The role of Big Data in innovative activities of start-ups:
A knowledge and technology perspective
Author: Niki-Iliana Katsaraki Student number: 11374748
Thesis Supervisor: Dr. M. P. Paukku
Submission Date: 23-06-2017- Final Version
Study field: M.Sc. in Business Administration-International Management track Institution: University of Amsterdam- Faculty of Economics and Business
1 STATEMENT OF ORIGINALITY
This document is written by Niki-Iliana Katsaraki 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.
2 TABLE OF CONTENTS Abstract………5 1. Introduction……….6 2. Literature Review………9 2.1 Big Data………9
2.1.1 Big Data as a resource………..10
2.1.2 Knowledge development……….12 2.1.3 Technology………...14 2.1.4 Innovation………16 2.2 Start-up firms………...17 2.2.1 Innovation-focused orientation………19 3. Theoretical Framework………..21 3.1. Development of propositions………..21 3.2. Conceptual Framework………...24 4. Research Methodology………..26 4.1. Research Approach……….26 4.2. Research Design……….27
4.2.1. Multiple Case Study………27
4.2.2. Case Selection………..27
4.2.3. Data Collection-Interviews………..28
4.3. Data Analysis………..29
4.3.1. Quality of the Research………....32
5. Results………34
5.1. Big Data………..34
5.1.1. Big Data-Knowledge………...34
5.1.2. Big Data-Technology………...38
5.1.2.1. Open Source Technologies………...40
5.2. Innovation………...40
6. Discussion………..43
6.1. Managerial Implications……….45
6.2. Limitations………..46
3 7. Conclusion……….48 References………...50 Appendix……….58
4 List of Figures and Tables
Figure 1: Conceptual Framework………..25 Table 1: Overview of the interviewees and the start-ups………..30 Table 2: Overview of the main codes and subcodes of our analysis……….31 Table 3: Examples of interviews' responses relating Big Data with knowledge…………..37 Table 4: Examples of interviews' responses relating Big Data with technology…………..39 Table 5: Examples of interviews' responses concerning innovation……….42 Table 6: Working Propositions………..43 Table 7: Results of working propositions………..45
5 ABSTRACT
Big Data and its capabilities have been of particular interest for both academic and managerial world. In our study, we are trying to investigate how Big Data as a knowledge and technological resource drive the innovation processes of start-up firms. In order to answer our research question, a qualitative research method is adopted. By interviewing employees of start-up firms, we are aiming to explain how these companies incorporate Big Data into their business. Our findings show a wide adoption of Big Data in the operations of the firms that we examined. The firms follow a highly innovative path and seek international markets. We observed that Big Data are translated into knowledge and technology in order to aid the innovation activities of the firms as well as their international steps. However, we found that, while the final products or services that were offered by the start-up firms were innovative, the technologies that were used by these firms were not. The adoption of open source technologies is also an important outcome that emerged from our study.
Keywords: Big Data, start-ups, technology, knowledge, innovation, resource-based view,
6 1. INTRODUCTION
In today's world, information can be generated from everywhere. The rapid expansion of new internet technologies has revealed a whole new world of data, characterized by great size and significant value. This data, commonly referred to as ―Big Data‖, has unique attributes in terms of value and information it can generate.
The emergence of this highly valued information has shifted business theories and processes to a new direction. Nowadays, access to big databases is much easier and analysis of information is quicker, providing companies with essential insights on fields that are crucial for their survival, performance and success and that previously demanded substantial use of resources and time to obtain. Information is easily obtained as political and economic barriers grow weaker every day. Managers do not have to seek for resources only from suppliers in close proximity. They can find what they need by searching for new opportunities, alliances and talents globally. All this new data combined with the modern ever-changing business environment, has facilitated the emergence of companies that do not follow a traditional expansion process. Thus, firms referred to as ―start-ups‖, are seeking to create competitive advantages by engaging in innovative activities that could open global paths, from an early stage.
In the modern world, it is difficult, if not impossible, to think of a company that does not depend on technology and software. There are a lot of ways to access these resources with little, or in some cases, no need of financial resource commitment. What is more, knowledge and market information are available for almost every part of the world, making international market access easier, quicker and cheaper than ever before. These availabilities have made international market access so easy, that companies are ―forced‖ to expand internationally from their emergence. Although this expansion path opposes traditional theory, that indicates
7 incremental expansion steps as the optimal internationalization model, there is a lot of evidence proving that this strategic decision can be quite successful. These ―born-global‖ companies that decided to adopt what was seen as a risky and aggressive strategy, have succeeded and prospered, initiating a new area of business research. Through the years, multinationality of firms has received significant attention by academia, leading to important researches in the field. However, most of the studies are focused on multinationality and firm performance (e.g. Lu & Beamish, 2004; Vermeulen & Barkema, 2002; Chang & Rhee, 2011). There are several reasons that motivate start-up firms in their expansion process. One is their need to catch up with their already established international rivals. These rivals usually have significant knowledge and experience of the foreign markets, possess the appropriate resources and skills and rely on bigger capital. Another reason is that managers of these firms are able to identify, and thus seize upon, new opportunities that either were not apparent before or that emerged from new markets and technologies. For example, Knight & Cavusgil (2004) have identified the importance of technological advances in driving international expansion of firms while decreasing costs. Gallo & Pont (1996), in their article connected technological capabilities with the international steps of family businesses in Spain. Although technology has always been a trigger for change, nowadays, its implications are even more apparent as the technological changes are faster, the access to technology is easier and the use of modern technologies is cheaper (Sofroniou, 2013).
The emergence of start-up firms has also made clear a need to change the traditional managerial strategic techniques. Expansion patterns have shifted to new, more rapid and fierce models that are characterized by speed and innovation. Companies have to be fast, identify the new opportunities early enough and adapt to new environments quickly.
Innovation is also an important part of start-up firms' philosophy (e.g. Bruyat & Julien, 2000 in Dimitratos et al., 2012; Cavusgil & Knigh, 2015; Knight & Cavusgil, 2004).
8 From an early age, these firms follow a highly innovative path, with technological excellence being on the center of attention. Digital revolution has also contributed in the emergence of these companies, as it brought to surface not only new emerging market opportunities but also new ways of business strategy and expansion. What is more, it provided companies as well as consumers with more information and evolutionary tools of communication, leading both sides to discover new sources of value (Edelman & Singer, 2015).
