Knowledge sharing in supply chain networks: Effects of collaborative innovation activities and capability on innovation performance pdf

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Technovation

journal homepage:www.elsevier.com/locate/technovation

Knowledge sharing in supply chain networks: Effects of collaborative innovation activities and capability on innovation performance

Changfeng Wang

^a,b,c,^⁎

, Qiying Hu

^c

aCollege of Economics and Management, Zhejiang Normal University, Jinhua, China

bSchool of Economics and Management, Shandong Jiaotong University, Jinan, China

cSchool of Management, Fudan University, Shanhai, China

A R T I C L E I N F O

Keywords:

Collaborative innovation Supply chain networks Knowledge sharing

Collaborative innovation capability Innovation performance

A B S T R A C T

Building on knowledge management and innovation capability theories, this paper aims to reveal the mechanisms of collaborative innovation processes by investigating the complex relationships among critical factors influencing firm's innovation performance in supply chain networks. Using hierarchical Multiple Regression (MR) and Moderated Multiple Regression (MMR) methods, results from a survey of 236 firms in China indicated that there are significant positive relationships between collaborative innovation activities, knowledge sharing, collaborative innovation capability, and firm's innovation performance. Moreover, it is expected that knowledge sharing plays a partial mediating role in the relationships between collaborative innovation activities and firm's innovation performance. Collaborative innovation capability exhibited a moderating effect on collaborative innovation activities - innovation performance relationship. These results contribute to collaborative innovation process management by offering a nuanced conceptualization of the collaborative innovation - performance relationship in supply chain networks.

1. Introduction

With increasing pressure to develop new products and services quickly and efficiently, firms have strived to foster greater supply chain collaborative innovation to maintain and improve their long-term performance (Gloor, 2006; Nieto and Santamaría, 2007; Davis and Eisenhardt, 2011; An et al., 2014; Burg et al., 2014; Bäck and Kohtamäki, 2015; Gao et al., 2015; Isaksson et al., 2016; Yasuyuki et al., 2016). Collaborative innovation denotes two or more supply chain members, such as suppliers, manufacturers, distributors, service providers, and even customers, sharing knowledge with each other and working jointly to plan and execute R&D in supply chain networks (Powell et al., 1996; Swink, 2006; Cao and Zhang, 2011). Extant research has suggested that collaborative innovation can stimulate mutual creativity, reduce R&D costs and risks, and improve innovation performance (Faems et al., 2005; Mishra and Shah, 2009; Davis and Eisenhardt, 2011; Mishra et al., 2015). Yet not all firms have truly ca- pitalized on the potential benefits thereof (Cao and Zhang, 2011). We still lack insights into the mechanisms of firm's collaborative innovation – performance relationships in supply chain networks.

Inside a multiproduct supply chain network, most of the collaborative innovation processes leverage the skills and resources of the

partners to exploit assets in a manner that neither could accomplish independently. It thus becomes possible for firms to learn from each other and benefit from new knowledge developed by collaborative innovation activities (Burg et al., 2014). A significant amount of research has demonstrated that knowledge sharing among these firms provides opportunities for mutual learning and at the same time enables all members in a supply chain network to work together in a way that creates truly new value (Dyer and Nobeoka, 2000; Hult et al., 2004;

Cheng et al., 2008; Nasr et al., 2015; Tan et al., 2016). However, some prior researchers have suggested that knowledge is possessed by individual firms, and cannot be easily shared across different members in a supply chain network (Hult et al., 2006; Dyer and Hatch, 2006).

While others contend that knowledge is usually embedded in the innovation process and often “sticky” or “leaky” and difficult to spread (Dyer and Nobeoka, 2000; Hansen et al., 2005; Le Dain and Merminod, 2014). Hence, without high level of knowledge sharing, a desired level of innovation performance cannot be guaranteed only by participating in collaborative innovation activities. Collaborative activities seem to have great potential for acquiring valuable knowledge and enhancing innovation performance, but further investigation is needed to understand this more fully.

To enrich the mechanisms and deepen our understanding of the

https://doi.org/10.1016/j.technovation.2017.12.002

Received 28 November 2015; Received in revised form 20 September 2017; Accepted 1 December 2017

⁎Correspondence to: College of Economics and Management, Zhejiang Normal University, Jinhua, China.

E-mail addresses:[email protected],[email protected](C. Wang).

Technovation 94–95 (2020) 102010

Available online 16 December 2017

T

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nature of firm's collaborative innovation - performance relationships, this study introduces another key variable, collaborative innovation capability, for explaining how collaborative innovation activities and knowledge sharing are materialized into innovation performance.

Previous studies have found collaborative innovation capability enables firms to successfully apply or replicate knowledge dispersed by interactive activities among individual firms and their supply chain networks (Lawson and Samson, 2001; Blomqvist and Levy, 2006; Mishra and Shah, 2009). This capability can not only enhance knowledge sharing among different firms but can also significantly contribute to increasing volume, variety, and engagement in innovation activities (Faems et al., 2005). Collaborative innovation capability plays an es- sential role in knowledge sharing by embedding innovation processes among supply chain network members to achieve favorable innovation results.

In sum, although prior literature has highlighted the separate im- portance of knowledge sharing and collaborative innovation capability for increasing innovation performance, much less attention has been focused on exploring the effectiveness of knowledge sharing and innovation capability from a holistic perspective. Moreover, little is known about how collaborative innovation activities, knowledge sharing, and collaborative innovation capability inter-relate to mediate different levels of innovation performance in supply chain networks.

Our study addresses this research gap by investigating the following questions: How do collaborative innovation activities, knowledge sharing, and collaborative innovation capability simultaneously affect firm's innovation performance? Or, more specifically, how can firms gain useful knowledge efficiently and effectively from other partners in the supply chain network to enhance their innovation performance?

This study posits that collaborative innovation activities may offer a learning opportunity for the participating firms in a supply chain network, but the learning outcome (innovation performance in this study) depends on the effectiveness of knowledge sharing and the level of collaborative innovation capability of individual firms (Lawson and Samson, 2001; Calantone et al., 2002; Blomqvist and Levy, 2006;

Mishra and Shah, 2009; Saunila et al., 2014).

