Cross-industry innovation : an exploratory study on cross-industry innovation at product companies

Cross-Industry Innovation

An exploratory study on cross-industry innovation at

product companies

Master Thesis

Author: Rozemarijn de Koomen

Student number: 10248382

Study: Msc in Business Administration – Entrepreneurship & Innovation

Supervisor: Dr. I. Maris-de Bresser

Second reader: Dr. A. Alexiev

Submission date: 24-06-2016

Statement of originality


This document is written by Student Rozemarijn de

Koomen who declares to take full responsibility for the

contents of this document.

I declare that the text and the work presented in this

document is original and that no sources other than those

mentioned in the text and its references have been used in

creating it.

The Faculty of Economics and Business is responsible

solely for the supervision of completion of the work, not

for the contents.

TABLE  OF  CONTENTS  

Abstract  ……….      5  

1.  Introduction  ……….      6  

2.  Literature  Review  ……….              9    

2.1.  Theoretical  framework………              9  

2.1.1.  Open  Innovation  ………..              9  

2.1.2.  Opportunity  recognition  for  CII………..            12  

2.1.3.  Assessment  and  adaption  of  a  cross-­‐industry  ………..          14    

2.2.  Conceptual  model  ………..          18  

3.  Research  Design  ……….   20  

3.1.  Multiple  case  study  analysis  ……….…………..          20  

3.2.  Sample………  ………...…………          20

3.3.  Data  collection…………  ……….   23  

3.3.1.  Interviews  ………        23  

3.3.2.  Secondary  data……….…………        25  

3.4.  Data  analysis  ………..        26  

3.5.  Validity,  reliability  and  generalizability  ………        28  

4.  Results  ……….……….        29  

4.1.  Brief  case  descriptions  ……….        29  

4.1.1.  Consultants  ………        29  

4.1.2.  Nike  ……….        30  

4.1.3.  Spectral  Industries  ……….      31

4.1.4.  BMW  ………      31  

4.2.  Empirical  findings  ……….      32  

4.2.1.  Existence  of  a  cross-­‐industry  solution/opportunity:  search  or    

                     no  search  ………..      32  

4.2.2.  Cross-­‐industry  opportunity  recognition  ……….        37  

4.2.3.  Assessment  and  adaptation  ………        42  

4.2.4.  Innovation  result  ……….        48  

5.  Discussion  ……….………..        50  

5.1.  The  research  question  answered  ………..        50  

5.2.  The  phases  of  CII  ………        52  

5.2.1.  Existence  of  a  cross-­‐industry  solutions/opportunity:  search  or    

                     no  search  ………      52  

5.2.2.  Cross-­‐industry  opportunity  recognition  ………..        53  

5.2.3.  Assessment  and  adaptation  ……….        55  

5.2.4.  The  innovation  result.………...        56  

5.3.  The  contributions  of  this  research  ……….        57        

6.  Conclusion  ……….        60  

References  ……….……….        62  

Appendix  ………..        66  

Appendix  1:  Interview  guide  ……….        66  

Appendix  2:  Codebook  ………..        72  

Appendix  3:  Background  documents  ………...      82  

Appendix  4:  Interview  transcripts  ………      92  

ABSTRACT  

Companies need to innovate continuously, otherwise they will die. That is one of the rare

certainties that companies can rely on. Cross-industry is a new phenomenon which has not

been addressed by the open innovation literature yet. Cross-industry innovation occurs when

a company takes an approach from a different industry, and adapts it so you it can be applied

in their own business. Some parts of cross-industry innovation have been covered in different

fields of research, but the literature has never described the whole process of cross-industry

innovation before. This study aimed to answer the question of how companies recognize

cross-industry opportunities, and how these opportunities are subsequently applied to achieve

product innovation. A qualitative research method was used to answer this broad question.

Empirical data was collected by conducting semi-structured interviews at three companies

who had applied cross-industry innovation, and with three cross-industry innovation

consultants. The empirical findings suggested new concepts in relation to cross-industry

innovation, as well as providing support for concepts as described by existing literature. A

generic process with the different phases of cross-industry innovation is proposed. The results

of this study help theorists to understand the complete process of cross-industry innovation,

and innovation managers can use the proposed process and the related concepts to improve

their cross-industry innovation activities. As the phenomenon is still rather unexplored in the

field of innovation, there is still room for further research that can use this study to examine

the suggested process and influencing factors.

1.  INTRODUCTION  

“You snooze, you lose” is a popular saying which is very true for companies in the current

business climate. If you are not alert and responsive to changes in the environment, you will

definitely be outcompeted. Companies that do not innovate will die. This is one of the few

certainties a company can rely on nowadays. Even though companies are aware of this fact,

the question of how they should innovate remains a tough one. The answer to this question is

constantly evolving. Through the years academic literature has been innovative in the area of

innovation itself, coming up with new approaches and new strategies to achieve company

growth through innovation.

The biggest change we have seen in the area of innovation is the move from closed to

open innovation (Chesbrough, 2006). In innovation theory it was recognized that much more

could be achieved when a company does not only recycle its own ideas in their internal R&D

activities, but also looks at external sources for innovation. Companies started to involve their

users, their distributors, suppliers and even competitors in their innovation process, but also

knowledge institutions outside their value chain, such as universities, research institutions

and consultants (Chesbrough, 2006). This lead to collaborations and knowledge sharing

between different organizations, helping each other to improve existing products, or to create

entirely new technologies for products.

Recently, a new phenomenon called cross-industry innovation entered the arena of

open innovation. The concept is simple: a company takes an approach from a different

industry, and adapts it so they can apply it in their business. The result is often a radical

innovation, using a completely new approach in your industry (Gassmann & Zeschky, 2008).

Cross-industry innovation (later to be referred to as CII) can be used for all types of

way of innovating already knows many examples. For instance, Nike Shox shoes have shock

absorbers in the soles that were originally used in Formula One racing. Another example is

the business model of Airbnb which is being adopted by boating, car and food businesses. In

services CII was applied to improve security procedures for surgeries by copying the

procedures used in the airline sector, such as security checklists (Vullings & Heleven, 2015).

