How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations

• 成果类型: Article
• 署名作者: Kayande, Ujwal; De Bruyn, Arnaud; Lilien, Gary L.; Rangaswamy, Arvind; van Bruggen, Gerrit H.
• 署名单位: Australian National University; ESSEC Business School; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.1080.0198
• 发表日期: 2009
• 页码: 527-546
• 关键词: decision-support-systems; perceived usefulness; cognitive feedback; INFORMATION; performance; IMPACT; task; complex; acceptance; guidance

摘要

Model-based decision support systems (DSS) improve performance in many contexts that are data-rich, uncertain, and require repetitive decisions. But such DSS are often not designed to help users understand and internalize the underlying factors driving DSS recommendations. Users then feel uncertain about DSS recommendations, leading them to possibly avoid using the system. We argue that a DSS must be designed to induce an alignment of a decision maker's mental model with the decision model embedded in the DSS. Such an alignment requires effort from the decision maker and guidance from the DSS. We experimentally evaluate two DSS design characteristics that facilitate such alignment: (i) feedback on the upside potential for performance improvement and (ii) feedback on corrective actions to improve decisions. We show that, in tandem, these two types of DSS feedback induce decision makers to align their mental models with the decision model, a process we call deep learning, whereas individually these two types of feedback have little effect on deep learning. We also show that deep learning, in turn, improves user evaluations of the DSS. We discuss how our findings could lead to DSS design improvements and better returns on DSS investments.