Accordingly, we offer a more nuanced conceptualization of the collaborative innovation - performance relationship in two important ways. First, we demonstrate empirically that knowledge sharing partially mediates the relationship between collaborative innovation activities and firm's innovation performance. This means that participating in collaborative innovation activities contributes more to innovation performance under higher knowledge sharing levels.

Second, we find support to suggest that the positively relationship between collaborative innovation activities and firm's innovation performance is stronger with higher levels of collaborative innovation capability. This suggests the existence of a moderator. These findings illustrate the nature of the collaborative innovation process and offer important implications for collaborative innovation management in supply chain networks.

The remainder of the paper proceeds as follows. The next section outlines and discusses relevant literature, providing a detailed exposi- tion of pertinent of theory, and sets out the hypotheses of this study.

Next, our methodology is elaborated before a presentation and ex- ploration of the results generated. Finally, we offer conclusions in the last section.

2. Theory and Hypotheses

We ground our model development in the knowledge management and innovation capability theories because these theories are complementary in focusing on the critical factors affecting firm's collaborative innovation performance. In general, scholars have recognized the variables of collaborative innovation activities, knowledge sharing, and collaborative innovation capability as the source of firm's innovation performance (Gloor, 2006; Swink, 2006; Blomqvist and Levy,

2006; Mishra and Shah, 2009; Cao and Zhang, 2011; Burg et al., 2014).

However, most of prior research has tended to emphasize the effects of isolated variables with less attention paid to the integrative effects of these variables. In this study, we focus on the interplay between collaborative innovation activities, knowledge sharing, and collaborative innovation capability. Firstly, involvement in collaborative innovation activities is regarded as a prerequisite of higher level innovation performance (Singh et al., 2016). These interactive activities may imply access to valuable knowledge, which is difficult to capture by firms acting alone. Importantly, such knowledge can be a source of successful new product or service R&D (Soosay et al., 2008; Cruz-González et al., 2015). Secondly, knowledge sharing is the core process of collaborative innovation projects (Gupta and Polonsky, 2014). It serves as a mediator between collaborative innovation activities and innovation performance. Finally, collaborative innovation capability is likely to moderate the effect of collaborative innovation activities and knowledge sharing on innovation performance (Carlile, 2004; Blomqvist and Levy, 2006).

In other words, the moderator can strengthen or enlarge the performance increasing effects of the other two variables. In the following sections, we develop arguments for hypotheses concerning these issues, starting with the individual effects of these variables, and following that, on the role of interaction effects of mediator and moderator.

2.1. Collaborative innovation activities

Collaborative innovation in supply chain networks has been viewed as an R&D process, whereby two or more supply chain partners work together toward introducing new products or services (Cao and Zhang, 2011). Supplier involving in collaborative innovation activities is regarded as one of the reasons why Toyota was able to launch new innovation products faster, with shorter R&D times and lower R&D costs (Liker et al., 1996;Lawson et al., 2015). From the present supply chain literature, it is clear that firms can improve their innovation performance by developing interfirm collaborations with various supply chain partners (Faems et al., 2005).

There are two specific reasons why participating interfirm collaborative innovation activities in supply chain networks can contribute to firms’ innovation performance. First, collaborative innovation activities constitute information channel resources that reduce the amount of time and investment required to gather information (Hildreth and Kimble, 2004). In the past, firms have developed “integrated” R&D approaches by themselves. Suppliers, customers, and other supply chain partners may cooperate with R&D efforts (when they are asked to do such favor), but their interactions are far beyond the reach of true collaborative innovation (Simatupang and Sridharan, 2002; Chapman and Corso, 2005). Information channels among them are almost closed or unidirectional. A truly collaborative innovation project involves rich forms of bi-directional communications inside the collaborative R&D team. Such communications, including mutual technical support, will stimulate and facilitate firm's new innovative activities by providing the external information necessary to generate new products (Soosay et al., 2008; Cruz-González et al., 2015). Second, and more importantly, firms participating in collaborative innovation projects is regard as the prerequisite part of a learning process, in which firms discover new opportunities and obtain new knowledge through interacting with others in the supply chain network (Chapman and Corso, 2005; Soosay et al., 2008; Cao and Zhang, 2011; Cruz-González et al., 2015). At the same time, the learning process will then benefit from access to new knowledge necessary to resolve design and manufacturing problems (Soosay et al., 2008). Participating such projects has also been recognized as the critical mechanism for knowledge combi- nation (Singh et al., 2016) and exchange to further achieve favorable collaboration (Simatupang and Sridharan, 2005).

To summarize, innovative ideas are often at the nexus of collaborative innovation activities. To foster innovation, information and knowledge should be deliberately distributed in the supply chain

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network. More collaborative innovation activities will provide more channels for distributing information and knowledge in such a way as to stimulate and support innovative products. A leading role in collaborative innovation projects is usually associated with higher innovation performance of individual firms within a supply chain network. A supply chain firm that participates in collaborative innovation activities is also likely to produce more innovations (Lawson et al., 2015;

Schleimer and Faems, 2016).

H1. Firms that engage in more collaborative innovation activities exhibit higher innovation performance.

2.2. Knowledge sharing

The observation that collaborative innovation activities have con- siderable potential to contribute to the innovation performance of firms does not mean that all collaborations are successful. Knowledge is usually unevenly distributed throughout the supply chain network.

Ernst and Kim (2002) find that knowledge transfer is not automatic;

that is, it requires a significant level of knowledge sharing in a complex process to internalize disseminated knowledge.

Knowledge sharing is one of the most important processes of knowledge management (Du et al., 2007). In a concluding article, Argote et al. (2003)posit a knowledge management framework where outcomes are delineated in terms of knowledge creation, retention and transfer. Knowledge sharing is the common thread in knowledge management processes. It creates opportunities to generate solutions and efficiencies that provide initial value to a successful innovation project (Lin, 2007). According toWang and NOE (2010), knowledge sharing differs from the similar term “knowledge transfer.” In their paper, knowledge sharing is only one part of knowledge transfer which typically has been used to describe the objective movement of knowledge between different units, divisions, or organizations rather than individuals. While we use the term “knowledge sharing” as a more subjective behavior generated by one or more supply chain firms.