The literature has started to recognize this phenomenon in the last decade, studying

different factors in relation to CII (Gassmann, Daiber & Enkel, 2011; Enkel & Gassmann,

2010; Enkel & Heil, 2014), but researchers have not yet been able to fully describe and

structurize the process of CII. There is not much empirical evidence on the full process of

CII. The open innovation literature still mainly focuses on innovation sources from within the

value chain (Chesbrough, 2006), while CII is about sources from outside the value chain.

Also there are studies in the entrepreneurship field (Shane, 2000), the organizational learning

field (Lane & Lubatkin, 1998), and in the organizational creativity field (Enkel & Gassmann,

2010) which have covered concepts that are involved in the process, such as analogical

thinking and cognitive distance (Enkel & Gassmann, 2010), absorptive capacity (Lane &

Lubatkin, 1998; Enkel & Heil, 2014), and alertness and opportunity recognition (Shane,

2000; Lim & Xavier, 2015). The gap lies between these areas, where the concepts from

entrepreneurship, organizational learning and creativity could be integrated with the open

innovation theory to describe the CII process which is situated under the umbrella of open

innovation.

The goal of this research is to contribute to existing literature by focusing on the

above presented gaps and expanding the knowledge we have about the process of CII. By

doing qualitative research through semi-structured interviews at three different companies

who have implemented CII for their products, as well as with consultants who work in this

area, more insight is provided into how companies recognize cross-industry opportunities and

how they subsequently apply them to their own context to achieve product innovation. The

research question that will guide this study is: How do companies recognize cross-industry

innovation opportunities and how are these applied to achieve product innovation? The

answer to this question can help theorists understand the complete process of CII and its

related concepts. On a practical level these findings could help companies to gain more

insights in how they can improve or start CII activities. This way they will not miss out on

any chances to attain product innovations from solutions that are actually already available.

This could save companies a lot of time trying to reinvent the wheel.

In the following chapter you will find a literature review, including an analysis of all

relevant literature, and the conceptual model based on that literature. After that, the research

design of this qualitative research is explained. This is followed by a chapter of the research

findings, where the inter-related themes from the collected data will be presented. In the

discussion the significance of these findings will be discussed in relation to the theoretical

concepts, together with the limitations of this research and recommendations for future

research. Finally, the conclusion will provide a summary and some final words about this

study.

2.  LITERATURE  REVIEW  

Since CII is a relatively new phenomenon, there is a limited amount of literature about the

concept itself. However, it has been recognized by several researchers (Gassmann, et al.,

2011; Enkel & Gassmann, 2010; Enkel & Heil, 2014), and it has been linked to cognitive

concepts in the areas of organizational learning, organizational creativity, and

entrepreneurship which can help explain the process of CII. CII falls under the umbrella of

open innovation. However, in the open innovation literature, the phenomenon of CII has not

appeared yet. It is important though to review this literature to get better insights in where CII

belongs in this area of research.

2.1  Theoretical  framework  

Firstly, the theory of open innovation will be discussed to get a broad overview of where CII

is situated in this area of research. The process of CII is roughly divided up into two main

phases: firstly the recognition of a cross-industry opportunity, and secondly the internal

process of how the opportunity is translated to fit into this company (Gassmann & Zeschky,

2008). After the theory on open innovation the concepts involved in these two phases are

explained.

2.1.1  Open  innovation  

Traditionally, firms engaged in internal R&D activities leading to internal innovations which

were then distributed as products by the firm. Most companies had relatively ‘closed’

innovation strategies as there was limited interaction with the external environment

(Lichtenthaler, 2011). Open and closed innovation are not mutually exclusive, and innovation

strategy combining the two approaches is implemented most often. According to Trott and

Hartmann (2009) there is a continuum of innovation approaches, with entirely closed

innovation on the one hand, and entirely open approaches at the other end. Open innovation

can be defined as “the use of purposive inflows and outflows of knowledge to accelerate

internal innovation, and expand the markets for external use of innovation” (Chesbrough, p.

2, 2006). The paradigm of open innovation builds on the idea that firms should use external

knowledge as well as internal knowledge, and internal as well as external ways to market,

when they wish to advance their technologies (Chesbrough, 2006). The process of open

innovation works in systems which combine internal and external sources to create new

products. These new systems come together in the new business model of open innovation,

which creates new value from both internal and external ideas. It finds ways to claim a

portion of that value internally, or takes it to the market through external channels outside the

firm’s current business to generate extra value (Chesbrough, 2006).

There are three core processes for opening up innovation: outside-in, inside-out and

coupled (Enkel, Gassmann & Chesbrough, 2009). These core process archetypes can

complement each other. However, it is often observed that only the outside-in process is used

for open innovation. Outside-in is inbound open innovation, using external sources for the

acquisition of new knowledge or technologies (Lichtenthaler, 2011). Companies that do

mostly inside-out as a key process implement outbound open innovation and are mostly

research-driven and have objectives for decreasing fixed costs for R&D, branding or setting

standards through spillovers. They bring out ideas to the market, they license out and/or sell

IP, and they multiply technology through different applications (Enkel, Gassmann &

Chesbrough, 2009). Lastly, the coupled process for open innovation is a combination of

outside-in and inside-out. These companies integrate external knowledge and competencies

while externalizing their own knowledge and competencies.

Even though CII falls under the umbrella of open innovation, this area of research has

not recognized the phenomenon yet. CII is similar to the general concept of open innovation

in the fact that external knowledge sources are used to achieve innovation, and the different

core processes can all be applied to CII as well. However, theory on open innovation remains

mainly focused on knowledge sources within the same market, business and value chain.

Current research has neglected the opportunities of multiplication of intellectual property into

new market fields (Gassman et al., 2010). This means that the type of innovation partners

discussed in the literature are present in the same industry, and by default do have a similar

knowledge base. Companies that try to innovate often look at best practices within their

industry. Even though they are committed to open innovation, they are blindfolded by

looking only at the best performing companies within their industry. This will never result in

breakthrough innovations, because it has already been done before (Vullings & Heleven,

2015).  

