PERSONNEL EXCHANGE AND KNOWLEDGE
SHARING IN SUPPLY CHAINS:
THE MODERATING EFFECT OF TRUST
By SIHENG CHEN
S2941066
University of Groningen Faculty of Business and Economics
Supply Chain Management Master Thesis
Supervisor: C.Xiao
Dr. Taco van der Vaart
Aug 2017
ABSTRACT
This study aims to empirically investigate the effect of personnel exchange on inter-organizational knowledge sharing, with competence-based trust and goodwill trust as moderators from the supplier’s perspective; it goes on to determine which kind of trust is more important to this effect. This study adopts a quantitative approach. Hypotheses were tested in SPSS by statistical methods including Person correlation and Hierarchical multiple regression. A total of 461 questionnaires were distributed, of which 126 valid questionnaires were returned. The results of this research show that more knowledge can be shared by proceeding personnel exchange between buyers and suppliers. Furthermore, when coming to the determination of whom to conduct personnel exchange, supplier firms are suggested to select the buyer firms based on the competence-based trust, as those competence-based trust can strengthen the cooperation longer and better than goodwill trust does. Those findings of this study focus on the knowledge-intensive industries in the Netherlands, China and Greece. The proposed model can be replicated in more countries settings to get more generality, and also can be broadened by involving the perspectives of all supply chain members.
Key Words: personnel exchange, employee mobility, inter-organizational trust,
competence-based trust, goodwill trust, inter-organization knowledge sharing, knowledge sharing
TABLE OF CONTENTS
1. INTRODUCTION ... 1
2. THEORETICAL BACKGROUND AND HYPOTHESES... 3
2.1 Inter-Organizational Knowledge Sharing ... 3
2.2 Effect of Personnel Exchange on Inter-Organizational Knowledge Sharing ... 4
2.3 Inter-Organizational Trust Between Suppliers and Buyers ... 5
2.4 Moderate Effect of Competence-based Trust and Goodwill Trust ... 6
3. METHODOLOGY ... 9
3.1 Development of questionnaire ... 9
3.2 Sample and Data Collection ... 10
3.3 Control Variables ... 11
3.4 Validity and Reliability ... 12
3.5 Common Method Bias ... 13
4. RESULTS ... 14
4.1 Descriptive Analysis ... 14
4.2 Correlation Analysis ... 14
4.3 Regression Analysis ... 15
4.3.1 Personnel exchange and inter-organizational knowledge sharing ... 15
4.3.2 The moderating effect of two types of trust ... 16
5. DISCUSSION AND CONCLUSION ... 18
5.1 Discussion ... 18
5.2 Limitations and Further Suggestions ... 19
5.3 Conclusion ... 20
REFERENCES ... 21
APPENDIX ... 26
Appendix A: Measurement Constructs ... 26
List of Figures Figure 1 Conceptual Model ... 8
Figure 2 Hypothesis Test ... 17
List of Tables Table 1 Respondent Companies ... 11
Table 2 Factor analysis and reliability test ... 13
Table 3 Descriptive Statistics ... 14
Table 4 Correlations ... 15
Table 5 Regression Analysis - Hypothesis 1 ... 16
1. INTRODUCTION
The turbulence of the business environment has recognized knowledge a critical resource (Peter, 2000). Sharing knowledge with business partners is regarded as a strategic means to acquire competitive advantage (Cao & Zhang, 2011), as the flow of knowledge across organizational boundaries can be streamlined (Shih, Hsu, Zhu, & Balasubramanian, 2012). Mawdsley (2016) found that the moving personnel among firms offers a path for spreading the knowledge. In this regard, employee mobility plays an important role in terms of knowledge sharing (DeCarolis & Deeds, 1999; Steensma & Wiley, 2017). For a long time being, buyers and suppliers are used to exchange human resources in order to share and capture more knowledge for accomplishing the joint tasks (Yan & Dooley, 2014). But in real life situation, when sending their personnel to the buyers, suppliers have to undertake a lot of risks such as high investment cost, lost of personnel or knowledge leakage (Aguiléra & Lethiais, 2016; Campbell, Ganco, Franco, & Agarwal, 2012; Ganco, 2013; Grandori, 2001; Marx, Strumsky, & Fleming, 2009; Tzabbar, 2009). Thus, suppliers have to be serious and selective in personnel exchange. Usually they tend to exchange personnel with trustworthy partners in most practices. However, since there are multiple types of trust among organizations, e.g. competence-based trust and goodwill trust, then it gives rise to the question: which type of trust is more important for selecting the right partner for personnel exchange?
extant literature, it remains blurry which type of trust is more important for choosing the right partner in order to conduct personnel exchange.
Additionally, Santoro and Saparito (2006) proposed that inter-organizational trust is strongly connected with inter-personal trust, meanwhile such kind of trust is clearly different from the trust that exists between two individuals (Zaheer, McEvily, & Perrone, 1998). In other words, those individuals working across boundaries may come to trust one another, and eventually affect the way that those individuals trust the other’s firm as a whole when it comes to knowledge sharing activities (Ring & Van De, 1994; Zaheer et al., 1998). Most extant research focuses on the direct influence of inter-organizational trust on knowledge sharing. But they all ignore one fact that it is the individuals, who are building, developing and experiencing the trust, and also being responsible for transferring, sharing, learning the knowledge across the organizations. Thus, those two gaps raise the interest of this research: to test the moderating effects of inter-organizational trust (competence-based trust and goodwill trust) toward personnel exchange on knowledge sharing and compare their influence. Thereby, a quantitative empirical research is conducted to test how seriously inter-organizational trust enhances this influence. A joint survey is carried out to collect data. So, it leads to the following research question:
To what extent does competence-based trust and goodwill trust strengthen the influence of personnel exchange on knowledge sharing respectively?
This study contributes to the existing body of research in several ways. A significant research literature has emerged about inter-organization knowledge transfer and acquisition (Becerra, Lunnan, & Huemer, 2008; Dayasindhu, 2002; Dushnitsky & Shaver, 2009; Szulanski, Cappetta, & Jensen, 2004; Yli-Renko, Autio, & Sapienza, 2001; Zheng, Zhang, Sheng, Xie, & Bao, 2014). With respect to the other inter-organization knowledge management issues, the study concerns about knowledge sharing has either not been paid enough attention to, or is merely from the perspective of buyers (Wu, Li, Chu, & Sculli, 2013). Hence, as one of the major focus of this paper, this research adopts supplier perspective. Besides, majority of existing literature has treated trust as a direct relational factor to knowledge sharing (Chen, Lin, & Yen, 2014; Şengün, 2010); meanwhile this research explores the moderating effect of trust, which is a relatively new concept. Furthermore, the results of this research is expected to solve the essential managerial problem for the firms who are struggle in choosing whom to cooperate with, in order to conducting personnel exchange activities.
2. THEORETICAL BACKGROUND AND HYPOTHESES
2.1 Inter-Organizational Knowledge Sharing
Inter-organizational knowledge sharing in the supply chains involves activities of transferring, supplying, and spreading knowledge to business partners (Cheng, 2011; Wang & Noe, 2010). By taking the valuable knowledge and sharing it with partners can develop new capabilities and opportunities (Cheng et al., 2008) and build best practices (Zhu & Sarkis, 2007). For instance, when a firm tries to provide value-added service such as warranty, delivery and other after-sale services, certain knowledge related to the products or services specification is required from his partner firm. With this regard, sufficient and efficient knowledge sharing between buyers and suppliers can help in integrating problem solving, utilizing each other’s expertise, and finally, enhancing the products and service for the end user (Takeishi, 2001).
