Acceptance of Wearable Fitness Trackers in the Consumer Market

Acceptance of Wearable Fitness Trackers in the

Consumer Market

Motivation Variables and Privacy Concerns on Usage Intention

and the Moderating Role of Health Concerns and Sportiness

Master Thesis, MSc Marketing Management

University of Groningen

Daniela Metilli Grela S3192903

e-mail: d.r.metilli.grela@student.rug.nl tel: (+34) 664559881

First Supervisor: Prof. dr. P. C. Verhoef Second supervisor: Mr. A. Onrust

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Executive Summary

The introduction of wearable devices to the market has been theorised to have the potential to transform markets, industries and society in different ways in the near future. Among them, wearable fitness trackers (WFTs) are the main wearable device in process of spreading in the consumer market. These, are devices in the form of a wristband designed to track biometrics (e.g. heart rate) and physical activity of the person that wears it (e.g. steps taken, distance travelled due to GPS trackers, exercise performed, calories burned, etc.). In spite of its imminent relevance in society, research regarding perception and attitude towards the adoption of these products from the potential user‟s point of view is lacking. Therefore, the objective of this paper is to investigate the main factors that motivate the usage of WFTs in the consumer market and the implications of perceived privacy concerns on intention to use them. Furthermore, given the potential implications of WFTs in terms of wellbeing and physical activity, health concerns and sportiness of individuals are taken into consideration as potential moderators.

After careful consideration of literature in the field, the conceptual model was designed and information was gathered by means of an online survey with total of 107 suitable responses (from non-users). The performance of multiple moderated regression analysis allowed to obtain the following results. Of the proposed main effects, Performance

Expectancy (perceived usefulness), Social Outcomes (social value) and Hedonic Motivation (enjoyment) were found to have a positive influence on intention to use

WFTs (the latter being the most representative of the three). On the contrary, Social

Norm and Privacy Concerns were not found to be significant. Furthermore, Health Concerns was found to have positive moderation effects in the relation of Performance Expectancy and Social Outcomes with Intention to Use, and negative effects in the case

of Hedonic Motivation. The second moderator Sportiness, did only show weak evidence of positive effects on the negative relation between Privacy Concerns and Intention to

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Preface

This master thesis is my last requirement to graduate from the Master of Science in Marketing Management at the University of Groningen. With it, I finish what is my last project at this university, putting an end to the beautiful chapter that has been life as a student. During this time, many are the things that I have learned and the inspiring people I have come across. And innumerable, the memories and valuable experiences that I will now treasure for the rest of my life.

The road to completing this project has not always been easy and it is with the help from many others that it has been possible. For that, I would like to thank every person that has been involved in this journey. My supervisor prof. Verhoef, for his guidance and ideas throughout these months of work. My fellow students and friends for their help and time shared. And last but not least, my family. For their constant and relentless support, for their kind words and encouragement in times of need and for the many times they contributed with laughter to difficult times. Without them, none of these would have been possible.

Daniela Metilli

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Content

List of Abbreviations ... 5

1. Introduction ... 6

2. Theoretical Framework... 10

2.1. Conceptualization of Wearable Fitness Technology ... 10

2.2. Theoretical Review ... 12

2.3. Hypotheses ... 15

2.3.1. Independent Variables ... 15

2.3.2. Moderating Variables ... 22

2.3.3. Control Variables: Sociodemographics ... 23

3. Methodology ... 24

3.1. Data Collection ... 24

3.1.1. Measurement: Survey Constructs ... 24

3.1.2. Procedure ... 25

3.2. Data Analysis ... 26

3.2.1. Sample description ... 27

3.2.2. Validity and Reliability ... 29

4. Results ... 32

5. Conclusion and Discussion ... 38

5.1. Discussion ... 39

5.2. Practical Implications ... 42

6. Limitations and recommendations for future research ... 45

References ... 47

Appendices ... 53

Appendix 1. Framework from UTATU 1 and UTAUT 2 ... 53

Appendix 2. Survey Design. Measurement Items. ... 54

Appendix 3. Complementary Result Tables from Factor Analysis. ... 57

Appendix 4. Complementary Graphs for Assumption Checks for Regression Analysis. ... 58

Appendix 5. Mean, Standard Deviation and Correlation Table. ... 60

Appendix 6. VIF Scores. ... 60

Appendix 7. Summary of results for the proposed hypotheses. ... 61

Appendix 8. Moderating Effects of Health Concerns ... 62

Appendix 9. Models 1 to 5. Standardized Coefficients ... 64

Figures

Figure 2. Conceptual Model: Results ... 37

Tables

Table 2. Rotated Component Matrix. ... 31

Table 3. Internal Consistency; Reliability Test. ... 31

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List of Abbreviations

Avg. Average

CA Cronbach‟s alpha.

Coef. Coefficient

e.g. Example given

FA Factor Analysis

IoT Internet of Things

i.e. id est; „that is‟

KMO Kaiser-Meyer-Olkin Measure of Sampling Adequacy MMRA Multiple Moderated Regression Analysis

n.s. Not significant p. Page SD Standard Deviation Sig. Significant Std. Standardised Unstd. Unstandardised

VIF Variance Inflation Factor

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1. Introduction

The market for wearable technology, one of the most extended applications of the Internet of Things (IoT), has seen a growth extensively forecasted to continue (European Commission 2016). Currently crowding this consumer market are Wearable Fitness Trackers (WFTs) in the form of intelligent wristbands. These, are devices designed to track biometrics (e.g. pedometer, heart rate, blood pressure) and physical activity of the person that wears it. By differentiating movement they identify the type of activity performed (e.g. steps, distance due to integrated GPS trackers, swimming strokes, sleep tracker) and infer information such as calories burned according to time and intensity. They also count with movement reminders (in the form of device vibration after certain periods of physical inactivity), personally tailored routines and even have notification system on their screen. These devices are connected to the user‟s smartphone, where the data is made available and supplemented with personalized feedback. In this way, it helps users to manage their health by encouraging the increment of their daily activity. Furthermore, options to share this content in media platforms, comment on friend‟s results and create groups to compete, allow for further enjoyment of the activity and increase the social component of this devices. Further options are available when information monitored is paired with a particular application that allows integrations and analysis of the data of particular purposes.

