Passive innovation resistance: The curse of innovation? Investigating consequences for innovative consumer behavior

Accepted Manuscript Passive Innovation Resistance: The Curse of Innovation? Investigating Consequences for Innovative Consumer Behavior Sven Heidenreich, Tobias Kraemer PII: DOI: Reference:

S0167-4870(15)00117-8 http://dx.doi.org/10.1016/j.joep.2015.09.003 JOEP 1857

To appear in:

Journal of Economic Psychology

Received Date: Revised Date: Accepted Date:

5 May 2014 18 August 2015 10 September 2015

Please cite this article as: Heidenreich, S., Kraemer, T., Passive Innovation Resistance: The Curse of Innovation? Investigating Consequences for Innovative Consumer Behavior, Journal of Economic Psychology (2015), doi: http:// dx.doi.org/10.1016/j.joep.2015.09.003

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Passive Innovation Resistance: The Curse of Innovation? Investigating Consequences for Innovative Consumer Behavior

Sven Heidenreicha*, Tobias Kraemerb

a

Faculty of Law and Economics, Saarland University, P.O. 15 11 50, 66041 Saarbruecken, Germany

b

Institute for Management, University of Koblenz-Landau, Universitätsstraße 1, 56070 Koblenz, Germany

*Corresponding Author (Correspondence to: [email protected]; Faculty of Law and Economics, Saarland University, P.O. 15 11 50, 66041 Saarbruecken, Germany, Phone: +49 (0) 681 / 302 – 71480)

1

Passive Innovation Resistance: The Curse of Innovation? Investigating Consequences for Innovative Consumer Behavior

ABSTRACT Empirical research reveals that many new products fail as a result of consumers’ passive resistance to innovation. Moreover, extant research suggests that high levels of stimulation induced by radical innovations even enhance negative effects of passive innovation resistance. However, empirical evidence for these propositions is still missing. Consequently, this study strives to enhance the current understanding (1) by investigating the inhibitory role of passive innovation resistance for different kinds of innovative consumer behaviors and (2) by examining the moderating role of perceived stimulation for effects of passive innovation resistance. Based on a large-scale empirical study (n=681), we provide first empirical evidence that passive innovation resistance inhibits both consumers’ tendencies to engage in innovative behavior and actual new product adoption. Furthermore, the results confirm that perceived stimulation increases the negative effects of passive innovation resistance. Our findings contribute to the ongoing discussion on a possible pro-change bias in adoption literature and to the current understanding on how to develop and market innovations to reach market success. Keywords: Passive innovation resistance, actualized innovativeness, hedonist innovativeness, social innovativeness

2

1.

Introduction

The continuous development and launch of new products are two major factors of companies’ longterm success (Prins & Verhoef, 2007). Consequently, companies must develop a constant stream of new products and successfully introduce these products to the market (Hess, 2009). However, the rate of innovations that are successfully introduced to the market is strikingly low and shows no sign of improvement (Andrew & Sirkin, 2003; Gourville, 2006). Recent studies propose that failure rates of innovations are approximately 40% to 55% (Castellion & Markham, 2013). Failed innovations lead to a negative return on investment and as a consequence might even expose a threat to the competitiveness of firms in the long run (Bayus, Erickson & Jacobson, 2003). Thus, consumers’ adoption behavior is a serious concern for firms because many new products fail due to consumers’ resistance to innovation (Hess, 2009; Heidenreich & Spieth, 2013). The relevance of innovation resistance has been acknowledged by both scientific research (Ellen, Bearden & Sharma, 1991; Laukkanen, Sinkkonen & Laukkanen, 2008; Ram, 1989; Reinders, 2010; Sheth, 1981; Heidenreich & Handrich, 2014) and management practice (Garcia, Bardhi & Friedrich, 2007; Gourville, 2006). Thereby, past research principally differentiates between (1) active innovation resistance which represents a negative attitude towards a new product that is caused by psychological and functional barriers during the evaluation of new products and (2) passive innovation resistance which refers to a predisposition to resist innovations due to an individual’s inclination to resist change and status quo satisfaction that already forms rather unconsciously prior to new product evaluation (Heidenreich & Handrich, 2014). Yet, scant research has investigated the toot causes and consequences of innovation resistance (Heidenreich & Spieth, 2013; Kleijnen, Lee & Wetzels, 2009; Laukkanen et al., 2008). Instead, past research on adoption behavior has focused on factors that foster the adoption and diffusion of innovations (e.g., Im, Bayus & Mason, 2003; Rogers, 2003) and largely neglected to examine factors that inhibit new product adoption and diffusion (e.g., Ellen et al., 1991; Heidenreich & Spieth, 2013; Nabih, Bloem, & Poiesz, 1997). As a consequence, only few studies have examined innovation-specific barriers to the adoption of new products and thus explicitly focused on

3

active innovation resistance as negative attitude formation that is driven by product-specific factors (e.g., Ellen et al., 1991; Kleijnen et al., 2009; Kuisma et al., 2007; Laukkanen et al., 2008). However, research that investigates consumers’ generic predisposition to resist innovations and thus passive innovation resistance remains largely neglected (e.g., Nabih, Bloem, & Poiesz, 1997; Talke & Heidenreich, 2014). Although past research on adoption behavior has at least implicitly recognized the role of passive innovation resistance as an important inhibitor of innovative consumer behavior, it still lacks empirical evidence for this proposition (Heidenreich & Spieth, 2013). Only two studies, one by Heidenreich & Spieth (2013) and another by Heidenreich & Handrich (2014) provide first empirical evidence that passive innovation resistance negatively affects new product evaluation and adoption. Yet, a better understanding of whether and how passive innovation resistance inhibits innovative consumer behavior might contribute to the ongoing discussion on a possible pro-change bias in adoption literature (Laukkanen & Kiviniemi, 2010; Rogers, 2003; Talke & Heidenreich, 2014). An empirical validation of passive innovation resistance as important inhibitor would imply that consumers with a high resistance to change disposition and/or a high satisfaction with the status quo might not always be open to innovations. If this is the case, some of the earlier empirical studies on consumer adoption behavior would have been indeed subject to pro-change bias, as these studies made the (biased) assumption that consumers are principally open to new products and willing to evaluate and adopt innovations. However, a thorough empirical validation of the inhibitory role of passive innovation resistance, accounting for possible effects on different types of innovative consumer behavior and across different research settings, is still lacking (Heidenreich & Handrich, 2014). Thus, debate remains about whether passive innovation resistance represents a neglected but important predisposition in innovation adoption research and whether previous adoption research was prone to pro-change bias. Furthermore, past research has shown that a behavioral response is not only influenced by a consumer’s optimal level of stimulation, which results from the degree of passive innovation resistance (Talke & Heidenreich, 2014), but also by the level of stimulation experienced by a consumer when exposed to the stimulus object itself (Wahlers & Etzel, 1985). Consequently, past

4

research suggests that the high amount of stimulation induced by a radical innovation might evoke high levels of passive innovation resistance, whereas low levels of stimulation induced by incremental innovations provoke much less passive innovation resistance (Heidenreich & Handrich, 2014; Heidenreich & Kraemer, 2015). Hence, the negative effect of passive innovation resistance on innovative consumer behaviour might be responsive to the amount of perceived stimulation experienced at the time of the exposure. In order to examine the inhibitory role of passive innovation resistance, it thus seems necessary to assess both the direct effect of passive innovation resistance and the moderating effect of perceived stimulation. Insights into this interaction would contribute to the current understanding on how to design optimal innovations that maximize market success. An empirical validation of the moderating role of perceived stimulation would suggest that maximizing product innovativeness might not be the most effective way to reach market success, as some consumers with high passive innovation resistance might react negatively to the amount of perceived stimulation induced by high product innovativeness. Yet, empirical proof on whether and how passive innovation resistance and perceived stimulation interact is also lacking. Consequently, we aim to enhance the understanding of the inhibitory role of passive innovation resistance for different types of innovative consumer behavior and its interaction with perceived stimulation. First, we develop the conceptual framework of this research and present relating hypotheses. Second, we empirically examine the effects of passive innovation resistance and perceived stimulation on innovative consumer behaviour. Thereby, we analyze both effects on innovative consumer behavior across several technological products as well as within specific new product evaluations. Finally, we outline theoretical and managerial implications of our results and present some avenues for further research.

5

2.

Conceptual Development and Research Model

2.1

Passive Innovation Resistance

According to a systematic literature review of Heidenreich & Handrich (2014), previous literature differentiates two types of consumers’ resistance to innovation: active and passive innovation resistance. Active innovation resistance represents a negative attitude formation driven by innovationspecific factors that evolves during new product evaluation (e.g., Kuisma et al., 2007; Laukkanen et al., 2009; Nabih et al., 1997). When evaluating new products, consumers form their attitude toward an innovation based on its attributes (Talke & Heidenreich, 2014). In case some of the perceived innovation attributes are not in line with their expectations, functional and psychological barriers arise (Heidenreich & Spieth, 2013; Ram & Sheth, 1989). As soon as these barriers exceed an adopterspecific tolerance level, consumers shape a negative attitude toward the new product which consequently results in active innovation resistance (Talke & Heidenreich, 2014). As a consequence, new products and services that encounter active innovation resistance most probably get rejected (Heidenreich & Spieth, 2013; Talke & Heidenreich, 2014). In contrast to active innovation resistance, which is caused by innovation-specific factors after a deliberate evaluation of the new product, passive innovation resistance refers to a generic predisposition to resist innovations, which already evolves rather unconsciously prior to new product evaluation (Talke & Heidenreich, 2014). Consumers generally strive to preserve the status quo (Zaltman & Wallendorf, 1983). Any innovation that is perceived as different or completely new will impose change, endanger consumers’ status quo and as a result provoke initial resistance (Laukkanen et al., 2008; Ram, 1987). Accordingly, passive innovation resistance represents the initial response of a consumer to the changes imposed by a new product, without having considered its characteritics. This means that instead of innovation attributes, passive innovation resistance is driven by the perceived degree of change or discontinuity that is connected to the adoption of the innovation (Heidenreich & Kraemer, 2015; Nabih et al., 1997). Consequently, a radical innovation that is perceived as completely different or new by the consumer might evoke higher levels of passive

