چشم انداز عمل منطقی نوآوری کاربر : مدل و آزمون تجربی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|2301||2012||12 صفحه PDF||سفارش دهید||12217 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Industrial Marketing Management, Available online 24 October 2012
Much research in the field of user innovation has followed two theoretical perspectives — the cost–benefit framework and the community perspective of user innovation. By adopting the theory of reasoned action (TRA) into the context of user innovation, this study establishes an integrative theoretical framework to accommodate both the cost–benefit perspective and the community perspective of user innovation. This TRA-extended framework embraces both the direct and the interactive influences of the cost–benefit factors (the perceived effort in innovation and the perceived benefit from innovation), the individual characteristics (personal innovativeness and experience) and the social interactions (the perceived social influence) in shaping user innovation at the individual level. The empirical results support the proposed theoretical model. The results also reveal that the moderating effect of experience (or perceived effort) on the intentional component of user innovation is different from the effect on the behavioral component of user innovation.
User innovators refer to those individual users or individual user firms who develop new products and services for themselves, without assistance from or involvement of the producers (von Hippel, 1988). Indeed, users (both firm users and individual users) are frequently the first to develop and use prototype versions of what later become commercially significant new industrial and commercial products (Baldwin et al., 2006, Enos, 1962, Urban and von Hippel, 1988, von Hippel, 1976, von Hippel, 1978, von Hippel, 1986 and von Hippel, 1988). Growing evidence from industrial products (e.g., Morrison et al., 2004, Morrison et al., 2000, Riggs and von Hippel, 1994, Urban and von Hippel, 1988, von Hippel, 1976 and von Hippel, 1988), consumer products (Baldwin et al., 2006, Franke and Shah, 2003, Franke et al., 2006, Füller et al., 2007, Hienerth, 2006, Hyysalo, 2009, Jeppesen and Frederiksen, 2006, Lüthje, 2004 and Lüthje et al., 2005) and new service development (Alam, 2006) research indicates that users may be a highly promising source of innovation. Much research in the field of user innovation has followed two theoretical perspectives. The cost–benefit framework is the most adopted perspective in previous studies and proposes that user innovation is jointly determined by the benefit an individual user expects to obtain from a required innovation and the costs associated with the innovation ( von Hippel, 1988 and von Hippel, 2005). The other perspective – the community perspective of user innovation – has attracted much research interest in recent years, including studies on the user innovator community (e.g., Franke and Shah, 2003, Füller et al., 2007, Hienerth, 2006 and von Hippel, 2005) and the open source community ( Bagozzi and Dholakia, 2006, Lerner and Tirole, 2002, von Hippel and von Krogh, 2003 and West and Lakhani, 2008). In this research stream,1 the social interactions among users and user innovators have a critical influence on user innovation activities. Given the importance of the two abovementioned theoretical perspectives, it is interesting to explore whether we are able to establish an integrative theoretical framework that accommodates both the cost–benefit perspective and the community perspective. As such, Bogers, Afuah, and Bastian (2010) observed, in a comprehensive review of the user innovation literature, that a major challenge for user innovation scholars is to develop a more coherent theoretical framework that is embedded in management theories to comprehensively explain user innovation. It is worth noting that, as stated in Hyysalo (2009), the existing studies have focused mainly on verifying the characteristics of the lead users2 and exploring whether and which specific user characteristics discriminate user innovators from non-innovating users (i.e., for end users, why do some of them innovate whereas others do not?) (e.g., Franke and Shah, 2003, Franke and von Hippel, 2003, Lüthje, 2004 and Morrison et al., 2000). A complementary, yet less examined research topic is why do user innovators develop some innovations further than others? In other words, for user innovators, what are the factors that determine their degree of involvement in user innovation? In answering this research question, this paper will focus on the driving factors of user innovators instead of the differences between innovating and non-innovating users. Furthermore, the previous studies on user innovation have examined the individual influences of the cost–benefit factors and the personal characteristics of user innovators. However, with few exceptions, the interactive effects among the cost–benefit factors, social influence and personal innovativeness in the context of user innovation have not been explored using a coherent theoretical framework. Therefore, by establishing an integrative model of user innovation behavior from a reasoned action perspective, this study attempted to fill in the gap in the literature. Specifically, by adopting the theory of reasoned action (TRA) to the context of user innovation and focusing on individual user innovators, this study will provide an integrative framework to combine the cost–benefit perspective and the community perspective of user innovation. Moreover, through modeling and empirically examining the integrative framework, both the direct and the interactive influences of the cost–benefit factors (the perceived effort in innovation and the perceived benefit from innovation), the individual characteristics (personal innovativeness and experience) and social interactions (the perceived social influence) in shaping user innovation will be explored. The user innovation construct is deconstructed into two parts, i.e., the intentional component and the behavioral component. By doing so, this study aims to better depict user innovation phenomena and examines whether there are differences between the influence of the user innovation determinants on the intentional component and the influence of those determinants on the behavioral component of user innovation. The remainder of this article is organized as follows. Section 2 presents the theory, the model and the hypotheses. Section 3 contains the sample, the data and the measures, and Section 4 presents the research results. Then, the discussion and conclusions are presented in the final section.
