Human Resource Analytics Research Articles

Impact of Human Capital Management on Organizational Performance With the Mediation Effect of Human Resource Analytics

Purpose: The main objective of the study is to examine the relationship between Human Capital Management, Human resource analytics and Organizational performance through systematic literature survey Theoretical Framework: The authors developed a conceptual framework to examine the relationship between the components of the Human Capital Management (HCM ) and its effect on organizational performance with the mediation of HR analytics Design/methodology/approaches: The study is based on extensive systematic literature review collected from previous studies Findings: The systematic review validated the proposed conceptual model and found that HR analytics help organizations track their human capital management and improves organizational performance Research practical social implication: This study makes significant contributions to the existing body of knowledge of Human capital management and application of innovative tool of HR analytics .There are several implications for practitioners based on the findings Originality/value: The study is more relevant and practically applied to the organization who are in the nurturing stage of implementing HR analytics towards Human Capital Management for increasing organizational performance

Employees' adoption of HR analytics – a theoretical framework based on career construction theory

PurposeDrawing upon the career construction theory, the present study aims to propose and test a mediation model exploring the association among technology readiness, adoption of human resource (HR) analytics by HR professionals, and organizational career growth.Design/methodology/approachA survey has been conducted to collect data from HR professionals (N = 347) working in various industrial sectors in India. The data collected is analyzed for mediation using SPSS PROCESS Macro (Model 4).FindingsThe study provided evidence about the mediating role of individual adoption of HR analytics between technology readiness (motivators, inhibitors) and organization career growth (career goal progress, professional ability development, promotion speed and remuneration growth).Research limitations/implicationsFirstly, the results provide evidence in relation to the career construction theory with respect to adoption of human resource analytics by HR professionals. Secondly, the study findings validate the technology readiness model in the context of adopting HR analytics. Thirdly and most significantly, the study proposes a novel theoretical framework for adoption of HR analytics by HR professionals in organizations.Practical implicationsThe findings imply that HR professionals' technology readiness (motivators and inhibitors) can facilitate better adoption of HR analytics in organizations. Moreover, the adoption of HR analytics shall promote better career growth for HR professionals.Originality/valueThe present study builds and tests a theoretical framework based on technology readiness, individual adoption of HR analytics, and organization career growth. The study is the first of its kind to the best of the authors' knowledge.

Human Resources Analytics: Leveraging Human Resources for Enhancing Business Performance

Human resources (HR) analytics enables managers to replace decision-making based on anecdotal experience, hierarchy, and risk avoidance with higher-quality data-driven decisions based on data analysis, prediction, and experimental research. Human Resources analytics underscores the value of HR data by emphasizing how people create value for the organization, so that value can be captured and leveraged. The research discusses transition of HR analytics with a linear three-stage maturity model and explains progression from traditional HR analytics to new age HR analytics. Research provides various key requirements for successful deployment of HR analytics in the organizations. Human Resources analytics focus on various HRM activities including selection, appraisal, compensation, rewards, and development to maximize their impact on organizational performance. Research also provides various illustrations of HR analytics deployment across various industries.

Technology Adoption and Human Resource Management Practices: The Use of Artificial Intelligence for Recruitment in Bangladesh

Artificial intelligence (AI) is now considered indispensable in undertaking operational activities, especially in the area of human resource analytics. However, in practice, the rate of the adoption of such modern algorithms in organisations is still in its early stages. Consequently, the primary objective of this study is to identify the main antecedents of the adoption of AI-based technologies in recruitment, using the lens of the unified theory of acceptance and use of technology (UTAUT) model, alongside perceived credibility and moderating variables, in the context of an emerging nation in South Asia, namely Bangladesh. Data were collected from 283 human resource professionals employed in different manufacturing and service firms in Bangladesh through the administration of a questionnaire, which was analysed by applying PLS-SEM. The outcomes of the study show that all the direct hypothesised relationships were found to be significant, apart from the extended variable of perceived credibility. However, no moderating effect of gender or firm size was found in any of the hypothesised propositions. Finally, policy implications and recommendations for future researchers are proposed.