6

innovation resistance than an incremental innovation that is perceived as quite similar to existing products or services (Heidenreich & Handrich, 2014; Laukkanen et al., 2009). Past research provides strong evidence that passive innovation resistance is primarily determined by consumers’ (1) inclination to resist change or (2) status quo satisfaction, or (3) both in combination (Heidenreich & Handrich, 2014). According to previous research, passive innovation resistance akin to personality traits like innate innovativeness as this predisposition is partly driven by adopter-specific factors that establish an individual’s resistance to change disposition (Heidenreich & Kraemer, 2015). Yet, passive innovation resistance also includes situation-specific factors that determine an individual’s satisfaction with the status quo. As a result, passive innovation resistance seems to be the more encompassing construct in comparison to innovation related personality traits. It represents a general disposition to act in a consistent way when confronted with innovations rather than an innate personality trait that more or less consistently leads to certain behaviors. Based on this conceptualization, types of passive innovation resistance are commonly categorized by the main root cause (Talke & Heidenreich, 2014). “Ccognitive passive resistance” is primarily determined by consumers’ inclination to resist change, whereas “situational passive resistance” is primarily driven by consumers’ status quo satisfaction. Both forms can interact in their creation of passive innovation resistance. Consumers highly inclined to resist change find it difficult to disrupt their established routines, are emotionally stressed when confronted with change, and have difficulties in cognitively changing their minds (Heidenreich & Speith, 2013; Oreg, 2003). Being perceived as different or new, innovations impose change on the consumer and thus provoke cognitive passive resistance for these types of consumers, disrupting innovative consumer behavior and inhibiting innovation adoption (Talke & Heidenreich, 2014). In addition, consumers with a desire for status quo satisfaction are highly satisfied with their tried and proven products and feel no further need for innovation such that situational passive resistance arises (Dethloff, 2004; Ram, 1987; Talke & Heidenreich, 2014). These consumers tend to prefer their established status quo because changing includes losses that might overshadow the potential gains (Hess, 2009). In order to maintain their current status quo, unconscious perceptual and cognitive mechanisms are triggered to disrupt the evaluation of information about

7

innovations (Heidenreich & Spieth, 2013; Zaltman & Wallendorf, 1983), leading to a reluctance to engage in innovative consumer behavior and subsequent adoption of new products and service innovations (Talke & Heidenreich, 2014). Therefore, passive innovation resistance is seen as a significant inhibitor of innovative consumer behavior (Bagozzi & Lee, 1999; Heidenreich & Handrich, 2014). However, empirical evidence for this proposition is still lacking. In the following we thus provide theoretical rationales for how passive innovation resistance might affect different types of innovative consumer behavior before empirically examining our hypotheses.

2.2

Innovative Consumer Behavior

In general, previous research acknowledges that innovative consumer behavior is reflected by inclinational and behavioral aspects of consumer innovativeness (Bartels & Reinders, 2011; Hirschman, 1980; Lee et al., 2012; Im, Bayus & Mason, 2003). However, extant research has produced many conceptualizations of consumer innovativeness that differ in their theoretical premise, internal structure, and purpose (Hauser, Tellis & Griffin, 2006; Roehrich, 2004; Bartels & Reinders, 2011). Yet, a systematical review on consumer innovativeness carried out by Roehrich (2004) synthesized research and findings across these different conceptualizations and led to a comprehensive typology of consumer innovativeness. According to Roehrich’s (2004) typology, two different types of consumer innovativeness that capture inclinational or behavioral aspects of innovative consumer behavior can be differentiated: (1) adoptive innovativeness reflects a tendency to purchase new products and thus captures inclinational aspects while (2) actualized innovativeness reflects the deree to which a consumer’s purchase of an innovation precedes that of other consumers and thus captures behavioral aspects. Hence, an examination of the inhibitory role of passive innovative resistance on innovative consumer behavior would require the evaluation of effects on both adoptive and actualized innovativeness. In the following, we thus provide theoretical rationales on how passive innovation resistance affects both types of innovative consumer behavior.

2.2.1

Actualized Innovativeness

8

Actualized innovativeness refers to new product adoption behavior, i.e. the actual acquirement of new ideas, information, and products (Im, Bayus & Mason, 2003; Midgley & Dowling, 1978). More specifically, it describes a measure of early adoption: that is, the extent to which consumers are relatively faster at adopting new products than other consumers (Bartels & Reinders, 2011; Lee, Khan & Mirchandani, 2013; Midgley & Dowling, 1978). Thus, actualized innovativeness is actual consumer behavior rather than individual traits or predispositions. With respect to passive innovation resistance and actualized innovativeness, it still lacks empirical evidence on the causal relationship between both constructs. Only Heidenreich & Handrich (2014) confirmed a negative correlation between passive innovation resistance and new product adoption within their scale development study. Similarly, Heidenreich & Spieth (2013) showed that passive innovation resistance indirectly reduces consumers’ intention to adopt via negative attitude formation within new product evaluation (active innovation resistance). Yet, some further evidence shows that both resistance to change and satisfaction with the status quo might inhibit actualized innovativeness. Findings of Oreg (2003) confirmed that individuals with higher levels of resistance to change were more reluctant to test a new IT system than individuals with a low inclination to resist changes. Nov & Ye (2009) came to similar results, showing that consumers’ inclination to resist changes represented an indirect antecedent to both effort expectancy and performance expectancy, which in turn determined their intentions to adopt digital library technology. Furthermore, in their experimental study, Ellen et al. (1991) showed that consumers who are satisfaction with a current manual method to complete an allocation task are less likely to adopt a superior computerized method (Heidenreich & Spieth, 2013). Similarly, Falk et al. (2007) showed that satisfaction with offline investment banking reduced the intention to use a new online self-service. Therefore, passive innovation resistance reflected by resistance to change as well as status quo satisfaction seems to reduce actualized innovativeness. Thus:

H1: Passive innovation resistance reduces actualized innovativeness

2.2.2

Adoptive Innovativeness

9

The concept of adoptive innovativeness centers on consumers’ tendency to buy new products (Roehrich, 2004). Whereas actualized innovativeness captures actual new product adoption behavior, adoptive innovativeness thus taps consumers’ eagerness to engage in innovative consumer behavior. According to Goldsmith & Hofacker (1991), consumers’ tendencies to adopt innovations (adoptive innovativeness) mediate both conceptually and empirically the linkage between innate innovativeness and specific innovative behaviors. Several studies have also empirically confirmed a mediating effect of adoptive innovativeness on the relationship of innate innovativeness and new product adoption (Chao, Reid & Mavondo, 2012; Im, Bayus & Mason, 2003). Since innate innovativeness and passive innovation resistance share the same level of abstraction (Heidenreich & Handrich, 2014), it can be assumed that adoptive innovativeness might also act as mediator in the relationship between passive innovation resistance and actualized innovativeness. In the following we describe in more detail how these constructs relate to each other and also provide corresponding theoretical rationales for their interrelations. According to Roehrich (2004), adoptive innovativeness refers to the tendency to engage in innovative behavior based on two central needs: the need for stimulation (Berlyne, 1960) and the need for uniqueness (Snyder & Fromkin, 1980). Therefore, adoptive innovativeness can be further differentiated in two related but distinct constructs: hedonist innovativeness is tied to the need for stimulation whereas social innovativeness is tied to the need for uniqueness. Consumers with high hedonist innovativeness seek variety, are interested in trying out innovative and new products, enjoy taking chances in buying unfamiliar products, and often alter their buying behavior to attain stimulating consumption experiences (Baumgartner & Steenkamp, 1996; Van Trijp, Hoyer & Inman, 1996). Roehrich (2004) found that hedonist innovativeness was positively correlated with the number of new products purchased. Furthermore, Baumgartner & Steenkamp (1996) confirmed a significant, positive correlation between exploratory acquisition of products and the possession of 46 new products. Therefore, hedonist innovativeness represents an antecedent to new product adoption, which increases actualized innovativeness (Heidenreich & Spieth, 2013). In contrast, Oreg et al. (2008) were able to confirm a negative correlation between consumers’ resistance to change disposition and

10

openness values, which, among other things, reflect the need for stimulation (Schwartz, 1999). Likewise, the results of a study by Heidenreich & Handrich (2014) confirmed a negative correlation between passive innovation resistance and consumers’ eagerness to seek novelty and variety. Therefore, we propose that passive innovation resistance might inhibit hedonist innovativeness. Thus:

H2: Hedonist innovativeness mediates the negative effect of passive innovation resistance on actualized innovativeness, such that (a) passive innovation resistance is negatively related to hedonist innovativeness and (b) hedonist innovativeness is positively related to actualized innovativeness.

Social innovativeness refers to the degree to which consumers are eager to adopt an innovation earlier compared to other members in their social system (Im, Bayus & Mason, 2003; Goldsmith & Hofacker, 1991). Therefore, consumers with high social innovativeness are eager to adopt new products before others (Bruner & Kumar, 2007). In his empirical study, Roehrich (2004) confirmed that social innovativeness positively influences the quantity of new products that were purchased. Similarly, results of Goldsmith & Hofacker (1991) provide empirical evidence for a positive relationship between social innovativeness and new product adoption. Thus, social innovativeness represents a precursor to new product adoption, that positively affects actualized innovativeness. With respect to the linkage of passive innovation resistance and social innovativeness, Oreg et al. (2008) confirmed that resistance to change and openness values are negatively correlated. As openness values represent an emphasis on the need for uniqueness (Schwartz, 1999) a similar relationship can be assumed for passive innovation resistance and social innovativeness (Heidenreich & Handrich, 2014). In support of this theoretical argumentation, Heidenreich & Handrich (2014) as well as Heidenreich & Spieth (2013) confirmed a negative correlation between passive innovation resistance and the eagerness of consumers to adopt new products earlier than others. Consequently, we propose that passive innovation resistance is likely to inhibit social innovativeness. Thus:

11

H3: Social innovativeness mediates the negative effect of passive innovation resistance on actualized innovativeness, such that (a) passive innovation resistance is negatively related to social innovativeness

and (b) social innovativeness is positively related to actualized

innovativeness.

