Mental simulation and innovation resistance : the effect of imagination-focused visualization on innovation resistance towards Really New Products

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Master Thesis

Mental simulation and Innovation resistance: The effect of

imagination-focused visualization on innovation resistance towards

Really New Products.

Name: Faraz Khattar Student number: 11121971 Date: 22-04-2017

MSc Business Administration: Marketing track Supervisor: Frank Slisser

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Statement of originality

This document is written by Student Faraz Khattar, 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

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Abstract

Every year firms throughout all industries invest enormous amounts of time and money in the research and development of technological innovations. The successful market introduction of these innovations is of major concern for the existence and growth of these firms.

However, the vast majority of these innovations fail to be commercially successful as they are rejected by consumers due to their resistance towards innovations. While previous literature has acknowledged innovation resistance as a critical barrier in the adoption process of innovations, research on strategies that help to overcome innovation resistance is still scarce. This study strives to address this gap by exploring the effect of what has been identified as one of the most effective marketing instruments for new product

communication: mental simulation. More specifically, this study tested the relationship of imagination-focused visualization on the different types of innovation resistance towards really new product innovations (RNPs) and two of the main drivers of innovation resistance: perceived risk and usage barrier. A quantitative online experiment amongst 144 respondents was conducted in order to test the proposed hypothesis. The results found no significant evidence for imagination-focused visualization to be an effective tool to reduce consumer innovation resistance. Neither did the results succeeded to find proof that imagination-focused visualization is a successful strategy in order to reduce perceived risk and usage barrier.

Keywords: Innovation resistance, mental simulation, imagination-focused visualization,

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1. Introduction

Throughout the last decades the world has been introduced to a tremendous amount of technological innovations. Some of these innovations have been responsible for major

changes in our current way of living (e.g. the internet, smartphones), but an extensive amount of the innovations that were introduced to the market faced high rates of failure and did not succeed to become commercially successful (Ram and Sheth, 1989). Innovations are believed to be the engine of economic growth and development (Chaney, Devinney and Winter, 1991). They are seen as the primary means for value creation, which enables firms to disturb the status quo in existing markets and displace current competitors (Rindova and Petkova, 2007). Through innovations, firms have the ability to find new solutions to problems and transform old markets and create new ones. However, the success of an innovation for a company depends ultimately on the consumers accepting them (Hauser, Tellis and Griffin, 2006).

Ram and Sheth (1989) argue that all technological innovations possess a risk barrier that evokes uncertainty. Such a risk barrier can lead to potential side effects that cannot easily be anticipated. An assumption is that in situations where consumers have to predict the benefits of innovations, especially those with a high level of radicalness, they experience much uncertainty (Hoefler, 2003). Such uncertainty and risk have been widely recognized as one of the most influential costs in the adoption process of innovations (Castano et al, 2008; Kleijnen et al., 2009). Another crucial factor within the decision process that inhibits

innovation adoption is the usage barrier that consumers face. When confronted with the adoption of an innovation, consumers are often worried that it is not compatible with existing workflows, practices or habits. Innovations that require adjustments in customers’ routine often face long development process before gaining consumer acceptance (Ram and Sheth, 1989).

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6 Previous literature in the field of innovations has predominantly focused on the

adoption process and the successful diffusion through the market, emphasizing the acceptance of innovations and aspects that influence the acceptance process (Ram, 1987; Kuisma et al., 2007; Mohtar and Abbas, 2012). Sheth (1981) argues that there is a need to look at the process of innovation adoption from another perspective. He states that most researchers have a pro-change bias and highlights the importance of understanding the psychology of innovation resistance and the individuals who resist change, in order to successfully develop and promote innovations. Since then, there has been an increase in the literature dedicated to the concept of innovation resistance by marketers and academics. The majority of the research on innovation resistance has focused on the drivers of innovations resistance and often stressed factors such as innovation characteristics, consumer

characteristics or the perception of innovation characteristics as the elements for success (Ram, 1987; Tornazky and Klein, 1982; Antioco and Kleijnen 2010; Cornescu, Adam, 2013). Only a relatively small number of studies have researched the different strategies that firms can implement in order to overcome innovation resistance (Ram and Sheth, 1989; Castano et al., 2008; Eng and Giulia, 2009; Laukannen et al., 2009; Heidenreich and Kraemer, 2015). Laukannen et al. (2009) state that in strategies, communication plays a crucial role in the process of diffusion and innovation resistance. Especially in the early stages of the diffusion process, communication is the main source of information and is of great value for reducing factors such as risk and uncertainty. Heidenreich and Kraemer (2015) created an overview of different communication strategies to overcome innovation resistance. They identified visualizing the new usage situation by employing a mental simulation task as the most effective marketing instrument for new product communication. Mental simulation refers to the imitative mental representation of events (Taylor and Schneide, 1989). The importance of mental simulation and its various effects have already been emphasized in

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7 different areas of psychology and marketing (Zhao, Hoeffler and Zauberman, 2011). The obtained evidence from previous studies suggests that imagery has a significant effect on consumers’ behavior. Imagining a consumption experience is proven to be an effective method to increase factors such as: memory enhancement, perceived likelihood of events and likelihood of product purchase (Petrova and Cialdini, 2007).

Zhao et al. (2011) state that imagining a product experience can also have a powerful effect on consumers’ product attitudes. By forming visual images of the process or outcome of a product usage, mental simulation has the ability to stimulate the adoption process of innovations by decreasing the uncertainties involved (Castano et al., 2008)

While there have been several studies dedicated to different forms of mental simulation and their effects on factors such as product evaluation and innovation adoption (Castano et al., 2008; Hoeffler, 2003; Zhao, Hoeffler and Dahl, 2012), there has been little empirical research done on the effectiveness of mental simulation as a tool for the reduction of innovation resistance (Heidenreich and Kraemer, 2015). More specifically, the effect of mental simulation on the resistance offered by consumers to a specific type of technological innovation has never been researched before. The aim of the current study is to address this research gap by exploring the relationship between mental simulation, innovation resistance and two of the main drivers of innovation resistance: usage barriers and perceived risk. This study will focus on providing an understanding how the process of mental simulation

influences the resistance offered by consumers to technological innovations. Accordingly, the following research question is formulated: What is the effect of mental simulation on

innovation resistance towards technological innovations? And how is that relationship mediated by usage barrier and perceived risk?

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Sub-questions

- What are technological innovations?

- How do perceived risk and usage barrier influence innovation resistance?

- What is mental simulation and how does it influence innovation resistance and its drivers?

