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Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.

The research paper aims to analyse the scholarly literature on advancing HR analytics as an intervention for attrition, a problem that lingers on organisational performance. This study aspires to provide an in-depth literature review and critically assess the knowledge gaps in HR analytics and attritions within organisational performance.

The review analyses the corpus of 196 research articles published in ostensible journals between 2011 and 2023. To identify research gaps and provide valuable insights, this study synthesises relevant studies using School of thought (S), Context (C), Methodology (M), Triggers (T), Barriers (B), Facilitators (F) and Outcomes (O) (SCM-TBFO framework). This study employs the R programming language to conduct a systematic literature review in accordance with the “preferred reporting items for systematic reviews and meta-analysis” (PRISMA) guidelines.

The emerging discipline of HR analytics encompasses the potential to manage attrition and drive organisational performance enhancements effectively. The study of SCM-TBFO encompasses a multidimensional approach, incorporating diverse perspectives and analysing its complex aspects compared to various approaches. The School of thought includes the human capital theory, expectancy theory and resource-based view. The varied research contexts entail the USA, United Kingdom, China, France, Italy and India. Further, the methodologies adopted in the studies are artificial neural networking (ANN), regression, structure equation modelling (SEM) case studies and other theoretical studies. HR analytics and attrition triggers are data mining decision systems, forecasting for firm performance and employee satisfaction. The barriers include leadership styles, cultural adaptability and lack of analytic skills, data security and organisational orientation. The facilitators were categorised into data and technology-related facilitators, human resource policies and organisational growth and performance-related facilitators. The study's primary outcomes are technology adoption, effective HR policies, HR strategies, employee satisfaction, career and organisational expansion and growth.

The primary goal of the literature review is to provide a comprehensive overview of the current state of HR analytics and its impact on organisational performance, particularly in relation to attrition. Further, the study suggests that attrition, a critical organisational concern, can be effectively managed by strategically utilising HR analytics and empowering data-driven interventions that optimise performance and enhance overall organisational outcomes.

This research intends to develop a model for predicting employees at a high-risk attrition and identify the most important factors affecting them.

In this study, using the triangulation technique of a mixed research method, the employee attrition problem is investigated by identifying its affecting factors. For that matter, data related to the human resources department of a pharmaceutical company in Iran are used. And to achieve the intended goal, advanced data mining algorithms and interviews with human resource managers are applied.

A model for predicting employees at a high-risk attrition is presented based on the gradient boosting machine algorithm with 89% accuracy. The use of the mixed research approach shows that qualitative and quantitative methods can be more effective in identifying the factors affecting employee churn or loss of staff. The results also contain a new situation arising out of the COVID-19 pandemic and remote working scenarios having impact on employee attrition. Finally, human resource policies are presented based on variables related to each of the identified factors.

The novel contributions of this study include real data related to a leading pharmaceutical company as well as a combination of two quantitative and qualitative methods. The hybrid approach can identify the reasons for attrition and, consequently, retention policies to benefit from the advantage of both approaches. Data mining can be useful to identify the factors, which are usually not mentioned in termination interviews, such as direct managers. On the other hand, the results obtained from termination interviews can also include features that the authors cannot identify through data mining, which are specifically related to the characteristics of the pharmaceutical industry such as building a more professional career path. From a practical perspective, since this company specializes in pharmaceutical marketing in a new way and is primarily comprised graduates, it is important to note that the churn of specialized people disperses organizational and technological know-how. On the other hand, the pharmacist community in Iran is small, and their attrition might adversely affect not only the reputation of an organization but the employer's brand as well. So, this research would help other similar firms in retaining their valuable human capital.

The purpose of this study is to develop tree-based binary classification models to predict the likelihood of employee attrition based on firm cultural and management attributes.

A data set of resumes anonymously submitted through Glassdoor’s online portal is used in tandem with public company review information to fit decision tree, random forest and gradient boosted tree models to predict the probability of an employee leaving a firm during a job transition.

Random forest and decision tree methods are found to be the strongest attrition prediction models. In addition, compensation, company culture and senior management performance play a primary role in an employee’s decision to leave a firm.

