The effects of testing an innovation in a living lab on the entrepreneurial process: A study of Dutch SMEs
Master Thesis
MSc BA Small Business & Entrepreneurship
University of Groningen
Faculty of Economics and Business
September 2018
By
Marije Abrahamse
S3270521
Supervisor: Prof. Dr. Aard Groen
Co-assessor: Dr. Arjan Frederiks
2 Abstract
In current business environments, new product development and innovation is important to survive and succeed. The aim of innovation is to create and deliver value to the customer and capture this value as a firm. Innovations often fail, because there is no market demand for the product. SMEs lack resources to invest in R&D, which makes it more difficult to identify exact customer needs and market demands. Customer involvement in the innovation process is known to be beneficial to develop a successful new product. This study focused on customer involvement through a living lab and assessed the effects of testing an innovation in a living lab on the entrepreneurial process. The research is conducted by using a mixed-methods approach, consisting of both qualitative and quantitative research.
The results indicate that SMEs can benefit greatly from testing their innovation in a living lab. First, it allows SMEs to understand customer needs and expectations. Second, it provides SMEs insight in what customers’ value and how to create value with their innovation. Third, SMEs gain insight in how to deliver value to the market and appropriate this as a firm. Furthermore, it stimulates both startups and established SMEs to commercialize their innovation. Additionally, we have tested if different test-, company- and innovation characteristics influence the magnitude of the effects. The SMEs that benefit most often have a technological innovation and are mature SMEs with significant entrepreneurial experience or startups with a high customer focus. To reach the positive outcomes mentioned above, SMEs have to test on different aspects and multiple times. Additionally, testing an innovation in a living lab benefits the SMEs in terms of brand awareness, network expansion and the attraction of new customers. To a lesser extent, it provides SMEs with new opportunities and allows SMEs to prove external parties that their product is valuable.
As an additional goal in this study, we have tested recent critique on the market orientation (MKTOR) scale by Narver and Slater. The well-established MKTOR scale is criticized as recent findings indicated that respondents often rate themselves overly positive on the scale, due to a lack of marketing knowledge and experience. This study compares the MKTOR scale by Narver and Slater (1990) to a semantic differential scale by Roersen et al. (2013). The results indicate that the respondents indeed rated themselves significantly higher on the MKTOR scale.
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Acknowledgements
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Table of contents
Introduction 5
Literature review 7
Startups and small- and medium sized enterprises 7
The entrepreneurial process as origin of innovation 8
Innovation and new product development 9
Customer involvement in innovation 11
Customer involvement: Testing and experimentation platforms 14
Insight in the entrepreneurial process 16
Additional antecedents 17
Scope of the study and conceptual model 19
Methodology 20 Research approach 21 Research context 22 Research method 22 Data collection 23 Measures 24 Data analysis 28 Quality criteria 33 Results 34 Descriptive statistics 34
Qualitative comparative analysis 37
Qualitative results 51
Narver & Slater (1990) vs. Roersen et al. (2013) scale 52
5 Introduction
Current business environments are characterized by emerging technological developments quickly changing customer needs and demands, and increasingly competitive markets (Anning-Dorson, 2017; Danneels, 2002; Menon, Chowdhury & Lukas, 2002; Moreno-Moya & Munuera-Aleman, 2016; Srinivasan, Lovejoy & Beach, 1997). Customers are more economical, responsible and demanding (Flatters & Willmott, 2009). These factors force organizations to continuously renew themselves to survive and succeed (Anning-Dorson, 2017; Danneels, 2002). Organizations aim to do this through innovation and regularly developing and marketing new products (Lantos, Brady & McCaskey, 2009). Successful new product development (NPD) is important, as it influences firm growth, increases sales and allows organizations to react to changing environments and marketplace pressures (Lantos et al., 2009). Innovation is often a source of competitive advantage (Brown & Eisenhardt, 1995).
Innovation is an essential, but risky activity. It generally involves high costs, although costs vary widely. The risk of failure when launching a new product is high (Lantos et al., 2009). Kotler and Armstrong (2010) state that ninety percent of new product launches fails. Likewise, Griffith (2014) states that ninety percent of the startups fail, predominantly because there is no market need for their product (McCarthy, 2017). A significant amount of cases exists where companies commercialize a product, but are unable to appropriate profits due to a lack of understanding of customer preferences (Gupta & Wilemon, 1990). A fit between the new product and customer and market needs is essential.
Innovations originate in the entrepreneurial process of discovery, evaluation and exploitation of opportunities (Shane & Venkataraman, 2000), a well-known process embedded in entrepreneurship literature. This process can take place through the creation of new firms or within existing firms. An innovation originates from the discovery of an opportunity and involves a sense-making process for determining whether market needs exist and if value can be created by satisfying these needs (Webb, Ireland, Hitt, Kistruck & Tihanyi, 2011). After the discovery, evaluated is if it is attainable to deliver value to the customer while at the same time appropriating value for the firm (Webb et al., 2011). When evaluated positively, the opportunity is exploited. Opportunity exploitation involves all activities to execute the new product development and commercialize the product (Webb et al., 2011).
Innovation varies in different types of companies. Startups or small- and medium sized enterprises (SMEs) often face scarce financial resources and are sensitive to disturbances in their cash flow (Welsh & White, 1981; Rothwell, 1989). Large firms often have large R&D budgets, but SMEs do not have the financial resources to invest in considerable R&D (Rothwell, 1989). Consequently, SMEs face a higher risk when pursuing an innovation as their vulnerability to project failure is bigger (Radas & Bozic, 2012. Not only do SMEs have to find other ways to obtain knowledge about customer demands and needs, it also highlights the importance of the fit between the new product and customer needs, as they cannot bear the risk of innovating an unpromising product.
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customer in the innovation process to create understanding of customer needs (Zirger & Maidique, 1990). Understanding customer needs is essential to the creation of new products and the innovation process (Joshi & Sharma, 2004). There are a few well-established concepts in both entrepreneurship and marketing literature on the importance of understanding customer needs, for example the customer orientation concept by Slater and Narver (1994), customer knowledge development by Joshi and Sharma (2004) and the lean startup method by Ries (2011). Even though these previously mentioned concepts are valuable, they lack a practical approach on how to involve the customer and create an understanding of customer needs. This study focuses on a practical approach of customer involvement, namely through testing the innovation in a real-life setting, called a living lab (Ballon, Pierson & Delaere, 2005).