Identification of human resource analytics using machine learning algorithms

Employee attrition is one of the most significant business issues in human resource (HR) analytics. This research aims to identify the most critical elements that contribute to employee attrition. Businesses operate heavily on employee training in order to maximize the returns they will offer to the company in the future. By utilizing the employee information value concept, it has been discovered that employee features such as overtime, the total number of projects and job level have a significant impact on attrition. To find the probability of new employee attrition, various classification algorithms such as decision trees (DT) classifier, logistic regression (LR), random forests (RF), and K-means clustering are used. A comparative analysis of the models with different rating scales is carried out for the highest accuracy. For prediction, four diverse machine learning (ML) algorithms such as LR, RF, DT classifier, and k-nearest neighbors (k-NN) are used. DT classifier outperforms with 97% of accuracy than other techniques. The effects of predictive ML techniques on the employee dataset show that RF evaluation outperforms other ML techniques followed by model of LR for the specific dataset if precision is the preferred metric. Identification of HR is forecasted using ML algorithms on employee data.

Designing Strategies for Reducing Employee Turnover: Human Resource Analytics Application

This paper aims to discuss the application of human resource analytics in designing Human Resource strategies, specifically employee retention. It explains the framework of HR analytics application in developing an employee retention strategy, which involves descriptive, diagnostic, predictive, and prescriptive analytics. The outcomes from the data analytics will lead to a data-driven solution and help an organisation come up with the most suitable intervention to increase employee retention.

Influence of performance expectancy on individual adoption levels of human resource analytics among human resource professionals in microfinance institutions in Kenya

Performance expectancy is when individuals believe an innovation supports their performance. Myriad challenges facing organizations have forced them to innovate to remain competitive. Human resource analytics is one such innovation which measures human resource functions for purposeful alignment with the organizations’ objectives. Earlier studies showed that 71% of respondents regarded human resource analytics as key. However, other studies found out that the uptake of human resource analytics was slow. The purpose of this study was to establish the influence of performance expectancy on individual adoption levels of human resource analytics among human resource professionals in Microfinance institutions in Kenya. The study was conducted in registered deposit taking Microfinance Institutions in Kenya. A cross-sectional survey was done on 500 human resource professionals. Stratified random and purposive sampling methods were used, obtaining a sample of 222 respondents. Questionnaires were used to collect data. The findings revealed that using human resource analytics improves job efficiency, making human resource professionals strategic. The study concludes there is a significant positive correlation (R = 0.754, p = 0.00) between performance expectancy and individual adoption levels of human resource analytics and that performance expectancy has positive significant effect on adoption levels (β = 0.855, p = 0.000). The study recommends that organizations align data analytical tools with management systems and also train its employees to increase the perceived usefulness of human resource analytics hence aiding with adoption.

Modelling Concept: Human Resources Professional Transformation in Manufacturing Company

This concept aims to meet the theoretical needs in answering the problem for the roles of the human resource management function in a manufacturing company which involve the HR Division Line Manager. The proposed update is an evolutionary concept of work in human resource management called “Human Resource Professional Transformation”. Specifically, this concept discusses the ability of the HR division line managers to make adaptive changes to the company’s business-oriented functional divisions with managerial competence, commitment, innovation capability, and readiness for changes. This concept was born by synthesizing various search related scientific literature. Based on the theoretical study, it can be concluded that the concept of human resources professional transformation has the potential to improve the performance of line managers in the HR division. Besides this, human resources professional transformation answers the role of the human resource function which integrates the theoretical basis of Human Resource Analytics, Human Resources Professional and Human Resource Transformation as a competitive advantage strategy in facing competition and promoting company success.
 Keywords: Managerial Competence, Commitment, Innovation Capability, Readiness

Moderating role of resistance to change in the actual adoption of HR analytics in the Indian banking and financial services industry

