Revisiting the challenges of disinvestment practices and central public sector enterprises (CPSEs): Indian empirical evidence
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.4.61Keywords:
Central public sector enterprises, Disinvestment, Profitability, State-Owned enterprises, Multiple regression analysisDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The current study investigates the challenges faced in disinvestment practices and conducts an empirical analysis of profitability measures in the selected CPSEs from an Indian perspective. The first part of the objective clarifies the efforts initiated by the disinvestment department concerning strategies adopted, utilization of proceeds, and the reasons for success, demonstrating the qualitative standpoint of the challenges defended to date. The second part examines the profitability measures return of assets (ROA), return of net worth/ equity (RONW/E), return of capital employed (ROCE), debt/equity (D/E), enterprise value (EV) and earnings before interest, taxes, depreciation, and amortization (EBITDA), & net profit margin (NPM), clarifying the decision criteria based on accounting-based metrics. The six measures were tested against the financial and operating performance regressors such as gross profit margin (GPM), technical analysis (TA), current liabilities (CL), concentration ratio (CR), and quick ratio (QR) using multiple regression analysis. The study considers 44 CPSEs leading to 218 firm-year observations, and the study period spans from 2018 to 2022. The former objective provides fresh insights into the challenges of disinvestment practices. The work clarifies that the success factors influencing the functioning of disinvestment practices are based on financial efficiency, modernization, employment generation, regional imbalance, utilization of proceeds, and methods of privatization. The latter objective reveals the model fit that confirms the profitability measures based on decision criteria {(outcome variable) [Adj. R2]} for the best fit {[(RoNW/E) [0.78] and (D/E) [0.75]}; good fit with normality issues {(NPM) [0.99]}; average fit {(ROA) [0.47]}; and poor fit {(EV/EBITDA) [0.06] and (ROCE) [0.01]} regressed on the predictor variables. This study offers insights for policymakers, regulators, academicians, corporate houses, and investors to refine disinvestment strategies, focusing on capital and ownership structures. It highlights the role of sound corporate governance, emphasizing transparency and accountability to enhance CPSEs' economic performance and reduce agency costs. By analyzing profitability measures through multiple regression, the research fills a gap in the literature, providing a comprehensive perspective on disinvestment and modern corporate finance within the Indian context.Abstract
How to Cite
Downloads
Similar Articles
- S. Mohamed Iliyas, M. Mohamed Surputheen, A.R. Mohamed Shanavas, Trust-based symmetric game theory for physical layer security in wi-fi communication , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- Bhavya Sathenapalli, Kali Charan Sabat, Unleashing entrepreneurial spirit: Driving innovation and growth in a rapidly changing world , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Tarandeep Kaur, Sangeeta Taneja, Kashmiri Embroidery: Sustaining Cultural Heritage in a Globalized World , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Viji Parthasarathy, Manikandasaran S S, Feature Selection Techniques for IOT Crop Yield Prediction Using Smart Farming Sensor Data , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Neeshma Jaiswal, Anshu Malhotra, Sandeep K. Malhotra, PREDICTATIVE HYPOTHESIS FOR PARASITE DISEASE OUTBREAKS OF ANISAKID NEMATODES , The Scientific Temper: Vol. 1 No. 01 (2010): The Scientific Temper
- KANAKLATA ., HOST PREDILECTION STUDIES IN RANGEENI STRAIN OF LAC INSECT (KERRIA LACCA KERR) , The Scientific Temper: Vol. 10 No. 1&2 (2019): The Scientific Temper
- Abhishek Dwivedi, Shekhar Verma, SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Harsha, Alwin S. Kumar, Srihari Jwalapuram, Sravan Kumar, Marketing strategies in the pharmaceutical industry , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Deepak K. Sharma, Vandana ., Pankaj Kumar, Ambrish Pandey, Jitender Pal, Investigating physico-chemical characteristics of water and wastewater in the printing industry , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- A. Jabeen, A. R. M. Shanavas, Hazard regressive multipoint elitist spiral search optimization for resource efficient task scheduling in cloud computing , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
<< < 39 40 41 42 43 44 45 46 47 48 > >>
You may also start an advanced similarity search for this article.

