How does open innovation affect firms’ innovative performance : The roles of knowledge attributes and partner opportunism

Huiping Zhou (Business School, Hunan University, Changsha, China)
Yanhong Yao (Business School, Hunan University, Changsha, China)
Huanhuan Chen (Business School, Hunan University, Changsha, China)

Chinese Management Studies

ISSN: 1750-614X

Publication date: 5 November 2018

Abstract

Purpose

This paper aims to explore the direct effects of open innovation (OI) on firms’ innovative performance, and to examine the moderating effects of knowledge attributes, including knowledge distance, knowledge embeddedness and partner opportunism on this relationship.

Design/methodology/approach

Survey data of 247 samples from China were used to test the proposed model through hierarchical regression analysis.

Findings

The findings indicate that the dimensions of OI are positively related to innovative performance. The results also reveal that knowledge distance positively moderates the relationship between inbound OI and innovative performance, whereas knowledge embeddedness negatively affects that relationship. Knowledge embeddedness negatively affects the relationship between inbound OI and innovative performance, whereas knowledge distance positively moderates that relationship. Thus, a new finding is proposed that knowledge attributes could align effectively with specific OI type to achieve superior innovation outcomes. In addition, the empirical results suggest that partner opportunism plays a negative moderating role on the relationship between outbound OI and innovative performance.

Originality/value

The proposed view that a firm’s innovation outputs will be superior when its knowledge attributes effectively align with OI enriches studies of the OI context and expands the literature of both the resource-based view and the knowledge-based view. Furthermore, this study provides insights into how OI benefits can be influenced by external contexts from the perspective of partners’ opportunistic behaviour.

Keywords

Citation

Zhou, H., Yao, Y. and Chen, H. (2018), "How does open innovation affect firms’ innovative performance ", Chinese Management Studies, Vol. 12 No. 4, pp. 720-740. https://doi.org/10.1108/CMS-05-2017-0137

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

In today’s dynamic and globalized business environment, many firms are enhancing their competitiveness and innovation through business networks or corporate collaboration (Popa et al., 2017). Chesbrough (2003) called this new approach to heightened performance “open innovation” (OI). The OI mode, which focuses on the integration and exploitation of both internal and external resources, is a common theme within the discourse of the academic press (Chesbrough, 2003). Chesbrough and Crowther (2006) divided OI into two dimensions: inbound and outbound. Inbound OI is defined as the exploration and acquisition of external knowledge; outbound OI refers to the external commercialization of internal technology (Chesbrough, 2006).

Resource-based view (RBV) states that organizations compete on the basis of their resources and capabilities (Barney, 1991). OI allows firms to explore outside knowledge and to exploit internal resources to obtain competitive advantages (Drechsler and Natter, 2012; Popa et al., 2017). However, scholars have not reached a consensus on how OI affects a firm’s innovation outputs and overall performance. While pretty number of extant literature demonstrates that OI has a positive impact on a firm’s performance indicators (Atuahene-Gima and Wei, 2011; Hung and Chou, 2013; Mazzola et al., 2012; Parida et al., 2012; Popa et al., 2017; Rangus et al., 2017), other studies suggest that it can have a negative or a curvilinear effect on performance (Laursen and Salter, 2006; Caputo et al., 2016). For instance, Cassiman and Valentini (2016) noted that OI could increase firms’ sales of new products, but at the same time, their R&D costs increase more than proportionally. Therefore, it is necessary to examine the different effects of inbound OI and outbound OI on firm’s innovative performance under various internal and external contexts.

This study investigates how OI affect innovative performance by considering internal knowledge attributes as a moderator. Knowledge attributes, including knowledge distance and knowledge embeddedness, are the characteristics that represent the specific state of knowledge within an organization. While prior literature notes that knowledge attributes can determine the success of knowledge acquisition and technological development (Gaffney et al., 2016), whether they can affect the effectiveness of OI on a firm’s innovative performance remains unclear. What is more, extant research on inbound and outbound OI seems to be primarily concerned with their direct impacts on performance indicators (Hung and Chou, 2013; Lichtenthaler, 2009; Cheng and Shiu, 2015). It is necessary to explore how OI affects firms’ innovative performance under different conditions. According to the RBV, organizations compete on the basis of their resources and capabilities (Barney, 1991). Because knowledge resources are a major component of OI (Urgal et al., 2013), OI research often uses the RBV and its extensions, such as the knowledge-based view (KBV), to suggest that firms also build collaborative networks with external partners. Internal knowledge factors are crucial in this regard as they provide capacities for OI practice and the selection of openness strategies. However, research on how internal knowledge is integrated with external knowledge sources to contribute to effective OI has been limited. Therefore, this research addresses whether a firm’s knowledge attributes determine the realization of innovative performance from OI.

This study also explores how OI affects innovative performance under opportunistic behaviours. The background of economic globalization, the demand for open communications and the disclosure of core knowledge represent challenges to technology-based companies engaged in business cooperation (Blomqvist et al., 2008). The OI mode, whether inbound OI or outbound OI, emphasizes companies should cooperate with external partners. Although prior literature has emphasized the consequences of opportunism and ways to reduce or avoid it among partners (Benton and Maloni, 2005; Chung, 2012; Kang and Jindal, 2015), empirical evidence on whether opportunistic behaviours decrease a firm’s innovative performance in OI practice is scant. Furthermore, in the Chinese business environment, lower marketization, imperfect industry norms and the lack of a foundation of good moral behaviour and business ethics all lead to frequent crises of trust. Thus, it is imperative to explore how OI affects firms’ innovative performance in light of partners’ opportunistic behaviours.

To address these identified research gaps, this paper uses survey data from 247 Chinese firms to:

  • examine the influence of inbound and outbound OI on innovative performance; and

  • investigate the moderating role of knowledge attributes and partner opportunism in such a linear link.

Our study contributes to the current literature in three ways.

First, this study divides OI into two dimensions, inbound and outbound OI, and sheds light on how the specific dimensions of OI serve as a catalyst for a firm’s innovative performance. Again, previous studies have reached contradictory conclusions about how inbound and outbound OI improve that performance (Hung and Chou, 2013; Cheng and Shiu, 2015; Bayona-Saez et al., 2017). Our findings complement those earlier findings by testing the dimensions of OI on innovative performance within the Chinese business context.

Second, using the RBV and KBV, we contribute to the innovation literature by proposing the moderating effect of knowledge attributes on the OI–performance relationship. The extant literature provides limited explanations of the role of internal contexts, such as corporate culture and knowledge in this relationship (Wang et al., 2015). Our study deepens the understanding of OI by showing how the characteristics of organizational knowledge contribute to the effectiveness of OI and thereby add new factors that substantially shape a firm’s ability to manage OI.

Third, through our evidence-based conclusion that partner opportunism negatively affects the relationship between outbound OI and innovative performance, this study provides insights into how external partnerships influence OI. From a managerial standpoint, our empirical conclusion emphasizes the effectiveness of close partner selection as a hedge against opportunism, steering managers to make better choices when implementing an outbound OI strategy.

