Overcoming innovation resistance: a comparative analysis

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Overcoming innovation resistance: a comparative analysis

Renjo van Bragt

11817844

Faculty of Economics and Business, University of Amsterdam

MSc BA: Entrepreneurship and Innovation

EC 20210419030423 Dr. Hesam Fasei 25th of June, 2021

Final version

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2 STATEMENT OF ORIGINALITY

This document is written by Renjo van Bragt who declares to take full responsibility for the contents of this document.

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

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

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3 TABLE OF CONTENTS

STATEMENT OF ORIGINALITY 2

Abstract 5

Introduction 6

Literature Review 11

2.1 Consumer Innovativeness 11

2.2 Perceived Value 12

2.3 Resistance to Innovation 13

2.4 Consumer Innovativeness and Resistance to Innovation 14

2.5 Perceived Value as Mediator 15

2.6 Conceptual Model 17

Methodology 18

3.1 Procedure and Research Design 18

3.2 Data Collection and Sample 19

3.3 Measures 20

3.3.1 Independent Variable 20

3.3.2 Dependent Variables 20

3.3.3 Mediating Variable 21

3.3.4 Control Variables 21

3.4 Analysis Procedure 23

Data Analysis and Results 24

4.1 Reliability Analysis 24

4.2 Factor Analysis 24

4.3 Normality Check 26

4.4 Descriptive Statistics 27

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4.5 Correlation Analysis 28

4.6 Regression Analysis 30

4.7 Mediation Analysis 36

Discussion 42

5.1 Discussion 42

5.2 Theoretical Implications 44

5.3 Practical Implications 45

5.4 Limitations and Future Research 46

Conclusion 48

References 49

Appendix 60

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5 Abstract

New services and new products fail often, which caused the way consumers respond to innovations gaining more attention from both organizations and researchers. However, most studies have focused on gaining an understanding of the reasons for adoption and not on what prevent consumer from adoption. Therefore the current study looks to examine the factors that lead to consumer resistance to the adoption of innovations, by considering consumer innovativeness and perceived value. Additionally, the study examines whether there is a difference between the factors that lead to the resistance of adopting service innovations compared to product innovations. An online questionnaire was sent to residents living in the Netherlands (n = 125), where it was found that consumer innovativeness only negatively affects the resistance to the adoption of both service and product innovations in combination with perceived value. There was no empirical evidence for a direct effect. Thus, organizations and marketeers should focus on increasing consumers’ perceived value by taking the practical and affective constructs into account. This can be done by leveraging the economic and rational values, but also the emotional and social values of the new service or new product. Meanwhile, they should limit their focus on consumer innovativeness since the current study accounted for innate innovativeness (personality trait).

Keywords: consumer innovativeness, perceived value, resistance to innovation, service innovation, product innovation

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6 Introduction

The performance of organizations is influenced by various factors, one of these factors is the ability to innovate in an environment that is constantly changing (Dess & Picken, 2000). The term innovation was first introduced by Schumpeter in the late 1920s and it argued that innovation is the implementation of novelty, for example a new good, new production method, new source of supply or new organizational structure (Hansen & Wakonen, 1997). However, innovations like new products and services fail approximately 50% of the time (Castellion &

Markham, 2013). Innovations that fail do not generate revenue which in turn can prevent companies from maintaining their competitive position (Hess, 2009). This leads to the way consumers respond to innovations being an important research topic in marketing science (Claudy, Garcia, & O’Driscoll, 2015). The way consumers respond to innovations and in particular the adoption of innovations, also known as the adoption process, has been applied in various frameworks like the diffusion of innovation theory (DOI; Rogers, 1962).

There are various factors which influence the adoption of innovation process, one of the factors that impacts the adoption process of new products and services is consumer innovativeness (Hauser, Tellis, & Griffin, 2006). However, the strength of the relationship between consumer innovativeness and the resistance to the adoption of innovations is inconsistent within the literature. There are studies where consumer innovativeness has no relation (Li, Zhang, & Wang, 2015), a weak relationship (Morton, Anable, & Nelson, 2016) and a moderate relationship (Chauhan, Yadav, & Choudhary, 2019; Kim, Kim, & Rogol, 2017) with the innovation adoption process. On top of that, the literature shows that consumer innovativeness can be both a predictor of the resistance to innovation (Al-Jundi, Shuhaiber, Augustine, 2019; Bartels & Reinders, 2010; Hong, Lin, & Hsieh, 2017; Jeong, Yoo, & Heo, 2009) and a moderator on consumer resistance to innovation (Leicht, Chtourou, & Youssef, 2018; Tomaseti, Sicilia, & Ruiz, 2004; Vandecasteele & Geuens, 2010; Zhang, Sun, Liu, &

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7 Chang, 2020) on consumer resistance to innovation. This lack of consensus within the literature creates a need to further explore the effect of consumer innovativeness on the innovation adoption process.

Additionally perceived value of the innovation also influence the adoption process (Cheung, Lam, & Lau, 2015; Jayashankar, Nilakanta, Johnston, Gill, & Burres, 2018; Yang, Yu, Zo, & Choi, 2016; Yu, Lee, Ha, & Zo, 2017). However the literature about the effect of perceived value only focus on the adoption of innovations and not the resistance of innovations.

The notion that existing literature mainly focus on the adoption of innovations and not on the resistance to the adoption of innovations is supported by Lapointe, Lamothe and Fortin (2002) where they found that since 1960 there have been over 26 300 articles about innovations, while only 26 of those articles include negative outcomes of innovations. This means there is a lesser understanding about the reasons for innovation resistance compared to the reasons for adoption of the innovation. The resistance to innovation is called the resistance to change (Gatignon, 1989), where “it is not an innovation in itself that people resist, but to its associated changes.”

(Ellen, Wiener, & Cobb-Walgren, 1991, p. 102). Moreover, the articles about the adoption of innovations assume that innovations are always good and that innovations are improvements of the existing product or service (Ram, 1987). Though this is not always the case, which is why innovation resistance researchers argue that managers and research should focus on what prevent consumers from adoption, instead of understanding the reasons for adoption (Antioco

& Kleijnen, 2010).

