Human Resource Analytics Research Articles

Privacy-Preserving Analytics in HR Tech- Federated Learning and Differential Privacy Techniques for Sensitive Data

This paper explores the application of privacy-preserving analytics in human resources (HR), focusing on the synergistic use of federated learning and differential privacy. As HR departments increasingly leverage data-driven insights, the protection of sensitive employee information becomes paramount. Federated learning enables collaborative model training without centralizing raw data, while differential privacy adds calibrated noise to ensure individual data remains indiscernible. Together, these techniques form a robust framework for safeguarding HR data while enabling advanced analytics. The paper discusses the challenges of handling sensitive HR information, examines the implementation of federated learning and differential privacy, and demonstrates their combined effectiveness in maintaining data utility while ensuring privacy. By adopting these approaches, organizations can derive valuable workforce insights, comply with data protection regulations, and foster employee trust. This research contributes to the growing field of ethical data use in HR, offering a blueprint for balancing analytical capabilities with privacy imperatives in the modern workplace. Keywords—Privacy-preserving analytics, Federated learning, Differential privacy, HR analytics, Data protection, Employee privacy, Decentralized learning, GDPR compliance, CCPA compliance, Sensitive data handling, Data-driven HR, Privacy-utility trade-off.

Designing employee benefits to optimize turnover: A prescriptive analytics approach

Employee turnover significantly impacts organizations, particularly those with substantial investments in training their workforce. To mitigate these effects, we propose a Prescriptive Human Resources Analytics approach that optimizes employee benefits to minimize total costs, focusing on turnover management The methodology models employee decision-making using a discrete choice model, with parameters estimated through maximum likelihood. We solve the resulting nonlinear optimization problem with a heuristic tailored to the problem’s complexity. We applied this methodology to a hospital case study, which was used to enhance the transportation system as an employee benefit, considering the associated turnover costs. The results demonstrate that our approach can reduce total costs, optimize the usage level of the designed benefits, and increase employee satisfaction. This research provides a robust framework for data-driven decision-making in HR, offering practical tools for improving employee retention strategies.

Transforming HR through Analytics: A Study of Performance Improvement Initiatives Driven by Data

ABSTRACT This study examines the transformative impact of facts analytics on Human Resources (HR) practices, especially focusing on overall performance development tasks. As agencies more and more apprehend the value of data-pushed choice-making, HR analytics has emerged as a strategic tool for reinforcing talent control, worker engagement, and ordinary organizational overall performance. By studying case research and empirical data, this studies identifies the important thing blessings of HR analytics, such as stepped forward recruitment processes, better employee retention, and heightened engagement ranges. However, the take a look at also addresses the challenges related to implementing analytics in HR, together with facts nice, change management, and talent gaps. Ultimately, the findings spotlight the need for HR professionals to include a facts-driven culture and invest in analytics abilities to reap sustained performance upgrades. This paper serves as a manual for companies in search of to leverage analytics for more suitable HR effects and strategic alignment.

A Human Resource Evaluation and Recommendation System based on Big Data Mining

This investigation presents a paradigm-shifting Human Resource (HR) Assessment and Recommendation Framework, leveraging progressed huge information mining procedures. Drawing bits of knowledge from cybersecurity, healthcare, education, and further detecting spaces, our framework utilizes a different cluster of calculations, counting Arbitrary Forests, Support Vector Machines, K-Means Clustering, and a Feedforward Neural Organize. The comparative investigation uncovers the Feedforward Neural Network as the standout entertainer, emphasizing its various levels including learning for complicated design acknowledgement inside HR measurements. Uniquely, this framework draws on methodologies and insights from varied domains such as cybersecurity, healthcare, and education, applying these rich, interdisciplinary perspectives to HR analytics. This cross-pollination of ideas enables the framework to adopt sophisticated data mining and pattern recognition techniques that are not traditionally utilized within HR, offering new avenues for detecting and interpreting complex employee data patterns. Result values illustrate the system’s adequacy, with a precision of 88%, an accuracy of 90%, a review of 87%, and an F1 score of 88%. These measurements emphasize the system’s capacity to comprehensively assess worker execution, giving exact suggestions for key HR decision-making. Ethical contemplations, innovation acknowledgement, and custom-fitted proposal frameworks, propelled by related works, are coordinates to guarantee the system’s reasonability over assorted organizational settings. This research contributes to the advancing scene of HR administration, offering a spearheading arrangement for organizations looking for data-driven, comprehensive, and morally sound approaches to workforce optimization.