External sources for open innovation as explained by the literature are: users,

suppliers, universities, knowledge brokers, competitors or partner firms within the industry

(Gassmann et al., 2010). Yet another external source of knowledge can come from a different

industry. CII means using existing solutions from other industries to meet the need of the

current market in your own industry. In order to do this, one cannot simply ‘copy-paste’ the

extra-industry solution (Vullings & Heleven, 2015). The solution needs to be creatively

imitated and translated to fit its new purpose (Enkel & Gassmann, 2010). Next to using

extra-industry knowledge for one’s company, one can also transfer own patents or technologies to

foreign industries. While the outside-in process leads to more innovativeness, the inside-out

process can help generate additional turnover for just a small amount of effort. For this

research the outside-in process of CII was studied: the process of how companies recognize

extra-industry ideas, but also how they actually translate them to fit their own purpose. Or

like Vullings and Heleven (2015) refer to this process: copy-adapt-paste.

2.1.2  Opportunity  recognition  for  CII  

The process of CII starts with an individual recognizing an opportunity for utilizing an

extra-industry approach.  Recognizing high potential opportunities can lead to great gains in profit,

growth and competitive positioning. According to Lim & Xavier (p. 106, 2015) opportunity

recognition can be defined as “ a discovery of an idea to create new businesses and the search

of information regarding market and technological possibilities”. Shane (2000) found that

not every individual is equally likely to recognize an opportunity. This is because not

everyone has the same prior knowledge. Prior knowledge affects how well a person is able to

recognize a new technology as an opportunity, as well as what approach one might choose to

exploit that opportunity. Of course, when one has a lot of knowledge about the source of the

opportunity, it is quite easy to recognize it. However, what if there is a knowledge gap

between the source of the opportunity and the person that is looking for it? Even if there is a

great opportunity to be seized, one would probably not recognize it if the individual does not

have any knowledge of the matter. The size of this knowledge gap between two people or

entities is also called cognitive distance. A high cognitive distance has the benefit of novelty,

but has the problem of incomprehensibility (Nooteboom, 2000).

The purpose of open innovation is to accelerate internal innovation by the use of

external knowledge (Chesbrough, 2006). External sources might have different knowledge

than a company’s own knowledge base, which can lead to new insights for own products. In

older studies a high cognitive distance was regarded as a counterproductive factor, due to the

incomprehensibility of the other entity (Enkel & Gassmann, 2010). However, in more recent

research it has been found that there is a positive relationship between cognitive distance and

innovation performance (Enkel & Gassmann, 2010), since the novelty of the innovation is

often very high. In cross-industry contexts, the cognitive distance between the knowledge

source and the innovation problem is high by default (Gassmann & Zeschky, 2008), which

means that cross-industry opportunities are often hard to understand and to recognize, but do

bring a lot of novelty.

In order to profit from an opportunity, it must first be discovered. Lim and Xavier

(2015) and Shane (2000) argue that people only discover opportunities when they are not

actively looking for them. According to these authors, systematic search is not possible when

you do not know what piece of information you are looking for. However, according to

Vullings & Heleven (2015), it is possible to improve the ability to see connections and

discover new opportunities through training. One could say that this improves the alertness of

the opportunity searcher, which in turn increases the chance to discover new opportunities

(Lim & Xavier, 2015). Alertness, as explained by Lim & Xavier (2015), is the ability to

notice changes, shifts and opportunities that have been overlooked. This ability also requires

the skill to consider various options and possibilities and to make connections between these.

A factor that can help making new connections is the individual’s network. This can enhance

the opportunity recognition since one gains access to support, information and assistance

(Lim & Xavier, 2015).

CII has been associated with analogical thinking, which is found to be important for

radical innovation, and can increase firm performance (Enkel & Gassmann, 2010). An

analogy is a cognitive mechanism to retrieve existing knowledge and to apply this to new

problems (Schild, Herstatt & Lüthje, 2004). This concept is slightly overlapping with

alertness for opportunity recognition as described by Lim and Xavier (2015). However,

unlike opportunity recognition, this process is not only about making the connection between

the two industries, but also about how this cross-industry knowledge is applied to a new

problem in an organization (Schild, Herstatt & Lüthje, 2004). In order to do this successfully,

one must see the similarities between the two situations, and be able to translate this

knowledge from the old to the new situation. Consequently, the first step of analogical

thinking, which is making the connection between two different contexts, is relevant for the

opportunity recognition phase. The second part of analogical thinking, which is to translate

knowledge from the old to the new situation, would be more relevant for the assessment and

adaptation phase of the CII process.

All in all, opportunity recognition is the first part of CII. It is the key to the rest of the

process, as without the discovery of a cross-industry opportunity, it is not possible to achieve

innovation through extra-industry solutions. Knowledge is of very big influence on this first

phase. When prior knowledge of the solution searching entity is very different from the

knowledge base of the entity who has already found a solution, it is very hard to recognize

this as a cross-industry opportunity. However, if this opportunity is recognized, the

innovation value can be very high due to the novelty of the solution. It is hard to look

structurally for cross-industry opportunities, but there are factors that can improve or have an

impact on opportunity recognition, such as alertness for new opportunities, a network that can

provide information and support, prior knowledge and cognitive distance. In this study, I

looked at all of these factors and examined whether they support or influence opportunity

recognition for CII, and if yes, how this happened.

2.1.3  Assessment  and  adaptation  of  a  cross-­industry  solution  

The previous sub-section was about the recognition of an opportunity for CII. Once this

opportunity is recognized, its success depends on how this knowledge is applied within the

company. As explained in the previous sub-section, CII has been associated with analogical

thinking (Enkel & Gassmann, 2010). The second step of the analogical thinking, which is

taking the knowledge from one context and applying it to a new context (Schild, Herstatt &

Lüthje, 2004), is relevant for the phase of assessment and adaptation of CII.

