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CHAPTER 3 Methodology
The following subchapters provide detailed descriptions with regards to our choices surrounding: research philosophy, research approach, methodological choice, research strategy, data collection and data analysis methods, as well as the ethical principles that guided this study
Research Philosophy
To ensure quality in management research, it is argued that researchers should consider their philosophical positions relating to the design of their study (Easterby-Smith et al., 2015). First, it is necessary for researchers to recognize their reflexive role in the research method. Thereafter it helps to clarify the research design of the study regarding how to gather, interpret and analyze empirical information, as well as allowing researchers to distinguish appropriate research designs from unsuitable ones. Lastly, it is claimed that recognizing philosophical positions can help researchers consider research designs outside of their past experiences. As such, philosophical positions primarily concern the researchers’ ontological and epistemological perspectives. (Easterby-Smith et al., 2015)
In summary, ontology refers to how researchers view the nature of reality and existence, whereas epistemology concerns to the researchers’ notion of knowledge and how to appropriately acquire it from the nature of reality (Easterby-Smith et al., 2015). Within ontology, there are 4 different stances: realism, internal realism, relativism, and nominalism. On one side of the spectrum, realism refers to there being one measurable truth for researchers to measure. On the other side of the spectrum, a nominalistic stance refers to a perspective where there is no truth, instead, facts are stated to be construed as human creations (Easterby-Smith et al., 2015).
Thus, with an aim to explore how logistics and transportation enterprises build dynamic capabilities to innovate their business models for digital transformation, through the perspective of various individuals in management positions, we affirmed an ontological stance where we perceived the nature of reality to include multiple different truths. This is aligned with a relativistic stance of ontology, which is described as a viewpoint where the researcher regards reality as including multiple subjective truths (Easterby-Smith et al., 2015). Additionally, to inquire into the nature of reality, there are two primary epistemological stances: positivism or social constructivism (Easterby-Smith et al., 2015). With an aim to create general understanding and explore the topic through a focused number of key individuals – as outlined in the following subchapters – rather than testing theory with statistical probability using a large random sample, we affirmed an epistemological position of a constructivist for this study (Easterby-Smith et al., 2015).
Research Approach
The common forms of logical reasoning when conducting research is through deduction, induction or abduction (Woo et al., 2017). As such, with the aim to develop an understanding of how to build dynamic capabilities for business model innovation via an exploratory research design, we affirmed an abductive approach throughout this study. This is based on collecting data to identify themes and patterns with the goal of modifying existing theory and eventually generating new ones which may be tested throughout further data collection (Saunders et al., 2012). Dubois & Gadde (2002) explain that the abductive approach, which is also called systematic combining, is alternating between empirical findings and theory and enables the researcher to develop the understanding of both the empirical phenomena and theoretical concepts.
The process of our abductive research is presented in the figure below and is similar to the procedures Lundin & Norrman (2010) have applied in their study based on the ideas of Dubois &Gadde (2002) as well as Kovacs & Spens (2005). After defining the problem based on gaps, in theory, we developed a frame of reference which discussed and exhibited the business model innovation as digital transformation, as well as its theoretical connection to dynamic capabilities. Afterward, empirical data were collected by means of qualitative interviews, which lead us to revise the frame of reference. In the end, we analyzed our empirical data, outlined our findings and consequently developed our final framework by building upon the existing theory.
Thus, the primary goal of our scientific study is to confront theory with empirical findings from practical cases and contribute to the theory building process. With the abductive research approach, we consider advancing our final results due to the continuous revision and adjustment throughout our study. Furthermore, to mitigate the challenges and limitations of the chosen research approach, we further acknowledged a strong need for transparency throughout the data collection and data analysis process. Thus, we assert additional emphasis on presenting and describing each step in clarity in the following subchapters under ‘data collection’ and ‘data analysis’ – an approach suggested by Weligodapola & Darabi (2018).
