The effect of inbound open innovation on firm performance: Evidence from high-tech industry

Technological Forecasting & Social Change 99 (2015) 222–230

Contents lists available at ScienceDirect

Technological Forecasting & Social Change

The effect of inbound open innovation on firm performance: Evidence from high-tech industry Chun-Hsien Wang, Ching-Hsing Chang ⁎, George C. Shen College of Management, National Chiayi University, Taiwan, ROC, No. 580. Sinmin Rd., Chiayi City 600, Taiwan, ROC

a r t i c l e

i n f o

Article history: Received 20 August 2014 Received in revised form 2 July 2015 Accepted 7 July 2015 Available online xxxx Keywords: Open innovation External knowledge Technology acquisition Horizontal technology collaboration Vertical technology collaboration

a b s t r a c t Academics and practitioners have demonstrated that open innovation is critical to superior performance; firms thereby connect their in-house R&D functions with external partners to enhance their innovation activities. This study extends the open innovation theory and the knowledge-based view to explain how a firm's external knowledge resource acquisition influences its innovation deployment as well as its performance. Specifically, we argue that technology scouting plays an important role as an antecedent to collaboration with horizontal and vertical technology acquisitions. Furthermore, the horizontal technology collaboration and vertical technology collaboration play mediators in determining firm performance returns from executing such acquisitions. Based on a large-scale survey of high-tech firms, the study finds that the ability to build well-developed external connection channels increases the efficacy of inbound open innovation in achieving superior performance. © 2015 Elsevier Inc. All rights reserved.

1. Introduction The sourcing of external knowledge is crucial to firm innovation activities (Cohen and Levinthal, 1990; Laursen and Salter, 2006; Powell et al., 1996). Indeed, scholars practicing in innovation management have long been interested in how firms acquire external resources. A central theme of the innovation process concerns what drives inbound openness innovation. To determine how firms access external knowledge and technology, one stream of research highlights the role of inbound open innovation (Chesbrough, 2003a; Parida et al., 2012; Sisodiya et al., 2013), in which external collaborative partners can complement in-house R&D activities and, in turn, increase firm performance (Cohen and Levinthal, 1990; Ahuja, 2000; Stuart, 2000; Powell et al., 1996). Moreover, many studies have shown that inbound open innovation is critical to a variety of positive outcomes, including greater in-house R&D, innovativeness, and performance (e.g., Chesbrough, 2003a; Laursen and Salter, 2006; Garriga et al., in press). Obviously, scholars have regarded inbound open innovation, which is often considered a key driver of firms' innovation, as a reflection of the variety of knowledge, technologies, and ideas among external partners. Thus, inbound open innovation can be defined as an outside-in process to access knowledge and technology that often resides beyond a firm's boundaries to complement the firm's internal innovation base. ⁎ Corresponding author. E-mail addresses: [email protected] (C.-H. Wang), [email protected] (C.-H. Chang).

http://dx.doi.org/10.1016/j.techfore.2015.07.006 0040-1625/© 2015 Elsevier Inc. All rights reserved.

While much of the prior literature focuses on inbound open innovation and has found evidence that extramural knowledge obtained from other various partners is beneficial to the focal firm's performance, Parida et al. (2012) identify horizontal technology collaborations (HTCs) and vertical technology collaborations (VTCs) as the crucial source of external knowledge resources for supporting innovation performance. Numerous studies on open innovation explain why these types of sources provide significant and valuable contributions (Belderbos et al., 2004; Brockhoff, 2003; Gassmann, 2006; Pisano, 1991). However few studies have explored the role of HTCs and VTCs as mediators in the development of inbound open innovation, which represents an important gap in the extant literature. Moreover, few theoretical and empirical studies of innovation have accounted for the conditions under which external knowledge partners boost innovation outcomes. To address these gaps in the literature, we use the knowledge-based view (KBV) of firms (Grant, 1996; Grant and Baden-Fuller, 2004) to leverage and extend the research on inbound open innovation (Parida et al., 2012; Chesbrough and Crowther, 2006), and we specify the conditions under which the external knowledge resources of HTCs and VTCs can be especially beneficial by establishing important fundamental and distinct components to guide research on sources of inbound open innovation. Studies have highlighted the fact that external technology acquisition has gradually become a key driver of firms' innovation performance (Chesbrough et al., 2006; Laursen et al., 2015; Moreira, 2014; Stuart, 2000; Van De Vrande et al., 2009). This study posits that, while the inbound open innovation role of a firm's innovation base necessarily depends on the amount of external knowledge, firms must

C.-H. Wang et al. / Technological Forecasting & Social Change 99 (2015) 222–230

create well-developed instruments to access the value of external knowledge. This study makes several theoretical contributions to innovation research. First, the study offers an integrative model that examines the influence exerted by technology scouting and horizontal and vertical technology collaboration on firm performance. This influence has led to an obvious result: increasing high-technology firms' ability to enhance their internal innovation and performance allows the firms to engage in openness strategies focused on external collaboration with different types of collaborators. As such, this study is one of the first to concurrently consider the relative contributions of the KBV and open innovation on these outcomes. Second, drawing on the KBV, this study enriches inbound open innovation theory by distinguishing between horizontal collaborative partners and vertical collaborative partners. This distinction is important because horizontal and vertical collaborative partners allow for the acquisition of two distinct facets of external knowledge and technology. In doing so, it potentially contributes to clarifying the elusive but important connection between external resources acquisition and firm performance. Many previous studies highlight the important role of external knowledge in innovation activities (Cohen and Levinthal, 1990; Chesbrough et al., 2006; Laursen et al., 2015; Moreira, 2014; Stuart, 2000; Van De Vrande et al., 2009), confirming the view that abundant external knowledge can provide benefits for innovation (e.g., Moreira, 2014; Schilling and Phelps, 2007). Third, relying on previous studies, this study specifies a measurement model of the determinants of inbound open innovation sources. Thus, we may contribute and link the open innovation perspective (Chesbrough, 2003a, 2003b) to the KBV theory (Grant, 1996; Grant and Baden-Fuller, 2004) in capturing useful and valuable knowledge resources. Specifically, very little research has attempted to conceptualize and empirically analyze inbound open innovation when different external knowledge-acquiring channels are used. 2. Theoretical background and hypotheses 2.1. Technology scouting and HTCs Technology scouting refers to a firm's innovation resource scanning and acquisition process; it implies both searching for technology acquisition channels and supporting the process of innovation efforts. Technology scouting characterizes an innovation process whereby external actors are involved as sources for ideas, new and crucial knowledge, technical solutions and acquisitions, or even discovery opportunities. For innovation processes, a firm that cannot fully develop its own knowledge and technologies often sources from outside its boundaries (Chesbrough, 2003b). Thus, the aim of technology scouting is to assist firms in building search mechanisms to identify opportunities and discover potential technologies in the external environment (Rohrbeck, 2012). According to Chesbrough (2003a) and Laursen and Salter (2006), external ideas, knowledge, and technology are valuable to internal innovation development. These arguments redefine innovation deployment between a firm and its surrounding environment, making firms more porous and embedded in collaboration with different competitors. This process allows for movement toward the creation of new solutions to current problems. By undertaking searches for available external sources, technology scouting by firms can play a crucial role in feeding innovation capability. In response to limitations in the capacity of complementary and advanced technological resources, a firm makes use of well-developed external technology searching instruments that allow the firm to exploit innovation resources by opting for different external sources. The rationale behind obtaining an external technology scouting advantage is that innovating more quickly than other firms permits the focal firm to discover and acquire crucial technological resources as rapidly as possible. The continued accumulation of complementary and advanced technologies from both allies and competitors (Pisano, 1991) is a viable way to

