On humans, algorithms and data

Journal Article (2022)
Author(s)

M. (Michela) Arnaboldi (Politecnico di Milano)

Hans M.C.J. de Bruijn (TU Delft - Organisation & Governance)

Ileana Steccolini (University of Essex)

H.G. van der Voort (TU Delft - Organisation & Governance)

Research Group
Organisation & Governance
Copyright
© 2022 Michela Arnaboldi, J.A. de Bruijn, Ileana Steccolini, H.G. van der Voort
DOI related publication
https://doi.org/10.1108/QRAM-01-2022-0005
More Info
expand_more
Publication Year
2022
Language
English
Copyright
© 2022 Michela Arnaboldi, J.A. de Bruijn, Ileana Steccolini, H.G. van der Voort
Research Group
Organisation & Governance
Issue number
3
Volume number
19
Pages (from-to)
241-254
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Purpose: The purpose of this paper is to introduce the papers in this special issue on humans, algorithms and data. The authors first set themselves the task of identifying the main challenges arising from the adoption and use of algorithms and data analytics in management, accounting and organisations in general, many of which have been described in the literature. Design/methodology/approach: This paper builds on previous literature and case studies of the application of algorithm logic with artificial intelligence as an exemplar of this innovation. Furthermore, this paper is triangulated with the findings of the papers included in this special issue. Findings: Based on prior literature and the concepts set out in the papers published in this special issue, this paper proposes a conceptual framework that can be useful both in the analysis and ordering of the algorithm hype, as well as to identify future research avenues. Originality/value: The value of this framework, and that of the papers in this special issue, lies in its ability to shed new light on the (neglected) connections and relationships between algorithmic applications, such as artificial intelligence. The framework developed in this piece should stimulate scholars to explore the intersections between “technical” as well as organisational, social and individual issues that algorithms should help us tackle.