7 focused on: 1) technical accuracy and reliability of WFTs (Diaz et al. 2015; Huang et al. 2016; Kaewkannate & Kim 2016); 2) the effects of use of WFTs on fitness related outcomes for adopters (Etkin 2016; Karapanos et al. 2016) and 3) the use of WFT for healthcare purposes, in which devices are specialized and intended for patients with particular medical conditions (Gao, Li & Luo Y. 2015; Nguyen et al. 2017; Wilson, Ramsay & Young 2017). This means that despite the popularity of WFTs being marketed, published research has neglected to address drivers of individual‟s adoption and usage intention in customer markets. In spite of perceived usefulness of this technology having been proven to play a role in usage intention, there are other relevant factors that affect adoption intention: mainly enjoyment and social factors. Researching these factors to understand what motivations non-users would have to use them in the future would have important implications in the acceptance of these devices in daily life of the population and further development to benefit society. On the other hand, it would reduce the considerable challenges in terms of product positioning and marketing and open an opportunity for new entrants, both of which would benefit from research on the motivation to use this technology. Therefore, the purpose of this study is to explore the association between motivation to use WFTs and intention to use a WFT in individual‟s daily lives in the future. The first research question proposed (RQ1) is:

What is the association between motivation to use a WFT and intention to use WFT in daily life among current non-adopters?

8 privacy for consumers, but the risks of potential information leakage due to the value of this data. This is because, similarly to smartphones, most WFTs integrate a GPS tracker and allow for constant retrieval of vast amount of real-time data, although much more personal (by including biometric information).

The potential integration of the physiological, location and context data surpasses the implications of currently collected big data. It has the potential to lead a revolution in market research that could account for new dimensions in the study of consumer behaviour, giving access to insights from individual‟s daily life, where actual experience takes place (Ng & Wakenshaw 2017; Stott 2017). It would have implications for numerous business areas, including marketing activities such as customization, device design and targeting strategies. Furthermore, it could take the already existing role of smartphones in customer journey, shopping behaviour and firm‟s targeting strategies (like real-time location based marketing) to a new level (Verhoef et al. 2017). This would also be the case in the use of other smart products, expected to become part of daily life in the future. Being able to sense and record their surroundings. In order to take advantage of this potential without risking future performance: learning the extent of consumer‟s perceived risks in the usage of WFTs and their tolerance in terms of trade-offs should be a priority. For these reasons, the second proposed research question (RQ2) of this study is: What is the association between privacy risks derived from the

use of a WFT and intention to use a WFT in daily live?

9 condition a moderator in the study.

At the same time, WFTs have been more heavily targeted at sporty individuals, as it makes sense that their characteristics would be more relevant for them. However, it remains unknown whether the level of sportiness of an individual affects the importance they give to the motivational variables that impulse the use this technology. For this reason, this study accounts for potential moderation effects of sportiness.

The contributions of this research to the literature are various. Firstly, it addresses the entrance of new technological products (smart products) to the market, in this case in the form of a product on the rise: wearable technology. Secondly, it expands the limited research in the understanding of adoption of WFTs in the customer market. By developing an integrated framework that includes variables from motivational and technology acceptance theory, it expands the consideration of perceived usefulness and enjoyment, while adding social influence and social outcomes as possible motivational factors. Additionally, it includes the consideration of information privacy risk perceptions as an important factor that has not been considered in the area of WFTs in consumer market1. Lastly, the level of sportiness and health concerns of potential users are taken into account as potential moderators for the first time in the research of WFTs in the consumer market1, expanding the practical and theoretical contributions of this paper to the field.

The paper is structured as follows. Section 2 contains the contextualization of smart products, the literature review used for the construct of the conceptual model and the proposed research model and hypotheses. Section 3 presents the methodology. Results are stated in Section 4, and Section 5 discusses findings and practical implications. Finally, limitation and Opportunities for future research are presented in Section 6.

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2. Theoretical Framework

2.1. Conceptualization of Wearable Fitness Technology

The term „Internet-of-Things‟ (IoT), coined by technologist and pioneer Kevin Ashton, can be semantically defined as „a world-wide network of interconnected objects uniquely addressable, based on standard communication protocols‟ (European Commission, Information Society and Media 2008). It involves a wide range of „smart products‟, some of which are originally created as such (like WFTs) or transformed by adding sensors and network connectivity (e.g. intelligent house thermostat and lights that can be automatically adjusted to environmental conditions and remotely managed via internet connection) (Verhoef et al. 2017). This increasing potential for device digitalization allows for interaction with each other and with consumers that has the potential to fundamentally transform markets, industries and society (Lucero 2016; Ng & Wakenshaw 2017).

The generalization of smart products leading to concepts such as „smart cities‟, allows for massive data sensing regarding not only location, but also environment and context. According to Verhoef et al. (2017), this is bringing connectivity to the next level as omnipresent, multifaceted, and multidimensional. As well as enhancing the evolution of an ever more connected consumers (to the internet via several devices, such computers, smartphones, tablets and know, wearable technology). As a consequence of this evolution, consumer behaviour is changing (Kannan & Li 2017).

11 expected to almost duplicate between 2017 (International Data Corporation 2017) and includes options such as smart clothing and smart glasses, which are expected to see an incredible increase in market share in the upcoming year (International Data Corporation 2017). However, most popular in the current market are smart watches and WFTs, both in the form of wristbands (European Commission 2016).

WFTs allow for instant access of data regarding personal biometrics, which are transferred to a software application on the consumer‟s smartphone or computer, where graphs are made available and it is possible to compare activity day by day‟ (Harvard Health Letter 2016). If massively adopted, this technology could mean a revolution in health and exercise activities, not only for medical purposes and athletes, but for everyday individuals. Such is the case when monitoring physical activity, speed and physical response to them. But also, with push notifications that impulse wearers to bust their exercise after certain periods of inactivity. These devices come in several different models, ranging from more traditional looking to innovative designs, comfortable rubber materials, waterproofed exterior, different battery duration, etc.

It is important to differentiate two types of wrist wearable devices with physical and activity tracking capabilities. On the one hand, health wearable devices are technologically more advance prototypes with the purpose of treating/monitoring health problems in patients with specific medical conditions. On the contrary, wearable technology with fitness tracking capabilities are devices designed to monitor more basic and daily fitness-related condition, such as physical characteristics, activities (steps, pulse, distance, calories burned, sleep, etc.) and diet in healthy users (Chan et al. 2012). Therefore, consumers characteristics are expected to have different impact in their perception of device characteristics, such as, enjoyment, battery life, appearance, price value, etc. (Gao, Li & Luo 2015). The current research takes this latter technology, known as wearable fitness trackers (WFTs), into account.