2. Literature review and hypotheses

2.1 Literature review on open innovation

As noted, OI has been divided into two dimensions, inbound OI and outbound OI (Chesbrough and Crowther, 2006). Previous studies emphasize the active role of inbound OI in acquiring external knowledge and technology and improving the firm’s crucial performance indicators (Hung and Chou, 2013; Mazzola et al., 2012; Parida et al., 2012; Cheng and Shiu, 2015; Rangus et al., 2017; Popa et al., 2017). For example, Hung and Chou (2013) identified two core OI processes – external technology acquisition and external technology exploitation – and explored their effects on firm performance. However, few studies examine how inbound innovations increase both the costs of seeking external knowledge (Laursen and Salter, 2006) and the risks of open activities (Lichtenthaler, 2010). This has resulted in a call for research on what kinds of internal organizational factors can be best adapted for OI (Cheng and Huizingh, 2014).

The process of outbound OI is being increasingly seen as a type of strategic selection by firms, which can profit from their own innovative outcomes without investing in complementary assets (Mazzola et al., 2012). Firms processing outbound activities gain economic and strategic advantages from outside organizations’ use of technology or from the co-development of valuable knowledge with external partners (Chesbrough, 2006; Hung and Chou, 2013; Mazzola et al., 2012; Parida et al., 2012; Popa et al., 2017; Rangus et al., 2017). Existing research highlights the positive impact that such outbound activities have on financial and innovation performance (Lichtenthaler, 2009; Cheng and Shiu, 2015). However, there are also downsides to openness. For example, Noh (2015) noted that open processes make it difficult to protect a firm’s intellectual property and its benefits.

2.2 Influence of inbound and outbound open innovation on innovative performance

2.2.1 Inbound open innovation and innovative performance.

According to Hung and Chou (2013), inbound OI assesses the degree to which a company accesses available external technologies to complement internal knowledge (Hung and Chou, 2013, p. 369). This process allows firms to acquire knowledge from external organizations, such as suppliers, customers and other sources, to enrich their own store of knowledge.

A considerable research proves that the extent of external knowledge exploitation helps firms boost innovation performances (Laursen and Salter, 2006; Hung and Chou, 2013; Cheng and Shiu, 2015; Bayona-Saez et al., 2017). They believe the extent of a firm’s exploratory learning behaviour to search for new and diverse knowledge beyond its boundary, which can facilitate them to create new and useful knowledge for more value with unique benefits. Thus, the capability to source and recombine knowledge from the external environment is becoming critical to sustain internal product innovation efforts (Chesbrough, 2003).

The RBV suggests that resources and capabilities are the main forces that drive firms to cultivate sustainable innovative benefits (Barney, 1991; Leskovar-Spacapan and Bastic, 2007). Following this logic, a firm can use its inbound OI to explore external knowledge and gain additional value through the synergy of knowledge resources. The complementary resources that it thus acquires can promote new product innovation and technological advancement (Gulati, 1999). An increasing knowledge pool can provide the firm with numerous choices for finding new and beneficial combinations of knowledge and renewing problem-solving capacities, which may create value with unique benefits (Hung and Chou, 2013). Furthermore, inbound activities allow firms to capture market development trends in a timely manner (Wang et al., 2015), thereby reducing their unnecessary product costs and enhancing their profit margins.

In short, technologies acquired from inbound activities can promote the exploitation of existing knowledge and the generation of new ideas. This outside-in process provides superior performance for firms’ innovation practices. Therefore, we propose the following hypothesis:

H1a.

Inbound open innovation has a positive effect on innovative performance.

2.2.2 Outbound open innovation and innovative performance.

Outbound OI refers to a firm’s purposive pursuit of commercialization or outward transfer of its technological knowledge to other firms to obtain monetary or non-monetary benefits (Hung and Chou, 2013). The RBV assumes that markets are highly imperfect, so firms not only need to tap into external resources but also exploit internal ones (Drechsler and Natter, 2012). A considerable body of research proves that internal knowledge commercialization engenders huge returns in terms of sales, profitability, market benefits and strategic benefits (Drechsler and Natter, 2012; Lichtenthaler, 2009; Hung and Chou, 2013; Mazzola et al., 2012; Noh, 2015; Popa et al., 2017; Rangus et al., 2017). For instance, various pioneering firms gain a great deal of additional revenues by exporting their internal innovation outcomes (Chesbrough and Crowther, 2006; Huizingh, 2011; Hung and Chou, 2013; Natalicchio et al., 2017), which, in turn, provides substantial funds for R&D to develop new products and technologies further. Firms that sell unused internal technologies to their partners can refocus on developing their core capabilities and hence outperform their counterparts who choose to do otherwise (Hung and Chou, 2013). Through outbound OI, firms can also gain strategic opportunities to obtain a high reputation and strong influence in the technology market, strengthen their brand, win customers’ trust and thereby increase their sales of new products.

In addition, outbound OI can generate new business opportunities for firms to become industrial leaders or to establish industrial standards for certain products on the basis of their technological advantage when their products or services become leading ones in industries through the fast pace of innovation. The process of establishing industry standards is likely to lower their costs of complying with new standards of products and also to provide them with opportunities to learn external new technologies for future innovation (Noh, 2015).

Obviously, the knowledge outflows in outbound OI not only generate huge economic and strategic interests for firms but also motivate their vitality through internal resource reorganization and exploitation, which constitutes a vital source of innovative benefits. Therefore, we propose the following hypothesis:

H1b.

Outbound open innovation has a positive effect on innovative performance.

2.3 The interaction between knowledge attributes and open innovation activities

Again, knowledge attributes are the characteristics that represent the specific state of knowledge within an organization. Previous studies of these attributes focus on implicit and explicit knowledge (Nonaka, 1994), or knowledge embeddedness, knowledge fuzziness and knowledge distance (Cummings and Teng, 2003). Using the research of Cummings and Teng (2003), we select embeddedness and distance as the dimensions of knowledge attributes to study.

Several previous studies state that the change of knowledge characteristics can have a profound impact on a firm’s performance and especially on the strategic consequences of innovation (Brusoni et al., 2001; Fixson and Park, 2008). For instance, Zahra and George (2002) considered that the characteristics of knowledge resources can determine the extent to which companies identify new knowledge and integrate it, along with old knowledge, into innovation activities. Knowledge attributes also affect knowledge transfer success, thus playing an important role in the formation and development of the core technical capabilities of enterprises (Birkinshaw et al., 2002).

However, scholars pay limited attention to how these attributes influence the implementation of OI to gain additional innovative value. To address this lack, we try to explore the effect of knowledge attributes on the OI–performance relationship.

2.3.1 The moderating effects of knowledge distance

2.3.1.1 The effect of knowledge distance in the inbound open innovation-performance relationship.

In keeping with the definition given by Cummings and Teng (2003), we use knowledge distance to mean the similarity of knowledge among the related parties of the company. The greater the knowledge distance, the less overlap there is in knowledge among related parties. Firms with a greater knowledge distance usually have less knowledge overlap and more diversified knowledge sources coming from different fields.

