How can universities of applied science organize
their departments in order to intensify and facilitate
cooperation with the industry.
Executive Program in Management Studies: Strategy track Student: Elga van der Spoel - 10687823
Supervisor: Dr. Ir. Jeroen Kraaijenbrink Date: 31-03-2016
II
Statement of Originality
This document is written by Student Elga van der Spoel, who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
III Acknowledgements
I have enjoyed the inspirational contacts with the lecturers and my fellow students over the past 2.5 years. During the final phase of writing this thesis various people supported me and I would like to express my gratitude for this. First of all, I would like to sincerely thank Dr. Ir. J. Kraaijenbrink for his guidance and for keeping me sharp by being critical, from my initial proposal until the final version of this thesis. He gave me useful insights, critical and
constructive feedback in a supportive way.
Furthermore, I would like to thank my employer, Stenden Hogeschool, for making it possible to do this study and not to mention, my colleagues and the management team, for your interest in my study and research. And a special thank you to all the respondents of the various universities of applied science for enthusiastically participating in this research and devoting their precious time.
Last but not least, I would like to thank my family for their love, support, interest and patience and just being there for me. Stefan, Kirsten and Lennart many thanks!
IV Table of contents 1. Introduction ... 2 2. Theoretical framework ... 5 2.1 Cooperation ... 6 2.1.1 University-Industry cooperation ... 7
2.1.2 Types of University-Industry cooperation ... 8
2.1.3 Factors facilitating or inhibiting university-industry cooperation ... 10
2.2 Organizational innovation ... 13
2.2.1 Type of innovation ... 15
2.2.2 Organizational structure and innovation... 20
2.2.3 Determinants of innovation ... 24 2.3 Conclusion ... 26 2.4 Conceptual model ... 27 3. Methodology ... 27 3.1 Research method ... 27 3.2 Data collection ... 28 3.3 Data analysis ... 31
3.4 Reliability and validity ... 33
4. Results ... 34
4.1 Results single case analysis: Department 1 ... 34
4.2 Results single case analysis: Department 2 ... 42
4.3 Results single case analysis: Department 3 ... 51
4.4 Results cross-case analysis ... 59
5. Discussion ... 74
5.1 Theoretical contribution ... 76
5.2 Practical implications ... 79
5.3 Limitations & future research ... 80
V List of tables, figures and abbreviations
Table 1: Types of University-Industry cooperation Table 2: Overview barriers and facilitators of cooperation Table 3: Mechanistic versus organic structure
Table 4: Determinants of innovation Table 5: Overview respondents
Table 6: Scores determinants department 1 Table 7: Scores determinants department 2 Table 8: Scores determinants department 3 Table 9: Overview cross-case analysis Figure 1: Conceptual model
List of abbreviations
UAS: university of applied science U-I cooperation: university – industry cooperation HEI: higher educational institution
HE: higher education
MOOC massive online open course
D1: department 1
D2: department 2
Abstract
Universities of applied science (UAS) are increasingly becoming more important as knowledge partner, innovator and contributor to regional development. UASs educate students for the professional practice which is changing fast due to technological
developments, global competition and changing consumer behavior. Knowledge development and knowledge circulation is essential to keep the curricula up to date and to maintain the fit with the industry. Although cooperation is not a new phenomenon, studies reveal that
university-industry cooperation is still in the development phase and therefore changes in the department to overcome the barriers that hinder cooperation are necessary. The purpose of this study is to explore how organizational innovation in departments of universities of applied science contributes to facilitating or intensifying cooperation with industry. For this cross-sectional study, a qualitative approach was taken in the form of a multiple case study. After establishing the theoretical framework, data collection took place through
semi-structured interviews, document analysis and observation. Results show that in the case where cooperation with the industry is successful and embedded in the curricula, centralization is low, management has a stimulating role, the internal- and external communication contributes to idea and knowledge sharing and professionalism is high. Lowering horizontal and vertical boundaries contributed to cooperation and innovation. Findings have suggested that
coordination (account manager/ representative) is needed to further intensify cooperation with the industry and industry cooperation should be structurally embedded in the educational programs. However, further research is needed to determine how university-industry
cooperation in a multi-level and multi-disciplinary setting in sustainable ecosystems should be developed.
Keywords: Organizational innovation, determinants, cooperation, university-industry cooperation,
2
1. Introduction
Universities and industry have been cooperating for long time, and due to the rise of a global knowledge economy, the need for strategic partnerships has intensified. Knowledge sharing, entrepreneurship and innovation are high on national and international agendas, (Freeman, 2014; OECD, 2014; WRR, 2013) and are becoming an important issue for universities and Universities of Applied Science (UASs). Universities contribute to the global knowledge economy and are providers of both research and teaching. However, are the traditional
universities able to survive in turbulent times of fast changes, or do they have to reframe their scope? The concept of the triple helix of university- industry- government relationship, initiated by Etzkowitz & Leyersdorff (1995), interprets the shift from a dual university- industry relationship to a triangular relationship between university, industry and government in knowledge based societies. This triple helix concept can be seen as an engine for economic growth and innovation in knowledge economies (Etzkowitz & Leyersdorff, 2000) and a shift in university’s role in society are noticeable. Ranga & Etzkowitz (2013) emphasize the role of universities as provider of innovative skills, generator of new institutional and social formats and transfer and application of knowledge. Associated with this, universities can also be the bridge-builder between firms with little or no experience in innovation especially with regard to small and medium enterprises (SMEs), as they lack the financial resources and knowhow to invest in innovation (Jongbloed & van der Sijde, 2008; Ranga, Miedema, & Jorna, 2008). SMEs can be seen as the backbone of economic development on regional and even national level, and therefore access to knowhow and innovation opportunities is of vital importance.
According to the Adviesraad Wetenschap, Techniek & Innovatie (AWTI, 2015), UASs are increasingly becoming more important as knowledge partner for SMEs because UASs educate students for the professional practice which is changing fast as a consequence of technological
3 developments. Knowledge development and knowledge circulation with the professional practice is therefore essential to keep the curricula up to date and to maintain the fit with the industry. In his article, ‘The call for innovation in Business Education’, Freeman (2014) argues that business education has fallen behind in the age of information. Business schools were unable to adapt to the changing demands from the industry. Three trends that are shaping business education are highlighted; new technology, with the focus on online
components, globalization and changing demands on graduates by employers. Therefore, the urge to innovate and redesign the existing curricula of UASs is a necessary operation to make it 21st century proof. This calls for closer ties with the professional practice. UASs are
pressured, by the ministry of Education and the European Union, to increase cooperation (OECD, 2014; WRR, 2013; Ministerie van OCW, 2015; AWTI, 2014). Due to technological development, it is noticeable that the speed of innovation in the firms is much higher than universities can anticipate on. The ambition is that in 2025, universities and UAS participate in sustainable ecosystems together with vocational education institutions, research institutes, governments, industry, associations, local retailers, and many others (Ministerie van OCW, 2015).
