Branding innovations : the Influence of brand awareness on innovation resistance towards smartwatches

Branding Innovations: The Influence of Brand Awareness on Innovation

Resistance towards Smartwatches

Larissa van Vuure (11032243)

Thesis Digital Business– Final version

MSc. Business Administration – Digital Business Track

University of Amsterdam

June 21st, 2018

First Supervisor: Dr. Somayeh Koohborfardhaghighi

Statement of Originality

This document is written by Larissa van Vuure, who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

Amsterdam, June 21st 2018 Larissa van Vuure (11032243)

Abstract

Technological innovations are often rejected by consumers, creating academic interest in the reasons of innovation adoption and resistance. Wearable technology and especially smartwatch technology are examples of new technologies which have attracted much attention during the last decade. The reasons for the adoption of smartwatches are researched multiple times. However, the area of smartwatch resistance is an under-investigated topic. According to the findings of the previous research in this area reasons of resistance towards smartwatches are twice as influential as the reasons for their adoption. Thus, this research will shed light on the factors influencing smartwatch resistance. This research consists of two studies. The first study examines which functional and psychological barriers influence the resistance towards smartwatches with the help of a survey analysis. Our obtained results show significant effects of the functional usage, value and risk barrier and the psychological image barrier on the resistance towards smartwatches. The higher the barriers, the higher the resistance. The second study investigates the influence of brand awareness and brand type on the resistance towards smartwatches. The results of a 2 (brand awareness: unknown vs. well-known) by 2 (brand type: fashion vs. technology) experimental study show no significant direct effect of brand awareness or brand type on the resistance towards smartwatches. However, there is an indirect effect of brand awareness through the risk barrier and an indirect interaction effect through the value barrier. That is to say, introducing smartwatches under a well-known brand reduces the risk barrier, however for fashion brands it increases the value barrier. Overall, this research shows there is dual-resistance towards smartwatches with a pronounced role for functional resistance barriers. Especially the risk barrier plays a special role in resistance towards smartwatches. Several implications from our findings can be derived. Since economic risk is among the factors which promote the emergence of resistance, new technological advancements in the design of smartwatches can lower the cost of production which ultimately leads to a lower price. Furthermore, a possible marketing communication strategy to reduce the risk barrier could be that smartwatch manufacturers introduce their products under a well-known brand. The introduction of smartwatches under well-known technology brands is most influential in reducing functional barriers.

Keywords: Wearable Technology, Smartwatches, Innovation Resistance, Brand Awareness,

Table of Content

2.4. Innovation research in the field of smartwatches 18

3. Theoretical framework 24

3.1. Study 1: The barriers influencing resistance 24

3.1.1. The usage barrier 24

3.1.2. The value barrier 25

3.1.3. The risk barrier 26

3.1.4. The tradition barrier 28

3.1.5. The image barrier 29

3.2. Study 2: The influence of branding on resistance 30

3.2.1. The effect of brand awareness 31

3.2.2. The moderating effect of brand type 33

4. Methodology 35

4.1. Study 1: The barriers influencing resistance 35

4.1.1. Procedure 35 4.1.2. Sample 36 4.1.3. Measurements 38 4.1.3.1. Barriers 39 4.1.3.2. Consumer resistance 40 4.1.3.3. Control variables 40 4.1.4. Statistical procedure 41

4.2. Study 2: The influence of branding on resistance 41

4.2.1. Research design and procedure 41

4.2.3. Pre-test 45 4.2.4. Measurements 47 4.2.4.1. Barriers 47 4.2.4.2. Consumer resistance 48 4.2.4.3. Manipulation check 48 4.2.4.4. Control variables 48 4.2.5. Statistical procedure 48 5. Results 49

5.1. Study 1: The barriers influencing resistance 49

5.1.1. Reliability and factor analyses 49

5.1.2. Normality test 51

5.1.3. Correlation matrix 51

5.1.4. Hypotheses testing 53

5.2. Study 2: The influence of branding on resistance 56

5.2.1. Reliability and factor analyses 56

5.2.2. Manipulation check 59

5.2.3. Normality tests 59

5.2.4. Correlation matrix 60

5.2.5. Hypotheses testing 62

6. Conclusion and Discussion 70

6.1. General discussion 70

6.2. Theoretical implications 73

6.3. Managerial implications 74

6.4. Limitations and future research directions 75

References 78

Appendices 87

Appendix I: Constructs and measurements study 1 87

Appendix II: Questionnaire study 1 88

Appendix III: Questionnaire pre-test study 2 96

Appendix IV: Constructs and measurements study 2 98

Appendix V: Questionnaire study 2 99

List of Tables

Table 1. Empirical research on smartwatch adoption and resistance ... 22

Table 2. Sample characteristics study 1 ... 38

Table 3. Sample charactersistics study 2 ... 44

Table 4. Mean and standard deviation of all brands in the pre-test... 47

Table 5. Means, standard deviations, correlations study 1 ... 52

Table 6. Hierarchical regression model of resistance towards smartwatches ... 54

Table 7. Hierarchical regression model of resistance towards smartwatches ... 56

Table 8. Means, standard deviations, correlations study 2 ... 61

Table 9. One-way ANOVA, brand awareness ... 63

Table 10. Outcomes PROCESS model 4, mediation analysis ... 64

Table 11. Factorial ANOVA, brand awareness and type of brand... 66

Table 12. Outcomes PROCESS model 8, moderated mediation analysis ... 67

List of Figures

Figure 1. Conceptual framework study 1 ... 24

Figure 2. Conceptual framework study 2 ... 30

Figure 3. Smartwatch picture for survey ... 36

Figure 4. Smartwatch advertisements for experiment ... 42

Figure 5. Statistical diagram study 1. ... 56

Figure 6. Statistical diagram study 2.. ... 68

1. Introduction

In the last decades a growing amount of technological innovations have failed, with

reported failure rates between 50 and 90 percent (Gourville, 2006; Talke and Heidenreich,

2014). One of the technological innovations that is struggling with attracting the attention of

consumers right now is wearable technology. Wearable technologies are sensors and

transmission chips embedded into ordinary objects such as clothing and accessories (Jung,

Kim & Choi, 2016). Wearable technology can be used as a fitness and health tracker, as a

remote, as a communication device, as a navigation tool and as a media device. Practitioners

expected wearable technologies to become the next big thing in the mobile device industry.

