Assimilation of Big Data in financial SMEs around the world

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Assimilation of Big Data in financial SMEs around the

world

Student: Pouya Zarchin

Student Number: 10915656

Date of submission: 21-06-2017

Institution: University of Amsterdam

Master’s Thesis: MSc. in Business Administration – International management

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Statement of originality

This document is written by Student Pouya Zarchin 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.

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1 Abstract

This paper investigates the assimilation of Big Data in financial SMEs around the world. According to the existing literature on the assimilation of new technologies, different attributes of organizations can influence the process of assimilation. Big Data seems to be a very promising new technology. However, despite being a very hot topic in both business and academic world, Big Data is a black box when it comes to its process of assimilation in organizations, especially SMEs. To address this gap and answer our research question, semi-structured interviews were held with the leaders of ten financial SMEs in ten different countries. The results of the study suggest that leader’s belief in the potential of Big Data is the most decisive attribute for SMEs during the assimilation process of Big Data. Furthermore, home country’s uncertainty avoidance seems to influence the process partially. This result can help to modify the existing theory on the assimilation of innovation and make it more accurate for new technologies like Big Data and organizations like financial SMEs. Furthermore, considering the importance of SMEs in today’s global economy, this result can assist them in taking more conscious steps within the framework of adopting Big Data.

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2 Introduction

Nowadays, Big Data is presumably one of the most important topics in both academic and business world. This popularity is partly due to the potential of Big Data. There are many studies that show how “Big” the impact of Big Data can be on our lives. Mayer-Schönberger and Cukier (2013), are taking a step further and suggest that Big Data is “A revolution that will transform how we live, work, and think”. According to Google Trends, the popularity of the search term “Big data” in Google search engine started rising five years ago, and has now reached top level.

Various studies have shown that Big Data is capable of improving a firm’s business performance. For instance, in terms of marketing, the study by Michael and Miller (2013) has shown that by using Big Data to analyze the consumers purchasing trends, firms can create more effective and personalized marketing strategies. Furthermore, real-time characteristics of Big Data can help firms to analyze huge amounts of data very fast and make quick and interactive decisions. Big Data seems to be very useful when it comes to predictive analysis. This can help firms having a better perception of the future (Waller & Fawcett, 2013). The predictive nature of Big Data can improve the efficiency in supply chain management (Gunasekaran, Kumar Tiwari, Dubey, & Fosso Wamba, 2016). A study by Popovič, Hackney, Tassabehji and Castelli (2016) suggests that proper use of Big Data can facilitate the manufacturing decision making and improve the business performance. Using Big Data can also improve the financial performance of the firms by boosting their customer retention sales growth and profitability (Wamba, et al., 2017).

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firms across the world that use Big Data, approximately 60% of the firms are able to generate revenue from the data they own. Nevertheless, these large firms are not representative of the whole business world. According to The World Bank (2015) Small and Medium Enterprises (SMEs) account for more than 99% of all enterprises, for 45% of total employment and for almost 33% of Gross Domestic Product (GDP). According to the existing literature, Big Data can help SMEs in their growth process (Sen, Ozturk, & Vayvay, 2016), but at the same time, the Big Data era is bringing a new set of challenges for SMEs.

This study aims to shed light on the assimilation process of Big Data by SMEs. To be more specific, the aim of the study is to gain a greater understanding of the decision by SMEs whether or not to start using Big Data. The study will contribute to the literature by providing a greater understanding of the assimilation of new technologies in organizations, most specifically in terms of Big Data and SMEs. Furthermore, considering the important role of SMEs in global economy and employment, the study could assist SMEs on their way to being better performing firms.

Due to the current study being master thesis project, the limitation in time is an issue, hence the focust must be kept narrow. Prior to the project, SMEs in the financial sector could be reached through networking. Due to two factors, namely their current stage of assimilation of Big Data and having strong international connections, the firms seemed to be a good match for the study. Therefore, it has been decided to focus on this type of SMEs. Furthermore, due to its quantitative nature, the financial sector seems highly accessible for Big Data (Seth, & Chaudhary, 2015). The mentioned financial SMEs are small and medium firms which have 10 to 25 employees and are mainly active in financial fields like merger and acquisition consulting, investment and financial advisory.

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In next chapters, the aim is to address the question: “Why is there a variance in the assimilation of Big Data among financial SMEs around the globe?”. First, the existing literature on the assimilation of new technologies is discussed in the literature review chapter. Next, the methodology chapter will focus on the design, the approach of analysis and the sample of our study in details. The results of the study will be presented in the results chapter, and the discussion and conclusion chapters will discuss how the research results relate to the existing literature and answer the research question.

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3 Literature Review

In this chapter, we will review the existing literature on both Big Data and the assimilation of new technologies in organizations. Firstly, we will discuss Big Data and try to conceptualize it. We will then underline Big Data literature in the financial sector and SMEs. Furthermore, we will focus on existing literature on theory of the assimilation of new technologies in general as well as within the framework of SMEs. Finally, we will try to link all the mentioned subjects and lay our path to answering the research question.

2.1 Big Data

Big Data is one of the main concepts of the current study. In this section, we will first elaborate on this concept and then review the existing literature on Big Data in both the financial sector and SMEs.

3.1.1 What is Big Data?

Oxford English Dictionary defines Big Data as “data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges”. In order to understand what Big Data really is, we must look at its characteristics, which are specified in volume, velocity and variety (McAfee, Brynjolfsson, Davenport, Patil, & Barton, 2012). Big Data has an enormously large volume. An example can be 2500 terabytes of data that Walmart collects from its customer transactions every hour (McAfee et al., 2012). Furthermore, the velocity of the creation of Big Data is extremely high: Big Data is created in real time. For example, Big Data allows gaining real-time insight into the crowdedness of areas in a city by using signals from mobile phones. The last characteristic of Big Data is its variety: Big Data has many different forms such as text, images, digits, GPS-signals and so on.

