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The Pathway to SME Success: The driving factors
of innovation. Insights from the Accenture
Innovation Awards
A Hybrid study (quantitative- and qualitative method) in the Dutch SMEsector
2 MSc BA Thesis
Submitted for partial fulfillment of the requirements for a Master of Science in Strategic Innovation Management at the Rijksuniversiteit Groningen.
The copyright belongs to the author.
The Rijksuniversiteit Groningen, Accenture and the author declare that any information provided by third parties, which these parties do not want to be made public, will be kept in confidence.
Supervisor
Dr. R.A. van der Eijk Co-assessor
Prof. dr. W.A. Dolfsma
Date
October, 2015
Word Count 17.935
University of Groningen
About the author
Alexander Thomas van der Valk (1991) is associated to the Faculty of Economics and Business (FEB) of Groningen in the Netherlands where he studies a double degree Honours MSc BA specialization courses ‘Change Management’ and ‘Strategic Innovation Management’. He has previously conducted research on the influence of the interaction between managers and subordinates in stimulating emergent change. Besides that he has been employed at the University of Groningen as a Student Assistant for multiple courses regarding Business Administration studies.
Copyright
Copyright © 2015. All rights reserved. No part of this publication may be reproduced or published in any manner whatsoever without permission of the author of this thesis.
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Abstract
Purpose- Many Small and Medium Enterprises (SMEs) fail to innovate successfully since innovation is regarded as an abstract and hard to grasp phenomena. Though, it is known that innovation contains two distinct processes: explorative activities, focusing on the research aspects of the innovation, and exploitative activities, emphasizing the commercialization and development of the innovation. Important to innovation success is the organizational configuration. The aim of this study is to investigate the interaction between the organizational configuration and the explorative and exploitative innovation activities.
Design/methodology/approach- The study uses data obtained from a survey of 1285 SMEs and from interviews with five innovators from different SMEs.
Findings- The outcomes show that the research aspect of innovation is no longer characterized by SMEs developing products in isolation. Rather the products, services and processes should be developed in cooperation with consumers in order to acquire tacit knowledge about the know ‘why’ of the new products, services or processes. Furthermore, the development and commercialization aspect of innovation is increasingly emphasizing collaborations with universities, though the emphasis is more on codified knowledge. SMEs do not have the same level of resources compared to established firms. In order to compete with established firms, SMEs will either have to incur the development costs of in-house asset development, or to collaborate with owners of these assets. SMEs successful in innovation seem to favor experimentation over elaborate planning, in-depth customer feedback over intuition and luck, and iterative design over traditional high risk “big design up front” development.
Practical implications – A major implication of this study is that practitioners have to be aware of the organizational configuration, as the organizational configuration highly impacts innovation performance. Furthermore, the findings assist SMEs by showing the distinct influence of the organizational configuration on explorative as well as exploitative activities.
Originality/value – This paper uses both quantitative and qualitative data to explore the interaction between the organizational configuration and innovation activities. Next to showing the interaction between organizational configuration and innovation activities, this paper shows in-depth insights into how and why this interaction is important.
Paper type- Research paper
Key words: Innovation, Accenture Innovation Awards, ambidexterity, exploration, exploitation, success-factors, organizational configuration & SMEs
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Executive Summary
In today’s dynamic environment innovation is increasingly recognized as the key factor driving SMEs success. Despite global awareness of the importance of innovation, not all organizations are successful in conducting innovative activities since it is considered as a rather abstract phenomenon. Though, it is known that it contains two distinct processes: explorative activities focusing on the research aspects of the innovation, and exploitative activities emphasizing the commercialization and development of the innovation. This research paper aims to address this innovation challenge by investigating the interaction between the organizational configuration and both explorative and exploitative activities. This aim is achieved by looking at the differences, in terms of organization configuration, between finalists and non-finalists of the Accenture Innovation Awards 2013 & 2014. The purpose of this research is to answer the following research question: ’’What is the role of the organizational configuration in stimulating and enhancing the process of innovation?’’. Additionally, two main sub-research questions were developed in order to distinguish between the two distinct activities of exploration and exploitation.
In order to answer the research question this study used data obtained from a survey of 1285 SMEs and from interviews with five innovators from different SMEs. In order to obtain the data this paper used a hybrid structure combing both a quantitative and a qualitative method. The SMEs successful in innovation seem to differentiate themselves by organizing differently for innovation in terms of: collaborating, entrepreneurial learning, human capital, R&D activities & expenditures, connectedness, and centralization. SMEs successful in innovation seem to favor experimentation over elaborate planning, in-depth customer feedback over intuition and luck, and iterative design over traditional high risk “big design up front” development. Rather than building a business model, SMEs successful in innovation aim to look for one.
Additionally, the research outcomes showed that there are differences as well as overlap in the interaction between the elements of organizational configuration and both explorative and exploitative activities. Firstly, the outcomes show that the research aspect of innovation is no longer characterized by SMEs developing products in isolation. Rather the products, services and processes should be developed in cooperation with consumers and universities in order to acquire tacit knowledge about the know ‘why’ of the new products, services or processes. Furthermore, the development and commercialization aspect of innovation is increasingly emphasizing collaboration as well, though the emphasis will be more on codified knowledge. SMEs do not have the same level of resources compared to established firms. In order to compete with established firms, SMEs will either have to incur the development costs of in-house asset development, or to collaborate with owners of these assets. The ability to acquire knowledge is influenced by the experience of the SME’s personnel.