However, the adoption of wearable technologies by consumers has been relatively slow

compared to other major technologies such as the smartphone (Choi & Kim, 2016; Kalantari,

2017). The amount of consumers using basic wearable technologies, such as health and

fitness trackers, is growing. However, smart wearable technologies, such as the smartwatch

and smart glasses, have difficulties attracting attention of consumers (BusinessWire, 2017).

Increasing the adoption rate of smart wearable technologies is important because it can

have multiple benefits for companies. Smart wearable technologies allow for easier and better

data collection through a constant flow of live consumer (location) data, which opens new

opportunities in marketing, R&D and human resource management (Leung, 2014). Smart

wearable technologies can create new business opportunities and improve the efficiency,

productivity, service, consumer experience and engagement (Leung, 2014; PwC, 2015). As a

result, wearable technologies can have a huge economic impact. In addition to that the

smartphone market is maturing and IT-companies who produce smartphones are trying to

create demand for new mobile devices to maintain their profitability (Jung et al., 2016).

Lastly, increasing the adoption rate of wearable technologies is important since many believe

Therefore, a postponement in the adoption of wearable technologies might delay the overall

adoption of the IoT among consumers (Ericsson Consumerlab, 2016).

In the existing literature there are two research perspectives on consumers adoption of

innovations: (1) the acceptance of innovation adoption and (2) the resistance towards

innovation adoption (Nabih, Bloem & Poiesz, 1997). The first research stream is investigated

multiple times from different perspectives (e.g., technology acceptance model (TAM), unified

theory of acceptance and use of technology (UTAUT) and innovation diffusion theory (IDT)

(Davis, 1989; Rogers, 1983; Venkatesh, Thong & Xu, 2012)) and in different contexts (e.g.,

mobile banking and social media (Luarn & Lin, 2005; Rauniar, Rawski, Yang & Johnson,

2014)). However, only a few researchers looked at the consumer resistance by looking at

barriers (Claudy, Garcia & O’Driscoll, 2015). Similar to other researchers in this area we

believe that researchers overlook one important aspect when only looking at the acceptance of

innovations. These researchers assume consumers are open to change and are interested in

new technologies, even though consumers often reject an innovation without looking at its

potential (Talke & Heidenreich, 2014). In line with other researchers we believe it is more

meaningful to understand the reasons of innovation resistance rather than the reasons of

innovation adoption (Laukkanen, Sinkkonen, Kivijärvi & Laukkanen, 2007; Sheth, 1981;

Ram, 1987). Especially since the reasons against adoption are twice as influential as the

reasons for adoption (Claudy et al., 2015).

This study examines the barriers that cause resistance towards adopting smartwatches.

The era of smartwatches has gained academic attention because smart wearable technology

has the potential to deliver a big and profitable market (e.g., Choi & Kim, 2016; Chuah et al.,

2016; Kim & Shin, 2015; Mani & Chouk, 2017). Looking at the research carried out in the

field of smartwatches, it appears most of the research focused on the investigation of reasons

and Kim and Shin (2015) explored the reasons for the adoption of smartwatches based on the

TAM and UTAUT framework. Mani and Chouk (2017) did investigate the resistance towards

smartwatches, but used the elements from the TAM and UTAUT framework. Although the

factors influencing the adoption of smartwatches are researched multiple times, there is a lack

of well-designed studies in the area of psychological and functional barriers influencing the

resistance towards adopting smartwatches.

When looking at the role of branding in innovation resistance there is also a lack of

well-designed studies on this topic. Research shows brands support the introduction and

adoption of innovations by providing means that reduce uncertainty and risks which can

stimulate the use of new products (Brexendorf, Bayus & Keller, 2015; Jung & Kim, 2015).

This indicates well-known brands might reduce the resistance towards smartwatches by

taking away the barriers. Therefore, the objective of this thesis is to see if brand awareness

can reduce the resistance towards smartwatches. This is done by identifying the degree of

innovation resistance, identifying which barriers are most influential on the resistance towards

smartwatches, and by investigating if the barriers and the innovation resistance are influenced

by brand awareness. Additionally, it is investigated if the role of brand awareness is different

for technology and fashion brands due to the fit between the parent brand and the smartwatch

as an extension (Aaker & Keller, 1990; Carter & Curry, 2013).

Based on the line of arguments presented so far the following research questions arise:

1) Which barriers cause resistance towards adopting smartwatches?

2) How does brand awareness and type of brand influence the presence of barriers and the

degree of resistance towards smartwatches?

This research has the following implications for practice. First, it gives marketers of

consumers are not adopting their smart wearable products. With the results of this study a

potential marketing strategy can be proposed on how to overcome the barriers consumers

have towards adopting smartwatches (Kleijnen, Lee & Wetzels, 2009). Second, this research

provides insights in the role of brand awareness in reducing barriers and resistance towards

smartwatches. Previous research has shown that the adoption of innovations is positively

influenced by the introduction under a well-known brand name (Florea, 2015). Before the

research performed in this thesis, it was unclear if brand awareness on its own is strong

enough to reduce the barriers and resistance as well. This insight would provide guidelines to

new and existing brands on if and how to enter the smartwatch market: as a brand extension

or under a new brand name.

The rest of this thesis is organized as followed. In chapter 2 the current literature on

consumer innovation resistance, the relationships between branding and innovations and the

current literature on smartwatch adoption is discussed. In chapter 3, we formulate our

theoretical model and present our hypotheses. This is followed by an extensive explanation of

the research design and method in chapter 4. The results of our analysis will be presented in

2. Literature review

This literature review is an overview of the relevant literature on innovation resistance,

the role of brands in innovation adoption and resistance and innovation research in the field of

smartwatches. First, I will describe the definition of innovation and innovation resistance.

This is followed by a description of the existing theories on innovation resistance. After that

the relationship between brands and innovation adoption and resistance is discussed. Lastly,

the current work on innovation research in the field of smartwatches is evaluated.

2.1. Innovation

In the academic literature various definitions for the concept of innovation are used.