PurposeThis study aimed to evaluate the factors that determine an individual's decision to adopt human resources (HR) analytics. This study attempts to extend Unified Theory of Acceptance and Use of Technology - 2 (UTAUT2) to identify the lag rate in adoption.Design/methodology/approachResponses were obtained from 387 HR employees of the Banking Financial Services and Insurance (BFSI) sector in metropolitan cities of India through nonprobabilistic purposive sampling. The analysis was performed through hierarchical regression, structural equation modeling and moderation of resistance to change.FindingsThe results suggest that performance expectancy, hedonic motivation and data availability are endorsed by proponents of the intention to adopt HR analytics. In contrast, effort expectancy, social influence, quantitative self-efficacy and habits did not influence behavioral intention (BI). Additionally, the actual use behavior (UB) of HR analytics was determined by BI and facilitating conditions. Furthermore, the moderating effect of resistance to change is explored.Practical implicationsThis study makes a significant contribution to the literature on the adoption of HR analytics. By appropriately concentrating on the adoption intention of HR analytics, organizations can intensify healthy employee relationships, thus encouraging the actual usage of HR analytics.Originality/valueThis study formulates a conceptual framework for the adoption of HR analytics that can be used by top management to formulate strategies for the implementation of HR analytics. Moreover, this study aimed to expand UTAUT2, emphasizing the concept of data availability and quantitative self-efficacy and examining the moderating role of resistance to change in the relationship between BI and UB.

HR analytics: A review, synthesis and research agenda

There is a rapid transformation happening in the corporate world where high end technologies like artificial intelligence, machine learning, robotic process automation, internet-of-things and big data analytics is being used to increase the proficiency and reduce the dependency on human interventions. The pandemic has also enforced the usage of automated systems in business functions. In the domain of human resource management (HRM), human resource analytics (HRA) is being extensively used to make unbiased and precise decisions using data analysis. However, this phenomenon is new and not much research work has been done on this topic. Our research work does a literature review and synthesis of the research work done in this domain to synthesize the findings of prior research, identify the constructs and variables used, find the research gaps and create a guide map for future research studies. For our research work, we have accessed research articles that are indexed in Scopus and analyzed the same using the VOS viewer software. Our research work will help the future research scholars to focus on specific areas of HRA that needs attention and exhaustive study to address issues that are creating challenges in the adoption of HRA in human resource department.

HR ANALYTICS: A Conceptual Study on Decision Making

Every organization is struggling to stay competitive since the pandemic has forced everyone into difficult situations. The only way to cope with this situation is to enhance development and advancement in human resources. HR Analytics, a critical tool for creating, analyzing, and storing a vast amount of employee data to make efficient decisions, played a significant role. Human resource analytics helps understand how the investments in human capital assets can contribute to the depreciation of expenses, reducing risk, generating revenues, and executing the strategic plan. In this paper the prime focus is on studying the existing literature on HR analytics and how it helps in making predictive decision-making. The paper also explores the periodic developments done in Human resource analytics as it is having a very crucial role in effective decision–making which gradually result in the success of an organization. Keywords: HR Analytics, HR information system, Human Resource Management, big data, decision making.

Exploring the phenomenon of HR analytics: a study of challenges, risks and impacts in 40 large companies

PurposeThis paper aims to respond to questions being raised about the challenges, risks and impacts of Human Resource Analytics (HRA). Based on a study of 40 companies, it discusses the implications of HRA for practitioners, praxis and practices in HRM and adds to this a concern with whether HRA is enhancing mutuality in employment relationships.Design/methodology/approachUsing an abductive approach, the authors analyzed data from semistructured interviews with an HRA leader or specialist in 40 large organizations.FindingsWhile wrestling with the challenge of data quality and integration, the practice of descriptive analytics is widely adopted in these companies and the majority can demonstrate improvement in the efficiency and effectiveness of HR activities through predictive analytics. The analytical competence of HR specialists is an ongoing issue. While much more research is needed, the companies in the authors’ sample demonstrated some valuable examples of mutual gains from HRA.Practical implicationsEducation in HRA must not only help to raise quantitative competencies among HR specialists but should also help them to ask critical questions about the theoretical propositions and subjective data points being built into HRA. Boundary spanning is important to enable effective HRA and processes for employee voice to be improved. Arguably, the time has come for a more formalized data analytics' strategy in large organizations.Originality/valueThis paper provides evidence on how HRA is being implemented in large companies, including how HRA leaders are managing its challenges and risks and the impacts it is having on business and employee outcomes.