Designing the Model of Human Resources Analytics in Health System with Grounded Theory

Background: Given the necessity of attracting, retaining, and developing human capital within the health system, the process of human resources analytics in this context is of paramount importance, and it plays a significant role in evidence-based decision-making and policy formulation. Therefore, the aim of the present study was to design a model for human resources analytics in the health system. Methods: This research is practical in nature and utilized a qualitative approach based on grounded theory. In 2023, semi-structured interviews were conducted with 14 experts and managers from Kerman University of Medical Sciences, using purposive sampling until theoretical saturation was achieved. Throughout the process, interviews were continuously compared, and notes were taken during and after the interviews. The collected data from each interview were then analyzed using open, axial, and selective coding with the assistance of MaxQDA20 software. Through categorizing the open codes, axial categories were identified and in the final step, relationships among core categories were established through selective coding. Additionally, the validity and reliability of the findings were assessed and confirmed based on the four criteria of credibility, dependability, confirmability and transferability. Results: In identifying the codes and categories related to human resources analytics, a total of 370 open codes, 61 subcategories, and 24 main categories were generated. These were ultimately examined across 6 main axes, including the central phenomenon, causal conditions, contextual conditions, intervening conditions, strategies and consequences. Conclusion: It is recommended that in order to enhance and develop the human resources analytics process within the health system, attention be paid to the extracted model and its dimensions and components, particularly the identified causal conditions, contextual conditions, and strategies

What influences the use of HR analytics in Human Resource management in Norwegian municipal health care services?

BackgroundThe centrality of human resources in the provision of healthcare suggests that Human Resource (HR) management and the use of Human Resource analytics – use of digital data to better understand, assess, plan and organize the workforce - can play an important role in this. However, data driven decision making in the field of human resource management is lagging, and the appropriation of HR analytics in the healthcare sector is limited.AimThe current study explores the role of HR departments and the adoption of Human Resource analytics in four municipalities in Norway to obtain insights into what influences the use or lack of use of HR analytics.MethodsEmpirical data were generated through qualitative interviews with fourteen individuals working in HR departments, the municipal administration, and the healthcare services. Structurational theory guided the analysis. The findings show that none of the municipalities made extensive use of data to inform decision making related to human resource management or workforce planning.Results and conclusionThree conditions hampered or made irrelevant the use of HR analytics: a decoupling between the services and HR, a weak data-culture, and HR and decision-making processes involving a plurality of stakeholders. However, there were changes underway in all municipalities related to the role of HR and HR analytics.

Impact of Human Resource Analytics Adoption on Organizational Agility and Operational Performance: Examining TOIE Model

Impact of Human Resource Analytics Adoption on Organizational Agility and Operational Performance: Examining TOIE Model

Leveraging Machine Learning Methods for Predicting Employee Turnover Within the Framework of Human Resources Analytics

Employee turnover is a critical challenge for organizations, leading to significant costs and disruptions. This study aims to leverage Machine Learning (ML) techniques within the framework of Human Resources Analytics (HRA) to predict employee turnover effectively. The research evaluates and compares the performance of six widely used models: Decision Trees, Support Vector Machines (SVM), Logistic Regression, Random Forest, XGBoost, and Artificial Neural Networks. These models were implemented using the R programming language on an open-source dataset from IBM. The methodology involved data preprocessing, splitting into training, validation and testing sets, model training, and performance evaluation using metrics such as accuracy, sensitivity, specificity, precision, F1-score, and ROC-AUC. The results indicate that the Logistic Regression model outperformed the other models, achieving high accuracy and a good F1-score. The study concludes by emphasizing the importance of HRA and ML techniques in predicting and managing employee turnover, while discussing limitations such as class imbalance and the need for more rigorous performance evaluation. Future research directions include exploring alternative models, feature selection techniques, and addressing class imbalance.