12 (push) notifications, as well as facilitating viewing and response of email, texts, calls social media notifications, calendar appointments, road directions, etc. WFTs are currently more common (including examples such as Fitbit and Jawbone). However, adoption rates are low at the moment (Business Insider 2017) and there are cases of brands leaving the consumer market (Jawbone and Microsoft) and companies (e.g. Fitbit) experiencing lower sales than expected (Heater 2017; Kleinman 2017; Russell & Biggs 2016). At the same time, smart watches are expected to dominate the market in the near future (International Data Corporation 2017). This tendency is explained by the migration of fitness tracking capabilities towards smart watches, which in combination with their more traditional functionalities allow for hybrid wristband devices (such as Apple watch, LG Watch Sport, Huawei watch) expected to win over the market (Beaver 2016; Framingham 2017). Since previous research, has considered the drivers of adoption and usage for smart watches (Choi & Kim, 2016; Chuah et al., 2016; Hong, Lin, & Hsieh, 2017; Hsiao, 2017; D. Kim, Chun, & Lee, 2014; K. J. Kim & Shin, 2015), similar research in the field of wearable fitness trackers would be relevant for the future market.

2.2. Theoretical Review

13 The extension of the Unified Theory of Acceptance and Use Technology (known as UTAUT2) from Venkatesh et al. (2012), builds on the findings of UTUAT from Venkatesh et al (2003) and presents a tested and modern framework in individual‟s adoption of IT. It combines numerous previous theories (that present similar or related constructs), some of which will be used as complementary information in the development of the model. Among them: 1) IS context: Theory of Acceptance Model (TAM) (Davis 1989), TAM 2 (Venkatesh et al. 2003); 2) Social psychology (Theory of Reasoned Action (TRA) by Arjen & Feisbein (1975), Theory of Planned Behaviour (TPB) by Ajzen (1991); 3) Human behaviour: Theory of Human Behaviour (THB) from Triandis (1977) and; 4) Sociology: Innovation Diffusion Theory (IDT) from Rogers (1983).

UTAUT and UTAUT 2 theorise that Beliefs (regarding outcomes associated with performing a behaviour) impact Behavioural Intention (intention to perform a behaviour), which in turn influences actual Behaviour (Jeyaraj, Rottman & Lacity 2006). Since the primary objective of this research is to understand non-user‟s perceptions of WFT and their consequent impact in their intention to use the device, Behavioural

Intention (referred to as Intention to Use in this paper) is the dependent variable of this

study. With equal meaning to Intention to Use from TAM and TAM2 (Davis 1989; Venkatesh & Davis 2000), it can be defined as „a person‟s intention to use or adopt an innovation in the future‟ (Jeyaraj, Rottman & Lacity 2006). According to Jeyaraj, Rottman & Lacity (2006) it is among the best predictors of actual behaviour.

UTAUT2 proposes seven variables to account for individuals‟ intention to use a technology2: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating

Conditions, Hedonic Motivation, Price Value and Habit. Since the interest of the study

is to account for the motivations to use the technology, only the variables that confound users motivation and that have been widely known to impact usage were taken into account after careful consideration. To make and account for these decisions, several

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14 theories and models were reviewed and compared (as will be explained in the following section “Hypotheses”). As a brief introduction, the perspective of motivation theory is relevant. In their paper, Davis, Bagozzi & Warshaw (1992) categorize motivation in two groups: extrinsic and intrinsic motivation. Extrinsic Motivation refers to the motivation to perform an activity to achieve a desired outcome, distinct from the activity itself (Davis, Bagozzi & Warshaw 1992). For example, better performance. This motivation is comparable to the variable from UTAUT and UTAUT2 Performance Expectancy, mostly known and referred to in numerous researches as Perceived Usefulness from TAM and TAM2. On the other hand, Intrinsic Motivation, relates to the realization of an activity for the process itself, not for the outcome it would achieve. In line with this, Davis et al. (1992) proves the relation of Enjoyment on intention to use (i.e. enjoyment of using WFTs), as does UTAUT2 (naming it Hedonic Motivation). Furthermore, UTAUT2 addresses this group by adding Social Influence, which is of similar meaning to Social Norm (from TAM2). The present research combines the concept of social norm with the concept Social Outcome (which refers to consumer‟s derive social image) to account for a second aspect of Social Influence, and therefore Intrinsic Motivations. Furthermore, in making these decisions the research of Jeyaraj et al. (2006) was consulted due to its analysis of the body of research that accounts for these predictors, linkages, and biases in IT innovation adoption research.

Due to the characteristics of WFTs, which are not the same as the traditional technologies accounted in the mentioned models, the construct of these variables contain diverse finding and theories to account for the differences. All of it is accounted for in the following section Hypotheses.

15 Figure 1. Conceptual Model

2.3. Hypotheses

2.3.1. Independent Variables

Performance Expectancy

The degree to which an individual perceives that using a technological device provides benefits in performing certain activities, is a concept that has been extensively used in technology acceptance frameworks (Venkatesh, Thong & Xu 2012). It is used to measure the interest/desire of consumers for a product based on the tasks it allows them to accomplish (external rewards). In the past, it has been captured as the variable

Performance Expectancy in the previously mentioned UTAUT/UTAUT2 (Venkatesh,

Thong & Xu 2012; Venkatesh et al. 2003) and is very similar in meaning to Perceived

Usefulness from the very frequently used model TAM/TAM2 (Davis 1989). These

16 devices and intention to adopt them (Choi & Kim 2016; Chuah et al. 2016; Gao, Li & Luo 2015; Kim & Shin 2015).

Wearable fitness trackers are devices designed to register user‟s vitals and physical activities, allowing them to track and improve health state and physical well-being in several forms. Therefore, health and fitness outcomes would be the most relevant benefits derived. By tracking biometrics, people with certain health problems can follow if their heart rate is within normal limits, making it easy to know the situations or moments in which it fluctuates and keep it monitored. Similarly, a person with sleeping problems could benefit from understanding what actually happens in their sleep, make sense of it using the app and improve it with the tips provided. At the same time, it can improve user‟s performance while exercising. For example, recording heart rate can be important for determining the intensity of one‟s daily activity or workout, not only to make it safe, but to make it more effective as well (Harvard Health Letter 2016). Additionally, measuring the activity an individual practices has been proven to positively affect the total amount of activity practiced (Etkin 2016). According to this, in the case of WFD, tracking exercise levels would increase actual exercise, which is good for the health state, but also for people that would like to lose weight. And reminders to move after certain periods of inactivity and frequent-and-easy-to-access updates on your activity are expected to encourage physical activity. Lastly, complementary services synchronised with the device (e.g. fitness app) can suggest personalized health and activity recommendations, tailored plans, etc.

