1. INTRODUCTION:

India is the world’s second-largest telecommunications market with a subscriber base of 1.16 billion and has registered strong growth in the last decade. In 2019, India surpassed the US to become the second largest market in terms of number of app downloads. The liberal and reformist policies of the Government of India have been instrumental along with strong consumer demand in the rapid growth in the Indian telecom sector. The total number of internet subscribers increased to 780.27 million in May 2021. India is also the world’s second-largest telecommunications market. The total subscriber base in the country stood at 1,198.50 million in May 2021. Gross revenue of the telecom sector stood at Rs. 68,228 crore (US$ 9.35 billion) in the third quarter of FY21. Over the next five years, rise in mobile-phone penetration and decline in data costs will add 500 million new internet users in India, creating opportunities for new businesses.

While the industry has been given a good boost by the Government, the performance of telecom companies has always been questionable, particularly with reference to profitability. Allowing the private participation is a good move which helps in the development of infrastructure in the country, GOI should understand the financial performance position and ascertaining the determinants of financial performance of the companies involved in the development of infrastructure. The study on profitability of companies undertaken by Frederick et.al (1984) studied the risk-return characteristics of the portfolios and compared them with fundamental performance measures and found that rankings are correlated with variability of returns. Titman and Wessel (1988) found that transaction costs are the important determinant of capital structure. Hasnath and Chatterjee (1990) analysed the patterns of public sector construction expenditures in the United States for the years 1957-1985. Their analysis of expenditure trends showed that demand and supply factors influence capital expenditures. Gnanavelu (1996) found that to increase profitability of the firm, there is a need for good financial performance and minimum borrowing. Pawaskar (2001) observed the operating performance of the companies and found that asset turnover and profit margins have major impact on companies’ performance. Ali et al (2004) studied BOT model in Turkey and found that there were problems related to coordination, land acquisition and use, water, operation time period, financing mix of the project, return on equity. Narware and Sharma (2004) used ratios, correlation analysis and multiple regression analysis to study the relationship between working capital and profitability and the study reports that working capital and profitability have both negative and positive association. Cinca et.al. (2005) argue that size of a firm and the location of the firm impact the financial ratio structure. Chen and Messner (2005) analysed BOT in water projects in China and found that many factors influence the capital structure and performance of these water projects. Dale et.al (2008) analysed infrastructure companies in the US and found that there are difficulties in understanding productivity growth because of the data revisions. Karadeniz et.al (2009) investigated the factors influencing capital structure decisions of the Istanbul Stock Exchange firms and found that a number of variables influence capital structure choice. Edward and Elizabeth (2009) observed corporate social responsibility and financial performance are positively related. Gurbuz et.al (2010) observed the corporate governance and financial performance are positively related in Turkey. Pratibha (2011) examined the level of general life satisfaction among women in different organizations and found that women employed in government sector organizations are more satisfied in their life than women’s employed in private sector organizations. Venkata et.al (2011) assessed the impact of liquidity management on profitability and result revealed that liquidity assets were not maintained sufficiently to meet the creditor’s obligation, current assets were not maintain properly, the average collection period is very high and negative correlation between liquidity and profitability. Saurabh and Sharma (2016) report that capital structure or financial leverage does not impact firm’s financial performance. Rakesh (2018) studied the determinants of capital structure and reported that profitability, size, age, debt service capacity growth and tax shield variables are the significant firm-level determinants of capital structure. Rupal & Udayan (2018) studied fundamental analysis to reach at a rational investment decision for Indian pharma stocks and found that the optimum choice for investment which will reduce the ambiguity in the minds of investors in the stock market. Praveen and Manjunatha (2018) analysed the relationship between operating performance and efficiency of information technology companies using DuPont Model and results show that there is significant relationship between return on sales, return on assets and return on equity.

