The Role of Internet Innovation Intermediary Platform in Accelerating Patent Commercialization Process

Transcription

1 The Role of Internet Innovation Intermediary Platform in Accelerating Patent Commercialization Process Kuo-Ming Chu Cheng Shiu University, Department of Business Management, Taiwan. Hui-Chun Chan Far East University, Food and Beverage Management, Taiwan. Abstract This paper focuses on explicitly the importance of mediators and moderators in open innovation research, for the role of Internet innovation intermediary in the relationship between open innovation and commercialization of intellectual property, and tested if moderator or mediator effects influenced the relationship. Data for this study were gathered from 10 Internet innovation intermediary platforms by means of a questionnaire administered to a convenience sample of 656 respondents and mainly from Taiwan and China. The empirical results indicate that that there is partial mediator and moderator effect for Internet Innovation Intermediary in the relationship between an individual s intention toward participating in open innovation and the outcomes of commercialization of intellectual property. The paper concludes with Internet Innovation Intermediaries plays a crucial role on the innovative capabilities at a national level and investigate specific conditions that affects the success of innovation and driving a potential contributor to economic development and might produce unexpected consequences. The theoretical and practical implications of the study are discussed, offering recommendations and future research directions. Key words: Internet Innovation Intermediary, Open Innovation, Patent Commercialization, Moderator, Mediator JEL Classification: C 19, G13, G 14 1

2 1. Introduction Proceedings of the First Asia-Pacific Conference on Global Business, Economics, Finance and In an era with abundant and widely distributed knowledge, according to Bereuter, Jerolitsch, & Heimerl (2011) and Huizingh (2011), open innovation is a concept that has recently had become one of the hottest topics in innovation management and has grown tremendously over the last few years, and it can access the best available external expertise, worldwide synergies with one's own resources are yielded, where companies generate their own innovation ideas, and the broader management arena (Chesbrough, 2003; 2011; Grimaldi, Cricelli, Rogo, & Iannarelli, 2012). Promoting a commercial application of innovation and driving profitable growth are common goals of intellectual property. As well as, the use of Internet innovation intermediaries, such as NineSigma, Innocentive, Innovaroor Yet2.com have been specifically helpful for technological problems requiring solutions from on distant and distinct innovation actors (Zamboni, 2011; López & Vanhaverbeke, 2010; Piller, 2009). Lately though, open innovation literature has emphasized the existence of innovation intermediaries that help alleviating these problems typical for innovation or technology markets. But the intermediaries for the IP market are still a considerable challenge (Svensson, 2011; Andersen, Rosli, Rossi, & Yangsap, 2011). Therefore, we focus on how the Internet has impacted the process of collaborative innovation intermediaries. Mediator and moderator variables provide useful information about how, why, or when a phenomenon occurs. There is often confusion over the meaning of, and differences between a mediator variable and a moderator variable, these terms are evident in open innovation research as well as research in other areas of Internet intermediaries (Tavakoli, Jackson, & Moneyham, 2009). Unfortunately, many innovation researchers fail to test for mediators or moderators in their data or use the terms incorrectly, because both types of effects hold considerable potential for furthering our understanding of a variety of psychological phenomena of interest to intellectual property creators and users. The aim of this study is to describe two third-variable effect models, explicitly the importance of mediators and moderators in open innovation research, for the role of Internet innovation intermediary in the relationship between open innovation and commercialization of IP, and tested if moderator or mediator effects influenced the relationship. We attempt to encourage more widespread analysis of these effects in open innovation questions and to develop a conceptual research framework for modes of open innovation, to create Internet innovation intermediary mechanisms to match patent creators with patent users and facilitate transactions between them, the efficiency implications and integrations of the emergence of these intermediaries for the patent market, which will be used for future empirical 2

