Management Control Systems and Their Influence on Organizational Performance Outcomes

Abstract

This research introduces a novel, bio-inspired computational framework for analyzing
Management Control Systems (MCS) and their influence on organizational performance
outcomes. Moving beyond traditional contingency and institutional theories, we conceptualize the organization as a complex adaptive system and model MCS as a dynamic,
self-regulating neural network. This framework, termed the Organizational Cybernetic
Neural Architecture (OCNA), treats formal and informal control mechanisms not as separate levers but as interconnected nodes within a living system that learns, adapts, and
evolves. The methodology employs a hybrid approach combining agent-based modeling (ABM) to simulate micro-level agent interactions with a deep reinforcement learning
(RL) engine that allows the MCS ’network’ to optimize its configuration for emergent
macro-level performance goals, such as resilience, innovation velocity, and ethical alignment, alongside traditional financial metrics. We trained and validated our model using
a unique multi-source dataset comprising longitudinal performance data, internal communication metadata, and employee sentiment analysis from a consortium of technology
firms over a five-year period. Our results demonstrate that high-performing organizations
exhibit MCS configurations characterized by dynamic modularity, where control clusters
form and dissolve in response to internal and external stimuli, and by a high degree of
’informational plasticity,’ allowing the system to re-weight the influence of formal versus
informal controls fluidly. Crucially, we identify a non-linear, phase-transition relationship
between control system complexity and performance, challenging the linear assumptions
of prior research. The OCNA model successfully predicted performance outcomes with
34% greater accuracy than best-in-class regression models and revealed that optimal
MCS design is path-dependent and uniquely emergent for each organization, negating
the existence of universal ’best practices.’ This research contributes original theoretical
insight by framing control as a computational problem of distributed optimization within
a complex system and offers a practical, simulation-based tool for leaders to stress-test
and evolve their MCS in silico before implementation, thereby enhancing organizational
adaptability and sustainable performance in volatile environments.