Board characteristics and firm value for Indian companies

Rakesh Kumar Mishra (Indian Oil Corporation Limited, Noida, India)
Sheeba Kapil (Department of Finance, Indian Institute of Foreign Trade, New Delhi, India)

Journal of Indian Business Research

ISSN: 1755-4195

Publication date: 19 March 2018

Abstract

Purpose

This paper aims to explore the relationship between board characteristics and firm performance for Indian companies.

Design/methodology/approach

Corporate governance structures of 391 Indian companies out of CNX 500 companies listed on National Stock Exchange have been studied for their impact on performance of companies. Panel data regression methodology has been used on data for five financial years from 2010 to 2014 for the selected companies. Performance measures considered are market-based measure (Tobin’s Q) and accounting-based measure (return on asset [ROA]).

Findings

The empirical findings indicate that the market-based measure (Tobin’s Q) is more impacted by corporate governance than the accounting-based measure (ROA). There is a significant positive association between board size and firm performance. Board independence is found significantly related to firm performance. Number of board meetings is found to be sending positive signal to the market creating firm value. Separation of chief executive officer and chairman of the board is found to be value-creating, and overburdened directors affect firm performance adversely.

Research limitations/implications

Limitations of the study are in terms of methodology and possible omission of some variables. It is understood that the qualitative dynamics happening inside board meetings impact corporate performance. The strategic decision-making process adopted by the boards to fight competition or to increase market share is not easily available in public domain. The decision-making processes and monitoring for implementation of those decisions could impact corporate governance performance relationship. These parameters and their impact on corporate performance are not covered under the scope of the present study.

Originality/value

The paper adds to the emerging body of literature on corporate governance performance relationship in the Indian context by using a reasonably wider and newer data set.

Keywords

Citation

Mishra, R. and Kapil, S. (2018), "Board characteristics and firm value for Indian companies", Journal of Indian Business Research, Vol. 10 No. 1, pp. 2-32. https://doi.org/10.1108/JIBR-07-2016-0074

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

Corporate governance is a system by which companies are directed and controlled (Cadbury Committee, 1992). Corporate governance is concerned with three aspects of decision-making process in a firm. First, who is empowered to take what decision; second, whose interest gets priority while taking a particular decision; and third, whether (and how) contextual factors such as social, political, economic and legal institutions are impacting the decision-making process and outcomes of these decisions.

In the context of emerging economies, corporate governance mechanisms have been found correlated with firm performance in various theoretical and empirical studies (Khanna and Palepu, 2000; Gibson, 2003; Klapper and Love, 2004; Young et al., 2008; Ehikioya, 2009; Claessens and Yurtoglu, 2013). Well-functioning corporate governance mechanisms in emerging economies are of crucial importance for both local firms and foreign investors that are interested in pursuing such tremendous opportunities for investment and growth that emerging economies provide (Rajagopalan and Zhang, 2008). From the perspective of local firms, there is evidence that firms in emerging economies (compared with their counterparts in developed countries) are discounted in financial markets because of their weak governance (La Porta et al., 2000). Improvements in corporate governance can enhance investor confidence in firms in emerging economies and increase these firms’ access to capital (Rajagopalan and Zhang, 2008).

The Indian Government initiated market reforms in 1991. Major elements of these reforms resulted in the opening of the Indian economy to multinational companies and foreign investment. Increased foreign investment in India intensified interest in good corporate governance and in particular the application of western governance structure to Indian firms (Jackling and Johl, 2009). India needed capital to finance the expansion of market spaces created by liberalization and outsourcing opportunities. This need for capital amongst other requirements led to corporate governance reforms and major initiatives in this direction. The initial step in this direction was the introduction of Clause 49 in the listing agreement by Securities Exchange Board of India (SEBI) that contained prominence of independent directors amongst other things. Government of India has taken yet another major step in this direction through the introduction of Companies Act 2013 (effective from 1 April 2014); wherein provisions of corporate governance have been made mandatory for Indian companies.