However, several literature points out that inter-organizational knowledge sharing between buyers and suppliers may not occur easily or automatically (Szulanski, 2000; Szulanski & Cappetta, 2003). Indeed, knowledge sharing is acknowledged as the most difficult subject in knowledge management field (Bakker, Leenders, Gabbay, Kratzer, & Engelen, 2006). There are several important relational factors for enriching and fostering inter-organizational knowledge sharing, such as relational embeddedness (Dayasindhu, 2002), having similar strategies or goals, and trust (Chen et al., 2014). All of them influence the effectiveness and sustainability of knowledge sharing between organizations (Chen et al., 2014). Specially for inter-organizational trust, it has a significant impact on the perceived ability, benevolence, integrity and the certainty of the business partners to abide by the business norms (Hong & Cho, 2011). It fuels the scope and depth of relationships with partner firms, thereafter, a better knowledge sharing process can be attained (Yang & Farn, 2009). It is well believed that knowledge sharing is greater when all the involved parties have a close and trusting relationship (Chang & Wong, 2010; Kim, Umanath, Kim, Ahrens, & Kim, 2012; Panteli & Sockalingam, 2005).
effectively spread through this network. Not only can the suppliers of Toyota be benefited in terms of production capabilities, but also Toyota itself, receive a better supply chain performance based on this solid network in the long run (Konecny & Thun, 2011; Lander & Liker, 2007). All those demonstrates that firms can take full advantage of their collective expertise and knowledge are tend to be more competitive, innovative in the marketplace (Bartholomée & Maarse, 2007).
2.2 Effect of Personnel Exchange on Inter-Organizational Knowledge Sharing
To cope with the competitive and fast paced business environment, firms have increasingly explored external sources to guard their overall competitiveness (Knudsen, 2007; Yeniyurt et al., 2014). Previous research indicated that firms with a large network of external experts are more competitive (Björk & Magnusson, 2009). Besides, social interaction also has a significant influence on knowledge sharing between supply chain partners (Howard et al., 2016). Firms with intensive social interaction with their business partners usually embrace greater opportunities to share knowledge and learn from each other. Thus, many firms expand their horizons by sending their personnel to partner firms for knowledge sharing and learning. For instance, after visiting Toyota’s plant, the employees of General Motors who work for NUMMY (A joint venture of General Motors and Toyota), immediately brought back General Motors knowledge about the highly efficient production system of Toyota. From this case, it can be seen that knowledge is transferred and shared by people, which is also in line with what Inkpen and Tsang (2005) said, that employee mobility leads to greater knowledge sharing.
However, the reality is far from ideal. Not every firm is willing to send their employees to partner firm, no matter for what kind of purposes. Researchers have identified three main reasons to explain why some supplier firms are hesitating in investing on the personnel exchange.
Talent losing. Buyer firms can seduce competent employees from supplier firms, or these
employees may leave their employers to set up their own business (Campbell et al., 2012; Ganco, 2013). Especially for those small entrepreneurial firms, poaching experienced employees from supplier firms is rather beneficial (Almeida, Dokko, & Rosenkopf, 2003). Nonetheless, losing employees may not be all that bad. Recent research found that when employees move between firms, they can spread knowledge from their former employers to the new employers, and from their new employers back to their former employers again, so the former employers actually receive more knowledge in this process (Corredoira & Rosenkopf, 2010). But still, losing employees can be particularly disconcerting to those supplier firms.
Knowledge leakage. As a coin always has two sides, knowledge sharing is not always good
personnel exchange is the knowledge leakage. Suppliers need to be aware of the occurrence of the worst situation: when the buyer firm master the core knowledge, they could deploy that knowledge on their products or service directly to compete against the former supplier firm. Thus, to protect the initial interests of those supplier company and secure the reliability of employee mobility, certain countries have laws that enforce non-compete agreements (Marx et al., 2009). Furthermore, some firms advocate to establish their patents to limit the consequence of knowledge leakage (Ganco, Ziedonis, & Agarwal, 2015).
High costs. Grandori (2001) introduced that high costs related to the physical movement of
those employees are driving way the needed for personnel exchange. When employees were sent to other company, they are harder to be managed than the previous moment when they still sit in front of their desks. Managing the exchanged employees requires more efforts from human resource management for both of two firms, which directly result in the higher costs. With regard to the higher costs for the money and time, personnel exchange is considered as an option only suitable for the most complex interactions, instead of those investments need to be returned in the short term (Aguiléra & Lethiais, 2016).
Indeed, it cannot be denied that the dark side of personnel exchange does exist. But if the firms and their business can manage these aforementioned risks very well, the personnel exchange is still advocated as a strategic way to share knowledge in the supply chain (DeCarolis & Deeds, 1999; Grandori, 2001). In a world where everything is interconnected, no firms want to be obsoleted, so they dedicate themselves to enrich their internal knowledge by approaching and absorbing external knowledge (Chesbrough, 2003; Helfat, 1997). Hence, many firms are still willing to strengthen their absorptive capacity by assigning their personnel to partner firms for technical training and learning (Cohen & Levinthal, 1990), and believing each other is open to share knowledge. So far, prior studies have already emphasized the risks of personnel exchange activities, while on the other side, the influence of personnel exchange in order to share knowledge with partners is still addressed positively by the majority of research (Inkpen & Tsang, 2005; Mawdsley, 2016). Base on the fact that employees send and receive knowledge when they are assigned to tasks in the partner firm, the flows of knowledge from two firms are expected to be increasing at the same time. In another word, the amount of knowledge shared increase. Hence, the following hypothesis can be posited:
H1: Personnel exchange has a positive influence on inter-organizational knowledge
sharing.
2.3 Inter-Organizational Trust Between Suppliers and Buyers
Practically speaking, trust is a multi-level phenomenon that exists at the personal, organizational and inter-organizational levels (Verbeke & Greidanus, 2009). Those different levels foster trust into different characteristics. Many scholars distinguished trust based on those characteristics (Ibrahim & Ribbers, 2009). McAllister (1995) claimed that trust can be either based on rational cognition or emotions. Similar to his findings, Sako and Helper (1998) argued that trust could be developed step by step if the trustee shows off the competence and openness, or keeps contractual promise. On the other hand, Das and Teng (2001) proposed to conceptualize trust as two-dimensional construct and emphasize the importance of competence and goodwill. Most trust-related research agrees that having enough competence in a certain domain and goodwill in business interaction is prominent (Ibrahim & Ribbers, 2009; Luhmann, 2000; Möllering, Harrington, & Harrington, 2009; Şengün, 2010). This research highlights the distinction between competence and goodwill, as it fits the aim of this research by further exploring different influences based on cognitive and calculative reasoning as well as the goodwill feelings (Ibrahim & Ribbers, 2009). Following two paragraphs give the description of competence-based trust and goodwill trust.
