A two-case study on the contribution of tie strength to the effectiveness and efficiency of the knowledge brokering process

1

THINKING IN BOXES OTHERS CANNOT SEE:

A two-case study on the contribution of tie strength to the effectiveness and efficiency of the knowledge brokering process

by

MONIQUE DANIËLLE TAVERNE

University of Groningen Faculty of Economics and Business

MSc BA Strategy & Innovation

December 2010

Nesciolaan 129 9752 HX Haren (06) 41531973

monique_taverne@hotmail.com

S1733281

2 ABSTRACT

This research studies the contribution of tie strength between the knowledge broker and seeker to the effectiveness and efficiency of the knowledge brokering process within the Northern part of the Netherlands. Knowledge brokering entails intermediating between relatively disconnected pools of ideas to facilitate the recombination of knowledge, resulting in innovation. Effective and efficient knowledge brokering is important to the region’s knowledge valorization. A conceptual model has been developed on the basis of knowledge brokering literature and social network theory, with emphasis on where they meet. Two embedded case studies with opposing tie strengths were conducted. Results from the case studies indicate that; it is likely that a weak tie contributes to the efficiency of the knowledge brokering process by requiring less time and engagement by the parties to develop and maintain the tie; it is likely that a strong tie contributes to the effectiveness of the knowledge brokering process by providing access more easily and lowering the risk of opportunistic behavior; and that it is likely that a strong tie contributes to the efficiency of the knowledge brokering process by being more motivated to help, enhancing cooperation and enhancing communication effectiveness. In order for these results to be generalized beyond its current context, future research should focus on lateral replication.

KEYWORDS: Knowledge Brokering Process, Tie Strength, Strength of Ties, Effectiveness, Efficiency

WORDCOUNT: 23.246 (Introduction – Recommendations)

3 PREFACE

My intention was to write a master thesis with both scientific and practical value, within an environment focused on sustainability and preferably dealing with open-innovation. I feel like I have been able to exactly reach that goal by conducting this research for the Energy Valley Foundation. Following upon my bachelor thesis, that dealt user-innovation and consequently the ‘not-invented-here’ syndrome, my master thesis regarding knowledge brokering is now all about a ‘nothing-is-invented-here attitude’ (Hargadon, Sutton, 2000:163).

Besides gaining a deeper understanding of knowledge brokering and tie strength, conducting research was a valuable experience. I enjoyed studying this topic more in depth and

combining existing theories. Moreover, relating theory to practice through conducting case studies has put what I have learned, during the master, in perspective. Overall, I am pleased with the result and think that this thesis represents me as a student.

I would like to thank the people that have allowed me to conduct research and helped me complete my master thesis. Firstly, I would like to thank Prof. Dr. W. A. Dolfsma for being my first supervisor and Dr. T. J. L. Broekhuizen for being my second supervisor. Secondly, a special thank you to Dr. K. Lok Eur. Ing. MBA and Drs. Ing. P. Cnubben for being wonderful mentors. Thirdly, I would also like to thank my colleagues at Energy Valley for answering all my complicated questions and making it enjoyable to attend work every day. Fourthly, all the people I have interviewed and those whom provided me with feedback deserve to be thanked as well. Last but not least, I owe my family and friends a big thank you for their support.

‘The future is already here, it’s just unevenly distributed’ (Hargadon, 2002:54)

4 TABLE OF CONTENTS

1. INTRODUCTION 7

1.1 Scope 8

1.2 Research Questions 8

1.3 Purpose 9

1.4 Structure 9

2. CONTEXT 10

2.1 National Innovation System 10

2.1.1 Sub-region 10

2.1.2 Research and development 11

2.1.3 Innovation 12

2.1.4 Relation between R&D and innovation 13

2.1.5 Government 14

2.1.6 Knowledge institutions 16

2.1.7 Companies 16

2.1.8 Intermediaries 17

2.1.9 Knowledge potential of people 17

2.2 Triple Helix 17

2.2.1 Obstacles to interaction 18

3. THEORETICAL FRAMEWORK 19

3.1 Knowledge Brokering 19

3.1.1 Definition 19

3.1.2 Role 20

3.1.3 Process 20

3.1.4 Accessing and bridging 24

3.2 Tie Strength 25

3.2.1 Definition 25

3.2.2 Strength of weak ties 26

3.2.3 Strength of strong ties 27

3.2.4 Balanced view 31

3.3 Effectiveness and Efficiency 31

3.3.1 Effectiveness 31

3.3.2 Efficiency 32

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3.3.3 Effectiveness versus efficiency 32

3.4 Conceptual Framework 33

3.4.1 Existing research 33

3.4.2 Success factors 34

3.4.3 Weak tie contributions 35

3.4.4 Strong tie contributions 37

3.4.5 Contributions per stage 40

3.4.6 Balanced view 43

3.4.7 Conceptual framework 44

4. METHODOLOGY 46

4.1 Research Design 46

4.2 Motivated Design Process 47

4.3 Analytical Strategy 48

4.4 Data Collection Methods 49

4.5 Design Quality 50

5. RESULTS 51

5.1 Knowledge Brokering Process 51

5.2 Tie Strength 52

5.2.1 Tie strength 52

5.2.2 Balanced view 53

5.3 Strengths of Ties 53

5.3.1 Weak tie strengths 53

5.3.2 Strong tie strengths 54

5.4 Contributions to Effectiveness and Efficiency 54

5.4.1 Weak tie contributions 55

5.4.2 Strong tie contributions 55

5.5 Additional Findings 58

6. CONCLUSION 60

6.1 Sub-research Questions 60

6.2 Main Research Question 61

7. DISCUSSION 64

7.1 Discussion 64

7.2 Limitations 69

7.3 Future Research 69

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8. RECOMMENDATIONS 71

9. REFERENCES 73

10. APPENDICES 83

I Employer knowledge Broker TCNN 83

II SMEs of knowledge seekers 84

III Case descriptions 85

IV Knowledge brokering process 87

V Tie strength 88

VI Interview questions 89

VII Additional questionnaire (Dutch) 90

7 1. INTRODUCTION

Within the Northern part of the Netherlands ‘academic research has been shown to only rarely translate directly into new products or services for industrial firms and is used by a limited number of firms as a source of information or knowledge for their innovation activities’

(Ranga, Miedema, Jorna, 2008: 705). Consequently, this region suits the description of both the European- (Dosi, llerena, Labini, 2006) and Dutch paradox (van der Duin, de Graaf, Langeler, 2009; Kreijen, van der Laag, 2003,). Put differently, the area does not succeed in valorizing its knowledge: creating economic and/or social value out of scientific results (Greenhuizen, 2010; Jonge, Louwaars, 2009; Sebök, 2007). Yet, because the measure of economic welfare and prosperity of the community is increasingly determined by its ability to innovate, knowledge valorization has become a necessity. Still, its necessity has become even more evident since today’s challenges, that call for innovative solutions, concern global warming, ageing of the population, exhaustion of our resources, mobility and pollution (Nederland Ondernemend Innovatieland, Innovatie Platform, 2009).

The major obstacle to transferring research into practice, according to Ward, House and Hamer (2009), is the gap between those who produce research and those who use it. This gap is mostly due to the fact that both parties reside in different worlds, with their own beliefs, values and practices. Therefore, a proposed solution is to employ knowledge brokers who, by facilitating the transfer of (scientific-) evidence between researchers and practitioners, help them to develop evidence-based solutions (Sheate, Partidário, 2009; Ward et al., 2009).