Methodological Choice
Following an exploratory research design, it is argued that conducting qualitative research is favorable if the researchers aim to develop an understanding of complex phenomena and where data from structured questions as well as surveys or alternative quantitative approaches, are not sufficient to obtain the necessary information (Malhotra & Birks, 2006). The same authors argue that qualitative research additionally permits researchers to acquire a comprehensive perspective of the phenomena, by exploring it from different angles as well as spotting patterns and themes that may emerge from observations.
Thus, in order to align with our purpose and answer our research question, we needed to obtain rich and comprehensive information by interviewing, elaborating and probing various employees at management levels in the logistics and transportation industry, concerning the phenomena of digital transformation. This would not have been possible by conforming to a quantitative research approach, where the sample would indeed have been larger, but consisted of information that undoubtedly would have been lacking the necessary depth to develop a holistic understanding of the phenomena, as stated by the literature (Malhotra & Birks, 2006; Bryman & Bell, 2011).
Research Strategy
The selection of a research strategy is dependent on the research purpose, question, philosophy, and approach. As explained in our methodological choice, we decided to conduct a qualitative study, which further lead to the chosen case-study strategy explained in the next chapters. The author Yin (2009, p. 18) has given the following definition of a case study: “empirical inquiry that investigates a contemporary phenomenon within its real-life context especially when the boundaries between phenomenon and context are not clearly evident”. Accordingly, our goal of this study was to holistically understand the phenomenon of building dynamic capabilities for business model innovation. The case study approach allows a deeper understanding of the dynamics present within a certain setting, with the aim to provide insights into unexplored concerns in the literature (Eisenhardt, 1989).
Case Design
Yin (2009) differentiates four different types of case study designs: single case vs. multiple case, and holistic case vs. embedded cases. While single-case studies are often used to provide extreme or unique examples of a certain phenomenon and involve a comprehensive analysis of one case, their goal is to develop, create and refine theoretical concepts (Yin, 2003). They are usually providing insights of individual cases, such as a certain company, a distinct project of an organization or a specific individual or group of people (Miles & Huberman, 1994; Stake, 2005; Diaz, 2009). Even though single-case studies may be compelling, a multiple-case design is used to expand the theoretical concepts from one case to a further number of cases, thereby learning about similarities with the final goal of creating new theoretical constructs (Eisenhardt, 1991; Yin, 2003; Eisenhardt & Graebner, 2007).
Multiple-case studies contribute with great possibilities of developing a theory by comparing the various findings from each case study by means of replication or elaborating on the differences and have therefore been described as a ubiquitous method in management research (Eisenhardt & Graebner, 2007; Eisenhardt et al., 2016). Authors commonly differentiate between literal replication, which composes comparing findings in multiple cases considering their similarities and contrary replication, which focuses on comparing the differences between the set of cases (Eisenhardt & Graebner, 2007). Yin (2003) adds that literal replication leads to compelling theoretical arguments, as these are often distinct with specific outcomes for the studied phenomenon. However, other authors argue that it is unlikely that identical cases will be found because of the versatility of organizations (Fitzgerald & Dopson, 2009). Further scholars have elaborated that setting up comparisons to outline differences on one key dimension such as entry time, market and performance facilitates the development of theoretical constructs (Langley & Abdallah, 2011).
We have conducted our study with a multiple case study approach focused on 6 case companies within the transportation and logistics industry. As proposed by Eisenhardt (1989) theory building is more valid from multiple case studies, especially in new areas of research with little prior theory and empirical evidence, which supports our research strategy, due to the limited scientific research in the area of dynamic capabilities in combination with business model innovation. This is further supported by Graebner et al. (2012) as well as Eisenhardt & Graebner (2007), who state the usefulness of case studies for theory generation with previously not systematically investigated areas and research questions that elaborate on how and why certain concepts are happening. The multiple case study approach was chosen to be able to primarily understand and learn about the phenomena from a holistic point of view, as well as potentially describing similarities and differences of each case to strengthen the emerging theory in this area (Eisenhardt & Graebner, 2007).
