared to 2017, the revenue earned with wearables in Germany grew by 4.2 percent in 2018 (Statista, 2019c).
Furthermore, when it comes to the type of wearable devices Germans prefer wrist mounted smartwatches and health monitors over devices which are built-in their clothes or eyewear (Mordor Intelligence, 2017). Specifically, in regard to smartwatch sales, Apple occupied the leading position in Germany in 2018 with their Apple Watch series (Euromonitor International, 2018).
A factor contributing to the sales growth of smartwatches in general was its design. According to Euromonitor International (2018), their elegant, versatile and hybrid designs did convince German consumers and let them buy those devices more frequently. In line with these findings an older study by Pricewaterhouse Coopers (2015) uncovered design as one of the six most important factors when purchasing a wearable device in Germany, the others being value for money, data security, intuitive usability, compatibility with other devices and social media access. In that regard, value for money has been indicated as the most important factor that influences the purchase decision by Germans (Pricewaterhouse Coopers, 2015). Similar to that, Euromonitor International (2018) found the average unit price of smartwatches to be an impediment for further success in Germany.
Furthermore, in 2017 the majority of the users of wearable technologies in Germany could be placed into the age group between 25-34 years (Statista Global Consumer Survey, 2018a) indicating that specifically younger people adopt those technologies. However, at the same time also new types of wearable devices that mainly target young children or elderly consumers (e.g. specific health and wellness trackers) entered the German market (Euromonitor, 2018; Russey, 2018; Mojapelo, 2018).
Consequently, similar to the world market, the German market for wearable devices is also growing which again underlines this thesis’ importance to focus on this market. Specifically, smartwatches are increasingly more valued by Germans. Furthermore, especially factors such as price, intuitive usability and design of smartwatches and other wearable devices play an important role for Germans when they decide about adopting such devices. This perfectly fits with this thesis’ research framework that is outlined in the following paragraphs.
Theoretical Background & Research Model
In the next paragraphs follows a detailed discussion of TAM and TPB, the two models that are used to construct this thesis’ research model. It is then explained how the underlying research model was built based on those two theoretical models. Finally, hypotheses for each of the dimensions are formulated.
Technology Acceptance Model (TAM)
In order to examine the factors that influence the purchase intention of smartwatches in Germany, similar to previous studies (e.g. Choi & Kim, 2016; Chua et al., 2016; Kim & Shin, 2015) this thesis uses TAM as the basic underlying framework (see Figure 1). This decision was made as, on the one hand, this model is one of the most used theoretical models to examine people’s acceptance of information technology and is therefore highly relevant in this context and, on the other hand, it is considered to be very robust (King & He, 2006; Venkatesh & Davis, 2000).
Originally, TAM is based on another psychological theory, the Theory of Reasoned Action (TRA) (see Appendix 2) (Fishbein & Ajzen, 1975). TRA postulates that people’s behavior is influenced by the behavioral intention which is again influenced by people’s attitude towards that behavior as well as the SN regarding the behavior (Fishbein & Ajzen, 1975).
Based on this theory, Davis (1989) developed TAM to facilitate an understanding of individual’s acceptance of new technologies. TAM hypothesizes that the attitude towards using a specific technology is determined by the perceived usefulness (PU) as well as the perceived ease of use (PEU) of that technology and finally leads to the intention to use as well as the actual use of the technology (Davis, 1989). So, TAM’s structure is undoubtedly very simple. However, despite its simplicity, empirical evidence underlined its robustness and relevance for technology acceptance research (King & He, 2006; Venkatesh & Davis, 2000). Therefore, TAM has been extensively and successfully used in previous studies about wearable technologies (Choi & Kim, 2016; Chua et al, 2016; Kim & Shin, 2015; Rauschnabel & Ro, 2016).
Nevertheless, the original TAM also has several weaknesses. Those were primarily addressed by Venkatesh, Morris, Davis and Davis (2003) as well as Venkatesh, Thong and Xu (2012) in their development of the “Unified Theory of Acceptance and Use of Technology” (UTAUT) and UTAUT2 respectively.
UTAUT & UTAUT2
One major weakness of TAM lies in the fact that solely the utilitarian aspects of PU and PEU were regarded as antecedents of individuals’ technology acceptance. Therefore, Venkatesh et al. (2003) saw a need to expand the model by including “Social Influence” and “Facilitating Conditions” as additional antecedents of the intention to use new technologies. They referred to this expanded model as the UTAUT (see Appendix 2). Building on that, in a later study Venkatesh et al. (2012) even further developed UTAUT by including “Hedonic Motivation”, “Price Value” and “Habit” as antecedents of individuals’ technology acceptance and referred to this model as UTAUT2 (see Appendix 3). So previous research has suggested ways to overcome TAM’s weaknesses and to get a bigger picture of the factors that influence consumers’ acceptance of technology.