Like other emerging economies, Indian organizations also face domination by family ownership and other forms of domination, such as government or a foreign group. These groups often exercise influence that is disproportionate to their actual shareholding (Pande and Ansari, 2013). In family-owned corporations that widely prevail in emerging economies (like India), boards are typically dominated by family members who enjoy substantial ownership and control and often hold top executive positions with an objective of controlling the firm (Carney and Gedajlovic, 2002). As an implication, board members of family-controlled firms may not be that much efficient in their monitoring role and may give benefit of doubt to incumbent mangers for low firm performance (Gomez-Mejia et al., 2003). In case of Indian firms, families (founders) are present on the boards in 63.2 (65.5) percent, and on an average, founders own over 50 per cent of outstanding shares (Jameson et al., 2014). This leads to different kind of corporate governance issues in India as compared to governance issue in the Anglo-Saxon economies, which is primarily disciplining management that may stop being accountable to the owners who usually are dispersed shareholders.

Denis and McConnell (2003) argued that to overcome problems in corporate governance, different internal or external mechanisms can be applied. Primary internal mechanisms are the board of directors and equity ownership structure of the firm, whereas primary external mechanisms are the external market for corporate control (the takeover market) and the legal system. External and internal governance mechanisms are complements, i.e. countries where market for corporate control are not that much prevalent and enforcement of corporate government regulations through legal system is weak, and provide a strong case for internal governance mechanisms to be at the forefront for improving corporate performance. Hence, considering the current stage of Indian economy where market for corporate control is still developing (Khanna and Palepu, 2000) and there exists a weak legal enforcement regime of corporate governance (Sarkar and Sarkar, 2000), it appears that internal governance mechanisms will have significant bearing on corporate performance.

This study attempts to investigate the impact of an internal governance mechanism, i.e. board of directors, on the firm value for Indian companies.

2. Objectives and methodology

This paper analyses data of CNX500 companies listed at National Stock exchange (NSE) for five financial years starting from financial year 31 March 2010 to 31 March 2014. We aim to find relationship of firm performance with different board characteristics such as:

  • board independence;

  • board leadership structure;

  • board size;

  • number of board meetings; and

  • busyness of directors.

In this paper, we also propose to find out:

  • whether factors such as firm size, age of firm, leverage used by firm and sales growth affect firm performance; and

  • whether the relationship between firm performance and different board characteristics is the same for different types of performance measures, namely, an accounting-based measure such as return on asset (ROA) and a market-based measure such as Tobin’s Q.

We used panel data methodology to analyze data across firms over the years. Panel data sets are able to identify the estimate effects that are not detectable in pure cross-sectional or pure time series analysis (Ahmed Sheikh and Wang, 2012). The regression has been carried out for complete set of data and also for data subsets to explore differential impacts of corporate governance variables on different types of companies, for example, small vs large companies, companies with small board vs those with large board.

The paper is organized as follows. In Section 3, we review theoretical arguments and empirical evidence with respect to the relationship between structure of board of directors and firm performance. In Section 4, we put forward our arguments for hypotheses development based on the review of previous findings. Section 5 provides description of model, variables and their measures. Section 6 presents data descriptive, followed by results and analysis. Finally, concluding remarks with discussion on limitations of our study and future directions is given in Section 7.

3. Literature review

3.1 The board of directors

As the relationship between board of directors and firm performance is substantially varied and complex, a single governance theory is not adequate to describe it (Nicholson and Kiel, 2007). Main theories developed to understand the relationship between structure of board, its role and firm performance are agency theory, resource dependence theory and stewardship theory. The relationship between board and firm performance has been studied in the context of different functions performed by the board. Board of directors serves two important functions for organizations:

  1. monitoring management on behalf of shareholders (agency theorists); and

  2. providing resources (resource dependence theorists) (Hillman and Dalziel, 2003).