1.2 Scientific contribution

From a scientific perspective, this study will contribute to the existing literature by modeling and test the relationship between mental simulation and innovation resistance. It will provide new insights to previous academic research on innovation resistance by empirically testing the relationship between two of the most important drivers of innovation resistance and the

various forms of resistance, which will be further elaborated in the literature review. The results of this study are also relevant for academics and marketers interested in

communication strategies to reduce innovation resistance and motivate the adoption of technological innovations. This study will add to recent research on mental simulation. It will not only test the relationship between mental simulation and innovation resistance toward technological products but also provide a better understanding of how that relationship works.

1.3 Managerial contribution

From a managerial perspective, this study is relevant because organizations can use the results to create communication strategies based on mental simulation in order to enhance the

adoption of their technological innovations. The results of this study provide an understanding for managers how they can implement mental simulation in their methods of communication and promotion, in order to decrease the usage barriers and perceived risk that consumers often face when confronted with new technological innovations. These results are not only

interesting for managers that want to decrease the chance of adoption failure but also for managers that are interested in influencing usage barriers and risk factors in general.

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9 Managers can use these insights for different marketing channels and tools such as print, television or even Internet advertisement. The outcomes of this study can contribute to the increase of the success rate of new product introductions, which can save a lot of time and money invested.

1.4 Outline

The structure of this research is as followed: first, a review of the existing literature will provide an insight into the concept of innovation resistance; it’s barriers and the

communication strategy: mental simulation. Second, several hypothesis will be developed and the corresponding conceptual framework will be presented. Third, the research methods will be described and the outline of the methodology will be provided. This will be followed up by the results, the discussion of the results and the conclusion chapter, including the limitations of the current research, areas for future research and theoretical and managerial implications.

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2. Literature review

2.1 The concept of innovation

In the academic literature, various definitions have been used for the concept of innovation. Many scholars define innovation as an idea, practice, or object that is perceived as new by an individual or another unit of adoption (Dewar and Dutton, 1986; Rogers, 2003). Others used a more comprehensive definition and explained innovation as a product, process, or service, either with unprecedented performance characteristics or familiar characteristics that offer significant improvements in performance or cost, which transforms the existing markets or create new ones (Cornescu and Adam, 2013). What is important is to not confuse innovation with an invention and to draw a distinction between the two concepts. Invention stands for the creation of something new, this can be a new idea for a product, a device or even a procedure (Denning, 2004). While innovation is the first commercialization of that new idea. Ruttan (1959) states that invention can be seen as an antecedent to innovation. In many cases, there is a time lag between the two and in order for a business to transform an invention into an innovation, it needs to combine different types of knowledge, capabilities, skills and resources (Fagerberg, 2004).

The traditional concept of innovation makes a distinction between product and process innovation, but since both of types of innovation are in most cases associated with the

development or application of new technologies, these innovations are usually named technological innovations (Schmidt and Rammer, 2004). Technological innovations can be seen as innovations that embody inventions from different fields such as industrial arts, engineering, applied sciences or pure sciences (Garcia and Calantone, 2002). Sjaaksjarvi (2003) argues that technological innovations are often more complex than other innovations

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11 and therefore require more consumer learning. Also, technological innovations possess a greater risk factor, which can influence the adoption process.

2.2 Classification of technological innovations

Another important aspect of the concept of innovations is how the different types of

innovation should be classified. In the scientific literature, there has been inconsistency in the typology used to classify the different forms of innovations. Garcia and Calantone (2002) write in their critical study on innovation typology: “Academics generally believe that they

have begun to understand the process of developing innovations and it doesn’t matter what they call them; new innovations smell just as sweet by any other name. The innovation process has been identified for radical, incremental, really new, discontinuous, and imitative innovations, as well as for architectural, modular, improving, and evolutionary innovations”.

Based on their findings, Garcia and Calantone (2002) classified innovations into three

categories: radical innovations, really new innovations (RNPs) and incremental innovations. Radical innovations can be described as innovations that represent a new technology that results in a new market structure, for example the World Wide Web.

An incremental innovation involves the adaptation, refinement and enhancement of existing products and/or production and delivery systems. Incremental innovations are defined as products that possess new features, benefits, or improvements to already existing technology in a market that already exists, for example Apple’s MacBook Air. RNPs lie between radical and incremental innovations and are seen as moderately innovative products. They can evolve into new product lines (e.g., Sony Walkman, product line extensions with new technology (e.g., Canon Laserjet), or new markets with existing technology (e.g, early fax machines). RNPs contain the majority of the innovations and are frequently misclassified as radical innovations.

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12 The criteria used by Garcia and Calantone (2002) in order to rule out incontinences and provide unambiguous labels for the different innovation types are based on the difference on a macro versus micro level and the discontinuous arisen from marketing versus technology perspective. At the macro level, the concern is how the characteristics of the product

innovation are new to the world, market, or industry, where at the micro level the

innovativeness is identified as new to the firm or the customer. Each type of technological innovation corresponds differently with these four criteria. Radical innovations are

innovations that incorporate a new technology that creates a new market infrastructure and which cause discontinuities on both a macro and micro level when introduced to the market. Incremental innovations cause either marketing or technological discontinuities only at a micro level. And RNPs can cause marketing or technological discontinuities on a macro level, but on a micro level, any combination of marketing and/or technological discontinuity is possible.

Urban, Weingberg and Hauser (1996) state that especially RNPs have the power to shift market structures and represent new technologies. They often create great opportunities for companies to differentiate themselves and increase their competitive advantage over other firms (Song and Weiss, 1998). Compared with incremental innovations, which are built on existing products, RNPs possess greater benefits and create the opportunity for consumers to do things they have never have been able to do before (Alexander et al., 2008; Feiereisen, Wong & Broderick, 2008). However, RNPs are also associated with greater risks and uncertainties (Herzenstein, Posavac and Brakus (2007).

Hoefler (2003) identified three potential sources of error that consumers may face when estimating the personal usefulness or RNPs. First, consumers can be more uncertain when evaluating the usefulness of the new benefits of the product. Second, the realization of the new benefits may require changes in the current consumption behavior. And third,

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13 consumers are forced to translate claimed benefits into realized benefits with the lack of personal experience or unbiased sources of information.

Based on the significant advantages that come along with RNPs and the many factors that can increase the resistance offered towards these innovations. This study will focus on RNPs innovations, instead of radical innovations, which are far less common, or incremental innovations, which possess less great advantages.

2.4 Consumer innovation resistance

The way consumers can respond to an innovation can be in favor or against. Before the adoption procedure can be established and consumers can form a positive attitude toward an innovation, it is essential that the primary resistance offered by consumers has been conquered (Ram, 1987). Consumers’ resistance to innovations is a behavioral response, which is based on a conscious choice (Szmigin & Foxall, 1989). Taken from an emotional point of view, resistance is a protest against alteration. It is an aversive motivational status that is established while an individual perceives that his or her choice is vulnerable, and alters opinions and behaviors towards retrieval of that vulnerable choice (Mohtar and Abbas, 2012).