Hereby, knowledge brokers actually make use of their in-between position to recombine existing knowledge from relatively disconnected domains, such as knowledge institutions and businesses, in order to innovate (Hargadon, 1989). Innovation in this case concerns

‘something new, which is presented in such a way that the value will be determined by its selectors’ (Jacobs, 2007:30) and is not bound to certain types, kinds or levels of newness.

In the context of knowledge valorization and innovation it would be valuable to find out about how knowledge could be brokered effectively and efficiently. Research by the founders of the knowledge brokering concept (Hargadon, 1998, 2002, 2003; Hargadon, Sutton, 1997, 2000) and other scholars (Bielak, Campbell, Pope, Schaefer, Shaxon, 2008; Kammen, Jansen, Bonsel, Kremer, Evers, Wladimiroff, 2006; Loew, Bleimann, Walsh, 2004; Sousa, 2008;

Sheate, Partidário, 2009;Verona, Prandelli, Sawhey, 2006; Wolpert, 2002) answer part of this

question by describing how various conditions, structures, cultures and people enable and/or

8 support knowledge brokering. Remarkably, it seems that no scholars have touched upon the contribution of tie strength to the effectiveness and efficiency of the knowledge brokering process (KBP) yet. This knowledge gap is particularly odd, because network ties essentially enable knowledge brokering to occur in the first place. Or as Fliaster and Spiess (2008:100) also state: ‘the development of creative ideas is mainly a social network process’. Hence, this study combines the need to understand how the KBP could be executed effectively and efficiently and the scientific knowledge gap by researching how tie strength could contribute to the effectiveness and efficiency of the KBP.

1.1 Scope

Tie strength has been confined to the relationship between the knowledge broker and the knowledge seeker and consequently the part of the process that takes place between these actors and/or could be influenced by this tie. Besides the limited time and resources available, regional circumstances justify the emphasis on this particular tie. Namely, while 75% of all employment within the region is provided by potentially knowledge seeking small- and medium sized enterprises (SMEs) (companies with less than 200 employees), they do not make a significant contribution to the valorization of knowledge the way they should (SNN, 2005; SNN, 2007A; SNN, 2007B). Possible reasons could be that SMEs experience difficulty in finding the right partners ( Kirkels, Duysters, 2010), do not collaborate enough with knowledge institutions (Nederland Ondernemend Innovatieland, Innovatie Platform, 2009, SNN, 2007A) or struggle to articulate their research demand, which makes it rather difficult for researchers to deliver the knowledge that meets their requirements (Klerx, Leeuwis, 2008). Thus, knowledge brokers could help the knowledge seeking SMEs within the region to valorize knowledge.

1.2 Research Questions

Consequently to the previously identified knowledge gap with practical value and the scope of this research the following research questions have been formulated. The main research question of this research is: ‘How could tie strength between the knowledge broker and seeker contribute to the effectiveness and efficiency of the knowledge brokering process?’. In order to answer this main research question three sub-research questions have been formulated.

1) Which part of the knowledge brokering process is relevant to the tie between the knowledge broker and seeker?

2) What are the relevant advantages of the various tie strengths between the knowledge

broker and seeker?

9 3) What does contributing to the effectiveness and efficiency of the knowledge brokering

process entail?

1.3 Purpose

Due to its practical and scientific incitement the purpose of this research is twofold. Firstly, this study intends to make a start towards filling the identified knowledge gap within the knowledge brokering literature and explore ways in which future research could contribute.

Secondly, this study aims to provide knowledge brokering organizations within the Northern part of the Netherlands with suitable advice on how to manage their ties with businesses in favor of their knowledge brokering efforts.

1.4 Structure

The remainder of this research is structured the following way. The second chapter elaborates on the context of this research according to the national innovation system- and triple helix model. The third chapter attends to answering the research questions by describing literature on knowledge brokering and tie strength, with emphasis on the part where they meet. The fourth chapter describes the methodology of how and why a “two-case” embedded case study has been conducted. The fifth chapter presents the results of this case study. The sixth chapter describes the conclusion per research question. The seventh chapter entails the discussion, limitations and suggestions for future research. The final chapter discusses the

recommendation towards local knowledge brokers.

10 2. CONTEXT

This chapter is dedicated to outlining the context of this research. Rather than using a pest- analysis, the national innovation system (NIS) model has been used as a tool to describe its context. Whereas intermediaries, like knowledge brokers, are at the centre of the national innovation system, the model clarifies which elements make up their environment. Interaction between these elements is described by making use of the triple helix model. Simultaneously, explaining the need for knowledge brokers by SMEs within the Northern region of the

Netherlands.

2.1 National Innovation System

Provided that the Netherlands are a member of the Organization for Economic Co-operation and Development (OECD) the following definition of the NIS- model has been used: ‘The national innovation system’s approach stresses that the flows of technology and information among people, enterprises and institutions are key to the innovative process. Innovation and technology development are the result of a complex set of relationships among actors in the system, which includes enterprises, universities and government research institutes’ (OECD, 1997: 7). Within the NIS -model, research and development represent the input, innovation the output and the remainder is related to the throughput of the innovation system (CBP, 2010; OECD, 1997). Although this model focuses on a national level, innovation systems can also be analyzed on a sub-regional level (Nauta, Gielen, 2009; OECD, 1997). Premises to a successful regional innovation system are; attracting and maintaining knowledge and talent within the region; connecting research to business; networking between businesses,

knowledge institutions and the government; focusing on specific specializations; and

managemental support (Nauta, Gielen, 2009). An adapted version of the NIS -model is shown in figure 1. This research focuses on the KBP taking place between the knowledge broker (intermediary), knowledge seekers (companies, mostly SMEs) and knowledge sources (knowledge institutions), with emphasis on the relation between the former.

2.1.1 Sub-region. In case of this research, the NIS is limited to the sub-region of the Northern

part of the Netherlands. This region consists of the provinces Groningen, Friesland and

Drenthe and covers 25% of the total country (SNN, 2010). The region is quite sparsely

populated, but urbanization has resulted in the establishment of four major cities: Groningen,

Leeuwarden, Emmen and Assen (SNN, 2007B). This geographical spread also accounts for a

11 historical explanation as to why knowledge institutions and companies are not well connected (SNN, 2007A).

FIGURE 1

National Innovation System (adapted from CBS, 2010)

2.1.2 Research and development. The input to innovation is not only represented by R&D expenditure as stated by Kleinknecht, Montfort and Brouwer (2002: 113). Therefore, figures on both R&D expenditure as well as total innovation expenditure will be presented next.

These figures are corrected by the number of companies per region, are based upon the Community Innovation Survey 2004-2006 (Copinga, de Jong, 2010) and concern companies with ten or more employees.

