How Deterministic and Probabilistic Information Systems Shape Complicatedness, Complexity, and Performance in Knowledge Work

Organizations increasingly use information systems to help knowledge workers manage portfolios containing many interconnected products and customers. Drawing on complexity science, we distinguish portfolio complicatedness, the number of interconnected components a worker carries, from complexity, which can emerge when interactions among those components become ad hoc and nonlinear. We theorize that deterministic CRM features impose predefined structure that should keep increasingly complicated portfolios tractable, whereas probabilistic GenAI features generate context-dependent outputs and iterative exchanges that can fuel complexity dynamics. Using agent- and deal-level data from U.S. independent insurance distribution and a shift-share instrument identification strategy that addresses endogenous portfolio composition and technology use, we find patterns opposite to these expectations. CRM use negatively moderates the performance effects of both product and customer complicatedness, while GenAI use positively moderates them. The shapes of these relationships reveal boundary conditions on the theorized mechanisms: predetermined structure is valuable when complicatedness creates interaction demands that require coordination and retrieval, but can constrain performance when those demands remain tractable; probabilistic interaction can impose costs at low complicatedness yet become valuable as informational demands exceed what a worker can readily maintain. The findings extend complexity science by showing that information systems can improve performance not only by taming complexity through structure but also by increasing a worker’s capacity to perform as complicatedness creates increasingly demanding interaction conditions.

Recommended citation: Jaiswal, A. and Tanriverdi, H. (2026). "How Deterministic and Probabilistic Information Systems Shape Complicatedness, Complexity, and Performance in Knowledge Work." ICIS 2026 Proceedings, Lisbon, Portugal.