The research design aligns with the pragmatic and interpretivist approaches (see Section 1.5.2) taken in this study. Design, in an organizational context, is an open-ended process with a focus on the analysis of needs together with the design of specific functionalities. Gregor et al. (2020, p. 1625) contend that the main form of theory in IS research should be theory for “design and action”, with DSR as one way of responding to calls for academics to engage in work that has greater impact outside of academia.
Hevner and Chatterjee (2010, p. 13) argue for the value of Design Science Research (DSR) in
“addressing the relevancy gap” in academia. Heeding this argument, Naidoo et al. (2012) assert that DSR’s intent to create an artefact through a balanced process that combines the highest standards of rigour with a high level of relevance has the potential to reduce the relevance gap between computing research and practical problems, thus fostering stronger relationships between researchers and practitioners. The use of DSR in the development of the eModeration evaluation framework combines theory and practice, thus ensuring a high level of rigour in the development of artefacts serving a practical purpose (Hevner & Chatterjee, 2010).
The evaluation of an artefact motivated the use of DSR in this study. Empirical evidence indicates that the focus of DSR is on the artefact, with very little attention being paid to the role-players in the various stages of the DSR process (Gregor et al., 2020; Van der Merwe et al., 2020). While acknowledging that several studies include users in the DSR process, Haj-
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Bolouri (2015, p.12) posits that the “techno-centric and problem-solving” focus of DSR precludes the user as a “central” component of DSR. Heeding Bodker and Pekkola's (2010) argument for user participation and the need for knowledge sharing in the design process, Participatory Design (PD), as a data capturing strategy (see Section 4.2.2.2) and a Participatory Action Design Research (PADRE) approach (see Section 4.2.2.8) are included to position the user and incorporate learning and reflection during each stage of the DSR process as advocated by Haj-Bolouri et al. (2016).
Van der Merwe et al. (2020) propose six guidelines for the development of a study using DSR.
The proposed guidelines, together with the method used to implement them in this thesis, are illustrated in Table 1-3.
Table 1-3: Guidelines for DSR Research
Guidelines Method Practical implementation
Contextualise DSR in the field of Information Systems and be able to distinguish between concepts such as design, Design Science, and DSR.
Literature Review on DSR. Chapter Four: Discussion of what makes DSR relevant to IS research (Section 4.2.2.1).
Understand the philosophical
underpinning of research and discourse on the nature of DSR.
Research Design: Literature Review on Philosophical Viewpoints.
Chapter Four: Discussion of ontological epistemological and axiomatic stance (Section 4.2.1).
Discussion of methods employed for data collection (Section 4.2.3.1).
Obtain a historical perspective of DSR and consult the work of pioneers in the field.
Literature Review on DSR. Chapter Four: Discussion of DSR contributions relevant to IS research (Section 4.2.2.1.1).
Consider the role of the artefact in DSR and the different views on design theory.
Artefacts:
eModeration Prototype
Evaluation framework.
Chapter Five: Discussion of prototype (Section 5.3.4).
Chapter Eight: Evaluation framework (Section 8.7).
Select an appropriate DSR method for
the execution of the study. Participatory Action design research (PADRE)
Participatory design for a collection of data within PADRE cycles of planning, implementing, evaluating, and reflecting, with learning occurring throughout all cycles of evaluating and reflecting on the knowledge.
Chapter Four: Data collection strategy using a PADRE approach (Section 4.2.2.8).
Strategize on how research in DSR
should be communicated in a report. Compilation of thesis. All chapters: Completion of thesis.
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The DSR process followed in this study is described in detail in Chapter Four (see Section 4.2.2.1). The following section describes the qualitative and quantitative data collection and analysis methods employed in this study.
1.6.1. Data collection and data analysis methods
This study employed both qualitative and quantitative data collection methods. Qualitative methods, as recommended by Creswell (2014), are useful in exploring new topics not covered by existing theories, as such providing in-depth understanding of participants' views. This approach values documenting real experiences in context, by considering user perspectives (Patton, 2015). The researcher’s experiences influence how this information is interpreted (Creswell, 2014; Patton, 2015).
Quantitative research is empirical and explains phenomena based on numerical data (Yilmaz, 2013). Quantitative data collection methods yield precise, numeric data. Statistical analysis of data by using software is more efficient than qualitative data analysis. Additionally, research results are researcher-independent, thus enhancing credibility (Johnson & Onwuegbuzie, 2004).
The choice between qualitative and quantitative research methods depends on the nature of the research problem (Creswell, 2014). If an intervention is required, a quantitative approach is appropriate, but if a concept requires exploration due to a lack of previous research, a qualitative approach is suitable. Qualitative data analysis seeks patterns and themes in the data without predetermined categories, leading to deeper, more detailed, and open analysis (Patton, 2015).
Quantitative data measures the prevalence of a phenomenon, while qualitative methods explain its meaning (Patton, 2015). A mixed methods design, combining both approaches, is useful when either method alone is insufficient to fully understand the research problem. This approach offers the greatest understanding (Creswell, 2014). Johnson and Onwuegbuzie (2004, p. 16) argue that research approaches “should be mixed in ways that offer the best opportunities for answering important research questions”. This study employed an exploratory sequential approach combining qualitative and quantitative data (Cresswell, 2014) starting with a qualitative phase to examine participants' views (see Section 4.2.2.2). The analyzed data (see
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Section 5.3) fed into the second, quantitative phase, using the qualitative phase to refine the