The effect of absorptive capacity on the relationship between knowledge distance and knowledge transfer

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The effect of absorptive capacity on the

relationship between knowledge distance and

knowledge transfer

Master thesis, Msc Human Resource Management University of Groningen, Faculty of Economics and Business

June, 2013 Chaiwat Tantipluptong Student number: S2330423 Plutolaan 329 9742 GK Groningen The Netherlands Tel: +31634576010 E-mail: c.tantipluptong@student.rug.nl Supervisor: Drs. Metha Fennis-Bregman

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ABSTRACT

This research examines the effect of absorptive capacity on the relationship between knowledge distance and knowledge transfer. A total of 100 respondents who have past experience in the university, participated in this study. The linear regression analysis indicates that low knowledge distance between a knowledge seeker and knowledge source increases knowledge transfer. Secondly, a positive relationship between absorptive capacity and knowledge transfer was found. However, based on the regression analysis, there is no significant moderating effect of absorptive capacity on the relationship between knowledge distance and knowledge transfer. The present findings suggest that it is important for highly innovative organizations especially HR managers, whenever possible, to hire individuals with high similar knowledge bases (low knowledge distance) and also with higher absorptive capacities. As a result, these individuals will help to promote knowledge creation and information sharing, which leads to higher internal knowledge transfer within the organization.

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1. INTRODUCTION

Promoting knowledge creation and information sharing within an organization is an increasingly important challenge for managers (Kogut & Zander, 1992). Organizations that can make full use of employees’ expertise and knowledge tend to be more innovative, efficient and effective in the marketplace (Argote, 1999). Therefore, facilitating transfer of knowledge within an organization is a key building block of organizational learning (Argote, 1999). Knowledge transfer refers to the dyadic exchanges of organizational knowledge between a knowledge seeker and a knowledge source (Granovetter, 1973; Szulanski, 1996) that involves direct communication processes between the two parties (Cumming & Teng, 2003). Knowledge transfer occurs at different levels between individuals, teams, groups or organizations (King, 2006). Moreover, Zahra and George (2002) also argued that the role of individuals in the organization is crucial for knowledge utilization and exploitation.

Knowledge transfer occurs through interactions among people who are in various social relationships (Yli-Renko et al., 2001). Recent studies (e.g. Fritsch & Slavtchev, 2007) suggest that social networks are crucial for an effective knowledge transfer in organization settings. A social network is a social structure made up of a set of actors (e.g. individuals or organizations) and the dyadic ties between these actors (Wasserman & Faust, 1994). Existing studies show that there are certain attributes of social networks that influence knowledge transfer, but more research in this area is called for (Levin & Cross, 2004). Furthermore, existing research tends to focus on either the actor or the relationship between the two actors involved in knowledge transfer. However, the relational contexts of knowledge transfer itself are largely missing (Szulanski, 1996). Knowledge distance refers to the difference between the source and the recipient in terms of their knowledge bases (Hamel, 1991).

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significant determinant of internal knowledge transfer within organizations. However, the process of gaining knowledge access is largely missing (Cohen & Levinthal, 1990). Absorptive capability is ‘the ability to recognize the value of new external information, assimilate it, and apply it to commercial ends’ (Cohen & Levinthal, 1990). Consequently, there is a need for new research that studies both the impact of an individual’s social network and the influence of an individual’s ability. Thus, we come up with the research question that this paper seeks to address: Does absorptive capacity weaken the negative influence of knowledge distance on knowledge transfer?

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2. THEORETICAL BACKGROUND

The distribution of labor, the specialization of knowledge bases, and the increasing relevance of knowledge to organizational performance have made it imperative for organizations to enhance their ability in transferring knowledge among organizational members. The individuals develop different ‘thought worlds’ that shape the way they prioritize and acknowledge (Dougherty, 1992). The knowledge transfer incurs costs (in terms of time and energy) when seeking out the appropriate knowledge source (Malmberg & Maskell, 2002). Researchers have taken the advantages of social networking that derives not only from the reduced transaction costs and risk but also from the access to valuable knowledge transfer (Malmberg & Maskell, 2002).

