Plane Thermoelastic Waves in Infinite Half-Space Caused Decision Making: Applications in Management and Engineering Vol. 5, Issue 2, 2022, pp. 140-175. ISSN: 2560-6018 eISSN: 2620-0104 DOI: https://doi.org/10.31181/dmame0306102022b * Corresponding author. E-mail addresses: sb.16ms1302@phd.nitdgp.ac.in (S. Biswas), gautam.bandyopadhyay@dms.nitdgp.ac.in (G. Bandyopadhyay), jnmukhopadhyay@gmail.com (J.N. Mukhopadhyaya) A MULTI-CRITERIA FRAMEWORK FOR COMPARING DIVIDEND PAY CAPABILITIES: EVIDENCE FROM INDIAN FMCG AND CONSUMER DURABLE SECTOR Sanjib Biswas1*, Gautam Bandyopadhyay1 and Jayanta Nath Mukhopadhyaya2 1 Department of Management Studies, National Institute of Technology, India 2 Finance Area, Army Institute of Management, India Received: 16 July 2022; Accepted: 29 September 2022; Available online: 6 October 2022. Original scientific paper Abstract: In this paper, we aim to carry out a comparative analysis of the dividend pay capabilities (DPC) of the selected organizations belonging to the Fast Moving Consumer Goods (FMCG) and Consumer Durables (CD) sectors listed in BSE, India during the period FY 2013-14 to FY 2019-20. We select top 25 companies from FMCG group and top 5 firms from the CD sector on the basis of average market capitalization. For comparison purpose, we have considered six aspects (grounded on the extant theories on dividend policy) such as ownership, size, profitability, growth, liquidity and risk. We have used a new integrated Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) and Evaluation based on Distance from Average Solutions (EDAS) framework for our analysis. The result shows that companies do not show consistent performance over the years. We further have noticed that FMCG organizations show comparatively better capabilities that CD firms vis-à-vis dividend payment. Since, there are considerable variations in the ranking, we apply aggregation methods like Borda Count (BC), Copeland method (CM) and Simple additive weighting (SAW). We use two other popular Multi- Criteria Decision Making (MCDM) methods like multi-attributive border approximation area comparison (MABAC) and the Complex Proportional Assessment (COPRAS) for comparison with our framework to ascertain the reliability of our result. Key words: Dividend payment, investment decision making, LOPCOW, EDAS, Borda count, Copeland method. mailto:sb.16ms1302@phd.nitdgp.ac.in mailto:gautam.bandyopadhyay@dms.nitdgp.ac.in mailto:jnmukhopadhyay@gmail.com A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 141 1. Introduction Dividend is a part of the profit distributed to the investors recognizing their stake in the business and cooperation. The remaining part of the profit (after paying the dividend) is retained by the firms for reinvestment in the ongoing and future activities. Dividends are paid primarily to allure the investors who perceive the same as a sign of company’s growth and steady income out of their investment (Khan et al., 2019). However, the decisions on dividend payment is a complex one that stands on conflicting perspectives. While a higher payment toward dividend is an indication of potential for monetary growth and a means for income for the investors, a lower dividend pay-out (DPO) enables the firms to use the surplus for future expansion of the business to provide a higher gain in future against the capital investment. The discussions on formulation of the policy decisions for determining the DPO keeping in mind two contradictory objectives such as providing income opportunities to investors for attracting them for further investment and retaining earnings for future expansions and growth of the continuing business have been progressing over many decades. The researchers have been able to put forth several theories in this regard. Literature on the agency problem has advanced hypotheses on the relation between free cash flow and business performance. Research papers have also included variables reflecting the agency problem in their explanation for DPO. As DPO reduces the free cash flow available to companies, it is expected that this will reduce the incidence of the agency problem. The general argument that is advanced is that managers keep free cash and invest them in growth of companies to consolidate their position as a larger company has more activities and requires more people and more supervision. It is possible that these investments may not be justified and is against the interest of the shareholders. In this regard, it is also emphasized that expansion through debt is desirable as there is better monitoring by lenders and acts as a disciplinary tool for managers. The dividend discount model of share price determination states that higher the DPO and its expected growth rate, higher is the value of the share of that company. That is DPO reflects income generation and helps in expectation formation for future growth. In other words, by declaring the dividends the companies provide an indication or signal to investors about the performance vis-à-vis income generation and prospects (Brigham and Houston, 2001). The purpose of declaration of dividends is to minimize the degree of asymmetry in information available to the internal parties (i.e., managers) and external (i.e., investors) shareholders (Lin et al., 2017; Hardy and Andestiana, 2019). A higher dividend transmits a positive signal to the investors while a lesser cash dividend payment provides a negative signal (Affandi et al., 2018). It is true that companies that skip dividends or lowers the rate of dividend, are penalized by the market. This approach, therefore, relates DPO to expected future growth and does not focus on the agency problem per se. The agency problems stem from the agency cost which is defined as sum of the expenditures related to monitoring (due to governance of the activities of the agents by the principal) and bonding (to ensure that the interests of the principals are met by the agents) and residual loss in form of the opportunity cost due to the difference in the interests of the agents and the principals (Jensen and Meckling, 1976). Payment of dividend also means less retained earnings for reinvestment purposes and can signify that the company does not have any expansion plans in the near future. Thus dividend payment can give conflicting signals. Rozeff (1982) made three propositions. First, the companies resort to paying lower dividends for reinforcing their investment plans and safeguarding from costly external finance. Secondly, in case of meeting Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 142 short term obligations and fixed charges, companies with higher debt-equity ratio tend to lower the dividend payment. Finally, to lower the agency cost companies prefer to have lower DPO if there is a higher external shareholding. The problem of agency cost gets escalated when there is a conflict of interest between the managers (i.e., agents) and the shareholders (i.e., owners) where the principal’s expectations are not reflected in the actions of the agents (Affandi et al., 2018). To this end, the agency cost can be minimized by striking a balance of the conflicting objectives of the agents and the principals. Dividend payment is one of the ways to reduce the agency conflict (Kilincarslan, 2021). In this connection, Easterbrook (1984) explained that although companies find dividend payments obvious, this is all cost and no benefit to them. Dividends are taxed at a higher rate than capital gains which would result from investments of the retained earnings. Further, in the presence of dividend payments, external finance for investments would add cost to the company. However, companies paying dividends and simultaneously raising funds from the market is very common. This he states is a way shareholders reduce the monitoring costs of the managers. As a single shareholder is not in a position to monitor the activities of the managers, they rely on external fund providers to do the job for them. Paying dividends and raising external funds leads to a check on the nature of investments undertaken by the managers, while keeping the leverage unaltered. This is further elaborated in Jensen (1986). According to him, companies that are involved in new activities are the ones that have not been yet subject to disciplinary forces of the market and hence generate higher free cash flow. Such companies may move into riskier ventures or unrelated diversification. Debt as a substitute for dividends can control this agency problem. Baker et al. (2002) mentioned about four explanations behind DPO such as signalling, tax-preference (i.e., transactional), agency problem and bird-in-hand. The theory of bird-in-hand relies on short-term gain in terms of payment of dividends rather waiting for long-term capital gains under uncertainty (Widiyanti et al., 2019). To sum up, it is evidenced that disclosure of dividends and building the capabilities to pay the dividends can converge the theories. Investors look for a consistent and increasing DPO to get confidence about the appropriate utilizations of the funds invested in the company (Chaniago and Ekadjaja, 2022). Therefore, we spot that there have been different schools of thoughts in explaining the motive and basis for taking dividend policies by the organizations. Further, it is an established fact that DPO has distinguished effect on firms’ valuation at the market place vis-à-vis investors’ behaviours and performance of the organizations. However, the evidence of a sizeable number of work continuously carried out over many decades in past suggest that the stated field has not been exhaustedly explored yet. This motivates us to undertake the current study that aims to find answers to the following research questions: - RQ1. How can a model be formulated to compare a group of companies on the basis of several influencing factors of DPO? - RQ2. To what extent do the firms differ from each other in terms of their capabilities to pay dividends subject to the influence of multiple indicative variables concerning the DPO? In this paper we intend to carry out a comparative analysis of the dividend pay capabilities (DPC) of the FMCG and CD organizations listed in BSE, India over a period of FY 2013-14 to FY 2019-20. DPC of a particular company is defined as the final appraisal score which is obtained considering the performance of the company with respect to the criteria (i.e., the factors that influence the dividend payment). A company with higher appraisal score is considered as having more capability (with A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 143 respect to the criteria) in paying dividend as compared with the other available alternative options (i.e., companies). Since, we consider multiple factors grounded on theoretical foundations of dividend policy that affect the decisions of DPO, our work aims to build a MCDM model for the comparative analysis. MCDM models are particularly useful when a set of alternative choices are compared subject to the influence of a number of conflicting attributes or features or criteria to select the best possible choice(s) (Pamucar et al., 2021; Laha and Biswas, 2019). For the purpose of such kind of analysis we use a very recently developed MCDM algorithm such as LOPCOW (Ecer and Pamucar, 2022) for calculating of criteria weights and EDAS method (Keshavarz Ghorabaee et al., 2015) for final ranking. The remaining part of the present paper is structured as follows. Section 2 is devoted for summarizing the observations and findings of some of the past work related to effect of the DPO on firm performance and determining factors for dividend payment. In section 3 we include a brief description of the data and methodology while section 4 provides the summary of the findings of the current work. Section 5 sheds light on the inferences and implications of the results through a brief discussion and in section 6 we make the concluding remarks alongside some scopes for future work. 2. Related Work A plethora of research spanning over last several decades have been carried out by the researchers and the practitioners in the field of dividend policy and its effect on firm’s performance (financial and market), value, shareholders’ sentiments vis-à- vis the disclosure of the dividends and the underlying factors that influence the decision on