نتیجه گیری انگلیسی
5.1. Summary of research findings By adopting the reasoned action perspective into the context of user innovation, this study provides an integrative and extended framework that combines the cost–benefit perspective and the community perspective of user innovation to model and empirically examine the direct and the interactive influences of cost–benefit factors (perceived effort in innovation and perceived benefit from innovation), individual characteristics (personal innovativeness and experience) and social interactions (perceived social influence) in shaping the intention and behavior of user innovation. The empirical results from a user innovation survey lend support to the theoretical model proposed in this study. Specifically, perceived benefit, personal innovativeness and social influence are positively related to user innovation with regard to leading edge status and the degree of engagement in innovation, and perceived effort is negatively related to user innovation with regard to the degree of engagement in innovation. Additionally, with respect to the moderating effects in the model, perceived effort negatively moderates the relationship between personal innovativeness and user innovation in terms of leading edge status and the degree of engagement in innovation, and personal innovativeness negatively moderates the relationship between social influence and leading edge status. Another interesting finding is that experience positively moderates the relationship between social influence and user innovation in terms of leading edge status, whereas it negatively moderates the relationship between social influence and user innovation in terms of the level of engagement in innovation. Together, the differences in the effects on the two components of the user innovation construct used in this study (i.e., leading edge status as the intentional component, and the degree of engagement in innovation as the behavioral component) should be critical in explaining and predicting the innovation-related behavior of user innovators. These differences are discussed below. 5.2. Discussion First, why does perceived effort have a significant influence on user innovation in terms of leading edge status, but not in terms of the level of engagement in innovation? According to Hypothesis 2, perceived effort is negatively related to user innovation. However, no significant relationship between perceived effort and user innovation was found for the level of engagement in innovation. The following reasons may account for this result. Leading edge status (the frequently used variable in prior research) is, by definition, an ex ante indicator of user innovation that measures the propensity of an innovator to undertake innovation activities. By contrast, the level of engagement in innovation is an ex post indicator of user innovation that measures how far user innovators develop their ideas into real innovative outcomes. Generally, user innovators will consider the perceived benefit more than the perceived effort before they begin to actually implement their innovation ideas. By contrast, during the process of developing ideas into real products, user innovators will attempt to evaluate more precisely the costs associated with the process of user innovation, and innovation-related costs will therefore be more seriously taken into consideration when making innovation-related decisions. Hence, it is a reasonable expectation that perceived effort would have a more significant influence on the actual behavior of user innovation than on the propensity of user innovation. Conversely, along with the increase in perceived effort (though a high level of perceived effort will impose a negative influence on the propensity for user innovation), it may be attractive for users to innovate because they enjoy challenge and have high needs for achievement, thereby inducing an increased inclination toward user innovation. Together, the above influences in opposite directions will offset each other. As a result, perceived effort exhibits no significant effect on leading edge status.5 Second, why does experience positively moderate the relationship between social influence and user innovation, in terms of leading edge status, but negatively moderate that relationship in terms of the level of engagement in innovation? One plausible reason is that experience may constrain user innovators' imaginations and creativity in seeking novel solutions to the problems that they encounter in product usage. As revealed in the classical research on problem solving, individuals are strongly constrained by their real world experiences via an effect called functional fixedness, i.e., those who use an object or see it used in a familiar way find it difficult to conceive of novel uses ( Adamson, 1952, Adamson and Taylor, 1954 and Allen and Marquis, 1964). The difficulties that those potential user innovators encounter will greatly weaken the impact of social influence on the extent to which they develop their ideas into real solutions. Moreover, unlike leading edge status as the intention aspect of user innovation, the degree of engagement in innovation, as a behavioral component of the user innovation construct, will be significantly influenced by perceived effort. Therefore, the costs associated with the functional fixedness would make some of the innovative users with high levels of experience more inclined to find an alternative way (e.g., giving advice on using, modifying or developing products based on their experience, instead of undertaking user innovations) to offer value to others in their user communities and to those who are important to them, thereby sustaining the social recognition in their social network. Third, the present study confirms the positive relationship between perceived benefit and user innovation in the literature. The greater the benefit a user expects to obtain from a needed innovation, the greater that user's