The intention of this thesis is to gather raw user perceptions of IoT technology which could be indicative towards IoT diffusion levels within a logistics organisation or likelihood to adopt IoT technologies. Therefore, the focus is only on user perceptions rather than the more popular IoT research themes such as opportunity and architecture. With user perceptions, we refer to IoT in regards to such considerations as its definition, utility and opportunity from the position of its direct logistics consumer. An evaluation of user perceptions on IoT technology might enhance IoT and logistics literature with new ideas which might be useful in future research or in practice such as IoT implementation. Our attempt to gather raw IoT perceptions and the meanings that users assign to the technology will be achieved by accepting that there is a subjective ‘social reality’ whereby facts are social constructions created by people (Jackson, Easterby-Smith & Thorpe, 2015). With being such an efficiency driven field, most logistics research tends towards more realist approaches in which fact or truths do exist and must be measured and revealed by ‘cause and effect’ testing. We argue that because humans govern logistics processes and because logistics is witnessing a lot of change with IoT revolutionising the discipline, that the ‘human factor’ should not be ignored. By employing more a relativist approach to our research study, logistics can benefit from fresher ideas and perspectives that could be especially important in such a time of change This chapter discusses our research design and how we employed a research philosophy and methodology to support our research initiatives. The chapter concludes with how this research study considered issues of validity, reliability and ethics.
In the effort of creating a quality research study, it is necessary to select a knowledge philosophy that underlies the research and sets a foundation. Philosophy is an overarching term which inquiries into nature and the development of knowledge for research (Saunders, et al., 2000). The philosophical assumptions that we adopted for this thesis, set the basis for how we formed the research study, how the data will be collected and interpreted, and finally how we make sense out of the data. Jackson, Easterby-Smith & Thorpe (2015) describe that in social research, methodology considerations usually start by taking positions in the central questions of ontology and epistemology.
Ontology sets assumptions about the nature of reality and consists of a debate between realism and relativism. The realist side of the spectrum is of the position that science can only progress with direct correspondence to that which is being investigated and the relativist position takes this further and suggests that the scientific phenomena under investigation are not only out there to be discovered, but are man-made (Jackson et al., 2015). Of these two positions, we include the latter position as part of our philosophical assumptions for this research study. Epistemology meanwhile, is concerned with how we perceive the world to be (Jackson et al., 2015). The two ends of the epistemology spectrum guide the ways in which researchers gain knowledge and therefore construct social research. The two sides of the spectrums are positivism and social constructionism. In the epistemology debate we employ the social constructionist take as it directly correlates with this study’s effort of discovering the different constructions and meanings that users place on the deployment of IoT technologies in their logistics organisational setting. As according to authors Jackson et al., (2015), human interests are the main drivers of science in social constructionism. We consider this to be the main advantage of the epistemology position in regards to how it can be useful to our study. Social constructionism is also good in examining change processes and thus theory generation which is important for purposes of this research study considering that IoT revolution is still in its infancy and its technologies are only starting to be introduced to logistics firms (Jackson et al., 2015). One of the main weaknesses of the social constructionism position however, is that the process can be very time-consuming and obtaining genuine interpretations and analysing them can be difficult. We have considered these weaknesses in our research study, and included them as part of our research limitations.
As discussed in the Problem Discussion (Section 1.2), there are not enough qualitative studies in logistics literature (Näslund, 2002; Näslund et al., 2010), and literature covering user perceptions of IoT is scarce. Therefore, the idea of conducting a qualitative study, as opposed to a quantitative research study, became very appealing and helped inspire this research study. A qualitative research study means that the data will be non-numerical and won’t be quantified (Saunders, et al., 2000). In social research studies, there is a tendency towards qualitative methods “…Because the crucial elements of sociological theory are often found best with a qualitative method, that is, from data on structural conditions, consequences, deviances, norms, process, patterns and systems” (Glaser & Strauss, 1967, p. 18). This is directly in line with our central motivation to use a qualitative methodology as it allows for flexibility by which we can efficiently capture and investigate subjective, personal and organisational stories, which will bring clarity to our fundamental research questions (RQ1) (RQ2). IoT is forcing change amongst businesses and the logistics industry at large, and users are forming perceptions around the change. A qualitative methodology is best and richest for theory generation (Glaser & Strauss, 1967) which is complementary to our research study of user perceptions in a setting of organisational change.
Specifically, in our research study we will be pursuing the Grounded Theory form of methodology. Grounded theory is a type of methodology founded by Ph.D. sociologists Glaser and Strauss. They define the methodology as the discovery of theory from data that is systematically obtained and analysed in social research (Glaser & Strauss, 1967). Key in this definition and central to the development of this form of methodology being that the theory produced is grounded in its data (Urquhart, 2012). Grounded theory contains key features which have been collected/ highlighted by author Urquhart (2012). These features provide a good starting point for its practice and are highlighted below:
- The aim of grounded theory is to generate or discover theory
- Theory focuses on how individuals interact with the phenomena under study
- The researcher has to set aside theoretical assumptions in order to let the substantive theory emerge
- Theory asserts a plausible relationship between concepts and sets of concepts
- The resulting theory can be reported in a narrative framework or a set of propositions
- Theory is derived from data acquired from fieldwork interviews, observation and document
- Data analysis proceeds from open coding (identifying categories, properties, and dimensions) through selective coding (clustering around categories) to theoretical coding.