223

improve the effect of innovation deployment on the development of new products/services. Furthermore, firms can use complementary and advanced technologies to develop their own innovation by relying on co-operative or co-developed capabilities (HTCs) with multiple partners. In doing so, HTCs can help firms to further strengthen the effect of their in-house technology development over competitors and external partners (Hamel et al., 1989) and thus discover new opportunities, especially when such opportunities are viewed as outside the realm of competition (Tether, 2002). The crux of the above argument is that a firm's decision to acquire external sources of technology and capability through HTCs involves pooling complementary resources with external partners and competitors to jointly develop innovation resources that they would be unable to produce internally. Therefore, HTC refers to a firm's ability to collaborate and connect with external partners and competitors, from which the firm can acquire new knowledge and technologies to spawn its own innovation efforts. A firm that possesses advanced scouting mechanisms is likely to search for new technologies from a wide variety of external sources that can be considered likely to aid the firm's innovation capabilities (Parida et al., 2012; Laursen et al., 2010). Hence, technology scouting reflects the importance of a firm's external technology monitoring ability (Laursen et al., 2010) to build, seek, sustain, exchange, and collaborate with external partners in the innovation process. However, without a well-defined technology scouting ability, the efforts of HTC mechanisms will not result in effective searching and monitoring of external knowledge that can be reflected in innovation input. Accordingly, we expect firms that draw deeply from HTCs with advanced competitors to be more innovative because they are able to acquire novel ideas that can lead to the development of new products/services. Consistent with the KBV, collaboration with external partners can deploy existing knowledge and thus create value (Grant and Baden-Fuller, 2004); however, the process requires a great deal of effort in identifying and acquiring the appropriate knowledge. Moreover, reliance on horizontal collaborations with competitors allows firms to tap into advanced technology, thereby providing a preemptive advantage that accelerates firms' innovation capabilities and allows for the monitoring of competitors' technology levels. This argument leads to the following hypothesis: Hypothesis 1. Technology scouting is positively related to horizontal technology collaboration.

2.2. Technology scouting and VTCs A growing body of research demonstrates that firm collaboration with customers is an important method of improving innovation efforts (Brockhoff, 2003; Gassmann, 2006; Von Hippel et al., 1999). Innovation ideas originate from the customer perspective of the value chain (Von Hippel, 1988) and have been declared one of most important openness strategies for firms (Chesbrough, 2007; Prahalad and Ramaswamy, 2004). Collaboration with customers enables firms to extract innovative ideas and novel knowledge from their customers to improve products/services during R&D and innovation processes. Scholars have demonstrated that collaboration with customers may yield significant benefits, such as improvement of existing core competencies, identification of market trends, and the ability to monitor technological development directions (Shaw, 1994; Von Hippel, 1988; Chesbrough et al., 2006). Firms that collaborate with customers have two distinguishing features. First, collaboration with customers allows a firm to develop appropriate technologies and customer-based innovation while allowing for improved interactions with the external customers who are embedded in the innovation development processes. Customers are often considered especially valuable and novel knowledge sources because their specific demands may anticipate the contribution of innovation efforts (Lukas and Ferrell, 2000;

224

C.-H. Wang et al. / Technological Forecasting & Social Change 99 (2015) 222–230

von Hippel, 1988). Second, firms' adoption of customers as collaborative partners is a significant trend in an era of value co-creation (Prahalad and Ramaswamy, 2004). Customers are viewed as important external sources of open innovation inputs when firms increase the use of customer knowledge in the modification of a product or in a service development (Gassmann, 2006). Therefore, vertical technology collaboration (VTC) refers to a collaborative relationship with customers that allows firms to capture technologies and knowledge of market development trends in a timely manner. Considering the customer perspective is important because customers typically share their innovation ideas, allowing firms to quickly understand their customers' needs. Customers often contribute their innovation ideas through communities (Frank and Shan, 2003), and they have first-hand experience using a firm's products and services (Schweisfurth and Raasch, 2015). Thus, customers can make significant contributions to the co-creation and innovation process (Frank and Shan, 2003; Jeppesen and Frederiksen, 2006; Chatterji and Fabrizio, 2014). To acquire external customer-related information, firms must build well-defined VTCs to act as a filter mechanism to allow them to search, acquire, assimilate, and internalize valuable and useful from customers to provide a link between the firms' internal innovation capabilities and customers' innovation-related information, thus providing feedback for product/services development processes. Customers are likely to be prolific innovators because of their experience pursuing advantages with respect to actual innovation needs and product realms. Therefore, as a determinant for the locus of innovations, customers are especially relevant for external information sourcing to potentially discover and access timing and value ideas, knowledge, and novel technologies (Von Hippel, 1988; Von Hippel, 2005; Chatterji and Fabrizio, 2014). This process is particularly important in high-technology firms that aim to enhance their innovativeness (Baum et al., 2000). Accordingly, firms can identify market trends and technological development levels by increasing their interactions with customers via VTC mechanisms. Furthermore, when customers are embedded in innovation processes, one would expect that external knowledge scouting would make firms more likely to be receptive to the creation of new or improved product offerings (Schweisfurth and Raasch, 2015; Von Hippel, 1988). Firms that capture customers' ideas and knowledge in products, processes, and services are likely to be able to identify problems early. However, there is no clear understanding of how customer requirements fit into the innovation process paradigms, which may lead to unmet customer needs for any problems. When this occurs, new products and services that are launched in the marketplace leave existing customers' important needs unresolved and unfulfilled. Therefore, the establishment of effective technology scouting to monitor customer requirements is important because it makes a firm more likely to identify a broader range of unmet needs, and the analysis of customers' needs, expectations and requirements can serve as a determinant for the locus of innovation to create solutions. As a result, well-developed VTCs are likely to effectively increase reciprocal interactions between firms and customers, which can lead to the accurate determination of customer demands and increased profit from innovation. This argument leads to the following hypothesis: Hypothesis 2. Technology scouting is positively related to vertical technology collaboration.