Agency theory (Jensen and Meckling, 1976; Fama, 1980; Fama and Jensen, 1983) considers agency relationship as a contract under which the principal (s) (shareholders) engage the agents (managers) to perform some service on their behalf which involves delegating some decision-making authority to the agent. If both the parties are utility maximisers, there is a good reason to believe that the agent will not always act in the best interest of the principal necessitating monitoring of agents’ behaviour by the principal (s). As the board monitors managers on behalf of principal, an independent board (comprising majority of outsiders) and separation of the post of chairman of the board and chief executive officer (CEO) would reduce agency cost, facilitating better performance.

Under the stewardship theory, objectives of both principals and agents are considered to be unidirectional, and hence, there is no conflict of interests. Managers are considered good stewards of resources entrusted to them (Donaldson, 1990; Donaldson and Davis, 1991, 1994). This theory considers managers trustworthy people (Donaldson and Preston, 1995). The agency cost will be minimal because, for fear of losing their reputation, managers will not act against interests of the shareholders (Donaldson and Davis, 1994). This theory indicates that to attain a superior performance by their corporations, CEOs should exercise complete authority over the corporation and that their role must be unambiguous and unchallenged (Donaldson and Davis, 1991). This situation is attained more readily if the CEO is also the chairman of the board. Hence, stewardship theory entails a better firm performance for companies with common role of CEO and chairperson of the board as a result of unidirectional strategic orientations provided by it.

The resource dependency theory provides a mechanism whereby firms have links to critical resources from the environment through affiliations of its directors and tends to emphasize on the economic nature of these resources (Barroso et al., 2011). The board has a role in provision of resources that include providing legitimacy, administering advice and counselling, acting as a link to important stakeholders or other significant bodies, facilitating access to resources such as capital, building external relations, etc. (Barroso et al., 2011). Board is a vital link between the company and the resources needed to maximize performance (Pfeffer and Salancik, 2003). Apart from this, the board itself is considered an important resource especially in relationship to its external environment as boards can manage environmental dependencies and would reflect the environmental needs (Hillman et al., 2009). This enables the board to provide a sustainable competitive advantage over its competitors (Barney et al., 2001). Thus, resource dependency theory would anticipate that board of directors with high level of external links would improve a company’s access to various resources, thus improving firm performance (Jackling and Johl, 2009). The linkage between resources and performance provided by the board of directors would depend on the activity and busyness of directors. Hence, in line with the resource dependency theory, it can be assumed that size and diversity of the board, number of board meetings and association of directors with other companies either as directors or as committee members will have a positive association with the firm performance.

Primary function of board of directors in corporations is to ensure maximization of shareholder value through a mechanism that includes activities pertaining to hiring, firing, monitoring and compensating the managers (Shleifer and Vishny, 1997; Hermalin and Weisbach, 2001). Theoretically, the board is an effective corporate governance mechanism, but empirical results do not exactly support this. Some of the reasons for such results are as follows:

  • Many a times, the board includes insiders whose monitoring is to be done by the board.

  • Selection of outsiders on the board is decided or influenced by inside managers.

  • Chairperson of the board is also the CEO of the firm.

Main issues highlighted in various empirical studies pertaining to the board have been certain specific variables and their impact on performance of the firm. These variables are size of the board, ratio of outside directors and inside directors, CEO duality, number of board meetings and internal and external busyness of directors.

3.1.1 Board composition.

Board composition may affect corporate performance in two ways; one way is to have more number of outside directors that will lead to better evaluation and monitoring, whereas on the other hand, more number of internal directors will lead to better corporate performance because of alignment of interests (as per agency theorists). An optimal board composition depends upon the kind of firm and the environment in which it is operating (Mishra and Kapil, 2016). Coles et al. (2008) found that complex firms that have greater advising requirements than simple firms have larger boards with more outside directors.

