INTRODUCTION:

In the past, Human Resource Management (HRM) was commonly perceived as an administrative function where historical knowledge, emotional factors, or intuition predominantly influenced decisions. Value creation heavily depended on the firm's human resources, from senior level to lower levels¹. For a significant duration, human resource managers had upheld the belief that employees were the most invaluable asset to any organization.² Consequently, it was deemed essential to clarify the conceptual understanding of HRM models, theories and perspectives. These HRM models comprised thorough descriptions of objectives, competencies, processes and benchmarks, related to human resources. It also facilitated the integration of HRM processes and structures by aligning HRM strategies with organisational goals. It undoubtedly played the most important role in any functioning of an organization.³ Additionally, explicit guidelines were provided for defining the roles and responsibilities of human resources, thus aiding organizations in achieving competitive positioning and effective management of administrative procedures.

Globally, organizations shifted their focus towards evidence-based management practices. These practices facilitated meticulous decision-making by effectively utilizing available evidence (data support) to benefit both the organization and its stakeholders.^4,5 In the past, HRM practices such as training and development, teamwork, HR planning, and performance appraisal exerted a positive and significant impact on business performance.⁶ HRM gained significant popularity during the 1980s. It was perceived as an integral aspect of strategic managerial functions in shaping business policies, where it assumed roles of both determination and contribution.⁷ Through HR analytics, decisions within organizations became increasingly evidence-based, fostering a more pragmatic cross-functional approach. Analytics-based HR decisions, grounded in evidence, were deemed more value-adding, forward-thinking and sustainable.

A survey conducted by The Economist Intelligence Unit (EIU) in 2014 confirmed that decisions across all organizational functions, including marketing, finance, sales, and human resources, were often reliant on personal experience and intuition. Despite the availability of information, many organizations lagged behind in adopting human resource analytics^8,9,10. Human resource analytics was frequently misperceived as solely statistical analysis^11,10 contrary to assertion that analytics constitutes a mental framework and logical progression, complemented by statistical tools¹². The implementation of human resource analytics in contemporary business organizations yielded better results across functions such as talent management, Return on Investment (ROI) enhancement and decision-making^13,8,14.

Nevertheless, the corporate world eventually recognized the significance of talent-related information and commenced fully embracing the information revolution in the field of human resource management. The focus was on developing a talent pool comprising high-potential and high-performing employees to fill these crucial positions.¹⁵

Contemporary organizations were confronted with formidable challenges, prompting a revaluation of HR departments and HRM systems beyond mere operational efficiency^16,17. In the face of relentless market dynamics, innovation emerged as a non-negotiable imperative for organizational survival^18,19 The stark reality of the then business landscape presented a binary choice: innovate or perish; a sentiment that echoed across industries²⁰.

Support activities within organizations, encompassing procurement, technology development, human resource management, and infrastructure, played pivotal roles in fostering strategic competitiveness²¹.Strategic management research highlighted the significance of leveraging internal strengths, mitigating external threats, and addressing internal weaknesses to attain sustainable competitive advantages. Recent scholarship predominantly focused on analysing firms' competitive landscapes, emphasizing the need for a nuanced understanding of variables driving workplace performance.

Maximizing returns on human capital necessitated an all-inclusive comprehension of the intricate interplay among staffing levels, competencies, compensation structures, workforce profiles, and other factors.^22,23 To fully grasp the impact of workforce dynamics on organizational outcomes, an integrated insight into pertinent measures from all HR and operational systems was indispensable^24,22.

HR analytics and metrics were frequently employed for enhanced decision-making and to adapt HRM strategies within organizations. There was a noticeable shift towards data-driven analyses in human resources, involving the utilization of large datasets²⁵. This innovative approach was expected to enable organizations to strike a balance between benefits and cost considerations, facilitating predictive analytics on staff sentiment, talent acquisition, capacity planning, and attrition risk management. Efficient approaches to utilizing HR analytics and measuring their impact on organizational procedures aimed at optimizing human resource utilization. This optimization was achieved by fostering the growth of HR analytics and tailoring it to meet the organisation's needs.