2.3. External collaboration and firm performance We expect HTCs to have a positive effect on firm performance, especially for firms in high-technology contexts. Most significantly, the primary challenges faced by high-technology firms are rapidly changing technologies, shortened product life cycles, increased R&D costs, and rapid innovation. Thus, high-technology firms must make

heavy investments in R&D activities to allow the convergence of multiple technologies and to emphasize the importance of technological standards (Gnyawali and Park, 2011; Gnyawali and Park, 2009). Central to this challenge is highlighting innovation inside the firm and how innovation gains collaborations with competitors. Because competing firms face similar technologies, customers, and markets, collaborations with competitors enable firms not only to acquire and create new technological knowledge but also to use and access other knowledge resources (Quintana-García and Benavides-Velasco, 2004; Gnyawali and Park, 2011; Wu, 2012). Thus, the similar challenges faced by competing firms (especially for high-technology firms) make them more likely to cooperate with competitors (Gnyawali and Park, 2011) in the pursuit of innovation. Essentially, in the globalization era, innovative firms often compete through collaborations among competitors (Jorde and Teece, 1990). In this respect, cooperation among competitors in innovation efforts might lead to the development of integrative technologies, the creation of new markets, the discovery of new business opportunities, and increased profits from striving for innovation. Well-developed HTCs can be considered a significant contributor to firms' innovation efforts to create and sustain superior performance and competitive advantage. In addition to its conventional contributions, firms that collaborate with competitors generally embed themselves in an inter-firm collaboration mechanism that may cause a firm to develop firm-specific features that increase its efficiency through complementary technological and knowledge acquisition. For example, a firm's openness to an inter-firm cooperation strategy may develop to fit with the firm's particular innovation strategy and its use of human capital, intelligence, and scarce resources to solve the dilemma of “how to make resources stretch to meet increasing needs” (Schermerhorn, 1976). As a result of this mechanism, the link between HTCs and firm performance is often easy to explain, and it is clearly appreciated. In support of this perspective, previous studies have shown that firms may pursue well-suited HTC instruments in external knowledge acquisition (Parida et al., 2012). HTCs also make firms more likely to leverage technology capabilities (Hagedoorn et al., 2006; Gnyawali and Park, 2009) and to experience an increasing learning effect in innovation development (Tether, 2002). The firm specificity of HTCs and the HTC-performance link thus enable a firm to obtain more stable and longer-term profits from its HTCs than is typical of other resources. HTCs thus have considerable potential to generate superior performance and a competitive advantage for firms (e.g., Belderbos et al., 2004; Fey and Birkinshaw, 2005; Laursen and Salter, 2006; Parida et al., 2012). This argument leads to the following hypothesis: Hypothesis 3. Horizontal technology collaboration is positively related to firm performance. As key external collaborative partners, customers and suppliers are embedded within, available through, and derived from a broad set of communities that allow firms to leverage external knowledge resources (Schweisfurth and Raasch, 2015). A firm's VTC capability affects the extent to which the firm can connect with external communities in the innovation process (Chesbrough et al., 2006; Gassmann, 2006; Von Hippel, 2005). VTCs are typically associated with firm performance advantages due to customers' voluntary sharing of their experiences and innovations to facilitate solutions that meet their own needs (Harhoff et al., 2003), so they are willing to transfer useful and novel knowledge outside the boundaries of their organizations to a firm's internal innovation process (Parida et al., 2012). VTCs help a firm gain access to valuable resources through high-frequency interactions with customers in open customer communities. Through frequent customer interactions, external knowledge and innovation-related information resources can be efficiently captured, combined, and exchanged among organizational divisions to create new one or many improved product/service offerings (Schweisfurth and Raasch, 2015; Von Hippel,

C.-H. Wang et al. / Technological Forecasting & Social Change 99 (2015) 222–230

1994). Empirical evidence also clearly indicates that firms that have high-frequency interactions with customers during their innovation processes are likely to be promoted as co-creating value (Prahalad and Ramaswamy, 2013; Payne et al., 2008) and are associated with positive performance outcomes (Parida et al., 2012). As noted previously, customers often play a central role in the innovation process. According to this stream of research, there are two main reasons for customers' contributions. First, in many cases, customers are the main beneficiaries of the innovation (von Hippel, 1988) to support their product/service development through their first-hand user experiences and their higher sensitivity to market trends. Second, customers often possess “free and novel” knowledge (Harhoff et al., 2003). This observation suggests that the knowledge and experience of customers that are freely acquired from open communities can be used to address their own needs. As a result, the importance of customer knowledge implies that it is advantageous for producer firms to provide service offers because they can allow firms to access knowledge that cannot be produced in-house. Thus, customer knowledge can provide experience- and evidence-based information for that can result in a firm's subsequent competitive advantage. We thus predict that customers are an important source of the knowledge that forms the basis for innovation. These arguments suggest the following hypothesis: Hypothesis 4. Vertical technology collaboration is positively related to firm performance.

2.4. HTCs and VTCs as mediators Earlier hypotheses proposed direct links for relationships between technology scouting and HTCs and VTCs, and imply that technology scouting would indirectly influence firms' performance through HTCs and VTCs. From a knowledge acquisition perspective, HTCs and VTCs are important mechanisms employed to acquire external resources and thus increase firms' innovation capabilities. The KBV holds that a critical advantage that firms have over markets is the provision of a superior context for supporting knowledge integration mechanisms (Grant and Baden-Fuller, 2004). Such a variety of knowledge sources provides opportunities for firms to open separate external channels in their quest to improve their ability to obtain innovation opportunities. Established HTCs and VTCs thus facilitate external knowledge reliability in the innovation development process, helping firms to more effectively acquire knowledge and capabilities from their competitors and customers. Together, these arguments shift attention toward hypotheses that link the relationships between technology scouting and HTCs and VTCs to firm performance; HTCs and VTCs can be assumed to have a mediating role in the technology scoutingperformance relationship. In addition to HTCs and VTCs, however, these mechanisms of technology collaboration also have implications for inbound openness. In a rapidly changing business environment, external knowledge and technology scanning can facilitate the capture of opportunities to keep up with novel developments. New innovative outcomes offered by competitors improve the possibilities of attaining more opportunities for the commercialization of a product or service. As the number of collaborations with competitors increases, complementary knowledge and advanced technology acquisition opportunities increase as well (Harhoff et al., 2003; von Hippel, 1988), improving innovation capability, especially in high-technology sectors in which cooperative/ competitive relationships frequently emerge for the purpose of innovation (Quintana-García and Benavides-Velasco, 2004; Sisodiya et al., 2013). Furthermore, from the KBV and the inbound open innovation perspective, external knowledge and technology sources can collaborate with customers to monitor and scan market development trends. That is, collaboration with customers can enhance firms' ability to