As already mentioned, data are generated from various sources, leaving firms with a variety of useful, yet unprocessed insights. The key activities that companies have to perform, are to find the ways by which the analysis of this data will contribute significantly to their success. Furthermore, it is also essential to define in what domains these contributions will take place. While a big number of large enterprises are still trying to integrate Big Data to their organization culture, start-up firms with their innovation philosophy, seem to have found the way. In a world where knowledge and technology are becoming more and more open and accessible with less money and time, data is used by companies for various strategic reasons as the invention of new ways to do things, the improvement of products and the monetization of knowledge (Petter & Peppard, 2013 in Frizzo-Barker et al., 2016). In other words, firms try to be innovative and differentiate themselves from their rivals.
Big Data is undoubtedly here to stay and is constantly revealing both new paths and new needs. These needs refer to strategy and business mentality change, knowledge and technological innovation. Big Data could be a resource that drives innovation, and within its analysis, companies could gain new knowledge and could thus, decide more accurately on what should be changed in terms of technological use and business plan, in order for them to innovate successfully.
9 2. LITERATURE REVIEW
2.1 BIG DATA
Big Data is undoubtedly the new trend that management sector is obsessed about. The importance of this data and the potential outcomes of its analysis have caught the attention not only of managers, but also of academics around the world, as there exists a wide list of scholars that have conducted Big Data related researches. However most of them agree that this data, while having huge potential for companies, is not yet fully understood, hence not thoroughly researched.
Braganza et al. (2017) state that there is limited research into the business significance of Big Data and its impact on firms' processes. They also point out the need for further study into the implication of Big Data in the resource-transformation processes of an organization. Choi et al. (2017) emphasize that there is open room for further investigation into the differences in technology used for different Big Data related initiatives. What is more, according to Côrte-Real et al. (2017) and their article on Big Data analytics in European firms, the studies in big data analysis mostly focus on developed countries. Thus, they state that it would be interesting to include southern European countries in future studies, or even a comparison of southern and northern European countries.
Doug Laney, Gartner's analyst, was the first to suggest that Big Data is characterized by its variety, value and velocity (3Vs-2001). His definition of this data attributes is commonly accepted and used as a base for a large part of academic research that has been conducted on the field. According to IBM, veracity is another important aspect of this data.
Through the years and due to the augmenting interest of business industry in the field, Big Data research has revealed significant new characteristics and potential. The rapid expansion of the Internet has also opened the road to new technological revolutions such as
10 social media platforms, which consequently revealed a whole new world of data (McAfee et al., 2012 in Fan et al., 2015). Companies and companies' managers are aware of the importance of collection and analysis of this data (Gandomi & Haider, 2015; Dutta & Bose, 2015). However, the challenges pertain to how the analysis will contribute to the company (Gandomi & Haider, 2015; Wamba et al., 2015). What is more, the integration of different insights generating from this analysis is also essential and challenging in order to provide a more holistic view (Fan et al., 2015).
In the following sections, we are going in depth on what is Big Data for a start-up firm, as well as on how it can provide value to it. We are going to follow a resource-related approach and more specifically, we are focusing on knowledge and technology resources. In addition, we are trying to examine its value for start-up firms' innovative activities.
2.1.1 Big Data as a resource
Big Data can be seen and examined through various points of view. In their study, Frizzo-Barker et al. (2016) look into the ways in which Big Data is examined by academia. According to the authors, some scholars view Big Data ―as a business topic, an
information-technology tool, a shift in architecture or new approach for collecting and using data, or simply a massive amount of data‖ (Frizzo-Barker et al., 2016, p. 406). The article also
highlights the differences among the academic researches that stem out of the ambiguous nature of Big Data. Access to and usage of this data can offer the firms significant information and insights that under certain transformation processes and codification, can lead to the creation of firm-specific advantages and assets. Thus, this data can also be seen as a resource for the firm.
Firm's resources and their contribution have been excessively studied by significant scholars that have brought up insights of high importance for academia as well as for
11 management. Resources that firms possess can, if combined appropriately, contribute in the development of the firm's competitive advantages and influence performance (Barney, 1991). However, resources per se are not sufficient for firms to differentiate unless they provide new strategic paths (Peteraf, 1993 in Lioukas, Reuer & Zollo, 2016), add value in the strategic options (Barney, 1991) and contribute to firm's performance in a way that is not easily imitated by the firm's competitors (Armstrong & Shimizu, 2007; Barney & Arikan, 2001; Sirmon et al., 2007 in Lioukas, Reuer & Zollo, 2016). Value, rarity and inimitability of the resources of a firm define its superiority compared to its rivals (Verbeke & Yuan, 2013).
Resources can be either tangible, i.e. possible to quantify, or intangible (Barney, 1995). Although, tangible resources and their outcomes are easier to measure, this is not the case for the intangible assets, as they, or their contribution, cannot be quantified. What is more, their potential value depends on the firm and its executives, hence it is a subject relying on the critical thinking and mindset of the individuals who examine it.
For academic as well as managerial purposes, Big Data based on its characteristics (3Vs), can be examined as a firm's intangible resource. There is a long list of intangible resources of firms. Some examples of such assets are knowledge, management skills, technology, patents and integration of operations and brands (Rugman et al. 2011). The information that Big Data offers as well as the technology that its emergence brought up could lead to the argument that Big Data can be seen as a resource, and thus, be analyzed as such. For the reasons that were developed above, as well as due to its newness as a research field and its nature, Big Data is mainly perceived as a technological and knowledge resource, for the purpose of this thesis.
12
2.1.2 Knowledge development
A significant amount of academic theories on firms are knowledge-centered, imposing that knowledge is if not the most important, one of the most essential resources of a firm. Knowledge creating is undoubtedly a fundamental driving force of a firm's expansion (e.g. Andersson et al., 2016; Johanson & Vahlne, 1975; Hutzschenreuter et al., 2015). By exploring new fields and domains, firms seek to develop knowledge that will facilitate the creation of firm-specific advantages, contributing in firm's superior performance and expansion steps. The knowledge gained can then be distributed through the whole firm network, can be transformed, if needed, and can be harvested for innovation (Pak et al., 2015). Firms that are able to collect and combine information from their environment as well as effectively transfer it across their units, are more risk-seeking and are able to foresee future market trends in their host countries (Li et al., 2015).
