IN INNOVATION NETWORKS
Katinka Bergema
Ka
ti
nka Ber
gema
by their different professional and organisational backgrounds, which
determine their goals, their roles and responsibilities, and their expectations.
People often struggle to deal with the critical situations that arise from these
differences. This thesis seeks to help practitioners deal with the critical
situations by providing them with an action repertoire for networked
innovation.
Based on an explorative literature study and a study of the collaboration
during the Senseo coffeemaker’s development, we found the factors that
influence the collaboration in innovation networks. When these factors
negatively influence the collaboration, they lead to critical situations.
In a simulation experiment, we asked 35 experienced networked innovation
managers what they would do in these critical situations. This provided an
overview of activities from which an action repertoire could be distilled.
This repertoire of actions can help practitioners to select an action in the
critical situation they experience. With the selection of an action,
practitioners can resolve the situation as it emerges and select an action
suited to the complexity of their situation.
Studying the collaboration in innovation networks extends design research
on the complexity of innovating in networks and the collaboration in
multidisciplinary teams. The action repertoire provides insights in how to
deal with these situations as they occur and enriches the literature that
provides guidance on what to do in networked innovation project.
All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author. Correspondence to: Katinka@Bergema.nl
Proefschrift
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus Prof. ir. K.C.A.M. Luyben, voorzitter van het College voor Promoties,
in het openbaar te verdedigen op maandag 4 januari 2016 om 12:30 uur
door
Catherina Paulina Anna Maria (Katinka) BERGEMA, Ingenieur Industrieel Ontwerpen,
Technische Universiteit Delft, geboren te Voorburg, Nederland
Prof.dr.ir. J. McDonnell Central Saint Martins University of the Arts London Prof.dr. L.T.M. Blessing University of Luxembourg
Prof.dr. P.G. Badke-Schaub Delft University of Technology
Dr.ir. J.P. Joore NHL University of Applied Sciences, Leeuwarden Prof.dr. H.M.J.J. Snelders (reserve) Delft University of Technology
This research was funded by the Innovation-Oriented Research Programme ‘Integral Product Creation and Realization (IOP IPCR)’ of the Dutch Ministry of Economic Affairs, Agriculture and Innovation.
1.1. The real world 12
1.2. The academic world 19
1.3. Problem definition 19
2: Research design 22
2.1. Introduction 22
2.2. Research questions 23
2.3. Research design to find barriers and enablers for networked innovation 24 2.4. Research design to find barriers and enablers for networked innovation in practice 25 2.5. Factors that influence networked innovation and lead to critical situations 28
2.6. Research design to find activities for critical situations 28
2.7. From activities to an action repertoire 30
2.8. Conclusion 30
2.9. Thesis structure 31
3: An explorative literature study on barriers and enablers for networked innovation 34
3.1. Introduction 34
3.2. Networked innovation 36
3.3. Research method for the explorative literature study 38
3.4. Barriers and enablers from the literature 43
3.5. Conclusion 57
4: Retrospective interviews on barriers and enablers for networked innovation 62
4.1. Introduction 62
4.2. Research method for the retrospective interviews 63
4.3. The Senseo project - a case narrative 65
4.4. Barriers and enablers for networked innovation 73
4.5. Conclusion 91
5: Critical situations in networked innovation 94
5.1. Introduction 94
5.2. Discussing the results per cluster 95
5.3. Factors that influence networked innovation 99
5.4. From factors to critical situations 99
7.4. Explanation for the differences in results 139
7.5. Implications and recommendations 139
7.6. Reflection on the method 142
7.7. How does this help practitioners 144
References 146
Appendices 156
1. Interview guide retrospective interviews 157
2. Fictitious case with fifteen critical situations 159
3. Research method simulation experiment 169
3.1. The experimental procedure 169
3.2. Pre-test simulation experiment 172
3.3. Final interview setup 175
3.4. Data collection 176
3.5. Data analysis 178
4. Analysis on cues and aims 19o
5. Coding guidelines 194
6. Reliability of the coding 197
7. Categorisation guidelines 203
8. Reliability of the categorisation 206
Summary 212
Samenvatting 220
INTRODUCTION
Preface
It happened during my master graduation project at Philips
Design. I was working on a project to help improve the sleep
of toddlers. At some point, I heard that another division within
Philips was also working on products for toddlers and sleep.
I asked my supervisor whether I could contact them to pool
our knowledge and see what we could do to integrate our
knowledge and create new knowledge. I was not allowed to
contact them until I had further elaborated concepts.
For some reason, this organisation was unwilling to share their knowledge even though this could help improve the quality of the designs. The reason for this unwillingness, I later learned, was that money streams in the organisation were organised in such a way that management needed to be able to trace which department owned the intellectual property. I then realised that such organisational aspects affect the collaboration in a design team and the collaboration with people from other departments.
Back then, as a graduation student, I saw no way to change the situation and accepted it as such, but till this day, I remain convinced that there should be a way to handle this situation so that the structure of an organisation does not prevent people from sharing knowledge between the different departments. In the end, the aim of an organisation and all its departments is to innovate and develop new products to, for example, increase profit, and to not let this aim be hampered by how the organisation is structured. From that moment on I wanted to find out how we can deal with such situations.
Soon after my graduation project, I was given an opportunity to do just that. Cees de Bont, then Dean of the Faculty of Industrial Design Engineering at Delft University of Technology, approached me to pursue a PhD in a large research project with the aim of ‘Developing a designerly approach to networked innovation.’ This project was part of the Innovation-Oriented Research Programme ‘Integral Product Creation and Realisation (IOP IPCR)’ and was funded by the Dutch Ministry of Economic Affairs, Agriculture and Innovation. The programme aimed to address the challenges of networked innovation and utilised
new approaches to design methodology, innovation management, and collaboration practices. This sought to provide the basis for better innovations and to help build sustainable cross-organisational networks across the Dutch economy. The main goal of this research programme was finding ways to support project managers, product managers, and designers in identifying synergies and risks from a long-term, systemic perspective (Kleinsmann et al., 2009).
Ten companies participated in the programme. The participating companies produce a wide range of products, offer different services and serve different markets; all of them engage in product creation and realisation and have experience in networked innovation projects. The consortium is composed of some larger companies as well as service providers and design consultancies. The participating companies are: Design Initiatief, DAF Trucks N.V., Philips, Driessen Aerospace Group, Friesland Campina, Volvo Aero Corporation (in Sweden), Indes, Sunidee, and Insights International NV.
