The Impact of <scp>AI</scp> Automation, Blockchain and Big Data on Sustainability and Performance: A Management Control Perspective

ABSTRACT This paper examines how emergent technologies, specifically Artificial Intelligence, Blockchain, and Big Data, transform Management Control Systems (MCS) design and use, focusing on organizational performance and sustainability capabilities of firms. Using a qualitative, multiple‐case approach, the study applies a thematic analysis on data collected from 10 semi‐structured interviews with managers of companies from diverse sectors. Results indicate that these technologies reshape MCS use by enabling more timely and comprehensive information, strengthening risk management, and increasing flexibility in control practices. However, the findings also highlight significant resistance arising from behavioral, environmental, and technical factors, suggesting that successful technological integration requires both top‐down strategic alignment and bottom‐up, actor‐level engagement. The paper contributes to existing theory by explicitly linking digital innovation, MCS, and change management frameworks with sustainability, responding to scholarly calls for empirical research into how control systems adapt in contexts of digital transformation. Practical implications for managers and policymakers underscore the importance of adaptive, transparent control structures in facilitating strategic development while providing accountability and ethical artificial intelligence use.

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Publication Details

Journal
Sustainable Development
Published
2025-10-15
DOI
https://doi.org/10.1002/sd.70327
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
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article

The Impact of AI Automation, Blockchain and Big Data on Sustainability and Performance: A Management Control Perspective

Raquel Flórez‐López, María Segovia Villarreal, Juan Manuel Ramón‐Jerónimo, Manuel García‐Nieto
Sustainable Development
Blockchain Technology Applications and Security
article

The Impact of AI Automation, Blockchain and Big Data on Sustainability and Performance: A Management Control Perspective

Raquel Flórez‐López, María Segovia Villarreal, Juan Manuel Ramón‐Jerónimo, Manuel García‐Nieto
article en

Abstract

ABSTRACT This paper examines how emergent technologies, specifically Artificial Intelligence, Blockchain, and Big Data, transform Management Control Systems (MCS) design and use, focusing on organizational performance and sustainability capabilities of firms. Using a qualitative, multiple‐case approach, the study applies a thematic analysis on data collected from 10 semi‐structured interviews with managers of companies from diverse sectors. Results indicate that these technologies reshape MCS use by enabling more timely and comprehensive information, strengthening risk management, and increasing flexibility in control practices. However, the findings also highlight significant resistance arising from behavioral, environmental, and technical factors, suggesting that successful technological integration requires both top‐down strategic alignment and bottom‐up, actor‐level engagement. The paper contributes to existing theory by explicitly linking digital innovation, MCS, and change management frameworks with sustainability, responding to scholarly calls for empirical research into how control systems adapt in contexts of digital transformation. Practical implications for managers and policymakers underscore the importance of adaptive, transparent control structures in facilitating strategic development while providing accountability and ethical artificial intelligence use.

Sustainable DevelopmentVol. 34(1)
University College Cork (IE), Universidad Pablo de Olavide (ES)
Openalex Percentile: Top 57%
Blockchain Technology Applications and Security
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The Impact of <scp>AI</scp> Automation, Blockchain and Big Data on Sustainability and Performance: A Management Control Perspective — Raquel Flórez‐López, María Segovia Villarreal, et al. · Sustainable Development (2025) | TGRS Research Map | TGRS