Competence-based trust. Sako (1992) defines competence trust as the expectation toward
another party to be capable of performing its role competently. They are fully convinced that the other party has the technical and managerial competence to complete what has been promised to do. Competence trust suggests a high probability of getting things accomplished successfully, which secures the sufficient flows of knowledge to be shared and acquired for both parties (Şengün, 2010).
Goodwill trust. Goodwill trust between organizations refers to the members of the
organization have the beliefs about a partner’s goodwill toward them and the existence of relational bonds between them (Zaheer et al., 1998). When a firm believes its partner is frank and genuine to them, the firm will have more faith that partner will put forth their best efforts to work with them (Yli-Renko et al., 2001). Such confidence lessens a firm’s fears about knowledge misappropriation, thereby fostering closer collaboration with their partner in order to share more knowledge (Rosell et al., 2014; Santoro & Saparito, 2006).
2.4 Moderate Effect of Competence-based Trust and Goodwill Trust
As previously stated, there always exists inevitable risks in the personnel exchange activities between buyers and suppliers (Campbell et al., 2012; Ganco, 2013; Grandori, 2001; Mawdsley, 2016). However, many research proposed that inter-organizational trust could support this process, because trust assists individuals, who are involved in the knowledge sharing process, to become more open and voluntary in the business relationship (Squire, Cousins, & Brown, 2009).
against them (Inkpen, 2000; Norman, 2002). Similarly, when a buyer firm engages in job training or education of personnel at the supplier firm, they will also keep their promise to their supplier: never transfer the care value of the activities to another relationship (Squire et al., 2009). The rational reason behind this phenomenon could be explained by the facilitating role of goodwill trust, as it constrains firms to behave with loyalty and conformity to expectations. In other words, goodwill trust essentially impairs the risks of knowledge leakage when conducting personnel exchange toward sharing knowledge between buyers and suppliers (Das & Teng, 2001; Kale et al., 2000). With facing lower risks of knowledge leakage, exchanged employees of two firms with high goodwill trust are motivated to share more knowledge. Above all, those arguments lead to the following hypothesis:
H2: Goodwill trust positively moderates the relationship between personnel exchange and
inter-organizational knowledge sharing.
However, despite having a motivation to engage in knowledge sharing, it does not fully guarantee that firms have enough capability to complete this process successfully. With the development of information and communication technologies, information asymmetry between buyers and supplier are reduced to a great extent (Squire et al., 2009). In consequence, buyers are less likely to carry suppliers with low supply chain performance (Squire et al., 2009; Subramani, 2004). On the other hand, suppliers are afraid of losing those buyers, so they put a lot of efforts to make themselves more competent in the market, which can meet buyer’s demand and win their trust. Focusing on the Toyota case again, most of the suppliers of Toyota are willing to visit the plant, form a study group to share their experience with Toyota, as they believe that moving employees could bring back advanced knowledge that Toyota has. By absorbing those valuable knowledge, they are able to offer Toyota better parts or service, and eventually stand in the fierce competition in the market. While from the perspective of Toyota, they are used to send their engineers to supplier firms, assisting those suppliers to better adopt total quality control system. However, due to thousands of suppliers they have, they will be selective regarding whom to send their personnel for technical help. To this regard, supplier performance is a selecting preference (Humphreys, Li, & Chan, 2004; Wilhelm & Kohlbacher, 2011). Indeed, personnel exchange is an expensive investment, most firms only tend to share their knowledge with those competent partners for receiving a higher return on investment (Squire et al., 2009). With high competence-based trust between two firms, the belief that the partner company is capable of finishing tasks lying into exchanged employees of both companies. The vanishing of any doubt about competence fades away the reluctance of exchanged employees to share knowledge. Hence, the effect of personnel exchange on knowledge sharing is strengthen. Hypothesis 3 is posited:
H3: Competence-based trust positively moderates the relationship between personnel
exchange and inter-organizational knowledge sharing.
it is clear that trust – from those moving individuals to the whole supply chain- is a facilitator for knowledge sharing (Das & Teng, 2001; Kale et al., 2000). In this research, trust comes in two types: competence-based trust and goodwill trust. Actually those trust can lead to different situations. Some suppliers prefer to corporate with big buyers, as they believed those buyers are more knowledgeable about the products and global market. On the other sides, some suppliers tend to share their knowledge only with those stable and benevolence buyers. In consequence, managers might face a universal business dilemma, to say the least, the managerial difficulty: which type of trust is more important for selecting the right partner for personnel exchange?
An investigation of a famous German manufacturing company Siemens provides a classic example of sharing knowledge with buyers by sending their experienced engineers to buyers’ companies. Those engineers bring advanced technology or measure to control the product stability, aiming to enhance the quality of the whole operations system in the supply chain. Due to those shared knowledge covers core technology, they only trust their verified partners, who surpass the baseline of the supply chain performance requirements. Another manufacturer Motorola also has the similar experience. Indeed, competence-based trust originates from the objective facts. Those facts are more convincing and not easy to be changed (Ibrahim & Ribbers, 2009). While goodwill trust is based on of beliefs and relation (Adler, 2001; Şengün, 2010), it sounds more like an attitude based trust, which makes it more vulnerable compared to the solid performance assessment. Suppose tone situation that every moving employee has the same goodwill trust as the firm has; on the other way around, a few exchanged employees may not have goodwill trust toward the partner as strong as the firm has. The underlying reason could be explained by the attitudes, which always vary from person to person. These employees are more likely to perform less cooperation or unwilling to share knowledge when they are transferred to the partner firm temporarily. As the pile of knowledge sharing of every exchanged employee contributes a lot in total knowledge sharing between two firms. Goodwill trust does not seem to have moderate effects as strong as competence-based trust has. According to the inference, it can be posited that:
H4: Competence-based trust more positively moderates the relationship between personnel
exchange and inter-organizational knowledge sharing than goodwill trust does.
To sum up, these four hypotheses form the following concept model.
3. METHODOLOGY
3.1 Development of questionnaire
The aim of this research is to quantify the moderating effects of competence-based trust and goodwill trust respectively on the personnel exchange and knowledge sharing. Thus, survey is the most suitable method to get empirical evidence by performing quantitative analysis effectively regarding the initial hypotheses. Multi-item scales are used to operationalize each variable. Each scale fits both of the qualitative description and operational definition of personnel exchange, inter-organizational knowledge sharing and two types of trust. All the items related to the four constructs are formed on extant research in knowledge-related filed, which demonstrates promising reliability and validity. Three constructs are measured on a seven-point Likert-type scale (i.e. ranging from strongly disagree “1” to strongly agree “7”). The full list of measurement constructs is shown in Appendix 1.
Inter-organizational knowledge sharing. Several studies have been investigated knowledge
sharing topic, but their scales are either focusing on the intra-organizational level, or rather than telling what kind of knowledge is shared instead of indicating how to share knowledge. Eventually, the scale developed by Moller & Svahn (2004) is selected. As the operational definition of inter-organizational knowledge sharing within this research is to support each other by providing information, in order to collaborate closely to solve problems, develop new ideas, or put new policies or procedures in practice, those five items express this definition in considerable detail. Essentially, this scale aims at measure knowledge sharing at inter-organization level. In addition, this scale has been deployed by Chen et al., (2014) to build a model about how knowledge sharing occurs across the supply chain; thus a certain level of reliability and validity has been manifested practically.