A previous study found weight loss, increase of physical activity and healthier lifestyle among important fitness goals to achieve through these devices (Lunney, Cunningham & Eastin 2016).

17 activity (e.g. excessively intense heart rate).

H1: Performance Expectancy of WFTs is positively related to Intention to Use a

WFT.

Hedonic Motivation

Hedonic Motivation, also known as Perceived Enjoyment in IT acceptance literature, is

understood as the fun or pleasure derived from usage of a particular technological device (Venkatesh et al. 2003). This pleasure or joy enhanced by the usage of a technology increases the probability of adopting such technology and using it more extensively (Davis, Bagozzi & Warshaw 1989). In some occasions, it is even considered as more important than Utilitarian Value in cases of non-organizational contexts like it is consumer markets (Venkatesh, Thong & Xu 2012). In previous research, it has been found to have a critical impact in individual‟s intention to adopt technology in similar areas of IT adoption, such as: mobile internet usage (Chong 2013; Kim, Chan & Gupta 2007); smartphone health apps (Wang, Dacko and Gad 2008) and smart watch adoption (Choi & Kim 2016; Hong, Lin & Hsieh 2017).

18 competitive behaviour that encourages a more active behaviour (Stott 2017). This is in line with gamification, a term used to describe the use of game mechanics and experience design to engage and motivate individuals to achieve their goals (Burke 2014). This engagement and competition can be considered as Hedonic Motivation, as it is perceived as enjoyable.

H2: Hedonic Motivation related to usage of WFTs is positively related to Intention

to Use a WFT.

Social Influence: Social Norm and Social Outcomes

Humans are fundamentally motivated to create and maintain meaningful social relationships with others, looking for social approval and affiliation (Cialdini & Goldstein 2004). Consequently, it is not uncommon for behaviour to be shaped and constrained by persons from one‟s social network (Granovetter 1973). This translates in social forces influencing or pressuring individuals to make certain adoption decisions, in many different ways. This influence or cohesion exists not only from people‟s own close network (which includes known individuals, like friends and family), but from individuals with which one has weak ties or that are not directly tied to the individual as well (e.g. a third persons, friend‟s friend) (Granovetter 1973). This dynamic is accentuated by the implications of the internet and social interactions, which allows for massive content and opinions of freely accessible (e.g. social media, reviews, ratings, etc.) that influence individual‟s decision marking. Given the nature of WFT, which allow for the sharing and commenting of personal scores and online competition with friends and other member of the fitness community, the effect of the social component on intention to adopt the technology can be further increased. As example of this, in recent times gamification3 has started to include digital tools (e.g. social media) to

amplify these effects. Furthermore, since products worn by individuals are used to infer information regarding them and create a person‟s image, wearing them in visible parts of the body, such as the wrist for wearable devices, it may result in the effects of social

19 influence being magnified (e.g. Chuah et al. 2016, proved it to be the case of smart watches).

In this paper, the proposed variable Social Influence distinguishes between two concepts:

Social Norm and Social Outcomes. To explain them, several perspectives of social

influence developed in different theories and interconnected among them will be considered to explain its influence on individuals‟ adoption behaviour.

Firstly, we include the variable Social Influence from UTAUT and UTAUT2 (Venkatesh, Thong & Xu 2012; Venkatesh et al. 2003), most commonly known as Social Norm or

Subjective Norm from TAM2 (Venkatesh & Davis 2000). It is defined as the extent to

which individuals perceive that certain referents or important others (e.g., family and friends) believe they should use a particular technology (Venkatesh, Thong & Xu 2012). In the context of IT acceptance, it has been used to reflect the idea that individuals may be sufficiently motivated to comply and choose to adopt the technology even if they are not favorable towards the behaviour and its implications, if they perceive that relevant referents believe they should (Venkatesh & Davis 2000). It has been relevant in consumer behaviour research (Venkatesh, Thong & Xu 2012) and it is considered to be one of the best predictors of individual of behavioural intention and promising for individual adoption research (Jeyaraj, Rottman & Lacity 2006).

According to UTAUT from Venkatesh & Davis (2000), this variable can affect individual behaviour through thee mechanisms. First one is compliance, causing individuals to simply alter their intention in response to the social pressure. Secondly,

internalization and identification, which include the modification of one‟s belief

20 that the explained outcome can be achieved by individuals: 1) actively searching for information from opinion leaders or a group with the appropriate expertise 2) making an inferences by observing the behaviour of significant others (Park & Lessig 1977).

Identification captures the concept that it is not uncommon for individuals to respond to

social influence to establish and maintain a favorable image with a particular reference group (Kelman, 1958 cited in Venkatesh & Davis, 2000). Image, can be defined as the extent to which using an innovation is believed to increase perceived status in a social network (Moore & Benbasat 1991, cited by Venkatesh & Davis 2000).

The second construct contained in Social Influence, Social Outcome, refers to the idea that individuals may acquire and use a product to convey a particular image about themselves, therefore, for a valuable social outcome. Self-image (Kim, Chun & Lee 2014) refers to the acquisition of product as a mean to self-express, which in turn allows achieving and displaying an increment in status and fashion sense. Self-expressiveness, closely related to individual‟s self-concept (Park & Lessig 1977), has been found to be a relevant factor in literature regarding IT adoption. For example, smartphones (Kim, Chun & Lee 2014) smart watches (Choi & Kim 2016; Kim & Shin 2015). Previous findings point to smart watches to be considered as innovation and a fashion (further sustained by Chuah et al. 2016) items that reflect one‟s identity and aids in the expression of one‟s values and uniqueness (Choi & Kim 2016; Kim & Shin 2015). Given the similarities among this product and WFT (and the increase in hybrid models), one could infer that WFTs cause similar perceptions in the population.

H3: Social Norm related to usage of WFT is positively related to Intention to Use a

WFT.

H4: Social Outcomes derived from usage of WFT are positively related to Intention

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Privacy Concerns

When considering a choice, individuals tend to perform a risk-benefit analysis. When asked to provide personal information to organizations, the comparison of the privacy costs associated with it, becomes more salient (Malhotra, Kim & Agarwal 2004). The term „information privacy‟, refers to “the claim of individuals, groups, or institutions to determine for themselves when, how, and to what extent information about them is communicated to others” (Westin 1967 cited in Malhotra, Kim & Agarwal 2004). On the other hand, „Information privacy concerns‟ refer to a person‟s subjective views of fairness in the context of information privacy (Campbell 1997, cited in Malhotra, Kim & Agarwal 2004). Information privacy concerns have been found to be related to the collection of the sensitive data, control over its use by firms and awareness of privacy practices (Malhotra, Kim & Agarwal 2004).

