The role of institutional ownership and industry characteristics on the propensity to pay dividend: An insight from company open innovation

Martono, S.; Yulianto, Arief; Witiastuti, Rini Setyo; Wijaya, Angga Pandu Article The role of institutional ownership and industry characteristics on the propensity to pay dividend: An insight from company open innovation Journal of Open Innovation: Technology, Market, and Complexity Provided in Cooperation with: Society of Open Innovation: Technology, Market, and Complexity (SOItmC) Suggested Citation: Martono, S.; Yulianto, Arief; Witiastuti, Rini Setyo; Wijaya, Angga Pandu (2020) : The role of institutional ownership and industry characteristics on the propensity to pay dividend: An insight from company open innovation, Journal of Open Innovation: Technology, Market, and Complexity, ISSN 2199-8531, MDPI, Basel, Vol. 6, Iss. 3, pp. 1-16, https://doi.org/10.3390/joitmc6030074 This Version is available at: https://hdl.handle.net/10419/241460 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Open Innovation: Technology, Market, and Complexity Article The Role of Institutional Ownership and Industry Characteristics on the Propensity to Pay Dividend: An Insight from Company Open Innovation S. Martono *, Arief Yulianto, Rini Setyo Witiastuti and Angga Pandu Wijaya Department of Management, Universitas Negeri Semarang, Semarang 50229, Indonesia; [email protected] (A.Y.); [email protected] (R.S.W.); [email protected] (A.P.W.) *Correspondence: [email protected] Received: 14 July 2020; Accepted: 29 August 2020; Published: 3 September 2020   Abstract: The purpose of this study is to test the free cash flow agency theory hypothesis; namely, (a) whether differences in industrial sector affect a company’s propensity to pay dividends, and (b) whether institutional ownership is able to substitute for the propensity to pay dividends as a bonding mechanism. The analysis uses logistic regression to explore the existence of institutional ownership as a substitute for paying cash dividends in companies belonging to different industrial sectors. The results show that companies in the manufacturing sector have a greater propensity to pay dividends compared to those in non-manufacturing sectors. The results also indicate that low institutional ownership, as an external monitoring mechanism, can substitute for increasing the propensity to pay dividends. Overall, the results are consistent with implications in dividend policy. The results support the notion that the propensity to pay dividends accommodates different behavioral factors, considering sectoral differences. In addition, the results illustrate the relevance of alternative theories in explaining dividend policy from the perspective of agency theory. The results show that sectoral comparisons, in addition to institutional ownership factors, play important roles in the propensity of Indonesian companies to pay dividends. This study shows that each industry sector has different income characteristics, which affect the differences in propensity to pay dividends. Keywords: propensity to pay dividend; industry sectors; Institutional ownership 1. Introduction Benefits are an essential factor that push the relationship between agency problems and free cash flow. Agents have a high propensity to maximize their benefit; however, if a conflict arises, the agent will prioritize their interests. Conflicts of interest appear due to significant differences in interest [ 1 ]. Agency problems occur due to differences in the interests of managers and shareholders in using free cash flow (FCF). Easterbrook argued that there are two causes of agency problems [ 2 ]: (a) shareholders preference for using FCF for investments with high returns despite a high level of risk, and opting to monitor the managers’ investment decisions; (b) managers dislike of risk, which prompts them to allocate FCF for investments with low risk. It is suspected that FCF is the cause of agency problems between managers and shareholders. Based on actual data, agency problems are not limited to disagreements between managers and shareholders but also between shareholders and debtholders. A manager who concurrently acts as a shareholder would have both the rights and control over their cash flow. Thus, they are more likely to make suboptimal investments [ 3 ]. Value transfer from debtholder to shareholder is ensued. They utilize debt to select suboptimal investment that is oriented to increasing equity value instead of company value. This situation could lead to overinvestments based on exaggerated decision [ 4 ]; J. Open Innov. Technol. Mark. Complex. 