- Data collection can stop when no new conceptualisations emerge
Grounded theory complements our underlying research questions and purpose by providing an inductive method by which raw IoT user perception data can be systematically developed into discovery theory. In our research, we are aiming to collect user perceptions about the innovation technology at its most genuine and unbiased form. With grounded theory, we will be able to record user perceptions that are free from constraints which a more inductive methodology might impose on it. The methodology was conceived by Glaser and Strauss (1967) under the grounds that sociology does not cover all the new areas of social life, and that it needs to be explored (Urquhart, 2012).
With its exploratory take, grounded theory allows our constructionist research study to evaluate user perceptions and their deeper social constructs formed by their experiences and daily interactions with IoT in their logistics setting, which has been largely left for granted in empirical IoT research. By in large, grounded theory is advantageous to and liberates our research study because (1) its focus is on building theory and encourages all aspects of social life to be explored and (2) through its encouragement not to focus on existing theory which helps to produce new ideas and theories (Urquhart, 2012).
Since grounded theory’s coining, the methodology has been modified and contradicted by researchers and even its own authors. For example, from its inception grounded theory has been questioned in regards to how specifically, researchers should practice its hallmark inductive approach, and remains a point of contention between researchers today (Bryman & Hardy, 2004; Jackson et al., 2015). Grounded theory author Glaser, believes that the methodology should demand that the researcher start with no prior pre-suppositions and that all data should emerge from the data collected. However, Strauss disagrees and contends that the researchers should at least familiarise themselves with prior research using a structured process (Jackson et al., 2015). This contradiction or varying ways in which a researcher could abide by the methodologies procedures could be seen as disadvantage to our research, as the deployment of either Glaser or Strauss’s stance could alter the outcome of our research. This thesis assumes Strauss’s take of grounded theory. For the sake of our research, we consider it beneficial to familiarise ourselves with prior research as is included in the Frame of Reference. However, the prior research has no tangible influence on the data collection.
Grounded Theory for Constructivist Research
The position of assuming some prior pre-suppositions in grounded theory research was also argued in the ‘Handbook of data analysis’ by authors Bryman and Hardy (2004). They emphasized this position as particularly relevant to grounded theory research with constructionist themes, since maintaining some degree of theoretical sensitivity is inherent to hermeneutic and constructivist interpretivist social research.
“Philosophically speaking, theory cannot simply ‘emerge’ from data, because interpretation and analysis are always conducted within some pre-existing conceptual framework brought to the task by the analyst.” (Bryman & Hardy, 2004, p. 631).
Authors, Bryman and Hardy (2004), used this statement to support their argument that in order to do discovery research, the researchers must use established disciplinary knowledge or theoretical sensitivities and that ‘knowing’ occurs within larger individual, institutional and other socio-cultural contexts. Without a pre-existing conceptual framework or other knowledges, the research will suffer from a lack of awareness that inherently structures emerging ideas and theory (Bryman & Hardy, 2004).
The ‘Handbook of data analysis’ (2004) further supports the use of grounded theory for constructivist approaches on the grounds that it allows the empirical world to be investigated without having to prescribe to a subsequent truth. Some credit the flexibility in the methodology to a postmodernist view of grounded theory whose development was inspired by the need to differentiate constructivist approaches from the more conventional grounded theory assumptions laid out by Strauss and Corbin. Charmaz (2000) listed these postmodern constructivist assumptions as (1) assuming the existence of multiple realities (2) involving the mutual creation of knowledge by viewer and viewed and (3) aiming towards interpretive understandings of participants’ meanings. The new postmodernist label to grounded theory has been questioned in regards to whether it adds anything unique in practice to conventional grounded theory, but is justified by confirming grounded theory’s flexibility to take on such constructivist approaches (Bryman & Hardy, 2004).
1.2 Problem Discussion
1.5 Thesis Outline
2. Frame of Reference
2.1 The Internet of Things
2.2 Supply Chain Management
2.3 Theoretical Background
3. Research Design
3.1 Research Philosophy
3.3 Data Collection
3.4 Data Analysis
3.5 Validity and Reliablity
4. Empirical findings
4.1 Sample and Interview
4.3 Theoretical Saturation
5.1 IoT Perceptions
5.2 Development of IoT Diffusion Model
5.3 Insight from IoT Diffusion Model
7.1 Research Ramifications
7.2 Limitations and Future Research
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Perceptions on the Internet of Things A study within the Swedish logistics sector, to provide new insight on Internet of Things technologies.