According to Inkpen (1998), knowledge transfer is not a random process and organizations can institute various internal policies, structures, and processes to facilitate learning. Recently, there has been more empirical research on intra-company knowledge transfer focusing on the different factors that hinder or stimulate knowledge transfer (Argote, 1999). The communications between people in the organization can facilitate knowledge flows within the organization (Ghoshal & Bartlett, 1988). In the past, research on cognitive structures and problem solving (Ellis, 1965; Estes, 1970; Bower & Hilgard, 1981) has concluded that an individual’s learning is greatest when the new knowledge to be assimilated is related to the individual’s existing knowledge structure. Moreover, Szulanski (1996) and Kostova (1999) have also mentioned the significant influence of relational factors such as physical distance, knowledge distance and norm distance on knowledge transfer.

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transmission channels, motivation to seek new knowledge and the capacity to absorb new knowledge. Furthermore, Szulanski (1996) suggested that along with causal ambiguity and the relationship between knowledge source and knowledge seeker, the lack of absorptive capacity by the knowledge seeker is considered to be the most significant determinant of knowledge transfer in many studies (e.g. Lane & Lubatkin, 1998; Gupta & Govindarajan, 2000).

Individual network characteristics and knowledge transfer

We based our selection of individual network variables on Cummings and Teng (2003). Cummings and Teng (2003) suggest that in order to understand the relationship between two actors in a social network, it is important to examine the relational context of knowledge transfer. Thus, we chose knowledge distance as an independent variable to measure the dyadic relationship between the two actors in the network and used knowledge transfer as the dependent variable.

Knowledge distance and Knowledge transfer

Knowledge distance is the difference between the knowledge seeker and knowledge source in terms of their knowledge bases. We propose that a high knowledge distance between the knowledge seeker and the knowledge source decreases knowledge transfer.

According to Hamel (1991), the organizational learning will be increased when the knowledge distance or ‘gap’ between two parties (i.e. knowledge seeker and knowledge source) is not too great. This is because too many learning steps are needed if the knowledge distance (or gap) is significant. In addition, Hamel (1991) concluded, “if the skill gap between partners is too great, learning becomes almost impossible”, as the knowledge seeker may not be able to “identify, if not retrace, the intermediate learning steps between its present competence level and that of its partner”. Additionally, Lane and Lubatkin (1998) reported that a large knowledge gap between a knowledge seeker and knowledge source would be less likely to assimilate the source’s knowledge. This means that when knowledge distance is high, the knowledge transfer between knowledge seeker and knowledge source will be low.

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knowledge source has an advantage in knowledge transfer. This is because it is easier for the knowledge seeker to understand the knowledge transfer by the knowledge source.

Hypothesis 1: High knowledge distance between the knowledge seeker and the knowledge source decreases knowledge transfer.

The effects of Absorptive capacity

Cohen and Levinthal (1990) defined absorptive capacity as ‘the ability to recognize the value of new external information, assimilate it and apply it to commercial ends’. It is the most significant determinant of internal knowledge transfer in the organization (Lane & Lubatkin, 1998). Existing research (e.g. Szulanski, 1996; Gupta & Govindarajan, 2000) found that absorptive capacity has a positive relationship with the knowledge transfer. We posit a moderating effect of absorptive capability on the connection between knowledge distance and knowledge transfer. We believe that the relationship between knowledge distance and knowledge transfer received, will be stronger for the knowledge seeker with a high absorptive capacity rather than those with a low absorptive capacity.

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when individuals are equipped with high absorptive capacities rather than low absorptive capacities (Figure 1). Thus, we come up with the second hypothesis.

Hypothesis 2: A bsorptive capacity moderates the relationship between knowledge distance and knowledge transfer. For individuals with high absorptive capacity, the negative influence of knowledge distance on knowledge transfer is weaker than for those with low absorptive capacity.

Figure 1: The expected effect of knowledge distance on knowledge transfer moderated by absorptive capacity.

The conceptual model below (Figure 2) illustrates my hypotheses:

Figure 2: The conceptual model of the influence of the relationship between

knowledge distance and knowledge transfer using absorptive capacity as a moderator.

Absorptive capacity H2

Knowledge distance Knowledge transfer

H1 High absorptive capacity Low absorptive capacity Knowledge Similarity

(Low knowledge similarity = High knowledge distance High knowledge similarity = Low knowledge distance)

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3. METHODOLOGY

Procedure

Data was collected from students who study in the Netherlands, especially in the University of Groningen (64%), Hanzehogeschool Groningen (10%) and other universities (26%). The research was conducted on universities, which is considered as a type of organization. This is because the students are different, for example, age, gender, and level of education making it a perfect sample for testing whether the proposed hypotheses on knowledge transfer holds true.