Ashok Kumar (2012) find in his study that there is moderate relationship between working capital management and firm’s profitability. Anitha and Nowfal (2014) show that there is significant positive correlation between liquidity and firm’s profitability. Anand and Subramanian (2015) in their study find that profitability of a company depends upon better utilisation of resources, cut-off of expenses and quality customer service. Jasminder and Harsh (2015) find that there is negative correlation between leverage and profitability. Rakesh and Janet (2018) find that there is weak negative relationship between capital structure and profitability. Manjunatha and Gujjar (2018a; 2018b) analyzed and found that net income of the organization is not enough to determine its efficiency unless profit margin, asset turnover, financial leverage is taken into consideration. In most of the developing countries there has been a debate on the level of efficiency of the state, public sector, and listed companies. Kavitha and Mohanraj (2019) found that capital structure is negatively related with liquidity while it is positively related with cost of debt, size of the business, liquidity, profitability and collateral value of asset. Manjunatha et.al (2020) found that return on equity is better in creating positive shareholders value and also found that return on sales, return on assets and assets turn over are positively correlated with ROE. Praveen and Manjunatha (2021) calculated return on equity for software and training services companies in India using three factors DuPont model and five factors DuPont model and found that there is a significant relationship between ROE, asset turnover and profit margin. Manjunatha et.al (2020) found that ROE is better in creating positive shareholders value and also found that ROS, ROA and ATO are positively correlated with ROE. Manjunatha and Vikas (2021) found that there is a significant difference in the financing pattern and independent variables have inverse relationship with the financing pattern of selected infrastructure sectors in India.

While many studies have been conducted on determinants of profitability of companies in the western countries, there are a few studies in the Indian context. Authors sample data used for their studies is also limited to one sector/few sectors. Studies by Karthikeyan (2000) and Rakesh (2018) have generally supported the determinants of profitability in India. There is no robust conclusive evidence that whether we can use particular variables to know the determinants of profitability in India and further Kavitha and Mohanraj (2019) suggested to use large sample for longer span of time to ascertain the relationship between profitability of firms and liquidity, leverage, profitability and efficiency ratios. Therefore, this study is undertaken on telecommunication industry. Bismark and Suresh (2018) studied the approaches for reviewing financial performance in telecommunication companies and found that ratio analysis was the widely used technique along with examining the impact of selected indicators like capital structure and operational practices. Further, the study also uses the ratios and regression analysis for getting the overall results for the telecommunication sector as in India. The paper is organized in four parts. Part 1 is the introduction; Part 2 presents objectives and methodology; Part 3 analyses the results; Part 4 presents the summary and conclusions. References and tables are given after Part 4.

2. OBJECTIVES AND METHODOLOGY:

2.1 We have set following objective based on the evidence of review of literature

· To test the determinants of profitability for telecommunication industry in India

2.2 Data and Sample:

We use the financial data of forty eight companies in telecommunication industry which are listed in the Indian stock exchanges. The required secondary data of annual reports are collected from the capital market line data base and prowess data base. This study uses the annual reports and the various corporate news releases of the companies to assess the determinants of the profitability of telecommunication industry. The companies are selected based on two criteria: a) the companies should have been listed and traded in Indian stock exchanges and b) annual reports and financial statements should be available for the years 1999-2000 to 2017-2018. The total number of companies included in this study, using the above criteria is forty-eight. The profitability measures of 10 dependent variables viz. a) Net Profit Ratio (NPR); b) Net Profit to Total Assets (NPTA); c) Operating Profit Ratio (OPR); d) Return on Investment (Long Term) ratio{ROI(LT)}; e) Return on Investment (Total) ratio (ROI); f) Return on shareholders’ Equity (ROE); g) Return on Total Assets (ROTA); h) Return on Fixed Assets(ROFA); i) Retained Earnings to Total Assets (RETA) and j) Operating Profit to Total Assets (OPTA) and 41 independent variables viz. 1) current ratio; 2) liquid ratio; 3) inventory to working capital; 4) current liabilities to net worth; 5) current liabilities to total assets; 6) working capital to net sales; 7) working capital to operating expenditure; 8) cash flow to current liabilities; 9) inventory turnover ratio; 10) receivables turnover ratio; 11) creditors turnover ratio; 12) total assets turnover ratio; 13) fixed assets turnover ratio; 14) working capital turnover ratio; 15) current assets turnover ratio; 16) long term debt to equity (net worth ratio); 17) total debt-equity ratio; 18) total debt (exclusive current liabilities) to debt + equity; 19) total debt (exclusive current liabilities) to total assets ratio; 20) capital gearing ratio; 21) proprietary ratio (fixed assets/shareholders equity); 22) leverage ratio; 23) long term debt to total capitalization (book value); 24) long term debt to total asset; 25) short term debt to total debt( including current liabilities); 26) EPS ; 27) pay-out ratio; 28) price to earnings ratio; 29) book value per share: 30) price to book value ratio; 31) net fixed assets to total Assets; 32) working capital to total assets; 33) retained earnings to total assets; 34) market value of equity to book value of debt; 35) market equity or market capitalization; 36) market value of firm; 37) logarithm of sales; 38) logarithm of total assets; 39) dividend to paid up capital; 40) PBIT to total assets; 41) cash profits to sales are computed from financial statements of telecommunication companies from the years 2000 to 2018 are aggregated for the telecommunication industry.