Although numerous management studies, academic and empirical research provides clear overviews of different viewpoints of university – industry cooperation, the studied domain in the literature is primarily focused on the technological and scientific collaborations. Studies on cooperation in other domains are limited. Furthermore, various scholars emphasize the importance of removing or reducing the barriers that inhibit cooperation (Ranga, Miedema, & Jorna, 2008; Jongbloed & van der Sijde, 2008; Bruneel, Salter, & D'Este, 2010), whereas other studies highlight best practices for U-I collaboration, entrepreneurial universities (Etzkowitz & Leyersdorff, 2000), and benefits/ drawbacks of university research centers (Gray, Sundstrom, Tornatzky, & McGowen, 2011; Bozeman & Boardman, 2013; Deel, 2012)
4 Cooperation with the industry is not a new phenomenon. However, studies focusing on how UASs should change at operational level to facilitate cooperation with the industry are limited. But at this level, the actual changes take place and the innovation is noticeable. Industry involvement requires specific skills and organizational capabilities that are different from those necessary in the academic domain (Bercovitz & Feldman, 2008). Furthermore, as research on organizational-level factors mainly focused on technology transfer or licensing offices, the role of organizational support has been neglected (Perkmann, et al., 2013). Additionally, current research failed to explore whether engagement is associated with diversification of skills, roles, and organizational departments adaptation in dealing with academic and industry requirements (Perkmann, et al., 2013).
This study focused on the university-industry collaboration part of the triple helix concept and was approached from the university perspective. Universities of applied science were studied as specific research in this domain is limited. Furthermore, the focus lies on departments (divisions within a university comprising one subject area, or a number of related subject areas) of universities of applied science as they can be characterized as pluralistic
organizations with various departments, each having its own strategy, values and focus. Another reason why the point of view from the departments was chosen is that organizational members of the department are able to shed light on the determinants of organizational innovation, whereas the industry can only reflect on the cooperation part.The aim of this study is to find out how organizational innovation in departments of universities of applied science contributes to facilitating or intensifying cooperation with industry, as it is one of the main goals in the current publication of the Ministry of Education (Ministerie OCW, 2015).
This leads to the following research question: How can universities of applied science organize their departments in order to intensify and facilitate cooperation with the industry.
5 It is relevant to research this phenomenon because organizational innovation is a means of changing an organization, whether to react to changes in the external or internal environment or to influence the environment. As competition for enrollment of students is high, and disruptive changes due to the rise of Massive Online Open Courses (MOOC’s) are present. This research will expand the theory on organizational innovation in relation to U-I cooperation, specifically in relation to the organizational design of departments, the
organizational processes and structures to facilitate cooperation with the industry. Knowledge about these factors will help UAS’s shed light on the implications for the organizational design.
For this cross-sectional study, a qualitative approach was taken in the form of a multiple case study. After establishing the theoretical framework, data collection took place through semi-structured interviews, document analysis and observation. The results were analyzed using pattern matching. The results of the single case and cross-case analysis were assessed against the theory. Finally, the implications, limitations and suggestions for future research will be discussed.
2. Theoretical framework
In this chapter the concepts of organizational innovation and cooperation will be discussed in depth in order to create a theoretical framework for this research. First, cooperation and specifically university-industry cooperation will be discussed with the purpose of elucidating the motives, successes and hindrances of cooperation. Furthermore, the concept of
organizational innovation, types of innovation, organizational structure and the determinants of innovation will be examined in order to understand the importance of organizational innovation.
6 2.1 Cooperation
Cooperation between individual organizations is a common phenomenon and has received a lot of attention in management and organizational literature. However, it is difficult to define cooperation, as scholars have offered numerous definitions. Most definitions of cooperation focus on the process by which individuals, groups and firms interact and form relationships in order to benefit from each other (Smith, Carroll, & Ashford, 1995).
Cooperation or alliances can be viewed from the transaction cost perspective and the resource based view. In transaction cost economics, a firm wants to minimize transaction - and
production costs (Coase, 1937). Transaction costs are the costs that are incurred from
activities necessary for an exchange (e.g. writing a contract, negotiating) whereas production costs refer to the costs of coordinating activities within the firm (e.g. managing, organizing production). When transaction costs are high, firms are likely to internalize activities. However, when transaction costs are low and production costs are high, firms will use the market. Strategic alliances combine market exchange and internalization (Das & Teng, 2000).
The resource-based view suggests that a firm has a competitive advantage if its resources are valuable, rare, imperfectly imitable, and non-substitutable (Barney, 1991). So, trading of resources becomes a strategic necessity. However, some resources are difficult to trade, because they are embedded in an organization. Therefore, the resource-based view suggests that cooperation or formation of strategic alliances can be used as a strategy to access other firm’s resources, which were otherwise difficult to obtain through market exchange. This can result in a competitive advantage and value creation for the firm (Das & Teng, 2000).
There are a variety of reasons for firms to cooperate either to enhance market position (Ebers, 1997; Kaats, Klaveren, & Opheij, 2009; Hagedoorn, 1993), to share costs and risks
7 competitive position (Eisenhardt & Schoonhoven, 1996; Ebers, 1997), and to gain access to and exchange resources, knowledge and skills (Eisenhardt & Schoonhoven, 1996; Das & Teng, 2000; Ebers, 1997; Gulati, 1998). It can be concluded that various reasons can be distinguished in the literature to establish why firms engage in cooperation.
2.1.1 University-Industry cooperation
Higher educational institutions interact on various levels (local, regional, national or
international) with stakeholders and communities and each of these stakeholders has different needs and demands from the institutions (Jongbloed, Enders, & Salerno, 2008).
According to different scholars, these interactions are beneficial because they stimulate (regional) economic development and knowledge- and technology transfer (OECD, 2014; WRR, 2013; Jongbloed, Enders, & Salerno, 2008; Etzkowitz & Leyersdorff, 1995). The idea of the triple helix concept of university-industry-government, initiated by Etzkowitz & Leyersdorff (1995) emphasizes how the links between the three spheres can drive innovation and create spin-offs and incubators.
As stated in the report of the WRR (2013), internationalization and innovation are key issues to increase the Dutch economy. Knowledge of these issues and circulation thereof are of major importance to enhance the knowledge economy (Vereniging Hogescholen, 2014). The development of knowledge is important, but even more, the application of ideas and
techniques of other industries, businesses or countries. From the knowledge-based view, knowledge can be seen as one of the resources which can give a firm competitive advantage, as knowledge is difficult to imitate, socially complex and heterogeneous (Grant, 1996). And in today’s knowledge-based society, this is becoming more important.