2020,6, 74; doi:10.3390/joitmc6030074 www.mdpi.com/journal/joitmc J. Open Innov. Technol. Mark. Complex. 2020,6, 74 2 of 16 human resource management development [ 5 ]; over confidence [ 6 ], and underinvestment due to moral hazard and asymmetric information [7]; the selling of additional assets [8]. Risk shifting, resulting from overinvestment, after debt contract will transfer to the higher risk investment. Hence, the debt market value undergoes devaluation. A manager who is a shareholder and another shareholder face less debt than they should when the beta is not adjusted. Shareholders obtain equity value growth benefits from risk shifting when a project is accomplished, and share risks with debtholders when the project fails. A contradictory situation appears when companies conduct risk avoidance and rejects projects with a positive net present value (NPV), provided that it is not significantly beneficial to shareholders. As a result, debtholders will increase lending rates and reduce lending, therefore complying with funding through the capital market. This situation results in higher premium demands from new shareholders due to self-protection in manager’s behavior, and the equity value declines due to dilution. Furthermore, in a project unable to be financed with equity, there is a conflict between current shareholders and previous shareholders. Debt usage results in increasing suboptimal investment risk, therefore debt utilization is expected to reduce discretional power in FCF, for managers’ as shareholders’ benefit. In addition, instead of debt, dividends are fruitful to reduce FCF [ 1 ]. Research on 669 units of observation in Vietnamese concluded that debt and dividends are substitution variables that could mediate between overinvestment and firm performance [ 9 ]. The impact of debt is more treacherous to the company, which leads to financial difficulties and bankruptcy, whereas cutting dividends will only reduce equity value. This situation encourages further understanding of substituted factors of dividends, considering weak minority investor protection in developing countries [4], such as Indonesia. When a manager receives an incentive for each project, having excess cashflow in investments with a positive net present value (NPV) could result in the allocation of funds to unprofitable projects [ 1 ]. Larger FCF could potentially trigger more problems between the manager and shareholders. Therefore, it is necessary to consider the role of substitution between dividends as a bonding mechanism, and institutional ownership as a monitoring mechanism. FCF is the remaining cash within a company once all investment projects with positive NPV have been implemented [ 1 ]. Therefore, the use of FCF has the potential to cause agency conflicts, occurring between managers (agents) and shareholders (principals). FCF can be used by companies as dividends, to reduce debt or issue equity, or as precautionary savings or additional investments [10]. A previous study identified free cash flow as one source of agency problems between managers and shareholders [ 11 ]. Managers of firms with high FCF and low growth opportunities tend to invest in marginal or even negative NPV projects and use income-increasing discretionary accruals to camouflage the effects of non-wealth-maximizing investments. FCF is determined by the characteristics of the industrial sector to which a given company belongs, which have differing characteristics and business scopes. FCF differences by industrial sector lead to variations in agency problems, such that dividends as bonding mechanisms differ [12,13]. In addition to dividends in the FCF hypothesis, institutional ownership can serve as a substitute to reduce agency problems. Companies with institutional ownership have the capability and resources to monitor agency problems [ 6 ]. Studies that have been conducted show that monitoring could reduce agency problems. More specifically, institutional ownership as a monitoring device, has a significant positive correlation with dividends [ 14 – 16 ]. In contrast, another piece of research explains that, from the signaling hypothesis perspective, institutional ownership is negatively correlated to dividends because they make it unnecessary to monitor the capital market for agency problems. Furthermore, different industrial sectors result in different FCF, agency problems, and impacts of using dividends as monitoring devices [ 17 ]. However, based on the signaling perspective, dividends are triggered by other companies in the intra-industry. This paper investigates how industrial sectors and institutional ownership affects dividends. We use logistic regression to discover how agency problems and signaling hypothesis variations in the industrial sector affect dividends. Furthermore, we also test the role of institutional ownership. J. Open Innov. Technol. Mark. Complex. 