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Table of Content
1. Introduction - The Importance of Innovation ... 6
1.1 The Innovation Process; Shifting Dominance ... 6
1.2 The Role of SMEs in Innovation ... 7
1.3 Scope & Domain; The Accenture Innovation Awards ... 7
1.4 Practical and Scientific Relevance ... 8
1.5 Research Question ... 8
1.6 Research Paper Outline ... 9
2. Theoretical Framework... 9
2.1 The Processes of Innovation related to Sub-research Questions I.I and II.I ... 9
2.2 Previous Research on Organizational Configuration ... 10
2.3 Organizational Configuration influencing Explorative Innovation Activities ... 11
2.4 Organizational Configuration influencing Exploitative Innovation Activities ... 12
2.5 Organizational Configuration influencing both Explorative- & Exploitative Innovation Activities .... 12
2.6 Specifying Sub-Research Questions ... 13
3. Research Methodology ... 13
3.1 Research Approach ... 13
3.2 Methodology Quantitative phase - Cluster Analyses ... 14
3.3 Methodology Qualitative phase - Case Analyses ... 18
4.Results ... Fout! Bladwijzer niet gedefinieerd. 4.1 Results of Study’s Quantitative Phase - Cluster Analyses ... 20
4.2 Results of Study’s Qualitative Phase - Case Analyses ... 23
4.3 Verification of Results ... 39
5. Discussion... 40
5.1 The Influence of the Predefined Factors on explorative activities; related to Sub-research Question I ... 41
5.2 The Influence of the Predefined Factors on the exploitative activities; related to Sub-research Question II ... 45
5.3 Overlapping Factors ... 46
6. Conclusion ... 47
6.1 Theoretical implications... 48
6.2 Managerial implications ... 49
7.Limitations & Future Research ... 49
8. References ... 51
9.Appendices ... 56
Appendix I. Questionnaire Accenture Innovation Awards ... 56
Appendix II. Cluster models and variable means per cluster. ... 60
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1. Introduction - The Importance of Innovation
“Success is not delivering a feature; success is learning how to solve the customer’s problem.” – Eric Ries
Most organizational practitioners and theorists agree that globalization, changes in customer demands and the emergence of technological innovations make the environment less predictable than ever before (Radas & Bozic, 2012). In a dynamic and ever changing environment, there can be little doubt that innovation is one of the most important issues which organizations will face in their challenge to survive (Konsti-Laakso, Pikala & Kraus 2012; Clancy and Moschini, 2013; Omri, Ayadi-Frikha & Chaiby, 2014). Additionally, organizational literature has increasingly shifted towards innovation rather than efficiency as the key driver of business success or failure (Bilton and Cummings, 2010; cited in Hotho & Champion, 2011).
Despite global awareness of the importance of innovation, not all organizations are successful in conducting innovative activities. Studies to date suggest that the small-and medium-sized (SME) business sector in general still considers innovation management a challenge and portrays an innovation management deficit (Kenny & Reedy, 2006; Hotho & Champion, 2011).Innovation seems to be a phenomena which is hard to grasp and is not clearly defined. Sometimes innovation is quick and incremental, whilst at other times innovation happens over a longer time period and is considered radical. However, there remains some overlap among definitions of innovation, since innovation is considered as ‘’a process that starts with an idea, proceeds with development of invention, and results in the introduction of a new product or service’’ (Omri et al. 2014, p.5).
This research paper compromises a combination of an empirical cluster analyses and a case study analyses on the factors which determine innovation success amongst SME’s, the latter being increasingly important nowadays. More specifically, the focus is on the influence of the organizational configuration which determines innovation success at SMEs. As will be shown in the following sections this can be considered as a key factor.
1.1 The Innovation Process; Shifting Dominance
The dominant prescription in innovation literature until the 1990s was that organizations should focus on either explorative innovation or exploitative innovation activities (Schoonhoven & Jelinek, 1990, Gibson and Birkinshaw, 2004; He and Wong, 2004 cited in Chang, Hughes & Hotho 2011). This implies that organizations should either focus on differentiation by innovating for the future, or they should focus on cost reduction by means of efficient production. Today it is recognized that this old formula will no longer work because firms competing in today’s hyper competitive world face the paradoxical challenge of ‘’dualism’’, that is: functioning efficiently today while innovating effectively for the future (Paap & Katz, 2003; p. 13). This implies that organizations should focus on ambidextrous innovation composing explorative innovation activities which captures the ‘’research’’ aspect of the innovation process and exploitative innovation activities which captures its ‘’development and commercialization’’ component.
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suggest that exploitative and explorative innovations may need two fundamentally different ways of managing the organizational configuration (Jansen et al., 2006; Raisch and Birkinshaw, 2008). In comparison with the traditional innovation approach, the relatively new concept of ambidexterity needs to be further researched so that it can be better understood (Chang et al., 2011).
The aim of this research is to identify the elements of the organizational configurations (referred to as factors) which enhance both processes of innovation (explorative- and exploitative activities). This aim is achieved by conducting research upon factors which influence the research and development/commercialization of innovations. Therefore the structure of this research paper will be based on the factors influencing the explorative- and the exploitative innovation activities.
1.2 The Role of SMEs in Innovation
In order to gain a better understanding of the innovation process this research paper will focus on the SME sector. Within organizational literature and practices there seems to be a consensus regarding the important role of SMEs in innovation (Radas & Bosic 2012). Konsi-Laakso et al. (2012) stress that SMEs can be considered a key factor for innovation as generators of new ideas, as entrepreneurs carrying out new ventures and as partners for other actors. This corresponds with the findings of Radas & Bosic (2009) who state that: ‘’One of the primary means through which SMEs are expected to accomplish this task is by developing and commercializing innovations’’ (p:438). Additionally, previous research has focused on explaining factors influencing success amongst SMEs concluding that innovation is one of the main factors influencing SME success. (Hopp & Stephan, 2015). SME’s are defined as organizations having a maximum of 250 employees, which is consistent with the current SME definitions (European Commission, 2015).
Despite the important role of SME’s in innovation, looking back at the last five years, more than 50% of all start-ups in the Netherlands seem to fail within the first five years and turn up bankrupt (KVK, 2015). This implies that though SMEs are well suited for conducting innovative activities, not all SMEs are equally successful in doing so. It can be stated that the elements related to the organizational configuration are among the most likely reasons as to why innovations at SMEs fail (Keskin, 2006; Unger, Rauch & Frese, 2011; Crema, Verbano, & Venturini, 2015). Hotho & Champion (2011) argue that if SMEs are to survive and prosper in the long run, a more strategic approach to innovation and effective innovation management skills regarding the organizational configuration is required.
1.3 Scope & Domain; The Accenture Innovation Awards
In spite of the fact that Dutch SMEs show high failure rates, they are globally recognized for their innovative capabilities. The innovative capabilities of the Netherlands is portrayed by the following quote of former prime minister of the Netherlands Jan Peter Balkenende (2007): ‘’From orange colored carrots, the microscope and the first megacorporation of the world (called the Verenigde Oostindische Company) to the inventions of Wi-Fi and Blu-ray, the Netherlands has made a significant mark on our world as we know it today’’. Despite its comparatively modest size, population and resources the Netherlands was able to contribute to the development of our modern society. Regarding its innovative capabilities, the Netherlands is an interesting field for innovation research.
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Accenture Innovation Awards (AIA). The setting of this study is the AIA which was organized for the first time in 2007. The AIA is a platform and contest where Dutch firms can showcase their innovation to a larger audience. The AIA aims at honoring, uniting and stimulating innovation in the Netherlands by focusing on new product, service and business model innovation, awarding those that stand out the most. The participant of the AIA are mostly SMEs including startups. These types of awards are evaluated and selected by a panel of industry experts that have a wide variety of backgrounds and expectations.
