A Critical Analysis of Inter-Relationship between Organs of Dividend Policy in Indian Pharmaceutical Industry

 

Dinesh S1*, Selvabaskar S2, Surulivel S. T3, Alamelu R4, Ananthi. M5

1Assistant Professor, School of Management, SASTRA Deemed to be University, Thanjavur, Tamilnadu

2Associate Professor, School of Management, SASTRA Deemed to be University, Thanjavur, Tamilnadu

3Senior Assistant Professor, School of Management, SASTRA Deemed to be University, Thanjavur, Tamilnadu

4Assistant Professor, School of Management, SASTRA Deemed to be University, Thanjavur, Tamilnadu

5Research Scholar, Department of Commerce, Madurai Kamaraj University, Madurai, Tamilnadu

*Corresponding Author E-mail: dinesh@mba.sastra.edu

 

ABSTRACT:

This study aims to analyse the inter-relationship between organs of the dividend policy in Indian pharmaceutical industry. This study implies the nature of empirical research design. Purpose of this study is to know the trends and patterns of dividend policy in the pharmaceutical industry. Find out the significant determinants that influence the dividend decisions in the pharmaceutical industry. The inter-relationship between the determinants of the dividend was identified and examined by applying appropriate statistical techniques. The extent of the relationship between the dependent and independent variables occurred and rediscovered. This study has tested twelve hypotheses to arrive the assumption for the formulation of the conceptual framework for this study. The results revealed from the value of the coefficient of determination known as R2 is equal to 0.956. It means that the variables are put together to explain the variations in DPS to the extent of 95.6% in the pharmaceutical industry. The Structural Equation Modelling has applied in this study to identify the dividend policies followed in the pharmaceutical industry, and it has discovered that the following mediating variables like Earnings Per Share, Debt-Equity Ratio, Sales Growth, Net Profit to Net worth Ratio, and Operating Profit Ratio as the essential determinants for dividend decisions.

 

KEYWORDS: Model validity, Earnings per share, Profitability.

 

 


1. INTRODUCTION:

The pharmaceutical industry always involves in discovers the new medicine for the diseases, develops the existing processes or solutions, produces the best quality medicines to withstand and outreach the competition. The topic of dividend policy remains one of the most controversial issues in corporate finance.  The dividend has defined as the distribution of created value to the shareholders. It may be in the form ‘Cash Dividend' or through the distribution of stocks of the company which is known as ‘Stock Dividend'.

 

 

Dividend policy has defined as the trade-off between the magnitude of retained earnings and distributed cash or securities.

 

The dividend payment of a company is looked upon differently by different sets of people associated with the company. For the investors, dividends are not merely means of regular earnings but also an essential input for determining the worth and credential of the firm. For managers, dividend payment might well determine the level of investment in profitable investment projects. Lenders look at it carefully because they feel that the more the dividend payment, the less will be the amount available for servicing and redemption of their claims.

 

 

 

 

2. STATEMENT OF THE PROBLEM:

In spite of the extensive research undertaken into dividend policy, gaps in research remain, from both theoretical and empirical perspectives. These gaps have noted by Allen and Michaely (2016)1 and DeAngelo, DeAngelo and Skinner (2017)2 amongst others. In particular, any attempt to achieve a consensus or to find a universal solution to specific dividend problems may not be attainable because dividend policy operates in a real-world environment that is multivariate and complicated. Adinath B. Kuchanur. (2012)3 and Ayan Majumdar (2017)4 note this difficulty of developing a one-size-fits-all explanation for dividend policy, and they explain that factors such as legal regulation, corporate governance, and firm characteristics vary across countries and Devika Rani P (2018)5 suggest that the ‘Dividend-payment patterns of firms are a cultural phenomenon, influenced by customs, beliefs, regulations, public opinion,  perceptions and hysteria, general economic conditions and several other factors, all in perpetual change, impacting different firms differently. Accordingly, it cannot be modelled mathematically and uniformly for all firms at all times.