willingness to devote resources to find a novel solution and the greater the level of investment will be (e.g., Franke et al., 2006, Lüthje and Herstatt, 2004, Morrison et al., 2000, Riggs and von Hippel, 1994, Urban and von Hippel, 1988 and von Hippel, 1988). Additionally, the social benefit obtained from innovating, with regard to the reputation effects (Füller et al., 2007) and signaling of competence (Glazer & Konrad, 1996), may be another important incentive for user innovators to participate in and contribute to product modifications, improvements and creation. This finding is consistent with the previous findings on user innovation from the community perspective of user innovation (e.g., Franke and Shah, 2003, Füller et al., 2007, Jeppesen and Frederiksen, 2006 and Lerner and Tirole, 2002). Fourth, this study confirms the finding in the literature that personal innovativeness is one of the important personal characteristics associated with user innovation (e.g., Agarwal and Prasad, 1998, Midgley and Dowling, 1978 and Morrison et al., 2004). As an information-intensive activity, the innovation process involves an active search for new ideas, knowledge and technologies that have commercial potential (Laursen and Salter, 2006 and von Hippel, 1994). Individuals with high levels of personal innovativeness are ‘active information seekers about new ideas’ (Rogers, 1995, p. 22) and are more capable of acquiring information and knowledge from a variety of external sources. Thus, innovating users are more likely to obtain the information that is required for implementing their innovation activities. Furthermore, innovation, by its very nature, is associated with great risk, uncertainty, and imprecision (Kirton, 1976). Because of their inherent desire to experiment with novel technologies and solutions, user innovators with high levels of personal innovativeness are able to cope with higher levels of uncertainty and are more prone to take risks (Rogers, 1983 and Rogers, 1995). Therefore, individuals with high levels of personal innovativeness will exhibit high levels of propensity for and the actual behavior of user innovation. 5.3. Contributions to the research on user innovation The present study differs from and contributes to the previous literature on user innovation in the following ways. First, by adopting the theory of reasoned action (TRA) to the context of user innovation, this study establishes an integrative theoretical framework to accommodate the cost–benefit perspective and the community perspective of user innovation, which are the two most influential research streams in the field of user innovation research. This TRA extended framework embraces both the direct and the interactive influences of the cost–benefit factors, individual characteristics and social interactions in shaping user innovation. Thus, this framework helps us to gain a comprehensive understanding of the user innovation phenomenon and has the potential to explain other innovation-related behavior in user communities, such as the free sharing of information among users and the question of why some users later go into business and commercialize their innovations as user-manufacturers. Second, with regard to the effect of innovativeness, the literature on innovation research focuses mainly on innovation adoption behavior rather than on innovating behavior (Fell, Hansen, & Becker, 2003). Extending from the classical theory of reasoned action, this study empirically confirms the significant influence of personal innovativeness on user innovation and offers a detailed explanation of the mechanisms by which personal innovativeness affects user innovation, which has been less discussed and empirically examined in the previous literature on user innovation. Specifically, personal innovativeness is positively related to user innovation, and this positive relationship is moderated by perceived effort. By contrast, personal innovativeness will negatively moderate the relationship between social influence and user innovation in terms of leading edge status. Third, in empirical studies on the determinants of user innovation, the construct of user innovation is often operationalized by leading edge status (e.g., Franke and Shah, 2003, Jeppesen and Frederiksen, 2006, Morrison et al., 2000 and Morrison et al., 2004). The present study reveals that there are two related aspects concerning the construct of user innovation: the intentional dimension (leading edge status) and the behavioral dimension (the degree of engagement in innovation). Although both of these dimensions may be predicted by the framework proposed in this study, they exhibit some differences in the effect of perceived effort on user innovation and the moderating effect of experience on the relationship between social influence and user innovation, as discussed above. Correspondingly, including both the intentional and behavioral components of user innovation in future research will help us to better understand the phenomenon of user innovation and the influencing mechanism(s) behind user innovation behavior. 