Confidentiality in knowledge sharing; knowledge
protection in intensive competitive settings
Master thesis
University of Groningen
Faculty of Economics and Business
MSc Supply Chain Management
June 26th ‘17 By Erik-Jan Hutten S2379376 e.j.hutten@student.rug.nl Supervisor C. Xiao Co-assessors prof. dr. J.T. van de Vaart
Abstract
1. INTRODUCTION
In the current supply chain environment, boundaries between organisations have become increasingly blurred (Danskin, Englis, Solomon, Goldsmith, & Davey, 2005), which has led to an increase in inter-organisational information and knowledge sharing (Potter & Beard, 2010). These collaborations are often enabled through the use of information technology, whereby Internet has become an integral part of everyday business (Reychav & Weisberg, 2010; Safa & Von Solms, 2016). This increased use of information technology has significantly improved supply chain coordination and its performance in practice (Cui, Allon, Bassamboo, & Van Mieghem, 2015). While most of the research is focused on these positive aspects (Li, Ragu-Nathan, Ragu-Nathan, & Subba Rao, 2006; Lin, 2007; Renzl, 2008; Spekman, Spear, & Kamauff, 2002), they are neglecting the increased risk of information and knowledge leakage to unauthorized parties.
With this rise in the number of inter-organisational exchanges, information and knowledge security has become a major issue (Kunz, Fabian, Marx, & Moller, 2011). Whereby, especially the repercussions of knowledge leakage to a competitor has severe devastating effects on organisational performance due to loss of competitive advantages (Tan, Wong, & Chung, 2016). Knowledge leakage hereby refers to “knowledge that is accidentally or forcibly transferred to any unauthorized parties either through verbal or written communications” (Tan et al., 2016, p. 622). The lack of awareness and skills from organisations, that thrive through inter-organisational knowledge flows, in dealing with these leakages is appalling (Tan et al., 2016). This has led to an increase in research studies around the concerns firms and managers have about the negative effects of knowledge leakage and its related costs (Casimir, Lee, & Loon, 2012; Husted & Michailova, 2010).
the nature of knowledge protection resources possessed by firms affect their knowledge sharing decisions (Liu, Ji, & Mookerjee, 2011). Even though knowledge protection seems an important factor to the degree of knowledge sharing between partners, there has been scant research on perceived knowledge protection in inter-organisational collaborations (Safa & Von Solms, 2016).
As mentioned above, in inter-organisational collaboration it might occur that sharing of knowledge causes a rival firm to become even more competitive (Perks & Easton, 2000). Moreover, the increased risk of unintended knowledge leakage in such a competitive setting might impede efforts to share knowledge (Hamel, 1991). This is also endorsed by Oxley and Sampson (2004) and Ramadhan & Samadhi (2016), which state that the competitive intensity of the industry where these collaborating firms are in, influences the decisions they make around the knowledge sharing process. An example flows from the research performed by Cheng (2011), in which green manufacturing firms would not share knowledge if they feel that the advantages of this collaboration are outweighed by the risk of knowledge leakage to competitors. So, in a highly competitive industry, mechanisms for protecting core knowledge are especially relevant (Ritala & Hurmelinna-Laukkanen, 2013).
However, gaining collaborative advantages requires the acquisition and application of knowledge across company boundaries (Inkpen & Crossan, 1995), and therefore raises the risk of knowledge leakage. Given this paradox, literature has stressed the need for more research into how the tension between knowledge protection and knowledge sharing can be managed within a competitive industry (Bengtsson & Kock, 2014). Especially in highly intensive industrial competition, this relationship will be more affected by this paradoxical nature. However, this fact has been overlooked in the existing literature.
This trade-off between the risk of knowledge leakage and the benefits of knowledge sharing in a competitive industry is interesting for managers to understand in order to optimize the efficiency of inter-organisational knowledge sharing while preventing knowledge leakage to their competitors. This has resulted in the following research question:
“What is the influence of knowledge protection on the degree of knowledge sharing, and how does competitive intensity impact this relationship?”
will be as follows. First, a conceptual model will be drawn up to and further elaborated through a literature review to get an understanding of the scope of the subject and explain the hypotheses. Secondly, the methods used for this research will be explained. Thirdly, the data will be gathered through a joint-survey and statistically analysed afterwards. Lastly, the results will be discussed, and the limitations and indications for further research will be clarified.