Knowledge can be generated from various sources and resources and can be redefined in order to serve certain needs of the firm. Its importance has been extensively examined through the years. According to Miller & Yang (2016), from their conception, firms are constantly trying to access and leverage already existing knowledge in order to form new, knowledge-based firm specific advantages by a constant upgrading process that aims to improve performance. Furthermore, this generated new knowledge is embedded in the firm and its operations, thus leading to the creation of a new competitive advantage that cannot be easily copied and, as long as the new knowledge becomes indissoluble from the firm, it can be easily transformed in order to serve the firm's ever-changing needs. For instance, when a firm is extracting insights from its social media pages, it develops a Big Data dataset with useful information relating to its potential customers, namely information of the people that visited the page, what are their interests or their demographic profiles (Xu et al., 2016). What
13 is more, there are also open databases, developed from other companies or organizations that contain market information that could be used as a knowledge resource for a firm.
Rugman (1980) stated that knowledge, although it is a ―public good‖, can aid the firms differentiate (Rugman, 1980 in Verbeke & Kano, 2016). The firm can absorb the knowledge and embed it in its core activities through internalization, hence creating a significant competitive advantage that will add value to its final product or service (Rugman, 1980 in Verbeke & Kano, 2016). Furthermore, the recombined knowledge resources are also transferred across the different departments of the firm through various formal and informal linkages (Hansen 1999; Tsai 2000, 2002; Hansen and Lovas 2004, in Karim & Kaul, 2015) in order to be used for the development of firm specific assets and innovative processes (Galunic and Rodan 1998, Rosenkopf and Nerkar 2001, Tsai 2001, Miller et al. 2007 in Karim & Kaul, 2015).
Deligianni et al. (2015), developed their research on examining how small technology firms use knowledge resources. According to their findings, the growth of small firms is significantly dependent on their ability to accumulate and process knowledge (Macpherson & Holt, 2007 in Deligianni et al., 2015). Moreover, they pointed out the importance of examining knowledge possession on different phases of firm growth, stating that firms follow an ongoing learning path (Hansen & Hamilton, 2011; Voudouris, Dimitratos & Salavou, 2007 in Deligianni et al., 2015). A firm's growth path is highly dependent on the reconfigured knowledge that could also lead to a change in the ways in which the company grows.
The authors mentioned above also focused on companies that operate in a market that faces significant fiscal problems, i.e. Greece. Firms that are founded in such markets which lack in consumers' demand, have to find ways to reach new, more prosperous international markets, thus they have to process and transform knowledge in such ways that it would serve as a driver of international growth and it would contribute to the formation of the firm's
14 competitive advantages. Thus, knowledge when possessed, could aid the firm in exploring and exploiting new market opportunities (Penrose, 1959 in Deligianni et al., 2015), as well as understand better its technological potential (Clarysse, Wright, & Van de Velde, 2011 in Deligianni et al., 2015). Finally, by investigating technologically intensive firms, their findings highlighted the importance of technology-related knowledge, and the different ways it contributes to the firms' growth when combined with other types of knowledge.
2.1.3 Technology
Technology is undoubtedly an essential resource for firms. Today's ever-changing contextual environment demands that firms possess superior technological assets. Technological position can determine and drive the expansion activities of an organization (Kimura, 1989 in Stuart & Podolny, 1996). According to Stuart & Podolny (1996), firms that have significant experience in the field they operate, are more likely to leverage and develop technology successfully.
However, recently founded firms, particularly the ones that seek international markets, have also realized the importance of technological adaptation in their core activities (Leten, Belderbos & Van Looy, 2016). Most of the firms that operate in fast-changing industries as, for example, that of Information Technology or automobile, rely highly on the utilization of advanced technology for their operations. This process requires substantial technological knowledge. According to well-known business scholars (e.g. Garrett, 2000), ever since the burst of information technology and its accessibility and ease in utilization, the expansion process of firms started to be quicker and their performance has been significantly better. The technological capabilities of firms also depend, by far, on the institutional environment and the market strength of each firm's home country (Santos-Arteaga et al.,
15 2017). Helfat & Campo-Rembado (2016) explain that the combination of firms' capabilities with advances in technology could explain the evolution of industries.
Technology has become so widely accessible that in most cases, it is standardized and available for everyone with significantly less cost than some years ago. However, while sometimes new technologies manage to become widely adopted, there are certain instances when due to bad timing, some technologies need more time or even fail to become institutionalized (Adner & Kapoor, 2016). What is more, old technologies might need time in order to get substituted by newer ones, depending on the different market and institutional environment of firms (Anderson & Tushman, 1991 in Adner & Kapoor, 2016).
Firms' ecosystems (Hughes, 1983; Rosenberg, 1976, 1982 in Adner & Kapoor, 2016) as well as competition between technologies are also determinants of technologies' life circles (Christensen, 1997; Foster, 1986; Utterback, 1994 in Adner & Kapoor, 2016). For this reason, access and use of technology itself as it is, cannot provide firms' with the necessary competitive advantages that are essential for their expansion and innovation processes (Prahalad & Hamel, 1990 in Leten, Belderbos & Van Looy, 2016). Thus, firms need to absorb these available technological resources, understand how they are functioning and transform them, by combining them with other resources, in new, original ways that can differentiate them from their rivals (Buckley & Casson, 2009). The aforementioned technological recombination also demands specific knowledge-related assets that are central to the firm‘s core operations and essential for its survival (Grant, 1996b; Liebeskind, 1996 in Karim & Kaul, 2015).
Firms need to define new ways of exploring and exploiting new technological opportunities, with less cost and less time needed, that could accouter them with the essential competitive advantages in order to achieve long term success (Belderbos et al., 2010; Danneels & Sethi, 2011; Levinthal & March, 1993; Markides & Williamson, 1994; Simsek,
16 2009; Teece et al., 1997; Uotila et al., 2009 in Leten, Belderbos & Van Looy, 2016). The technological environment where a firm operates also provides opportunities for firms, that could lead to the development of innovations (Breschi et al., 2000).