Just as in opportunity recognition, cognitive distance also plays a role in analogical

thinking during the application of the new knowledge. Non-obvious analogies from more

distant pieces of knowledge (like a different industry) enhance creativity, which gives greater

potential for breakthrough innovations (Schild, Herstatt & Lüthje, 2004). Distant analogies

are associated with higher innovative potential, while at the same time limiting risk and

uncertainty (Gassmann & Zeschky, 2008). In several studies, an inverted u-shaped

relationship between cognitive distance and innovation performance was found (Nooteboom

et al., 2007: Enkel & Heil, 2014). This means that there is an optimal amount of cognitive

distance for innovation performance; the information source should not be too related, but

also not too unrelated. Enkel and Gassman (2010) however did not find that the degree of

cognitive distance influenced the quality of innovation. A higher or lower cognitive distance

does not cause a more explorative or exploitative innovation outcome. What they did find is

that CII in general leads mainly to breakthroughs and radical innovation instead of

incremental innovation. Although distant analogies have more innovation potential, there is

also a downside to it. Because there are less similarities in context and situation than with

near analogies, it is harder to retrieve the knowledge and to apply it to the new situation

(Enkel & Heil, 2014).

Gassmann and Zeschky (2008) have done a multiple case study about the role of

analogical thinking in respect to breakthrough innovations. They argue that drawing a distant

analogy is the most critical part when looking for cross-industry opportunities. After the

analogy is drawn, the companies have a simple but powerful solution, which can be adapted

to its new context. They found that this adapting phase is quite easy once the analogy is

drawn, supporting their prediction that CII entails limited risks. However, for both stages of

the process, which were recognition of the problem and the solution (stage 1) and the

assessment and adaptation of the solution (stage 2), prior knowledge and building new

knowledge to get more understanding of the problem and the solution was critical for

success. Also, based on the cases that were successful in achieving radical or breakthrough

innovation through drawing extra-industry analogies, they proposed a generic process of

analogical thinking which they think might help companies to search for cross-industry

opportunities for innovation. Up to date the process is still unproven, but it does entail stages

as suggested by other academics. The process is presented in Figure 1.

For this research, Gassmann and Zeschky’s (2008) process of product innovation

through analogical thinking was used as a rough guideline, meaning that in answering the

research question I also separated the process into two phases; before and after the

recognition of the solution. This generic process was used as a guide to examine the practice

of cross-industry innovation in empirical cases. Several interview questions were based on

the different phases of the process.  

Another process that is important in relation to successful application after

opportunity recognition has taken place is the firm’s absorptive capacity (Lane & Lubatkin,

1998; Enkel & Heil, 2014). According to Lane and Lubatkin (1998), this is the capability to

value, to assimilate and to utilize external information. In other words, it is about the

capability to acquire new knowledge and apply this in the company, which is practically the

assessment and adaptation phase of CII.  Similar to opportunity recognition and analogical

thinking, absorptive capacity is found to be a function of the firm’s prior related knowledge.

This knowledge is often developed and maintained as a byproduct of routine activities such

as manufacturing, marketing and R&D. When a firm wants to acquire distant knowledge that

is unrelated to its current activities, it must also invest in accumulating relevant knowledge

(Lane & Lubatkin, 1998; Enkel & Heil, 2014). By gaining new relevant knowledge the firm’s

absorptive capacity is improved, and new information from outside of the company can be

assimilated and applied through the firm more easily (Lane & Lubatkin, 1998). Next to a

firm’s knowledge base, absorptive capacity is also dependent on the communication systems

through which knowledge can be transferred across and between subunits of the firm. (Cohen

& Levinthal, 1990; Enkel & Heil, 2014). As accumulating new knowledge and the internal

communication systems have an effect on absorptive capacity, they are also expected to have

an effect on the assessment and adaptation phase of CII, since the two are similar.

To summarize the relevant concepts in the second phase of CII and their influencers,

literature describes two mechanisms: analogical thinking and absorptive capacity. For both of

these mechanisms prior knowledge is an important factor. Success of the application of a

cross-industry solution seems to be very dependent on the knowledge base that the firm

already has. This is similar to the first phase of the CII process, cross-industry opportunity

recognition. However, literature about absorptive capacity also stresses that the firm can still

work on accumulating new knowledge during the process, and also assimilate this through

communication systems in the company (Lane & Lubatkin, 1998). This will improve their

ability to implement a new solution throughout the company. Unlike general open innovation

initiatives, the CII process is often initiated by coincidence (Gassmann & Zeschky, 2008).

Gassmann and Zeschky (2008) recognized this and proposed a process which should promote

structural search for CII solutions, in which analogical thinking plays a critical role. This

generic process, seen in Figure 1 above, will be used as a rough guideline to structure this

research. The factors of cognitive distance, accumulating new knowledge, communication

systems, and analogical thinking are all relevant to the second phase of CII. Consequently,

this research examines these concepts and their effects on CII.

2.2  Conceptual  model  

In Figure 2 the conceptual model is presented. From the literature can be concluded that the

opportunity recognition for CII is the most critical and essential part of the overall process.

To recognize such an opportunity, one must first discover similarities between two

completely different contexts, before the process can continue. The ability to to do this, is

influenced by the prior knowledge (Shane, 2000), the cognitive distance between the

innovating company and the extra-industry source for the solution and analogical thinking

(Enkel & Gassmann, 2010), as well as the network and the organization’s alertness for

opportunities (Lim & Xavier, 2015). After opportunity recognition, the assessment and

adaptation of the solution form a phase together. Influencing factors on this side are again

prior knowledge, cognitive distance (Nooteboom et al., 2007), and analogical thinking (Enkel

& Gassmann, 2010), as well as accumulation of new knowledge (Lane & Lubatkin, 1998),

and the internal communication systems (Lane & Lubatkin, 1998). For the conceptual model

the 3 steps of the process are chosen which came back in many cognitive studies. With the

process as proposed by Gassmann and Zeschky (2008) as starting point, throughout which

the concepts that were identified as important in the literature review were added. Before the

process there is the existence of an extra-industry solution somewhere, and after the process

is done, it is expected to have a radical innovation as a result (Enkel & Gassmann, 2010).