Firstly, total R&D expenditure of the Northern part of the Netherlands in 2006 was

€160.489.000, which is 2.1% of the national total. When correcting this figure by the number of companies within the region (4,956), this means that €32.46 was spend on R&D per

company. Compared to the other regions this is the least amount of money spend on R&D per company (R&D expenditure per company in 2006 per region: Northern-wing €90.88,

Research & Development Innovation

Relation between R&D and Innovation Knowledge potential of People Knowledge Institutions

(Knowledge Seekers)

Intermediaries (Knowledge Brokers)

Within Companies Between Within Companies (Knowledge Seekers)

G o v e r n m e n t

Knowledge-flows

12 Southern-wing €112.35, East €62.62, South-West €53.80, South-East €280.65) (Copinga, de Jong, 2010).

Secondly, the total innovation expenditure of the Northern part of the Netherlands in 2006 was €282,463,000, which is 2.8% of the Dutch total (Copinga, de Jong, 2010). When correcting this figure by the number of companies within the region, this means that €56.99 was spend on innovation per company. Compared to the other regions this is clearly the least amount spend on innovation, in fact, innovation expenditure per company is almost twice as much in all other regions (R&D expenditure per company in 2006 per region: Northern-wing

€138.86, Southern-wing €148.84, East €110.09, South-West €104.61, South-East €327.41) (Copinga, de Jong, 2010).

Finally, it can be concluded that the Northern region of the Netherlands does invest in research & development and other innovation inputs the least. Moreover, 55% of the companies, within the Northern region of the Netherlands, that wanted to innovate in 2006, noted the lack of information to be a barrier to innovation (compared to 52.6% nationally in 2006) (Copinga, de Jong, 2010).

2.1.3 Innovation. Because patent applications are often used as a measure of innovation, this research has also collected data on patent applications. However, since patents exclude non- patented innovations, include patents that might never be commercialized and are not the most important means of appropriation by companies, the share of products new to the market and new to the firm in total sales has been used as well (Kleinknecht, Montfort and Brouwer, 2002).

Firstly, the Northern part of the Netherlands has requested the second least amount of patents (2.9% of the national total 2003-2005) compared to the other regions (patent applications per region as percentage of the national total 2003-2005: Northern-wing 14.8%, Southern-wing 15.5%, East 9.7%, South-West 1.9%, South-East 55.2% in 2006) (Louter, Eikeren, 2008).

More specifically, according to research by PatentVista the region’s SME share in patents is also low (3.7% of the national total in 2003).

Secondly, innovation as a percentage of the total sales of the Northern region of the

Netherlands in 2006 was 7.8% compared to 8.8% nationally. Even though below the national

13 percentage, the region does outperform three regions (share new or renewed products in total sales per region: Northern-wing 7.2%, Southern-wing 10.7%, East 7.3%, South-West 4.4%, South-East 11.4% in 2006) (Copinga, de Jong, 2010).

In conclusion, the figures give mixed signals about innovation within the Northern part of the Netherlands. Namely, even though the region applies for the second least amount of patents it outperforms three other regions in the share of new or renewed products in total sales.

Nevertheless, these mixed signals per indicator of innovation are not uncommon as Kleinknecht et al. (2002) state that all indicators tell a different story.

2.1.4 Relation between R&D and innovation. The relation between R&D and innovation is of great importance to the knowledge economy (CBS, 2010). However, the innovation input output ratio for the Northern part of the Netherlands is difficult to establish with the

previously presented figures since different measurements have been used as well as different time periods. What is possible is to compare the input in the form of R&D and innovation expenditure per company per region with the output in the form of the share of new and renewed products in total sales per region. First, a conclusion on the basis of the number of patent applications within the region is drawn.

Firstly, what stands out is the fact that the number of patent applications in the Northern part of the Netherlands during the time period 2003-2005 was the second lowest of the entire country. In combination with the next argument this could be explained by the fact that the Northern part of the Netherlands does not patent all its innovations, prefers other means of appropriation or that the patents are more valuable than in other regions.

Secondly, while R&D and innovation expenditure (input) per company in the Northern part of

the Netherlands was the least, the share of new and renewed products in total sales (output)

was higher than that of three other regions within the Netherlands (Northern-wing, East and

South-West). On the basis of these figures one could say that the Northern part of the

Netherlands has turned its input more effectively/efficiently into output than three other

regions within the Netherlands. Nevertheless, there is quite a large gap between the share of

new or renewed products in sales of the Northern region with that of the Southern wing and

South-East of the Netherlands and much room for improvement. One way to improve is by

expanding the key areas and thus their percentage of sales of the total sales of the region, as

14 these have a larger share of new or renewed products in their sales. Key areas within the region include energy (Energy Valley), water (Friese Water Alliantie) and sensor technology (ASTRON/LOFAR), and sectors with potential are the life sciences, ICT, chemistry,

agribusiness, metal/marine engineering, tourism and commercial care (SNN, 2007B).

Moreover, the region could aspire to become more effective and efficient in converting input into output by translating existing knowledge into innovation by for example improving triple helix interaction.

2.1.5 Government. The Samenwerkingsverband Noord- Nederland (SNN) is a partnership between the three Northern provinces to jointly strengthen their economical position. Within this partnership the four major cities Groningen, Leeuwarden, Asssen and Emmen have an important advisory role (SNN, 2010). The SNN, in cooperation with other parties, has

developed three programs to accomplish its objectives. The parts of the programs that relate to innovation, knowledge valorization and knowledge brokering are described next.

The first program to be discussed, in line with ‘Pieken in de Delta’ and following upon

‘Kompas voor het Noorden’, is called ‘Koers Noord’. This program is developed by SNN in cooperation with the Ministry of Economic Affairs and concerns the Northern part of the Netherlands for the period of 2007-2013. This program focuses on strengthening the (inter-) national competitiveness of the Northern region of the Netherlands by making the transition to a knowledge intensive economy, extending commercial and knowledge intensive clusters and increasing the innovative and knowledge valorizing ability of SMEs. Programs for each high potential cluster (Energy, Water, and Sensor Technology) include, extending the development of knowledge in general, establishing knowledge centers for joint research and development, stimulating the knowledge transfer from knowledge institutions to SMEs, stimulating

knowledge valorization and innovation development. The programs regarding the high potential sectors (agri-businesses, life sciences and tourism) are directed at increasing knowledge, implementing knowledge, cooperation between companies and knowledge

institutions, establishing cluster organizations, integration of knowledge from different sectors

and innovation through combining existing knowledge. Finally, within the program special

attention is given to strengthening SME’s innovativeness, stimulating strategic activities by

SMEs, stimulating the diffusion of knowledge from the knowledge institutions among

companies and supporting SME’s innovation initiatives. (SNN, 2007A)

15 The second program to be discussed is called the ‘Strategische Agenda’ and is written by the general management of SNN for the period of 2007-2013 regarding the Northern region of the Netherlands. One of the main goals of the ‘Strategische Agenda’ is to manage the transition towards a dynamic knowledge economy. To realize this goal SNN has identified three key areas (Energy Valley, Friese Water Alliantie, and ASTRON/LOFAR) and seven sectors (agribusiness, chemistry, commercial care, life sciences, ICT, marine engineering and tourism) that may have the potential to grow through innovation. More general measures are taken for SMEs, which can contribute to both the specified high potential areas and sectors.