An online questionnaire with 19 questions was used for this research (Appendix A). The questions were divided into 5 sections. The first section was about some general questions related to the demographic profile of participants. The second, third and fourth sections were about the past experiences of participants in relation to knowledge distance, absorptive capacity and knowledge transfer respectively. The last section was about the face validity, which measures the understanding of the questions by participants.

Sample

A total of 102 respondents completed the questionnaire. Two respondents indicated that they have difficulties in answering the question because the questionnaire is conducted in English. In order to alleviate biased results these two respondents were removed from this study. Moreover, 100% of our respondents were students at the university.

As shown in Table 3.1, the sample subjects were predominantly of ages between 23 and 25 (69%) and female (59%). The average age of participants were 24.31 years. With regard to the level of education, 36% of respondents were in Bachelor’s degree programs, 56% of respondents were in Master’s degree programs and 8% of respondents were in Doctorate or Professional degree programs.

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Table 3.1: Demographic Profile of Respondents

Demographic Item Frequency Percentage (%)

Gender Male Female 41 59 41 59 Age (Range: 20-34 years) 20-22 23-25 Above 25 17 69 14 17 69 14 Education Bachelor’s degree

Master’s degree

Doctorate or Professional Degree

36 56 8 36 56 8 Measures

The items measured in the questionnaire were developed by adopting existing scales that has been used in prior studies, modifying them to fit our context (See Appendix A). Theses multi-item scales were already tested for validity and reliability. The answers of the items were assessed by a 5-point Likert scale range from 1 = ‘Not at all’ to 5 = ‘Extremely’. All variables were standardized prior to the development of indices.

Knowledge transfer (Dependent Variable)

In this paper, we define the level of knowledge transfer based on the level of knowledge utilization from the knowledge source by the knowledge seeker assuming both acquisition and use of new knowledge (Minbaeva et al., 2003). Minbaeva et al. (2003) argued that the important element in knowledge transfer is not the underlying (original) knowledge, but rather “the extent to which the receiver acquires potentially useful knowledge and utilizes this knowledge in its own operations”.

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knowledge from colleagues in other departments. (4) I applied knowledge that I gained from colleagues in other departments.

Within this study, the internal consistency of the scale had a Cronbach’s alpha value of 0.63, which is above the threshold level of 0.60 recommended for a new scale (Nunnally, 1978). Thus, the final measure of the variable knowledge transfer was a weighted average of the four items.

Knowledge Distance (Independent Variable)

In this paper, we define knowledge distance as the differences between the knowledge seeker and knowledge source in terms of their knowledge bases (Cumming and Teng, 2003).

The concept of knowledge distance has been discussed by a number of researchers due to its crucial impact on the knowledge transfer (Hamel, 1991; Lane and Lubatkin, 1998, Nonaka and Takeuchi, 1995). Lane and Lubatkin (1998) measured the overlap between the knowledge seeker and knowledge source by measuring the number of research communities, in which both partners had published during a defined period of time using a bibilometric database from the Center for Research Planning. However, in this study, the measurement system would not be practical. Therefore, this paper follows Cumming and Teng (2003) in defining knowledge distance as the degree to which the knowledge seeker and knowledge source possess similar knowledge.

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lower knowledge distance between the knowledge seeker and the knowledge source. Within this study, the internal consistency of the scale had a Cronbach’s alpha value of 0.60, which is above the threshold level of 0.60 recommended for a newly developed scale (Nunnally, 1978).

Absorptive Capacity (Moderating Variable)

In this paper, we define absorptive capacity as the ability to recognize the value of new external information, assimilate it and apply it to commercial ends’ (Cohen & Levinthal, 1990).

Cohen and Levinthal (1990) stated that individual absorptive capacity is truly an important management issue. The organizational absorptive capacity is based on individual absorptive capacity. Therefore, this paper focused on individual absorptive capacity. This paper follows Minbaeva et al. (2003), absorptive capacity was classified into two dimensions – employee ability and learning motivation. Hence, this paper proposed that a high level of individual absorptive capacity moderate the effects of knowledge distance on knowledge transfer. In order to capture this concept, we asked the respondents to assess on a five-point Likert-type scales (ranging from 1= ‘Not at all’ to 5= ‘Extremely’) on the following five items: (1) I can grasp the knowledge other members provide (2) I can communicate and exchange ideas with other members easily (3) I can hold the latest progress in my field (4) I can distinguish and collect new knowledge and information rapidly (5) I can study new knowledge effortlessly. Within this study, the internal consistency of the scale had a Cronbach’s alpha value of 0.72, which is above the threshold level of 0.70 recommended for testing theory (Nunnally, 1978). Therefore, the variable absorptive capacity is the average of the scores on the five items.