2.3

Moderating Effects of Perceived Stimulation

As already laid out in the beginning, it seems necessary to consider both the direct effect of passive innovation resistance and the moderating effect of perceived stimulation to fully assess the inhibitory role of passive innovation resistance for innovative consumer behavior. In general, stimulation refers to the degree of activation of an individual due to incoming stimuli from any external or internal situation, event, agent or object. Perceived stimulation is commonly referred to as the amount of stimulation from all sources that an individual perceives at a certain point of time (Steenkamp & Baumgartner, 1992; Steenkamp, 1996; Wahlers & Etzel, 1985). Accordingly, an individual that experiences many new things and changes in his or her daily life will have higher levels of perceived stimulation than an individual with a steady life. The concept of perceived stimulation has been a major component in behavioral theories (e.g., Berlyne, 1960; Steenkamp & Baumgartner, 1992; Wahlers & Etzel, 1985), particularly in investigations of the relationship between the state of an organism and its reaction to stimuli. Extant research differentiates between an individual’s actual stimulation level (ASL) and the optimal stimulation level (OSL) (Steenkamp & Baumgartner, 1992; Steenkamp, 1996; Wahlers & Etzel, 1985). In general, OSL refers to the person’s preferred level of stimulation, including any possible internal and external sources throughout all possible situations and over time (Menon & Kahn, 1995; Steenkamp, 1996; Zuckerman, 1979). The ASL refers to the amount of stimulation that an individual experiences from all sources at a specific point in time (Menon & Kahn, 1995; Steenkamp, 1996; Wahlers & Etzel, 1985). According to Howard & Sheth (1969), people strive to maintain an optimal level of stimulation. If the ASL is equal to the OSL, the person is satisfied. However, when circumstances change this level, the person expends effort to re-establish congruity between the ASL and the OSL (Gierl, Helm & Stumpp, 1999; Walters & Etzel, 1985;

12

Zuckerman, 1979). If ASL is greater than OSL, theory suggests that the person is receiving too much stimulation from the environment (Gierl et al., 1999; Wahlers & Etzel, 1985). In this situation, the person attempts to avoid or moderate the stimulation to restore congruity (Gierl et al., 1999; Orth & Bourrain, 2005). Because stimulation seeking and stimulation avoidance depend on whether ASL falls short of or exceeds OSL, the effects of both low levels of OSL and high levels of ASL on actualized innovativeness should be negative (Orth & Bourrain, 2005). Transferring these findings of optimal stimulation theory to the context of passive innovation resistance leads to the following theoretical rationale for the interaction effect with perceived stimulation. Because people with greater passive innovation resistance show a weaker need for stimulation and novelty (Oreg, 2003; Talke & Heidenreich, 2014), the amount of stimulation considered most comfortable and, thus, their OSL are considerably lower than for people with lesser passive innovation resistance. In addition, the higher the amount of perceived stimulation, the higher is the person’s ASL. Thus, the degree to which ASL exceeds OSL increases with rising levels of both passive innovation resistance and perceived stimulation. As pointed out above, in the case that ASL exceeds OSL, individuals will engage in stimulation avoidance to re-establish congruity, which leads to a negative effect on actualized innovativeness. Conclusively, passive innovation resistance and perceived stimulation are likely to interact negatively on actualized innovativeness. Thus:

H4: The negative effect of passive innovation resistance on actualized innovativeness increases with higher levels of perceived stimulation.

2.4

Covariates

While the focus of this study is on the relationship between passive innovation resistance and adoptive as well as actualized innovativeness, past research suggests that sociodemographic characteristics might also influence actualized innovativeness (e.g., Heidenreich & Spieth, 2013; Im et al., 2003; Venkatraman & Price, 1990). Therefore, we included age, level of education, and annual income as

13

covariates in our framework. Including these key sociodemografics should enhance the robustness of our hypotheses testing. ------------------------------Insert Figure 1 about here -------------------------------

3. 3.1

Effects on Innovative Consumer Behavior across Technological Products Data

In order to investigate the proposed relationships within our research model, we employed a multistep process to develop a web-based questionnaire. Initially, a small sample of marketing and innovation experts evaluated the questionnaire with respect to wording, format and layout as well as comprehension. We made slight revisions according to the experts’ comments. The scales’ validity and reliability were checked based on a convenience sample of students. We then administered the instrument to 1526 people who we initially acquired by a data collection procedure similar to the one applied by Hennig-Thurau et al. (2007). Accordingly, potential participants were recruited by trained marketing students from their social network based on quota criteria regarding age, gender and education. After selection, the potential participants received an email that directed them to the webbased survey. Because the survey was self-administered, students did not collect the data themselves (Heidenreich & Handrich, 2014). The quota character is an accepted characteristic for empirical research (Hennig-Thurau et al., 2007), and recent marketing research has employed online surveys successfully (e.g., Völckner et al., 2010). Finally, we received a response rate of 44.6 %, which resulted in 681 complete and usable responses. 58.4 % of the participants are male with an average age of 29.2 years, whereas 41.6 % are female with an average age of 30.1 years. The descriptive analysis further shows that most participants (44.3 %) were in possession of either a high-school diploma or a secondary school certificate (6.8 %) without university education. Yet, a significant amount of the participants (33.0 %) reported “university or some other graduate degree” as their educational

14

category. The distribution of income within the sample was quite usual as 26.2 % of the respondents reported an annual income of more than 35000 Euro.

3.2

Measures

We define passive innovation resistance as a predisposition of individuals to resist innovations. In line with Heidenreich & Handrich (2014), we operationalize passive innovation resistance as a molar thirdorder construct, with reflective measures for the first-order constructx (Chin, 2010; Chin & Gopal 1995). Using the measurement inventory of Heidenreich & Handrich (2014), passive innovation resistance consists of two second-order factors: (1) inclination to resist changes and (2) satisfaction with the status quo. Inclination to resist changes as second-order dimension consists of a 12-item scale with four factors, each representing one of our first-order factors of inclination to resist changes: (1) routine seeking, (2) emotional reaction to imposed change, (3) short-term focus, and (4) cognitive rigidity. These factors reflect behavioral, affective, and cognitive aspects of inclination to resist change (Oreg, 2003). The second-order factor status quo satisfaction consists of a 6-item scale with two factors: (1) satisfaction with extent of innovations and (2) satisfaction with existing products. We define actualized innovativeness as the extent to which consumers actually adopt new products more quickly and more often than other members in their social network (Im et al., 2003). Of the many different measures of actualized innovativeness in the literature, we use the most prominent one, namely the "cross-sectional" method (e.g., Heidenreich & Handrich, 2014; Im et al., 2003). Accordingly, we operationalized actualized innovativeness by assessing the number of 14 preselected high-tech products a respondent owned at the time of the survey. Following Heidenreich & Handrich (2014), we slightly modified the common approach, by accounting for the lifetime stage of the product and the adoption timing of the consumer. Based on this information, we then calculated a respondent’s score for each product and summarized all individual product scores. This process led to one index for each respondent as a measure of his or her actualized innovativeness. As social and hedonist innovativeness are two related but distinct constructs we implemented them as separate variables within our structural model. To measure social innovativeness, we use

15

Bruner & Kumar’s (2007) technological innovativeness inventory, which captures the eagerness of consumers to adopt new technological products before others. For the operationalization of hedonist innovativeness, we use Van Trijp et al.’s (1996) exploratory acquisition of new products scale, which captures consumers’ eagerness to seek novelty and variety. Each item was measured on a seven-point rating scale. Finally, perceived stimulation was defined as the amount of stimulation that an individual experienced from all sources in the past (Steenkamp, 1996). We use the subjective difference between change-seeker indices proposed by Helm (2001) to assess perceived stimulation. This inventory was developed in line with the change-seeker index of Garlington & Shimota (1964) and was confirmed to exhibit a high reliability and validity (Helm, 2001). The original item battery consists of seven reflective items, four of which we adapted to operationalize perceived stimulation in our study. With respect to our controls, the respondents’ age was assessed in years. The educational level was measured and grouped into six categories (from lower to higher educated). Finally, the annual income before taxes was captured in Euros.

3.3

Analysis

In order to test our research model and the corresponding hypotheses, component-based structural equation modeling was applied (partial least squares path modeling [PLS-PM]; Vinzi & Russolillo, 2013). We decided to against the use of covariance-based methods (e.g., LISREL) as (1) PLS-PM

has less strict assumptions regarding the sample distribution and size (Chin & Newsted, 1999) and (2) even more important, molar higher-order constructs cannot be modeled with covariance-based techniques but can easily be implemented in PLS-PM (Chin, 2010). To investigate the moderating effect of perceived stimulation on the relationship between passive innovation resistance and actualized innovativeness (Hypothesis 4 in Figure 1), we employed the product term approach. Following this approach, we created an interaction term by multiplying each indicator of the independent construct by each indicator of the moderating construct (Wilson, 2010). We furthermore standardized all predictive and moderator variables to help minimize

16

multicollinearity that develops when creating the product terms (Low & Mohr, 2001). Subsequently, the model including all direct and interaction effects was estimated using the PLS-PM algorithm (Hair, Hult, Ringle, & Sarstedt, 2014). We applied PLS-PM using a centroid weighting scheme and nonparametric bootstrapping with 1000 replications and individual-level preprocessing to estimate of the outer and inner model parameters (Hair et al., 2014; Tenenhaus et al., 2005). Furthermore, we used The higher-order latent variable of passive innovation resistance was implemented by using the repeated indicator approach (Wetzels, Odekerken-Schroder & van Oppen, 2009). Within this approach each indicator of a firstorder construct is repeatedly used at the subsequent order levels. As a consequence, the standard PLSPM algorithm can be used to estimate the model (Chin et al., 1996). The repeated indicator approach is most suitable for hierarchical models with equal numbers of manifest variables for every first-order variable (Chin, 2010). As hierarchical model fulfills this requirement, we proceed with the evaluation of the measurement and structural model.

