Trust in complex task environments: results of an agent-based simulation on incomplete contracts

Abstract

Purpose Incentive contracts often do not govern all task elements for which a subordinate is responsible. In these situations, trust among the contracting parties is crucial for the effort the subordinate exerts on non-contracted task elements. This paper aims to examine the emergence of trust and its performance effects, accounting for the complexity of the task environment and the different cognitive bases of trust. Design/methodology/approach The paper employs an agent-based simulation following the framework of NK fitness landscapes and builds on the integrated model of leader-member exchange of Brower et al. (2000). In the simulation model, artificial organizations search for superior organizational performance where decision-makers' contracts are incomplete, i.e. not governing all task elements contributing to organizational performance. In the simulation experiments, the complexity of the task environment and the cognitive bases of trust-building, especially on the superior's side, are varied. Findings The simulation results suggest that the complexity of task environments plays a twofold role in trust-building and its performance effects: first, as complexity increases, dynamics arising from interrelated feedback loops affect trust among contracting parties; and second, this, in turn, reinforces frequent intra-organizational adjustments. These effects are moderated by the cognitive bases of trust-building. Consequently, in highly complex task environments, higher trust among parties does not universally improve organizational performance. Originality/value The paper contributes to the literature by analyzing the interplay between the complexity of an organization's task environment and the dynamics of reciprocal trust that unfold in pursuit of superior organizational performance. Moreover, by using agent-based simulation, the paper showcases how computational methods can further advance trust research in organizational contexts.

The authors' abstract, as published at the source. Kybernetes, 2026 · DOI ↗