2020,6, 74 3 of 16 Previous studies have predicted dividends using ordinary least square (OLS) and generalized least squares (GLS), techniques used for estimating the unknown parameters in a linear regression model when there is a certain degree of correlation between the residuals in the regression model. The research proposes that the differences between industrial sectors impact the differences in the propensity to pay dividends. Companies with low institutional ownership have an increased propensity to pay dividends and vice versa. 2. Literature Review 2.1. FCF Hypothesis: Institutional Ownership, Industry Sector, and Dividends Agency problems escalate as free cash flow increases. This stage occurs when a company is in the growth stage. A higher percentage of dividends diminishes fund availability. Therefore, managers tend to restore free cash, becoming more reluctant to use funds carelessly [ 1 ]. When a company needs external funding (equity) and has a risky debt as a monitoring device, it tends to increase its dividend payment propensity. The dividend not only acts as a monitoring device, but also helps in decreasing agency costs that cover managers’ consumption of perks and overinvestment [ 1 ]. However, institutional shareholders are unlikely to be involved directly in monitoring, because they typically have long-term investment planning [ 18 ]. Moreover, institutional shareholders tend to suggest that companies pay higher dividends and, for financial needs, seek future external capital markets. Investors with institutional ownership conduct strict monitoring of the company and expect reward in the form of large dividends. Agency cost is related to free cash flow and overinvestment [ 19 ]. Higher institutional ownership increases capital market monitoring activities, resulting in bigger dividends. A previous study reinforced the notion that institutional ownership causes an escalation in company monitoring [20]. Based on the FCF hypothesis, it can be concluded that investors would like to make their institutional ownership more intensive, because monitoring the company’s actual income status would lead to a more substantial shareholding. As a result, institutional investors take a critical role in governing corporations that seek to receive more dividends. Managers as shareholders have discretional power in determining the use of FCF, which could result in suboptimal investments. This prediction is based on the overinvestment hypothesis due to risk factors [ 1 ], and an underinvestment presumption due to assets ownership and growth opportunities [ 7 ]. Future studies are advised to explore these overinvestment and underinvestment factors because they could differ across types of industries. Firms with a larger size, higher interest coverage ratio, profitability, and low business risk and debts are likely to distribute higher dividends in India [ 17 ]. The profitability of an institution could indicate the applicability of the free cash flow hypothesis in India. Growth opportunities and FCF differ between sectors in the Indian capital market. The type of risk that managers tend to avoid is within the area of FCF allocation. As one of the causes of agency problems, FCF can be caused by differences in the scope of business and operations in the industrial sector. FCF refers to the excess money that a company generates in a given project [ 1 ]. This cash could be used to cover dividends, debts, and sweeteners when issuing equity, to retain it as precautionary savings, or as additional investments. A study conducted in Jordan described that industrial sectors impact FCF, but that these types of sectors do not impact dividend decisions [ 21 ]. The consumer noncyclical industry sector seeks to maintain a higher FCF than other industries (e.g., the aviation industry). Previous research explained that agency cost does not significantly influence dividend decisions in various industrial sectors [ 22 ]. In the basic industry sector, companies with high FCF tend to pay lower dividends [23]. Prior research highlighted that companies in the diversified, industrial, and basic materials industries in the US tend to pay lower dividends [ 12 ]. Previous research described different dividend payments in the manufacturing sector. Dividend payments in the manufacturing sector and the miscellaneous sector were higher compared to other sectors. The shipping sector tends to have large J. Open Innov. Technol. Mark. Complex. 