Rogers (1983) defines innovation as an idea, practice or object that is perceived as new by an

individual or other unit of adoption. While Ram (1987) defines innovation as a product which

is perceived as new by consumers. The definitions have one thing in common, namely that an

innovation is about newness. This newness is the result of change(s) in either attributes of the

product (e.g., when mobile phones introduced touchscreens) or the product concept itself

(e.g., when traditional mobile phones were replaced by the introduction of the smartphones)

(Ram, 1987; Ram & Sheth, 1989). When a product attribute changes, it is called an

incremental innovation, resulting in either marketing or technological discontinuities. When

the product concept changes it is called a radical innovation, resulting in both marketing and

technological discontinuities (Garcia & Calantone, 2002).

One of the emerging radical innovations at this moment is the Internet of Things (IoT).

The IoT connects people, machines and objects in the exchange of information by allowing

them to communicate over the internet (Hsu & Lin, 2016). Businesses are embracing the IoT

and its benefits. However, the consumer adoption of IoT is yet to come. Right now, the most

traditional watch and allow for the installation and use of applications (Chuah et al., 2016, p.

277). The installation of applications is possible because of the sensors and transmission chips

that are embedded into the watch, these chips also allow communication with other products

in the IoT (Jung et al., 2016). The smartwatch is an innovative product combining the aspects

of a traditional watch and a smartphone. This is a change in the product concept, resulting in

both marketing and technological discontinuities. Therefore, smartwatches are a radical

innovation which usually leads to high innovation resistance (Ram & Sheth, 1989). The

concept of innovation resistance is discussed in the following chapter.

2.2. Innovation resistance

Almost every new technology deals with innovation resistance slowing down the

adoption process. Innovation resistance is defined as ‘the resistance offered by consumers to

an innovation, either because it poses potential changes from a satisfactory status quo or

because it conflicts with their belief structure’ (Ram & Sheth, 1989, p. 6). When a new

technology deals with innovation resistance from consumers the adoption process will slow

down until the resistance is overcome (Ram, 1989). If the resistance cannot be overcome, the

innovation is likely to fail. Even when there are pro-adoption arguments, which means there

are arguments in favor of adopting the innovation, the innovation resistance drivers outweigh those arguments (Gourville, 2006). Understanding consumers’ resistance drivers can help a

firm with reducing the innovation resistance, which reduces the chance of innovation failure

(Ram, 1989) and makes place for pro-adoption arguments (Gourville, 2006).

There are two forms of innovation resistance: passive and active resistance (Talke &

Heidenreich, 2014). Passive resistance is the tendency to resist innovations prior to the

evaluation of new products, which means someone would resist smartwatches before trying or

consumers become aware of the existence of a new product. Active resistance is an attitudinal

outcome following an unfavorable product evaluation, which means the resistance towards

smartwatches appears after trying out a smartwatch. This form of resistance appears in the

persuasion stage where consumers actively evaluate the product, resulting in either innovation

adoption or innovation resistance (Talke & Heidenreich, 2014). Active resistance can be

further broken down into three categories: rejection, postponement and opposition (Ram &

Sheth, 1989). Rejection of the innovation is a strong attitude against adopting the innovation.

Postponement of an innovation is a delay of the decision. It means consumers find an

innovation acceptable but decide not to adopt it at that moment, however they are not ruling

out adoption in the future. Lastly, opposition is the most active form of innovation resistance

where a consumer is launching an attack against the adoption (Ram & Sheth, 1989).

Ram (1987) was the first to come up with a theory on innovation resistance. Based on

past literature Ram (1987) found consumer innovation resistance depends on three factors:

perceived innovation characteristics, consumer characteristics and characteristics of

propagation mechanisms. The perceived innovation characteristics are based on the model of

Rogers (1983) and are relative advantage, compatibility, perceived risk, trialability and

communicability. An innovations newness is perceived by consumers. Therefore, resistance

depends on the following consumer characteristics: personality, attitude, value orientation,

previous innovative experience, perception, motivation and beliefs. Lastly, the propagation

mechanisms are the contact between firms and consumers. The propagation mechanisms are

the extent of marketer control and type of contact with the consumer (Ram, 1987).

Two years later Ram and Sheth (1989) came up with a framework that explains

innovation resistance is caused by functional and psychological barriers. An innovation

barrier is every factor that influences the innovation adoption process negatively (Piatier,

through the existence of a new technology (Ram & Sheth, 1989). The three functional barriers

are the usage, value and risk barrier. The usage barrier appears when an innovation is

incongruent with existing workflows, practices or habits and asks for changes in consumers

daily routines. The value barrier is the performance-to-price value of the product compared to

alternatives. When a new product does not offer more performance-to-price value than an

existing product, consumers feel no need to change their existing behavior. The risk barrier is

the risk coming with the use of an innovation. In the beginning all innovations have

uncertainty, leading to feelings of risk among consumers (Ram & Sheth, 1989). These

perceived risks can be physical (harm to a person or property, among which risks regarding

privacy), economic (the first product line within a new innovation has a high economic risk

because consumers expect there will be a better and cheaper alternative in the future),

functional (consumers worry a new product is not functioning properly) or social (resisting an

innovation because someone feels their peer-circle will not accept the new product). The

psychological barriers arise when innovations conflict with prior beliefs (Ram & Sheth,

1989). The two psychological barriers are the tradition and image barrier. The tradition barrier

appears when the innovation requires someone to deviate from traditions or do things that are not in the social norms. For example, in the 90’s there was resistance against the use of

mobile phones because consumers did not understand why they would want to be reachable

all the time, this was the social norm back then. The image barrier appears when the

associations (extrinsic cues: manufacturing country, product class or industry) with a product

are unfavorable, leading to an unfavorable image about the product, which will lead to

resistance to adopt the product (Ram & Sheth, 1989). Depending on the context consumers

can feel between none to all five barriers at the same time.

Laukkanen, Sinkkonen and Laukkanen (2009) proposed a framework with a typology

non-resistance (no barriers at all), functional non-resistance (only functional barriers), psychological

resistance (only psychological barriers) or dual resistance (both functional and psychological

barriers).