The existence of innovation awards and competitions testifies the increasing focus on SMEs and innovation as a tool for survival in today’s fast changing business environment. Paradoxically, despite the recognition of the role of SMEs in generating innovation, knowledge about which factors influence SMEs innovation success remains unclear (Hotho & Champion, 2011; Omri et al. 2014). Though, research on the differences between innovative and non- innovative small businesses has suggested that differences in innovativeness stem mainly from the organizational configuration from the companies rather than the external circumstances (e.g. Hodgkinson & Healey, 2011; Forsman & Rantanen, 2011 cited in Konsti Laakso et al., 2012). In a similar vein Bloom & Van Reenen (2010) argue that organizational literature regarding firm performance neglects the role of the management practices perspective. The purpose of this study is to analyze the difference between successful and less successful SMEs in terms of the organizational configuration, by analyzing participants from the AIA. Note that, though this research will distinguish between explorative and exploitative activities, its aim is not to research how organizations can combine both activities at the same time. The aim of this research is to identify the elements of organizational configuration which enhances the explorative- and/or exploitative activities.
1.4 Practical and Scientific Relevance
Within this study, the organizational configuration of SMEs are researched. Research on elements, which are composing the organization configuration of SMEs, will enable organizations to increase the probability of appropriate organizational configuration which will enhance the process of innovation. Because innovation compromises two types of activities/processes, explorative and exploitative, understanding of how the organizational configuration influences these processes will enhances our understanding of managing innovation processes.
Previous researchers tend to focus SME innovate capabilities in a one-way dimensional fashion in that they focus almost exclusively on obstacles and hampering factors for SMEs to innovate (Radas & Bosic, 2012). This study will research the SME innovation success by focusing on both factors that allow them to conduct innovation activities successfully and factors that form an obstacle for innovation. Therefore, this paper is of managerial interest, because it is important to enhance our understanding on managing innovation amongst SME’s in complex and uncertain environments.
The theoretical interest of this paper resides in the fact that it provides more insight into the innovation processes at SMEs. Additionally, more explanatory research about the business phenomena of emergent change is needed due to the fact that the theoretical field of innovation at SMEs is considered immature and vague.
1.5 Research Question
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innovation. The question to be answered by the research presented in this paper thus reads: ’’What is the role of the organizational configuration in stimulating and enhancing the process of innovation?’’
Since the research question will be answered by dividing the innovation into the two distinguishable parts, the organizational configuration will be related to events of explorative and exploitative activities.
Sub-research question I - ‘What is the role of the organizational configuration in stimulating and enhancing the process of explorative innovation activities?’
I.I What are explorative innovation activities
Sub-research question II - ‘What is the role of the organizational configuration in stimulating and enhancing the process of exploitative innovation activities?’
II.I What are exploitative innovation activities?
According to the research question, the unit of analysis will be the organization which is at a micro level. More specifically, the focus will be on how elements, composing the organizational configuration, contribute to (blocked) innovative processes. Note that this research paper is not about finding ways to manage or balance both explorative and exploitative activities within one organization.
1.6 Research Paper Outline
This research paper is structured as follows: it first outlines the theoretical framework regarding innovation and the elements of organizational configurations. Subsequently, it provides the research design. Thereafter, results of the data analysis are provided. Additionally, findings are discussed with respect to the role of the organizational configuration in stimulating and enhancing the process of innovation which are linked to existing literature. In the conclusion, implications, limitations and suggestions for future research are presented.
2. Theoretical Framework
Within this section, the concepts of innovation and the organizational configuration are elaborated on by means of a literature review. First, the processes of innovation will be discussed in order to answer sub research questions I.1 and II.1. Secondly, the elements composing the organizational configurations will be explored by focusing on previous research on these concepts. Next, the elements derived from previous research are described further and are categorized by factors influencing the exploration innovation activities and or exploitation innovation activities. Finally, the research question will be further specified.
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to as commercialization activities. Examples of this include production and refinement, which have the aim of capturing rents/interest from the invention. In the next section, the organizational configuration will be explored by focusing on previous research on this concept.
In exploration there is uncertainty about which technical standards will become the ‘dominant design’ and there is much volatility of prototyping. During exploration the emphasis of competition lies on the technical feasibility and a so called ‘race to the market’. The volatility of prototyping implies a great deal of trial and error and knowledge is often tacit. One can also think of this as the probe and learn process (Groen, Wakkee & De Weerd-Nederhof, 2008). Volberda and Elfring (2001) argue that ‘’exploitation concerns the accumulation of information and the exploitation of experience to estimate both the current and expected organizational developments and environment and to facilitate its optimization’’ (p:248). In exploitation the technical development has consolidated in a dominant design resulting in less uncertainty in demand and supply. Additionally, knowledge will be more codified and diffused and competition will shift towards efficient production and distribution (Nooteboom and Gilsing, 2004, p:10).
In order to cope with the increasing dynamic and complex environment many scholars have suggested that organizations should seek help from external actors for innovation as an important part of managerial strategy (Coombs et al., 2003; Chesbrough et al., 2006). This implies that organizations should not innovative in isolation, rather they should work with and draw knowledge from actors outside their organization in order to find commercially exploitable new combinations of knowledge or technology (Katila, 2002; Fleming and Sorenson, 2004; Laursen and Salter, 2006; Laursen, 2012). Cohen and Levinthal (1990) argue that, in order to render these benefits effectively, organizations need to align their internal processes to the external environment: they need to configure their organization to enable successful absorption of knowledge and information form external sources.
2.2 Previous Research on Organizational Configuration
As presented in the previous subsections, research on the differences between innovative and non-innovative SMEs has suggested that differences in innovative performance stem mainly from organizational configuration rather than the external circumstances. Therefore, organizational configuration is defined as the composition and organization of different internal components, in order to be aligned with the external environment for innovative activities (adopted from Chang et al., 2011). By synthesizing the existing literature on elements of organizational configuration and obstacles and enablers of innovation, several categories and classifications of those elements of organizational configuration were established. Ultimately, this selection was narrowed by focusing on elements of organizational configuration relevant for SMEs and the processes of explorative and exploitative innovation (see table 1).
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be referred to as Human Capital. This factor will be elaborated upon in the next subsection.
Another important dimension of organizational configuration is entrepreneurial learning. According to Hopp & Stephan (2015) the importance of information institutions for SMEs is of ongoing interest. To survive selection pressure SMEs need to recognize new external knowledge, assimilate it, and apply it to commercial ends since this is critical to its innovative capabilities (Cohen & Levinthal, 1990, cited in Jansen, Van den Bosch & Volberda, 2005). In the literature it is widely accepted that collaborative relationships can be proved as a tool in order to achieve successful innovation for SMEs. Collaborative relationships can provide SMEs with new knowledge and information derived from these collaborations. This emphasizes the importance of factors that may influence the innovation process which will be referred to as Entrepreneurial Learning and Collaborating. These factors will be elaborated upon in the next subsection.