5.4. Managerial implications As indicated in Table 5, approximately 80% (103 of 130 usable responses) of the respondents in this study reported that they had never interacted with manufacturers during their innovation activities. This finding was similar to that of Lüthje's (2004) study on sports-related products, in which nearly two-thirds (62.5%) of the consumers were without manufacturer contact in implementing their ideas and solutions for their own outdoor activities. Obviously, the user innovators are satisfied with the realization of their ideas for personal use and do not aspire to develop products in cooperation with manufacturers, even though established manufacturers may have complementary resources in the form of distribution channels, established brands and existing manufacturing facilities (Baldwin et al., 2006). This finding strongly indicates that, to make use of the potential business value of user innovations, manufacturers must explore effective approaches and measures for enhancing the degree of interaction between user innovators and manufacturers.The empirical results reveal that manufacturers are able to increase the propensity for and the level of user involvement in innovation by increasing the perceived benefit for the user innovators. In particular, the results reveal that, in addition to innovation-related benefits, including the psychological enjoyment of innovating, social benefits are an important motivation for user innovation. Thus, manufacturers may be able to help user innovators attain more benefits from social recognition (in terms of peer recognition and firm recognition) to motivate them to actively take part in user innovation. As suggested in Jeppesen and Frederiksen (2006), obtaining acknowledgment from the firm for a given innovation may be an important additional benefit of making an innovation for innovative users. Next, it is found that a decrease in perceived effort will directly facilitate user innovation, and perceived effort is associated with a high level of user innovation through the moderating effect of perceived effort on the relationship between personal innovativeness and user innovation. Because personal innovativeness is a relatively steady personal trait, it is more feasible for manufacturers to facilitate user innovation by reducing the user innovators' perceived effort in a given product field. As suggested in the literature, the modularized design of products (Franke & Piller, 2004) and free information sharing through firm-hosted on-line communities (Füller et al., 2007 and Jeppesen and Frederiksen, 2006) may greatly reduce the perceived effort for user innovators. Moreover, recent studies demonstrate that toolkits for user innovation and design may help integrate customers into new product development and design (Franke & Piller, 2004) by allowing users to design novel products based on trial-and-error experimentation and by delivering immediate feedback on the potential outcome of their design ideas (von Hippel, 2001 and von Hippel and Katz, 2002), thereby facilitating user innovation by lowering the perceived effort of innovative users (Franke and Piller, 2004, von Hippel, 2001 and von Hippel and Katz, 2002). Finally, the findings on social influence highlight the importance of social influence on user innovation. The development and application of information technology have provided many opportunities for manufacturers to take advantage of the effect of social influence on user innovation. Manufacturers are able to host or sponsor communities of practice with on-line or off-line user communities in which users are able to actively and easily exchange their ideas, exchange advice and feedback, foster member embeddedness through personal interactions and freely share their product improvements and modifications (Franke and Shah, 2003, Jeppesen and Frederiksen, 2006 and Porter and Donthu, 2008). By doing so, close social and psychological links between users in a community will be established, thereby strengthening the positive effect of social influence on user innovation. 5.5. Limitations and future research directions This study has some limitations that must be considered. First, the data in this study were cross-sectional, and future studies could use longitudinal research designs to examine how the antecedent variables (i.e., perceived effort and perceived benefit, social influence and personal innovativeness) in this study would affect the continuance of user innovation in a given product field and the potential changes in these effects over time. Furthermore, this study uses a convenience sample (by snowball sampling) instead of a random sample, and the research sample in this study is relatively small to make a more generalized conclusion on user innovation behavior. Although the effectiveness of snowball sampling in user innovation research has been empirically supported by Morrison et al. (2000) and Lüthje and Herstatt (2004), additional studies with a large sample size will be required to test the validity and robustness of the TRA-extended framework on user innovation. Second, the degree of engagement in innovation was introduced to operationalize the behavioral dimension of the user innovation construct in the present study. As an interesting topic worthy of future investigation, additional aspects for measuring the behavioral component of the user innovation construct should be taken into consideration and employed in further empirical analyses. By doing so, we will gain further insight into the phenomenon of user innovation. Some of these behavioral variables may include the frequency of user innovation, the degree of technological improvement and the commercial attractiveness of user innovation. Despite these limitations, this study makes a contribution to the user innovation literature by complementing the existing lead user studies and by offering a comprehensive understanding of user innovator behavior. Third, this study focused on user innovation at the individual level. Thus, additional surveys and quantitative studies will be required to determine whether the findings may be generalized into broader contexts, such as user innovation at the organizational level and the free revealing of information among innovative users in the context of on-line user communities. Moreover, when considering the moderating effects of various factors on user innovation, future studies could investigate a broader set of variables. For instance, Arts, Frambach, and Bijmolt (2011) find that perceived uncertainty exhibits a stronger effect on intention than on adoption behavior. It will be interesting to empirically examine whether and how perceived uncertainty would moderate the relationships between the antecedents (i.e., perceived benefit, personal innovativeness, and social influence) and user innovation.