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Prabandhan: Indian Journal of Management logo

Prabandhan: Indian Journal of Management

ISSN: 0975-2854 Frequency: Monthly Peer Review: Double-blind Published since: 2008 Language: English
A publication of AMCPL
amcon.co.in
New Delhi, India
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Issue Articles

Vol. 17, No. 10 · October 2024 OPEN ACCESS
PIJM Vol. 17
2024
Prabandhan: Indian Journal of Management
Volume 17, Issue 10 · October 2024
ISSN 0975-2854
Articles 4
DOI Prefix 10.17010/pijom
Access Open Access
Table of Contents 4 ARTICLES
01 Original Article
Open Access
Do Corporate Cash Holdings Matter for ESG Performance? Empirical Evidence from India

Purpose : The study investigated the relationship between a firm’s cash holdings and its ESG performance, recognizing environmental, social, and governance (ESG) as a driver of long-term value creation.Methodology : The study examined a sample of 98 Indian firms listed …

Keywords: ESG performance, cash holdings, Indian firms, precautionary motive, slack resource theory
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02 Original Article
Open Access
Exploring the Impact of Indian and Non-Indian Holiday Schedules on Organizational Stress

Purpose : The study examined the impact of work-holiday schedules on organizational stress levels among Indian employees. We discovered the significance of sustainability reports in the minds of shareholders as a result of the increased importance of responding to global …

Keywords: organizational stress, workplace stress, holiday schedule, work calendar
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03 Original Article
Open Access
Financial Sustainability : The Influence of Internal Efficiency in Indian Commercial Banks

Purpose : The paper examined the relationship between internal bank efficiency and financial sustainability within the specific context of Indian commercial banks. The study aimed to provide empirical evidence on the determinants and consequences of internal bank efficiency and its …

Keywords: financial sustainability, bank efficiency, performance, profitability, bank stability
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04 Original Article
Open Access
A Hybrid Approach for Customer Segmentation and Loyalty Prediction in E-Commerce

Purpose : This study aimed to enhance e-commerce customer segmentation and loyalty prediction by integrating machine learning (ML) with traditional statistical methods.Design/Methodology/Approach : The research adopted a hybrid approach, utilizing k-means clustering for customer segmentation based on recency, frequency, and …

Keywords: customer segmentation, loyalty prediction, e-commerce, k-means clustering, XGBoost
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