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3.1.2 Why is Big Data a hot trend nowadays?

One of the drivers of the current study, is that nowadays Big Data is a very hot trend in both business and academic world. Before 2010, however, Big Data was a relatively unknown topic. This dramatic change is caused by two main factors. The first factor relates to the volume characteristic of Big Data. Nowadays, there are enormous amounts of data available in comparison with just five years ago. In fact, approximately 90% of the available data today was produced within the last two years. Furthermore, we are creating 2.5 exabytes (2.5 billion gigabytes) of new data every day and this number continues to grow (IBM, 2017). Moreover, the Cloud technology has made the storage of data easier than ever.

The second factor which has laid the ground for Big Data is the improvement of computers in both processing and storing data. Today’s common smartphones are stronger than supercomputers of the last decade. By using an Internet connection and frameworks like Hadoop, we are able to combine the processing power of computers to easily analyze very large sets of data

3.1.3 Conceptualization of Big Data

Big Data is a relatively new technology. In order to conduct our research properly, first, we must make it clear how we conceptualize Big Data in our study. According to Resource Based View, firms can create competitive advantages by application of a bundle of both tangible and intangible strategic resources (Barney, 1991). Amit and Schoemaker’s (1993) contributed to Resource Based View by adding the concept of capabilities. This concept is related to the importance of an appropriate level of coordination while trying to create value from different resources. Thus, capability refers to the fact that the available resources need to be efficiently

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a black box. This is because there are a few studies that evaluate the process adoption of Big Data in organizations, especially in SME’s. Big Data could be a strategic resource for the firms. For us, this is an empirical question. However, this question exceeds the scope of the current study. Therefore, to keep the concept of Big Data clear in this paper, we conceptualize Big Data as a strategic resource for the firms, which requires capabilities in order to create value from it.

3.1.4 Big Data in financial sector

In order to justify the assumption which claims that Big Data has huge potential for the financial sector, we will review the existing literature on this subject. Due to quantitative nature of finance, data, in general, is one of the crucial aspects of it. Therefore, efficient use of Big Data is one of the essential success factors in this data-driven industry (Seth, & Chaudhary, 2015). By analyzing large datasets, Big Data can help firms in the financial sector to find new market opportunities (Peat, 2013; Fang, & Zhang, 2016). Major financial firms are already creating value from Big Data by for instance “reducing the response time to real-time data streams and improving the scalability of algorithms and software stacks on novel architectures” (Fang, & Zhang, 2016, pp.1).

3.1.5 Big Data and SMEs

Despite the importance of SMEs in the global economy, there is a gap in the literature when it comes to Big Data and SMEs. The only available published paper on this subject is the study by Sen, Ozturk and Vayvay (2016). Their study aimed to propose ground for the utilization of Big Data for the growth of SMEs. Besides arguing that the use of Big Data can help SMEs to grow faster, they assume, due to their position in the global economy and their size, a small change in SMEs can have a major effect. Again, due to the small size, SMEs are quicker and more flexible when it comes to adoption of new technologies. The latter can be related to

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organizational attributes influencing the assimilation of new technologies, which will be discussed later.

3.2 Assimilation of a new technology

The process of the adoption and assimilation of new technologies in organizations has been addressed by numerous studies. However, considering that Big Data is a relatively new technology, there is a gap in literature when it comes to the process of assimilation of Big Data in organizations. In our study, we will use the theory of assimilation of innovations as a base. Theory of assimilation of innovations (Meyer, & Goes, 1988) explains the process of adoption of a new technology in organizations by categorizing the process in different stages and trying to understand which factors are influencing this process. According to the theory of assimilation of innovations, Assimilation is a process that starts with the knowledge-awareness stage in which, firms’ initial awareness and evaluation of the innovation begins. The next stage of assimilation is the evaluation-choice stage in which a firm makes the decision to acquire the innovation. The final stage of assimilation is the adoption-implementation stage, which refers to fruition of innovation: full acceptance, utilization, and institutionalization (Meyer, & Goes, 1988).

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Figure 1: Stages of the theory of assimilation of innovations (Meyer, & Goes, 1988)

Stages of the assimilation of new technologies Knowledge-Awareness Stage

Discussion concerning adoption Consideration of the suitability of the innovation Apprehension: Awareness of the existence of an innovation

Evaluation-Choice stage

Strategic and political evaluation of proposal Financial and sector specific evaluation of proposal

Acquisition proposal of the innovation

Adoption-Implementation Stage

Expansion: the innovation is expanded or upgraded Acceptance: Innovation become accepted and frequently used

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Different studies have shown that the theory of assimilation of innovations, is an important theory when it comes to understanding the process of assimilation of new technologies in organizations. The theory can be applied to different fields. For instance, a study by Damanpour and Schneider (2006) have employed the theory of assimilation of innovations to examine the effects of environmental, organizational and top managers’ characteristics on the process of adoption of innovations in public organizations in the United States. The theory could help them to come with the results that suggest that organizational characteristics and top managers’ attitudes toward innovation have a strong influence on the process of adoption of innovations in organizations. This study has shown that the theory of assimilation of innovations can also be used in public sector studies and that it is not only limited to the medical organizations.

In a more recent study in information technology (IT) field, which was also based on the theory of assimilation of innovations, Hameed, Counsell and Swift (2012), have reviewed 59 existing studies on the assimilation of IT technology. In their meta-analysis, they focused on the impact of ten different organizational characteristics on the process of assimilation of IT in organizations. Their findings demonstrate that organizational readiness is the most important organizational characteristic that affects the process of assimilation of IT in organizations. Similar to the original theory, they have identified three stages of assimilation, namely the stages of initiation, adoption-decision and implementation. However, there is a difference between the initiation stage of Hameed, Counsell and Swift (2012) and the knowledge-awareness of Meyer and Goes (1988). In the initiation stage, firms first identify the need for an innovation and then start actively to gather knowledge on the new possible innovation, but the knowledge-awareness stage starts with the awareness of a new technology through external sources. By applying the modified version of the theory of assimilation of innovations to the IT

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field, this meta-analysis has shown that the theory can also be used in new technological fields like IT.