This research is carried out in cooperation with Innofest, which is a foundation that gives SMEs the opportunity to test their innovation on festivals in The Netherlands. The tests have different aims, for example to identify customer needs or preferences, examine if there is demand for the product or to test technological aspects of the product. Innofest believes that early prototype testing increases quality and decreases risk. The foundation wishes to assess what the influence is on the SMEs when they test their innovation in a real-life setting. Hence, this study aims to investigate what the effects of testing an innovation in a living lab are lab on the entrepreneurial process. This research is conducted in cooperation with the SMEs that have tested their innovation through Innofest.
Customer involvement encompasses a wide range of activities. Previous studies have focused on the importance of customer orientation (Slater & Narver, 1994), customer roles in the innovation process (e.g. Agrawal & Rahman, 2015; Cui & Wu, 2016; Moeller, Ciuchita, Mahr, Odekerken-Schro & Fassnacht, 2013) and the intensity of customer involvement (Bitner, Faranda, Hubbert & Zeithaml, 1997). Additionally, there is a considerable amount of literature on online customer involvement (e.g. Füller, Mühlbacher, Matzler & Jawecki, 2009; Xie & Jia, 2016). To the best of my knowledge, there is scarce research on customer involvement through living labs and, additionally, its effects on the entrepreneurial process. Consequently, research on this subject is valuable for the abovementioned reasons. As a result, we aim to answer the following research question: What effects does testing a new product or innovation in a living lab has on the entrepreneurial process?”
By answering this research question, this study aims to contribute to literature on customer involvement in the innovation process. More specifically, this research focuses on customer involvement through the use of living labs and assesses the effects on the entrepreneurial process. Furthermore, we aim to discover if this is a valuable way for SMEs to obtain sufficient knowledge on customer needs and demands.
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Literature review
To answer the research question, a thorough understanding of existing literature is needed as a foundation for this study. This chapter provides existing literature on startups and SMEs, the entrepreneurial process and its relation to innovative activities, innovation and new product development and the involvement of customers in the innovation process. Furthermore, it discusses different factors that possibly influence the effects of testing an innovation in a living lab.
Startups and small- and medium sized enterprises
The focus of this study is on startups and small- and medium sized enterprises (SMEs). SMEs are considered key to ensuring economic growth, innovation, job creation and social integration in the European Union (EU) (Eurostat, 2015). In the EU, SMEs are defined as companies with fewer than 250 employees. SMEs are divided into three categories based on the number of employees: Micro enterprises (<10), small enterprises (10-50) and medium enterprises (51-250). SMEs represent 99% of all enterprises in the EU (Eurostat, 2015). Occasionally, SMEs are categorized based on turnover, which does not make sense in this study, as it includes SMEs that have not commercialized any product yet.
In comparison to large firms, SMEs are generally more efficient in internal communication, able to reorganize rapidly to adapt to change, do not face bureaucracy problems and owners are often willing to accept risk (Rothwell, 1989). On the other hand, SMEs often face resource constraints, do not have large R&D capital and at times lack specialists (Rothwell, 1989). In line with the abovementioned characteristics, SMEs are known to be more innovative than large firms, due to their lack of structural inertia, targeted innovation and risk-seeking leadership (Dean, Brown & Bamford, 1998). While SMEs are known to be more innovative, they frequently face problems successfully bringing their innovation to the market. Due to their limited R&D capital, they have less insight in market demands and needs (Abernathy & Utterback, 1978). SMEs face a higher risk when pursuing an innovation due to their vulnerability to project failure (Radas & Bozic, 2012).
8 The entrepreneurial process as the origin of innovation
Since Schumpeter’s (1934) ‘gale of creative destruction’ the concepts of entrepreneurship and innovation have been intertwined. Often entrepreneurs are seen as the ones coming up with innovations (Autio, Kenney, Mustar, Siegel & Wright, 2014). Shane & Venkataraman (2000, p. 218) define the field of entrepreneurship as the “examination of how, by whom and with what effects opportunities to create future goods and services are discovered, evaluated and exploited”. The field involves the study of sources of opportunities, the processes of discovery, evaluation and exploitation of opportunities and the set of individuals who discover, evaluate and exploit the opportunities (Shane & Venkataraman, 2000, p. 218). Innovations arise from the discovery of opportunities in the market. An entrepreneurial opportunity is a situation in which new goods, services, raw materials and organizing methods can be introduced and sold at greater cost of production (Shane & Venkataraman, 2000). Once this opportunity is discovered, evaluated and, when evaluated positively, exploited, it results in a new product. The exploitation of these opportunities can be executed through the creation of new firms or within existing firms. The focus of this study is not on the sources of opportunities, nor on the individuals who discover, evaluate and exploit the opportunity, but on the processes of discovery, evaluation and exploitation. The process of entrepreneurship includes the set of activities through which individuals, independently or within a firm, seek to satisfy customer needs through innovation that provides a more efficient or effective means and/or ends (Shane & Venkataraman, 2000; Webb et al., 2011).
The process of entrepreneurship exists of three steps: (1) Opportunity discovery; (2) Opportunity evaluation, and; (3) Opportunity exploitation. Opportunity discovery occurs when an individual or organization assumes more value can be created by providing more efficient of effective means and/or ends (Casson, 1982). Regarding the first step of the entrepreneurial process there are some tensions within established literature. Scholars distinguish between opportunity discovery, opportunity creation and opportunity recognition. According to George, Parida, Lahti and Wincent (2016) opportunity creation occurs when there is no existing product or market demand and everything is invented from scratch. Opportunity discovery is when there is either product or market demand and the other condition must be identified to satisfy the other. Opportunity recognition is when product and market demand exist, but new ways are explored to satisfy this demand (George et al., 2016). In this study, all three distinctions are included within what we call the ‘opportunity discovery’ process. The designation of terms and if the opportunity is either discovered, recognized or created is less relevant. The focus of this step is on the creation of value (Teece, 2010) in whatever way possible, which can result from either discovery, recognition or creation of an opportunity.
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that value (e.g. time, money, resources, and risk) (Shane & Venkataraman, 2000). Entrepreneurs tend to evaluate opportunities on three broad categories, namely those that relate to demand-side considerations (e.g. windows of opportunities), those that relate to supply-side considerations (e.g. resources) and those that involve the entrepreneur’s personal considerations (e.g. goals, consequences, risk-reward consideration) (Wood & Williams, 2014). Wood & Williams (2014) state that entrepreneurs evaluate opportunities based on rule-based thinking, which involves developing a series of rules via formal education, previous experiences and interaction with others. In the opportunity evaluation process, the question is if and how value can be delivered to the customer and how the business can appropriate profits from delivering that value (Teece, 2010). Teece (2010) states that innovators often fail to deliver or capture value from their innovations, because they have no clear idea how to do so. Teece (2010) proposes to design a business model when innovating. This can clarify how a business can create value, deliver value to customers and appropriate value as a firm. A business model translates technological innovation into a commercially successful innovation (Teece, 2010).
