Data Visualizations and Human Resource Management: The State of Science and Practice

Abstract

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

Citation

Caughlin, D.E. and Bauer, T.N. (2019), "Data Visualizations and Human Resource Management: The State of Science and Practice", Buckley, M.R., Wheeler, A.R., Baur, J.E. and Halbesleben, J.R.B. (Ed.) Research in Personnel and Human Resources Management (Research in Personnel and Human Resources Management, Vol. 37), Emerald Publishing Limited, Leeds, pp. 89-132. https://doi.org/10.1108/S0742-730120190000037004