2. LITERATURE REVIEW
2.1. Knowledge sharing
2.2. Knowledge protection
According to Tan et al. (2016), the increase of inter-organisational knowledge sharing through rapidly advancing information technology and the subsequent increasing volume of confidential information passing through the supply chain, brings greater risk of knowledge leakage. In inter-organisational collaborations, knowledge sharing allows the participants to build collective intellectual capital and valuable new knowledge assets. However, any organisation that conducts knowledge sharing outside the boundaries of their company exposes itself to the risk that this knowledge will be leaked to an unauthorized party (e.g., a direct competitor), therefore worsening the organisation’s competitive position (Soper et al., 2007). Knowledge leakage refers to “knowledge that is accidentally or forcibly transferred to any unauthorized parties either through verbal or written communications” (Tan et al., 2016, p. 622). These knowledge leakages allow competitors to imitate a firms’ processes, services or innovative products without investing into its development. Thus, managers have a good reason to protect as much knowledge as possible (Mansfield, Rapoport, Romeo, Wagner, & Beardsley, 1977). These privacy risks pertaining to knowledge leakage restrain organisations’ willingness to share knowledge (Eurich, Oertel, & Boutellier, 2010). To mitigate these risks, firms can use knowledge protection mechanisms. Knowledge protection in this research refers to “defending information from unauthorized access, disclosure, use, modification, disruption, inspection, and perusal” (Safa & Von Solms, 2016, p. 442). Thus confidentiality, reliability, integrity, availability and accountability are important factors in knowledge protection (Mukundan & Prakash Sai, 2014; Von Solms & Van Niekerk, 2013).
To minimize the occurrence of knowledge leakage, organisations can formally protect their knowledge (Ceccagnoli, 2009). Important formal knowledge protection instruments are patents, industrial designs, copyrights, and trademarks in this regard (Estrada, Faems, & de Faria, 2014). These provide the company with the exclusive usage of this knowledge and raise their sustained competitive advantage (Gelabert, Fosfuri, & Tribó, 2009). These mechanisms for knowledge protection also enable the organisation to define the knowledge sharing boundaries and help mitigate the risks of knowledge leakage (Estrada et al., 2014). So, if the organisation has their knowledge protected in knowledge transfers, they will more likely get involved in knowledge sharing behaviour. This leads to the following hypothesis:
2.3. Competitive intensity
Knowledge is of utmost importance in the existence of organisations, managers have been aware of competitive advantages that result from knowledge over the last decades (Nooshinfard & Nemati-Anaraki, 2014). However, as competition intensifies, organisations need higher quality information and knowledge to achieve these competitive advantages (Luo, 2003). Competitive intensity in this research refers to the extent of competition in a specific industry (Porter, 1985). Organisations in highly intensive industries have a greater tendency towards collaborating to acquire important resources that are unavailable to their competitors (Mahapatra, Das, & Narasimhan, 2012). In general, higher competitive intensity within an industry leads to a bigger need to achieve competitive advantage through relevant inimitable knowledge and resources (Hernández-Carrión, Camarero-Izquierdo, & Gutiérrez-Cillán, 2017). This would imply that an organisation increases their degree of knowledge sharing in these situations of high competitive intensity. However, this also introduces a higher potential risk for knowledge leakage (Ahmad, Bosua, & Scheepers, 2014).
Taken together, high competitive intensity strengthens the degree of knowledge sharing due to a bigger need for relevant inimitable knowledge. But it also enhances the negative effects of knowledge sharing through greater risk of knowledge leakage. So, organisations might proceed with caution in these situations, and sometimes will impede on sharing knowledge due to this risk (Cheng, Yeh, & Tu, 2008). The main reason for this behaviour is that organisations perceive sharing of knowledge or confidential information as a risk of losing core knowledge and competitive advantages to a competitor, which is in conflict with their own interests in a highly competitive industry (Cheng, 2011). Whereas in settings of low competitive intensity, knowledge sharing is less needed for competitive advantages, and the repercussions of leakage are a lot less severe (Tan et al., 2016). Jaworski and Kohli (1993) argue that under conditions of high competitive intensity, customers have many alternative options to satisfy their needs and wants. This suggests that in highly competitive industries, the mechanisms to protect knowledge are more relevant than in markets with lower competitive intensity (Knudsen, 2007).