225

recognize market opportunities, manage unanticipated events during the innovation development process, and boost firms' business activities (Enkel et al., 2009; Lettl et al., 2006). Using various available external knowledge resources, firms can combine knowledge and other resources to produce new knowledge and innovation results (Cohen and Levinthal, 1990; Kogut and Zander, 1992). Technologyand science-based firms are heavily dependent on enhancing their ability to innovate by combining their specific knowledge with that of external partners (Schilling and Phelps, 2007; Stuart, 2000). Therefore, a firm possesses HTC and VTC abilities that may allow for co-creation with complementary partners through alliances, cooperation, acquisition, and joint ventures, during which innovation processes for joint development and commercialized innovation success occur. Thus, collaboration with competitors, and customers might enhance firms' capability to solve technology problems and encourage technology breakthroughs and discovery opportunities that ultimately facilitate improvement in firm performance. In light of the aforementioned discussion, the following hypothesis can be developed: Hypothesis 5a. The link between technology scouting and firm performance is mediated by horizontal technology collaboration. Hypothesis 5b. The link between technology scouting and firm performance is mediated by vertical technology collaboration. The conceptual framework that appears in Fig. 1 proposes that technology scouting influences the return from HTCs and VTCs. We use firm performance as the dependent variable because HTCs and VTCs are crucial sources of external knowledge that lead to improved firm performance through the acquisition of the external knowledge and its integration with internal activities (Brockhoff, 2003; Cohen and Levinthal, 1990; Tether, 2002). This conceptual model is in line with the KBV perspective (e.g., Carayannopoulous and Auster, 2010; Grant, 1996; Grant and Baden-Fuller, 2004) through “external knowledge searching–collaboration process (capability)–output (firm performance)”. The model predicts that the more resource-rich the outside knowledge is in providing complementary resources to internal operations, the better the firm performance. Thus, technology scouting may have a positive impact on HTCs and VTCs. These process mediators (i.e., VTCs and HTCs) are important links between a firm's technology scouting and firm performance. From the perspective of inbound open innovation theory, such internal innovative knowledge generation is an outside-in process in which firms need to integrate their abundant external resources with their in-house efforts (Grant, 1996; Carayannopoulous and Auster, 2010) to improve their performance. Hence, in our theoretical model, both HTCs and VTCs have positive influences on the firm performance. 3. Research methods 3.1. Sample and data collection The high-technology sector provides an appropriate setting for testing open innovation hypotheses for two reasons. First, in a fastInbound open innovation

+

Technology

Horizontal technology collaboration

+

Firm performance

scouting +

Vertical technology collaboration

+

Fig. 1. The conceptual model.

226

C.-H. Wang et al. / Technological Forecasting & Social Change 99 (2015) 222–230

changing technology environment, an openness strategy is necessary for high-technology firms to access the abundant external resources that can complement in-house R&D and innovativeness (Chesbrough, 2003b; Hagedoorn, 2002). Second, high-technology sectors in Taiwan occupy a crucial position in the global high-technology supply chain for the development of advanced technologies. Thus, well-developed collaborative innovation relationships support such technology development and commercialization (THT Research, 2005). Altogether, high-technology sectors provide a rich context for supporting and promoting open innovation empirical requirements. High-technology firms were selected for the sampling frame using the following criteria: (1) registration with the Industrial Development Bureau (IDB) of the Ministry of Economic Affairs (MOEA) and the Taiwan Over-the-Counter Securities Exchange (TOSE), (2) availability of detailed financial data from the Taiwan Economic Journal (TEJ), and (3) status as R&D and innovation-oriented manufacturers that collaborate with global partners, suppliers, and customers to upgrade the innovative capabilities of their internal systems. These criteria allowed us to control for variability in firm age, size, industry types, R&D expenses, and capital, which should increase the external validity of our findings. In total, this study identified 1079 firms that matched the selection criteria. The data were obtained from multiple sources, including MOEA, TOSE, and TEJ, to increase validity and accuracy. Data on R&D expenditures were available for most of the firms in the sample. However, until now, no database recording open innovation data at the firm level has been available. Consequently, open innovation data were collected using a questionnaire survey in a two-stage process. First, a semi-structured interview was employed, a revised preliminary version of which was discussed and created by high-technology experts. Five high-technology experts with over seven years of experience in hightechnology sectors were required to agree with executives on each question to ensure that no problems existed with terminology or interpretability. Second, this study designed the final version of the questionnaire by drawing on the high-technology experts' feedback. The questionnaire was thoroughly pretested, and several items were carefully revised to improve their clarity. A stratified random sampling method was used to select 650 (approximately 60%) high-technology firms. Then, questionnaires were sent to the 650 high-technology firm CEOs and senior managers following the previously described sampling frame. To enhance the response rate, all of the CEOs or senior managers of the sample firms were first contacted by telephone and e-mail, and two follow-up e-mails were then sent to them to solicit their participation in our survey. Then, two weeks later, the same questionnaire was sent as a reminder to all of the participants using fax or e-mail. Four weeks after the initial mailing, we sent replacement questionnaires to nonrespondents. After the initial mailing and the two follow-ups, a total of 150 usable responses were returned, of which 32 were excluded because of incomplete answers and missing data, resulting in a response rate of 22.56%. On average, the hightechnology firms in the sample were 27 years old, had 87.22 million in annual R&D expenses in new Taiwan dollars (TWD), and had 3530 employees.

3.2. Constructs and measures All of the constructs were researched in the extant literature and discussed with the high-technology experts when compiling the measurement items. As noted, some items were modified to reflect the specific context of the study. New questions were developed based on a review of the inbound open innovation literature. All of the constructs were measured by the average of the responses on a sevenpoint Likert scale (from 1 = strongly disagree to 7 = strongly agree). The Appendix provides all of the measurement details.