Thus human resource analytics (HRA) served as a tool for organizations to leverage current strategic and operational data, transforming it into a proactive approach to address future HR challenges. HR analytics emerged as a crucial mechanism for achieving success, leveraging existing data to forecast future ROI and gain strategic advantage²⁶.

REVIEW OF RELATED LITERATURE:

Kale, Aher, and Anute(2022) in their paper stated that, in the contemporary business landscape, managing employees within organizations was no longer a solitary endeavour. Advancements in technology facilitated the online management of employees and tracking of their performance through HR analytic tools. The adoption of HR analytics had yielded improvements in employee performance and enhanced business efficiency, such as enhancing recruitment quality, managing talent effectively, boosting employee productivity, and reducing employee turnover. It delved into the diverse uses of HR analytics in different organizational settings and elucidated the benefits associated with its implementation. By leveraging analytical tools, organizations could identify issues related to performance, employee turnover, and behaviour using available data. The paper also examined the utilization of HR analytics in five different organizations and elucidated how its implementation had positively impacted both the organizations and the employees, leading to monetary gains and a shift in business strategy towards a people-centric approach.²⁷

Nagpal, Jaiswal and Panchal(2022) in their study aimed to explore the application of HR analytics in human resource practices and to assess employee satisfaction with HR Analytics. HR Analytics played a crucial role in addressing and assisting in the resolution of HR-related issues, ensuring compliance with organizational policies and objectives. The findings of the study indicated that HR analytics contributed to increased employment, expedited decision-making processes, and enhanced productivity, among other benefits Furthermore; it aided HR researchers in positioning their papers more explicitly within current debates and encouraged them to explore future research avenues based on emerging questions.²⁸

Ekka,(2021) in her study, aimed to provide an overview of the current developments in HR analytics, focusing on the evolving roles of HR from various perspectives. The paper underscored the importance of comprehending the implications of HR analytics and emphasized its future relevance in the dynamic landscape of the business industry.²⁹

Parimlam and Dhanabagiyam, (2023) in their study the swiftly evolving arena of contemporary business, the fusion of Artificial Intelligence (AI) and Electronic Human Resource Management (EHRM) emerged as a focal point of both scholarly exploration and practical implementation within the domain of human resource management (HRM). This systematic review endeavoured to probe into and scrutinize the strategic implications of AI and the transformative potential of EHRM in nurturing innovative approaches within HRM. It embarked upon its journey by delving into existing literature on AI and EHRM, untying their potential benefits and the challenges they posed in augmenting HRM practices. Through embracing AI and harnessing the capabilities of EHRM systems, organizations were poised to optimize HR processes, elevate decision-making competencies, enrich employee experiences, and ultimately chart a path toward organizational success in the epoch of digital transformation.³⁰

Das and Dash, (2023) in their study over the past few decades, businesses faced pressure from investors to adopt environmentally friendly practices, posing a challenge for management to maintain a competitive edge. The human resources department had to implement green practices to uphold its strategic image with stakeholders. The research outlined the significant impact of these green HRM practices on ecological sustainability performance. Although beneficial, the research had limitations, including a lack of mixed studies and constraints imposed by Odisha's circumstances, potentially affecting analysis validity.³¹

The trajectory of information technology evolution transitioned from management information systems to enterprise resource planning and eventually embraced cloud computing environments. Across various organizations, the adoption of distinct HR software modules facilitated the integration of their HR functions. This research endeavour focused on discerning the extent of usage of diverse HR tools within selected organizations in Odisha. Additionally, it delved into uncovering discrepancies in the utilization patterns of electronic HRM tools within this specific context. The findings illuminated the heterogeneous landscape of electronic human resource management tools across different organizations. Major entities implemented a spectrum of e-HRM tools, including HR functional applications, integrated HR software suite applications, Interactive Voice Response systems, HR intranet applications, self-service applications, employee self-service applications, manager self-service applications, HR extranet applications, and HR portal applications. This diversity underscores the multifaceted approach adopted by organizations in leveraging technological advancements for HR management.

