Date of submission: March 24, 2021; date of acceptance: May 5, 2021. * Contact information: kaur.navleen4@gmail.com, Shri Atmanand Jain (P.G.) Col- lege, Ambala City, Haryana, India, 134003, phone: +917206691999; ORCID ID: https:// orcid.org/0000-0002-2643-0506. Copernican Journal of Finance & Accounting e-ISSN 2300-3065 p-ISSN 2300-12402021, volume 10, issue 2 Kaur, N. (2021). Determinants of Dividend Payout Decisions of Original Equipment Manufactur- ers from Indian Automobile Industry. Copernican Journal of Finance & Accounting, 10(2), 9–26. http://dx.doi.org/10.12775/CJFA.2021.005 navleen Kaur* Shri Atmanand Jain (P.G.) College, Ambala City determinants of dividend payout decisions of original equipment manufacturers from indian automobile industry Keywords: dividend, panel data, automobile, original equipment manufacturers, Bom- bay Stock Exchange. J E L Classification: G35, C23. Abstract: The purpose of this study is to identify and analyze the variables that signi- ficantly affect dividend payout decisions of Original Equipment Manufacturers (OEMs) from Indian automobile industry listed on the Bombay Stock Exchange (BSE). Analy- sis is based on balanced panel data with 180 observations of 12 companies over a pe- riod of 15 years i.e. from 2003-04 to 2017-18. Descriptive analysis, correlation analysis, and static panel data regression analysis including regression diagnostics have been used as statistical tools to achieve the purpose of the study. STATA software was used to analyze the data in the present study. The findings indicate that the significant de- terminants of dividend payout decisions of sample companies during the study period were profitability, size, book value per share, tangibility of assets, leverage and price earnings ratio. The findings of the study support various theories of dividend policy viz. Signalling, Pecking Order, and Transaction Cost. As per authors’ knowledge, this is the first study focusing on the determinants of dividend payout decisions of OEMs in In- dia using the data from 2003-04 to 2017-18. The empirical findings of the present study Navleen Kaur10 will provide useful insight pertaining to dividend payout decisions to various stakehol- ders of different companies and will also be helpful to the future researchers. Introduction Dividend payout decisions are an integral part of financial management. Suc- cessful companies make profits and these profits can be retained in the busi- ness for investment in future growth opportunities or distributed as dividends among the shareholders. The decision with regard to what percentage of prof- its should be retained in the business and what percentage should be distrib- uted as dividends is known as dividend policy of a company. Determinants of dividend payout decisions of the corporate sector have been researched exten- sively in the past but still it is one of the controversial topics in the literature of finance. Various theories like Signalling, Pecking Order, and Transaction Cost etc. guide the dividend decision of the corporate sector. Indian automobile in- dustry plays a vital role in the overall growth and development of the country because growth of many other industries viz. Iron & Steel, Lead, Chemicals, Capital Goods, and Service Sector etc. is linked with the growth of automobile industry. After the liberalization of the Indian economy in 1991, the number of automobile manufacturing facilities increased rapidly in the country. As per the annual report (2018-19) of Department of Heavy Industries, Government of India, turnover of Indian automobile industry is equivalent to 7.1 per cent of overall GDP. After reviewing the literature, it was noticed that meager research has been conducted on the factors affecting dividend payout decisions of origi- nal equipment manufacturers (OEMs) from Indian automobile industry. So, the present study bridges the research gap by studying the same. Literature review and hypotheses formulation The review of the previous studies used for the formulation of hypotheses for this study is presented below. Profitability and Dividend Payout Signalling theory of dividend policy advocates that companies with higher profits pay higher dividends to their shareholders. Many previous research determinAnts of dividend PAyout decisions… 11 studies found a positive and significant relationship between profitability and dividend payout ratio of a company (Rehman & Takumi, 2012; Kumar & Su- jit, 2017; Thirumagal & Vasantha, 2017; Bostanci, Kadioglu & Sayilgan, 2018; Chakraborty, Shenoy & Kumar, 2018; Franc-Dąbrowska, Madra-Sawicka & Ul- richs, 2019; Pinto & Rastogi, 2019). Another theory with regard to the relation- ship between profitability and