3.4

Results

To assess the psychometric properties of the higher-order variable passive innovation resistance, a null model was set up without any structural relationships (Wetzels et al., 2009). In a first step, we used exploratory principal components analysis (PCA) to evaluate content validity of the reflective firstorder constructs (Wilson, 2010). All indicator loadings in our first-order measurement model were above the threshold value of 0.7 and composite reliabilities of all first-order constructs were high, ranging between 0.88 and 0.97 (see Table 1; Chin, 2010). The average variances extracted (AVEs) of all measures exceeded the cutoff value of 0.50 that Fornell and Larcker (1981) recommend. Additionally, all first-order constructs satisfy the Fornell–Larcker criterion, as the AVE of each latent variable turned out to be higher than the common variance with any other latent variable, completing the reflective measurement model’s validation process of first-order constructs.

-------------------------------

17

Insert Table 1 about here -------------------------------

In a second step, we assessed the goodness of all molar second- and third-order constructs. First, the significances of the second-order weights were determined by means of bootstrapping (Chin, 1998). For the second-order factor “inclination to resist changes,” all second-order weights could be confirmed as significant. Consequently, all first-order constructs, namely short-term focus, emotional reaction to imposed change, routine seeking, and cognitive rigidity, were significant contributors to the second-order construct “inclination to resist changes”. With respect to the second-order construct “status quo satisfaction”, both satisfaction with extent of innovations and satisfaction with existing products showed significant weights of 0.56 and 0.52, respectively. At third order level, significant weights of 0.85 and 0.43 could be confirmed for both second-order constructs, namely inclination to resist changes and status quo satisfaction, providing further empirical evidence for their importance in forming passive innovation resistance. Subsequently, the degree of multicollinearity was assessed (Grewal, Cote & Baumgartner, 2004). Of all the molar higher-order constructs, the highest variance inflation factor (VIF) value was 3.13, providing additional assurance that the hierarchical construct was adequately operationalized (Henseler, Ringle & Sinkovics, 2009). We thus proceeded to the assessment of the remaining reflective constructs. We initially used exploratory PCA to evaluate the content validity of hedonist, social, and actualized innovativeness as well as perceived stimulation. The cutoff threshold of 0.707 was exceeded by all loadings. Furthermore, after having eliminated three items of hedonist innovativeness, all items of each construct correlated more highly with like items, providing additional evidence for unidimensionality (Wilson, 2010). In addition to unidimensionality, indicator loadings were far above 0.6 and each calculated composite reliability exceeded 0.80 (see Table 2; Hulland, 1999; Nunnally & Bernstein, 1994). Moreover, since all AVEs were higher than the cutoff value of 0.50 (Fornell & Larcker, 1981) and their corresponding square roots exceeded the intercorrelations with other latent variables in the model, discriminant validity should be given (Fornell & Larcker 1981; Hair et al.,

18

2014). In summary, the results of the measurement model evaluation are encouraging enough to proceed with the structural model evaluation.

------------------------------Insert Table 2 about here -------------------------------

Smart-PLS 2.0 (Ringle, Wende & Will, 2005) was applied to test the hypotheses at structural model level by examining the corresponding path coefficients and significances. A summary of the results based on 681 observations can be found in Figure 2.

------------------------------Insert Figure 2 about here -------------------------------

The results on the explained variance in the model indicate a good fit of the estimations and the data; the R-square is 0.20 for social innovativeness, 0.41 for hedonist innovativeness, and 0.52 for actualized innovativeness. The predictive power of the model was assessed using a blindfolding approach to calculate Q2 values of 0.16 (social innovativeness), 0.29 (hedonist innovativeness), and 0.50 (actualized innovativeness). Since all Q2 values are different from 0, the predictive power of the model is confirmed (Geisser, 1974; Stone, 1975). Furthermore no multicollinearity should be present as the calculated VIF value was 1.84. Finally, to generate t-statistics and standard errors a bootstrapping procedure was applied (Chin, 1998). In support of Hypothesis 1, passive innovation resistance has a significant, negative effect on actualized innovativeness (β = –0.23, p < 0.01). Consistent with Hypothesis 2, hedonist innovativeness significantly mediates the effect of passive innovation resistance on actualized innovativeness. More specifically, hedonist innovativeness is negatively related to passive innovation resistance (β = –0.64, p < 0.01) and significant positively but very weakly related to actualized innovativeness (β = 0.08, p <

19

0.05). To test the mediation effect, we followed PLS-PM procedures that were developed in line with Baron and Kenny’s (1986) suggestions for evaluating the mediating role of variables (Helm, Eggert, & Garnefeld, 2010). Following these procedures, the z-statistic was calculated to test the significance of the mediation (Sobel, 1982). The calculated z-value was 2.43 (p < 0.05), which confirms the formation of a mediation effect. To estimate the magnitude of the indirect effect, we use the variance accounted for (VAF) value, which states the ratio of the indirect to the total effect. A calculated VAF value of 0.18 indicates that the indirect effect explains approximately 18% of the total effect on actualized innovativeness (Helm et al., 2010). In support of Hypothesis 3, the mediating effect of social innovativeness could also be confirmed. More specifically, social innovativeness is negatively related to passive innovation resistance (β = –0.43, p < 0.01) but positively related to actualized innovativeness (β = 0.53, p < 0.01). The corresponding z-value of 11.29 (at p < 0.01) confirms the mediation effect. Furthermore, the VAF value turned out to be 0.49, indicating that the indirect effect explains almost half of the total effect of passive innovation resistance on actualized innovativeness (Helm et al., 2010). In summary, the total effect on actualized innovativeness equals β = –0.51, which underscores the strong influence of passive innovation resistance on new product adoption. In support of Hypothesis 4, perceived stimulation moderates the effect of passive innovation resistance on actualized innovativeness by significantly but only marginally increasing the negative effect (β = –0.07, p < 0.05). We used the statistical software “Interaction!” to draw the interaction effect and graphically illustrate this finding (see Figure 3).

------------------------------Insert Figure 3 about here -------------------------------

20

4.

Effects on Innovative Consumer Behavior within New Product Evaluations

4.1

Data

We employed the same sample as before, with its effective sample size of 681 observations. To investigate the effects of passive innovation resistance in new product evaluations, we selected mobile phones as the research object because almost every German citizen possesses a mobile phone (Heidenreich & Kraemer, 2015). Consequently, our research context should be highly relevant to potential participants. In a pilot study, experts in the field of innovation management identified nine mobile phones as adequate stimuli from a larger set of innovative mobile phone concepts.1 We developed a short product description for each mobile phone in accordance to Hoeffler’s (2003) suggestions using the original descriptions associated with mobile phones. Within all descriptions, a standard image of the mobile phone was accompanied by three paragraphs that described the characteristics of the product. The first paragraph listed the primary novel attribute of each mobile phone, followed by descriptions of the corresponding benefits in the subsequent paragraphs. In each description the number of times the product itself was mentioned was held constant. In a subsequent pretest, 44 consumers evaluated these product descriptions on radicalness of the stimuli (“This technological product is a minor variation of an existing product”) and perceived amount of behavioral change (“The use of this product requires a significant amount of behavioral change”) using two items, which we adapted from Moreau, Lehmann & Markman (2001) and Hess (2009). According to the test consumers’ comments, small revisions were made. Finally, three mobile phones (one incrementally new product, one dynamically continuous product, one radically new product) were taken as adequate stimuli for the final online experiment. Note that unique associations with specific products are avoided by means of a fictional label (i.e., “T13,” “T23,” “T33”; Mukherjee & Hoyer, 2001). Participants were randomly assigned to one of the three products, in line with Drèze & Nunes’s (2007) recommendations.

1

Initially, we identified a large set of mobile phone concepts by scanning several Internet databases for mobile phone concepts (e.g., www.concept-phones.com; www.conceptmobiles.com; www.cellphonebeat.com; www.coolest-gadgets.com; www.designyourway.net; www.geeky-gadgets.com; www.loopycellphones.com). More than 40 concepts were identified during this process.

21

4.2

Measures

We employed the measurement inventory of Heidenreich & Spieth (2013) to operationalize passive innovation resistance as a molar second-order construct, with formative first-order constructs (Chin, 2010; Chin & Gopal 1995). We used Goldsmith & Hofacker’s (1991) domain-specific inventory to operationalize social innovativeness as reflective construct. To operationalize hedonist innovativeness, we used a domain-specific version of Van Trijp et al.’s (1996) exploratory acquisition of new products scale, which consists of four reflective items. We assessed actualized innovativeness by measuring participants’ intention to adopt a new product using a three-item, seven-point scale anchored by “unlikely/likely,” “improbable/probable,” and “impossible/possible” (Kulviwat et al., 2007). Because the amount of perceived stimulation in new product evaluation is primary determined by perceived product innovativeness, we used a scale from Moreau et al. (2001) to assess perceived stimulation.

4.3

Analysis

We again applied PLS-PM with a centroid weighting scheme and nonparametric bootstrapping with 1000 replications and individual-level preprocessing. Because the repeated indicator approach only works best when equal numbers of first-order indicators are present and this requirement is violated in our new product evaluation model, we employed the two-stage approach to model our hierarchical construct passive innovation resistance (Chin, 2010). Therefore, we estimated the latent variable scores of the first-order construct without having the second-order construct present. Afterwards, the latent variable scores are employed as second-order indicators in a subsequent structural model analysis. To explore the interaction effect of perceived stimulation and passive innovation resistance on actualized innovativeness (Hypothesis 4 in Figure 1), we again employed the product term approach.