Given that the current study aims to understand why SMEs decide to whether or not start to use Big Data, we are focusing on the knowledge-awareness stage, where the initial steps are taken to making this decision.

3.2.1 Knowledge-Awareness Stage

The knowledge-awareness stage, consists of three steps: Apprehension, Consideration and Discussion (Meyer, & Goes, 1988). During the first step, firm members start to learn about the existence of the new technology. In the case of Big Data, this can be interpreted as managers and employees of an SME hearing about Big Data through different sources. Consideration comes to picture when members of the firm consider the new technology’s suitability to their firm. This step is very crucial: for instance, if the managers and directors of the firm who determine the strategy of the firm do not consider the suitability of the new technology for the firm, the new technology will not make its way to the firm. The next step of the knowledge-awareness stage is the discussion step. In this step, the members of the firm start engaging in conversations concerning the adoption of the new technology and finally make the decision on whether or not the process of the adoption of the new technology will be started.

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Figure 2: Three steps of the knowledge-awareness stage of assimilation (based on Meyer, & Goes, 1988).

3.2.2 Attributes that influence assimilation

Another aspect of the theory of assimilation of innovations is the categorization of factors which influence the assimilation process of new technologies and cause differences in adoption of new technologies among different organizations. Since we are addressing the differences in the assimilation of Big Data among SMEs, this aspect is crucial for our research. In their study on the assimilation of new technology’s in the medical sector, Meyer and Goes (1988), have discovered that during the assimilation process, there are different attributes that influence the process. They have categorized the attributes under five clusters: attributes of environments, organizations, leaders, innovations, and innovation-decision processes. Environmental

Knowledge-awareness stage of assimilation of new technologies

Apprehension: Learning of existence of the new technology

Consideration: considering suitability of the new technology

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study is about the medical organizations, they use the external factors for hospitals such as patients and capital funds as an example for environmental attributes. Organizational attributes refer to the characteristics of the organization like the size of it, whereas leadership attributes refer to the leader’s attributes like support for the acquisition of a new innovation. Innovation attributes are referring to the characteristics of an innovation. An example for the innovation attributes in the medical concept can be the level risk of using a new medical instrument. Innovation-decision attributes refer to the context related attributes of an innovation, such as its compatibility with the task and the experience of potential users.

Another major study that addresses the attributes that influence assimilation, is technology-organization-environment (TOE) framework (Tornatsky, & Fleischer, 1990). TOE framework explains the assimilation process using three categories of determinants. First category is related to characteristics of the new technology (Raymond, Bergeron, & Blili, 2005; Xu, Zhu, & Gibbs, 2004; Zhu, Kraemer, & Xu, 2003), which can be seen as a combination of innovation attributes and innovation-decision attributes, introduced by Meyer and Goes (1988). The second category of determinants is regarding the characteristics of the organization, including the characteristics of the leader or the decision-maker (Raymond, et al., 2005; Kuan and Chau 2001). Due to the inclusion of leader attributes, this category is a combination of organizational and leader attributes of Meyer and Goes (1988). The last category of characteristics of TOE framework is referring to the environmental context; For example, the effect of home countries culture or the external pressures from the firm’s business partners (Raymond, et al., 2005; Raymond and Bergeron 1996). This category is similar to the environmental attributes of Meyer and Goes (1988).

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A brief look at the related studies shows that TOE framework is way more popular among the scholars than Meyer and Goes (1988) framework when it comes to categorizing attributes influencing the assimilation process of new technologies. The TOE framework is namely more cited by scholars than the theory of assimilation of innovations. However, the TOE framework mainly aims to categorize the characteristics of the firms, whereas the theory of assimilation of innovations focuses on the whole process of assimilation. Furthermore, the TOE framework is a general model which can be used for different technologies. In the current study, we are focusing on a single technology: Big Data. Moreover, our research question addresses the differences of SMEs in the adoption of Big Data and not a range of new technologies. Therefore, in the current study, we will only focus on organizational and environmental characteristics of the firms and assume technology characteristics as a fixed factor. This also is in the same line with Fitchman’s (2000) statement that argues when the general theory is not sufficient, researchers should tailor the theory of assimilation to specific classes of technology.

3.2.3 Assimilation of new technologies in SMEs

When we look at the existing literature on the assimilation of new technologies in SMEs, we can see that there is a clear research gap in this field. As discussed before, the SMEs and their role in the global economy are getting more important. Therefore, it is necessary to understand the different aspects of SMEs. There are two major studies which have focused on the process of the assimilation of new technologies in SMEs. Before examining the same concept in Big Data theme, we will review these two studies.

In their study about the assimilation of e-learning in SMEs, Raymond, Uwizeyemungu, Bergeron, and Gauvin, (2012), tried to propose an integrative conceptual framework of the

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organizational and environmental characteristics of the firms such as the SMEs business network and employees, leaders of SMEs are playing a key role and can be seen as the most important determinant that influences the assimilation of new technology in SMEs. Furthermore, the study by Raymond, Bergeron and Blili (2005), about determinants of the assimilation of e-business in Manufacturing SMEs, shows that characteristic of the leaders in SMEs is the most important determinant of the assimilation of e-business.

In the same line with existing literature, in the current study, we will pay extra attention to the leaders of SMEs in our sample.