3.2.1. Firm performance Firm performance was measured by asking respondents to assess their firm's sales growth, market share growth, return on investment, and profit level relative to their major competitors (Slater and Narver, 1994). New product performance was measured as the degree to which a product performs well in the market relative to its major competitors in terms of sales, market share, ROI, and profit (Kim et al., 2012). 3.2.2. Technology scouting Consistent with Parida et al. (2012), five scale items were adopted to measure the external technology sources and trends for hightechnology firms. A confirmation factor analysis with varimax rotation and Kaiser normalization (Hair et al., 2006) resulted in a five-factor solution in which the items showed clean loadings on the technology scouting constructs (see Appendix A). 3.2.3. Horizontal technology collaboration A three-item scale was used to capture a high-technology firm's activities collaborating with competitors, partners, and other actors for external resource acquisition. The scale for HTCs draws on Parida et al. (2012). 3.2.4. Vertical technology collaboration The measure of VTC was adapted from Parida et al. (2012) to integrate collaboration with suppliers, present and potential customers, and end users into innovation efforts. 3.3. Control variables Five control variables were included in this study. Firm size may affect innovation and performance because larger firms usually have larger knowledge bases. This study controlled for firm size by using a log transformation of the number of employees in each firm. The study also controlled for firm age by calculating the number of years since the firms were founded. For the same reasons, the study also controlled for R&D expenses and capital. R&D expenses and capital were measured in millions of new Taiwan dollars. In this study, we controlled for industry effects because different high-technology sectors may have different knowledge and technology strategies. Three dummy controls were used to represent different high-technology sectors. 4. Analysis and results To test the common method variance, Harman's one-factor test was used (Podsakoff and Organ, 1986). According to Podsakoff and Organ (1986), if one factor can explain the majority of the variance, then the common method variance may occur. A principal components factor analysis including all of the variables in the study was conducted, and it resulted in a four-factor solution. The first and largest factor only accounts for 23.30% of the variance. Therefore, the common method variance does not appear to be a problem (Podsakoff and Organ, 1986). Table 1 presents the correlations and descriptive statistics for the study variables. In Table 1, technology scouting, firm performance, and new product performance are highly positively correlated. In addition, both HTCs and VTCs are positively associated with firm performance and new product performance. Consistent with expectations, the three main measures (i.e., technology scouting, HTCs, and VTCs) are positively correlated with firm performance and new product performance. Table 2 provides ordinary least square (OLS) regression estimates for the hypotheses tests. After mean-centering the interacting variables, the variance inflation factor (VIF) values are far below the threshold of 10; the VIF scores range from 1.07 to 3.34, suggesting that multicollinearity is not a problem (Aiken and West, 1991). In Table 2, both Model 1 and Model 3 contain only the control variables. As shown in Models 2 and 4, technology scouting is positively and significantly related to HTCs

C.-H. Wang et al. / Technological Forecasting & Social Change 99 (2015) 222–230

227

Table 1 Correlation matrix (n = 150). Variables

1

2

3

4

5

6

7

8

9

1. Firm age 2. Firm size .10 3. R&D expense −.01 4. Capital .03 5. Technology scouting

.59⁎ .79⁎

.53⁎⁎

.17⁎

.27⁎⁎

.25⁎⁎

.06

.18⁎

.09

.56⁎⁎

.15

.17⁎

.17⁎⁎

.61⁎⁎

.49⁎⁎

.17⁎

.14

.16⁎

.32⁎⁎

.30⁎⁎

.10

.06

.08

.45⁎⁎

.30⁎⁎

3530.64 10,706.31

87.22 351.32

3.67 1.44

.03 6. HTCs −.08 7. VTCs −.01 8. Firm performance .09 9. New product performance .01 27.28 18.08

Mean S.D.

5.45 .85

01

. .78⁎

.09

4.95 .97

4.97 1.07

4.39 1.14

4.47 1.02

⁎ p b 0.1. ⁎⁎ p b 0.05.

(β= .65, p b .001) and VTCs (β= .62, p b .001); therefore, the results support Hypotheses 1 and 2. In Table 3, Model 7, the relationships among HTCs, VTCs, and firm performance are examined. The results show that both HTCs (β=.21, p b .01) and VTCs (β= .30, p b .01) are significantly positively related to firm performance. Both Hypothesis 3 and Hypothesis 4 are therefore supported. Then, by integrating Tables 2 and 3, the effects of the mediation analysis of HTCs and VTCs' effect on firm performance can be examined. As shown in Tables 2 and 3, after controlling for the effects of the control variables, HTCs (β= .21, p b .01) and VTCs (β= .30, p b .01) have a significantly positive effect on firm performance. Finally, as Model 8 demonstrates, the previously significant linkage between technology scouting and firm performance is no longer significant when HTCs and VTCs are added to Model 8, but HTCs (β=.15, p b .01) and VTCs (β=.23, p b .1) remain significant with regard to firm performance, suggesting partial support for Hypotheses 5a and 5b. Thus, HTCs and VTCs partially mediate the technology scouting and firm performance relationships.

4.1. Robustness check Table 4 presents robustness checks that seek to verify the robustness of the results with respect to the model specification. We use an OLS regression model for the examined new product performance. Model 9 examines the effect of a series of control variables on the dependent variables. Model 10 focuses on the main effect on the dependent variables. The coefficient for technology scouting is similar and highly significant for Model 6 in Table 3 and for Model 10 in Table 4. Model 7 in Table 3 and Model 11 in Table 4 examine the effects of HTCs and VTCs on firm performance, respectively. The coefficients across these models are in the same direction, and the significance levels remain stable across models. In addition, the mediating effects of HTCs and VTCs on new product performance are examined in Models 11 and 12, respectively, and the results support the partial mediation of technology

Table 3 OLS regression results (n = 150).

Table 2 OLS regression results (n = 150). Variable

Industry dummy Firm size (ln) Firm age R&D expense (ln) Capital

HTCs Model 1

Model 2

Model 3

Model 4

Yes −.05 (.07) −.00 (.00) .09⁎⁎ (.04) .05 (.09)

Yes .00 (.06) −.01 (.00) .03 (.04) −.06 (.08) .65⁎⁎⁎

Yes .00 (.06) −.00 (.00) .04 (.04) .08 (.08)

Yes .06⁎

Technology scouting R2 Adjusted R2

VTCs

0.044 0.011

(.08) 0.337⁎⁎⁎ 0.306

Standardized error coefficients are in parentheses. ⁎ p b 0.1. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

.046 .013

Variable

Firm performance Model 5

Model 6

Model 7

Model 8

Industry dummy Firm size (ln)

Yes .09 (.08) −.01 (.00) .01 (.05) .07 (.10)

Yes .13⁎ (.08) −.01 (.00) −.02 (.05) .00 (.10) .41⁎⁎⁎ (.11)

Yes .11 (.07) −.01 (.00) −.01 (.05) .01 (.10)

Yes .09 (.08) −.01 (.00) −.02 (.05) −.01 (.10) .18 (.15) .15⁎

Firm age R&D expense (ln)

(.05) −.00 (.00) −.01 (.03) −.03 (.06) .62⁎⁎⁎

VTCs

(.07) .382⁎⁎⁎ .356

R2 Adjusted R2

Capital Technology scouting HTCs

.09⁎⁎ .059

.175⁎⁎⁎ .140

Standardized error coefficients are in parentheses. ⁎ p b 0.1. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

.21⁎ (.10) .30⁎⁎

(.11) .23⁎

(.12) .163⁎⁎⁎ .128

(.13) .17⁎⁎⁎ .131

228

C.-H. Wang et al. / Technological Forecasting & Social Change 99 (2015) 222–230

Table 4 Robustness checks using OLS (n = 150). Variable

Industry dummy Firm size (ln) Firm age R&D expense (ln) Capital

New product performance Model 9

Model 10

Model 11

Model 12

Yes .07 (.07) −.00 (.00) −.04 (.04) .045 (.09)