22 Therefore, the risks associated with privacy would be more salient in the case of WFTs in comparison with other technological products, thus, of a greater importance in the consideration to adopt and use them.

In this context, lack of privacy is a perceived as a risk, which has been proven to have a negative impact on individual‟s intention to adopt (Lancelot, Popovic & Oliveira T. 2013). Previous findings, have shown that privacy concerns are perceived as risks that affect consumer‟s intention to adopt e-services (Featherman & Pavlou 2003) and biometric identification systems (e.g. face recognition, fingerprints, iris recognition, hand geometry, and voice recognition) (Lancelot, Popovic & Oliveira 2013). It is theorised that privacy concerns regarding usage of WFT will exist and negatively influence usage intention.

H5: Privacy Concerns are negatively related to Intention to Use WFTs. 2.3.2. Moderating Variables

Health Concerns

The health state of an individual is likely to affect their perception and judgement of health related technological tools. Therefore, we include the variable Health Concerns as a moderator.

A previous researh by Gao et al. (2015) shows significant differences when comparing motivation to use WFTs among consumer market and wearable devices in the medical field specifically designed for patients with certain medical conditions such as cancer or diabetes (wearable medical devices). From their research we can espect that the more salient the health concerns an individual has, the more the importance he/she gives to

Performance Expectancy when evaluating intention to use a WFT and less to Hedonic Motivation and Social Influene.

H6a: The relation between Performance Effectiveness and Intention to Use will

23 H6b: The relation between Hedonic Motivation and Intention to Use will be weaker

when the respondent has higher levels of Health Concerns.

H6c: The relation between Social Norm and Intention to Use will be weaker when

the respondent has higher levels of Health Concerns.

H6d: The relation between Social Outcomes and Intention to Use will be weaker

when the respondent has higher levels of Health Concerns.

Less healthy individuals may be more concerned that disclosing their health information could damage their status, employment opportunities, or social standing (Bansal, Zahedi and Gefen 2010). Therefore, poor health status would heighten information sensitivity, which, in turn, influences privacy concerns.

H6e: The relation between Privacy Concerns and Intention to Use will be stronger

when the respondent has higher levels of Health Concerns. Moderator: Sportiness

The moderator Sportiness accounts for the exploration of potential differences in the relation between the motivation factors and privacy concerns with intention to use, among individuals with different performance levels of physical activity.

2.3.3. Control Variables: Sociodemographics

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3. Methodology

3.1. Data Collection

The present study has been carried out by means of an online survey. This method was chosen due to its potential to reach a high number of respondents from different parts of the world in a short period of time, with low effort and cost for the researcher. Furthermore, this method was considered convenient for respondents, allowing access in their own preferred time and facilitating their responses (Malhotra et al. 2004).

3.1.1. Measurement: Survey Constructs

The measurements included in this study, except for the control variables, were adapted from validated and reliable constructs from prior research and measured using multi-item scales (see Appendix 2).

The construct items from UTAUT2 (Performance Expectancy, Hedonic Motivation,

Social Norm, Social Outcome and Intention to Adopt) were mainly adapted from scales

validated from this model (Venkatesh et al. 2012). However, since two of them did not capture the nature of wearable fitness technology on their own, they were complemented with scales from different authors to achieve a more adequate outcome. Therefore, Performance Expectancy was adapted in combination with Markland & Ingledew (1997) and Social Influence: Social Norm and Social Outcome were complemented with constructs from Kim, Chun & Lee (2014) and Moore & Benbasat (1991). Privacy Concerns, on the other hand, were added to complement current IT literature, by using the adaptation of constructs proposed by Malhotra et al. (2004) and Featherman & Pavlou (2003). Each of the previously mentioned item was measured with a seven-point Likert scale, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”).

25 Benyamini 1997; Quesnel-Vallée 2007) and reliability (Lundberg & Manderbacka 1996) has been previously proved, and it has had an extensive use in healthcare-related studies. The seven-point Likert scale ranged from 1 (“excellent”) to 7 (“terrible”).

The moderator variable Sportiness was measured by means of a single-response rating regarding physical activity on the previous week. In spite of the simplicity and shortage of this question, validity and reliability of this scale has been checked and concluded to be approximate to long-questionnaires regarding physical activity (Milton, Bull & Bauman 2011; Wanner et al. 2014). The validated item was replicated in the survey without need for modification. Therefore, participants were asked to specify the number of days they have done a total of 30 min or more of physical activity that was enough to raise their breathing rate (excluding housework or physical activity that may be part of your job).

The control variables Age, Gender, Income were additional questions in the survey to measure any differences in effect. Age was measured as a continue variable and made into intervals. Gender was dummy coded were 0=Female and 1=Male. Income was measured in categories (“Less than average”; “Average”; “Twice the average” and “More than twice the average”) and dummy coded for the regression analysis, as well as the variable WFT User which classified individuals as: users and non-users of WFTs.

3.1.2. Procedure

The survey was designed as an online (anonymous) survey in the platform Qualtrics, which allows access to the web-based survey by clicking on a link provided. This link was directly distributed among family and friends of the researcher and indirectly through personal posts on social media, where individuals were encouraged to complete and share the survey. At the same time, collaboration with Palestra Fitness4 (in Roden, the Netherlands) was arranged and the survey was shared among the members of such fitness centre, as well as though their Facebook page.

26 The administered survey was designed following the general guidelines of the Handbook of Marketing Research (Vriens & Grover 2006), which states that the sequence of the survey should be structured in three parts: a short introduction, the questionnaire (ranging from less to more specific items) and identification information at the end. The introduction to the survey contained an explanation of what the survey consisted on, the amount of time it would take to be completed, the topic to be researched and a short description of WFT. Furthermore, assurance of anonymity of responses was stressed in order to prevent socially desirable responding or impression management5 and reduce systematic error (Vriens & Grover 2006). The following section, the questionnaire, started by presenting questions regarding Intention to Use, in order to prevent responses regarding the independent variables to influence the response. The last section of the survey was dedicated to demographics. These questions were placed at the end because asking sensitive questions after individuals have given their opinions prevents carryover effects to following questions (Van Wonensel & Archer 2015).