Table 3.2: Outcome of reliability analysis

Variable Number of items Cronbach’s alpha (α)

Knowledge transfer 4 0.63

Knowledge distance 4 0.60

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Control Variable

We included a number of control variables in order to capture potential exogenous effects stemming from heterogeneity in the sample. Prior researchers have identified that demographic characteristics, such as age and gender can affect knowledge transfer behavior (Reagan et al., 2012). Firstly, in order to capture the potential gender differences, we added the variable male and female. Secondly, we also included age. Thirdly, the moderating variable ‘absorptive capacity’ also includes the current skills and capabilities of the individual. Therefore, we control the individual ability by adding the education level, which has been used by educational psychologists to predict learning ability. Education was measured on a three-point scale (1=Bachelor’s degree, 2=Master’s degree, 3 = Doctorate or Professional degree). Data analysis

The aim of this research was to investigate the relationship between knowledge distance and knowledge transfer while looking at the moderating effects of absorptive capacity.

Before testing my hypothesis, a Cronbach’s alpha test was conducted to test the reliability of the scales used. The Cronbach’s alpha showed that all scales show internal consistency and measure the underlying construct for each of the items.

Pearson correlations were used to test for the relationship between the control variables (i.e. age, gender and education) and the other variables (i.e. knowledge distance, knowledge transfer and absorptive capacity). Moreover, the Pearson correlations were also used to test the correlation between the dependent variable (knowledge transfer) and the independent variable (knowledge distance) for my hypothesis 1. Then we will include only the control variables that have significant effect on the main variables (i.e. knowledge distance, knowledge transfer and absorptive capacity) in the regression analysis.

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4. RESULTS

The descriptive statistics and Pearson correlations among the variables in this study are shown below in Table 4.1. The correlation analysis shows that there is no significant relation of gender and age on the relationship with knowledge transfer. However, Table 4.1 shows some significant and positive correlations. Among them, knowledge transfer and absorptive capacity had the strongest positive correlation (r=.39; p<.01). There was also a significant correlation between knowledge distance1 and knowledge transfer (r=.38; p<.01) meaning that lower knowledge distance goes with more knowledge transfer. Thus, this is as expected in hypothesis 1. Furthermore, there is also a positive correlation between age and education (r=.54; p<.01).

Moreover, Table 4.1 also presents a significant correlation between absorptive capacity and education (r=.54; p<.01). This means that the control variable of education is significant to include in the regression analysis to control the effect of absorptive capacity on the relationship between knowledge distance and knowledge transfer.

Table 4.1: Descriptive statistics and correlations.

Variable Mean SD 1 2 3 4 5 1. Gender 1.59 2. Age 24.31 2.66 0.15 3. Education 1.72 0.60 0.15 0.54** 4. KD 3.29 0.71 -0.04 -0.09 -0.05 5. AC 3.95 0.55 -0.16 -0.10 0.36** -0.07 6. KT 3.53 0.66 -0.02 -0.03 0.14 0.38** 0.39**

Note: N=100. KD=Knowledge Distance; A C=A bsorptive Capacity; KT=Knowledge Transfer. ** Correlation is significant at the 0.01 levels (2-tailed).

1_{High score on knowledge distance scale means in fact high knowledge similarity}

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To test the hypothesis 2, Table 4.2 presents the results of the hierarchical multiple linear regression with knowledge transfer as the dependent variable. Before conducting the regression analysis, all the item scores of the independent variables (knowledge distance and absorptive capacity) were transformed to z-scores in order to create the interaction term. The purpose is to reduce the possibility of multicollinearity between the predictor variables and the interaction term.