2020,6, 74 4 of 16 debts due to the high demand for using FCF for investment in ship facilities and fixed assets [ 24 ]. Considering that dividend regulations vary, the current study conducted a proxy to the proportion of pay dividends. Additionally, the proxy of cash dividends is used in this study to account for the inconsistent literature results concerning the causes of dividend distribution. A study of non-financial companies listed on the Abu Dhabi Securities Exchange discovered that dividends are positively associated with income and negatively associated with leverage, although differences do exist between sectors [ 25 ]. These sectors include the manufacturing, telecommunications, service, food, energy, telecommunications, and property and real estate sectors. Institutional ownership acts as a signaling device for dividends, particularly to reduce information asymmetry. It signals two conditions: (1) better insight regarding the company’s future growth due to having access to direct monitor resources, and (2) a positive mechanism to reduce agency problems [ 16 ]. Several studies also added that dividend policy is negatively and significantly associated with institutional ownership [26,27] . Without perilous debt, the provision of sweeteners (i.e., dividends) will be marked as positive news for the market. Namely, that the value of equity will increase. This condition could lead to improvements in the monitoring of the capital market. However, institutional shareholders (e.g., pension funds, insurance companies, investment companies, and banks) can carry out better monitoring, contributing to reducing the propensity to pay dividends. Therefore, dividends and institutional ownership are substitutes [ 28 ]. Companies do not need to pay dividends if institutional investors’ presence alleviates concerns, if management dissipates the company’s FCF. Investors could allocate more resources to monitoring company’s management, and to comprehensively analyzing company’s prospects [29]. The difference between FCF and the signaling hypothesis lies in the role that institutional ownership has on dividends. Based on the FCF hypothesis, the use of debt to limit managers’ use of FCF increases the risk of financial difficulty and bankruptcy instead of suboptimal investment. Companies that need external funding will access the capital market, causing institutional ownership with better monitoring resources to ask for more dividends. In contrast, the signaling hypothesis explains that institutional ownership can substitute dividends as a monitoring mechanism because companies can monitor their resources better. Thus, the present study formulates the first hypothesis: institutional ownership affects company’s dividend policy with a different direction of relationship between the FCF and the signaling hypothesis. For the first hypothesis, earning per share (EPS), the control variable, is used as a proxy for corporate income. The decision to use EPS as the control variable was made because EPS is considered as a signal in the signaling hypothesis. 2.2. Signaling Hypothesis: Institutional Ownership, Industry Sector, and Dividends From the perspective of the FCF hypothesis, the cause of agency problems lies in the way that dividends can limit discretionary power. In this situation, management can act in its own interest, in contrast to the signaling hypothesis. Dividend information can sometimes be rejected by the market due to information asymmetry, leading to an unfavorable response from the investors. It has become a consensus among the academic and financial communities that managers have more, and superior information than other interested parties. The manager may use dividends as a signal to be conveyed to the market. Previous signaling research does not support the FCF hypothesis or wealth transfer