Also shown is how CII is situated under the umbrella of open innovation. Focus of this

research was on the outside-in process of open innovation, since this is the core process that

is most frequently used.    

As it can be seen, it is still a very broad model, based on the many different

perspectives in the literature. This is appropriate, since the research will be exploratory and

qualitative, and the model needs to be open to new insights. This conceptual model only

gives a theoretical idea of what the process might look like, and what to focus on during

research.  

3.  RESEARCH  DESIGN  

In order to answer the research question, a qualitative research method was used. This area of

research is still relatively unexplored, and the research question is a ‘how’ question, making

it very open and broad in nature. In depth qualitative research is helpful to do this kind of

exploratory research, and to understand the process from the perspective of the ones involved

(Perry, 1998). The conceptual model, presented in the previous chapter did not serve as a

hypothesis, but as a guideline. It provided a structure for this research, and was still open to

changes in case the empirical data provided new insights. In the following paragraphs the

research approach will be explained.

3.1  Multiple  case  study  analysis  

The strategy for this qualitative research was a multiple case study. A case study is defined as

a form of social science which “investigates a contemporary phenomenon in its real-world

context, especially when the boundaries between the phenomenon and the context may not be

clearly evident” (Yin, p. 2, 2013). Case studies are very helpful in gaining more insights in

the real-life context and the perspective of the company (Yin, 2009). This way more

understanding could be gained about the stages the company went through in this process.

Also, by studying multiple cases, there is the possibility of comparison between them and

direct replication, which makes the analytic conclusions more powerful and improves

external validity (Yin, 2009).

3.2  Sample  

The sample for this multiple case study was a set of CII consultants, and three companies that

achieved product innovation through CII. The consultants were together treated as one

separate case, since they had the same profession and had the same high-experience

background. The decision to also interview consultants was made because they one of the

few who have seen this process at many different companies, and have developed a mental

database of what such a process generally looks like.  

One criterion for selection of the companies was that they had already gone through

the process of CII, so it would be a retrospective study and all steps of the process could be

discussed. This was beneficial because this way there was a clear overview within the

company of the whole process, and more secondary documents were available for analysis.

Another criterion for the selection of the sample was that the company had achieved product

innovation. This was done particularly for cross-case analysis reasons; the processes of

different types of innovations could be more difficult to compare and to find patterns in.

Furthermore the companies were not restricted to one industry. Three companies were chosen

that were different in industry, different in size, different in location, and different in the

end-user of their product. These characteristics were not expected to have major influence on the

process of CII, and to find patterns across these three different cases would make the results

more generalizable, since that would suggest that in not just one, but all types of different

industries this is the case. In the Table 1 an overview of the cases is given. A more detailed

presentation of these cases and the types of cross-industry innovations they engaged in is

offered in the Results chapter.

Table 1: Representation of the cases

Case

Product

Description

Number of

employees

Location

Number of

interviews

Consultants

Consultancy

-

Belgium

3

Nike

Sports footwear and

apparel

62,600

United States

2

Spectral

Industries

Spectrometers (optic

instruments)

3

Netherlands

2

BMW

Cars

122,244

Germany

2

Multiple units of data were used for each case. For selecting the individual

interviewees, I used my personal network to find the right persons, as well as writing

messages through LinkedIn and e-mail to people who might be suitable for this research, and

cold calling to organizations. For LinkedIn I acquired a LinkedIn Premium trial account so I

could find the right people at the right companies through extended search options, and

approach these people through inMail. Also I called organizations and people who might

know companies or consultants involved in CII, such as the ESA BIC, the track coordinator

of Entrepreneurship & Innovation at the UvA, the authors of the CII book ‘Not Invented

Here’ and the Innovation Centre of ABN AMRO. For the consultants the only criterion was

that they were active consultants in CII. For the company employees it was important that

they had been involved in at least one CII process. In Table 2 the nine individuals that were

interviewed are presented. The respondents did not oppose to their names being published for

this research.

Table 2: Representation of the individual respondents  

Name

Company

Job description

Ramon Vullings

Independent Consultant

Speaker / cross-industry expert /

ideaDJ

Mathieu Mottrie

CREAX (CII consultancy firm)

Managing Partner / CEO

Marc Heleven

Independent Consultant

Cross-industry innovation expert /

innovation websearch / ideaDJ

Rory Fraser

Nike

Product / Consumer Insights

Manager

Carsten Franke

Nike

Color Designer Footwear Running

Ad Maas

Spectral Industries

CEO

Marijn Sandtke

Spectral Industries

CTO

Bernhard Schambeck

BMW

Head of Technology Transfer &

Scouting

Markus Müller

BMW

Specialist for Technology Foresight

& Technology Scouting

3.3  Data  collection  

The main data source for this study is interviews. This data collection method allows the

researcher to gain detailed and deep knowledge about the process from the interviewees’

perspective, and also enables to ask about the context, conditions and factors that were

influencing their decisions (Leech, 2002). In addition to interviews, secondary data such as

company documents were used to get more rigorous data, this data triangulation will improve

the overall quality of research (Gibbert & Ruigrok, 2010).

3.3.1  Interviews  

Before the official interviews, two informal interviews were done with one of the consultants

Ramon and with Rory from Nike. From these interviews I gained insights into what topics of

CII they found important and what I should pay attention to in the official interviews. Also,

this allowed me to already get an idea how CII worked in real-life. For the official interviews,

semi-structured questions were used to gather the data. The interviews took place either

through Skype, or face-to-face. The Skype interviews I deducted from my home, and for the

face-to-face interviews the location was selected by the respondent. It was made sure that the

setting was quiet and private. This ensured good quality of the recording, and the interviewee

was more comfortable telling every detail when no other people could hear the interview. The

interviews took between 45 and 90 minutes and were recorded and transcribed afterwards.