According to the KBV, innovation depends on an organization’s knowledge base, as well as on its ability to acquire, manage and create knowledge (Zhou and Li, 2012). Diverse knowledge resources facilitate external knowledge seeking through inbound activities, providing a sufficient foundation of complementary knowledge to enable effective communication, and help firms effectively integrate newly acquired external knowledge with existing knowledge to produce more innovative outputs through knowledge asset synergy (Afuah, 2002). A broader knowledge base also provides firms with more unique technological fields, which lead to greater market opportunities to create new products or services through the knowledge-seeking process (Laursen and Salter, 2006). In addition, a diverse knowledge set can provide more novel problem-solving solutions to resolve unexpected problems encountered in inbound OI (Levitt and March, 1988), thereby enabling firms to execute complex tasks quickly and smoothly within the knowledge-seeking process.

Generally, high knowledge distance is conducive to the assimilation and exploitation of new knowledge, thereby enriching firms’ knowledge store of inbound OI and providing an important foundation for superior innovative performance.

Therefore, we propose the following hypothesis:

H2a.

Knowledge distance positively affects the relationship between inbound open innovation and innovative performance. Specifically, the greater the knowledge distance, the more inbound OI is related to innovative performance.

2.3.1.2 The effect of knowledge distance on the outbound open innovation−performance relationship.

Outbound OI emphasizes the external commercialization of internal knowledge and technology. Firms with greater knowledge distance usually have different thinking and communication methods among organizational members; thus, they cannot reach a consensus on the use of knowledge, which leads to difficulties in organizational knowledge sharing and transfer, as well as to a lack of the core technical capabilities needed to promote the external use of internal knowledge (Nelson and Winter, 1982). This hinders them from carrying out outbound OI activities. In this situation, such firms should reintegrate their existing knowledge base to promote the commercialization of technological knowledge, given that this process will increase the cost of knowledge integration (Laursen and Salter, 2006). High knowledge distance does not appear to be conducive to companies promoting the commercialization of internal knowledge, and thus it reduces the innovation outcomes of outbound OI. This argument leads us to propose the following hypothesis:

H2b.

Knowledge distance negatively affects the relationship between outbound open innovation and innovative performance. Specifically, the greater the knowledge distance, the less that outbound OI is related to innovative performance.

2.3.2 The moderating effects of knowledge embeddedness

2.3.2.1 The effect of knowledge embeddedness on the inbound open innovation–performance relationship.

According to the definition of knowledge embeddedness cited by Argote and Ingram (2000), knowledge embeddedness refers to the degree to which knowledge is indivisible or inseparable from its carrier (Argote and Ingram 2000). Knowledge can be embedded in people, tools and routines, as well as in related sub-networks among these elements. Embedded knowledge mainly centres on the knowledge of a particular field, so companies with high knowledge embeddedness usually have highly specialized knowledge and unique technological advantages in the specific field. When an inbound OI strategy is implemented, the specific knowledge resources in a certain field may suffer from a confined technological trajectory and result in greater cognitive inertia, requiring the firm to constantly exert more effort to break the existing knowledge-seeking routines, as well as entrenched methods and solutions (Katila and Ahuja, 2002). Thus, a firm will be less likely to pursue significantly different solutions to integrate external knowledge sources into its internal knowledge base (Levitt and March, 1988), which, in turn, will restrain it from acquiring different areas of knowledge from inbound OI to generate good innovative outcomes. Furthermore, highly specialized knowledge may hamper the flexibility of knowledge and can lead to a series of technical innovation risks, such as a “competency trap” (Levitt and March, 1988), thereby undercutting the absorption and combination of external knowledge in inbound OI. Thus, we can reasonably hypothesize that:

H3a.

Knowledge embeddedness negatively affects the relationship between inbound open innovation and innovative performance. Specifically, the higher the knowledge embeddedness, the lesser that inbound OI is related to innovative performance.

2.3.2.2 The effect of knowledge embeddedness on the outbound open innovation−performance relationship.

As noted, firms with a high level of embedded knowledge usually have a highly specialized knowledge base and technical advantages in a specific field. These firms can take advantage of a wealth of knowledge resources to promote the external exploitation of knowledge by outbound OI to achieve the rapid commercialization of valuable innovations (Chesbrorigh and Crowther, 2006), which can result in good performance returns. High-level knowledge embeddedness also enables firms to distinguish among special technical issues and solve any problems within their specialties in the outbound OI process and actualization, which can increase their effectiveness and viability for knowledge commercializing activities.

In addition, as the KBV maintains, effective acquisition and use of external knowledge sources are vital for firms to create new knowledge (Grant, 1996). Thus, when a firm possesses higher embedded knowledge, it is more likely to offer external organizations sufficient technological knowledge to accumulate related knowledge to boost its own new knowledge creation in outbound OI (Hung and Chou, 2013) and thereby gain innovative benefits from co-exploiting technology with external partners.

Therefore, highly specialized embedded knowledge helps accelerate the process of commercializing internal outcomes and the formation of innovative benefits in outbound OI practice. In this regard, we suggest the following hypothesis:

H3b.

Knowledge embeddedness positively affects the relationship between outbound open innovation and innovative performance. Specifically, the higher the knowledge embeddedness, the more that outbound open innovation is related to innovative performance.

2.3.3 The moderating effects of partner opportunism.

In transaction cost theory, opportunism is defined as “self-interest seeking with guile” and “the incomplete or distorted disclosure of information, especially to calculated efforts to mislead, distort, disguise, obfuscate, or otherwise confuse” (Williamson, 1985, p.48). When a firm collaborates with other firms, it faces challenges in the demand for open communications and the disclosure of technological knowledge (Blomqvist et al., 2008). Generally, information and knowledge are critical sources of opportunism risk – that is, risks that knowledge will be copied or leaked from the focal firm to its partners or even its competitors (Williamson, 1985); this risk is a major threat for OI. Extant literature mainly focuses on the consequences of opportunism and ways to reduce or avoid it among partners (Benton and Maloni, 2005; Chung, 2012; Kang and Jindal, 2015). For example, scholars suggest that opportunism has negative effects on relational constructs such as trust and long-term orientation (Chung, 2012), and that to protect against such threats, business partners should work together to establish equitable governance mechanisms that would stabilize business cooperation (Benton and Maloni, 2005). Such mechanisms provide a set of norms and shared expectations to regulate interfirm behaviour.

Transaction cost literature holds that the risk of opportunism may produce substantial opportunity costs in the form of “valuable deals that won’t be done” (Calfee and Rubin, 1993, p. 164). OI creates major risks of knowledge leakage and bears a high risk of opportunistic exploitation by the collaboration partners (Drechsler and Natter, 2012). Inbound OI tends to acquire knowledge resources from external partners through partnerships, technology purchase agreements and other methods. But as many participants are involved in a firm’s innovation processes, outsiders have more opportunities to access the firm’s technologies and proprietary knowledge, which will consume a firm’s resources for external knowledge seeking, thereby undercutting firm’s efforts to integrate externally acquired knowledge with existing knowledge and reducing its innovative outputs in knowledge seeking.