Personnel exchange. Five items from the scale of Backmann et al. (2015) are selected to
measure personnel exchange. Personnel exchange within this research is limited to the physical movement of employees to the partner’s facilities in order to share and achieve knowledge, which is in line with the study of Backmann et al., (2015). Additionally, this scale has been used by Zahra and George (2002) to measure the absorptive capacity. Hence, the reliability and validity of this construct has been built on solid foundation.
Competence-based trust. The scale used to measure competence-based trust is adopted
from Ganesan (1994). For the purpose of this research, the operational definition of competence-based trust is: one organization hold the belief that the other party has the technical and managerial competence to complete what it is promised to be done, which is in consistent with the definition given by Ganesan (1994). Besides, those four items also reflect and cover the operational definition of competence-based trust comprehensively.
Goodwill trust. The scales of Zaheer et.al (1998) and Liu et.al (2015) are compensated and
take the opportunism behaviour into consideration, such as the item “This partner may use opportunities that arise to profit at our expense”. Furthermore, the scale of Zaheer et. al (1998) did not emphasize the positive intention toward the other party, while the scale of Liu et.al (2015) made it quite clear on the item “We feel that the dominant partner will keep its word.” Thus, in order to meet the demand of this research, their scales are combined and thereafter, it is considered to be suitable and sufficient for this research.
The original survey questions were translated from English to Chinese and Greek, as this joint survey involved organizations which are located in the Netherlands, China and Greece respectively. In addition, owing to most of the Dutch have a proficient level of English, the original English questionnaire was distributed in the Netherlands. As for the Chinese and Greek version, several academic researchers were invited to edit and verify the translation. Subsequently, survey was pretested for the structure, clarity of the instructions and language by a group of eight researchers, who are also doing this master thesis project from University of Groningen. The survey was improved based on the feedback on the relevance, consistency, and accuracy. It is worth to mention that during the pretest, researchers found it is impractical for the respondents, who are the suppliers in this case, to answer the questions with regard to all of their buyers. Thus, the questionnaire specifically and kindly requests all the respondents to keep their largest buyer in mind when answering the questions.
3.2 Sample and Data Collection
The knowledge-intensive industries such as manufacturing, healthcare, semiconductors and so on are chosen as the sample frame for this research. It is believed that those industries play major roles in terms of the supply chain context. Besides, intensive communication and interaction occur between suppliers, wholesalers, and retailers (Chen et al., 2014), which is expected to achieve more specified collaborations and knowledge sharing (Takeishi, 2001; Tatikonda & Stock, 2003).
The target respondent organizations were first contacted by emails, phone calls or social media such as LinkedIn, depending on the contact information. Once the organization was willing to participate, they would receive another email attached with a link to the on-line survey. Each respondent organization was asked to identify one of their key buyer and answered the questions based on their relationship with that specific buyer, and the relevant knowledge sharing activities between them.
A total of 461 questionnaires were distributed, and altogether, 126 out of 153 responses were proved to be valid. The response rate is 33.2%. In order to reduce single-respondent bias, each firm was asked to have two respondents at the same time to participate the survey. A basic profile of those respondent companies is shown in Table 1.
Table 1 Respondent Companies
Number Percentage Location of the organization
The Netherlands 27 21.42%
China 56 44.44%
Greece 43 34.12%
Age of the organization in years
0~20 71 56.35%
21~50 32 25.40%
51~100 16 12.70%
≥101 7 5.55%
Length of relationship with major buyer in years
0~5 26 20.63%
6~25 81 64.29%
26~50 15 11.91%
≥51 4 3.17%
Number of employees in this organization
0~50 38 30.16% 51~250 30 23.81% 251~500 22 17.46% ≥501 36 28.57% Type of ownership State-owned 9 7.14% Domestic private 84 66.67% Joint-venture 17 13.49% 100% foreign invested 16 12.70% 3.3 Control Variables
Five control variables were controlled that might affect inter-organizational knowledge sharing but were not of direct interest to this study. These can be split as the location of the organization, the age of the organization, the length of relationship with major buyer in years, the number of employees in this organization, and the type of ownership.
Previous research has considered the age of organization as an important determinant, as they argued that the younger the organization is, the more advantage they have (Frost, Birkinshaw, & Ensign, 2002). The compelling reason is an aging organization becomes inert to share, while younger organization are more open toward sharing and acquiring knowledge. In the second place, the length of the relationship with major buyer in years is widely been discussed, yet no clear or convincing evidence to show that it has positive influence on inter-organization knowledge sharing (Wijk, Jansen, & Lyles, 2008). In this research, it is predicted that trust in a buyer-supplier relationship is built slowly over the years, and the longer relationship leads to more favourable knowledge sharing outcomes. Thirdly, the number of
of organizational size on knowledge related issues are positive (Chen et al., 2014; Wijk et al., 2008). Dummy variables were conducted to recode and sort original data for the rest of two control variables: the location of the organization and the type of ownership. Accordingly, the Netherlands and domestic private are select as reference category.
3.4 Validity and Reliability
To test for the validity and reliability of the construct, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted. The result of factor analysis is summarized in the Table 2, which only represents the result after reduction.
Before starting the factor analysis, sampling adequacy needs to be measured. Sampling adequacy gives information regarding whether factor analysis can be conducted with the current data. It is determined through Kaiser-Meyer-Olkin (KMO) statistics and the value should be 0.6 or above so that data is considered suitable for factor analysis (Cerny & Kaiser, 1977). Results indicate the KMO value as 0.829 (>0.6), stating that factor analysis can be conducted. A principle component analysis (PCA) with Varimax rotation method was performed. Wang et al. (2016) advocated that PCA is one of the most popular techniques for dimension reduction and extraction, which is based on calculating a set of eigenvectors that span a linear subspace. Varimax method is specially helpful to extract the redundant items in each scale, therefore enable the grouping of each variable much easier (Li, 2005). In factor analysis, 0.5 or higher loadings for each factor are required (Hair et al., 2006). Our results of factor analysis reveal the loadings of most of the items were above the threshold value, ranging between the lowest loading of 0.669 and highest loading of 0.883, stating that the validity of the instrument has been demonstrated. Except for two items of knowledge sharing and two items from personnel exchange, they are excluded due to cross loading. The nest step is to perform CFA on software AMOS, the results show that all the factor loading coefficients are over 0.5, which guarantees the convergent validity. The initial eigenvalues for all factors are greater than 1. The total combination of explained variance for four factors is 66.04%, which is over the benchmark number 50%.
Table 2 Factor analysis and reliability test
Construct Factor loading
1 2 3 4
Factor 1: Goodwill trust: α =0.876
We feel that the partner negotiates with us honestly. 0.880
We feel that the dominant partner will keep its word. 0.863
We feel partner attempts to escape from its commitments. 0.856
We are hesitant when contract specifications are vague. 0.883
Partner may arise to profit at our expense. 0.857
Factor 2: Personnel exchange: α =0.863
Our company frequently interacted with the buying company. 0.739
Our company regularly visited our project buying partner. 0.680
Our company collected new information from buying companies. 0.781
Factor 3: Inter-organizational knowledge sharing: α =0.863
My company organizes job training to enhance knowledge. 0.831
My company and business partners share experiences. 0.834
My company shares new knowledge and views with each other. 0.669
Factor 4: Competence-based trust: α =0.834
This partner has been frank in dealing with us. 0.724
Promises made by this partner are reliable. 0.697
This partner does not make false claims. 0.705
Eigenvalues 3.927 3.515 2.766 2.377
Percent variance explained 20.67 18.51 14.56 12.30
KMO 0.829
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 5 iterations.
