38 Decision Support System

Dr R. Baskaran

Decision Support System

A decision support system (DSS) is a computer-based application that collects, organizes and analyzes business data to facilitate quality business decision-making for management, operations and planning. A well-designed DSS aids decision makers in compiling a variety of data from many sources: raw data, documents, personal knowledge from employees, management, executives and business models. DSS analysis helps companies to identify and solve problems, and make decisions.

The purpose of this session is to discuss how characteristics of a decision support system (DSS) interact with characteristics of a task to affect DSS use and decision performance. This discussion is based on the motivational framework developed by Chan (2005) and the studies conducted by Chan (2009) and Chan et al. (2009). The key constructs in the motivational framework include task motivation, user perception of DSS, motivation to use a DSS, DSS use, and decision performance. This framework highlights the significant role of the motivation factor, an important psychological construct, in explaining DSS use and decision performance.

While DSS use is an event where users place a high value on decision performance, the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) do not explicitly establish a connection between system use and decision performance. Thus, Chan (2005) includes decision performance as a construct in the motivational framework rather than rely on the assumption that DSS use will necessarily result in positive outcomes (Lucas & Spitler, 1999; Venkatesh et al., 2003).

This is an important facet of the framework because the ultimate purpose of DSS use is enhanced decision performance. Chan (2009) tests some of the constructs in the motivational framework. Specifically, the author examines how task motivation interacts with DSS effectiveness and efficiency to affect DSS use. As predicted, the findings indicate that individuals using a more effective DSS to work on a high motivation task increase usage of the DSS, while DSS use does not differ between individuals using either a more or less effective DSS to complete a low motivation task. The results also show significant differences for individuals using either a more or less efficient DSS to complete a low motivation task, but no significant differences between individuals using either a more or less efficient DSS to perform a high motivation task only when the extent of DSS use is measured dichotomously (i.e., use versus non-use).

These findings suggest the importance of task motivation and corroborate the findings of prior research in the context of objective (i.e., computer recorded) rather than subjective (self-reported) DSS use. A contribution of Chan’s (2009) study is use of a rich measure of DSS use based on Burton-Jones and Straub’s (2006) definition of DSS use as an activity that includes a user, a DSS, and a task. Chan et al. (2009) extends the motivational framework by investigating the alternative paths among the constructs proposed in the framework. Specifically, the authors test the direct effects of feedback (a DSS characteristic) and reward (a decision environment factor), and examine these effects on decision performance. The results indicate that individuals using a DSS with the feedback characteristic perform better than those using a DSS without the feedback characteristic. The findings also show that individuals receiving positive feedback, regardless of the nature (i.e.,informational or controlling) of its administration perform better than the no-feedback group. These results provide some evidence supporting the call by Johnson et al. (2004) for designers to incorporate positive feedback in their design of DSS. Positive feedback is posited to lead to favorable user perception of a DSS which in turn leads to improved decision performance. The findings also suggest that task-contingent reward undermine decision performance compared to the no reward condition, and performance contingent reward enhance decision performance relative to the task-contingent reward group. The study by Chan et al. (2009) demonstrates the need for designers to be cognizant of the types of feedback and reward structures that exist in a DSS environment and their impact on decision performance.

DSS characteristics

The characteristics of a DSS include ease of use (Davis, 1989), presentation format (Amer, 1991; Hard & Vanecek, 1991; Umanath et al., 1990), system restrictiveness (Silver, 1990), decisional guidance (Silver, 1990), feedback (Eining & Dorr, 1991; Gibson, 1994; Stone, 1995), and interaction support (Butler, 1985; Eining et al., 1997).

Ease of use

DSS use is expected to occur if users perceive a DSS to be easy to use and that using it enhances their performance and productivity (Igbaria et al., 1997). Less cognitive effort is needed to use a DSS that is easy to use, operate, or interact with. The extent of ease of use of a DSS is dependent on features in the DSS that support the dimensions of speed, memory, effort, and comfort (Thomas, 1996). A DSS is easy to use if it reduces user performance time (i.e., the DSS is efficient), decreases memory load with the nature of assistance provided (memory), reduces mental effort with simple operations (effort), and promotes user comfort (comfort). An objective of developers is to reduce the effort that users need to expend on a task by incorporating the ease of use characteristic into a DSS so that more effort can be allocated to other activities to improve decision performance. DSS use may decline if increased cognitive effort is needed to use a DSS because of lack of ease of use.

System restrictiveness and decisional guidance

Two DSS attributes, system restrictiveness and decisional guidance, have been examined to show what users can and will do with a DSS (Silver, 1990). System restrictiveness refers to the degree to which a DSS limits the options available to the users, and decisional guidance refers to a DSS assisting the users to select and use its features during the decision-making process. If a decision-making process encompasses the execution of a sequence of information processing activities to reach a decision, then both the structure and execution of the process can be restricted by a DSS. The structure of the process can be restricted in two ways: limit the set of information processing activities by providing only a particular subset of all possible capabilities, and restrict the order of activities by imposing constraints on the sequence in which the permitted information processing activities can be carried out. User involvement is often essential during the execution of information processing activities after the structure of the process has been determined. The structure in the decision-making process is also promoted with the use of a restrictive DSS; in this respect, users are not overwhelmed with choices among many competing DSS. In certain cases,additional structure may actually enhance DSS use when ease of use is facilitated. However, lesser system restrictiveness may be preferred to enhance learning and creativity. Users may not use a DSS that is too restrictive because they may consider DSS use to be discretionary (Silver, 1988).