Although knowledge sharing is often subject to false starts (Zellmer- Bruhn, 2003), a firm's innovation performance is enhanced when the firm communicates information, effective practices, and preferences with other partners in a supply chain network. For example, on the one hand, knowledge sharing offers an excellent opportunity to explore and test the potential value of the knowledge (Chesbrough, 2006) shared by the collaborative partner. On the other hand, sharing knowledge is an efficient way for a firm to signal to collaborative innovation partners that it possesses knowledge of potential value to them (Husted and Michailova, 2010). This signal increases the attractiveness of the firm as a potential collaborator in innovation-related interfirm projects (Ritala et al., 2015). Thus, firms that share knowledge in a supply chain network are more likely to establish and engage in more interfirm collaborative innovations with higher levels of performance.

Knowledge sharing can be defined as a social interaction (Lin, 2007) that involves the exchange of R&D knowledge, experiences, and skills through the supply chain network. Groups of people from different supply chain firms share a concern, a set of problems, or a passion about a new product or service, and deepen their knowledge and expertise in this area by interacting in the context of an ongoing collaborative innovation project. They operate as “social learning systems,” where practitioners connect to solve technical problems, share new ideas, set new standards, and build new tools. Firms and researchers use a variety of terms to describe similar phenomena, such as “knowledge commu- nities,” “thematic groups,” and “learning networks” (Liao et al., 2007).

A community of knowledge sharing practitioners is a particular type of network that features peer-to-peer collaborative innovation activities to build new skills and manage the knowledge assets of the supply network. It is believed that sharing knowledge based on mutuality, trust, and respect yields long-term benefits, such as higher innovation performance and profit.

Overall, knowledge sharing can generate opportunities for firms to accrue further profits from their innovative endeavors (Alnuaimi and George, 2016). We formalize this argument in the following hypothesis:

H2. Knowledge sharing is positively associated with innovation performance.

2.3. Mediating effect of knowledge sharing

Knowledge sharing in collaborative innovation activities often demands resources, patience, and numerous iterations (Ritala et al., 2015). Firms need to repeatedly engage in such collaborative innovation activities to avail of higher levels of knowledge sharing and performance. Knowledge sharing may also serve as a mediator between collaborative innovation activities and innovation performance. Few previous studies have addressed this point.

Specifically, we suggest that knowledge sharing is a mechanism that helps to realize the knowledge benefits of collaborative innovation activities for innovation performance because functionally diverse supply chain partners can acquire information, know-how and per- spectives from each other through knowledge sharing (Soosay et al., 2008; Cruz-González et al., 2015). More collaborative innovation activities can build closer ties and generate mutual trust between partners (Dodgson, 1993). These activities provide supply chain partners the opportunity to access diverse valuable knowledge resources in the network (Hall and Andriani, 1998; Soosay et al., 2008; Cruz-González et al., 2015). While those valuable Knowledge is frequently character- ized by tacitness, and it is difficult to spread across different partners (Grant, 1996; Hansen, 1999; Huang and Li, 2009). To fully leverage the tacitness knowledge resided in individual supply chain partners, the firm needs to develop higher level of knowledge sharing to generate cross-fertilization of ideas (Cheung et al., 2016). Through knowledge sharing, knowledge accumulated by close contacts and interactions (Dyer and Nobeoka, 2000) can be diffused throughout the whole supply chain networks and be converted into common language and memory shared by supply chain members (Myers and Cheung, 2008). When knowledge can be shared effectively, supply chain members are more inclined to utilize knowledge together to develop new product (Sakakibara, 2003), improve efficiency and further achieve favorable collaborative innovation results and performance. Finally, supply chain partners can also develop a better understanding of, and response to, the market and competitive environment by knowledge sharing in the same collaborative innovation platform (Malhotra et al., 2005; Cao and Zhang, 2011). They can easily bundle such knowledge and information into an integrated whole, bringing coordination effects and increase their innovation performance.

At the same time, based on the previously described analysis, sharing knowledge effectively and efficiently drives innovation performance (Tsai, 2001; Lin, 2007; Sáenz et al., 2009; Gupta and Polonsky, 2014). Therefore, knowledge sharing serves to improve firms’

innovation performance. Collectively, the above analyses provide a basis for the proposition of a mediating role of knowledge sharing in the collaborative innovation activities–innovation performance relationship (Huang and Li, 2009).

H3. Knowledge sharing mediates the collaborative innovation activities–innovation performance relationship such that collaborative innovation activities has a positive impact on knowledge sharing, which, in turn, has a positive impact on firms’ innovation performance.

2.4. Collaborative innovation capability

Collaborative innovation capability is discussed in the recent literature on inter- and intra-organizational relationships (Blomqvist and Levy, 2006). Theoretical approaches thereof are closely related to dynamic capability (Teece et al., 1997; Eisenhardt and Martin, 2000;

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Winter, 2003), combinative capability (Kogut and Zander, 1992; Van Den Bosch et al., 1999), and absorptive capacity (Cohen and Levinthal, 1990; Zahra and George, 2002). Individual firms differ in their ability to assimilate and replicate new knowledge gained from collaborative innovation activities. Mishra and Shah (2009) labeled such ability

“collaborative competence.” They argue that this ability to simultaneously collaborate with other firms in a supply chain network is a valuable—yet rare—firm-level capability. Followed their definition, we define collaborative innovation capability as the ability to simultaneously involve key supply chain partners in the innovation process and examine its effect on innovation performance (Mishra and Shah, 2009).

It is not a new idea that an individual firm needs to externally integrate with their collaborative innovation partners in a supply chain network to achieve high innovation performance (Simatupang and Sridharan, 2002; Soosay et al., 2008; Mishra and Shah, 2009).Soosay et al. (2008), using case studies, demonstrate that a firm's ability to work together with collaborative innovation partners enables them to integrate and link innovation processes for increased effectiveness as well as embark on innovation.Swink (2006)states that a firm's ability to collaborate is key to its innovative success. Building on Swink's work, Mishra and Shah (2009)also find empirical evidence for collaborative competence and its impact on collaborative innovation performance. They highlight the superior collaborative innovation benefits of simultaneously involving multiple partners in the project process.