Kamardin and Haron (2011), using factor analysis, extracted two dimensions of monitoring roles: management oversight and performance evaluation. Non-independent, non-executive directors and managerial ownership are positively related to both dimensions of monitoring roles, while multiple directorships of non-executive directors are negatively related to management oversight roles. Boards with a high representation of outside and foreign directors were associated with better performance compared to those boards that had a majority of insider executive and affiliated non-executive directors (Ameer et al., 2010).

Petra (2006) explained that a dispersed nature of shareholding pattern results into a situation where an individual shareholder does not have either potential or incentive to monitor the behaviour of management directly. In such a situation, board of directors are entrusted to monitor the behaviour of management on behalf of shareholders. Majority of outside independent directors in the board would reduce conflict of interest and increase its monitoring potential. Reforms brought out in different countries indicate towards providing more independence to the board. As per Aguilera (2005), corporate governance reforms are increasingly focusing on non-executive directors/independent outside directors with a hope that they will bring greater transparency, accountability and efficiency to corporate governance.

In India, Clause 149(4), Chapter XI of Companies Act 2013, states that every listed public company shall have at least one-third of the total number of directors as independent directors, and the Central Government may prescribe minimum number of independent directors in case of any class or classes of public companies. Every company existing on or before the date of commencement of this Act (1 April 2014) shall, within one year, comply with the requirements of the provisions.

3.1.2 Chief executive officer duality.

There are two different views on board leadership structure. Agency theorists argue that roles of CEO and chairperson combined into a single person (i.e. CEO duality) will lead to domination of board by that person making the board ineffective in monitoring managerial opportunism (Jensen, 1993). As a result, CEO duality enhances CEO entrenchment and reduces board independence (Rhoades et al., 2001). Separating these two roles is desired so that the CEO is responsible for executing company’s policies and running the company, and chairperson of the board is responsible for running the board and monitoring and evaluating managerial activities (Yan Lam and Kam Lee, 2008). Higgs (2003) recommends that separating these two roles, “avoids concentration of authority and power in one individual and differentiates leadership of the board from running of the business”. The board is also responsible for the process of hiring, firing, evaluating and compensating the CEO, and thus, the chairperson should preferably not be the same person whose performance is being assessed (Jensen, 1993). CEO duality is expected to lower the performance because CEOs would gain much power to further their own interests rather than the interests of shareholders (Weisbach, 1988). On the other hand, steward theorists accept that managers are good stewards of company resources (Davis et al., 1997). The supporters of stewardship theory advocate that there is no conflict of interest between the mangers and shareholders; hence, CEO duality would promote a unified and strong leadership with a clear sense of strategic direction.

3.1.3 Board size.

Literature suggests that an increased board size has two competing effects: greater monitoring versus more rigid decision-making (Harford et al., 2012). Impact of board size on corporate performance is a trade-off between two competing aspects; first, a large board leads to wide experience and more linkages to external environment (which may help in access to resources and stakeholders) and second, a large board slows down the decision-making process. Yermack (1996) found an inverse relationship between board size and firm value. In contrast, Harris and Raviv’s (2008) model of boards trades off additional monitoring services with free-riding predicting larger boards to provide optimal monitoring when managers’ opportunities to consume private benefits are high. Abor and Biekpe (2007) found that board size has significant positive impact on profitability. Empirical results indicate that board size is positively related to the ROA, earnings per share and market-to-book ratio. (Ahmed Sheikh et al., 2013). Geraldes Alves (2011) predicted a non-linear relationship between board size and earnings management. Kumar and Singh (2013) found a negative relationship between board size and firm value in the Indian context. Kota and Tomar (2010) found that small boards are more effective in enhancing firm value.

Regarding board size in India, Clause 149(1), Chapter XI of Companies Act 2013, says that every company shall have a board of directors consisting of individuals as directors and shall have:

  • a minimum number of three directors in the case of a public company, two directors in the case of a private company and one director in the case of a one-person company; and

  • a maximum of 15 directors; a company may appoint more than 15 directors after passing a special resolution.