All interviews were conducted in English, except for one, which was conducted in Dutch.

The Dutch interview was transcribed in the same language, and the relevant parts that were

later used as evidence were translated to English. The transcripts are presented in Appendix

4. Based on the theory, the following subjects were covered in the interview questions:

-  

Recognition of a cross-industry opportunity

-  

_{Cognitive distance between the source of the extra industry-solution and the company}

-  

_{Organizational network knowledge access & support}

-  

_{Overall alertness for the opportunity}

-  

_{Assessment of the opportunity for CII}

-  

_{Adaptation of the extra-industry solution to fit the company’s purposes}

-  

_{Prior knowledge that could be used during this project}

-  

_{New knowledge that was gained during this project}

-  

_{Internal communication during the project}

-  

_{The result that was achieved by using a CII solution for innovation purposes}

In Appendix 1 the interview guides that were used for the interviews can be found. Since the

consultants are involved in a different way than the company employees that were

interviewed, the questions for them were slightly changed to fit their context. Hence, there

were two separate interview guides for consultants and for company employees. Additional

questions were sometimes used, depending on the respondent’s background. Apart from these

questions probes and follow up questions were used to get more details from the respondent’s

answers. This increases the richness of the data obtained (Patton, 1987). Questions starting

with “who”, “what”, “where”, “why” and “how”, were all used in the interview, depending

on the type of elaboration sought. The following three questions are examples of questions

that were asked during the interview:

1.   Why was specifically the … industry used for this innovation? Could other industries

have provided the same kind of solution?

2.   How does (company)’s prior knowledge base affect the way they are able to recognize

solutions in different industries?

-   Are there big differences between (company) and the industries they work with?

-   Do you feel (company) always easily understands and processes the information

coming from new industries?

3.   Can you describe the steps made to adapt the original solution to fit your product?

-   Is this considered to be a complicated process?

A pilot interview was done with Rory Fraser. He was informed that he was the first

respondent for the interview, and that I might get back to him with additional questions later.

Luckily, this has not been necessary since the interview and the questions asked resulted in

enough richness in data.

3.3.2  Secondary  data  

To support the data from the interviews, additional documents were collected. From the

consultants the following data was obtained: documents including the standardized CII

process steps, documents describing the general approaches for different types of clients,

useful CII inspiration websites, and examples of the innovation projects that they helped their

clients with. From the companies I received documents such as: internal company website

updates about the innovated product, news updates about the new product, videos about the

innovation process, technological reviews of the innovation, external blog updates about the

new products, and the company’s own website description about the innovations.

These additional documents were all analyzed in the same manner as the primary data

from the interviews, in order to find patterns which were also found in the interviews, or even

new insights which could not be obtained through interviews. The next section will elaborate

on the data analysis.

3.4  Data  analysis  

After conducting and transcribing the interviews, the transcriptions were coded. Since this

research is a combination of an inductive and a deductive approach, both building upon

existing theory and building new theory from the data, I used a hybrid approach as described

by Fereday and Muir-Cochrane (2006). This means that before the fieldwork I had already

developed a list of codes based on the theoretical framework, but this list was not set in stone,

and open for changes based on the collected data.

As shown in the theoretical framework in Figure 2 in the previous chapter, the general

steps of the CII process were expected to be: 1. the existence of an innovation problem, 2.

cross-industry opportunity recognition of an extra-industry solution, 3. assessment of the

opportunity, 4. adaptation of the extra-industry solution, 5. radical innovation. Additional to

that, influencing factors for this process concluding from the literature are: the network,

alertness for cross-industry opportunities, prior knowledge, cognitive distance, the

accumulation of new knowledge, and the internal communication of the company. All

together these elements resulted in the following categories.

-  

_{The innovation goal/problem}

-  

_{Cross-industry opportunity recognition}

-  

_{Use of the network}

-  

Prior-knowledge

-  

_{Cognitive distance}

-  

_{Assessment of the opportunity}

-  

_Adaptation

-  

_{Accumulating new knowledge}

-  

_{Internal communication}

-  

_{Innovation result}

Upon these theory-based categories the codes were built. I started out with these codes above

as open codes. During analysis of the data new codes and categories were found, since the

literature-based codes did not cover everything that happened in reality. Examples of

inductive codes are: having an open attitude, source inspiration, CII storytelling, prototyping

and assessment criteria. Also, I started using axial coding when I noticed that some of the

initial open codes could be the axial codes for several subcodes, whereas other open codes fit

better as subcode under a different axial code. For instance, ‘assessment’ became an axial

code for ‘prototyping’, ‘criteria’ and ‘decision making’. In Appendix 2 the final codebook

that was used to analyze all interviews can be found. Here the definitions of the codes are

explained, and an example quote of each code is provided.

Initial coding was done with pen and paper, and a frequently changing codebook in a

computer document. First everything was coded by hand, and the computer document of the

final codebook was finalized. Subsequently, all transcripts were uploaded in NVivo, and the

final codes could all be entered at once, and the code analysis could be copied from the paper

versions of the transcripts. Cases were created so one could view what one case or one

person said about certain topics, and to compare what different people said about the same

topic. This was done by using cross-tabulation. This was very useful for cross analyzing the

cases, and looking for patterns. The secondary data was analyzed according to the same

codes as the transcripts, providing support for the interview data.

Since this is a qualitative research method, the results could not be visually presented

using graphs or numbers. In order to make the results comprehensible and visually attractive,

tables were used explaining certain patterns and relationships found in the data.

3.5  Validity,  reliability  and  generalizability  

Certain measures were taken to ensure validity and reliability. To ensure construct validity, I

used multiple sources of evidence for triangulation purposes. This makes the research

stronger. Also I established a clear chain of evidence, so the reader is able to reconstruct how

I went from the research questions to the final conclusions (Gibbert & Ruigrok, 2010).