Especially in high-tech clusters, or hotspots, universities are seen as an important source of knowledge. Some leading universities like Stanford in Silicon Valley, MIT in Boston and
8 TUE in Eindhoven, play an important role in these regions and their development. This again emphasizes the role of higher education (HE) as an important driver of the knowledge-based economy and an important contributor for society.
Cooperation can take place on various levels in a university, ranging from institutional level, to individual academics that interact with industry for different reasons. D’Este & Perkmann (2010) identified four main motives why academics engage with industry which are mostly related to further their own research either through learning (e.g. gaining new insights, accessing new knowledge) or through access to funds and other resources (e.g. new
technology). However, academics’ motives to engage in joint research or contract research are purely research driven and commercialization plays no role. Siegel et al. (2003) add that academics also value recognition of the scientific community as motive because this leads to presentations at conferences and publications in high-rated journals which can result in access to finances for either personal gain or funding for students. From the perspective of the university, the motives to interact with the industry deal with the protection of intellectual property and securing additional funding (Siegel et al, 2003).
2.1.2 Types of University-Industry cooperation
Knowledge transfer can take many forms, e.g. contracting work, consulting, or informal relations, and is likely to be transferred by the knowledge creator (Ponomariov & Boardman, 2012). It is noticed that the type of knowledge exchange differs (e.g. degree of complexity) and therefore also requires a different type of relationship. Furthermore, Ponomariov & Boardman (2012) argue that the transformation of knowledge is dynamic and social and is carried out through personal interactions and communication between individuals.
Universities and industry have mutual interests to cooperate in the transfer of knowledge because universities want to engage in innovative research and valorize their knowledge and organizations want to use this knowledge for market/product innovations.
9 There are many different types of U-I collaboration and the focus of these types of
interactions can vary, depending on the objective of specified type.
These can range from passive mechanism (e.g. scientific publishing), to informal interactions and staff mobility. Ponomariov & Boardman (2012), focus on three underlying characteristics of the knowledge transfer channels in U-I cooperation. First, they address knowledge
transformation and relational intensity, which implies that the use of a knowledge transformation channel depends on personal involvement. The next characteristic is the relative significance of the knowledge transfer channels and goal congruence, and finally the degree of knowledge finalization, which refers to the realization of the knowledge transfer via the appropriate channel. In table 1, the eight most common types of U-I cooperation are explained.
Type of cooperation Explanation
Collaboration in R&D Cooperation including joint R&D activities, contract research, R&D consulting, cooperation in innovation, informal and personal networks, joint publications with firm scientists/researchers, joint supervision of theses with firm scientists/researchers in cooperation with business and student projects in cooperation with business. Academic mobility Temporary or permanent movement of teaching staff or researchers
from HEIs to business; and employees, managers and researchers from business to HEIs.
Student Mobility Temporary or permanent movement of students from HEIs to business.
Commercialization of R&D results Commercialization of scientific R&D results with business through spin-offs, disclosures of inventions, patenting or licenses.
Curriculum development and delivery The process of creating a learning environment and the development of human resources relevant to modern society with members of the business community. This includes the development programs or planned experiences.
Life-long Learning Refers to all learning activities undertaken throughout life through a HEI, whether formal or informal.
Entrepreneurship Actions involving HEIs towards the creation of new ventures or developing and innovative culture within the HEI in cooperation with business.
Governance Cooperation between HEI and business at a management level of the HEI or firm.
Table 1: Types of U-I cooperation ( Science-to-Business Marketing Research Centre 2011, p. 43)
As the research of the Science of Business Marketing Research Centre (2011) has shown, U-I cooperation in Europe is still in the development stage, but there are exceptions. Cooperation
10 is mainly initiated on personal, individual level. Personal relations increase communication and trust, which are beneficial for long-term relationships.
A study by Bekkers & Bodas Freitas (2008) revealed that a discrepancy exists in the importance of the different type of channels between universities and industry. Firms value patenting and activities by technology transfer office as least important whereas participation in conferences, recruitment of students are valued as a better means to transfer knowledge. To better serve the industry, this might lead to a shift in focus on other channels, but universities have to find ways to stimulate this transformation, as academic publishing is still one of the top priorities.
2.1.3 Factors facilitating or inhibiting university-industry cooperation
However, for most universities cooperation with industry is not business as usual. Most European academics do not engage with industry and a just a few to a high degree. Although U-I is stimulated by politics, there are still barriers which hinder this cooperation. Based on the studies of Bruneel, Salter & D’Este (2010), D’Este & Perkmann (2010), Science|Business Innovation Board (2012), Ramli & Senin (2015), and Siegel et al (2003) different factors which hinder or facilitate cooperation will be discussed.
Bruneel, Salter & D’Este (2010) identified two types of barriers; 1) orientation-related barriers, which refer to the orientation of both partners, and 2) transaction-related barriers, which are related to conflicts over IP and dealing with university administration. The
orientation-related barriers include cultural differences, and differences in mission, according to Ramli & Senin (2014) universities strive for knowledge acquisition whereas industry aims at making profit. As the missions are not aligned, this can lead to conflicts (Siegel et al, 2003; Bruneel, Salter & D’Este, 2010). Furthermore, time limitation can also hinder the cooperation because academics have to focus on students, teaching and administrative tasks and research has most of the time a long horizon.
11 As opposed to the industry, where the focus is on short-term results in order to compete in the market and gain a competitive advantage (Ramli & Senin, 2015).
Resource- or transaction related barriers include the limitation of funding, human resources and infrastructure and processes. As universities are mostly funded through governments and funding from industry is relatively small, it is difficult for academics to engage in applied research as universities focus on funding fundamental research. Furthermore, academics avoid involvement with the industry as it might restrain their academic freedom; it might threaten their creativity as applied research is aimed at commercial gain. Also, the lack of
commercializing ideas might be due to the fact that in the academic world publishing is still valued higher than contract research. The amount of publications in high-rated journals plays an important role in university rankings, and therefore receives more attention of academics due to pressure of the university (D'Este & Perkmann, 2010).
Another factor which hinders the cooperation is the administrative organization of the university as discussed by Ramli & Senin (2014), Science|Business Innovation Board (2012) Siegel et al. (2003), Bruneel, Salter & D’Este (2010). These scholars mention that
bureaucracy, inflexibility of systems and procedures restrict cooperation with industry. Universities are risk-averse and are unable to respond to changes in the competitive environment fast, which can frustrate the cooperation partner. Finally, ineffective management, and a lack of coordination can also be seen as a factor influencing the U-I cooperation.