3.1.4 Board committees.

Eberhart (2012) reported a significant increase in firm valuation (measured by Tobin’s Q) for companies that adopted an alternative of the Anglo-American type committee system. This finding was attributed to “signal sending”, as companies that adopted this system signal a choice towards transparency via monitoring by outsiders, suggesting a reduction of asymmetric agency costs. The above-mentioned paper finds that the committee corporate governance system produces higher corporate value than the traditional auditor governance, citing evidence that it is the signal provided by the adoption of the credible system, not the financial performance variables that account for this difference. Veronica and Bachtiar (2014) found that audit committee has a significant negative relation with discretionary accruals indicating effectiveness of audit committee in constraining the level of earnings management. Further, they found that proportion of independent directors on board and existence of audit committee increases the positive relationship between discretionary accruals and stock return, thereby indicating that earnings management conducted by firms have higher proportion of independent directors, and firms having audit committee will be valued higher by the market. Raja and Kumar (2007) found that committee component has statistically significant relationship with firm performance.

3.1.5 Board meetings and participation of directors.

Directors on board discharge their responsibilities of monitoring and providing resource linkage through their active participation in the board meetings. Board effectiveness is dependent on behaviour of directors in board meetings. Active directors’ behaviour – i.e. challenging, questioning, informing, encouraging, etc. – is an important driver of board effectiveness (Roberts et al., 2005). Board members’ commitment are far more important than board demographics for predicting board task performance (Minichilli et al., 2009). The commitment of board members will depend upon their involvement in the meeting, which refers to their effort during discussions and in the follow-up of decisions taken during the board meetings (Judge and Zeithaml, 1992). Involvement also includes board members’ willingness and ability to advance useful questions and to intervene constructively in the board decision-making process. Additionally, for increasing their involvement, the board members must be prepared for the board meeting, which refers to their willingness and ability to participate in board meetings with a deep knowledge of the topics to be discussed to actively contribute to the decision-making process. Preparation is related to the degree to which board members examine information prior to the meetings and take initiatives to collect further information (Forbes and Milliken, 1999). Hence, number of board meetings and an effective participation of directors in these meetings are expected to impact firm performance positively.

3.1.6 Outside busyness of directors.

Number of directorship/chairmanship or committee positions in other companies held by directors of a company indicates degree of linkage with external environment and resources. Fama (1980) and Fama and Jensen (1983) note that the market for outside directorships provides an important source of incentives for outside directors to develop reputation as monitoring specialists. This reputation hypothesis tells that by sitting on many boards, an executive learns about different management styles or strategies used in other firms (Perry and Peyer, 2005). Because of their competence and extensive experience, they are more likely to serve on a larger number of board committees than those not holding multiple directorships. This hypothesis, thus, predicts a positive relation between the number of board seats and the number of board committees (Jiraporn et al., 2009).

Ferris et al. (2003) has termed directors holding directorship position in multiple companies in terms of busyness hypothesis. Multiple directorships permit a firm to use its directors to form or solidify advantageous contracting relations with other firms, such as important suppliers or customers (Ferris et al., 2003). However, individuals holding more outside board seats have less time to spend serving on board committees. At the cost of shareholders, executives may seek outside directorship because it improves their visibility and enhances their status. Large number of appointments can make directors over committed and consequently compromise their ability to monitor company management effectively on behalf of shareholders and adversely affect firm value (Fich and Shivdasani, 2006).

3.2 Uniqueness of Indian corporate governance system

Indian corporate governance system is unique because of certain specific issues as compared to much researched corporate governance system of developed economies. Although Indian corporate governance codes and systems are largely modelled on the developed economies, it is substantially different in terms of sources of corporate governance ills and structures to deal with those ills.

