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A research agenda for studying project decision-behaviour through the lenses of simple heuristics

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  • Stingl, Verena
  • Geraldi, Joana

Abstract

Management practitioners often preconsciously rely on simple heuristics when approaching ill-structured decision problems. Simple heuristic research suggests that those simple cognitive strategies do not only constitute a fast mode of deliberation but may also be effective. Nonetheless, empirical research using simple heuristics as a theory of managerial or organisational cognition remains sparse. To stimulate empirical research, we propose concrete avenues for research, starting with the empirical problems and then considering how simple heuristics can be used as a lens to address these issues. We illustrate our argument by focusing on empirical problems involved in project decisions. Specifically, we discuss three problems that both pose a challenge and offer an opportunity for simple heuristic research: decision (or problem) framing, acquisition and use of unstructured information, and identification of options. We discuss these challenges along two views: the use of heuristics through the practitioner and the development of heuristics in the context of the organisational environment. Our article contributes to the research on project decision making through concrete guidance for designing empirically relevant research within the simple heuristic paradigm, as well as to the simple heuristic community by extending the research into novel empirical problems and methodological approaches.

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  • Stingl, Verena & Geraldi, Joana, 2021. "A research agenda for studying project decision-behaviour through the lenses of simple heuristics," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
  • Handle: RePEc:eee:tefoso:v:162:y:2021:i:c:s0040162520311938
    DOI: 10.1016/j.techfore.2020.120367
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    1. Krawinkler, Andreas & Breitenecker, Robert J. & Maresch, Daniela, 2022. "Heuristic decision-making in the green energy context:Bringing together simple rules and data-driven mathematical optimization," Technological Forecasting and Social Change, Elsevier, vol. 180(C).

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