Policy is directed at proactively bringing together companies and knowledge institutions in order to stimulate/facilitate cooperation between them. Furthermore, knowledge centers will be established, innovation stimulated, organizing ability supported, possibilities to use external experts increased and knowledge institutions will be made more accessible and transparent to SMEs. Finally, SNN will work on a suitable regional system of knowledge circulation, by which universities (HBO and WO) will be developed into recognizable players within knowledge networks. (SNN, 2005)

The third program to be discussed is called ‘Operationeel Programma Noord-Nederland’

(2007-2013), which elaborates on the previously explained ‘Strategische Agenda’ and

allocates European funding (EFRO) to specific actions. This program was developed by SNN in cooperation with Gemeente Groningen, Gemeente Emmen, Gemeente Leeuwarden,

Gemeente Assen and the European Union. The main purpose of this program is to accomplish a transition from the existing economy to a knowledge economy, in which development and implementation of innovation and technology strengthen the territorial qualities of both the cities and rural areas. Operationalisation of this goal resulted in sub-goals, which include the development and implementation of knowledge and innovation, the stimulation and

facilitation of entrepreneurship, the stimulation of cluster forming within high potential sectors and the development and extension of the knowledge infrastructure. Sectors with growth potential are: energy, water technology, (multi-)sensor technology, life sciences, chemistry, agribusiness, tourism, commercial care and metal/marine engineering. In general, the program focuses on combining and implementing the regions own qualities by stimulating and supporting cooperation and network- and cluster- forming. Moreover, SMEs within the region are stimulated and supported to valorize the knowledge from knowledge institutions.

The region will invest in bringing together the supply and demand of knowledge and

transferring knowledge to SMEs. Knowledge transfer and circulation is central and promoted

16 through collaborative initiatives as well as intermediaries, which also facilitate demand

articulation by SMEs (SNN, 2007B).

2.1.6 Knowledge institutions. The Northern part of the Netherlands has a reasonable amount of educational institutions. The Rijksuniversiteit Groningen (RUG), Hanzehogeschool, Stenden, Noordelijke Hogeschool Leeuwarden, Van Hall Institute and smaller educational institutions within the region educate 60.000 students (SNN, 2007B). Moreover, within the clusters of energy, water and sensor technology, there are also various large research institutions active, which have the capabilities to compete on an international level (SNN, 2007B). Knowledge institutions that contribute to the energy sector are, for example, Energieonderzoekcentrum Nederland, Kenniscentrum Windturbine Materials and Contructions Wieringerwerf, Proefboerderij Nij Bosma Zathe, Energie Kennis Centrum, Centrum voor decentrale energie, Energy Delta Research Centre, TNO Groningen, Proces Groningen, Energy Delta Institute, KEMA Groningen, Hogeschool Van Hall Larenstein, Noordelijke Hogeschool Leeuwarden, Hogeschool Stenden Emmen and Hanze Institute of Technology (Energy Valley, 2010).

2.1.7 Companies. Within the three Northern provinces there are 125,700 companies (2010) operative, this accounts for 9.1% of the total number of businesses in the Netherlands (Kvk, 2010). The region offers 650,000 jobs, of which 75% is provided by SMEs, making up for 9%

of the national employment (SNN, 2007A; SNN, 2007B). Also, the share of industrial high- tech and medium tech job in the total employment is average or low and only high or extremely high in a few municipalities (SNN, 2007A). The gross regional product per

inhabitant within the region is 20% below the national average and the disposable income per inhabitant is almost 10% below the national average (SNN, 2007B). The three provinces are limited in their contribution to the gross domestic product and are among the least productive regions. Nevertheless, the economic growth of the region is higher than the national average (SNN, 2007B). The four major cities are most important in economic development. The agriculture, industry and non-commercial services are highly represented within the Northern part of the Netherlands, but the commercial service sector is falling behind (SNN, 2007B).

Finally, from the total number of Northern companies with ten or more employees 55.6%

could be called innovative in 2006. This is below the Dutch average of 56.3% and in the

bottom three when compared to the other regions (percentage innovative companies per

17 region: Northern wing 56,1%, Southern wing 54,8%, East 56.8%, South West 55.2%, South East 58,8%) (Copinga, de Jong, 2010).

2.1.8 Intermediaries. Several intermediaries operate within the Northern part of the

Netherlands. Some examples of organizations that bring together disconnected local parties in order to valorize knowledge and innovate are Technologie Centrum Noord Nederland

(TCNN), Syntens, Investerings- en Ontwikkelingsmaatschappij voor Noord-Nederland (NOM), branch organizations, the Chambre of Commerce and Liaison offices of the universities and colleges. On top of that there are also intermediaries that focus on one specific cluster or sector; some examples are Energy valley, Friese Water Alliantie and Wetsus. Nevertheless, general numbers on how many of this kind of intermediaries exist and how they perform could not be found.

2.1.9 Knowledge potential of people. At the moment, the Northern part of the Netherlands houses 1,713,954 people (CBS, 2010B). Altogether, the three Northern provinces provide 60.000 students with higher vocational and university education. Nevertheless, compared to the rest of the Netherlands the labor force has a lower educational level because of a brain- drain. On top of that, the region suffers from an aging population, resulting in little supply of employees on the labor market and a greater demand for facilities. This also shows in the low percentage of highly educated employees within the region (23% in 2005) compared to the national average (30.8% in 2005) (Louter, Eikeren, 2008). Also, the unemployment rates are above the Dutch average and the degree of workforce participation is below average (SNN, 2007B). Finally, from the companies within the region that wanted to innovate in 2006, 58.2% indicated that a bottleneck to innovation was the lack of qualified personnel (against 60% nationally) (Copinga, de Jong, 2010).

2.2 Triple Helix

The model that explains the underlying structure and dynamics of the previously described

NIS is called triple helix (Leydesdorff, Zawdie, 2010). In case of this research, triple helix

specifically refers to ‘the dynamics of reciprocal university-industry-government relations in

the commercialization of new knowledge’ (Suvinen, Konttinen, Nieminen, 2010: 1366). Or

also, the way in which the government, knowledge institutions and SMEs, interact in order to

valorize knowledge. The following numbers give an idea of interaction among these actors

within the Northern part of the Netherlands. Namely, in 2006 8.5% of all companies with ten

18 or more employees cooperated with other companies or institutions (against 8.9% nationally) (Copinga, de Jong, 2010). Furthermore, the lack of cooperation was identified as a barrier to innovation by 35.4% of all the companies that wanted to innovate within this region

(compared to 35.3% nationally in 2006) (Copinga, de Jong, 2010). While interaction among the different spheres is crucial to knowledge-based development, removing the blockages to interaction clears the path to sustainable development (Dzisah, Etzkowitz, 2008:102). One way to remove these blockages and support the commercialization of science-based

innovation is by employing intermediaries (Johnson, 2008; Suvinen, Konttinen, Nieminen, 2010; Yuwawutto, Smitinont, Charoenanong, Yokakul, Chatratana, Zawdie, 2010) or bridging organizations (Johnson, 2008; Suvinen, Konttinen, Nieminen, 2010: 1366), such as

knowledge brokers.

