From Managing People to Managing Interfaces between Humans and Technology

22.09.2022 12:00 – 13:00

MANAGEMENT BROWN BAG SEMINAR / SHORT ABSTRACT

There is a potential for a paradigm shift that we have rarely discussed in human resource management (HRM) research. It relates to the very definition of HRM as a field of inquiry into the processes of “developing, applying and evaluating policies, procedures, methods and programs relating to the individual in the organization” (Miner & Crane, 1995: 5). With the arrival of digital technologies, focusing on individuals alone may be too limited. We need to consider broadening the scope of HRM as a field of research and areas of practice to the interfaces between humans and technology.

However, when shifting from managing people to managing interfaces, HR professionals will face several traps brought about by digital technologies. For example, in response to the promise of AI and machine learning, many organizations revamped their talent strategies and refocused on hiring data scientists. This created and then accelerated a talent gap for data scientists, resulting in hundreds of unfilled positions and difficulties in competing with tech start-ups for talent. However, this gap is only temporary, as advances in machine learning mean that the tasks handled by data scientists will become automated. What remains relevant in the era of automated machine learning are competencies related to understanding the business challenges, translating those challenges into mathematical terms, and deploying the results of the new models into existing business processes. This will require a very different approach to talent management and leadership development.

Another trap may be associated with the fact that the interactions between humans and technology are based on the assumptions introduced by humans. Those assumptions may be incomplete, biased, or not evident, and very often lack contextualization. One way of dealing with this trap is to use own data and invest in building highly contextual in-house HR algorithms. However, analytics poses major challenges for HR, including challenges associated with data quality and data integration. HR practitioners generally lack key analytical competencies, ranging from the ability to ask business-relevant research questions to the ability to build and run analytical models. The function needs some big-time upskilling (Huselid and Minbaeva, 2019). For HR, managing interfaces between humans and technology requires becoming data literate, acquiring data-analytics competencies, becoming “consumers of analytics,” and acting as “boundary spanners” and change agents when implementing the results of analytics projects (Minbaeva, 2017, 2018).

The presentation will review the dominant theoretical frameworks in strategic HRM conducive for analyzing challenges of managing interfaces between humans and technology. It will present several work-in-progress empirical studies to illustrate the application of these frameworks. The presentation will also refer to the previously published paper: Minbaeva, D. (2021) Disrupted HR? Human Resource Management Review https://doi.org/10.1016/j.hrmr.2020.100820

Lieu

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