In outbound OI, open processes make it difficult to protect a firm’s intellectual property and its benefits (Noh, 2015). To maintain the scarcity of intellectual property rights, external partners may engage in opportunism that fails to follow the cooperation agreement. Obviously, maintaining relationships with opportunistic partners is difficult because the control of opportunistic behaviour requires high coordination costs (Samaha et al., 2011). Once partners behave like free riders or engage in knowledge theft or other opportunistic behaviours, the key knowledge resources within the organization may be revealed to participants, strengthening competitors. This can reduce the rarity of knowledge assets and even lead to weakened economic benefits. Accordingly, we propose the following hypotheses:

H4a.

Partner opportunism negatively affects the relationship between inbound open innovation and innovative performance.

H4b.

Partner opportunism negatively affects the relationship between outbound open innovation and innovative performance.

In summary, the research framework is shown in Figure 1.

3. Research method

3.1 Sampling and data collection

This study used survey data to test the proposed hypotheses. To capture OI and knowledge-intensive characteristics, we chose firms from the machinery manufacturing, automobile, energy and materials and medical equipment industries. We collected data in Hunan, Guangdong, Liaoning and other provinces in China.

Following a valid method that previous OI studies have used (Naqshbandi, 2016), we targeted primary managers, middle and senior managers and members of the core staff who take responsibility for the development of new products and services and application of OI. To guarantee the validity of the sample sources, no more than three participants from each firm were allowed to participate.

Data were collected in two steps. The first step entailed pretesting, mainly to validate the questionnaire’s items; this step was conducted in MBA and EMBA classrooms and yielded 90 usable valid samples. The second step was the formal survey for hypothesis testing. We asked the target firms’ human resource departments to recruit experienced interviewers, set up appointments to present the questionnaire and collect the surveys after their completion. In total, we handed out 400 questionnaires; after excluding 153 incomplete and invalid questionnaires, we collected 247 effective samples for a recovery rate of 61.75 per cent. The distribution of samples is shown in Table I.

3.2 Measurements

Questionnaire items, unless stated otherwise, were measured using a five-point Likert scale. All measures are adapted mature scales; three researchers who are proficient in the English and Chinese languages translated and adjusted the content of these measures to be appropriate for the Chinese context.

We adapted the OI measures from Hung and Chou’s study (2013).We used five items to measure inbound OI; these reflect the firm’s approaches to gaining and exploring knowledge from external partners. We also used five items to measure outbound OI; these capture the firm’s approaches to commercialize internally developed ideas.

We measured innovative performance – a firm’s performance outcomes for new products – using five items identified by Han and Li (2015).

We adapted the knowledge attributes measure from Cummings and Teng’s study (2003). We used four items to assess the degree of embeddedness of specific knowledge in a firm, and adapted four items to measure knowledge distance – the similarity of knowledge across members within the organization.

We adapted measures of partner opportunism from the studies by Wuyts and Geyskens (2005), Lai et al. (2012) and Chung (2012). We used five items to assess the opportunistic behaviour of partners to achieve their own interests. The fit indicators of the confirmatory factor analysis (CFA) are as follows: χ2/df = 1.449, RMSEA = 0.048, GFI = 0.929, CFI = 0.992, TLI = 0.985, AGFI = 0.953, IFI = 0.993 and NFI = 0.976.Thus, the scale of partner opportunism has good reliability and validity.

Finally, we used five control variables to exclude any factors that might interfere with the study results. We included firm age because older firms lack the flexibility needed to adopt openness (Van de Vareska et al., 2010). We divided firm age into four groups: less than three years, three to five years, five to ten years and more than ten years. We also included firm size, as there is widespread belief in the positive relationship between firm size and innovation (Huizingh, 2011), and we accordingly divided it into four groups as well: fewer than 100 staff, 100-499 staff, 500-1,000 staff and more than 1,000 staff. In addition, we included market turbulence (Han et al., 1998), technological turbulence (Citrin et al., 2007) and competitive intensity (Zhou et al., 2005) as control variables because they were found to influence OI-related performance in previous research (Cheng and Huizingh, 2014).

4. Data analysis and results

4.1 Validity and reliability tests

After data collection, we carried out a validity analysis for each construct. First, we conducted a CFA to test the unidimensionality of the measurement models. The model fit indices are as follows: χ2 = 373.524, df = 218, χ2/df = 1.713, RMSEA = 0.061, CFI = 0.908, IFI = 0.910 and TLI = 0.923. The results show that the confirmatory model fits well.

Second, we conducted an exploratory factor analysis. This showed that all the constructs’ Cronbach’s α and composite reliability (CR) coefficients are well above the cut-off point of 0.70 (Table II), demonstrating that the construct reliability is sufficient to enable hypothesis testing.

In addition, most factor loadings are greater than the recommended value of 0.7 (Table II), and the average variance extracted (AVE) for each latent variable is greater than 0.5. This indicates that the measurement model has ideal convergent validity.

As shown in Table III, the square root of the AVE for each construct is higher than the off-diagonal correlation coefficients between the two constructs, providing additional support for discriminant validity.

4.2 Non-response bias and common method bias

We addressed the potential problem of non-response bias by comparing the key attributes (firm age and firm size) of the first 50 respondents with those of the last 50 respondents through a t-test according to Mohr and Spekman (1994). No significant differences were found between the two samples, indicating that non-response bias does not exist.

As only a single interviewee completed the survey, following Podsakoff et al. (2003), we conducted Harman’s single-factor analysis to check for common method bias; the factor with the highest variance explanation out of four accounts for 21.698 per cent of the total variance explained. Furthermore, we also used the marker variable method. As Lindell and Whitney (2001) suggested, common method bias can be assessed by identifying a marker variable that is not theoretically related to at least one other variable in the research model. Thus, respondents’ age was used as the marker variable. According to the results shown in Table III, this variable was not significantly related to any of the variables in the model. Moreover, the correlations between the constructs that were hypothesized to be significant remained significant after controlling for the effect of the marker variable. Therefore, both tests indicate that common method bias is not a serious issue in this study.

4.3 Hypotheses tests

Table III shows the basic information for each construct and its correlations. The analysis results indicate a significant positive correlation between each variable. In addition, the multicollinearity is tested by the variance inflation factor (VIF) statistic, and the findings show that the VIF of each variable is less than the cut-off point of 10, satisfying the assumption of multicollinearity.

4.3.1 Inbound and outbound open innovation on firms’ innovative performance.

This study uses software SPSS 21.0 to test the hypothesis. A regression analysis is appropriate for testing direct effects (Aiken and West, 1991). The regression results are presented in Table IV. We first centralize the independent variables to reduce potential multicollinearity, and regress innovative performance against the control variables in Model 1. Then, we add predictors in Model 2. The results indicate that both inbound OI (β = 0.152, p < 0.001) and outbound OI (β = 0.243, p < 0.001) have a positive impact on innovative performance, supporting H1a and H1b.