Next to the barriers that hinder cooperation, different studies focus on, or highlight the success factors of U-I cooperation (Pertuzé, Calder, Greitzer, & Lucas, 2010;
Science|Business Innovation Board, 2012; Baycan & Stough, 2012). The orientation- and resource-related barriers mentioned in the previous part can be reduces through trust,
12 experience, effective communication and resources (Ramli & Senin, 2015; Tartari, Salter, & D'Este, 2012; Bruneel, Salter, & D'Este, 2010).
Personal trust and trust-based relationships between academics and industry partners are very important for efficient knowledge transfer activities and to make mutual commitments to the cooperation. Furthermore, trust enables both parties to talk freely and feel that they receive a fair treatment in case problems arise. So in this sense, it helps to overcome opportunistic behavior of one of the parties to take advantage of the cooperation (Tartari, Salter, & D'Este, 2012; Bruneel, Salter, & D'Este, 2010). Through cooperation, knowledge and information will be shared, and if this knowledge is tacit, high level of trust between partners facilitate the transfer of this knowledge. Therefore, trust may help to lower the barriers to collaboration.
Academics who have prior research experience with industry will perceive fewer barriers to cooperation. This is because the academic knows he can learn from the cooperation and develop routines and practices which can be used again in subsequent cooperation (Bruneel, Salter, & D'Este, 2010; Tartari, Salter, & D'Este, 2012).
Furthermore, the relational capital of the academic, as discussed by Ponomariov & Boardman (2012) positively influences the extent to which an academic engages in knowledge transfer. As people determine the success of a cooperation, experienced academics are able to cross organizational boundaries and understand what is needed (Science|Business Innovation Board, 2012). So, personal experience of academics in engaging with industry can lower the transaction-based barriers.
Another factor to improve the success of cooperation is effective communication between the partners. Open and transparent two-way communication should be encouraged to prevent problems to arise. Therefore, requirements, objectives and benefits of the cooperation should be discussed regularly as transparent and effective communication can solve problems, which
13 in turn increases trust (Ramli & Senin, 2015; Pertuzé, Calder, Greitzer, & Lucas, 2010;
Science|Business Innovation Board, 2012). If these factors are not managed and taken care, they can turn into barriers which hinder cooperation. In table 2, the barriers and facilitators are summarized.
Barriers Facilitators
Differences in culture between U and I
Differences in mission between U and I
Lack of time
Limited funding
Lack of commercialization
Restraining of academic freedom
Administrative organization, bureaucracy, inflexibility
University risk-averse
Universities unable to respond to changes fast
Ineffective management in universities
Lack of coordination
Personal trust, trust based relations
Experience of the academics
Effective communication
Sufficient resources
Mutual commitment
Table 2: Overview barriers and facilitators of cooperation
2.2 Organizational innovation
Studied by scholars from a variety of academic disciplines and from various perspectives, innovation can be seen as a complex construct. At the organizational level, researchers have defined “innovation” as the development of a new product or service, a new technology or process, a new structure or system, or program, which is new to the adopting organization (Damanpour, 1991; Crossan & Apaydin, 2010; Daft, 1978). Innovation can be seen as one of the most important challenges an organization faces in the 21st century. Stable external environments have changed into more dynamic and turbulent ones and because of increasing competition, new technological developments, rapidly changing markets and changing
consumer demands, organizations are forced to innovate faster (Volberda, Jansen, Tempelaar, & Heij, 2011). Due to the dynamism in the environment, current products and services
14 organizational level, team or individual level but also on governmental level, where
innovation is seen as a catalyst for economic growth, creator of employment opportunities and trade.
Organizations adopt innovation to respond to the aforementioned changes and serve as a means for change in order to achieve the organization’s goal. According to scholars, innovation is considered as a source of competitive advantage and innovation capability is seen as the most important determinant of firm performance (Crossan & Apaydin, 2010). Schumpeter, in the 1920’s, was the first who defined innovation as something new.
The term ‘organizational innovation’ refers to the creation or adoption of an idea, system, program, product or service that is new to the organization (Damanpour, 1991; Daft, 1978). The adoption of innovation should result in improving the performance or efficiency of an organization adopting the innovation. Organizational innovation is a means of changing an organization, whether to react to changes in the external or internal environment or to influence the environment. Furthermore, as Hage (1999) argues, besides the new products, services or technologies, the study of organizational innovation advanced other disciplines as science, the military and higher education. Also businesses have started to realize the
importance of innovation to survive in a worldwide competitive environment.
The changes in the external environment also affect UASs. In their report, ‘an avalanche is coming’ of Barber et al., (2013), the authors state that higher education faces enormous challenges as the pressure of competition on universities is great, not only because of the global competition between universities, but also because of the rise of new players like massive open online courses (MOOC’s) offered by Coursera, edX, Stanford online etc.
Although online and distance learning are not new, the Open Universiteit in the Netherlands is an example of this, the quality of the MOOC’s have improved through technology and design.
15 At national level, governments can be directive in influencing universities, as universities have to meet the requirements they have agreed upon. This can be a constraining factor for higher educational institutions to adapt to change. Furthermore, technology and globalization transform the global economy and the pace of innovation is accelerating (Clark, 1998; Christensen & Eyring, 2011). As Christensen & Eyring (2011) state, universities have to rethink their educational programs and business models as changes will affect future employment opportunities and therefore required skills and knowledge.
2.2.1 Type of innovation
Innovation can be demonstrated in a variety of ways, and therefore it can be divided into different types. According to Daft (1978), organizational innovation can be divided into administrative innovation or technological innovation. Administrative innovations refers to the internal mechanisms of an organization and relate to the social structure, including innovation in organizational structure, allocation of resources, policies of recruitment, the structuring of tasks, authority and rewards. It is also related to the members of the
organization and their relationship among them and their communication with each other and their external environment. An administrative innovation does not lead to new products or services, but it is likely to influence it. Technological innovation focuses on the end-user to provide new products, processes or services and will usually be related to technology (Daft, 1978). They mostly occur in the operating part of the organization and affect the technical system. A technological innovation can be described as a new idea leading to new products/ services, or the introduction of new elements to enhance or facilitate the operating process. In his study among school organizations in the US, Daft (1978) identified that majority of
innovations were technical innovations, meaning changes in the educational context, method, the development of new courses and adjustments in the curricula. Damanpour et al. (2009) support the view of Daft, and mention research on innovation types mainly focused on
16 technological innovations in the goods industry and that research on administrative innovation has been scarce. As argued by Damanpour et al. (2009), technological innovations can only be implemented successfully if the administrative part of the organization is open to new ideas and adoption is facilitated.