Furthermore, based on the existing literature it can be concluded that there is a need to further explore consumer resistance to the adoption of innovation, specifically on service innovations. Most literature regarding consumer resistance to innovations focus on product innovation (Chang & Zhang, 2019; Chen, Anders, & An, 2013; Garcia, Bardhi, & Friedrich, 2007; Heidenreich & Kraemer, 2016; Ionela-Andreea, 2019; Ju, & Lee, K. 2020; Juric, &

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8 Lindenmeier, 2019; Kushwah, Dhir, & Sagar, 2019; Sadiq, Adil, & Paul, 2021), while studies that apply consumer resistance to innovation on service innovation have only gained scientific attention in the last few years (Kaur, Dhir, Singh, N., Sahu, & Almotairi, 2020; Matsuo, Minami, & Matsuyama, 2018; Mani & Chouk, 2018) with exception to the study of Laukkanen, Sinkkonen, Kivijärvi and Laukkanen (2007). Furthermore, there is also a lack of literature that analyze the effect of consumer resistance on both product and service innovations (Claudy et al., 2015; Reinhardt, Hietschold, & Gurtner, 2019). Meaning that there is a lesser understanding about the effect of consumer resistance to the adoption service innovations compared to product innovations.

Taking previous research in consideration, the present study looks to analyze what effect consumer innovativeness has on the resistance of adoption to service innovations compared to product innovations. Additionally, perceived value will be used as a possible mediator on this main effect. This leads to the following research question:

How does consumer innovativeness affect consumer resistance to the adoption of service innovations compared to product innovations?

On top of that, there is one sub-question to this research question: Does perceived value influence this effect via mediation?

The current study contributes to the existing literature by analyzing consumer resistance to the adoption of service innovations, which has been strongly underrepresented in comparison to product innovations (Lapointe et al., 2002). Meaning that the findings of this study will directly contribute to the information gap regarding the antecedents of resistance to service innovations.

Also, the present study looks to analyze the resistance to the adoption of service innovations

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9 outside of an online payment context which previous studies have focused on (Kaur et al., 2020;

Laukkanen, Sinkkonen, & Laukkanen; Matsuo et al., 2018). This is important to research in order to get a better understanding whether the results of resistance to service innovations are generalizable to different services or product categories. Additionally, there is lack of comparative studies that focus on both product innovations and service innovations in the context of consumer resistance (Claudy et al., 2015; Reinhardt et al., 2019), by which this study looks to contribute its findings to make an overview of the effect that consumer innovativeness has on these types of innovations. Furthermore, the existing literature has mainly focused on the effect that perceived value has on the adoption of innovation (Cheung et al., 2015; Yu et al., 2016; Yu et al., 2017), while the present study examines whether perceived value has a mediating role on the effect of consumer innovativeness on the resistance to the adoption of service and product innovations.

There are also practical contributions regarding this study. First and foremost, the results of this study will help society by gaining more information about the factors that prevent consumers from adopting both service and product innovations, which in turn can help avoid the failure of future innovations. More successful innovations can accelerate the growth of society as a whole, examples are having more education platforms or less polluting vehicles.

Additionally, by limiting the amount of innovations that fail various resources will be prevented from being wasted, examples of these resources are natural resources (e.g. oil, metals and coal), money and time (spent on the innovations). Firms in particular will benefit from a better use of their resources, since a majority of the innovations succeeding means that they will generate a bigger revenue as opposed when a majority of the innovations fail. Furthermore, marketeers will also benefit from the results of this study. Namely, by gaining more information about the reasons that prevent consumers from adopting both service and product innovations can help marketeers with creating better persuasive advertising.

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10 The remainder of this study is structured as follows: chapter two will give an overview of the current literature and theoretical framework, chapter three will talk about the data and method used, while chapter four summarizes the results. Finally, chapter five and six discuss the results and give a conclusion of the study.

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11 Literature Review

2.1 Consumer Innovativeness

The speed in which consumers adopt new products or services is dependent on their innovative behavior (Im, Mason, & Houston, 2007). This innovative behavior includes the degree to which a consumer adopts an innovation earlier than others which is also referred as consumer innovativeness (Rogers & Shoemaker, 1971). On top of that, Midgley and Dowling (1987) found that there are two categories of consumer innovativeness: innate innovativeness and actualized innovativeness. Innate innovativeness is related to a personality trait where innovators and early adopters have different personality traits than the majority that is late or the laggards (Al-Jundi et al., 2019). Examples of these personality traits are curiosity and ambition (Morton et al., 2016). Additionally, innovative consumers can be stimulated by the need of novelty seeking or the need for uniqueness (Roehrich, 2004). However, there is also actualized innovativeness which displays the actual innovative behavior represented by purchase intentions, relative time to adopt the new product and number of new products owned (Lassar, Manolis, & Lassar, 2005). This actualized innovativeness can be divided between domain-specific innovativeness, which is the adoption of new products within a specific category; and product-specific innovativeness, which is the adoption of a single product (van Rijnsoever & Donders, 2009). Besides the adoption process, consumer innovativeness is also found to have an effect on product evaluations of new brands (Klink & Athaide, 2010) and seller–buyer relationships (Athaide & Klink, 2009). Within the present study, it regards consumer innovativeness as innate innovativeness, by using innovativeness as a personality trait.

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12 2.2 Perceived Value

The term consumer perceived value was first coined by Zeithaml (1988) with the purpose to predict consumer behavior. His definition of perceived value was the consumers’ assessment about the usage of a good, in particular what they received from that good. Meaning, it is perceived by consumers and can’t be influenced by the seller (Roig, Garcia, Tena, & Monzonis, 2006). However, throughout the years the concept of perceived value has been split up in two major approaches. The first major approach regards perceived value as a purely practical construct consisting of two factors: the benefits received and sacrifices made by the consumer (Cronin, Brady, & Hult, 2000; Grewal, Monroe, & Krishnan, 1998). The component of benefits received consists of economic and social benefits, for example perceived quality of the product or service and psychological benefits (Zeithaml, 1988). Meanwhile, the component of sacrifices made by the consumer are the monetary and non-monetary prices that the customer has to pay (Roig et al., 2006).

