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Assessing the impact of motivation and ability on team-based productivity using an agent-based model

Josef Di Pietrantonio, Rachael Miller Neilan () and James B. Schreiber
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Josef Di Pietrantonio: Duquesne University
Rachael Miller Neilan: Duquesne University
James B. Schreiber: Duquesne University

Computational and Mathematical Organization Theory, 2019, vol. 25, issue 4, No 5, 499-520

Abstract: Abstract It is common for organizations to hire workers based on their knowledge, skills, and abilities. However, despite capable workers being hired, productivity may suffer if employees’ motivational needs are not satisfied. We developed an agent-based model to simulate the completion of tasks by teams of workers in an organization. Each worker is described by an ability value and a 3-parameter motive profile expressing the individual’s needs for affiliation, achievement, and power. During each time step, each worker contributes to an assigned task at a rate determined by the worker’s ability and motive profile, the task’s difficulty and proximity to completion, and the team’s experience. When a task is completed by a team, the workers are re-assigned to a new team and task. At the end of 365 time steps, the model outputs the total number of completed tasks, which is the primary measurement of productivity. Model simulations demonstrate that hiring workers based on their ability and motivational strengths can lead to increased productivity. Additional model simulations illustrate the benefit of identifying failing tasks and re-assigning new teams to these tasks in real-time.

Keywords: Agent-based models; Simulation; Motivation theory; Organizational performance; Productivity (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10588-019-09295-4

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