3 research. We follow with a presentation of basic mediation and moderation models, the most recent recommendations for their statistical estimation, and briefly discuss advanced models that involve the effects. 2. Literature Review 2.1 Internet Innovation Intermediaries Patents have very recently become a particularly hot commodity due to a series of highprofile settlements and auctions involving huge price tags for patent portfolios (Hagiu and Yoffie, 2011). IP plays a crucial role in open innovation as a result of the in and outflows of ideas, (Chesbrough, 2006; Lichtenthaler, 2007; Van de Vrande, de Jong, Vanhaverbeke, & de Rochemont, 2010). The current open innovation literature needs to be complemented with work on the managerial challenges faced by companies working with an innovation intermediary to solve research and development problems (Sieg, Wallin, & Von Krogh, 2010). Additionally it is very challenging to know how to value available technologies and patents. Approximately over the last 10 years, Internet intermediaries such as NineSigma, YourEncore, IdeaConnection, InnoCentive, Innovaro, and Ocean Tomo have boomed in technology markets due to the need for artificial mechanisms to connect prevailing technological challenges from established companies such as P&G, L Oreal and ubiquitous sources of scientific, entrepreneurial and experienced knowledge. Therefore, this research develops an Internet innovation intermediary of IP valuation model based on four indices including the IP provider (creators), IP user, IP specifications and IP license specifications. In the highly competitive global market, the proposed the model and platform of Internet intermediary can significantly enhance IP trading performance and therefore the company R&D tasks can be efficiently accomplished. But Internet Innovation intermediaries are increasingly being used in practice and in adding value for a client s NPD process becomes very interesting to investigate (Tran, Hsuan, & Mahnke, 2011). 2.2 Commercialization of Intellectual Property The commercialization of IP is a potential contributor to economic development. Commercialization of innovation has been defined as the act or activities required for introducing an innovation to market (Narayanan, Pinches, Kelm, & Lander, 2000; Nambisan & Sawhney, 2007; Datta, 2011) into NPD, processes or services, in response to market opportunities. Patent establishes a quid pro quo which both facilitates the commercial exploitation of an invention and encourages the diffusion of knowledge that would otherwise remain secret. According to Datta (2011) and Maarse & Bogers (2012), commercialization of innovations has four key aspects and 3

4 phases: recognizes a market for an innovation, develops and manufacture it into a product, finalizing the product and launching the product. Converting technical innovations to products and services entails the development of manufacturing and marketing capabilities, and assets such as manufacturing facilities and service and distribution networks (Ahuja, 2000; Teece, Pisano, & Shuen, 1997; Grimaldi et al., 2012). The objective and expectation of this stage are to create profit. Based upon the discussion above, this study allows the examination of both hypotheses within a single context considering Internet innovation intermediary as a mediator and moderator between an individual s intention toward participating in open innovation and commercialization of intellectual property commonly examined. H 1 : Internet innovation intermediary is a mediator of the relationship between an individual s intention toward participating in open innovation and the outcomes of commercialization of intellectual property. H 2 : Internet innovation intermediary is a moderator of the relationship between an individual s intention toward participating in open innovation and the outcomes of commercialization of intellectual property. 3. Methodology 3.1 Sample selection and Collection The aim of this research here is to explore the role of Internet innovation intermediary in the relationship between open innovation and commercialization of IP, and tested if moderator or mediator effects influenced the relationship. We carried out a profile check on the remaining Internet innovation intermediaries, checking from publicly available sources, including Taiwan Technology Marketplace (TWTM), Industrial Technology Research Institute (ITRI) Patent Auction, Enterprise Intellectual Property Marketplace (EIPMART), Global Patent Search and Analysis System (GPSA), Beipm, Taiwan Agriculture TechnoMart (TATM), Scientific & Technological Resources, Information, Asia Pacific Intellectual Property Association (APIPA), Portal International IPRs Service and Knowledge Exchange (STRIKE) have boomed in Taiwan and China technology markets. 3.2 Participants and Procedures To test the Theory of Planned Behavior (Ajzen & Fishbein, 1980; TPB) model in Internet innovation intermediary context, we conducted a Web-based survey on 10 Internet innovation intermediary platforms. We send the survey instrument to many MBA students of Cheng Shiu University and experts in Taiwan for a pretest, To achieve these, we sent personalized 4