Corporate governance approach in developed economies is hinged on disciplining the management and making them accountable to the owners who are usually dispersed shareholders, whereas in India, it is the stronghold of the majority or dominant shareholder(s) who may use the majority of company resources to serve their own interests. Hence, the “agency cost”, which arises out of difference in the interests of managers and shareholders in developed economies, arises out of difference in the interests of majority or dominant shareholders and minority shareholders in India. This applies across the Indian companies with dominant shareholders – public-sector undertakings (government as dominant shareholder), multinational companies (parent company incorporated abroad as dominant shareholder) and private-sector companies (family or business groups and sometimes non-listed holding companies as dominant shareholder).

Apart from this, there is one additional issue in the form of promoter-controlled companies. Promoter(s) in general is(are) person(s) who is(are) involved in incorporation and organization of a corporation. Promoters constitute an important part of companies in Indian context, as most of the companies are of family origin. Promoters (even though may not be the majority shareholders in many cases) are usually present on the board of Indian companies and exercise powers disproportionate to their shareholding.

In the case of developed economies, redressal of the corporate governance issues is addressed through boards and their committees, independent directors, managing CEO succession and the disclosures. In the Indian corporate culture, boards are not as empowered as their counterparts from developed economies, and often, functioning of the boards is fully controlled by the majority or dominant shareholders.

In India, corporate governance reforms started in 1990s with the formation of SEBI in 1992 and subsequent committees (K M Birla committee in 1999, Naresh Chandra Committee in 2002 and Narayan Murthty committee in 2003). Reforms recommended by these committees culminated into insertion of Clause 49 into the listing agreement in 2000 and subsequent insertion of penalty clause in 2004. These reforms called for prominence of independent directors and formation and functions of different board committees as measures to improve corporate governance.

Significant difference also exists in enforcement of corporate governance systems in India and developed economies. Chakrabarti et al. (2008) have noted that while on paper, the framework of the country’s legal system provides some of the best investor protection in the world, enforcement is a major problem in view of the slow functioning of over-burdened courts and the widespread prevalence of corruption.

The following section considers the above unique aspects of Indian corporate governance system in understanding the hypothesized relationship between board structure and firm performance.

4. Hypotheses development

Impact of board composition on firm performance varies across different studies depending upon the theoretical bases considered by these studies. Agency theory, which is based on the inherent conflict between the firm’s owners and its management (Fama and Jensen, 1983), indicates that a greater proportion of outside independent directors help boards to efficiently monitor in the situation of conflict.

Independent directors are invited on the board to oversee management on behalf of shareholders (Baysinger and Butler, 1985). Higher proportion of independent directors on the board may lead to superior financial performance (Baysinger and Butler, 1985) and greater firm value (Mak and Kusnadi, 2005). Hutchinson et al. (2008) found that board independence is associated with lower performance-adjusted discretionary accruals, a commonly used measure of earnings management. Outside directors were found impacting firm value positively (Black and Kim, 2012). Kumar and Singh (2012) found that independent director’s proportion has an insignificant positive effect on firm value for Indian companies.

Ehikioya (2009) found no evidence to support the impact of board composition on performance. Yammeesri and Kanthi Herath (2010) reported that neither independent directors nor grey directors are significant determinants of firm value. Gill (2013), in an analysis of the central public-sector enterprises in India, has shown that non-compliance with the corporate governance provisions with regard to required number of independent directors on the board did not have any concomitant effect on their performance. Certain studies even indicate that outside directors are negatively related to ROA, earnings per share and market-to-book ratio (Ahmed Sheikh et al., 2013). Kota and Tomar (2010) found that non-executive independent directors fail in their monitoring role.

Significant development has taken place in India for empowering boards through the introduction of Clause 49 of listing agreement in which independence of board has been emphasized. Companies Act 2013 also has provisions for specified number of independent directors ensuring independence of the board. However, true independence of the boards in India is yet to be fully ensured in spirit. This is because of two things, first, supply side constraint for qualified independent directors and second, appointment of directors based on kinship and social and family ties in India (Khanna and Rivkin, 2001).