BIBLIOMETRIC ANALYSIS OF HR ANALYTICS LITERATURE

Human resource analytics (HR analytics) research has been popular in recent years and is a newly emerging research area. Seeing in which frame the work done in this field is progressing will shed light on new future studies in the field. This study examines how HR analytics work is built on the basis of the intellectual framework. This research aims to contribute to the literature by examining the references, authors, topics, citations and journals of the studies. For this purpose, bibliometric techniques were used to examine 178 articles published between 2010 and 2021. A wide variety of disciplines have been used in the journals that publish these articles to address the issues of HR analytics. Main themes gathered in the articles are around the concepts of big data, talent management and workforce analytics. The study results show that research interest in HR analytics has increased in recent years. While the competencies of HR professionals, data quality, technological developments, cooperation with the IT department are the main topics, the literature seems to neglect the issue of ethics.

The role of the capability, opportunity, and motivation of firms for using human resource analytics to monitor employee performance: A multi‐level analysis of the organisational, market, and country context

AbstractThe digitalisation of business processes has led to the availability of (big) data which increasingly allows firms to analyse their workforce using HR analytics. On the basis of a cross‐national multi‐level analysis and a data set that covers more than 20,000 firms in all member states of the European Union we investigate the reasons why some firms make use of human resource (HR) analytics to monitor employee performance while others refrain from doing so. We show that the use of HR analytics depends upon firm characteristics as well as contextual factors. In terms of firm characteristics, we find that firms require the structural and managerial capability to make use of HR analytics. For contextual factors, our findings show that some market factors motivate firms to make use of HR analytics while the institutional, that is, juridico‐political, and cultural environment in which firms are embedded influences firms' opportunities to use HR analytics.

(How) do advanced data and analyses enable HR analytics success? A neo-configurational analysis

PurposeEnabled by increased (“big”) data stocks and advanced (“machine learning”) analyses, the concept of human resource analytics (HRA) is expected to systematically improve decisions in human resource management (HRM). Since so far empirical evidence on this is, however, lacking, the authors' study examines which combinations of data and analyses are employed and which combinations deliver on the promise of improved decision quality.Design/methodology/approachTheoretically, the paper employs a neo-configurational approach for founding and conceptualizing HRA. Methodically, based on a sample of German organizations, two varieties (crisp set and multi-value) of qualitative comparative analysis (QCA) are employed to identify combinations of data and analyses sufficient and necessary for HRA success.FindingsThe authors' study identifies existing configurations of data and analyses in HRM and uncovers which of these configurations cause improved decision quality. By evidencing that and which combinations of data and analyses conjuncturally cause decision quality, the authors' study provides a first confirmation of HRA success.Research limitations/implicationsMajor limitations refer to the cross-sectional and national sample and the usage of subjective measures. Major implications are the suitability of neo-configurational approaches for future research on HRA, while deeper conceptualizing and researching both the characteristics and outcomes of HRA constitutes a core future task.Originality/valueThe authors' paper employs an innovative theoretical-methodical approach to explain and analyze conditions that conjuncturally cause decision quality therewith offering much needed empirical evidence on HRA success.

Estimating Successful Internal Mobility: A Comparison Between Structural Equation Models and Machine Learning Algorithms

Internal mobility often depends on predicting future job satisfaction, for such employees subject to internal mobility programs. In this study, we compared the predictive power of different classes of models, i.e., (i) traditional Structural Equation Modeling (SEM), with two families of Machine Learning algorithms: (ii) regressors, specifically least absolute shrinkage and selection operator (Lasso) for feature selection and (iii) classifiers, specifically Bagging meta-model with the k-nearest neighbors algorithm (k-NN) as a base estimator. Our aim is to investigate which method better predicts job satisfaction for 348 employees (with operational duties) and 35 supervisors in the training set, and 79 employees in the test set, all subject to internal mobility programs in a large Italian banking group. Results showed average predictive power for SEM and Bagging k-NN (accuracy between 61 and 66%; F1 scores between 0.51 and 0.73). Both SEM and Lasso algorithms highlighted the predictive power of resistance to change and orientation to relation in all models, together with other personality and motivation variables in different models. Theoretical implications are discussed for using these variables in predicting successful job relocation in internal mobility programs. Moreover, these results showed how crucial it is to compare methods coming from different research traditions in predictive Human Resources analytics.