22

4.4

Results

We initially assessed the relevance of all indicators, their discriminant validity, and the degree of multicollinearity (Chin, 2010) to evaluate the goodness of the hierarchical measurement models. Table 3 shows that values of all first-order indicators for both resistance to change and satisfaction with the status quo were above the threshold of 0.1 (Chin & Newsted, 1999). Moreover, the corresponding significances of all weights exceeded the critical value of t > 1.98. Moreover, all weights of passive innovation resistance at the second-order level exceed both critical thresholds. Because the maximum VIF value at both the first- and second-order level was 2.11 and all construct correlations were below the threshold (>.90), multicollinearity is no concern (see Table 3). In summary, these results confirm that the hierarchical construct of passive innovation resistance provides good measurement model fit.

------------------------------Insert Table 3 about here -------------------------------

For the evaluation of the remaining reflective constructs, we again used exploratory PCA to assess content validity of hedonist, social, and actualized innovativeness and perceived stimulation. Every indicator loadings exceeded the critical value of 0.70 and no item correlated more highly with unlike items, providing assurance for unidimensionality (Wilson, 2010).. After having eliminated one item of social innovativeness, which fell below the common threshold of 0.4, all indicator loadings were above 0.60 (Hulland, 1999; see Table 4). As it is also shown in Table 4, all composite reliabilities exceeded 0.70 (Nunnally & Bernstein, 1994). Moreover, the AVE of all measures were above 0.50 and the corresponding square roots exceeded the intercorrelations with the other latent variables in the model, supporting discriminant validity (Chin, 1998; Fornell & Larcker 1981).

------------------------------Insert Table 4 about here

23

-------------------------------

Figure 4 presents the results of the estimation using an effective sample size of 681 observations. The R-square is 0.45 for social innovativeness, 0.34 for hedonist innovativeness, and 0.26 for actualized innovativeness. The blindfolding procedure resulted in Q2 values of 0.39 (social innovativeness), 0.29 (hedonist innovativeness), and 0.24 (actualized innovativeness, indicating high predictive power (Geisser, 1974; Stone, 1975). Furthermore, multicollinearity should not be present at the structural model level as the calculated VIF value was 3.22 ------------------------------Insert Figure 4 about here -------------------------------

In line with Hypothesis 1, passive innovation resistance has a significant, negative effect on actualized innovativeness (β = –0.25, p < 0.01). In support of Hypothesis 2, hedonist innovativeness mediates the effect of passive innovation resistance on actualized innovativeness. Hedonist innovativeness is negatively related to passive innovation resistance (β = –0.56, p < 0.01) and significant positively but very weakly related to actualized innovativeness (β = 0.09, p < 0.10). The zstatistic (Sobel, 1982) was again applied confirms that hedonist innovativeness significantly mediates the negative effect of passive innovation resistance on actualized innovativeness (z-value = 1.66, p < 0.10). The calculated VAF value of 0.17 indicates that the indirect effect explains approximately 17% of the total effect of passive innovation resistance on actualized innovativeness (Helm et al., 2010). Furthermore, we found support for Hypothesis 3; the mediating effect of social innovativeness was established. More specifically, social innovativeness is negatively related to passive innovation resistance (β = –0.66, p < 0.01) but positively related to actualized innovativeness (β = 0.16, p < 0.01). The significance of the mediation effect is also confirmed by a z-value of 2.78 (p < 0.01). Moreover, a VAF value of 0.30 shows that the indirect effect explains one-third of the total effect on actualized innovativeness (Helm et al., 2010). Overall, the total effect on actualized innovativeness equals β = –

24

0.40, which underscores the strong effect of passive innovation resistance on new product evaluation.

In support of Hypothesis 4, perceived stimulation moderates the relationship of passive innovation resistance on actualized innovativeness by increasing the negative effect (β = –0.17, p < 0.01). We again used the statistical software “Interaction!” to draw the interaction effect and graphically illustrate this finding (see Figure 5). ------------------------------Insert Figure 5 about here ------------------------------5.

General Discussion

Innovation management research continues to dedicate considerable attention to consumers’ adoption behavior (Heidenreich & Kraemer, 2015). An important but largely overlooked research area is consumers’ resistance to innovations and specifically whether their predisposition to resist innovations (passive innovation resistance) actually inhibits innovative consumer behavior. The goals of the current study were twofold: (1) to determine whether passive innovation resistance represents an inhibitor of innovative consumer behavior, and (2) to investigate whether perceived stimulation enhances the negative effects of passive innovation resistance. Our empirical studies produced several interesting results. First, our findings indicate that, in general, passive innovation resistance represents a strong inhibitor of innovative consumer behavior, reducing both adoptive and actualized innovativeness. In both studies, we showed that it substantially decreases the eagerness of consumers to seek out novelty and variety (hedonist innovativeness) and to buy innovations more often and quickly than others (social innovativeness), thereby inhibiting new product adoption (actualized innovativeness). In addition to these indirect effects of passive innovation resistance on actualized innovativeness, we found a strong direct effect, confirming that consumers high on passive innovation resistance are more reluctant to adopt new products. Our results indicate that consumers overrate products that are currently in use, while the benefits offered by a new product are underestimated due to the changes

25

entailed to the adoption (Heidenreich and Spieth, 2013). High passive innovation resistance thus leads to a high rejection probability of new products, reducing consumers’ actualized innovativeness. These findings are in line with previous empirical research showing that passive innovation resistance has negative effects on new product evaluation and adoption (Heidenreich & Spieth, 2013; Heidenreich & Handrich, 2014). Yet our findings expand extant research by providing first empirical evidence that passive innovation resistance inhibits different kinds of innovative consumer behavior and that the individual effect of passive innovation resistance varies across social, hedonist and actualized innovativeness. Furthermore, validating the inhibitory role of passive innovation resistance in two studies with different settings enhances the external validity and the substance of previous findings that already pointed out to the inhibitory role of passive innovation resistance. Second, notable results are also provided by the moderator variable perceived stimulation. The results confirm a significant interaction effect of passive innovation resistance and perceived stimulation on actualized innovativeness in both studies. Thus, passive innovation resistance is more likely to reduce actualized innovativeness when consumers’ perceive high levels of stimulation from their environment. This finding is in line with both our expectations and empirical evidence of previous studies that indicates negative correlations between actual stimulation and consumers’ exploratory tendencies (Kahn & Isen, 1993). However, we expand extant research by showing that passive innovation resistance influences OSL and thus interacts with perceived stimulation and ASL on actualized innovativeness. Berlyne (1960) originally suggested that when people are confronted with an innovation characterized by complexity, novelty, or irregularity, their perceived stimulation and, thus, ASL increase. However, psychological pleasantness is highest at the OSL, which typically is low for consumers with greater passive innovation resistance. Thus, when consumers with greater passive innovation resistance are confronted with high levels of stimulation due to a radical innovation, they attempt to decrease this stimulation by rejecting its source and, thus, the innovation. In conclusion, our findings from the moderation analysis provide first empirical evidence for the applicability of optimal stimulation theory to the concept of passive innovation resistance.

26

6.

Implications

From a theoretical perspective, the findings of our study contribute to adoption theory in three major ways. First, our findings contribute to the ongoing academic debate on whether passive innovation resistance represents an important predisposition in adoption research (Heidenreich & Handrich, 2014). Our results conclusively demonstrate that passive innovation resistance represents an important inhibitor of innovative consumer behavior. However, most studies follow a novelty-seeking rather than novelty-resistance paradigm, using constructs that focus on the need for stimulation or uniqueness as well as novelty seeking as root causes for innovative consumer behavior (Chau & Hui, 1998; Menon & Kahn, 1995; Roehrich, 2004). Yet, empirical research indicates that the relationship between traditional constructs and innovative consumer behavior, especially new product adoption, is, if significant, weak (e.g., Goldsmith & Hofacker, 1991; Im et al., 2003). Given that both studies consistently show strong individual effects of passive innovation resistance on innovative consumer behavior, it seems appropriate to approach innovative consumer behavior from a resistance perspective to achieve greater explanatory power in adoption models. Thus, our results are in line with the findings of Heidenreich & Handrich (2014), and clearly support the importance of passive innovation resistance as a predisposition in innovation adoption research. Second, the findings of our research contribute to the ongoing discussion on a possible prochange bias in adoption literature. Several researchers argue that adoption literature is largely subject to a pro-change bias, as some authors assume that individuals are principally open to change and thus interested in the evaluation and adoption of innovations (e.g., Rogers, 2003; Sheth, 1981; Talke & Heidenreich, 2014). As a consequence, most studies investigate positive predispositions and outcomes within the adoption process, like innate innovativeness or innovation acceptance (e.g., Im et al., 2003; Rogers, 2003; Speier & Venkatesh, 2002). However, more recent research suggests that many consumers lack interest in innovative consumer behavior due to a predisposition to resist innovations (i.e. passive innovation resistance; Heidenreich & Spieth, 2013, Heidenreich & Handrich, 2014). So far though, empirical evidence on the inhibitory role of passive innovation resistance is scarce and further empirical validation is needed to provide substantiation for the existence of a pro-change bias.

