Cognitive process as a basis for MIS and DSS design
Management Science
Managerial influence in the implementation of new technology
Management Science
Mental models: theory and application in human factors
Human Factors
A study of personal computer utilization by managers
Information and Management
Decisional guidance for computer-based decision support
MIS Quarterly
A psychological approach to decision support systems
Management Science
The effect of multimedia on perceived equivocality and perceived usefulness of information systems
MIS Quarterly - Special issue on Intensive research in information systems: using qualitative, interpretive, and case methods to study information technology—third installment
Past, present, and future of decision support technology
Decision Support Systems - Special issue: Decision support systems: Directions for the next decade
Evaluating the Impact of Dss, Cognitive Effort, and Incentives on Strategy Selection
Information Systems Research
Providing Decisional Guidance for Multicriteria Decision Making in Groups
Information Systems Research
Forecasting software in practice: use, satisfaction, and performance
Interfaces - Wagner prize papers
Convincing DSS users that complex models are worth the effort
Decision Support Systems
DSS Effectiveness in Marketing Resource Allocation Decisions: Reality vs. Perception
Information Systems Research
Applied Stochastic Models in Business and Industry - Bridging the Gap between Academic Research in Marketing and Practitioners' Concerns
Students as Surrogates for managers in a decision-making environment: an experimental study
Journal of Management Information Systems - Special section: Strategic and competitive information systems
The effects of structural characteristics of explanations on use of a DSS
Decision Support Systems
Perceived Information Quality in Data Exchanges: Effects on Risk, Trust, and Intention to Use
Information Systems Research
System Design Features and Repeated Use of Electronic Data Exchanges
Journal of Management Information Systems
Factors influencing decision support system acceptance
Decision Support Systems
Decision space visualization: lessons learned and design principles
HCI'13 Proceedings of the 15th international conference on Human-Computer Interaction: interaction modalities and techniques - Volume Part IV
Evaluating and understanding text-based stock price prediction models
Information Processing and Management: an International Journal
Explaining data-driven document classifications
MIS Quarterly
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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.