Ten researchers from different universities and research institutes collaborated to study the challenges of collaboration in innovation networks, using new approaches to study design methodology, innovation management and collaboration practices. As one of these ten, I studied the collaboration between people from different organisations in innovation networks and learn what we can do to help them deal with situations where organisational aspects harm the collaboration as they strive to make today’s complex products and services.
mater, the Faculty of Industrial Design Engineering at the Delft University of Technology, the effect of which was a clear research focus and aim. This faculty, and specifically the research sections Design Methodology and Management & Organisation, has a history of design research, from studying design activity (e.g. Cross, Christiaans & Dorst, 1996; Dorst, 1997) and design collaboration (e.g. Valkenburg, 2000; Kleinsmann, 2006), to its methodical ways of doing design (e.g. Roozenburg & Eekels, 2001; Buijs & Valkenburg, 2005). This history directed the focus of my research efforts to the design research community. The faculty’s research has ‘doing design’ at its core; the theories and methods they develop help designers in practice (Faculty of Industrial Design Engineering, 2014). This directed the aim of my research; an action repertoire to support designers in the collaboration in innovation networks.
1.1. The real world
When design symbolically speaking matured from making chairs to products with more moving parts, designers began working together in teams (Valkenburg, 2000, p.20). The design of complex products required that mono-disciplinary teams became increasingly multi-disciplinary. As this complexity increased further, a hierarchy was needed of collaborating (multi-disciplinary) subteams (Gerwin & Moffat, 1997; Chiu, 2002). Figure 1.1 gives an example of such a structure for the teams involved in the development of a complex product: a car (Gerwin & Moffat, 1997). There is an overall team, responsible for the car, and subteams responsible for the body, the transmission, the engine, the interior, and the assembly. The engine, in turn, requires teams for the cylinder head, the cylinder block, the lower intake manifold assembly, etc. The lower intake manifold assembly team requires subteams
Figure 1.1: Structure of the different teams in a complex product in 1997, an example for the development of a traditional car (Gerwin & Moffat, 1997).
for the lower intake, the fuel rail, and the fuel injection.
The structure of the teams often resembles the product architecture, and interdependencies between parts of the product require collaboration between the teams. High up in the hierarchy, the activities mainly involve coordination by managers; lower in the hierarchy, the activities involve detailed design done by engineers (Gerwin & Moffat, 1997). Lower in the hierarchy, the teams have specific design tasks involving the design of specific parts. As a result, the teams are more mono-disciplinary in nature (Kleinsmann, 2006, p.21). Higher in the organisational structure, the teams consist of representatives, leaders, or managers of the subteams (Gerwin & Moffat, 1997; Kleinsmann, 2006, p.21) and, consequently, are more multi-disciplinary (Kleinsmann, 2006, p.21).
Although we can all agree that a car
is more complex than a chair, it is quite another thing to suggest that product design is increasingly complex. Is this really the case? Or has what was once considered complex become simpler merely because we learned how to do it? I would argue that yes, product design is more complex today than before and, what is more, the speed at which this complexity has increased is also increasing. The (cellular) telephone is an often used example to illustrate this increase complexity (Granstrand, Bohlin, Oskarsson & Sjöberg, 1992; Leonard & Sensiper, 1998; Valkenburg, 2000, p.19; Kleinsmann, 2006, p.11). Valkenburg (2000) represented the increasing complexity in her thesis with figure 1.2.
Compare the cellular telephone with its wired ancestor. When we look at its functionality and its product architecture, the regular, landline phone did not change all that much in the period between the 1940s and the 1990s. Not so with the cellular
telephone (see Figure 1.3). In the late 1970s and early 1980s, during the development of the first mobile phones (e.g., the NMT 450), the main challenge engineers faced was to make the base functions work, ensuring people could communicate from mobile locations without too much noise in the communication. The second generation of mobile phones (e.g., the NMT900, introduced in 1987) was hand portable. This portable aspect required a lower weight and reduced volume, while still offering people fairly long speaking time without the need for them to recharge the battery. To achieve this, its designers needed additional knowledge about, for example, battery technology, assembly design, and advanced display technology. With the introduction of the third generation mobile telephones (or GSM-terminals), the cellular communication systems changed from analogue to digital technology. As mobile phones became a product for the masses in the mid 1990s, the frequency spectra and speech quality were further improved. The development of this third generation depended on knowledge about the
conversion from digital to analogue signals (and vice versa) and software design. This third generation was much more complex than first two generations (Granstrand et
al., 1992).
Kleinsmann (2006) expands the example of the mobile phone and describes their increased complexity. Phones are no longer used only for making phone calls, but also for text messaging, taking pictures, making movies, playing games, and listening to music. It has become a product that expresses someone’s personality (Kleinsmann, 2006, p.11-12). With today’s phones, the number of functions has increased even more (e.g., with video calling and web browsing) and users can connect to stores to buy applications, music, and books, to further personalise their phones with the functions they require. In addition to these extra functions, mobile phones have also become a fashion statement. In 2013, for instance, Mediatek, a Taiwanese semiconductor company, launched the ‘Sugar’ phone, which integrated Swarovski crystals into their phone.
The previous example illustrates that these days people often buy products that are supported with services and service platforms (e.g., Apple’s Appstore). In looking at this expansion beyond the actual product itself, Joore & Brezet (2015) propose a multilevel Design Model, consisting of four levels. Technological products form the base level. When these products are
accompanied by services, we get to the product-service system level. The third level is the socio-technical system level: innovation at this level is often referred to as system innovation. Here, the product-service systems are accompanied by infrastructures, government legislation, as well as cultural and social aspects. The highest level is the societal system where the changes take place at a societal level (Joore & Brezet, 2015).
The ‘Sugar’ phone might still be
described as a technological change whereas
the iPhone with iTunes and the Appstore can be seen as a product-service system. It might even be seen as a socio-technical system: it changed the way people buy music, people personalise their phones. and Apple provides a platform where others can develop applications for iPhone users. In addition to the product’s increased functionality, the system behind the product has also become more complex. The product, the service, and the system all have to be designed (Joore & Brezet, 2015).
Let’s take a closer look at an example of
a socio-technical system: the development of electric vehicles. BetterPlace provided electric vehicle networks and services (they were founded in Israel in 2007 and filed for bankruptcy in 2013). They offered services to enable the adoption and use of electric vehicles by building and operating the infrastructure and systems to optimise energy access and use. To ensure a compelling and, in the end, successful service, BetterPlace needed to provide an integrated solution to electric transportation. They worked together with governments, businesses, and utility companies, such as A123 Systems, Renault, and the Automotive Energy Supply Corporation, to accelerate the transition towards sustainable transportation (see Figure 1.4). It is not just the electric cars that needed to be developed, BetterPlace also depended on long-lasting batteries, charge spots, battery switching stations, driver services, additional electricity generation, and communication systems. The result? A complex network of stakeholders: a car manufacturer, a battery developer, but also
an operator for battery charging stations, and local government support (Betterplace). All stakeholders in the network need to collaborate, depend upon each other, need to be fully committed to develop a sustainable solution and, in the end, strive for a successful implementation of electric transportation. If one partner were to leave, the project would come to a grinding halt. In such collaborations, where people from different organisations work together, this is referred to as networked innovation (Kleinsmann et al., 2009).