When looking at the extent of an innovation, scholars also tend to distinguish between radical and incremental innovation. Radical innovation brings fundamental changes to the
organization and has a significant impact on the activities of the organization. Innovation can change existing systems and structures and make existing products and services obsolete. Contrary, incremental innovation is associated with minor adjustments and variations in existing routines and practices (Damanpour, 1991; Crossan & Apaydin, 2010). As incremental innovation uses existing knowledge to create improvements to products,
processes and services, radical innovations requires new knowledge to create completely new products of services or make fundamental changes in existing ones. Furthermore, radical innovations are riskier than incremental innovations. Radical innovations can create new markets and solutions to existing problems and can lead to a new way of working within organizations or industries, mobile internet and 3-D printing are a few examples of this (McKinsey Global Institute, 2013). Related to universities, Christensen & Eyring (2011) argue that universities need to change their DNA if they want to survive in the future, because online technologies are disrupting traditional universities. This is in line with Barber et al., (2013) who also indicate, that universities are forced to make changes in order to survive due to several external challenges.
As dynamic environments are characterized by changes in technologies, changes in consumer behavior and preferences and market opportunities, this can result in the obsolescence of products and services. Jansen et al., (2006) distinguish between exploratory and exploitative innovation, where exploratory innovation refers to organizations pursuing new knowledge and
17 develop new products and services for new or emerging markets or customers. On the other hand, organizations pursuing exploitative innovation, they build upon existing knowledge and try to extend their current assortment of products and services for already existing markets or customers. Jansen et al., (2006) support the view of Tushman & O’Reilly (1996) that
organizations should simultaneously develop exploratory and exploitative innovation which will lead to ambidextrous organizations. It leads to organizations that, in periods of relative stability, incrementally innovate and sometimes operate in an environment characterized by radical changes. This means that the management of organizations needs to be aware that they have to destroy something that has been created to make new organizations (Tushman & O'Reilly, 1996). This is also relevant for universities as they are entering a difficult century, facing an overload of demands put on them; governments reducing full covering of costs, treat of new entrants disrupting the market with different business models, adapting and creating new structures and programs (Clark, 1998; Barber, 2013; Christensen & Eyring, 2011). However, as discussed by Clark (1998), universities have shown their existence because throughout centuries they have managed to overcome various difficulties and adapted to every new situation they have faced, and transformed themselves to adaptable organizations.
As for the most of the twentieth century, the success of organizations could be attributed to the economies of scale, role clarity, control and specialization (Ashkenas, 1999). Larger organizations were able to attain more efficiency, influence its suppliers and attract more capital. As a result, a clear division of tasks and authority increased the efficiency and created specialties leading to distinct functional departments. To meet the performance standards, a strong form of control was needed. However, in today’s dynamic environment, successful organizations should possess other characteristics like flexibility, speed, innovation and integration to respond to customer demands and change strategies (Ashkenas, 1999). This view is supported by Miles et al. (1997) who argue that in the latter part of the twentieth
18 century, with the rise of the network organization, the ability to respond to market demands quickly and efficiently extending the customization process in the entire value chain were the key contributions. Through the rise of the network form, organizations refocused on areas where knowledge and assets added the greatest value. This organizational form allowed value to be added across and within firms along the value chain. As organizational boundaries in this form may shift (vertically and horizontally), and become more permeable, this will ease the flow of information and sharing of ideas (Ashkenas, 1999, Miles et al. 1997). As Santos & Eisenhardt (2005) denote, the boundaries set by the organization can be framed by the sphere of influence. This entails that boundaries are not only used to protect existing (market) positions, but also to expand offensively into emerging markets. Furthermore, influence of organizational actors on other organizations is not only through ownership mechanisms that expand horizontal and vertical boundaries, but also through nonownership mechanisms, such as lobbying and friendship ties. If organizations have a central position in a network structure, their sphere of influence may be extended, which may lead to higher performance (Santos & Eisenhardt, 2005). Ashkenas (1999) denotes that organizations that have rigid top-down control are less likely to respond quickly to changing environments. Lowering horizontal boundaries will facilitate cooperation, communication and integration. Miles et al. (1997) introduce a cellular organization, which will fit the age of innovation. They describe the cellular form as a living, adaptive organization, consisting of cells (self-managing teams, autonomous business units) that can operate alone but interact with other cells to generate business itself but also for the overall organization. The independence and interdependence allow the organization to innovate as each cell must combine entrepreneurship,
self-organizing, and member ownership in mutual reinforcing ways (Miles et al., 1997). Cellular organizations add value ‘through its unique ability to create and utilize knowledge’ (Miles et al, 1997, p 16), whereas matrix- and divisionalized organizations have to create special
19 mechanisms (e.g. project groups) to generate and share knowledge. As the authors denote, implementing the cellular form is challenging, as investments, risk-taking and member ownership is required to make it successful. Therefore, this kind of organizational form is mostly associated with new, rapidly expanding businesses.
Another view on innovation research that has gained attention of several scholars in recent years is management innovation. However, the majority of the innovation research is related to the understanding how organizations can stimulate technical innovation and the meta-analysis of Crossan & Apaydin (2010) reveals that only 3% of innovation papers is related to management innovation (Volberda, Van den Bosch, & Heij, 2013). Technological innovations are aimed at changes in technology relating to an organization’s core activity (Daft, 1978; Volberda, Van den Bosch, & Heij, 2013) whereas management innovation is related to how management work is done and associated with changes in practices, processes, structures and techniques (Birkinshaw, Hamel, & Mol, Management Innovation, 2008). Volberda et al. (2013) argue that organizational innovation and management innovation appear to have some overlap, they are not identical. Organizational innovation has often a broader focus as changes can be administrative and technical (Daft, 1978; Damanpour, 1991; Crossan & Apaydin, 2010; Volberda, Van den Bosch, & Heij, 2013) whereas management innovation is more comprehensive because it relates to the way management work is accomplished. Birkinshaw et al. (2008, p. 825) define management innovation as: ‘the innovation and implementation of a management practice, process, structure or technique that is new to the state of the art and is intended to further organizational goals’ and focus on the operational level of the
organization as changes usually take place at this level and it is therefore easier to observe. As management innovation is mainly tacit, more diffuse and complex, it becomes more difficult to imitate, which can lead to a sustained competitive advantage for the organization (Vaccaro, Jansen, Van den Bosch, & Volberda, 2012; Birkinshaw & Mol, 2006).