3.5 Common Method Bias
Common method bias is a typical issue in survey research. It is recognized to have a random or systematic measure items, thereafter threatens the construct validity (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Several proactive steps were launched to prevent the common method bias for this research.
In case of the common method bias still exist, Herman’s single factor test and a common latent factor (CLF) were conducted. The result of Herman’s single factor test is 33.53%, which is below the benchmark 50%. A CLF was added to the CFA model to compare the results of standardized regression weights when it with and without the CLF. It shows that the difference for one item of goodwill trust is larger than 0.2. Thus, this item was excluded in order to avoid the common method bias. After all of those preventive action and reliable analysis, common method bias is unlikely to be a serious concern for this study.
4. RESULTS
4.1 Descriptive Analysis
To start with, the mean value of items in construct personnel exchange, competence-based trust, goodwill trust and inter-organizational knowledge sharing were computed. A set of values for each construct of analysis was delivered. In this research, personnel exchange is regarded as independent variables, while inter-organizational knowledge sharing is treated as dependent variable. Competence-based trust and goodwill trust are regarded as moderators. Table 3 below shows the descriptive statistics about means and standard deviations for each variable. There is no mean and standard deviation for two control variables organization location and
ownership type, as they are categorical variables.
Table 3 Descriptive Statistics
Variable Mean Std. Deviation
Inter-org knowledge sharing 5.31 0.96
Personnel change 5.71 0.86 Competence-based trust 5.59 0.77 Goodwill trust 5.17 1.98 Employee number 4415.13 27930.47 Organization age 34.47 48.70 Relationship length 16.25 18.83 Organization location - - Ownership type - -
It can be seen from the Table 3 that the standard deviation for one control variable
organization size is extremely high, which can be inferred that the distribution of sample is
abnormal. Hence, a base ten logarithm was computed to let the adjusted value distribute normally, which could further lead to a more logical and rational regression analysis.
4.2 Correlation Analysis
can be seen from the table that inter-organizational knowledge sharing is positively correlated with personnel exchange (β =0.266, p<0.01), competence-based trust (β =0.383, p<0.01), and goodwill trust (β =0.186, p<0.05). Similarly, personnel exchange is also positively correlated with competence-based trust (β =0.395, p<0.01), and goodwill trust (β =0.271, p<0.01). In addition, competence-based trust does not have a strong correlation with goodwill trust (β =0.164). As for the five control variables, the organization location (β =0.391, p<0.01) and employee number (β =0.326, p<0.01) is strongly correlated with inter-organizational knowledge sharing. Table 4 Correlations Variable 1. 2. 3. 4. 5. 6. 7. 8. 1. Employee number - 2. Organization age .446** - 3. Relationship length .268** .549** - 4. Ownership type .160 .125 .015 - 5. Organization location .504** -.095 .002 -.117 - 6. Goodwill trust .352** .236** .197* -.022 .162 - 7. Competence-based trust .012 .161 .248** -.071 .125 .164 - 8. Personnel exchange .080 .192* .165 .057 -.149 .271** .395** -
9. Inter-org knowledge sharing .326** .058 .103 -.145 .391** .186* .383** .266**
**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
4.3 Regression Analysis
4.3.1 Personnel exchange and inter-organizational knowledge sharing
Hierarchical multiple regression analysis is implemented in two models to test hypothesis 1.
Table 5 summarizes the results. The first model involves all the control variables, so their
Table 5 Regression Analysis - Hypothesis 1 Knowledge sharing Step Variable 1 2 1 CONTROL Employee number 0.208 0.188 Organization age -0.024 -0.079 Relationship length 0.060 0.034 Greece -0.130 -0.226* China 0.156 0.107 Stated own -0.006 0.019 Joint venture -0.005 0.013 Foreign invested -0.179* -0.182* 2 MAIN EFFECT Personnel exchange 0.325*** R square 0.209 0.304 Adjust R square 0.155 0.250 F 3.859*** 5.622***
***. Significant at the 0.001 level (2-tailed). **. Significant at the 0.01 level (2-tailed). *. Significant at the 0.05 level (2-tailed).
4.3.2 The moderating effect of two types of trust
The second regression model aims to examine the moderating effect of competence-based trust and goodwill trust. The detail results are present in Table 6. In order to test the moderating effect, the values of independent variable and moderator are standardized in advance to avoid the occurrence of multicollinearity.
Step 1 shows the regression result of control variables. It reveals that one of the organization type, foreign invested, has a negative influence on knowledge sharing. In step 2, personnel exchange, competence-based trust and goodwill trust are included as additional variables. It can be seen that personnel exchange and competence-based trust is significantly correlated to knowledge sharing (β =0.232, p<0.01; β =0.260, p<0.01). While the correlation between goodwill trust and knowledge sharing is not significant. Based on the results of step 3, goodwill trust does not have significant moderating effect (β =0.025, p=0.289), whereas competence-based trust has significant moderating effect (β =0.267, p<0.05).
Table 6 Regression Analysis - Hypothesis 2, 3 Knowledge sharing Step Variable 1 2 3 1 CONTROL Employee number 0.208 0.283* 0.287* Organization age -0.024 -0.103 -0.111 Relationship length 0.060 -0.011 -0.031 Greece -0.130 -0.116 -0.144 China 0.156 0.102 0.071 Stated own -0.006 0.038 0.057 Joint venture -0.005 -0.006 0.007 Foreign invested -0.179* -0.179* -0.188* 2 MAIN EFFECT Personnel exchange 0.232** 0.214* Goodwill trust -0.051 -0.043 Competence-based trust 0.260** 0.248** 3 INTERACTION EFFECT
Personnel exchange X Goodwill trust 0.025
Personnel exchange X Competence-based trust 0.267*
R square 0.209 0.348 0.358
Adjust R square 0.155 0.250 0.285
F 3.859*** 5.530*** 4.794***
***. Significant at the 0.001 level (2-tailed). **. Significant at the 0.01 level (2-tailed). *. Significant at the 0.05 level (2-tailed).
In conclusion, hypothesis 1, 3 and 4 are supported. Only hypothesis 2 is rejected. The possible reason for those finding will be discussed in next section. The Figure 2 below summarizes the results.
5. DISCUSSION AND CONCLUSION
5.1 Discussion
The results of this study conform to the initial thought that personnel exchange can facilitate knowledge sharing between buyers and suppliers, and the positive moderating influence of competence-based trust toward personnel exchange and knowledge sharing is also supported. However, the positive moderating influence of goodwill trust on this relationship is rejected.