3.2.4 The international aspect of assimilation of new technologies

Among all of the environmental attributes of the firms, which are addressed in the literature of assimilation of new technologies there is one that did not get much attention from the scholars, namely the effect of home country on the process of assimilation of new technology. However, there are studies which address this characteristic, not in the concept of assimilation, but in related concepts. For instance, Shane (1995) and Strychalska-Rudzewicz (2016) have studied the impact of national culture on openness for innovations. The findings of their studies suggest that the organizations in countries with a relatively lower level of uncertainty avoidance are more open for new innovations. These results could also be applied to the concept of assimilation of Big Data in SME in different countries.

Another country-related characteristic which could affect the adoption of innovations is the level of development of the home country (Gu, 1999; Evenson, & Westphal, 1995). To be more specific, firms in developed countries are more open and successful when it comes to the adoption of a new innovation in comparison with firms in developing or less developed

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countries. This approach can also be used while studying the process of assimilation of Big Data in SME in different countries.

3.3 Why assimilation of Big Data and financial SMEs?

Before taking another step in this paper, it is useful to sum up why we are interested in answering “why is there a variance in the assimilation of Big Data among SMEs in financial sector around the globe?”.

First of all, Big Data is a very hot topic these days and many firms are considering to start using it. However, Big Data is a relatively new technology and firms which assimilate it could be considered early adopters of Big Data. The implications of these assimilation process can lay the path for the future of Big Data. Therefore, we are in a critically important era. Furthermore, due to the quantitative nature of finance, Big Data seems to be very promising for the financial sector. This potential of Big Data for the financial sector is shown by different studies (Peat, 2013; Fang, & Zhang, 2016; Seth, & Chaudhary, 2015). However, these Big Data themed studies in the financial world are focusing on the large and major financial firms, whereas SMEs are also very important for the world economy and the global society. Therefore, in the current study, we are focusing on the financial SMEs to fill this gap in the literature and at the same time through our finding, helping SMEs to take more conscious steps on their Big Data journey.

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4 Theoretical framework

In this chapter, we will present the theoretical background and talk about our expectations regarding the differences in the assimilation of Big Data in financial SMEs. Furthermore, we will present our conceptual model.

4.1 Impact of organizational determinants on assimilation

Organizational attributes refer to a range of characteristics such as the number of employees (size of the firm), recency of employees’ education and last but not least, the leader attributes. According to different studies discussed in the previous chapter (Raymond, et al., 2005; Kuan and Chau 2001; Meyer, & Goes, 1988) organizational determinants have an impact on the assimilation of a new technology in organizations. Thus, we expect organizational determinants also will influence the assimilation of Big Data in financial SMEs, which is presented in the form of working propositions below:

Working proposition 1: Larger financial SMEs are more likely to assimilate Big Data.

Working proposition 2: If the employees of a financial SME have a more recent education, the firm is more likely to assimilate Big Data.

Working proposition 3: If the leader believes that Big Data can create value for the financial SME, the firm is more likely to assimilate Big Data.

Working proposition 4: If the leader of a financial SME has more recent education, the firm is more likely to assimilate Big Data.

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4.2 Impact of environmental determinants on assimilation

In this context, the environment refers to the attributes of the environment in which the firm exist, like firm’s network and the firm’s home country. The latter is a clear research gap when it comes to SMEs. The current study will take the SMEs and the assimilation of new technologies out of the national concept and use an international perspective. We will focus on two factors namely the culture of the home country and the development level of the country. To be more specific about the culture, uncertainty avoidance of the home country seems to have an impact on firms’ openness for new technologies and innovations (Shane, 1995; Strychalska-Rudzewicz, 2016). Furthermore, the level of development of the home country (Gu, 1999; Evenson, & Westphal, 1995) is expected to influence the assimilation of new technology. Finally, different studies have shown that the firm’s network and the presence of facilitator parties in it, can influence the assimilation of a new technology in organizations (Meyer, & Goes, 1988; Raymond, et al., 2005; Raymond and Bergeron 1996). Therefore, we expect environmental determinants to also influence the assimilation of Big Data in financial SMEs, which is presented in the form of Working propositions below:

Working proposition 5: If there are parties that can facilitate the assimilation of Big Data in the network of the financial SME, the firm is more likely to assimilate Big Data.

Working proposition 6: If the financial SME is located in a country with a low level of uncertainty avoidance, the firm is more likely to assimilate Big Data.

Working proposition 7: If the financial SME is located in a developed country, the firm is more likely to assimilate Big Data.

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4.3 Conceptual model

Based on the theory of assimilation of innovations and TOE framework, our conceptual model is depicted in figure 1.

Figure 3: The Conceptual model - Determinants of assimilation of Big Data in SMEs active in financial sector Firm’s size Recency of employees’ education Leader’s belief in Big Data

Assimilation of Big Data in financial SMEs Recency of leader’s education O rga ni za ti ona l de te rm ina nt s Uncertainty avoidance level of

the home country Big Data facilitators in network E nvi ronm ent al de te rm ina nt s Development level of the home country

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5 Research Methodology

In this chapter, we will describe the design, the sample and the procedure of the current study and finally, we will discuss the strengths and limitations of our research design.

5.1 Description of design and sample

In this section, we will explain our research design. Further, we will elaborate the sample and selection of participants of the current study.

5.1.1 Design

The current study was a multiple case study which was based on semi-structured interviews with the leaders of the firms in our sample. This method was chosen over quantitative methods with the aim to gain a deeper knowledge through interviews. By using the multiple case study method, a better understanding of differences and similarities among the cases can be reached (Yin, 2009). Furthermore, since our starting point was the theory of assimilation of innovations (Meyer, & Goes, 1988) and TOE framework (Tornatsky, & Fleischer, 1990) the current study can be categorized as deductive.