2.3 Tools of analysis:

There are numerous factors both qualitative and quantitative, including the subjective judgment of financial managers which conjointly determine the profitability of a firm. The main determinants of the profitability are many. In this study we use forty one different financial ratios to ascertain how these ratios influence the profitability of the telecommunication sector. We use financial statement analysis tools and regression for the paper. Ten ratios representing as profitability are dependent variables and forty one ratios are taken as independent variables for telecommunication industry. The following regression equations are designed to test the relationship and significance.

NPR = α_i + β_1* variable_i +e_i…1

NPTA = α_i + β_1* variable_i +e_i…2

OPR = α_i + β_1* variable_i +e_i…3

ROI(LT) = α_i + β_1* variable_i +e_i…4

ROI = α_i + β_1* variable_i +e_i…5

ROE = α_i + β_1* variable_i +e_i…6

ROTA = α_i + β_1* variable_i +e_i…7

ROFA = α_i + β_1* variable_i +e_i…8

RETA = α_i + β_1* variable_i +e_i…9

OPTA = α_i + β_1* variable_i +e_i…10

We present results of the regression co-efficients and their corresponding probability values (p-values) for telecommunications industry which results in 410 regression lines (41x10) in Tables 1A & 1B.

3. RESULTS AND ANALYSIS:

The findings of the determinants of profitability for telecommunication industry are presented in the following paragraphs.

Table 1A: Determinants of Profitability for Telecommunications Industry in India (N=48)