On the other hand, the second major approach regards perceived value also as a practical construct, but also as an affective construct. The practical construct consists of economic and rational value of the consumers, while the affective construct consists of an emotional and a social dimensions (Roig et al., 2006). Here the practical construct is purely utilitarian with the attributes (e.g. quality) of the product or service. However, the affective construct consists of the feelings that are generated by the product or service, while social value is the utility the consumer has with his or her social environment (Sheth, Newman, & Gross, 1991).

Overall, the assessment of perceived value is dependent on different factors, examples are the knowledge about the product and the consumer’s financial resources (Leroi-Werelds, Streukens, Brady, & Swinnen, 2014). Regarding the influence of perceived value, it has been found that it influences both consumers’ purchase intentions (Zhuang, Cumiskey, Xiao, &

Alford, 2010) and consumers’ trust (Kim, Zhao, & Yang, 2008). This makes that perceived

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13 value being regarded as important for companies, since they can affect purchase intentions by providing more value on their products or services (Steenkamp & Geyskens, 2006). Within the present study, perceived value will be regarded as both a practical and affective construct by using a measure that includes both constructs.

2.3 Resistance to Innovation

Consumers can respond in two different ways to innovations, consumers either adopt the innovation or consumers resist the innovation (Laukkanen, 2016). Researchers focus on the process itself within the adoption process with regard to the first type of response. Multiple theoretical models have been design to investigate and explain the adoption process of innovations. Examples of these models are the theory of planned behavior (Ajzen, 1991) and the technology acceptance model (Davis, 1989). On the other hand studies on the resistance to innovations focus on the factors that lead consumers to reject new products or services (Mani

& Chouk, 2018). Ultimately, the factors that lead to innovation resistance are not explained by the same factors that explain the adoption of innovations (Gatignon & Robertson, 1989).

The factors that lead to resistance to innovations are caused by different barriers associated with the adoption of innovations (Claudy et al., 2015). Studies show that there are two types of barriers, namely functional and psychological barriers (Kleijnen, Lee, & Wetzels, (2009). Functional barriers can be divided into three barriers: risk, value and usage barriers.

Risk barriers refer to the degree that innovations lead to either financial or social consequences (Posavac, Brakus, & Herzenstein, 2007). Value barriers refer to the value the consumer gets for the price, in comparison to alternatives (Molesworth & Suortti, 2002). Lastly, usage barriers are the amount of changes that the consumer has to make to its current routines (Ram & Sheth, 1989).

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14 Psychological barriers, however, can be divided into two barriers: tradition and norm barriers, and image barriers. Tradition and norm barriers is the degree to which consumers have to accept changes in their culture (Day & Herbig, 1992). Meanwhile image barriers are associated with the perception that the innovation will lead to an unfavorable image (Ram &

Sheth, 1989). Within the present study, resistance to innovation will be measured using both functional and psychological barriers.

2.4 Consumer Innovativeness and Resistance to Innovation

Researchers have found that the resistance to the adoption of innovations is caused by functional and psychological barriers (Joachim, Spieth, & Heidenreich, 2018). Therefore, in order to overcome resistance to innovation is it important to take these types of barriers into account (Mani, & Chouk, 2018). According to Claudy et al. (2015) there is a need to further explore the effects that personality traits, like consumer innovativeness, have on the behavioral responses to innovations. Most literature conclude that consumer innovativeness has a positive effect on the adoption of new products, with some studies showing no effect (Li et al., 2015).

Examples of these new products are MP3 and CD players (Hirunyawipada & Paswan, 2006), autonomous cars (Leicht et al., 2018), electric vehicles (Morton et al., 2016) and smart toys (Zhang et al., 2020). On the other hand, previous findings show that consumer innovativeness has a negative effect on the adoption of new services. For example, it was found that consumer innovativeness is negatively related to online banking adoption (Aldás‐Manzano, Lassala‐

Navarré, Ruiz‐Mafé, & Sanz‐Blas, 2009; Chauhan et al., 2019; Lassar et al., 2005), where Lassar et al. (2005) found it was influenced via psychological barriers, while Aldás-Manzano et al. (2009) found that it was influenced via both psychological and functional barriers.

However the mentioned studies have focused on the adoption of products and services and not on the resistance to the adoption process. As mentioned before the factors that lead to the

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15 resistance to innovation are not explained by the same factors that explain the adoption of innovations (Gatignon & Robertson, 1989). Therefore present study will use different measures, namely the measures used in resistance to innovations studies instead of measures used in studies about the adoption of innovations. Nevertheless, the present study looks test the results achieved in the studies about the adoption of innovations in the context of resistance to the adoption of new innovations. Since the previous literature shows that there is a difference in the effect of consumer innovativeness on the adoption of both new service and product innovations, this leads to the following hypotheses:

H1a: Consumer innovativeness positively impacts the resistance to the adoption of new service innovations.

H1b: Consumer innovativeness negatively impacts the resistance to the adoption of new product innovations.

2.5 Perceived Value as Mediator

Consumer innovativeness is an individual’s willingness to try a new technology (Agarwal &

Prasad, 1998). When a consumer is more innovative he or she tends to try new products or services. On the other hand, more traditional consumers have a lesser tendency to try these new products or services and often wait until they become popular (Jeong, Kim, Park, & Choi, 2017). Additionally, it is found that consumer innovativeness leads to perceived value (Lin, 2015), where consumers that are more innovative understand benefits of an innovation easier than more traditional consumers. Researchers also found that consumer innovativeness is lead to affective value, namely hedonic value (Kim, Fiore, Niehm, & Jeong, 2010). Thus consumer innovativeness has a positive effect on consumers’ perceived value.

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16 On top of that researchers found that perceived value influences consumer behavior, where it influences the emotional response and consumption (Dumana & Mattilab, 2005).