When an opportunity is evaluated positively, the decision is made to exploit the opportunity. Opportunity exploitation involves the activities to execute the innovation, such as gathering, bundling and leveraging resources, developing a commercialization strategy and acting upon the developed business model (Webb et al., 2011). The innovation is introduced to the market and, if successful, the customer needs associated with the initially recognized opportunity are satisfied (Webb et al., 2011). In this step, the goal for the firm is to capture a portion of the value it delivers (i.e. value capturing) (Teece, 2010).
In all three steps of the entrepreneurial process, different information is needed to assess whether or not to persevere. Customers can provide the firm with useful information about the value of the opportunity. The scope of this research is what effects customer involvement through testing in a living lab has on the three abovementioned entrepreneurial processes, corresponding to the research question. In the following paragraphs, a detailed overview of the different relevant concepts is presented, along with antecedents that possibly influence the testing trajectory.
Innovation and New Product Development
10 stage of the innovation process.
Innovations are often distinguished based on their level of novelty to the market and the firm executing the innovation. The most well-known distinction is incremental versus radical innovations (Dewar & Dutton, 1986; Green, Gavin & Aiman-Smith, 1995; Knight, 1967). Other renowned distinctions are architectural versus modular (Henderson & Clark, 1990), simple versus complex (Singh, 1997) and competence enhancing versus competence destroying (Gatignon, Tushman, Smith & Anderson, 2002; Tushman & Anderson, 1986). All concepts are based on a continuum and have differences in levels of novelty, impact on the organization or impact on the environment. Another categorization is that by Heany (1983) who provides a checklist for the categorization of innovations, resulting in six different categories (i.e. style change, product-line extension, product improvement, new product, start-up business and major innovation). This categorization is based on if the market is established, to what extent customers know features and utility of the product and the design efforts (Heany, 1983). Another additional concept is the newness of the innovation by Docter, Van der Horst & Stokman (1989) who distinguish the innovation degree by assessing if an innovation is new to the world, new to the country, new to the sector, new to the company or non-innovative.
This study focuses on customer involvement in the innovation process. Innovations also differ regarding the source of innovation; they can originate from a perceived need in the market or the recognition of a new technology. Well-established concepts are the so-called technology-push and market-pull modes of innovation (Rothwell, 1992; Walsh et al., 2002). The technology-push model of innovation assumes that an innovation starts with a scientific discovery and ends in a marketable new product or process (Carter & Williams, 1957; Rothwell, 1992). Walsh et al. (2002) developed a definition conforming to this by stating that an innovation resulting from the recognition of technological feasibility is called a technology-push innovation. There is not much known about the market demand of such a product. The market-pull model means that innovations arise from a perceived or articulated customer need, eventually resulting in a new product (Myers & Marquis, 1969; Rothwell, 1992). Walsh et al. (2002) conform to this by stating that an innovation resulting from the recognition of potential demand is a market-pull innovation. Another source of innovation can be an individual who solves a problem for themselves; this is called a lead user. They often attempt to fulfill a need they experience and to do so; they come up with a new concept, product or service (Von Hippel, 1986). The user is the customer as well as the innovator (Brait, 2004).
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Additionally to levels of innovativeness and sources of the innovation, innovations differ in readiness. A well-known concept to measure readiness of products prior to commercialization is the Technology Readiness Level (TRL). TRL is officially developed by NASA, but is now used by both government agencies and major companies (Lee, Chang & Chien, 2011). The primary use of TRL is to clarify the readiness of the technology and consequently, enable to company to make decisions concerning the development of the product. Lee et al. (2011) developed a framework of innovation readiness levels (IRL) based on the existing TRL developed by the NASA in 1980s. In this study, when referring to TRL, it is an adjusted TRL scale based on the IRL by Lee et al. (2011). More information on the developed scale is presented in the methodology. We believe the readiness of the product at the moment of testing possibly influences the effects of testing an innovation in a living lab. It is hard to assess potential effects due to limited theory on the subject, but it is possible that, the less ready the innovation, the larger the effects and vice versa. We aim to investigate the effects of the TRL on testing an innovation in a living lab.
Customer involvement in innovation
Until now we have discussed the entrepreneurial process and innovation and NPD, this paragraph focuses on customer involvement in the innovation process. As specified previously, in the current marketplace it is important for firms to constantly renew to survive and succeed (Anning-Dorson, 2017; Danneels, 2002) by regularly introducing new products (Lantos et al., 2009). The introduction of innovative products, services or processes is an opportunity for SMEs to stand out from competition (Brown & Eisenhardt, 1995; Ndubisi & Iftikhar, 2012). NPD often fails, predominantly because there is no market need for the new product or due to a lack of understanding of customer demands (Gupta & Wilemon, 1990; McCarthy, 2017). Consequently, an understanding of customer needs and wants is important for successful NPD. Rothwell (1977) validates this, as he identified nine success factors for innovation or new products based on a wide range of studies. An important success factor is having a market orientation, which means putting emphasis on customer needs and, where possible, involving potential customers in the development process. Likewise, Zirger and Maidique (1990) state that the best way to decrease the risk of developing an unsuccessful product is to involve the customer in the innovation process to create an understanding of their needs and wants. In line with that, Danneels (2002) implies that it is valuable to seek input from customers in a firm’s product development.
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customers and access to communication channels to exchange information between the organization and potential customers during development and commercialization of the product (Danneels, 2002). This study aims to find out the effects of customer involvement during development and commercialization of the innovation. Obviously, customer involvement has been researched before. In the following paragraphs a few well-established theories and their use in this study are discussed.
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Customer knowledge development. Another concept that focuses on customer involvement is the concept of ‘customer knowledge development’ (CKD) introduced by Joshi and Sharma (2004, p. 48) and is defined as “the process of developing an understanding of customer new product preferences that unfolds through the iteration of probing and learning activities across stages of the prelaunch phase of development”. NPD projects involve two major stages: pre-launch and post-launch. The development of knowledge about the customer is more beneficial in the prelaunch stage (Cooper, 1998). The prelaunch stage includes idea generation, concept refinement, product development, product testing and ends with the introduction of the product (Joshi & Sharma, 2004). Probing activities consist of the distribution of new product ideas, concepts and prototypes among target customers. Learning activities encompass the analysis of customer feedback and the development of subsequent probes based on this (Joshi & Sharma, 2004). Engaging in these activities creates favorable circumstances to obtain up-to-date customer preferences.
