Analytics capabilities and organizational competitiveness: Unveiling the impact of management control systems and environmental uncertainty - Centre de Droit des Affaires et de Gestion
Article Dans Une Revue Decision Support Systems Année : 2022

Analytics capabilities and organizational competitiveness: Unveiling the impact of management control systems and environmental uncertainty

Résumé

In this study, we investigated how management control systems (MCS) aid in translating analytics capabilities (AC) generated insights into enhanced organizational competitiveness under conditions of environmental uncertainty. The survey data from 405 manufacturing firms offers several unique contributions. First, it reveals that the link between AC and organizational competitiveness is not necessarily direct and may occur through MCS. Thus, it offers interesting insights concerning the mechanisms through which AC can generate value and competitiveness. Second, this study borrows from Simons' [58,59] levers of control (LoC) framework and offers a holistic and more in-depth understanding of the connections between AC, MCS, and organizational competitiveness. Third, by considering the nonlinear moderating role of environmental uncertainty, it offers a response to recent calls for research on the circumstances under which AC-generated insights can lead to organizational competitiveness. It also provides a response to recent calls for further research on AC - MCS relationships. Fourth, the study draws on a dynamic capabilities perspective to analyze how MCS can help translate AC-generated insights into improved organizational competitiveness. While this perspective has proven its worth in the information systems literature, it has received less attention with regards to management control processes.
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hal-03636923 , version 1 (22-07-2024)

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Issam Laguir, Shivam Gupta, Indranil Bose, Rebecca Stekelorum, Lamia Laguir. Analytics capabilities and organizational competitiveness: Unveiling the impact of management control systems and environmental uncertainty. Decision Support Systems, 2022, 156, pp.113744. ⟨10.1016/j.dss.2022.113744⟩. ⟨hal-03636923⟩
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