This leads to believe that competitive intensity strengthens the relationship between knowledge protection and the degree of knowledge sharing. Hence the following hypothesis is proposed:
Figure 1: Conceptual model 3. METHODOLOGY
3.1. Research design
This study adopted a survey as quantitative research method to verify theoretical constructs and investigate the relationships between variables. Initial constructs were developed and validated via prior research on these subjects. The survey instrument totalled 96 items (including the items for the control variables) and was organized in sections by factor, not randomized. Two professors of the university of Groningen, with substantial experience in this subject area, helped to examine the appropriateness and clarity of the content wording for each item. The research constructs were validated through theoretical and construct examinations. Followed by a measurement of the Cronbach’s α to test the reliability and consistency of these measurement constructs. Cronbach’s α higher than 0.7 is usually accepted as a reliable construct (Hair, Black, Babin, & Anderson, 2010). Furthermore, an exploratory factor analysis was performed to check for independent latent variables. A common rule of thumb in principal component analysis is that each item needs to have a factor loading of above 0.7 for each construct (Hair et al., 2010). The items used for each of the constructs can be found in Table 1.
3.2. Sample and data collection
different countries; Greece, China, and the Netherlands. This was done in order to see if there were substantial differences in the approach towards knowledge sharing among different cultures. These industries are interesting because sharing of intellectual capital has become a critical factor affecting a company's ability to remain competitive in the new global marketplace, especially in these knowledge-intensive industries (Tan, Plowman, & Hancock, 2008). Furthermore, little research has focused exclusively on individual analysis of knowledge-intensive industries (Wen, Tien, Hung, & Wen, 2016). Focus of the data collection was on the automotive industry, healthcare industry and semiconductor industry as these are prevalent in the three countries under investigation. In addition, due to the intensive interaction and collaborations between suppliers, wholesalers and retailers, this can be seen as an ideal target (Chen, Lin, & Yen, 2014). In general, the selected firms had sufficient experience with knowledge sharing. One of the major causes of common method bias in survey research is obtaining the measures from the same rater. One way of controlling for this is to collect the measures of these variables from different sources (Podsakoff, Mackenzie, Lee, & Podsakoff, 2003). Therefore, two managers from each company were utilized as respondents for this research, because they are involved in knowledge sharing activities when they interact with business partners and possess the necessary information around knowledge protection mechanisms.
In order to address multiple companies, this research is conducted by means of a joint survey. Hereby, eight students have each retrieved response from approximately fifteen companies in the three countries under investigation. This method is used to increase the number of respondents by combining resources and thereby increasing the statistical power of the research. To get the companies to respond a data collection process provided by Dillman, Smyth, and Christian (2014) was used. This suggested to contact a company multiple times. At first, an e-mail explaining the purpose and importance of this research was provided with the addition of the link to the online survey on Qualtrics. In addition, a request was made to forward the survey to the managers responsible for the inter-organisational knowledge sharing if they had not yet received it. Second, two weeks after the e-mail follow-up calls were conducted to further inspire participation.
3.3. Measures
measured using multiple items. All items were measured using a seven-point Likert-type scale (ranging from 1 = strongly disagree to 7 = strongly agree). Before conducting a factor analysis, the correlation tables of each of the items needs to be generated, and checked if the items do not correlate too highly or too lowly with the other items of the same factor (Field, 2009). If items correlate too highly (r > 0.8 or r < -0.8), it becomes impossible to determine the unique contribution to a factor (Field, 2009). If an item correlates lowly with many other variables (-0.3 < r < (-0.3), the item probably does not measure the same underlying construct as the other items. This was the case for the last item of competitive intensity, which correlated lowly with all other items measuring this construct (r < 0.3). Furthermore, the first item of knowledge sharing also correlated lowly (r < 0.3) with two other items of this construct. This was taken into account while conducting the factor analysis. All scales and corresponding items except the control variables, are listed in Table 1 below.