dividend payout mentions that the companies with high profits pay less dividends because these companies retain their prof- its for investment in future growth opportunities. Kaźmierska-Jóźwiak (2015) found empirical evidences in support of a negative and significant relationship between these variables. After reviewing the literature, it was found that dif- ferent proxies have been used to represent the profitability position of a com- pany while studying its relationship with the dividend payout ratio of different companies viz. return on assets, return on equity, return on capital employed, and earnings per share etc. The present study uses return on capital employed as a proxy of profitability of selected companies because this ratio measures the overall profitability of a company (Tulsian, 2009). H1: There is a positive relationship between profitability (ROCE) and divi- dend payout ratio of Indian automobile companies. Liquidity and Dividend Payout Previous research studies found different results while testing the relation- ship between liquidity position and dividend payout ratio of the corporate sec- tor. The studies which found a negative and insignificant relationship between these two variables include Kaźmierska-Jóźwiak (2015) and Franc-Dąbrowska et al. (2019). Kumar and Sujit (2017), Bostanci et al. (2018) and Sumathy and Rajasekaran (2019) found positive significant relationship while Rehman and Takumi (2012) discovered positive insignificant relationship between liquidity position of a company and its dividend payout ratio. Generally, either current ratio or quick ratio is used as a proxy to represent liquidity position of a com- pany. The present study uses quick ratio as the proxy for liquidity because it is a better measure of liquidity as compared to current ratio because the former takes into consideration only liquid assets of a company and ignores inventory, prepaid expenses, etc. The study also assumes a negative relationship between liquidity and dividend payout ratio because high liquidity suggests lower divi- Navleen Kaur12 dend payments to the shareholders resulting in high availability of cash or oth- er liquid assets in a company. H2: There is a negative relationship between liquidity (QR) and dividend payout ratioof Indian automobile companies. Size and Dividend Payout With regard to the relationship between size of a company and its dividend payout ratio, it is generally said that, bigger the company higher dividends it will pay to its shareholders. Ghosh (2010), Kumar and Sujit (2017), Franc- Dąbrowska et al. (2019) and Thapa (2021) identified positive and significant relationship between these two variables. In previous studies, size of a com- pany is calculated either by taking natural logarithm of total sales or natural logarithm of total assets. The presents study calculates this variable by taking natural logarithm of total assets of the selected companies. H3: There is a positive relationship between size and dividend payout ratio of Indian automobile companies. Book Value per Share and Dividend Payout The author did not find any previous study which empirically investigated re- lationship between these two variables. So, this study introduces empirical ex- amination of the relationship between book value per share and dividend pay- out ratio. Book value per share shows the value which will be distributed among the shareholders, if the company goes bankrupt. This study assumes a negative relationship between book value per share and dividend payout ratio of a com- pany because payment of dividends by a company reduces its reserves and sur- plus and thereby reducing the book value per share of the company. H4: There is a negative relationship between book value per share and divi- dend payout ratio of Indian automobile companies. Tangibility of Assets and Dividend Payout The literature consists of mixed results regarding relationship between tangi- bility of assets and dividend payout by companies. Ghosh (2010) found empiri- cal evidence in support of negative relationship between these two variables. determinAnts of dividend PAyout decisions… 13 Pinto and Rastogi (2019) found significant positive relationship between tan- gibility of assets and dividend policy of the companies from agro, mining engi- neering, textiles, construction and infrastructure, logistics and consumer good and appliances sectors while they found significant negative relationship be- tween these variables in the case of banking sector. It is assumed that a nega- tive relationship exists between tangibility of assets and dividend payout ratio of the automobile manufacturers in India because companies with more tangi- ble assets pay less dividends as more