Internal validity was ensured by matching patterns from all of the interviews and the

documents, which shows relationships between certain factors. Also, a clear research

framework was used to build explanations, by providing tables and clear descriptions about

the relationships between different factors. The external validity or generalizability is already

a strength since in a multiple case study this is already much better due to replication of the

cases. Also, the cases I selected all have very different settings. Consequently, the patterns

found between these are more generalizable than patterns found in just one specific context.

A limitation of a case study is that from only a few cases I am not able to tell anything about

the whole population. However, I am able to generalize my empirical observations to theory

(Gibbert & Ruigrok, 2010). For reliability I tried to be as transparent as possible, by

recording all interviews and transcribing these, and establishing a case study database in

which all of my data is organized, so they can be retrieved for future investigators. Lastly, I

made sure my research is replicable by clarifying every step along the way.

4.  RESULTS  

In this chapter the results will be presented and analyzed. First, a brief description of each

case will be provided. Next, the analysis of the results will be presented in chronological

order of the process as emerged from the data.

4.1  Brief  case  descriptions  

To fully understand the information that is presented in the analysis, some background

information of the cases is required. The paragraphs below will briefly describe the cases in

terms of the case characteristics, examples of cross-industry innovation within this case, and

the way the interview respondents are involved in these CII processes. See the Appendix 3

for the documentation that these background descriptions are based on.

4.1.1  Consultants  

This is the only case that does not have the company as a binding factor, but the profession of

the respondents. All three respondents have experience in CII, in product innovation, and also

process, service and business model innovation. Ramon helps companies in their innovation

activities, either on the ‘fuzzy front end’ of an innovation project, where inspiration and

brainstorm sessions are key, or he helps on the implementation side. Marc Heleven is mostly

active in the ‘fuzzy front end’ of innovation. He provides companies who are struggling with

a certain innovation problem with inspiration for innovation. Marc and Ramon together wrote

the book ‘Not Invented Here’, which provides examples and tools for CII. Mathieu Mottrie

owns his own CII consultancy company named CREAX. His company provides consulting

services based on the notion that ‘someone else has already solved your problem’.

Examples of innovation projects these respondents were involved in are: transferring

pressure technology from disposable beer barrels to deodorant sprays, new ways of

digitalization for Volkswagen Audi cars, and transferring knowledge from the flowerculture

to Engie, an energy company.

4.1.2    Nike    

In their communications to the consumer, Nike prides itself in making the most innovative

sportswear. In their innovation activities, they do not restrain themselves to just their own

industry. One example of CII at Nike is a material called Lunarlon foam. Many Nike sport

shoes have this material in their soles, making a lightweight and responsive cushioning

experience (Appendix 3) The material originated the space industry, where NASA used it for

shock impact reduction. The foam can take the shape of impressed objects, and then returns

to its original shape (Appendix 3). Another example is NikeGRIP, an anti-slip solution used

in socks. To improve traction of the sock, they looked at geckos, which can climb walls

thanks to the traction provided by tiny hairs on their toes. The nanofiber that Nike developed

does the same thing, by creating far more contact points between the foot and the shoe

(Appendix 3).

One of the respondents was Rory Fraser, working in the running department of Nike.

His job is to be the link between the athlete and the development of new products at Nike. He

drives an innovation process before it started, by talking to athletes and taking their input to

the innovation team. He is also involved when the innovation project is already in the

prototyping phase, and the prototype needs to be tested with athletes. Carsten Franke, the

second respondent of this case, is a color designer, and comes in in the innovation process

when it is a bit further along and new color ways need to be designed for new types of

materials. He cooperated on one big innovation project in which he also worked closely with

the innovation team.

4.1.3  Spectral  Industries  

Spectral Industries is a startup, with only 2 fulltime employees. It is an entrepreneurial

spin-off of the Netherlands Organization for Applied Scientific Research (TNO). Their whole

company is based on one CII project, which is transferring the technology from an optical

instrument which was made for the European Space Agency to applications that can be used

here on earth. About 10 years ago, the spectrometer was originally developed with the

purpose to find life on Mars. Even though the spectrometer itself was not a new invention,

the one made for ESA was special in the sense that it was made to endure severe

environmental conditions, such as a rocket liftoff impact, dust, and very high or low

temperatures. Also it was low weight and compact. This made the spectrometer more suitable

for different industries. The instrument can be very valuable in for instance the mining

industry, the medical diagnostic industry or the steel-making industry. The first respondent

for this case, Ad Maas, was involved in the development of the instrument for ESA ten years

ago, and since Spectral Industries was launched in 2015 he has been CEO of the startup.

Martijn Sandtke is CTO, also has a background in optics, and has been with Spectral

industries since it started a little over a year ago.

4.1.4  BMW  

The philosophy of BMW is always staying one step ahead and to drive long term

technological leadership (BMW). To do this, they are constantly scanning new trends and

technologies which might influence the automotive industry. An example of cross-industry

innovation at BMW is the iDrive controller that was developed in the end of the nineties

(Appendix 3). Back then, the round knob which could be pushed in eight directions, was a

new and intuitive way to navigate through the car’s computer system. The inspiration for the

innovation came from one of their designers, who used such a knob in his workstation to

control a designing program. Also, they are working with augmented reality for their car

brand Mini (Appendix 3).

The two respondents for this case were Bernhard Schambeck and Markus Müller.

Both work at the Technology Transfer and Scouting division of BMW. The goal of this

division is to scout new technology trends from outside the automotive industry and to find

ways to incorporate these within BMW. Also, for the development department they act as a

technology consultant. Bernhard is head of Technology Transfer and Scouting, and Markus is

one of the specialists working there.

4.2  Empirical  findings  

In the sub-sections that follow, the inter-related factors are identified and described for each

phase of the CII process. These will be supported by quotes from the collected empirical data.

The phases are discussed in chronological order of the process; the search phase, the

cross-industry opportunity recognition phase, the assessment and adaptation phase, and the

innovation result phase.