hypothesis. The distribution of FCF to shareholders only increases the prosperity of the shareholders and does nothing for corporate profits [ 30 ]. Due to information asymmetry, manager incentives are needed when giving real information from the market [ 31 ]. Dividends tend to be viewed as good news as compared to bad news [32]. This situation shows the market reaction changes as expected. As shareholders with suboptimal investment, managers can be inhibited from reducing FCF through debt and dividends. Companies prefer dividends because they are less risky than debt, although it depends on the company’s characteristics, which are attached to the industrial sector. Previous research explained that companies with high FCF do not always distribute dividends, like investment opportunities and growth opportunities. Similarly, companies with low FCF also do J. Open Innov. Technol. Mark. Complex. 2020,6, 74 5 of 16 not regularly distribute dividends. This situation indicates that the use of FCF does not entirely cause agency problems, but instead depends more on the characteristics of the industry and country. In other words, dividends as a substitute for debt cannot always be used in all general conditions but are more dependent on industry characteristics. The signaling hypothesis is different from the FCF hypothesis. Theoretically, with asymmetric information, the signaling hypothesis explains that dividends could be utilized to send information to the market. Research on intraand inter-industry relations with dividends is currently limited. A previous study found that dividend policies relate to the dividend policies upheld by external companies [ 33 ], with the relationship being: (a) positively related, (b) no relationship, and (c) negatively related to other companies. Meanwhile, dividend policy depends on industry specifications, so it does relate to the FCF agency problem. Research in the general, financial, and energy sectors, with the results in the energy industry having a contagion effect (contagious), showed dividend announcements made one company will be followed by another company, but not in the general sector [34]. The second hypothesis cannot apply the use of a control variable due to the heterogeneity of the company’s characteristics in each industry. This consideration is essential partly because the use of FCF will differ profoundly depending on the company’s characteristics, be it an intraor inter-industry company. Additionally, both types of past study (i.e., with or without control variables) have discovered that a firm’s nature does not significantly impact company performance [ 35 ]. This situation indicates that the company’s environment or nature cannot be measured clearly. 2.3. Shifting from Closed Innovation to Open Innovation Closed innovation (CI) is a linear model of an organization’s dependence on internal competencies, such as strategy and processes, as well as research and development (R&D), value, and idea creation practices. Researchers working at a CI company are designing and developing products according to customer needs. Rarely or never are new inventions the aftermath of closed innovation. This results in the perspective of innovation as an isolated process, due to the mechanism of creating values and ideas depending on the internal capacity of certain individuals and small groups within the company [ 36 ]. This situation encourages the company to use its internal competence independently for R&D, to produce and use distribution channels to convey values and ideas to the market in situations of uncertainty and company limitations. The situation of uncertainty drives a paradigm shift from CI to open innovation (OI) through conceptualizing and commercializing inventions, therefore there are no company boundaries and this allows integrating several external parties such as universities, research organizations, suppliers, customers, and competitors in the innovation process [ 37 ]. The transition from the authoritative and individualistic innovative processes in the CI model to the open innovation (OI) model is currently required in the organization, and prominently in financial management [38]. Company characteristics, such as ownership structure, the industry the company operates in, and size determine involvement in open innovation [ 39 ]. The type of company ownership affects R&D policy. Each type of ownership has a different perspective regarding incentives, investment