Considering the time horizon, our study was conducted retrospectively as our goal was to explore dynamic capabilities of organizations facing digital transformation at one particular moment in time. Saunders et al., (2012) framed this as “a snapshot” which we capture by collecting empirical material solely once and in a rather short period of around one month. This can be contrasted with studies focused on change processes and development over time (longitudinal) – as we aim to collect data based on previous experiences, which shape the current state of how organizations build dynamic capabilities for business model innovation in the context of digital transformation (Payne & Payne, 2004).
Case Selection
Considering case study research, several authors have described the importance of sampling strategy and argued that appropriate selection of cases might be of crucial importance when conducting multiple case studies (Eisenhardt, 1989; Stake, 2005). Authors have outlined that cases shall be selected purposefully to explore the purpose of the study thoroughly and with a certain depth (Pratt, 2008). This is confirmed by Eisenhardt (1989), who supports the importance of selecting cases for theoretical reasons and not randomly or because of statistical purposes.
Accordingly, our sampling method for the case-selection during this study was through purposive sampling and judgmental sampling, which is a non-probability sampling method, where the sample is chosen based on a set of prerequisite criteria (Malhotra & Birks, 2006; Easterby-Smith et al., 2015). This sampling method is favorable when the research does not require inferences from a broad population, when time and cost are of concern, as well as when the aim of the study is of an exploratory nature (Malhotra & Birks, 2006).
Thus, to be included for our study, the case companies had to be (1) operating within the transportation and logistics industry and (2) to experience digital transformation and therefore also taking steps to innovate their business models. Furthermore, (3) the case companies had to meet the requirements of being an ‘enterprise’, which entailed having more than 1000 employees and operating in more than 1 geographic market. The reasoning for this was to investigate large companies and particularly explore how digital transformation is or has changed their business, rather than exploring small and medium-sized enterprises (SME’s) or startups who may have emerged a result of it. Additionally, given that our paper focuses on acquiring information related to strategy – as well as having adapted the theoretical dynamic capabilities framework, which emphasizes the role of management – our purposive sampling method included targeting individuals solely in various management roles, who were able and capable of sharing strategy-related insights.
The table below provides an overview of the 6 different case companies which have been interviewed during our study. We have purposefully decided to interview these particular companies as they are well-known players in the market and all of them presented distinct information considering the expansion of digital offerings, investment into new digital businesses as well as changes within their operational set-up in their online presences. As the logistics and transportation industry is quite fragmented, where multiple actors are often involved throughout the supply and value chain process of these companies, we decided to interview 6 case companies to get a holistic picture of the industries’ digitalization endeavors, as mentioned previously.
Data Collection
The following subchapters describe our choice of data collection, which consisted of primary data via semi-structured interviews, followed by secondary data for the express purpose of gathering information for providing company descriptions We outline the reasons for choosing these methods and provide a table consisting with practical information about the conducted interviews.
Interviews
With the purpose of exploring digital transformation through the theoretical framework of dynamic capabilities – using a multiple-case study approach – we collected our data by conducting 11 semi-structured interviews. The reason for our choice was to be able to discover meaning, common and contrasting patterns emerged from the interviews based on a loose framework of topics that were presented in the frame of reference of this paper. Thus, this level of depth would not have been possible by conducting highly structured interviews, as it would have involved utilizing predefined responses and provided less flexibility for asking potential probing questions during the interviews (Easterby-Smith et al., 2015; Malhotra & Birks, 2006). Similarly, our choice of not conducting unstructured interviews was also due to the purpose of this paper. As mentioned previously, our aim was to explore digital transformation through the framework of dynamic capabilities. Thus, our interviews were designed to follow a loose framework of topics derived from the literature, thereby not adhering to the format of unstructured interviews, which is comparatively spontaneous, open, and based without a general guideline in mind (Easterby-Smith et al., 2015; Blumberg et al., 2011).