There are numerous definitions of innovation resistance in the existing literature. The most frequently used is the definition by Ram and Sheth (1989). They define innovation resistance as: ‘the resistance offered by consumers to an innovation, either because it poses

potential change from a satisfactory status quo or because it conflicts with their belief

structure’. Kleijnen et al., (2009) address the lack of consistent terminology applied in studies

on innovation resistance and argue that such a definition might be too broad as it defines the concept of innovation resistance simply as ‘resistance to innovation’. Also, such definition includes ‘not trying the innovation at al', which can be problematic because the initial objection towards an innovation can be overthrown by simply giving the consumers the

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14 opportunity to try the innovation for a certain period of time (Rogers, 2003). Scholars have tried to address such matters in subsequent studies, which has resulted in more comprehensive research on the concept of innovation resistance. In many studies, there has been made a distinction between active and passive resistance (Bagozzi and Lee, 1999; Mohtar and Abbas. 2012; Heidenreich and Spieth, 2013).

Active innovation resistance refers to negative attitude formation that follows new

product evaluation and is generally driven by the different attributes of an innovation.

Consumers evaluate the perceived innovation attributes of a new product and eventually form an attitude towards the innovation (Heidenrech and Spieth, 2013). Active resistance occurs when consumers decide to express their dissatisfaction with regard to the innovation and can be triggered by the perceived risk of an innovation (Ram and Sheth, 1989; Kleijnen et al., 2009)

Passive innovation resistance refers to the consumers’ predisposition to resist

innovations prior new product evaluation. It is the first response of consumers who are confronted with an innovation without considering any product-specific factors (Heidenrech and Spiet, 2013). Passive resistance often occurs due to inertia. For example, when men skincare products initially were introduced to the market, men failed to adopt the products simply because it was not a habit for men (Ram and Sheth, 1989). Other studies have elaborated the concept of innovation resistance by making a distinction between three different types of resistance: rejection, postponement and opposition (Szmigin and Foxall, 1989; Kleijnen et al., 2009)

Rejection is the ultimate form of resistance where a consumer decides to not adopt the

innovation. The decision to reject something requires often an active evaluation by the consumer, which eventually leads to the objection of the innovation (Szmigin and Foxall, 1989; Kleijnen et al., 2009; Cornescu and Adam, 2013).

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Postponement is the simplest form of resistance and it refers to the situation where an

individual delays the decision to adopt an innovation. Even when consumers find an innovation acceptable, they can still postpone the decision to adopt (Szmigin and Foxall, 1989; Kausma, Laukkanen and Hiltunen, 2007). Cornescu and Adam (2013) state that postponement is often caused by situational factors such as timing, acquiring necessary knowledge or ensuring that the product or innovation works effectively. After a period of time, the status of postponement can change and consumers can take the decision to accept or reject an innovation.

Opposition is the strongest form of resistance and is defined as ‘effective behaviors

actively directed in an opposing way to the introduction of innovations’ (Cornescu and Adam, 2013). Kleijnen et al. (2009) describe opposition as innovation sabotage, where consumers actively engage in creating and implementing strategies (e.g. negative word-of-mouth) in order to prevent an innovation to be successful. Opposition eventually always leads to rejection although the consumer may try out the innovation before making the decision to reject it (Szmigin and Foxall, 1989). This study will measure innovation resistance by focusing on the three various forms of resistance: postponement, rejection and opposition.

2.5 Drivers of innovation resistance

Sheth (1981) identified two psychological constructs, which are fundamental in the process of understanding the psychology of innovation resistance: 1) Habit toward an existing practice or behavior and 2) Perceived risk associated with innovation adoption. Habit toward existing behavior is the most influential determinant in generating resistance to change. It incorporates all the behavioral steps involved in the process of selecting, acquiring and actually adopting an innovation or existing alternative. According to Sheth (1981), human beings have the tendency to strive for consistency in their behavior rather than adjusting and searching for

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16 new behaviors. This means that when an innovation requires more change from the total stream of behavior, the resistance to change will be stronger because it demands to give up or adjust existing habits. The second determinant that plays a crucial role in the process of innovation resistance is the perception of risk associated with an innovation. The higher the perceived risk in the perspective of a consumer, the higher the resistance will be.

Following these psychological constructs, Ram (1987) and Ram and Sheth (1989) further developed the concept of innovation resistance by adding functional and psychological barriers to adoption The functional barriers relate to product usage patterns, product value and the risks associated with product usage, whereas psychological barriers include the tradition and image barriers.

While these conceptual models have been great tools for better understanding

innovation resistance and its drivers, there is still an insufficient amount of attention dedicated to the thorough conceptualization of consumer resistance based on empirical evidence.

Kleijnen et al. (2009) tried to address this issue by developing a new model based upon previous literature and through qualitative methods. In their study, innovation resistance is a hierarchical construct that can lead to the three forms of resistance that are earlier discussed: postponement, rejection and opposition. Kleinen et al. (2009) further identified two main groups of antecedents of innovation resistance: (1) the degree of change required; and (2) conflicts with the consumer’s prior belief structure and just like Ram and Sheth (1989), they broadly distinguish between functional and psychological barriers. In total, they identified four barriers to consumer resistance: (1) Risks, (2) traditions and norms, (3) usage patterns and (4) perceived image. Again the most common drivers of innovation resistance have been perceived risk and usage pattern. The model is presented in Figure (1).

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Figure 1: The resistance hierarchy. Reprinted from ‘’An exploration of consumer resistance to

innovation and its antecedents”, by Kleijnen, M., Lee, N., & Wetzels, M., 2009, Journal of Economic

Psychology, 30(3), 344-357.

2.6 Mental simulation

In the existing literature the terms, mental simulation, mental imagery and visualization have been used interchangeably to describe the same process. Taylor and Schneider (1989) defined mental simulation as the imitative representation of the functioning or process of some events or series of events. Through the cognitive construction of hypothetical scenarios such as the visualization of likely future events, fantasizing about less likely future events or

reconstructing past events, mental simulation has the ability to positively change attitudes, evaluations and actual behavior (Escales, 2004). Taylor et al. (1998) write that mental simulation in general, can be used as an effective method for problem solving because it matches the way social reality occurs. Also when people attempt to imagine a set of events in their minds in a concrete and specific way, it often makes those events seem more real and true. Mental simulation in general has been recognized as a well-established cognitive tool for influencing product evaluations and product adoption decisions. When consumers elaborate on product decisions, they automatically form visual images of product related behaviors and the consequences involved (Hoeffler, 2003).