Firms in supply chain networks with high levels of collaborative innovation capabilities are likely to harness more new knowledge from other firms to facilitate their innovative activities. Collaborative innovation activities bring the suppliers and customers in a supply chain network together onto the same innovation platform; hence, these stakeholders understand and appreciate each other's concerns and work toward mutually agreed solutions (Mishra and Shah, 2009). Firms must have the capacity to absorb collaborative inputs in order to generate innovative products in this platform. Without such capacity, they cannot learn or transfer knowledge from one firm to another. Collective involvement in these collaborative innovation activities helps develop a common language of understanding around the critical interdependencies at boundaries in settings where innovation is desired (Carlile, 2004). This language, including other collaborative innovation capabilities, strengthens jointly produced knowledge and accom- modates dynamic local interests, thereby enabling suitable resolution of new demands in the market. For instance, on the one hand, putting suppliers and customers together in the same collaborative innovation platform is beneficial because it allows the suppliers to integrate specific customer needs or requirements in a dynamic market into a successful new design. At the same time, manufacturers of this innovation platform find it easier and faster to initiate changes in manufacturing technology and adhere to customer specifications. On the other hand, the bespoke manufacturing of new technology is taken into considera- tion by suppliers when decisions are made regarding the complexity, dynamics and variety of components within new products or services (Mishra and Shah, 2009). As a result, if all supply chain members in a collaborative innovation project have higher levels of collaborative capabilities, then a higher level of innovation performance will be more easily achieved.

These arguments lead us to propose the following hypothesis:

H4.A firm's collaborative innovation capability is positively related to its innovation performance.

2.5. Moderating effect of collaborative innovation capability

Collaborative innovation capability is also likely to moderate the effect of innovation activities and knowledge sharing on a firm's innovation performance. Although participating in collaborative innovation activities provides important access to new knowledge and gives opportunities to share it, the impact on innovation performance

may rely on the extent to which a firm can absorb and apply such new knowledge. A firm may be able to access certain new knowledge, but not enhance its innovation performance if it does not have enough capacity to exploit such knowledge in innovation activities. Regular participation in innovation activities and knowledge sharing increases the positive impact on the firm's innovation performance if the firm has adequate capacity with which to effectively transfer and make full use of knowledge from other partners. The interaction between collaborative innovation activities and innovation capability is critical to interfirm knowledge sharing (Tamer Cavusgil et al., 2003; Blomqvist and Levy, 2006). The more extensive the participatory innovation activities and sharing of knowledge, the broader the knowledge sources the firm has and the higher the innovation capacity needed to transfer and make full use of such knowledge to ensure higher innovation performance.

Firms with a high level of collaborative innovation capability are also likely to dynamically respond to environmental changes (Lawson and Samson, 2001). This responsiveness is based on the ability of collaborating firms to quickly adapt and apply shared knowledge to innovate new features of a product or service (Blomqvist and Levy, 2006).

Such benefits derived through collaborative innovation capability may not be immediately visible; however, the potential long-term rewards are enticing (Soosay et al., 2008) and eventually facilitate cooperation among participating members along the supply chain network to improve innovation performance.

In summary, a high collaborative innovation capability can help supply chain partners combine complementary and related knowledge to achieve supernormal innovation performance.Tzabbar et al. (2008) suggest that bundling knowledge stocks can produce a combined return on knowledge that is greater than the sum of individual parts (1 + 1 > 2). This collaborative effect results from the process of making better use of knowledge in the supply chain network. Hence,

H5a.Participating in innovation activities is more positively related to innovation performance when the firm has high collaborative innovation capability than when the firm has low innovation capability.

H5b. Sharing knowledge is more positively related to innovation performance when the firm has high collaborative innovation capability than when the firm has low innovation capability.

Fig. 1summarizes the arguments regarding collaborative innovation activities, knowledge sharing, collaborative innovation capability, particular control variables, and the hypotheses derived from them to assess innovation performance.

3. Methods

We use Multiple Regression (MR) and Moderated Multiple

Control variables Firm Age Number of Employees Annual Turnover Collaborative

Innovation Activities

Knowledge Sharing

Innovation Performance Collaborative

Innovation Capability

H1(+)

H2(+) H3(+)

H4(+) H5a(+)

H5b(+)

Fig. 1.Summary of hypotheses regarding knowledge sharing, collaborative innovation activities, innovation capability, and innovation performance.

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Regression (MMR) (Wang and Han, 2011) to measure and test our conceptual framework and hypotheses using survey data. All descriptive and regression analyses are conducted using SPSS (22.0). The antecedents of the research framework in this study are collaborative innovation activities and knowledge sharing, and the consequence is innovation performance whereas the moderator is collaborative innovation capability. Knowledge sharing is also a mediator variable. The model is hypothesized, and estimates of the parameter values are used to develop an estimated regression equation between a dependent variable (innovation performance) and independent variables (collaborative innovation activities, knowledge sharing, and collaborative innovation capability). A one-way ANOVA is applied to the control variables to determine whether they can be used in the regression equation.

3.1. Sample and data collection

We test the validity of the model and research hypotheses using data collected in a questionnaire survey of 310 firms operating in China.

Over the past three decades, China has been moving aggressively from a strategy of imitation to one of innovation and establishing itself at the forefront of technological innovation. According to the reports by United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics, China had spent 2.046% of GDP on research and development expenditure in 2014. This percentage is increased dramatically since 1996. Also, the transformation of the Chinese economy from a centrally planned economy to a free market has had great impact on the Chinese supply chains’ innovation system.

Currently, China is also the world's second largest economy and glo- balization results in both pressures and drivers for Chinese firms to acquire external knowledge by engaging with more collaborative innovation activities in global supply chain networks to improve their performance. In addition, Chinese culture has strong implications for interpersonal and inter-organizational dynamics in supply chain networks. China is an economy based on relationships, which is an important factor influencing collaborative innovation in supply chain networks. Thus, China provides a rich context to test the interplay among the variables in this research. Examinations of the mediating role of knowledge sharing and the moderating role of collaborative innovation capability are particularly meaningful in the Chinese context. We mainly used snowball (Biernacki and Waldorf, 1981) to select our sample firms most of which are located in the Shandong Province.

The selected firms represent a wide range of industries, including Health Care, Energy, Information Technology, Materials, Tele- communication Services and Utilities.

To carry out our research, we submitted a survey project proposal to Shandong Province. The project was approved. Then we acquired a letter of recommendation from the local government bureau. In order to collect enough data, we first contacted our cooperative partners, which have some sort of prior relationships with us, and used the Yellow Pages to identify big firms (with annual turnover > 1000 million RMB Yuan).

