Success and failure of decision support systems: Learning as we go

Abstract

Much effort and money have been devoted to the development of decision support systems (DSS) to enhance the decision-making capabilities of primary producers and their advisers. These initiatives have been partly driven by the increased complexity of primary production brought on by market globalization, the need for sustainable production practices, and the increasing rate and volume of information exchange. Decision support systems can assist producers in making better decisions by integrating information into a more useable form, altering production systems, enhancing management skills, and reducing costs of production. Despite these benefits, the adoption of DSS by producers has been limited. Reasons include the lack of end user evaluation preceding and during DSS development, as well as the fact that the DSS output may not fit the producer's decision-making style or because the complexity needed to operate the DSS is great and requires considerable data input. Information systems-development methodologies are investigated as a way of increasing DSS adoption. These methodologies, including “hard” and “soft” systems approaches, are discussed as ways to provide greater concordance between developer and end user. A case study of “HotCross,” a DSS under development to evaluate crossbreeding systems in northern Australia, was used to identify issues involved in DSS development and use. Issues highlighted include industry consultation, target audience focus, evaluation of DSS success, user participation, support and availability, and participatory learning processes. The case study provided evidence of a perceptible shift in the development process because greater emphasis was put on the learning process of breeding program design by end-users rather than emphasis on learning how to use the DSS itself. Greater end user involvement through participatory learning approaches (action learning, action research, and soft systems methodologies), iterative prototyping (evolving development processes), as well as keeping DSS development manageable and small in scope, will provide avenues for improving the rate of DSS adoption.

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