DV	a		b		c		d		e
IV	i	ii	i	ii	i	ii	i	ii	i	ii
1	257.6	0.3	0.0	0.5	26.4	0.0*	-1.0	0.7	6.0	0.7
2	256.6	0.3	0.0	0.5	26.0	0.1*	-0.6	0.8	8.4	0.6
3	464.2	0.8	0.0	0.8	-13.3	0.9	-3.5	0.8	-619.7	0.0*
4	-8.2	0.9	0.0	1.0	0.7	0.8	0.3	0.5	-1.1	0.7
5	-1296.1	0.0*	-0.1	0.0*	-42.2	0.0*	0.7	0.7	0.2	1.0
6	7.8	0.0*	0.0	0.1	0.2	0.0*	0.0	0.9	0.0	0.8
7	30.7	0.0*	0.0	0.0*	1.1	0.0*	0.0	0.9	-0.1	0.8
8	-2.0	1.0	0.0	0.3	-1.6	0.8	1.0	0.3	-1.5	0.8
9	0.0	0.1	0.0	0.2	0.0	0.8	0.0	0.4	0.0	1.0
10	0.7	0.8	0.0	0.9	0.1	0.4	0.0	0.7	0.0	1.0
11	1.6	0.9	0.0	0.4	0.2	0.7	-0.1	0.7	0.1	0.9
12	439.7	0.3	0.0	0.5	33.5	0.2	16.0	0.0*	-32.4	0.3
13	2.1	0.8	0.0	0.8	0.3	0.3	0.2	0.0*	0.2	0.7
14	6.8	0.8	0.0	0.5	1.8	0.3	0.9	0.0*	-1.1	0.6
15	484.5	0.1	0.0	0.2	11.1	0.4	2.4	0.4	-10.0	0.6
16	-4.6	0.9	0.0	0.7	0.0	1.0	-0.1	0.7	-0.6	0.8
17	-7.0	0.9	0.0	1.0	0.7	0.8	0.3	0.6	-1.0	0.8
18	-0.3	0.9	0.0	0.8	0.0	1.0	0.0	0.0*	0.0	1.0
19	1734.6	0.0*	0.1	0.1	80.3	0.0*	-15.2	0.0*	36.6	0.5
20	-9.4	1.0	0.0	0.6	0.4	1.0	-0.2	0.9	-0.8	0.9
21	-1.7	1.0	0.0	0.7	0.1	1.0	-0.2	0.5	-1.1	0.6
22	1944.4	0.0*	0.1	0.0*	66.7	0.0*	0.1	1.0	1.0	1.0
23	3.5	1.0	0.0	0.8	2.0	0.6	-1.0	0.3	0.3	1.0
24	-1414.4	0.1	-0.2	0.0*	-25.1	0.6	-9.8	0.3	27.7	0.6
25	101.2	0.6	0.0	0.9	11.2	0.3	-2.8	0.1	2.2	0.9
26	0.0	0.2	0.0	0.4	0.0	0.8	0.0	1.0	0.0	0.7
27	-0.4	0.9	0.0	0.9	9.3	0.2	-0.4	0.9	0.3	1.0
28	1849.5	0.3	12.0	0.1	3032.8	0.2	1635.0	0.2	1330.9	0.5
29	0.0	0.2	0.0	0.4	0.0	0.9	0.0	1.0	0.0	0.8
30	465.1	0.5	3.6	0.2	1054.1	0.2	812.8	0.1	763.4	0.3
31	1128.3	0.4	0.1	0.5	71.3	0.3	-23.1	0.1	10.1	0.9
32	1274.2	0.0*	0.1	0.0*	42.6	0.0*	0.2	0.9	-2.1	0.9
33	1067.2	0.0*	0.1	0.0*	35.1	0.0*	0.8	0.7	0.9	0.9
34	-14939.5	0.9	-3.2	0.7	-1431.3	0.7	449.8	0.6	-841.1	0.9
35	-13.3	1.0	0.0	0.9	-6.0	0.7	1.2	0.7	-0.8	1.0
36	0.0	0.7	0.0	0.8	0.0	1.0	0.0	0.5	0.0	0.9
37	333.4	0.0*	0.0	0.0*	21.2	0.0*	-0.9	0.5	7.1	0.3
38	111.1	0.4	0.0	0.2	2.3	0.7	-2.2	0.1	10.0	0.2
39	2174.1	0.5	0.5	0.1	121.8	0.5	-10.6	0.8	133.0	0.5
40	-210.1	0.7	0.0	0.7	-4.6	0.8	6.4	0.1	17.3	0.6
41	-231.2	0.2	0.0	0.0*	-6.1	0.4	-2.1	0.3	1.0	0.9
42	27	33	30	33	31	31	21	36	23	40
43	14	8	11	8	10	10	20	5	18	1
44		20		25		22		18		23
45		13		8		9		18		17
46		7		5		9		3		0
47		1		3		1		2		1

Source: Computed by the researcher using the ratios and fitting the simple linear regression.

Note 1: First row of the Table 1 A serial number a to d represents dependent variables which are explained

under the heading ‘data and sample’.

Note 2: First column of the Table 1A serial number 1 to 41 represents independent variables

Note 3: Second and third column of the Table1A serial number i and ii indicates co-efficient and p values

respectively. Same explanation holds good for column fourth to ninth.

Note 4: Forty second row of the table indicates the number of positive coefficients (N+ve/ P>0.05).

Note 5: Forty third row of the table indicates the number of negative coefficients (N –ve/ P<0.05).

Note 6: Forty fourth row of the table indicates the number of positive coefficients and their statistical

significance (N +ve, P>0.05).

Note 7: Forty fifth row of the table indicates the number of negative coefficients and not statistically significant

(N-ve, P>0.05).

Note 8: Forty sixth row of the table indicates the number of independent variables having statistically

significant association with dependent variable (N +ve, P>0.05).

Note 9: Forty seventh row of the table indicates the number of independent variables which do not have statistically

significant association with dependent variable (N -ve, P>0.05).

Note 10: The*mark in the p-value column denotes that the corresponding coefficients of the independent variables

are statistically significant at 5% level of significance. N at the top of the table represents the number of

observations taken for fitting the regression. For example, the number of companies taken for analysis in

telecommunication industry is forty-eight and therefore, this is the value of N.

Note 11: The source and notes hold good for the Table 1B also.

