Positioning This DSR Study

Gregor and Hevner (2013) argued that DSR is yet to attain its full potential because of gaps in the understanding and application of its concepts and methods. To address this issue, the authors suggested positioning a DSR study according to a taxonomy derived from the DSR literature.

Given this context, the next sections will position this DSR study in terms of the philosophical grounding that underpins it, the level of artefact abstraction and the type of knowledge contribution.

3.3.1 Philosophical Grounding

Philosophical grounding for research is usually synthesised into two dominant research traditions (Purao, 2002): positivism and interpretative. The former is based on the view that observation and measurement are at the core of the scientific endeavour, while the latter is concerned with gathering an in-depth understanding of the phenomenon—usually human-related (Healy and Perry, 2000).

However, DSR differs from these traditional views, as it can incorporate aspects of both (Vaishnavi and Kuechler, 2004). DSR is a problem-driven method (Baskerville, 2008), where knowledge is created from iterations between the design and the explanation of artefacts (Nunamaker et al., 1991).

Different worldviews are expressed in terms of ontology, epistemology, methodology and axiology elements (Vaishnavi and Kuechler, 2012). Ontology is the study that deals with the reality of the phenomenon under investigation (Shadish et al., 2002; Healey and Perry, 2000). That is, in order to understand this world, the researcher must represent or reconstruct it as seen by others (Sedoglavich, 2008). Epistemology is the study that deals with the ways of knowing this phenomenon (Shadish et al., 2002; Rossman and Rallis, 2003). It describes the nature of the relationship between the researcher and the reality (Sedoglavich, 2008). Methodology is the technique used by the researcher to discover that reality (ontology) (Sedoglavich, 2008). Finally, axiology is the study

of values that individuals and groups hold for sharing with others (Vaishnavi and Kuechler, 2012).

This study assumes that the phenomenon of software development projects can be viewed as a systematic process whose behaviour is governed by interconnected factors that that impact project planning. It also assumes that the software development process can be enhanced through measurement over the project lifecycle and lessons learnt from past projects developed by the organisation. This assumption is consistent with the worldview for design (Vaishnavi and Kuechler, 2012). In terms of ontology, it assumes that there are different aspects of the reality (multiple realities). In terms of epistemology, this view deals with both objective and subjective factors that can be analysed through quantitative and qualitative methods for understanding this phenomenon. This improved knowledge can lead to enhance the success rate of projects (axiology). Table 3.1 summarise the philosophical grounding that underpins this study.

Table 3.1: Philosophical grounding that underpins this study

Positivism Interpretative

Ontology Multiple realities

Epistemology Objective

(factors that impact project planning)

Subjective

(factors that impact project planning) Methodology

Qualitative

(measurement over the project lifecycle)

Quantitative

Axiology Understanding

3.3.2 Level of Artefact Abstraction

Hevner et al. (2004) and March and Smith (1995) stated that DSR studies should contribute to the literature through a viable artefact in terms of a construct, a model, a method or an instantiation. Walls et al. (1992) and Gregor and Jones (2007) proposed that DSR studies should produce a design theory. These apparent contradictions can be addressed by distinguishing research contributions through levels of contribution using Purao’s (2002) framework (Gregor and Hevner, 2011, 2013).

Purao’s (2002) framework has three levels of abstraction, which range from specific artefacts at Level 1 in the form of products and processes, to more general contributions at Level 2 in the form of nascent design theory, such as constructs, methods and models, and more abstract contributions in the form of emergent design theories about the phenomena at Level 3 (Gregor and Hevner, 2013).

This study provides two contributions: the QPEM model for the project management literature and the QPLAN tool for the software industry (Section 1.7). Hence, according to Purao’s (2002) framework, the former contribution (a model) is classified in the second level of artefact abstraction, while the latter (a software product) is classified in the first level of artefact abstraction.

3.3.3 Type of Knowledge Contribution

Gregor and Hevner (2013) proposed a framework for classifying knowledge contributions in four quadrants: invention, improvement, exaptation and routine design (Figure 3.2). Improvement is a quadrant dedicated to contributions that provide new solutions for known problems—that is, better solutions in the form of more efficient artefacts (much of the previous and current DSR in ISs can be classified as improvement research). Invention is a quadrant dedicated to contributions that provide new solutions for new problems—that is, recognisably novel artefacts that can be applied and evaluated in a real-world context. Routine design is a quadrant dedicated to contributions that provide existing solutions for existing problems. In this case, research opportunities are not obvious, but this work may lead to new findings. Finally, exaptation is a quadrant dedicated to contributions that provide known solutions extended to new problems—that is, the design knowledge that already exists in one field is extended in a new field.

Figure 3.2: Knowledge contribution framework (adapted from Gregor and Hevner, 2013)

Improvement Invention

Routine Design Exaptation

High Low

Hi

gh

Low

Application Domain Maturity

Sol

uti

on

Maturi

In this study, the knowledge contribution from this research should be classified as improvement (second quadrant), as both QPEM and QPLAN are new artefacts that are designed to fill gaps found in the literature (known problems) and in the software industry (Section 1.3).