Ahuja (2014) observed that in the modern dynamic world, technology played a pivotal role as perhaps the most crucial resource for any nation. The study conducted at an automobile ancillary unit aimed to analyse the process of technology upgrading through technology adoption and adaptation. The findings of the case study have been discussed, highlighting the implications of technology upgrades through initiatives of technology adoption and adaptation on the manufacturing performance of the organization.¹⁶

Vyas and Junare,(2020) in their study aimed to raise awareness about the necessity of advanced HRMS features for enhancing strategic HRM. The paper delved into the conceptual understanding of HRMS applications, serving as a crucial tool for decision support and enhancing organizational financial performance. Emphasis was placed on leveraging the important features of HRMS to improve strategic HRM practices, including skill inventories, absenteeism management, turnover ratio, retention ratio, job satisfaction, and succession planning. Through secondary data analysis and literature review, valuable insights were derived. The paper provided HR professionals with precise insights into utilizing HRMS applications to enhance organizational productivity, employee development, and satisfaction. Additionally, it underlined the value addition of HRMS to existing systems like HRIS and HCM, suggesting the upgrade of the current HR system to HRMS as a feasible and decision.³²

Mohammed’s, (2019) through his exploration, the study aimed to provide insights into the role of HR analytics in enhancing decision-making processes and optimizing organizational performance. This study delved into the extant literature regarding HR analytics and its impact on predictive decision-making within organizations. Additionally, it critically examined literature concerning the incorporation of HR analytics into organizational frameworks, emphasizing the implementation of pertinent IT infrastructure and resources.¹³

In recent years, there had been a noticeable surge in scholarly attention towards human resource analytics (HR analytics) within business entities. HR analytics entailed the examination of the correlation between human resource policies and practices and the resultant outcomes for both employees and organizations. Its utilization offered organizations a competitive edge over their counterparts. Telu and Verma, (2019) in their article endeavoured to investigate the significance of HR analytics in the business sector. Additionally, it outlined potential obstacles that could impede the adoption and integration of HR analytics and proposed strategies to mitigate them. Furthermore, it delineated the procedural steps involved in HR analytics implementation and deliberated on its prospective significance in the business landscape.³³

OBJECTIVES OF THE STUDY:

The study aimed:

· To trace the development in the concepts practice in the field of HR Analytics.

· To highlight roles of Human Resource Analytics and its practical implementation within business organizations.

· To study the relationship between Human Resource Analytics and Human Resource Metrics

· To analysing to the various stages of Human Resource Analytics

RESEARCH METHODOLOGY:

The research method employed in this study was descriptive research, entailing the collection of necessary data and information using the secondary method of research. This encompassed written sources such as books and journal articles. Furthermore, the researcher meticulously incorporated information from trusted websites and examined doctrinal theories, consulting with experts, professionals, and insights from former experts. The overarching goal of this comprehensive approach was to enhance our understanding of the rise of HR analytics in modern era and its need.

Transitioning from Descriptive Metrics to Predictive Analytics in Hr:

Future outcomes were anticipated by utilizing predictive analytics within human resources. Proficiency in mathematics and statistics was deemed paramount for the practical application of predictive analytics. One critical domain where predictive analytics became indispensable was in organizational decision-making with reference to talent management. Admittedly, talent-related decisions entailed a multitude of variables that were challenging for manual interpretation. By harnessing the power of mathematics, statistics and big data; HR managers devised algorithms capable of processing vast datasets enabling informed and potentially error-reducing HR decisions. This underscored the significance of predictive analytics in HR.