Perceived value influences the adoption of new products and services (Fornell, Johnson, Anderson, Cha, & Bryant, 1996), while it also influences the continuation of using innovative products like smart watches (Hong et al., 2017). The assessment of perceived value can be influenced by various factors like the innovation’s economic and rational value to the consumer, but also emotional and social dimensions (Roig et al., 2006). For example, Mashal and Shuhaiber (2019) found that factors like perceived cost, perceived enjoyment and social influences had a positive influence on the adoption of smart home devices. Meanwhile another factor like perceived usefulness influences the adoption of media tables (Yu et al., 2017). Thus, the judgement of overall attributes of an innovation is called perceived value, where the judgement includes both qualitative and quantitative measures (Susilowati & Sugandini, 2018).

With the existing literature in mind it can be concluded that perceived value has an influence on the adoption process, namely an positive effect on the adoption of new products and services.

Summarized, the literature shows that consumer innovativeness has a positive effect on consumers’ perceived value, while perceived value has a positive effect on the adoption of new products and services. This means that there is an indirect effect of consumer innovativeness on the adoption of innovations via perceived value. The mediation of perceived value is supported by the findings of Hong et al. (2017) and Al-Jundi et al. (2019), where they found that perceived value mediates the effect of consumer innovativeness on the adoption of new products. However the findings of the mediation effect are inconsistent within the literature since Al-Jundi et al. (2019) found a partial mediation and the study of Hong et al. (2017) found a full mediation. The inconsistency within the literature leads to a need to further explore the mediation effect of perceived value on consumer innovativeness and the resistance to the adoption of new products. This leads to the following hypothesis:

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17 H2a: Perceived value mediates the effect of consumer innovativeness on the resistance to the adoption of new service innovations.

H2b: Perceived value mediates the effect of consumer innovativeness on the resistance to the adoption of new product innovations.

2.6 Conceptual Model

Figure 1

Conceptual Model

Resistance to the adoption of service innovations

Resistance to the adoption of product innovations Consumer innovativeness

Perceived value of service innovations

Perceived value of product innovations

H1a

H1b H2a

H2a

H2b

H2b

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18 Methodology

3.1 Procedure and Research Design

The present study investigated the effect of consumer innovativeness on the resistance to the adoption of service innovations compared to product innovations. Additionally, it analyzed whether perceived value has an influence on this effect. A quantitative research method has been chosen to answer the research question and hypotheses, namely with the use of a cross- sectional survey method. The survey consisted of measures for the independent, dependent and mediator variables which were all adopted from previous research about the topic (Al-Jundi et al., 2019; Mani & Chouk, 2018). Additionally, the questionnaire was available in both Dutch and English where the English questionnaire has been made with the use of direct translation by the researcher. The English translation has been checked by a native English speaker in order to increase the internal validity of the questionnaire.

Furthermore, since the study looks to compare the groups of service and product innovations it was important to have the same sample among the two groups. In order to achieve this each respondent had to fill out the questions of both groups. However, to avoid survey fatigue the choice was made to have 50% of the respondents start with the questions about service innovations, while the other 50% of the respondents started with the questions about product innovations. Furthermore, in contrary to previous research, the present study did not ask about one specific service or product innovation (e.g. internet banking; Chauhan et al., 2019). Instead the present study asked about consumers’ general opinion of both service and product innovations, while including 2 examples of these types of innovations. The two examples for service innovations were: supermarkets that are beginning to provide home- delivery, and introduction of live online music performances and sport events. While the two examples for product innovations were: introduction of new smartphone, and a new drink (for example a healthy sports drink or energy drink). On top of that, the questions in the

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19 questionnaire could be answered using a 7-point Likert scale as adopted from Al-Jundi et al.

(2019), and Mani and Chouk (2018): 1: strongly disagree, 2: disagree, 3: somewhat disagree, 4: neither agree nor disagree, 5: somewhat agree, 6: agree, 7: strongly agree.

3.2 Data Collection and Sample

In order to test the hypotheses, the most suited research sample would include any consumers of products or services. For example, previous studies about the topic have used university students (Al-Jundi et al., 2019; Li et al., 2015) or existing survey websites (Zhang et al., 2020).

There are also studies that made use of purposeful sampling in order to include consumers with smart watches (Hong et al., 2017). Hence, the sample can consist of all people that have buying power. With research ethics in mind, all people over 18 years old could participate in the study.

In total the study aimed to invite over 250 respondents for the questionnaire. The reason for this amount is to optimize external validity of the results since a larger sample size is more generalizable, but also to take into account respondents that will not finish the questionnaire.

However, the actual sample size is lower than the mentioned goal amount. Namely, only a total of 131 questionnaires were started, where 125 questionnaires were successfully finished.

Regarding the sample method, the chosen sample method was convenience sampling which is followed by snowball sampling. The reason for this is the goal of time efficiency in mind, while also aiming for a high reach. However, this sampling method is a major limitation to the study where it harms the generalizability and thus external validity of the results. The limitation section within the discussion will explain more about the effects of this limitation.

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20 3.3 Measures

3.3.1 Independent Variable

Consumer Innovativeness. The present study uses consumer innovativeness as an independent variable. Consumer innovativeness refers to the degree to which a consumer adopts an innovation earlier than others (Rogers & Shoemaker, 1971). The concept of consumer innovativeness was in the present study measured with a 7-point Likert scale consisting of 6 items which were adopted from the construct used by Al-Jundi et al. (2019). However, changes were made to the formulation of the questions to fit both products and services. An example of one of these changes is from “Using new products would improve my image” to “Using new products or services would improve my image”. Regarding the reliability of the measurement in the study of Al-Jundi et al. (2019), it was tested with the use of Cronbach’s Alpha where consumer innovativeness (ɑ = 0.826) was reliable. The construct (6 items) that has been used can be found in the appendix.