horizons, abilities to monitor, and control firm management, including R&D decisions in closed or open innovation [ 40 ]. The research sample involves large companies listed on the Indonesia Stock Exchange, which have stronger R&D competencies compared to SMEs (Small Medium-sized Enterprises) [ 39 , 41 ]. Larger companies that have sufficient R&D resources tend to disclose and commercialize OI more than SMEs with limited resources. Companies with corporate or institutional ownership have more external cooperation capabilities resulting in more adaptions for OI than companies with private ownership [ 42 ]. Private companies have limited financial, technology, and knowledge resources, which restricts innovation [ 43 ]. The benefits of OI for manufacturing companies listed on the New York Stock Exchange are profitability, production processes, improvements, and market profit [ 44 ]. There is a potential relationship between long-term shareholders value and open innovation in companies that pay dividends for more than 60 years [ 45 ]. Companies that practice OI can create higher value through dividends, and further encourage positive reactions from shareholders [44]. J. Open Innov. Technol. Mark. Complex. 2020,6, 74 6 of 16 Open innovation is useful for organizational dynamics, especially for improving company performance [ 46 ]. When the company’s performance increase, especially in terms of increasing revenue, shareholders expect higher returns through dividends. The prospects of the company are the primary attraction for an investor to invest in a company. Open innovation is an exertion that continues to grow, especially in companies through R&D and business model development [ 47 ]. The key to the success of open innovation is located in the collaboration that is achievable through a combination of organizational resources [ 48 ]. The relationship between divisions within a company aims to achieve company goals and targets based on the vision and mission. Open Innovation is a strategy that is fruitful to achieve these targets and objectives of company revenue. The relationship between shareholders and the company is reciprocal. Shareholders increase company ownership through the capital market investment. Shareholders require delineated and comprehensive effort to monitor a company due to their interest in maintaining a profit. Open innovation is directly related to finance and policy dynamics related to the company’s vision and mission [ 49 ]. Open innovation needs to involve a quadruple helix, which consists of researchers and technology infrastructures, company, government, and society, to produce significant innovations [50]. 3. Data and Methodology Table 1depicts the data of the study. The data consists of 2596 company observations listed on the Indonesian Stock Exchange (IDX) between 2011 and 2018, specifically regarding their institutional ownership and shared cash dividends per share. The amount of data for each period has the same distribution tendency. The institutional ownership and debt–equity ratio are measured by the ratio of shares held by the institutional ownership, to the total number of outstanding shares [ 51 ] and cash dividends per share (DPS), recognized from the firm annual reports. Dividends are considered to be closely related to propensity to pay. Despite the fact that dividends are mandatory, this regulation could not be implemented in Indonesia because policies regarding the matter rely heavily on ownership and company structure [52]. Table 1. Number of companies per period and per sector. Code Sectors Sub-Sectoral Years 2011 2012 2013 2014 2015 2016 2017 2018 Total 11 Agriculture Crops 2 2 1 1 1 1 1 1 10 12 Agriculture Plantation 8 10 14 11 15 13 13 16 100 13 Agriculture Animal Husbandry 0 0 0 0 0 0 0 0 0 14 Agriculture Fishery 2 2 2 3 3 2 1 2 17 15 Agriculture Forestry 0 0 0 0 0 1 0 0 1 19 Agriculture Others 1 0 0 0 0 0 0 0 1 21 Mining Coal Mining 12 16 13 11 17 13 16 18 116 22 Mining Crude Petroleum and Natural Gas Production 4545647944 23 Mining Metal and Mineral Mining 6 7 7 5 6 9 7 6 53 24 Mining Land/Stone Quarrying 1 2 1 2 2 2 1 1 12 29 Mining Others 0 0 0 0 0 0 0 0 0 31 Basic Industry and Chemicals Cement 3 1 3 2 4 5 5 6 29 32 Basic Industry and Chemicals Ceramics, Glass, Porcelain 5 6 6 5 6 4 5 6 43 33 Basic Industry and Chemicals Metal and Allied Products 11 11 14 10 14 11 11 10 92 34 Basic Industry and Chemicals Chemicals 6 7 9 7 8 8 9 9 63 35 Basic Industry and Chemicals Plastics and Packaging 11 9 9 10 10 9 10 12 80 36 Basic Industry and Chemicals Animal Feed 1 3 3 2 4 4 5 3 25 37 Basic Industry and Chemicals Wood Industries 1 1 2 2 1 1 1 2 11 38 Basic Industry and Chemicals Pulp and Paper 8 3 4 6 9 8 7 8 53 39 Basic Industry and Chemicals Others 0 0 0 0 0 1 2 1 4 41 Miscellaneous Industry Machinery and Heavy Equipment 0 0 0 1 13 2 3 3 22 42 Miscellaneous Industry Automotive and Components 9 7 8 6 0 10 12 11 63 J. Open Innov. Technol. Mark. Complex. 