tangible assets means less current assets or cash available to pay dividends to the shareholders. H5: There is a negative relationship between tangibility of assets and divi- dend payout ratio of Indian automobile companies. Leverage and Dividend Payout Both Pecking Order and Transaction Cost Theories suggest negative relation- ship between leverage and dividend payout ratio of a company. According to first theory, companies with high fixed interest obligations make less divi- dend payments and according to second theory highly leveraged companies pay lesser dividends to their shareholders in order to reduce their transaction costs. The studies which found negative and significant relationship between leverage and dividend payout ratio include Ghosh (2010), Kaźmierska-Jóźwiak (2015), Kumar and Sujit (2017) and Chakraborty et al. (2018). Rehman and Takumi (2012) identified positive and significant relationship between these variables. The studies which found empirical evidences in support of positive relationship between these variables concluded that high dividend payments indicate towards good financial position of the companies which enables them to raise debt funds easily. Majority of the previous studies used debt equity ra- tio as a proxy for leverage and the present study also uses the same variable to represent leverage of the selected companies. H6: There is a negative relationship between leverage (DER) and dividend payout ratio of Indian automobile companies. Price Earnings R atio and Dividend Payout Price earnings ratio measures risk in the future earnings of a company. High price earnings ratio means low risk and vice versa. There exists a negative Navleen Kaur14 relationship between business risk and dividend payments because high risk in future earnings of a company will lead to low dividend payments by the company. So, it can be deduced that price earnings ratio and dividend pay- out ratio are positively related to each other. Kaźmierska-Jóźwiak (2015) and Franc-Dąbrowska et al. (2019) identified positive but insignificant relation- ship between these two variables. Thirumagal and Vasantha (2017) and Su- mathy and Rajasekaran (2019) found positive and significant relationship be- tween these variables concluding that high risk companies pay low dividends because these companies prefer internal financing, thereby reducing the divi- dend payments. H7: There is a positive relationship between price earnings ratio and divi- dend payout ratio of Indian automobile companies. Objectives of the Study 1. To identify the variables that significantly affect dividend payout deci- sions of OEMs from Automobile Industry in India listed on the Bombay Stock Exchange (BSE). 2. To explain the magnitude of change in the dividend payout ratio of the selected companies due to these variables. Need of the Study After the introduction of liberalization in Indian economy in 1991, a large num- ber of global auto manufacturers entered India’s automobile industry putting the industry into a higher growth trajectory. This industry has been defined as the next sunrise sector for the Indian economy but still there is enough scope for its growth and development. Government of India is actively focusing on formulating various plans and policies for the development of this industry. The literature is full of studies on determinants of dividend payout decisions of corporate sector all over the world but very limited research has been con- ducted on dividend payout decisions of OEMs from Indian automobile industry. So, the present study bridges the gap in the literature by studying the firm-spe- cific determinants of dividend payout decisions of automobile manufacturers in India. The study will provide useful insight pertaining to dividend policy de- cisions to the managers and other stakeholders related to different companies determinAnts of dividend PAyout decisions… 15 all over the world. As per authors’ knowledge, this is the first study focusing on the determinants of dividend payout decisions of Indian automobile manufac- turers using the data for the period from 2003-04 to 2017-18. Research Methodology and Research Process Sample, Study Period, Data Collection and Variables The study uses company-level data consisting of all OEMs from Indian automo- bile industry listed on the BSE. Thirteen OEMs from the industry were listed on BSE as on April, 2020 viz. Ashok Leyland Ltd., Atul Auto Ltd., Bajaj Auto Ltd., Eicher Motors Ltd., Force Motors Ltd., Hero Motocorp Ltd., Hindustan Motors Ltd., Mahindra & Mahindra Ltd., Maruti Suzuki India Ltd., Scooters India Ltd., SML Isuzu Ltd.,Tata Motors Ltd., and TVS Motor Co Ltd. These companies cover four major segments of the industry