2.1.4 Innovation
All of the above lead to the conclusion that just the availability of knowledge- and technology-related resources is not sufficient in order for companies to develop competitive advantages and succeed. Firms need to recombine these resources in novel, creative ways that can open new paths towards success (Paruchuri & Eisenman, 2012 in Vera et al., 2016). Thus, they innovate. Verbeke & Yuan (2013) state that companies that combine their resources in the right ways are more likely to seize upon new opportunities. Le Breton-Miller & Miller (2015), give the example of Corning, which by leveraging its resources has achieved significant innovations. Paladino (2007) also investigates how resources affect innovative activities of firms, stating that ―innovations refer to new combinations of existing resources
and skills‖ (Paladino, 2007, p. 539). According to the Organization for Economic
Cooperation and Development, innovation is ―the implementation of a new or significantly
improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations‖
(OECD, 2005, p. 55 in Oura et al., 2016, p. 924)
Knowledge and technology are considered the main drivers of innovation processes by a significant number of scholars. According to Schilling (2015), huge technological changes, also referred to as ―technological shocks‖, could force companies to start innovating in order to cope with the new circumstances. In addition to this, firms that innovate in order to adapt to technological changes are more likely to successfully face and overcome potential problems that could arise (Ahuja & Lampert, 2001; Hargadon & Sutton, 1997 in Leten, ,
17 Belderbos & Van Looy, 2016), thus reduce uncertainty and risk. Karim & Kaul (2015) consider innovation as ―the creation of new knowledge‖ (p. 439) and relate innovative activities of firms with knowledge resources. Moreover, Kaplan & Vakili (2015) explain that the recombination of technologically diverse knowledge could lead to more break-through technological innovations (Phene et al., 2006; Rosenkopf & Nerkar, 2001; Trajtenberg et al., 1997 in Kaplan & Vakili, 2015).
It is essential for firms to understand where and when to innovate, as well as in what ways. In other words, firms have to identify the phases of technological changes, examine whether there is a problem to be solved, and gain the required knowledge in order to successfully face it (Teece, 1992 in Roy & Sarkar, 2016). Wrong choices would make innovative processes harder and would harshen the problem-solving procedure and thus, the company's position (Roy & Sarkar, 2016). McElheran (2015), emphasized on both internal and external resources recombination in order for firms to innovate in technology- and knowledge-intensive situations.
2.2 START-UP FIRMS
Business' and economics' scholars have been developing theories for many years now. As the business world evolves so does the existing theories. Firm size and knowledge were seen as two of the most important drivers of a firm‘s expansion. For instance, stage theory (Uppsala model) developed by Johanson & Vahlne (1975), sees the expansion process as small steps towards international expansion that commence with exports and lead to the establishment of a subsidiary. However, the emergence of internet, signifying the start of a new era of information availability and facilitation of communication, demanded a shift in the existing theories.
18 The aforementioned new era brought up a new firm model. Young and small firms can expand and claim market shares that could normally only be pursued by firms with significant size and operating history. These firms, referred to as international new ventures (Oviatt & McDougall, 1994 in Presutti et al., 2007), born-global firms (Madsen & Servais, 1997 in Presutti et al., 2007) or ―start-ups‖ are in their majority, small- and medium-sized enterprises (SMEs) that possess knowledge- and technology-related resources from an early age (Presutti et al., 2007). Howbeit, start-ups are not just smaller multinational enterprises (MNEs). In contrast to large corporations that execute already examined business plans for known markets and needs, start-ups are seeking for business models by exploring market potentials (Blank, 2012). Due to their young age, these firms lack of tangible and intangible resources as well as experience, aspects that are crucial and that are commonly possessed in excess by the corporations that start their international expansion. However, they are able to leverage innovativeness, knowledge, and technology to achieve considerable market success early in their evolution (Knight & Cavusgil, 2004).
Start-ups are certainly highly dependent on international markets and their international operations generate a large part of their profits. Although there is considerable independence in each international market's operations, experiential knowledge gained is usually transferred and transformed through the company's network. The aforementioned knowledge flows constitute an essential part of the start-up's capability of superior performance (Johanson & Martín, 2015).
Nowadays, the increasing openness and integration of the modern world has provided an ideal and fruitful base for start-ups to emerge and succeed. According to the European Union, around 20 percent of newly established firms start to internationalize from their conception (Knight & Liesch, 2016).
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2.2.1 Innovation-focused orientation
It is already mentioned that knowledge creation is a crucial factor that leads a firm's expansion process. The knowledge that start-ups' possess from the start of their operation is converted into firm-specific capabilities that are essential for the firms' next steps into a global presence (Knight & Cavusgil, 2004). These firms' entrepreneurship is based on their highly innovative nature. In academia, innovation has been examined from various different perspectives. For example, some scholars associate it with firm size (e.g. Lewin & Massini, 2003 in Knight & Cavusgil, 2004; Camisón-Zornoza et al., 2004), others with market leadership (e.g. Gilbert & Newbery, 1982; Athey & Schmutzler, 2001 in McElheran, 2015) and others with knowledge (e.g. Roy & Sarkar, 2016; Wang & Wu, 2016) and technology (e.g. Schilling, 2015; Helfat & Campo-Rembado, 2016).
Innovation in terms of knowledge creation and technological capabilities is transferred throughout the firm's whole network (Johanson & Martín, 2015) and is, if not the most important, one of the fundamental factors responsible for the firms' competitive advantages (Blomkvist et al., 2017). Lessard et al. (2016) emphasize on the importance of innovation processes for the superior performance of modern firms. 21st century's firms, especially the ones that address to a global market, integrate innovative techniques that are developed everywhere, through open innovation networks and resources. Global innovation clusters and co-invention are driving forces that aid modern firms to develop and expand, and thus to succeed internationally (Wang & Wu, 2016; Lessard et al., 2016).
It can be inferred from the above that knowledge and technology are generators of innovation for start-ups. Due to the limited resources that these firms have, there are ways that can enhance these factors, providing them with essential insights and speeding up the process. Additionally, their small size, their flexibility and their willingness to learn and gain market experience, impels to the discovery of an innovative modus operandi (Love & Roper,
20 2015). Firms that have innovation-related experience are more apt to choose to expand internationally (Love & Roper, 2015). What is more, start-ups‘ innovating activities depend more on external knowledge rather than on internal resources of research and development (Ganotakis & Love, 2011; Piergiovanni et al., 1997 in Love & Roper, 2015).
To sum up, although Big Data analysis is already widely used in business industry and their potential contribution is understood, there is still little academic research on the field. There are various reasons why we are still in a very early stage on exploring Big Data's influence on business industry. It is still the dawning of a new era for business and difficulty in separating this data from other organizational resources and examining its impact as well as lack of existing databases on its outcomes are impeding the research on the outcomes of its usage by firms. What is more, as mentioned above firms of 21st century are highly dependent on knowledge and technology of our era.
So how can Big Data, as a knowledge and technological resource, drive the innovation processes of start-up firms?