The innovation processes in such projects is often seen as a closely-coupled process in which people work together intensively as shown on the left in figure 1.5 (based on Kvan, 2000). During this process, they work closely together to realise a design and jointly go through the process. In reality, many of the processes are more loosely-coupled. The different people in teams collaborate because they have specialised knowledge relevant to the innovation that is being developed. Each person works in their own expertise and
shares their knowledge on their domain of expertise when it is relevant in the process. In figure 1.5 on the right, we see two people working with their own expertise on a shared problem. Here, people contribute to the project at those stages where their knowledge is needed. In this process, team members have phases of individual work and phases of collaborative work (Kvan, 2000).
The Betterplace example resembles such a loosely-coupled innovation process. The people at A123 systems developed the long lasting batteries, the people at Renault Nissan developed the car, the people at Dong Energy made sure that there was enough electricity during the night, etc. All of them had both ‘individual’ work at the different companies and collaborative work, where people from the different organisations shared and integrated their knowledge on the interface between the car and the battery, the battery and the switch stations, the car and the charge spots, etc. These phases of individual work and collaboration are shown in figure 1.6.
But who are these people? Who do we mean when we point to people in an organisation? In discussing the hierarchical team structure in the development of a car, we pointed out that higher up in
the hierarchy, teams are often multi-disciplinary because the representatives of subteams meet to integrate their knowledge. The same seems to occur in these networked innovation projects. These representatives collaborate with each other, as presented in figure 1.7 in the hatched area (the project), but also collaborate with their subteams, lower in their own organisations, as well as with their leaders or managers, higher in their organisations (the dotted lines in Figure 1.7).
De Man and Duysters (2005) studied the effect of different forms of collaboration on innovation. They found that projects in which organisations collaborate in innovation networks have a failure rate of around 50%, even when there is a technological success. While their definition of collaborations as well as for the success rate are rather broad, their study gives an idea of the failure rate of these projects. They explain that the high failure rate of these projects is due in large part to strategic, operational, and cultural differences between partners (de Man & Duysters, 2005). The difference in people’s professional and organisational backgrounds challenge the collaboration in innovation networks. These challenges take the form of, for example, different goals
(Kalay, 2001; van der Duin, Kleinsmann & Valkenburg 2014a), different roles & responsibilities (van der Duin et al., 2014a; van der Duin, Kleinsmann & Valkenburg, 2014b), different expectations (Sonnenwald, 1996; Maurer & Valkenburg, 2014) and different specialised work languages (Sonnenwald, 1996). These different goals, roles and responsibilities, and expectations influence the collaboration and lead to critical situations. People need to act in these situations to resolve or deal with these challenges, but often struggle in choosing what to do (Kleinsmann et al., 2009).
Even though people are motivated to work on networked innovation projects, research by Maurer & Valkenburg (2014) indicates that people lack the
understanding in how to turn these projects into a success. People encounter problems whose novelty prevents them from being solved using existing methods (Maurer & Valkenburg, 2014). Research also suggests that there is also a lack of methods, tools, and techniques to support people in networked innovation and help them deal with the complexity of networked innovation (Maurer & Valkenburg, 2014; van der Duin et al., 2014a). Although people in practice are trying to develop these methods themselves, these methods are still at a trial stage (Maurer & Valkenburg, 2014). Some methods and tools are available that describe the process or help map the different stakeholders and their needs (e.g. The Value Flow model of den Ouden,
2012) or define the scope of the project (e.g. Blizzard & Klotz, 2012), but these do not support the people who have to deal with the challenges when they emerge. These people need a repertoire of actions that they can use in such situations.
1.2. The academic world
When it comes to academic publications on the topic of opening up the innovation process, few have done as much as Chesbrough to garner people’s interest. Since the publication of his book in 2003, Chesbrough and Bogers (2014) analysed the studies on open innovation in other fields, and found that most of these studies take an organisational level of analysis; other levels, such as the individual and team, received less attention. He suggests that further research should focus on these levels. The design research community has long taken in interest in how designers go about designing. Similar to how design practice has evolved, design research has moved from looking at the activity of individual designers (e.g., Cross, et al., 1996; Dorst, 1997) to designers in mono-disciplinary and multi-disciplinary teams (e.g., Sonnenwald, 1996; Chiu, 2002; Kleinsmann, Valkenburg & Buijs, 2007; Valkenburg 2000), to designers as part of networked innovation (e.g., Charnley, Lemon & Evans, 2011; Blizzard & Klotz, 2012). We reviewed the publications on collaboration from the last ten years published in three design journals (CoDesign, Design Studies and Journal of Engineering Design, see Section 3.3).
Although the publications certainly have their merits, there also are some important limitations to point out here, as these define the scope of our inquiry. The publications that look at the collaboration
in mono-disciplinary and
multi-disciplinary teams (e.g., Sonnenwald, 1996; Chiu, 2002; Kleinsmann et al., 2007) do not explicitly distinguish between innovation inside and outside an organisation, but look mainly at collaboration within an organisation. Publications that look at designers as part of networked innovation (e.g., Charnley et al., 2011; Blizzard & Klotz, 2012) are interested mainly in how designers go about and should go about designing complex systems. They take the collaborative aspect of these design projects into account, but it is not their explicit focus. Although Blizzard & Klotz (2012) propose a framework that can help designers in designing for complexity, it does not go into detail regarding the collaboration nor does it help people solve issues as they emerge.
On the whole, much of the available literature can be grouped into two categories: studies either look at collaboration, but not at networks, or they look at innovating in networks, but not at how people collaborate in such projects. When it comes to literature on the collaborative challenges in networked innovation, there is a clear gap (Deken & Lauche, 2014).