20 In view of the complexity of management innovation several scholars underline the crucial role of leaders as change agents (Birkinshaw et al. 2008; Vaccaro et al. 2012). In the light of Birkinshaw et al. (2008), these change agents can be either external or internal change agents. The change agents are not necessarily the organizational leaders, but due to the prominent position of leaders, they affect the organizational conditions for management innovation to happen. Vaccaro et al. (2012) emphasize that leaders can influence management innovation as they can reduce uncertainty and complexity, to communicate transparently and support the change. In large organizations, it is more difficult to shift direction due to the bureaucracy and formalization because it requires more time. Therefore, as argued by Vaccaro et al. (2012), transformational leaders who inspire and motivate the team and individual, emphasize
creativity and innovation, empower employees, develop trust and build relationships can bring the organization to the next level in pursuing changes in managerial practices, processes and structures.
2.2.2 Organizational structure and innovation
The classical organizational design theory reasoned that there were universal forms for designing organizations and that there was only one best way to organize this (Lam, 2004). The bureaucratic model of Weber (1947, in Lam, 2004) can be seen as a classic model of organizational design and involves structuring an organization hierarchically, with clear authority, formal rules and procedures, and division of labor. However, the concept of division of labor is not new, as it was already introduced by Adam Smith in 1776 in his famous work of the pin factory. Another influential study in structuring organizations is work of Chandler (1962) on the multidivisional form, where the organization is divided in semi-autonomous units who can operate semi-autonomous and flexible and are responsible for realizing their own profits. The units are controlled by a parent who is also responsible for the strategy of the entire organization. The M-form has the advantages of economies of scale and
21 decentralization. However, the allocation of power and the levels of management can be seen as a disadvantage of the M-form. Chandler (1962) showed in his study how changes in strategy affected the structure an organization.
Burns & Stalker (1961) differentiate between mechanistic and organic organizations and demonstrate how changes in the external environment (technological and market) affect the organizational structure and innovation management. In their study on manufacturing firms, they found that firms could be grouped into one of the main types, mechanistic and organic, based on the classification of the (in)stability and (un)predictability of the environment. Mechanistic type of organizations have a more rigid and hierarchical structure and can be found in more stable and predictable environments. Organic type of organizations can be described as organizations having a more fluid set of arrangements and are able to adapt to rapidly changing environments which require emergent and innovative responses (Burns & Stalker, 1961). The characteristics of both polar types of organizations are listed in table 3.
Mechanistic Organic
Tasks broken down into specialized functionally differentiated duties. Individual has a specified task. Abstract nature of each task
Individuals contribute to the common task of the organization. Continual adjustment and re-definition of individual tasks through interaction with others Hierarchical structure of control and authority and
communication, many rules and procedures
Less hierarchy of control and authority, few rules, flexible
Decision-making centralized at the top of the organization.
Decision-making more participatory
Communication mostly vertical Communication lateral
Table 3: Mechanistic versus organic structure, based on Burns & Stalker (1961) and Lam (2004)
The work of Burns & Stalker is still relevant in today’s society because of the present-day challenges organizations are facing. As innovation becomes more important, this implies that organizations must move away from being mechanistic to become more organic to be able to respond to environmental changes. Universities are facing similar challenges as the external environment becomes more uncertain and is rapidly changing.
22 Another important contribution to the strand of literature on organizational structure is the work of Mintzberg (1979) who proposed a series of archetypes that provide the basic configurations of firms operating in different environments. Mintzberg states that organizations belong to a specific configuration based on the characteristics of the organization and the contingency factors associated with it, like size, age, type of
environment. This implies that successful organizations design their structure in accordance with their situation and vice versa. Therefore, effective structuring requires consistency of design parameters and contingency factors. Mintzberg distinguishes six basic configurations: simple structure, machine bureaucracy, professional bureaucracy, divisionalized form, adhocracy and missionary. The key features of the configuration vary, even as the innovative potential. The main issue is that bureaucratic structures function well in stable environments but the innovative power is relatively low and organizations have difficulties in dealing with changes. On the other hand, the more entrepreneurial forms, like the adhocracy, are more organic and flexible and therefore capable of dealing with the dynamism of the environment.
The organization configuration of universities is based on the professional bureaucracy of Mintzberg (1979). The procedures are formal and standardized; decisions are made according to standardized processes. The organization is vertically organized with a large and
autonomous operating core of high professionals, who exercise control over their own work. In the professional bureaucracy, organizational members work rather autonomously, relatively free of control of managers and are able to decide on work-related issues. As stated by
Mintzberg (1979), the power lies at the bottom of the hierarchy. The professional bureaucracy can be found in a stable but complex environment. Stability enables the organizational
members to apply standardized skills to work with a great deal of autonomy and complexity requires decentralization. Looking at the structure of universities, it can be classified as complex, with different levels of management (vertical differentiation), faculties (horizontal
23 differentiation), and support offices. The innovative potential of the professional bureaucracy can be classified as having individuals who are highly innovative within a specific domain. The educational level of the organizational members of a professional bureaucracy is relatively high, and therefore are an important asset for the organization. The organizational members possess unique knowledge and skills to create new products or services. Because of their valuable knowhow, they are involved in a variety of activities within the organization (Volberda, Jansen, Tempelaar, & Heij, 2011). However, constraining factor is that the professionals are focused on their own area of expertise and this makes coordination across functions or disciplines difficult and therefore limits the innovative capability of the
organization. As Gibb, Haskins & Robertson (2013) add, it will require flexibility in
organizational design to allow interdependencies among departments, projects or individuals, as the external stakeholders demand more flexible institutions. Eventually, this can lead to Schumpeterian ‘creative destruction’ (Schumpeter, 1934 in Gibb, Haskins & Robertson, 2013), where departments adapt or merge into new units.
As horizontal differentiation is present, universities can be characterized as pluralistic organizations with various departments. The difficulty is that all departments have their own external orientation and values and are more focused on justifying their existence within the organization rather than dealing with innovation (Gibb, Haskins, & Robertson, 2013). As indicated by Rhys & Boyne (2014), controlling the fragmentized departments in professional bureaucracies created excessive overheads as the top wanted to exercise a form of managerial control over its departments.
Universities can be classified as mature organizations and some have a long history of existence. As Christensen & Eyring (2011) state in their research among US universities, the traditional university faces no serious competition apart from those with similar operating models. Universities were able to grow in scale and scope and continuously increase the
24 number of new degree programs. The decision-making system, culture and reward system, combined with the risk-averse behavior of the top management, resulted in universities getting bigger-and-better tendencies (Christensen & Eyring, 2011). The focus on reinforcing existing practice blinded them to disruptive technologies, and gave innovative new entrants time to enter the market, without being interfered by the traditional universities. This tendency led to universities being inert. Concluding, the success of innovation requires an
organizational structure in which organizational members are challenged, empowered and professional development is stimulated. This means a shift from the more traditional structures to more flexible forms of organizing.
