Conclusion

Chan's motivational framework provides a foundation for facilitating understanding of DSS use and decision performance. Instead of relying on the assumption that DSS use necessarily results in improved decision performance, the motivational framework proposes a link between DSS use and decision performance. Chan also identifies the significant role of the motivation factor in explaining DSS use and decision performance. The author proposes examination of motivation as two separate components; namely, task motivation and motivation to use a DSS. Separation of these two effects assists researchers in identifying the underlying reasons for lack of DSS use.

Additionally, the motivational framework developed by Chan presents abundant future research possibilities. Future work can examine factors that affect task motivation, a key construct in the motivational framework. Task-related factors such as interest, utility, importance or the opportunity cost of engaging in a task can be manipulated to obtain a measure of self-reported task motivation to provide additional insight into future research findings. It might be interesting to investigate factors (e.g., the users' motivational orientation, decision environmental factors and task characteristics) that influence task motivation.

The motivation theory may provide insight into the findings by Todd and Benbasat on why users do not translate the effort savings from use of a DSS to perform a task into increased information processing. An examination of task motivation also helps us consider ways for increasing DSS use. DSS use is posited to occur when the benefits (i.e., effectiveness and efficiency) outweigh the costs (i.e., cognitive effort) associated with usage. For example, features can be incorporated into a DSS to reduce the cognitive effort involved in the use of a strategy and to encourage DSS use.

A rich measure of DSS use consistent with Burton-Jones and Straub's definition of a DSS (that includes a user, a DSS, and use of the DSS to complete a task) is a more relevant construct than behavioral intention. Caution should be exercised to avoid the misleading assumption that behavior would follow intention. For example, one might intend to lose 20 pounds; however, the individual might not engage in actual behavior (i.e., exercise or cut down on calories) to lose the intended weight. TAM posits that behavioral intention leads to system use; however, prior research findings on the relationship between intention to use systems and system use are mixed. Lack of a strong correlation between self-reported and objective usage data and the low correlation between intention and system use present a challenge to the use of intention as a proxy for system use. Further, many TAM studies have used the intention (i.e., self-reported) measure as a proxy for system use although the focus of these studies is on system use. Since most TAM studies measure the variance in self-reported use, future research should measure system use rather than usage intention.

Further, empirical evidence in the behavioral decision-making literature suggests that decision makers make tradeoff between accuracy and effort in their formulation and subsequent use of DSS. Although accurate decision strategies such as additive difference (AD) can lead to improved decision performance, the effort required for using these strategies may discourage use of such strategies. Use of the more accurate AD strategy is expected to increase when the effort required for using the strategy is reduced; that is, when a DSS provides high support for the strategy.

Insights can also be gained from future work on whether user perception of a DSS might affect motivation to use a DSS, and whether task motivation interacts with DSS characteristics (e.g., ease of use, presentation format, system restrictiveness, decisional guidance, feedback or interaction support) to affect DSS use. Research can assist system developers in understanding the types of characteristics that can be incorporated into a DSS to create favorable user perception of the DSS to increase motivation to use the DSS, DSS use, and decision performance.

Finally, alternative paths among the constructs are implicit in the motivational framework developed by Chan. Chan conducts a study to examine how task motivation interacts with DSS effectiveness and efficiency to affect DSS use. Chan et al. also examine the effects of feedback (a characteristic of a DSS) and reward (a characteristic of the decision environment) on decision performance. These studies demonstrate the existence of alternative paths in the motivational framework. Future work can explore other possible alternative models from the framework.