Acquiring knowledge from external partiers is recognized as a challenge amongst SMEs. In order to gain knowledge from external channels, Tsai (2001) argues that firms need to develop absorptive capacity. Absorptive capacity refers to the ability of a firm to recognize new, valuable information from outside, assimilate it and apply it to commercial ends (Cohen & Levinthal, 1990). Makri, Hitt & Lane (2010) argue that the absorptive capacity is merely a function of prior related knowledge which is gained by Research & Development (R&D). This highlights the importance of a factor that may influence the innovation process which will be referred to as R&D Activities & Expenditures. These factors will be elaborated upon in the next subsection.
2.3 Organizational Configuration influencing Explorative Innovation Activities
Entrepreneurial learning is defined as the commitment to learn, to be open- minded
and stimulate interorganizational knowledge sharing (adopted from Keskin, 2006). Farrel (2000) argues that ‘’entrepreneurial learning orientation fosters a set of knowledge-questioning and knowledge-enhancing values that leverage the adaptive behaviors provided by market-orientation to a higher-order learning that leads to the development of breakthrough products, services, and technologies, and the exploration of new markets’’ (cited in Keskin, 2006, p.399). Knowledge, like marketing intelligence, has been acknowledged as one of the most important critical skills required in ensuring innovation success (Wren, Sounder & Berkowitz, 2000, cited in Larsen & Lewis, 2007). In this perspective, learning-orientation lays a foundation for a desire to assimilate new ideas, and leverage customer intelligence for firm innovativeness since the innovation process is regarded a process of ‘’know-how’’ accumulation (Zeng, Xie & Tam, 2010). From previous research it is expected that entrepreneurial learning enhances explorative innovation capabilities by influencing the firm’s knowledge base.
R&D activities & expenditures is defined as the resource commitment towards
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2.4 Organizational Configuration influencing Exploitative Innovation Activities
Collaborating is defined as the use of purposive inflows and outflows of resources to
others/partners (both horizontal and vertical) for innovative activities (adopted from Chesbrough, Vanhaverbeke & West, 2006). The crucial role of networks and collaborations for SMEs come to the fore in investigations in innovative performance (Laursen & Salter, 2013). One set of reasons for this phenomenon is that large firms are more likely to possess relevant specialized and co specialized assets at the time of new product introduction in comparison with SMEs (Teece, 1986). SMEs on the other hand will either have to incur the expenses of trying to build the assets themselves, or try to establish collaborations with owners of the specialized assets. For example, SMEs are increasingly obtaining knowledge from external sources, like suppliers and universities etc., in their innovative activities (Crema et al., 2015). The early Schumpeterian ideas about the lone entrepreneur bringing innovations to markets has been replaced by models of different actors working together in iterative processes of trial and error in order to bring about successful commercialization of a new idea (Larusen & Salter, 2013). Additionally, Chesbrough (2003) argues that organizations with an open innovation approach gain access to external ideas by deploying outside (as well as in-house) pathways to the market. Within the open innovation approach the trend toward interorganization innovation processes is explicitly considered (Vanhavereke, van der Vrande & Chesbrough, 2008, cited in Lichtenhaler, 2011). An open innovation approach allows firms to discover technical and market information that would otherwise be hard to discover (Almirall & casadesus-Masanell, 2010). From previous research it is expected that collaborating enhances the exploitation of new ideas by having access to outside resources and knowledge.
2.5 Organizational Configuration influencing both Explorative- & Exploitative Innovation Activities
Human capital is defined as skills and knowledge that individuals acquire through
investments in schooling, on the-job training and other types of experience including the ability to learn from new venture creation, as well as once the new business is established (adopted from Becker, 1964, cited in Unger et al., 2011). Within the entrepreneurship literature there are a number of arguments about how human capital increases SME innovation success. Unger et al. (2011) argue that specific knowledge and skills acquired through their past professional activities and training, increases the ability of entrepreneurs to discover business opportunities that are not noticeable to others. Suanders & Gray (2014) found that entrepreneurs are more likely to come up with ideas and initiatives for new products, services or processes when they are more experienced or higher educated, which enhances the exploration innovation activities. Additionally, higher levels of skills and experience are associated with superior knowledge of customers which allows entrepreneurs to commercialize new product or services (Sethi, Smith & Park, 2001;Omri et al. 2014). Similarly, Baum, Locke & Smith (2011) found a positive relation between high level of entrepreneurial knowledge and planning strategy, which in turn positively affects with business implementation success. From previous research it is expected that human capital can influence both processes of innovation. First, human capital enhances creation of new products by increasing entrepreneurial creativity. Second, entrepreneurial experience and skills stimulates the process of development and commercialization.
13 Factors – elements of Organizational configuration- Influencing Innovation process Definition
–of the elements composing organizational configuration-
Example of studies on organizational configuration -who used this concept- 1.Collaborating Exploitative
activities
The use of purposive inflows and outflows of resources to others/partners for innovative activities Chesbrough et al., 2006; Laursen et al., 2013; Teece, 1986; Crema, et al., 2015 2.Entrepreneurial
learning Explorative Activities The commitment to learn, to be open- minded and stimulate interorganizational knowledge sharing
Keskin 2006; Farrel, 2000; Wren et al. 2000
3.Human Capital Both
processes Skills and knowledge that individuals acquire through investments in schooling, on the-job training and other types of experience
Unger et al., 2011; Suanders et al. 2014; Omri et al. 2014; Baum et al. 2011
4.R&D activities & expenditures
Explorative Activities
The resource commitment towards innovation
Cooper et al. 2007; Oxley et al., 2004; Cohen et al., 1990
* These elements will be referred to as predefined factors and are derived from previous research on the innovation processes in section 2.3, 2.4 & 2.5
**The elements are alphabetically ordered
2.6 Specifying Sub-Research Questions
The factors mentioned in the previous subsection lead to a further specification of the already mentioned sub-research questions, by focusing on the proposed relations mentioned in table 1. This leads to the following specified sub research questions:
Sub-research question I - ‘What is the role of the organizational configuration in stimulating and enhancing the process of explorative innovation activities?’
I.I How does entrepreneurial learning influence explorative innovation activities? I.II How does human capital influence explorative innovation activities?
I.III How does R&D activities & expenditures influence explorative innovation activities? Sub-research question II - ‘What is the role of the organizational configuration in stimulating and enhancing the process of exploitative innovation activities?’
II.I How does collaborating influence exploitative innovation activities? II.II How does human capital influence exploitative innovation activities?
3. Research Methodology
This section starts with explaining the overall research approach. Secondly, a distinction will be made between the two different analyses, starting with the cluster analyses and thereafter the case study. For each of the two analyses the sample description, data content, data collection method and data analysis are explained.