2.2.3 Determinants of innovation
The determinants of innovation, which emerged from reviewing existing literature are based on the organizational level and derived from the review of Damanpour (1991) and the multi-dimensional framework of Crossan & Apaydin (2010) on organizational innovation. The Damanpour review examined the relationship between thirteen organizational factors, mainly structural variables, and organizational innovation. He also studied the impact of four major contingencies (type of innovation, type of organization, scope of innovation, and stage of adoption) on the determinants and innovation. Crossan & Apaydin based their
multi-dimensional framework on extensive review of literature on innovation, published in past 27 years, and viewed it from various perspectives to connected three meta-constructs of
innovation determinants: innovation leadership, managerial levers, and business process and viewing innovation as a process and an outcome. This research is based on the determinants of Damanpour (1991) and Crossan & Apaydin (2010) and summarized in table 4.
Determinant Explanation of variable Effect on innovation Formalization The degree to which rules, procedures,
tasks and communications are written down or formalized (Damanpour, 1991)
In organizations where
formalization is high, flexibility and creativity are lacking, these factors facilitate innovation and encourage new ideas.
25
influence on innovation. Centralization Hierarchy of authority and
decision-making and the way it is concentrated in an organization (Damanpour, 1991; Mintzberg, 1979).
Centralization has a negative impact on innovation (Damanpour, 1991). Organizations with a hierarchical structure and where the decision-making is centralized resemble the mechanistic structure of Burns & Stalker (1961) and innovation is likely to occur in organizations with an organic structure
Organizational complexity Specialization, differentiation and vertical differentiation represent the complexity of an organization, where specialization refers to the number of occupations/ specialties in an organization; differentiation to the number of departments in an organization, and vertical
differentiation to the number levels in an organization’s hierarchy (Damanpour, 1991; Hage, 1999) Complexity, especially specialization contributes to increase in innovation (Damanpour, 1991). However, vertical differentiation has the adverse effect, the higher the levels of vertical differentiation, the less innovation occur.
Professionalism Professional knowledge of organizational members, based on education, experience and/or
involvement in professional activities and personal development
(Damanpour, 1991; Hage, 1999)
Professionalism has a positive effect on innovation as
organizational members increase their knowledge/ experience.
Administrative intensity Indicator of administrative overhead Administrative overhead has a negative effect on innovation. Managerial tenure and attitude
towards change
Extend to which managers are receptive to change which leads to a climate (Crossan & Apaydin, 2010) (Damanpour, 1991)
Length of service and experience within an organization
If managers are receptive to change, it positively influences innovation
The longevity of managers has a positive influence on innovation as they have experience and
knowledge how to do the job and to reach desired outcomes. Slack resources Resources available in the
organization beyond what is required to maintain regular operations
Availability of slack resources allows for investing in new ideas, absorbs failure of innovation and therefore has a positive effect on innovation.
External communication Ability to be in contact with external environment and participation in extra organizational activities
Organizations, who work together with others, bring in new ideas and are willing and able to exchange information with external parties. Internal communication Extend of communication among
organizational members, units.
Communication across the boundaries of the unit increases cross-fertilization of ideas which stimulates innovation. Open and informal communication creates an environment in which innovation will increase.
Organizational culture Innovative culture by having a shared vision, autonomy and support for experimentation (Crossan & Apaydin, 2010)
An organizational climate in which leaders stimulate and encourage innovation, leads to empowerment and creation of ideas.
Table 4: Determinants of innovation (Damanpour, 1991; Crossan & Apaydin, 2010; Hage, 1999; Mintzberg, 1979).
26 2.3 Conclusion
Cooperation and innovation can be seen as phenomena that have received a lot of attention in scientific and managerial research. Not only in the private sector, organizations engage in cooperation for a variety of reasons, but also in the public sector cooperation is becoming an important issue. Stable external environments have changed into more dynamic and turbulent ones and because of increasing competition, new technological developments, rapidly
changing markets and changing consumer demands, organizations are forced to innovate faster. Universities can be seen as catalysts for regional development and knowledge transfer with the industry is beneficial for both the university and the industry. However, as industry cooperation is high on the strategic agenda of the Ministry of Education, Culture & Science, there are still barriers that inhibit the collaboration. Bureaucracy, inflexibility, inability to react to changes in the environment and incapable management are mentioned as the most important factors. Therefore, organizational innovation is a way of changing an organization and breaking down the barriers that hinder cooperation might lead to intensified cooperation. UASs face enormous challenges as the pressure to innovate is great, not only because of global competition, decrease in funding but also because of new entrants and the
governmental regulation which encourage the cooperation with the industry. UASs are large, mature organizations, in which changes take time and the focus on own department hampers cooperation. Furthermore, centralization and control suppress cooperation with the industry. In order to cooperate successful with the industry, departments of UASs should increase their flexibility, speed and innovativeness. This requires adaptations in practices, processes and structures of UASs.
27 2.4 Conceptual model
This research will be aimed at the university – industry collaboration part of the triple helix concept, the research was approached from the perspective of universities and focused on departments of UASs instead of the UAS as an entire entity. To be able to answer the overall research question: “How can universities of applied science organize their departments in order to intensify and facilitate cooperation with the industry”, the determinants of
Damanpour (1991) and Crossan & Apaydin (2010) serve as the framework on which this research will be based.
Figure 1: Conceptual model
3. Methodology
3.1 Research method
A qualitative approach was taken to conduct the research. A multiple case study was applied, in which three departments of UASs were examined. Using this design, gave the possibility to gain in-depth knowledge on the research topic, to find out how UASs cooperate with the industry and which determinants of organizational innovation had an impact on the internal organizational design of the department in order to increase cooperation. A multiple case study was conducted because the research question refers to a contemporary phenomenon that
Factors hindering cooperation
Determinants of
organizational innovation
Cooperation with the industry
28 can only be studied in its real life environment (Yin, 2013). According to Yin (2013), in a multiple case study, a small number of cases can be combined to predict literal replication, which means that similar results are predicted from the chosen cases.
In this study, a department of a medium-sized UAS was chosen as unit of analysis and specifically those departments with studies operating in the economic/marketing domain (BBA). The preference of a department over a university as a whole was based on the fact that there might differences between departments within the same UAS and therefore an analysis on corporate level might not reveal the actual determinants influencing the internal
organization down to the bottom-line.