The answer to this question will give useful insights on the currently emerging field of Big Data and its implications. Although academic research in the field has not yet been broad, this research can still provide a starting point from where further research can be developed. Furthermore, it will serve as an example, illustrating the potential and the impact of Big Data application on firms' strategic processes. Managerial implications will also be of high significance as the insights that will eventually emerge from this thesis could point at new and prosperous domains for managers to explore and exploit.
21 3. THEORETICAL FRAMEWORK
As mentioned in the literature review, resource-based view can serve as a mean of examining and evaluating the impact of Big Data (Erevelles et al., 2016). Firms need to sense the environments where they operate in and adapt by transforming their resources (Teece et al., 1997). By combining and transforming resources through unique ways, the firm can develop certain capabilities (Teece et al., 1997). Innovation can be seen as a resource-based capability of a firm (Chandler & Hanks, 1994 in Wang & Ang, 2004) that derives from the recombination and transformation of these resources.
When examined as a resource, Big Data is by definition, one of significant importance. According to Wong (2012), data nowadays is available in all formats, sizes and ranges and firms are constantly trying to gain value from it and add its analysis to their everyday business operations. The business and informatics world agree that the amount of data will continue to rise and access to them will be easier and cheaper, day by day.
The challenges that modern firms face are to find out the ways in which this data can benefit them (Wong, 2012; Tan et al., 2015). For start-ups, these challenges are significantly greater. The global competitive environment, the fast technological changes and the limited resources almost impose the development of innovation in order to succeed or even sometimes, survive (Damanpour & Wischnevsky, 2006). Thus, in order to fill in for this lack of resources, start-ups have to discover the means to create them.
3.1 DEVELOPMENT OF PROPOSITIONS
While there is a lot of controversy among academics and business people in defining what a start-up is, there is one area where most of them agree: their high concentration on innovation processes from the start or even before their launch.
22 Damanpour & Wischnevsky (2006), explain that there are two major academic views on innovation. According to the first one, the corporate view, bigger enterprises are more likely to innovate, due to their great resources' possession (Barras, 1986; Hagedoorn and Roijakkers, 2002 in Damanpour & Wischnevsky 2006). However, based on the second view, the entrepreneurial view, small new firms are the ones that through their innovation-generating processes, change and reshape the industries in which they operate (Stevenson and Jarillo, 1990; Hagedoorn and Roijakkers, 2002 in Damanpour & Wischnevsky 2006).
For this thesis, we are going to follow the entrepreneurial view. Small firms are more willing to explore new opportunities, adopt new technologies and knowledge and are more flexible to change and adapt. For their exploration, they use data generated from various sources to which they have access and they use various methods in order to transform it into valuable resources. Therefore, our first two working propositions for research are the following:
Working Proposition 1: Start-ups that better incorporate the use of Big Data into their business operations are more likely to innovate successfully.
Working Proposition 2: Start-ups that successfully use Big Data-generated resources are more likely to seek international markets.
Focusing on software firms, de Souza Bermejo et al. (2015), proposed that the analysis and the transformation of data into knowledge points out the need of bridging the created knowledge with the knowledge that is already used within a firm. As a result, all the knowledge that exists in the firm is integrated and thus, is aiding the innovation development. For this thesis, we will consider knowledge as all the information that is generated from Big
23 Data analysis. Thus, taking also into account the existing studies analyzed in the literature review, we are led to the third working proposition:
Working Proposition 3: Start-ups that generate knowledge from Big Data analysis are more likely to follow an on-going innovation process.
Technology is undoubtedly one of the most important resources that a firm possesses. In most of cases, it is indissolubly connected with the company's core business and its modus operandi, and its proper use often leads to successful outcomes in various levels. However, technology by itself is not enough.
In their research, Rose et al. (2016), focused on small software firms. They explained that the high-technology is nowadays, one of the most-profit generating industries and is highly dependent on innovation. What is more, it is a prosperous field for small technology-intensive start-ups that have been able to overcome the barriers posed by their large rivals, by engaging in innovative activities which is facilitated by their ability to be flexible. These innovative activities have aided them in successfully operating in the industry, as well as in discovering and developing new fields that have not yet been dominated by large enterprises.
Furthermore, the emergence of new technologies has revealed a whole new level of potential for companies. For example, in their research, Al-Fuqaha et al. (2017) have explored the potential of the shift of Internet connection from traditional devices to almost every device and physical object, which is called the Internet of Things (IoT). By exploiting the available technologies, traditional objects are now producing a variety of data, making it necessary for companies to try to create new paths in order to catch-up and gain advantage of this information, thus, to innovate. This new internet potential has made obvious the need for the architecture, coordination and management of a new generation of technology, which in
24 most cases, is freely available. Howbeit, while almost all companies have access to this open-source reopen-sources, these reopen-sources have to be transformed in order to fit in each firm's specific needs and desirable outcomes. Modern firms, and especially start-ups, are hiring skilled personnel as software developers and business analysts in order to make use of these open sources.
In their article, Andersson et al. (2016) define technology as ―the tools and machines
that are used to solve real-world problems‖ (Andersson et al., 2016, p. 154). For the purpose
of this thesis, we are focusing more on computing, web-based technologies as well as tools for Big Data analysis, with a special interest in intangible technological resources, meaning software-related ones. On this basis, our fourth working proposition is the following:
Working Proposition 4: Start-ups that analyze successfully Big Data are more likely to use more complex, innovative and hardly imitable technology.
3.2 CONCEPTUAL FRAMEWORK
The aforementioned propositions are based on the outcomes of the existing research on the field as well as our personal interests and our identifications of what is still missing and needs further explanation. As already mentioned above, given that Big Data and its use and outcomes are still in a preliminary research stage, there are many significant things to uncover.
By discovering if and in what ways start-ups try to incorporate Big Data in their business operations, as well as what mechanisms they use, we are aiming to shed light in the field and uncover the processes and their implications and contributions in the ever-changing modern business environment.
25
Figure 1. Conceptual Framework
Knowledge
Innovation
Technology Big Data
26 4. RESEARCH METHODOLOGY
In this section, an overview of the methods that are chosen in order to serve our research and the reasons behind these choices is presented. Next, we are going to illustrate the sample choice and data gathering processes, and elaborate on the means of analysis that we chose as well as the analysis itself.