3.1 Research Approach
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method is chosen since literature on innovation in SME’s is relatively recent and many aspects are almost not addressed in current academic literature (Creme, Verbano & Venturini, 2015). Additionally, the literature regarding innovation in SME’s lacks an in-depth account of which elements of the organizational configuration influence innovation and how this interaction is constituted (Konsti-Laakso et al., 2013). From the examination of the components of the experience from AIA finalists of 2013 and 2014, theory is developed that can enrich current literature with new insights.
In order to enhance the quality of this research two distinct methods were chosen. A cluster analyses method (quantitative phase) followed by a case study method (qualitative phase). This was applied in order to accomplish the generation of practical and relevant grounded theory by focusing on real world situations and phenomena in a broad environmental view (Charmaz, 2006; Locke, 2002). The advantages of a quantitative phase preceding a qualitative phase lies in a more in-depth and detailed clarification of the resulting quantitative outcomes (Jabbour et al., 2010).
The cluster analyses is chosen to identify clusters of AIA 2013 & 2014 participants based on their organizational configuration, which is a sets of firms that share a common profile along conceptually distinct variables (Meyer, Tsui, and Hinings,1993; Miller and Mintzberg, 1983). By means of a cluster analysis the participants of the AIA 2013 and 2014 are grouped in such a way that the statistical variance among elements grouped together is minimized while between-group variance is maximized. By following a deductive approach, the number of clustering variables, as well as the expected number and nature of groups in a cluster solution are tied to the elements of organizational configurations mentioned in the theoretical framework, summarized in table 1 (Ketchen et al., 1993). The goal of the cluster analyses is to research whether the finalists of AIA 2013 & 2014 have elements of their organizational configuration in common opposed to non-finalists participants. This quantitative phase of the research will focus on the ‘’what’’ influences success. Figure 1 displays the methodological organization of this study. It should be noted that the researcher was aware of the limitations of the cluster analysis, such as a lack of objectivity and a static representation of the results as stated by Kaufman and Rousseeuws (1990). According to Kaufman and Rousseeuws (1990) the lack of subjectivity results from the decisions regarding the objects and attributes, the method for standardizing the data matrix and the resemblance coefficient.
3.2 Methodology Quantitative phase - Cluster Analyses 3.2.1 Data Content & Sample Description
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becomes evident from the comparison between both databased is the uniqueness of the AIA database regarding the characteristics and richness of its database. In 2013, 968 SME’s signed up for the AIA. In 2014, there were 729 SME’s that applied for the AIA competition. In total this adds up to 1697 applications. From these 1697 only 1285 survey were filled in completely. The missing SMEs who did not fill in the questionnaire completely were eliminated during the analysis. Therefore 75.76% were included from the total amount of applications for the AIA 2013 and 2014.
Table 2: Sample description
AIA edition
2013 AIA edition 2014 Total –of 2013 & 2014 editions- CIS – organized by CBS- Firm size – number of employees- 1-250 1-250 n.a. 10-50 Participants -total number- 968 729 1697 5761 Participants – nr. which completed the survey- 853 (88%)* 432 (59%)* 1285 1124 (19%)* Non-finalists – nr. which filled in the survey- 789 385 1175 n.a. Finalists – nr. which filled in the survey- 64 47 110 n.a.
* Percentage of the total number of participants
3.2.2 Data collection Method
In line with Bacharach’s (1989) statement, each variable used in this empirical part of the study must be defined in terms of measurement. Therefore, all variables will be described in the following section. Most of the elements of organizational configuration are operationalized based upon the before mentioned Community Innovation Survey (CIS4), organized by Eurostat. Innovation success and the element of human capital are based upon innovation literature and are further operationalized by the Accenture Research department. Additionally, table 3 provides an overall summery including supplementary information about each variable included.
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Collaboration- For collecting data about the collaboration, respondents were asked to whether they were affiliated with an incubator (0=no, 1=yes). Next, respondents were asked if they collaborated with different parties (0=no, 1=yes) and whether they perceived these collaborations as important (0=no, 2=yes).
R&D Expenditures and Activities- Additionally, in order to collect data about the R&D expenditures and activities, the respondents were asked about their total investment in R&D by using a 5-point Likert Scale (0-10.000=1, 10.000-100.000=2, 100.000-1.000.000=3, 1.000.000-10.000.000=4, 10.000.000 or more=5), and the percentage of R&D of the total investment by using a 5-point Likert Scale (0%-20%=1, 20%-40%=2, 40%-60%=3, 60%-80%=4, 80%-100%=5). Next, the respondents were asked about the investment in terms of man days by using a 5-point Likert Scale (0-100=1, 100-1000=2,
1000-10000=3, 10000 or more=4) and FTE (0-20=1, 20-40=2, 40-60=3, 60-80=4, 80-100=5).
Human Capital- The human capital of the respondents was investigated by asking about their experience in the industry of their SME (No=1, less than 3 years=2, 3-10 years=3, 3-10 years or more=4) and their experience with founding startups (No=1, 1 startup=2, 2 startups=3, 3 startups=4, 4 startups=5, 5 startups or more=6). Both variables are measured by a Likert scale.
Entrepreneurial Learning- Lastly, in order to gather data about the entrepreneurial learning, respondents were asked about their knowledge sources with different parties on a 4-points Liker Scale (very important=1, important=2, somewhat important=3, not used=4).
Table 3: Variable overview
Variable -Measured in the qualitative phase- Operationalization -defining the measurement of the element incl. question nr in Questionnaire- Data type -of operationalization per element- Level of measurement -measurement scale per variable- Reference- source of information- Innovation
success Competition status (-) Binary (0=non-finalist, 1=finalist)
Nominal Accenture Research Department Collaboration Affiliated with an
Incubator (nr.1) Binary (0=no, 1=yes) Nominal Eurostat (CIS4) Types of collaborations
for innovative activities (nr.9a)
Binary
(0=no, 1=yes) Nominal Eurostat (CIS4) Importance of
collaborations (nr.9b) Binary (0=no, 1=yes) Nominal Eurostat (CIS4) R&D
Expenditures and Activities
Total investment R&D
(nr. 2) 5- point Likert scale (0-10.000=1, 10.000.000 or more=5)
Ordinal Eurostat (CIS4)
Percentage (R&D) of
total investment (nr.3) 5-point Likert scale (0%-20%=1, 80%-100%=5).
Interval Eurostat (CIS4)
Investment in man-days
(nr.4) 4-point Likert scale (0-100=1, 10000 or more=4)
Ordinal Eurostat (CIS4)
Investment in FTE (nr.7) 5-point Likert scale (0-20=1, 80-100=5).