On the one hand, the control variable age and gender are not included because the previous test shows that there is no significant effect on knowledge transfer. On the other hand, the influence of the control variable education level is significant on absorptive capacity, thus included in step 1. However, the level of education does not significantly contribute to the prediction of knowledge transfer (∆R2=.02, p-value is not significant). In step 2, the determinant of knowledge transfer (i.e. knowledge distance) is added. The regression analysis shows that there is a positive relationship between knowledge distance and knowledge transfer (b=.25, p<.01). Moreover, knowledge distance and education (control variable) are significantly contributed to the prediction of knowledge transfer (∆R2=.15, p<.001). Inspection of the b-coefficients showed that education and knowledge distance in relation to knowledge transfer was significant (b=.17, p<.1 for education and b=.25, p<.01 for knowledge distance). This corresponds with the relation found in the correlation analysis and is consistent with hypothesis 1 that the lower knowledge distance between knowledge seeker and knowledge source (high degree of knowledge distance from items) increases knowledge transfer. Thus, hypothesis 1 was supported.

In step 3, I added absorptive capacity to determine any direct relationship of absorptive capacity on knowledge transfer. As shown in step 3, absorptive capacity contributes to the predication of knowledge transfer (∆R2_{=.08, p<.001). Although not}

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moderation effect of absorptive capacity was performed (see Figure 3). According to figure 3, both lines go up reflecting the positive main effect of knowledge distance on knowledge transfer (b=.17; p<.01). Since the dotted line is steeper than the solid line, which demonstrates the interaction effect (b=.08; p=.17), it can be argued that there is a positive relationship between knowledge distance and knowledge transfer, and this relationship is stronger for high levels of absorptive capacity. This means that when absorptive capacity is high, the knowledge transfer tends to be higher at higher level of knowledge distance (higher degree of knowledge distance from items means lower knowledge distance between knowledge seeker and knowledge source). However, hypothesis 2 was rejected.

Table 4.2: Result of regression analysis showing absorptive capacity as a moderator on the relationship between knowledge distance and knowledge transfer

Step Variable Knowledge transfer (b) R2 ∆R2 Sig 1 Education 0.16 0.02 0.02 0.16 2 Education KD 0.17 0.25 0.17*** 0.15*** 0.09 0.00 3 Education KD AC 0.19 0.18 0.20 0.25*** 0.08*** 0.05 0.01 0.00 4 Education KD AC KD x AC 0.16 0.17 0.23 0.08 0.26*** 0.01*** 0.11 0.01 0.00 0.17 Note: N=100. b=unstandardized coefficients; KD=Knowledge distance;

A C=A bsorptive capacity. *** p < .001.

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Figure 3: Post-Hoc Probing of Absorptive capacity moderation effect on the relationship between knowledge distance and knowledge transfer.

1 1.5 2 2.5 3 3.5 4 4.5 5 Knowledge T

ransfer Low Absorptive _capacity

High Absorptive capacity

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5. DISCUSSION AND CONCLUSION

Findings

The aim of this study was to examine the role of knowledge distance on knowledge transfer and whether absorptive capacity will moderate the relationship between knowledge distance and knowledge transfer. The results of the questionnaire study among 100 respondents showed that knowledge distance is positively related to knowledge transfer. We found that the low knowledge distance between the knowledge seeker and the knowledge source increases knowledge transfer. This means that individuals with low knowledge distance (high knowledge similarity with other members) will be able to gain more knowledge than those with high knowledge distance (low knowledge similarity with other members). This finding is in line with our reasons described in the theoretical framework. Therefore, hypothesis 1 is confirmed.

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kind of situation, when individuals with high knowledge distance (low knowledge similarity with other members) are equipped with high absorptive capacity, their learning may be increased and their ability to absorb knowledge being more positive which might enhance their knowledge transfer.

However, the results from post-hoc analysis presented the tendency of a moderating role of absorptive capacity (Hypothesis 2). It appears that the individuals with higher absorptive capacity will have less impact from the negative influence of knowledge distance on knowledge transfer than those with a lower absorptive capacity. This is because when there is knowledge distance between the knowledge seeker and knowledge source, individuals with high absorptive capacity are likely to gain higher knowledge transfer than those with low absorptive capacity. Furthermore, the overall explanation of knowledge transfer becomes more significant by adding the moderating effect of absorptive capacity on the relationship between knowledge distance and knowledge transfer. This result indicates that the model including all significant variable works well, explaining more than one fourth of the observed variation in the knowledge transfer (R2=0.26). This R2 is used to measure the overall goodness of fit of the model.

