麻豆传媒AV

Supervisor: Dr Monica Franco-Santos, Reader in Organizational Governance and Performance

Background

The use of algorithms in artificial intelligence (AI) technologies permeates our society (Alaimo & Kallinikos, 2021; Gritsenko & Wood, 2022; Vassilopoulou, Kyriakidou, Özbilgin, & Groutsis, 2022). These devices are gradually transforming the internal governance mechanisms of organisations (Kellogg, Valentine, & Christin, 2020)–i.e., the formal and informal mechanisms managers use to facilitate the delivery of organisational goals. Algorithms have been defined as computer-programmed procedures that transform input data (“big data” or otherwise) into an outcome using a predetermined formula (Gillespie, 2014).

Their use is promoted to benefit learning, decision-making and the selection, allocation, and coordination of complex tasks (Jarrahi et al., 2021; Parent-Rocheleau & Parker, 2022; Van den Broek, Sergeeva, & Huysman, 2021; Waldkirch, Bucher, Schou, & Grünwald, 2021). As a result, more and more algorithms are being developed to enhance and modernise management practices. However, we still know little about how algorithms work and their consequences within and beyond organisations.

Possible Research Areas

We are interested in supervising keen and passionate individuals—with backgrounds in business or organisation studies, sociology, economics, law or psychology—who wish to understand phenomena related to:

• Internal governance with algorithms.
• The use of algorithms in performance management practices.
• The use of algorithms in rewards-related practices (base pay increases, bonuses/incentives).
• The intended and unintended consequences of using algorithms to influence people’s decision-making and behaviour.

Suggested Reading

Alaimo, C., & Kallinikos, J. (2021). Managing by Data: Algorithmic Categories and Organizing. Organization Studies, 42(9), 1385–1407.

Gillespie, T. (2014). The relevance of algorithms. In Media technologies: Essays on communication, materiality, and society (p. 167). Cambridge, MA: MIT Press.

Gritsenko, D., & Wood, M. (2022). Algorithmic governance: A modes of governance approach. Regulation and Governance, 16(1), 45–62.

Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data and Society, 8(2).

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366–410.

Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), 1–17.

Van den Broek, E., Sergeeva, A., & Huysman, M. (2021). When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quarterly, 45(3), 1557–1580.

Vassilopoulou, J., Kyriakidou, O., Özbilgin, M. F., & Groutsis, D. (2022). Scientism as illusio in HR algorithms: Towards a framework for algorithmic hygiene for bias proofing. Human Resource Management Journal, (September 2021), 1–15.

Waldkirch, M., Bucher, E., Schou, P. K., & Grünwald, E. (2021). Controlled by the algorithm, coached by the crowd – how HRM activities take shape on digital work platforms in the gig economy. International Journal of Human Resource Management, 32(12), 2643–2682.

Contact

Dr Monica Franco-Santos, Email: monica.franco@cranfield.ac.uk