2.2.1 Obstacles to interaction. In relation to this research, describing the obstacles SMEs experience when interacting with other triple helix partners, explains why they struggle to

‘access and make optimal use of external knowledge’ (Ranga, Miedema, Jorna, 2008:703) and, thus, could use the help of knowledge brokers. The obstacles are presented next. Firstly, obstacles to interaction between SMEs and the government within the Northern region of the Netherlands contain: poor communication, the perceived bureaucracy of institutions by SMEs, different time perspectives (short- vs. long term), cultural differences, attitude and language barriers, SMEs lack of time or interest to invest, unawareness of the needs and interests of the SMEs by the institutions and unawareness of the SMEs towards the services of the institutions (Ranga, Miedema, Jorna, 2008:709). Secondly, obstacles that make it difficult for SMEs to collaborate with universities, within the Northern part of the Netherlands,

include: communication difficulties, image and attitude differences and language- and thinking level differences (Ranga, Miedema, Jorna, 2008:710). Finally, SMEs within the Northern part of the Netherlands say that ‘knowledge institutions do not understand SMEs procedures and activities’ (Ranga, Miedema, Jorna, 2008:710), which makes it difficult for them to form a realistic image of their collaboration. As well as that SMEs have trouble to make the first contact with suitable people, because of the different language- and time horizons. Moreover, contact with the government is mostly directed at getting to know one another instead of disseminating knowledge. Additionally, the specific expertise of the

government and knowledge institutions are not made clear to SMEs. Also, SMEs experienced

resistance and reluctance by the staff of universities in relation to the commercialization of

their knowledge (Ranga, Miedema, Jorna, 2008:710).

19 3. THEORETICAL FRAMEWORK

In order to answer the previously mentioned research questions, this chapter connects theory on knowledge brokering with that of tie strength. The first part of this chapter describes the definition, roles and process of knowledge brokering. The second part of this chapter describes tie strength, the strengths of both weak and strong ties and their importance. The third part defines the contribution to effectiveness and efficiency in terms of the KBP. Finally, the contribution of tie strength to the effectiveness and efficiency of the KBP is presented in the form of propositions and conceptual framework.

3.1 Knowledge Brokering

While so-called ‘invention factories’, like IDEO and Edison’s laboratory, solely produce new solutions and consult to others in various forms, they provide valuable insights into the innovation process (Hargadon, 1998). Likewise, they inspired Hargadon to come up with the concept of knowledge brokering. This part of the theoretical framework answers the first research question: ‘Which part of the knowledge brokering process is relevant to the tie between the knowledge broker and seeker?’. In order to do so, the definition and roles of knowledge brokers will be discussed, followed by a description of the KBP.

3.1.1 Definition. While knowledge brokering could be described as thinking in boxes others cannot see (Hargadon, 2002), a more complete definition is the following: knowledge

brokering entails intermediating between relatively disconnected pools of ideas to facilitate the recombination of knowledge, resulting in innovation (Bielak et al., 2008; Cillo, 2005;

Dobbins, Robeson,Ciliska, Hanna, Cameron, O’Mara, DeCorby, Mercer, 2009; Carnabuci, Bruggeman, 2009; Hargadon, 1998; Hargadon, Sutton, 2000; Hargadon, 2002; Kimble, Grenier, Goglio-Primard, 2010; Kammen et al., 2006; Kirkels, Duyster, 2010; Meyer, 2010;

Loew et al., 2004; Michaels, 2009; Sousa, 2008, Sheate, Partidário, 2009; Verona et al., 2006;

Ward et al., 2009). Naturally, the party that executes knowledge brokering is referred to as a knowledge broker. A knowledge broker can be an individual, group, organization, structure or even an entire country (Dobbins et al., 2009; Hargadon, 2002; Meyer, 2010; Sheate,

Partidário, 2009; Sousa, 2008) and works either for-profit or non-profit (Hargadon, 1998;

Krikels, 2010). Also, knowledge brokers can be hired by parties that are in need of knowledge from an unfamiliar field (Hargadon, Sutton, 2000) or work within the boundaries of an

organization (Cillo, 2005). During this research, the party in need of knowledge will be

20 referred to as the knowledge seeker and the parties providing the knowledge in order to come to a solution will be called knowledge sources (Sousa, 2008).

3.1.2 Role. To gain an understanding of how knowledge brokers position themselves this study turns to research by Gould and Fernandez (1989), who have identified five different roles in brokering.

Brokering within their study is defined as ‘a process by which intermediary actors facilitate

transactions between other actors lacking access or trust in one another’ (Gould, Fernandez, 1989:91).

Explaining the position of knowledge brokers in terms of these roles, leads to the following result.

Since knowledge brokers intermediate between two disconnected parties from different domains (Hargadon, 1998) they take on a Liaison role: ‘the broker is an outsider with respect to both the initiator of the brokerage relation and the receiver of the relation’ (Gould, Fernandez, 1989:93). Figure 2 shows the role and position of the knowledge broker.

FIGURE 2

Knowledge Brokering Liaison Role: the ellipses correspond with domain boundaries, the circles represent the different actors and the arrows the ties between them (adapted from Kirkels, 2010)

Knowledge Broker

Knowledge Seeker Knowledge Source

3.1.3 Process. The model that explains the underlying process of knowledge brokering has known many different forms. Its roots can be found in the model describing the process of technology brokering that consists of accessing, acquiring, storing and retrieving technologies (Hargadon, Sutton, 1997). This concept emphasizes on a brokering strategy that coordinates

‘ action or information between distant parties who have no immediate prospect for direct introduction or connection’ (Obstveld, 2005: 104). Also it is oriented towards a tertius iungens strategy of

‘facilitating, locating and even forging coordinated action between disparate network

members’ (Obstveld, 2005, 121). Eventually, the technology brokering model evolved into a more elaborate KBP model through support by research from different fields and multiple case studies. The different fields included: ‘historical studies of technology, social network analysis, organizational learning, innovation research, and cognitive psychology’ (Hargadon, 1998: 211). Furthermore, the case studies concerned companies like IDEO, Design

Continuum, Anderson Consulting, McKinsey & Company, Hewlett-Packard, Boeing

21 Company, Edison & Co.’s, Elmer Sperry and 3M (Hargadon, 1998; Hargadon, 2000). Much alike the technology brokering process, the KBP is iterative, not always linear and boundaries between the stages are sometimes difficult to distinguish (Hargadon, Sutton, 1997: 725). The most up to date version of this model describes the process of knowledge brokering to include an accessing, bridging, learning, linking and building stage (Hargadon, 2002). However, in order to answer the research question the different stages have been critically reviewed, evaluated for their relevance and scanned for general descriptions. This has resulted in the following findings and consequently adaptation of the KBP.

Firstly, when critically reviewing the different stages one cannot help noticing that two of them are actually not process stages. Namely, Hargadon (2002) describes that the accessing stage contains of a structural pre-condition and that the bridging stage describes the general strategy. Additionally, he mentions that ‘yet while such conditions explain the potential for innovation they are incomplete in describing the process that exploits these conditions’

(Hargadon, 2002: 57). Hence, this research will not include an accessing or bridging stage in its KBP. However, these stages will be explained at a later stage in order to show how tie strength enables knowledge brokering.

Secondly, Hargadon (2002) does not provide a clear overview of, when the knowledge broker, deals with what party, to execute which activity. In fact, many of the process stages include simultaneous involvement of knowledge sources, knowledge seekers, fellow knowledge brokers and relevant third parties. While this research is confined to the tie between the knowledge broker and seeker only the process stages and activities relevant to and of influence by this tie have been filtered out.

Thirdly, rather than a general description of how the stages could be executed, Hargadon (2002) mostly provides examples. Hence, this research has resorted to other scholars in order to describe the relevant activities making up the learning, linking and building process stages.

The KBP executed and/or influenced by the tie between the knowledge broker and seeker is

presented in figure 3, a more detailed explanation follows afterwards.