Yes .12⁎ (.06) −.00 (.00) −.06 (.04) −.05 (.08) .58⁎⁎⁎ (.09)

Yes .07 (.05) .00 (.00) −.03 (.04) −.02 (.08)

.16⁎ (.09) .40⁎⁎⁎

Yes .08 (.06) .00 (.00) −.05 (.04) −.07 (.08) .41⁎⁎⁎ (.13) .04 (.09) .25⁎

(.10) .197⁎⁎⁎ .164

(.11) .254⁎⁎⁎ .217

Technology scouting HTCs VTCs R2 Adjusted R2

.05 .020

.262⁎⁎⁎ .231

Standardized error coefficients are in parentheses. ⁎ p b 0.1. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

scouting on the new product performance relationships. Model 12 shows that the results of the mediating terms are similar when we use new product performance as our dependent variable. The results demonstrate that the empirical results of this model are quite consistent with the aforementioned findings, as the effects of the primary predictors and the mediating terms are the same as those in the OLS model.

5. Discussion This study presents a theoretical framework of inbound open innovation based on the KBV and proposes the use of several concepts to guide our understanding of the different collaborative mechanisms underlying the acquisition of useful external resources. An important mediating mechanism of technology collaboration is the connection between technology scouting and firm performance in a high-technology context. Consistent with the KBV, the findings obtained from the present study suggest that abundant external knowledge and technology can facilitate increases in internal innovation capabilities and thus lead to superior performance (Grant, 1996; Grant and Baden-Fuller, 2004). Following this line of thought, inbound open innovation based on the KBV and abundant external knowledge and technology facilitates internal innovation efforts and allows firms to obtain a competitive advantage and superior performance. In addition, this study develops an integrated model in which technology scouting affects firms' different technology acquisition instruments and thus can exert an influence on firm performance. Our results indicate that technology scouting does have a significant effect on HTCs and VTCs which may be that collaborations with suppliers, customers, and competitors can improve innovation processes. That is, the development of a well-designed HTC and VTC mechanism may be much easier to operationalize than solo innovation efforts in the hightechnology context. Turning to the return on the execution of HTCs and VTCs, if a firm engages in inbound open innovation processes to achieve superior performance, the firm should use VTC instruments as the primary approach to acquiring timing and novel market information and knowledge. This result implies that in the high-technology context, collaborations with customers in acquiring abundant market-oriented information and internalizing it into in-house innovation is more likely to yield higher firm performance. As argued by Chesbrough (2003a), a firm's overemphasis on an internal focus may cause it to lose opportunities because external sources such as open customer communities allow

firms to extract useful information, knowledge, and technology to facilitate innovation. In addition, we observed that technology scouting has significant effect on HTCs. This is expected result may reside in the competitive and complex environment in which high-technology firms may find it need to acquire functionally equivalent and compatible technology for their in-house innovation deployment. Hence, we believe that the HTCs and VTCs may help firms expand and devote their in-house innovation efforts to discover functionally equivalent and compatible technology from other competitors and customers to enhance the innovation deployment. In addition, evidence provided by the empirical results provides strong support for the KBV hypothesis in terms of the acquisition of external knowledge and technologies that can enhance the positive relationship between an inbound openness strategy and firm performance. Furthermore, this study supports Laursen and Salter (2006), Parida et al. (2012), and Sisodiya et al. (2013) contention that inbound open innovation is more than an outside-in concept; rather, it can facilitate the identification and acquisition of external resources and enable a firm to achieve superior performance. 6. Managerial implications This study has important implications for managers. It offers a useful theoretical and empirical framework that can be applied to a manager's specific industry as an analytical tool because the constructs and propositions extend managers' perceptions of open strategic management beyond closed innovation retention. The constructs and propositions may serve as a heuristic that allows firms to draw knowledge from external sources, open up boundaries to exploiting novel outside ideas, and provide firms the ability to discover innovative opportunities. As argued by Chesbrough et al. (2006), open innovation is a set of practices for profiting from innovation and a cognitive model for creating, interpreting, and researching those practices. Thus, an openness strategy can enable firms to gain and exploit innovative opportunities. Furthermore, our results indicate the importance of inbound open innovation in boundary-spanning activities, and in particular, the degree to which technology scouting plays an antecedent role to HTCs and VTCs. This study extends and integrates the KBV theory perspective (Carayannopoulous and Auster, 2010; Grant, 1996; Grant and Baden-Fuller, 2004) and the inbound open innovation theory perspective (Parida et al., 2012; Laursen and Salter, 2006), thus providing an external knowledge searching-collaboration mechanisms-superior performance framework. Our findings suggest that HTCs and VTCs are mediating roles that can make external knowledge sources more effective linking between external sources and superior performance. Consequently, our study suggests that to maximize the benefits from external knowledge resources and to strengthen in-house innovation processes and routines, a firm with a broad knowledge base should take concerted efforts to build and refine the relationships between its external linkages and innovation outcomes. Where this can be done, we further suggest that firms should focus on the deployment of HTCs and VTCs, which is a feasible strategy for high-technology contexts because the results indicate that most technology collaborations originate from external actors and sources. In using this openness strategy, firms can attempt to find innovative opportunities beyond their existing capabilities to significantly improve their performance. To enhance inbound openness strategies, a firm must actively access abundant external knowledge resources through HTCs and VTCs. Open innovation through multiple collaboration channels involves horizontal and vertical collaboration and, consequently, can result in the development of more innovative products/services. Thus, understanding the nature of HTCs and VTCs' capabilities is the first step in open innovation processes to improve firm performance. In addition, managers should strive to find the right “fit” between the types of collaboration instruments and technology scouting to effectively leverage external technology searching and monitoring capabilities in the crucial

C.-H. Wang et al. / Technological Forecasting & Social Change 99 (2015) 222–230

resources acquisition process to facilitate the development and return on innovation efforts. That is, high-technology firms with welldeveloped knowledge acquisition strategies that rely more on multiple channels for their innovativeness are better equipped to generate superior performance. 7. Limitations and future directions Several limitations to this research indicate fruitful directions for future research. First, this study measures only a single high-technology industry, which could lead to limitations in understanding openness strategies in other sectors. Future research could examine other sectors, taking into account inbound open innovation in the current theoretical framework to enhance the understanding of the effects of openness strategies on firms' outcomes in different industries. Second, this study is unable to account for the other contingency effect (i.e., absorption capability), which would provide a fuller test of the present theories and allow the opportunity to explore how absorption capability might affect performance convergence and divergence. External knowledge integration may depend on firms' absorption capability effects in their innovation activities. Third, this study focuses on high-technology firms' level of innovation openness rather than interfirm open collaboration networks. Finally, we encourage future research to examine links to social network theory (e.g., Gulati, 1998) to explore interfirm open innovation strategies. Acknowledgments The authors would like to thank the Ministry of Science and Technology (MOST) of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC102-2410-H-415012 and MOST103-2410-H-415-051-MY3. The authors would also like to acknowledge the contribution of the Associate Editor, Dr. Tugrul Daim, and two anonymous referees for their valuable comments and constructive suggestions that have contributed significantly to this paper.