Since this survey was targeted at respondents from different nationalities, translation into Spanish was conducted in order to improve the response rate among people with lower level of the English language and non-speakers. At the same time, translation into Dutch was required in order to be distributed among members of the Palestra Fitness.

A pre-test was undertaken with 3 respondents for each version of the survey (language version) to test whether the items proved clear and understandable. Slight modifications were necessary and the final version (in English) is presented in Appendix 2.

3.2. Data Analysis

In this research, the relationship between the dependent variable (Intention to Use) and the independent variables was analysed and quantified. Furthermore, the study accounts for the two moderating effects on these main effects of the two moderators (Health

5

27

Condition and Sportiness). In order to achieve this, Moderated Multiple Regression

Analysis (MMRA) was chosen as the appropriate statistical procedure to be performed, as it accounts for the change in value of the dependent variable when one of the dependent variables varies, while the other independent variables remain constant. After the main effects of the independent variables were assessed, interaction terms were added between the moderators and the main effects, first checking for individual effect and later adding the moderators to the model. Results are presented in the form of five models in order to expose the gains in variance explained by the additions of: 1) Control Variables; 2) Main Effects; 3) Moderating Variable Sportiness; 4) Moderating Variable

Health Concerns (excluding Sportiness); 5) Moderating Variable Health Concerns and Sportiness. The corresponding stages of the research allowed for the comparison of

results and appropriateness of the variables included.

Before this test is performed, several procedures need to take place. In the following subsection information regarding sample data is presented. The subsection after that displays the evaluation of the quality of the measurement model carried out by means of validity and internal consistency tests (factor analysis and reliability analysis, respectively). In the section Results, several statistical checks and assumptions were performed and the final results presented for interpretation.

The treatment of the data was made possible by using SPSS Statistical Software, one of the most favoured software tools for the analysis of models such as UTAUT constructs (Williams, Rana & Dwivedi 2015), highly present in this research.

3.2.1. Sample description

28 the originally planned analysis containing only non-users. Furhtermore, the small amount of respondents that owned a WFT did not allow for a significant representation of results, sustaining this decision. Therefore, these 17 respondents were not taken into account. Lastly, one response was eliminated because of extreme answers (outliers), which would have distorted results significantly, leaving a total of 107 valid cases. Following the minimum6 and the preferred ratio of observations to variables7 proposed by Hair et al. (2010) for determining the preferred sample size for MMRA, the number of respondents taken into account (n=107) is appropriate (with more than the preferred number of 105; containing more than 15 respondents per predictor variable; 5 independent variables and 2 moderators).

Other than the previous mentioned, there were no specific needs to be met by the participants to be included in the final sample. However, minimum requirements of diversity in terms of demographics were required to ensure the suitability of conclusions drawn from the statistical analysis.

The demographics for the final sample are shown in Table 1. The sample consists of 50 (46.7%) male and 57 (53.3%) female respondents. The respondents ages range from 19 to 70 years old, with an average of 35 years old (Mage=35.18, SD=14.139). Observation of Table 1 shows an under-representation of older adults and elderly people, accounting for a total of 28.1% of participants being over the age of 41 and an over-representation of younger adults aged under 41 (71.9%). A possible explanation for these figures is convenience sampling, since distribution of the survey was mainly among personal network of the researcher. The distribution of income level shows that most participants pertain to the „average‟ income level group (43%), while having an under representation of „more than twice the average‟ group (4.7%). This is explained by the fact that most of the population are youngsters under the age of 30, still studying

6 _{The minimum ratio of observations to variables by Hair et al. (2010) 5:1 refers to the minimum sample} size desired to perform MMR analysis should be of 5 respondents per predictor variable.

29 or recently joining the labour market, these numbers.

Table 1. Sample Characteristics

Of the total participants, most respondents were Spanish (55.1%), Argentinian (26.2%) and Dutch (5.6%), while the remaining respondents (13.2%) belonged to different countries.

3.2.2. Validity and Reliability

Factor analysis was performed to achieve an initial assessment of the measurement model, allowing to establish whether each of the items used in the survey measures the same underlying dimensions they were theorized to belong to.

After the first factor analysis was performed, the Rotated Component Matrix made salient that the item Performance Expectancy 7 had high cross loadings. Comparing the stated questionnaire item with other ones from the construct, it is possible to perceive a certain difference in terms of what it measures. Factor analysis for the variables

Performance Expectancy 1-7 and 1-6 separately, showed significant improvements in

Number of respondents (N) Percentage (%) Gender Male Female 50 57 46.7 53.3 Age <25 26-40 41-60 >60 34 43 19 11 31.8 40.2 17.8 10.3 Income

Less than average Average

Twice the average

More than twice the average

30 the loading of the items into one variable, increasing loadings to one factor and eliminating cross loadings. Furthermore, internal consistency test concluded that the removal of Performance Expectancy 7 would increase Chronbach‟s alpha (from .882 to .883). Therefore, the item was eliminated from the final model.

Results obtained from running a new factor analysis showed a KMO statistic of .820 and a significant Barlett‟s Test of Sphericity (p=.000). On the other hand, communalities were above .5. These figures confirmed the goodness-of-fit and appropriateness to interpret the results from factor analysis. Furthermore, the eigenvalues of the first six factors were above one, the first five factors explained at least 5% of the variance and the fourth item was the first one with a cumulative percentage over 60% (refer to Appendix 2.) In combination with the scree plot and theory in which the research was embedded, 5 factors were chosen to be appropriate.

31 Table 2. Rotated Component Matrix.

Component 1 2 3 4 5 Performance Expectancy 1 ,728 Performance Expectancy 2 ,789 Performance Expectancy 3 ,635 Performance Expectancy 4 ,836 Performance Expectancy 5 ,723 Performance Expectancy 6 ,633 Hedonic Motivation 1 ,744 Hedonic Motivation 2 ,710 Hedonic Motivation 3 ,887 Hedonic Motivation 4 ,857 Hedonic Motivation 5 ,842 Hedonic Motivation 6 ,829 Social Norm 1 ,894 Social Norm 2 ,912 Social Norm 3 ,873 Social Outcome 1 ,502 Social Outcome 2 ,553 Social Outcome 3 ,700 Social Outcome 4 ,825 Social Outcome 5 ,758 Social Outcome 6 ,680 Social Outcome 7 ,604 ,488 Social Outcome 8 ,667 Privacy Concerns 1 ,904 Privacy Concerns 2 ,961 Privacy Concerns 3 ,912 Privacy Concerns 4 ,932 Privacy Concerns 5 ,923

Internal consistency showed good reliability (well above .8) for the five factors (refer to Table 3), which meant that the items theorized to measure a particular construct, did belong together. Therefore, it was appropriate to proceed with regression analysis.