2020,6, 74 7 of 16 Table 1. Cont. Code Sectors Sub-Sectoral Years 2011 2012 2013 2014 2015 2016 2017 2018 Total 43 Miscellaneous Industry Textile, Garment 12 12 16 11 16 13 12 16 108 44 Miscellaneous Industry Footwear 1 1 2 0 2 2 2 2 12 45 Miscellaneous Industry Cable 4 4 4 3 4 6 6 6 37 46 Miscellaneous Industry Electronics 0 0 1 0 0 0 0 1 2 49 Miscellaneous Industry Others 0 0 0 0 0 0 0 0 0 51 Consumer Goods Industry Food and Beverages 11 13 13 9 13 14 18 15 106 52 Consumer Goods Industry Tobacco Manufacturers 3 3 3 3 3 4 3 3 25 53 Consumer Goods Industry Pharmaceuticals 6 8 9 7 8 8 8 9 63 54 Consumer Goods Industry Cosmetics and Household 4 6 4 5 6 6 6 5 42 55 Consumer Goods Industry Houseware 3 0 3 2 3 3 4 4 22 59 Consumer Goods Industry Others 0 0 0 0 0 0 1 0 1 61 Property, Real Estate and Building Construction Property and Real Estate 27 32 32 30 36 30 35 40 262 62 Property, Real Estate and Building Construction Building Construction 5 7 8 6 8 9 14 12 69 69 Property, Real Estate and Building Construction Others 0 0 0 0 0 0 0 0 0 71 Infrastructure, Utilities and Transportation Energy 2 2 2 1 4 5 5 5 26 72 Infrastructure, Utilities and Transportation Toll Road, Airport, Harbor, and Allied Products 1232224521 73 Infrastructure, Utilities and Transportation Telecommunication 5 5 5 2 4 4 5 4 34 74 Infrastructure, Utilities and Transportation Transportation 14 20 20 18 23 22 24 30 171 75 Infrastructure, Utilities and Transportation Non Building Construction 4 4 5 4 6 7 8 12 50 79 Infrastructure, Utilities and Transportation Others 0 0 0 0 0 0 0 0 0 91 Trade, Services and Investment Wholesale (Durable and Non-Durable Goods) 24 22 26 23 27 27 29 27 205 93 Trade, Services and Investment Retail Trade 16 18 19 18 18 19 21 21 150 94 Trade, Services and Investment Tourism, Restaurant, and Hotel 12 14 13 15 18 15 24 22 133 95 Trade, Services and Investment Advertising, Printing, and Media 9 8 8 9 13 12 13 16 88 96 Trade, Services and Investment Health Care 1 1 3 2 4 6 3 5 25 97 Trade, Services and Investment Computer and Services 3 2 2 2 4 4 5 4 26 98 Trade, Services and Investment Investment Company 5 7 6 6 5 5 6 8 48 99 Trade, Services and Investment Others 2 3 4 3 3 4 5 7 31 265 286 311 270 349 335 377 403 2596 The finance sector was not included in the data, because (a) the policies of the financial sector differ from those of other sectors, and (b) besides the policy differences, the finance sector also generally pays greater dividends. In Indonesia, policies in the financial sector (in addition to referring to IDX regulations) are also based on the Financial Services Authority (OJK) regulations. The equation model used in this study indicate in Table 2: Table 2. Research Equation. The first model: the propensity of manufacturing companies, compared to non-manufacturers, in cash dividend payouts versus not paying cash dividends. The scale of the variable in the predictor and response is binomial. logp 1−p=β0+β1X1 The second model: propensity to pay cash dividends, compared to not paying cash dividends, as determined by the percentage of institutional ownership. The scale of the variable in the predictor is a ratio and the response is binomial. logp 1−p=β0+β1X1 The third model: propensity to pay cash dividend as determined by the sector (X 1 ) and the percentage of institutional ownership. The response variable scale is binomial, predictor X1is binomial, and X2is a ratio. logp 1−p=β0+β1X1+β2X2 J. Open Innov. Technol. Mark. Complex. 2020,6, 74 8 of 16 The ‘propensity to pay cash dividends’ was selected as a variable based on two considerations. First, this variable affects the influence of FCF on sectoral differences and agency problems regarding the use of opportunistic managerial cash. As such, it becomes more appropriate to use cash dividends compared to other variables such as stock dividends. Second, evidence suggesting the definite significant impact of the predictor variable on the dependent variable concerning dividend policies is still lacking. Thus, the current research decided to explore the propensity to pay cash dividends. 