Knowledge sharing is one of the strategic ways for suppliers and buyers to let themselves stay competitive in the market. In order to share more knowledge with each other, suppliers and buyers form the joint project group, ally learning group, proceed personnel exchange. Nowadays, more and more firms are willing to participate in those activities. To this regard, the results of this study further confirm that conducting personnel exchange activities with business partners can lead to greater amount of knowledge to be shared. One common phenomenon lies in practices that those moving talent can bring and share market or technical related knowledge with each other, and in the end, resulting in more fruitful knowledge database between buyers and suppliers, which could lead to better supply chain performance (Cheng, 2011; Wang & Noe, 2010; Zhu & Sarkis, 2007).
Despite the fact that personnel exchange brings along the opportunities of sharing more knowledge, there exists inescapable risks. Hence, trust is expected to ease those risks and motivate the firms to be open toward their business partners. The results of this study show that competence-based trust plays an important moderating role in how the personnel exchange contributes knowledge sharing between suppliers and buyers. On the contrary, goodwill trust does not have positive moderating influence. Although McEvily et.al (2003) advocated that goodwill trust can loose the fear of knowledge misappropriation and encourage the openness in the relationship, it seems that this effect still far from enough to support knowledge sharing. As mentioned before, knowledge leakage is a negative derivative consequence of personnel exchange (Kale et al., 2000), thereafter, formal and social interaction mechanisms are suggested to be adopted to reduce the risks of opportunism and unintended knowledge leakages (Easterby-Smith, Lyles, & Tsang, 2008). While in this regard, goodwill trust tends to be considered as an informal safeguards (Ian Stuart, Verville, & Taskin, 2012). Imagine that when the supplier company send their talented employee to the buyer company, even though in the beginning, goodwill trust does enable the supplier company have strong faith toward buyer company that they will never reveal the core knowledge to other competitors, however, under the great temptation of interests day by day, nobody can fully guarantee to deny it all the time. In this case, only having those informal goodwill faith or trust is insufficient for maintaining a good and sustain knowledge sharing process in real business relationship.
facts and the following rational analysis and judgement. For example, many firms are used to evaluating their partners annually based on the supply chain performance indicators: speed, cost, reliability, dependability and flexibility. If all those performance indicators show satisfying results, the cooperation will be continued and even further developed, as the sufficient capacity of partner firms are ensured. This kind of evaluation makes firm feel secured because it is based on the solid, strong data instead of the capricious, vague feelings. Depending on the evaluation results, supply chain partners can learn from each other’s strong points to offset the weakness. In return, a better supply chain performance can be achieved. Consequently, more valuable and reliable knowledge can be created and shared with the whole chain partners. From this point of view, it makes sense that during the personnel exchange, with the competence-based trust, supplier firms can find the right partner for knowledge sharing, and in the end, make each other stay competitive in the current market.
Last but not the least, the results of this research also point out that employee number and one of the organization types, foreign invest, could also influence the personnel exchange and knowledge sharing. It shows that employee number has positive influence. Admittedly, the more employees a firm has, the harder it is to manage, no matter the intra hierarchy or inter relationships. On the other hand, foreign invest has negative influence. Inventec (2014) once mentioned that most foreign buyers want to keep their suppliers within OEM relationships, even they have already invested these suppliers. Imposing the restriction on knowledge sharing is an effective way to limit the amount of knowledge acquired by suppliers, which protect the foreign buyer from nurturing potential competitors.
5.2 Limitations and Further Suggestions
This study has several limitations that provide suggestions for future research. First of all, it is not persuasive enough that only involving three countries for the data collection. As majority of data was gathered in China, which may cause over representation of Chinese firms. Thus for the further research, it is recommended to conduct research in more countries to achieve more generalized results and get fair comparison.
On the other hand, the total number of valid responses is rather small, as 27 from the Netherlands, 56 from China and 43 from Greece. The analysis based on small sample size could easily be spurious. Further study need to extend the research scope, and collect more valid data to reinforce the results.
5.3 Conclusion
REFERENCES
Adler, P. S. (2001). Market, Hierarchy, and Trust: The Knowledge Economy and the Future of Capitalism. Organization Science, 12(2), 215–234.
Aguiléra, A., & Lethiais, V. (2016). Explaining the Relative Frequency of Face-to-face Meetings in Cooperative Relationships among Companies: An Econometric Analysis.
Growth and Change, 47(2), 218–235.
Almeida, P., Dokko, G., & Rosenkopf, L. (2003). Startup size and the mechanisms of external learning: Increasing opportunity and decreasing ability? Research Policy, 32(2 SPEC.), 301–315.
Anderson, J., & Gerbing, D. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411–423.
Backmann, J., Hoegl, M., & Cordery, J. L. (2015). Soaking It Up: Absorptive Capacity in Interorganizational New Product Development Teams. Journal of Product Innovation
Management, 32(6), 861–877.
Bakker, M., Leenders, R. T. A. J., Gabbay, S. M., Kratzer, J., & Engelen, J. M. L. Van. (2006). Is trust really social capital? Knowledge sharing in product development projects. The Learning Organization, 13(6), 594–605.
Bartholomée, Y., & Maarse, H. (2007). Empowering the chronically ill? Patient collectives in the new Dutch health insurance system. Health Policy, 84(2–3), 162–169.
Becerra, M., Lunnan, R., & Huemer, L. (2008). Trustworthiness, risk, and the transfer of tacit and explicit knowledge between alliance partners. Journal of Management Studies,
45(4), 691–713.
Björk, J., & Magnusson, M. (2009). Where do good innovation ideas come from? Exploring the influence of network connectivity on innovation idea quality. Journal of Product
Innovation Management, 26(6), 662–670.
Campbell, B. A., Ganco, M., Franco, A. M., & Agarwal, R. (2012). Who leaves, where to, and why worry Employee mobility, entrepreneurship and effects on source firm performance. Strategic Management Journal, 33(1), 65–87.
Cao, M., & Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of Operations Management, 29(3), 163–180.
Chang, H. H., & Wong, K. H. (2010). Adoption of procurement and participation of e-marketplace on firm performance: Trust as a moderator. Information and Management,
47(5–6), 262–270.
Chang, Y.-W., Hsu, P.-Y., Shiau, W.-L., & Tsai, C.-C. (2015). Knowledge sharing intention in the United States and China: a cross-cultural study. European Journal of Information
Systems, 24(3), 262–277.
Chen, Y. H., Lin, T. P., & Yen, D. C. (2014). How to facilitate inter-organizational
knowledge sharing: The impact of trust. Information and Management, 51(5), 568–578. Cheng, J.-H. (2011). Inter-organizational relationships and information sharing in supply
chains. International Journal of Information Management, 31(4), 374–384.
Chesbrough, H. W. (2003). Open Innovation: The New Imperative for Creating and Profiting
from Technology. Harvard Business School Press, Boston. (Vol. 2006).
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128–152.
Corredoira, R. A., & Rosenkopf, L. (2010). Should auld acquaintance be forgot? The reverse transfer of knowledge through mobility ties. Strategic Management Journal, 31(2), 159– 181.
Das, T. K., & Teng, B.-S. (2001). Trust, Control, and Risk in Strategic Alliances: An Integrated Framework. Organization Studies, 22(2), 251–283.
Dayasindhu, N. (2002). Embeddedness, knowledge transfer, industry clusters and global competitiveness: A case study of the Indian software industry. Technovation, 22(9), 551–560.
