Abstract
This empirical project investigates the complex and dynamic process of Management Control System (MCS) adoption within start-up environments. While extant literature acknowledges the importance of MCS for managing growth, a significant gap remains in understanding the underlying conditions and the detailed process by which these systems are adopted in startups. Existing studies predominantly focus on the ‘What’ aspects of adoption, failing to identify the generative mechanisms—the ‘How’. Therefore, the central research question guiding this inquiry is: “How do startups adopt management control systems?”.
The research is philosophically grounded in Critical Realism (CR). This positioning supports the study's objective to move beyond description toward explaining the "How" of MCS adoption. To achieve methodological rigor , the research design employs a multi-methodological approach. This approach combines Visual Mapping to capture the vivid, messy, and contextually rich process data from individual companies, with the Gioia Method for performing rigorous cross-company thematic inductive analysis to identify generalized patterns and aggregated dimensions. Data collection involved semi-structured interviews with 31 key personnel across 17 startups in the Indian ecosystem apart from meeting 4 startups in the pilot phase and 5 academic experts and practitioners in the final phase of sensemaking.
The findings reveal a multi-layered process, structured around three core dimensions: organizational evolution, the changing composition of MCS, and the mechanisms driving adoption and maturity. The analysis proposes a new typology for MCS in this context: "Business Hygiene MCS" and "Core MCS". This process-based classification links directly to the ‘Build vs. Buy’ decision framework utilized by founders. Furthermore, the study extends the literature on MCS as a "package" by theorizing its emergence through two interconnected mechanisms: the organizational dimension (e.g., functional build-up and founders distancing themselves from daily operations) and the technical dimension (e.g., integrating independent SaaS tools via APIs or consolidating into enterprise systems like ERPs to establish a single source of truth).
The contribution of this research lies in providing a robust, process-based framework that integrates contingency factors, organizational dynamics, and system characteristics to explain the mechanisms governing MCS evolution in startups. For practice, the findings offer actionable insights, particularly concerning system selection criteria and how to manage the transition from informal controls to formal, interconnected MCS packages. The research concludes by identifying opportunities for future work, including the impact of Artificial Intelligence on accelerating MCS evolution cycles.