4.1 RESEARCH APPROACH
For the purpose of this thesis, a qualitative, multiple case study, in-depth research approach is selected in order to aid us in understanding the mechanisms that result in data-driven business innovation. This qualitative data gathering methodology is suitable for shedding light to a domain that has not been fully examined yet.
According to Noor (2008), many scholars examine issues by gathering relevant data and then analyzing them, trying to form causal relations that explain the occurrence of a certain phenomenon. However, not all phenomena can be understood and explained through quantitative measurements. Gephart (2004), describes qualitative research method as an interpretive method that aims at describing meanings and in enhancing understanding of the subject matter.
In this case, the lack of numerical databases together with the subjective understanding of Big Data and its meaning for start-up executives, are indicating that a qualitative research approach better serves the purposes of this thesis. In addition, the important attributes that the qualitative approach has, i.e. its flexibility as well as its focus on context, depth and detail, is more suitable as it will help us understand the phenomena under examination in their home ground. Finally, Big Data is a field that is so far, well researched from an Information Technology (IT) perspective. Yet, there is a need for further research in
27 the managerial part of this domain and its implications. A qualitative approach constitutes a springboard from where we can understand the phenomenon via the examination of the opinions, the thoughts and the experiences of people that are connected to it.
4.2 RESEARCH DESIGN
4.2.1 Multiple Case Study
In qualitative research, there are two ways of presenting the analysis of the gathered and processed data (Yin, 1984 in Eisenhardt, 1989). The first one is constructing a single case study, focused on a specific case. However, for this research, we are going to use a multiple case study approach. The companies that we are going to choose will be highly innovative and technologically developed start-ups that have been very successful in their respective industries.
We are going to present our data in multiple parallel case studies (Thomas, 2011), with different cases being examined concurrently, evaluating each case separately and concluding in a connection and comparison of all the different cases. We find this approach as the most appropriate for this research on the grounds that case studies will provide a thorough insight on the phenomenon, examining it from a perspective of the people involved and affected by it and on the environment where it took place (Gall et al. 1996).
4.2.2 Case Selection
Our sample consists of ten up firms that have global presence. The chosen start-ups have their headquarters in various different markets as, for example, in Greece or in the Netherlands, and have shown positive financial results as well as continuous growth. The products or services that they offer are addressed to a global market. Highly innovative
28 approaches and the incorporation of Big Data usage in their everyday operations are strong qualities of the companies in our sample.
4.2.3 Data Collection - Interviews
The data that will provide an answer to this research question were obtained through semi-structured interviews with companies' executives. Through the interviews, we aim to understand how Big Data have helped these companies prosper and expand, identify new opportunities and, finally, how the use of Big Data has shifted the strategies and the norms of each firm. We interviewed one executive in each company, except for one firm, where we interviewed two. The questions that were asked concern the use of Big Data, and focus on two domains: knowledge and technology. The interviews are structured in such way that they would lead to our understanding of how the two aforementioned domains have aided the firm to develop innovations and succeed. They also aim at revealing the requirements that the company has to fulfill in order to effectively utilize this data, as well as the drawbacks of their use. A semi-structured approach was adopted, as it was considered more appropriate because it provides a basis for the interviewees to understand the concept, while offering space for further development of their thoughts and opinions.
The interviews of the companies would provide the chance to examine and study the subject matter on its natural environment. The case studies of the specific cases will give important and original insights and context-dependent knowledge (Flyvbjerg, 2006) on the phenomenon of Big Data's influence on a company. This qualitative method will provide managers the opportunity to explain the phenomenon from their perspective, namely tell their story (Crick & Jones, 2000). Given that the field requires further research, these cases could be the start of a database that could be further expanded.
29 4.3 DATA ANALYSIS
As already mentioned, the data were gathered via semi-structured interviews. The interviews were recorded with the interviewees' assents. Each interview was transcribed and then analyzed with Atlas.ti program for qualitative data analysis. The majority of the interviews were conducted via Skype. From the remaining ones, two were conducted face to face and the other two were conducted via e-mail. The interviews lasted approximately thirty to forty five minutes. The data were collected between the first week of May of 2017 and the second week of June of 2017. Before the arrangement of the interviews, the potential interviewees were acquainted with the research subject and purpose. All of the interviews were conducted in Greek, except three that were conducted in English. A general overview of the interviewees and their role in their respective companies is presented in Table 1.
30 Respondent Company Industry Founded Year Location Job Role of the Interviewee
Scientific Background of the interviewee 1 Application Social Entertainment Media & 2016 Athens, Greece CEO, Founder Computer Science
2 Software and Hardware Development Firm Software and Hardware 2015 Amsterdam, The
Netherlands CEO, Founder
Software Science & Engineering, Mathematics and Computer Science 3 Code Learning and Skills' Development Application Education and
Learning 2015 London, UK Software Engineer
Artificial Intelligence and Computer Science
4 Application & Retail Analytics
Apparel &
Fashion 2014
Amsterdam, The
Netherlands CEO, Founder
Business Administration and
Management
5 Consultancy Big Data Technology and Information Services 2016 Amsterdam, The Netherlands Co-Founder Business Intelligence and Big Data, Econometrics 6
Big Data & Content Analytics Information Technology and Services 2013 New York,
USA CSO, Co-Founder
Finance, Economics,
Statistics 7 Daycare Services Day Care 2014 Dublin, Ireland Co-Founder Management Quality
8 Optimization Content Platform Internet & Content Solutions 2012 Athens,
Greece CEO, Founder
Electrical & Electronic Engineering
9 Housing Hotel & Services
Hospitality 2006 Amsterdam, The Netherlands Social Media Engagement Coordinator Applied Informatics, Business Administration 10 Hotel & Housing Services Hospitality 2006 Amsterdam, The Netherlands Marketing Product Manager Graphic & Communication Design
11 Science-based Consultancy Healthcare 2010 Amsterdam, The Netherlands Director Health Economics and Reimbursement, Founder Health Technology Assessment & Chronic Disease Management, Health Sciences
31 The codes that were used for the analysis emanated not only from the review of the existing literature, but also from the interview conduction and their transcription and analysis. We used three basic codes, namely ―big data-knowledge‖, ―big data-technology‖, and ―innovation‖. These three basic codes were then associated with subcodes that are related and attached to the meanings of the former, and enhance their value for the research outcomes.