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18 Previous literature has identified different forms of mental simulation, each with different effects on subsequent evaluations and behavior. A number of studies have made a distinction between self-relevant and other-relevant imagery, according to whether consumers imagine themselves or another person performing a certain task (Petrova, 2007). Other studies have made a distinction between process simulation and outcome simulation (Pham and Taylor 1999; Zhao, Hoeffler and Zauberman, 2011). When engaging in process-focused mental simulation, an individual emphasizes the actions that are necessary to achieve a goal by creating a step-by-step story or narrative. While outcome-focused mental simulation emphasizes the outcomes of the goal such as the positive benefits of consuming a certain product (Petrova, 2007). Zhao et al. (2009) argue that when it comes to using mental simulation as a tool to evaluate RNP, people seem to underestimate the usefulness of the radically new features. That is why they also made a distinction between memory-focused visualization and imagination-focused visualization.

Memory-focused visualization is when consumers are asked to “picture themselves

making use of the product”. According to Zhao et al. (2009), memory-focused visualization

can be an effective way to generate more stable preferences because it helps consumers to evaluate products, but it can also generate negative side effects. In situations where consumers simply have to picture themselves using a certain product, they will take the easiest route and place limitations on their imagery by visualizing more accessible usage situations and

outcomes. The reason here for is that humans tend to be “cognitive misers”, who are reluctant to participate in the extensive cognitive thinking that is often required for mental simulation (Zhao et al., 2009). This may lead to consumers underestimating the perceived value of the benefits provided by an RNP and highlighting negative learning cost inferences often associated with adopting an RNP. As a result, the overall evaluation of an RNP can be

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19 the study of Dahl, Chattopadhyay and Gorn (1999): imagination-focused visualization. Imagination-focused visualization is more oriented on an imaginative emphasis and enhances the focus of a consumer on new, not discovered and never before experienced outcomes of a product. According to Zhao et al. (2009), imaginative focus leads to an

increased perceived value of product benefits of an RNP by reducing memory fixation and by better highlighting the new opportunities. It also reduces the identification of the learning cost associated with the RNP, which eventually leads to a better overall evaluation of the RNP. Perceived benefits and perceived risk or cost involved in adopting an innovation have been both identified as important drivers in the innovation adoption process (Ellen, Bearden and Sharma, 1991). Based on the information above, this study will focus on imagination-focused visualization.

2.6.1 Imagination-focused visualization and innovation resistance

Individuals who engage in mental simulation engage in narrative processing. This process helps to transfer the direction away from critical thoughts and contributes to the creation of positive affect (Escales, 2004). The lack of critical thoughts and the presence of positive feelings toward an innovation can play an important role in the reduction of innovation

resistance. When consumers spend less time contemplating on the possible negative aspects of an innovation, they unconsciously create less negative feelings, which in turn can lead to the reduction of innovation resistance. Heidenreich and Kraemer (2014) confirmed that mental simulation was able to significantly reduce the negative effect of passive innovation resistance on new product adoption. Due to the cognitive constructions of hypothetical usage scenarios individuals are able to focus on the fact that the usage of an innovation is compatible with familiar practices. The reduction in perceived changes in behavior necessary to use a new product subsequently can lead to a decrease in the passive resistance offered by consumers.

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20 This is especially the case for innovations with high radicalness. If the product is perceived to be an incremental innovation, the effect of mental simulation can become insignificant. The study of Heidenreich and Kraemer (2014) was dedicated to passive innovation resistance. Nevertheless, it can be assumed that the results also apply to innovation resistance in general. Passive innovation resistance is part of innovation resistance and while the effects of mental simulation on innovation resistance might slightly vary in degree, there is no evidence in the previous literature suggesting that the results found for passive innovation resistance are not generalizable for the concept of innovation resistance and its different types. Also while most of the studies are dedicated to mental simulation in general. It is only natural to assume that these assumptions are applicable for imagination-focused visualization. This is because just like any other form of mental simulation, imagination-focused

visualization gives the consumer the ability to envision a set of events in their minds in a concrete way, but it adds an extra dimension that stimulates consumers to use their imagination to visualize different usage situations and benefits an innovation. This is especially important for RNPs, because these possess’ high risks and consumers are often unfamiliar of the potential value of the products. Accordingly, the following hypothesis is formulated:

H1: Imagination-focused visualization has a negative relationship with (a) postponement, (b) rejection and (c) opposition of RNPs.

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2.6.2 Imagination-focused visualization and perceived risk

Kleijnen et al. (2009) state that perceived risk plays a crucial role when it comes to decreasing consumer resistance toward innovative products. In their study, risk appeared to be the most common antecedent of innovation resistance. The resistance offered by consumers is often influenced by many uncertainties, which mainly are experienced by consumers’ inability to predict the performance of a product. Perceived risk is therefore a function of the unexpected outcomes of a new product adoption and the results that deviate from expectation that

consumers have (Hirunyawipada and Paswan, 2006).

In the previous literature, perceived risk has been seen as physical, social, economic or

functional uncertainty and the perceived side effects associated with certain innovations (Ram

and Sheth; 1989;Kuisma, Laukkanen and Hiltunen, 2007;Kleijnen et al., 2009 ). Physical risk is related to consumer perception of the potential damage that an innovation can cause to an individual or his or her property. Economic risk concerns consumer perception of how an innovation can result in a waste of economic resources. Functional risk is one of the fundamental types of risk when it comes to innovation resistance. It is related to the

uncertainty about the performance of an innovation and thus one of the main motives why a consumer can choose to reject the adoption of an innovation. The last type of risk is social

risk, which refers to the perception of a consumer that their social environment will stand

behind their choice of adoption or whether they will not support their decision. As the

different types of risk increase, diffusion rate and the adoption level decreases (Roger, 2003). Casnato et al. (2008) state that mental simulation has the ability to reduce different kind of uncertainties. For example, through the process of mental imagery consumers can reduce performance anxiety and bolster positive feelings towards an innovation. The uncertainties involved in the adoption of an innovation can shift from performance and symbolic uncertainties to switching costs and affective uncertainties, depending on the

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22 moment of adoption and the different kind of mental simulation. Feiereisen, Wong and Broderick (2008) write that visual mental simulation can also act as a surrogate for a product-in-use demonstration of innovations. This is especially the case for RNPs, which are not always available for demonstration. Through demonstration, mental simulation can enhance consumer understanding of innovations features and result in the reduction of perceived risk (Heiman and Muller, 1996). Based on this information, the following hypothesis is proposed:

H2: The negative relationship of imagination- focused visualization on (a) postponement, (b) rejection and (c) opposition of RNPs is mediated by perceived risk.

