Paper Type
Complete
Paper Number
PACIS2026-2171
Description
This study addresses the persistent ambiguity surrounding the business analytics and firm performance relationship by replacing static empirical measures with a dynamic agent-based simulation on an NK fitness landscape. Grounded in contingency and organizational ambidexterity theories, we evaluate five strategic cohorts ranging from pure exploitative to pure explorative orientations under distinct environmental configurations of complexity, dynamism, and resource constraints. Our results robustly validate the presence of a stability paradox, demonstrating that under highly stable environmental conditions, an exclusive focus on exploitative analytics creates a local optimum trap that severely depresses terminal performance. Conversely, cultivating broader explorative and ambidextrous search architectures provides a structural defense against optimization myopia, unlocking superior global peaks over long horizons. We establish concrete, temporal boundary conditions for strategic analytics deployment and offer a data-driven blueprint for digital leadership.
Recommended Citation
Vinekar, Vishnu PhD, "Business Analytics and Firm Performance: An Analytical Agents Simulation" (2026). PACIS 2026 Proceedings. 12.
https://aisel.aisnet.org/pacis2026/data_analtyics/data_anltics/12
Business Analytics and Firm Performance: An Analytical Agents Simulation
This study addresses the persistent ambiguity surrounding the business analytics and firm performance relationship by replacing static empirical measures with a dynamic agent-based simulation on an NK fitness landscape. Grounded in contingency and organizational ambidexterity theories, we evaluate five strategic cohorts ranging from pure exploitative to pure explorative orientations under distinct environmental configurations of complexity, dynamism, and resource constraints. Our results robustly validate the presence of a stability paradox, demonstrating that under highly stable environmental conditions, an exclusive focus on exploitative analytics creates a local optimum trap that severely depresses terminal performance. Conversely, cultivating broader explorative and ambidextrous search architectures provides a structural defense against optimization myopia, unlocking superior global peaks over long horizons. We establish concrete, temporal boundary conditions for strategic analytics deployment and offer a data-driven blueprint for digital leadership.
Comments
05-DataAnalytics