DeCarolis, D. M., & Deeds, D. L. (1999). The impact of stocks and flows of organizational knowledge on firm performance: an empirical investigation of the biotechnology industry. Strategic Management Journal, 20(10), 953–968.
van Dijk, A., Hendriks, P., & Romo-Leroux, I. (2016). Knowledge sharing and social capital in globally distributed execution. Journal of Knowledge Management, 20(2), 327–343. Dushnitsky, G., & Shaver, J. M. (2009). Limitations to interorganizational knowledge
acquisition: The paradox of corporate venture capital. Strategic Management Journal,
30(10), 1045–1064.
Dyer, J. H., & Nobeoka, K. (2000). Creating and managing a high-performance knowledge-sharing network_ the Toyota case. Strategic management journal.
Easterby-Smith, M., Lyles, M. A., & Tsang, E. W. K. (2008). Inter-organizational knowledge transfer: Current themes and future prospects. Journal of Management Studies, 45(4), 677–690.
Fawcett, S. E., Jones, S. L., & Fawcett, A. M. (2012). Supply chain trust: The catalyst for collaborative innovation. Business Horizons, 55(2), 163–178.
Frost, T. S., Birkinshaw, J. M., & Ensign, P. C. (2002). Centers of excellence in multinational corporations. Strategic Management Journal, 23(11), 997–1018.
Ganco, M. (2013). Cutting the Gordian knot: The effect of knowledge complexity on employee mobility and entrepreneurship. Strategic Management Journal, 34(6), 666– 686.
Ganco, M., Ziedonis, R. H., & Agarwal, R. (2015). More stars stay, but the brightest ones still leave: Job hopping in the shadow of patent enforcement. Strategic Management
Journal, 36(5), 659–685.
Ganesan, S. (1994). Determinants of Long-Term in Buyer-Seller Orientation Relationships.
The Journal of Marketing, 58(April), 1–19.
Gaur, A. S., Mukherjee, D., Gaur, S. S., & Schmid, F. (2011). Environmental and firm level influences on inter-organizational trust and SME performance. Journal of Management
Studies, 48(8), 1752–1781.
Grandori, A. (2001). Neither hierarchy nor identity: Knowledge-governance mechanisms and the theory of the firm. Journal of Management and Governance, 5(3–4), 381–399. Ha, B. C., Park, Y. K., & Cho, S. (2011). Suppliers’ affective trust and trust in competency in
buyers Its effect on collaboration and logistics efficiency. International Journal of
Operations & Production Management, 31(1–2), 56–77.
Helfat, C. E. (1997). Know-how and Asset Complementarity and Dynamic Capability Accumulation: The Case of R&D. Strategic Management Journal, 18(5), 339–360. Hong, I. B., & Cho, H. (2011). The impact of consumer trust on attitudinal loyalty and
purchase intentions in B2C e-marketplaces: Intermediary trust vs. seller trust.
International Journal of Information Management, 31(5), 469–479.
Howard, M., Steensma, H. K., Lyles, M., & Dhanaraj, C. (2016). Learning to collaborate through collaboration : how allying with expert firms influences collaborative innovation within novice firms, 2092–2103.
Humphreys, P. K., Li, W. L., & Chan, L. Y. (2004). The impact of supplier development on buyer-supplier performance. Omega, 32(2), 131–143.
Ian Stuart, F., Verville, J., & Taskin, N. (2012). Trust in buyer-supplier relationships. Journal
Ibrahim, M., & Ribbers, P. M. (2009). The impacts of competence-trust and openness-trust on interorganizational systems. European Journal of Information Systems, 18(November 2007), 223–234.
Inkpen, A. C., & Tsang, E. W. K. (2005). Social capital networks, and knowledge transfer.
Academy of Management Review, 30(1), 146–165.
Inkpen, A. C. (2000). Learning through joint ventures: A framework of knowledge acquisition. Journal of Management Studies, 37(7), 1019–1043.
Inventec, T. (2014). Learning by supplying, 223(June 2013), 204–223.
Kale, P., Singh, H., & Perlmutter, H. (2000). Learning and protection of proprietary assets in strategic alliances: Building relational capital. Strategic Management Journal, 21(3), 217–237.
Ke, W., & Wei, K. K. (2007). Factors affecting trading partners’ knowledge sharing: Using the lens of transaction cost economics and socio-political theories. Electronic Commerce
Research and Applications, 6(3), 297–308.
Kim, K. K., Umanath, N. S., Kim, J. Y., Ahrens, F., & Kim, B. (2012). Knowledge
complementarity and knowledge exchange in supply channel relationships. International
Journal of Information Management, 32(1), 35–49.
Knudsen, M. P. (2007). The relative importance of interfirm relationships and knowledge transfer for new product development success. Journal of Product Innovation
Management, 24(2)1, 17–138).
Konecny, P. A., & Thun, J. H. (2011). Do it separately or simultaneously - An empirical analysis of a conjoint implementation of TQM and TPM on plant performance.
International Journal of Production Economics, 133(2), 496–507.
Krishnan, R., Martin, X., & Noorderhaven, N. G. (2006). When does trust matter to alliance performance? Academy of Management Journal, 49(5), 894–917.
Lander, E., & Liker, J. K. (2007). The Toyota Production System and art: making highly customized and creative products the Toyota way. International Journal of Production
Research, 45(16), 3681–3698.
Li, L. (2005). The effects of trust and shared vision on inward knowledge transfer in
subsidiaries’ intra- and inter-organizational relationships. International Business Review,
14(1), 77–95.
Liu, H., Ke, W., Wei, K. K., & Hua, Z. (2015). Influence of Power and Trust on the Intention to Adopt Electronic Supply Chain Management in China. International Journal of
Production Research, 53(1), 70–87.
Luhmann, N. (2000). Familiarity, Confidence, Trust: Problems and Alternatives. Trust:
Making and Breaking Cooperative Relations, 94–107.
Möller, K., & Svahn, S. (2004). Crossing East-West boundaries: Knowledge sharing in intercultural business networks. Industrial Marketing Management, 33(3), 219–228. Marx, M., Strumsky, D., & Fleming, L. (2009). Mobility, Skills, and the Michigan
Non-Compete Experiment. Management Science, 55(6), 875–889.
Mawdsley, J. K. (2016). Employee Mobility and Organizational Outcomes : An Integrative Conceptual Framework and Research Agenda, 42(1), 85–113.
McAllister, D. J. (1995). Affect-and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of management journal, 38(1), 24–59. Möllering, G., Harrington, B., & Harrington, B. (E2009). Leaps and lapses of faith:
Exploring the relationship between trust and deception. Deception: From ancient
empires to Internet dating., 137–153.
Norman, P. M. (2002). Protecting knowledge in strategic alliances resource and relational characteristics. Journal of High Technology Management Research.
alliances: A framework for facilitating knowledge sharing. Decision Support Systems,
39(4), 599–617.
Peter, D. (2000). Putting rore Now Into Knowledge. Forbes, 165(11).
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.
Ring, P. S., & Van De, A. H. (1994). Developmental processes of cooperative
interorganizational relationships. Academy of Management Review, 19(1), 90–118. Rosell, D. T., Lakemond, N., & Wasti, S. N. (2014). Integrating knowledge with suppliers at
the R&D-manufacturing interface. Journal of Manufacturing Technology Management,
25(2), 240–257.