3.3.2 Dependent Variables

Resistance to Product and Service Innovations. Consumer resistance to innovations is defined as a form of resistance to change cause by innovation (Ram, 1987). Previous studies about consumer resistance to innovation used theoretical frameworks which included determinants like perceived innovation characteristics (Rogers, 1962) or consumer characteristics (Zaltman & Wallendorf, 1983). This study, however, took approach by researching consumer innovativeness on resistance to innovations. The measurement of both resistance to product and service innovations was created by adopting the construct used in Mani and Chouk (2018). However, changes were made to the formulation of the questions in order to have a separate scale for each dependent variable. An example of change is from “I'm likely to be opposed to the use of smart bank services” to “I'm likely to be opposed to the use

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21 of [product / service innovation]”. The minimal difference between the scales for both dependent variables makes the comparison more valid. Additionally, the reliability of the measurement in the study of Mani and Chouk (2018) was reliable (ɑ = .950). The construct (6 items) that has been used can be found in the appendix.

3.3.3 Mediating Variable

Perceived Value. Consumer perceived value viewed as the consumers’ assessment about the usage of a good, in particular what they received from that good (Zeithaml, 1988).

Just like the concept of consumer innovativeness, perceived value was measured with a 7-point Likert scale consisting of 6 items which were adopted from the construct used by Al-Jundi et al. (2019). However, to measure the mediating effect of perceived value on both product and service innovations it was made sure that perceived value was measured twice: once for product innovations and once for service innovations. The measurement used in the study of Al-Jundi et al. (2019) was reliable (ɑ = .878). The construct (6 items) that has been used can be found in the appendix.

3.3.4 Control Variables

The present study controls for a total three variables, namely gender, age and education level. Each variable was used to analyze whether they influence the main effect and mediation effect.

Gender. Previous research has shown that gender influences the adoption of new innovations, where Mattilia, Karjaluoto and Pento (2003) found that men use internet banking more often than women. The notion that the internet is mostly used by men is supported by the findings of Venkatesh and Morris (2000). Thus, there is a difference in the adoption of new innovations when looking at gender. Therefore the first control variable in the present study is

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22 gender. Within the present study respondents could answer gender with the following: male, female, other, or rather not say.

Age. On the other hand the existing literature shows an relationship between age and the adoption process of new innovations. Mutengezanwa and Mauchi (2013) found a relationship between age and the adoption of internet banking, with consumers below 45 years more likely to adopt internet banking. These findings are similar to previous research that show that younger people are early adopters of innovations (Gattiker, 1992). Based on this the second control variable is age. With regard to the present study the respondents had a text entry to fill in their age.

Education level. Lastly, it was found that education influences the adoption of electric vehicles (Javid & Nejat, 2017). A higher education level has a higher probability of buying an electric vehicle compared to lower education levels. On top of that, Dickerson and Gentry (1983) found that adopters of home computers are likely to be more educated than non-adopters of home computers. Thus, the last control variable, education level, has an influence on the adoption process of product innovations. Within the present study education level was measured by using the education system that is used in country where the study was held, which was the Netherlands. Respondents had the option to choose between primary school, pre- vocational secondary education (VMBO), senior general secondary education (HAVO), pre university education (VWO), secondary vocational education (MBO), higher professional education bachelor (HBO Bachelor), higher professional education master (HBO Master), university education bachelor (WO Bachelor), university education master (WO Master) and other, which included a text entry. All translation for the demographic question about education are adapted from the government website (Government, w.d.).

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23 3.4 Analysis Procedure

Before testing the hypotheses both a reliability and factor analysis were performed. The reliability analysis was done in order to test whether the used scales were reliable, while the factor analysis had an exploratory purpose to check whether different scales could be made.

However in order to test the hypotheses a total of three different analyses were performed. First of all, a bivariate correlation analysis was perform to examine the relationships between the variables used in the study. This included the control variables. Secondly, a total of four hierarchical regression analyses were performed. The first pair of hierarchical regression analyses were used to predict the value of each mediator (perceived value of service/product innovation) based on the value of the independent variable (consumer innovativeness). While the second pair of hierarchical regression analyses were used to predict the value of each dependent variable (resistance to the adoption of service/product innovations) based on the value of the independent variable (consumer innovativeness) and mediators (perceived value of service/product innovation). Lastly, the mediation was tested by performing two PROCESS models where each dependent variable was tested separately.

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24 Data Analysis and Results

4.1 Reliability Analysis

The measurements used in this research were adopted from previous studies of Al-Jundi et al.

(2019), and Mani and Chouk (2018). However, the current research had both an English and Dutch version of the survey. The translation of the original measures to Dutch may have impacted the reliability of the measures. Therefore a reliability analysis of all the measures was being done with the use the of Cronbach’s alpha which has a threshold of α > .70 in order for it to be reliable. Consumer innovativeness did not meet the threshold and had a Cronbach’s alpha of α = .69. Deletion of any of the items would not improve the scale. Although the measure of consumer innovativeness did not meet the required threshold of α > .70 and is thus deemed unreliable, it was still used in this research. The discussion section will elaborate the implications of this choice.

On the other hand, the other measurements exceeded the required Cronbach’s alpha threshold where perceived value of service innovations equaled α = .78, resistance to service innovations α = .82, perceived value of product innovations equaled α = .76, and resistance to product innovations α = .78. Concluding, all measures except consumer innovativeness were reliable and none of the measures could be improved by deleting an item.

4.2 Factor Analysis

An exploratory factor analysis has been applied in order to further evaluate the scales that have been used in this research. This factor analysis examines similarities between the items and looks to cluster these items, where these clusters are called factors. Within this analysis it makes use of Kaiser-Meyer-Olkin (KMO) test which has a threshold where the KMO > .60. While the analysis also examines how many factors to extract by using Kaiser criterion of eigenvalues with eigenvalue > 1. Consumer innovativeness equaled KMO = .67, with Bartlett’s test of

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25 sphericity χ² (15) = 147.09, p < .001, indicating a sufficiently large correlation between items.

However, Kaiser’s test and the scree plot showed that there two components with an eigenvalue over 1, which together explained 58,92% of total variance. Following this the two factors were rotated with an Oblimin with Kaiser normalization rotation. Table 1 shows the factor loadings after rotation. The items that formed clusters suggest that factor 1 represents consumer image (CI), while factor 2 represents consumer experience (CE). Meaning that consumer innovativeness could actually be split into two different scales, consumer image (CI) and consumer experience (CE).