 

Most previous studies have dealt with the dividends policy concept but most of them have dealt with one type of dividend (the cash dividend) (Miller and Modigliani, 19616, Abhin. J, Diljith. P, A. Ananth. 20187, and  Holder et al., 20148) and do not make a clear distinction between the dividends policy concept and dividend types. The dividends policy concept consists of three kinds of dividends (cash, share and repurchase) (Moyer et al., 20149 and Nilam Panchal. 201810). Some studies deal with either share dividend (Bali, 2016)11 or share repurchase (Ikenberry et al., 201412 and Rupal Muduli, Udayan Das. (2018)13). The company may distribute profits in the form of regular cash dividends, or shares dividends to shareholders. However, both types might be distributed at the same time. On the other hand, shareholders can also obtain profits (as a capital gains) when the company repurchases its shares and considers the regular cash dividend as something quite common (Broyles, 201614 and Rajesh Khurana, 201815). Therefore, there is a lack of studies covering all three types of dividends together at the same time.

 

This research has focused on the study of dividend trends such as the percentage of dividend-paying companies over the past twelve years. This study is to find the patterns among the determinants cutting across the pharmaceutical industry. Girish. S, Dr Kavitha Desai (2018)16 and Thummala. Sudheer, Bodduluri. Sudhir. (2018)17, The decision to pay or change dividends is affected by vital firm characteristics such as firm size, Dividend per share, Earning per share, Existence of organization, growth of sales, tangible assets in the organization, debt and equity ratio, Operating profit ration, net profit ratio, Net profit to net worth ratio, Dividend payout ratio and operating cost ratio.

 

3. OBJECTIVES OF THE STUDY:

·        To find out the interrelationship between the organs of dividend  in the pharmaceutical industry.

·        To find out the extent of the relationship between the dependent and independent variables by applying multiple regression analysis.

·        To contribute to the philosophy of the subject by offering unique and exclusive models for the pharmaceutical industry that determine dividend decisions.

 

4. RESEARCH DESIGN:

The research design is the blueprint of research work. Basically, since the nature of the study is Empirical and analytical. Several pieces of evidence from the earlier researches, company records are taken into consideration, and multivariate analysis is used to arrive at the appropriate determinants that have more impact on dividend decisions. This research design is also Analytical because of several financial data like Dividend Per Share (DPS) Debt Equity Ratio (DER) Tangibility, Existence-of-the-organization, Size of the firm, Sales growth, Profitability of the firm, Operating cost ratio, net profit, Networth, Dividend payout ratio and operating profit ratio have taken into consideration for the analysis.

 

4.1 Tools used in this study:

Structural Equation Modelling has applied through IBM SPSS AMOS.

 

4.2 Period of the study:

This research has focused on the study of dividend trends such as the percentage of dividend-paying companies over the past twelve years from 2006-2018 financial years.

 

4.3 Companies included for representing the pharmaceutical industry:

1     Sun Pharma

2     Cipla

3     Dr Reddy's Laboratories

4     Lupin Limited

5     Cadila Healthcare

6     Divis Labs

7     GlaxoSmithKline Pharmaceuticals Ltd

8     Ipca Labs

9     Aurobindo

10   Ranbaxy

 

 

5. DATA ANALYSIS AND DISCUSSION:

5.1   AMOS graphically exhibits the Dividend Determinants of the pharmaceutical industry

 

Fig. 1: AMOS graphically exhibits the Dividend Determinants of the pharmaceutical industry

 

Analysis of path diagram reveals that dividend has positive relationship EPS, DPS, TANG, DER, DPR and OCR which is significant at 1 per cent and 5 per cent levels and negative relationship with AGE, SIZE, GROW, OPR, NPR and NPNW. The analysis of the model suggests that all the measured variables except AGE, SIZE, GROW, OPR, NPR and NPNW influence the dividend policy of the select companies in the pharmaceutical industry

 

5.2 Testing of Hypotheses:

The following table depicts the results of the testing of the hypotheses.

