Research Foundations

4.2.1 Research Paradigm and Time Horizon

Realism was followed as a research paradigm (Saunders, Lewis and Thornhill, 2009), as realism was assumed to be the most appropriate paradigm to develop valid, reliable and realistic research results. The use of codes and categories makes this paradigm scientific (Leplin, 1984; King and Horrocks, 2010).

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The time horizon of this research is cross-sectional (Saunders, Lewis and Thornhill, 2009), while some longitudinal aspects are also examined (Taris, 2000), e.g. long-term factory developments.

4.2.2 General Research Approach and Logic

The research is conducted qualitatively, as various complex fields of study and knowledge are examined (Creswell, 2009).

This project is mainly conducted inductively due to the model and concepts’

development; however it also involves a deductive approach due to the permanent theory and model application/testing and reflection, which led to their further development. This combination of inductive and deductive approaches is in line with Easterby-Smith, Thorpe and Jackson (2008). The relations between the ROs also indicate this combination.

The limitations of today’s factories and technical characteristics of area systems make RO3-results partly deductive. One example is that pipes in terrestrial areas are not transformable, but should be; those in TASs must be transformable. Directly usable interview statements were used to induce and deduce concepts and theory.

BFPSs were developed based on literature and concepts which come from reality (e.g. UHPs), and very clear assumptions are derived from these. The BFPSs were initially largely empty frameworks, and it was necessary to test their validity; this could be ensured through interview data, as these data could be clearly assigned to each BFPS. The use of BFPSs was top down and deductive, while numerous concepts emerged bottom-up from the interview data, and thus inductively. The BFPSs and these concepts could be combined into the model and theory.

What is (technically) feasible with TASs? What potential can be gained through their use? Which developments speak for TASs? How can the limitations of today’s factories be converted to develop TAS-requirements? To answer these and further questions required furthermore abduction. The best explanations from a logical perspective were developed based on facts (Burks, 1946; Hanson, 1958). Abduction is crucial for all research results and particularly for the RO3- and RO4-results.

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The dynamic in factory planning and real-world factory requirements which emerge through this dynamic emanated from the interviews, and are recognisable throughout sections 6.1 and 6.2. The same applies to the limitations of today’s factories. A mix of induction, deduction and abduction led to the development of the TAS-requirement profile. Content and relational analyses were required to identify concepts and their relationships. Analyses of cause-and-effect relationships play an important role in this regard. These are explained next. Further details about abduction are provided in sections 4.3 and 5.4.

4.2.3 Systematic Research and Analysis

Systematic research is planned (Dixon-Woods et al., 2006). A systematic research approach allows the interpretation of data and is reproducible (Tranfield, Denyer and Smart, 2003). New search terms were constantly identified and considered during the research project (see appendix 4.2.3 for search terms).

Clear aims are required to identify cause-and-effect relationships based on a systematic and analytic procedure. One aim of this study was to identify relevant objects and structures and relations among these objects and structures. A cause leads to an effect/impact, while the cause can be backtracked (Schlick, 1925). King and Horrocks (2010, p. 9) argue that “the world is made up of objects and structures that have identifiable cause[-]and[-]effect relationships.” They (p. 14) argue further:

Causal relationships are not only linearly interconnected. The complexity of factory projects disables the detailed definition of all cause-and-effect relationships.

Dunleavy (2003, p. 69) argues that:

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Thus, ‘analytic plus descriptive’ and ‘analytic, argumentative plus descriptive’

approaches were used (Dunleavy, 2003, pp. 72–75) to create memos. Analytic means that causal analyses were based on systematic accounts under category headings. Independent and dependent variables could be differentiated (Rath, 2008). Changes in factory environments impact on factories. Actions lead to consequences. The interviews provided numerous cause-and-effect relationships.

These real-world data have ensured the validity and reliability of the research results. Confounding variables were not identified, but dilutive effects were (see subsections 4.6.1 and 4.7.4).

All research results were developed based on systematic research and analysis. This is particularly recognisable through the relation of RO2 and RO3 (and the other RO-relations). The model development and testing also took place systematically.

Furthermore, the interviews followed a systematic approach (see section 4.7).

The functions of Citavi® (4) (Swiss Academic Software GmbH), Microsoft® Excel®

and Word® were used for data analyses. Thus, the author could organise and analyse data systematically in order to identify relationships and patterns. The keywords and categories in Citavi® in particular assisted the author to perform causation-coding and coding and to develop the concepts, model and theory.

Word® was used to sort data, the same as Excel®, which was furthermore used to create mind, concept and process maps. This was supported by hand-written memos. Thus, relations between research elements (i.e. categories and concepts) could be identified and defined (see axial coding and further coding procedures in section 4.5).

The interview transcripts were analysed line-by-line. Codes gave meaning to text segments (open coding). Words that were used by the interviewees were partly

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used as codes. BFPSs were labelled (deduction), as were new/emerging codes (induction). Codes were constantly compared (constant comparison) and their relationships defined (axial coding). This process was repeated as new codes emerged from the interviews. Thus, the author returned to already analysed/coded transcripts.

Codes were grouped, which led to concepts such as eBFPCs and difficulty factors.

EBFPCs can be differentiated through the BFPSs and then be broken down and further differentiated with the difficulty factors. BFPSs were critically reflected and did not change. Based on the interview data and real-world factory layout developments, it could be concluded that BFPSs are sensible.

The concept of ‘difficulty factor(s)’ was developed from codes such as ‘small displacement’ and ‘large displacement’. Initially, other codes were used, and these were changed during analyses. Some initial codes were ‘area size’, ‘area shape’,

‘area characteristics and required processes’ and ‘substructures and required processes’. These were partly adapted and led finally to the concept of

‘fundamental enabler(s)’. The final codes are recognisable throughout this document, as these are equivalent to the final categories and concepts.

BFPSs provided good support in analysing the large amount of interview and other data, as they provided a framework which helped to bring occurrences and research elements together and to identify their relationships. Finally, the concepts and relationships between the research elements could be identified and defined and the text developed (selective coding). This procedure is also comparable with the framework method or analysis (Ritchie and Spencer, 1994; Mason, Mirza and Webb, 2018; Gale et al., 2013). The following sections provide further information and evidence for these and further process steps (see subsection 4.3.2 for details about the development of BFPSs, section 4.5 for coding-related sources and section 4.7 for interview analyses-related sources).

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