Different studies showed the effectiveness of these five barriers, however some

researchers argue for a more extensive set of barriers (Joachim, Spieth & Heidenreich, 2018;

Laukkanen & Kiviniemi, 2010; Zsifkovits & Günther, 2015). Building on to this Talke and

Heidenreich (2014) came up with seventeen potential barriers. They came up with nine new

barriers, divided the risk barrier into the four types of risks proposed by Ram and Sheth

(1989) and renamed the tradition barrier. The proposed framework consists of nine functional

and eight psychological barriers. The nine functional barriers arise when consumers perceive

a product attribute as dysfunctional for their personal needs and usage expectations (value,

complexity, trialability, compatibility, co-dependence, visibility, communicability,

amenability and realization barrier). Whereas the eight psychological barriers arise when an

innovation conflicts with social norms, values or individual usage patterns (norm, image,

usage, information, physical risk, functional risk, economic risk and social risk barrier).

Joachim et al. (2018) explored if the framework suggested by Talke and Heidenreich

(2014) was comprehensive and examined the relative importance of each barrier in the

acceptance of mobile service innovations. Their findings show the framework is indeed

comprehensive. Similarly, their findings show all 17 elements negatively impact the intention

to adopt the innovation. The three most influential functional barriers are the value,

complexity and co-dependence barriers. The three most influential psychological barriers are

the functional, usage and information barrier (Joachim et al., 2018).

Most researchers until now still look at the five barriers proposed by Ram and Sheth

(1989) and found these to be accurate predictors of the resistance towards innovations. The

(Jansukpum & Kettem, 2015; Laukkanen, 2016; Lian & Yen, 2013). Therefore, this research

will also look at these five barriers. However, following the method of Talke and Heidenreich

(2014) and Joachim et al. (2018) we divide the risk barrier into four different barriers:

physical, economic, functional and social risk barrier.

2.3. Branding innovations

Nowadays the relationship between branding and innovations is becoming more

important. Both innovations and brands can benefit from each other. Brands support the

introduction and adoption of innovations, whereas successful innovations improve brand

perceptions, attitudes and usage (Brexendorf et al., 2015). Brands can support the introduction

and adoption of innovations by providing means that reduce uncertainty and risks that come

with innovations, which can stimulate the use of new products (Brexendorf et al., 2015).

Branding innovations, meaning that the product is part of a coherent brand strategy, can make

all the difference in failure or succeeding. It can create or improve the offering, create a new

subcategory to change customers buying behavior and it can improve an innovations

credibility and consumers respect towards the innovation (Aaker, 2007).

To reduce the risks coming with new product introduction an often-used strategy is to

introduce the new product under a well-known brand name, this is called a brand extension.

In this case the associations and attitudes of the well-known brand are expected to transfer to

the extension. Until the new product becomes more familiar, consumers judge the product

based on the knowledge about the parent brand (Bhat & Reddy, 2001). When looking at the

influence of brand awareness, which is the ability to recognize or recall that specific brand

name or remember it as a part of a certain product category (Aaker, 1991), research shows

that higher brand awareness increases trust in the product (Smith & Wheeler, 2002, in Lu,

innovation (Lüders, Andreassen, Clatworthy & Hillestad. 2017). This is because trust is part of consumers’ risk reduction strategies and can reduce the uncertainty coming with

innovations (Groß, 2016).

However, the influence of a well-known parent brand on a brand extensions sales

depends on the level of fit (Carter & Curry, 2013). Fit can be described as the perceived

similarity between the parent brand and the extension product (Aaker & Keller, 1990) and

consists of two elements (Bhat & Reddy, 1997, in Bhat & Reddy, 2001; Carter & Curry,

2013): (1) Whether an extension and its parent are from the same product category and satisfy

the same need, and (2) whether the extension and the parent brand share the same

associations. When consumers perceive a low fit between the existing product and its

extension they might react unfavorable (Truong, Klink, Simmons, Grinstein & Palmer, 2017).

Thus, the lower the fit between the smartwatch as an extension of the parent brand, the less

strong the positive effect of introducing the product under a well-known brand name is.

2.4. Innovation research in the field of smartwatches

When looking at the research carried out in the field of smartwatches, it appears that

most research looked at the reasons for adopting smartwatches and no-one looked at the

barriers influencing the resistance towards smartwatches.

By looking at TAM and visibility, Chuah et al. (2016) surveyed 226 business students

from a Malaysian university and found perceived usefulness (PU), perceived ease of use

(PEOU) and visibility positively influence the adoption of smartwatches. However, they

found PEOU is only indirectly related to adoption via PU. Within the same sample of 226

business students, Krey et al. (2016) also looked at the influence of TAM and visibility on the

adoption intention. In addition, they looked at the difference of the importance of each

complement to the results of Chuah et al. (2016). In addition, the results show that usefulness

is more important when smartwatches are perceived as technology and visibility is more

important when smartwatches are perceived as fashion.

The results of Choi and Kim (2016), who performed an online questionnaire under

562 Korean respondents, confirm the findings of Chuah and colleagues (2016). Choi and Kim

examined the effect of perceived enjoyment and self-expressiveness on the adoption of

smartwatches. They found enjoyment is positively related to the attitude towards adoption,

whereas self-expressiveness directly influences the intention to adopt smartwatches. Kim and

Shin (2015) looked at the role of psychological determinants on the adoption of smartwatches.

They performed a survey under 363 smartwatch users and found affective quality and relative

advantage are associated with PU, while mobility and availability influence PEOU. They also

found subcultural appeal and costs influence the attitude against adoption directly.

Jeong, Byun and Jeong (2016) also looked at the influence of TAM on the acceptance

of smartwatches. In addition, they looked at the influence of smartphone use and similarity on

the results. Their study shows PU, PEOU and perceived aesthetics (PA) positively influence

the acceptance of smartwatches. In addition, they found that a positive user experience of

smartphones and similarity between the smartphone and smartwatch have a positive effect on

PU, PEOU and PA.