We then encouraged the top managers of these big firms to recommend collaborative innovation partners in their supply chain networks. These supply chain partners may or may not on the Yellow Pages. Only a few (roughly less than 15%) firms were identified and selected directly using the Yellow Pages.

In our project team, there were eleven members from our university.

Two of them were teachers, who were responsible for designing the survey, providing necessary training and leading the nine students in carrying out the interviews, distributing and collecting questionnaires, and subsequent data analysis. There was another member, from the local government, who helped us to coordinate with the respondent firms for the purposes of executing successful interviews. Data were collected from July 2014 to September 2015, via face-to-face interviews using a structured questionnaire. We obtained 236 completed and usable questionnaires from these firms, representing a response rate of

76%. Our respondents mostly comprised CEOs (45%) and heads of R&D departments (25%).Table 1lists the respondent firm characteristics, including firm age, number of employees, annual turnover, and total assets.

Prior to distribution of the formal questionnaire survey, a preliminary version of the survey instrument was pre-tested among a group of five executives and three heads of R&D departments from enterprises in the above industries. Feedback from them was incorporated into a revised version of the survey instrument, along with comments and suggestions from industry experts, local government officials, and several colleagues knowledgeable in survey design. We also subsequently interviewed of these pre-testers, who are responsible for collaborative innovation projects, given that CEOs and heads of R&D departments are best able to respond to questions regarding their firms’ innovation issues. This approach is consistent with the selection of key informants knowledgeable regarding organizational matters by virtue of their po- sition (John and Weitz, 1988).

3.2. Measures

3.2.1. Dependent variable

3.2.1.1. Innovation performance. Innovation performance refers to the degree of success attained by the supply firm at achieving its goals pertaining to product-market or technological innovation (Goodale et al., 2011). We measured this dependent variable, innovation performance of the supply chain firm, with a composite 7-point Likert-type scale. The respondents were asked to assess their firm's performance in common terms of innovation, such as technological competitiveness, response to customer demand, number of new products or services, profitability, and speed to market of new products or services against their principal competitors operating in the same sector. No objective indicators, such as patents, were used to measure innovation performance. Our purpose was to examine the overall innovation performance of firms; we believe that this relative measurement approach is a feasible approach to satisfy this purpose (Ritala et al., 2015). These innovation performance measures have been frequently used in the extant product development literature (Chen and Huang, 2009).

Table 1

Descriptive statistics for survey sample.

Number of firms Percentage (%) Firm age (years)

≤ 5 27 11.4

6–10 37 15.7

11–15 38 16.1

16–20 44 18.6

21–25 40 16.9

6–10 20 8.5

> 10 30 12.7

Number of employees

≤ 100 51 21.6

101–1000 96 40.7

1001–10000 57 24.2

＞10000 32 13.6

Annual turnover (million RMB Yuan)

< 10 66 28.0

10–50 62 26.3

51–100 29 12.3

101–300 22 9.3

301–1000 22 9.3

> 1000 35 14.8

Total assets (million RMB Yuan)

< 40 72 30.5

40–100 56 23.7

101–400 36 15.3

> 400 72 30.5

Total 236 100

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3.2.2. Independent variables

3.2.2.1. Collaborative innovation activities. In the questionnaire items, firms indicate whether they had engaged in supply chain collaborative activities involving innovation in the last few years. Respondents specified whether they participated in collaborative R&D or other innovation-related projects with their customers or suppliers. We used five items on a 7-point Likert-type scale (1 = strong disagreement, 7 = strong agreement) to measure firm involvement in supply chain collaborative activities. The scale items measure both the extent and frequency of involvement of each firm and are developed using the existing literature on collaborative innovation (Soosay et al., 2008;

Mishra and Shah, 2009). For instance, the measurement items CIA2 (we frequently provide technical support to other partners in the supply chain network) and CIA5 (new product R&D teams have frequent interaction with customers and suppliers) are each a measure of the extent to which the firm collaborates with suppliers, customers, and R&

D partners in the collaborative innovation process.

3.2.2.2. Knowledge sharing. We developed a composite measure for knowledge sharing involving a three-item scale based on knowledge sharing intention measured following Bock et al. (2005) and Ritala et al. (2015). We chose this measure because it covered different aspects of knowledge that may be shared among different collaborative innovation members in supply chain networks. The original instrument was designed to measure the intention to share knowledge in an interfirm context. This was modified herein to measure supply chain firms’ perceptions of the degree to which their collaborative innovation partners share different forms of knowledge. We dropped three items in order to simplify the questionnaire and improve the reliability of the scale based on our preliminary data analysis.

3.2.2.3. Collaborative innovation capability. A firm's collaborative innovation capability in a supply chain network is what enables the firm to effectively integrate with their collaborative innovation partners to achieve high innovation performance (Mishra and Shah, 2009). The essence of collaborative innovation capability is that returns obtained from jointly using collaborative innovation practices with higher capability are greater than the sum of returns obtained from using individual innovation practices in isolation. The literature proposes several different measures of collaborative innovation capability (Blomqvist and Levy, 2006), and no single measure is superior to all others under all circumstances. This study defines collaborative innovation capability as the ability to ensure that the knowledge or technology generated by any firm in the supply chain network is captured and eventually exploited, not re-generated later or left unrecognized. The authors also view collaborative innovation capability as identifying the key collaborative innovation partners along with their roles and responsibilities and cooperating with them to complete a collaborative R&D project.

3.2.3. Control variables

Firm age may influence innovation performance because innovation culture and resource deployment may be a function of longevity. We calculate firm age as the number of years from the founding date. We use six dummy variables to measure firm age. Moreover, previous studies suggest that firm size may be a latent issue (Laursen and Salter, 2006); hence, we also include the size of collaborative innovation participating firms as a control variable. To some degree, firm size re- flects investment ability for R&D projects. This study measures firm size as the number of employees and annual sales in million RMB Yuan using three and six dummy variables, respectively.

4. Analyses and results

4.1. Reliability and validity

This study used SPSS 22.0 to estimate the model's reliability and validity and to test the proposed hypotheses. After the questionnaires were collected, we operationalized composite reliability using Cronbach's alpha (Cronbach, 1951). Cortina (1993) argues that the alpha coefficient is one of the most important and pervasive statistics in research involving test construction and use. Most studies that have used alpha regard values thereof equal to or exceeding 0.70 as adequate without comparing it with the number of items in the scale (Cortina, 1993). As shown inTable 2, the Cronbach's alpha values of individual constructs are all greater than 0.85, suggesting that the items reflect the underlying phenomena well.