Knowledge sharing (Cronbach’s α = 0.791) was operationalized through conducting
the widely-used five items of Moller and Svahn (2004). By using these items the extent to which the supplying company shares knowledge with their biggest customer can be obtained. This measurement has generated validating evidence in several research publications in the knowledge sharing discipline (Chen et al., 2014; J. Cheng et al., 2008). Four items measuring inter-organisational knowledge sharing were retained after factor purification.
Knowledge protection (Cronbach’s α = 0.878) measures were elicited from Norman
(2002), these four items clearly measure the protection mechanisms that are in place in the supplying company and if these mechanisms are deemed necessary. These items were also adopted by Jean, Sinkovics, and Hiebaum, (2014) and are therefore also validated through contemporary research. All four items measuring the protection of knowledge were retained after factor purification.
Competitive intensity (Cronbach’s α = 0.836) was operationalized through the use of
of the items (Schmitt & Stults, 1982). Thus, this negatively worded item may have been a source of common method bias.
*Items omitted from analysis due to low score on factor analysis and higher score on Cronbach’s α when deleted
Table 2: Descriptive statistics
Respondent profile (N = 126)
Number of employees Frequency Percentage
<100 46 36.5%
101-500 46 35.7%
501-1000 17 13.5%
1001-3000 6 4.8%
Above 3000 12 9.5%
Table 1: Measurement scales
Measurement construct Factor loadings Cronbach’s α when deleted Knowledge sharing (Cronbach’s α = 0.788)
My company provides relevant knowledge to our business partners. .600* .791* My company teams up with business partners to enhance inter-firm
learning.
.680 .750
My company and business partners jointly organize job training to enhance each other’s knowledge.
.709 .745
My company and business partners share successful experiences with each other.
.822 .709
My company and business partners share new knowledge and viewpoints with each other.
.796 .734
Knowledge protection (Cronbach’s α = 0.878)
My company has formal and systemized processes for protecting knowledge, e.g., contracts, regulations, and procedures.
.752 .852
My company relies on patents and trademarks to protect our critical knowledge from inappropriate use.
.814 .842
My company has processes to protect knowledge from inappropriate use inside or outside the organization.
.821 .826
My company has incentives that encourage the protection of knowledge. .827 .852
Competitive intensity (Cronbach’s α = 0.762)
Organisational age 0-15 years 49 38.9% 16-30 years 40 31.7% 31-45 years 12 9.6% Above 45 years 25 19.8% Organisational type State-owned 8 6.3% Domestic private 86 68.3% Joint venture 17 13.5% 100% foreign invested 15 11.9% Nation of residence The Netherlands 27 21.1% Greece 43 34.1% China 56 44.4%
Length of relationship with largest buyer
0-10 years 64 50.8%
11-20 years 36 28.6%
21-30 years 13 10.3%
31-40 years 6 4.7%
Longer than 40 years 7 5.6%
3.4. Descriptive data analysis
From the total of 461 contacted companies, 166 have started the questionnaire. However, out of these only 126 responses were eventually valid. This means that there was a dropout rate of 31.7% and the total response rate was 27.3%. Table 2 depicts the firm characteristics of these 126 responses. Interestingly, most companies indicate that the relationship with their largest customer is between 0 and 10 years. This is a fairly short period and gives to think that the buyers are quite easy to replace, and that this happens often. However, most companies also do not exist for a long time, as most of them have not aged beyond 30 years, so this statistic might be a little biased. The vast amount of the respondents has a domestic private organisation, in contrast with a low number of state-owned organisations.
4. DATA ANALYSIS
while still taking all control variables into account. Lastly, the moderation effect of Hypothesis 2 will be assessed in model 3, where the interaction effect is included, next to the control variables and the independent variables. To check for potential multicollinearity between the constructs and the interaction term, the variance inflation factor (VIF) was calculated for both the independent variables and the interaction effect (Zhou, Zhang, Sheng, Xie, & Bao, 2014). The largest variance inflation factor emerged from the interaction between knowledge protection and knowledge sharing, with a value of 1.265, which is considerably lower than the critical threshold of 10. Therefore, multicollinearity did not appear to be a significant issue.