 

Table 1: Testing of Hypotheses

Hypotheses

Hypothetical Relationship

Result

H11: There is a positive impact of EPS on the dividend in the pharmaceutical industry.

Positive

Confirmed

H12: There is a negative impact of DPS on the dividend in the pharmaceutical industry.

Positive

Confirmed

H13: There is a positive impact of AGE on the dividend in the pharmaceutical industry.

Negative

Not

Confirmed

H14: There is a positive impact of SIZE on the dividend in the pharmaceutical industry.

Negative

Not

Confirmed

H15: There is a positive impact of GROW on the dividend in the pharmaceutical industry.

Negative

Not

Confirmed

H16: There is a positive impact of TANG on the dividend in the pharmaceutical industry.

Positive

Confirmed

H17: There is a positive impact of DER on the dividend in the pharmaceutical industry.

Positive

Confirmed

H18: There is a positive impact of OPR on the dividend in the pharmaceutical industry.

Negative

Not

Confirmed

H19: There is a positive impact of NPR on the dividend in the pharmaceutical industry.

Negative

Not

Confirmed

H110: There is a positive impact of NPNW on the dividend in the pharmaceutical industry.

Negative

Not

Confirmed

H111: There is a positive impact of DPR on the dividend in the pharmaceutical industry.

Positive

Confirmed

H112: There is a positive impact of OCR on the dividend in the pharmaceutical industry.

Positive

Confirmed

Chi-square =5752.9

Degrees of freedom = 78, Probability level = .000


Table 2: Regression estimates

Latent Variable

 

Measured Variables

Estimates

SE

R2

CR

P

F1(ED)

<---

EPS

6.679

.700

.61

9.547

***

F1(ED)

<---

DPS

16.809

2.163

.97

7.770

***

F2(CD)

<---

AGE

.495

.027

.99

18.162

***

F2(CD)

<---

SIZE

.138

.025

.99

29.219

***

F2(CD)

<---

GROW

-3.238

.089

.92

37.358

***

F2(CD)

<---

TANG

3.224

.072

.04

44.578

***

F2(CD)

<---

DER

36.500

1.041

.25

35.073

***

F3(PR)

<---

OPR

77.63

1.082

1.00

21.666

***

F3(PR)

<---

NPR

.415

.047

.22

8.797

***

F3(PR)

<---

NPNW

4.230

.261

.20

16.213

***

F3(PR)

<---

DPR

3.096

.181

.43

17.092

***

F3(PR)

<---

OCR

25.309

.972

1.00

26.043

***

***- Significant at 1% level

 


Structural Equation Model (using the package) AMOS graphically explains the relationship between the variables that have an influence on the determination of dividend. Dividend is determined by three important latent variables namely, Earnings and Dividend (ED), Capital Structure Determinants (CD) and Profitability of the Firm (PR).

 

Dividend Per Share (DPS) has a positive relationship with Earnings and Dividend by 16.81 and Earning per share also has a positive connection with earnings and dividend by 6.68.

Debt equity ratio has the highest influence in determining capital-structure of the firm by 36.50 followed by Tangibility that controls by 3.22, "Existence-of-the-organization" influences by .50, Size of the firm has significantly measured by .138 and Sales growth has a negative impact on determining capital structure determinants of the firm by -3.238. The other variables like the existence of the firm, size of the organisation, sales growth, tangibility, and debt-equity ratio have significance for determining the capital structure of the firm at 1% level of importance.

 

Profitability of the firm has determined by operating profit ratio by 77.63 with 1% level of significance. Operating cost ratio is also a key variable for determining the profitability of the firm by 25.31 followed by net profit to net worth influenced by 4.23, Dividend payout ratio is controlled by 3.10 and Net profit ratio also has a lower level of influence in determining the profitability of the firm by .42. The other variables like operating profit ratio, net profit ratio, net profit to net worth, dividend per share and operating cost ratio have significance in determining the profitability of the firm at 1% of the level of importance. The most critical coefficients of all the manifest variables are mentioned in the above the table with a value of 2.962 that is significant at 1 per cent level. Among the selected variables, twelve variables are the most influencing variables that determine the dividend policy of select companies in the pharmaceutical industry.


