Call for Papers: Special Issue on “The Ethics of Digitalization and Emerging Corporate Responsibilities in the Digital Age”
Prof. Dr. Philipp Schreck, Martin-Luther-University Halle-Wittenberg, Germany
Prof. Dr. Laura Marie Edinger-Schons, University of Mannheim, Germany
Prof. Dr. Matthias Uhl, Technische Hochschule Ingolstadt, Technical University of Munich, Germany
Digital technologies are transforming the way we are doing business. Whether bank loans should be granted to applicants, jobseekers be invited to interviews, employees be promoted, or customers be paid special attention to: Decisions that were previously taken by humans alone, are now prepared or taken autonomously by machines (Balasubramanian et al. 2020; Rahwan et al. 2019). Technical developments have led to an unprecedented computing capacity, allowing for precise analyses and predictions of human behavior. Artificial Intelligence (AI) can solve problems which, until recently, were believed to be solvable by human beings only. For example, AI can master complex strategic games such as Chess and Go, write journalistic texts, compose music, and write poems. Tech companies often have access to big data and new technologies which could be leveraged to, beyond making profits, solve social and environmental issues through digital social innovation. Finally, new technologies such as face-recognition technologies and predictive analytics may have unintended side-effects that the producing companies may be held responsible for.
These developments raise numerous ethical issues which have led to emerging fields of research in various disciplines. For example, in an effort to mitigate the risks of biased and untransparent autonomous systems, scholars have started to develop normative frameworks for the design of AI systems (Dignum 2018; Floridi et al. 2018; Glikson/ Woolley 2020) and algorithms (Martin 2019; Mittelstadt et al. 2016). In a similar vein, normative and conceptual analyses have explored how digitalization changes our understanding of corporate responsibility and responsible innovation (Lobschat et al. 2021; Yoo et al. 2010). Empirical research, in turn, has investigated human perceptions of and behavioral responses to machine decisions. Such research has included studies on a human aversion against, and trust in algorithms (Castelo et al. 2019; Dietvorst et al. 2018; Ibrahim et al. 2021; Kawaguchi 2021; Logg et al. 2019); the diffusion of responsibility between humans and machines (Gogoll/ Uhl 2018; Kirchkamp/ Strobel 2019; Parasuraman et al. 2000), and the role of AI in People Analytics (Newman et al. 2020; Tursunbayeva et al. 2021).
Against the background of these issues, this special issue of the Journal of Business Economics invites original research in the broad fields of the ethics of digitalization and emerging corporate responsibilities in the digital age. We seek original theoretical and empirical contributions that address challenges that the ethics of digitalization poses for corporations. The special issue is intended to deepen the debate about the often-ambiguous ethical implications of the use of AI and other digital technologies in the workplace and in our daily lives. Examples of specific questions which may be addressed are: To what extent does digitalization present companies with genuinely new normative questions, e.g., on privacy, autonomy, and security? What intended and unintended effects do automated decisions and predictive analytics have on the individual and the organizational level? How do individuals perceive of and respond to machine decisions, and what are the implications for an ethically aligned design of human-machine interactions?
The objective of this Special Issue is to bring together state-of-the-art research on the ethics of digitalization and emerging corporate responsibilities in the digital age, and to stimulate existing research in these fields. We welcome contributions from various disciplines such as business management, economics, philosophy, sociology, and related fields. We are open to a wide range of methodological approaches, including innovative vignette, lab, and field experiments, textual analyses, normative research, and literature reviews.
Submission deadline for manuscripts is December 31, 2021.
Manuscripts need to be submitted via the journal’s website: http://www.springer.com/11573 (click "Submit Online" there). The recommended manuscript length for Journal of Business Economics is a maximum of 13,000 words. Submitted papers should adhere to the format requirements of the Journal of Business Economics. Please consult the “Instructions for Authors” at http://www.springer.com/11573.
Submission of a manuscript implies that the work described has not been published before; that it is not under consideration for publication anywhere else; that its publication has been approved by all co-authors, if any, as well as by the responsible authorities – tacitly or explicitly – at the institute where the work has been carried out.