Hence, involving customers by letting them test a prototype of the product can be used to acquire customer feedback on the product and increase knowledge about customer preferences. A prototype is a first or preliminary version of a product from which other forms are developed (“prototype,” n.d.). An early version of the product is introduced to a credible target group to teach the company more about customer preferences and technological feasibility (Lynn, Morone & Paulson, 1996). Missing features or design failures are identified, giving the firm the opportunity to make alterations prelaunch (Melton & Hartline, 2010). Thus, by introducing the prototype to the market, the firm can learn from the experience, modify or refine the product based on customer feedback and in the end, bring a winning product to the market (Lynn et al., 1996). Joshi and Sharma (2004) developed a scale for the level of customer knowledge development (CKD), measuring the extent to which the company engaged in probing and learning activities during the development of the product. It is interesting to measure if the level of CKD influence the effects of testing in a real-life environment with potential customers.
Lean startup method. A popular concept that also conforms with the abovementioned concepts is the lean startup method by Ries (2011). The lean startup method is an ad hoc solution for starting up a successful business through building a viable product and interacting with potential customers. Ries (2011) states that the products that startups build are experiments and the learning about how to build a sustainable business is the outcome of those experiments. The method contains a three-step feedback loop containing the steps ‘build’, ‘measure’ and ‘learn’. The goal is to minimize the time of completing the loop to create a successful business.
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to move on to the next step, namely building the minimum viable product (MVP). MVPs can differ in complexity (e.g. ranging from a demonstration video of the product to an actual prototype). Building the MVP helps entrepreneurs start the process of learning. The lean startup method does not only focus on testing a product design, but the goal is to test fundamental business hypothesis and to win over customers (Ries, 2011). After building and introducing the MVP it is time to figure out if the firm is actually achieving validated learning (i.e. the measure step) by comparing the starting point to the ideal product based on customer feedback. The company has to move closer to the ideal by changes and optimizations to the product. When this is done, the company decides if it wants to pivot or persevere (i.e. third step in the feedback loop). Changes might be needed, which are called “pivots” (Ries, 2011).
Different pivots are: Zoom-in pivot (a single feature of the product becomes the whole product), zoom-out pivot (the whole product becomes a single feature of a larger product), customer segment pivot (the product was made for another customer than anticipated), customer need pivot (other customer problems are identified and the company is able to solve these), value capture pivot (the company realizes it has to capture value in another way), channel pivot (the basic solution can more efficiently be delivered through another channel) or technology pivot (the basic solution can be solved by using a completely different technology) (Ries, 2011). Firms can evaluate whether they are getting closer to the fit between the product and the customer needs and preferences by evaluating their trips through the feedback loop (Ries, 2011).
The lean startup method gives insight into what information is needed throughout the process and focuses on reaching the fit between the product and customer needs. It is not necessarily a measurable concept, but we decided to use the different categorizations of pivots to figure out what it is that the companies have changed after testing their innovation on the festival.
Even though the three abovementioned concepts are interesting to use in this study, they lack a practical approach on how to involve the customer in the innovation process. In this study, we focus on involvement of the customer using a living lab, which we will discuss in more detail in the following paragraph.
Customer involvement: Testing and experimentation platforms
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new technology, product or service; (2) Testbed: A standardized laboratory environment used for testing new products protected from the hazards of testing in a live or production environment; (3) Field trial: A test of technical and/or other aspects of a new product in a limited, but real-life environment; (4) Living lab: An experimentation environment in which technology is given shape in real life contexts and in which (end) users are considered ‘co-producers’; (5) Market pilot: A pilot in which new products that are considered to be rather mature, are released to a number of end users in order to obtain marketing data or to make final adjustments before the commercial launch; (6) Societal pilot: A pilot project in which the introduction of new products into a real-life environment is intended to result in societal innovation. Ballon et al. (2005) has clarified the differences by putting the TEPs within a framework based on three central characteristics: Technological readiness (low vs. high maturity), focus of the tests (technological tests vs. design and development tests) and the degree of openness of the innovation process to users. This framework is shown in figure 1.
Figure 1 Framework of test and experimentation platforms (TEPs) (Ballon et al., 2005)
16 Insight in the entrepreneurial process
Until now we have aimed to clarify why it is important to innovate, why SMEs and startups face challenges obtaining sufficient information on customer demands and needs, why customer involvement in the innovation process can be useful and how this can be executed in practice. One more subject needs to be reviewed, namely what information is important in each step of the entrepreneurial process while innovating and what information can possibly be obtained through testing innovations in a living lab. First, as previously mentioned, Danneels (2002) stated that introducing an innovation takes two key tasks: To physically make the new product (technological competence) and to sell the product to customers (customer competence). Logically, information is needed on both aspects. Furthermore, the innovation process is embedded in the entrepreneurial process resulting in information needed in all three processes (i.e. opportunity discovery, evaluation and exploitation). Opportunity discovery is associated with value creation, opportunity evaluation with value delivery and appropriation and opportunity exploitation is associated with value capturing (Shane & Venkataraman, 2000; Teece, 2010). In the opportunity discovery process the focus is on figuring out if and how value can be created through the recognized opportunity (i.e. value creation). This corresponds with the leap-of-faith assumption by Ries (2011), who states that by having early contact with customers, a basic level of customer needs can be defined through introducing them to an early version of the intended product (Ries, 2011). To define what customer needs encompass, we look at the most basic concept within the marketing literature. The basic idea of marketing is identifying customer needs, wants and demands, offering a product to satisfy these customer needs and wants and deliver value to the market by doing so, while also appropriating profit for the firm at the same time (Kotler, Wong, Saunders & Armstrong, 2005). The most basic concept underlying marketing and new product development is that of human needs, which are “states of felt deprivation” and are a basic part of humans (Kotler et al., 2005, p. 8). Human wants are forms of human needs, shaped by culture and individual personality and are described in terms of objects that will satisfy those needs (e.g. products, services). Human needs are limited and basic, but human wants are almost unlimited. However, humans only possess limited resources to satisfy their wants; therefore, humans choose products that provide the most satisfaction and benefits for their resources (e.g. money). These wants backed up by buying power are the demands (Kotler et al., 2005, p. 8). Those human needs, wants and demands are the base of a new market offer.
