Journal of Contemporary Research in Management (JCRM)

Impact of Employee Wellness Programs on Employee Mental Health in the IT Sector of Coimbatore

2026-05-17T14:21:18+02:00

Employee wellness programs (EWPs) are becoming more important as companies identify the central role that employee mental health plays in productivity and overall workplace health. This research investigates the influence of wellness programs on the mental health of IT workers in the Coimbatore area, involving interventions such as mental health counselling, stress management workshops, physical fitness, and flexible working hours. Employing a quantitative survey-based approach, data were gathered from employees to determine participation levels and perceived impact. The research finds mental health counselling and stress management workshops to be the most participated and effective programs, followed by financial wellness programs and nutrition counselling as less participated. The programs, although made available, are restricted by low awareness, non-participation, and perceived ineffectiveness from achieving their full impact. The research urges organizations to personalize wellness programs to employee needs and to establish a culture that actively fosters participation. The research adds to the current debate on workplace wellness and offers practical implications for IT companies to promote employee mental wellbeing.

The Role of Anti-Dumping Duties and Its Impact on Chennai Industrial Sector: An Analytical Study

2026-05-17T14:24:03+02:00

Trade between India and China which has concerns like dumping of Chinese goods at low price in the Indian market which causes problems to Chennai Industrial sector since Chennai was known as hub of Industries. Because of this problem, the government implemented various anti-dumping duties upon China to ensure fair competition between India and China and also to protect the domestic industries from this dumping issue. Henceforth, Chennai, known as an important industrial hub for automobile products acts as a significant solution to observe the measures taken on by the local manufacturers. This study is to analyze the impact of these duties on the Chennai Industrial sector. The study found that the Disruption in supply chain operations and decline in market competitiveness as the most significant challenge and Balanced growth with greater market opportunities as the long-term consequences and local employment as the impact. It is concluded that Anti-dumping duties implemented by the Indian government plays a significant role in India’s trade policy to counter unfair trade practices committed by China on Indian domestic Industries. And the trade remedies should be made in the manner that it goes well with the domestic capacity-building.

A study on how ERP systems transform hospital Decision-Making

2026-05-17T14:27:48+02:00

Decision-making in hospitals is very crucial, and for the process to happen effectively, leveraging of information technology is essential. Enterprise resource planning systems is one such application which could be deployed and utilized effectively to aid decision making. While large hospitals have the financial resources to quickly adopt new technologies, the small and medium category of hospitals still struggle to do so. This study aims to understand the factors that influence the adoption of ERP and how the adoption impacts decision-making in small and medium hospitals. Ease of use, top management attitude, employee involvement, data security, and data storage are factors that have been analyzed in this study. Data for the empirical study was collected from 150 hospitals in the southern state of India. Once the convergent and discriminant validity of the questionnaire was ensured, structural equation model was built using Warp PLS software. The results show that ease of use, top management attitude, data security and data storage have a positive relationship with the adoption of ERP. The study also found that the adoption of ERP has a positive relationship with the decision-making process in hospitals. This study contributes to the domain knowledge of ERP implementation in hospitals.

A Study on the Transportation Industry Customer Churn using Machine Learning: A Systematic Literature Review

2026-05-17T14:30:26+02:00

Machine Learning (ML) has become a crucial technology for addressing customer churn by enabling businesses to predict customer attrition, identify at-risk customers, and develop proactive retention strategies. Although the application of ML in customer churn management has gained momentum, existing research remains scattered across different disciplines and publication outlets. This study conducts a systematic literature review to consolidate and synthesize the fragmented knowledge in the transportation sector on the use of ML in customer churn prediction and prevention. The review examines peer-reviewed publications from the ranked journals between 2019 and 2024. The search strategy identified 67 studies, of which 34 were selected as primary papers relevant to this research. The findings contribute to the literature by (i) assessing the current state of ML applications in customer churn, (ii) identifying key ML techniques employed across different stages of customer churn management (prediction, prevention, and intervention), and (iii) summarizing the reported benefits of ML in reducing customer attrition and improving retention outcomes. This study offers valuable insights for both researchers and practitioners aiming to leverage ML technologies to mitigate customer churn and enhance customer loyalty.

Predicting Small Business Longevity and Competitiveness Through Self-Random Manhattan Botox-Coupled Attention Network Driven Strategic Entrepreneurship

2026-05-17T14:38:29+02:00

Predicting the survival and competitiveness of small businesses is an intricate problem owing to the multidimensional nature of strategic, financial, and operational aspects. Most current models do not accurately capture the complex dynamics between firm dynamics and survival patterns over the long run. In view of this, the study presents a new deep learning model known as Self random Manhattan Botox coupled Attention network (Self-RanMB-CAN), whose goal is to improve the accuracy of prediction for small business survival and competitiveness. A structured dataset named "Small Business Longevity Dataset" consisting of 500 records with 12 critical features has been created to enable this analysis. The data is preprocessed with Cumulative Curve Fitting Approximation (CCFA) to provide normalized trend representation, and then the Discrete Cosine-Krawtchouk–Tchebichef Transform (DCKTKT) is applied for feature extraction to maintain spectral and spatial information. The Self-RanMB-CAN model proposed incorporates a Random-Coupled Neural Network (RCNN) coupled with Manhattan Self- Attention (MaSA), enabling targeted attention on high-impact features, while the Botox Optimization Algorithm (BOA) optimally adapts network parameters to drive performance to its maximum potential. Experimental results reflect a prediction accuracy level of 99.9%, reflecting the stability of the proposed approach. The proposed model has twofold advantages: (i) it greatly improves feature discrimination for better strategic decision-making and (ii) guarantees optimal learning of parameters in complex business environments.