Appendix A. Constructs and measurement items.a

SFL Firm performance (α = .925; CR = .947; AVE = .816) Our company's overall performance compared with major competitors over the past year in: Sales growth rate Market share growth Return on investment Growth rate of profit New product performance (α = .919; CR = .943; AVE = .805) Compared with major competing products in the market, our new product has significantly higher than other firms. Sales Market share Return on investment Profit Technology scouting (α = .816; CR = .869; AVE = .573) Our company can observe technology trends. Our company can view external sources for ideas and knowledge as important. Our company can collect deep information on our industry. Our company has adopted contract agreements with research institutions and universities for acquiring new technologies. Our company can search and buy new technology from other firms and institutions. Horizontal technology collaboration (α = .747; CR = .855; AVE = .663) Our company cooperates and co-develops with other firms.

.892

.903 .897 .922

.883

.922 .915 .867

.823 .819 .760 .723 .644

.787

229

Appendix A. (continued) (continued) SFL Our company possesses a regular network to exchange experiences/knowledge with partners. Our network partners contribute important input. Vertical technology collaboration (α = .885; CR = .929; AVE = .845) Our company has involved present customers in supporting innovation. Our company has involved future potential customers in supporting innovation. Our company has involved end-users in supporting innovation.

.891 .759

.897 .932 .879

a

Items are measured using seven-point Likert scales (1 = strongly disagree and 7 = strongly agree). Initial loading was fixed to 1 to set the scale of the construct. CR = construct reliability; AVE = average variance extracted; SFL = standardized factor loading.

References Ahuja, G., 2000. Collaboration networks structural holes and innovation: a longitudinal study. Adm. Sci. Q. 45, 425–455. Aiken, L.S., West, S.G., 1991. Multiple Regression: Testing and Interpreting Interactions. Sage, Newbury Park, CA. Baum, J.A.C., Calabrese, T., Silverman, B.S., 2000. Don't go it alone alliance network composition and startups' performance in Canadian biotechnology. Strateg. Manag. J. 21, 267–294. Belderbos, R., Carree, M., Lokshin, B., 2004. Cooperative R&D and firm performance. Res. Policy 33 (10), 1477–1492. Brockhoff, K., 2003. Customers' perspectives of involvement in new product development. Int. J. Technol. Manag. 26, 464–481. Carayannopoulous, S., Auster, E.R., 2010. External knowledge sourcing in biotechnology through acquisition versus alliance: a KBV approach. Res. Policy 39, 254–267. Chatterji, A.K., Fabrizio, K.R., 2014. Using users: when does external knowledge enhance corporate product innovation? Strateg. Manag. J. 35 (10), 1427–1445. Chesbrough, H., 2003a. Open Innovation. Harvard University Press, Cambridge, MA. Chesbrough, H., 2003b. Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press, Boston, MA. Chesbrough, H., Crowther, A.K., 2006. Beyond high tech: early adopters of open innovation in other industries. R&D Manag. 36, 229–236. Chesbrough, H., Vanhaverbeke, W., West, J. (Eds.), 2006. Open Innovation: Researching a New Paradigm. Oxford University Press, New York. Chesbrough, H., 2007. Open Business Models. Harvard Business School Press, Boston. Cohen, W.M., Levinthal, D.A., 1990. Absorptive capacity: a new perspective on learning and innovation. Adm. Sci. Q. 35 (1), 128–152. Enkel, E., Gassmann, O., Chesbrough, H., 2009. Open R&D and open innovation: exploring the phenomenon. R&D Manag. 39 (4), 311–316. Fey, C., Birkinshaw, J., 2005. External sources of knowledge, governance mode and R&D performance. J. Manag. 31 (4), 597–621. Frank, N., Shan, S., 2003. How communities support innovative activities: an exploration of assistance and sharing among end-users. Res. Policy 32 (1), 157–178. Garriga, H., von Krogh, G., Speath, S., 2013. How constraints and knowledge impact open innovation. Strateg. Manag. J. http://dx.doi.org/10.1002/smj.2049 (wileyonlinelibrary.com, in press). Gassmann, O., 2006. Opening up the innovation process: towards an agenda. R&D Manag. 36, 223–228. Gnyawali, D.R., Park, B.J., 2009. Co-opetition and technological innovation in small and medium-sized enterprises: a multilevel conceptual model. J. Small Bus. Manag. 47 (3), 308–330. Gnyawali, D.R., Park, B.J., 2011. Co-opetition between giants: collaboration with competitors for technological innovation. Res. Policy 40, 650–663. Grant, R.M., 1996. Toward a knowledge-based theory of the firm. Strateg. Manag. J. 17, 109–122. Grant, R.M., Baden-Fuller, C., 2004. A knowledge accessing theory of strategic alliance. J. Manag. Stud. 41 (1), 61–84. Gulati, R., 1998. Alliances and networks. Strateg. Manag. J. 19, 293–317. Hagedoorn, J., 2002. Inter-firm R&D partnerships: an overview of major trends and patterns since 1960. Res. Policy 31 (4), 477–492. Hagedoorn, J., Roijakkersand, N., Van Kranenburg, H., 2006. Inter-firm R&D networks: the importance of strategic network capabilities for high-tech partnership formation. Br. J. Manag. 17 (1), 39–53. Harhoff, D., Henkel, J., Von Hippel, E., 2003. Profiting from voluntary information spillovers: how users benefit by freely revealing their innovations. Res. Policy 32 (10), 1753–1769. Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., 2006. Multivariate Data Analysis. Prentice-Hall, Upper Saddle River, NJ. Hamel, G., Doz, Y., Prahalad, C.K., 1989. Collaborate with your competitors and win. Harv. Bus. Rev. 67 (1), 133–139. Jorde, T.M., Teece, D.J., 1990. Innovation and cooperation: implications for competition and antitrust. J. Econ. Perspect. 4 (3), 75–96. Jeppesen, L.B., Frederiksen, L., 2006. Why do users contribute to firm-hosted user communities? The case of computer-controlled music instruments. Organ. Sci. 17 (1), 45–63.