viz. Commercial Vehicles, Passenger Vehi- cles, Two-wheelers, and Three-wheelers. These companies represent universe in the Indian context when it comes to the listed companies. The present study does not take into consideration non-Indian origin automobile manufacturing companies which have established their subsidiaries in India. There are few other OEMs from automobile industry in India which are also of Indian origin viz. Asian Motor Works, Chinkara Motors Pvt. Ltd., Deccan Auto Ltd., Hradyesh, and JS Auto Pvt. Ltd. but these companies are not listed on either Bombay Stock Exchange (BSE) or National Stock Exchange (NSE). As a result, their financial results are not available. Out of the automobile companies listed on the BSE, Bajaj Auto Ltd. did not form part of sample of the present study because there was a demerger of this company into three companies in the year 2007-08. The business of existing Bajaj Auto Ltd. was divided into three divisions’ viz. auto business, investment business, and finance business. So, due to the non availability of consistent data of the auto business of the company throughout the study period, it was not possible to include this company in the sample of this study.Also, till the year 2016 LML Ltd. completely shut down its operations in March, 2018. So, twelve companies formed a part of the final sample for the present study. List of com- panies listed on the BSE as on April 2020 is presented in table 1. Navleen Kaur16 Table 1. Automobile Manufacturers listed on Bombay Stock Exchange of India as on April, 2020 S. No. Name of Company Year of Incorporation BSE Code 1 Ashok Leyland Ltd. 1948 500477 2 Tata Motors Ltd. 1945 500570 3 SML Isuzu Ltd. 1983 505192 4 Atul Auto Ltd. 1986 531795 5 Scooters India Ltd. 1972 505141 6 Bajaj Auto Ltd. May, 2008 (not included in the present study, due to demerger) 532977 7 Eicher Motors Ltd. 1982 505200 8 Hero Motocorp Ltd. 1984 500182 9 TVS Motor Co. Ltd. 1911 532343 10 Force Motors Ltd. 1958 500033 11 Hindustan Motors Ltd. 1942 500500 12 Maruti Suzuki India Ltd. 1981 532500 13 Mahindra & Mahindra Ltd. 1945 500520 S o u r c e : website of the Bombay Stock Exchange, India, www.bseindia.com. The study covers the time period from 2003-04 to 2017-18. The financial year 2003-04 is selected as the beginning year for the time period of this study because Government of India launched and implemented its first comprehen- sive policy framework named ‘Auto Policy, 2002’ for automobile industry of the country in the year 2002 which resulted into major changes in the indus- try. Annual reports of the sample companies, Prowess Database of Centre of Monitoring Indian Economy and Ace Equity Database of Accord Fintech Pri- vate Limited were utilized for the purpose of data collection for the present study. Selected variables and formulae used for their computation are shown in table 2. determinAnts of dividend PAyout decisions… 17 Table 2. Variables and Formulae used for their computation Variable Proxy Symbol Formula Literature Dividend Payout Dividend Payout Ratio DPR (Total Dividend to equity shareholders/ Total net profit belonging to equity share- holders) * 100 Khan and Jain, 2011 Profitability Return on Capital Employed ROCE (Profit before interest and tax/ Average Capital employed)*100 Tulsian, 2009 Liquidity Quick Ratio QR Liquid Assets/ Current Liabilities Bostanciet et al., 2018 Size Log of Total Assets SIZE LN(Total Assets) Thirumagal and Vasantha, 2017 Book Value per Share Book Value per Share BV (Equity Capital + Reserves – Debit Balance of Profit & Loss) / Total Number of Equity Shares Malhotra and Tandon, 2013 Tangibility of Assets Tangibility of Assets TOA (Total Assets-Current Assets)/ Total Assets Pinto and Rastogi, 2019 Leverage Debt-equity Ratio DER Total Debt/Shareholder’s Equity Horne and Wachowicz (2001) Price Ear- nings Ratio Price Earnings Ratio PER Market price per share/Earnings per share Sinha, 2012 S o u r c e : based on the review of previous studies. Tools used for Analysis To identify and analyze the variables that significantly affect dividend pay- out decisions of the sample companies during the study period ordinary least square (OLS) regression and static panel data regression including fixed effects model (FEM) and random effects model (REM) were used. First of all, Breusch and Pagan Lagrangian multiplier test was applied to select between OLS re- gression, and random effects regression. The result of the test indicated the presence of significant random effects. As a next step, fixed effects regression was run which indicated that no significant fixed effects were present. Re- gression diagnostics with the help of Breusch-Pagan test, Wooldridge test and calculation of variance