4.3.2 The moderating effects test for knowledge attributes.

We adapt a regression analysis to test the interaction effects between knowledge attributes and OI (Aiken and West, 1991); the results are presented in Table IV. Independent variables are centralized to reduce potential multicollinearity; on the basis of Model 1, we add predictors in Model 3, and include all variables and interaction terms in Model 4.The results indicate that the interaction (β = 0.235, p < 0.001) between knowledge distance and inbound OI is positively related to innovative performance, but that the interaction (β = −0.245, p < 0.001) between knowledge embeddedness and inbound OI is negatively associated with innovative performance. Thus, H2a and H3a are supported. Performing similar operations, we obtain Models 5 and 6.We find that the interaction (β = −0.145, p < 0.01) between knowledge distance and outbound OI is negatively associated with innovative performance, but that the interaction (β = 0.222, p < 0.001) between knowledge embeddedness and outbound OI has a positive effect on innovative performance, supporting H2b and H3b.

In addition, to shed further light on the moderating role of knowledge attributes, we followed Aiken and West (1991) to plot the two-way interaction effects of inbound OI, outbound OI, knowledge distance and knowledge embeddedness. As illustrated in Figure 2, H2a, H2b, H3a and H3b are also supported.

On the basis of the analysis results, we can conclude that knowledge distance positively affects the relationship between inbound OI and innovative performance, whereas knowledge embeddedness negatively affects that relationship. Moreover, knowledge embeddedness positively affects the relationship between outbound OI and innovative performance, whereas knowledge distance negatively affects it. Thereby, a firm processing inbound OI benefits more from knowledge distance than from knowledge embeddedness to achieve innovative outcomes, whereas a firm processing outbound OI benefits more from knowledge embeddedness than from knowledge distance to cultivate innovative benefits. Accordingly, we propose a new conclusion: the strong fitness between knowledge attributes and OI has a positive effect on innovative performance.

4.3.3 The moderating role test for partner opportunism.

We also adopt a regression analysis to test the interaction effects between partner opportunism and OI (Aiken and West, 1991).The regression results are presented in Table V. We first centralize the independent variables to reduce potential multicollinearity. Similarly, on the basis of Model 1, we then add predictors in Model 2. Finally, we include all variables and interaction terms in Model 3. The results indicate that the interaction (p > 0.05) between inbound OI and partner opportunism is not related to innovative performance; therefore, H4a is not supported. However, the interaction (β =−0.141, p < 0.01) between outbound OI and partner opportunism has a positive influence on innovative performance; therefore, H4b is supported.

To shed further light on the moderating relationships, we also plot the two-way interaction effects of OI and partner opportunism, as illustrated in Figure 3.

5. Discussion and conclusion

5.1 Conclusion

In accordance with the RBV and KBV, this study aims to identify the direct and indirect effects of OI on innovation performance in the context of internal knowledge and external partnerships. Our empirical findings show that both inbound and outbound OI are positively related to innovative performance. By revealing that knowledge distance and knowledge embeddedness play positive or negative moderating roles in shaping OI outcomes, the results also demonstrate the strategy fit between OI dimensions and knowledge attributes to cultivate superior innovative outputs. Knowledge is a key resource to establish and sustain the competitive advantage of firms (Grant, 1996). Hence, it clearly emerges that knowledge assumes a pivotal role in the OI paradigm. By adding partner opportunism to the OI literature, this study reveals the potentially negative role of external partnerships in the outbound OI–performance relationship, and encourages firms to build reciprocal cooperation relationships with outsider organizations when implementing an OI strategy.

5.2 Theoretical implications

In seeking to provide insights into how OI affects innovative performance, as well as whether knowledge attributes and partner opportunism influence this relationship, our study contributes to the OI literature in three ways.

First, using the RBV, this study provides empirical evidence to show how inbound OI and outbound OI positively affect innovative performance. Inbound OI helps companies to acquire complementary resources to generate new ideas, whereas outbound OI makes the company’s superior resources more valuable. This is consistent with the RBV that emphasizes the importance of resources and verifies the applicability of RBV in the context of OI. Furthermore, the results are consistent with the findings that OI practices are beneficial to a firm’s innovation outputs (Chesbrough and Crowther, 2006; Parida et al., 2012; Urgal et al., 2013; Cheng and Shiu, 2015; Popa et al., 2017; Rangus et al., 2017).Our empirical analysis also supports the claims that inbound and outbound OI activities are not mutually exclusive (Chesbrough and Crowther, 2006); both types of openness activities contribute to the formation of innovative results. Therefore, firms should combine their resources with a capacity to facilitate the synergistic effects between inbound and outbound activities.

Second, by applying the KBV, we provide insights into the role of internal knowledge attributes in the process of OI from a contingency perspective. Knowledge attributes represent the specific state of knowledge within an organization. The KBV states that innovation can be affected by an organization’s ability to acquire, manage and create knowledge (Zhou and Li, 2012), which is related to knowledge attributes. Previous studies pay limited attention to the extent to which an organization’s internal knowledge elements affect the absorption of external knowledge in this respect (Chesbrough and Crowther, 2006). To the best of our knowledge, our paper stands as one of the first attempts to examine the link between OI and knowledge attributes with empirical evidence. It deepens the understanding of OI from a new perspective while extending the findings of previous studies of OI, which are largely focused on external factors (Lichtenthaler, 2009; Cheng and Shiu, 2015; Bayona-Saez et al., 2017). It also extends OI theory by underscoring the importance of achieving strategic fit or alignment between OI operation and knowledge attributes to gain superior innovative outputs. These results further explain why OI strategy sometimes has a counterproductive effect on innovation performance, as noted in some existing studies (Laursen and Salter, 2006; Noh, 2015; Cheng and Shiu, 2015).

Third, this study provides new empirical evidence that partner opportunism negatively affects the relationship between outbound OI and innovative performance and thus expands transaction cost theory in the OI context. By revealing the negative role that partners’ opportunistic behaviour plays in shaping outbound OI outcomes, the findings help explain why firms are reluctant to transfer their technology, as they fear strengthening their competitors by selling the “corporate crown jewels” (Noh, 2015). Unexpectedly, our results indicate that partner opportunism has no effect on the inbound OI–performance relationship. This maybe because information is becoming more transparent with the rapid development of science and technology, making it more difficult for partners to conceal information. Therefore, the cost of partner selection is declining, and it is becoming easier for firms to select or replace their partners.

5.3 Practical implications

This paper offers several implications for OI practitioners in the Chinese business environment. First, our findings indicate that both inbound OI and outbound OI have positive effects on innovative performance (Chesbrough and Crowther, 2006). This finding is consistent with an emphasis in the prior literature that the “closed innovation” model, which relies on internal resources, cannot adapt to complex and volatile market demands. Many small and medium enterprises in China have begun to explore suitable innovation models (Zhou et al., 2017), but a segment of these enterprises have not yet realized that openness activities may improve their innovative performance. Compared with large firms, small and medium enterprises are usually less bureaucratic, are more inclined to take risks, have more professional knowledge and are faster in adapting to market demands, which may enable them to benefit more from OI (Parida et al., 2012). Therefore, these enterprises should take full advantage of this opportunity and engage in openness activities according to business and market conditions.