Table 3displays the correlation coefficients of the research variables. The results fromTable 3indicate that the correlations between factors are all significant.

4.2. Hypotheses Tests

To see how much additional variance was explained by the independent variables after controls, we tested our hypotheses with hierarchical MR and MMR analyses. To begin with, we conducted a three-step regression analysis to examine the mediating effects of knowledge sharing (Table 4). We first examined the effects of the control variables (dummy variables) on innovation performance by regressing innovation performance on these variables (Model 1). Then, in Step 1, we added one independent variable to test the effect of collaborative innovation activities on a firm's innovation performance. The results in Model 2 show the hierarchical regression analyses estimating the effects of collaborative innovation activities. Hypothesis 1states that firms that engage more in collaborative innovation activities are associated with higher levels of innovation performance. As shown in Table 4, the coefficient for collaborative innovation activities is positive and significant (P < 0.01), indicating that collaborative innovation activities contribute to firm's innovation performance. Hence, Hypothesis 1is supported.

In Step 2, we regressed knowledge sharing on collaborative innovation activities and the control variables to test their effects on knowledge sharing (Model 3b). Then we regressed innovation performance on knowledge sharing and the control variables to examine its effect on innovation performance. The results in Model 3a suggest that knowledge sharing has a significantly positive effect on innovation performance (P < 0.01), indicating that sharing more knowledge contributes to a firm's collaborative innovation performance. Hence, Table 2

Descriptive statistics, and internal consistency of scale constructs.

Latent variables Means S.D. Cronbach's alpha

Collaborative innovation activities CIA1 5.52 1.469 0.869 CIA2 4.94 1.479 CIA3 5.36 1.403 CIA4 4.93 1.365 CIA5 5.06 1.474

Knowledge sharing KS1 5.44 1.439 0.889

KS2 5.30 1.410

KS3 5.61 1.346

Collaborative innovation capability CIC1 5.78 1.424 0.883 CIC2 5.78 1.297

Innovation performance IP1 5.69 1.331 0.875

IP2 5.65 1.268

IP3 5.15 1.402

IP4 4.59 1.303

IP5 5.50 1.367

IP6 5.24 1.460

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Table3 Correlations. Variables12345678910 1.Collaborativeinnovationactivities1 2.Knowledgesharing0.733**1 3.Collaborativeinnovationcapability0.709**0.664** 4.Innovationperformance0.748**0.678**0.752**1 5.Firmage1−0.266**−0.189**−0.228**−0.206**1 6.Firmage2−0.101−0.062−0.119−0.160*−0.155*1 7.Firmage3−0.0490.0490.0270.000−0.157*−0.189**1 8.Firmage50.078−0.029−0.0320.021−0.162*−0.195**−0.198**1 9.Firmage60.0950.0620.0600.068−0.109−0.131*−0.133*−0.137*1 10.Firmage70.159*0.1060.139*0.165*−0.137*−0.165*−0.167*−0.172**−0.1161 11.Numberofemployees1−0.266**−0.162*−0.227**−0.318**0.361**0.0850.078−0.127−0.160*−0.200** 12.Numberofemployees30.128*0.1170.0330.168**−0.203**−0.053−0.1130.088−0.0300.201** 13.Numberofemployees40.223**0.1150.179**0.189**−0.142*−0.069−0.072−0.0140.1020.220** 14.Annualturnover1−0.306**−0.202**−0.172**−0.307**0.280**0.0690.010−0.005−0.122−0.238** 15.Annualturnover20.014−0.0270.0140.021−0.154*−0.0720.1050.064−0.0090.061 16.Annualturnover40.151*0.0970.0680.145*−0.069−0.0580.0580.1270.0070.009 17.Annualturnover50.0080.007−0.053−0.002−0.028−0.002−0.055−0.109−0.0070.187** 18.Annualturnover60.153*0.1210.0790.136*−0.072−0.0250.033−0.031−0.0610.057 19.Annualturnover70.264**0.196**0.183**0.267**−0.1130.082−0.150*−0.0300.130*0.127 Variables111213141516171819 1.Collaborativeinnovationactivities 2.Knowledgesharing 3.Collaborativeinnovationcapability 4.Innovationperformance 5.Firmage1 6.Firmage2 7.Firmage3 8.Firmage5 9.Firmage6 10.Firmage7 11.Numberofemployees11 12.Numberofemployees3−0.296**1 13.Numberofemployees4−0.208**−0.223**1 14.Annualturnover10.613**−0.285**−0.192**1 15.Annualturnover2−0.173**−0.022−0.040−0.372**1 16.Annualturnover4−0.133*0.0570.086−0.200**−0.191**1 17.Annualturnover5−0.1270.081−0.041−0.150*−0.144*−0.0771 18.Annualturnover6−0.1050.198**−0.079−0.124−0.119−0.064−0.0481 19.Annualturnover7−0.219**0.210**0.322**−0.260**−0.249**−0.134*−0.101−0.0831 *p<0.05. **p<0.01.

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Hypothesis 2is supported. The results in Model 3b suggest that collaborative innovation activities has a significantly positive effect on knowledge sharing (P < 0.01), indicating that participating more in collaborative innovation activities contributes to greater knowledge sharing by firms.

In Step 3, we regressed innovation performance on collaborative innovation activities and knowledge sharing, controlling for firm age, number of employees, and the firm's annual turnover. The results in Model 4 show that the effects of collaborative innovation activities on innovation performance is reduced, but still significantly positive (P < 0.05). This indicates that knowledge sharing partially mediates the linkage between collaborative innovation activities and innovation performance, thus supportingHypothesis 3.

We conducted another three-step regression analysis (MMR) to examine moderating effects by first entering the control variables (firm age, number of employees, and annual turnover) and one independent variable (collaborative innovation activities) in Step 1; one independent variable (collaborative innovation activities or knowledge sharing) and moderator variable (collaborative innovation capability) in Step 2; and interactions in Step 3. Changes in the multiple squared correlation coefficient (R²) were traced from step to step (Tsai, 2001). To minimize the potential threat of multi-collinearity, we mean-centered all variables, including collaborative innovation activities, knowledge sharing, collaborative innovation capability, and innovation performance, con- stituting interaction terms, and then created interaction terms by multiplying the relevant mean-centered variables (collaborative innovation activities × collaborative innovation capability, knowledge sharing × collaborative innovation capability) (Tsai, 2001).