Interval Eurostat (CIS4)
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(nr.5) (No=1, 10 years or
more=4) Gray Accenture (2014); Research Department Experience with
founding start-ups (nr.6) Likert scale (No=1, 5 startups or more=6)
Ordinal Suanders & Gray (2014); Accenture Research Department Entrepreneurial
Learning Knowledge (nr.8) sources Likert scale (very important=1, not used=4).
Ordinal Eurostat (CIS4)
3.2.3 Data Analyses
This research paper will focus on common characteristics, in terms of elements composing the organizational configuration, of SMEs who were finalists in comparison to non-finalists of the AIA 2013 and 2014 editions. It is assumed that the competition progress indicates the degree to which a SME is successful in terms of innovation since participating in the competition is demonstrated to be a signaling of quality (Van der Eijk et al. 2013). Therefore finalists of the AIA are considered successful and non-finalists less successful.
A Two-Step Clustering technique in SPSS was performed in order to create the clusters. The first step involves a nonhierarchical pre-clustering of the data that results in a number of pre-clusters. The second step of the two-step clustering technique is the hierarchical clustering technique which was applied to the pre-clusters, resulting in a solution with an optimal division of heterogeneity between the clusters and homogeneity within clusters. An advantage of the two step clustering technique is that it allows both categorical and continuous data (Hair, Tatham, Anderson & Black, 2006). In this way it could be researched whether the finalists and non-finalists of the AIA 2013 and 2014 would form different cluster, which means that they would have different profiles when it comes to their organizational configuration.
Lastly, a discriminant analyses is undertaken in order to check how well the model performs with regards to the cluster analysis finding and profiling the clusters. This is done since the cluster analyses does not exhibit inbuilt performance, fit measures or tests of significance. The goal of this analysis is to figure out what variables would help differentiate group membership on a significance level. Note that this technique only compares the finalists cluster (group one) with the non-finalists clusters (group two) which implies that the discriminant analyses tries to predict group membership (finalist or not) on the basis of certain variables.
Judgment sampling, which is sampling based on the judgment of the researcher, was applied in order to select the cases derived from the cluster analyses (Cooper & Schindler, 2008). The judgment for selecting the case was based on the type of innovation and the availability of interviewees of the participants of the AIA 2013 and 2014.
3.2.4 Quality Criteria of Research
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et al. (2011), regarding research quality criteria, was taken into consideration. This implies that the industry level, the quality of the sample and the timeliness of the data all needed to be assessed (all regarded validity criteria).
With regard to the quality of the data collected, the quality of the data source is based upon the before mentioned database and questionnaire of Accenture. As already have been mentioned in the previous subsections, the database and questionnaire of Accenture are adopted from the CIS4 questionnaire organized by Eurostat. Additionally, this questionnaire has been validated and approved by the Rijksuniversiteit Groningen and the research department of Accenture. In order to assess the quality of the selected sample from the survey, the response rate of the sample will be analyzed. As already has been stated in the previous subsections the final sample contained 1285 valid cases which is 75.76% of the total sample. Additionally, the SMEs will be screened on the same criteria in corporation with Accenture innovation specialists, which assures the accuracy and the precision of the questionnaire. Next, in order to validate the results of the cluster analysis so called split sampling validation tests (at 75% and 50% of the cases) will be performed. In order to enhance the representation and reliability of the split sampling validation tests, the split sample validation will be performed proportioned to the finalists and non-finalists group (e.g. 75% of the finalists and 75% of the non-finalists).
Finally, in assessing the generalization of the study, the timelines of the data and the newness of the sample are relevant. Since this study collected data from the AIA 2013 and 2014 editions this data is very relevant. Additionally, the results are not limited by one single industry since the sample contained SMEs active in eleven different industries.
3.3 Methodology Qualitative phase - Case Analyses 3.3.1. Data Content
The aim was to gather data content from the 5 interviewees ,who participated in the AIA 2013 & 2014 final, which focused on the experiences and understandings of ‘how’ the organizational configurations influenced the innovative activities. More specifically, the goal of the researcher was to collect information about incidents where elements of the organizational configurations were influencing factors enhancing or blocking the explorative and/or exploitative innovation activities.
3.3.2 Data Collection Method
The sources of data for the case analyses were five semi-structured interviews with open ended questions meant for individual respondents, in order to enable deeper probing into the responses of entrepreneurs of SMEs (Cooper & Schindler, 2008). The semi-structured interviews helped to understand the influence of the organizational configuration on the distinctive processes of innovation (explorative and exploitative) since it allowed for extraction of a variety of factual experiences that show the influence of the factors on the activities of exploration and exploitation. Additionally, semi-structured interviews were used because they allow questioning to be guided and it was more easy to ask for clarification of points. In order to increase the appropriateness of the interviews an interview protocol was used (see Appendix III).
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researcher actively stimulated the interviewees to come up with stories and examples of incidents about the innovation process.
Prior to the start of the interview the researcher introduced himself along with information about the background and objective of the research. The interview itself started with questions about the background of the respondent and the company. The first set of questions focused on the content of the innovation and was aimed at bringing about factors, experienced by the interviewees, that were of influence on processes of exploration and exploitation not accounted for in the theoretical framework section.
The second set focused on the innovation process, including the outcomes and the factors influencing these outcomes. Additionally, probing questions were asked about how these factors arose by asking if the interviewees could exemplify this by means of concrete examples. In the final part of the interview, questions were presented based on the predefined factors presented in the theoretical framework section (see figure 1). In this way the predefined factors were tested and validated. In summary, by starting off with exploring what was innovated, then asking what factors made the innovation happen and lastly by asking if the interviewee could give a concrete example, the influence on the processes innovation was investigated.
Lastly, since only five interviews were conducted the learning curve of conducting interviews was limited. In order to compensate for this shortcoming the first three interviews were contacted by mail after all five interviews were conducted. This allowed the researcher to ask the most crucial questions, derived from the overall learning experience of conducting all five interviews. By contacting the first three interviews again, the intermediary findings were tested and strengthened.
3.3.3 Sample Description
Five persons, involved in the research, development and commercialization of the innovations, were interviewed stemming from a total of five SMEs (AIA finalists). According to Eisenhardt (1989) a number of cases between four and ten works well with regard to the complexity and volume of the data and related to convincing empirical grounding. Table 4 displays the characteristics of the five SMEs, whereby each interview is represented with a code (a combination of a letter and number) in order to ensure anonymity. Note that the interviewees were chosen based upon the results of the quantitative phase of this research. As already have been mentioned, the interviewees are derived from finalists of the AIA finale in order to increase the change of in-depth insights regarding the interaction between the organizational configuration and the process of (successful) innovation.
