Due to pressures from different stakeholders aimed at alleviating societal inequities related to
pay and income, organizations are increasingly being confronted with the need to open up
about pay. For example, New York City recently became the largest U.S. city to require private
employers to disclose salary ranges in job ads, the Austrian government’s “transparency act”
mandates all employers to state the minimum salaries and overpayment options in job ads,
and employees in Germany have the right to request information on the average salaries of
coworkers in the same job category. However, recent literature has demonstrated that the
consequences/outcomes of pay transparency are complex and not universally positive, and
asked for much more research on this topic (Arnold and Fulmer, 2018; Bamberger, 2021;
Brown, Nyberg, Weller et al., 2022; Lam, Cheng, Bamberger et al., 2022; Marasi and Bennett,
2016).
In this Special Issue we aim to expand existing research by focusing attention on contributions
addressing the implications of pay information disclosure for human resource management.
We seek to stimulate the field of HRM to develop knowledge on managing pay information
disclosure that is both rigorous and relevant. We welcome contributions from different fields
and perspectives on HRM and related disciplines, and invite empirical as well as
conceptual/theoretical papers.

Contributions could address the following or similar questions – but are not limited to these:

• When are organizations more likely to disclose which kind of pay information?
• What is the role of different actors – employees, HR professionals and line managers
  – in pay disclosure processes?
• What are the outcomes of pay disclosure for employees and organizations (e.g.
  regarding satisfaction, pay dispersion, performance)?
• When are positive or negative outcomes of pay disclosure on the employee, team
  and organizational level more likely?
• How are changes in pay disclosure managed by HR professionals or managers, and
  how do employees respond to pay disclosure in the short and/or long-term?

Guest editors

• David G. Allen is the Luther A. Henderson University Chair in Management and Leadership at TCU (U.S.) and Distinguished Research Environment Professor at the University of Warwick (UK).
• Julia Brandl is Professor of HRM and Employment Relations at the University of Innsbruck, Austria.
• Christian Grund is Professor of HRM and Personnel Economics at RWTH Aachen University.
• Anna Sender is a Senior Researcher and Lecturer in Strategic HRM at the University of Lucerne and Lucerne University of Applied Sciences and Arts, Switzerland.

Submission Deadline: 30 September 2023.
Publication date: Issue 1/2025 (accepted manuscripts available online first).
View the complete Call here.

Research can face artificial intelligence (AI) as an issue of technology development but also as an issue of enacted technology at work. Human-centered design of AI gives emphasis to the expertise and needs of human beings as a starting point of technology development or as an outcome of AI-based work settings. This is an important goal, as expressed, for example, by the international labor organization’s call for a „human-centered agenda“ for the future of AI and automation collaboration. This Research Topic raises the question of what human-centricity means, i.e. what are the criteria and indicators of human-centered AI and how can they be considered and implemented?

The aim of this Research Topic is to open a floor for an interdisciplinary discourse elaborating on theoretical concepts and methods in use that specify the human-centricity of AI work settings. A common ground for theory development and discourse on empirical results is necessary as human-centered AI is being theorized, investigated, and developed in various disciplines. The range of disciplines is from information science, machine learning, engineering and robotics, medicine up to ergonomics/work science, psychology, sociology, philosophy, business studies, law and labor relations just to mention the core disciplines involved in the current debate. These disciplines provide different basic understandings of what human-centered AI at work exactly means. Basic assumptions are not necessarily routed in explicit theories but also result from theories in use leading to a set of methods and instruments applied in R&D projects and transferred to practice. As different perspectives co-exist and are currently not well integrated there is a need to further specify and systemize what human-centered AI in the workplace exactly means and what the underlying criteria are. Based on that, we can gain an overview, search for a common ground and distinguish between communities that move forward the discourse in parallel.

We invite a wide range of empirical and theoretical contributions that investigate:

• Work settings making use of AI
• The connection and loops between AI development and AI use fields
• Regulations for AI implementation
• Issues of decision making with and/or about AI at work

We equally appreciate theoretical or empirical work on how to specify human-centricity as a (normative) input factor, as a throughput in process descriptions and/or as an measurable output of AI-based work settings. Invited research contributions might contribute from different starting points:

1. Theoretical outlines giving emphasis to concepts for specifying the normative basis of human-centered AI at work
2. Methods, instruments, and standards in terms of theories in use of what human-centricity means
3. Empirical studies investigating the antecedents and/ or outcomes of human-centered AI use at work and mediating or moderating mechanisms;
4. Case study analysis representing practices of human-centered AI where qualitative field work allows to specify criteria in an inductive manner

Topic Editors:

• Annette Kluge (Ruhr University Bochum)
• Corinna Pfeifer (University of Lübeck)
• Uta Wilkens (Institute of Work Science, Ruhr University Bochum)
• Verena Nitsch (Institute of Industrial Engineering and Ergonomics, Faculty of Mechanical Engineering, RWTH Aachen University)

Submission Deadline for Abstract: 06 February 2023.
Submission Deadline for Manuscript: 06 July 2023.
View the complete CfP here.