230

C.-H. Wang et al. / Technological Forecasting & Social Change 99 (2015) 222–230

Kim, N., Im, S., Slater, S.F., 2012. Impact of knowledge type and strategic orientation on New product creativity and advantage in high-technology firms. J. Prod. Innov. Manag. 30 (1), 136–153. Kogut, B., Zander, U., 1992. Knowledge of the firm, combinative capabilities, and the replication of technology. Organ. Sci. 3, 383–397. Laursen, K., Salter, A., 2006. Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms. Strateg. Manag. J. 27 (2), 131–150. Laursen, K., Leone, M.I., Torrisi, S., 2010. Technological exploration through licensing: new insights from the licensee's point of view. Ind. Corp. Chang. 19 (1), 1–27. Laursen, K., Moreira, S., Markus, A., 2015. Knowledge diversity, transfer and coordination: the effect of intrafirm inventor networks on the speed of external knowledge recombination. The 36th DRUID Celebration Conference 2015, Rome, Italy (June 15–17). Lettl, C., Herstatt, C., Gemuenden, H.G., 2006. Users contributions to radical innovation: evidence from four cases in the field of medical equipment technology. R&D Manag. 36 (3), 251–272. Lukas, B.A., Ferrell, O.C., 2000. The effect of market orientation on product innovation. J. Acad. Mark. Sci. 28, 239–247. Moreira, S., 2014. Technology licensing and firm innovation: the moderating effect of slack and organizational myopia. Acad. Manag. Proc. 2014 (1), 13765. Parida, V., Westerberg, M., Frishammar, J., 2012. Inbound open innovation activities in high-tech SMEs: the impact on innovation performance. J. Small Bus. Manag. 50 (2), 283–309. Payne, A.F., Storbacka, K., Frow, P., 2008. Managing the co-creation of value. J. Acad. Mark. Sci. 36 (1), 83–96. Pisano, G., 1991. The governance of innovation: vertical integration and collaborative arrangements in the biotechnology industry. Res. Policy 20, 237–249. Prahalad, C.K., Ramaswamy, V., 2013. The Future of Competition: Co-creating Unique Value with Customers. Harvard Business School Press, Boston, MA. Podsakoff, P.M., Organ, D.W., 1986. Self-reports in organizational research: problems and prospects. J. Manag. 12, 531–544. Powell, W.W., Koput, K., Smith-Doerr, L., 1996. Interorganizational collaboration and the locus of innovation: networks of learning in biotechnology. Adm. Sci. Q. 41, 116–145. Prahalad, C.K., Ramaswamy, V., 2004. The Future of Competition: Co-creating Unique Value with Customers. Harvard Business School Press, Boston, M. A. Quintana-García, C., Benavides-Velasco, C., 2004. Cooperation, competition, and innovative capability: a panel data of European dedicated biotechnology firms. Technovation 24 (12), 927–938. Rohrbeck, R., 2012. Exploring value creation from corporate-foresight activities. Futures 44, 440–452. Schermerhorn Jr., J.R., 1976. Openness to interorganizational cooperation: A study of hospital administration. Acad. Manage. J. 19 (2), 225–236. Schilling, M.A., Phelps, C.C., 2007. Interfirm collaboration networks: the impact of large-scale network structure on firm innovation. Manag. Sci. 53 (7), 1113–1126. Schweisfurth, T.G., Raasch, C., 2015. Embedded lead user — the benefits of employing user for corporate innovation. Res. Policy 44, 168–180. Sisodiya, S.R., Johnson, J.L., Grégoire, Y., 2013. Inbound open innovation for enhanced performance: enables and opportunities. Ind. Mark. Manag. 42 (5), 836–849. Shaw, B., 1994. User/supplier links and innovation. In: Dodgson, M., Rothwell, R. (Eds.), The Handbook of Industrial Innovation. Edward Elgar, Brookfield, UK. Slater, S.F., Narver, J.C., 1994. Does competitive environment moderate the market orientation — performance relationship? J. Mark. 58, 46–55.

Stuart, T.E., 2000. Interorganizational alliances and the performance of firms: a study of growth and innovation rates in a high-technology industry. Strateg. Manag. J. 21 (8), 791–811. Tether, B.S., 2002. Who co-operates for innovation, and why: an empirical analysis. Res. Policy 31, 947–967. THT Research, 2005. Why Taiwan Matters, U.S. Business Week Magazine. pp. 1–5 (6 March). Van De Vrande, V., De Jong, J.P.J., Vanhaverbeke, W., De Rochemont, M., 2009. Open innovation in SMEs: trends, motives and management challenges. Technovation 29 (6/7), 423–437. Von Hippel, E., 1988. The Sources of Innovation. Oxford University Press, New York. Von Hippel, E., Thomke, S., Sonnack, M., 1999. Creating breakthroughs at 3 M. Harv. Bus. Rev. 4, 47–57. Von Hippel, E., 1994. Sticky information and the locus of problem solving: implications for innovation. Manag. Sci. 44, 629–644. Von Hippel, E., 2005. Democratizing Innovation. The MIT Press, Cambridge, MA. Wu, J., 2012. Technological collaboration in product innovation: the role of market competitive and sectoral technological intensity. Res. Policy 41, 489–496. Chun-Hsien Wang is a professor in the Department of BioBusiness Management, National Chiayi University, Taiwan. He received his Ph.D. in Business Administration in 2006 from National Sun Yat-sen University, Taiwan and his M.S. in International Business from National Dong Hwa University, Hualien, Taiwan, R. O. C., in 2002, as well as his M.S. from the Department of Marketing at National Chung Hsing University, Taichung, Taiwan, R. O. C., in 1999. His current research interests include open innovation, service innovation management, biotechnology management, R&D innovation management, and social networking and collaboration as well as its applications. His papers have appeared in Technovation, International Journal of Production Economics, OMEGA-International Journal of Management Science, Technological Forecasting and Social Change, Innovation: Management, Policy & Practice, Computers in Industry, Information and Software Technology, International Journal of Technology Management, Asia Pacific Management Review, Computers & Education, Applied Economics Letters, Science Technology and Society, and Applied Mathematical Modeling in addition to international conference proceedings. [email protected]. Ching-Hsing Chang is an assistant professor in the Department of BioBusiness Management, National Chiayi University, Taiwan. He received his Ph.D. from Department of Agricultural, Environmental, and Development Economics, The Ohio State University in 2011. His scholarly interests range from applied econometrics, environmental economics, and corporate governance. His research in environmental economics focuses on examining whether government-sponsored voluntary environmental programs spur innovation in environmental technology. Research in corporate governance empirically tests the causal relationship between managerial incentive scheme and technological innovation, and takes a further step by identifying if these innovations are related, in larger proportion, to explorative or exploitative activities within firms. George C. Shen is an associate professor of marketing at National Chiayi University in Taiwan. His research interests include service marketing and Internet marketing. His work has been published in Journal of Business Research, Behaviour & Information Technology, Internet Research, Computers in Human Behavior, Industrial Marketing Management, and Journal of Service Management, and others.