4.1. Control variables
* p .05, **p < .01
4.2. Correlations
Table 4 shows the correlation coefficients between every pair of variables. Among the control variables, organisational size was significantly correlated with organisational age (r = .44, p < .01), length of relationship (r = .24, p < .01), ownership type of a joint venture (r = .29, p < .01) and both the organisational location of China (r = .49, p < .01) and Greece (r = -.43, p < .01). Furthermore, organisational size is unsurprisingly strongly correlated with the length of the relationship (r = .56, p < .01). Except for the control variables, the largest correlation coefficient, between knowledge protection and knowledge sharing was .42 (p < .01) followed by .36 (p < .01), the coefficient between partners’ knowledge protection and the competitive intensity in an industry.
4.3. Regression analysis
To test Hypothesis 1, a regression analysis was performed as depicted in Table 3. In order to analyse whether or not there was a significant effect, a linear regression analysis was performed, in which knowledge protection was regressed on knowledge sharing, while controlling for the above stated control variables. The results show that there is a positive significant effect of the knowledge protection in an organisation and the degree of knowledge
Table 3: Regression of control variables on knowledge sharing
Knowledge sharing
Model 1 Model 2 Model 3
Step and variables ß t-Value ß t-Value ß t-Value
Intercept 4.51** 11.07 5.06** 12.46 4.79** 11.34
Control
Organisational age (OA) .04 .31 .05 .35 .07 .53 Organisational size (OS) .09 1.42 .06 1.02 .07 1.11 Length of relationship (LR) .00 .25 .00 -.39 .00 -.47
State Owned (OT) .12 .29 .12 .31 .16 .44
Joint Venture (OT) .08 .26 .02 .08 .09 .33
Foreign Invested (OT) -.47 -1.59 -.47 -1.68 -.45 -1.63
China (OL) .43 1.35 -.02 -.05 .13 .39
Greece (OL) -.13 -.50 -.55 -2.00 -.40 -1.43
Main effects
Knowledge protection (KP) .35** 3.31 .38** 3.63
Competitive intensity (CI) .12 1.21 .16 1.56
Two-way interaction
CI x KP .16* 1.98
R Square 0.19 0.29 0.32
sharing (β = .35, p < .01). This means that an increase in knowledge protection leads to more knowledge sharing. Therefore, Hypothesis 1 is supported.
4.4. Moderation analysis
To test for Hypothesis 2, a moderation analysis is required. To perform such an analysis, both the variables of knowledge protection and competitive intensity were standardized to facilitate interpretation. The interactive effect of knowledge protection and competitive intensity on knowledge sharing was significant (β = .16, p = .05).
To test whether the form of this interaction corresponds with the hypothesized pattern, Figure 2 was created, which depicts the two-way interaction of knowledge protection (KP) and competitive intensity (CI) on knowledge sharing (KS). The slope of the relationship between knowledge protection and knowledge sharing for a low level of competitive intensity is indicative of a weak but positive relationship, whereas the slope for high competitive intensity indicates a strong and positive relationship between knowledge protection and knowledge sharing, see Figure 2. Thus, competitive intensity significantly moderates the relationship between knowledge protection and knowledge sharing, this finding is in line with the expectation. Therefore, supporting Hypothesis 2.
5. DISCUSSION
The purpose of this study was to gain a better understanding of the underlying factors that drive the key processes of inter-organisational collaboration and knowledge sharing in the context of supply chain management. This study measured the effect of knowledge protection and the influence of the intensity of competition on collaboration and knowledge sharing. Theoretical and practical contributions are discussed below.
5.1. Theoretical contributions
Knowledge sharing in a supply chain represents an important opportunity for firms to achieve competitive advantages (Dyer & Singh, 1998; Hult, Ketchen, Cavusgil, & Calantone, 2006). This study adds to our understanding of why firms should maintain but be cautious about their knowledge sharing behaviour with supply chain members, especially in highly competitive settings. By revealing a linear relationship between knowledge protection and knowledge sharing, it is clear that in order to gain competitive advantages from inter-organisational collaborations, knowledge protection mechanisms should be in place.
According to recent indications of supply chain management research, there has been a call for more assessment of the environmental factors influencing knowledge sharing practices (Patel, 2011). By demonstrating the moderating effect of competitive intensity, we extend this part of supply chain management research and offer new evidence of how the competitive intensity of an industry influences decision making around knowledge sharing behaviour. Thereby building on the recent research performed by Zhou et al. (2014), which looked at the curvilinear effect of competitive intensity on the relational ties between buyers and suppliers. Whereas this study focuses mainly on the moderating effect of competitive intensity on the relationship between knowledge protection and knowledge sharing. So, this study also addresses the existing need for more insights on the paradoxical situations of high competition surrounding inter-organisational exchanges as voiced by Bengtsson and Kock (2014).