The practice of utilizing descriptive and historical metrics in human resources had been customary in organizations. Metrics such as time to hire, cost per hire, training program participation rates, and employee satisfaction survey results exemplified this practice. In contrast, adopting HR and predictive analytics enabled organizations to forecast future outcomes, making it a potent decision-making tool. Given the complexities of contemporary businesses, aligning HR decisions with organizational strategies and objectives was imperative. Instead of dwelling solely on past and present data, a forward-looking approach leveraging predictive analytics was necessary for informed decision-making. This was aptly distinguished between metrics and predictive metrics, wherein the latter identified a problem and suggested an actionable plan.

The evolution of HR decision-making was shifting from reactive to predictive paradigms. Reactive decision-making, rooted in historical inputs and crisis responses, contrasted with predictive decision-making that anticipated future outcomes. While decisions based on dashboards were reactive, predictive HR analytics established statistical relationships between proposed HR actions and organizational outcomes, thereby informing strategic planning and fostering organizational sustainability and growth.

Before computerized systems, HR primarily focused on manual record-keeping, involving paper files stored in physical cabinets. Tasks included tracking attendance, managing payroll manually, and keeping detailed records of employee information, job roles, and performance reviews. This labour-intensive process was time-consuming and prone to human error, making it difficult to maintain accurate and up-to-date records.

The digitization of HR began in the 1970s and 1980s with the introduction of HR Information Systems (HRIS)³⁴. Early HRIS systems, which emerged in the late 1970s and early 1980s, offered basic functionalities like electronic record-keeping and payroll processing. These systems provided efficiency gains by reducing the administrative burden on HR professionals and improving data accuracy and retrieval times. Throughout the 1990s, HRIS continued to evolve, integrating more HR functions such as recruitment, training, and performance management. Enhanced reporting tools allowed HR to generate standard reports on metrics like turnover rates, headcount, and employee demographics. The ability to manage large databases of employee information also improved, enhancing data management and security.

By the 2000s, HR analytics had advanced beyond basic record-keeping and reporting. HR professionals gained access to sophisticated reporting tools that enabled detailed analysis of workforce trends³⁵. The use of dashboards became common, allowing real-time monitoring of key HR metrics like employee engagement and absenteeism. Additionally, organizations began using internal and external benchmarking to compare performance against industry standards and historical data.

The 2010s marked a shift from descriptive to predictive analytics in HR. Organizations began developing and implementing predictive models to forecast future HR trends, such as predicting employee turnover and identifying high-potential employees. Machine learning algorithms were increasingly used to enhance the accuracy of these models. Companies conducted pilot projects to test predictive analytics in areas like recruitment, retention, and performance management.

In the 2020s, predictive analytics became a mainstream tool in HR³⁶. Widespread adoption of predictive analytics tools and platforms led to their integration into HR processes across various organizations. HR professionals used predictive insights to make proactive decisions, such as implementing targeted retention strategies before turnover occurred. This evolution enhanced HR's strategic role within organizations, allowing it to leverage data-driven insights to drive business outcomes and improve overall performance.

Looking ahead, HR analytics continues to evolve. Predictive analytics will increasingly integrate with broader business intelligence systems, providing a holistic view of organizational performance. The use of advanced AI will offer deeper insights and further automate routine HR tasks, enhancing efficiency and strategic impact. Predictive analytics will also enable highly personalized employee experiences, tailoring interventions to individual needs and preferences.

The Role of Hr Analytics:

In addressing critical HR functions, HR managers confronted a series of inquiries before making decisions. For instance, decisions regarding talent management entailed considerations of talent sourcing, job profile alignment, talent utilization optimization and retention strategies. Traditionally viewed as an administrative function, HR management now necessitated data-driven insights to enhance employee capabilities, mitigate fraud risks and leverage social network information. Similarly, in areas such as performance evaluation, training and development, data-driven HR decisions were pivotal in supporting organizational strategies and achieving business objectives. HR analytics emerged as a valuable ally for organizations, empowering informed decision-making and strategic alignment with business goals.

Defined as the application of analytical logic within HRM functions, HR analytics served to enhance employee performance, rationalize decision-making processes and optimize ROI from human resources. From an employee’s perspective, HR analytics offered a platform to evaluate contributions to the organization and assess career progression. By leveraging HR analytics for critical HR decisions, organizations mitigated the risk of erroneous decisions and enhanced decision quality and processes. Moreover, HR analytics elevated organizational HR decision-making to a strategic and business-aligned level.