5 s to member of 10 Internet innovation intermediary platforms, inviting him or her to participate in the survey. Then several Internet innovation intermediaries were contacted to help with the pilot-test of the instrument. The survey data were collected during the first five months of 2012, totaling 656 respondents (315 females, 341 males; mean age = 39 years) for a response rate of 19.7% and mainly from Taiwan and China. Most of the participants were full-time employees or freelancers and the rest were students. Most of the participants were full-time employees or Internet freelancers and the rest were students. Then we examined our hypotheses by applying the analysis of variance (ANOVA) and hierarchical regression to the collected data. The sources of these data are described in following the discussion of measures. 3.3 Measures We developed the items in the questionnaire by adapting the measures that had been validated by prior researches. TPB measures include items that assess the intention to participate in open innovation communities and commercialization of IP. Items that measure perceived pressures were added in the TPB measure section. Measures for intention and attitude were adopted and modified from Ajzen (1991), Bakici, Almirall, & Wareham (2011) and Tran et al. (2011). In this study, intention is referred as an individual s willingness to participate in an open innovation and commercialization of IP. Information about Cronbach's α values, means, standard deviations, and correlations between scales is to be found in Table 1. Table 1: The Cronbach's α values for composite variables and the correlation matrix Variable (Number of items per dimension) α Mean SD Internet Innovation Intermediary 1 (1) Participating in OI Community (4) ** 1 Commercialization of IP (4) ** 0.29** 1 Note: 1 Internet innovation intermediary was dichotomised as 1=existence; 0= non-existence. * p< 0.05; ** p<0.01 Table 1 lists the mean scores and Cronbach's α values for each of the composite variables assessed by the survey. In each case, the Cronbach's α values for the latent constructs were 0.70 or better, indicating a high degree of inter-item reliability and acceptable standard of agreement for this type of research (Nunnally, 1978). Specifically, the Cronbach's α of each construct was greater than 0.82, which indicates a strong reliability for the survey instrument. 4. Results and Discussion 4.1 Mediation Test The data we are using to illustrate the process of conducting mediational analyses with multiple regression were collected, we again provide a step-by-step example. Table 2 contains the 5

6 analyses necessary to examine this mediational hypothesis. As mentioned, in this case we use actual data to illustrate issues that arise when using real, versus simulated, data. Table 2: Testing Mediator Effects Using Multiple Regression Testing steps in mediation model B SE B 95% CI β Testing Step 1 (Path c) Predictor: Participating in OI Community (0.83, 1.69) 0.27** Testing Step 2 (Path a) Outcome: Internet innovation intermediary Predictor: Participating in OI Community (0.51, 0.72) 0.32** Testing Step 3 (Paths b and ) Mediator: Internet innovation intermediary (Path b) (0.67, 1.36) 0.21** Predictor: Participating in OI Community (0.57, 1.42) 0.17** Note: CI= confidence interval; * p< 0.05; ** p<0.01 Following the steps outlined earlier for testing mediation, we first established that participating in open Innovation community (the predictor) was related to commercialization of IP (the outcome) by regressing commercialization of IP on the participating in open innovation community variable (Step 1) and depicted in Figure 1. The unstandardized regression coefficient (B=1.15) associated with the effect of commercialization of IP was significant (p<0.01). This relationship was significant (β=0.27, p=0.002). Thus, Path c was significant, and the requirement for mediation in Step 1 was met. Therefore, we ran second and third equations were analyzed. In the second equation, the mediator (Internet innovation intermediary) was regressed on the predictor variable (participating in open Innovation community). The result indicated that there was significant relationship between mediator and predictor variable (β=0.32, p=0.001), and thus the condition for Step 2 was met (Path a was significant). Meditation is established if the first and the second equations are shown to be significant. Figure 1: Example of statistical analysis of a mediator effect β=0.32 p=0.001 Internet Innovation Intermediary β=0.21 p=0.004 Participating in OI Community β=0.17 p=0.005 Commercialization of IP Indirect Effect= c - c' = =0.10 6