Strengths and limitations

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However, there is also a limitation in this research. To reduce the complexity in the current study, we used only one distance measure, which is knowledge distance. Current research has revealed that distance is considered to be a heterogeneous concept in the relation context of knowledge transfer, which can be defined in various ways (e.g. Cumming and Teng, 2003). Many studies have revealed that different distance measures have significant effects on knowledge transfer. For example, Cumming and Teng (2003) examined four different distance measures and their effects on behavioral approach. This study has shown that different distance measures have different effects on knowledge transfer.

Moreover, the online questionnaire had self-reported questions, which means that the participants rated themselves. Therefore, it is common that respondents tend to over-report behaviors perceived as appropriate and under-report behaviors that perceive as inappropriate. Thus, the high scores on absorptive capacity and knowledge transfer could have been influenced by the self-report bias. In contrast, according to Miller and Panjikaran (2001), the absence of an interviewer in online surveys allows the participants to provide more sensitive information rather than the offline surveys (e.g. telephone survey).

Theoretical Implications and Future research

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because it is embedded in cultures and routines. For learning culture, the need for learning of culture in the organization to facilitate knowledge transfer, has been mentioned in many literatures (Aubrey & Cohen, 1995; Huber, 1991).

A bsorptive capacity. In this study, I proposed that absorptive capacity would moderate the relationship between knowledge distance and knowledge transfer. Such that this relationship should be more prominent and significant when participants have high absorptive capacity than those with low absorptive capacity. Unfortunately, this study found that the moderation effect of absorptive capacity was not significant if the control variable of education is included. In addition, this study found that the individuals with a high absorptive capacity are able to gain higher level of knowledge transfer than those with a low absorptive capacity. Consequently, it is also consistent with existing researches (e.g. Szulanski, 1996; Gupta and Govindarajan, 2000) that absorptive capacity is positively related to knowledge transfer.

Knowledge transfer. In this study, the dependent variable refers to the degree of knowledge transfer between a knowledge seeker and knowledge source in the organization. I based the results on the extent of knowledge transferred as a knowledge seeker perspective (See in Appendix A). According to Kogut and Zander (1992), organization learning requires the reconstruction and adaptation of transferred knowledge received by the knowledge seeker. Therefore, this helps to enhance the concept of organizational learning. However, there are different types and dimensions of knowledge transfer (Nonaka and Takeuchi, 1995; Cowan et al., 2000). For further research, the knowledge transfer can be analyzed further in two perspectives, which are the extent of knowledge transferred as a knowledge source and the extent of knowledge received as a knowledge seeker. This will help to provide more understanding of knowledge transfer in the organization.

Practical Implications

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needed, individuals with low knowledge distance (high knowledge similarity with other members) will be able to gain more knowledge than those with high knowledge distance (low knowledge similarity with other members). Thus, it is important for HR managers to hire people that do not possess a vast difference in their knowledge bases to increase knowledge transfer within their organizations.

The results from this study also present that absorptive capacity, which is the significant determinant of internal knowledge transfer within organizations (Gupta and Govindarajan, 2000) have direct effect on knowledge transfer. Individuals with high absorptive capacity imply that they are able to absorb more knowledge transferred than those with low absorptive capacity. According to Minbaeva et al. (2003), the organization needs to consider the importance of absorptive capacity to facilitate more internal knowledge transfer. For instance, when individuals in the organization have low knowledge distance (high similarity of knowledge bases between individuals), thus individuals with a high absorptive capacity are able to promote higher knowledge transfer within the organization than those with a low absorptive capacity. Therefore, the HR managers should consider hiring individuals with high absorptive capacity rather than those with a low absorptive capacity when their organizations would like to promote internal knowledge transfer in the organization.

Conclusion

Knowledge distance and absorptive capacity have been identified as important aspects of knowledge transfer. These factors have been mentioned in prior research separately, but a combined approach has been lacking. Such joint examination is necessary because both variables seem to be relevant in terms of the organizational learning aspects (Cohen and Levinthal, 1990; Cumming and Teng, 2003; Minbaeva et al., 2003).

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In this study, I found that knowledge distance has an impact on knowledge transfer. The individuals with low knowledge distance increase the knowledge transfer in the organization. Moreover, absorptive capacity had direct effect on knowledge transfer. Individuals with a high absorptive capacity will be able to gain higher knowledge transfer than those with a low absorptive capacity. Furthermore, based on the outcomes of the regression analysis, I can argue that absorptive capacity does not affect the relationship between knowledge distance and knowledge transfer. However, if the control variable of education is left out, there is a significant moderating effect of absorptive capacity. Moreover, based on the post-hoc analysis, when an individual’s absorptive capacity is high, the knowledge transfer is higher for an individual’s with low knowledge distance between the knowledge seeker and the knowledge source than those individuals with a low absorptive capacity. By focusing on individuals with a high absorptive capacity, the organization could therefore reduce the negative effects of knowledge distance. As a result, the organization turns knowledge distance into a good account for their organization.