DPO. The principal objective of corporate financial management is to maximize the market value of equity shares. The key question of interest is: What is the relationship between dividend policy and market price of equity shares? The jury is still out on this unresolved issue in corporate finance. According to the traditional position enunciated by Graham and Dodd (1934), the stock market places considerably more weight on Dividends than on retained earnings. The Gordon model (Gordon, 1959) has shown that for firms, where the rate of return generated by the firm is greater than the rate of return required by shareholders, the price per share increases as the dividend payout ratio decreases and vice versa. Miller and Modigliani (1961) expounded that the value of a firm depends solely on its earnings power and is not influenced by the manner in which its earnings are split between dividends and retained earnings. According to them dividends matter because of the uncertainty characterizing the future, the imperfections in the capital market and the existence of taxes. In real life different investors hold different views about future prospects and managers are better informed about future prospects than investors. Consequently, the information or signaling content of such dividend announcements. Muth's paper (Muth, 1961) says that what matters in economics is not what actually happens but the difference between what actually happens and what was supposed or expected to happen. Consequently, only surprises in policy would have the kind of effect the policy maker is striving to achieve. What happens if the dividend announced is higher than what was expected by the market? In such a situation the market revises its assessment of future earnings and would lead to an upward price movement in the share and vice versa. The academic thinking is that the price changes that occur look like responses to dividends themselves, though they are caused by an underlying revision of the earnings potential. Mathematical models like the Walter model (Walter, 1963) have shown that the optimal payout ratio for a Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 144 growth firm is nil. Clearly this leads to an extreme course of action which makes limited sense in the real world. In most countries dividends are taxed more heavily than capital gains. Hence it can be argued that firms should pay little dividend so that investors earn more by way of capital gains. The tax laws in all countries favor capital gains in one more way. Taxes on dividends are payable immediately but taxes on capital gains are payable only when shares are sold. Consequently, the effective tax rate on capital gains diminishes as the period of holding increases. Brennan (1971) attempted to provide a connectivity between the Gordon’s model and Miller and Modigliani framework. Lintner (1956) made some very important observations. Mature firms with significant stable earnings have higher payout ratios, whereas fast growing firms have low payout ratios. Large FMCG companies may fall under this category. Dividends tend to follow earnings, but Dividends follow a smoother path than earnings. Transitory changes in earnings are not likely to have an impact on Dividend payment. Moreover, Dividends are sticky in nature as managers are reluctant to have a Dividend payout that may have to be reversed. A subsequent study by Fama and Babiak (1968) supported the Lintner model. In the subsequent studies, the authors (Black and Scholes, 1974; Asquith and Mullins, 1983; Lease et al., 1999) extended the explanations on how dividend yield and policy influence the stock price movements and impact of dividend intimations on stock price hike at the market place. There were a number of early contributions to discern the influence of industry, managers’ views and other subsequent factors on dividend policy (Michel, 1979; Baker et al., 1985; Miller and Rock, 1985; Baker and Powell, 1999; Baker et al., 2001) In the following sub-sections, we present a summary of some of the recently published research available in the extant literature where the first one discusses how are the dividend policy and DPO relevant and important for firms’ market performances and valuations while in the second sub-section we enfold the findings of the past work to explore various determinants of the DPO. 2.1. Effect of dividend policy and DPO on firm performance There has been a number of past research that attempted to establish the impact of dividend payment not only to bring new investments but also the enhance the firm’s value and performance. For instance, Jiang et al. (2019) conducted an analysis over 210 stocks listed in Shanghai and Shenzhen 300 index, China and noted that the drop in the share prices is lower on the days of dividend payment. In another study, Taofeek et al. (2019) focused on dividend management on stock price movement in long run as well as short run. Five variables were used namely stock price volatility, dividend pay-out ratio, dividend yield, earnings volatility and firm size. This research considered non-financial sectors listed in Nigerian stock exchange. In this study the researcher highlighted that low dividend pay-out ratio serves as good signal to investors for expectation of return which increases the firm value. The work of Pandey and Narayani (2019) focused to explore the impact of DPO on the share price in Auto sector of India for a longitudinal spectrum of 12 years ranging from 2004 to 2016 encompassing the recession period 2008-09. Ten auto companies listed in NSE were considered and six variables were contemplated namely market share price as dependent variable and dividend yield, dividend pay-out ratio, earning retention ratio, earning per share and leverage. The researchers found out that dividend yield and DPO have a significant effect on share price in given time period. Odum et al. (2019) attempted to find out the impact of DPO on firm’s value. The research employed panel ordinary least square regression techniques on 11 beverages and breweries companies listed on Nigeria Stock Exchange covering ten years from 2007. A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 145 As a manifestation of the values of the firm, five indicating variables such as profit after tax, cash holding, leverage ratio, dividend pay-out ratio and firm size were considered. Based on the findings form the study author recommended into order to increase the value of the firm, manager must ensure to increase PAT and leverage ratio. Puspitaningtyas (2019) tried to determine the effect of dividend announcement on stock return on the period 2017 in Indonesia stock market. 53 companies were considered in this research which were listed in Indonesia stock market who have announced cash dividend in consecutive during 2016-2017 and do not conduct corporate action other than announcement of dividend. Four criteria were considered namely actual return, expected return, abnormal return and average abnormal return for three time periods such as pre event i.e. 5 days before the event, event day and post event i.e. 5 days after the event. Researcher found that the market reacts to the announcement of dividend which is indicated by the existence of abnormal return value which is directly proportion to increase and decrease in dividends which strengths the perspective of signalling theory. The work of Omar and Echchabi (2019) examined the potential role of dividend pay-out plays in influencing the fund managers in selecting and recommending a stock. Semi-structured interview method was conducted with six Malaysian investment manager and the results indicates that other factors coupled with dividend pay-out pays a catalyst for investors and fund managers to select a stock in their portfolio. In the context of signalling theory, Salman (2019) worked on investigating the influence of shareholder preference and dividend signalling on the dividend policy of the corporations in Pakistan. Through a structured questionnaire based survey of 61 executives, the study reported that there are significant positive relationships between dividend policy and shareholder’s preferences and dividend signalling. In a recent work, Yin and Nie (2021) attempted to predict the returns of the stocks listed in Chinese market using raw and multiple adjusted dividend pay-out ratios (DPR). The research showed that stock returns can be positively predicted by DPR during the study period (2002-2018). In a different study, the researchers observed a moderating effect of the dividend policy on the causal relationship between profitability and value of the firm (Setyabudi, 2021). In the context of Nigeria, Ifeanyichukwu and Yusuf (2021) worked on examining the effect of the share dividends and cash dividend on the market price of the share during the time period 2014-2018. The authors observed a positive effect of cash dividend on share price at the market and also recommended the organizations to work for increasing the price- earnings ratio. Paying dividends to shareholders not adds to increase their wealth, but also helps to paying organizations to achieve sustainability in the long run (Sami and Abdallah, 2021). A policy with higher dividend payment increases the corporate value significantly (Dang et al., 2021; Gupta & Arora, 2021). Dividend payment is associated with investors’ sentiments that enhances the demand of the investors and eventually escalates the market return (Kumar et al., 2022). Seth and Mahenthiran (2022) further extended the growing volume of the literature to establish the relevance of the signals of CSR disclosure and DPO for maintaining long-term relationship with the shareholders that eventually enables the firms to become sustainable in future. 2.2. Determinants of DPO Over the years the researchers from various countries have conducted several studies from various perspectives to find out the determinants of the DPO. In Amidu and Abor (2006), the authors used financial statements for six consecutive years to find out the factors that affect the DPO decision for the organizations listed on the Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 146 Ghana Stock Exchange. The authors considered two perspectives such as agency cost and opportunity for investments. It is evidenced in their work that profitability and cash flow hold positive associations with DPO while risk maintains the inverse relationship. In a later work, Hamill and Al-Shattarat (2012) tested the hypothesis of agency cost to discern the effect of ownership structure, free cash flow and firm size on DPR and observed significant influence. Mui and Mustapha (2016) had worked on public organizations in Malaysia and used multiple regression to conclude that investment opportunity, liquidity and size of the firm bear significant effect on DPO. The study of Khan et al. (2017) on Pakistani firms advocated for taxes and cash flow in addition to profitability as enablers of dividend policy. The authors conducted the study during 2003-2012 using panel regression. Based on their analysis over Chinese state controlled and non-state-controlled firms during a period of 10 years, Lin et al. (2017) realized that information asymmetry lowers the DPO. However, the authors observed evidence that for state controlled firms, higher information asymmetry leads to higher DPO. Continuing in the same direction, Malik and Sattar (2018) applied the Ordinary Least Square (OLS) method to figure out notable influence of governance related variables such as size of the board, CEO duality, ownership structure, size of the firm and operating cash flow on DPO for the companies in Pakistan. While working on 19 companies from Indonesian stock exchange during 2013-2015, Tumiwa and Mamuaya (2019) noted significant impact of firm size, profitability, and leverage on the DPO and stock price. The work of Le et al. (2019) supported the growing strand of work and revealed that profitability is positively related with DPO. However, the authors did not notice any notable influence of firm size, free cash flow, financial leverage and liquidity on dividend payment. Nidar et al. (2019) found positive influence of ownership structure and presence of independence in the board on DPR while they noticed insignificant and negative effect of board size. Budiarso (2019) extended the literature