A Design Science Method for Emerging Decision Support Environments

📝 Abstract

Emerging technologies and business models require organisations to continuously deal with complex, dynamic and unstructured issues, leading to the need for newer forms of decision support systems (DSS). However, in emerging environments the existing knowledge base can be scattered, unstructured, and sometimes conflicting, which challenges any efforts in designing DSS. This paper highlights the role of design science methods in developing these emerging areas, and suggests a design science method that focuses on consolidating the knowledge base by ontologically grounding experience and expertise. The proposed method is illustrated in the context of a published case, and validated by practically applying it to develop a crowdsourcing decision tool. The study contributes with recommendations on how to consolidate the knowledge base and design DSS artefacts.

💡 Analysis

Emerging technologies and business models require organisations to continuously deal with complex, dynamic and unstructured issues, leading to the need for newer forms of decision support systems (DSS). However, in emerging environments the existing knowledge base can be scattered, unstructured, and sometimes conflicting, which challenges any efforts in designing DSS. This paper highlights the role of design science methods in developing these emerging areas, and suggests a design science method that focuses on consolidating the knowledge base by ontologically grounding experience and expertise. The proposed method is illustrated in the context of a published case, and validated by practically applying it to develop a crowdsourcing decision tool. The study contributes with recommendations on how to consolidate the knowledge base and design DSS artefacts.

📄 Content

Australasian Conference on Information Systems

Thuan et al.
2015, Adelaide, Australia A Design Science Method for Emerging Decision Support Environments

A Design Science Method for Emerging Decision Support Environments Nguyen Hoang Thuan School of Information Management
Victoria University of Wellington Wellington, New Zealand Thuan.Nguyen@vuw.ac.nz Pedro Antunes School of Information Management Victoria University of Wellington Wellington, New Zealand Pedro.Antunes@vuw.ac.nz David Johnstone School of Information Management Victoria University of Wellington Wellington, New Zealand David.Johnstone@vuw.ac.nz Abstract Emerging technologies and business models require organisations to continuously deal with complex, dynamic and unstructured issues, leading to the need for newer forms of decision support systems (DSS). However, in emerging environments the existing knowledge base can be scattered, unstructured, and sometimes conflicting, which challenges any efforts in designing DSS. This paper highlights the role of design science methods in developing these emerging areas, and suggests a design science method that focuses on consolidating the knowledge base by ontologically grounding experience and expertise. The proposed method is illustrated in the context of a published case, and validated by practically applying it to develop a crowdsourcing decision tool. The study contributes with recommendations on how to consolidate the knowledge base and design DSS artefacts in areas lacking strong theoretical foundations, and where expertise and experience are dominant sources of knowledge. Keywords: Decision Support System, Design Method, Design Science, Knowledge Base 1 Introduction Decision Support Systems (DSS), despite of its long history in the Information Systems (IS) discipline, is still an interesting ‘alive and well’ research area. This is because new technologies and business models are continuously emerging, which involve new business forms, large amounts of information, complex and unstructured issues. Thus, newer forms of decision-support development are continuously demanded (Hosack et al. 2012). This demand can be seen via many calls for further DSS in the emerging areas, like big data, social media, mobile computing, and crowdsourcing (Arnott and Pervan 2014; Geiger and Schader 2014), which provide great opportunities for DSS research. On the other hand, they raise challenges on how to rigorously develop DSS when promoting unestablished business structures and may lack strong theoretical foundation.
Aligning with the high percentage of DSS research adopting the design science paradigm (Arnott and Pervan 2014), we suggest that design science should play important role in developing DSS in the emerging environments. There are (at least) three reasons for this suggestion. First, design science emphasises a rigorous approach to advance current knowledge on design and development (Hevner et al. 2004). This is highly applicable to emerging DSS environments, where decision-support tasks involve consolidating domain knowledge for better decision (Nemati et al. 2002). Second, design science aims at developing innovative artefacts to address unstructured issues, which are also the major target of DSS research in the emerging areas. Third, design science is more focussed on utility than truth (Hevner and Chatterjee 2010), aligning to the purpose of support and improvement of DSS (Arnott and Pervan 2012). All in all, this combination of rigor, innovation and utility places design science as an appropriate paradigm to guide research addressing the emerging DSS environments. Australasian Conference on Information Systems

Thuan et al.
2015, Adelaide, Australia A Design Science Method for Emerging Decision Support Environments

Several methods for guiding design science research have already been proposed in the IS discipline, covering from broad principles and guidelines (Hevner et al. 2004) down to prescriptive accounts on how to plan (Peffers et al. 2007) and manage a design project (Hevner and Chatterjee 2010). Nevertheless, the application of several existing design methods may not to be suitable in emerging DSS environments, where the large wickedness and dynamism exist. Many existing methods require either established theories or meta-artefacts related to the problem (e.g. Carlsson et al. 2011; Pries- Heje and Baskerville 2008). However, this requirement is hardly met in emerging areas, as explained by Paré et al. (2015) that in such emerging issues/areas “an accumulated body of research exists but there is a lack of appropriate theories or current theories are inadequate in addressing existing research problems” (p. 188). Furthermore, the existing methods do not address the issue of lacking common understanding due to the emerging unestablished nature of the areas. For instance, when designing decision tools for crowds

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