As Table 5shows, to test the moderating effect of collaborative innovation capability on the relationship between collaborative innovation activities and innovation performance, the analysis first includes the control variables and independent variable ZCIA (standardized value of collaborative innovation activities) in the model (Model 5), then adds the moderator variable ZCIC (standardized value of collaborative innovation capability) (Model 6), and finally includes the interaction terms (ZCIA × ZCIC) (Model 7). As shown inTable 5, the

coefficient for collaborative innovation capability is positive and significant (P < 0.01), indicating that a high level of collaborative innovation capability contributes to firm's innovation performance.

Hence,Hypothesis 4is supported. As predicted, the coefficient of interaction is positive and significant (P < 0.1), indicating that the effect of collaborative innovation activities on innovation performance is dependent on a firm's collaborative innovation capability. Hence, Hypothesis 5ais supported.

Hypothesis 5bstates that sharing knowledge is more positively related to innovation performance when the firm has high collaborative innovation capability than when the firm has low innovation capability.

To test this hypothesis, we used a similar method. The analysis first includes the control variables and independent variable ZKS (standardized value of knowledge sharing) in the model (Model 8), then adds the moderator variable ZCIC (standardized value of collaborative innovation capability) (Model 9), and finally includes the interaction terms (ZKS × ZCIC) (Model 10). As shown inTable 5, the coefficient of interaction is not statistically significant, indicating that the effect of knowledge sharing on innovation performance is not dependent on firm's collaborative innovation capability. Hence,Hypothesis 5bis not supported.

The results of hypotheses testing are summarized inTable 6. All hypotheses for the main, mediating and moderating effects are supported exceptH5b.

To better explain the form of interactions reported in the above hierarchical regression moderated multiple analyses, we plotted the trend showing the relationship between collaborative innovation activities and innovation performance at both high and low levels of collaborative innovation capability. This interaction effect is shown in Fig. 2 using one standard deviation above and below the mean to capture high and low collaborative innovation capability. The plot shows that when collaborative innovation capability is high, a firm's participatory innovation activities are more positively related to innovation performance; conversely, when collaborative innovation capability is low, a firm's participatory innovation activities are less positively related to innovation performance.

Table 4

Mediation regression models.

Variables Innovation performance Knowledge sharing

Model 1 Model 2 Model 3a Model 4 Model 3b

Block 1: Control variable

Firm age 1 − 0.10 (0.62) − 0.03 (0.60) − 0.06 (0.61) − 0.05 (0.60) − 0.07 (0.26)

Firm age 2 − 0.18*(0.54) − 0.13 (0.51) − 0.15*(0.52) − 0.15 (0.51) − 0.06 (0.22)

Firm age 3 − 0.08 (0.54) − 0.05 (0.51) − 0.09 (0.52) − 0.09 (0.51) 0.00 (0.22)

Firm age 5 0.02 (0.53) 0.02 (0.50) 0.04 (0.51) 0.01 (0.51) − 0.12 (0.22)

Firm age 6 − 0.10 (0.65) − 0.10 (0.62) − 0.10 (0.63) − 0.10 (0.62) 0.00 (0.27)

Firm age 7 − 0.09 (0.58) − 0.09 (0.55) − 0.09 (0.57) − 0.09 (0.56) 0.01 (0.24)

Number of employees 1 − 0.07 (0.51) − 0.07 (0.48) − 0.07 (0.49) − 0.06 (0.49) 0.05 (0.21)

Number of employees 3 0.00 (0.44) 0.02 (0.42) 0.00 (0.43) 0.00 (0.42) 0.01 (0.18)

Number of employees 4 − 0.08 (0.55) − 0.10 (0.52) − 0.08 (0.53) − 0.10 (0.52) − 0.04 (0.23)

Annual turnover 1 − 0.03 (0.59) − 0.02 (0.56) − 0.02 (0.57) − 0.02 (0.56) − 0.02 (0.24)

Annual turnover 2 0.09 (0.54) 0.05 (0.51) 0.08 (0.52) 0.09 (0.52) 0.03 (0.23)

Annual turnover 4 0.08 (0.68) 0.01 (0.66) 0.05 (0.67) 0.05 (0.66) 0.09 (0.29)

Annual turnover 5 0.03 (0.81) 0.01 (0.77) 0.03 (0.78) 0.03 (0.77) 0.03 (0.34)

Annual turnover 6 0.07 (0.92) 0.00 (0.89) 0.04 (0.90) 0.03 (0.89) 0.07 (0.39)

Annual turnover 7 0.33^**(0.64) 0.23*(0.62) 0.27^**(0.62) 0.27^**(0.61) 0.15 (0.27)

Block 2: Independent variable

Collaborative innovation activities 0.35^**(0.15) 0.13*(0.15) 0.56^**(0.05)

Knowledge sharing 0.25^**(0.13) 0.22^**(0.13)

Block 3: Model statistics

R Square 0.148 0.239 0.225 0.233 0.388

Adjusted R Square 0.090 0.183 0.146 0.173 0.343

F value 2.550^** 4.299^** 3.520^** 3.888^** 8.669^**

①Number of observations (n) is 236;②Each path coefficient is standardized;③The values in parentheses are standard errors.④There are no missing item scores in the analysis.

* p < 0.05.

** p < 0.01.

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5. Discussion

In order to meet dynamic market demands, it is necessary for firms to collaborate with partners in supply chains to innovate new products or services quickly. The critical factors affecting firm's collaborative innovation performance have been stressed in the literature (Cao and Zhang, 2011), however, most such studies focus on limited aspects of involvement and examine sources of synergy obtained from these do- mains in an isolated or fragmented manner. Such a focus does not represent the collaborative innovation process in practice because it ig- nores the factor interdependencies among collaborative innovation participants in a supply chain network.

The primary objective of this study is to understand whether or not collaborative innovation activities, knowledge sharing, and collaborative innovation capability affect a firm's innovation performance in the framework of supply chain networks. Theoretically, this study over- comes weaknesses observed in prior collaborative innovation research.