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needs, the problems customers try to solve and the value customers are looking for (Osterwalder et al., 2015).
In the opportunity evaluation step, the focal point is how to deliver value to the customer and how to appropriate a part of this value as a firm (e.g. value delivery and appropriation) (Shane & Venkataraman, 2000; Teece, 2010). The decision whether or not to act upon the opportunity is made based on demand-side considerations (e.g. windows of opportunity), supply-side considerations (e.g. resources) and personal considerations (e.g. goals, consequences, risk or return) (Wood & Williams, 2014). Customers are less involved in supply-side and personal considerations, but they can be valuable on the demand side. Therefore, when evaluating an opportunity, it is important to look at customer demands as described above. People demand products with the benefits that add up to the most satisfaction (Kotler et al., 2005). Not only is it important to evaluate if the new product will deliver value to the customer, it is also important to see if the company can appropriate profits from introducing this new profit or else it is not worth exploiting it. The expected value of exploiting the opportunity has to be large enough to compensate for costs to generate that value (Shane & Venkataraman, 2000). To get clarification on this, Teece (2010) states it is effective to develop a business model, which is a tool to display logic and evidence of how a business creates and delivers value to customers (Teece, 2010). This allows the innovator to identify needed resources, sources of revenue, the most important costs, the intended customer base and the preferred channels of the customers to deliver the product (Osterwalder & Pigneur, 2010). Testing an innovation in a living lab can possibly provide insight in the abovementioned concepts.
In the opportunity exploitation step, the activities to introduce the innovation to the market are executed. Strategies are developed and carried out, business models are executed and resources are assigned. It is important to have a clear strategy of how to carry out the commercialization (Teece, 2010; Webb et al., 2011). Exploitation mostly includes the launch of the product (possibly through new venture creation) and, as testing a product in a living lab is done pre-launch, it is not clear what testing an innovation in a living lab can have on the exploitation of the opportunity. This study aims to investigate if and what effects there are on the exploitation step.
Additional antecedents of insight into customer information
In the previous paragraphs concepts that could influence the effects of testing an innovation in a living lab on the entrepreneurial process are introduced (e.g. size of the company, startup vs. SME, innovativeness, source of the innovation, customer orientation, and customer knowledge development). However, looking at existing literature and the process that this study focuses on, some additional concepts are interesting to look at.
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used interchangeably. Reuber and Fischer (1999) differentiate between those terms by considering entrepreneurial experience as direct observation of, or participation in, activities for new venture creation and the knowledge an individual gained is entrepreneurial knowledge. Extant research has revealed that experienced entrepreneurs have more knowledge about relevant concepts, enhancing their ability to recognize entrepreneurial opportunities (Politis, 2005). Furthermore, the lessons learned from a prior entrepreneurial experience might enhance the ability exploit entrepreneurial opportunities (Ronstadt, 1988). Accordingly, Shane and Venkataraman (2000) suggested that the possession of prior information influences the likelihood that certain individuals discover entrepreneurial opportunities. Wood and Williams (2014) state that opportunities are partly evaluated based on previous experience. Consequently, the amount of prior experience seems to be highly associated with an entrepreneur’s ability to recognize, evaluate and exploit entrepreneurial opportunities (Politis, 2005). Expected is that it will influence the ability of entrepreneurs to successfully gather the needed information during the test in the real-life setting. However, this can turn out different ways. On the one hand, the entrepreneur might already possess sufficient knowledge and therefore, the tests provide the entrepreneur with less new information. On the other hand, it is possible that the experienced entrepreneur has a clearer idea of what information is needed and can run the test with a clear focus.
Characteristics of the firm. As discussed in the first paragraph of this chapter, the differentiation is made between size and age in SMEs. More company characteristics are interesting to investigate. Reuber and Fischer (1999) state that the needs of ventures differ through different business stages. Kazanjian and Drazin (1990) confirm this by stating that in every growth stage companies have their own characteristics, challenges and advantages. For example, early stage firms are often extremely informal and unstructured, which consequently allows for great flexibility and easy adjustments to the market. Scott and Bruce (1987) endorse this and have made a growth model for small businesses. The model consists of five growth stages, each with its own distinctive characteristics. The five stages are: (1) Inception; (2) Survival; (3) Growth; (4) Expansion and; (5) Maturity. In the first stage, the main effort is to develop a commercially viable product and establishing the product in the market. However, for example in the second stage, the focus is more on market entry by competitors, forcing the company to obtain and sustain a competitive advantage. In the third growth stage, it is important to keep this competitive advantage, which requires a greater focus on customer needs and adapting product to those needs (Scott & Bruce, 1987). In this sense, every stage has its own characteristics and different kinds of relevant information are needed. Therefore, this study examines if the growth stage of the firm influences the effects of testing the innovation in a living lab.
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product. This categorization is identified through internal classified documents from Innofest, in which they keep track of all the companies that tested their innovation through Innofest until now. In this ‘Innofest Monitor’ as it is called, it is stated what the goal of each company with testing the prototype is. By looking at the different sorts of tests the SMEs have performed, we aim to find out if certain tests are influencing the extent to which SMEs gained in the entrepreneurial process.
Scope of the study
To recapitulate, in current business environments new product development and innovation is needed to survive and succeed. The aim of innovation is to create value, deliver value to the customer and capture value as a firm. The process of innovation lies within the entrepreneurial process of opportunity discovery, evaluation and exploitation. An opportunity is discovered and an innovation originates to take advantage of that opportunity. Innovations can arise from customer needs or demands (i.e. market-pull innovation), from a scientific, technological discovery (i.e. technology-push innovation) or by solving a problem for themselves (i.e. lead user innovation). Innovations or new products need to match customer needs and demands, which is, within large firms, often identified through R&D. Small firms do not have the resources for this, often resulting in products that do not satisfy customer needs and market demands. Involving the customer in the innovation process provides SMEs with the opportunity to develop a successful product. One way of involving the customer in the innovation process is through testing the innovation in a living lab, which is the focus of this study.
