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Guiding the Algorithm: Harnessing artificial intelligence to nurture SMEs management control systems

Laura Broccardo et al.

Management Control2025https://doi.org/10.3280/maco2025-001-s1002article
ABDC C
Weight
0.37

What the paper says

The diffusion of artificial intelligence (AI) amongst organizations is profoundly revolutionizing business processes, such management control (MC) practices. However, the existing literature reveals a significant gap in understanding the contribution of AI-empowered tools to management control systems (MCSs), particularly within small and medium enterprises (SMEs), where these systems are often rudimental, informal, subjective, and short-period-oriented. To address this gap, the authors employed a mixed-methods approach, featuring an open-coding and thematic analysis of AI-generated answers to MC questions related the analysis of liquidity and profitability ratios from 37 Italian SMEs, as well as a qualitative investigation, with in-depth interviews with 15 respondents, to highlight their perceptions. Our investigation is informed by the social construction of technology theory, to explore the relevant social groups, interpretative flexibility, technological frames and patterns of closure related to AI-empowered tools and their contribution to SMEs' MCSs. We connect our findings to two frames, which highlight, on the one hand the positive contribution of such tools to SMEs' MCSs through accurate and comprehensive responses; on the other hand, a negative frame highlights concerns related to inconsistency and verbosity of the answers, which may affect trust and usability. This manuscript aims to provide an overview of the possible contribution of AI-empowered tools to SMEs' MCSs. Both scholars and practitioners can benefit from the theoretical and practical implications provided, which complements the limited number of studies in the field. Future research avenues are discussed.

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https://doi.org/https://doi.org/10.3280/maco2025-001-s1002

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@article{laura2025,
  title        = {{Guiding the Algorithm: Harnessing artificial intelligence to nurture SMEs management control systems}},
  author       = {Laura Broccardo et al.},
  journal      = {Management Control},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.3280/maco2025-001-s1002},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.