A human resources analytics and machine-learning examination of turnover: implications for theory and practice

PurposeWhat do antecedents of turnover tell us when examined using human resources (HR) analytics and machine-learning tools, and what are the respective theoretical and practical implications? Although the turnover literature is expansive, empirical evidence on turnover antecedents studied using data science tools remains limited.Design/methodology/approachTo help reinvigorate research in this field, the authors propose a novel examination of turnover antecedents—competencies, commitment, trust and cultural values—using big data tools to develop a granular, case-dependent measure of turnover.FindingsUsing archival data from 700,000 employees of a large organization collected over a period of ten years, the authors find that turnover is generally associated with varying levels of these antecedents. However, in more fine-grained analysis, their relation to turnover is contingent upon role, person and cultural background.Originality/valueThe authors discuss the implications on turnover and strategic HR research and the potential of Artificial Intelligence and machine-learning methods in the design and implementation of managerial and HR planning initiatives.

Bridging the gap: why, how and when HR analytics can impact organizational performance

PurposeDespite the growth and adoption of human resource (HR) analytics, it remains unknown whether HR analytics can impact organizational performance. As such, this study aims to address this important issue by understanding why, how and when HR analytics leads to increased organizational performance and uncover the mechanisms through which this increased performance occurs.Design/methodology/approachUsing data collected from 155 Irish organizations, structural equation modeling was performed to test the chain mediation model linking HR technology, HR analytics, evidence-based management (EBM) and organizational performance.FindingsThe study's findings support the proposed chain model, suggesting that access to HR technology enables HR analytics which facilitates EBM, which in turn enhances organizational performance.Originality/valueThis research contributes significantly to the HR analytics and EBM literature. First, the study extends our understanding of why and how HR analytics leads to higher organizational performance. Second, the authors identify that access to HR technology enables and is an antecedent of HR analytics. Finally, empirical evidence is offered to support EBM and its impact on organizational performance.

Can HRM predict mental health crises? Using HR analytics to unpack the link between employment and suicidal thoughts and behaviors

PurposeThe aim of this research is to determine the extent to which the human resource (HR) function can screen and potentially predict suicidal employees and offer preventative mental health assistance.Design/methodology/approachDrawing from the 2019 National Survey of Drug Use and Health (N = 56,136), this paper employs multivariate binary logistic regression to model the work-related predictors of suicidal ideation, planning and attempts.FindingsThe results indicate that known periods of joblessness, the total number of sick days and absenteeism over the last 12 months are significantly associated with various suicidal outcomes while controlling for key psychosocial correlates. The results also indicate that employee assistance programs are associated with a significantly reduced likelihood of suicidal ideation. These findings are consistent with conservation of resources theory.Research limitations/implicationsThis research demonstrates preliminarily that the HR function can unobtrusively detect employee mental health crises by collecting data on key predictors.Originality/valueIn the era of COVID-19, employers have a duty of care to safeguard employee mental health. To this end, the authors offer an innovative way through which the HR function can employ predictive analytics to address mental health crises before they result in tragedy.

An exploratory bibliometric analysis of the evolution of HR analytics as a popular management concept

Human resource (HR) analytics is one of the hottest concepts in the field of HR management. In recent years, it has also started to attract the attention of academic researchers. In this paper, we carry out an exploratory bibliometric analysis to map the emerging academic field of HR analytics. By doing this, we provide more insight into the role of academic researchers in the evolution of HR analytics as a popular management concept. The bibliometric analysis draws on data extracted from the Scopus database, and analyses of the overall publication volume and growth trajectory, influential documents, authors, journals, and countries are performed. The results show that the academic discourse around HR analytics emerged relatively late and several years after the initial debate based on the practitioner-based literature. The findings are discussed in relation to the literature about the emergence and evolution of popular management concepts.