HR analytics encompassed a spectrum of methodologies, including statistical analysis, query development, research design, big data integration, results evaluation, and decision translation. Behavioral modelling, predictive modelling, impact analysis, cost-benefit analysis and ROI assessment were among the tools utilized in HR analytics, transcending traditional HR metrics, scorecards, and dashboards.

Different Types of Human Resources Systems:

Human Capital Management:

Human Capital Management (HCM) was utilized by organizations to effectively manage their workforce through a set of practices, processes and technologies. HCM included a diverse array of tasks associated with human resources, such as recruiting, orienting new employees, managing performance, providing training and development opportunities, administering compensation and benefits and fostering employee engagement. Overall, the aim was to optimize the utilization of human capital within an organization, ensuring that the right people were in the right roles, equipped with the necessary skills and resources to contribute effectively to the organization's goals and objectives.

Human Resource Management System:

Human Resource Management System (HRMS) was a software solution designed to automate and streamline various HR processes within an organization. Various functionalities to manage employee data, payroll, benefits administration, recruiting, performance management and other HR-related tasks were typically encompassed by HRMS systems. Overall, HRMS systems streamlined HR processes, improved efficiency and enhanced the employee experience by centralizing data, automating routine tasks and providing tools for better workforce management and decision-making.

Human Resource Information System:

Human Resource Information System (HRIS) was a software solution that integrated various HR functions and processes by providing a centralized database for storing, managing and retrieving employee data. HRIS systems streamlined HR operations, enhanced data accuracy and improved decision-making within organizations. Overall, HRIS systems centralized HR data, automated routine tasks, improved efficiency and enabled better workforce management and decision-making across the organization. HR processes were enhanced, administrative overhead was reduced, and contributions to the overall success of the business were made. Various terms, such as web-based human resources, HRIS, and virtual human resource management, were utilized to denote the use of Information and Communication Technology in Human Resource Management. However, e-HRM emerged as the dominant trend. In the Indian context, it garnered considerable global attention, influenced by broader global shifts. These features reflected the evolving landscape of HR practices in response to technological advancements and changing global dynamics.³⁷

Rise of Human Resource Metrics:

Human Resource metrics were the pivotal aspects tracked by organizations regarding Human Capital Management (HCM). These metrics served as quantifiable indicators of various HR functions and their impact on organizational performance. Key HR metrics covered a broad spectrum of domains, comprising recruitment, employee engagement, retention, training and development, performance management, and overall workforce efficacy.

Recruitment Metrics:

Recruitment metrics such as time-to-fill, cost-per-hire and quality of hire was utilized to evaluate the efficiency and effectiveness of talent acquisition processes.

Employee Engagement Metrics:

A level of employee satisfaction, motivation and commitment was measured by employee engagement metrics; providing insights into organizational culture and morale.

Training and Development Metrics:

Turnover rates and employee tenure shed light on workforce stability and loyalty, while the investment in employee growth and skill enhancement was assessed using training and development metrics.

Performance Management Metrics:

Performance management metrics monitored both individual and team performance in relation to predefined goals and objectives, thereby enabling ongoing enhancement and alignment with organizational strategies.

Overall, valuable insights into the health and effectiveness of an organization's human capital management practices were provided by HR metrics. By tracking and analyzing these metrics, areas for improvement could be identified, informed decisions could be made, and HR strategies could be optimized to drive business success and achieve goals.

In the realm of strategic human resource management³⁸, HR analytics and HR metrics were emerged as indispensable components. HR metrics, in the past, played a pivotal role in offering quantitative measurements across various HR functions and processes, encompassing aspects such as recruitment costs, turnover rates, and training expenses. Conversely, HR analytics delved deeper into this data employing statistical methodologies and predictive modeling to discern underlying patterns, correlations, and improvement opportunities.