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REFERENCES

Adler, P. S. & Kwon, S. W. (2002). Social capital: Prospects for a new concept,

Academy of Management Review, 27: 17-40.

Allen, T. J. (1977). Managing the flow of technology: Technology transfer and the dissemination of technological information within the R&D organization, MIT Press, Cambridge, MA.

Argote, L. (1999). Organizational learning: Creating, retaining, and transferring knowledge, Kluwer Academic Publishers, Boston, MA.

Aubrey, R. & Cohen, P. (1995). Working Wisdom: Timeless skills and vanguard strategies for learning organizations, Jossey-Bass, San Francisco, CA.

Bower, G. H. and Hilgard, E. R. (1981). Theories of learning. Prentice-Hall, Englewood Cliffs, NJ.

Cohen, W. M. and Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation, Adminsitrative Science Quarterly, 35: 128-152.

Cowan, R., David, P. A. & Foray, D. (2000). The explicit economics of knowledge codification and tacitness, Industrial and corporate change, Oxford University Press, 9(2): 211-253.

Cumming, J.L. & Teng, B.S. (2003). Transfering R&D knowledge: The key factors affecting knowledge transfer success, Journal of Engineering and Technology

Management, 20: 39-68.

(27)

Ellis, H. C. (1965). The transfer of learning, Macmilian, New York.

Estes, W. K. (1970). Learning theory and mental development, Academic Press, New York.

Fritsch, M. & Slavtchev, V. (2007). Universities and innovation in space, Industry

and Innovation, 14: 201-218.

Ghoshal, S. & Bartlett, C. (1988). Creation, adoption, and diffusion of innovations by subsidiaries, Journal of International Business Studies, 19(3): 365-388.

Granovetter, M. (1973). The strength of weak ties, American Journal of Sociology, 78: 1360-1380.

Gupta, A. and Govindarajan, V. (2000). Knowledge flows within MNCs, Strategic

Management Journal, 21: 473-496.

Hamel, G. (1991). Competition for competence and inter-partner learning within international strategic alliances, Strategic Management Journal, 12: 83-103.

Holmbeck, G.N. (2002). Post-hoc probing of significant moderational and meditational effects in studies of pediatric populations, Journal of Pediatric

Psychology, Special Issue on Methodology and Design, 27(1): 87-96.

Huber, G. P. (1991). Organizational learning: The contributing processes and literatures, Organization Science, 2(1): 88-115.

Inkpen, A. C. (1998). Learning and knowledge acquisition through international strategic alliances, Academy of Management Executive, 12(4): 69-80.

(28)

Kogut, B. & Zander, U. (1992). Knowledge of the firm, combinative capabilities and the replication of technology, Organization Science, 3: 383-397.

Kostova, T. (1999). Transnational transfer of strategic organizational practices: a contextual perspective, Academic of Management Review, 24: 308-324.

Lane, P. J. & Lubatkin, M. (1998). Relative absorptive capacity and interorganizational learning, Strategic Management Journal, 19: 461-477.

Levin, D. Z. & Cross, R. (2004). The strength of weak ties you can trust: the mediating role of trust in effective knowledge transfer, Management Science, 50(11): 1477-1490.

Malmberg, A. & Maskell, P. (2002). The elusive concept of localization economies: towards a knowledge-based theory of spatial clustering, Environment and Planning, 34: 429-449.

Miller, T. W. & Panjikaran, K. J. (2001). Studies in comparability: The propensity scoring approach, A.C. Nielsen Center for Marketing Research, University of

Winconsin-Madison, 23(2): 23-25.

Minbaeva, D. B., Pedersen, T., Bjorkman, I., Fey, C. F. & Park, H. J. (2003). MNC Knowledge transfer, Subsidiary Absorptive Capacity, and HRM, Journal of

International Business Studies, 34: 586-599.

Minbaeva, D. B., Makela, K. & Rabbiosi, L. (2010). Explaining intra-organizational knowledge transfer at the individual level, SMG Working paper 1/2010, Copenhagen Business School, Center for Strategic Management and Globalization.