Table 1 B: Determinants of Profitability for Telecommunications Industry in India(N=48)

DV	f		g		h		i		j
IV	i	ii	i	ii	i	ii	i	ii	i	ii
1	16.1	0.2	-1.2	0.4	-27.5	0.4	0.2	0.3	0.1	0.5
2	17.0	0.2	-1.3	0.4	-29.2	0.4	0.2	0.4	0.1	0.5
3	15.1	0.9	1.8	0.9	74.1	0.7	0.4	0.8	-0.7	0.2
4	-1.2	0.7	0.0	0.9	3.5	0.6	0.0	0.9	0.0	0.1
5	-2.1	0.8	0.0	1.0	-44.4	0.1	-1.1	0.0*	0.0	0.9
6	0.0	1.0	0.0	0.6	0.0	0.9	0.0	0.0*	0.0	0.6
7	0.1	0.7	0.0	0.6	0.6	0.3	0.0	0.0*	0.0	0.8
8	13.8	0.0*	1.1	0.1	11.0	0.4	0.0	0.6	0.0	1.0
9	0.0	0.4	0.0	0.1	0.1	0.3	0.0	0.2	0.0	0.9
10	0.1	0.6	0.0	0.8	-0.2	0.6	0.0	1.0	0.0	0.7
11	0.1	0.9	0.1	0.4	0.8	0.7	0.0	0.5	0.0	0.7
12	16.9	0.5	0.0	1.0	7.7	0.9	0.3	0.5	0.7	0.0*
13	0.3	0.4	0.0	0.9	0.4	0.6	0.0	0.8	0.0	0.0*
14	1.3	0.4	0.1	0.7	4.3	0.3	0.0	0.7	0.1	0.0*
15	1.5	0.9	0.2	0.9	65.7	0.1	0.3	0.4	0.2	0.1
16	1.0	0.6	-0.1	0.5	-0.9	0.9	0.0	0.9	0.0	0.9
17	-1.2	0.7	0.0	0.9	3.7	0.6	0.0	1.0	0.0	0.1
18	-0.1	0.4	0.0	0.7	0.0	0.9	0.0	0.9	0.0	0.8
19	-11.6	0.8	1.6	0.7	217.3	0.0*	1.1	0.1	-0.4	0.1
20	-19.2	0.0*	0.7	0.6	22.9	0.4	0.0	0.9	0.0	0.9
21	-2.9	0.1	-0.1	0.5	0.1	1.0	0.0	0.9	0.0	1.0
22	11.7	0.5	-0.3	0.9	61.3	0.2	1.6	0.0*	0.0	0.9
23	-0.3	0.9	-0.3	0.6	-9.5	0.5	0.0	0.9	0.0	0.9
24	-5.9	0.9	-2.5	0.7	47.8	0.7	-2.1	0.0*	-0.2	0.4
25	-1.3	0.9	-0.1	0.9	20.5	0.4	0.0	1.0	0.0	0.6
26	0.0	0.6	0.0	0.4	0.0	0.8	0.0	0.5	0.0	1.0
27	2.9	0.7	0.1	1.0	-6.1	0.8	-0.1	0.8	0.1	0.5
28	568.1	0.8	1012.4	0.1	13227.9	0.1	62.2	0.3	45.1	0.0*
29	0.0	0.8	0.0	0.4	0.0	0.8	0.0	0.6	0.0	0.9
30	502.0	0.5	394.5	0.0*	2622.9	0.3	4.8	0.8	20.4	0.0*
31	-113.2	0.1	2.7	0.7	299.2	0.1	0.3	0.8	-0.6	0.1
32	5.7	0.6	-0.2	0.9	33.1	0.2	1.1	0.0*	0.0	0.6
33	6.8	0.4	1.3	0.2	42.9	0.1	-39.5	0.6	0.0	0.7
34	-711.3	0.9	-88.6	0.9	-6562.2	0.6	-0.1	0.8	2.6	0.9
35	-1.0	1.0	0.0	1.0	-14.2	0.7	0.0	1.0	0.0	0.9
36	0.0	1.0	0.0	0.8	0.0	0.7	0.2	0.0*	0.0	0.6
37	-2.2	0.7	0.8	0.3	40.1	0.0*	0.1	0.4	0.0	0.6
38	-3.8	0.5	0.6	0.5	30.9	0.1	3.5	0.2	0.0	0.7
39	128.1	0.5	33.0	0.1	301.2	0.5	0.0	1.0	-0.5	0.6
40	-21.3	0.3	3.9	0.0*	-19.0	0.7	-0.2	0.2	0.0	0.8
41	5.2	0.5	-1.7	0.1	-49.8	0.0*	28.0	33.0	0.0	1.0
42	25	39	21	39	29	38	12	7	25	36
43	16	2	20	2	12	3		23	16	5
44		24		19		27		10		20
45		15		20		11		5		16
46		1		2		2		2		5
47		1		0		1				0

Note 1: First row of the Table1B serial number f to j represents dependent variables which are explained data and sample.