The conducted interview lasted 60 minutes on average and were all conducted by one researcher. All interviews were recorded (saved as sound file) on two electronic devices, using a program called: Dictaphone. ‘Dictaphone’ is an application available on Apple Inc. devices which can be used to record audio. After conducting the five interviews, the recordings were transcribed verbatim (saved as Word file). The interviews were conducted during several-day site visits to the SME’s.
Table 4: Characteristics of the SMEs mentioned in the interviews
Interview code A1 B2 C3 D4 E5
Year –of participation in AIA- 2014 2014 2013 2013 2014
Industry –of SME- Medical Mechanics Traffic Energy Aviation
20 3.3.3 Data Analyses
The data derived from the five interviews was transcribed and coded based on both deductive and inductive coding. The transcribed fragments concerned thoughts, experiences, ideas and meanings derived from the interviews were coded by using Atlas.ti software. The coded data are fragments indicating the presence or absence of factors influencing the processes of exploration and exploitation of innovative products and services. During the analyses, a distinction between assumptions and concrete events was made whereas only thoughts, experiences, ideas and meanings concerning concrete events were coded in order to increase the reliability.
In advance, main deductive categories were developed and derived from previous literature discussed in the theoretical framework section. These predefined codes were: entrepreneurial learning, human capital, R&D activities & expenditures and collaboration. A description of these predefined codes can be found in table 6 and 7. Inductive codes were used to capture issues raised by interviewees not accounted for by the deductive codes, allowing contextual matters not accounted for to come forward. The inductive coding process resulted in a list of additional codes resulting from insights gained from personal experiences of the interviewees. First, all five interviews were coded and additional codes were added to the predefined coding scheme. The development of inductive codes led to reconsideration of the previous coded interviews. In order to increase the reliability and validity of this research and to increase the quality of the coding scheme one fellow researcher, with the same study background, was asked to code all five interviews independently (Aken et al., 2012). Thereafter, the coded interviews of both researchers were compared. Discrepancies were discussed in order to reach consensus so names of codes could be optimized and to make sure that the codes were correctly assigned to the text fragments. In order to ensure the generalizability of the outcomes, the outcomes were presented to an innovation specialists of Accenture who verified the outcomes. The outcomes of the interview analysis are presented in the next sections. Additionally, protocols for both a case study and the interviews were used in combination with explicit procedures, increasing standardization and in turn research reliability (Van Aken et al., 2012). Lastly, the researcher has to be aware of interviewee bias, especially with positive answers to questions (Van Aken et al., 2012).
4. Results
This section presents the results of both the quantitative and the qualitative phases. Section 4.1 presents the results of the quantitative phase, based on the questionnaire, and section 4.2 shows the results of the quantitative phase, based on the interviews. Lastly, the results will be verified with an innovation expert in section 4.3.
4.1 Results of Study’s Quantitative Phase - Cluster Analyses
The results of the cluster analysis are presented in this section. Within the cluster analyses the reported cluster scores are the mean and median (in percentages) scores of the clusters (Appendix II). The results of the cluster analyses will be presented based on the related factors/elements of organizational configuration.
Year -of concept launch- 2013 2012 2013 2014 2012
21 4.1.1 Cluster profiles
In order to gain in-depth insights in the interaction between the organizational configuration and (successful) innovation the results of the cluster analyses will be that the interviewees will be chosen based upon the profile of the finalist clusters. From the finalists of the AIA who scored highest on the variables determining the clusters the interviewees will be randomly selected, which is limited by the availability of the SMEs. The cluster analysis showed that 4 clusters were formed based upon the 38 variables. Additionally, the results indicate that the quality of the cluster cohesion is poor to fair. A fair result equates to data that reflects Kaufman and Rousseeuws (1990) rating as fair significant evidence for clustering. This implies that a large part of the cluster member cases are located on their cluster centers. Appendix II portrays the clusters sizes together with the average scores on the comparative variables per cluster. Additionally, appendix II shows the variables by within-cluster importance. This overview indicates which variables contributes to the variance between the finalists-cluster and the non-finalists clusters.
What becomes most evident from the cluster analysis is that of the 4 clusters only one contained AIA finalists. Accordingly, this cluster profile showed that all AIA 2013 & 2014 finalists are clustered together. Table 5 shows a summary of the variables from which the finalists of the AIA 2013 & 2014 differ from the non-finalist.
4.1.2 Collaborations for innovation
With regard to the collaboration factor the collaborating with customers for innovative activities (89,1%) and collaborations with universities and other knowledge institutions for innovative activities (78,2%) variables showed a significant mean difference between the finalists and non-finalists clusters. Additionally, the variables indicating the importance of collaborating with customers for innovative activities (80%) and were significantly higher amongst finalists when compared with the non-finalists clusters.
This implies that finalist of the AIA on average collaborate more with customers, universities and other knowledge institutions when compared with non-finalists. Additionally, finalists identify the collaboration with open innovation platforms and customers as more important in comparison with non-finalists.
4.1.3 Entrepreneurial Learning
For the entrepreneurial learning factor, the variables indicating the importance of the universities and other knowledge institutions as a source of knowledge (2,85) and customers as sources of knowledge (3,57) showed a significant mean difference between the finalists and non-finalists clusters. This means that finalists identified the universities and other knowledge institutions & customers as more important source for knowledge for their innovation activities in comparison with non-finalists.
4.1.4 Human Capital
With regard to the human capital factors, the variables of experience in the industry (3,85) and experience with former startups (5,43) showed a significant mean difference between the finalists and non-finalists clusters. This means that finalists indicated to have more experience in their industry and more experience with establishing former startups compared with non-finalists.
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For the R&D activities & expenditures factor, the variables of total investment (3,42) and investment in percentage (4,60) showed a significant mean difference between the finalists and non-finalists clusters.
This implies that finalist indicated to have higher investments for their innovative activities in terms of total investment and investment in percentage of revenue compared with non-finalists.
4.1.6 Identifying Clusters
The previous sections provide a description of the characteristics of the first cluster called finalists. In order to gain a better understanding of the other clusters derived from the cluster analyses this section will take a closer look these clusters (based upon appendix II). The second cluster, called the affiliated collaborating non-finalists, is characterized by an organization configuration displaying a focus on collaborating with multiple different entities. This cluster showed a significant mean difference with other clusters on the elements of organizational configuration of collaboration. With regard to the element of collaboration, the variables of affiliated with an incubator for innovative activities (17,4%), collaborating with consultants for innovative activities (14,4%), collaborating with the government for innovative activities (11,3%), importance of collaborating with other companies within the concern (37,6%) and the importance of collaborating with consultants (18%) showed significant mean differences with the other three clusters.