with their work on Indonesian consumer durable firms to investigate the footprints of profitability (variable: return on asset), efficiency (variable: growth of asset), risk (variable: debt ratio) and non-discretionary accruals and discretionary accruals on dividend policy using logistics regression over a period of 2010-2017. The author reported the consequential role of profitability for deciding the DPO. The extant literature further evidenced with the work of Lloren-Alcantara (2020) on Philippine-listed organizations during 2014-2018. The study pointed out the affirmative effects of profitability, liquidity and firm size but negative impact of the insider ownership on dividend payment. The authors argued for further work in this regard. In the recent works (Setyabudi, 2021; Salim and Aulia, 2021) the authors reflected in tune with past work and noted the significant associations of profitability, liquidity and leverage with DPO. Yakubu et al. (2021) reported a positive causal association among working capital management through cash conversion cycle and days inventory outstanding with DPO based on a study made on a group of non-financial firms in Ghana during 2007 to 2016. Bakri et al. (2021) conducted a two period comparison of the determinants of DPO with respect to formal corporate governance mechanisms in Malaysian context and noted that profitability, lagged of dividends and firm size remain as a constant factor. Al Sawalqa (2021) worked on life cycle theory of DPO on selected Jordanian non-financial firms and noted the importance of asset value and shareholders’ equity on determination of the dividend policy. The study of Taher and Al-Shboul (2022) has focused on delving into the relationship of liquidity and dividend policy and found that an increase in liquidity decreases the DPO. Chaniago and Ekadjaja (2022) figured out positive and significant impact of return on equity and ownership structure on DPR while they discovered A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 147 insignificant and positive effect of cash ratio for the Indonesian firms. Novia and Marlina (2022) provided a contrasting result as they observed no effect of leverage and liquidity and negative impact of profitability on the dividend policy. In Indian context, there have been a number of work in sync with the research at global platform. For example, Labhane and Das (2015) investigated for the trend and determinants of dividend policy for 239 firms listed on the National Stock Exchange (NSE), India over a period of 20 years. The authors put forth some interesting observations. First, the authors observed a decline in the number of companies that pay dividend while there had been a rise in the total amount paid in form of dividends over the study period. Secondly, the pattern of DPO varies across the industries. Finally, the authors concluded that given a conditions of higher free cash flow, better investment opportunities, larger size, age and profitability, and lower leverage, the firms tend to pay more dividends. In a later work (Singla and Samanta, 2018), it was found that profitability, life cycle and size lead to increase in dividend payment while cash flow exhibits negative relationship with DPO due to the presence of agency problem. Thakur and Kannadhasan (2018) applied quantile regression model to establish the differences in dividend payments by the companies due to changes in the profitability, growth, and size. In the work of Labhane and Mahakud (2019), 781 Indian organizations listed in NSE were examined for a period of 1995 to 2015 based on 14 variables related to profitability, efficiency, risk, liquidity, size, market capitalization and nature of business. The results highlighted the notable effect of the business group and profitability on DPO. Garg and Bhargaw (2019) diverted the stream of ongoing work by using Lintner’s model and noted the effect of current year’s earning on dividend payment for the Indian firms listed in the Bombay Stock Exchange (BSE). Katakwar et al. (2021) pointed out the positive impact of return on equity on DPO while they found risk and tax rate negative influence the dividend payments for NSE listed firms. 2.3. Motivations and Contributions of the Research From the literature review we make out that the subject area is not an unknown one. There has been a continuous effort in introspecting the motives behind formulating the dividend policy and its impact on financial and market performance of the stocks and investors’ behaviours. A steady growth in the volume of the literature is observed that deal with unveiling the determinants of the dividend payment in the context of leading indices of the global stock market while considering different types of the industries and firms. However, there is a scantiness in the work that considers multiple perspectives and provide a comprehensive multi-criteria based evaluation of a number of organizations to enfold the competitive positions with respect to their relative capabilities for paying dividends. It is evidenced in the extant literature that most of the past research have utilized time series based predictive models and frameworks to detect the causal associations. In this regard, the current work adds value to the growing literature in two ways. Firstly, in Indian context the present paper may be considered as a work of its kind that provides a multi-period, multi-criteria based comparison of FMCG and CD companies with respect to the features rooted through the theoretical base of the dividend policy and findings of the previous work. Secondly, we present a new integrated framework of LOPCOW-EDAS methods for carrying out MCDM based analysis wherein we utilize the multiple aggregation methods. LOPCOW has not been explored for variety of applications yet. Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 148 3. Data and Methodology In this paper, we aim to carry out a comparative analysis of the DPC of the selected organizations belonging to the FMCG and CD sectors listed in BSE, India. The present section discusses the selection of the sample, description of the criteria and methods used in the paper. Figure 1 depicts the flow of the steps followed in the current study. 3.1. Sample In the present paper, we consider the FMCG and CD companies listed in BSE. We apply two filtrations. First, we discard all companies which are not listed in BSE during April 1, 2013 to March 31, 2020 (our study period is FY 2013-14 to FY 2019- 20). Second, we calculate the average market capitalizations (over the study period) of the companies shortlisted at the first stage by using geometric mean. We select top 25 companies from FMCG group and top 5 firms from the CD sector. Therefore, our final sample consists of total 30 organizations (see table 1). These 30 organizations are the alternative options in our paper. In the present paper we have adopted convenience sampling. We have considered the FMCG and CD sectors. FMCG aka consumer packaged products are regularly bought by the consumers and consumed by households in daily use. FMCG sector is characterized by a huge variety of household products with higher consumption and variable price range (lowest may be below INR 10), a large number of consumers (both from urban and rural markets), a diverse distribution network, lower penetration level (that lowers the entry and exit barriers), and a higher level of competition with presence of many domestic as well as multinational firms alongside unorganized players (Dhingra et al., 2018). In the decade the sector has undergone a transformational change because of technological progress, e-commerce, enhanced penetration to rural markets, Covid-19, and changing nature of the consumer behaviours which have posited promises for potential future growth and challenges for the organizations to design and deliver unique value propositions (TOI report, 2022). According to the recent report by Indian Brand Equity Foundation (IBEF, 2022a) the estimated market potential for FMCG is USD 220 billion by 2025 with a CAGR of 14.9% while the projected value of the packaged food market in India is USD 70 billion. The observed rural spending is around 50 percent of the total spending in FMCG products. The FDI inflow in the last two years has been USD 20.11 billion. On the other hand, CD refers to a group of products consumed by the household over a period of time such as kitchen appliances, electronic gadgets, home furnishing and leisure items etc. The products are classified under three broad categories: White Goods, Brown Goods and Consumer Electronics. The sector is also characterized by wide variety, a higher level of technology dependency, a mix of several domestic and multinational firms in addition to numerous unorganized and/or organized support firms and higher level of competition on brands. Given the developments in the software and hardware technology and enhanced disposable income, CD sector has emerged as one of dynamic and happening industry having a widespread awareness. With government initiatives (e.g., rural electrification and affordable housing schemes), CD products have a notable rural penetration too. In recent time, the sector has witnessed a FDI inflow of USD 3.19 billion (IBEF, 2022b; Sarangi, 2019). Considering the growth potential, familiarity to the households, variety of products, higher level of competition within the industry, promising amount of FDI, and increased level of use at all levels of the society, have made the FMCG and CD sectors the sectors of interest for the investment decision analysis. A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 149 In the FMCG and CD sectors there are 72 and 10 listed companies respectively. Since, our study period starts from April 01, 2013, at the first level of filtration, we discard the companies that do not appear in the listing throughout the study period, i.e., got enlisted after April 01, 2013 and/or got discontinued before March 31, 2020. After the first level of filtration we obtain 60 companies from the FMCG and 09 firms from the CD sector. Now, we calculate the average market capitalization for all companies qualified at the first stage (i.e., 69 companies). We use geometric mean (GM) for calculating the average as GM is acceptable than the arithmetic mean in presence of outliers, if any. Since any of the 69 companies did not have any missing and/or zero value for the market capitalization, GM is also justified in use. After obtaining the average market capitalization, we select top 25 organizations from the FMCG (out of 60) and top 5 companies from the CD (out of 9) sectors. Here, our final sample consists of more than 30 percent of the total elements available in the population. The total size of the final sample is 30. The extant literature has advocated for 30 as a minimum standard size of the sample in sync with the central limit theorem, n-hat and n-omega methods (for example, Roscoe, 1975; Luanglath and Rewtrakunphaiboon, 2013; Louangrath, 2014; Luanglath, 2014; Agresti and Kateri, 2021). Hence, the sample size used in this paper satisfies the minimum requirement. 3.2. Criteria Description In line with past work, we select the criteria for carrying out the comparative analysis of DPC of the sample organizations. For example, the extant literature shows that Institutional ownership (IO) plays a momentous role in corporate governance. The distribution pattern of IO is one of the significant enablers for supporting the organizations in maintaining the optimum cash holding vis-à-vis agency cost issue to safeguard the interest of the investors. with the cash holding by the organizations. In this context, a higher % of non-promoter ownership reduces the cash holdings and thereby support the objective of “Efficient Monitoring Hypothesis (EMH)” as observed by Gupta and Bedi (2020). The size of the organization has a positive impact on the profitability of the firm (Hirdinis, 2019). Profitability indicates the earnings prospect of the firms that favours the dividend pay-out (Dewasiri et al., 2019). However, earning is supported by the growth. A growing organization has a better prospect of earnings in future. Hence, growth is an important enabler of dividend pay-out. Liquidity in terms of free cash flow (FCF) on the other hand has a positive effect on the dividend policy (Rochmah and Ardianto, 2020; Pattiruhu and Paais, 2020). According to the signalling theory, dividend is an indicator of the potential earnings in future. However, the uncertainties due to business risk blur the future earning prospect. Therefore, risk negatively influences the DPO (Hamill and Al- Shattarat, 2012). Therefore, leverage as a measure of risk undermines DPO. The criteria that are used in the current work for comparing the FMCG and CD organizations are summarized in table 2. Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 150 Table 1. List of companies under comparison S/L Company Category S/L Company Category A1 Avanti Feeds Ltd. FMCG A16 I T C Ltd. FMCG A2 Bajaj Consumer Care Ltd. FMCG A17 Jyothy Labs Ltd. FMCG A3 Bombay Burmah Trdg. Corpn. Ltd. FMCG A18 K R B L Ltd. FMCG A4 Britannia Industries Ltd. FMCG A19 Marico Ltd. FMCG A5 C C L Products (India) Ltd. FMCG A20 Nestle India Ltd. FMCG A6 Colgate-Palmolive (India) Ltd. FMCG A21 Procter & Gamble Hygiene & Health Care Ltd. FMCG A7 Dabur India Ltd. FMCG A22 Radico Khaitan Ltd. FMCG A8 E I D-Parry (India) Ltd. FMCG A23 Tata Consumer Products Ltd. FMCG A9 Emami Ltd. FMCG A24 United Breweries Ltd. FMCG A10 Future Consumer Ltd. FMCG A25 Zydus Wellness Ltd. FMCG A11 Gillette India Ltd. FMCG A26 Rajesh Exports Ltd. CD A12 Godfrey Phillips India Ltd. FMCG A27 Symphony Ltd. CD A13 Godrej Consumer Products Ltd. FMCG A28 Titan Company Ltd. CD A14 Hatsun Agro Products Ltd. FMCG A29 Voltas Ltd. CD A15 Hindustan Unilever Ltd. FMCG A30 Whirlpool Of India Ltd. CD Figure 1. Research Framework A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 151 It is evident from the literature review that for better governance and utilization of the surplus earned from the business operations, the organizations need to be under independent vigilance. An increase in the percentage of shareholding by the non-promoters may reduce the excessive cash holding and the possibility of misuse by the agents (i.e., managers) and hence address the agency cost problem, if any. Hence, in this study we take % ownership by non-promoters as a proxy of IO. For an effective management of cash and earnings, IO should be maximized. A company with greater amount of total assets is likely to operate with freedom. It is also an indication of company’s financial wellbeing and future prospect. Hence, size which is a natural log of total assets is treated as beneficial for building DPC. It is amply evident from the past work that a more profitable firm is likely to be capable for enhancing dividend payout. Therefore, all profitability indicators are considered as of maximizing nature with respect to DPC. The same explanations hold true for the growth variables for having a better DPC. Hence, all growth indicators are mentioned in the maximizing direction. If an organizations are having greater liquidity, the short run obligations can be made. Further, liquidity also indicates efficiency in business operations in generating cash. Therefore, NCF is considered in the maximizing direction. Finally, a firm can operate with stability for long run growth, if the debt is lower than the profit. To this end, leverage (considered as a proxy indicator of risk) is considered as a non-beneficial criterion with respect to DPC in this paper for which we set minimizing objective. Table 2. List of criteria Dimension Criteria Definition Code Effect Direction UOM Ownership Institutional Ownership (IO) % ownership by Non- promoters C1 Maximize % Size Size of the Firm (S) Natural Log of total assets C2 Maximize Value Profitability Net Profit Margin (NPM) (Net Profit/ Revenue)*100% C3 Maximize % Return on Capital Employed (ROCE) (PBIT/Capital Employed)*100% C4 Maximize % Growth Sales Growth (SG) Natural Log of (Sales at t / Sales at (t-1)) C5 Maximize Value Market Cap/ Enterprise Value (MCEV) Market capitalization/ Enterprise Value C6 Maximize Times Liquidity Net Cash Flow (from operating activities) (NCF) Net amount of money being generated from regular business operations C7 Maximize Rs. Million Risk Leverage (L) Debt/ PBITDA C8 Minimize Times 3.3. Data The total spectrum for study has been selected as 10 years, i.e., FY 2012-13 to FY 2021-22. However, FY 2012-13 has been considered as a base year for the calculation of the year on year growth attributes (for example, Sales Growth). Further, FY 2020- 21 and 2021-22 have been the periods affected by the “black swan” event, Covid-19 which impacted the stock market unprecedentedly and yet we believe may not be suitable to be considered for a stable analysis. Hence, the study period is effectively selected as FY 2013-14 to FY 2019-20. The data for finding out various indicating criteria for the companies under study have been collected from CMIE Prowess IQ (version 1.96). Accordingly, the decision matrices for the financial years (i.e., FY 2013-14 to FY 2020-21) have been constructed using the definitions as mentioned in the table 2. The decision matrices for the various FYs are given in the Appendix A. The Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 152 study period spans over FY 2013-14 to FY 2019-20. In our paper we have not considered the period FY 2020-21 as the same is characterized by an unprecedented disruption because of the rapid spread of Covid-19. During this ‘black swan’ period there has been a massive impact on socio-cultural and economic environment across the globe. Hence, for a deeper understanding of the comparative DPC of the companies under study, we have selected a considerably uninterrupted period. 3.4. Criteria Weight Calculation: LOPCOW Method The LOPCOW method calculates the criteria weights based on objective information (Ecer and Pamucar, 2022). It provides the following advantages - The criteria weights are comparatively even in distribution - Negative performance values of the alternatives can be used in deriving the criteria weights. This is a useful feature in many complex real-life scenarios such as stock returns. - Ability to work efficiently with a large number of criteria and alternatives Let, ij m n X x      be the decision-matrix where, m is the number of alternatives (i.e., companies under comparison; 30m ) and n is the number of criteria (in our case, 8n  ). In what follows are the computational steps (Ecer and Pamucar, 2022) Step 1. Normalization of the decision-matrix Using the linear max-min type of normalization, we obtain the normalized decision matrix as given by ij m n R r      where, min max min j ij ij j j x x r x x    (when j j   , desired effect: maximizing) (1) max max min j ij ij j j x x r x x    (when j j   , desired effect: minimizing) (2) Step 2. Derive the Percentage Value (PV) for the criteria The PV for each criterion is given by the natural log of the mean square value as a proportion of the standard deviation expressed in percentage. This step helps to reduce the uneven distribution of the weights. Accordingly, PV is calculated as 2 1 ln .100 m rij i m Pj                   (3)  denotes the standard deviation Step 3. Computation of criteria weights The weight for the th j criterion is given by A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 153 1 ij j n ij j P w P    (4) Where, 1 1 n j j w   (i.e., sum of the weights of all criteria = 1) 3.5. EDAS Method EDAS considers average solution as a yardstick for figuring out the suitability of the alternatives. In this method, two distances used such as PDA (positive distance from the average) and NDA (negative distance from the average) are calculated subject to the desired effect of the corresponding criterion, i.e. maximizing and minimizing. The alternative, which has higher PDA and/or lower NDA, is considered as the best alternative among the others (Keshavarz Ghorabaee et al., 2015). EDAS has been applied in various real-life problems concerned with selection of best possible alternatives subject to influence of a set of criteria, for example, performance based selection of mutual funds (Karmakar et al., 2018), carpenter manufacturer selection (Stević et al., 2018), resource selection under dynamic environment for crowd computing for smartphones (Pramanik et al., 2021), green supplier selection (Wei et al., 2021), strategic decision for international market selection (Zolfani et al., 2021), 3D printer selection in digital manufacturing (Lei et al., 2022), and green financing (Su et al., 2022) among others. In what follows are the advantages of the EDAS method: - EDAS is useful method in the situations with fluctuations in the performance values - It does not consider the extreme solutions for benchmarking and therefore, it works fine in realistic situations - Provides stable and reliable solutions (even with larger alternative and criteria sets) that are free from the rank reversal issues. The procedural steps of the algorithm are as under. Step 1. Formation of the decision matrix The decision matrix is represented as 11 1 1 n ij m n m mn x x X x x x                 (5) Where, is the number of alternatives and is the number of criteria. ij x is the performance value of the alternative subject to the criterion. Step 2. Derive the average solution The average solution is derived as Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 154 ; 1, 2, ... 1 j n m xij i x j m     (6) Step 3. Derive the PDA and NDA The PDA and NDA are calculated by using the following expressions PDA: (0,( )) ; (max ) (0,( )) ; (min ) Max x xij j j j imizing x j ij Max x xj ij j j imizing x j d                  (7) NDA: (0,( )) ; (max ) (0,( )) ; (min ) Max x xj ij j j imizing x j ij Max x xij j j j imizing x j d                  (8) Step 4. Calculation of the weighted sum of PDA and NDA values for all alternatives subject to the criteria The weighted sums are calculated as 1 n S w ji ij j d     (9) 1 n S w ji ij j d     (10) Here, w j is the weight of the criterion. Step 5. Normalization of the weighted sum of PDA and NDA values The normalization is done as under For weighted sum of PDAs: ( ) Si NS i Max Si i     (11) For weighted sum of NDAs: 1 ( ) Si NS i Max Si i      (12) A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 155 Step 6. Computation of the appraisal score of the alternatives The appraisal score of the alternative is computed as 1 ( ) 2 S NS NS ai i i     (13) Here, 0 1S ai   Step 7. Ranking of the alternatives The alternatives are ranked as per their appraisal scores. Higher is the score, more preferred is the corresponding alternative. 3.6. Aggregation of the MCDM results In many real-life MCDM applications arriving at a consensus decision is a critical issue (Biswas, 2020a). The problem arises when a group of opinion makers or a set of different MCDM algorithms are involved in selection of a best possible alternative. To aggregate the outcomes of different decision making frameworks, the researchers have developed a number of algorithms. In this section, we discuss some of the approaches. 3.6.1. Borda Count (BC) BC is an age old established preference based aggregation method (Borda, 1784) that has been applied for consolidation of the ranking results of various MCDM algorithms (Lansdowne and Woodward, 1996; Wu, 2011; Pourjavad and Shirouyehzad, 2011; Gandhi et al., 2018; Barak and Mokfi, 2019; Ecer, 2021). In what follows are the steps for this aggregation method. Step 1. The ranking of the alternatives (subject to the influence of the criteria) is made by each opinion maker or method. Step 2. Suppose, there are alternative options. Each alternative is given a point equal to the number of options succeeding the considered one. Hence, the most preferred or best alternative receives points while the second best alternative gets points and so on. Step 3. Calculation of the sum of the points obtained by each alternative option Step 4. Ranking of the alternatives based on the total points. The alternative which obtains the highest points would be ranked first and so on. 3.6.2. Copeland Method (CM) The CM is the extended and modified form of BC. The CM starts after the BC. This method puts emphasis on the number of other alternative options subordinated to the given alternative (Lestari et al., 2018; Dortaj et al., 2020; Ecer, 2021). The procedural steps are as follows. Step 1. Computation of the win score for each alternative (vis-à-vis the others) Step 2. Computation of the loss score (after subtracting of the score obtained in the first stage from majority wins’ score) Step 3. Calculation of the final score which is the difference between the win and loss scores. The alternative that obtains the highest overall score will be ranked first and so on. Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 156 3.6.3. Grade Average Method (GAM) This is a simple method of aggregation of the ranks by various models. According to this method, the alternatives are ranked using the different methods. Then, for each alternative, an average of ranks or grades (obtained by using various models) is calculated. The alternative that scores least grade average, overall is the first preferred one (Dortaj et al., 2020). In the present paper, for calculation purpose, we have used MS Office (2016) and SPSS (version 25) software tools on a computer with Intel(R) Core(TM) i3-1005G1 CPU @ 1.20GHz 1.19 GHz, 8GB RAM. 4. Results In this section, we briefly highlight the findings step by step. First, we carry out the calculations for year wise criteria weights using the procedural steps of the LOPCOW method (see expressions (1) to (4), section 3.4). Table 3 provides the normalized decision matrix while table 4 exhibits the calculations of the criteria weights using LOPCOW for FY 2013-14. The calculations of the criteria weights (using LOPCOW method) for other FYs are given in Appendix B. Table 3. Normalized decision matrix (for LOPCOW method) for FY 2013-14 Company Criteria C1 C2 C3 C4 C5 C6 C7 C8 A1 0.4344 0.0577 0.1501 0.2905 1.0000 0.2794 0.2799 0.9185 A2 0.0000 0.1219 0.5079 0.2556 0.4407 0.2912 0.2874 0.9878 A3 0.1216 0.0990 0.0193 0.0045 0.4562 0.2843 0.2774 0.3190 A4 0.3245 0.3660 0.1313 0.3296 0.4598 0.2892 0.3377 0.9632 A5 0.4075 0.1086 0.2871 0.1359 0.3695 0.2735 0.2836 0.9028 A6 0.3211 0.4183 0.2979 0.7412 0.4771 0.2892 0.3218 0.9554 A7 0.0851 0.5086 0.3227 0.3064 0.4549 0.2892 0.3484 0.9019 A8 0.3979 0.5389 0.0000 0.0000 0.1891 1.0000 0.2893 0.2103 A9 0.0302 0.3151 0.4217 0.2989 0.3670 0.2931 0.3136 0.9695 A10 0.4530 0.2476 0.1680 0.0293 0.4402 0.2902 0.2639 0.0000 A11 0.0000 0.2481 0.0613 0.0555 0.4896 0.2902 0.2842 0.6220 A12 0.0528 0.3728 0.1069 0.1140 0.4908 0.2912 0.3041 0.8387 A13 0.1564 0.5793 0.3124 0.1506 0.4711 0.2873 0.3523 0.7436 A14 0.0003 0.1994 0.0757 0.1078 0.4892 0.2735 0.2950 0.8825 A15 0.1037 0.7721 0.2792 1.0000 0.4151 0.2902 0.6624 0.9976 A16 1.0000 1.0000 0.4348 0.3062 0.4525 0.2912 1.0000 0.9688 A17 0.1100 0.3675 0.1883 0.0664 0.5628 0.2775 0.2895 0.6293 A18 0.2188 0.4402 0.2255 0.1121 0.6764 0.2353 0.2521 0.7968 A19 0.2048 0.5085 0.3594 0.1907 0.4128 0.3078 0.3009 0.8562 A20 0.1638 0.6208 0.2833 0.2935 0.4254 0.2873 0.4567 0.9738 A21 0.0583 0.3215 0.3401 0.2890 0.5574 0.2892 0.3091 0.9539 A22 0.4621 0.3827 0.0504 0.0332 0.5637 0.2569 0.2884 0.6911 A23 0.5116 0.5671 0.1995 0.0704 0.4926 0.3039 0.2895 0.7106 A24 0.0024 0.5193 0.0752 0.0653 0.4458 0.2824 0.3044 0.7568 A25 0.0329 0.0609 1.0000 0.2661 0.2452 0.2961 0.2833 0.8777 A26 0.2940 0.7343 0.0265 0.0365 0.0000 0.0000 0.0000 0.1537 A27 0.0000 0.0000 0.5091 0.4015 0.7822 0.2931 0.2827 1.0000 A28 0.2937 0.6119 0.1668 0.2441 0.4027 0.2873 0.2166 0.8155 A29 0.5981 0.5341 0.0802 0.0842 0.2180 0.2961 0.3089 0.3508 A30 0.0000 0.3300 0.0938 0.1544 0.3287 0.2980 0.2954 0.7640 A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 157 Table 4. Criteria weights (FY 2013-14) – LOPCOW method C1 C2 C3 C4 C5 C6 C7 C8 Mean Square 0.1042 0.2119 0.0976 0.0904 0.2351 0.1100 0.1308 0.6358 SD 0.2325 0.2344 0.2044 0.2143 0.1771 0.1427 0.1593 0.2742 PV 32.8284 67.4634 42.4134 33.8469 100.7452 84.3134 81.9621 106.7331 Wj 0.0597 0.1226 0.0771 0.0615 0.1831 0.1532 0.1489 0.1940 Table 5 provides the summary of the criteria weights for all years. The orders of the criteria (as per their relative importance) for different years are given in table 7. It is noticed that leverage (i.e, risk) obtains the higher priority while liquidity in most of the cases holds the less weight. We now use these weights to compare and rank the companies under study using EDAS method. Table 5. Year wise criteria weights – summary FY Criteria Weights C1 C2 C3 C4 C5 C6 C7 C8 2013-14 0.0597 0.1226 0.0771 0.0615 0.1831 0.1532 0.1489 0.1940 2014-15 0.0986 0.1891 0.2256 0.1537 0.1479 0.1272 0.0394 0.0186 2015-16 0.0596 0.1148 0.1515 0.0984 0.1533 0.1779 0.0165 0.2279 2016-17 0.0749 0.1216 0.1643 0.1411 0.1908 0.0074 0.0241 0.2759 2017-18 0.0601 0.1060 0.1818 0.1124 0.1553 0.0019 0.0990 0.2835 2018-19 0.0564 0.0952 0.1225 0.0765 0.2130 0.2299 0.0199 0.1867 2019-20 0.0751 0.1072 0.1788 0.0840 0.0919 0.2119 0.0157 0.2355 2020-21 0.0459 0.0624 0.1604 0.0734 0.1653 0.1774 0.1359 0.1792 Table 7 exhibits the average solution for the criteria for the FY 2013-14 (using the expression (6)). Table 8 provides the calculation of the appraisal scores of the alternatives (using the expressions (7) to (13)) for the FY 2013-14. The ranking order of the alternatives are also included in table 8. In the similar way we find the ranking order of the alternatives for all other financial years (see Appendix C). Table 6. Year wise criteria weights – priority order FY Priority order 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 158 Table 7. Average solution (FY 2013-14) Criteria C1 C2 C3 C4 C5 C6 C7 C8 Avg. Sol. 42.0386 9.9983 9.8703 25.081 0.1114 1.1347 4970.837 6.488 Table 8. Ranking of alternatives (FY 2013-14) Company S+ S- NS+ NS- Si Rank A1 0.8600 0.2105 0.3817 0.9113 0.6465 3 A2 0.2170 0.1798 0.0963 0.9242 0.5103 12 A3 0.0049 0.5360 0.0022 0.7740 0.3881 27 A4 0.1920 0.0527 0.0852 0.9778 0.5315 10 A5 0.1101 0.3097 0.0489 0.8695 0.4592 22 A6 0.3161 0.0274 0.1403 0.9885 0.5644 6 A7 0.2028 0.0306 0.0900 0.9871 0.5385 8 A8 0.9898 0.8649 0.4393 0.6354 0.5374 9 A9 0.1923 0.1861 0.0853 0.9215 0.5034 13 A10 0.0239 0.6651 0.0106 0.7196 0.3651 29 A11 0.0496 0.3346 0.0220 0.8590 0.4405 24 A12 0.0948 0.1677 0.0421 0.9293 0.4857 16 A13 0.1351 0.0475 0.0600 0.9800 0.5200 11 A14 0.1144 0.2460 0.0508 0.8963 0.4735 20 A15 1.3503 0.0775 0.5994 0.9673 0.7834 2 A16 2.2528 0.0128 1.0000 0.9946 0.9973 1 A17 0.1476 0.2699 0.0655 0.8862 0.4759 19 A18 0.3251 0.3370 0.1443 0.8580 0.5011 14 A19 0.1083 0.1346 0.0481 0.9432 0.4957 15 A20 0.5365 0.0613 0.2382 0.9742 0.6062 5 A21 0.2954 0.0865 0.1311 0.9635 0.5473 7 A22 0.1737 0.3120 0.0771 0.8685 0.4728 21 A23 0.0985 0.1788 0.0437 0.9246 0.4842 17 A24 0.0103 0.2165 0.0046 0.9087 0.4567 23 A25 0.3267 0.4470 0.1450 0.8116 0.4783 18 A26 0.0268 2.3722 0.0119 0.0000 0.0059 30 A27 0.7069 0.1821 0.3138 0.9232 0.6185 4 A28 0.0602 0.4227 0.0267 0.8218 0.4243 26 A29 0.0473 0.6561 0.0210 0.7234 0.3722 28 A30 0.0067 0.3502 0.0030 0.8524 0.4277 25 Table 9 provides the summary of the year wise rankings which reflect that there have been some considerable irregularities in the ranking orders of the alternatives. A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 159 Table 9. Summary of year wise ranking of the companies Company Rank 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 A1 3 8 2 7 2 26 9 A2 12 7 11 6 11 10 8 A3 27 29 30 30 30 25 29 A4 10 14 7 9 14 15 13 A5 22 17 21 18 12 27 10 A6 6 4 10 10 19 19 11 A7 8 11 17 19 18 12 14 A8 9 1 9 27 28 29 28 A9 13 5 12 14 21 22 21 A10 29 30 27 29 5 13 1 A11 24 22 23 17 23 16 25 A12 16 25 26 12 25 30 7 A13 11 15 19 20 17 9 16 A14 20 26 5 11 26 20 24 A15 2 3 4 4 3 5 5 A16 1 2 3 2 1 6 3 A17 19 19 20 21 27 23 27 A18 14 20 16 25 22 3 15 A19 15 9 13 16 9 11 18 A20 5 13 29 5 10 8 6 A21 7 12 14 8 15 2 12 A22 21 27 1 23 6 1 19 A23 17 21 22 26 20 24 4 A24 23 24 6 24 8 17 26 A25 18 6 8 3 4 14 30 A26 30 28 25 28 29 4 20 A27 4 10 18 1 13 28 2 A28 26 16 28 15 7 7 22 A29 28 23 24 22 24 18 23 A30 25 18 15 13 16 21 17 We notice that the alternatives do not hold consistent positions over the years of the study period. To set the overall preferential order, it is necessary to arrive at a consensus. To meet this objective, we apply the widely used aggregation of voting technique such as BC as described in section 3.6.1. We also use another popular method like CM to carry out the aggregation to validate the result of BC. Further, we formulate a new decision matrix using the appraisal scores of the alternatives each year. In this newly formed decision matrix all the years are assigned same weights (i.e., equal importance). Table 10 presents the decision matrix used for obtaining the overall ranking of the alternatives. We apply Simple Additive Weighting (SAW) method for deriving the overall ranks as followed in many past research (for instance, Biswas, 2020b, Pramanik et al., 2021). Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 160 Table 10. Decision matrix for overall ranking of the alternatives Weight 0.1429 0.1429 0.1429 0.1429 0.1429 0.1429 0.1429 Models Score Values 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 A1 0.6465 0.5059 0.7904 0.6126 0.7993 0.5276 0.6147 A2 0.5103 0.5195 0.6576 0.6204 0.6235 0.7946 0.6173 A3 0.3881 0.0466 0.0000 0.1170 0.0000 0.5295 0.1992 A4 0.5315 0.4417 0.7217 0.6001 0.6042 0.7130 0.5810 A5 0.4592 0.3752 0.5353 0.5023 0.6065 0.5057 0.6134 A6 0.5644 0.5633 0.6684 0.5807 0.5506 0.6584 0.6098 A7 0.5385 0.4802 0.5905 0.4953 0.5676 0.7643 0.5799 A8 0.5374 0.8558 0.6924 0.2756 0.3544 0.3277 0.2807 A9 0.5034 0.5423 0.6307 0.5360 0.5237 0.5667 0.5116 A10 0.3651 -0.0342 0.4138 0.1395 0.7495 0.7582 0.8963 A11 0.4405 0.3029 0.4669 0.5188 0.5183 0.6890 0.4955 A12 0.4857 0.2362 0.4211 0.5457 0.4995 0.0642 0.6229 A13 0.5200 0.4384 0.5813 0.4922 0.5681 0.8194 0.5449 A14 0.4735 0.2026 0.7413 0.5656 0.4755 0.5892 0.5021 A15 0.7834 0.6031 0.7545 0.6856 0.7962 0.8985 0.7209 A16 0.9973 0.6479 0.7768 0.7339 0.8843 0.8878 0.7911 A17 0.4759 0.3611 0.5734 0.4655 0.4666 0.5407 0.4430 A18 0.5011 0.3575 0.5928 0.3601 0.5196 0.9232 0.5477 A19 0.4957 0.4949 0.6180 0.5234 0.6356 0.7791 0.5423 A20 0.6062 0.4680 0.3949 0.6206 0.6275 0.8371 0.6969 A21 0.5473 0.4698 0.5965 0.6028 0.6001 0.9278 0.5950 A22 0.4728 0.1913 0.9223 0.3799 0.6752 0.9285 0.5319 A23 0.4842 0.3254 0.5146 0.3512 0.5290 0.5406 0.7687 A24 0.4567 0.2692 0.7303 0.3696 0.6473 0.6781 0.4616 A25 0.4783 0.5383 0.7043 0.6883 0.7915 0.7196 0.0006 A26 0.0059 0.1620 0.4382 0.1747 0.2546 0.9225 0.5190 A27 0.6185 0.4912 0.5825 0.9695 0.6060 0.4094 0.8245 A28 0.4243 0.3918 0.4030 0.5352 0.6501 0.8469 0.5095 A29 0.3722 0.3025 0.4642 0.4323 0.5067 0.6764 0.5030 A30 0.4277 0.3700 0.5934 0.5360 0.5913 0.5780 0.5428 Table 11 exhibits the overall ranking of the alternatives using the BC method. We proceed to calculate the win score and loss score for each alternatives using the findings presented in table 9 and the steps described in section 3.6.2 (CM) to derive the Copeland score and accordingly, rank the alternatives. A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 161 Table 11. Overall Rank (using Borda Count) Company Borda Count Final Rank_ BORDA Company Borda Count Final Rank_ BORDA A1 153 3 A16 192 1 A2 145 4 A17 54 27 A3 10 30 A18 95 16 A4 128 9 A19 119 11 A5 83 19 A20 134 6 A6 131 8 A21 140 5 A7 111 13 A22 112 12 A8 79 21 A23 76 24 A9 102 15 A24 82 20 A10 76 23 A25 127 10 A11 60 26 A26 46 29 A12 69 25 A27 134 7 A13 103 14 A28 89 17 A14 78 22 A29 48 28 A15 184 2 A30 85 18 Table 12 provides the findings of the CM. We apply the regular procedural steps of the SAW method (Simanaviciene and Ustinovichius, 2010) and obtain the overall (after aggregating) ranks of the alternatives (refer table 13). Table 12. Overall Rank (using Copeland approach) Company Wins Losses Final Score Final Rank Company Wins Losses Final Score Final Rank A1 153 2892 -2739 3 A16 192 2853 -2661 1 A2 145 2900 -2755 4 A17 54 2991 -2937 27 A3 10 3035 -3025 30 A18 95 2950 -2855 16 A4 128 2917 -2789 9 A19 119 2926 -2807 11 A5 83 2962 -2879 19 A20 134 2911 -2777 6 A6 131 2914 -2783 8 A21 140 2905 -2765 5 A7 111 2934 -2823 13 A22 112 2933 -2821 12 A8 79 2966 -2887 21 A23 76 2969 -2893 24 A9 102 2943 -2841 15 A24 82 2963 -2881 20 A10 76 2969 -2893 23 A25 127 2918 -2791 10 A11 60 2985 -2925 26 A26 46 2999 -2953 29 A12 69 2976 -2907 25 A27 134 2911 -2777 7 A13 103 2942 -2839 14 A28 89 2956 -2867 17 A14 78 2967 -2889 22 A29 48 2997 -2949 28 A15 184 2861 -2677 2 A30 85 2960 -2875 18 Table 13. Overall Rank (using SAW method) Company Final Rank_ SAW Company Final Rank_ SAW A1 4 A16 1 A2 5 A17 24 A3 30 A18 16 A4 8 A19 11 A5 20 A20 7 A6 9 A21 6 A7 12 A22 10 A8 25 A23 22 Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 162 Company Final Rank_ SAW Company Final Rank_ SAW