Other researchers took a different perspective by looking at the influence of the

innovation diffusion theory (IDT) and other models on the adoption of smartwatches. Wu,

Wu and Chang (2016), who performed a survey questionnaire under 212 respondents from

Taiwan, look at the influences of IDT, TAM and UTAUT elements on the adoption intention

of smartwatches. They found relative advantage, perceived result demonstrability and

impacts the intention to adopt. Lastly, they found PEOU and perceived compatibility do not

influence attitude.

Hsiao (2017) looked at the IDT, new product adoption model (NPA) and

task-technology fit model (TTF) on the adoption of smartwatches. Hsiao performed an online

survey under 341 Taiwanese respondents and made a distinction between Apple and

non-Apple smartwatches. His results show that for the non-Apple smartwatch condition the

smartwatch tasks significantly influence TTF, which significantly influences adoption

intention. Looking at the IDT only relative advantage positively influences TTF, while

perceived compatibility, relative advantage and design aesthetics positively influence the

adoption intention. Looking at the environmental variables online word-of-mouth positively

impacted the adoption. For non-Apple smartwatches, TTF does not influence the adoption

intention. However, all product attributes (compatibility, relative advantage, complexity and

design aesthetics) have positive effects on the adoption intention. Word-of-mouth and

openness to experience have positive effects on the adoption.

Other researchers took a different perspective and looked at the product design and its

influence on the intention to adopt (Hsiao & Chen, 2018; Jung et al., 2016; Kim, 2016). Jung

et al. (2016) researched the preference structure towards smartwatches by looking at brand,

price, communication, display shape and size. Their study, performed under a sample of 123

South Korean respondents, shows fashion attributes are more influential than functional

factors (price and brand). Looking at the differences between brands, they found that

functional aspects are more important for adopting new brands than for existing brands. In

addition to Jung et al. (2016), Kim (2016) also looked at the influence of screen shape. With

the help of a between-subjects experiment, 200 participants trialed a smartwatch with either a

round or a square screen. Their results show the adoption is higher for a round screen than for

Lastly, Hsiao and Chen (2018) also looked at the influence of design elements on the adoption

intention. With a questionnaire under 260 Taiwanese potential users of the Apple watch they

look at the influence of perceived content and infrastructure, interface convenience, design

aesthetics, and social, performance, emotional and value for money value on the adoption

intention. Their results show perceived content and infrastructure, interface convenience and

design aesthetics have strong effects on the attitude towards smartwatches, which influences

purchase intention. Perceived infrastructure and design aesthetics influence the performance

and social value. However, only emotional and value for money value have a strong positive

effect on purchase intention.

Mani and Chouk (2017) are the only ones looking at the reasons against the adoption

of smartwatches. However, they use the elements from the TAM and UTAUT2 framework to

do this. Their findings show that PU, perceived price, intrusiveness, perceived novelty and

self-efficacy impact consumers’ resistance towards smartwatches.

All empirical smartwatch adoption and resistance studies are summarized in table 1.

The table shows the adoption of smartwatches is researched multiple times by using TAM,

UTAUT2, IDT and other additional factors. The resistance of smartwatches is only researched

once by Mani and Chouk (2017). The overview shows no-one looked at the role of barriers on

Table 1

Empirical research on smartwatch adoption and resistance

Authors Method Smartwatch adoption Smartwatch resistance

Pe rc e iv e d u se fu ln e ss Pe rc e iv e d e ase of u se Pe rc e iv e d e n joy m e n t V isi bi lit y ( D e si gn ) ae st h e tic s Task -te ch n ol ogy f it S e lf -e xpre ss iv e n e ss S oc ial c o n form ity Pri c e /c ost s R e lat iv e adv an tag e B ran d A v ai labi lit y C om pat ibi lit y O pe n n e ss t o e xp e ri e n c e S m art ph on e si m ilari ty Pe rc e iv e d u se le ss n e ss N ov e lty Pri c e Pri v ac y c on c e rn s In tru si v en e ss D e pe n de n ce S e lf -e ff ic ac y U sage barr ie r V al u e barr ie r R isk bar ri e r Tradi tion barr ie r Im age barr ie r B ran d aw are n e ss Ty pe of bra n d

Choi & Kim (2016) Survey under 562 Koreans X X X X Chuah et al. (2016) Survey under 226 Malaysian students X X X

Hsiao (2017) Survey under 341 Taiwanese respondents

X X X X X X X

Hsiao & Chen (2018) Survey under 260 Taiwanese potential Apple watch users X X X X X X Jeong et al. (2016) Survey under 355 Korean respondents X X X X Jung et al. (2016) Survey under 123 Korean respondents X X X Kim (2016) Experiment with 200 East Asian students X X X

Kim & Shin (2015)

Survey under 363 Korean smartwatch

users Krey et al. (2016) Survey under 226 Malaysian students X X X Mani & Chouk (2017) Survey under 402 French students X X X X X X X Wu et al. (2016) Survey under 212 respondents from Taiwan X X X X X Contribution of this study Survey study under 210 and experimental study under 162 participants. X X X X X X X

3. Theoretical framework

3.1. Study 1: The barriers influencing resistance

This chapter will visualize and explain the hypothetical relationships in the conceptual

framework of study 1. In this study we look at which barriers: usage, value, risk, tradition and

image barrier, influence resistance towards smartwatches. This leads to the following

conceptual framework (figure 1):

Figure 1. Conceptual framework study 1

The hypotheses are based on the theory of innovation resistance barriers by Ram and

Sheth (1989). According to this theory, consumers feel between none to five barriers at the

same time depending on the context. This article helps to find out which barriers are relevant

in the case of resistance towards smartwatches.

3.1.1. The usage barrier

The usage barrier appears when an innovation is incongruent with existing workflows,

practices or habits (Ram & Sheth, 1989). Especially for technological innovations, consumers

costs and wanting to stay with what is familiar (Burnham, Frels & Mahajan, 2003; Heide &

Weiss, 1995). Wearing watches is closely related to consumers daily routines and habits.

However, smartwatches are different from the existing habit of wearing a wristwatch because

it has the possibility to monitor activities, receive notifications and use services as payments

(Choi & Kim, 2016). This leads to switching costs from the current pattern to the new pattern,

which leads to a usage barrier that prevents consumers from using the new product.