inf lation factors was also carried out to check the as- sumptions of the heteroscedasticity, autocorrelation, and multicollinearity re- spectively. Descriptive analysis and correlation analysis was also carried out in this study. The STATA software was used to apply all these tests and empiri- cally analyze the data used in the present study. Navleen Kaur18 Empirical Results Descriptive Analysis The study has 180 firm-year observations. All the variables depicted positive average value during the study period. The average quick ratio was much be- low the standard for this ratio indicating that the liquidity position of the se- lected automobile companies was not so satisfactory during the study period. Average debt equity ratio was also less than the standard for this ratio showing that the Indian automobile companies used lower percentage of debt in their capital structures. Table 3. Descriptive Statistics Variables Average Maximum Minimum Standard Deviation Observations DPR 2.73 5.36 0.69 1.29 180 ROCE 4.83 5.74 0.65 0.40 180 QR 0.54 4.6 0.03 0.46 180 SIZE 9.95 13.4 5.63 2.06 180 BV 202.73 1970.74 311.78 -22.44 180 TOA 0.64 5.81 0.09 0.59 180 DER 0.56 6.62 -0.34 0.88 180 PER 2.71 7.08 0.69 1.10 180 S o u r c e : author’s own calculations using STATA software. Correlation Analysis The correlation matrix shown in table 4 revealed that out of the entire explan- atory variables price earnings ratio depicted highest positive correlation with the dependent variable while liquidity position of the sample companies meas- ured by quick ratio showed lowest positive correlation with the same. Dividend payout ratio depicted positive correlation with three other explanatory var- determinAnts of dividend PAyout decisions… 19 iables viz. return on capital employed, size, and book value per share of the selected companies. Both tangibility of assets and debt-equity ratio displayed negative correlation with the dividend payout ratio of the selected companies during the study period. No high correlations were noticed among the vari- ous explanatory variables indicating that the selected model was free from the problem of multicollinearity. Table 4. Correlation Matrix Variables DPR ROCE QR Size BV TOA DER PER DPR 1.0000 ROCE 0.2500 1.0000 QR 0.0095 0.0921 1.0000 SIZE 0.4119 0.1595 -0.0931 1.0000 BV 0.0477 0.1534 0.0846 0.3498 1.0000 TOA -0.3405 -0.0479 -0.1789 -0.2793 -0.1459 1.0000 DER -0.2204 -0.1262 -0.1501 -0.0872 -0.2655 0.0620 1.0000 PER 0.4867 0.1103 0.0948 0.3452 0.2443 -0.3490 -0.3386 1.0000 S o u r c e : author’s own calculations using STATA software. Regression Diagnostics Heteroscedasticity Initially, natural log of certain variables viz. dividend payout ratio, return on capital employed, and price earnings ratio was taken to standardize the data under consideration. After that, ordinary least square regression (OLS) was run on the above variables, and then, Breusch-Pagan test was performed to check the assumption of homoscedasticity in the present regression model. Null Hypothesis for the Test: Constant Variance (Homoscedasticity). The results of the test revealed that the p-value was 0.2169. Since, the p-val- ue was more than 0.05; it resulted in the acceptance of the null hypothesis. This shows that the present model is free from the problem of heteroscedasticity. Navleen Kaur20 Autocorrelation For checking the presence of autocorrelation, Wooldridge test for serial corre- lation was conducted. Null Hypothesis for the test: No first-order autocorrelation. It was found that the p-value was 0.7754 which is more than 0.05. So, the present model is free from the problem of autocorrelation. Multicollinearity Multicollinearity refers to a situation where very high inter-correlations ex- ist among the various explanatory variables included in a regression model. The presence of multicollinearity in the data can be identified with the help of correlation matrix or by calculating the variance inf lation factors (VIFs) for the variables included in the regression. According to Gujarati and Sangeetha (2011) high values of VIFs depict the presence of high multicollinearity. They further stated that if the value of VIF for a variable is more than 10, then that variable is considered as a highly collinear variable, resulting into the presence of severe multicollinearity in the data. Table 5 shows that VIFs of all the variables included in the regression model used in the present study were less than 2. Thus, the data used in the present study is free from the problem of multicollinearity. Table 5. Variance Inf lation Factors Variables VIF 1/VIF ROCE 1.05 0.947877 QR 1.10 0.909137 SIZE 1.35 0.740564 BV 1.24 0.808603 TOA 1.23 0.813954 DER 1.22 0.822991 PER 1.39 0.719191 Mean