Hr Metrics and Hr Analytics:

HR metrics acted as crucial performance indicators furnishing invaluable insights into the efficacy and efficiency of HR endeavors. Meanwhile, HR analytics undertook a more profound analysis leveraging statistical techniques and predictive modeling to unveil intricate patterns, relationships and avenues for enhancement.

The amalgamation of HR metrics and analytics empowered organizations to craft informed decisions, optimizing their strategies for human capital management. This fusion facilitated the identification of areas ripe for improvement, predicted forthcoming workforce trends and aligned HR interventions with organizational objectives. Eventually, this systematic method, grounded in data, drove organizations to increase employee productivity, promote engagement and improve overall business performance.

Stages of Analytics:

a) Descriptive Analytics:

Descriptive analytics involved analyzing historical data to understand what had happened in the past. It focused on summarizing and presenting data in a meaningful way such as charts, graphs and reports. Descriptive analytics provided insights into trends, patterns and key performance indicators (KPIs); allowing organizations to gain a better understanding of their current state. Gathering raw data didn’t make sense and wasn’t always useful. However, it proved to be useful once it was sorted and put in a systematical order. Descriptive analysis (also known as observing and reporting) was the most basic type of analysis and was frequently used. Basically, all the historical data available was collected and summarized it into something understandable. A headcount of employees in the organization or some specific department would come under Descriptive Analytics. More complicated metrics like turnover rates also came under descriptive analytics as well.

b) Diagnostic Analytics:

Diagnostic analytics delved deeper into understanding why certain events had occurred by identifying the root causes of past outcomes. It involved analyzing historical data to uncover patterns and relationships that explained the observed phenomena. Diagnostic analytics enabled organizations to pinpoint areas requiring enhancement and to make better-informed decisions regarding how to tackle underlying issues. If descriptive analytics told what had happened, then diagnostic analytics told why it had happened. It went beyond merely describing events to searching for their underlying causes. This process involved making observations, conducting descriptive analysis and then proceeding with diagnostic analysis. Diagnostic analytics used various techniques including data drilling and data mining, to investigate the root causes of problems and find their solutions. Companies needed to understand why problems were occurring in order to effectively address them.

c) Predictive Analytics:

Predictive analytics utilized historical data and statistical algorithms to forecast future outcomes or trends. By analyzing past patterns and trends, predictive analytics models could predict future events, such as customer behavior, sales trends or employee turnover. This facilitated organizations in foreseeing upcoming challenges and opportunities, empowering them to proactively mitigate risks or leverage emerging trends. Unlike descriptive analytics, which relied on past data, predictive analytics looked forward, employing different statistical models and forecasts to predict potential outcomes. The goal was to meet the organization's needs by building models based on patterns identified in descriptive analytics. For example, predictive analytics could forecast employee tenure or assist the talent acquisition team in assessing cultural fit within the organization.

d) Prescriptive Analytics:

Prescriptive analytics elevated predictive analytics by not only foreseeing future outcomes but also recommending actions to achieve desired results or circumvent potential issues. Utilizing advanced algorithms and optimization techniques, it provided actionable insights and decision recommendations based on predictive models. This enabled organizations to make well-informed decisions by suggesting the most effective course of action to attain their objectives.

After predicting future scenarios, the focus shifted to determining actionable steps. Prescriptive Analytics offered recommendations based on predictions and historical data. Especially advantageous for businesses experiencing fluctuations in demand throughout the year, like retailers during peak holiday seasons; it provided guidance for staffing choices. Additionally, it aided in optimizing the hiring process by identifying required skills across the employee lifecycle.

This analysis integrated information from preceding levels and prescribed necessary actions. Notably, various professional HR analytical tools, including Visier, Tableau, QLIK, SPSS, and Microsoft Excel, were commonly used in contemporary organizations to facilitate these analytics.