Meanwhile, perceived value of service innovations equaled KMO = .83, with Bartlett’s test of sphericity χ² (10) = 149.83, p < .001, indicating a sufficiently large correlation between items. Resistance to service innovations equaled KMO = .80, with Bartlett’s test of sphericity χ² (10) = 216.24, p < .001, also indicating a sufficiently large correlation between items.

Furthermore perceived value of product innovations equaled KMO = .80, with Bartlett’s test of sphericity χ² (10) = 135.40, p < .001, again indicating a sufficiently large correlation between items. Finally, resistance to product innovations equaled KMO = .79, with Bartlett’s test of sphericity χ² (10) = 162.04, p < .001, once more indicating a sufficiently large correlation between items.

Table 1

Factor Loadings Consumer Innovativeness

Item (CI) (CE)

The design of new products or services are attractive to me .01 .81

Using new products or services would provide a novel experience .01 .35

I feel more important when using new products or services .45 .12

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26

Item (CI) (CE)

I would like to follow global trends rather than sticking to traditions -.05 .47

Using new products or services would improve my image .77 -.07

People think positively of me when I use a new product or service. .82 .01

4.3 Normality Check

All variables were controlled whether they followed a normal distribution. These normality checks are necessary since many statistical test assume that a variable is normally distributed (American Psychological Association, w.d.). The first normality check show that consumer innovativeness had skewness of -.06 and kurtosis of 2.39. The kurtosis of consumer innovativeness is not close to zero which indicates that the variable is slightly peaked.

Meanwhile perceived value of service innovations had skewness of .14 and kurtosis of .23, resistance to service innovations had skewness of .18 and kurtosis of -.72, perceived value of product innovations had skewness of .22 and kurtosis of .29, lastly resistance to product innovations had skewness of .06 and kurtosis of -.44. Meaning that all variables except consumer innovativeness were normally distributed. Consumer innovativeness is the only variable which peaked.

The second normality check tests whether the residuals per dependent variable were normally distributed. For the first dependent variable, resistance to service innovations, a normal P-P plot and histogram were created. Appendix 2 shows the normal P-P plot of standardized residuals which indicate that the data has normally distributed errors, as did Appendix 3 with the histogram. On top of that, the Shapiro-Wilk test for resistance to service innovations was nonsignificant, meaning that the null hypothesis of normally distributed

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27 residuals is accepted, W(125) = .99, p = .934. Furthermore, the variance of the residuals of resistance to service innovations were controlled on being homogeneous via a scatterplot.

Appendix 4 shows a scatterplot where the variance is fairly evenly distributed, though there are some outliers, meaning that it is homogenous.

Regarding the second dependent variable, resistance to product innovations, another normal P-P plot and histogram were created. Appendix 5 shows the normal P-P plot of standardized residuals which indicate that the data has mostly normally distributed errors, though some points are not on the line. Meanwhile Appendix 6 indicate that the data has normally distributed errors with the histogram. Also, the Shapiro-Wilk test for resistance to product innovations was nonsignificant, meaning that the null hypothesis of normally distributed residuals is accepted, W(125) = .99, p = .807. Similarly like the other dependent variable, the variance of the residuals of resistance to product innovations were controlled on being homogeneous via a scatterplot. Appendix 7 shows a scatterplot where the variance is fairly evenly distributed, though there are some outliers, meaning that it is homogenous.

4.4 Descriptive Statistics

In total 131 questionnaires were started and a total of 125 questionnaires were successfully finished, meaning that there was a 95% response rate. Gender was fairly even distribution where 51% was male, with no respondents choosing a gender besides male or female and no respondents that didn’t want to share their gender. The age of the respondents ranged from 18 to 75 years old with the average age of the respondents being 29 years old. Additionally, a majority of the respondents is highly educated where 33% of the respondents is currently following or has finished a higher professional education (HBO) bachelor or master, while 52%

is currently following or has finished an university education (WO) bachelor or master.

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28 4.5 Correlation Analysis

A correlation analysis was performed in order to investigate the relationships between all variables. Table 2 shows an overview of all correlations. Furthermore, table 2 shows that consumer innovativeness is negatively correlated to resistance to the adoption of service innovations (r = -.31, p < .01). Meanwhile mediating variable perceived value of service innovations also has a negative correlation to perceived value of service innovations (r = -.70, p < .01), but has a positive correlation to consumer innovativeness (r = .44, p < .01).

Furthermore, consumer innovativeness is also negative correlated to the resistance to the adoption of product innovations (r = -.41, p < .01). Mediating variable perceived value of product innovations is also negatively correlated to resistance to the adoption of product innovations (r = -.68 p < .01), but is positively correlated to consumer innovativeness (r = .48, p < .01). The significant correlations between the variables indicate that there is evidence that support most of the hypotheses of the present study. Nevertheless different analyses will be used to further investigate the relationships between the variables.

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29

Table 2

Means, Standard Deviations and Correlations

Variables M SD 1 2 3 4 5 6 7 8

1. Gender 1.49 0.50 -

2. Age 29.17 12.93 -.07 -

3. Education 7.10 1.60 .05 -.50** -

4. Consumer inn. 3.32 0.57 .07 .20* .02 -

5. Perceived value of service innovations 3.34 0.65 .06 .01 .06 .44** -

6. Resistance to service innovations 2.49 0.71 -.11 .05 -.12 -.31** -.70** -

7. Perceived value of product innovations 3.27 0.62 -.03 .02 .15 .48** .47** -.41** -

8. Resistance to product innovations 2.48 0.68 ,07 .01 -.11 -.41** -.42** .46** -.68** -

Note. **. Correlation is significant at the .01 level (2-tailed).

