Cause of Attrition in an Information Technology Enabled Services Company

The purpose of this study is to identify the human resource and the contextual factors that catalyze employee attrition in an ITES organization. A triangulation approach used to understand the reasons for employee attrition including: conducting structured interviews with the employees upon the intimation of their intention to leave the organization as well as a follow up semi-structured interview six months post their official resignation. The study was analyzed using the word cloud qualitative data analysis technique, radar chart, correlation, paired sample t-test, chi-square, and ANOVA. This exploration affirms that human resource factors impact attrition while contextual factor generation influences the human resource factors. The study also indicates that employees feel comfortable to reveal their actual reason for leaving the organization only after a certain period post resignation. Managers should prioritize maintaining relationships with employees; ensure to provide employees with enriching job content and learning opportunities for career growth.

Predictive Path Modelling of Indicators of Secondary School Instructors’ Affective, Continuance and Normative Job Commitment

There is a growing body of literature investigating the impact of retraining and motivation on employee work efficiency. However, little seems to be understood about the effects of employee placement on the commitment of teachers to their jobs. To the best of the researcher's awareness, the partial and composite impact of staff placement, retraining, and motivation on the three aspects of job commitment (affective, continuance and normative) among secondary educators have scarcely been examined. This research was intended to fill this vacuum by using a predictive path modelling approach to analyse the association between these endogenous and exogenous variables. A random sample of 500 secondary school principals was surveyed using two forms of questionnaires. Collected data were analysed and formulated hypotheses tested using Path and multiple linear regression analyses, with the aid of Amos and SPSS packages. Findings indicated that staff placement and motivation were highly predictive of instructors' commitment (at the affective and continuance dimension), but not at the normative dimension; Employee dedication in the three dimensions was not predicted by personnel retraining, but it did result in employee attrition; workers retraining only increased teachers' work commitment when it was augmented with placement and motivation. The combined effect of staff placement, retraining, and motivation was statistically significant in two dimensions of teachers’ job commitment (affective and continuance), but not on the normative dimension. Based on these findings, policy and theoretical ramifications for effective instructional management, assessment, classroom practice, and future study are discussed. Received: 30 March 2021 / Accepted: 27 July 2021 / Published: 5 November 2021

Employee Attrition Prediction Using Deep Neural Networks

Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset.

Employee Attrition Dashboard Using Tableau

Tableau is a powerful and fastest growing data visualization tool used in the Business Intelligence Industry. It helps in simplifying raw data in a very easily understandable format. It also allows non-technical users to create customized dashboards. The purpose of this paper is to visualize and analyse the employee attrition rate using the Tableau visualization tool by considering various important factors that play crucial role in affecting the attrition rate. In order to visualize and predict the attrition rate of employees of an organization, we proposed an intelligent, flexible and effective system that helps the managers to identify the valuable employee and try to retain them. The attrition rate can be analysed based on various factors such as job roles, years since last promotion, gender and number of companies worked. Keywords: Employee Attrition, Tableau, Attrition Analysis, Visualization, Employee Turnover, Dashboards