Nonaka, I. & Takeuchi, I. (1995). The knowledge creating company: how Japanese companies create the dynamics of innovation, Oxford University Press, New York.

(29)

Reagan, R., Singh, P. V. & Krishman, R. (2012). Community Cost?: Analyzing the contingent association between internal cohesion and external knowledge transfer relationships, Working Paper, Tepper School of Business.

Simonin, B. L. (1999). Transfer of marketing know-how in international strategic alliances: An empirical investigation of the role and antecedents of knowledge ambiguity, Journal of International Business Studies, 30(3): 463-490.

Szulanski, G. (1996). Exploring internal stickiness: Impediments to the transfer of best practice within the firm, Strategic Management Journal, 17: 27-43.

Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications, Cambridge University Press.

Yii-Renko, H., Autio, E. and Sapienza, H. J. (2001). Social capital, knowledge acquisition, and knowledge exploitation in young technology-based firms, Strategic

Management Journal, 22: 587-613.

Zahra, S. A. and George, G. (2002). Absorptive capacity: a review, reconceptualization, and extension, Academy of Management Review, 27(2): 185-203.

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APPENDIX

Online questionnaire

Dear Respondent, Welcome to this study on knowledge transfer behavior

I am a MSc student from the Human Resource Management department at the University of Groningen in the Netherlands. The purpose of this survey is to gain more insight into the relationship between knowledge distance, knowledge transfer and absorptive capacity.

I sincerely invite you to complete this survey that will only take up to 10 minutes of your time.

The information gathered will be used for research purposes only. Please rest assured that all data will be kept strictly confidential and will not link back to you as an individual.

Please answer all questions as frankly as possible. This is not a test! There are no right or wrong answers. I am simply interested in your own past experiences. Your participation is very important to this research and for future studies about this topic. Thank you for your time and co-operation.

Section 1 – Background Information

First, I would like to ask some general questions about you. 1) How old are you? ______

2) What is your gender? ( ) Male

( ) Female

3) What is your education level? ( ) Bachelor’s degree

( ) Master’s degree

(31)

4) Which university are you studying? ________ Or the university that you were studying at before?

5) What is your nationality? ________

Section 2 – Past experience on Knowledge distance (Independent Variable)

Concerning your past experience at the University or Hanze Hogeschool, please choose your level of agreement with the following statements.

6) The other members and I have similar education and professional backgrounds. (The other members refer to your colleagues and friends.)

Not at all 1 2 3 4 5 Extremely

7) The other members and I grasp similar tools and methods. Not at all 1 2 3 4 5 Extremely

8) The other members and I have similar project experience. Not at all 1 2 3 4 5 Extremely

9) The other members and I can find many common topics in experience exchange. Not at all 1 2 3 4 5 Extremely

Section 3 – Past experience on your Absorptive capacity (Moderating Variable)

Please indicate how strongly agree/disagree with each of the following statements about your past behavior regarding ability to recognize the value of new external information in the university or Hanze Hogeschool.

10) I can grasp the knowledge other members provide. Not at all 1 2 3 4 5 Extremely

(32)

12) I can gather the latest progress in my field. Not at all 1 2 3 4 5 Extremely

13) I can find and collect new knowledge and information rapidly. Not at all 1 2 3 4 5 Extremely

14) I can absorb new knowledge effortlessly. Not at all 1 2 3 4 5 Extremely

Section 4 – Past experience on Knowledge Transfer (Dependent Variable)

The following statements represent the relationship between you as a knowledge seeker and your colleagues, and other members as a knowledge source. Please indicate your opinions by circling the appropriate level of agreement on the scale next to each statement.

15) I gained knowledge from colleagues in my own department. Not at all 1 2 3 4 5 Extremely

16) I applied knowledge that I gained from colleagues in my own department. Not at all 1 2 3 4 5 Extremely

17) I gained knowledge from colleagues in other departments. Not at all 1 2 3 4 5 Extremely

18) I applied knowledge that I gained from colleagues in other departments. Not at all 1 2 3 4 5 Extremely

Section 5 – Face Validity

As this questionnaire is conducted in English, there may be some difficulties in answering the question for non-native English speakers.

19) Did you have difficulties in understanding this questionnaire? ( ) Yes

The effect of absorptive capacity on the relationship between knowledge distance and knowledge transfer