VIF 1.23 S o u r c e : author’s own calculations using STATA software. determinAnts of dividend PAyout decisions… 21 Selection of the Final Model from OLS, REM and FEM At first, random effects model was run and then Breusch and Pagan Lagrangi- an multiplier test for random effects was conducted to find out whether there are significant random effects or pooled OLS regression needs to be preferred. Results of Breusch and Pagan Lagrangian multiplier test for random effects showed a p-value of 0.00 which indicated that the significant random effects were present. After that, F test was conducted and results of the F test showed a p-value of 0.1979 which indicated that significant fixed effects were not pre- sent. As a result, REM was selected to study the factors affecting dividend pay- out decisions of the selected companies during the study period. Panel Data Analysis The regression model framed to study the inf luence of various firm-specific ex- planatory variables on the dividend payout decisions of the selected companies is presented below: DPRit= β0 + β1ROCEit + β2QRit + β3SIZEit+ β4BVit + β5TOAit + β6DERit + β7PERit + uit Where, DPRit = Dividend payout ratio for company i in period t, ROCEit = Return on capital employed ratio for company i in period t, QRit = Quick ratio for com- pany i in period t, SIZEit = Size for company i in period t, BVit = Book value per share for company i in period t, TOAit = Tangibility of assets for company i in period t, DERit = Debt-equity ratio for company i in period t, and PERit = Price earnings ratio for company i in period t. β0 is the intercept; β1 is the slope (co- efficient or parameter estimate) of profitability of a company measured by re- turn on capital employed ratio; β2 is the slope of liquidity measured by quick ratio; β3 is the slope of size of the company; β4 is the slope of book value per share; β5 is the slope of tangibility of assets; β6 is the slope of leverage measured by debt-equity ratio; β7 is the slope of price earnings ratio; and uit is the error term. i= 1, 2, 3 …. 12 companies, t= Time 1, 2, 3 …15 years. Navleen Kaur22 Table 6.Panel Data Analysis Using Random Effects Model Random Effects GLS regression Number of Observations = 180 Group variable: company-id Number of Companies = 12 R2: Within = 0.0234 Observations per Company : Minimum = 15 Average = 15 Maximum = 15 Between = 0.6556 Overall = 0.3854 Prob. > chi2 = 0.0000 Variables Regression Coefficient Standard Error Z P> |z| C -.8552511 1.004725 -0.85 0.395 ROCE .3071646 .1825755*** 1.68 0.092 QR -.1110894 .1572276 -0.71 0.480 SIZE .1729584 .0517767* 3.34 0.001 BV -.0005941 .0002722** -2.18 0.029 TOA -.2744477 .1399673** -1.96 0.050 DER -.1555669 .0888975*** -1.75 0.080 PER .3034171 .0757587* 4.01 0.000 *Significant at 1 per cent level of significance, ** Significant at 5 per cent level of significance, ***Sig- nificant at 10 per cent level of significance. S o u r c e : author’s own calculations using STATA software. The panel data regression results in table 6 clearly show that all the explanato- ry variables except liquidity, were statistically significant during the study pe- riod from 2003-04 to 2017-18. Return on capital employed depicted a positive relationship with the dividend payout ratio, which was statistically significant at 10 percent level of significance. Profitability position is the strongest factor affecting dividend payout decision of the sample companies during the study period because 1 percent increase in the profitability of an Indian automobile company resulted in 0.31 percent increase in the dividend payments of the se- lected companies. Price earnings ratio which measures the risk level depicted positive relationship with the dependent variable which was statistically sig- nificant at 1 percent level of significance Price earnings ratio comes out to be determinAnts of dividend PAyout decisions… 23 the second most important factor affecting the dependent variable. With 1 per- cent increase in price earnings ratio, a 0.30 percent increase was noticed in the dividend payout ratio of the selected companies. Size also depicted statistical- ly significant positive relationship with the dividend payout ratio, at 1 percent level of significance. A percentage increase in size of an Indian automobile com- pany showed an increase of 0.17 percent in its dividend payments. Book value per share and tangibility of assets depicted negative relationship with the divi- dend payout ratio which was statistically significant at 5 percent level of sig- nificance. An increase of 1 percent in the tangibility of assets depicted 0.27 per- cent decrease in the dividend payments by the selected companies. Further, if the book value per share of