Qian Song

The moderating roles of subjective (perceived) and objective task complexity in system use and performance

We distinguish between subjective (perceived) and objective task complexity.We distinguish between task motivation and DSS motivation.High task motivation enhances DSS motivation.Perceived task complexity moderates the effect of DSS motivation on DSS use.Objective task complexity moderates the effect of DSS use on performance. This study builds on previous research by separating the effects of subjective (perceived) and objective task complexity to examine perceived task complexity as a positive moderator of the impact of decision support system (DSS) motivation on DSS use, and objective task complexity as a positive moderator of the effect of DSS use on performance. We manipulated task motivation and task complexity, and measured perceived task complexity, task motivation, and DSS motivation. The DSS also captured actual DSS use and the essential information for deriving the performance construct. The findings suggest that individuals report increased motivation in the high than low motivation task, and high task motivation leads to increased motivation to use the DSS to perform the task. Further, the results reveal that perceived task complexity decreases in the presence of the DSS and this highlights the importance of the DSS in alleviating cognitive resources which enhances user motivation to use the DSS, resulting in increased usage of the DSS to complete the task. The findings also accentuate the pivotal role of the DSS in assisting information processing of an objectively complex task which promotes the positive effect of DSS use on performance.

Decision support system (DSS) use and decision performance: DSS motivation and its antecedents

Abstract We developed an experimental decision support system (DSS) that enabled us to manipulate DSS performance feedback and response time, measure task motivation and DSS motivation, track the usage of the DSS, and obtain essential information for assessing decision performance through conjoint analysis. The results suggest the mediating role of DSS use in the relationship between DSS motivation and decision performance. Further, DSS motivation is highest in the presence of high task motivation, more positive DSS performance feedback, and fast DSS response time. The findings have important implications for both DSS research and practice.

The Efficacy of a Decision Support System in Enhancing Risk Assessment Performance

A pervasive challenge for decision-makers is evaluating data of varying form (e.g., quantitative vs. qualitative) and credibility in arriving at an overall risk assessment judgment. The current study tests the efficacy of a Decision Support System (DSS) for facilitating auditors’ evaluation and assimilation of financial and nonfinancial information in accurately assessing the risk of material misstatements (RMM) in financial information. Utilizing the proximity compatibility principle, the DSS manipulates the display of cues either in an integral (where pieces of information are displayed on one computer screen) or separable (where pieces of information are displayed on different computer screens) format. Based on cognitive fit theory, we expect that the integral (separable) display best supports financial (nonfinancial) information processing, leading to enhanced risk assessment performance. In addition, we predict that consistent DSS display of financial and nonfinancial information facilitates risk assessment performance. Further, this study accentuates the importance of auditors’ preference for presentation of financial and nonfinancial information and consistent presentation of all the information in strengthening the effect of DSS display format on risk assessment performance. We design a case which includes a seeded high fraud risk. A total of 112 audit seniors participated in the experiment where the DSS display format was manipulated and the auditors’ RMM assessments and display preferences were measured. The results support the hypotheses and highlight the value of the DSS in enhancing risk assessment performance.

Motivational Framework: Insights into Decision Support System Use and Decision Performance

The purpose of this chapter 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