A9 15 A24 19 A10 26 A25 14 A11 23 A26 29 A12 28 A27 3 A13 13 A28 17 A14 21 A29 27 A15 2 A30 18 Figure 2 pictorially represents the comparison of the overall ranking of the alternatives using BC, CM and SAW methods which reflects a consensus. We also calculate the correlations among the overall ranking by using BC method and others (see table 14) which indicates the consistency of BC method with others. Table 14. Correlation Test among the rankings by BC, CM and SAW methods Final_Rank_Copeland Final_Rank_SAW Final_Rank_ BORDA Spearman's rho 1.000** .977** Sig. (2-tailed) 0.000 0.000 ** Correlation is significant at the 0.01 level (2-tailed). Figure 2. Comparison of overall ranks by BC, CM and SAW methods We further test the consistency of the year wise ranking of the alternatives (obtained by using EDAS method) and the overall ranking (obtained by using BC method) as given in table 15. We note that the correlation is statistically significant. Further, it is evident that FY 2013-14, FY 2016-17 and FY 2017-18 show higher consistency, FY 2015-16 and FY 2019-20 are moderately consistent and FY 2018-19 exhibits low consistency with the final ranking. A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 163 Table 15. Correlation test among the year wise rankings and the overall ranking Rank_ 13_14 Rank_ 14_15 Rank_ 15_16 Rank_ 16_17 Rank_ 17_18 Rank_ 18_19 Rank_ 19_20 Overall Rank Spearman's rho .816** .744** .588** .780** .720** .370* .507** Sig. (2-tailed) 0.000 0.000 0.001 0.000 0.000 0.044 0.004 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). MCDM methods are dependent on the given conditions such as selection of alternative and criteria sets, effects of the criteria on the alternatives, criteria weights, computational steps of the algorithms and so on. Therefore, it is imperative to examine whether the result obtained by using a specific MCDM method is reliable or not (Biswas et al., 2019; Gupta et al., 2019; Gupta et al., 2022; Biswas et al., 2022a). The extant literature shows several instances (e.g., Biswas and Pamucar, 2021; Biswas et al., 2021; Biswas et al., 2022b; Biswas and Anand, 2020) wherein the authors use a group of widely used methods to compare with the selected framework for the given problem. In our paper, we rank the alternatives using two other popular and extensively used MCDM models such as multi-attributive border approximation area comparison (MABAC) (Pamučar and Ćirović, 2015) and the COmplex PRoportional ASsessment (COPRAS) method (Zavadskas et al., 1994) for all years. Next, we use the BC method to derive the final ranks for both MABAC and COPRAS method. Then, we examine the correlations among the rankings (year wise and overall) provided by our framework (using EDAS), MABAC and COPRAS (see tables 16-18). The tables 16-18 suggest that our EDAS based ranking is comparable and in sync with the other methods. Hence, there is a reason to consider our result as a reliable one. Table 16. Rank correlation test (year wise) between EDAS and COPRAS EDAS_ 13_14 EDAS_ 14_15 EDAS_ 15_16 EDAS_ 16_17 EDAS_ 17_18 EDAS_ 18_19 EDAS_ 19_20 COPRAS_13_14 Spearman's rho .993** Sig. (2-tailed) 0.000 COPRAS_14_15 Spearman's rho .892** Sig. (2-tailed) 0.000 COPRAS_15_16 Spearman's rho .928** Sig. (2-tailed) 0.000 COPRAS_16_17 Spearman's rho .957** Sig. (2-tailed) 0.000 COPRAS_17_18 Spearman's rho .964** Sig. (2-tailed) 0.000 COPRAS_18_19 Spearman's rho .960** Sig. (2-tailed) 0.000 COPRAS_19_20 Spearman's rho .821** Sig. (2-tailed) 0.000 ** Correlation is significant at the 0.01 level (2-tailed). Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 164 Table 17. Rank correlation test (year wise) between EDAS and MABAC EDAS_ 13_14 EDAS_ 14_15 EDAS_ 15_16 EDAS_ 16_17 EDAS_ 17_18 EDAS_ 18_19 EDAS_ 19_20 MABAC_13_14 Spearman's rho .905** Sig. (2-tailed) 0.000 MABAC_14_15 Spearman's rho .683** Sig. (2-tailed) 0.000 MABAC_15_16 Spearman's rho .453* Sig. (2-tailed) 0.012 MABAC_16_17 Spearman's rho .916** Sig. (2-tailed) 0.000 MABAC_17_18 Spearman's rho .731** Sig. (2-tailed) 0.000 MABAC_18_19 Spearman's rho .663** Sig. (2-tailed) 0.000 MABAC_19_20 Spearman's rho .749** Sig. (2-tailed) 0.000 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Table 18. Rank correlations among the final results by EDAS, MABAC and COPRAS MABAC_Final COPRAS_Final EDAS_final Spearman's rho .782** .972** Sig. (2-tailed) 0.000 0.000 ** Correlation is significant at the 0.01 level (2-tailed). We further conduct a non-parametric statistical test such as Kruskal Wallis Test (KWT) to examine whether the distribution functions of EDAS, COPRAS and MABAC are significantly different. We find the value of the Asymp. Sig. as 1.00 which strongly supports the null hypothesis that the distribution functions of all methods are equal. Hence, the result obtained by EDAS method is further validated statistically. 5. Discussion The current study reveals some interesting observations. Firstly, we see that when multiple criteria (ownership, size, profitability, growth, liquidity and risk) are considered for comparing the companies (i.e., FMCG and CD), the rankings are not consistent over the years. We observe that as per overall aggregated ranking, top two organizations such as A16 (ITC limited) and A15 (Hindustan Unilever Ltd) hold their positions more or less consistent. The same behavior is noticed for the bottom three organizations such as A3 (Bombay Burmah Trdg. Corpn. Ltd), A26 (Rajesh Exports Ltd.) and A29 (Voltas Ltd). Looking at the nature of these organizations, we find that market capitalization (see table D1 in Appendix D) for the top two capable organizations are higher than others. In addition, both A16 and A15 are having multi- product portfolio with strong global presence. Further, it is seen that FMCG organizations are in the top bracket as far as DPC is concerned. The companies in the bottom bracket are mostly CD firms. The result is an indication that organizations producing luxury goods may tend to be less capable in paying the dividends. Further, A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 165 we figure out that FY 2018-19 shows considerably higher variations in the usual rankings of the companies. It may be an indirect effect of the declaration of the GST bill in India and demonetization initiative by the Government of India. The present study has some significant implications. From the theoretical point of view, the current study is a distinguished work for comparing the firms on the basis of their DPC. So far, studies have been made to explore the effect of dividend payment on the firms’ performance and their values and to enfold the determinants of the dividend policy. But, the question arises, are the firms capable enough to pay dividends? Therefore, this paper provides a holistic multi-perspective analysis framework to gauge the capabilities of the firms beforehand. We have noticed that DPC varies significantly over the years. Hence, although the firms may realize the importance of paying dividend as a positive signal to the investors, they may not be equally capable of the providing the same over the years. Hence, there is a need of striking a balance between the principal’s interest and manager’s decisions. Further, the present paper has its importance from the perspectives of behavioral finance also. Despite the tax disadvantage of Dividends (typically dividend income is taxed more than capital gains) and issuance cost associated with new equity, firms pay Dividends and investors generally regard such dividend payments positively. Information signaling, clientele effect, agency costs are some important reasons. In addition, investors have preference for dividends due to behavioral reasons. Lack of self-control and aversion for regret could be important reasons. Consequently, dividends and capital receipts are not perfectly substitutable. The experts of the behavioral finance field (e.g., Kahneman and Tversky, 1979; Thaler and Shefrin, 1981; Shefrin and Statman, 1984) regarded the internal conflict as one of the major reasons behind such mismatch. The individual wishes to deny himself a present indulgence, yet simultaneously finds that he yields to the temptation. In the area of personal finance, individuals would like to protect their principal from their wasteful spending tendencies. A simple way to do this is to limit their spending to the dividend income so that the capital amount is preserved. Such behavioral nature explains a preference for dividend by those who otherwise have difficulty in exercising self-control. The individuals who set aside funds for their children's college education at one interest rate, yet borrow to finance their consumer goods at a higher interest rate, are not acting as standard utility maximizers. Yet the underlying rationale seems quite straightforward. Similarly, it implies that an individual may be better off by allowing current consumption to be determined by the dividend payout from his stock portfolio. In other words, this individual may wish to follow a rule stipulating that portfolio capital is not to be consumed, only dividends. Empirical evidence suggests that most investors feel more regret when they sell their stock to generate income compared to using the dividend income. Regret shall be more, if stock prices rise subsequently. Therefore, despite all arguments and counter discussions related to dividend policy, DPO holds its importance in attracting the investors over the years. Hence, this paper puts forth a notable extension to the growing strand of work that renders a new direction to the individual investors and policy makers. 6. Conclusion The present study has been designed to add a new dimension to the ongoing strand of literature on dividend policy and DPO. The current work has provided a multi-period, multi-criteria based framework to compare the DPC of 25 FMCG and 5 CD organizations (listed in BSE, India) for the period FY 2013-14 to FY 2019-20. For comparison purpose, we have considered six aspects (grounded on the extant Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 166 theories on dividend policy) such as ownership, size, profitability, growth, liquidity and risk. We have used a new integrated LOPCOW-EDAS framework for our analysis. The result shows that companies do not show consistent performance over the years. However, the aggregate overall performance is in sync with the market capitalization for most of the organizations. We further have noticed that FMCG organizations show comparatively better capabilities that CD firms vis-à-vis dividend payment. For aggregation (of the ranks for different years) we have used widely used techniques such as BC, CM in addition to SAW. The aggregated overall ranking shows consistency with the same obtained for individual years. As per the aggregated ranking the companies like A16: ITC limited; A15: Hindustan Unilever Ltd. A1: Avanti Feeds Ltd. A2: Bajaj Consumer Care Ltd. A21: Procter & Gamble Hygiene & Health Care Ltd. hold the top positions while A3: Bombay Burmah Trdg. Corpn. Ltd. A26: Rajesh Exports Ltd. A29: Voltas Ltd. A17: Jyothy Labs Ltd. A11: Gillette India Ltd. fall into the lower bracket. However, the present work posits a number of further scope of research. Firstly, in this paper we have not considered subjective opinions of the investors in deriving the criteria weights. The criteria weights, though have been found by using objective information, but the susceptible to abrupt variations in the performance values of the alternatives. One general drawback of the opinion-based decision making is subjective bias. Hence, one future study may also take opinions of some seasoned investors and experts to derive the criteria weights which shall be aggregated with the weights found by using objective values. Then the same weights may be used to compare the companies for different years during the study period. Secondly, in our study we observe considerable variations in ranking for different years. One future work may attempt to gauge the impact of macroeconomic events during each FY and shall draw a causal association with the variations in the ranking. Thirdly, it shall be an interesting future work to examine whether DPC has any positive association with the stock market performance of the organizations under study here. Further, the present study may be extended to test whether sales