 

5.4 Model fit summary:

Table 3: Baseline Comparisons

Model

NFI Delta1

RFI rho1

IFI Delta2

TLI rho2

CFI

Default model

.72098990

.7178897

.84568798

.66768956

.96231234

Saturated model

1.00000000

1.0000000

1.0000000

Independence model

.00000000

.0000000

.00000000

.00000000

.00000000

 


Normed Fit Index (NFI), Models with overall fit indices of less than 0.8, can usually be improved substantially. These indices and the general hierarchical comparisons described previously are best understood. (Bentler and Bonett, 1980, p. 600TLI)18.

 

NFI value is less than .88, which implies that this model is substantially improved the excellent model. Relative Fit Index (RFI)- the rho1 value is 0.72. Hence it is inferred that it is a good fit. Incremental Fit Index (IFI)- the value of delta2 is 0.84. Therefore it is concluded that there is a perfect fit. Tucker Lewis Index (TLI)- the rho2 value 0.67 is close to the high range. Above indicators indicates an ideal fit. Confirmatory Fit Index (CFI) is also close the value of 1. CFI states the perfect fit for the model.


 
Table 4: Root Mean Square Error of Approximation (RMSEA)

Model

RMSEA

LO 90

HI 90

PCLOSE

Default model

.02989762

.01678560

.06278523

.00289783

Independence model

.08787656

.07876567

.09897876

.00098978

 

 

 


Browne and Cudeck 199319,, It is infers that the RMSEA value if fewer then.05. Which; indicates the low level approximation of error in this model and it closely fits the model toward to the degree of freedom. P close value used to test the hypothesis with the model and degree of freedom.

 

The above table depicts that the RMSEA value is less than .05 which indicates the low level of approximation error in this model and the model's close fit concerning the degree of freedom. P close value has been used to test the hypothesis with the model and degree of freedom. Hence it can be inferred that this model achieves a significant fit in measuring the dividend determinants in the pharmaceutical industry.

 

 

 

6. FINDINGS OF THE STUDY:

6.1 Multiple regressions for The pharmaceutical industry:

·        The analysis of variance of multiple regression models for DPS of the pharmaceutical sector indicates that the overall significance of the model is not that high. The coefficient of determination R2 value showed that these variables put together explaining the variations of DPS to the extent of 95%.

 

6.2 Model prediction and fitness for measuring dividend determinants of the pharmaceutical industry:

·        The critical ratios of all the manifest variables except OPR and OCR are above the table value 2.962 and significant at 1 per cent level. Among the selected variables, ten variables are the most influential factors in determining the dividend policy of select companies in the pharmaceutical industry

 

7. CONTRIBUTION OF THE RESEARCH:

7.1 Contribution to the conceptual framework:

Structural Equation Model (AMOS) graphically explains the relationship between the variables influencing the determination of dividend. Dividends have determined by three critical latent variables namely, Earnings and Dividend (ED), Capital Structure Determinants (CD) and Profitability of the Firm (PR). Thus, the research throws light on the most critical factors that affect dividend policies in the pharmaceutical industry. The analysis can be extended to other sectors to identify the determinants of dividends in these industries.

 

7.2 Contribution to the Financial Decision-making Process:

Finance involves three major decision areas-investment decisions and dividend decisions. The outcome of the research will help the finance managers in dividend decisions. Companies that want to achieve a target payout ratio can use the models developed in this research to manage the essential determinants that have been identified in the result and achieve the desired effect.

 

8. CONCLUSION:

The trends and patterns of dividends and the major determinants that influence the dividend decisions are explored and studied in the pharmaceutical industry. The interrelationship between the determinants of the dividend was identified and examined. The extent of the relationship between the dependent and independent variables was identified and rediscovered and twelve hypotheses were tested to arrive at the tentative assumption in the formulation of the conceptual framework. Validity of dividend determinant variables for the pharmaceutical industry has been identified. The extent of risk connected to each determinant of dividend in the pharmaceutical industry was manifested by calculating Beta coefficients and finally to contribute to the philosophy of the subject a set of unique and exclusive Structural Equation models has presented to the pharmaceutical industry.