22 FIGURE 3

Knowledge brokering process between the knowledge broker and knowledge seeker (adapted from Hargadon, 2002)

The learning stage according to Hargadon (2002) ‘describes the activities that individuals engage in to bring knowledge from a particular domain into the organization’ (Hargadon, 2002:49). However, all but one of these activities contribute to future projects or are not relevant to the tie between the knowledge broker and seeker. Nevertheless, a relevant first step of a knowledge broker towards innovation is to pursue alternative definitions of the problem experienced by the knowledge seeker (Hargadon, 2002). It should be noted that this activity is particularly important, because no one will benefit from solving the wrong problem (Büyükdamgaci, 2003; Kilmann, Mitroff, 1979). As well as that, ‘how a problem is defined often determines what solutions are considered’. (Hargadon, 1998: 217). Furthermore, a problem can be defined as a ‘discrepancy between what is and what could or should be’

(Kilmann, Mitroff, 1979, 27) and thus can also include an opportunity. However, since

Hargadon (2002) has not described how to come to such an alternative problem definition this research has resorted to Büyükdamgaci (2003), who describes two major empirical studies by Mintzberg et al. (1976) and Lyles (1981) concerning the problem definition process in

organizations. In case of this research, the knowledge seeker has probably become aware or recognized the existence of a problem, experienced stimuli to take action and possibly defined the problem before approaching the knowledge broker for help. Nevertheless, once hired, the knowledge broker will define an alternative problem in concertation with the knowledge seeker in order to find a suitable solution. Hence, drawing from research by Büyükdamgaci (2003) the learning stage entails the following activities:

LEARNING STAGE:

Define alternative problem

• Gather information

• Form different views

• Come to

understanding of the problem

LINKING STAGE:

Innovate through analogical reasoning

• Gain deeper understanding of problem and context

• Search for analogies

• Evaluate and filter knowledge

• Translate and transfer knowledge

BUILDING STAGE:

Facilitate new tie formation

• Give advice on who to include

• Introduce parties to one another

• Facilitate the

connection through

building trust

23 - Gathering information;

- Form different views of the problem;

- Come to an understanding of the problem.

The linking stage ‘describes those activities of individuals and groups that lead them to recognize how past learning can apply to the current situation, getting at least some of the right knowledge into the right hands at the right time’ (Hargadon, 2002:63). The main activity described by Hargadon (2002) to result in recombination is analogical reasoning, which can be explained as ‘the transfer of knowledge from a base domain to a target domain as a function of correspondence between these two’ (Kalogerakis, Lüthje, Herstatt, 2010:418).

Even though Hargadon (2002) provided several examples of how companies have employed analogical reasoning to come to innovative solutions, he does not describe a systematic process. Hence, this research employs the A4 Innovation Process, by Gassman, Zeschky (2008) for product innovation by analogical reasoning, to describe the activities within the linking stage. Since knowledge brokering may result in all kinds of innovations the process has been adapted in a way so that it is more generally applicable. Also, only the activities that are relevant to the tie between the knowledge broker and seeker and/or could be influenced by it have been included. This has resulted in the following description of the linking stage:

- Gain a deep understanding of the problem and its context;

- Search for analogies;

- Evaluate and filter relevant knowledge;

- Translate and transfer the relevant knowledge.

The building stage of the KBP ‘describes the activities that individuals and teams use to construct new networks around those new combinations in order to ensure their success’

(Hargadon, 2002:69). Examples by Hargadon (2002) on possible ways to build network ties include bridging previously disconnected parties, building ties with new parties or path

creation. In case of this research, the goal of this stage is to facilitate the formation of new ties

between the knowledge seeker and third parties. Although Hargadon (2002) briefly mentions

some different activities knowledge brokers could engage in, he does not elaborate on how

they could facilitate the tie formation process. Thus, this research has resorted to other

scholars for a more general process. Analyzing and combining the work of Kelly, Peters and

O’Connor (2008), Elfring and Hulsink (2010) Hanna and Walsh (2002) and Perry (1996) on

24 network brokering/intermediating related topics has resulted in the following description of the building stage:

- Give advice on who to include;

- Introduce parties to each other;

- Facilitate the connection by building trust between them.

3.1.4 Accessing and bridging. Even though the accessing and bridging stage have been excluded from the KBP during this research, they do explain how knowledge brokering is enabled by tie strength. For this reason, these two stages are described next.

Firstly, the structural pre-conditions making up the accessing stage are the recombinant nature of innovation and the fragmented nature of social worlds (Hargadon, 2002). The recombinant nature of innovation refers to the fact that innovations are often the result of combining existing elements in new ways (Hargadon, 2002). Because the social world is fragmented in many isolated domains, existing elements in one domain often stay unknown to another (Hargadon, 2002:53). However, once transferred from one domain to another, existing elements could become new and valuable in combination with the elements of that domain.

Secondly, the bridging stage actually describes the general strategy used by knowledge brokers to exploit their structural position in between domains. Bridging disconnected domains increases the likelihood of recombinant innovation to occur, when the ideas in one domain are valuable but unknown to others (Hargadon, 2002; Hargadon, 2003). To overcome structural isolations knowledge brokers pursue many weak ties across multiple, relatively disconnected domains. The way in which knowledge brokers bridge domains is by moving resources between them (Hargadon, 2002).

Overall, tie strength contributes to creating the conditions that explain the potential for

innovation and enable knowledge brokers to exploit these. Namely, pursuing many weak ties

across relatively disconnected domains grants the knowledge brokers not only access to the

raw materials of innovation, it also brings them in the position to function as a bridge between

these relatively disconnected domains. However, ‘moving between many small worlds does

not ensure that an organization will recognize their resources nor recombine them in novel

ways’ (Hargadon, 2002: 57). Hence, in order to exploit the favorable conditions knowledge

brokers have to learn about the existing resources within the different domains, link that

25 knowledge to new situations in other domains and build new networks around the innovations that result from recombination (Hargadon, 2002). Nevertheless, while playing an important role during accessing and bridging, this research has been unable to identify scholars that have intended to relate tie strength to the learning, linking or building stage. Before this research will attempt to exactly do that, the remaining sub-research questions will be answered first.

3.2 Tie Strength

The importance of tie strength has been confirmed by several scholars in different fields, e.g.

in the field of entrepreneurship (Jack, 2005), knowledge transfer (Levin, Cross, 2004) and new product development (Oke, Idiagbon-Oke, Walumbwa, 2008). Similarly, Hagardon (2002) has also recognized the importance of tie strength in relation to the KBP’s pre- condition and strategy. In order to answer the second sub research question: ‘What are the relevant advantages of the various tie strengths between the knowledge broker and seeker?’

this chapter will first provide definitions of relevant terms, outline both the strengths of weak and strong ties and finally discuss a balanced view between the two.

3.2.1 Definition. The building blocks of a network are the individual actors within it, also called nodes, and the relationships between them, called ties (Dolfsma and Aalbers, 2008).