*. Correlation is significant at the .05 level (2-tailed)

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30 4.6 Regression Analysis

A total of four regression analyses were executed, namely one for each mediator and one for each dependent variable. However, in order to account for the control variables, gender, age and education, four hierarchical regression analysis were applied. Each of the four hierarchical regression analysis had two models. The first model only included the control variables, while the second model included both the control variables and the other predictor variables. Table 3 shows the results of the hierarchical regression analysis for the mediator variable perceived value of service innovations. The first model with only the control variables had a total variance explained of 1% and was not significant, F(3,121) = .36, p = .359. On the other hand, in the second model when adding predictor variable consumer innovativeness the total variance explained was 20%, F(4,120) = 7,65, p < .001. The addition of consumer innovativeness explained additional 19% of variance in perceived value of service innovations, after controlling for gender, age and education (R2 change = .19, F(1,120) = 29,25, p < .001).

Furthermore in the second model only consumer innovativeness was significant (β = .46, p <

.001). Meaning that if the unit for consumer innovativeness increases with one, the unit for perceived value of service innovations increases with .52.

Table 3

Hierarchical Regression Model of Perceived Value of Service Innovations

Dependent variable

Perceived value of service innovations

Independent variables β t

Model 1: control variables

Gender .06 .60

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31

Dependent variable

Perceived value of service innovations

Independent variables β t

Age .06 .58

Education .09 .82

R2 .01

F .36

Model 2: predictor variable

Gender .02 .21

Age -.07 -.70

Education .02 .17

Consumer inn. .45*** 5.41

R2 .20

∆R2 .19

F 7.65***

Note. ***. Significant at p < .001.

Moreover, table 4 shows the results of the hierarchical regression analysis for the dependent variable resistance to the adoption of service innovations. The first model with only the control variables had a total variance explained of 2% and was not significant, F(3,121) = .88, p = .452.

However, in the second model when adding predictor variables consumer innovativeness and perceived value of service innovations the total variance explained was 50%, F(5,119) = 23,76, p < .001. The addition of consumer innovativeness and perceived value of service innovation

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32 explained additional 48% of variance in resistance to the adoption of service innovations, after controlling for gender, age and education (R2 change = .48, F(2,119) = 56,87, p < .001).

Furthermore in the second model only perceived value of service innovations was significant (β = -.69, p < .001). Meaning that if the unit for the perceived value of service innovations increases with one, the unit for resistance to the adoption of service innovation decreases with .76.

Table 4

Hierarchical Regression Model of Resistance to the Adoption of Service Innovations

Dependent variable

Perceived value of service innovations

Independent variables β t

Model 1: control variables

Gender -.10 -1.12

Age -.02 -.15

Education -.11 -1.05

R2 .02

F .88

Model 2: predictor variable

Gender -.06 -.96

Age .03 .34

Education -.05 -.64

Consumer inn. .00 -.02

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33

Dependent variable

Perceived value of service innovations

Independent variables β t

Perceived value of service inn. -.69*** -9.55

R2 .50

∆R2 .48

F 23.76***

Note. ***. Significant at p < .001.

Now for table 5, it shows the results of the hierarchical regression analysis for the mediator variable perceived value of product innovations. The first model with only the control variables had a total variance explained of 4% and was not significant, F(3,121) = 1,46, p = .230. In the second model when adding predictor variable consumer innovativeness the total variance explained was 25%, F(4,120) = 10.20, p < .001. The addition of consumer innovativeness explained additional 19% of variance in perceived value of product innovations, after controlling for gender, age and education (R2 change = .22, F(1,120) = 35,20, p < .001). Also, in the second model only consumer innovativeness was significant (β = .48, p < .001). Meaning that if the unit for consumer innovativeness increases with one, the unit for perceived value of product innovations increases with .53.

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Table 5

Hierarchical Regression Model of Perceived Value of Product Innovations

Dependent variable

Perceived value of service innovations

Independent variables β t

Model 1: control variables

Gender -.03 -.31

Age .13 1.22

Education .21* 2.06

R2 .04

F 1.46

Model 2: predictor variable

Gender -.07 -.85

Age -.01 -.10

Education .14 1.51

Consumer inn. .48*** 5.93

R2 .25

∆R2 .22

F 10.20***

Note. ***. Significant at p < .001.

*. Significant at p < .05.

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35 Finally, for the dependent variable resistance to the adoption of product innovations, table 6 shows the results of the hierarchical regression analysis. The first model with only the control variables had a total variance explained of 2% and was not significant, F(3,121) = .78, p = .505.

However, in the second model when adding predictor variables consumer innovativeness and perceived value of product innovations the total variance explained was 48%, F(5,119) = 21,70, p < .001. The addition of consumer innovativeness and perceived value of service innovation explained additional 46% of variance in resistance to the adoption of product innovations, after controlling for gender, age and education (R2 change = .46, F(2,119) = 52.07, p < .001).

Additionally, in the second model only perceived value of product innovations was significant (β = -.62, p < .001). Meaning that if the unit for the perceived value of product innovations increases with one, the unit for resistance to the adoption of product innovation decreases with .68.

Table 6

Hierarchical Regression Model of Resistance to the Adoption of Product Innovations

Dependent variable

Perceived value of service innovations

Independent variables β t

Model 1: control variables

Gender .08 .86

Age -.05 -.45

Education -.13 -1.28

R2 .02

F .78

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36

Dependent variable

Perceived value of service innovations

Independent variables β t

Model 2: predictor variable

Gender .07 1.06

Age .07 .85

Education .02 .23

Consumer inn. -.13 -1.61

Perceived value of service inn. -.62*** -8.09

R2 .48

∆R2 .46

F 21.70***

Note. ***. Significant at p < .001.

4.7 Mediation Analysis

In total two mediation analyses (PROCESS) were performed, one for each dependent variable.

For the mediation analyses ordinary least squares (OLS) estimates for direct effects, whereas bootstrap results for indirect effects by using 5.000 bootstraps and bias-corrected method. Table 7 shows the results of the mediation analysis for the dependent variable resistance to the adoption of service innovations. It was found that consumer innovativeness indirectly influences resistance to the adoption of service innovations via its effect on perceived value.