Table 3. Internal Consistency; Reliability Test.

Dimension No. of Items Cronbach’s Alpha

Performance Expectancy 6 .883

Hedonic Motivation 6 .922

Social Norm 3 .955

Social Outcome 8 .884

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4. Results

Assumptions for regression analysis were checked and results confirmed its suitability (refer to Appendix 3). The histogram and normal probability plot supported the normality assumption and the scatterplot sustained the linear distribution assumption. The correlation matrix, with no significant correlations or significant ones below .6, sustained the independent observations assumption. In combination with low Variance Inflation Factor (VIF) scores below 3, it was sustained that the dataset presented no multicollinearity concerns.

As explained in the section Methodology, MMRA was performed in five stages and the results are shown in the following models. Please refer to Table 4 to follow the results for the different models.

4.1. Model 1. Control variables

Model 1 of the regression analysis accounts for the control variables Age, Gender and

Income. Statistically significant (p=.021), it explained 12.3% of variance in the

dependent variable Intention to Use. While the variables Age and Gender were not significant, results for the variable Income showed a significant value only at one level. „Below average income‟ (β=-1.302, p<.001) had a significant, different impact on

Intention to Use a WFT when compared to individuals that belong to the group „average

income‟. Given that the beta coefficient presents a negative value, interpretation is that having an income below the average level has a negative impact on Intention to Use. Therefore, individuals with lower income (below the average) have lower Intention to

34 4.2. Model 2. Main effects.

In this model the main effects are added to the control variables for the regression analysis. These additions increased the explained variance in the dependent variable

Intention to Use by the model to 49.8% (p<.001).

While Social Norm and Privacy Concerns do not have significant effects on Intention to

Use, Performance Expectancy (p<.05; β=.425), Hedonic Motivation (p<.05; β=.303)

and Social Outcomes (p<.01; β=.380) do. Therefore, higher levels of these significant variables lead to an increase in Intention to Use a WFT. At the same time, the effects of the control variables remained similar as in the previous model, containing only „below average income‟ as (negatively) significant (p<.01).

4.3. Model 3. Moderator: Sportiness

In model three the first moderator Sportiness was added to the variables contained in the previous model, both as form of interaction with the independent variables, as well as another independent variable to check for potential directs effects in Intention to Use. These additions resulted in an increase in the variance explained by the model, to 54.1% (p<.001). Sportiness was not found to have a significant impact on Intention to Use. In terms of moderation, only interaction effects with the independent variable Performance

Expectancy was significant. The interaction with the moderator presents a negative Beta

(p<.05), sustaining that higher levels of sportiness reduce the positive relation of

Performance Expectancy with Intention to Use.

The other variables included (independent and control variables) maintained similar results (in terms of significance and direction of the effect) as in the previous models.

4.4. Model 4. Moderator Health Concerns.

35 showed that, as theorized, the variable Health Concerns does not have a direct effect on

Intention to Use. It did however, had significant moderation effects on three variables.

Two of those interactions, in particular with Performance Expectancy and Social

Outcomes (p<.01, β=.449 and p<.05, β=.263 respectively) had positive betas. This

means that Health Concerns would increase the already positive relation between them and Intention to Use. The third significant moderation effect was with Hedonic

Motivation. With a negative beta (significant at .05), Health Concerns negatively

moderates the positive relation between Hedonic Motivation and Intention to Use.

Variables contained in previous models (independent and control variables) maintained their overall values (in terms of significance and direction of the effects).

4.5. Model 5. Full Model

Model 5 contained all the mentioned control, independent and moderator variables, accounting for the complete conceptual model proposed in this research. The total variance explained by it, was the highest, reaching 61.6% (p<.001). Both main effects and moderation effects showed similar results from previous models. The previously mentioned positive effect of „below the average‟ income, continues to show a negative effect on Intention to Use (β=.-786, p<.05), while Age and Gender continue as not-significant. Consistent with theoretical foundation, significant positive effects were found for the independent variables Performance Expectancy (β=437, p<.05), Hedonic

Motivation (β=.438, p<.001) and Social Outcome (β=.375, p<.05). Therefore hypotheses

1, 2 and 4 are supported (refer to Appendix 7 for a summary of results for the hypotheses). Social Norm and Privacy Concerns did not reach the minimum significant level; thus hypotheses 3 and 5 were not sustained. In comparison with model 3,

Sportiness did not maintain the previously found moderation effects on Performance Expectancy), but results did report week evidence of moderation effects on Privacy Concerns with a p-value near the limit (β=.103, p=.055). However, this relation has a

36

Concerns, results were relatively similar to model 4. While non-significant results of Social Norm and Privacy Concerns were in line with previous findings, non-significant

results for Hedonic Motivation were not. In comparison with significant results at .05 level in Model 4, this variable presents a p=.058, which is slightly over the cut-off point. Given the circumstances, the acceptance of this result as week evidence and hypothesis can be argued. Performance Expectancy is positively moderated by Health Concerns (β=.536, p<.01), in concordance with H6a, increasing the positive relation with

Intention to Use (refer to Appendix 8 for graphic representation). At the same time, the

moderator positively influences the relationship of Social Outcome on Intention to Use (p<.05), sustaining hypothesis H6d.

When comparing these results, it is made salient that the strongest predictor variable for intention to use is Hedonic Motivation (see Appendix 9), accounting for the highest significance level (p=.000) and the highest standardized coefficient (std. β= .323). On a second and third place there are Social Outcomes and Performance Expectancy with lower significance level (p<.05) and standardized betas of .262 and .235, respectively. Of the moderation effects, the influence of Health Concerns on Performance

Expectancy is the most salient of all (std. β=.306; p<.01), followed by Social Outcomes

37

38

5. Conclusion and Discussion

The current research examined the factors that influence adoption of wearable fitness trackers (WFTs). The first goal of the article was to find the factors that drive the usage WFT and understand their impact on future intention to use. After consideration of previous literature and research, concordance among theories regarding the most relevant variables to explain adoption and usage intention were taken into account to design the conceptual model. Of them, Performance Expectancy, Hedonic Motivation,

Social Norm and Social Outcomes were found to be more relevant and introduced into

the conceptual model as independent variables for the later statistical analysis.

