The weight of organizational factors on heuristics

Abstract

Purpose The purpose of this paper is to investigate the influence of organizational factors on individual decision-making under conditions of uncertainty and time pressure. A method to assess the impact of individual and organizational factors on individual decisions is proposed and experimented in the context of triage decision-making process. Design/methodology/approach The adopted methodology is based on the bias-variance decomposition formula. The method, usually applied to assess the predictive accuracy of heuristics, has been adjusted to discriminate between the impact of organizational and individual factors affecting heuristic processes. To test the methodology, 25 clinical scenarios have been designed and submitted, through simulations, to the triage nurses of two Spanish hospitals. Findings Nurses’ decisions are affected by organizational factors in certain task conditions, such as situations characterized by complete and coherent information. When relevant information is lacking and available information is not coherent, decision-makers base their assessments on their personal experience and gut feeling. Research limitations/implications Discriminating between the influence of organizational factors and individual ones is the starting point for a more in-depth understanding of how organization can guide the decision process. Using simulations of clinical scenarios in field research does not allow for capturing the influence of some contextual factors, such as the nurses’ stress levels, on individual decisions. This issue will be addressed in further research. Practical implications Bias and variance are useful measurements for detecting process improvement actions. A bias prevalence requires a re-design of organizational settings, whereas training would be preferred when variance prevails. Originality/value The main contribution of this work concerns the novel interpretation of bias and variance concepts to assess organizational factors’ influence on heuristic decision-making processes, taking into account the level of complexity of decision-related tasks.

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