1.3. Problem definition
The preceding overview has given us an understanding of the problem designers and the design research community are faced with. Products are part of an increasingly complex system, which requires that designers work together in networked innovation teams to design those systems. People in these projects, each from different organisations, bring in their own specialised knowledge. They
have to share and integrate this knowledge with others involved in the innovation project in an often loosely-coupled process. During the collaborative phases of this process, the representatives of the organisations involved have to collaborate with other representatives but are often hampered in their efforts due to their different professional and organisational backgrounds. These differences
determine people’s goals, their roles and responsibilities, and their expectations. People often struggle to deal with the (critical) situations that arise from these differences. There are few tools available to help people in networked innovation; the tools that are available are often ill-suited to resolve issues as they emerge. Although the design research community has looked both at collaboration and innovating in networks, there is a gap when it comes to the challenges that arise from collaboration in innovating in networks and how to deal with these challenges as they emerge. This provides the problem statement as shown at the right.Chapter 2 explains the research design and the setup of this thesis to answer to the demands from design practice.
When people from different
organisations work together
in order to innovate, their
collaboration is challenged
by the differences in
their professional and
organisational backgrounds.
When these differences
affect the collaboration this
leads to critical situations
where people often struggle
to act.
RESEARCH DESIGN
2.1. Introduction
In networked innovation, people from different organisations
work together to innovate. These people work in teams
and often have different professional and organisational
backgrounds which jeopardises the collaboration in innovation
networks. This thesis uses the term critical situations to refer
to the situations where the collaboration is hampered by these
differences. When situations arise where these differences
affect the collaboration, people often struggle to deal with
these critical situations (see Section 1.1). In answer to the
demands from design practice, this thesis aims to gather an
action repertoire for networked innovation.
This action repertoire can be used by practitioners to deal with these situations and provides them with alternative actions. An important step in assembling this action repertoire is uncovering what activities people experienced in these projects do when they have to deal with such critical situations. Before we can do that, though, we need to find out what these critical situations in networked innovation are. However, although much has been said about collaboration in multi-disciplinary design teams, little has been published on collaboration in innovation networks. Little, too, is known about the factors that influence networked innovation and when these result in critical situations. Because people and literature mostly do not talk about factors that influence the collaboration, but instead describe the barriers and enablers for collaboration, a first step in describing the critical situations is finding the barriers and enablers for networked innovation (see Figure 2.1). In this model, the barriers and enablers form the input to describe the factors that influence networked innovation and lead to critical situations. With these critical situations, we can search for the activities people do when confronted with such situations. Based on these activities, we can provide an action repertoire for networked innovation.
2.2. Research questions
The aim of this thesis is to formulate an action repertoire for networked innovation. The main research question for this thesis, then, is:
What is an action repertoire
for networked innovation?
To answer this question, as described in section 2.1, we first need to know what activities people do when they experience critical situations and what these critical situations are. To find the critical situations, we need to know what factors influence networked innovation and lead to these situations. We find these factors by searching for the barriers and enablers in networked innovation. Thus, before we can answer the main question, we first need to answer the following questions:
1. What are the barriers and enablers for networked innovation?
2. What are the factors that influence networked innovation?
3. What are the critical situations in networked innovation?
4. What are the activities for critical situations?
We answered these questions through three studies. A first study sought to find the barriers and enablers for networked innovation that were already known from previous studies. Because these studies did not explicitly examine the collaboration in innovation networks, we needed empirical data to learn about the barriers and enablers people experience in practice. This empirical data was collected in a second study. An interpretation of these barriers and enablers from both studies provided an overview of the factors that influence networked innovation and led to the critical situations. With these critical situations, we designed a third study to find the activities for the critical situations. Interpreting these activities resulted in an action repertoire for networked innovation. Figure 2.2 provides an overview of the different studies, their results, and the interpretive steps between the studies. This chapter describes the research design of each of the three studies.
2.3. Research design
to find barriers and
enablers for networked
innovation
The first study aims to find the barriers and enablers for networked innovation. As explained in the introduction, the design research community has long taken an interest in how designers design and in the collaboration in multi-disciplinary design teams, but less is known about the collaboration in networked innovation. Earlier publications either look at collaboration, but not in the context of networks, or look at innovating in networks, but not the collaboration. This does not mean, though, that these publications do not contain relevant information. As Verschuren and Doorewaard (1999) suggest, earlier publications provide valuable information
for further research (such as this thesis). Literature also describes what is considered to be ‘known’ about the object of study, or what is not known yet (Dul & Hak, 2008).
Before we can find the barriers and enablers for networked innovation, we need to define networked innovation in more detail. So far, networked innovation has been defined as the activity where people from different organisations work together (Kleinsmann et al., 2009). In networked innovation, all team members have knowledge that is needed for the joint project. In the collaboration in design teams, the team members share their knowledge (Kleinsmann & Valkenburg, 2008), create new knowledge (Deken, Kleinsmann, Aurisicchio, Lauche & Bracewell, 2012), and integrate their knowledge (Kleinsmann & Valkenburg, 2008). We need to be clear about what we mean by knowledge, knowledge sharing, knowledge creation, and knowledge integration. This provides the first subresearch questions for this study:
1.1. What is networked innovation? By answering this question we define networked innovation and it different components (knowledge, knowledge sharing, creation, and integration) and explore the literature to learn about the barriers and enablers for networked innovation. The additional level of complexity of collaboration in networked innovation in which the actors come from different organisations is likely to add new barriers and enablers, but the barriers and enablers found in multi-disciplinary design also influence the collaboration in innovation networks. For example
Badke-Schaub and Frankenberger (1999) describe the presence of communication and a good group climate as factors that influence the collaboration in multi-disciplinary design. It stands to reason that these factors can also influence the collaboration in innovation networks. This provides us with the second subresearch question for this study:
1.2. What are the barriers and enablers for networked innovation from other collaborative contexts?
To answer this question, we needed to find the barriers and enablers for networked innovation and multi-disciplinary design identified by previous studies. We looked for these answers in an explorative
literature study in which we selected several relevant journals from the design research community and reviewed the issues from the last ten years for articles that could provide barriers and enablers for networked innovation. Section 3.3 describes in more detail how these journals and articles were selected and how we distilled the barriers and enablers from these studies.
2.4. Research design
to find barriers and
enablers for networked
innovation in practice
The aim of the second study was to find barriers and enablers in practice. The explorative literature study provided barriers and enablers for networked innovation. Because the literature did not explicitly examine the collaboration in innovation networks, we needed empirical
data to learn about the barriers and enablers people experience in practice. This provided the research subquestion for this study:
1.3. What are the barriers and enablers for networked innovation in practice? To answer this question, we needed an in-depth understanding of what happens in networked innovation. Qualitative methods serve to investigate the nature of phenomena (Blessing & Chakrabarti, 2009, p.79). With qualitative methods, a relatively small number of cases can provide an in-depth understanding of a specific phenomenon (Gerring, 2007, p.1). As such, a qualitative approach is most effective to answer these two questions.