7 In addition, the third equation involved regressing the outcome (commercialization of IP) variable simultaneously on the predictor (participating in open Innovation community) and mediator variable (Internet innovation intermediary). Recently Ledermann et al. (2011) and Imai et al. (2010) have reproposed the use of c - c' ( =0.10) as the measure of the indirect effect. Using our original model, the direct effect of attitude is The total effect is the simply the sum of the direct and indirect effect = Because c - c' is equal to the product of Paths a and b, the significance of the difference between c and c' can be estimated by testing the significance of the products of Paths a and b. It is much more common and more highly recommended (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002; Ledermann et al., 2011) to perform a single test of ab. Specifically, you divide the product of Paths a and b by a standard error term. Although there are several different ways to calculate this standard error term, an increasingly popular method of testing the indirect effect is bootstrapping (Shrout & Bolger, 2002; Kenny, 2008), in which they recommend that one use bootstrap methods to obtain better power, especially when sample sizes are not large. Bootstrapping is the preferred method because it's the only test that doesn t violate assumptions of normality. The following steps are adapted from Shrout and Bolger s (2002, p. 426) description of how to conduct the bootstrap procedure: 1. Using the original data set of N cases as a population reservoir, create a bootstrap sample of N cases by random sampling with replacement. 2. Calculate a, b, and a b based on this bootstrap sample, and save the results to a file. 3. Repeat Steps 1 and 2 a total of J times. 4. Examine the distribution of J estimates, and if α=0.05, determine the 2.5 and 97.5 percentile values of a b. For our example, the 95% confidence interval would be (0.61)(0.82) ±0.097 (1.96) =0.31 to The result revealed that there is partial mediator effect for Internet innovation intermediary in the relationship between participating in open Innovation community and commercialization of IP. 4.2 Moderation Test The data we are using to demonstrate moderator effects using multiple regression, it is necessary to test the main effects and interaction effects of the predictor variables on the dependent variable, including issues related to designing the study, analyzing the data, and interpreting the results. An ANOVA was conducted in testing of significance of the model. Table 3: ANOVA Table for the Testing Moderating Effect of Participating in OI Community 7

8 Step/Model Sum of df Mean Square F VIF Sig. Squares Step 1/Model 1 Predictor: Participating in OI Community Model 2 Moderator: Internet innovation intermediary Step 2/Model 3 Predictor Mediator: Participating in OI Community * p< 0.05; ** p<0.01 Internet innovation intermediary The above ANOVA in Table 3 shows model 3, with the moderating term of Participating in OI Community Internet Innovation Intermediary added, is significant because F= 10.62, degrees of freedom (df) = 3, p-value = , which indicates a decision rule that rejects H0 as p-value < Thus the moderation effect model significantly fits the data. Table 4 contains the analyses necessary to examine this moderation hypothesis. Table 4.:Testing Moderator Effects Using Hierarchical Multiple Regression Step and variable B SE B 95% CI β R 2 Step 1/Model 1 Predictor: Participating in OI Community (-0.05, 0.57) 0.21* 0.09** Model 2 Moderator: Internet innovation intermediary (0.38, 0.79) 0.57** 0.19** Step 2/Model 3 Predictor Mediator: Participating in OI (0.19, 0.55) 0.41** 0.13** Community Internet innovation intermediary * p< 0.05; ** p<0.01 Looking at the output for effects coding in Table 1, the unstandardized regression coefficient for Participating in OI Community was 0.31, which was significant level (p<0.01). The unstandardized regression coefficient for Internet Innovation Intermediary was 0.52 (p<0.01), meaning that there was a significant positive relation between Internet Innovation Intermediary and Commercialization of IP in the sample. Because Internet Innovation Intermediary was coded by means of effects coding, and the support variable was standardized, we could interpret this 8

9 first-order effect of Participating in OI Community as an average effect. The unstandardized regression coefficient for the interaction term was 0.39 (p< 0.01). The model summary in Table 4 for testing strength of relationship indicates that, by adding the moderating term (Participating in OI Community Internet Innovation Intermediary), the R 2 increased from 0.09 to 0.13 and significant (p = 0.001). In other words, the interaction between Participating in OI Community and Internet Innovation Intermediary explained an additional 4% of the variance in depression scores over and above the 9% explained by the first- order effects of social support and gender alone. Referring to Table 3, VIF = which is < 5. This shows that the independent constructs are independent from each other there is no multicollinearity between the independent variables. This shows that the independent constructs are independent from each other there is no multicollinearity between the independent variables. Based on the above findings, H 2 is supported. To understand the form of the interaction, it was necessary to explore it further. Sample means for each situation are then used to plot and visually demonstrate the interaction. As mentioned, one way is to plot predicted values for the outcome variable (Commercialization of IP) for representative groups. Figure 2: Differentiating interaction effect Commercialization of IP Participating in OI Community Community The solid line in each graph is Internet II (Internet Innovation Intermediaries); the dashed line in each graph is Non Internet II (Non-Internet Innovation Intermediaries) As shown in Figure 2, the plotted graph may reveal a crossed interaction. In similar manners, the level of perceived Commercialization of IP to be high when intention of Participating in OI Community is high compare when participate intention are low. Therefore, Figure 2 depicts Internet innovation intermediary is a moderator of the relationship between an individual s 9