 

The results revealed from the Structural Equation Models applied to the pharmaceutical industry discovered that the following mediating variables like Earnings Per Share, Debt-Equity Ratio, Sales Growth, Net Profit To Net worth Ratio, and Operating Profit Ratio as the critical determinants for dividend decisions.

 

 

 

9. REFERENCES:

1.       Allen and Michaely, Do Dividends Matter?, International conference on financial analysis, Graduate School of Business, The University of Chicago, 2016.

2.       DeAngelo, DeAngelo and Skinner, Security Analysis: Principles and Techniques,11th ed., New York, Mc Grew Hill Book Company, 2017.

3.       Adinath B. Kuchanur. Consistent Performance in Key Operating Dimensions: A Study of the Selected Public Sector and Private Sector Banks in India. Asian J. Management 3(2): April-June, 2012 page 62-72.

4.       Ayan Majumdar. Empirical Modeling of Corporate Dividend Policy: A Study on Nifty 50 Companies. Asian J. Management; 2017; 8(3):718-722.

5.       Devika Rani P. Dividend Declaration and its Impact on the Stock return of some select firms of Cement Industry. Asian Journal of Management. 2018; 9(3):1085-1089.

6.       Miller and Modigliani, Dividend Policy, Growth and the Valuation of Shares, Journal of Business, vol. 34, October 1961.

7.       Abhin. J, Diljith. P, A. Ananth. Impact of Dividend Policy Determinants on Indian Capital Market.Asian Journal of Management. 2018; 9(2):880-884.

8.       Holder et al., The Investment, Financing and Valuation of the Corporation, Homewood, III, Richard Irwin, 2014.

9.       Moyer et al., Dividend Policy of Indian Corporate Firms; An Analysis of Trends and Determinants, NSE Research Initiative, Dec. 2014,

10.     Nilam Panchal. How does Dividend Policy Impact the Value of the Firm? – An analysis of selected Indian Sectors. Asian Journal of Management. 2018; 9(1):99-106.

11.     Bali, Trends in Dividend Payout – A Study of Selected Indian Companies, Journal of Management Research, Vol. 5, No. 3, Dec. 2016.

12.     Ikenberry et al., Dividend payout trends in the post-liberalisation era: A Case Study of Colgate Palmolive (I) Ltd. Management Accountant, March 2014,

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

14.     Broyles, Journal of Business Finance and Accounting, 36(3) and (4), 496–522, April/May 2016, 0306-686X doi: 10.1111/j.1468-5957.2009. 02126 .x

15.     Rajesh Khurana, D. P. Warne, Stock Price Adjustments to Selected Corporate Announcements: A Study of Dividend Announcements. Asian Journal of Management. 2018; 9(1):649-659.

16.     Girish. S, Dr Kavitha Desai. Analysis of Accounting Variables and its impact on the Market price per Share: Evidence from Nifty Pharma Index Companies of India. Asian Journal of Management. 2018; 9(1): 333-336

17.     Thummala. Sudheer, Bodduluri. Sudhir. Consumer Attitude towards E-tailing: An empirical study on Rural and Urban Areas. Asian Journal of Management. 2018; 9(1):17-22.

18.     Bentler, P. M., and Bonett, D. G., Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin,1980, 88, 588–606.

19.     Browne, M. W., and Cudeck, R.. Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models, 1993, pp. 136–162. New- bury Park, CA: Sage.


 

 

 

 

 

Received on 31.10.2018         Modified on 19.11.2018

Accepted on 14.12.2018      © RJPT All right reserved

Research J. Pharm. and Tech. 2019; 12(3): 990-994.

DOI: 10.5958/0974-360X.2019.00163.X