Second, our new view that a firm’s innovation outputs will be superior when its knowledge attributes effectively align with OI suggests that firms should select appropriate OI types on the basis of the characteristics of internal knowledge resources. Managers must realize that simply performing different OI activities without considering the knowledge attributes within the organization may hinder the effectiveness of OI performance. Therefore, managers should pay special attention to the fit between OI strategy and knowledge attributes, and should adjust one activity to fit with the characteristic of firm knowledge. More accurately, for firms with diversified knowledge, managers could take advantages of inbound activities to acquire and integrate external knowledge for high innovative performance. This process is especially significant for small and medium enterprises to increase their store of core technical knowledge and promote the creation of new ideas through inbound OI. Firms with rich embedded knowledge could implement outbound activities to facilitate the inside–out process of knowledge commercialization, as this presents a good opportunity to form an industry standard based on their technology path.

Finally, by showing the negative effects of partner opportunism on the relationship between outbound OI and innovative performance, this study supports practitioners who intend to govern outside partnerships in openness processes. In the Chinese business environment, where there is less trust, a considerable amount of cooperation among enterprises occasionally comes from private relations, so opportunistic behaviour is very common in innovation practice. Thus, it is vital for firms to manage outside partnerships in openness processes (Drechsler and Natter, 2012). Managers should establish interfirm governance mechanisms (including transactional and relational mechanisms) to stabilize business partnerships (Yam and Chan, 2015) and avoid economic and strategic losses through knowledge leakage. For example, companies should pay attention to patents or the registration of brands and copyrights.

5.4 Limitations and future study

As with most studies, this research has several limitations that provide direction for future studies. First, the sample data come from China. Although Chinese companies have been actively seeking cross-border cooperation in open activities, it is undeniable that culture and thought processes may affect their OI processes and performance. Future studies are recommended to explore OI in different cultural contexts.

Second, although our analysis results demonstrate that the fit between knowledge attributes and specific OI strategy generates superior innovative performance, to the best of our knowledge, prior literature has not developed an effective evaluation index to measure the extent of knowledge distance and knowledge embeddedness within organizations. Future research is encouraged to explore these attributes to provide more constructive guidance for firms to select appropriate OI practice.

Third, this paper studies the moderating effect of knowledge attributes on OI–performance relationship. However, firm’s knowledge structure is also associated with innovation (Fixson and Park, 2008). What is more, internal knowledge resources may play the mediating role in the complex mechanism of OI. Future research could further explore the mechanism of organization’s knowledge base on OI–performance relationship to increase the understanding of OI strategy.

Figures

Research framework

Figure 1.

Research framework

The moderating effects of knowledge distance and knowledge embeddedness

Figure 2.

The moderating effects of knowledge distance and knowledge embeddedness

The moderating effects of partner opportunism

Figure 3.

The moderating effects of partner opportunism

Distribution of sample features

Variables Items (%)
Gender Male 72.1
Female 27.9
Position Senior managers 25.15
Middle managers 14.91
Line managers 27.09
Core staff 32.85
Firm type State-owned companies 40.52
Private companies 35.41
Foreign companies 12.47
Joint ventures 11.6
Firm age < 3 years 4.81
3-5 years 10.19
5-10 years 11.79
= 10 years 73.21
Firm size < 100 staff 11.87
100-499 staff 16.38
500-1.000 staff 28.79
=1000 staff 42.96

Results of the exploratory factor analysis and reliability

Construct Items Loading
Inbound OI (a = 0.821; CR = 0.876; AVE = 0.587) We often acquire technological knowledge from outside for our use 0.701
We regularly search for external ideas that may create value for us 0.796
We have a sound system to search for and acquire external technology and intellectual property 0.806
We proactively reach out to external parties for better technological knowledge or products 0.805
We tend to build greater ties with external parties and rely on their innovation 0.716
Outbound OI (a = 0.818; CR = 0.873; AVE = 0.579) We are proactive in managing outward knowledge flow 0.667
We make it a formal practice to sell technological knowledge and intellectual property in the market 0.811
We have a dedicated unit to commercialize knowledge assets 0.810
We welcome others to purchase and use our technological knowledge or intellectual property 0.778
We often co-exploit technology with external organizations 0.730
Knowledge distance (a = 0.767; CR = 0.851; AVE = 0.589) The difference of knowledge bases between our staff is small; it is easy for us to get knowledge from each other 0.778
We can understand the new knowledge gained from other employees and apply it to work 0.717
We know how to pass on our own knowledge to other employees 0.784
Because the knowledge base of our staff is not much different, it is easy for us to communicate with each other 0.789
Knowledge embeddedness (a = 0.841; CR = 0.896; AVE = 0.683) It is easy for us to identify in our firm who could help us learn the knowledge when we need new knowledge 0.792
It is easy for us to identify in our firm who could help us learn the tools and technologies when we need new tools 0.866
It is easy for us to identify the equipment or technology needed to complete at ask in the company 0.818
It is easy for us to identify the information needed to complete at ask in the company 0.827
Partner opportunism (a = 0.814; CR = 0.872; AVE = 0.577) Our partners often exaggerate their needs to get what they desire 0.658
2. Our partners often alter the facts to get what they want 0.789
Our partners often promise to do things, even though they actually have no intention of following through 0.833
Our partners will not provide a completely truthful picture when negotiating 0.758
Given the chance, our partners might try to take unfair advantage of our business unit 0.750
Innovative performance (a = 0.829; CR = 0.881; AVE = 0.597) Compared with our competitors in the past three years, we have a:
Higher amount of new products 0.739
Faster speed of new product launching 0.827
Lower new product operating costs 0.809
Higher new product sales revenue 0.786
Increasingly higher new product market share 0.694

Descriptive statistics, correlations and average variances extracted

Variables Mean SD Inbound OI Outbound OI KE KD PO IP
Inbound OI 3.427 0.761 0.766
Outbound OI 3.013 0.857 0.583*** 0.761
KE 3.500 0.733 0.330*** 0.370*** 0.826
KD 3.435 0.663 0.384*** 0.406*** 0.607*** 0.767
PO 2.511 0.625 −0.271*** −0.421*** −0.524*** −0.534*** 0.760
IP 3.200 0.733 0.364*** 0.490*** 0.441*** 0.363*** −0.445*** 0.773
MV Marker (respondent age) −0.012 −0.077 −0.081 −0.176* −0.009 0.037
Notes:

N = 247; KE = knowledge embeddedness; KD = knowledge distance; PO = partner opportunism; IP = innovative performance; Italic figures on the diagonal are the square root of AVE;

***

p < 0.001

Regression on OI, innovative performance and knowledge structure for the moderation effect

Innovative performance
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Controls
Firm age 0.012 0.032 0.014 −0.003 0.016 0.005
Firm size 0.229*** 0.147*** 0.219*** 0.202*** 0.211*** 0.208***
Market turbulence −0.040 0.112 −0.061 −0.096 −0.091 −0.110
Technological turbulence 0.475*** 0.389*** 0.324*** 0.337*** 0.247*** 0.251***
Competition degree 0.120* 0.125* 0.037 0.066 0.080 0.094
Predictors
Inbound OI 0.152*** 0.170** 0.166**
Outbound OI 0.243*** 0.280*** 0.271***
Knowledge distance 0.039 0.051 0.031 0.042
Knowledge embeddedness 0.253*** 0.240*** 0.255*** 0.245***
Interactions
Inbound OI × Knowledge distance 0.235***
Outbound OI × Knowledge distance −0.145**
Inbound OI × Knowledge embeddedness −0.245***
Outbound OI × Knowledge embeddedness 0.222***
F 27.511*** 41.115*** 27.976*** 24.349*** 31.652*** 26.048***
R2 0.363 0.486 0.474 0.497 0.505 0.514
Adjusted R2 0.350 0.474 0.457 0.477 0.489 0.495
Notes:
***

p < 0.001;