The selection of a research method Our qualitative approach took the form of retrospective interviews around a case. Case studies are a common approach in qualitative research (Denzin & Lincoln, 2008, p.119) and have previously been used to study barriers and enablers for collaboration in multi-disciplinary design teams (e.g., Chiu, 2002; Kleinsmann, Valkenburg & Buijs, 2007; Charnley, Lemon & Evans, 2011). Case studies are a preferred method in a real life context in which the researcher has little control over events (Dul & Hak, 2008, p.4; Yin, 2009, p.2). The case study approach focuses on understanding the dynamic nature present in a single setting and develops detailed and rich knowledge about a single or a small number of related cases (Eisenhardt, 1989; Verschuren & Doorewaard, 1999, p.163; Robson, 2002, p.89). We chose one networked innovation project, as one case can provide valuable
insights for exploratory research (Blessing & Chakrabarti, 2009, p.268; Patton, 2002, p.46).
Case selection
There are several options in selecting a case, but when you only select one case, that selection should be done purposefully. The case is the input for the data collection and defines the entities on which the analysis is done and affects the findings. It also helps define the limits for generalising the findings (Eisenhardt, 1989). To choose an information rich case regarding the barriers and enablers, we use purposive sampling (Patton, 2002, p.230; Silverman, 2010, p.141). Selecting a case purposefully can be done in different ways (e.g. extreme case sampling or intensity sampling) (Patton, 2002, p.230; Gerring, 2007, p.88). The current study used intensity sampling (Patton, 2002, p.234), which uses information rich cases that represent the phenomena of study intensely. Using intensity sampling provides us with rich examples of the barriers and enablers for networked innovation which will later on help us describe the critical situations (Patton, 2002, p.234).
The explorative nature of this study also influences its case selection, as we are interested in the barriers and enablers for networked innovation during the entire development process. A retrospective study is one way to provide this insight (Kleinsmann, 2006). It has the additional advantage that respondents are freer to speak and time has filtered out those factors that only influence networked innovation to a small degree.
We selected the case based on the following criteria:
...for the partnering organisations: • The organisations are large, not SMEs. • The organisations have their own
product portfolio (which excludes design agencies).
• The organisations have different knowledge bases.
• The organisations all have content knowledge about the product to be developed (and not, for example, only about the market).
• The businesses of the organisations are in different product categories. • At least one of the organisations has a
background in product development. ...for the networked innovation project: • The actors from the different
organisations collaborated with the aim of innovation.
• The actors from the different
organisations went through a product development process collaboratively. • The product is an integration of
different knowledge bases. • The partnering organisations
collaborate on a reasonably equal footing (this excludes a buyer-supplier relationship).
• The actors have gone through a whole cycle of the innovation process which has resulted in an introduced product. ...for the product:
• The product has to be innovative for the different organisations in the sense that it shows a ‘gap’ with the existing product portfolio.
• The product/service is introduced in the market (retrospective case). ...for the people:
• The people worked at the interface between the organisations.
• People from diffferent organisations are willing to cooperate for interviews.
Retrospective interviews
We collected the data in the single case by means of retrospective interviews, which have the advantage that they can be focused directly on the case study topics and can give causal explanations (Yin, 2009). Interviews can also provide information about what cannot be observed (Patton, 2002, p.340-341, Blessing & Chakrabarti, 2009, p.271). In this study, this can provide insight in the barriers and enablers that prevent actors from sharing, creating, and integrating their knowledge. Also, interviews are an appropriate method when exploratory work is required and they have the potential of providing rich and highly illuminating material (Robson, 2002, p.272), which can help us to provide insight in the critical situations people experienced.
This study used semi-structured interviews with an interview guide to ensure that a basic line of inquiry is pursued with each person interviewed, while the interviewer is free to explore, probe, and ask questions that will elucidate a particular topic (Patton, 2002, p.343).
Thematic analysis
We distilled the barriers and enablers from the interview transcripts using thematic analysis. Thematic analysis is a common method for studies in which the content has an explicit focus and is a useful method for stories that develop in interview, conversations, and group meetings (Riesman, 2008, p.53). To do this thematic analysis in a systematic manner, we selected ‘Framework’ (Ritchie & Spencer, 2002) as a method which consists of five key stages to qualitative data analysis: 1. Familiarisation, 2. Identifying a thematic framework, 3. Indexing, 4. Charting, and 5. Mapping and
interpretation (Ritchie & Spencer, 2002). The content of each of these steps and how this was applied to this study is explained in section 4.2.
2.5. Factors that
influence networked
innovation and lead to
critical situations
When we have an understanding of the barriers and enablers for networked innovation from both the explorative literature study and the retrospective interviews, we can describe the factors that influence networked innovation and answer the second research question:
2. What are the factors that influence networked innovation?
To answer this second research question, the barriers and enablers from both the explorative literature study and the retro-spective interviews were clustered to make an overview of the barriers and enablers found. These barriers and enablers were transformed into neutral factors, i.e. factors can affect the collaboration either positively or negatively. Section 4.2 describes how this clustering was done. When such factors hamper the collaboration in a negative way, they lead to critical situations in networked innovation. Based on an overview of the factors that influence networked innovation, we can describe critical situations in networked innovation and answer the third research question:
3. What are the critical situations in networked innovation?
The data from the retrospective interviews provided us with the rich and highly illuminating material, which helped us describe the critical situations for networked innovation. These critical situations were used as starting point for the third study.
2.6. Research design to
find activities for critical
situations
The third study helped us find an action repertoire for networked innovation. To arrive at this action repertoire, we first needed to know what activities people do in each of the previously described critical situations. We were not only interested in the activities of people but also in what people want to achieve with their activities and why they select a certain activity. With this in-depth understanding of the different activities, we came to an action repertoire for networked innovation, which is more than the sum of the different activities people do in each of the critical situations. This provided the following subquestions for this study:
4. What are the activities for critical situations?
4.1. What is the aim of those activities? 4.2. Why do they select those activities? Here too, as in the previous study, a qualitative approach was the most suitable form to answer these questions and gain an in-depth understanding of people’s activities and the reasoning for these activities (see Section 2.4).
Simulation experiment
There are several qualitative research approaches available and commonly used to learn more about people’s activities and their motivations for these activities. Verbal protocol analysis, for instance, gives people an assignment and asks them to verbalise the choices they make and their reasoning while they are doing their assignment (Ericson & Simon, 1993). We presented participants with critical situations and asked them what they would do in that situation. By giving all participants the same situations, we learn about their activities and learn whether they would select similar or different activities in the same situation and we get an in-depth understanding about these activities. This provides the opportunity to compare activities of different participants for the same situation and gain in-depth knowledge about people’s activities. Another advantage of this technique is that we get the input from all the participants on all situations and do not have to wait for the situations to unfold (as would be the case with an observational study).