5.1.2 Unit and approach of the analysis

The current study aims to understand why financial SMEs differ in the assimilation of Big Data across the globe. As we mentioned before, the focus was on differences in organizational and environmental attributes of the firms. Therefore, the unit of analysis – or in other words – the cases of our study, were separate financial SMEs in our sample. To find the answer to our research question, we conducted an across cases analysis. Thus, we examined which

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5.1.3 Sample and Selection process

The sample of the current study consisted of ten SMEs. These SMEs are involved in international financial advisory across the world (Europe, North-America, South-America, Middle-East, Asia and Australia). The process of selection started with one of the SMEs which was located in the Netherlands and the researcher already knew them. This financial SME was a member of an alliance of global financial SMEs with more than 31 members. Due to the profile and their global presence of this alliance, these firms were a perfect match for our study. After the Dutch firm has agreed to participate in our study, we have reached all of the other members of the alliance to ask them to participate too. Finally, ten financial SMEs agreed to participate. Due to the privacy concerns, the firms requested anonymity through the protection of their names and the identity of the employees. Therefore, we will only use the home country as distinguishing variable between the firms. For instance, instead of the name of the firm, we will use “Dutch SME”. It is worth mentioning that all of the firms were at the first stage of assimilation of Big Data or in other words, they were all aware of the existence of Big Data, which matches the subject of our study. From each firm, the leader was interviewed. Our decision to interview the leader of each firm is because of the critical importance of leaders in SMEs’ strategies (Raymond, Bergeron, & Blili, 2005; Raymond, Uwizeyemungu, Bergeron, & Gauvin, 2012). As mentioned before, our “gate keeper” in this network of financial SMEs was the Dutch SME. However, the contact information of other firms was collected from the website of the alliance. The connection to respondents in each firm was accomplished by calling each firm and asking to make an interview appointment with the leader of the firm. During this first connection with the firm, it was also asked who the leader of their firm is (the person who has the most influence on the firm’s strategy).

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5.2 Description of research instrument and procedures

In this section, we will describe our research instrument and data collection procedure.

5.2.1 Research instrument

In the current study, data was collected using semi-structured interviews with open-ended questions. This method was employed to gain a broader understanding of the firms (Cohen, & Crabtree, 2006). All interviews, except one with Dutch SME’s leader – which was conducted face-to-face – were conducted via Skype and phone.

Firm’s location Method of interviewing the leader

Netherlands Face-to-face

Germany Phone call

France Skpye

Turkey Phone call

India Phone call

China Skpye

Korea Skpye

Japan Skpye

Canada Skpye

Mexico Skpye

Table 1: Methods of interviewing each leader 5.2.2 Data Collection

A total of ten interviews has been conducted with the leaders of the firms. This phase started in April 2017 and ended in May 2017. The duration of the interviews averaged around 40 minutes and the interviews were recorded as voice records for further transcription. Each interview covered these topics:

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Covered topic Related concept in study

Leader’s characteristics: Demographics, educational background etc. Organizational determinants Leader’s perception of Big Data and it potential for firm’s business. Organizational determinants Firm’s characteristics: Size, financial facts Organizational determinants Employees’ characteristics: Demographics, educational background etc. Organizational determinants Firm’s plans concerning Big Data. Decision on adoption of Big Data Environment of the firm: Network etc. Environmental determinants

Table 2: Covered topics by interview questions and concept measured by them.

5.3 Strengths and limitations of the research design

To ensure a sufficient level of validity and reliability in the study, we have used four quality tests that are most commonly used by scholars (Yin, 1984): construct validity, external validity, internal validity and reliability.

5.3.1 Construct validity

According to Gibbert, Ruigrok and Wicki (2008), construct validity is the extent to which a study examines what it claims to examine. In order to increase construct validity of our study, we used a method which is suggested by two major sources (Yin, 1994; Denzin and Lincoln, 1994), namely using multiple sources of evidence. Since we have only interviewed one person from each firm in each country, it is difficult to use multiple sources of evidence of the findings. However, the firms in our sample are all members of the same alliance. Therefore, we have compared the patterns of each interview with other interviews. In other words, we have used interviews of other leaders as an alternative source of evidence to check the findings.

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5.3.2 External validity

External validity refers to the generalizability of the findings (Yin, 2009). It can always be questioned whether ten SMEs active in the financial advisory sector are representative for all financial advisory SMEs. However, in order to increase the external validity of our study, we aimed to explain the case selection as explicit as possible (Gibbert, et al., 2008). In the Sample and Selection process section, we have tried to present all available information about the selection process.

5.3.3 Internal validity

According to Yin (1984), internal validity discusses if the causal relation is being concluded correctly or if it is based on systematic errors. In the current study, we tried to increase the internal validity by pattern matching (Yin, 1984). We have compared the patterns of different interviews with each other. This process is very similar to what we have done to increase the construct validity of our study. However, the aim is here to check our interpretation on the findings and not the findings itself.

5.3.4 Reliability

According to Denzin and Lincoln (1994), a reliable research provides the same results when repeated by another researcher. One of the tactics to make a research more reliable is to keep a protocol (Yin, 2009). In the current study, the whole process was recorded in the protocol. Prior to each step, we have described all of the future steps in the protocol and tried to stay on the line with this protocol. In the case of any change in plans, we have modified the protocol to keep it consistent.

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6 Analysis strategy

According to Yin (2009), the data analysis part is one of the most difficult and least developed aspects of qualitative research, therefore it is important to have a clear analysis strategy. In this chapter, we will describe our strategy of analysis.

6.1 Relying on theoretical propositions

In the current study, we have used the relying on theoretical propositions strategy. This strategy is the most preferred strategy by scholars (Yin, 2009). In this strategy, theoretical orientation serves as a guide for data analysis (Yin, 2009). For our study, we have formed seven working propositions, which will be used to analyze the data.