H2a: There is a negative relationship between imagination-focused visualization and perceived risk.

In the research of Kleijnen et al. (2009) perceived risk has been linked to all three types of innovation resistance. However, each type of risk had a different effect on the specific forms of resistance. Functional risk was an important antecedent of innovation rejection, together with economic risk and social risk. While physical risk was the main antecedent for

opposition and economic risk was a common antecedent to postponement. Measuring the effects of the different types of risk is beyond the scope of this study, that is why perceived risk is measured as a combination of all the different types of risk. Also, while the study of Kleijnen et al. (2009) was not specifically focused on RNPs, it can be concluded that these findings also apply to RNPs, since RNPs share the same comparisons as other technological innovations but score much higher on risk and uncertainty (Aggarwal, Cha and Wilemon, 1998). Accordingly, the following hypothesis is formulated:

H2b: Perceived risk has a positive relationship with (a) postponement, (b) rejection and (c) opposition of RNPs.

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2.6.3 Imagination-focused visualization and usage barrier.

The usage barrier is one of the most widely investigated and influential barriers to innovation resistance. It relates to the inconsistencies caused by an innovation, which are not in line with past experiences and threaten to disrupt established usage patterns (Talke and Heidenreich, 2013). Kuester et al. (2010) identified three types of usage barriers that have an effect on innovation adoption: usage difference, required behavioral change and the difficulty of use. To overcome usage barriers, consumers need to change their behavior and habits and learn how to make use of an innovation. The more different, or behavioral change required or complex an innovation is, the longer the process takes before an innovation is adopted (Ram and Sheth, 1989).

By employing mental simulation consumers can imitate a particular usage situation and gain knowledge that helps in understanding how to use a certain innovation (Feiereisen, Wong and Broderick, 2008). Shiv and Hubar (2000) write that the use of visual processing enables to simulate a product experience and to better understand the consequences of product usage. The perception of usage barriers depends on contextual factors such as product

newness (Kuester et al., 2010). This means that in the case of RNPs, consumers will face high usage barriers because they are not able to easily assess its compatibility towards habits or practices. Mental simulation is in such a situation the ideal tool to reduce usage barriers by giving consumers the ability to challenge their presumptions, which are often formed by unfamiliarity with an innovation and its possibilities. Kuester et al. (2010) confirm the

effectiveness of mental simulation on the usage barriers by stating that mental simulation was the only instrument capable of reducing usage barriers to RNPs. Again, the above-mentioned studies have focused on mental simulation in general or on process and outcome mental simulation. But it is safe to say that these effects also apply to imagination-focused visualization. Zhao et al. (2009) wrote in their study that when consumers employ

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24 imagination-focused visualization they envision more new usages of the product. This leads to opening up new perspectives, which help to overcome the constraints of any current usage and unleashes participant’s imagery in a way that they have never experienced before. In line with the theory, the next hypothesis is formed:

H3: The negative relationship of Imagination-focused visualization on (a) postponement, (b) rejection and (c) opposition of RNPs is mediated by usage barrier.

H3a: Imagination-focused visualization has a negative relationship with the usage barrier.

Several previous studies on technological innovations have proven the importance of usage barriers on innovation adoption and resistance. Kuisma et al. (2007) found in their study on Internet banking, that many consumers who used traditional ATM for their payments faced usage barriers, which caused for them to perceive Internet banking as unsuitable for them. The usage barrier was a consequence of concrete and functional elements of Internet channel such as the lack of internet connection, official receipt, barcode reader etc.

In the study of Kleijnen et al. (2009) conflict with existing usage patterns was the primary driver of postponement and was positively linked to rejection. The usage barrier played a minor role in opposition. Nevertheless, one can argue that with the presence of extreme usage barriers and certain levels of opposition might be formed. Again it is safe to say that these results also apply to RNPs. Consumers often don’t have any or minor experience with RNPs, that means when adopting an RNP established usage patterns are disrupted and behavioral change is required in order for consumers to be able to use the product.

In line with the theory the following hypothesis is proposed:

H3b: Usage barrier has a positive relationship with (a) postponement and (b) rejection of RNPs.

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2.7 Conceptual model

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3. Research method

3.1 Research design

This study was explanatory in nature and attempted to answer the research question by conducting an online experiment. The main advantage of an experiment is that it has the ability to test for effects and causal explanation in a controlled context. This often leads to a higher internal validity. The experiment was (Imagination mental simulation vs. no mental simulation) between-subject factorial design. In order to collect the data, this study used a cross-sectional survey approach. In total six main variables were identified: imagination-focused visualization, perceived risk, usage barrier and the innovation resistance variables: postponement, rejection and opposition.

3.2 Sample

According to Rogers (2003) every member of the society can be fitted into one of the

innovator’s categories: early adopters, early majority, late majority or laggards, depending on the degree to which an individual is relatively earlier in adopting new ideas than the other members of a system. Based on this information, it can be concluded that when researching innovative products, every consumer is a feasible target as long as controlled for the different way they react to innovations. The target population of this study consisted out of Dutch consumers with the age eighteen and up. The minimum age of eighteen is chosen in order to make the settings more realistic since people younger than eighteen are not often confronted with the purchase of RNPs.

This study used non-probability convenience sampling in order to retrieve the participants for the experiment. Convenience sampling does have disadvantages since it is prone to bias and influences that are beyond control because the cases in the sample appear only due to the fact that they are easy to obtain. However, due to budget and time constraints,

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27 convenience sampling still is a viable method since it provides the opportunity for the

generalization of the population. In order to have an adequate amount of respondents for the results to be analyzable, this study strived to have a minimum of 100 respondents and a maximum of 200 respondents.

3.3 Pre-test

The selection of the RNP stimuli for the experiment was done through a pre-test.

First, twelve technological innovations that could be considered as RNPs were selected. These products were chosen after a thorough search on the Internet and through an evaluation

process based upon the definition of RNPs by Hoeffler (2003) and the micro/macro

discontinuity criteria set by Garcia and Calantone (2002). Only those technological products have been selected that can be categorized as consumer goods and which will appeal to a broad consumer segment. Products such as functional products for niche markets or products for businesses have been excluded since they would not apply to a large consumer group and thus were not relevant for this study.