27

The findings of this research address this issue, by empirically validating passive innovation resistance as important inhibitor for innovative consumer behavior. Our results confirm that individuals with high passive innovation resistance might not always be open to innovations. Consequently, the assumption that individuals are principally open to innovation and thus interested in the evaluation and adoption of new products seems indeed pro-changed in its bias (Heidenreich and Handrich, 2014). Third, our research contributes to the current knowledge on the advantages and disadvantages of product innovativeness. Prior research commonly suggested that higher levels of product innovativeness might be advantageous for market success as radical new products are associated with higher benefits compared to products with low levels of product innovativeness (Hess, 2009; Ziamou, 1999). However, our results point out that there also might be a dark side to high levels of product innovativeness. According to our findings, high product innovativeness also involves high levels of stimulation and thus might evoke high passive innovation resistance. Thus, a high level of product innovativeness might be beneficial for the market success in terms of high perceived relative advantage that fosters adoption but detrimental in terms of high perceived levels of stimulation that fosters negative effects of passive innovation resistance. From a managerial perspective, our research provides important implications for the development of new products as well as the design of measures to counteract negative effects of passive innovation resistance when launching radical innovations. With respect to new product development, innovation managers must realize that individuals sometimes act irrationally when confronted with innovations and overrate products currently in possession, while advantages of innovations are consistently underestimated due to the changes entailed. Although prior research indicates that radical new products are associated with more objective benefits than incrementally new products (Hess, 2009; Ziamou, 1999), our results show that product innovativeness also increases the amount of stimulation and, thus, the negative effects of passive innovation resistance on actualized innovativeness. The negative effects of passive innovation resistance thus might supersede the positive effects connected with a superior relative advantage of a new product as a result of the high amount of perceived changes entailed to adoption. For this reason, it might not be enough to offer radical

28

innovations with superior relative advantage, as literature on innovation adoption has long suggested (e.g., Ostlund, 1974; Ram, 1987; Rogers, 2003). Instead, marketers should first assess the potential market resistance that a new product will face before deciding to go ahead (Heidenreich & Spieth, 2013). When targeting consumer segments with greater passive innovation resistance, companies should keep product innovativeness within limits. However, when facing consumer segments with lesser passive innovation resistance, companies should draw on the positive effects of a superior relative advantage by enhancing product innovativeness. With respect to countermeasures for passive innovation resistance in case of radical new product launches, innovation managers could accompany product launches with marketing campaigns that focus on reducing the amount of perceived stimulation entailed to the adoption of a new product (Heidenreich & Kraemer, 2015). Within product announcements companies might use categorization cues, to indicate the membership of the radical innovation to a certain category (Gregan-Paxton & Moreau, 2003). This should help consumers to better align the product with familiar practices, reducing perceived stimulation (Moreau, Markman & Lehmann, 2001). Furthermore, companies might use advertisement to highlight that the use of the radical innovation is compatible with current usage patterns. This in turn was shown to enable consumers to align the product with existing consumption practices, which should also decrease perceived stimulation (Hess, 2009; Hoeffler, 2004). Within this respect, findings of Heidenreich & Kraemer (2015) empirically confirmed that such advertisements are highly effective in reducing negative effects of passive innovation resistance, especially for radical new products. Besides these strategies, companies could also use product demonstrations or product trials to help customers get accustomed to the radical new product (Heimann & Muller, 1996). Finally, companies may bundle a radical innovation with a traditional complementary product to increase the understanding of the new product’s functions and how to use it, which in turn reduce perceived stimulation (Heidenreich and Spieth, 2013; Reinders, Frambach & Schoormans, 2010).

29

7.

Limitations and Future Research Directions

As it is the case with most research, our study also has some limitations. Our results are from the category of consumer electronics products, which is commonly knwon as an adequate research context to study consumers’ adoption behavior (e.g., Heidenreich and Kraemer, 2015; Im et al., 2003). In addition, we validated our results in two studies using distinct research designs. However, we must be cautious in generalizing our findings; consumers are commonly highly involved in the decision making process when purchasing electronic products because of the relatively high costs in this category (Im et al., 2003). Further research could strive to replicate our findings for different product categories (e.g., consumer, nondurable, service) and in different contexts to enhance external validity. The generalizability of the results to a broader population might also be somewhat restricted due to our sample’s rather low average age. Specifically, passive innovation resistance might represent an even stronger predisposition to resist innovations among older consumers, and thus innovative consumer behavior might be more negatively affected by passive innovation resistance than in the case of younger consumers (Heidenreich & Spieth, 2013; Laukkanen et al., 2007). Further research could replicate our findings using a sample of an older population to further advance external validity. Recent research suggests that in case of a higher-order measurement model like that of passive innovation resistance which is not unidimensional (i.e., its lower-order measures capture conceptually different latent variables), the lower-order constructs should either be (1) treated as separate variables, or (2) aggregated in a molar higher-order construct with reflective measures at the first-order level (Lee & Cadogan, 2013). As previous conceptual research suggests that passive innovation resistance is a multidimensional construct that is formed by an individuals’ inclination to resist changes and his or her satisfaction with the status quo (Talke & Heidenreich, 2014), we believe that an aggregated operationalization as molar higher-order construct is the most adequate choice. However, future research might employ the alternative approach suggested by Lee & Cadogan (2013) and treat both second-order constructs as separate variables to determine the individual effects of cognitive and situational passive innovation resistance on innovative consumer behavior.

30

Within our new product evaluation study, we found strong empirical support that passive innovation resistance inhibits different types of innovative consumer behavior. While we included some control variables to provide for a stronger test of our hypotheses, the inclusion was restricted to three key sociodemografics as covariates. However, an empirical study of Heidenreich & Spieth (2013) confirmed that alongside with passive innovation resistance also product-specific barriers and active innovation resistance significantly affect adoption intention in new product evaluations. In order to fully avoid an omitted-variable bias, future research might replicate our findings while additionally controlling for effects of active innovation resistance and product-specific barriers (such as value, complexity or usage barrier; for an overview please see Talke & Heidenreich, 2014) on actualized innovativeness. Finally, we studied the dynamic phenomenon of consumers’ adoption behavior using crosssectional data. Since it is often difficult to capture the adoption process over time, we decided to take a “snapshot” of the adoption behavior at a single point in time. However, some of the effects might turn out to be more longitudinal in nature and thus require an observation over time. For instance, the negative effects of passive innovation resistance on newly introduced technology products (e.g., use of touch screens) could change over time as consumers get more familiar with the innovation as visibility in their social system increases. Thus, an empirical investigation of passive innovation resistance and its effect on the adoption of a specific product over time could yield useful insights into how passive innovation resistance hinders possible changes in attitude formation toward a product. Furthermore, research could examine the moderating role of perceived stimulation in a longitudinal study, because the ASL may vary over time. An assessment of perceived stimulation and its effect on new product adoption at different points in time would expand current knowledge from our results. Consequently future research might employ longitudinal date to also capture effects that are more longitudinal in nature. To facilitate further research in the area of innovation resistance, we subsequently outline some possible future research directions. First, future research could use different behavioral measures to operationalize innovative consumer behavior when examining the effects of passive innovation

31

resistance (Heidenreich and Handrich, 2014). Whereas we used the actual quantity of new products that a consumer possesses from a particular product category and the intention to buy a new product for actualized innovativeness in our study, other measures, such average time of adoption, product trial, or repeated purchases could provide alternative ways of validating our basic results (Heidenreich & Handrich, 2014; Im et al., 2003). Research could also examine the ability of passive innovation resistance to explain variables other than adoptive innovativeness that describe explorative consumer tendencies (e.g., independent judgment making, technological anxiety). Finally, to profile passive resistors, it would be fruitful to examine the relationship between passive innovation resistance and personality variables (e.g., Big Five personality dimensions), personal values (e.g., Schwartz’s value system), individual lifestyle (VALS typologies), and geodemographic segment descriptors (e.g., PRIZM; Im et al., 2003). Insights into these relationships might provide marketers and researchers as well with guidelines to segment a sample into consumers with low and high levels of passive innovation resistance based on easily accessible individual differences and demographics rather than on complex dispositions and personality traits (Heidenreich & Spieth, 2013).

32

REFERENCES Andrew, J., & Sirkin, H. (2003). Innovating for cash. Harvard Business Review, 81(9), 76-83. Bagozzi, R.P. & Lee, K-H. (1999). Consumer resistance to, and acceptance of, innovations. Advances in Consumer Research, 26 (1), 218-225. Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration. Technology Studies, 2(2), 285309. Baron, R., & Kenny D. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182. Baumgartner, H., & Steenkamp, J. (1996). Exploratory consumer buying behavior: conceptualization and measurement. International Journal of Research in Marketing, 13 (2), 121-137. Bayus, B. L., Erickson G., & Jacobson, R. (2003). The financial rewards of new product introductions in the personal computer industry. Management Science, 49(2), 197-210. Berlyne D.E. (1960). Conflict, arousal and curiosity. New York: McGraw-Hill. Bruner, G. C., & Kumar, A. (2007). Gadget lovers. Journal of the Academy of Marketing Science, 35 (3), 329-339. Castellion, G. & Markham, S. K. (2013). Perspective: New product failure rates: Influence of argumentum ad populum and self-interest. Journal of Product Innovation Management. 30 (5), 976-979. Chao, C., Reid, M. and Mavondo, F. (2012). Consumer innovativeness influence on really new product adoption. Australasian Marketing Journal, 20 (3), 211-217. Chau, P.Y.K., & Hui, K.L. (1998). Identifying early adopters of new IT products: a case of Windows 95, Information and Management, 33(5), 225-230. Chin, W. W. (1998). The partial least squares approach for structural equation modelling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research, Hillsdale, NJ: Lawrence Erlbaum Associates Chin, W. W. (2010). How to write up and report PLS analysis. In Vinzi, V.E., Chin, W. W., Henseler, J., & Wang, H. (Eds.). Handbook of partial least squares: concepts, methods and applications (Springer Handbooks of Computational Statistics Series, Vol. II, pp. 655-690). Heidelberg, Dordrecht, London, New York: Springer. Chin, W. W., & Gopal, A. (1995). Adoption intention in GSS: relative importance of beliefs. ACM SigMIS Database, 26(2-3), 42-64. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (1996). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo Simulation Study and Voice Mail Emotion/Adoption Study. Proceedings of the 17th International Conference on Information Systems, Cleveland, Ohio. December, 1996. Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling: analysis with small samples using partial least squares. In R. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307–341). Thousand Oaks, CA: Sage.