6.2 Data analysis process

The process of analyzing data of the current study was started by listening to the interview records and summarizing them. These summaries were made based on theoretical propositions to eliminate the superfluous and irrelevant information. Each interview summary consisted of one page of information including the key quotes of the interviewee. Finally, we have coded and interpreted the data using the seven theoretical propositions of the current study.

6.3 Showing data

According to Pratt (2009), in addition to discussing and interpreting the data, it is important to present and visualize the data. This can assist the reader in understanding how the researcher moved from data to interpretation. Therefore, despite the qualitative nature of our study, we have visualized our data in the form of tables and graphs to bring more clarity in our process of data interpretation.

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7 Results

In this chapter, we will discuss the findings of our study. First, we will discuss our findings of the level of assimilation of Big Data among the firms in our sample. Furthermore, in order to find the answer to our research question, we will try to present and interpret the findings related to each of our working propositions. The summary of the data is presented in the table below.

Table 3: Summary of the data

* Plans for the adoption of Big Data: measured by asking firms if they have any plans regarding assimilation of Big Data.

** Leader Big Data perspective: measured based on the level of leader belief in the potentials of Big Data. -- : the leader sees no potential in Big Data at all; - : the leader sees no potential at the moment; -+ : the leader believes in the limited potential of Big Data, but not for their business at the moment; + : the leader believes in potentials of Big Data for their business. ++ : the leader believes in potentials of Big Data for their business and is very enthusiastic about it.

*** Big Data network: leader’s self-reported strength of the Big Data facilitators in the firm’s network. - : No facilitators; Location Plans for

adoption of Big Data * Size of the firm (number of employees) Education recency of employees (average years since last education) Education recency of leader (year of last education)

Leader Big Data perspective ** Big Data network*** 1 Netherlands Yes 15 11.5 1989 ++ ++ 2 Germany No 11 12 1982 - - 3 France No 25 16.5 1993 - - 4 Turkey No 10 17 1983 - - 5 India Yes 25 11.3 1998 ++ ++ 6 China Yes 16 13 1984 + -+ 7 Korea No 10 13 2000 - - 8 Japan No 17 12.5 1977 - - 9 Canada No 10 21 1989 -- - 10 Mexico No 18 10.5 2002 -+ -+

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7.1 Assimilation of Big Data

All of the interviewed leaders admit that their firm is aware of the existence of Big Data and they could easily explain what Big Data is. Therefore, it can be considered that these firms have completed the first stage of the assimilation process, namely the apprehension phase. However, only three financial SMEs in our sample have reached the consideration phase of the assimilation process: Financial SMEs in The Netherlands, India and China. These financial SMEs are already confident about the potential of Big Data and its suitability for their firms. They could even be considered as being one step further: the discussion phase. These firms have started looking actively for ways of adoption of Big Data in their business. For instance, the firm in India has already made appointments with facilitator parties in Bangalore to discuss the process of adoption. The rest of the financial SMEs in our sample, have stopped in the apprehension phase and do not see any potential in Big Data for their business.

7.2 Impact of organizational attributes on assimilation of Big Data

As we have discussed in the theoretical framework chapter, we were expecting that organizational attributes of the financial SMEs will influence their assimilation process of Big Data. Here we will discuss the findings categorized by working propositions related to the organizational attributes, namely firm’s size, recency of employees’ education, recency of leader’s education and the leader’s belief in the potential of Big Data.

7.2.1 Firm’s size and assimilation of Big Data

As we discussed in the theoretical framework chapter, it was expected that larger financial SMEs are more likely to assimilate Big Data, where size is referring to the number of employees in a firm. The firms in our samples range between 10 to 25 employees. Two of the leaders

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(Japan and Korea) in our sample considered the size of the firm as an important factor for not adopting Big Data:

“Big Data is more applicable for larger firms, we are small and our scale is not fitting with Big Data”.

However, when the firms are compared, there is no notable size difference between the firms which are in the discussion phase and the firms that are stuck in the apprehension phase of the assimilation of Big Data (see table 3). Thus, based on the findings of the current study we can assume that the size of financial SMEs does not influence the assimilation process of Big Data.

7.2.2 Recency of employees’ education and assimilation of Big Data

Our expectation was that financial SMEs in which people with more recent education are working are more likely to assimilate Big Data in their practices. Almost all of the employees of the firms in our sample had a university (graduate) degree in either finance or business administration. There was no employee with university level IT or Data Science education in any of the firms. In seven out of the ten firms in our sample, more than half of the employees have (less than ten years since their graduation). Furthermore, one of the leaders, who was not very interested in adopting Big Data, was considering employees’ education as one of the major issues and challenges that can hinder the assimilation of Big Data:

“We have no one in our team who has the needed skills or education of Big Data, the majority of the employees are graduated more than 15 years ago. Furthermore, there is no one who is interested in going back to school. If I decide to adopt Big Data, I need to make a revolution in my firm”.

As it can be concluded from the perspective of this leader, employees’ education seems to be an issue which hinders the process of assimilation of Big Data. However, within the firms in

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education between the financial SMEs at the discussion phase and the firms that stuck in the first phase of the assimilation of Big Data. Therefore, based on the findings of our study it can be assumed that the recency of employees’ education of financial SMEs has no effect on their assimilation of Big Data.

7.2.3 Recency of leader’s education and assimilation of Big Data

It was expected that the recency of leader’s education will influence the assimilation process of Big Data in financial SMEs. All of the leaders in our sample graduated from university more than ten years ago (Table 4). Most of them have finished their education in 80’s and 90’s. Furthermore, there is no notable difference in terms of the recency of leader’s education between the financial SMEs at the discussion phase of assimilation of Big Data and the firms that are stuck in the apprehension phase of the assimilation of Big Data. Therefore, based on the findings of the current study, it can be assumed that the recency of leader’s education of financial SMEs does not influence the assimilation of Big Data.