the selected companies increases by 1 percent a fall of 0.0006 percent was witnessed in their dividend payments. A statisti- cally significant negative relationship was found between leverage and divi- dend payout ratio of the selected automobile companies at 10 percent level of significance. A percentage increase in debt-equity ratio showed a decrease of 0.16 percent in the dividend payments. Conclusion The study estimated random effects panel data regression model which con- firms the significant inf luence of profitability, size, book value per share, tangi- bility of assets, leverage and price earnings ratio on the dividend payout deci- sions of the OEMs from Indian automobile industry. Liquidity did not inf luence the dividend payout ratio of the sample companies significantly during the study period. This result is in line with the findings of Kaźmierska-Jóźwiak (2015) and Franc-Dąbrowska et al. (2019). Profitability, size, and price earnings ratio showed a positive relationship, while book value per share, tangibility of assets, and leverage depicted a negative relationship with the dependent variable. The results indicate that more profitable automobile companies have steady and sta- ble earnings, which result into payments of lager dividends to their sharehold- ers akin to the findings of Rehman and Takumi (2012), Kumar and Sujit (2017), Thirumagal and Vasantha (2017), and Bostanci et al. (2018), Chakraborty et al. (2018), Franc-Dąbrowska et al. (2019) and Pinto and Rastogi (2019). It is found that the companies with big size pay higher dividends as compared to the com- panies with small size like in previous studies by Ghosh (2010), Kumar and Sujit (2017) and Franc-Dąbrowska et al. (2019). The study identifies that companies Navleen Kaur24 with low risk in their future earnings are more likely to make large dividend pay- ments. A similar conclusion was reached by Thirumagal and Vasantha (2017). It is also found that higher percentage of tangible assets in the asset structure of Indian automobile companies’ results in lower dividends payments to the share- holders, similar to the findings of Ghosh (2010). The results also suggest that high degree of leverage in the capital structure of automobile companies in In- dia results in less dividend payments to their shareholders as purported by var- ious previous empirical studies viz. Ghosh (2010), Kaźmierska-Jóźwiak (2015), Kumar and Sujit (2017) and Chakraborty et al. (2018). Furthermore, fall in the book value per share of the automobile companies makes more reserves and surplus available to make higher dividend payments to their shareholders. The results of this study are also in accordance with Signalling theory, pecking order theory and transaction cost theory. Limitations and Scope for Further Research The present study focused only on the OEMs from Indian automobile industry which are listed on the BSE but there are many other automobile manufactur- ers of non-Indian origin which established their subsidiaries in India and these manufacturers are not listed on the Indian stock exchanges. These automobile manufacturers were not included in the present study due to non availability of the data. So, these non-Indian origin manufacturers can also be included in the sample for future studies if the data relating to these manufacturers is avail- able. The sample of this study can also be extended by taking into considera- tion Indian auto-component manufacturers to have understanding of the divi- dend payout decisions of the Indian automotive industry. This study included only the firm-specific explanatory variables. Thus, macro-economic variables can also become part of the future research. The present study did not include Bajaj Auto Ltd. due to non availability of data on the account of demerger of the company. This renowned company can also become part of the sample for fu- ture research studies. Funding: This research received no external funding. Conf licts of Interests, Ghostwriting and Guest Authorship: The authors de- clare no conf lict of interests, ghostwriting and guest authorship. determinAnts of dividend PAyout decisions… 25 References Bostanci, F., Kadioglu, E., & Sayilgan, G. (2018). Determinants of Dividend Payout Deci- sions: A Dynamic Panel Data Analysis of Turkish Stock Market. International Journal of Financial Studies, 6(93), 1-16.http://dx.doi.org/10.3390/ijfs6040093. Chakraborty, S., Shenoy, S.S., & Kumar, S.N. (2018). Empirical Evidence on the Deter- minants of Dividend Pay-outs in the Auto Components Sector in India. Investment Management and Financial Innovations, 15(4), 356-366. http://dx.doi.org/10.21511/ imfi.15(4).2018.29. 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