The second goal was to investigate the impact of potential privacy concerns regarding the usage of WFTs. Given the characteristics of the data collected and the nature of digital data, which includes GPS location, physical activity and biometrics (e.g. heart rate), concerns regarding the potential distribution and misuse of this data had the potential to play an important role. Privacy Concerns were then introduced as a fifth independent variable.

Lastly, given the implications of WFTs in well-being and physical activity of users, it was determined to account for the potential implications of these characteristics. Therefore, Sportiness and Health Concerns among respondents were introduced in the conceptual model as moderators of the relation between the independent variables and the dependent variable Intention to Use a WFT.

39 5.1. Discussion

The variable Performance Expectancy, accounted for the benefits that WFTs provide in service of successfully performing a desired activity. Consistent with previous research in the field of IT acceptance literature, this study found it to be a strong predictor of intention to use. In this case, it would include monitoring capabilities of heart rate and amount of physical activity performed, and achieving goals such as increasing sportiness, health state, weight loss, etc. All of this, complemented with apps that provides plans and feedback to enhance the results. In other words, finding a WFT as useful, in particular for monitoring health and improving physical activity, influences in a positive manner the decision to use the device.

At the same time, Hedonic Motivation has shown a relevant effect on decision to use a WFT. Despite the clear usefulness of a WFT, this device and its complementary functions have the potential to be considered fun and pleasurable to use, which in turn leads to higher usage intention. What is more, in line with previously reviewed literature from Venkatesh, Thong & Xu (2012), perceived enjoyment was the strongest determinant of usage intention, significantly more than Performance Expectancy (i.e. perceived usefulness). Not only for usage itself; the numerous possibilities to share results on different platforms, follow other‟s user‟s progress, commenting on them and competing with others, allows for amplification of enjoyment. In particular given the current tendency in society to connect with friends and other individuals through social media platforms. This possibility is a salient figure that contributes to its recreational value of todays‟ population. This relation of usage with consumer‟s social live, contributes not only to enjoyment but to findings regarding social influence as well.

40 means to express oneself and displaying a certain image to others. In this case, representing connotations of status, innovation, fashion/style and uniqueness, among others. In line with set expectations, this variable is found to be a strong driver of intention to use a WFT, even more than usefulness of the devices. An additional explanation for this is that these devices are worn in visible parts of the body, which makes social outcomes more salient. This makes sense given that it is common for individuals to make decisions based on others opinions (Granovetter 1973). In other words due to social influence. The present study theorised that social aspects influence individuals through two related dimensions. The first one was the already explained Social Outcomes. The second one was Social Norms.

Contrary to previous research, consumer behaviour and motivational variables; Social

Norm was not found to play a significant role in the decision to use a WFT. Therefore,

the belief that referents and important others (e.g., family and friends) should use a WFT does not affect individuals‟ intention to use it.

41

Health Concerns

In the case of the proposed moderator Health Concerns, two interaction effects are found to be significant. As expected, for individuals with more salient concerns regarding their health, the perception of usefulness of WFTs is a stronger determinant of intention to use. Contrary to theory, this is the case as well for the variable Social

Outcomes, found to be a stronger determinant of intention to use for individuals with

higher levels of health concerns. In other words, health concerns have a (positive) moderating effect in the relationship between Performance Expectancy and Social

Outcomes with Intention to Use.

Furthermore, results show week evidence of negative moderating effects of individual‟s health concerns on Hedonic Motivation. This would be in line with set expectations, and introduce the possibility that higher levels of health concerns weaken the importance of enjoyment as a driver of intention to use.

On the other hand, the variable does not have moderating effects on Privacy Concerns. This is not in line with previous research supporting that poor health status would heighten concerns for privacy (Bansal, Zahedi and Gefen 2010).

Sportiness

42 5.2. Practical Implications

Companies with interest in the market of WFTs would benefit from exploding this research‟s findings in terms of product specifications and marketing strategies. Results emphasise the relevance of enjoyment, usefulness and social value perceptions, which can be enhanced in the marketing of the product in several ways.

First of all, the current focus of WFTs as complements for exercise, while important in terms of perceived usefulness, should be broaden in order to better account for the potential of social implications. Generally speaking, companies would benefit from enhancing the idea of a WFT as a constant in the population‟s daily live, not only for particular situations, but for an ample range of social contexts as well (i.e. formal social gatherings). For this, aesthetics and design play an important role. Marketing an appealing WFT that serves as a fashion complement, as well as a well-being gadget, would enhance continuous use for consumers more concerned with physical appeals. For example, by creating more jewellery-looking devices, or allowing transformation of more sportive devices (i.e. by changing the rubber wrist band for more appropriate one in formal contexts).

At the same time, results sustain that image should be emphasised. Creating lines of WFTs that target different social groups in terms of appearance and social standing (i.e. hipsters, athletes, fashionistas, etc.) would appeal to their sense of belongingness to the group. Choosing the „right‟ influencers to promote then would facilitate reaching large amounts of like-minded individuals and influencing their perception. Furthermore, designs that slightly enhance levels of uniqueness, such as allowing small modifications of the product or personalization (i.e. customization) could prove to have great results.

43 Perceived enjoyment can be enhanced by promoting contests and open fitness challenges in exchange for rewards related to the use of the device or external rewards (e.g. attending an sports event, premium subscriptions, etc.).

Promotion of usefulness should not be forgotten. For example, through promotion of practices such as „before and after experiences‟. Furthermore, opportunities to bring WFTs to the public through collaboration with institutions should be taken (e.g. collaboration with gyms, allowing members to try and familiarise with the product). Another idea would be taking timing into account with promotions and deals for „bikini season‟ and the after-Christmas period.

On the other hand, recognising the importance of factors valuable to potential users of this target markets is relevant not only for companies selling WFTs, but for other private or public entities. The constant retrieval of vast amount of real-time data that can combine physiological, location and context data has the potential to infer emotional states. In the area of marketing, the correct analysis of this information would allow to perfectly understand individual‟s context and respond appropriately. This would include not only strategies such as the already existing real-time and location-based advertisements, but targeting according to human‟s physical state and emotions.

In a society with overstimulation, information overload and increasing lack of time, efforts to benefit from wearable technology as communication platforms should be translated into convenient, concise and relevant exchanges. A related term to this that arose with the appearance of wearables, is that of „glanceable marketing‟8. Integrating wearable technology as part of the customer journey by following the previous recommendations would play a significant role as well.

Given the numerous advantages of this technology in the marketing field, it is recommended that it is introduced as a topic in marketing departments. Furthermore, devoting part of the efforts on mobile devices not only to smartphones, but to WFTs and

8