Decision Support System

Decision Support System (DSS) is perceived as interactive computer-based systems that uses knowledge and theory from various disciplines such as database research, decision theory, artificial intelligence, cognitive science, mathematical modelling and management science to support decision making activities (Kou, Shi, & Wang, 2011). DSS assist decision-makers in making a choice, rendering a judgement, or drawing a conclusion. The decision-makers organisational operations are subordinated to the human user, who remains central to and in control of the decision-making process (Forgionne, Kohli, & Jennings, 2002). In other words, DSS is a process by which people, procedures, methods, equipment, and tools are integrated to produce a desired result (Gachet & Haettenschwiler, 2006). The user interface (or user), the model and the database (knowledge base) are three major components of DSS (Power, 2002). This study describes decision support system as information system based system that supports enterprise or business’s decision-making process. DSS is as a tool that support SE owner-managers to decide on structured and unstructured problems that influence business operations and organisational processes (Richard, 2009).

The research in the field of decision support system has grown given the complexities and uncertainties of decisions that managers struggle to overcome (Power & Sharda, 2007). Decision support systems are extensively adopted and in use in many large organisations (Çagˇdaç Arslan, Çatay, & Budak, 2004). Therefore, there is tendency to apply experience and techniques gained from large organisations directly to small businesses, without taking into cognisance the peculiarity and the different decision support needs of the small enterprises. The setback with DSS in SEs is perception of small businesses as miniature versions of large businesses ignoring that problems differ, and even similar problems require different solutions. SEs have higher failure risks and commonly do not have access adequate and timely information can pose further challenges to small organisations (Duan & Xu, 2009).

Duan and Xu (2015) further states that the problems inherent in providing support for small business management are commonly studied from a social or economic viewpoint. Only very few studies indeed have addressed decision support needs in the context of SEs (Delisle & St- Pierre, 2004). There are more than thirty different approaches to the design and construction of decision support methods that have been developed over the last two decades (Gachet & Haettenschwiler, 2006). The methodologies for achieving successful DSS design in SEs should

accommodate human reasoning from strategic to lowest levels of granularity of action decided by SE owner-managers. Human reasoning have not been applied in the practice of DSS development and focus has been on high-level decision making (Pomerol & Adam, 2006). This study proposed an artefact that holistically evaluate small enterprises from highest to lowest level of granularity using the principle of enterprise architecture to guide structured and unstructured IT problems in SEs.

Phillips- Wren (2013) categorised problems that requires to be decided on as structured, semi –structured and unstructured. The structured decisions have known optimal solutions, while the unstructured decisions have no agreed-upon criteria or solution and decision-makers rely on the personal preferences. In between the structured and unstructured decision-problems, there is a range of semi-structured problems that have some agreed-upon parameters and yet require human preferences for a decision within a specific set of criteria (Phillips-Wren, 2013). DSS is a broad range of interactive computer system that support decision maker to utilise data, model and knowledge to solve semi-structured, ill-structured or unstructured problems (Richard, 2009). Gachet & Haettenschwiler (2006) states that: “There will always be a tension – ideally a creative one – in the DSS field between Decision and Systems but the link between the two is - Support. The quality of the support we can provide managers depends on our understanding of both decision-making and system building”.

Knowing the objectives of the decision-makers allow the evaluation of decision. Thus, it is important to know the utility of decision-maker and understands the expected outcome of the decision-maker regarding probabilities of future events (Pomerol & Adam, 2006). It is paramount to characterise the decision-makers’ problem before one can begin to understand how to support or assist the decision-makers. Phillips- Wren (2013) argued that semi- structured decision problems are amenable to decision support through interaction with decision makers and developing alternatives based on criteria and optimal solutions. DSS are designed to support different decisions, e.g. single, multiple or decisions that that ranges from managerial to creative problem solving.

This study acknowledges the debate on the categorisation of DSS discussed in (Alter, 1980; Kuljis & Paul, 2001; Power & Sharda, 2007). However, the Power & Sharda (2007) presents an expanded categorisation of DSS into five types of DSS.

i. Communications-Driven DSS get functionalities that support the shared decision- making from information technology and communications.

ii. Large structured database drives Data-Driven DSS. For example, Executive Information Systems and Business Intelligent Systems.

iii. Document-Driven DSS supports decision making process by integrating different storage and processing technologies to provide enhanced document retrieval and analysis.

iv. Knowledge-Driven DSS preserve knowledge that recommend actions based on artificial intelligence, case-based reasoning and Bayesian networks.

v. Model-Driven DSS allows the user to manipulate the parameters of a given model to evaluate the output of a decision problem.

The advantage of a model driven DSS is the accessibility of the DSS to non-technical specialist like manager through user friendly interface. The decision maker provides data and parameters for analysing a problem situation. The model-driven DSS is often built based on decision analysis, mathematic programming and simulation techniques (Power & Sharda, 2007). Several model-driven DSS applications such financial and accounting systems have been developed for managers to support their decision making process using spreadsheet and web-based (Kuljis & Paul, 2001; Power & Sharda, 2007). The spreadsheet-based and web-based DSS has become the most common techniques of modelling DSS applications (Ragsdale, 2000). In any case, DSS should be driven by exigencies encountered in the real-world problems, and not a means to overcome some conception of a theoretical model (Delisle & St-Pierre, 2004).

2.7 Summary

This chapter is part of the problem awareness phase of the DSR methodology. This chapter enlightened the researcher on the challenges faced by SE decision-makers, the peculiar characteristics of SME, the trend and gap of research in IT decisions, and the concepts of enterprise architecture in SEs. The researcher emphasised the attributes and the challenges of SEs to elucidate the significance of the problems surrounding the phenomenon in this study. The researcher’s pragmatic philosophic assumption influences the adoption of a problem- centric approach to conducting the literature review. The gap in the adoption of EA as IT decision-making strategies in SEs is also identified a finding of the literature review.