33

Drèze, X., & Nunes, J.C. (2004). Using combined- currency prices to lower consumers’ perceived cost. Journal of Marketing Research, 41(1), 59–72. Ellen, P. S., & Bearden, W. O., & Sharma, S. (1991). Resistance to technological innovations. An examination of the role of self-efficacy and performance satisfaction. Journal of the Academy of Marketing Science, 19 (4), 297-307. Engel, J.F., Blackwell, R.D., & Miniard, P.W. (1993). Consumer behavior. Orlando: The Dryden Press. Vinzi, E. V. & Russolillo, G. (2013). Partial least squares algorithms and methods. Wiley Interdisciplinary Reviews: Computational Statistics, 5(1), 1-19. Falk, T., Schepers, J., Hammerschmidt, M., & Bauer, H.H. (2007). Identifying cross channel dissynergies for multichannel service providers. Journal of Service Research, 10(4), 143-60. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement errors. Journal of Marketing Research, 18, 39–50. Foxall, G.R. (1995) Cognitive styles of consumer initiators. Technovation, 15(5), 269-88. Garcia, R., Bardhi, F., & Friedrich, C. (2007). Overcoming consumer resistance to innovation. MIT Sloan Management Review, 48(4), 82-88. Garlington, W.K., & Shimota, H.E. (1964). The change seeker index: a measure of the need for variable stimulus input. Psychological Reports, 14, 919-924 Gatignon, H., & Robertson, T.S. (1991). Innovative decision processes. In Robertson, T. S. & Kassarjian, H. H. (Eds.). Handbook of consumer behavior. Englewood Cliffs, NJ: Prentice Hall, 316-348. Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101-107. Gierl, H., Helm, R., & Stumpp, S. (1999). Erklärung des Konsumentenverhaltens durch die Optimum Stimulation Level Theorie. Marketing ZFP, 21(3), 217-235. Goldsmith, R. E., & Hofacker, C. F. (1991). Measuring consumer innovativeness. Journal of the Academy of Marketing Science, 19, 209-221. Gourville, J.T. (2006). Eager sellers and stony buyers. Understanding the psychology of new-product adoption. Harvard Business Review 84(6), 98-106. Gregan-Paxton, J., Hibbard, J.D., Brunel, F.F. & Azar, P (2002). So that's what that is: Examining the impact of analogy on consumers' knowledge development for really new products. Psychology & Marketing, 19(3), 533-50. Grewal, R., Cote, J. A., & Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: Implications for theory testing. Academy of Marketing Science Journal, 23, 519–529. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage. Hauser, J., Tellis, G.J., & Griffin, A. (2006). Research on innovation: A review and agenda for "marketing science." Marketing Science, 25(6), 687-717.

34

Heidenreich, S. & Handrich, M. (2014). What about passive innovation resistance? Investigating adoption-related behaviour from a resistance perspective. Journal of Product Innovation Management, doi:10.1111/jpim.12161. Heidenreich, S. & Kraemer, T. (2015). Innovations – Doomed to Fail? Investigating Strategies to Overcome Passive Innovation Resistance, Journal of Product Innovation Management, doi:10.1111/jpim.12273. Heidenreich, S. & Spieth, P. (2013). Why innovations fail - The case of passive and active innovation resistance. International Journal of Innovation Management, 17 (5), 1-42. Heiman, A. & Muller, E. (1996). Using demonstration to increase new product acceptance: Controlling demonstration time. Journal of Marketing Research, 33(6), 422-30. Helm, R. (2001). Planung und Vermarktung von Innovationen. Stuttgart. Helm, S., Eggert, A., & Garnefeld, I. (2010). Modelling the impact of corporate reputation on customer satisfaction and loyalty using PLS. In Vinzi, V.E., Chin, W. W., Henseler, J., & Wang, H. (Eds.), Handbook of partial least squares. Heidelberg: Springer. Hennig-Thurau, T., Henning, V., Sattler, H., Eggers, F., & Houston, M. (2007). The last picture show? Timing and order of movie distribution channels. Journal of Marketing, 71(4),63-83. Henseler, J., Ringle, C.M., & Sinkovics, R.R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277-320 Hess, S. (2009). Managing consumer's adoption barriers. Doctoral dissertation. University of Mannheim. Hirschman, E.C. (1980). Innovativeness, novelty seeking and consumer creativity. Journal of Consumer Research, 28, 283-295. Hoeffler, S. (2003). Measuring preferences for really new products. Journal of Marketing Research, 40(4), 406-20. Howard, J.A. & Sheth, J.N. (1969). The theory of buyer behavior. New York: Wiley. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 20(2), 195–204. Im, S., Bayus, B.L., & Mason, C.H. (2003). An empirical study of innate consumer innovativeness, personal characteristics, and new-product adoption behaviour. Journal al the Academy of Marketing Science, 31(1), 61-73. Kahn, B., & lsen, A.M. (1993). The influence of positive affect on variety seeking among safe, enjoyable products. Journal of Consumer Research, 20(5), 257-270. Kleijnen, M. & Lee, N., & Wetzels, M. (2009). An exploration of consumer resistance to innovation and its antecedents. Journal of Economic Psychology, 30(3), 344-357. Kuisma, T., Laukkanen, T. & Hiltunen, M. (2007). Mapping the reasons for resistance to internet banking: A means-end approach. International Journal of Information Management, 27 (2), 7585. Kulviwat, S., Brunder, G.C., Kumar, A., Nasco, S.A., & Clark, T. (2007). Toward a unified theory of consumer acceptance technology. Psychology & Marketing, 24(12), 1055-1084.

35

Laukkanen, T. & V. Kiviniemi. (2010). The role of information in mobile banking resistance. International Journal of Bank Marketing, 28(5), 372-388. Laukkanen, T., Sinkkonen, S., Kivijärvi, M., & Laukkanen, P. (2007). Innovation resistance among mature consumers. Journal of Consumer Marketing, 24(7), 419-427. Laukkanen, P., Sinkkonen, S., & Laukkanen, T. (2008). Consumer resistance to internet banking: postponers, opponents and rejectors. International Journal of Bank Marketing, 26(1), 440-455. Laukkanen, T., Sinkkonen, S., Laukkanen, P. (2009) Communication strategies to overcome functional and psychological resistance to internet banking. International Journal of Information Management, 29 (2), 111-118. Lee, N. & Cadogan, J.W. (2013). Problems with formative and higher-order reflective variables. Journal of Business Research, 66, 242-247. Lee, K., Khan, S. A. & Mirchandani, D. (2013). Balancing hierarchical effects of product attributes on actualized innovativeness in the context of high-tech products. Journal of Business Research, 66 (12), 2634–2641. Low, G. S., & Mohr, J. J. (2001). Factors affecting the use of information in the evaluation of marketing communications productivity. Journal of the Academy of Marketing Sciences, 29(1), 70–88. Menon, S., & Kahn, B.E. (1995). The impact of context on variety seeking in product choices, Journal of Consumer Research. 22 (3), 285-295. Midgley, D. F., & Dowling, G.R. (1978). Innovativeness: The concept and its measurement. Journal of Consumer Research, 4(2), 229–242. Moreau, C.P., Lehmann, D.R., & Markman, A.B. (2001). Entrenched knowledge structures and consumer response to new products. Journal of Marketing Research, 38(1), 14-29. Mukherjee, A., & Hoyer, W.D. (2001). The effect of novel attributes on product evaluation. Journal of Consumer Research, 28(3), 462-472. Nabih, M.I., Bloem, J.G., & Poiesz, T.B.C. (1997). Conceptual issues in the study of innovation adoption behaviour. Advances in Consumer Research, 24, 190-196. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill. Oreg, S. (2003). Resistance to change: developing an individual differences measure. Journal of Applied Psychology, 88(4), 680-693. Oreg, S., Bayazit, M., Vakola, M., Arciniega, L., Armenakis, A., Barkauskiene, R., Bozionelos, N., Fujimoto, Y., Gonzalez, L., Han, J., Hrebickova, M., Jimmieson, N., Kordacova, J., Mitsuhashi, H., Mlacic, B., Feric, I., Kotrla, M., Ohly, S., Saksvik, P., Hetland, H., Saksvik, I., & van Dam, K., (2008). Dispositional resistance to change: Measurement equivalence and the link to personal values across 17 nations. Journal of Applied Psychology, 93(4), 935-944. Orth, U.R., & Bourrain, A. (2005), Optimum stimulation level theory and the differential impact of olfactory stimuli on consumer exploratory tendencies. Advances in Consumer Research, 32, 613619. Osgood, C. E., & Tannenbaum, P. H. (1955). The principle of congruity in the prediction of attitude change. Psychological Review, 62, 42-55.