Namely, the effect from consumer innovativeness to perceived value of service innovations was positive and significant (b = .52, p < .001), indicating that consumers that score high on

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37 innovativeness are more likely to have higher perceived value of service innovations. On top of that, the effect from perceived value of service innovations to resistance to the adoption of service innovation was negative and significant (b = -.76, p < .001), indicating that consumers with higher perceived value of service innovations are more less likely to have resistance to the adoption of service innovations. Furthermore based on 5.000 bootstrap samples the confidence interval for the indirect effect was entirely below zero (-.46 to -.21), meaning that consumers who are innovative will have increased perceived value, which in turn will reduce the resistance to the adoption of service innovations.

Table 7

Mediation Model of Resistance to the Adoption of Service Innovations

Dependent variables (service innovation)

Perceived value Resistance to innovation

Independent variables B 95% CI B 95% CI

Constant 1.64*** [.72; 2.55] 5.28*** [4.44; 6.11]

Gender .02 [-.19; .23] -.09 [-.27; .09]

Age .00 [-.01; .01] .00 [-.01; .01]

Education .01 [-.07; .08] -.02 [-.09; .05]

Consumer innovativeness (path a) .52*** [.33; .71] (path c’) .00 [-.19; .18]

Perceived value (path b) - - -.76*** [-.92; -.60]

R2 = .20, F(4,120) = 7,65, p < .001 R2 = .50, F(5,119) = 23,76, p < .001

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38

Direct effect Path c’

Indirect effect (unstandardized) Path ab

Indirect effect (completely standardized)

Path ab

.00

-.39

-.32

[-.19; .18]

[-.59; -.21]

[-.46; -.17]

Note. ***. Significant at p < .001.

For the second mediation analysis, table 8 shows the results of the mediation analysis for the dependent variable resistance to the adoption of service innovations. Regarding the dependent variable resistance to the adoption of product innovations, it was found that consumer innovativeness indirectly influences resistance to the adoption of product innovation via its effect on perceived value. Namely, the effect from consumer innovativeness to perceived value of product innovations was positive and significant (b = .54, p < .001), indicating that consumers that score high on innovativeness are more likely to have higher perceived value of product innovations. On top of that, the effect from perceived value of product innovations to resistance to the adoption of service innovation was negative and significant (b = -.68, p < .001), indicating that consumers with higher perceived value of product innovations are more less likely to have resistance to the adoption of product innovations. On top of that, based on 5.000 bootstrap samples the confidence interval for the indirect effect was entirely below zero (-.43 to -.17), meaning that consumers who are innovative will have increased perceived value, which in turn will reduce the resistance to the adoption of product innovations.

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39

Table 8

Mediation Model of Resistance to the Adoption of Product Innovations

Dependent variables (product innovations)

Perceived value Resistance to innovation

Independent variables B 95% CI B 95% CI

Constant 1.28*** [.43; 2.12] 4.89*** [4.09; 5.70]

Gender -.08 [-.28; .11] .10 [-.08; .28]

Age .00 [-.01; .01] .00 [-.01; .01]

Education .05 [-.02; .13] .01 [-.06; .07]

Consumer innovativeness (path a) .53*** [.35; .70] (path c’) -.15 [-.33; .03]

Perceived value (path b) - - -.68*** [-.84; -.51]

R2 = .25, F(4,120) = 10.20, p < .001 R2 = .48, F(5,119) = 21,70, p < .001

Direct effect Path c’

Indirect effect (unstandardized) Path ab

Indirect effect (completely standardized)

Path ab

-.15

-.36

-.30

[-.33; .03]

[-.53; -.20]

[-.43; -.17]

Note. ***. Significant at p < .001.

All hypotheses, results and effect sizes that were calculated in this section are summarized in table 9 and figure 2 below.

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40

Table 9

Overview Hypotheses and Results

Hypothesis Result

1a Consumer innovativeness positively impacts the resistance to the adoption of new services.

Rejected

1b Consumer innovativeness negatively impacts the resistance to the adoption of new products.

Rejected

2a Perceived value mediates the effect of consumer innovativeness on the resistance to the adoption of new services.

Accepted (path a: b = .52, p <

.001; path b: b = -.76, p < .001)

2b Perceived value mediates the effect of consumer innovativeness on the resistance to the adoption of new products.

Accepted (path a: b = .53, p <

.001; path b: b = -.68, p < .001)

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41

Figure 2

Overview Hypotheses and Results

Note. ***. Significant at p < .001.

Resistance to the adoption of service innovations

Resistance to the adoption of product innovations Consumer innovativeness

Perceived value of service innovations

Perceived value of product innovations

H1a: rejected

H1b: rejected H2a: .52***

H2a: -.76***

H2b: .53***

H2b: -.68***

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42 Discussion

This chapter discusses the results of the analyses, while also providing a summary of the theoretical and practical implications. After this the limitations and recommendations for future research will be discussed.

5.1 Discussion

The purpose of this study was to examine how consumer innovativeness affect the resistance to the adoption of service innovations compared to product innovations. The results indicate that consumer innovativeness does not influence both service innovations and product innovations. However, perceived value was found to be a mediator of the relationship between both consumer innovativeness and the resistance to the adoption of service innovations, and consumer innovativeness and the resistance to the adoption of product innovations.

The results of this study suggest that the main hypothesized relationships (H1a and H1b) are rejected. Where the degree to which a consumer adopts an innovation earlier that others (consumer innovativeness), neither increases nor decrease the consumers’ resistance to adopt new service or product innovations. However, this means that the present study is contradictory to the majority of the literature where they found that consumer innovativeness has positive effect on the adoption of new products (Citrin, Sprott, Silverman, & Stem, 2000;

Hirunyawipada & Paswan, 2006; Kim et al., 2017; Leicht et al., 2018; Morton et al., 2016;

Thakur & Srivastava, 2015; Zhang et al., 2020) and a negative effect on the adoption of new services (Aldás‐Manzano et al., 2009; Chauhan et al., 2019; Lassar et al., 2005). One possible explanation for the difference in the findings is that the studies where an effect (either positive or negative) was found looked to understand the factors that lead to the adoption of innovations, while the present study focused at the factors that lead to the resistance to innovations. This is relevant since they are not explained by the same factors (Gatignon & Robertson, 1989).

Figure

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References

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