Codes Subcodes
Big Data - Knowledge
Feedback Information Lifelong Learning Previous Experience
Big Data - Technology
Big Data - Open source Analytics
Software
Program languages Cloud
Internet of Things (IoT) Innovation Scale Product Optimization
International Expansion
Table 2. Overview of the main codes and subcodes of our analysis
The main codes are basically the three main domains under examination in this research. For the subcodes we followed two different processes. Some of the coding was in vivo, namely we used the exact word that the interviewee had said, and we assigned it to a specific phrase with a relative meaning. For example, the word ―feedback‖ was used by the majority of the interviewees, associating it with the concept of information that emerged from their customers. Thus, we linked it with the basic code ―Big Data-Knowledge‖. The other subcodes were created through careful examination of the phrases of the interviewees, where we tried to comprise the meaning of a phrase into one word. In addition, some of the codes
32 were combine into one that could cover the meaning of all. For instance, some of the interviewees mentioned Java, Python or other programming languages. For avoiding having a big set of different codes that could lead to confusion or mistakes, and due to the words being part of the same category, namely languages used for programming, we grouped them into one subcode, that of ―Program Languages‖. During the analysis process, some of the codes were discarded as we saw that they did not contribute to the purposes of the research.
Based on the codes and subcodes, we identified the most important quotations in each interviews and we proceeded in listing them in excel sheets. We also took advantage of the linkage-creating graphs that the analysis program that we used offers.
4.3.1 Quality of the Research
Qualitative methods are increasingly used in academia, especially when the research in progress addresses relatively unexplored domains that require new insights and different perspectives (Noor, 2008). Yin (1984) proposes four criteria that determine the quality of the research. These criteria are construct validity, external validity, internal validity and reliability.
In a qualitative research, the determination of whether it fulfils all the four criteria is challenging. First of all, construct validity is about ―establishing correct operational
measures for the concepts being studied‖ (p. 36, Yin, 1984). In this study, we follow a precise
and robust path, while trying to explain all the questions as well as tools and methods for their answer. We are also making a clear presentation of the final outcomes, dedicating a separate section to each important part.
Secondly, internal validity pertains to ―establishing a causal relationship‖ (p. 36, Yin, 1984). We focus on the reasons behind the interviewees‘ answers so as to ensure the internal validity of our research design (Eisenhardt, 1989). We also followed the proposition of Yin
33 (1984) to ensure internal validity, that of pattern-matching, meaning that we compare ―an
empirically based pattern with a predicted one‖ (p. 103, Yin, 1984).
Thirdly, external validity concerns the ability of the generalization of the research outcomes (Yin, 1984). External validity constitutes a significant challenge in qualitative research. The diverse cases examined in our sample make generalization easier. Howbeit, the number of interviews can bring into question the ability to generalize the outcomes, although the findings and the answers of the interviewees were not, in their majority, diverse.
Finally, reliability is the fourth criterion proposed by Yin (1984). Reliability refers to whether the research procedures, if repeated in the future, could result in the same outcomes (Yin, 1984). The explicit explanation of the steps we followed, as well as the supervision that we had during the conduction of our research, aid in the reassurance of the study being reliable.
34 5. RESULTS
In this section, the findings of the interviews and our analysis are presented. The outcomes of the research will give answers to our research question: How can Big Data, as a
knowledge and technological resource, drive the innovation processes of start-up firms?
In the following paragraphs, the main findings from the qualitative study that are related to each working proposition are going to be presented and connected and in the final part we are going to summarize the main outcomes of the research and conclude on whether our propositions are supported.
5.1 BIG DATA
In this part, we are going to present the interviews' outcomes that are related to Big Data and its use by the start-ups under investigation. As it was already mentioned in the literature review, there is not one specific working definition of what this data is.
On this phase, it is important to state that all the interviewees were familiar with Big Data and had their own perception of what this resource is. What is more, all of them were exposed, directly or indirectly to Big Data and its analysis, in their working environment or personal life. Furthermore, as already mentioned above, we are examining Big Data from a managerial perspective.
5.1.1 Big Data - Knowledge
Though we interviewed start-ups that operate in different industries, as for example, in the Media and Entertainment industry or in the Information Technology and Services industry, we observed that every interviewee agreed that Big Data is a knowledge-related resource.
35 Specifically, Respondent 1 (2017) and Respondent 3 (2017) said that users' feedback and the statistics that were generated from the first launch of their product opened up new paths that led them in optimizing their offerings and also in expanding to a new market. What is more, as their respective products were developing, they increasingly relied on Big Data and its analysis (Respondent 1, 2017; Respondent 3, 2017; Respondent 9, 2017, Respondent 11, 2017). Respondent 3 (2017) also stated that before they add a new feature to their product, they examine the data gathered in order to assess whether the potential addition will add value to the already existing product. Respondent's 4 (2017) answers coincide with what is mentioned above and he also added that they try to base their decisions “on data and not
on instinct”. The insights developed above are linked to Deligianni et al. (2015) as they
propose that small firms' growth paths relate to the knowledge gained as well as its reconfiguration. Respondent 5 (2017) also mentioned the value of knowledge generated from data produced from customers, emphasizing that it is important to be fast in launching new products or product features in order to quickly receive feedback from the customers and optimize your offering.
Respondent 2 (2017) works in a company that, among other projects, they try to develop software that aids the creation of Big Data databases, in order to create and store knowledge, that could lead to further product development or optimization of the existing ones. Furthermore, Respondent 7 (2017) added that they examine everything that is related to their users or clients because “many times what we thought was then useless, proved of high
importance as we continued to grow”. Respondent 7 (2017) also stated that their company‘s
international expansion steps were made after careful examination of all the available data for the prospective markets. This statement contradicts the theory developed by Johanson & Vahlne (Uppsala model – 1975) that wants companies to follow small, incremental internationalization steps in order to gain the appropriate knowledge of new foreign markets.
36 For Respondent 5 (2017), as well as for Respondent 11 (2017), Big Data analysis is a part of their service offerings. The respondents stated that they have been working with publicly available data from the start of their firms‘ operations, combining them with the data provided by their clients in order to uncover its hidden value. In the following table (Table 3), there is an overview of some examples of responses relating Big Data and its analysis with knowledge.