Ties between nodes can be added, upgraded or dropped (Elfring, Hulsink, 2007) and can be strong, weak or absent (Krackhardt, 1992). Like many other studies, this research will be based on the following definition of tie strength by Granovetter (1973:1361): ‘Tie strength is a (probably linear) combination of the amount of time, the emotional intensity, the intimacy (mutual confiding), and the reciprocal services which characterize the tie. Each of these is somewhat independent of the other, though the set is obviously highly intracorrelated’. The way in which tie strength is measured is mostly chosen by the scholar researching a specific network (Dolfsma, Aalbers, 2008; Evald, Klyver, Svendsen, 2006). Based upon the

organizational context of this research and evidence by an empirical study of Campbell and Marsden (1984), tie strength will be determined by frequency of contact and work-related closeness (Hansen, 1999; Levin and Cross, 2004). Although this operationalization may differ from that of the scholars referred to during this research, ‘there are no direct

contradictions in the different definitions, but they emphasis different elements’ (Evald et al.,

2006:11). Additionally, it should be noted that members of a dyad can experience differences

in tie strength, because some of its criteria are subjective (Fliaster, Spiess, 2008; Krackhart,

1990). Finally, while Granovetter (1973) questioned his own cut-off method to distinguish

26 between strong and weak ties, this research, much alike Capaldo’s (2007) and that of most scholars (Evald et al., 2006), operationalizes strong and weak ties as degrees of one another on a continuous basis. Moreover, since the KBP could not be executed if the tie between the knowledge broker and seeker was absent, the advantages of the various tie strengths is limited to weak and strong ties. Therefore, the strengths of weak and strong ties are discussed next.

3.2.2 Strength of weak ties. The concept of the ‘strength of weak ties’ comprises the

argument that weak ties are more likely to provide access to non-redundant information (Burt, 2000; Granovetter, 1973; Granovetter, 1983; Krackhardt, 1992; Levin, Cross, 2004; Tiwana, 2008). Namely, ‘those to whom we are weakly tied are more likely to move in circles

different from our own and will thus have access to information different from that which we receive’ (Granovetter, 1973:1371). Besides the original concept of the ‘strength of weak ties’, weak ties provide other advantages in certain circumstances. Table 1 provides an overview of the identified strengths of weak ties. By presenting these strengths, this research does not rule out that there could possibly be more strengths. Further explanations of the strengths of weak ties are provided afterwards.

TABLE 1 Strength of Weak Ties

Source(s) Strengths of weak ties

Burt, 2000; Granovetter, 1973; Granovetter, 1983; Krackhardt, 1992; Levin, Cross, 2004; Tiwana, 2008

Being more likely to provide access to non- redundant information

Granovetter, 1973; Granovetter, 1983;

Levin, Cross, 2004

Being better means for diffusion

Evald et al., 2006; Granovetter, 1983;

Hansen, 1999

Being more likely to serve as bridges and more cost-efficient bridges

Fliaster, Spiess, 2008; Hansen, 1999;

Reagans, Evily, 2003

Enable the transfer of simple knowledge and more cost-efficient in transferring

Fliaster, Spiess, 2008 Requiring less time investment and less engagement by parties

Firstly, weak ties are particularly suitable for diffusion purposes since they will ‘reach a larger

number of people and traverse greater social distance’ (Granovetter, 1973:1366; Granovetter,

1983; Levin, Cross, 2004 ). Diffusion, in this case, is ‘the process in which an innovation is

27 communicated through certain channels over time among the members of a social system’

(Rogers, 2003:5). Hence, reaching more and different people helps organizations and individuals getting their new ideas widely adopted.

Secondly, weak ties are more likely than strong ties to serve as bridges (Evald et al., 2006;

Granovetter, 1983) and are more cost-efficient bridges compared to the high investment cost necessary to build strong ties (Hansen, 1999). In brief, since bridging ties provide ‘the only path between two otherwise disconnected nodes’ (Bian, 1997: 368) they have the opportunity

‘to broker the flow of information between people, and control the projects that bring together people from opposite sides of the hole’ (Burt, 2000: 353). This is beneficial because the structural holes, also known as the weaker connections between two groups, separate sources of non-redundant information which are central to creativity and learning (Burt, 2000).

Thirdly, weak ties enable and are most cost-efficient in transferring simple (codified and independent) knowledge (Hansen, 1999; Reagans, Evily, 2003). This is because the transfer of explicit knowledge does not require frequent communication in order for the receiving party to understand it and the development and maintenance of a weak tie requires less time investment and engagement by the parties (Fliaster, Spiess, 2008). Hereby, the level of

codification concerns ‘the degree to which the knowledge is fully documented or expressed in writing at the time of transfer’ (Hansen, 1999:87) and independence relates to ‘the extent to which the knowledge to be transferred is independent or is an element of a set of

interdependent components’ (Hansen, 1999: 87).

Fourthly, the indirect transaction cost of developing and maintaining weak ties is significantly lower than that of strong ties since weak ties require ‘lower time investment and less

engagement of the parties’ (Fliaster, Spiess, 2008:111).

3.2.3 Strength of strong ties. Despite the fact that Granovetter (1973, 1983) does not

elaborate on the role of strong ties and their benefits in his studies, he does mention that they

are more motivated to provide help. Nowadays scholars regularly use the term ‘strength of

strong ties’ to describe these kinds of advantages (Evald at al., 2006). Table 2 presents an

overview of the identified strengths of strong ties. While many strengths are included, this

research does not rule out that there could be more strengths. Further explanations of the

strengths of strong ties are provided afterwards.

28 TABLE 2

Strengths of Strong Ties

Source(s) Strengths of strong ties

Granovetter, 1973; Granovetter, 1983; Krackhardt, 1992; Levin, Cross, 2004; Reagans, McEvily, 2003

Being more motivated to help

Fliaster, Spiess, 2008; Granovetter, 1983;

Krackhardt 1992; Levin, Cross, 2004; McFadyen, Semadeni, Canella, 2009

Being more easily accessible

Capaldo, 2007; Fliaster, Spiess, 2008; Tiwana, 2008; Uzzi, 1996

Enhancing cooperation

Dolfsma, Aalbers, 2008; Hansen, 1999; Jenssen, Koenig, 2002; Levin, Cross, 2004; Tiwana, 2008

Enhancing communication effectiveness

Capaldo, 2007; Dolfsma, Aalbers, 2008; Fliaster, Spiess, 2008; Hanssen, 1999; Jenssen, Koenig, 2002; Reagans, McEvily, 2003; Tiwana, 2008

Enabling the transfer of complex knowledge

Atterton, 2007; Dolfsma, Aalbers, 2008; Evald et al., 2006; Fliaster, Spiess, 2008; Jack, 2005;

Jenssen, Koenig, 2002; Rindfleish, Moorman, 2001; Shi, Adamic, Strauss, 2007

Enabling transfer of sensitive and/or valuable knowledge

Capaldo, 2007; Fliaster, Spiess, 2008; Reagans, McEvily, 2003; Tiwana, 2008

Lowering the risk of opportunistic behavior

Krackhardt, 1992 Reducing resistance

Atterton, 2007; Fliaster, Spiess, 2008, Lowering direct transaction costs Batterink, Wubben, Klerkx, Omta, 2010; Capaldo,

2008; Elfring, Hulsink, 2010; Jack, 2005; Ozcan, Eisenhardt, 2009, Vissa, Anand, 2006

Helping others to get introduced to relevant parties

Jack, 2005; Wong, Boh, 2010 Helping others to build a trustworthy reputation

Firstly, strong ties are more motivated to help (Granovetter, 1973; Granovetter, 1983; Levin,

Cross, 2004; Krackhardt, 1992) because ‘motivation may stem from social considerations,

such as the desire to reciprocate or even it may be rooted in psychological considerations such

as the desire to maintain balanced relationships’ (Reagans, McEvily, 2003:244).