However, those two models have also several weaknesses. UTAUT, for example, focuses primarily on organizational contexts and therefore neglects the dimension “Hedonic Motivation” which can be considered as a highly important aspect influencing consumer intention to adopt new technologies (Venkatesh et al., 2012). A detailed discussion of this dimension and its relevance follows in next chapter (see 126.96.36.199 TAM’s need for Extension – Hedonic Aspects). Moreover, UTAUT does not include the dimension “Attitude towards using” which previous studies on smartwatch adoption found to be an important and necessary mediator between the different antecedent dimension and the intention to adopt smartwatches (Chua et al., 2016; Hsiao & Chen, 2018; Wu, Wu & Chang, 2016).
In addition to that, by incorporating “Habit” UTAUT2 includes a dimension that was considered not to be applicable to smartwatch adoption research. This can be claimed as a habit could not influence the purchase of smartwatches; in contrast, it could only develop if a consumer already possesses a smartwatch. Apart from that, although the model includes “Hedonic Motivation”, it still neglects another aspect that was regarded as particularly important in the adoption process of smartwatches, namely their design aesthetics (e.g. Hsiao & Chen, 2018). A detailed discussion of this dimension and its relevance follows in next chapter (see 188.8.131.52 TAM’s need for Extension – Hedonic Aspects). Moreover, similarly to UTAUT, UTAUT2 also neglects the dimension “Attitude towards using” and thus a potential mediating influence between different antecedents and the behavioral intention.
Therefore, it can be concluded that neither UTAUT nor UTAUT2 would be suitable as a research model for the underlying study. That is why, it was decided to develop an own model consisting of an extended version of TAM and TPB for this research.
Table of contents :
1.2 Problem Formulation
1.5 Contribution to Theory & Practice
2 LITERATURE REVIEW
2.1 Approach to Literature review
2.2 Overview of Wearable Technologies
2.2.1 Benefits for Consumers
2.2.2 Benefits for Society
2.4 Smartwatches on the German Market
2.5 Theoretical Background & Research Model
2.5.2 Technology Acceptance Model (TAM)
184.108.40.206 UTAUT & UTAUT2
220.127.116.11 TAM’s need for Extension – Hedonic Aspects
2.5.3 Theory of Planned Behavior (TPB)
2.5.4 Research Framework and Hypotheses Development
18.104.22.168 Perceived Usefulness (PU)
22.214.171.124 Perceived Ease of Use (PEU)
126.96.36.199 Perceived Enjoyment (PE)
188.8.131.52 Design Aesthetics (DA)
184.108.40.206 Attitude towards Using
220.127.116.11 Subjective Norm (SN)
18.104.22.168 Perceived Behavioral Control (PBC)
22.214.171.124 Behavioral / Purchase Intention
3.1 Research Philosophy
3.2 Research Approach
3.3 Research Purpose
3.4 Research Design and Research Strategy
3.5 Data Collection Method
3.6 Survey Design
3.7 Population and Sampling
3.8 Analyses of Data
3.9 Limitations of Methodology
3.10 Reliability and Validity
3.10.3 Pilot Testing
3.11 Ethical Considerations
4 EMPIRICAL FINDINGS
4.1 Demographic Sample
4.2 Descriptive Statistics
4.3 Reliability Analysis
4.4 Factor Analysis
4.5 Hypotheses Testing
4.5.1 Correlation Analysis
126.96.36.199 Hypothesis 1
188.8.131.52 Hypotheses 2a & 2b
184.108.40.206 Hypotheses 3a & 3b
220.127.116.11 Hypotheses 4a & 4b
18.104.22.168 Hypothesis 5
22.214.171.124 Hypothesis 6
126.96.36.199 Hypothesis 7
4.5.2 Multiple Regression Analysis
188.8.131.52 Hypotheses 1, 2b, 3b & 4b
184.108.40.206 Hypotheses 2a & 3a
220.127.116.11 Hypothesis 4a
18.104.22.168 Hypotheses 5, 6 & 7
5.1 Attitude towards Using
5.1.1 Perceived Usefulness
5.1.2 Perceived Ease of Use
5.1.3 Perceived Enjoyment
5.1.4 Design Aesthetics
5.2 Purchase Intention
5.2.1 Attitude towards Using
5.2.2 Subjective Norm
5.2.3 Perceived Behavioral Control
6.1 Research Question
6.2 Theoretical Implications
6.3 Managerial Implications
6.4 Social and Ethical Issues of Smartwatches
6.6 Future Research
Appendix 1: Article Search
Appendix 2: Theory of Reasoned Action (TRA)
Appendix 3: UTAUT & UTAUT2
Appendix 4: Survey English
Appendix 5: Survey German
Appendix 6: Frequency Tables
Appendix 7: Normal Probability Plots & Scatterplots