Purpose – The paper aims to identify, synthesise, and critically examine the extant academic research on the relation between big data analytics (BDA), corporate accountability and non-financial disclosure across several disciplines. Design/methodology/approach – The paper employs a structured literature review methodology and applies “insight-critique-transformative redefinition” framework to interpret the findings, develop critique and formulate future research directions. Findings – The paper identifies and critically examines 12 research themes across four macro categories. Our insights indicate that the nature of the relationship between BDA and accountability depends on whether an organisation considers BDA as a value creation instrument or as a revenue generation source. The paper discusses how non-financial disclosure can effectively increase corporate accountability for ethical, social and environmental consequences of BDA. Originality/value – This paper is the first one to provide a comprehensive synthesis of academic literature, identify research gaps and outline a prospective research agenda on the implications of big data technologies for non-financial disclosure and corporate accountability along social, environmental and ethical dimensions. Social impact - The paper discusses how non-financial disclosure can reduce negative social and environmental impact stemming from the corporate use of BDA. Practical implications - This study presents the results of a structured literature review exploring the state-of-the-art of academic research on the relation between BDA, non-financial disclosure and corporate accountability. The paper employs a systematic approach, to provide an exhaustive analysis of the phenomenon with rigorous and reproducible research criteria. The paper also presents a series of actionable insights of how corporate accountability for the use of big data and algorithmic decision-making can be enhanced.

Corporate Accountability and Big Data Analytics: Is Non-financial Disclosure a Missing Link?

Agostini Marisa;Arkhipova Daria;Chiara Mio
In corso di stampa

Abstract

Purpose – The paper aims to identify, synthesise, and critically examine the extant academic research on the relation between big data analytics (BDA), corporate accountability and non-financial disclosure across several disciplines. Design/methodology/approach – The paper employs a structured literature review methodology and applies “insight-critique-transformative redefinition” framework to interpret the findings, develop critique and formulate future research directions. Findings – The paper identifies and critically examines 12 research themes across four macro categories. Our insights indicate that the nature of the relationship between BDA and accountability depends on whether an organisation considers BDA as a value creation instrument or as a revenue generation source. The paper discusses how non-financial disclosure can effectively increase corporate accountability for ethical, social and environmental consequences of BDA. Originality/value – This paper is the first one to provide a comprehensive synthesis of academic literature, identify research gaps and outline a prospective research agenda on the implications of big data technologies for non-financial disclosure and corporate accountability along social, environmental and ethical dimensions. Social impact - The paper discusses how non-financial disclosure can reduce negative social and environmental impact stemming from the corporate use of BDA. Practical implications - This study presents the results of a structured literature review exploring the state-of-the-art of academic research on the relation between BDA, non-financial disclosure and corporate accountability. The paper employs a systematic approach, to provide an exhaustive analysis of the phenomenon with rigorous and reproducible research criteria. The paper also presents a series of actionable insights of how corporate accountability for the use of big data and algorithmic decision-making can be enhanced.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5011020
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