When looking at technological innovations, the construct of usage barrier also includes

the degree to which an individual considers an innovation to be relatively difficult to

understand and use (Laukkanen & Kiviniemi, 2010). Research of Chuah et al. (2016, Kim

(2016), Krey et al. (2016) and others confirmed that when smartwatches are easy to use the

adoption increases. Hsiao and Chen (2018) show that when the interface is convenient and the

infrastructure is easy to use in the eyes of the users this will improve the adoption of

smartwatches. Thus, when the ease of use is low, the usage barrier is high and this will

increase the resistance towards smartwatches. Based on this line of arguments, it is expected

that the usage barrier will arise and will influence the resistance towards smartwatches:

H1. The usage barrier positively influences the resistance towards smartwatches.

3.1.2. The value barrier

The value barrier is the performance-to-price value of the product compared to the

existing product or other alternatives. Unless a product offers more performance-to-price

value than an existing product, consumers feel no need to change their existing behavior

(Ram & Sheth, 1989). When there is no incentive (value) to change, there is no desire to

change in the eyes of consumers. Indicating a positive relation between value barrier and

resistance (Antioco & Kleijnen, 2010). When a product is perceived to be better than the

smartwatches have no advantages, resistance to its adoption will be more pronounced (Mani

& Chouk, 2017). Research has shown that early adopters of smartwatches do not see the real

benefit of smartwatches, indicating that the average consumer will attribute even less value to

the use of smartwatches (Cecchinato, Cox & Bird, 2015). Therefore, the value barrier is

expected to arise and positively influence the resistance towards smartwatches.

H2. The value barrier positively influences the resistance towards smartwatches.

3.1.3. The risk barrier

All innovations have uncertainties regarding the expected outcomes for the users. This

leads to feelings of risks for consumers. These perceived risks can be economic (the first

product line within an innovation has a high economic risk because consumers expect there

will be a better and cheaper alternative in the future), functional (consumers worry a new

product is not functioning properly), social (resisting an innovation because someone feels

their peer-circle will not accept the new product) or physical (harm to a person or property,

among which risks regarding privacy) (Ram & Sheth, 1989). Thus, the following hypothesis

is formulated:

H3. The risk barrier positively influences the resistance towards smartwatches.

According to Dhebar (1996) economic risks are especially relevant for high

technology innovations. These innovations require high investments in the eyes of consumers.

That makes consumers worry more about how well their money is spend in the long-term,

leading to feelings of economic risk (Dhebar, 1996). The findings of Mani and Chouk (2017)

showed perceived price is one of the factors that influences consumer resistance towards

smartwatches. This is because smart products are still in an early stage of development and

product, price will become less influential (Mani & Chouk, 2017). In addition to that,

previous research in the field of smartwatches showed the importance of price and value for

money in the intention to adopt smartwatches (Hsiao & Chen, 2018; Jung et al., 2016; Kim &

Shin, 2015). Therefore, the following hypothesis is formulated:

H3a. The economic risk barrier positively influences the resistance towards smartwatches.

Additionally, functional risk is particularly relevant for high-technology innovations

such as smartwatches, because consumers cannot evaluate the functionality of the product

(Claudy et al., 2015). Additionally, popularity of the product is a cue for functional risks.

When a product is more popular it is associated with lower levels of functional risks

(Rauschnabel, Brem & Ivens, 2015). At this moment smartwatches are not widely adopted in

Europe and therefore are not popular yet (Bamburic, 2017). Thus, it is expected that a

functional risk barrier arises and is expected to influence the resistance towards smartwatches.

Based on this line of arguments the following hypothesis is formulated:

H3b. The functional risk barrier positively influences the resistance towards smartwatches.

There are also social problems with the use of smartwatches (Atzori, Iera & Morabito,

2010). Research of Kim (2016) shows factors such as attractiveness, coolness and affect

influence the adoption of smartwatches, showing the importance of social environments.

Research of Rauschnabel et al. (2015) shows that when consumers do not believe that other

consumers in their social environment (would) use smart glasses in public, there is a high

social risk. When peers also use the product the social risk is lower. Rauschnabel and Ro

(2016) confirmed the importance of peer opinions for adopting smart glasses. This can work

two-ways, when other expect them to use the product they will use it and when others expect

alike in the sense that they are both different from the original product (normal glasses or

wristwatches) and are visible to others (Chuah et al., 2016), it is expected that social

confirmation is as important for smartwatches as it is for smart glasses. Thus, the social risk

barrier is expected to influence the resistance towards smartwatches.

H3c. The social risk barrier positively influences the resistance towards smartwatches.

Lastly, when ordinary objects such as wristwatches become part of the IoT this leads

to increasing information security risks (Atzori et al., 2010). According to this research, the

increasing risks can hinder the widespread adoption of smartwatches because it leads to

physical risks. Looking at research in the field of privacy risks on wearables, results show the

majority of adults feel wearables, among which smartwatches, have privacy risks (Rackspace,

2013). According to this research, around 51 percent of consumers have privacy insecurities

when adopting wearable devices and see this as a barrier to adoption (Rackspace, 2013). In

the age group of 45 years and older, 63 percent of consumers are not interested in wearables

due to privacy issues (Page, 2015). Thus, it is expected that a physical risk barrier will arise

and influence the resistance towards smartwatches:

H3d. The physical risk barrier positively influences the resistance towards smartwatches

3.1.4. The tradition barrier

The tradition barrier appears when the innovation requires someone to deviate from

traditions and existing culture or do things that are not in the social norms (Ram & Sheth,

1989). When an individual is interested in buying smartwatches, social norms play a powerful

role in deciding to buy the product (Venkatesh, Morris, Davis & Davis, 2003). Traditions are

strongly embedded in society and conflicts with traditions lead to strong negative reactions

smartwatches since adoption rate of smartwatches is quite low. Only 3.8 percent of European

consumers wore smartwatches in 2017 with a much lower adoption rate in the Netherlands

(Bamburic, 2017). Therefore, it is likely that consumers do not know peers that use

smartwatches and might feel that it is against the norm/tradition to use it.

