With the increasing interest and use of people analytics and algorithmic management (PAAM), in recent years, this workshop aims to provide a forum for academic debate in order to advance our knowledge of this phenomenon, which involves the use of analytics to drive strategy and algorithms that can augment and/or automate HR-related decision making in areas such as recruitment and selection, training, appraisal, compensation and workforce planning. This forum will serve researchers as a grass-roots community where a greater theory-informed scholarship is developed.
Existing research on PAAM has been broadly confined to the proper boundaries of certain disciplines. On the one hand, there is a growing scholarship on this topic in management subject areas, primarily, human resource management, work and organisational psychology, organization studies and business analytics. On the other hand, research contributions in other disciplines such as sociology, law and philosophy have shed solid insights around those social implications attached to the use of analytics in people management.
The PAAM workshop is designed to bring together leading and emerging scholars working in the area of PAAM across disciplines. We therefore would like to emphasise the multidisciplinary nature of this forum and hence, we welcome contributions from a diverse array of management subject areas such as human resource management and employment relations, work and organisational psychology, business analytics, information systems, organization studies and business ethics as well as from other disciples such as sociology, law and philosophy. We believe a multi-disciplinary approach will help us advance the field in order to provide scientific evidence for an effective and socially responsible use of analytics in HR and People Management, taking into account a diverse group of stakeholders in the organisation.
The workshop has a strong developmental focus and as such encourages research which may be in progress as well as completed research.
https://www.eiasm.org/frontoffice/event_announcement.asp?event_id=1599%20.
DEADLINE:
A 3-page abstract: March 28, 2022
Notification: as of April 19, 2022