If you have questions regarding relevance and submission of your work to this special issue, please contact Philipp Schreck at firstname.lastname@example.org, Laura Marie Edinger-Schons at email@example.com, or Matthias Uhl at firstname.lastname@example.org.
Balasubramanian, Natarajan/ Ye, Yang/ Xu, Mingtao (2020): Substituting Human Decision-Making with Machine Learning: Implications for Organizational Learning, in: Academy of Management Review (online first).
Castelo, Noah/ Bos, Maarten W/ Lehmann, Donald R (2019): Task-Dependent Algorithm Aversion, in: Journal of Marketing Research 56 (5), pp. 809-825.
Dietvorst, Berkeley J./ Simmons, Joseph P./ Massey, Cade (2018): Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them, in: Management Science 64 (3), pp. 1155-1170.
Dignum, Virginia (2018): Ethics in Artificial Intelligence: Introduction to the Special Issue, in: Ethics and Information Technology 20 (1), pp. 1-3.
Floridi, Luciano/ Cowls, Josh/ Beltrametti, Monica/ et al. (2018): Ai4people—an Ethical Framework for a Good Ai Society: Opportunities, Risks, Principles, and Recommendations, in: Minds and Machines 28 (4), pp. 689-707.
Glikson, Ella/ Woolley, Anita Williams (2020): Human Trust in Artificial Intelligence: Review of Empirical Research, in: Academy of Management Annals 14 (2), pp. 627-660.
Gogoll, Jan/ Uhl, Matthias (2018): Rage against the Machine: Automation in the Moral Domain, in: Journal of Behavioral and Experimental Economics 74, pp. 97-103.
Ibrahim, Rouba/ Kim, Song-Hee/ Tong, Jordan (2021): Eliciting Human Judgment for Prediction Algorithms, in: Management Science (online first).
Kawaguchi, Kohei (2021): When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business, in: Management Science 67 (3), pp. 1670-1695.
Kirchkamp, Oliver/ Strobel, Christina (2019): Sharing Responsibility with a Machine, in: Journal of Behavioral and Experimental Economics 80, pp. 25-33.
Lobschat, Lara/ Mueller, Benjamin/ Eggers, Felix/ et al. (2021): Corporate Digital Responsibility, in: Journal of Business Research 122, pp. 875-888.
Logg, Jennifer M/ Minson, Julia A/ Moore, Don A (2019): Algorithm Appreciation: People Prefer Algorithmic to Human Judgment, in: Organizational Behavior and Human Decision Processes 151, pp. 90-103.
Martin, Kirsten (2019): Ethical Implications and Accountability of Algorithms, in: Journal of Business Ethics 160 (4), pp. 835-850.
Mittelstadt, Brent Daniel/ Allo, Patrick/ Taddeo, Mariarosaria/ et al. (2016): The Ethics of Algorithms: Mapping the Debate, in: Big Data & Society 3 (2), pp. 1-21.
Newman, David T/ Fast, Nathanael J/ Harmon, Derek J (2020): When Eliminating Bias Isn’t Fair: Algorithmic Reductionism and Procedural Justice in Human Resource Decisions, in: Organizational Behavior and Human Decision Processes 160, pp. 149-167.
Parasuraman, Raja/ Sheridan, Thomas B/ Wickens, Christopher D (2000): A Model for Types and Levels of Human Interaction with Automation, in: IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans 30 (3), pp. 286-297.
Rahwan, Iyad/ Cebrian, Manuel/ Obradovich, Nick/ et al. (2019): Machine Behaviour, in: Nature 568 (7753), pp. 477-486.
Tursunbayeva, Aizhan/ Pagliari, Claudia/ Di Lauro, Stefano/ et al. (2021): The Ethics of People Analytics: Risks, Opportunities and Recommendations, in: Personnel Review.
Yoo, Youngjin/ Henfridsson, Ola/ Lyytinen, Kalle (2010): Research Commentary—the New Organizing Logic of Digital Innovation: An Agenda for Information Systems Research, in: Information systems research 21 (4), pp. 724-735.
Download PDF here.