Firm’s location Field of last education (leader) Graduation year (leader)

Netherlands Master of Economics 1989

Germany Master of Business Administration 1982

France MBA in Finance & Economy 1993

Turkey Bachelor of Economics 1983

India Master in Accountancy 1998

China MBA in Finance 1984

Korea MBA in Finance 2000

Japan Bachelor of Law 1977

Canada MBA in Finance 1989

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7.2.4 Leader’s belief in potentials of Big Data

As we discussed in the literature review chapter, we were excepting that if the leader believes that Big Data can create value for the financial SME, the firm is more likely to assimilate Big Data. The leaders of three firms in our sample which are at the discussion stage of assimilation and making plans to adopt Big Data in their business, were quite enthusiastic about Big Data and believed in potentials of Big Data. For example, one of these leaders expressed his vision of Big Data as:

“each period of time has its technological revolutions, I believe Big Data is the next revolution, especially for business world and especially for financial consultancy. Human brain is limited when it comes to analyzing large amount of information and Big Data will dominate the market soon”.

According to our findings, leader’s belief in the potential of Big Data seems to be the most determining organizational attribute when it comes to the assimilation of Big Data. In all of three firms which entered the third stage of assimilation, leaders believe that Big Data can create value for the firm. We see the opposite when it comes to the firms that are stuck in the first stage of the assimilation process. These leaders are namely not very enthusiastic and sometimes even skeptic about Big Data:

“I think Big Data is being overrated. It is only a new technology that maybe can be useful in the future, but it’s not going to change the game.”

Thus, based on the findings of our study, we can assume that the leader’s belief in the potential of Big Data is one of the attributes influencing the assimilation of Big Data in financial SMEs.

7.3 Impact of environmental attributes on assimilation of Big Data

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regarding environmental attributes, namely the presence of the facilitator parties in the firm’s network and impact of the home country.

7.3.1 Facilitating parties in firm’s network and assimilation of Big Data

We were expecting that if in the financial SME’s network, parties exist that can facilitate the assimilation of Big Data, a firm is more likely to assimilate Big Data. However, during the interviews, we have discovered that the firms start to look actively for facilitator parties right after they have made the decision to adopt Big Data. Thus, having facilitator parties in the network is not a reason for firms to consider adoption of Big Data. Moreover, since there are three firms in this alliance of financial SMEs, which are advancing in the assimilation of Big Data, other firms have the opportunity to cooperate with these firms and leverage from the experience and existing knowledge of these firms. The following quote is summarizing this issue:

“I know there are some firms in our alliance who are taking some initial steps regarding Big Data, I also know some IT firms which are active in Big Data, but here in Mexico and especially in our sector no one is using Big Data, as the trend goes by maybe we can start thinking to adopt, but at the moment it is not easy to create value for us from Big Data.”.

In other words, all of the firms in this alliance already have facilitator parties in their network, however, this attribute doesn’t seem to affect their decision to whether or not assimilate Big Data in their practices. Furthermore, the home country seems to be important when it comes to the assimilation of Big Data and this attribute is addressed in the next paragraph.

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7.3.2 Home country’s culture and assimilation of Big Data

As we discussed before, the culture of financial SME’s home country – and to be more specific – the level of uncertainty avoidance is expected to influence the assimilation of Big Data. We have used the Hofstede’s measure of uncertainty avoidance (Hofstede, Hofstede, & Minkov, 2010) to determine the level of each country’s uncertainty avoidance. The results are presented in the graph 1. As can be seen, the three firms that are at the last stage of assimilation of Big Data, (blue colored bar: Netherlands, India and China), have the lowest levels of uncertainty avoidance.

Graph 1: Uncertainty avoidance level per county

However, there is one exception: Canada. Despite having a low level of uncertainty avoidance, the firm did not have any plans regarding Big Data. This can be explained by this firm’s leader’s vision of Big Data:

0 10 20 30 40 50 60 70 80 90 100 China India Canada Netherlands Germany Mexico Turkey Korea France Japan

Uncertainty avoidance

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“I know that everyone in the financial and consultancy world is panicking about Big Data, but I think we are immune to any harm from Big Data because our business is based on our relationship with the clients and their data is bounded to their business”. Among all leaders, the leader of Canadian SME was the least interested in Big Data and was not convinced of its potential. Despite this exception, it can be partly assumed that financial SMEs located in countries with lower uncertainty avoidance are more likely to adopt Big Data. However, the leader’s belief in the potential of Big Data seem to suppress the impact of this attribute.

7.3.3 Home country’s development level and assimilation of Big Data

We were expecting that if the financial SME is located in a developed country, the firm is more likely to assimilate Big Data. We have used the United Nations’ country classification to classify the countries in our sample based on their level of development (United Nations, 2014).

Firm’s home country Level of development

Netherlands Developed Germany Developed France Developed Turkey Developing India Developing China Developing Korea Developing Japan Developed Canada Developed Mexico Developing

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As it can be seen in table 5, half of the home countries in our sample were developed countries and the other half were developing countries. In contradiction to our expectation, there was no evidence that supported the claim that firms located in a developed country are more likely to assimilate Big Data. In fact, two of the three financial SMEs in the discussion phase were located in a developing country. Based on our findings, we can assume that the level of development of the home country does not impact the assimilation of Big Data in financial SMEs which are located in a developing or developed country.

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8 Discussion

In this chapter, we will link the findings to the existing literature. Further, we will discuss the future research possibilities.

8.1 Linking the findings to the literature

In this section, we will link the findings to the related literature and try to address the similarities and the differences. First, we will discuss the base theories for the current research, namely the theory of assimilation of innovations (Meyer, & Goes, 1988) and the TOE framework (Tornatsky, & Fleischer, 1990). Further, we will focus of each of the literature related to organizational and environmental attributes.

