Unlocking the Potential of HR Analytics: A Review of the Evidence

Introduction

Human Resource (HR) analytics is a hot topic in the field of human resource management. It involves using data and technology to make decisions about hiring, retention, and overall success of an organization. However, despite the growing interest in this area, there is still a lot of debate about its effectiveness. The purpose of this article is to provide a simple and relatable review of existing research on HR analytics and identify areas where more research is needed.

Background

HR analytics has been around for a long time, with the concept of measurement in human resources dating back to the early 1900s. Despite its long history, interest in HR analytics has recently surged.

Purpose

• To provide a simple and relatable review of existing research on the value of HR analytics
• To identify areas where new research is needed

Methodology

• Evidence-based review guidelines are used to improve decisions by focusing on the quality of evidence.
• Specific questions are framed, and a high-quality source of evidence is retrieved (published and peer-reviewed articles).
• Integrative synthesis is used to provide the best available evidence from multiple sources.

Current state of HR Analytics

• Only 16% of organizations report using HR analytics.
• There are very few empirical studies on the topic.

Critical evaluation of evidence

To ensure that the best evidence is used in this study, a thorough evaluation of the research was conducted. This involved separating the articles into those published in scholarly peer-reviewed journals and those published in non-peer-reviewed periodicals. The latter are considered less objective and may represent the interests of consultants or vendors.

Out of a sample of 60 articles, 32 were classified as appearing in peer-reviewed journals. These were further evaluated by whether they were published in journals on the Journal Quality List (JQL), which is a guide to identifying journals of appropriate standard for academic assessment. 16 articles were eliminated because they did not appear on the JQL, leaving 14 articles for analysis.

Categorization

The remaining 14 articles were categorized by the research question they addressed and by research approach. The five research questions guiding this study are:

• What is HR Analytics?
• How does it work?
• Why does it work?
• What are the outcomes of HR Analytics?
• What are moderating factors that affect these outcomes?

In evaluating the question of why HR Analytics works, four categories of theoretical perspectives were created to explain cause and effect relationships.

These are:

• Strategic management
• Macro-organizational behavior
• Micro-organizational behavior
• Other

The articles were also categorized by methodological approach, which includes: Empirical, Conceptual, and Case Study. Empirical research involves collecting data and analyzing it to draw conclusions. Conceptual research focuses on understanding and explaining a concept, while case studies involve in-depth analysis of a specific situation or organization.

This thorough evaluation of the research ensures that the conclusions drawn from the study are reliable and that the best evidence is used to answer the key questions guiding this study.

1.What is HR Analytics??

HR Analytics, also known as Human Resource Analytics, is a relatively new concept in the field of human resource management. It first appeared in published literature in the early 2000s. HR Analytics is defined as the use of descriptive, visual, and statistical analyses of data related to HR processes, human capital, organizational performance, and external economic benchmarks to establish business impact and enable data-driven decision-making. It is different from HR metrics, which are measures of key HR outcomes such as efficiency, effectiveness, or impact.

HR Analytics involves the use of more sophisticated analysis techniques and technologies to collect, manipulate, and report data. It also involves integrating data from different internal functions and external sources to make better decisions on the people-side of the business. It is also linked to strategic HRM and aims to connect HR processes and decisions to organizational performance, thus elevating the role of HR in an organization.

2. How does HR Analytics work?

HR Analytics works by using a combination of different models and processes to collect, analyze, and interpret data related to HR processes, human capital, organizational performance, and external economic benchmarks. This data is then used to make evidence-based decisions on the people-side of the business.

One of the most commonly used models in HR Analytics is the LAMP model, which stands for logic, analytics, measures, and processes. This model, first introduced in the book Beyond HR: The New Science of Human Capital, argues that these four elements are critical in understanding the cause-effect relationship between HRM processes and strategic HRM and business outcomes. It also suggests that these four components are necessary to uncover evidence-based relationships and motivate enhanced decisions based on those analyses.

Another model used in HR Analytics is the HR Scorecard, which links HRM processes and people to business outcomes. This model is described in detail in the book The HR Scorecard: Linking People, Strategy and Performance. This model helps organizations in understanding their performance and making data-driven decisions.