Another benefit of this technique is that in solving the task participants will retrieve their earlier experiences (van Someren, Barnard & Sandberg, 1994). As such, we gain insight in participants’ activities, without losing the value of their earlier experiences gained in practice. We wanted to retrieve their experience in such situations, which are full of rich information, without us having to deal with issues of confidentiality. Participants, provided with a set of
situations, do not have to talk about their own, often confidential, projects and situations, but use their experience to solve the task.
Thinking aloud with structured open-ended questions
Another element adopted, in part, from the verbal protocol analysis technique was the ‘thinking aloud’ part. With participants’ thinking aloud, we could get their
reasoning to select a certain activity and not only our observation of their activities. In the simulation experiment, the participants were asked to think aloud while responding to the situations and questions. With these ‘concurrent protocols’, the participants think aloud while solving the task; in ‘retrospective protocols’ participants reflect on their activities in retrospect (Ericson & Simon, 1993). By using ‘concurrent protocols’, we learn about people’s activities in critical situations, instead of their retrospective interpretation on how they dealt with the situations.
In this study, we do not only want to learn about people’s activities, but also why they select a certain activity and what they want to achieve with the activity. In his book, Schön (1983) describes ‘reflection-in-action’. By forcing participants to make their ‘framing’ explicit, we can learn what activities people do in the given situation and also their reasoning to select an activity and its aim. In addition, the reflection in action may trigger ‘reflection-on-action’ (Schön, 1983). In doing so, the participants may refer to a situation in which they had to deal with the factors as in the given situation and reflect on what they did in that situation. Based on these earlier experiences, they decide on how they act in the given situation.
For experienced practitioners, the reason why they choose a certain activity might rely on tacit knowledge, i.e. knowledge that they cannot make explicit. Both Ericson & Simon (1993, p.xiii) and van
Someren et al. (1994, p.34) describe the limitations of using experts as participants for verbal protocols. After several years of experience the behaviour of the experts becomes automatised and verbal protocols will only present the results of the experts’ act of recognition, and not the detail of their internal processes (Ericson and Simon, 1993, p.xiii). The experts can perform the task very well, but can experience difficulties in explaining how they got to the ‘right’ answer. The verbal protocol illuminates some of the knowledge, but in some cases it seems they are better able to make their knowledge explicit in a discussion afterwards (van Someren et al., 1994, p.34). To make this knowledge explicit, the thinking aloud was complemented with interview questions. Interview questions serve to make explicit what is on experienced people’s mind (Patton, 2002, p.341). Interviewing experts requires an effective organisation of their knowledge to be reliable (Ericson & Simon, 1993, p.xiii). This is why we decided to ask the participants standardised open-ended questions with each situation in the simulations experiment to gain the reasoning for the selection of activities and the aims of their activities. With standardised open-ended questions, the wording and sequence of questions are determined in advance and all interviewees are asked the same questions. This makes it possible to compare data and the dataset is complete for each interviewee and all the topics addressed in the interview. In addition, it facilitates the organisation and analysis of the data (Patton, 2002, p.349).
In analysing the data, we needed to distill the activities with their reasoning and aim from the data. The activities of the different participants needed to be clustered into a set of activities for each of the critical
situations. In order to do so, we used two coding cycles in which the data was chunked and labeled in the first cycle and clustered in the second cycle. The content of each of these steps and how this was applied to this study is explained in section 6.3.
2.7. From activities to
an action repertoire
An overview of all the activities for all the critical situations alone does not provide an action repertoire. To find this action repertoire, another interpretative step was needed to answer the last and main research question:
What is an action repertoire
for networked innovation?
The activities for each of the critical situations needed to be analysed across the critical situations to uncover recurring themes. How this interpretation was done is described in section 5.4.2.8. Conclusion
In order to arrive at an action repertoire, we needed different insights: the barriers and enablers for networked innovation, the factors that influence networked innovation, the critical situation in networked innovation, and people’s activities in these critical situations. To arrive at these results, we formulated the following research questions:
1. What are the barriers and enablers for networked innovation?
1.1. What is networked innovation? 1.2. What are the barriers and enablers for networked innovation from other collaborative contexts?
1.3. What are the barriers and enablers for networked innovation in practice? 2. What are the factors that influence
networked innovation?
3. What are the critical situations in networked innovation?
4. What are the activities for critical situations?
4.1. What is the aim of those activities? 4.2. Why do they select those activities? 5. What is an action repertoire for
networked innovation?
This chapter described the research design for each of the three studies that led to these results.
For the first study, an explorative literature study was set up to define networked innovation and find the barriers and enablers for networked innovation. This explorative literature study reviewed previous studies in design research in different collaborative contexts. This resulted in an overview of barriers and enablers for networked innovation and answered questions 1.1 and 1.2.
Because this explorative literature study had its limitations and practice is often ahead of research, we conducted retrospective interviews around an empirical case to find the barriers and enablers that people experience in practice. This answered question 1.3 and formed the input for the second part. The barriers and enablers found in the explorative literature review and the retrospective interviews led us to the factors that influence networked
innovation (question 2) and the critical situation in networked innovation (question 3).
Part three used the resulting situations in a simulation experiment. In this experiment, experienced practitioners answered how they act in a number of challenging situations. This resulted in an overview of activities for each of the critical situations (question 4). An interpretation of the activities and analysis across the situations led to an action repertoire for networked innovation (question 5).
2.9. Thesis structure
This thesis began with an overview of the challenge designers face in collaborating in innovation networks in chapter 1. Chapter 2 described the research design of three studies: an explorative literature study, retrospective interviews, and a simulation experiment. Chapter 3 describes the explorative literature study, which resulted in a number of barriers and enablers for networked innovation. We expand on these barriers and enablers in chapter 4 with the results from the retrospective interview study and explain in chapter 5 how these were described as factors and result in critical situations for networked innovation. Chapter 6 describes the simulation experiment that resulted in an action repertoire for networked innovation based on the activities for the critical situations found in the previous studies. Chapter 7 concludes this thesis and discusses the results and implications. Figure 2.3 gives an overview of the studies, their results, and the setup of this thesis.
AN EXPLORATIVE LITERATURE
STUDY ON BARRIERS AND
ENABLERS FOR NETWORKED
INNOVATION
3.1. Introduction
In innovation projects, when the required knowledge cannot
be found internally, some organisations choose to collaborate
with other organisation to jointly develop innovations
(Granstrand, Bohlin, Oskarsson, Sjõberg, 1992; Sonnenwald,
1996; Charnley & Lemon & Evans, 2011; Blizzard & Klotz, 2012).