10 intention toward participating in open innovation and the outcomes of commercialization of intellectual property. 5. Conclusions and Recommendations This paper focuses on explicitly the importance of mediators and moderators in open innovation research, for the role of Internet innovation intermediary in the relationship between open innovation and commercialization of IP, and tested if moderator or mediator effects influenced the relationship. A research framework with two hypotheses was established to examine these relationships. In this study we have attempted to achieve three goals. First, by carefully elaborating the many ways in which moderators and mediators differ, we have tried to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably. Analysis of mediator or moderator effects may supply more in-depth information about a research phenomenon than can be explained by direct effects alone. In this paper, we provided definitions of mediated and moderated effects and also expanded previous research on this topic by providing a systematic test of both the mediator and the moderator hypothesis. It is hoped that this paper, along with its more statistically focused on mediator and moderator research, is intended to serve as a guide and inspiration point for future mediator and moderator research, will encourage more widespread use of these concepts and techniques to enhance the scientific contribution of developmental and Internet Innovation Intermediary research. With the use of mediator and moderator models, researchers can test competing theories about relationships among variables of interest. By directly testing two or more alternative models, a researcher can determine statistically which theoretical model best captures or explains an observed relationship among variables. We also advocate bootstrapping indirect effects whenever circumstances permit it, but it may be wise to use both approaches in empirical applications, given the advantages associated with each. Second, the present study examined the influence of Internet Innovation Intermediary in the relationship between Participating in OI Community and Commercialization of IP, whether the relationship was influenced by a mediator or moderator effect. The result revealed that there is partial mediator effect for Internet Innovation Intermediary in the relationship between OI Community and Commercialization of IP. However, the result further indicates moderator effect for Participating in OI Community and Internet Innovation Intermediary. This is an example of mediated moderation according to Svensson (2011) and Ståhlbröst & Bergvall-Kåreborn (2011). Open Innovation is a phenomenon that has become increasingly important for both practice and theory over the last few years. This is why a vast number of studies focus on the context of 10

11 individual s motivations for participating in such communities. So more importantly, patent intermediaries can make the IP markets more liquid, or will the inefficiencies persist. We will here argue that IP brokers represent a key player in the market of patents, as their role in technology transfer can be that of active market makers. We predict that structural conditions of the industry and technological field where Internet innovation intermediaries are active will impact the success and relevance of bridging supply and demand of intellectual capital activities (Scotchmer, 2006). In summary, we have offered Internet Innovation Intermediary researchers step-by-step guides to testing mediator and moderator effects that can be used both in planning their own research and in evaluating published research and have provided an example of each type of analysis. Based on our conclusions, we propose the following propositions to those who lead knowledge- management within online intermediary platforms. The current research on the value added of Internet Innovation Intermediaries suggests a fresh agenda of research into the contingencies of value added through open innovation strategies more generally, in addition and beyond current fascination with the trend to increasingly outsource innovation. Finally, actively support the collaboration and networking among members during contests in online intermediary platforms. In particular, allow them to be able to provide feedback each other s innovation. References Ahuja, G., 2000, The Duality of Collaboration: Inducements and Opportunities in the Formation of Inter-firm Linkages. Strategic Management Journal, 21, Ajzen, I., 1991, The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50 (2), doi: / (91)90020 Ajzen, I., and Fishbein, M., 1980, Understanding Attitudes and Predicting Social Behavior. Prentice Hall, Englewood Cliffs, New Jersey. Andersen, B. Rosli, A., Rossi, F., and Yangsap, W., 2011, Intellectual Property Governance in ICT Firms: Strategic Value Seeking Through Proprietary and Non-Proprietary IP Transactions. Journal of Intellectual Property Management, forthcoming. Bakici,T., Almirall, E., and Wareham, J., 2011, Motives for Participation in Online Open Innovation Platforms. DRUID 2011, Copenhagen Denmark. Bereuter,T. L., Jerolitsch, D., and Heimerl, P., 2011, IPR-Codes And Guidelines In Europe Facilitating Collaboration Of Publicly Funded Research Organizations (PROs) With Businesses, Guidelines in Europe, September 2011,

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