and operational performance, innovativeness, financial stability and economic sustainability have any positive influence on DPC or not. Fourthly, in this paper we have not examined the impact of Covid-19 on DPC. A near future research may be designed in this regard by considering the FY 2020-21 and FY 2021-22. Fifthly, it may also be a notable work if an investigation may be made to find out the association of DPC and DPR and dividend yield. Sixthly, the current work focuses on FMCG and CD sectors. The same framework as used in this paper may be modified/extended for assessing the comparative DPC of the constituent firms belong to other sectors. Seventh, from the technical point of view, LOPCOW is very recently introduced. The method may be tested in other complex scenarios, especially under uncertainty wherein future research shall extend the model to work with imprecise information. Eighth, a future work may be done to compare a group of companies at the different phases of the business cycle and are managed differently (e.g., by professionals, promoter dominated, multinational governance etc.) with our model to obtain their DPC and subsequently relate to their DPO. Ninth, there is a possibility to examine the performance of the companies from behavioural perspectives vis-à-vis DPC. Tenth, there may be other measures of risk, for instance, cost of the capital employed, degree of operating leverage among others that may also be considered in further analysis. The EDAS method has some limitations. EDAS method is more appropriate for risk neutral situations as it considers the average solution point as a benchmark. The average solution may not always portray the true picture in all real-life scenarios. A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 167 Further, in some occasions, it is seen that the NDA or PDA values for some alternatives equal to zero. In those cases, the weighted sum values become undefined. Nevertheless, the above-mentioned scopes, we hope, will not lessens the value and potential of the present work. We believe that the current work shall contribute new dimensions and perspectives for the policy makers in business organizations and government and help the investors in investment decision making. Author Contributions: Conceptualization: SB, GB, DP, JNM; methodology: SB, GB, DP; software: SB; validation: SB, GB, DP; formal analysis: SB.; investigation: SB, GB; data curation: SB; writing—original draft preparation: SB; writing—review and editing: JNM.; supervision: GB. All authors have read and agreed to the published version of the manuscript Funding: This research received no external funding. Data Availability Statement: Necessary data is provided. Acknowledgments: The authors express their sincere thanks to all anonymous referees whose valuable comments helped to improve the quality of this paper. Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Affandi, F., Sunarko, B., & Yunanto, A. (2019). The Impact of Cash Ratio, Debt To Equity Ratio, Receivables Turnover, Net Profit Margin, Return On Equity, and Institutional Ownership To Dividend Payout Ratio. Journal of Research in Management, 1(4), 1-11. Agresti, A., & Kateri, M. (2021). Foundations of Statistics for Data Scientists: With R and Python. Chapman and Hall/CRC. Al Sawalqa, F. A. (2021). Life-Cycle Theory of Corporate Dividend Policy in Jordan: The Role of Equities, Assets, and Age during the Period 2015-2019. The Journal of Asian Finance, Economics and Business, 8(6), 1-11. Amalia, S., & Hermanto, S. B. (2018). Effect of Profitability, Managerial Ownership, Leverage and Growth on Dividend Policy. Journal of Accounting Science and Research (JIRA), 7(8), 1-21. Amidu, M., & Abor, J. (2006). Determinants of dividend payout ratios in Ghana. The journal of risk finance. 7(2), 136-145. Asquith, P., & Mullins Jr, D. W. (1983). The impact of initiating dividend payments on shareholders' wealth. Journal of business, 77-96. Baker, H. K., & Powell, G. E. (1999). How corporate managers view dividend policy. Quarterly Journal of Business and Economics, 38(2), 17-35. Baker, H. K., Farrelly, G. E., & Edelman, R. B. (1985). A survey of management views on dividend policy. Financial management, 14(3), 78-84. Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 168 Baker, H. K., Powell, G. E., & Veit, E. T. (2002). Revisiting the dividend puzzle: Do all of the pieces now fit?. Review of Financial Economics, 11(4), 241-261. Baker, H. K., Veit, E. T., & Powell, G. E. (2001). Factors influencing dividend policy decisions of Nasdaq firms. Financial Review, 36(3), 19-38. Bakri, M. A., Abd Jalil, M. I., & Hassan, Z. (2021). Dividend Policy in Malaysia: A Comparison of Determinants Pre and Post Malaysian Code on Corporate Governance. International Journal of Banking and Finance, 16(2), 1-22. Barak, S., & Mokfi, T. (2019). Evaluation and selection of clustering methods using a hybrid group MCDM. Expert Systems with Applications, 138, 112817. Biswas, S. (2020a). Exploring the Implications of Digital Marketing for Higher Education using Intuitionistic Fuzzy Group Decision Making Approach. BIMTECH Business Perspective, 2(1), 33-51. Biswas, S. (2020b). Measuring performance of healthcare supply chains in India: A comparative analysis of multi-criteria decision making methods. Decision Making: Applications in Management and Engineering, 3(2), 162-189. Biswas, S., & Anand, O. P. (2020). Logistics Competitiveness Index-Based Comparison of BRICS and G7 Countries: An Integrated PSI-PIV Approach. IUP Journal of Supply Chain Management, 17(2), 32-57. Biswas, S., & Pamučar, D. S. (2021). Combinative distance based assessment (CODAS) framework using logarithmic normalization for multi-criteria decision making. Serbian Journal of Management, 16(2), 321-340. Biswas, S., Bandyopadhyay, G., Guha, B., & Bhattacharjee, M. (2019). An ensemble approach for portfolio selection in a multi-criteria decision making framework. Decision Making: Applications in Management and Engineering, 2(2), 138-158. Biswas, S., Majumder, S., & Dawn, S. K. (2022b). Comparing the socioeconomic development of G7 and BRICS countries and resilience to COVID-19: An entropy– MARCOS framework. Business Perspectives and Research, 10(2), 286-303. Biswas, S., Majumder, S., Pamucar, D., & Dawn, S. K. (2021). An extended LBWA framework in picture fuzzy environment using actual score measures application in social enterprise systems. International Journal of Enterprise Information Systems (IJEIS), 17(4), 37-68. Biswas, S., Pamučar, D., Božanić, D., & Halder, B. (2022a). A New Spherical Fuzzy LBWA-MULTIMOOSRAL Framework: Application in Evaluation of Leanness of MSMEs in India. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/5480848 Black, F., & Scholes, M. (1974). The effects of dividend yield and dividend policy on common stock prices and returns. Journal of financial economics, 1(1), 1-22. Borda, J. D. (1784). Mémoire sur les élections au scrutin. Histoire de l'Academie Royale des Sciences pour 1781 (Paris, 1784). Brennan, M. (1971). A note on dividend irrelevance and the Gordon valuation model. The Journal of Finance, 26(5), 1115-1121. https://doi.org/10.1155/2022/5480848 A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 169 Brigham, E. F., & Houston, J. F. (2001). Financial Management. Book 1 issue 8. Jakarta: Erlangga. Budiarso, N. S. (2019). Agent, Steward, and Dividend Policy. European Research Studies, 22(3), 83-94. Chaniago, Y. F., & Ekadjaja, A. (2022). Determinant of Dividend Payout Ratiosin Consumer Goods Company. Jurnal Ekonomi, Special Issue, March 2022, 100-118. Dang, H. N., Vu, V. T. T., Ngo, X. T., & Hoang, H. T. V. (2021). Impact of dividend policy on corporate value: Experiment in Vietnam. International Journal of Finance & Economics, 26(4), 5815-5825. Dewasiri, N. J., Koralalage, W. B. Y., Azeez, A. A., Jayarathne, P. G. S. A., Kuruppuarachchi, D., & Weerasinghe, V. A. (2019). Determinants of dividend policy: evidence from an emerging and developing market. Managerial Finance, 45(3), 413- 429. Dhingra, R., Dev, K., & Gupta, M. (2018). Performance Analysis of FMCG Sector in India. International Journal of Business Analytics and Intelligence, 6(2), 12-23. Dortaj, A., Maghsoudy, S., Ardejani, F. D., & Eskandari, Z. (2020). A hybrid multi- criteria decision making method for site selection of subsurface dams in semi-arid region of Iran. Groundwater for Sustainable Development, 10, 100284. Easterbrook, F. H. (1984). Two agency-cost explanations of dividends. The American economic review, 74(4), 650-659. Ecer, F. (2021). A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renewable and Sustainable Energy Reviews, 143, 110916. Ecer, F., & Pamucar, D. (2022). A novel LOPCOW-DOBI multi-criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690 Fama, E. F., & Babiak, H. (1968). Dividend policy: An empirical analysis. Journal of the American statistical Association, 63(324), 1132-1161. Gandhi, N. S., Thanki, S. J., & Thakkar, J. J. (2018). Ranking of drivers for integrated lean-green manufacturing for Indian manufacturing SMEs. Journal of Cleaner Production, 171, 675-689. Garg, M. C., & Bhargaw, V. (2019). The Determinants of Dividend Policy in Indian Corporate Sector. IUP Journal of Accounting Research & Audit Practices, 18(1), 31-81. Gordon, M. J. (1959). Dividends, earnings, and stock prices. The review of economics and statistics, 99-105. Graham, B., & Dodd, D. L. F. (1934). Security analysis. New York: McGraw-Hill. Gupta, C. P., & Bedi, P. (2020). Corporate cash holdings and promoter ownership. Emerging Markets Review, 44, 100718. https://doi.org/10.1016/j.ememar.2020.100718 Gupta, K., & Arora, R. (2021). Three dimensional bounded transportation problem. Yugoslav Journal of Operations Research, 31(1), 121-137. https://doi.org/10.1016/j.omega.2022.102690 https://doi.org/10.1016/j.ememar.2020.100718 Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 170 Gupta, S., Bandyopadhyay, G., Bhattacharjee, M., & Biswas, S. (2019). Portfolio Selection using DEA-COPRAS at risk–return interface based on NSE (India). International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(10), 4078-4086. Gupta, S., Bandyopadhyay, G., Biswas, S., & Mitra, A. (2022). An Integrated Framework for Classification and Selection of Stocks for Portfolio Construction: Evidence from NSE, India. Decision Making: Applications in Management and Engineering, https://doi.org/10.31181/dmame0318062021g Hamill, P. A., & Al-Shattarat, W. (2012). What determines the dividend payout ratio for Jordanian Industrial Firms?. Journal of Emerging Market Finance, 11(2), 161-188. Hardy, S., & Andestiana, R. (2019). The Effect of Profitability, Debt Policy and Asset Growth on Dividend Policy (In Food And Beverage Companies Listed on the Indonesia Stock Exchange for the 2013-2017 Period). Journal of UMT Dynamics, 2(2), 44-58. Hirdinis, M. (2019). Capital structure and firm size on firm value moderated by profitability. International Journal of Economics and Business Administration, 7(1), 174-191. IBEF report (2022a). https://www.ibef.org/industry/fmcg (last accessed July 31, 2022) IBEF report (2022b). https://www.ibef.org/industry/indian-consumer- market/infographic (last accessed July 31, 2022) Ifeanyichukwu, N. C., & Yusuf, L. (2021). A. Effects of Dividend Policy On Market Share Price of Listed Industrial Goods Companies in Nigeria. International Journal of Innovative Research and Advanced Studies (IJIRAS), 8(11), 14-21. Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. The American economic review, 76(2), 323-329. Jensen, M., & Meckling, W. (1976). Theory of The Firm: Managerial Behavior, Agency Cost and Ownership Structure. Journal of Financial Economics, 3, 305-360. Jiang, Y., Pan, X., & Zhou, Q. (2019). The Effect of Dividend on Stock Returns in Chinese Market: Is There a Clientele Phenomenon? Journal of Accounting and Finance, 19(2), 96-114. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-292. Karmakar, P., Dutta, P., & Biswas, S. (2018). Assessment of mutual fund performance using distance based multi-criteria decision making techniques-An Indian perspective. Research Bulletin, 44(1), 17-38. Katakwar, K., Tenguriya, S., Chhajer, P., Mehta, V., & Gandhi, V. (2021). Determinants of Dividend Policy in India–Special Reference to Nifty 50 Companies. Shanlax International Journal of Management, 9(2), 37-42. Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi- criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435-451. https://doi.org/10.31181/dmame0318062021g https://www.ibef.org/industry/fmcg https://www.ibef.org/industry/indian-consumer-market/infographic https://www.ibef.org/industry/indian-consumer-market/infographic A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 171 Khan, K., Lamrani, H. C., & Khalid, S. (2019). The impact of dividend policy on firm performance: A case study of the industrial sector. Risk Governance & Control: Financial Markets & Institutions, 9, 23–31 Khan, R., Meer, J. K., Lodhi, R. N., & Aftab, F. (2017). Determinants of dividend payout ratio: A study of KSE manufacturing firms in Pakistan. IBT Journal of Business Studies (JBS), 13(1), 12-24. Kilincarslan, E. (2021). The influence of board independence on dividend policy in controlling agency problems in family firms. International Journal of Accounting & Information Management, 29(4), 552-582 Kumar, A., Lei, Z., & Zhang, C. (2022). Dividend sentiment, catering incentives, and return predictability. Journal of Corporate Finance, 72, 102128. Labhane, N. B., & Das, R. C. (2015). Determinants of dividend payout ratio: Evidence from Indian companies. Business and Economic Research, 5(2), 217-241. Labhane, N. B., & Mahakud, J. (2019). Impact of Business Group Size and Diversification on Dividend Policy and Pay-outs: Evidence from Indian Companies. South Asian Journal of Management, 26(1), 50-75. Laha, S., & Biswas, S. (2019). A hybrid unsupervised learning and multi-criteria decision making approach for performance evaluation of Indian banks. Accounting, 5(4), 169-184. Lansdowne, Z. F., & Woodward, B. S. (1996). Applying the Borda ranking method. Air Force Journal of Logistics, 20(2), 27-29. Le, T. T. H., Nguyen, X. H., & Tran, M. D. (2019). Determinants of Dividend Payout Policy in Emerging Markets: Evidence from the ASEAN Region. Asian Economic and Financial Review, 9(4), 531 Lease, R. C., John, K., Kalay, A., Loewenstein, U., & Sarig, O. H. (1999). Dividend Policy:: Its Impact on Firm Value. Oxford University Press. Lei, F., Wei, G., Shen, W., & Guo, Y. (2022). PDHL-EDAS method for multiple attribute group decision making and its application to 3D printer selection. Technological and Economic Development of Economy, 28(1), 179-200. Lestari, S., Adji, T. B., & Permanasari, A. E. (2018, May). Performance comparison of rank aggregation using borda and copeland in recommender system. In 2018 International Workshop on Big Data and Information Security (IWBIS) (pp. 69-74). IEEE Lin, T. J., Chen, Y. P., & Tsai, H. F. (2017). The relationship among information asymmetry, dividend policy and ownership structure. Finance Research Letters, 20, 1-12. Lintner, J. (1956). Distribution of incomes of corporations among dividends, retained earnings, and taxes. The American economic review, 46(2), 97-113. Lloren-Alcantara, P. A. (2020). Determinants of Dividend Payout Policy: Evidence from a Philippine Context. Philippine Management Review, 27, 1-16. Louangrath, P. (2014). Sample size determination for non-finite population. Southeast-Asian J. of Sciences, 3(2), 141-152. Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 172 Luanglath, I. (2014). Innovation Analysis for Business Productivity. Executive Journal, 34(1), 23-39. Luanglath, P. I. and Rewtrakulpaiboon (2013). Determination of Minimum Sample Size for Film-Induced Tourism Research. Silapakorn 70th Anniversary International Conference 2013. Towards the Next decade of Hospitality and Creative Economics: Looking Forward to 2020. December 1st – 3rd, 2013, Bangkok, Thailand. Conference Proceeding, pp. 127-139. Malik, W., & Sattar, A.R. (2018). Corporate Governance Characteristics and Operating Cash Flow as Determinants of Dividend Payout: Evidence from Pakistan. Pacific Business Review International, 11(5), 123-130. Michel, A. (1979). Industry influence on dividend policy. Financial Management, 8(3), 22-26. Miller, M. H., & Modigliani, F. (1961). Dividend policy, growth, and the valuation of shares. the Journal of Business, 34(4), 411-433. Miller, M. H., & Rock, K. (1985). Dividend policy under asymmetric information. The Journal of finance, 40(4), 1031-1051. Mui, Y. T., & Mustapha, M. (2016). Determinants of dividend payout ratio: evidence from Malaysian public listed firms. Journal of Applied Environmental and Biological Sciences, 6(1), 48-54. Muth, J. F. (1961). Rational expectations and the theory of price movements. Econometrica, 29(3), 315-335. Nidar, S. R., Masyita, D., & Anwar, M. (2019). Analysis of Determinant Factors towards Dividend at Manufacturing Companies Listed in Indonesia Stock Exchange. Academy of Accounting and Financial Studies Journal, 23(2), 1-10. Novia, Y. D., & Marlina, M. (2022). Determinants of Dividend Policy in Compass 100 Index Companies. Budapest International Research and Critics Institute (BIRCI- Journal): Humanities and Social Sciences, 5(2), 9831-9842. Odum, A. N., Odum, C. G., Omeziri, R. I., & Egbunike, C. F. (2019). Impact of Dividend Payout Ratio on the Value of Firm: A Study of Companies Listed on the Nigerian Stock Exchange. Indonesian Journal of Contemporary Management Research, 1(1), 25-34. Omar, M. M. S., & Echchabi, A. (2019). Dividend policy and pay-out practices in Malaysia: A qualitative analysis. Journal of Accounting, Finance and Auditing Studies, 5(1), 226-240. Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert systems with applications, 42(6), 3016-3028. Pamucar, D., Žižović, M., Biswas, S., & Božanić, D. (2021). A new logarithm methodology of additive weights (LMAW) for multi-criteria decision-making: Application in logistics. Facta Universitatis, Series: Mechanical Engineering, 19(3), 361-380. Pandey, N. S., & Narayani, S. D. (2019). Impact of Dividend Policy During the Global Economic Recession: Automobile Industries in India. SCMS Journal of Indian Management, 16(2), 18-29. A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 173 Pattiruhu, J. R., & Paais, M. (2020). Effect of liquidity, profitability, leverage, and firm size on dividend policy. The Journal of Asian Finance, Economics and Business, 7(10), 35-42. Pourjavad, E., & Shirouyehzad, H. (2011). A MCDM approach for prioritizing production lines: a case study. International Journal of Business and Management, 6(10), 221-229. Pramanik, P. K. D., Biswas, S., Pal, S., Marinković, D., & Choudhury, P. (2021). A comparative analysis of multi-criteria decision-making methods for resource selection in mobile crowd computing. Symmetry, 13(9), 1713. Puspitaningtyas, Z. (2019). Empirical evidence of market reactions based on signaling theory in Indonesia Stock Exchange. Investment Management and Financial Innovations, 16(2), 66-77. Rochmah, H. N., & Ardianto, A. (2020). Catering dividend: Dividend premium and free cash flow on dividend policy. Cogent Business & Management, 7(1), 1812927. Roscoe, J.T. (1975) Fundamental Research Statistics for the Behavioural Sciences, 2nd edition. New York: Holt Rinehart & Winston. P. 163. Rozeff, M. S. (1982). Growth, beta and agency costs as determinants of dividend payout ratios. Journal of financial Research, 5(3), 249-259. Salim, M. N., & Aulia, S. (2021). Analysis determinant of dividend payout ratio and its impact to the firm value (empirical study on food and beverage industry issuer 2016- 2019), International Journal of Engineering Technologies and Management Research, 8(9), 46-59. Salman, A. (2019). Determinants of dividend policy. Investment management and financial innovations, 16(1), 167-177. Sami, M., & Abdallah, W. (2021). Assessing the impact of dividend policy on the sustainability of distressed firms. Journal of Modelling in Management, 16(3), 987- 1001. Sarangi, P. R. A. S. A. N. T. (2019). The Indian Consumer Durable Market and an Analysis of Demand Pattern for Major Durables. Unpublished manuscript. Retrieved from https://icsi.edu/media/portals/86/Major%20Durables.pdf (Last accessed July 31, 2022) Seth, R., & Mahenthiran, S. (2022). Impact of dividend payouts and corporate social responsibility on firm value–Evidence from India. Journal of Business Research, 146, 571-581. Setyabudi, T. (2021). The Effect of Institutional Ownership, Leverage, and Profitability on Firm Value with Dividend Policy as an Intervening Variable. Journal of Business and Management Review, 2(7), 457-469. Shefrin, H. M., & Statman, M. (1984). Explaining investor preference for cash dividends. Journal of financial economics, 13(2), 253-282. Simanaviciene, R., & Ustinovichius, L. (2010). Sensitivity analysis for multiple criteria decision making methods: TOPSIS and SAW. Procedia-Social and Behavioral Sciences, 2(6), 7743-7744. https://icsi.edu/media/portals/86/Major%20Durables.pdf Biswas et al./Decis. Mak. Appl. Manag. Eng. 5 (2) (2022) 140-175 174 Singla, H. K., & Samanta, P. K. (2018). Determinants of dividend payout of construction companies: a panel data analysis. Journal of Financial Management of Property and Construction, 24(1), 19-38 Stević, Ž., Vasiljević, M., Zavadskas, E. K., Sremac, S., & Turskis, Z. (2018). Selection of carpenter manufacturer using fuzzy EDAS method. Engineering Economics, 29(3), 281-290. Su, Y., Zhao, M., Wei, G., Wei, C., & Chen, X. (2022). Probabilistic uncertain linguistic EDAS method based on prospect theory for multiple attribute group decision-making and its application to green finance. International Journal of Fuzzy Systems, 24(3), 1318-1331. Taher, F. N. A., & Al-Shboul, M. (2022). Dividend policy, its asymmetric behavior and stock liquidity. Journal of Economic Studies, (ahead-of-print). DOI 10.1108/JES-10- 2021-0513. Taofeek, O., Kajola, S. O., & AKINBOLA, O. А. (2019). Influence of Dividend Policy on Stock Price Volatility of Non-Financial Firms Listed Nigerian Stock Exchange. Journal of Varna University of Economics, 63(1), 35-49. Thakur, B. P. S., & Kannadhasan, M. (2018). Determinants of dividend payout of Indian manufacturing companies: A quantile regression approach. Journal of Indian Business Research, 10(4), 364-376. Thaler, R. H., & Shefrin, H. M. (1981). An economic theory of self-control. Journal of political Economy, 89(2), 392-406. TOI report (2022). https://timesofindia.indiatimes.com/blogs/voices/the-state-of- indian-fmcg-sector-and-future-prospects-for-fy23/ (last accessed July 31, 2022) Tumiwa, R. A. F., & Mamuaya, N. C. (2019). The Determinants of Dividend Policy and Their Implications for Stock Prices on Manufacturing Companies Listed on the Indonesia Stock Exchange. KnE Social Sciences, 778-793. Walter, J. E. (1963). Dividend policy: its influence on the value of the enterprise. The Journal of finance, 18(2), 280-291. Wei, G., Wei, C., & Guo, Y. (2021). EDAS method for probabilistic linguistic multiple attribute group decision making and their application to green supplier selection. Soft Computing, 25(14), 9045-9053. Widiyanti, M., Adam, M., & Isnurhadi, I. (2019). Effect of Company Performance on Earing per Share with Dividend Payout Ratio as Intervening Variable in LQ 45 Companies. Acta Universitatis Danubius. Œconomica, 15(4), 286-292. Wu, W. W. (2011). Beyond Travel & Tourism competitiveness ranking using DEA, GST, ANN and Borda count. Expert Systems with Applications, 38(10), 12974-12982. Yakubu, I. N. (2021). The effect of working capital management on dividend policy: An empirical analysis of listed firms in Ghana. International Journal of Industrial Management, 9, 25-31. Yin, L., & Nie, J. (2021). Adjusted dividend-price ratios and stock return predictability: Evidence from China. International Review of Financial Analysis, 73, 101618. https://timesofindia.indiatimes.com/blogs/voices/the-state-of-indian-fmcg-sector-and-future-prospects-for-fy23/ https://timesofindia.indiatimes.com/blogs/voices/the-state-of-indian-fmcg-sector-and-future-prospects-for-fy23/ A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian … 175 Zavadskas, E. K., Kaklauskas, A., & Sarka, V. (1994). The new method of multicriteria complex proportional assessment of projects. Technological and economic development of economy, 1(3), 131-139. Zolfani, S. H., Ebadi Torkayesh, A., Ecer, F., Turskis, Z., & Šaparauskas, J. (2021). International market selection: a MABA based EDAS analysis framework. Oeconomia Copernicana, 12(1), 99-124. © 2022 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).