Exploring the Role of HR Analytics in Enhancing Talent Acquisition Strategies

Hiring and selecting the right candidate is a critical business process in today’s cutthroat competition. HR Analytics, also known as Human Resource Analytics, provides unique insights into talent acquisition and helps organisations to improve their strategies based on fact. This research paper aims to establish the use of the HR Analytics in improving the talent acquisition practices since this will help in identifying the different candidates sourcing, screening, selection, and onboarding processes. In doing so, the work employs the study of different techniques of HR Analytics including but not limited to the predictive analytics, data mining, and talent market analysis with the main view of understanding how different techniques can be used to give the organizations competitive advantage in talent management and attraction. The research will focus on a few real-world solutions such as the use of HR Analytics for social media talent acquisition, the use of prediction models for screening and the use of data analytics for selection. Also, the paper will discuss the problems and drawbacks that may be encountered while adopting HR Analytics in recruiting. In it, we will highlight the data quality issue, the privacy issue, and the need that HR professionals must have skills and knowledge to use the HR Analytics tools. Lastly, this research aims at contributing to the existing body of knowledge that seeks to help organizations that aim at increasing on their Strategic Human Resources Management Talent Acquisition approach based on the design and use of HR Analytics. Day by day organizations are becoming more rational and by using this technique organizations can arrive to better decisions about employer branding and, consequently, about attracting and retaining the best talent in today’s job market.

Analíticas de recursos humanos para la gestión del cambio y de la felicidad

owadays, companies are in a constant process of change, and they need to be flexible and innovative and take care of the well-being of their employees. Events such as the pandemic COVID19 have highlighted the need to consider new perspectives to address Human Resources Management. Human Resource Analytics (HRA) are tools that help to understand and implement better Human Resources policies and strategies. However, many of the literature reviews on HRA only analyse what has been published up to 2021 and, moreover, do not usually considerdifferent time periods for the identification of the issues studied, which would help to better understand the evolution of the issues. Therefore,the aim of this paper is to present a structured and period-based picture of the main Human Resource Analytics themes studied and to proposenew themes for future research. The results obtained have been grouped into the following thematic categories: context, internal aspects, tools,applications and effects. From these results, two novel themes have been identified: change management and happiness management. And twotheoretical models for the adoption of HRA have been proposed, one on decision-making and one on organisational change. These models canserve as a starting point for future research and have a direct application for decision making in companies.

Exploring the individual adoption of human resource analytics: Behavioural beliefs and the role of machine learning characteristics

The technological capabilities of Human Resource Analytics (HRA), enhanced by recent innovations in Machine Learning (ML), offer exciting opportunities. However, organisations often fail to realise these potentials because of a limited understanding of why individuals choose to adopt or disregard respective tools. Prior research on innovation adoption offers preliminary insights but fails to aggregate the determinants of individual adoption into actionable suggestions for decisions in the ML adoption process. Our study applies focused interviews to examine non-ML experts' reasoning for using a specific tool tailored to a public sector organisation, which corresponds to the usual end-user perspective of ML-based HRA adoption. By drawing from the HRA adoption framework, provided by Vargas et al. (2018), we contribute to the literature by identifying relevant beliefs and experiences influencing one's intention to adopt ML-based HRA and by qualitatively linking these beliefs to ML characteristics such as transparency, automation and fairness. For practitioners, we provide actionable guidance emphasising the need to ensure fairness proactively, as interviewees do not consider this aspect when deciding to adopt ML-based HRA.

Adoption of HR analytics to enhance employee retention in the workplace: A review

Human resources (HR) analytics is garnering increasing interest each year and is set to play a pivotal role in the development of human resources. In the present era, numerous companies are harnessing the power of analytics to gain a competitive advantage by comprehending all the vital aspects of their workforce by enhancing employee retention through leveraging HR analytics to inform strategic HR choices. Many companies are now incorporating analytical tools into their HR function as a fact-based approach to develop relevant strategies and make informed decisions in managing their workforce more effectively. However, HR faces several challenges in implementing data analytics. Talent management commonly utilizes data analytics to enhance employee engagement, including retention rates, recruitment, job satisfaction, and happiness. This paper discusses the adoption of HR data analytics to enhance employee retention in the workplace. This study delves into the significance of HR data analytics in the realm of employee retention, aiming to assess the efficacy of data-driven decisions. A thorough examination of scholarly publications was undertaken, encompassing both indexed and non-indexed papers sourced from reputable electronic databases to gain insights into the present understanding of HR analytics and its influence on employee retention. The discussion uncovers that HR analytics has a noteworthy impact on improving employee retention in the workplace.