Sako, M., & Helper, S. (1998). Determinants of trust in supplier relations: Evidence from the automotive industry in Japan and the United States. Journal of Economic Behavior &
Organization, 34(3), 387–417.
Santoro, M. D., & Saparito, P. A. (2006). Self-interest assumption and relational trust in university-industry knowledge transfers. IEEE Transactions on Engineering
Management, 53(3), 335–347.
Selnes, F., & Sallis, J. (2003). Promoting relationship learning. Journal of Marketing, 67(3), 80–95.
Şengün, A. E. (2010). Which Type of Trust for Inter-firm Learning? Industry & Innovation,
17(2), 193–213.
Shih, S. C., Hsu, S. H. Y., Zhu, Z., & Balasubramanian, S. K. (2012). Knowledge sharing-A key role in the downstream supply chain. Information and Management, 49(2), 70–80. Elsevier B.V.
Spear, S., & Bowen, H. K. (1999). Decoding the DNA of the Toyota Production System by.
Harvard Business Review, 77(5), 96–106.
Squire, B., Cousins, P. D., & Brown, S. (2009). Cooperation and knowledge transfer within buyer-supplier relationships: The moderating properties of trust, relationship duration and supplier performance. British Journal of Management, 20(4), 461–477.
Steensma, H. K., & Wiley, J. (2017). Employee mobility , spin-outs , and knowledge spill-in : how incumbent firms can learn from, 1626–1645.
Subramani, M. (2004). How do suppliers benefit from information technology use in supply chain relationships? MIS Quarterly, 28(1), 45–73.
Szulanski, G. (2000). The Process of Knowledge Transfer: A Diachronic Analysis of Stickiness. Organizational Behavior and Human Decision Processes, 82(1), 9–27. Szulanski, G., & Cappetta, R. (2003). Stickiness: Conceptualizing, Measuring, and Predicting
Difficulties in the Transfer of Knowledge within Organizations. Blackwell Handbook of
Organizational Learning & Knowledge Management (pp. 513–534).
Szulanski, G., Cappetta, R., & Jensen, R. J. (2004). When and How Trustworthiness Matters: Knowledge Transfer and the Moderating Effect of Casual Ambiguity. Organization
Science, 15(5), 600–613.
Takeishi, A. (2001). Bridging inter- and intra-firm boundaries: Management of supplier involvement in automobile product development. Strategic Management Journal, 22(5), 403–433.
Tatikonda, M. V., & Stock, G. N. (2003). Product Technology Transfer in the Upstream Supply Chain. Journal of Product Innovation Management, 20(6), 444–467.
Taylor, R. (1990). Interpretation of the Correlation Coefficient: A Basic Review. Journal of
Diagnostic Medical Sonography, 6(1), 35–39.
Tzabbar, D. (2009). When does scientist recruitment affect technological repositioning?
Verbeke, A., & Greidanus, N. S. (2009). The end of the opportunism vs trust debate:
Bounded reliability as a new envelope concept in research on MNE governance. Journal
of International Business Studies, 40(9), 1471–1495.
Wang, S., & Noe, R. A. (2010). Knowledge sharing: A review and directions for future research. Human Resource Management Review, 20(2), 115–131.
Wang, T., Han, X., Xu, R., Chen, Y. W., Ishizaki, Y., Miyamoto, M., & Hattori, T. (2016). Automatic inspection of throw-away tips based on principal component analysis. 2015
12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015
(pp. 1741–1746).
Wijk, R. Van, Jansen, J. J. P., & Lyles, M. A. (2008). Inter- and Intra-Organizational
Knowledge Transfer : A Meta-Analytic Review and Assessment of its Antecedents and Consequences, (June).
Wilhelm, M. M., & Kohlbacher, F. (2011). Co-opetition and knowledge co-creation in Japanese supplier-networks: The case of Toyota. Asian Bus Manage, 10(1), 66–86. Wu, F., Li, H. Z., Chu, L. K., & Sculli, D. (2013). Supplier selection for outsourcing from the
perspective of protecting crucial product knowledge. International Journal of
Production Research, 51(1), 1508–1519.
Yan, T., & Dooley, K. J. (2014). Buyer-supplier collaboration quality in new product development projects. Journal of Supply Chain Management, 50(2), 59–83.
Yang, S. C., & Farn, C. K. (2009). Social capital, behavioural control, and tacit knowledge sharing-A multi-informant design. International Journal of Information Management,
29(3), 210–218.
Yeniyurt, S., Henke, J. W., & Yalcinkaya, G. (2014). A longitudinal analysis of supplier involvement in buyers’ new product development: Working relations, inter-dependence, co-innovation, and performance outcomes. Journal of the Academy of Marketing
Science, 42(3), 291–308.
Yli-Renko, H., Autio, E., & Sapienza, H. J. (2001). Social capital, knowledge acquisition, and knowledge exploitation in young technology-based firms. Strategic Management
Journal, 22(6–7), 587–613.
Zaheer, A., Lofstrom, S., George, V. P., & Ghauri, P. N. (2002). Interpersonal and interorganizational trust in alliances. Cooperative strategies and alliances (pp. 347– 377).
Zaheer, A., McEvily, B., & Perrone, V. (1998). Does Trust Matter? Exploring the Effects of Interorganizational and Interpersonal Trust on Performance. Organization Science, 9(2), 141–159.
Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of Management Review, 27(2), 185–203.
Zheng, K., Zhang, Q., Sheng, S., Xie, E., & Bao, Y. (2014). Are relational ties always good for knowledge acquisition ? Buyer – supplier exchanges in China. Journal of Operations
Management, 32(3), 88–98. Elsevier B.V.
Zhu, Q., & Sarkis, J. (2007). The moderating effects of institutional pressures on emergent green supply chain practices and performance. International Journal of Production
Research, 45(18–19), 4333–4355.
APPENDIX
Appendix A: Measurement Constructs
Construct Items
Inter-organizational knowledge sharing (Möller & Svahn, 2004)
1. My company provides relevant knowledge to our business
partners.
2. My company teams up with business partners to enhance
inter-organizational learning.
3. My company and business partners jointly organize job training to
enhance each other’s knowledge.
4. My company and business partners share successful experiences
with each other.
5. My company and business partners share new knowledge and
views with each other. Personnel exchange
(Backmann et al., 2015)
1. Our company frequently interacted with the buying company.
2. Our company regularly visited our project buying partner.
3. Our company collected new information and ideas from buying
companies.
4. Our company regularly conducted meetings with buying
companies to acquire new knowledge.
5. Our company regularly approached buying companies to obtain
new knowledge. Competence-based
trust
(Ganesan, 2011)
1. This partner has been frank in dealing with us.
2. Promises made by this partner are reliable.
3. This partner is knowledgeable regarding his products.
4. This partner does not make false claims.
Goodwill trust (Liu Et Al., 2015; Zaheer Et Al., 1998)
1. We feel that the partner negotiates with us honestly.
2. We feel that the dominant partner will keep its word.
3. We feel that the dominant partner attempts to escape from its
commitments (reverse coded).
4. We are hesitant to transact with this partner when contract
specifications are vague (reverse coded).
5. This partner may use opportunities that arise to profit at our