Table 1B show that inventory to working capital as an independent variable is the determinant of the ROE. The two independent variables viz. price to book value ratio and logarithm of PBIT by total assets are the determinants of the ROTA. The three independent variable viz. working capital to net sales, total debt (exclusive of current liabilities) to total assets ratio and logarithm of sales are the determinant of the ROFA. The five independent variables viz. working capital to net sales, working capital to operating expenditure, working capital to total assets, leverage ratio and logarithm of sales are the determinants of the RETA. The 5 independent variables viz. total assets turnover ratio, fixed assets turnover ratio, working capital turnover ratio, price to book value ratio and price to book value ratio are the determinants of the OPTA.

4. SUMMARY AND CONCLUSION:

This paper has attempted to test the determinants of profitability for telecommunication industry in India. The summary and conclusions of these results are presented in this section. We found that A) working capital to total assets as an independent variable emerge as determinant for seven dependent variable viz. NPR, NPTA, OPR, ROI (LT). ROTA, ROFA and OPTA; b) Working capital to operating expenditure as independent variable emerge as determinant for six dependent variable viz. NPR, NPTA, OPR, ROI (LT), RETA and OPTA; c) Retained earnings to total assets as independent variable emerge as determinant for six dependent variable viz. NPTA, OPR ROTA ROFA, RETA and OPTA d) Logarithm of sales as independent variable emerge as determinant for five dependent variable viz. NPR, NPTA,OPR, ROI and ROTA and e) logarithm of PBIT by total assets as an independent variable emerge as determinant for five dependent variable viz. NPTA, OPR, ROTA, ROFA and OPTA. We conclude that working capital to total assets, working capital to operating expenditure, retained earnings to total assets, logarithm of sales and logarithm of PBIT by total assets emerge as major determinants of the financial performance of telecommunication industry in India. The results of the study may be used by researchers to compare with other foreign infrastructure companies to understand the determinants of financial performance of the infrastructure industries. We have analysed only the listed companies and further studies can include unlisted companies. Further studies can be undertaken for company wise analysis and also bivariate, trivariate and multivariate regressions models may be designed for better understanding of the relationship and significance in telecommunication industry in India and western countries.

5. CONFLICT OF INTEREST:

The author has no conflict of interest regarding this investigation.

6. REFERENCES:

1. Ali Senturk H, Gokhan Yazici and Burcin Kaplanoglu S (2004), Case Study: Izmit Domestic and Industrial Water Supply Build–Operate–Transfer Project, Journal of Construction Engineering and Management, 130/ 3, 449-454.

2. Ananda Kumar, V. Subramanian (2015), Profitability Analysis (A Study Conducted in Whirlpool of India Limited and Compared with Selected Competitors). Asian J. Management; 6(3): July-Sept. 241-246

3. Ashok Kumar Panigrahi (2012), Impact of Working Capital Management on Profitability –A Case Study of ACC Ltd. Asian J. Management 3(4): Oct.-Dec., 210-218.

4. Bismark Maka, N. Suresh (2018). Review of Financial Performance analysis of Corporate Organizations. Asian Journal of Management. 9(1):500-506.

5. Chen Chuan and John I Messner (2005), An investigation of Chinese BOT projects in water supply: a comparative perspective, Construction Management and Economics, 23/9, 913–925.

6. Cinca C.S, Molinero C.M, and Larraz J.L.G. (2005), Country and size effects in financial ratios: A European perspective. Global Finance Journal, 16/8, 26–47.

7. Dale W. Jorgenson, Mun S. Ho, and Kevin J. Stiroh (2008), A Retrospective Look at the U.S. Productivity Growth Resurgence, Journal of Economic Perspectives, 22/1, 3–24.

8. Edward Nelling and Elizabeth Webb (2009), corporate social responsibility and financial performance, the virtuous circle revisited, Review of Quantitative Finance and Accounting, 32/2, 197- 209.

9. Frederick L, Muller Bruce D, Fielitz Myron T, Greene (1984), Portfolio Performance in Relation to Quality, Earnings, Dividends, Firm Size, Leverage, and Return on Equity, The Journal of Financial Research, 7/1, 17-26.

