Abstract

Today’s digital economy presents HRM with new challenges related to changes in the “language” of business. In fact, firms rely ever more heavily on employees skilled in understanding both the language of business operations and that of business-related data. However, often employees do not have the combined skills of data analytics and management. With the relative ease with which data is available from digital sources and the increase in computational power, employees are often called to make data driven decisions that affect firm standard operations, functions, and eventually performance. Consequently, value creation through (big) data does not solely depend on a firm’s ability to analyze big data, but it also requires management capabilities such as leadership, training, talent management, employee upskilling and the creation of an evidence-based, data-driven culture (Mcafee et al. 2012; Rodgers et al. 2023).

۱ The process

We advertised the open call for papers through our networks and at major conferences such as the European Academy of Management annual meeting. Here in particular, we encouraged contributors to the track on “New forms of work, data analytics and big data” to consider submitting their work. Overall, we received a total of 41 submissions. Of those, 17 manuscripts (i.e., 41%) were desk rejected, primarily because they did not fit with the aims of the Special Issue. Another 12 manuscripts (i.e., 29%) were evaluated negatively and thus eventually rejected. One manuscript was withdrawn. Ultimately, we accepted 11 manuscripts (29%). Before final acceptance was granted, the handling guest editor shared the manuscript with the guest editorial team for final approval and potential further comments. In our decision making we tried to strike a balance between conceptual and empirical works, both quantitative and qualitative. We hope that our selection of manuscripts will find the interest of RMSC’s readership.

۲ The contributions

Our Special Issue starts off with three literature review articles that, to some extent, lay the foundations for the topics discussed in some of the empirical works also present in this collection. These contributions are then followed by two conceptual contributions. Next the section of empirical papers has a focus on HRM and its connection to AI and data analytics. We conclude with the topic of innovative behavior. In the following sections we briefly introduce the articles.