Innofest offers a solution to this, by giving SMEs and startups the opportunity to test their innovation on a festival. This research aims to find out the effects of testing an innovation in a living lab (i.e. festival) on the entrepreneurial process (i.e. opportunity discovery, evaluation and exploitation). Based on the established literature, we have identified different test-, company- and innovation characteristics that could possibly influence these effects. First, regarding test characteristics, there are three sorts of tests: Technological test, market test and design test. Second, concerning company characteristics, we have identified quite some aspects that could be of impact: Size, established SME vs. startup, firm orientation (level of customer orientation, market orientation and customer knowledge development) and the growth stage. Third, with regard to innovation characteristics, aspects we look at are the source of the innovation, the level of innovativeness and the readiness level (TRL). For that reason, three sub questions are formulated additionally to the research question in the introduction: (1) What are the effects of the different test characteristics on the three steps in the entrepreneurial process?; (2) What are the effects of the different company characteristics on the three steps in the entrepreneurial process?; and (3) What are the effects of the different innovation characteristics on the three steps in the entrepreneurial process?
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Figure 2 Conceptual model
Methodology
In this chapter, the research method and reasoning behind it are presented. We discuss the research approach, context of the research, research method, data collection, measurements, quality criteria of the research and an extensive explanation of the data analysis.
Research approach
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that recently have pursued new product development or are pursuing new product development and tested their innovation on a festival in cooperation with Innofest. Elaboration on the specific research context and Innofest can be found in the next paragraph. This approach allows to generalize to theory and offers the possibility to expose patterns that lead to successful testing.
The following steps are taken in this certain research: Definition of research question, development of a theoretical foundation, selection of respondents, development of research instruments, entering the field to collect data, analysis of data, formulation of propositions and comparison to existing literature (Eisenhardt, 1989).
Research context
As clarified before, this study is executed in cooperation with the foundation “Innofest”. Innofest offers SMEs and startups an opportunity to test their innovation on festivals in the north of The Netherlands. The tests are performed with different goals, for example to identify customer needs and preferences, improve the fit between the product and market or identify missing features or technological failures. Innofest believes, in line with the lean startup method and design thinking, that early prototype testing increases quality and decreases risk. Festivals are seen as a ‘mini-society’. As described by Van Vliet (2012) a festival is a reflection of society, because it reflects concepts as social interaction and narrative discourse to economical processes, leisure activities and disciplinary behavior. Furthermore, festivals require a physical infrastructure, energy and water, a sewage system and there are challenges for waste, logistics and safety. Therefore, it is an appealing context for companies to run early tests of their innovations as it gives them the opportunity to identify customer needs and demands, but also provides information about technological feasibility or quality.
Innofest is founded in January 2016 and so far tested 58 innovations in two festival seasons. Companies can apply, but Innofest also scouts companies with interesting innovations. Innofest has four requirements to be selected: (1) The prototype is innovative; (2) Testing the prototype on a festival is relevant and has clear added value; (3) The prototype has a (future) business case; (4) The prototype contributes to a better world (Innofest, 2018). When Innofest decides it is valuable to test the innovation on the festival, the innovation programmers link the innovation/company with a certain festival. The next step is to set up a plan to test the prototype in a way that fits the festival infrastructure. After the testing day, there is an evaluation on the test.
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Table 1 Classification companies
The CEO of Innofest selected respondents for the interviews based on these criteria. Furthermore, the aim was to make the same differentiation based on questions asked in the questionnaire. However, after the interviews were conducted and the results from the questionnaire came in, the distinction turns out not to be that clear in practice. There were young companies that have not commercialized any product yet, but have a large customer base waiting for the product to be ready. There were also companies that are slightly older, have a product that is ready, but never sold anything yet. I have tried to find a logic to distinguish between groups, but no clear distinction is present. Therefore, decided is to distinguish SMEs based on age and size, more information can be found in the paragraph about measures.
Research method
The method used to perform this research is a mixed-methods approach, also called methodological triangulation (Erzberger & Prein, 1997). The reason is that the phenomena of customer involvement through a living lab has, to the best of my knowledge, not been (properly) studied. The formulated research questions require a qualitative, in-depth approach to be able to understand the effects on the entrepreneurial processes. However, qualitative research on itself gives limited insight, as the sample is small and the risk of subjectivity or bias of the researcher is present. Only using quantitative methods will give less insight into possible effects, as questionnaires are limited and do not give the opportunity to research effects extensively and in-depth (Erzberger & Prein, 1997). Limiting this study to traditional forms of quantitative or qualitative research will not provide sufficiently valid answers to the research question. The core of mixed-methods or triangulation is that all methods have inherent biases and limitations, so use of one method will result in biased and limited results (Greene, Caracelli & Graham, 1989). Therefore, a mixed-methods approach is used to improve quality of the study. This often means that the research design is split into two distinct parts by studying different facets of a phenomenon (Greene et al., 1989).
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combines qualitative and quantitative elements in one interview format. Geurts and Roosendaal (2001) conducted interviews during which the interviewees had to respond to statements and the researchers were asking probe questions to figure out the reasoning behind the answers. This research design is based on the same method, however it slightly differs due to limited time and resources as there was no time to interview all 58 companies that have tested their innovation with Innofest.
A question format is developed organized around topics matching the different variables in the conceptual model. Both the interview guide and questionnaire (see appendix 1 and 2) are developed based on these topics and corresponding existing theory presented in the previous chapter. The survey is sent to as many companies as possible that have tested through Innofest to increase the sample. However, as mentioned before, questionnaires give limited insight into the reasoning behind the answer. Hence, we have conducted ten in-depth interviews the same way as Geurts and Roosendaal (2001). During the interview, the respondents filled in measurement scales and had to respond to statements by the interviewer. The interviewer asked probing questions to get insight into the reasoning behind the interviewees answer. Doing this has three utilities: (1) It allows the interviewer to check if the explanations and real-life situation matches the scores; (2) It allows the interviewer to see if the respondents understand the scales; and (3) It allows the interviewer to gather additional information on the reasoning behind statements. This specific design aims to increase the validity of the research and is a more systematic application of a mixed-methods approach for data collection and data analysis (Geurts & Roosendaal, 2001). Additionally, the interviews were conducted before the rest of the surveys were sent, which resulted in an additional dependent variable in the survey and research (i.e. pivots based on the tests). Adding additional variables during research corresponds with the grounded theory approach. More information can be found in ‘measures’.
Data collection
Interviews and questionnaires are used to collect data. The quantitative data is used to demonstrate relationships and the qualitative data allows us to gain more insight in the phenomenon. The data collection took from the beginning of May until the end of June 2018.