The research results indicate that when competition is high, a high level of knowledge protection can improve the quality of the collaboration, whereas in a setting of low level competitive intensity the organisation would suffer from the lengthy application process and the substantial required resource commitments for knowledge protection mechanisms (de Faria & Sofka, 2010). As Figure 2 shows, a high level of protection is best for knowledge sharing when competition is high. Low levels of protection instead relate to lower degrees of knowledge sharing and this is not dependent on the amount of knowledge protection.
The links between knowledge sharing, knowledge protection and competitive intensity are a relevant topic not only in knowledge management research but also in supply chain practice. This study has important implications for practitioners, particularly those with management responsibilities involved in arranging inter-organisational collaborations and the organisations’ protection measures to prevent the occurrence of knowledge leakage.
This research argues that organisations that reciprocally share knowledge with external partners for their own competitive advantage purposes must be aware of the potential risk of knowledge leakage and the repercussions such leakages can cause. To prevent these harmful events from occurring, this research contributes by assessing the importance of knowledge protection mechanisms. Through the results of this research it becomes clear that an increase in knowledge protection mechanisms enhances the degree of knowledge sharing. However, for practicality an organisation should also take the costs of these mechanisms into account before implementing them. It could be the case that the costs exceed the benefits.
Results show that in settings of highly intensive competition, it would be a wise decision to invest in knowledge protection mechanisms. This way, the organisation is able to gain competitive advantages from knowledge sharing while mitigating the risk for unintended knowledge leakage. Contrary to this, in settings of low intensive competition, it would not be necessary to invest a lot of time and resources in increasing knowledge protection, as it would not have a strong effect on the competitive gains made through knowledge sharing.
5.3. Limitations and further research
This study has several limitations which could be addressed in future research. First, due to the length of the questionnaire there is a decent chance that there is some form of bias through respondent fatigue and carelessness (Hinkin, 1995). This is especially relevant because the items of interest for this research were at the bottom of the questionnaire.
protection could be an interesting subject for further research regarding inter-organisational collaboration and knowledge sharing behaviour, especially in the context of risk mitigation.
Third, through the low number of respondents from each country, it is impossible to draw conclusions towards knowledge sharing behaviour based on cultural differences. However, this would be interesting to be investigated in further research since the approach towards knowledge sharing is different for each culture (Chang et al., 2015). Whereas agreements in western business environments are mainly a matter of business relationships above personal relationships, Chinese regard a good relationship as a pre-condition before entering a business arrangement (Hofstede, 1983). Furthermore, the southern Europe culture of Greece also differs from the western Europe culture of the Netherlands, as the Greek focus on building strong and long-lasting relationships through trust and loyalty, while Dutch are often not so patient when it comes to relationship building as it usually is or a roaring success or an outright failure.
6. CONCLUSION
Do knowledge protection mechanisms enhance the degree of knowledge sharing, and what is the impact of competitive intensity on this relationship? Despite the increasing importance of inter-organisational knowledge sharing to gain competitive advantages and the subsequent increased risk around knowledge leakage, existing literature does not provide a clear answer to this question (Bengtsson & Kock, 2014; Ritala & Hurmelinna-Laukkanen, 2013; Safa & Von Solms, 2016). The aim of this paper is to shed new light on this paradoxical nature of knowledge sharing in competitive settings.
Through building on insights from existing literature on knowledge management and leakage, the argument is developed that under circumstances of high intensive competition knowledge sharing depends on the knowledge protection mechanisms that are in place in the organisation. Using a sample of 126 organisations in knowledge-intensive industries, the data analysis show that the degree of knowledge sharing is indeed impacted by knowledge protection. In addition, the analysis shows that this relationship is positively moderated by the competitive intensity of the industry. Thereby making knowledge protection mechanisms essential for organisations in these intensive industries, through increasing the degree of knowledge sharing whilst also mitigating the risk of knowledge leakage.
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