Cognitive biases in implementing a performance management system: behavioral strategy for supporting managers’ decision-making processes

Purpose: The purpose of this paper is twofold: to provide a clear picture on the cognitive biases affecting managers' decision-making process of implementing a performance management system (PMS), and to identify managerial practices, measures and the key challenges to manage the cognitive biases in the corporate strategy. Design/methodology/approach Semi-structured interviews, based on theoretical milestones of performance management and cognitive psychology, gathered from 104 experienced professionals' evaluations on the likelihood and impact of managers' cognitive biases in PMS implementation, potential solutions as well as drivers and connected criticalities. Findings: Recurring cognitive biases, together with considerable impacts, emerged in the first, and most strategic, phases of the PMS implementation. The authors developed a roadmap to support corporate transition to integrate behavioral strategy into the PMS implementation aiming to achieve economically and efficiently sound performance. Research limitations/implications: From the view of proper behavioral strategy affirmation in performance management literature, in a small way, the authors contribute to a desirable taxonomy of cognitive biases so differentiated decision-making scenarios may be built to compare results and draw new observations. Behavioral studies could transversally connect the cognitive biases of performance management to actors' sociodemographic features and personality types. Practitioners may check biases affecting their organizations by means of the questionnaire and, consequently, adopt the framework illustrated to reduce them. Originality/value: Performance management literature has constantly investigated positive and negative behavioral factors related to the PMS. This study, instead, makes a theoretical and methodological contribution to the PMS implementation as a decision-making process. The authors propose a theoretical framework that integrates cognitive psychology insights and applies measures to reduce biases.