The activity in this form of collaboration, where people from
different organisations work together, is called networked
innovation (Kleinsmann et al., 2009) (see Figure 3.1). In
networked innovation, all team members have knowledge that
is needed for the joint project.
Lower in the organisational hierarchy, the teams are mono-disciplinary in nature, while at the interface between the organisations, the team is multi-disciplinary in nature (for a more detailed description, see Section 1.1).
In networked innovation, people need to share and integrate their knowledge into a new product (or service). They all have a different professional and organisational backgrounds which influences how they understand the importance, meaning and value of objects, concepts, and situations (Kalay, 2001). For example, they (according to Sonnenwald, 1996) speak specialised work languages and have different expectations and (according to Kalay, 2001) have different goals. These barriers and enablers influence networked innovation. In the design research community, little research has been done into the barriers and enablers for networked innovation. Some articles look at whole system design (e.g., Charnley et al, 2011; Blizzard &
Klotz, 2012), but these articles focus on the design processes, design methods, and approaches and do not explicitly focus on the collaboration between people. Other researchers do focus on the collaboration, but do not have an explicit focus on the collaboration between actors from different organisations (e.g., Sonnenwald, 1996; Chiu, 2002; Kleinsmann, Valkenburg & Buijs, 2007). This study aims to explore what the barriers and enablers for networked innovation are. Sonnenwald (1996) argues that an improved understanding of these barriers and enablers can form the basis to develop tools and models that support and facilitate the collaboration. Blizzard and Klotz (2012) also express the need for the development of tools to help designers collaborate in networked teams. Due to the limited number of studies that look into collaboration in innovation networks, we will broaden our scope and also explore what the barriers and enablers are for
multi-disciplinary design. The teams in networked innovation are multi-disciplinary in nature and are likely to experience similar barriers and enablers as in multi-disciplinary design projects, but will also experience additional barriers and enablers due to the increased complexity arising from the different organisations.
3.2. Networked
innovation
The literature uses many different terms to describe the collaboration between different people or organisations with the aim of innovation, such as ‘open innovation’, ‘whole system design’, ‘collaborative design’, ‘multi-disciplinary design’, ‘co-design’, and ‘co-creation’. With that in mind, what do we mean when we say networked innovation? When people collaborate in design teams, they share their knowledge (Kleinsmann & Valkenburg, 2008), create new knowledge (Deken, Kleinsmann, Aurisicchio, Lauche & Bracewell, 2012), and integrate their knowledge (Kleinsmann & Valkenburg, 2008). But how do we distinguish between knowledge itself, the sharing of knowledge, its creation, and its integration?
Knowledge
In collaboration in innovation networks, people have different pieces of knowledge. These pieces of knowledge are embedded in each person’s mental model. Badke-Schaub, Neumann, Lauche and Mohammed (2007) describe several distinct mental models when it comes to design teams. A mental model can relate to knowledge about the task, the process, the group, competence, and context. Here, we are mainly interested in knowledge about the task and the process
as these relate to the design activity. The task mental model includes the knowledge about technology and all the knowledge related to the object to be designed, e.g., its materials, its form aspects, its usage. The process mental model includes knowledge about how to solve and how to handle a task and describes problem-solving strategies and design methods. The word ‘task’ in the work of Badke-Schaub et al. (2007) relates to work that needs to be done, while it is defined as the knowledge about the content of the task. Because the term addresses the content of a task, rather than the task itself, this thesis adopts the word content, similar to Kleinsmann (2006).
Another distinction is made between tacit and explicit knowledge (Polanyi, 1966). Explicit knowledge is knowledge that can be transferred using language. Tacit knowledge, on the other hand, only exists in the minds of people, which makes it impossible to transfer or communicate (Polanyi, 1966). Explicit knowledge is observable in studying the collaboration, where implicit knowledge is not. This thesis will look only at explicit knowledge. In this thesis:
Knowledge refers to information about either the content of a design task or the process of designing that is explicitly made available for other people.
Knowledge sharing
Valkenburg (2000) and Chiu (2002) both state that knowledge in design projects is shared by means of design communication. McDonnell (2009) elaborates on this and describes knowledge sharing as conver-sational turns in which actors contribute from their own expertise. In this thesis:
Knowledge sharing is the activity where different team members express their knowledge in turns.
Knowledge creation
Knowledge sharing can turn into knowledge creation. In design research, many authors adopt Nonaka’s (1994) definition of knowledge creation (e.g., Luck, 2003; Dong, Kleinsmann & Deken, 2013; Kleinsmann, 2006). He argues that knowledge is created by individuals. Knowledge is created through a continuous transferral between tacit and explicit knowledge. The transformation of tacit knowledge into explicit knowledge is seen as key to knowledge creation in innovation, because it creates new explicit concepts from tacit knowledge that can be used in the organisations (Nonaka, 1994). The focus on this transformation is also adopted in design research (e.g., Kleinsmann, 2006). The transformation from tacit into explicit knowledge occurs in the mind of an individual actor, but can be triggered by the interaction with other actors (Nonaka, 1994) or other media, e.g., documents (Court, 1997). The interaction with others, as seen in (multi-disciplinary) teams where actors approach each other and ask each others feedback, creates new knowledge (Nonaka, 1994; Lindkvist, Bengtsson & Wahlstedt, 2011). Based on this understanding, knowledge creation for an individual can be defined as the conversion of tacit knowledge into explicit knowledge.
However, we are interested in knowledge creation as a team activity. The research of Nonaka (1994) and Polanyi (1966) focus only on the individual actor as the source of newly created knowledge (Lindkvist et al., 2011). This is also recognised by researchers
from other fields (e.g., Bereiter, 2002; Stahl, 2006; Lindkvist et al., 2011) and some make an effort to redefine knowledge creation. Lindkvist et al. (2011) describe knowledge creation in a team as an “interactive and communicative process wherein [team members] help each other imagine new ideas and expand on each other’s ideas”. However, what happens during this knowledge creation process remains unclear. Cohen, Lotan, Abram, Scarloss & Schutlz (2002) state that for creative open-ended tasks, such as designing a complex product, a team is more than the collection of individual actors that bring their knowledge, skills, and abilities. By sharing their knowledge, people create new knowledge that results from the interaction between them and goes beyond the sum of the individual knowledge, nor can it be attributed to a single actor (Bereiter, 2002; Cohen et al., 2002). The different knowledge of the actors is shared and explored at the group level which leads to novel results. One actor shares his knowledge, this is picked up by the other actors, interpreted, and established as new knowledge in the team. The meaning is synergetic and arises through the dialogue in the team in which the actors shared their individual perspectives (Stahl, 2006). In this thesis:
Knowledge creation is the activity in which a team explores the different available pieces of knowledge, and expands this knowledge by building on the different ideas. This leads to new knowledge that is more than the sum of the individual pieces of knowledge as shared by the team.