36

Ostlund, L.E. (1974). Perceived innovation attributes as predictors of innovativeness. Journal of Consumer Research, 1, 23-29. Pallister, J.G., & Foxall, G.R. (1995). Psychometric properties of the Hurt – Joseph – Cook scales for the measurement of innovativeness. Technovation, 18(11), 663– 75. Prins, R., & Verhoef, P. (2007). Marketing communication drivers of adoption timing of a new eservice among existing customers. Journal of Marketing, 71(3), 169-183. Ram, S. (1987). A model of innovation resistance. Advances in Consumer Research 14(1), 208-212. Ram, S. (1989). Successful innovation using strategies to reduce consumer resistance. An empirical test. Journal of Product Innovation Management, 6(1), 20-34. Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing, 6, 5–14. Reinders, M. J. 2010. Managing Consumer Resistance to Innovations. Doctoral Thesis, Amsterdam. Reinders, M.J., Frambach, R. & Schoormans, J.P.L. (2010). Using product bundling to facilitate the adoption process of radical Innovations. Journal of Product Innovation Management, 27(7), 11271140. Ringle, C., Wende, S., & Will, A. (2005). Smart PLS 2.0 (beta). University of Hamburg, http://www.smartpls.de. Roehrich, G. (2004). Consumer innovativeness - Concepts and measurements. Journal of. Business Research, 57(6), 671-677. Rogers, E.M. (1976). New product adoption and diffusion. Journal of Consumer Research, 2(4), 290301. Rogers, E.M. (2003). Diffusion of innovations (5th Ed.). New York: Free Press. Schwartz, S. (1999). A theory of cultural values and some implications for work. Applied Psychology: An International Review, 48, 23–47. Sheth, J.N. (1981). Innovation resistance. The less developed concept (LDC) in diffusion research. Research in Marketing 4, 273-282. Snyder, C.R., & Fromkin, H.C. (1980). Uniqueness: the human pursuit of difference. New York: Plenum. Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13(1), 290–312. Speier, C. & Venkatesh, V. (2002). The hidden minefields in adoption of sales force automation technologies. Journal of Marketing, 66(3), 98-111. Steenkamp, J.-B.E.M., & Baumgartner H. (1992). The role of optimum stimulation level in exploratory consumer behavior. Journal Consumer Research, 19(4), 434-48. Steenkamp, J.-B.E.M. (1996). The relationships among arousal potential, arousal and stimulus evaluation, and the moderating role of need for stimulation, International Journal of Research in Marketing, 13 (4):319-329. Stone, M. (1975). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B, 36(2), 111-133.

37

Szmigin, I., & Foxall, G. (1998). Three forms of innovation resistance: the case of retail payment methods. Technovation, 18(6/7), 459–468. Talke, K. and S. Heidenreich. 2014. How to overcome pro-change bias: Incorporating passive and active innovation resistance in innovation decision models. Journal of Product Innovation Management, forthcoming. Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48, 159–205. Van Trijp, H. C. M., Hoyer, W. D., & Inman, J. (1996). Why switch? Product category-level explanations for true variety-seeking behavior, Journal of Marketing Research, 33(3), 281-292. Venkatraman, M.P., & Price, L.L. (1990). Differentiating between cognitive and sensory innovativeness: concepts, measurement, and implications. Journal of Business Research, 20(4), 293-315. Völckner, F., Sattler, H., Hennig-Thurau, T., & Ringle, C.M. (2010). The role of parent brand quality for service brand extension success. Journal of Service Research, 13, 379-396. Wahlers, R. G., & Etzel, M. J. (1985). A consumer response to incongruity between optimal stimulation and lifestyle satisfaction. In Advances in Consumer Research, 12, 97-101. Wetzels, M., Odekerken-Schroder, G., & van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Quarterly, 33(1), 177-195. Wilson, B. (2010). Using PLS to investigate interaction effects between higher order branding constructs. In Vinzi, V.E., Chin, W. W., Henseler, J., & Wang, H. (Eds.). Handbook of partial least squares: concepts, methods and applications (Springer Handbooks of Computational Statistics Series, Vol. II, pp. 655-690). Heidelberg, Dordrecht, London, New York: Springer. Zaltman, G., & Wallendorf, M. (1983). Consumer behavior: Basic findings and management implications. New York: Wiley. Ziamou, P. (1999). The Effect of the Degree of Newness of a "Really New" Product on Consumers' Judgments. Advances in Consumer Research, 26 (1), 368-71. Zuckerman, M. (1979). Sensation seeking: beyond the optimal level of arousal. Hillsdale, NJ: Lawrence Erlbaum Associates.

38

39

40

41

42

43

TABLES Table 1: Hierarchical measurement model results First-order Construct

Routine Seeking CR=0.916 AVE=0.786

Emotional Reaction to imposed Change CR=0.921 AVE=0.795

Short-Term Thinking CR=0.945 AVE=0.852

Cognitive Rigidity CR=0.884 AVE=0.727

Satisfaction with Extent of Innovations CR=0.957 AVE=0.881

Satisfaction with Existing Products CR=0.974 AVE=0.926

Indicator label

Item

Loading (λi)

Significance (t-value)

MV 1

I generally consider changes to be a negative thing.

0.872

66.986

MV 2

I like to do the same old things rather than try new and different ones.

0.894

68.740

MV 3

I’d rather be bored than surprised.

0.892

89.167

MV 4

If I were to be informed that there’s going to be a significant change regarding the way things are done at work, I would probably feel stressed.

0.895

97.939

MV 5

When I am informed of a change of plans, I tense up a bit.

0.915

114.310

MV 6

When things don’t go according to plans, it stresses me out.

0.864

68.558

MV 7

Often, I feel a bit uncomfortable even about changes that may potentially improve my life.

0.921

123.982

MV 8

When someone pressures me to change something, I tend to resist it even if I think the change may ultimately benefit me.

0.931

140.636

MV 9

I sometimes find myself avoiding changes that I know will be good for me.

0.917

114.499

MV 10

I often change my mind.

0.777

36.142

MV 11

I don’t change my mind easily.

0.895

106.873

MV 12

My views are very consistent over time.

0.881

98.479

MV 13

Overall, my personal need for innovations in the field of technological products has been by far not covered in the past.

0.903

228.796

MV 14

Overall, I consider the number of innovations in the field of technological products as being too low.

0.961

258.927

MV 15

Overall, I consider the pace of innovations in the field of technological products as being too low.

0.951

171.917

MV 16

In the past, I was very satisfied with available technological products.

0.964

87.299

MV 17

In my opinion, past technological products were completely satisfactory so far.

0.970

242.925

44

MV 18

Past technological products fully met my requirements.

0.953

163.000

Table 2: Global measurement model results of structural model 1 First-order Construct Actualized Innovativeness CR=1 AVE=1

Indicator label

Item

Loading (λi)

Significance (t-value)

MV 19

(Index)

1.000

-

Hedonist Innovativeness CR=0.882 AVE=0.714

MV 20

0.849

56.605

0.860

56.771

0.827

47.843

0.915

88.012

0.940

135.415

0.956

212.572

0.875

74.694

0.859

58.802

0.728

13.881

0.864

30.231

0.876

41.813

0.874

44.352

MV 21 MV 22 MV 23

Social Innovativeness CR=0.959 AVE=0.827

MV 24 MV 25 MV 26 MV 27 MV 28

Perceived Stimulation CR=0.911 AVE=0.721

MV 29

MV 30 MV 31

If I like a brand, I rarely switch from it just to try something different. When I go to an electronics retail store, I feel it is safer to order products I am familiar with. I enjoy taking chances in buying unfamiliar brands just to get some variety in my purchases. I get a kick out of buying new high tech items before most other people know they exist. It is cool to be the first to own new high tech products. I get a thrill out of being the first to purchase a high technology item. Being the first to buy new technological devices is very important to me. I want to own the newest technological products. In the past, I was not occupied doing same things too often. (1) / I was occupied doing same things too often. (5). In the past, I have not experienced far too seldom new things and changes. (1) / I have experienced far too seldom new things and changes. (5) In the past, I was not making insufficient new experiences. (1) / I was making insufficient new experiences. (5) In the past, my life did not run far too steady. (1) / my life did run far too steady. (5)

45

Table 3: Hierarchical measurement model results

Item

First-Order Measurement Model Inclination to resist Changes VIF = 2.105 Significance Weights (t-value)

Status Quo Satisfaction VIF = 1.848 Weights

Significance (t-value)

Routine Seeking (MV 32) “I generally prefer to use mobile phones with which I am familiar over starting to use a new technological product”

0.301

11.471

-

-

Emotional Reaction to Imposed Change (MV 33) “I find it exciting to try out new mobile phones” [r]

0.534

17.978

-

-

-

-

-

-

0.636

13.984

0.452

9.340

-

-

-

-

-

-

Short-Term Focus (MV 34) “I often feel a bit uncomfortable to try out new 0.306 11.941 mobile phones, even though it may be beneficial to me” Cognitive Rigidity (MV 35) “Once I’ve started using certain mobile phones, 0.135 4.560 I’m not likely to switch” Satisfaction with extent of Innovations (MV36) “Overall, my personal need for innovations in the field of mobile phones has been by far not covered in the past” [r] Satisfaction with existing Products (MV 37) “In the past, I was very satisfied with my mobile phones” Second-Order Measurement Model Passive Innovation Resistance VIF = 1.086 Significance Construct Weights (t-value) 0.811 24.650 Inclination to resist Changes 0.401 10.062 Status Quo Satisfaction

46

Table 4: Global measurement model results of structural model 2 Construct Actualized Innovativeness CR=0.972 AVE=920

Indicator label MV 38 MV 39 MV 40 MV 41

Hedonist Innovativeness CR=0.959 AVE=0.856

MV 42 MV 43 MV 44

Social Innovativeness CR=0.946 AVE=0.854

MV 45 MV 46 MV 47

Perceived Stimulation CR=1 AVE=1

Item

MV 48

How likely do you feel it is that you would purchase this product?

Very unlikely – Very likely Highly improbable – Highly probable Impossible – possible

I would rather try a mobile phone I am not very sure of, than stick with a mobile phone I am used to. If I like a mobile phone, I often still switch from it just to try something different. I enjoy taking chances in buying unfamiliar mobile phones just to get some variety in my purchases. When I go to a mobile phone shop, I am primary interested in new and unfamiliar mobile phones. In general, I am among the first in my circle of friends to buy a new mobile phone when it appears. If I heard that a new mobile phone was available, I would be interested enough to gather information. In general, I am among the first in my circle of friends to know the newest mobile phones. This technological product is a minor variation of an existing product (r)

Loading (λi)

Significance (t-value)

0.942

116.864

0.973

214.808

0.962

224.912

0.920

125.858

0.909

106.568

0.942

137.891

0.928

90.269

0.894

76.363

0.920

109.810

0.957

222.944

1.000

-

47

Highlights

  

Passive innovation resistance is a strong inhibitor of innovative consumer behavior Passive innovation resistance decreases adoptive and actualized innovativeness Perceived stimulation increases the negative effects of passive innovation resistance

48