3.2 Data collection
In this multiple case study, the cases were purposefully selected based on theoretical and confirming and disconfirming sampling (Patton, 1990). Additional criteria for selecting the cases were: medium-sized UASs, with bachelor degree programs in the economic domain and located in peripheral areas of the country. The first case, the department of the UAS in
Finland was selected because of its long-term sustainable cooperation with the industry and serves as an example on how industry cooperation is integrated in the educational programs and in the organization. The second case was chosen because the researcher is employed at this UAS and works in the chosen department. The department wants to intensify industry cooperation but has difficulties accomplishing this. Additionally, it provided access to data and respondents. The third case was selected because a professorship which focuses on public-private cooperation is part of the department and can serve as an information rich case (Patton, 1990). There were differences in level of U-I cooperation between the three
29 part of the curricula, as opposed to department 2 where cooperation is hardly present. Case 3 was just in between the other two extremes
Per case, a member of the management team, and at least three members of the teaching staff from each department were be selected, leading to an initial sample of 12 respondents. Additionally three participants were added, two for case 1 to provide more insight into the already existing cooperation and one for case 2to gain deeper understanding of the factors inhibiting cooperation. Apart from the member of the management team, snowball sampling was applied to select the participants per case. This sampling method was used because it was expected that the respondents had knowledge on which colleagues fit the criteria of this research which were: being a member of the teaching staff, a minimal tenure of 5 years in the current organization and experience in cooperation with the industry. By conducting 15 interviews, enough data was gathered to reach saturation. In table 5, an overview of the respondents per case is given. Because of privacy reasons the names of the UASs,
departments and respondents were anonymized. The cases are labeled department 1, 2 and 3 and the respondents are labeled as respondent 1 to 15.
Case Total nr. of respondents per case
Gender Function Tenure (years)
in current organization
Department 1 6 4 females, 2
males
Head of Campus, program director, principal lecturer, lecturer
Between 7 and 30 years
Department 2 5 3 females, 2
males
Program director, senior lecturer, lecturer Between 8 and 27 years
Department 3 4 3 females,
1male
Team leader, coordinator, researcher, senior lecturer
Between 7 and 26 years Table 5: Overview respondents
Semi-structured interviews were used as they provide more in-depth information about the determinants of organizational innovation which influences the cooperation with the industry. This data collection method was applicable because the respondents could talk freely about the factors and features related to the topic (Saunders, Lewis, & Thornhill, 2012). The prospective participants for the interviews were invited via email to take part. After
30 confirmation to participate in the research, a date and location were scheduled for the
interview by the interviewee. At the end of the each interview, the respondent could identify possible participants (snowball sampling). Again, these prospects were invited via email to participate, when agreed, a time and location was set by the interviewee. The interviews were conducted between the end of October 2015 and mid-December 2015. The location and time for the interview were chosen by the participant. Fourteen interviews were conducted
individually and face-to-face and were voice recorded. One interview was conducted via email, sending the participant the interview questions as it appeared difficult to make an appointment for a face-to-face or skype interview. The duration of the interviews lasted between 42 minutes and 1 hour and 14 minutes. The format of the interview was pre-tested prior to conducting the interviews to assure that the relevant constructs were covered.
Next to interviews, observation of the departments took place where the office space and the organizational members in daily routine and meetings, were observed. The role of the observers could be described as complete participant (Saunders, Lewis, & Thornhill, 2012). This provided insight in how organizational members interact, communicate, and about organizational culture in the department.
Furthermore, several documents were analyzed such as educational plans and programs, website of the UASs, annual plans and publications of the ministries of Education to gain insight in funding, industry cooperation, and administrative intensity.
Using semi-structured interviews had several advantages. First, it provided the opportunity to probing further into the topics and opinions and provided useful background information. Furthermore, the researcher had the possibility to adapt the questions during the research based on the initial outcomes. Finally, by using a semi-structured format, it provided a structure for coding and analyzing the data. Using participant observation had the advantage
31 of observing the behavior of the organizational members in a real context and provided
additional information about this topic. The analysis of the documents added additional information or confirmed the information provided by the respondents.
3.3 Data analysis
For this research, a multiple case study design was applied where three cases were studied and 15 respondents were interviewed. Each interview was transcribed word-by-word, leaving out the contextual information as this was not relevant for the research subject.
Next to the interviews, documents of the selected UASs were analyzed (annual reports and educational plans) and scanned for information related to the determinants of Damanpour (1991) and the university-industry cooperation. Furthermore, participant observation took place, again related to the determinants, to find out how employees interacted, communicated. The findings were written down or summarized and documented.
To analyze the collected data, a content analysis was used (Miles & Huberman, 1984). Based on the determinants of Damanpour (1991) and Crossan & Apaydin (2010) and the facilitators of cooperation mentioned by several scholars the underlying codes were determined.
The transcribed interviews, the summaries of the document analysis and the observations were coded with the aid of the software program QDA miner light.
The strategy used to analyze the data is based on the concept of relying on theoretical propositions combined with examining plausible rival explanations of Yin (2013). Based on the dimensions of Damanpour (1991) and Crossan & Apaydin (2010) as a framework, all fragments from the document analysis and interviews were coded as exactly as possible. Based on this initial labeling of words, phrases and sections, several sub codes emerged. These sub codes were appropriated to the right main category, which was based on the
32 determinants. This combined deductive approach and inductive approach was suitable for this research as prior knowledge based on the determinants was combined with themes brought up by the respondents (Miles & Huberman, 1984).
The analytic technique used was pattern matching (Yin, 2013). As the determinants of Damanpour (1991) serve as the main framework, the data of interviews and documents were compared with this theory to explain the findings. If the pattern of the data matched that with the empirical patterns, an explanation could be found and this strengthened the internal validity (Saunders, Lewis, & Thornhill, 2012).
The following steps have been taken from the preparation of the data to the final results and were derived from the theories of Eisenhardt (1989) and Miles & Huberman (1984). The first step was the preparation of the data, transcribing the interviews word-by-word and making summaries of the documents which were analyzed and the observations. All the transcripts and summaries were uploaded in the QDA miner program. Next step was the composition of the main categories, as the coding tree was based on the existing framework of Damanpour (1991) and Crossan & Apaydin (2010), in the QDA miner software program. The third step was coding of the data per interview, reading the transcripts and labeling the relevant words, phrases and sections e.g. “personal development of organizational members”, “freedom to take decisions”. This type of coding is referred to as open coding, where all relevant words and fragments are labelled. Strauss & Corbin (1990) argue that open coding is ‘the process of breaking down, examining, conceptualizing and categorizing date’. While labeling the
relevant parts, the coded parts were appropriated to the relevant category or sub-category. If sections or phrases could not be appropriated to the predetermined category, additional (sub)categories were added. The next step was axial coding, the process of merging and splitting codes and adding new categories. Next, selective coding was used to establish