In addition, the tradition barrier especially arises when innovations can potentially

transform future life (Bredahl, 2001). The tradition barrier is not only based on the disruption

of the specific innovation now but also the consequences it can have for society in the future

(Kleijnen et al., 2009). The acceptance of smartwatches now, might encourage people to

consider the acceptance of wearable computers or devices in the future. If people do not want

to accept wearable computers and devices in the future, they might reject smartwatches now.

Following this reasoning, the following hypothesis is formulated:

H4. The tradition barrier positively influences the resistance towards smartwatches.

3.1.5. The image barrier

Image barriers are unfavorable associations towards an innovation based on the

product class, manufacturer or brand behind the innovation (Ram & Sheth, 1989). As it is

often hard to evaluate a product based on intrinsic cues (product characteristics), it is often

observed based on extrinsic cues in the form of stereotypes. In this case information is

obtained from product class, industry, brand and country of origin. If the image regarding one

or multiple of these elements is negative it will lead to resistance (Ram & Sheth, 1989). The

image barrier is especially influential for technological innovations, since these products are

purchased to retain certain status (Ismail, 2012; Venkatesh & Brown, 2001). When a product

image is negative this can damage a person’s status (Berger & Heath, 2007). As a result, a

low perceived image through negative media attention or unfavorable peer recognition can

damaging one’s own status (Kleijnen et al., 2009). Smartwatches are part of the category

technological innovations that are purchased to enhance social status. Therefore, when a

person expects the image of the extrinsic cues to be negative this will lead to a higher

resistance towards smartwatches. Thus, the following hypothesis is proposed:

H5. The image barrier positively influences the resistance towards smartwatches.

3.2. Study 2: The influence of branding on resistance

This chapter visualizes and explains the hypothetical relationships in the conceptual

framework of study 2. In this study the effect of brand awareness (unknown or well-known

brands) and different types of brands (fashion or technology brands) on resistance towards

smartwatches is measured. It is expected that this effect is mediated by the barriers. This leads

to the following conceptual framework (figure 2):

Figure 2. Conceptual framework study 2

The hypotheses regarding the influence of brand awareness and brand type on

innovation resistance and innovation resistance barriers are based on the theory of Aaker on managing brand equity (1991) and Aaker and Keller’s brand extension model (1990).

3.2.1. The effect of brand awareness

In previous literature there has been an interest among researchers in finding the

relationship between the role of branding on product purchase intention and innovation

adoption of new products. Most arguments favor the use of brand extensions over new brands

for the introduction of new products (Florea, 2015). In case of a brand extension the

awareness of the parent brand and its associations will transfer to the new product (Carter &

Curry, 2013; Florea, 2015). This leads to higher trial rate, conversion rate and loyalty rate

(Florea, 2015) and supports the introduction and adoption of innovations (Brexendorf et al.,

2015). This can be explained by the fact that brand awareness increases the trust in the brand

(Smith & Wheeler, 2002, in Lu, Chang & Chang, 2014) and trust reduces the resistance

towards the innovation (Lüders et al., 2017). Thus, the following hypothesis is formulated:

H6a. There are significant differences in the resistance towards smartwatches for well- known and unknown brands, in a way that the resistance is lower (higher) for a well-known (unknown) brand.

The relation between brand awareness and resistance towards smartwatches is

expected to be mediated by the barriers. It is expected that the introduction of an innovative

product under a well-known brand reduces the barriers, which leads to lower resistance

towards the product.

It is expected that the usage barrier is lower for well-known brands because

consumers know that the existing products of the brand are popular and thus conclude the

products are easy to use. As a result, it is expected that the new product, in this case a

smartwatch, will also be easy to use and no usage barrier will appear. Which in turn decreases

Introducing a product under a well-known brand name can affect perceptions and

attitudes towards the product; a product from a well-known brand is often perceived as having

higher value because of the brand name (Aaker, 1996). Indicating branding can enhance the

perception of value and quality of the product (Delgado-Ballester & Hernández-Espallardo,

2008; Rao, Lu & Ruekert, 1999), which in turn leads to a higher purchase intention (Chi, Yeh

& Yang, 2009). As a result, it is expected that a well-known brand will reduce the value

barrier towards smartwatches, which in turn decreases the resistance towards smartwatches.

In addition, a well-known brand is expected to increase trust and reduce uncertainties

and risks coming with innovations (Brexendorf et al., 2015; Lüders et al., 2017; Truong et al., 2017). This is because trust is part of consumers’ risk reduction strategies (Groß, 2016). As a

result, it is expected that a well-known brand will reduce the risk barrier towards

smartwatches, which in turn decreases the resistance towards smartwatches.

The tradition barrier appears when the innovation requires someone to do things that

are not the tradition or in the social norms. When someone feels it is not in the norms to do

something, they might not do it out of fear for negative reactions from peers (John & Klein,

2003). However, well-known brand names can act as a form of insurance against potential

negative peer evaluations (DelVecchio, 2001). Thus, it is expected that well-known brands

will reduce the tradition barrier, which in turn decreases the resistance towards smartwatches.

Lastly, when purchasing a product, consumers cannot always evaluate relevant

intrinsic attributes, relying more on extrinsic attributes such as brand name (Zeithaml, 1988).

A well-known brand name can serve as a positive extrinsic cue, whereas an unknown brand

cannot serve as a (positive) cue. Heavily advertised brands, in other words brands that are

well-known, tend to be evaluated more positively than brands with less advertising (Zeithaml,

resistance towards smartwatches. Based on the line of arguments presented for the influence

of brand awareness on each of the barriers, the following hypotheses are formulated:

H6b-f. The effect of brand awareness on resistance towards smartwatches is mediated by the barriers (H6b: usage barrier; H6c: value barrier; H6d: risk barrier; H6e: tradition barrier; H6f: image barrier), in a way that the barriers are lower (higher) for a well-known (unknown) brand.

3.2.2. The moderating effect of brand type

Launching an innovation under an existing brand name is expected to reduce the

resistance towards smartwatches both directly and through the mediating effect of barriers.