Participants. The participants are SMEs that have tested their innovation through Innofest in the last two years. From the 58 companies, ten companies were interviewed. Forty-eight SMEs remained for the survey, from which 36 were e-mailed to fill in the questionnaire. Unfortunately, not all companies could be contacted as their contact information was missing. The selection of the interviewees is based on theoretical sampling, namely the requirements mentioned in table 1. Partially, the interviewees are selected on pragmatic grounds, as the interviewees are all part of the portfolio of Innofest. All respondents were individuals that are either the (co)-owner or manager from the SMEs that have tested their innovation through Innofest in the last two years.
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in perceived causes, inferences and explanations (Yin, 2013). Furthermore, it gives the opportunity to understand reasoning behind the potential relationships or it can corroborate relationships found in the quantitative data (Eisenhardt, 1989). A semi-structured interview helps to create a more systematic and comprehensive understanding and offers the benefit of easily comparing interviews (Patton, 1987). The CEO of Innofest helped contacting companies for the interviews. Apart from the semi-structured interview, as mentioned before, the interviews also involved statements and scales for the interviewees to fill in. This is also displayed in the interview guide in Appendix 1.The measurements can be found in the next paragraph. All interviews were held face to face, in their own company or on an external location. The interviews were conducted in Dutch and were all recorded on tape. Afterwards, all interviews were transcribed. The transcripts can be found in the attached document.
Questionnaires. Apart from the ten interviews, a questionnaire was sent to 36 people by e-mail. They received an anonymous link through which they could fill in the questionnaire. The questionnaire consisted of all the topics relevant for this research, based on the literature review and conceptual model. More information on the measures can be found in the next paragraph. The director of Innofest sent the e-mail, because more responses were expected if it was sent through the organization. In the end, ten people filled in the questionnaire, which is a response rate of 27,8%. The questionnaire can be found in Appendix 2.
Measures
In this paragraph, we discuss all independent and dependent variables based on the conceptual model and used in both the interviews and questionnaires.
Independent variables. The independent variables consist of the different test-, company- and innovation characteristics.
Test characteristics.
Sort of test. There are three sorts of tests identified based on internal documents: (1) Technology test: To test if the technology/product functions and if the technology is feasible for a larger market; (2) Market test: To test is there is a customer demand or market need for the product; and (3) Design test: To test the products aiming to get the opinion of the customer on usability and features of the product. We have simply asked the respondents which tests they performed.
Number of tests. The number of tests the SMEs performed (based on the abovementioned classification) is counted.
Number of festivals. The number of festivals to measure the number of ‘test moments’. Company characteristics.
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base, contrarily, it also came forward that the products of a five year old company are on the market, but no products were sold yet. Therefore, the decision is made to measure size and age of the SMEs. Age is measured in years, starting from the registration date at the Chamber of Commerce. Size is measured in the number of employees.
Growth stage. The designation of SMEs in a certain growth stage is based on the growth model for SMEs developed by Scott and Bruce (1987). Scott and Bruce (1987) have developed a five stage growth model (i.e. inception, survival, growth, expansion, maturity) with corresponding distinctive characteristics of SMEs for each stage. We have showed a summarized version of the five stages to the respondents, allowing them to choose the one their company is currently in.
Entrepreneurial experience. Entrepreneurial experience and knowledge has been found to enhance the ability to recognize and exploit entrepreneurial opportunities (Politis, 2005; Ronstadt, 1988). To measure entrepreneurial experience, a scale based on the article by Politis (2005) is made. The scale contains seven statements, such as “I have been a (co-)founder of a business in the past” or “I have experience in managing a business”. In this way, indicated is what sort of entrepreneurial experience the people that have executed the testing through Innofest have. We have made it measurable by rating every kind of experience with a number from one to eight, with appointing the highest score to “I have been a (co-)founder in the past” and the lowest score to “I do not have any entrepreneurial experience”. In the end, the score is obtained by summing up the different sorts of experience.
Level of customer orientation. The level of customer orientation is measured by the MKTOR scale by Narver and Slater (1990) as it has been cited most often and it is known to outperform other scales (Gray et al. , 1998; Roersen et al., 2013). The official MKTOR scale by Narver and Slater (1990) consists of fifteen statements based on three components: Customer orientation, competitor focus and inter-functional coordination. In this particular study the decision is made to focus on customer orientation, hence, chosen is to only use the part of the MKTOR scale that represents customer orientation. This consists of six statements that are rated on a 7-point Likert scale ranging from “strongly disagree” to “strongly agree”. The internal consistency, measured with Cronbach’s alpha, found by Narver and Slater (1990) varies between .8547 and .8675. The internal consistency of this scale in this study is .826.
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situation in the firm (Roersen et al., 2013). The statements representing a market orientation are based on the MKTOR scale by Narver and Slater (1990). The other statements, representing non-market management orientations are based on the article by Shoham (1999). The scale consists nine trade-offs, with six unlabeled scale points between both statements. To control for acquiescence bias (Friborg, Martinussen & Rosenvinge, 2005) four of the statements representing market orientation are placed left and the other five are placed right. The scores of the four market orientation statements on the left side were reversed after collecting the data (i.e. 1 = 6, 2 = 5, 3 = 4, 4 = 3, 5 = 2, 6 = 1). The eventual market orientation score is obtained by summing up the scores. The internal consistency is .546.
Level of customer knowledge development. The level of customer knowledge development is measured by the scale created by Joshi and Sharma (2004), who developed a scale drawn on previous studies. This is based on the three defining characteristics of the concept, namely an ongoing, emergent trial-and-error process (Joshi & Sharma, 2004). The scale consists of five statements on which the respondent rates the extent to which it characterizes the NPD process, based on a 5-point Likert scale ranging from “strongly disagree” to “strongly agree”. Joshi and Sharma (2004) have found a construct reliability of .89. This study has an internal consistency, based on Cronbach’s alpha, of .634.
Innovation characteristics.
Source of the innovation. As mentioned in the previous chapter, it is interesting to see what the source of the innovation is (i.e. technology-push, market-pull or lead user). Preferred was to use the scale by Walsh et al. (2002), but we have not been able to get insight into the scale. Therefore, the origin of the idea is obtained by simply asking the respondents how the idea originated, based on four options representing market-pull, technology-push, lead user or other.
Degree of innovativeness. The aim was to measure the degree of innovativeness in two ways, by using the categorization by Heany (1983) and, additionally, the newness of the innovation based on Docter et al. (1989). However, due to incomplete data in this study on the scale by Docter et al. (1989) the decision is made to leave this scale out. Consequently, the degree of innovation is measured by Heany’s (1983) categories of innovation, based on a checklist consisting of five questions about the market for the product, knowledge of the customer about functions and features of the product and design efforts. The checklist results in six categories: Style change, product-line extension, product improvement, new product, start-up business and major innovation (Heany, 1983).