37
Respondent Examples of relative Responses
1
―When we launched our prototype and we got customers‘ feedback, new horizons opened.‖
―We are thinking to add features for business clients, so we need data for that because then the client could know anytime who, when and how many people are looking at their advertisement.‖
2 ―Big Data is the collection of big amounts of information and its processing so as to produce new insights.‖
3
―You have to put key metrics in your app so you can see your drop-off rate, for example. Like last month we did not have that step and we had a 10% drop-off. If, with a new feature, we have a 15% drop-off, maybe it is not worth having it.‖ ―We do a lot of textual analyses in feedback.‖
4
―By using Big Data from various sources, online, offline or combined, we can learn, patent, improve our algorithms or whatever we use.‖
―I try to base my decisions on data and not on instinct‖. 5
―There is a lot of opportunities for companies to do something with their data.‖ ―Yes, sure [there is a lot of knowledge hidden in Big Data]. But now it is more accessible as well because it is all digital.‖
6
―[Big Data] is huge metrics in terms of depth as well as in terms of width, i.e. historical width, and this is happening across multiple features […]‖
―[…] formerly, a marketer had access to information like [name] is male, living in New York, 30-40, single, educated, that was it, ten characteristics. A marketer nowadays may know thousands of things about me.‖
7
―We analyze data in order to see user‘s behavior, how we can improve user‘s experience, how we can increase conversions depending on what we want. We use feedback so our database is clarified, because there is evaluation from both sides.‖
8 ―You get a lot of information concerning your company.‖ 9
―Big Data can offer useful information and insights.‖
―Firms can learn a lot, particularly in respect to customer relationship management and to marketing.‖
10
―Data sets map human behavior. When done right, you are able to uncover insights that people cannot tell you themselves. When asked, people will respond with a biased answer, yet data allows you to uncover the unequivocal truth.‖
11 ―[…] we try to collect as much evidence as we can through databases that are publicly accessible or that we have access to as a company […]‖
38
5.1.2 Big Data - Technology
The research findings concerning technology were in accordance with our expectations relating to their type. According to what was developed in the literature review, open source technologies, as well as modern technologies or technological hardware were expected to be present in our findings. As it was anticipated, every respondent stated that they used a variety of open source tools and technologies that were interweaved with Big Data and their analysis.
Respondent 1 (2017) said that they use technology provided by Google or Apple, that allows their product to be functional everywhere. Respondent 2 (2017) stated that they use LoRa protocol as well as other protocols that are related to IoT. He then continues by adding that they take advantage from every open source technological resource that Google offers, meaning, for example, open software and cloud servers. Respondent 3 (2017) also emphasized on their company's great use of open source software, open databases and cloud servers and diagrams.
Technology that is used or created by the start-ups incorporates features that either emerged from Big Data analysis or that have certain features in order to gather and analyze existing data or data produced by their use (Respondent 3, 2017; Respondent 4, 2017; Respondent 6, 2017; Respondent 7, 2017, Respondent 11, 2017). Open source technologies and tools were also incorporated in the start-ups' core products (Respondent 2, 2017; Respondent 5, 2017; Respondent 7, 2017; Respondent 8, 2017). An overview of some examples of technologies used is presented in Table 4.
39
Respondent Examples of relative Responses
1 ―Nobody can reach Google's way of mapping the world.‖
2
―We do embedded software. […] Internet of things, yes. Basically, Internet of Things is a part of the apps that use embedded code.‖
―[..] in general, Google services, open source software from Google, open libraries, we incorporate all these to our products[...]‖
3
―Usually, when we say 'Big Data' we mean […] everything that infrastructurely has to do with technologies like Hadoop, Google Clusters, MAPI use [...]‖
―When you build a new feature […] you have to think of what data you will need behind all this, so you could know if the feature is a success, if it is useful for you, if you finally have to keep it or not.‖
―[…] we wrote a script that took the most frequently used words and formed a cloud diagram.‖
4 ―A big part and the main reason that we founded [start-up's name] was the analytics.‖
5
―[…] we use pretty much only open source software or programming languages. Python or PHP and stuff like that. […] you can do a lot when you have the raw program. Sky is the limit pretty much. But it is so much more versatile than for example, Microsoft Power BI or Tableau or something.‖
6
―[...] companies are now technologically able to record incredible amount of information about consumers [...]‖
[…] there is an explosion also in the number of available tools [...], it is easy to download ready tools and use them out of the box, open source [...]‖
7 ―All the data, all the statistics are on our backend.‖
8 ―This works also in offline companies due to the Internet of Things [...].‖
9 ―We have a lot of different databases and we try to unitize them in order to be able to proceed in analyzing them.‖ 10 ―We use only Google analytics of our digital platforms as it stands.‖
11 ―[…] more like standard analytics, I mean we have some decision tree modeling, that's actually some kind of open source packaging, […] or other programs but there is nothing super-fancy […]‖
40
5.1.2.1 Open Source Technologies
During the conduction of the interviews, we observed that the majority of the interviewees mentioned open source software and technologies. Due to the limited financial capital that start-ups have, we were anticipating that there were going to be some responses of firms that pertain to open source technologies, though their usage was greater than expected.
There was a big number of respondents that indicated the use of open source software and analytical tools (Respondent 1, 2017; Respondent 3, 2017; Respondent 5, 2017; Respondent 7, 2017; Respondent 8, 2017). What is more, the give-and take philosophy of the open source community impelled a number of start-ups to develop open source technologies themselves. Respondent 3 (2017) indicated that ―when you give something to people,
everything is developing faster‖. Respondent 2 stated that ―in essence, [open source] helps everyone […]. It helps us, because we can show the quality, and [it helps] the clients because it aids potential customers in choosing them […]”.
Furthermore, many respondents indicated that they use open source tools such as courses or training programs in order to fill in for any potential lack in knowledge or experience with analytical tools and software (Respondent 2, 2017; Respondent 3, 2017; Respondent 4, 2017; Respondent 5, 2017; Respondent 8, 2017).
5.2 INNOVATION
Innovation is the main focus of our research and certainly, one of the main activities of start-ups (Bruyat & Julien, 2000 in Dimitratos et al., 2012). All the respondents stated explicitly that the product or service that their respective companies offer is an innovation itself. The products or services are either unique in their respective markets (Respondent 1, 2017; Respondent 2, 2017; Respondent 7, 2017; Respondent 10, 2017) or are executed in a
41 different manner from the predominant one (Respondent 4, 2017; Respondent 5, 2017; Respondent 8, 2017, Respondent 11, 2017).
The start-ups that constitute our sample are very successful in the industries where they operate and have managed to achieve significant results, both financial and market-related. This supports the proposition developed by Lessard et al. (2016), that innovation is an important determinant of modern firms' success. A list of the responses that are related with innovation is presented in Table 5.