This study may be used by human resources staff to better understand factors which influence employee attrition. In addition, techniques developed in this study may be applied to company-specific data sets to construct customized attrition models.

This study contains several novel contributions which include exploratory studies such as industry job transition percentages, distributional comparisons between factors strongly contributing to employee attrition between those who left or stayed with the firm and the first comprehensive search over binary classification models to identify which provides the strongest predictive performance of employee attrition.

Need of the Study: Artificial intelligence (AI) can be regarded as a big leap in the case of technological advancement. Developments in AI have profound implications for economic sectors and on the societal level. In contemporary times, AI is applied widely in assisting organisations in informing managerial decisions, organisational goals, and business strategies. One can very well witness the interest of human resource (HR) professionals in the implementation of AI for the formulation of HR policies and future frameworks. In the past few years, various research works have been carried out on how these two critical branches can be combined for bringing out the best in human resource management (HRM). The fundamental explanation for this is found in every organisation’s most important management aim is employee retention and elevation.

Purpose: In this direction, this chapter will try to analyse the probability of employees leaving the company, the key drivers behind it, recommendations or strategies that can be implemented in improving employee retention, elevation predictions with the help of different features of machine learning, and the possibility of some other techniques other than key performance indicators (KPI), and rating and training score in this field.

Methodology: The goal will be achieved with the help of implementing machine learning-based classification tools and an ensemble learning approach to the data set of the corporate sector.

Findings: Machine learning techniques can be utilised to develop reliable models to find different factors for elevation and employee attrition.

This paper aims to focus on the issue of high employee turnover in the Indian tech industry. An integrative review is conducted to analyse the past and current state of literature, as well as prepare a research agenda for future studies.

A pool of 72 articles published between 2010 and 2022 is reviewed with a special focus on Indian tech employees. This study elucidates the extent and impact of employee retention strategies through content analysis.

Two broad perspectives have been established in the literature: the reasons for quitting and the explanations for staying. By means of a comprehensive review, this paper combines these two aspects of literature and suggests factors under organization’s control to retain competent tech employees.

The study is designed to integrate the two theoretical viewpoints of employee turnover literature by consolidating the reasons behind quitting behaviour and staying intention. Codes combining the two aspects are presented as a valuable resource to retain tech talent.

With talent management becoming an area of growing concern in the literature, the purpose of this paper is to investigate talent management and its relationship to levels of employee engagement using a mixed method research design.

The first phase was a survey on a sample of 272 BPO/ITES employees, using Gallup q¹² or Gallup Workplace Audit. Focus group interview discussion was based on reasons for attrition and the unique problems of employee engagement. In the second phase, one of the BPO organizations from the phase I sample was chosen at random and exit interview data was analyzed using factor analysis and content analysis.

The results were in the expected direction and fulfilled the research aims of the current study. In the first phase low factor loadings indicated low engagement scores at the beginning of the career and at completion of 16 months with the organization. High factor loadings at intermediate stages of employment were indicative of high engagement levels, but the interview data reflected that this may mean high loyalty, but only for a limited time. In the second phase factor loadings indicated three distinct factors of organizational culture, career planning along with incentives and organizational support. The first two were indicative of high attrition.

A limitation of the research design was a sample size of 272 respondents. Some of the Cronbach's alpha scores of the subscales of Gallup q¹² were low. The strength of the study lies in data triangulation, which was obtained through a mixed method approach, a survey and unstructured focus group interviews. There are theoretical implications for the construct of employee engagement. There seems to be a construct contamination from the fields of employee satisfaction, employee commitment and employee involvement, which is beyond the scope of this paper. Future studies in India may look into this area and construct an independent scale of employee engagement, focusing on the antecedent variables and testing them for theoretical underpinnings.

The present study indicated that a good level of engagement may lead to high retention, but only for a limited time in the ITES sector. The need for a more rigorous employee engagement construct is indicated by the study. Practical implications for retention in the BPO/ITES sector are referred to.

The purpose of this paper is to examine where high rates of employee attrition are a problem and put forward some possible solutions.

The paper concentrates on the experience of information technology (IT) firms in India.

The paper describes how employees seem most likely to leave a company after around five years. The longer an employee remains with a company, the less likely he or she is to leave.