Additionally, theories of innovation, social influence, and cognition can be used to guide and explain the cause-effect relationships between HR Analytics antecedents, outcomes, and moderators. Industrial psychology has some history addressing this question with regard to the adoption of 'utility analysis' in the 1970s and 1980s.

3. Why does HR Analytics work?

In summary, the study found that HR Analytics works because it allows for the combination of pay for performance compensation, Human Capital Management software, and HR Analytics to align incentives and monitor employee behavior, resulting in higher productivity. The study also suggests that HR Analytics alone may not enhance productivity, but when combined with other strategies, it can be a valuable tool for organizations. Theoretical frameworks such as agency theory, strategic management theories and the Resource Based View were found to be consistent with the results of the study. It is also suggested that the LAMP model, which focuses on the four critical components of a measurement system, may be a key to understanding the cause-effect relationship between HRM processes and strategic HRM and business outcomes.

4. What are the outcomes of HR Analytics??

The outcomes of HR Analytics include improved financial performance, increased employee engagement, and higher customer satisfaction and revenue. However, despite the early evidence supporting a causal link between HR Analytics and business outcomes, there is a low level of HR Analytics diffusion across companies. Studies show that only a small percentage of firms use HR Analytics to determine or implement HR strategy, and many companies only use it to analyze employee survey data. There is a disconnection between the persuasive evidence of positive business impact and decisions to adopt and implement effective HR Analytics. The reasons for this are not fully understood but further research may provide possible explanations.

5. What moderating factors affect HR Analytics outcomes??

HR Analytics outcomes can be affected by several moderating factors. These include having HR professional analytical skills, gaining managerial buy-in, and having HR information technology. The shortage of analytically skilled HR professionals is often cited as the primary reason why HR Analytics is not more widely adopted. The lack of analytical skill not only impedes the uptake of HR Analytics within companies but also raises concerns that HR Analytics, if adopted, will not be controlled by HR professionals but by others who may misinterpret, or mis-specify the analyses. Additionally, having the necessary IT acumen and financial skills is important for HR professionals to effectively use analytic software tools and access and use measures of business results. Gaining managerial buy-in is also crucial for HR Analytics success as it allows HR professionals to access cross-functional data needed for their analyses. Finally, having HR information technology is necessary for collecting, manipulating, and reporting data. The importance of these moderating factors highlights the need for organizations to invest in developing analytical skills and technology within their HR departments and for HR professionals to work closely with other functions to gain support and access to necessary data.

Discussion and future research

The review of the literature on HR Analytics shows that there is a lack of scientific evidence to support the adoption of HR Analytics. Of the 14 articles reviewed, only 4 involved empirical analyses and only one of these provided evidence linking HR Analytics to company performance. The remaining 10 studies provided little to no evidence supporting their conclusions.

A striking finding was the lack of scholarly articles on HR Analytics and the even smaller number of empirical studies in the field. This suggests that the topic of HR Analytics has not yet caught the attention of the management research community. The low number of articles also suggests that management scholars have shown little interest in examining the antecedents and consequences of HR Analytics.

A major conclusion that emerges from this literature review is the need for more scientific research on HR Analytics. Future research should draw upon and expand on the study of Aral et al. (2012) which approached a well-accepted issue in the HR field and examined the additional effects of HRM information technology and HR Analytics. Other research topics could include the relationship between specific HR practices and business outcomes, the impact of HR Analytics on employee engagement and satisfaction, and the role of technology in HR Analytics.

Conclusion

In conclusion, while HR Analytics has the potential to improve decision-making and organizational performance, there is a lack of scientific evidence to support its adoption. More research is needed in order to fully understand the potential benefits and limitations of HR Analytics.

Reference: An evidence-based review of HR Analytics by Janet H. Marler & John W. Boudreau

Note: This article is based on the original research article cited above published in The International Journal of Human Resource Management. The article is written as a part of the study assignment with the intent of simplifying the findings of the research for the understanding of students and HR practitioners.