Each of the twelve products was presented to the participants by a picture of the product itself and a picture of the product in a usage situation together with a brief product description such as smartphone projector or portable washing machine. After each picture the respondents had to answer three questions, assessing the familiarity of the respondent with the product, the product innovativeness and product newness. In the existing literature these three constructs have been used interchangeably to qualify an innovation as an RNP (Garcia and Calantone, 2002). Familiarity was measured by asking a (1) yes or (2) no question. Product newness and product innovativeness were measured by two items according to a seven-point Likert scale, ranging from totally disagree (1) to totally agree (7). The questionnaire for the pre-test is presented in appendix 2.

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28 The questionnaire was distributed via WhatsApp and email to close friends and relatives. The people contacted for the pre-test were excluded from the real experiment in order to prevent any biases. In total 30 respondents participated in the pre-test. The results are presented in table 1.

Table 1: Consumers familiarity, perceived product newness and product innovativeness (means)

Product Familiarity Product newness Product innovativeness

Mean Mean Mean

1 Foldable TV 1.70 5.30 5.60 2 Fitring 1.93 5.80 5.57 3 Earplugs 1.67 5.40 5.20 4 Smartphone bracelet 1.90 5.73 5.60 5 Portable translator 1.63 5.60 5.33 6 VR Headset 1.00 5.27 4.87 7 Laser keyboard 1.33 5.23 5.00 8 Wireless charger 1.07 4.97 4.30 9 Tanning printer 1.93 5.77 5.40 10 Wireless earplugs 1.07 4.47 3.90 11 Portable washingm. 1.97 5.07 4.93 12 3D Camera 1.30 5.40 4.63

3.4 Really new product stimuli

The product that eventually was chosen based on the total scores from the pre-test is the concept phone, Nokia fit ring created by Issam Trabelsi (Technoworm, 2015). The Nokia fit ring is a lightweight Bluetooth enabled phone concept that allows consumers to use the basic

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29 functions of a phone. The Fitring is made from soft silicon and flexible rubber, which provide nonslip features and make the product waterproof so it can be used in all situations, even in a swimming pool or on the beach. The ring gives consumers the ability to answer calls, see notifications for incoming calls or unread text messages.

The Fitring scored very high on familiarity (M = 1.93) (SD = 0.25), which means that almost none of the respondents were familiar with the product. The Fitring was also rated high on product newness (M=5.80) (SD=1.40) and product innovativeness (M = 5.57) (SD = 1.41). This is an indication that the participant’s rate the product as new compared with other

products available on the market and believe that it is an innovative product.

The Fitring also complies with the definition of Hoeffler (2003) and criteria of Garcia and Calantone (2002). The product innovation is new to the industry, as well as new to the consumers since it provides the ability to make phone calls in a way that has never been able before. Also adopting the product requires changes in behavior since consumers have to wear a ring to answer their phone.

3.5 Procedure

The experiment and the questionnaire were designed and conducted via the Internet by using the online survey software Qualtrics. The experiment has been available online from

14/12/2016 until 29/12/2016. By using the Internet to distribute the experiment many

participants could be reached in a relatively short time period. The link to the experiment has been shared through various media. Friends, family and relatives have been contacted via Facebook, email and Whatsapp, with a personal request if they would be so kind to participate in the experiment and share the link with their own friends and relatives.

By clicking on the link the respondents would be routed to the start page of the experiment where they could read a short introduction and find further instructions. The start

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30 page also emphasized the anonymity and confidentiality of the answers given by the

respondents and also indicated the duration of the experiment. The experiment has been developed in the Dutch language to avoid language barriers. Existing measures that originally have been written in English, have been translated into Dutch and back translated into English in order to avoid misinterpretations and increase understandability of the questions and

instructions.

By clicking continue, the respondents could start with the experiment. The allocation of participants to the different groups is done through randomization to ensure an equivalent spread of attributes across both groups. Respondents in the two groups received the same visual images of the Fitring with a short product description. In order to prevent prior brand knowledge and reduce external influence to the dependent variables, the brand Nokia has been removed from the images to isolate the effect of mental simulation.

After being exposed to the pictures and the product description, the control group directly received questions that measured the usage barrier, perceived risk, consumer innovativeness and resistance. The experimental group was asked to read the visualization instructions carefully and execute the steps described before answering any questions. A timer was included in this section in order to prevent participants to continue with answering

questions without carefully reading the instructions or executing the visualization task. The timer enabled the participants only to continue after a period of 30 seconds. The reason why there is chosen for a minimum of 30 seconds is too prevent the visualization task period to become too long and avoid irritations amongst the participants, which could result in stopping half way through the experiment. Also, a minimum of 30 seconds is long enough in order for the participants to visualize a few new activities with one single product.

On the next page, the participants of the experimental group were asked to write down a minimum of two new activities where they just thought off. This section was included in

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31 order to again stimulate the participants to actually execute the visualization task and as a seriousness check to verify if respondents thought of new activities. By entering two activities the participants could continue with answering the questions that measured the different variables. At the last section of the questionnaire, all the respondents were asked if they were familiar with the Fitring. Those respondents with a familiarity with the product were deleted in order to eliminate any biases in the analysis. After answering several demographic

questions, the respondents arrived at the end of the survey where they were thanked for their participation and saw further contact details in case they had any comments or questions. The results were analyzed by using the statistics program SPSS.

3.6 Mental simulation instructions

The participants in the experimental group received instructions in order to elicit imagination- focused visualization. These instructions have been based on the study of Zhao et al. (2009), where they simply asked the participants to unleash their imagination and visualize new activities. Such instructions have been shown to motivate imagination-focused visualization and limit misinterpretation due to the simplicity of the task and the language used.

The participant received the following instructions: “When thinking about whether to

buy new products, many consumers find that using imagination to form visual images (pictures in the mind) of potential uses of the product can help them evaluate it.

Unleashing your imagination and visualizing new activities that you have never been able to do before may help you evaluate the Fit Ring. Please push yourself to visualize these new activities (i.e., think about new ways you will use this product) as you evaluate the Fit Ring.

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3.7 Measurement of variables

In order to maximize the reliability and positively stimulate the internal validity, the measures for this research have been borrowed from previous studies. Some of the measures were adjusted to suit the topic of this study.

Translation, back-translation procedure

All the items used in this research have been derived from English studies. In order to increase the understandability of the questions and decrease any language biases, the original items have been translated into Dutch. To make sure the content of the items was not affected; the Dutch items were translated back into English by a third person with sufficient knowledge of both the Dutch as the English language.

Innovation resistance

Innovation resistance is measured by the three variables: postponement, rejection and opposition. Each element will be measured by a validated seven-point Likert scale ranging from totally disagree (1) to totally agree (7). This scale is taken from the study of Van der Heijden (2006). Postponement is measured by four items (a = 0.94), rejection is measured by three items (a = 0.71) and opposition is measured by four items (a = 0.89). Examples of the items are “I will probably not use this product” (rejection) and “I think it is a bad thing that

this product is being sold and I’m willing to do something about it” (opposition).