Hr Analytics as a Better Tool for Hr Decisions:

HR analytics stood as the paramount strategic tool guiding HR decision-making processes at that time. While HR metrics and tools like HR scorecards and dashboards oversaw operational HR functions, HR analytics, leveraging these metrics, crafted algorithms aligning HR decisions with organizational objectives. By amalgamating data from diverse sources, HR analytics formulated predictive algorithms, driving strategic human capital management. At that time, numerous global organizations prioritized human resources through strategic HR analytics. Amidst stressful circumstances where individuals harbored job insecurity, HR took proactive steps to cultivate key skills and bolster competencies among employees.³⁹ Its strategic benefits encompassed talent attrition management, performance anticipation, efficient compensation and benefits programs, and enhancing employee motivation and morale. The genesis of HR analytics traced back to Fitz-enz, (1984) who pioneered the measurement of HRM services with metrics, initially focusing on employee performance metrics.⁴⁰ Over time, HR analytics evolved into a comprehensive strategic tool for holistic HRM or human capital management. Organizations harnessing HR analytics strategically not only measured but also enhanced critical HR aspects like employee satisfaction, retention, compensation design, workforce planning, and leadership identification. By gathering workforce data, developing algorithms and feeding them into advanced computer models, HR analytics provided insights into past, present and future trends, facilitating informed decision-making. For instance, predictive analytics enabled organizations to forecast the impact of incentive plan modifications on talent retention, thereby refining the decision-making process. Enhanced HR decisions, in turn, aligned human resources more closely with business objectives, rendering organizations more business-aligned.

Two prominent examples of an HR decision-making process using HR analytics have been explained as follows:

Talent Retention:

HR analytics played a pivotal role in enhancing decision-making processes, particularly in talent retention strategies. Organizations erroneously believed that increasing compensation and benefits alone would suffice to retain talent, which proved to be inaccurate. While HR metrics, scorecards and dashboards provided insights into turnover rates and resignation rates, they were limited in understanding trends across departments, divisions, and employee levels. Such comprehensive insights necessitated the utilization of HR analytics and predictive models. By leveraging metrics, algorithms were developed to identify strategies for improving talent retention within the organization.

Performance-related pay (PRP):

PRP-related decisions involved rationalizing the distribution between fixed and variable pay, determining essential KPIs for performance measurement and allocating weights between individual and group performances. These decisions on aligning performance with pay couldn't rely solely on HR metrics. While HR metrics documented pay differentials, HR analytics was necessary to align business value with pay and optimize pay at risk, striking a balance between fixed and variable pay.

Summing Up:

In the past, the adoption of HR analytics presented numerous advantages for HR functions, propelling them towards growth opportunities. It was evident that a thorough understanding of the business and strategy context was essential for HR professionals to legitimize their role as strategic business partners. HR analytics offered insights that enabled HR functions to align closely with organizational objectives and contribute directly to business success.

Anticipated changes in HR functions were expected to be significant, driven by technological advancements and the evolving nature of the work environment. Experts predicted a decrease in routine HR jobs, with companies increasingly outsourcing such tasks. This shift was fuelled by the integration of new technologies, which made HR functions more inclusive and participative, transforming them into integral components of business processes.

The advent of self-service computer systems empowered employees across all functions to handle data entry tasks, reducing the need for manual HR interventions. While some entry-level and transactional HR roles became redundant, it also opened up opportunities for HR professionals to concentrate on more intricate tasks demanding specialized expertise, such as compensation and benefits management.

In conclusion, the future of HR functions was characterized by a shift towards automation, outsourcing, and specialization. While certain traditional HR roles became redundant, new opportunities emerged for HR professionals to add value through strategic initiatives and specialized expertise. With HR analytics at the forefront, organizations were poised to navigate the complexities of the modern workplace and drive sustainable growth through effective human capital management.

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Received on 09.03.2024 Revised on 12.11.2024

Accepted on 11.04.2025 Published on 28.05.2025

Available online from May 31, 2025

Asian Journal of Management. 2025;16(2):73-81.

DOI: 10.52711/2321-5763.2025.00012

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