To better represent these critical factor interdependencies, our study combined knowledge management and innovation capability theory to propose knowledge sharing as the mediating mechanism and collaborative innovation capability as the moderating mechanism vis-à-vis the influences of collaborative innovation activities on a firm's innovation performance. The empirical findings provide several valuable and interesting academic and practical implications.

5.1. Implications for management research

This study has attempted to find empirical evidence for the idea that collaborative innovation activities are associated with the high levels of innovation performance. The analyses conducted herein support the posited hypothesis. Firms that engage more in collaborative innovation activities within the framework of collaborative innovation projects

perform better in terms of the proportion of turnover realized by means of new products or services. The results strongly support the claim that supply chain collaborative innovation activities increase innovation performance. Since participating in collaborative innovation activities offers a number of advantages (Slowinski et al., 2015), both customers and suppliers in a supply chain network likely already view each other as important strategic partner and already have some knowledge of each other's resources and innovation capabilities. This existing collaborative relationship may also ease negotiations over intellectual property rights, risk sharing, and cost recovery in collaborative innovation projects. So, engaging more in collaborative innovation activities with other supply chain partners enhances innovation prospects (Hall and Andriani, 1998; Rothaermel, 2001; Tamer Cavusgil et al., 2003; Faems et al., 2005; Simatupang and Sridharan, 2005; Cao and Zhang, 2011). For instance, customer firms may seek to cultivate mutually beneficial relationships with trusted suppliers in which the supplier is engaged early in the customer's R&D process to find the right direction of innovation with shorter times than other inter-organizational relationships (Schiele, 2012).

Collaborative innovation among supply chain partners is not merely a pure transaction of resources and information, but leverages new knowledge creation and sharing. A great diversity of knowledge is distributed across the supply chain network, collaborative innovation projects provide ideal platforms for knowledge sharing and learning (Cao and Zhang, 2011). Collaborative innovation in supply chain networks will reach across different disciplines to consolidate broad knowledge regarding new product and service technologies. It is often difficult for a firm to buy and use such particular knowledge in the marketplace because of its tacit nature; however, a firm may have a better chance of accomplishing its objective of acquiring new knowledge and then improving innovation performance by collaborating with other supply chain firms (Soosay et al., 2008; Cruz-González et al., Table 5

Moderation regression models.

Variables ZIP^a

Model 5 Model 6 Model 7 Model 8 Model 9 Model 10

Block 1: Control variable

Firm age 1 − 0.04 (0.22) − 0.02 (0.21) − 0.02 (0.18) − 0.05 (0.19) 0.03 (0.16) 0.03 (0.16)

Firm age 2 − 0.15^**(0.18) − 0.13*(0.18) − 0.12 (0.19) − 0.15 (0.16) − 0.07 (0.14) − 0.08 (0.14)

Firm age 3 − 0.04 (0.18) − 0.03 (0.18) − 0.02 (0.18) − 0.07 (0.16) − 0.02 (0.14) − 0.03 (0.14)

Firm age 5 − 0.12 (0.18) − 0.10 (0.18) − 0.09 (0.18) − 0.05 (0.16) 0.01 (0.13) 0.01 (0.13)

Firm age 6 − 0.08 (0.22) − 0.07 (0.22) − 0.07 (0.22) − 0.05 (0.19) − 0.01 (0.16) − 0.02 (0.16)

Firm age 7 − 0.06 (0.20) − 0.06 (0.20) − 0.05 (0.20) − 0.02 (0.17) − 0.02 (0.15) − 0.01 (0.15)

Number of employees 1 − 0.07 (0.17) − 0.05 (0.17) − 0.05 (0.17) − 0.14 (0.15) − 0.07 (0.13) − 0.08 (0.13)

Number of employees 3 0.02 (0.15) 0.03 (0.15) 0.03 (0.15) 0.00 (0.13) 0.05 (0.11) 0.04 (0.11)

Number of employees 4 − 0.02 (0.19) − 0.01 (0.18) − 0.01 (0.18) 0.03 (0.16) 0.02 (0.14) 0.02 (0.14)

Annual turnover 1 − 0.05 (0.20) − 0.06 (0.20) − 0.04 (0.20) 0.04 (0.17) 0.00 (0.15) − 0.01 (0.15)

Annual turnover 2 0.01 (0.18) 0.02 (0.18) 0.02 (0.18) 0.10 (0.16) 0.09 (0.14) 0.09 (0.14)

Annual turnover 4 0.06 (0.32) 0.07 (0.23) 0.07 (0.24) 0.12*(0.20) 0.11*(0.17) 0.11*(0.17)

Annual turnover 5 0.00 (0.28) 0.02 (0.27) 0.01 (0.28) 0.02 (0.24) 0.05 (0.20) 0.06 (0.20)

Annual turnover 6 0.06 (0.32) 0.07 (0.32) 0.07 (0.32) 0.08 (0.28) 0.08 (0.23) 0.08 (0.23)

Annual turnover 7 0.10 (0.22) 0.10 (0.22) 0.10 (0.22) 0.18^**(0.19) 0.15^**(0.16) 0.15^**(0.16)

Block 2: Independent variable

ZCIA 0.52^***(0.06) 0.40^***(0.08) 0.42^***(0.08)

ZKS 0.59^***(0.05) 0.28^***(0.05) 0.48^***(0.06)

ZCIC 0.16^**(0.08) 0.20^**(0.08) 0.51^***(0.05) 0.26^***(0.05)

ZCIA×ZCIC 0.07*(0.04)

ZKS×ZCIC − 0.08 (0.34)

Block 3: Model statistics

R Square 0.400 0.412 0.415 0.545 0.675 0.678

Adjusted R Square 0.356 0.366 0.366 0.512 0.650 0.653

F value 9.110^*** 8.968^*** 8.537^*** 16.382^*** 26.662^*** 25.528^***

①Number of observations (n) is 236;②Each path coefficient is standardized;③The values in parentheses are standard errors.④There are no missing item scores in the analysis.

aZIP=Standardized value of Innovation Performance (standardized Zscore); ZCIA=Standardized value of Collaborative Innovation Activities; ZKS=Standardized value of Knowledge Sharing; ZCIC=Standardized value of Collaborative Innovation Capability.

* p < 0.1.

** p < 0.05.

*** p < 0.01.