Usage barrier

To measure the usage barrier the scale of Laukkanen et al. (2009) (Cronbach’s a = 0.94) has been used. The scale consists of five items and assesses the usage barriers that consumers face

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33 when confronted with the adoption of new innovations. The original study of Laukkanen et al. (2009) was dedicated to resistance towards Internet banking. Therefore, the questions were adjusted to fit the RNP used in this study. An example item is “in my opinion, this product is

easy to use”.

Perceived risk

The items that are included in this study for measuring perceived risk are financial, functional, social risk and physical risk. The existing scales of Mieres, Martín and Gutiérrez (2006a) have been used in order to measure financial and social risk. For each of the items, a seven-point bipolar scale ranging from “strongly agree” to “strongly disagree’ has been used. Financial risk was measured by three items (a = 0.89) and social risk was measured by four items (a = 0.94). To measure functional risk the scale of Dholakia (2000) was used, consisting out of three items (a = 0.79). Physical risk was measured by the scale of Yoo and Kim (2012), including three items (a = 0.91). Both functional risk and physical risk were measured by a 7-point Likert-type scale, ranging from strongly disagree (1) to strongly agree (7).

Examples of the items are “When buying this product, I would worry about how reliable the

product will be” (functional) “I am afraid that this product may not be safe for me” (physical).

Control variables

In the last section of the survey, the participants were asked about their age, gender, income, and education. These are included in the survey to describe the characteristics of the study and control for their influence on the other variables. Another control variable that was included in this survey has been consumer innovativeness. Innovativeness is defined by Rogers (2003) as the degree to which an individual is willing to adopt new ideas in comparison to others and

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34 has been often been related to the adoption process of new products and innovation resistance (Manning, Bearden and Madden, 1995; Heidenreich, 2013). Jin (2016) writes innovativeness is a crucial factor in new product adoption behaviors because it influences the adoption itself and the rate of adoption. Also, significant evidence has been found for the interaction between mental simulation and the degree of innovativeness. Consumer innovativeness is measured by six-item construct based on the consumer novelty-seeking scale of Fort-Rioche and

Ackermann (2013) (a = 0.94). For each of the items, a seven-point bipolar scale has been used, ranging from “strongly agree” to “strongly disagree’. Examples of the items are “I am

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4. Results

4.1 Descriptive data of sample

In total 187 subjects participated in the online experiment. From the 187 respondents that started with the experiment, 155 completed the questionnaire (completion rate = 83%). There were seven missing values discovered during the examination of the data. The missing values were all from respondents that failed to complete the questionnaire. These cases have been removed after careful consideration. No single missing values were detected, which can be explained by having a relatively short survey with forced answers. In the experimental group, respondents were asked to write down a minimum of two new activities they could think of after the mental simulation instructions. There was one case where the participant had answered with None, None. After a thorough examination of the answers of the following participant, the decision had been made to exclude this case from this study because the respondent had answered with the same response to most of the questions. In total three participants had answered that they were already familiar with the Fitring, in order to prevent any biases these three cases were also excluded from the analysis. This resulted in 144 valid questionnaires.

The participants were almost equally assigned to the two experimental conditions. 71 participants were assigned to the experimental group and 73 participants were assigned to the control group. Among the respondents 81 (56%) of were males and 63 (44%) were females. The highest percentage of the respondents (55%) was between the 25-34 years old. The majority of the respondents had a higher educational background. 77 people (54%) had an HBO (higher professional education) and 41 people (29%) had a WO (scientific education). The distribution among the yearly income levels were as following: 19% had a yearly income of €10.000 or less, 26% had an income between the €10.000 and 25.000, 25% had an income

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36 between the €26.000 and the €40.000, 9% had an income between €41.000 and €50.000 and 21% had an income of €50.000 or more.

4.2 Normality Check

Normality checks have been performed by inspecting the data for skewness and kurtosis. The empirical criteria set for the acceptable range of values differs amongst researchers. While some consider values between -2 and +2 as acceptable in order to prove normal distribution, more conservative researchers accept a range from -1 to +1 (George and Mallery, 2010). Only opposition showed signs of skewness, exceeding the empirical criteria for acceptance with a value of 1.012. After inspection of the q-q plots and the shapes of the histograms, the decision has been made for no further actions. This decision is based on the study of Ghasemi and Zahediasl (2012), who wrote that with large enough sample sizes (>30 or 40) the

violation of normality assumption should not cause major problems. This is confirmed by Field (2009), who writes that with large samples the skew and kurtosis are likely to be significant even though the skew and kurtosis are not necessarily too different from a normal distribution.

4.3 Control variables

In order to check for any biases in the allocation of the participants in the experimental and control group, a few preliminary analyses were conducted. First of all, the allocation of males and females in the two groups was checked by conducting a Chi-square test of independence. The results showed that there was no significant difference in the number of males and females in the two conditions χ² (1) = 0.099, p = .753. Second, the difference in the age of the participants in the two groups was checked by performing a one-way ANOVA. There was no significant difference in age between the two groups F (1, 142) = .482, p = .49, meaning that the age distribution is similar in the groups that are being compared. Three more one-way

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37 ANOVAS were conducted to check the distribution of education, income and consumer innovativeness between the two groups. No significant differences were found between the two conditions for education F (1, 142) = .724, p = .40, income F (1, 142) = .002, p = .97 and consumer innovativeness F (1, 142) = .508, p = .48. Based upon these results in can be concluded that the randomization among the participants was successfully accomplished. In addition, the effect of the control variables on the depended variables was checked. The control variables didn’t have a significant effect on the dependent variables and therefore these were left out of the study since no confounding effects were discovered.

4.4 Reliability Analysis

In order to investigate the internal consistency of the measurement scales, reliability analysis has been performed including all the items of the variables. Table 2 shows the Cronbach’s Alpha for each variable. All the Cronbach’s alphas of the variables were above the minimum requirement of α = 0,70. This is an indication that the reliability is acceptable

Also, the Cronbach’s alpha if item deleted and the Corrected item-Total correlation have been examined (all above .30). None of the original items have been deleted since this would not result in a marginal increase in the Cronbach’s alpha.

The Cronbach’s alphas of the scales used in this research were originally higher in the existing studies, where they have been taken from. A reason for the decrease can be the intermixing of the questions when designing the survey. In the previous studies the questions that measure a single construct have often been grouped. In this study, there has been chosen to intermix the questions randomly in order to increase the reliability. Goodhue and Loiacono (2002) state that the intermixing of questions in a questionnaire can indeed result in a lower Cronbach’s alpha but at the same time create measures with higher actual reliability.