Knowledge integration
Building on the work of McDonnell (2009), Kleinsmann, Deken, Dong & Lauche (2012) define knowledge integration for a collaborative setting as “the collaborative negotiation process of aligning the individual knowledge bases in establishing a shared orientation within the team.” According to McDonnell (2009), this process occurs through “tentative excursions, where one party invokes the position or knowledge of the other to propose or justify a design decision, provoking, in turn, an expert response”.
Knowledge integration, in this thesis, is the activity in which the team negotiates on their different knowledge basis to arrive at a shared and aligned orientation of the innovation process and the innovation content.
Networked innovation
Based on these definitions we could define networked innovation.
Networked innovation is
the activity of actors from
different organisations
working together, on a
reasonably equal footing, in
order to innovate. They all
have different knowledge
that is needed in the joint
project.
During the innovation project, the different actors in the team share their knowledge on the innovation content and the innovation process and create new knowledge. The team integrates the knowledge into an aligned orientation for the content and the process.
3.3. Research method
for the explorative
literature study
This section expands on the research design as described in section 2.3. It describes the research method for the explorative literature study in more detail and describes the selection of the literature, how the barriers and enablers were distilled from the literature and also discusses the six cluster for which barriers and enablers were found.
Selection of the literature
The review of Gemser, de Bont & Hekkert (2012) provides an overview of design journals. From that overview, we selected three design journals that contain studies on the collaboration of design teams; Design Studies, Journal of Engineering Design and CoDesign. Design Studies and the Journal of Engineering Design were selected based on their relevance to the research in this thesis. Although not in Gemser et al. (2012) top three, CoDesign was also included because of its explicit focus on design collaboration.
The three journals, CoDesign, Design Studies, and the Journal of Engineering Design, were reviewed for articles
published in the last ten years which could provide barriers and enablers for networked innovation. This initial selection was based on the titles and abstracts of the articles.
The three journals provided a multitude of articles on collaboration and the increasing complexity of product development, but there are just a few articles that combine those two and also describe barriers and enablers for the collaboration in the design of (complex) products and/or services in practice. The articles that describe barriers & enablers for the collaboration in practice are presented in table 3.1.
These articles describe different colla-borative contexts. The study by Charnley et al. (2012) was the only one that examined innovation networks and the collaboration that occurs within these networks. There were a couple more articles that describe
the collaboration in multi-disciplinary teams and also include the collaboration with actors from other organisations. In table 3.1, these studies are labeled as multi-disciplinary & networked innovation in the last column. There were also studies that described the collaboration in multi-disciplinary design within an organisation. These are labeled as multi-disciplinary design in table 3.1.
The selected articles also referred to other articles that studied collaboration and provided barriers and enablers for collaboration. These studies were also added to the explorative literature study (see table 3.2).
Author Year Journal Focus
Charnley, Lemon & Evans 2011 Design Studies Networked Innovation
Kleinsmann, Valkenburg & Buijs 2007 CoDesign Multi-disciplinary design &
Networked innovation
Larsson 2007 Design Studies Multi-disciplinary design &
Networked innovation
Ostergaard & Summers 2009 Journal of Engineering
Design
Multi-disciplinary design & Networked innovation
Feast 2012 CoDesign Multi-disciplinary design &
Networked innovation
Pei, Campbell & Evans 2010 CoDesign Multi-disciplinary design
Kleinsmann, Deken, Dong & Lauche 2012 Journal of Engineering
Design Multi-disciplinary design
Yang, Dong & Helander 2012 Journal of Engineering
Design Multi-disciplinary design
Yoshimura 2012 Journal of Engineering
Design Multi-disciplinary design
Table 3.1: Articles in the design research community that describe barriers & enablers for the collaboration in design between 2005 - 2015.
This explorative search resulted in a selection of articles that study different collaborative contexts:
• Networked innovation (Charnley et al., 2011).
• Multi-disciplinary design & networked innovation (Sonnenwald, 1996; Kalay, 2001; Chiu, 2002; Kleinsmann et al., 2007; Larsson, 2007; Ostergaard & Summers, 2009; Feast, 2012).
• Multi-disciplinary design (Dougherty, 1992; Badke-Schaub & Frankenberg, 1999; McDonough, 2000; Bucciarelli 2002; Pei, Campbell & Evans, 2010; Kleinsmann et al., 2012; Yang, Dong & Helander, 2012; Yoshimura, 2012). All these articles provide barriers and enablers for networked innovation (see Figure 3.2), mostly from the design research community. The scope of this thesis is on the design research community (see Preface of Chapter 1), in line with my own
background and the faculty’s history in design research. Obviously, the design research community is not the only
community that addresses the collaboration in innovation networks. Examples of other communities are innovation management, psychology and HR-management. These communities also describe barriers and enablers for the collaboration in innovation networks. These communities call these barriers and enablers, for example
dissatisfiers and satisfiers or hygiene factors and motivators. Interestingly, these forms of barriers and enablers show that the negative of a barrier is not necessarily an enabler (and the other way around). We deal with the effect of this notion in the implications and recommendation section in the concluding chapter of this thesis (Section 7.5)
The explorative literature study will describe the barriers and enablers for each
Author Year Journal Focus
Sonnenwald 1996 Design Studies Multi-disciplinary design &
Networked innovation
Kalay 2001 Automation in
construction
Multi-disciplinary design & Networked innovation
Bucciarelli 2002 Design Studies Multi-disciplinary design &
Networked innovation
Chiu 2002 Design Studies Multi-disciplinary design &
Networked innovation
Dougherty 1992 Organisational science Multi-disciplinary design
Badke-Schaub & Frankenberger 1999 Design Studies Multi-disciplinary design
McDonough 2000 Journal of product
innovation management Multi-disciplinary design
collaborative context. Section 3.4 starts with a description of the studies that explicitly focus on networked innovation. This is followed by a description of the studies that focus on multi-disciplinary design and also include the collaboration with people from other organisations. Those studies do not explicitly distinguish between
these two forms of collaboration. Lastly, section 3.4 describes the studies on multi-disciplinary design. From this explorative literature study, this chapter concludes with an overview of the barriers and enabler for networked innovation from the three collaborative contexts described above.
Figure 3.2: The studies from different collaborative context provide barriers and enablers for networked innovation.