4. Findings and Results In terms of the institutional ownership percentage based on propensity to pay cash dividends, the current findings reveal that, among companies with institutional ownership, as many as 1448 of them (53.78%) did not pay dividends while the remaining 1148 companies (44.22%) paid dividends. Table 3depicts the differences in standard deviation, interquartile, and mean relative between the percentage of institutional ownership in companies that did not pay dividends and paid dividends. Table 3. Data description: propensity to pay or not pay cash dividends based on institutional ownership. Pay Div Not Pay Min 10 23.32 J. Open Innov. Technol. Mark. Complex. 2020, 6, x FOR PEER REVIEW 8 of 16 The ‘propensity to pay cash dividends’ was selected as a variable based on two considerations. First, this variable affects the influence of FCF on sectoral differences and agency problems regarding the use of opportunistic managerial cash. As such, it becomes more appropriate to use cash dividends compared to other variables such as stock dividends. Second, evidence suggesting the definite significant impact of the predictor variable on the dependent variable concerning dividend policies is still lacking. Thus, the current research decided to explore the propensity to pay cash dividends. 4. Findings and Results In terms of the institutional ownership percentage based on propensity to pay cash dividends, the current findings reveal that, among companies with institutional ownership, as many as 1448 of them (53.78%) did not pay dividends while the remaining 1148 companies (44.22%) paid dividends. Table 3 depicts the differences in standard deviation, interquartile, and mean relative between the percentage of institutional ownership in companies that did not pay dividends and paid dividends. Table 3. Data description: propensity to pay or not pay cash dividends based on institutional ownership. Pay Div Not Pay Min 10 23.32 Q1–Min 49.61 40.80 Med–Q1 11.43 12.86 Q3–Med 12.42 9.24 Max–Q3 16.28 13.75 Mean 71.72 74.78 Min 10 23.32 Q1 59.61 64.12 Median 71.05 76.99 Q3 83.47 86.23 Max 99.76 99.99 Mean 71.72 74.78 Mean 71.72 74.78 Standard Error 0.43 0.39 Median 71.05 76.99 Mode 65 80 Standard Deviation 14.74 14.68 Sample Variance 217.44 215.64 Kurtosis −0.58 −0.14 Skewness −0.09 −0.48 Range 89.76 92.10 Maximum 99.76 99.99 Minimum 10 7.88 Sum 82,340.28 108,283.4 Count 1148 1448 The data description of the percentage of institutional ownership by the manufacturing sector is as follows: (a) 34.78% comes from the manufacturing sector; (b) 65.22% comes from the nonmanufacturing sector. Table 4 shows the variations in the average flat, interquartile, and standard deviation of the percentage of institutional ownership between the manufacturing and nonmanufacturing companies (see Table 1). Most companies, both those share DPS or not, have similar characteristics. Namely, the proportion of institutional ownership is greater than the average. As such, the ownership proportion tends to vary. The number of companies that distribute DPS has a proportion of institutional ownership that is greater than the average and more than those that do not distribute dividends. Similarly, companies that distribute DPS are more homogeneous in institutional ownership proportion than those that do not share DPS. Proposed provisional evidence is that companies tend 0 20 40 60 80 100 120 Pay Not Pay Box Plot Q1–Min 49.61 40.80 Med–Q1 11.43 12.86 Q3–Med 12.42 9.24 Max–Q3 16.28 13.75 Mean 71.72 74.78 Min 10 23.32 Q1 59.61 64.12 Median 71.05 76.99 Q3 83.47 86.23 Max 99.76 99.99 Mean 71.72 74.78 Mean 71.72 74.78 Standard Error 0.43 0.39 Median 71.05 76.99 Mode 65 80 Standard Deviation 14.74 14.68 Sample Variance 217.44 215.64 Kurtosis −0.58 −0.14 Skewness −0.09 −0.48 Range 89.76 92.10 Maximum 99.76 99.99 Minimum 10 7.88 Sum 82,340.28 108,283.4 Count 1148 1448 The data description of the percentage of institutional ownership by the manufacturing sector is as follows: (a) 34.78% comes from the manufacturing sector; (b) 65.22% comes from the non-manufacturing sector. Table 4shows the variations in the average flat, interquartile, and standard deviation of the percentage of institutional ownership between the manufacturing and non-manufacturing companies (see Table 1). 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