TRL. Officially, the TRL scale was developed by NASA in the 1980s and is now often used by governmental organizations (e.g. European Commission). Consequently, the official scale contains complicated technological terms. Lee et al. (2011) developed a framework of innovation readiness levels (IRL). However, this IRL does not only account for the level until commercialization, but until the innovation has become obsolete. Consequently, both scales did not fit the context of this study correctly. As a result, based on both the existing TRL and IRL by Lee et al. (2011) a new TRL is constructed in a way that is easy to understand for the target audience of this study.
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Insight into opportunity discovery. The goal in the opportunity discovery process is to figure out if the innovation can create value for the customer (Teece, 2010). The basic idea of value creation is identifying customer needs and demands and offering a product to satisfy those needs by delivering value (Kotler et al., 2005). We measured the extent to which the SMEs gained insight into how to create value for the customer with their innovation. This is measured by a self-developed scale based on the value proposition canvas by Osterwalder et al. (2015). The value proposition canvas looks at customer’s needs and problems on one side and the value proposition (i.e. how to create value) on the other side (Osterwalder et al., 2015). The customer side of the canvas focuses on customer jobs (e.g. what needs is the customer trying to satisfy; what functional jobs is the customer trying to get done), customer pains (e.g. what does the customer dislike or try to prevent) and customer gains (e.g. what are the customer’s expectations, what would the customer like). The value proposition side of the canvas focuses on the actual product or service (e.g. what product can satisfy customer needs and how), pain relievers (e.g. how the product/service alleviates customer pains) and gain creators (e.g. how the product/service creates customer gains) (Osterwalder et al., 2015). Based on these items, a scale is created by asking to what extent the SMEs that tested their innovation got insight into six topics, which they could rate on a 4-point Likert scale ranging from “not at all” to “to a great extent”. The internal consistency, measured by Cronbach’s Alpha, is .802.
Insight into opportunity evaluation. In this step, the focus is on how to deliver value to the customer and how to appropriate value as a firm (i.e. value delivery) (Shane & Venkataraman, 2000; Teece, 2010). Teece (2010) proposes to develop a business model to gather evidence on this subject. Therefore, to measure to what extent the respondent got insight into the opportunity evaluation process, a scale is made based on the business model canvas by Osterwalder and Pigneur (2010). The business model canvas consists of nine building blocks: Customer segments, value propositions, channels, customer relationships, revenue streams, key resources, key activities, key partnerships and cost structure (Osterwalder & Pigneur, 2010). The value proposition is discussed above in the opportunity discovery variable and thus, excluded in this scale. For the remaining eight blocks, ten questions are formulated to assess to what extent the respondent got clearer insight into those items by testing their innovation through Innofest. The ten questions are asked to be rated on a 4-point Likert scale ranging from “not at all” to “to a great extent”. The internal consistency, based on Cronbach’s Alpha, is .719.
Insight into opportunity exploitation. The opportunity exploitation step consists of introducing the product to the market, which can be done in many different ways. This involves the commercialization of the product. As mentioned before, the testing through Innofest is done pre-launch, which makes it unclear in what way, and to what extent the tests will have an influence on this. Therefore, we simply asked if testing the innovation in cooperation with Innofest had any influence on the commercialization process. This questions could be answered with “yes” or “no” and had to be specified (as an open answer) when answered yes.
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out it was interesting to find out the number and what kind of pivots the respondents executed after testing. Therefore, the number of pivots is added as an additional dependent variable. The categorization of pivots is based on the pivots mentioned by Ries (2011), which are discussed in the theory section, and contains pivots both on the product and the company. The list can be found in appendix 2 in the format of the questionnaire.
Data analysis
As the study uses a mixed-method approach, both a quantitative and qualitative data analysis are done. This paragraph divides into three sections: Quantitative data analysis, qualitative data analysis and the analysis of the market orientation scales. The latter is discussed individually as it requires some additional explanation.
Quantitative data analysis. Most often, SPSS is used for analyzing quantitative data, but we believe another analysis tool fits this study better. SPSS (24.0) is still used for descriptive statistics, an inter-correlation analysis and tests for reliability. The main analysis of the quantitative data is done by using Fuzzy set Qualitative Comparative Analysis (fs/QCA), which is a non-parametric method. Fs/QCA is an analytical method that allows dealing with a limited number of cases in a configurational way (Berg-Schlosser, De Meur, Rihoux & Ragin, 2009). Sample representativeness is less of an issue, because fs/QCA does not assume that data are drawn from a given probability distribution (Fiss, 2011). Furthermore, many studies often assume there is only one way to achieve a desired outcome, but fs/QCA allows for finding multiple combinations that lead to the same desired outcome, i.e. equifinality. Both features fit the context of this study, as the sample is relatively small and it is interesting to see if combinations between different kinds of characteristics can lead to the same outcome. In practice, there is a lot of diversity between different companies and innovations and there is probably not “one best way”.
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alphas, have (largest) eigenvalues higher than 3.00. Unfortunately, it is not possible to change sample size or measurement anymore, but it is important to keep in mind for future research.
Continued is by performing the Fuzzy set Qualitative Comparative Analysis in the Fs/QCA software 2.0. The methods by Fiss (2011), Ragin (2008) and Rihoux and Ragin (2009) are followed. The method of fs/QCA is based on Boolean algebra and truth tables (Lowik, Kraaijenbrink & Groen, 2016). Fs/QCA consists of five steps, which will be discussed in detail below: (1) Definition of the property space; (2) Calibration of the data into fuzzy sets; (3) Constructing a truth table; (4) Reduce the number of truth table rows based on the conditions of the minimum number of cases and the minimum consistency level; and (5) Reduce the number of truth table rows by simplifying combinations through an algorithm based on Boolean algebra.
(1) Definition of the property space. The first step is defining the property space, which is done by defining the conceptual model and research questions based on the theoretical background.
(2) Calibration of the data. Fs/QCA treats each case as a combination of causal and outcome conditions based on the degree of membership within those conditions. Scores obtained in the questionnaire on the different variables need to be transformed to fuzzy values. To use Fs/QCA values of conditions are transformed into a range anywhere from 0 to 1, to indicate to which extent they fall into the category of full-non membership, full membership or an intermediate category. For the calibration of the data, the direct method of calibration by Ragin is used (2007, 2008, 2009).
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