10. Gnanavelu N (1996), Case Study of Financial Performance of Sakthi Sugars Limited, M.Phil Dissertation, Bharathiar University, Coimbatore-46.

11. Hasnath, Syed Abu & Chatterjee Lata (1990), Public Construction in the United States: An Analysis of Expenditure Patterns, The Annals of Regional Science, Springer; Western Regional Science Association, 24/2, 133-145.

12. Jasminder Kaur, Harsh Vineet Kaur (2015), Impact of Financial Leverage on Profitability of Fast Moving Consumer Goods Companies Listed on BSE. Asian J. Management; 6(4): Oct. -Dec., 276-282

13. Karadeniz Erdinc, Serkan Yilmaz Kandir, Mehmet Balcilar and Yildirim Beyazit Onal (2009), Determinants of capital structure: evidence from Turkish lodging companies. International Journal of Contemporary Hospitality Management, 21/5, 594-609.

14. Karthikeyan (2000), Financial performance of selected automobile companies-an analytical study, Journal of Financial Economics, 31/2, 135-175.

15. Kavitha S.K and Mohanraj V (2019), Determinants of Capital Structure with Special Reference to Selected Automobile Companies. International Journal of Scientific Research and Review, 8/1, 275-280.

16. Manjunatha and Vikas (2021), An Empirical Study on Financial Pattern of Infrastructure Sectors in India, Asian Journal of Management, 12/2, 221-227.

17. Manjunatha T and Praveen Gujjar J (2018a), Performance Analysis of Indian Information Technology Companies Using DuPont Model, IUP Journal of Management Research, 17/ 4, 1-6.

18. Manjunatha T and Praveen Gujjar J (2018b), Extended DuPont Ratio Analysis of Indian Information Technology Companies, Pacific Business Review International, 11/5, 5-14.

19. Manjunatha T, Vikas K M and Praveen Gujjar J et.al (2020), A Study on Profitability Analysis of Infrastructure Companies in India, Pacific Business Review International, 13/ 3, 57-63.

20. Narware and Vivek Sharma (2004), Liquidity management of HPC Ltd-An Empirical Study, The Management Accountant, 2/7, 201 – 203.

21. Pawaskar V (2001), Effects of Mergers on Corporate Performance in India, Vikalpa, Vol. 26, Issue 1, pp. 19-32

22. Pratibha Barik (2011), General Life Satisfaction of Female Professionals Across Different Organizations. Asian Journal Management 2(4), 197-201.

23. Praveen Gujjar J and Manjunatha T (2018), Profitability Analysis of Indian Information Technology Companies using DuPont Model. Asian Journal of Management. 2018; 9(3), 1105-1108.

24. Praveen Gujjar J and Manjunatha (2021), Testing of Dupont Model for Software and Training Services Companies in India, Asian Journal of Management, 12/2, 169-180.

25. R. Anitha, K. Nowfal (2014). Impact of Liquidity Management on Profitability: A Case Study of Malappuram Co-Operative Spinning Mills Limited, Kerala. Asian J. Management 5(3): July-September, 354-358.

26. Rakesh Kumar Sharma (2018), Factors affecting financial leveraging for BSE listed real estate development companies in India. Journal of Financial Management of Property and Construction, 23/3, 274-294.

27. Rakesh M, Janet Jyothi D Souza (2018), Impact of Capital Structure on Profitability. Asian Journal of Management. 9(3):1067-1072

28. Rupal Muduli and Udayan Das(2018). Investment in Pharma Stocks in BSE: A Performance Analysis. Asian Journal of Management. 9(1), 351-358.

29. Saurabh Chadha and Anil K Sharma (2016), Capital Structure and Firm Performance: Empirical Evidence from India, Vision, 19(4), 95–302.

30. Titman Sheridan and Roberto Wessel (1988), The Determinants of Capital Structure Choice, The Journal of Finance, XLIII/1, 1-19

31. Venkata Ramana N, S. Md. Azash and K. Ramakrishnaiah (2011), Impact of Liquidity Management on Profitability: A Case Study of Lanco Industries Limited (LIL), Srikalahasthi (AP). Asian J. Management 2(4), 182-185.

Received on 22.10.2021 Modified on 16.01.2022

Asian Journal of Management. 2022;13(2):127-133.

DOI: 10.52711/2321-5763.2022.00023