29 Secondly, strong ties are more easily accessible (Fliaster, Spiess, 2008; Krackhardt, 1992;

Levin, Cross, 2004; McFayden, Semadeni, Canella, 2009) because strong ties are ‘more easily called on and willing to help however limited the information they could provide’

(Granovetter, 1983: 211).

Thirdly, strong ties enhance cooperation (Fliaster, Spiess, 2008) because of several reasons.

Namely, due to participants trustworthiness’ cooperation through strong ties can reach beyond contracts, are more open-ended and effective (Capaldo, 2007). Also, cooperation is enhanced since strong ties are no longer solely based upon economic returns and exceed those of selfish behavior (Tiwana, 2008:257; Uzzi, 1996:681,682).

Fourthly, communication effectiveness is enhanced through a strong tie since they develop a shared context (Fliaster, Spiess, 2008:109), a common language ( Levin, Cross, 2004; Tiwana, 2008 ) and because they allow for two-way communication (Hansen, 1999). Overall, one could say that knowledge communicated through a strong tie is more likely to be understood by the receiving party (Dolfsma, Aalbers, 2008: 395 ; Fliaster, Spiess, 2008; Levin, Cross, 2004 ).

Fifthly, strong ties enable the transfer of all kinds of knowledge, but are most efficient in transferring complex (tacit and dependent) knowledge (Dolfsma, Aalbers, 2008; Hansen, 1999; Reagans, McEvily, 2003:262). An explanation as to why s trong ties are more capable of transferring complex knowledge is because this requires some shared context and mutual knowledge as developed through strong ties (Fliaster, Spiess, 2008; Reagans, McEvily, 2003;

Tiwana, 2008). Also, transferring complex knowledge requires frequent interaction, which is one of the characteristics of strong ties ( Fliaster, Spiess, 2008 ; Hansen, 1999; Jenssen, Koenig, 2002;

Reagans, McEvily, 2003). Furthermore, while the transfer of complex knowledge is time intensive and not effortless, it is important that both parties are motivated to help, which is yet another

characteristic of strong ties ( Fliaster, Spiess, 2008 ; Reagans, McEviliy, 2003). Additionally, trust as

a characteristic of strong ties will further eases the transfer of knowledge since opportunistic behavior

is less likely to occur (Reagans, McEvily, 2003). Finally, strong ties even have a cost lowering

effect when knowledge needs to be transferred on a frequent and intensive basis (Fliaster,

Spiess, 2008). Remarkably, a strong tie across a structural hole could even ease the knowledge

transfer between the disconnected parties (Reagans, McEviliy, 2003).

30 Sixthly, an important attribute and resultant of a strong tie is trust (Jack, 2005; Oke, Idiagbon- Oke, Walumbwa 2008; Levin and Cross, 2004; Rowley, Behrens, Krackhardt, 2000) . E nhanced trust between strongly tied nodes leads to certain benefits. Firstly, because the transfer of sensitive and/or valuable knowledge could make the sender vulnerable, this type of knowledge is mostly transferred through strong ties who are trusting ( Atterton, 2007; Dolfsma, Aalbers, 2008; Evald et al., 2006;

Fliaster, Spiess, 2008; Jack, 2005; Jenssen, Koenig, 2002; Rindfleish, Moorman, 2001; Shi, Adamic, Strauss, 2007). Secondly, trust between two parties connected through a strong tie lowers the risk of opportunistic behavior ( Capaldo, 2007; Fliaster, Spiess, 2008; Reagans, McEvily, 2003; Tiwana, 2008). In other words, it is more likely that parties will behave in their partner’s best interest through strong ties. Thirdly, change can best be facilitated by strong ties because their base of trust can provide comfort and reduce resistance during times of uncertainty

(Krackhardt, 1992, Granovetter, 1983 ). Finally, direct transaction costs may be lowered due to having a trusting relationship ( Atterton, 2007; Fliaster, Spiess, 2008).

Seventhly, strong tie connections can introduce actors to relevant parties (Elfring, Hulsink, 2010; Jack, 2005). Ozcan and Eisenhardt (2009), for example, state that firms often use existing (in-)direct ties, as stepping stones to form new ties. As well as that Vissa and Anand (2006) explain that strong ties are more motivated to provide referrals. Moreover, strong ties develop mutual knowledge and a deep understanding of each other’s cultural traits and long- term objectives over time, which enables them to select appropriate partners for further collaboration (Capaldo, 2008). Additionally, the connection between parties is facilitated more easily when an intermediary is strongly tied to both parties, because this provides the intermediary with access and credibility and reduces the concerns about the quality of the other party (Batterink et al. 2010; Capaldo, 2008; Elfring and Hulsink, 2010; Jack, 2005;

Ozcan, Eisenhardt, 2009, Vissa, Anand, 2006).

Finally, strong ties can become advocates who communicate positive information to their contacts and consequently enhance the actor’s personal and business reputation (Jack, 2005;

Wong, Boh, 2010). This is based upon the fact that past experiences, provide information on

expected behaviors in the future (Wong, Boh, 2010). For example, trust between two strongly

tied actors can be transferred to another actor, when the first two actors endorse each other’s

trustworthiness (Wong, Boh, 2010). Eventually, having a trustworthy reputation increases the

likelihood of the other actors’ willingness to cooperate (Wong, Boh, 2010).

31 3.2.4 Balanced view. Within different contexts researchers have found support to back up the importance of both weak and strong ties. One could even speak of ‘the battle between strong and weak ties’ (Evald et al., 2006: 8), aiming at the disagreement existing within research on the importance of strong and weak ties. Nevertheless, research in general has started to shift towards a more balanced view of the importance of strong and weak ties (Evald et al., 2006).

Hansen (1999), for instance, identifies the importance of both weak and strong ties during different stages of the knowledge sharing process. Likewise, Evald at al. (2006), describe the importance of tie strength throughout the entrepreneurial process. Furthermore, Tiwana (2008) argues that both bridging and strong ties make up the ideal configuration of ties for innovation-seeking project alliances. Also Harryson, Dudkowski and Stern (2008) and

Harryson (2008) argue that balancing exploration and exploitation requires the transformation from weak to strong ties. Finally, Kleinbaum and Tushman (2007) describe the importance of (bridging) weak and strong ties in relation to the need of businesses to balance their

exploration and exploitation.

Evidently, both strong and weak ties, either separately or combined, can play an important role during different stages of one and the same process. The way in which parties are able to have different tie strengths during the course of one process is either simply due to the passing of time or by being involved with different parties simultaneously (Evald at al., 2006; Hansen, 1999; Kleinbaum, Tushman 2007; Tiwana, 2008). Applying this more balanced view to the research topic it becomes even more obvious that the theory is incomplete.

3.3 Effectiveness and Efficiency

This part of the theoretical framework intends to answer the third sub-research question:

‘What does contributing to the effectiveness and efficiency of the knowledge brokering process entail?’. While one could contribute to the KBP’s effectiveness, efficiency, both or none (Ostroff, Smitt, 1993), their definitions in relation to this research are described next.

3.3.1 Effectiveness. According to Ostroff and Smitt (1993:1345) effectiveness ‘refers to an absolute level of either input acquisition or outcome attainment’ in the context of

organizational performance. Pérez-Nordtvedt, Kedia, Datta and Rasheed (2008:717) described effectiveness as ‘the degree to which goals are attained’ in the context of cross border

knowledge transfer. Golany and Tamir (1995: 1172) define effectiveness as ‘the observed