Ergo, our semi-structured interviews were conducted utilizing a topic guide containing a list of questions connected to different overarching topics that emerged from the frame of reference for this paper. Please refer to Appendix 1 for the topic guide that we used when conducting the interviews. The advantage of using semi-structured interviews for our research was so that we could cover the areas identified in the frame of reference. Also, it aided us to better understand the potential themes we wanted to analyze, such as how organizations build dynamic capabilities to innovate their business models for digital transformation and the potential accompanied challenges this brings forth. Besides that, semi-structured interviews enabled us with the goal of making the answers comparable, but still with the flexibility to ask the respondents additional follow-up questions, as mentioned previously (Blumberg et al., 2011; Easterby-Smith et al., 2015). The topic guide, see Appendix 1, includes open questions, which motivated the respondents to describe their experiences of digital transformation and potential follow-up questions, which motivated the respondents to talk further about specific questions with the aim to receive further details to a certain part of the interviewee’s answer.
As mentioned, we conducted 11 semi-structured interviews through 6 case companies, which were between 55 minutes to 75 minutes long, as outlined in Table 6. Several measures were taken in order to avoid interview biases during the data collection procedure. First of all, the themes of the interview guide were developed in advance of the interviews and the questions asked during the interviews were thrived to be neutral and free from any personal beliefs. Secondly, we avoided questions that would touch upon sensitive information, such as financial rewards, budget targets or other sensitive data. This was due to sensitive questions potentially leading to an unwillingness to answer or discuss but the interviewees, which could lead to interviewee bias as suggested by Saunders et al. (2012).
Finally, all interviews were conducted in a virtual setting, using tools such as Skype and other equivalent alternatives that were most convenient for the interviewees. This was due to the fact that the respondents were geographically dispersed and there was no opportunity to physically conduct the interviews with all of the participants. Table 6 shows a summary of all the conducted interviews. Company names and personal names have been removed and replaced with a dedicated company number, as well as an accompanying alphabetical letter to the respective interviewees. This was done due to the wishes of our respondents of remaining anonymous when signing our consent form. Please refer to Appendix 2 for the consent form. Furthermore, the classifications of the respondents and companies in Table 6 is used uniformly in both the empirical and analysis chapters of this paper.
Table of Content
Introduction
1.1. Background
1.2. Problem Statement
1.3. Purpose .
Frame of Reference
2.1. The Digital Transformation Context
2.2. Digital Transformation Strategy
2.3. Business Model Innovation as Digital Transformation
2.4. Dynamic Capabilities
2.5. Microfoundations
2.6. Managerial Capabilities
2.7. Dynamic Capabilities & Business Model Innovation
Methodology
3.1. Research Philosophy
3.2. Research Approach
3.3. Methodological Choice
3.4. Research Strategy
3.5. Case Design
3.6. Case Selection
3.7. Data Collection
3.7.1. Interviews
3.8. Case Analysis
3.9. Research Ethics
3.10. Research Limitations & Reflections
Empirical Findings & Analysis
4.1. Digital Transformation in Logistics & Transportation Industry
4.2. Business Model Innovation: Empirical Findings
4.3. Business Model Innovation: Analysis
4.4. Sensing Capabilities: Empirical Findings
4.5. Sensing Capabilities: Analysis
4.6. Seizing Capabilities: Empirical Findings
4.7. Seizing Capabilities: Analysis
4.8. Transforming Capabilities: Empirical Findings
4.9. Transforming Capabilities: Analysis
4.10. Managerial Capabilities: Empirical Findings
4.11. Managerial Capabilities: Analysis
4.12. Challenges
4.13. Building Dynamic Capabilities for Business Model Innovation
Conclusion, Contribution & Implications
5.1. Conclusion
5.2. Theoretical Contributions
5.3. Practical Implications & Recommendations
5.4. Future Research
References
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Digital Transformation: How enterprises build dynamic capabilities for business model innovation