AI in HR: A Comprehensive Analysis and Framework for Success

Artificial Intelligence (AI) is transforming the field of Human Resources (HR), revolutionizing how organizations manage their workforce. This paper provides a comprehensive analysis of AI's impact on HR practices, from recruitment and talent management to employee engagement and decision-making. It explores the benefits and challenges of AI adoption in HR and offers a practical framework for successful implementation. By examining real-world case studies and current trends, this paper equips HR professionals and organizational leaders with the knowledge and strategies needed to harness the power of AI for more effective and data- driven HR management. Key Words: AI in HR, HR automation, Talent management, Employee engagement, Ethical AI, HR analytics

Exploring Causal Complexity in the Adoption of Human Resource Analytics – Necessary and Sufficient Conditions (WITHDRAWN)

Exploring Causal Complexity in the Adoption of Human Resource Analytics – Necessary and Sufficient Conditions (WITHDRAWN)

Measuring HR analytics maturity: supporting the development of a roadmap for data-driven human resources management

PurposeToday, companies are struggling to develop their human resources analytics (HRA) capability, although interest in the subject is rapidly increasing. Furthermore, the academic literature on the subject is immature with limited practical guidance or comprehensive models that could support organisations in the development of their HRA capability. To address this issue, the aim of this paper is to provide a maturity model – i.e. HRAMM – and an interdependency matrix through which an organisation can (1) operationalise its HRA capability and assess its organisational maturity; (2) generate harmonious development roadmaps to improve its HRA capability; and (3) enable benchmarking and continuous improvement.Design/methodology/approachThe research described in this paper is based on the popular methodology proposed by Becker et al. (2009) and the procedure for maturity evaluation developed by Gastaldi et al. (2018). This method combines academic rigour and field experience in analytics, in a process spanning eight main phases that involves literature reviews and knowledge creation techniques.FindingsWe define HRA maturity through four areas and 14 dimensions, providing a comprehensive model to operationalise HRA capability. Additionally, we argue that HRA maturity develops through an evolutionary path described in four discrete stages of maturity that go beyond traditional analytics sophistication. Lastly, the interdependency matrix reveals specific enablers for the development of HRA.Practical implicationsThis paper provides practitioners with useful tools to monitor, evaluate and plan their HRA development path. Additionally, our research helps practitioners to prioritise their work and investment, generating an effective roadmap for developing and improving their HRA capability.Originality/valueTo the best of the authors’ knowledge, this study is the first to provide a model for evaluating the maturity of HRA capability plus an interdependency matrix to evaluate systematically the prerequisites and synergies among its constituting dimensions.

Public sector health analytics capacity before and after Covid-19: A case study of manager perspectives in New Brunswick, Canada

BackgroundDemand for health data and analytics to support research, policy, and practice continues to rise, accelerated by the Covid-19 pandemic. Despite the importance of the government analytics workforce in driving academic-based data sharing and linkage platforms, little is known about how public sector managers assess capacity in health analytics. This case study describes findings from consultations among middle managers of analytics services in a Canadian provincial health ministry. MethodsData collection involved a mixed-questions survey to gauge the functional perspective of managers on organisational and human resource analytics capacity within the New Brunswick Department of Health. The repeated cross-sectional survey was implemented in two rounds, with a baseline collected before the Covid-19 global outbreak (in 2016) and a follow-up after the pandemic emergency response (in 2022). ResultsThe post-pandemic period was associated with perceptions of a growing role for public service personnel in handling analytics. Recruitment and retention of skilled analytics professionals emerged as the top priority for capacity building, including needs-based planning, competitive compensation packages to address skills shortages, professional development and promotion opportunities, and tracking key performance indicators for employee satisfaction. ConclusionsGovernment health analytics professionals play a critical role in advancing administrative data use and re-use. Enhanced knowledge sharing is needed on best practices in supply--demand monitoring for analytics professionals and planning for human resources surge capacity in the public service, lest significant innovation potential for health system improvement be left untapped.