**

p < 0.01;

*

p < 0.05 (the same below)

The moderating results for partner opportunism

Innovative performance
Variables M1 M2 M3 M4 M5 M6
Controls
Firm age 0.012 −0.006 −0.006 0.012 0.006 0.013
Firm size 0.229*** 0.206*** 0.209*** 0.229*** 0.194*** 0.201***
Market turbulence −0.040 −0.099 −0.099 −0.040 −0.114* −0.111*
Technological turbulence 0.475*** 0.316*** 0.315*** 0.475*** 0.283*** 0.274***
Competition degree 0.120* 0.048 0.047 0.120* 0.090 0.104
Predictors
Inbound OI 0.219*** 0.211***
Outbound OI 0.225*** 0.195**
Partner opportunism −0.270*** −0.261*** −0.258*** −0.207***
Interactions
Inbound OI × Partner opportunism −0.029
Outbound OI × Partner opportunism −0.141**
F 27.511*** 28.817*** 25.173*** 27.511*** 28.443*** 26.377***
R2 0.363 0.458 0.458 0.363 0.454 0.470
Adjusted R2 0.350 0.442 0.440 0.350 0.438 0.452
Notes:
***

p < 0.001;

**

p < 0.01;

*

p < 0.05

References

Afuah, A. (2002), “Mapping technological capabilities into product markets and competitive advantage: the case of cholesterol drugs”, Strategic Management Journal, Vol. 23 No. 2, pp. 171-179.

Aiken, L.S. and West, S.G. (1991), Multiple Regressions: Testing and Interpreting Interactions, Sage, Newbury Park, CA.

Argote, L. and Ingram, P. (2000), “Knowledge transfer: a basis for competitive advantage in firms”, Organizational Behaviour and Human Decision Processes, Vol. 82 No. 1, pp. 150-169.

Atuahene-Gima, K. and Wei, Y. (2011), “The vital role of problem-solving competence in new product success”, Journal of Product Innovation Management, Vol. 28 No. 1, pp. 81-98.

Barney, J. (1991), “Firm resources and sustained competitive advantage”, Journal of Management, Vol. 17 No. 1, pp. 99-120.

Bayona-Saez, C., Cruz-Cázares, C., García-Marco, T. and García, M.S. (2017), “Open innovation in the food and beverage industry”, Management Decision, Vol. 55 No. 3, pp. 526-546.

Benton, W.C. and Maloni, M. (2005), “The influence of power driven buyer/seller relationships on supply chain satisfaction”, Journal of Operations Management, Vol. 23 No. 1, pp. 1-22.

Birkinshaw, J., Nobel, R. and Ridderstrtde, J. (2002), “Knowledge as a contingency variable: do the characteristics of knowledge predict organization structure”, Organization Science, Vol. 13 No. 3, pp. 274-289.

Blomqvist, K., Hurmelinna-Laukkanen, P., Nummela, N. and Saarenketo, S. (2008), “The role of trust and contracts in the internationalization of technology-intensive born globals”, Journal of Engineering and Technology Management, Vol. 25 Nos 1/2, pp. 123-135.

Brusoni, S., Prencipe, A. and Pavia, K. (2001), “Knowledge specialization, organizational coupling, and the boundaries of the firm: why do firms know more than they make?”, Administrative Science Quarterly, Vol. 46 No. 4, pp. 597-621.

Calfee, J.E. and Rubin, P.H. (1993), “Nontransactional data in economics and marketing”, Managerial and Decision Economics, Vol. 14 No. 2, pp. 163-173.

Caputo, M., Lamberti, E., Cammarano, A. and Michelino, F. (2016), “Exploring the impact of open innovation on firm performances”, Management Decision, Vol. 54 No. 7, pp. 1788-1812.

Cassiman, B. and Valentini, G. (2016), “Open innovation: are inbound and outbound knowledge flows really complementary?”, Strategic Management Journal, Vol. 37 No. 6, pp. 1034-1046.

Cheng, C.C.J. and Shiu, E.C. (2015), “The inconvenient truth of the relationship between open innovation activities and innovation performance”, Management Decision, Vol. 53 No. 3, pp. 625-647.

Cheng, C.C.J. and Huizingh, E.K.R.E. (2014), “When is open innovation beneficial? The role of strategic orientation”, Journal of Product Innovation Management, Vol. 31 No. 6, pp. 1235-1253. No

Chesbrough, H.W. (2003), Open Innovation: The New Imperative for Creating and Profiting from Technology, Harvard University Press, Boston.

Chesbrough, H.W. (2006), Open Business Models: How to Thrive in the New Innovation Landscape, Harvard University Press, Boston.

Chesbrough, H.W. and Crowther, A.K. (2006), “Beyond high-tech: early adopters of open innovation in other industries”, R&D Management, Vol. 36 No. 3, pp. 229-236.

Chung, J.E. (2012), “When and how does supplier opportunism matter for small retailers’ channel relationships with the suppliers?”, Journal of Small Business Management, Vol. 50 No. 3, pp. 389-407.

Citrin, A.V., Lee, R.P. and McCullough, J. (2007), “Information use and new product outcomes: the contingent role of strategy type”, Journal of Product Innovation Management, Vol. 24 No. 3, pp. 259-273.

Cummings, J.L. and Teng, B.S. (2003), “Transferring R&D knowledge: the key factors affecting knowledge transfer success”, Journal of Engineering and Technology Management, Vol. 20 Nos 1/2, pp. 39-68.

Drechsler, W. and Natter, M. (2012), “Understanding a firm’s openness decisions in innovation”, Journal of Business Research, Vol. 65 No. 3, pp. 438-445.

Fixson, S.K. and Park, J.K. (2008), “The power of integrality: linkages between product architecture, innovation, and industry structure”, Research Policy, Vol. 37 No. 8, pp. 1296-1316.

Gaffney, N., Karst, R. and Clampit, J. (2016), “Emerging market MNE cross-border acquisition equity participation: the role of economic and knowledge distance”, International Business Review, Vol. 25 No. 1, pp. 267-275.

Grant, R.M. (1996), “Toward a knowledge-based theory of the firm”, Strategic Management Journal, Vol. 17 No. S2, pp. 109-122.

Gulati, R. (1999), “Network location and learning: the influence of network resources and firm capabilities on alliance formation”, Strategic Management Journal, Vol. 20 No. 5, pp. 397-420.

Han, J.K., Kim, N. and Srivastava, R. (1998), “Market orientation and organizational performance: is innovation a missing link”, Journal of Marketing, Vol. 62 No. 4, pp. 30-45.

Han, Y. and Li, D. (2015), “Effects of intellectual capital on innovative performance: the role of knowledge-based dynamic capability”, Management Decision, Vol. 53 No. 1, pp. 40-56.