The theory of assimilation of innovations (Meyer, & Goes, 1988), formed the basis of the current study. Meyer and Goes (1988), have created this theory initially to explain the assimilation of medical innovation in hospitals. For the current study, we have adopted this model to the context of financial SMEs and assimilation of Big Data with a focus on the first stage of the theory. Throughout this process and with the help of the TOE framework (Tornatsky, & Fleischer, 1990), we were able to categorize the data properly and interpret it successfully. The current study has shown that the theory and especially the first stage of the three-stage model also can be applied to other industries and different types of innovations and new technologies. However, considering that we could only use the first stage of the theory, we cannot make any assumptions about the other stages. We have modified the knowledge-awareness stage, by replacing the medical innovation with Big Data and it helped us to understand the process better. Furthermore, we have discovered that in the case of financial SMEs, there are two attributes, which are more important than the others. This aspect will be

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As discussed before, we have tried to examine if the organizational attributes like the size of the firm, recency of employees’ and leader’s education and leader’s belief in the potential of Big Data, which can explain the variance in the assimilation of Big Data among financial SMEs around the globe. According to different studies (Raymond, et al., 2005; Kuan and Chau 2001; Meyer, & Goes, 1988) these organizational determinants have an impact on the process of the assimilation of a new technology in organizations. However, findings of our research suggest that among all these organizational attributes only leader’s belief in the potential of Big Data influences the assimilation of Big Data among financial SMEs. This difference can be explained by the fact that our sample consists of SMEs. When we narrow our focus on the literature down to the literature related to the assimilation of the new technologies in SMEs, we can see that the leaders’ role is the most important attribute which impacts the assimilation process in SMEs (Raymond, et al., 2012; Raymond, et al., 2005). Thus, the difference of findings with the existing literature can be explained by SME nature of our sample. In the same line with the existing literature, the current study has shown that the leaders are playing a very important role in SMEs when it comes to the assimilation of new technologies.

Another set of attributes that we have taken into consideration were environmental attributes of the financial SMEs, namely the impact of the home country and presence of facilitator parties in firm’s network. Similar to the existing literature (Shane, 1995; Strychalska-Rudzewicz, 2016) the current study suggests that uncertainty avoidance of the home country has an impact on SME’s assimilation process of Big Data, however this effect it limited to the firms where the leader believes that Big Data can create value. The latest can also be related to fact that the leaders are playing a crucial role when it comes to SMEs.

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We have also focused on the impact of the level of home country’s development on assimilation process of Big Data. In contrast with the existing literature (Gu, 1999; Evenson, & Westphal, 1995), the findings suggest that there is no difference between the firms in developed and developing countries regarding the assimilation of Big Data.

Furthermore, in contrast with the existing literature (Meyer, & Goes, 1988; Raymond, et al., 2005; Raymond and Bergeron 1996), the findings of the current study suggest that the firm’s network and presence of facilitator parties in it, has no influence on the assimilation of Big Data in financial SMEs. Moreover, we have discovered that firms start actively looking for facilitator parties in their direct and indirect network, after making the decision to adopt Big Data. This can be seen as a finding in the same direction with the existing literature, in the sense that firm the impact of facilitator parties start right after the first stage of assimilation, namely in the evaluation-choice stage of the assimilation process.

The current study has shown that during the first stage of the process of assimilation of Big Data in financial SMEs, two attributes have a high importance: 1. leader’s belief in the potential of Big Data 2. the uncertainty avoidance level of the home country. Moreover, the first attribute can suppress the impact of the second one.

8.2 Future research possibilities

As mentioned before, the sample of the current study was limited to the firms in the first stage of the assimilation process. However, the theory of assimilation of new technologies (Meyer, & Goes, 1988) has two other stages, namely the evaluation-choice stage and the adoption-implementation stage. For future research, it can be interesting to study deeply a larger sample

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which also includes firms in other stages of assimilation. This could help to gain a better perspective of the impact of different attributes on assimilation process of the new technologies.

In the current study, due to the lack of time, we were only capable of interviewing the leaders of the companies. Future research could also take the employees’ perspective of Big Data into account and examine if it also plays a role in the assimilation process of Big Data.

Finally, future research could also focus on the pre-assimilation phase, namely on how the firms receive knowledge about the new technology through different sources and if these differences affect the assimilation process.

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9 Conclusion

In this chapter, we will firstly summarize the current study and try to answer the research question based on the findings which were presented in the result chapter.

The main question of the current research was: “Why is there a variance in the assimilation of Big Data among financial SMEs around the globe?”. On the basis of the theory of the assimilation of innovations (Meyer, & Goes, 1988) and with the help of the TOE framework (Tornatsky, & Fleischer, 1990), we have tried to address this question. The sample of current research consists of ten financial SMEs which were members of the same international alliance of financial SMEs. Leaders from each firm were interviewed in a semi-structured format. After summarizing and coding the data of interviews, we have analyzed the differences and similarities of the firms in terms of the organizational and environmental attributes. During all of these steps, we have used the working propositions which were based on the existing literature. The findings of the current study suggest that the most important attribute which can influence the assimilation process of Big Data in financial SMEs is the leader’s belief in the potential of Big Data. Furthermore, the findings also suggest that the level of uncertainty avoidance of the home country also influences the assimilation process of Big Data in financial SMEs. However, this effect is limited to the firms with a leader who believes in the potential of Big Data. Taking these two key findings into account, we can assume that the variance in the assimilation of Big Data among financial SMEs around the globe can be explained by the leader’s belief in the potentias of Big Data and partially by the level of uncertainty avoidance of the home country. These findings can help to improve the theory and make it more accurate when it comes to understanding the assimilation process of new technologies in SMEs.

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Assimilation of Big Data in financial SMEs around the world