Human Resource Management

Publisher:: Wiley
Publication date:: 2021-02-01

ISBN:: 0090-4848

Issue Number

Latest documents

The science and practice of workforce analytics: Introduction to the HRM special issue
This special issue of Human Resource Management is focused on the latest thinking, research, and practical advances in the emerging field of Workforce Analytics. The eight diverse papers in this issue present new theoretical developments, methodological and statistical tools, and examples of innovative workforce analytics in practice. Taken as a whole, the findings show that workforce analytics can significantly enhance the ability of leaders and managers to achieve their operational and strategic objectives through more effective workforce management. But capitalizing on these opportunities will require both HR and line managers to develop a comprehensive understanding of how the workforce contributes to their firm's strategic success—and this understanding must be reflected in the workforce metrics and analytics they develop and deploy.
Expanding the methodological toolbox of HRM researchers: The added value of latent bathtub models and optimal matching analysis
Researchers frequently rely on general linear models (GLMs) to investigate the impact of human resource management (HRM) decisions. However, the structure of organizations and recent technological advancements in the measurement of HRM processes cause contemporary HR data to be hierarchical and/or longitudinal. At the same time, the growing interest in effects at different levels of analysis and over prolonged periods of time further drives the need for HRM researchers to differentiate from traditional methodology. While multilevel techniques have become more common, this article proposes two additional methods that may complement the current methodological toolbox of HRM researchers. Latent bathtub models can accurately describe the multilevel mechanisms occurring in organizations, even if the outcome resides at the higher level of analysis. Optimal matching analysis can be useful to unveil longitudinal patterns in HR data, particularly in contexts where HRM processes are measured on a continuous basis. Illustrating the methods’ applicability to research on employee engagement, this paper demonstrates that the HRM community—both research and practice—can benefit from a more diversified methodological toolbox, drawing on techniques from within and outside the direct field to improve the decision‐making process.
Issue Information
Using multi‐item psychometric scales for research and practice in human resource management
Questionnaires are a widely used research method in human resource management (HRM), and multi‐item psychometric scales are the most widely used measures in questionnaires. These scales each have multiple items to measure a construct in a reliable and valid manner. However, using this method effectively involves complex procedures that are frequently misunderstood or unknown. Although there are existing methodological texts addressing this topic, few are exhaustive and they often omit essential practical information. The current article therefore aims to provide a detailed and comprehensive guide to the use of multi‐item psychometric scales for HRM research and practice, including their structure, development, use, administration, and data preparation.
Building credible human capital analytics for organizational competitive advantage
Despite the enormous interest in human capital analytics (HCA), organizations have struggled to move from operational reporting to HCA. This is mainly the result of the inability of analytics teams to establish credible internal HCA and demonstrate its value. In this article, we stress the importance of conceptualizing HCA as an organizational capability and suggest a method for its operationalization. We argue that the development of HCA within an organization requires working with three dimensions of HCA: data quality, analytics capabilities, and strategic ability to act. Moreover, such work must be undertaken on three levels: individual, process, and structure.
Analytical abilities and the performance of HR professionals
Recent years have shown an increased focus on workforce analytics and the importance of workforce analytics in helping HR professionals to be more useful business partners. This suggests that HR professionals may need to become more and more data savvy and develop better analytical abilities if they hope to perform well and contribute meaningfully in the future. Despite this emphasis, there has been no research explicitly connecting the individual level analytical abilities of HR professionals to their job performance. Using a proprietary sample of 360 feedback surveys from 1,117 HR professionals in 449 unique organizations we test this general relationship. We also test whether the relationship varies by industry‐, company‐, and job‐level factors. We find support for our main hypotheses that HR professionals with higher analytical abilities will also have higher perceived job performance. We also find that the strength of this relationship varies by some job roles. We explore and discuss these empirical results.
Putting human capital analytics to work: Predicting and driving business success
Workforce analytics have evolved considerably in the past two decades and yet application in many organizations is scant. Some organizations, however, are reaping the benefits of using more‐sophisticated, but practical, workforce tools and measures to understand, predict, and control important business outcomes. In this article, we will share some of the work that has been done using several human capital and business frameworks, including the service‐profit chain and People Equity, to show how such frameworks can be used to understand and predict revenue, profit, customer satisfaction, and employee turnover. We also provide recommendations for how organizations can secure senior leader support that leads to actions and improvements.
Workforce analytics: A case study of scholar–practitioner collaboration
Drawing on a case study of a large multinational fashion company, we describe the process of development of a Workforce analytics initiative within the corporate HR department resulting from the collaboration of practitioners and researchers. The article elaborates on three main points: (a) how social science research methods and the competences of management researchers may act as the basis for a rigorous Workforce analytics infrastructure and support the development of such practice along time, (b) some of the key levers and limitations for the creation of a Workforce analytics initiative within a company, and (c) how this emerging practice illustrates a symbiotic relationship between academics and practitioners. After presenting the case, we close with a set of lessons learned both for practitioners and scholars in the field.
Beyond Moneyball to social capital inside and out: The value of differentiated workforce experience ties to performance
The differential impact of social capital among employees in strategic and support roles has received far less attention than that of human capital in talent management literature. Building on network closure theory and differentiated workforce theory, we examine the effect of strategic and support teams’ experience ties on team performance while controlling for human capital using current Moneyball‐inspired metrics for workforce quality. Using an 111‐year longitudinal data set of 15,837 Major League Baseball players from all 30 teams and 3,475,778 experience ties, we find that after accounting for the effect of team quality, managerial stability and reputation, and era effects, organizational experience ties and subsequent team performance have an inverted U‐shaped relationship for strategic roles and a U‐shaped relationship for support roles. Competitor experience ties have an inverted U‐shaped relationship on performance for strategic roles, yet the hypothesized U‐shaped relationship showed differences for different competency areas among support roles. This study highlights the value of social capital to team performance and the importance of differentiating human resource management (HRM) practices for strategic and support roles in 20 different competency areas. It also showcases how workforce analytics with big data can be applied to HRM and have value added impact on workforce and firm strategy execution.
Using workforce analytics to improve strategy execution
In this article, I introduce an approach to conducting workforce analytics that is designed to improve strategy execution and organizational effectiveness through the application of systems diagnostics. What differentiates the approach are two analytic steps that precede the analyses that are typical of workforce analytics today: competitive advantage analytics and enterprise analytics. Conducting these two additional steps enables the analyst to identify the critical business issues that are the biggest problems for senior business leaders, and to determine if structural issues coming from the organization design and culture are at play. First conducting those analyses best enables traditional workforce analytics to provide insights the organization's leadership views as truly valuable.