The paper highlights the importance of making employees feel valued, keeping them informed of what is going on in the company, relating their work to the aims of the organization as a whole and ensuring that they have a clear career‐development path.

The paper stresses that high rates of employee attrition can harm not only individual companies, but also national economies as a whole.

The paper focuses on the IT industry in India to recommend how to reduce employee attrition.

The purpose of this paper is to enhance the readers’ understanding of the domain of talent management and employee engagement, and how the former culminates in the latter, specifically as a tool for retention of employees. The paper also attempts to probe and aid readers in understanding of the domain of talent management and employee engagement, specifically as a tool for retention of employees.

The paper is based on secondary research in the areas of talent management practices, employee engagement and talent retention. The authors have reviewed the existing body of work on these topics in order to arrive at an analysis of the practice of talent management and employee engagement and to successively measure its impact on the retention of employees. In order to aid the endeavor to bring some clarity to and explore the trends in talent management, the authors have based this paper on a systematic review of the existing literature which seeks to examine the modern thought process and evidence. Current examples are drawn from many leading companies and their practices are emphasized for better understanding. Various drivers responsible for engaging and retaining talent are studied by reviewing the existing literature. After analyzing the existing literature, the authors have arrived at talent retention and engagement practices that may aid and assist industry practitioners and policy makers in designing and implementing successful talent management initiatives. The authors have proposed a model for arriving at improved retention from talent management practices.

One of the most effective tools in ensuring that employees stay engaged and committed to their work is talent management. This sense of engagement or commitment toward their work ensures in turn that these employees stay with the organization in the long run. As the authors have begun analyzing the costs associated with recruitment, selection and the opportunity cost related to attrition, organizations have been concentrating their talent management efforts in the direction of retention. The paper concludes that a synchronization of talent management practices and employee engagement initiatives leads to improved talent retention and proposes a model to this end.

Owing to the fact that the paper is conceptual, the model and hypotheses the authors arrive at must be empirically tested by relevant stakeholders in the organization, specifically the stakeholders specializing in the area of talent management, in order to add further weight to the literature.

Derived from an extensive study of current and impending talent management strategies, the findings may aid organizations and policy makers to develop and refine talent management practices in order to engage human capital, with the ulterior aim of talent retention – a concern that plagues most contemporary organizations.

Preempting or preventing attrition is a priority in several organizations, and more often, in some industries as a whole. The authors examine how talent management practices can contribute to the efforts in preempting or preventing attrition in organizations. Adding to the existing literature on talent management, the paper explores the areas of talent management which directly affect employee engagement and in turn make a difference in talent retention. This link connecting talent management practices with retention deserves a greater amount of investigation, which the paper explores.

The purpose of this study is to explore the human capital (HC) value creation practices of knowledge‐based software and service exporter industry in India.

The study used HC disclosure attributes as a tool to the contents of the annual reports for the year 2003‐2004, to evaluate the type and amount of HC disclosed by the software firms. The study also conducted semi‐structured interviews with the heads of human resources (HR) of 14 software firms to obtain a greater understanding of the similarities between reporting and managed HC practices.

The study identified most reported and least reported attributes of HC using content analysis and explained their reporting of value creation using interviews and resource‐based view. The findings suggest that the HC reporting practices were consistent with interview findings. The frequency of HC attributes reported followed the extent of the management's perception of HC value creation to the firm.

The limitations of the study are: first, it is a cross‐sectional study; second, the findings may be applicable exclusively to the software and service industry in India and cannot be generalised to other industries in India or software industry in other countries; third, the assertions by the HR heads are assumed to faithfully reflect the firm's HC practices; fourth, the study assumes that annual reports are the primary documents available to public (stakeholders) requiring information on the firms.

This study provides an insight into the HC reporting practices of the nascent software and service exporter industry in India that is experiencing an economic boom that is positively influenced by the information technology software industry. This study throws light on the utilization of HC for value creation by the top software firms. This may help countries having tie‐up with Indian firms to understand the value creation process of these firms to sustain growth. The study would enable other software firms to understand HC reporting practices of the industry, and could use a framework that is similar to the framework used in this study.