This implies that the members of the third cluster on average are more often affiliated with an incubator and collaborate more with consultants and the government and identify the collaboration with other companies within the concern and consultants as more important when compared with the other AIA participants. Therefore this cluster is referred to as the affiliated collaborating non-finalists.
The third cluster, called the supplier oriented non-finalists, is characterized by an organizational configuration aimed at collaborating with and gaining information from suppliers. This clusters showed a significant mean difference with the other clusters on the elements of organizational configuration of collaboration and entrepreneurial learning. With regard to the element of collaboration, the variables of collaborating with suppliers for innovative activities (96,8%) and importance of collaborating with suppliers (100%) showed a significant mean difference with the other three clusters. Additionally, the variable of suppliers as a knowledge source (3,35), as a variable of entrepreneurial learning, was significantly higher amongst members of this clusters when compared with the other clusters.
This implies that the members of the second cluster on average collaborate more with suppliers and identify the collaboration with suppliers as more important when compared with the other AIA participants. Additionally, the members of the second clusters use suppliers as a knowledge source for their innovation activities. Therefore this cluster is referred to as the supplier oriented non-finalists.
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industry associations as a knowledge source (2,00), as variables of entrepreneurial learning, were significantly higher amongst members of this clusters when compared with the other clusters.
This implies that the members of the fourth cluster on average collaborate more with competitors and identify the collaboration with competitors as more important when compared with the other AIA participants. Therefore this cluster is referred to as the competition oriented non-finalists.
Table 5: Variables from which finalists of the AIA differ from non-finalists
Elements –
composing the organizational configuration-
Variables
–on which finalists (cluster nr.1) differ significantly from the non-finalists ( clusters nr.2-5)- Finalists –scores on variables - Affiliated collaborating non-finalists - - scores on variables - Supplier oriented non-finalists scores on variables - Competitor oriented non-finalists - scores on variables - R&D Expenditures and Activities I. Investment total II. Investment in percentage I. 3.42**
II. 4.60** I. 2.26 II. 3.32 I. 2.43 II. 3.31 I. 2.34 II. 3.27 Human Capital I. Experience in the
industry
II. Experience with establishing former start-ups
I. 3.85**
II. 5.43** I. 2.20 II. 3.35 I. 2.18 II. 3.31 I. 2.22 II. 3.16
Entrepreneurial
Learning I. Universities and other knowledge institutions as a source of knowledge II. Customers as a source of knowledge
I. 2.85**
II. 3.57* 1. 2,18 II. 3.65 1. 1.85 II. 3.10 1. 1.88 II. 3.28
Collaborating for innovative activities
I. Collaborations with universities and other knowledge institutions regarding innovative activities
II. Collaboration with customers for innovative activities
III. Importance of collaborating with customers for innovative activities. I. 78,2%** II. 89,1%** III. 80%** 1. 22,9% II. 0,6% III. 0,3% 1. 1,3% II. 8,8% III. 0% 1. 2,8% II. 17,5% III. 15,3%
* Significantly different from non-finalists clusters at 5% level of significance ** Significantly different from non-finalists clusters at 1% level of significance
*** The results were confirmed by a split sample validation test (75% and 50% of all case, Appendix IV)
4.2 Results of Study’s Qualitative Phase - Case Analyses
The results of the case analysis are presented in this section by means of coding schemes based on their influence on the exploration and exploitation activities of AIA 2013 and 2014 innovation finalists. The coding scheme is designed to distill experiences which show the effects of the elements composing the organizational configuration on the exploration and exploitation activities. The subsequent subsection will elaborate upon the process in which and how these innovative ideas for new products, services or processes emerged.
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predefined and additional codes, followed by an elaboration on the findings including an analysis of the interview outcomes. Next, the outcomes related to sub-research question II are presented following the same structure as the results related to sub-research question I. These last two sections will highlight the results from the factors perspectives.
Interview Focus & Highlights
Table 6 presents the focus, in terms of elements of the organizational configuration, of the five interviewees individually during the interview.
Table 6: Summary of focus per interview
4.2.1 Interview Outcomes related to Sub-research Question I
Within this subsection the outcomes and codes related to factors influencing the exploration process are elaborated upon. The findings show 5 relations related to sub-research question I, of which three relations are derived from predefined codes, and
Focus - in terms of elements- Highlights –quotes illustrating focus-
A1 Collaboration & Human Capital ‘’The collaboration with the Erasmus UMC was of critical importance since it allowed us to experiment with our product. In this way valuable information about the key features of our product became clear in real-life
situations. […] it allowed us to find a business model after figuring out who our customers really were and what they truly desired’’
B2 Collaboration, Connectedness
& Human Capital ’The collaboration between people from different expertise and departments leads to improved refinement of the products since together they see more possibilities for improvements and market applications’’
C3 Collaboration, Entrepreneurial
Learning & Human Capital ‘’Due to my experience with founding startups I knew that the first phases are about creating a dominant design. A product type, which contained only core features, was tested and improved in cooperation with customers [municipality] and the university of Delft. In this way the product was improved based upon practical experiences of customers instead of expectations derived from the drawing table, which resulted in a product really valued by our customers: a win-win situation.’’
D4 Entrepreneurial Learning, R&D activities and expenditures, connectedness & centralization
‘’My experience in this [energy] sector allowed me to combine both techniques into one, new and disruptive product. The moment I encountered the problem I
wondered if this resource (salt) would make a sustainable new kind of battery’’
E5 Collaborations,
Entrepreneurial Learning, Centralization
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from additional codes two relations showed up from the interviews. The coding scheme is presented below (see tables 7a to 7e) including detailed descriptions of the codes supported by quotes. Within each subsection (4.2.1 & 4.2.2) a recap of the coding schema will be presented. Additionally, the main quote/argument is marked with italic text followed by a supportive quote, in order to clarify the main argument further. Finally, a quote exemplifying the absence of elements is provided.
4.2.1.1 Impact of Entrepreneurial Learning on Explorative Activities
Table 7a: The Influence of Entrepreneurial Learning on the Explorative Activities Factor
-Predefined factors are marked with
Italic text-
Codes
-Manifestations of independent factors developed during coding process for identifying relevant interview experiences-Frequency -Number of times code was mentioned in interviews-Depth -Number of interviews
mentioning code and frequency per interview (indicated with interview code and percentage of total frequency)-A1 13% B2 15% C3 33% D429% E510% 1.Entrepreneurial Learning Defined as: The commitment to learn, to be open- minded and stimulate interorganizational knowledge sharing Presence of learning from customers has a positive impact on explorative activities Absence of learning from customers has a negative impact on explorative activities Presence of learning from universities has a positive impact on explorative activities Presence of
commitment to learn has a positive impact on explorative activities Absence of
