Dr Vimal Babu, Associate Professor in the Department of Management along with his PhD scholar Ms Rukma R, have co-authored a research paper titled “Enhancement of New Random Forest Algorithm to Predict the Employee Attrition Rate.” Published in the International Journal of Enterprise Network Management (ABDC-B), the paper also includes contributions from Dr Vijaya Prabhagar M, Assistant Professor at IIM Jammu.

Their work focuses on improving machine learning techniques to better predict employee attrition, that is, the employees who are likely to leave the organisation. The study focuses on the impact of HR analytics adoption by applying algorithms to predict employee attrition. It uses the Random Forest algorithm to identify reasons for attrition. However, this algorithm can be slow due to the large number of decision trees it uses. To improve it, the study proposes a new method that selects the best decision trees using a technique called fractional factorial design, which outperforms all others in predicting employee attrition.

Abstract

The problem of employee attrition in every organisation concerns the employee turnover ratio, thereby increasing the cost of investment in human resources. Various factors are reasonable for the rapid attritions at different phases. The purpose of the current study is to predict the employees who are likely to leave the organization. The factors that lead to attrition are identified using the Random Forest algorithm. The Random Forest algorithm is a widely used supervised machine-learning technique for classification and prediction. However, the random forest algorithm has certain issue like it is too slow and ineffective for real time predictions. i.e., the large number of trees can make the algorithm, which results in a slower model. Therefore, the study proposes, a new alternative for choosing the appropriate decision trees based on the concept of fractional factorial design of experiments. The different performance criteria were compared across the modified random forest algorithm, existing random forest algorithm, Support Vector Machine (SVM), Logistic Regression (LR), Navie Bayes (NB), K – Nearest Neighbour (K-NN), Decision Tree, XG Boost tree and Neural Network (NN). It was found that the modified random forest algorithm excelled in all criteria and performed better than the existing ones.

Practical Implementation/Social Implications of the Research

Practical implication: It emphasises the importance of focusing on factors like Business Travel, Job Role, Over Time, Department, Marital Status, and Education to reduce employee attrition. Organisations should design retention programs that support frequent travellers, manage overtime effectively, and address specific departmental needs. Tailoring job roles, promoting work-life balance through flexible hours, and providing educational opportunities are key strategies for enhancing employee satisfaction and retention.

Social Implication: The measures of this study can transform work culture into a more supportive and inclusive environment, promote gender equality by supporting employees with family responsibilities, and contribute to economic stability through reduced attrition. By investing in employee well-being, organizations can also strengthen their community engagement and corporate social responsibility initiatives for both employees and the broader society.

Future Research Plans

Future research could explore other factors influencing employee attrition, such as organizational culture, leadership style, and employee engagement. Longitudinal studies could track changes over time to understand the long-term effects on attrition rates. Additionally, investigating the interplay between identified factors, such as how Job Role and Over Time influence attrition, could provide deeper insights. Expanding the study across different industries and locations may reveal specific trends, allowing for tailored retention strategies. Lastly, incorporating qualitative methods like interviews and focus groups could offer a richer understanding of employees’ experiences and perceptions, complementing the quantitative findings.

With the increasing emphasis on startup culture and enhancing entrepreneurial ventures among the youth, Dr Aftab Alam, Assistant Professor from the Department of Management has published a paper “Developing a Reflective-formative-formative Scale for Measuring University Entrepreneurial Ecosystem from Students’ Viewpoints” in the Q1 journal IEEE Transactions on Engineering Management having an impact factor of 4.6.

Dr Aftab and his team have conducted research on how universities can create the best environment to support entrepreneurship among students and staff. The team has identified three key elements that help an entrepreneurial ecosystem thrive:

1. Skill Development – Helping people learn how to start and manage businesses.
2. Resources – Providing tools, funding, and guidance.
3. Culture – Encouraging creativity, risk-taking, and innovation.

They have also created a way to measure how well universities are doing in building this supportive environment, helping them improve and compare with others.

Abstract

This research addresses the gap in the literature on University Entrepreneurial Ecosystems (UEE) by conceptualizing UEE as an ecology-inspired system with dimensions like Entrepreneurial Skill Development, Resources, and Culture. Unlike prior studies focusing on isolated aspects, we provide a comprehensive framework and develop robust measurement scales using a rigorous four-step methodology, ensuring nomological validity. The paper contributes to entrepreneurial ecosystem literature by offering a novel conceptualisation and practical tool for evaluating and comparing UEE performance. This study aids scholars in understanding UEE’s holistic impact and supports managers in enhancing university-driven entrepreneurship for regional development.

Practical Implication of the Research

This paper contributes to the entrepreneurial ecosystem literature by conceptualising and measuring performance of university entrepreneurial ecosystems (UEE). Beyond academic circles, it could also serve as a valuable tool for managers seeking to evaluate and enhance the performance of these ecosystems.

Collaboration

1. Dr Arpita Ghatak, Kent Business School, University of Kent
2. Dr Bhaskar Bhowmick, Indian Institute of Technology Kharagpur
3. Dr Swagato Chatterjee, Queen Mary University of London

Future Research Plan

1. Longitudinal Validation: Conduct longitudinal studies to evaluate how UEE dimensions evolve over time and their sustained impact on entrepreneurial outcomes like spinoffs, startups, and regional development.
2. Comparative Analysis: Compare UEEs across diverse geographic, cultural, and institutional contexts to identify patterns, best practices, and challenges unique to different ecosystems.
3. Entrepreneurial Outcomes Measurement: Develop advanced metrics to assess the direct and indirect outcomes of UEEs on entrepreneurial intentions, skill-building, and economic growth.

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Confirmatory first-order measurement model results for the subscales

Hierarchical Third-Order Measurement Model Results