4.2.1  Existence  of  a  cross-­industry  solution/opportunity:  search  or  no  search  

The CII consultants all work based on the idea that other industries can offer opportunities or

solutions that are suited for your industry as well. Like Mathieu said: “We believe your

problem has been solved by someone, somewhere.” Even if you are not aware of it,

somewhere else, an opportunity or a solution for your company already exists. Unlike the

conceptual model suggested, the CII process might have already taken off before the

cross-industry opportunity recognition stage. The process of CII either starts at this phase, at the

existence of a cross-industry solution or opportunity, or at the point of cross-industry

opportunity recognition. This depends on whether the company has actively searched for the

opportunity/solution or not. The respondents described different ways in which their CII

projects started, and two influencing factors were mentioned. The first one is the driving

force behind the innovation project: is it opportunity driven, or problem driven? The second

factor which was often mentioned in relation to the starting point of the CII project, was

search or no search. Has the company been structurally searching for new opportunities or

solutions, or did it just accidentally stumble upon a very interesting solution or opportunity?

These two conditional factors result in four different starting points for CII, each of these was

encountered by at least one of the respondents. In Table 3, a matrix of exemplary quotes of

these four different situations is presented.

Table 3: Four different starting points for CII

Active search

No search

Problem driven

“One approach is we have an

innovation, and we know what

we like to do, and what we need

to provide, but we don’t have

the capabilities or the

technology. Then we are looking

for a concrete solution for this

kind of innovation.” (Bernhard,

BMW)

(About a running shoe, Lunar Epic) “I

think it solved quite a few problems,

traction was actually one which we

weren’t necessarily trying to solve for

this shoe, it just something that we

learned along the way.” (Rory, Nike)

Opportunity driven

“And the other approach is

technology push, that we

constantly scout what’s

happening outside, what might

affect our business. And then we

transfer this knowledge or

technology to our company. And

we put it in our strategy, or not.”

(Bernhard, BMW)

“I don’t know if you’re familiar with

Adidas Boost? It’s a material they used

in their soles. That material is made,

it’s a really unique material and it was

made by a completely different

industry. So they went to all of these

different companies and said ‘hey

we’ve built this great new technology,

this amazing foam, do you guys want to

buy it’. […] Adidas said yes, and they

paid the most amount of money for it.

(Rory, Nike)

The findings of this study indicate that the companies BMW and Nike and also the

consultants follow the view that opportunities can be created. All three have developed ways

to structurally search for these opportunities. Especially BMW is very clear in the way they

look for new opportunities outside of their industry. Like Markus describes it: “We try to

identify white spots in the research or innovations for BMW, by taking a look at what’s

happening outside, and comparing that to what’s our internal agenda, so to say.” At Nike,

this view was illustrated by Rory, who explained that their designers are constantly looking

‘outside the box’ for new trends, whether it is in fashion, electronics or nature. Even though

BMW and Nike probably do not know what exactly they are looking for, they did find a way

to look for cross-industry opportunities. At Spectral Industries none of the respondents talked

about search for CII opportunities, since their CII project was one of which they

coincidentally found the opportunity.

As mentioned, the process for CII either starts before opportunity recognition, or

after. This depends on whether the company is already actively searching for the opportunity

(as in the case of BMW and Nike). If there occurred no search whatsoever, the process will

only start at the point of opportunity recognition, when the company discovers a

cross-industry opportunity or solution by coincidence (like Spectral Industries). It did seem that the

larger companies mostly depended on structured search, and not on coincidental

opportunities. Like consultant Ramon mentioned: “In many cases people do set out to do

something new or innovative. The serendipity of actually stumbling upon something and think

hey, this could be a new combination, is more in the entrepreneurial field.” Looking at the

other cases, this statement holds up. At Spectral Industries, Ad and Marijn acted upon an

opportunity that was right under their nose, there was no search necessary. Marijn explained

that they knew from the start that the space instrument would also be applicable in other

industries. BMW, Nike and also the consultants often talked about the process steps being

that first there is a certain question, problem or innovation goal, and only then they set out to

structurally search for these. Table 4 shows some examples of this chronological order of CII.

Table 4: CII chronological order: innovation goal à structured search

1. Innovation goal/problem

2. Structured Search

“For example for a big food and beverage

company we have been working on the

processing of cheese. If you cut it with a knife,

the cheese sticks to your knife, right. So they

have this problem on an industrial scale, […]

So there again we try to look at who else in the

world has faced similar challenges. So we found

a technology in the rubber industry. To cut

rubber. Which apparently was a good solution to

cut cheese in a more efficient way.” (Mathieu,

Consultant)

“So those guys were asking us to find new

approaches for how to produce coating that is as

great as that coating, and as lasting as that one,

but maybe possibly cheaper.

[…] for an anti-fingerprint coating I went up

value chain, which also means that they deliver to

different industries. One example, there is a

company which is the world market leader in

fluor coatings. It’s.. You know Teflon pans. It’s

Teflon coated. And it basically kind of works like

an anti fingerprint coating works.” (Markus,

BMW)

“For instance an athlete says, ‘I need more

traction in my shoes, what you’re giving me isn’t

giving enough traction on wet surfaces’.

So you can look at what animals out there have

really good grip when they are climbing a wet

surface or whatever it is, you know.” (Rory,

Nike)

The companies facilitate this kind of search by employing innovation teams that are

actively looking at these opportunities. These teams are continuously scanning technology

trends and studying the industries that might be relevant for the innovation goal/problem. For

companies, the search includes utilizing the organizational network. According to Mathieu

they first go to their network to look for opportunities, since this is still in their comfort zone.

Bernhard from BMW acknowledged this: If we can’t find a solution in-house at BMW or at

our automotive suppliers, then we go outside automotive to find the solution. So before

considering to look outside, they first stick to what they know. Rory also mentioned that the

organizational network was the reason that the Nike + app was first only available for iPhone,

since Tim Cook does not only work for Apple, but is also on the board of Nike. When Nike

set out to make an app, they logically first asked Apple for help.