However, if consumers perceive inconsistency between the existing product and its extension,

introducing a product under a well-known brand does not affect resistance and consumers

might even react unfavorable (Florea, 2015; Truong et al., 2017). Smartwatches can be

marketed under a well-known fashion brand (e.g., Fossil, Diesel) or a well-known technology

brand (e.g., Google, Samsung) and can be seen as both computer devices and fashion

accessories. The research of Jung et al. (2016) implies smartwatches are more often seen as

digital devices rather than as fashion accessories. Even though wearables look like ordinary

objects consumers do recognize them as extraordinary products with smart technologies

imbedded. Other research shows almost half (49.5%) of respondents identify smartwatches

predominant as a technology, whereas only a small percentage (8%) sees it predominant as

fashion. The others (43.5%) see it as a combination of both, called fashnology (Chuah et al.,

2016). Therefore, it is expected that the fit between smartwatches launched under a

well-known technology brand is higher than for smartwatches launched under a well-well-known

smartwatches and the indirect effect through the barriers is expected to be stronger for

technology brands than for fashion brands. Thus, the following hypotheses are formulated:

H7a. Brand type moderates the negative effect of brand awareness on the resistance towards smartwatches, in a way that the effect of high brand awareness is stronger (weaker) for technology (fashion) brands.

H7b-f. Brand type moderates the effect of brand awareness on the resistance towards smartwatches through the barriers (H7b: usage barrier; H7c: value barrier; H7d: risk barrier; H7e: tradition barrier; H7f: image barrier), in a way that the effect of high brand awareness is stronger (weaker) for technology (fashion) brands.

4. Methodology

In this chapter the methodologies of both the first and second study are discussed.

First, the research design and procedure of the first study will be outlined. After that the

sample, measurements and statistical analysis of the first study are discussed. The

methodology of the second study is discussed in the same order. In addition to that, the results

of the pre-test from the second study are discussed in the methodology.

4.1. Study 1: The barriers influencing resistance 4.1.1. Procedure

In order to answer the research question on which barriers cause resistance towards

adopting smartwatches, a quantitative research was carried out in the form of an online

self-administered survey. The use of a survey was a suitable method for this research since it is a

frequently used method in opinion and behavior research. The survey was administered online

via Qualtrics and consisted of closed questions with a pre-determined set of responses.

The participants were invited to participate in this study via Facebook, personal

contact and email. The invitation included a short introduction about the study and a link to

the survey. The invitation also contained the conditions for filling in the survey, namely that

the participants had to be European citizens of 18 years and older that did not own and had

never owned a smartwatch. Next to that the time needed to fill in the survey (5-10 minutes)

was mentioned. When clicking the link the survey started. At first a short introduction about

the purpose of this study was included. Respondents were informed that the purpose of this

research was to understand consumers opinions towards smartwatches. This was followed by

an anonymity statement the participants had to agree upon. After the participants agreed with

The survey consisted of different sections. First, a picture of a smartwatch (see figure

3) accompanied by a short description of the functionalities of a smartwatch was shown to the

participants. The description included elements such as connection to the internet, running

mobile apps, making calls, messaging via text or video, accessing weather updates, providing

fitness monitoring, offering location directions and many more. The participants were asked

to carefully read the description. After seeing the picture and the description of smartwatches

the respondents had to answer several questions upon the presence of barriers and innovation

resistance towards smartwatches. Lastly, age, gender, education, consumer innovativeness,

European citizenship and smartwatch ownership (and if so, which one) were measured.

Figure 3. Smartwatch picture for survey

4.1.2. Sample

The population of interest was adult consumers from Europe who had never owned a

adoption and resistance was performed with a sample from Asia except for the study of Mani

and Chouk (2017). In addition, the adoption of smartwatches is lower in Europe than in other

continents (Bamburic, 2017). Next to that, it was important for this study that consumers had

never owned a smartwatch before, otherwise they would not feel any barriers towards

adopting smartwatches because they already adopted smartwatches in the past. Before

collecting the responses, the sample size was determined at a minimum of 200 responses.

In order to collect the data for the questionnaire a non-probability convenience sample

of people familiar to the researcher has been used. The participants were recruited through

self-selection and snowball sampling techniques via Facebook, personal contact and email. In

10 days a total of 259 participants participated in this study, of which 215 completed the

entire survey. The 44 participants that did not finish the survey completely were excluded

from the study. All participants were European citizens. However, four of the participants

indicated they had a smartwatch and were deleted from the dataset. Next to that, one of the

participants was 17 years old and was also deleted from the dataset. Lastly, the Mahalanobis

distance analysis was executed to find outliers in the data. According to this analysis there

were three outliers in the data. However, since Mardia’s Coefficient is 1.998, < 3, the

multivariate distribution is normal and the three outliers were not deleted from the data. This

resulted in a total sample of 210 participants.

The gender distribution of the sample was 139 females (66.2%), 69 males (32.9%) and

two others who did not indicate their gender (1.0%). The average age was 30,4 years. The

education ranged from high school (16.2%), vocational education (13.8%), bachelor degree in

university of applied science (23.3%), bachelor degree in university (28.6%), master degree in

university (15.2%), doctoral degree (0.5%) to other (2.4%). The level of innovativeness in this

sample was late majority on average. The further distribution of consumer innovativeness was

percent were early majority, 36.2 percent were late majority and 22.9 percent were laggards.

The details are further listed in table 2.

Table 2

Sample characteristics study 1, n = 210

Variable Amount Percentage

Gender Male 69 32.9%

Female 139 66.2%

Other 2 1.0%

Education level High school graduate 34 16.2%

Vocational education 29 13.8%

Bachelor degree applied science 49 23.3%

Bachelor degree in university 60 28.6%

Master degree 32 15.2%

Doctoral degree 1 0.5%

Other 5 2.4%

Age 29 and under 158 75.2%

30 - 39 8 3.8% 40 - 49 4 1.9% 50 - 59 27 12.9% 60 and older 12 5.7% unknown 1 0.5% Consumers innovativeness Innovators 0 0% Early adopters 24 11.4% Early majority 62 29.5% Late majority 76 36.2% Laggards 48 22.9% 4.1.3. Measurements

During this study, validated scales from previous research were used to measure the

constructs in this research. In this study the usage, value, risk, tradition and image barrier,

consumer resistance and control variables (gender, age, education, consumer innovativeness,

European citizenship and smartwatch ownership) were measured. A complete overview of