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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.

Given a growing literature indicating that downsizing is not an effective way to address financial decline, having either little impact or negative impact on the financial health or market valuation of financially troubled companies, what is the alternative for those companies in financial trouble? Three sets of alternatives to downsizing are available to companies suffering financial trouble: strategies addressing personnel/fix costs, strategies focused on addressing cost cutting/variable costs and strategies addressing strategic planning/revenue. Although alternatives to downsizing have been identified, little research has been conducted comparing the impact of downsizing vs alternatives to downsizing on firm performance. The paper aims to discuss this issue.

This present study looked solely at strategies focused on addressing personnel/fix costs. Focusing primarily on forced attrition (downsizing) vs temporary attrition and/or natural attrition, this research attempts to determine whether specific groupings of alternatives to downsizing are more effective at addressing financial decline that companies find themselves in as compared to downsizing. This included relying on temporary attrition, natural attrition or doing nothing at all.

The research presented here indicates that various alternatives to downsizing have an immediate positive impact on measures of profitability and a positive long-term impact on one measure of efficiency: revenue per employee. Evidence shows that temporary attrition leads to better financial outcomes than natural attrition than forced attrition or downsizing.

The research presented here indicates that various alternatives to downsizing have an immediate positive impact on measures of profitability and a positive long-term impact on one measure of efficiency: revenue per employee. This has implications for managers put in the position of having to make a decision whether to downsize or not.

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.

The purpose of this paper is to identify the attributes that predict customer attrition behavior in the brokerage and investment banking sectors.

The authors analyze the complete stock trading records and customer profiles of 458,098 retail customers from a Korean brokerage house. The authors develop customer attrition prediction models and further explore the practicality of these models using statistical classification techniques.

The results from three different binary selection models indicate that customer transaction patterns effectively explain the attrition of active retail customers in subsequent periods. The study results demonstrate that monetary value variables are the most critical for predicting customer attrition in the securities industry.

This study contributes to the customer attrition literature by documenting the first large-scale field-based evidence that confirms the practicality of the canonical recency, frequency and monetary (RFM) framework in the investment banking and brokerage industry. The findings advance previous survey-based studies in the financial services industry by identifying the attributes that predict customer attrition behaviors in the securities industry.

The outcomes can be easily operationalized for attrition prediction by practitioners in financial service firms. Moreover, the ex post density of inactive customers in the top 10 percent most-likely-to-churn group is estimated to be five to six times the ex ante unconditional attrition ratio, which ascertains that the attributes recognized in this study work well for the purpose of target marketing.

While the securities industry is regarded as one of the most information-intensive industries, detailed empirical investigation into customer attrition in the field has lagged behind partly due to the lack of suitable securities transaction data and demographic information at the customer level. The current research fills this gap in the literature by taking advantage of a large-scale field data set and offers a starting point for more elaborate studies on the drivers of customer attrition in the financial services sector.

Research that follows people over a period of time (longitudinal or panel studies) is increasingly recognised as of great importance in helping us to understand the ageing process and changes over time in the lives of older people. If people drop out of studies ‐ which older people are more likely to do ‐ the value of the study diminishes. This research draws on evidence from ongoing and previous longitudinal studies of people aged 55 and over to examine what factors encourage the retention of participants and what causes them to drop out. The research is synthesising existing evidence, drawing together the experiences of researchers involved in longitudinal studies, and collecting some new evidence about the views of survey participants. This article reports on the first part of the research by drawing together evidence from other studies. These show that there are some factors that are related to attrition whereas for others the evidence is mixed. Methods employed by these studies to reduce attrition and retain participants are examined. It must be noted that apart from the consistent finding that attrition is associated with age, education, socio‐economic status and cognitive impairment, not all studies examined the same variables; some only being explored by one study. This makes it difficult to draw any further conclusions and indicates that attrition needs to be addressed in a uniform manner by more studies. This article identifies some implications for policy‐makers and practitioners.

The prediction of student attrition is critical to facilitate retention mechanisms. This study aims to focus on implementing a method to predict student attrition in the upper years of a physiotherapy program.

Machine learning is a computer tool that can recognize patterns and generate predictive models. Using a quantitative research methodology, a database of 336 university students in their upper-year courses was accessed. The participant's data were collected from the Financial Academic Management and Administration System and a platform of Universidad Autónoma de Chile. Five quantitative and 11 qualitative variables were chosen, associated with university student attrition. With this database, 23 classifiers were tested based on supervised machine learning.

About 23.58% of males and 17.39% of females were among the attrition student group. The mean accuracy of the classifiers increased based on the number of variables used for the training. The best accuracy level was obtained using the “Subspace KNN” algorithm (86.3%). The classifier “RUSboosted trees” yielded the lowest number of false negatives and the higher sensitivity of the algorithms used (78%) as well as a specificity of 86%.

This predictive method identifies attrition students in the university program and could be used to improve student retention in higher grades.

The study has developed a novel predictive model of student attrition from upper-year courses, useful for unbalanced databases with a lower number of attrition students.

Teacher attrition/retention seems to be a wicked issue: it has strong face validity and common-sense meaning, but the literature does not provide a clear definition. In the first section, the author analyzes the different ways in which the issue of teacher attrition and retention is problematized as the basis for a definition: as an educational issue teacher attrition and retention refers to the need to prevent good teachers from leaving the job for the wrong reasons. Arguing that teacher attrition/retention constitutes both a problem and a challenge, he continues in the second part to foreground lessons learned. The conclusion outlines an agenda for teacher education, teacher induction, and school development to positively deal with the challenge to keep the good teachers in teaching.

The purpose of this paper is to determine whether teachers with high value‐added scores (as a measure of teacher quality) stay or left test grades and subjects in a medium‐sized school district.

Panel data for this paper encompass teachers providing math and reading instruction and link to individual students in grades 3‐10 from a single Florida school district (2000‐2001 to 2004‐2005). Value‐added modeling is used to estimate a measure of teacher quality, which is entered into binomial logistic regression models.

This paper finds a negative relationship between reading teachers' value‐added scores and attrition (p<0.05) – a finding consistent with the few that have examined the relationship between value added and teacher attrition. A significant relationship is not found between math value added and attrition. There is also no significant relationship between value added and transferring. Secondary and alternatively certified teachers are more likely to exit tested grades/subjects. Classroom percentages of students enrolled in the free/reduced lunch program (a proxy for poverty) are associated with leaving among math and reading teachers.

Not all turnover is negative. Evidence from this paper suggests that schools are not losing the best teachers from tested subjects and grades – those in which schools and school leaders are held accountable. While there are costs associated with turnover, it can serve as an important matching function between workers and employers.

Only, a few published studies have utilized value‐added scores as the measure of teacher quality and tested their relationship with teacher attrition.