Decoding Employee Loyalty: Unravelling The Impact of Human Resource Analytics in Uganda's Commercial Banking Sector

The study examined the effect of human resource analytics on staff retention in commercial banks in Uganda, a case of Finance Trust Bank, main branch. The study objectives included; (i) to evaluate the effect of HR data mining analytics on staff retention in Finance Trust Bank; (ii)to determine the effect of HR data interpretation analytics on staff retention in Finance Trust Bank and (iii) to examine the effect of HR performance management analytics on staff retention in Finance Trust Bank. The study design was a cross sectional design and the approaches used were both qualitative and quantitative. The study population was 65 people, a census was adopted; 53 respondents responded, thus a response rate of 82%. Questionnaires were the main tools used in data collection. The findings indicated that performance management analytics is the greatest contributor to employee retention in Finance Trust Bank (β = 0.566; p value = 0.001), data mining analytics is the second contributor to employee retention (β= 0.373; p value= 0.006), and data interpretation analytics is the least contributor to employee retention (β = 0.211; p value= 0.039). The study concluded that data mining analytics, data interpretation analytics and performance management analytics are all strong, positive and significant predictors of employee retention at the bank (R2 =.647; p value = 0.000). Conclusion were drawn and recommendations given.

Modeling challenges to implement HR analytics in IT sector using ISM

This research paper delves into the intricate landscape of modeling challenges that hinder the integration of Human Resources (HR) analytics within the Information Technology (IT) sector. Employing a qualitative research methodology, the study conducts structured interviews to unravel the nuanced layers of impediments faced by organizations aspiring to harness the power of HR analytics. The research primarily employs Interpretive Structural Modeling (ISM) to map the interdependencies among these challenges, offering a comprehensive understanding of their hierarchical nature. The findings of this study contribute significantly to the existing body of knowledge by identifying key challenges ranging from data privacy concerns to the integration of analytics into HR decision-making processes. By illuminating the intricacies of these challenges, the research aims to guide the creation of strategic frameworks capable of overcoming them. Ultimately, this study seeks to pave the way for the effective implementation of HR analytics in the IT sector, fostering a culture where data-driven insights drive organizational decisions and enhance workforce management. Keywords: HR Analytics, Challenges of Implementing HR analytics, HR in IT sector, ISM technique.

Prescriptive Decision Making Model for Contextual Intelligence in Human Resource Analytics

Prescriptive Decision Making Model for Contextual Intelligence in Human Resource Analytics

Reshaping the future of HR: Human Resource Analytics and Talent Management

Purpose Strategic human resource management is needed in the fast-changing 21st century company environment. Organizations must find new ways to maximize their personnel since human resources management is crucial to success. Human Resource (HR) Analytics has gained significant recognition as a powerful tool in this pursuit, offering valuable insights into HR-related data that facilitate well-informed decision-making. This study analyzes HR analytics and organizational performance, with talent management as a mediating variable. Design / methodology /Approach It is a descriptive and explanatory study conducted to analyze the relationship among studied variables. A mixed method approach has been used in which qualitative data was collected through twenty-four interviews and quantitative data from a sample of 245 respondents was drawn through purposive sampling. SPSS analyses quantitative data, while NVivo analyzes qualitative data. Findings Current research helps organizations obtain a competitive edge in a world that is increasingly competitive. The significant findings of the study indicated that HR Analytics plays an important role in organizational performance in the presence of talent management as a mediating variable. Research Limitations The cross-sectional design was used; a longitudinal investigation could provide more information. The understudied variable relationships are studied in the corporate sector only. Practical implications The research findings emphasize the importance of HR analytics on the success of the corporate sector in Pakistan. This study demonstrated the effectiveness of human resource analytics in enhancing organizational performance by employing talent management. Originality /value The research findings will make an important contribution to resource-based theory in the context of Pakistan as a developing South Asian country.