Huizingh, E.K.R.E. (2011), “Open innovation: state of the art and future perspectives”, Technovation, Vol. 31 No. 1, pp. 2-9.

Hung, K.P. and Chou, C. (2013), “The impact of open innovation on firm performance: the moderating effects of internal R&D and environmental turbulence”, Technovation, Vol. 33 Nos 10/11, pp. 368-380.

Kang, B. and Jindal, R.P. (2015), “Opportunism in buyer-seller relationships: some unexplored antecedents”, Journal of Business Research, Vol. 68 No. 3, pp. 735-742.

Katila, R. and Ahuja, G. (2002), “Something old, something new: a longitudinal study of search behavior and new product introduction”, Academy of Management Journal, Vol. 45 No. 6, pp. 1183-1194.

Lai, F., Tian, Y. and Huo, B. (2012), “Relational governance and opportunism in logistics outsourcing relationships: empirical evidence from China”, International Journal of Production Research, Vol. 50 No. 9, pp. 2501-2514.

Laursen, K. and Salter, A. (2006), “Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms”, Strategic Management Journal, Vol. 27 No. 2, pp. 131-150.

Leskovar-Spacapan, G. and Bastic, M. (2007), “Differences in organizations’ innovation capability in transition economy: internal aspect of the organizations, strategic orientation”, Technovation, Vol. 27 No. 9, pp. 533-546.

Levitt, B. and March, J.G. (1988), “Organizational learning”, Annual Review of Sociology, Vol. 14 No. 1, pp. 319-338.

Lichtenthaler, U. (2009), “Outbound open innovation and its effect on firm performance: examining environmental influences”, R&D Management, Vol. 39 No. 4, pp. 317-330.

Lichtenthaler, U. (2010), “Technology exploitation in the context of open innovation: finding the right ‘job’ for your technology”, Technovation, Vol. 30 Nos 7/8, pp. 429-435.

Lindell, M.K. and Whitney, D.J. (2001), “Accounting for common method variance in cross-sectional research designs”, Journal of Applied Psychology, Vol. 86 No. 1, pp. 114-121.

Mazzola, E., Bruccoleri, M. and Perrone, G. (2012), “The effect of inbound, outbound and coupled innovation on performance”, International Journal of Innovation Management, Vol. 16 No. 6, pp. 277-303.

Mohr, J. and Spekman, R. (1994), “Characteristics of partnership success: partnership attributes, communication behavior, and conflict resolution techniques”, Strategic Management Journal, Vol. 15 No. 2, pp. 135-152.

Naqshbandi, M.M. (2016), “Managerial ties and open innovation: examining the role of absorptivecapacity”, Management Decision, Vol. 54 No. 9, pp. 2256-2276.

Natalicchio, A., Ardito, L., Savino, T. and Albino, V. (2017), “Managing knowledge assets for open innovation: a systematic literature review”, Journal of Knowledge Management, Vol. 21 No. 6, pp. 1362-1383.

Nelson, R.R. and Winter, S.G. (1982), An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge.

Noh, Y. (2015), “Financial effects of open innovation in the manufacturing industry”, Managemen tDecision, Vol. 53 No. 7, pp. 1527-1544.

Nonaka, I. (1994), “A dynamic theory of organizational knowledge creation”, Organization Science, Vol. 5 No. 1, pp. 14-37.

Parida, V., Westerberg, M. and Frishammar, J. (2012), “Inbound open innovation activities in High-Tech SMEs: the impact on innovation performance”, Journal of Small Business Management, Vol. 50 No. 2, pp. 283-309.

Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003), “Common method biases in behavioral research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903.

Popa, S., Soto-Acosta, P. and Martinez-Conesa, I. (2017), “Antecedents, moderators, and outcomes of innovation climate and open innovation: An empirical study in SMEs”, Technological Forecasting and Social Change, Vol. 118, pp. 134-142.

Rangus, K., Drnovšek, M., Minin, A.D. and Spithoven, A. (2017), “The role of open innovation and absorptive capacity in innovation performance: empirical evidence from Slovenia”, Journal of East European Management Studies, Vol. 22 No. 1, pp. 39-62.

Samaha, S.A., Palmatier, R.W. and Dant, R.P. (2011), “Poisoning relationships: perceived unfairness in channels of distribution”, Journal of Marketing, Vol. 75 No. 3, pp. 99-117.

Urgal, B., Quintás, M.A. and Arévalo-Tomé, R. (2013), “Knowledge resources and innovation performance: the mediation of innovation capability moderated by management commitment”, Technology Analysis and Strategic Management, Vol. 25 No. 5, pp. 543-565.

Van de Vareska, V., Vanhaverbeke, W. and Gassmann, O. (2010), “Broadening the scope of open innovation: past research, current state and future directions”, International Journal of Technology Management, Vol. 25 Nos 3/4, pp. 221-235.

Wang, C.H., Chang, C.H. and Shen, G.C. (2015), “The effect of inbound open innovation on firm performance: evidence from high-tech industry”, Technological Forecasting and Social Change, Vol. 99, pp. 222-230.

Williamson, O.E. (1985), The Economic Institutions of Capitalism, The Free Press, New York, NY.

Wuyts, S. and Geyskens, I. (2005), “The formation of buyer-supplier relationships: detailed contract drafting and close partner selection”, Journal of Marketing, Vol. 69 No. 4, pp. 103-117.

Yam, R.C.M. and Chan, C. (2015), “Knowledge sharing, commitment and opportunism in new product development”, International Journal of Operations and Production Management, Vol. 35 No. 7, pp. 1056-1074.

Zahra, S.A. and George, G. (2002), “Absorptive capacity: a review, reconceptualization, and extension”, Academy of Management Review, Vol. 27 No. 2, pp. 185-203.

Zhou, K.Z. and Li, C.B. (2012), “How knowledge affects radical innovation: knowledge base, market knowledge acquisition, and internal knowledge sharing”, Strategic Management Journal, Vol. 33 No. 9, pp. 1090-1102.

Zhou, K.Z., Yim, B. and Tse, D.K. (2005), “The effects of strategic orientations on technology and market-based breakthrough innovations”, Journal of Marketing, Vol. 69 No. 2, pp. 42-60.

Zhou, Q., Fang, G., Yang, W., Wu, Y. and Ren, L. (2017), “The performance effect of micro-innovation in SMEs: evidence from China”, Chinese Management Studies, Vol. 11 No. 1, pp. 1-16.

Acknowledgements

This study is supported by National Natural Science Foundation of China (grant numbers 71573078; 71473076).

Corresponding author

Huanhuan Chen can be contacted at: chhnicole@hnu.edu.cn

About the authors

Huiping Zhou is a PhD Candidate in Business School of Hunan University, China. Her research interests focus on organizational behaviours of employee, knowledge management and technological innovation in enterprises in China.

Yanhong Yao (PhD) is a Professor in Business School of Hunan University, China. Her research interests focus on organizational behaviours of employee and knowledge management in enterprises in China.

Huanhuan Chen is a PhD Candidate in Business School of Hunan University, China. Her research interests focus on organizational behaviours of employee, knowledge management and technological innovation in enterprises in China.