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Tabel 2: Scales, items per scale, Cronbach’s alphas, means and standard deviations.

4.5 Correlation matrix

Table 3 presents the correlations between the various variables used in this study. In order to determine the correlations between the variables, Pearson’s correlation coefficients were examined. The Pearson’s coefficients can result in a value between the -1 and 1, indicating a positive or a negative relationship between the two variables. Values above the .30 are an indication of a medium effect, while values above the 0.50 indicate a strong relation (Field, 2009).

The analysis presents several significant correlations between certain dependent, mediating and control variables. The dependent variable rejection was found to have a weak negative relationship with age (r = .-249, p < .01). This indicates that the older the

participants were, the less rejection they showed towards the product. Postponement was found to have a moderate negative relation with income (r = .-304, p < .01), suggesting that the higher the income of a respondent, the less they postponed the decision to adopt the presented innovation. The mediating variable perceived risk seemed to have a somehow strong significant positive relation with rejection (r = .448, p < .01) and a significant weak relationship with postponement (r = 268, p < .01) and opposition (r = 265, p < .01). This means that the riskier the product is perceived to be, the more tendency the respondents will

Variable Cronbach’s Alpha Items M SD

Rejection 0.76 3 4.15 1.40 Postponement 0.71 4 3.98 1.28 Opposition 0.73 4 0 1 Perceived Risk 0.77 12 0 1 Usage Barrier 0.71 3 4.6 1.08 Consumer innovativeness 0.88 6 4.4 1.26

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39 have towards rejecting the innovation, postponing the purchase decision, or showing signs of opposition. Moreover, there has been a moderate positive relationship found between

perceived risk and usage barrier (r = 380, p < .01). Hence, the more complex the perception of the usage of an innovation, the more the participants will perceive the product itself to be risky. Additionally, weak significant correlations exist for the moderating variable consumer innovativeness and gender (r = -221, p < .01), income (r = 179, p < .01) and perceived risk (r = 206, p < .01). This is an indication that high consumer innovativeness is more related with males than females. Also, the higher the participants scored on consumer innovativeness the higher their income was but at the same time the riskier they perceived the product to be.

Tabel 3: Means, Standard Deviations and Correlations.

**. Correlation is significant at the 0.01 level (2-tailed) *. Correlation is significant at the 0.05 level (2-tailed)

M SD 1 2 3 4 5 6 7 8 9 10 11 1. Gender 1.44 0.49 - 2. Age 3.38 1.11 -.249** - 3. Education level 4.95 1.07 -.039 -156 - 4. Income 2.87 1.40 -.310** .486** .052 - 5. Rejection 4.15 1.40 -.108 -249** .117 -.139 (.76) 6. Postponement 3.98 1.28 .159 -.030 .003 -.304** .-.063 (.71) 7. Opposition 0.00 1 -.061 -.026 -.061 -.015 .185** -.046 (.75) 8. Usage barrier 4.6 1.08 .078 .089 -.067 .156 -.194* -.078 .112 (.77) 9. Perceived Risk 0.00 1 .000 -.213* .240** -.227** .488** .268** -.265** .380** (.71) 10. Consumer Inn. 4.37 1.26 -.221** -.010 .139 .179* -.121 -.155 -.021 -.134 .206* (.88) 11 Mentalsim. .49 .502 .026 -.058 .071 .004 -.135 -.010 -.135 .044 -.120 -.060 -

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4.6 Hypothesis testing

In order to test the proposed model and hypothesis, the PROCESS macro for SPSS written by Andrew F. Hayes has been used. PROCESS uses an ordinary least squares or logistic

regression-based path analytical framework in order to estimate direct and indirect effects in simple and multiple mediator models. Bootstrap methods are implemented in PROCESS for inference about indirect effects. The bootstrapping analysis requests a bootstrap confidence interval for the indirect effect using 5000 bootstrap samples and a bias corrected 95% confidence interval (Preacher and Hayes, 2008).

Model 6 was selected in PROCESS in order to perform a multiple mediation analysis. This model tested a multiple mediation model with imagination-focused visualization as an independent variable (x), postponement, rejection and opposition as dependent variables (Y) and usage barrier and perceived risk as mediators (M). Since PROCESS only allows to test models with one dependent variable at the time, three different multiple mediation analyses were performed to test the effects on the three dependent variables postponement, rejection and opposition. Figure 3 illustrates the mediation model and the different paths.

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41 Hypothesis 1 proposed that imagination-focused visualization has a direct negative effect on (a) postponement, (b) rejection and (c) opposition of RNPs. The results showed no significant effect for path c’ in the different models, (a) postponement c’ = .058, t (140) = .2768, p = > .05, (b) rejection c’ = .229, t (140) = 1.082, p = > .05 and (c) opposition c’ = -.209, t (140) = -.1.286, p = > .05. Hence, H1 was rejected.

Hypothesis 2 proposed that the negative relationship of imagination-focused visualization on (a) postponement, (b) rejection and (c) opposition of RNPs is mediated by perceived risk. This is the first indirect effect and it indicates that the people that encaged in with imagination-focused visualization, experience significant decrease in perceived risk, which is further associated with decrease in postponement, rejection and opposition,

independently of the usage barrier. This indirect effect was found to be non-significant in all three models because the bootstrap confidence interval was not entirely above zero, (a) postponement, b = -.086, 95 % CI [-.24, .03], (b) rejection b = -.143, 95 % CI [-.36, .05] and (c) opposition b = -.049, 95 % CI [-161, .010]. Further examination of the results showed an absent relationship between with imagination-focused visualization and perceived risk as proposed by hypothesis 2a in all three models. Therefore, also H2a was rejected. Hypothesis 2b proposed that perceived risk has a positive relationship with (a) postponement, (b)

rejection and (c) opposition of RNPs. This was the only hypothesis that was found to be significant in all three models, meaning that H2b could be accepted, (a) postponement b1 = .358, p = < . 05, (b) rejection b1 = 594 = p < .05 and (c) opposition b1 = .202 = p < . 05. Hypothesis 3 proposed that the negative relationship of imagination-focused visualization on (a) postponement, (b) rejection and (c) opposition of RNPs is mediated by usage barrier. This is the second indirect effect and it suggests that people that engage in with imagination-focused visualization, perceive less usage barrier, which is also related to lower levels of postponement, rejection and opposition. The inspection of the bootstrap analysis

Mental simulation and innovation resistance : the effect of imagination-focused visualization on innovation resistance towards Really New Products

Master Thesis