In this study, monitoring role of the board has been considered of prominence. Agency theory has been taken as a base to analyze the monitoring role of the board and its impact on firm performance. Monitoring by independent directors is supposed to be efficient as it will not involve the clash of interest. Independent directors, because of their experience and to maintain their reputation, do better scrutiny of managerial behaviour, hence ensuring all shareholders’ interests. This leads to better firm performance. Considering the above, we propose following hypothesis:

H1.

Board independence is positively related to firm performance.

Prior studies have indicated that the leadership structure of the board, particularly role of the CEO, may influence the firm performance. This relationship is contingent upon the ability of a CEO to influence decisions (Adams et al., 2005), which in fact is based on the power a CEO has (Finkelstein, 1992). Corporate governance reforms worldwide advocated separation of the role of CEO and chairman of the board. This was based on the premise that the one of the important tasks of the board is to evaluate and control the behaviour of top management including that of the CEO. In that case, if CEO himself/herself is the leader of the group responsible for evaluating and taking decisions in this regard, the process may get biased impacting firm performance negatively. Agency theory predicts that the separation of the chairman and CEO roles leads to a greater scrutiny of managerial behaviour, which further leads to a better performance (Lorsch and MacIver, 1989). On the other hand, stewardship theory predicts that decision-making under the unified leadership (having both the roles chairman and CEO) leads to better performance (Donaldson and Davis, 1991).

Empirical evidence suggests that the relationship between CEO duality and accounting performance is contingent on the presence of family control factor. CEO duality is good for non-family firms, and while non-duality is good for family-controlled firms. CEO duality has significant positive impacts on profitability (Abor and Biekpe, 2007). CEO duality is positively related to earnings per share (Ahmed Sheikh et al., 2013; Kamal Hassan and Saadi Halbouni, 2013).

On the contrary, Ehikioya (2009) reported that there is significant evidence to support that CEO duality adversely impacts firm performance. CEO duality leads to a higher incidence of bad news disclosure, suggesting increased scrutiny works (Collett and Dedman, 2010). This may lead to lower market valuation and increased cost. Considering the Indian corporate scene with dominance of promoters and business groups having family-related CEOs possessing disproportionate power in the board, we arrive at our next hypothesis:

H2.

Chief executive officer duality is negatively related to firm performance.

Prior studies have studied the relationship between board size and firm performance from two different theoretical perspectives; resource dependency theory and theory of group cohesiveness. Resource dependency theory predicted that a board of directors with high level of links to the external environment would improve company’s access to various resources, thus improving corporate governance and firm performance (Jackling and Johl, 2009). However, theory of group cohesiveness indicates that with increased size, board faces problems of poor coordination and slow decision-making. Jensen (1993) indicated that when the board size is more than seven or eight, the board is less likely to function effectively.

There is an inverse relationship between board size and firm performance measured by Tobin’s Q (Yermack, 1996; Eisenberg et al., 1998), because of lack of coordination and communication associated with a large board. It becomes more difficult for all directors to express their ideas and opinions in limited time available when a board has more than ten members (Lipton and Lorsch, 1992). On the other hand, small boards augment monitoring capabilities (Yermack, 1996; Khanchel, 2007) and are more efficient (Garg, 2007).

As indicated in various studies, there is an optimum size of the board for maximum performance, so below the optimum size, it is expected that board size will have a positive relationship with performance, whereas above optimum size, board size will have a negative relationship. This is in line with earlier studies, for example, Cormier et al. (2010), Golden and Zajac (2001) and Vafeas (1999). Larger boards are likely to have higher coordination costs, which reduces their ability to effectively monitor management (Fauzi and Locke, 2012). Differences in findings of the relationship between board size and firm performance may be also due to firm-specific factors such as firm size and firm age (Bennedsen et al., 2008).

In the Indian context, prominence of promoter-based and family-based companies generally restrict director’s position to kinship and family members. So, more number of directors is expected to add more resource capability in line with resource dependency theory. However, inducting directors beyond kinship may not add value. First, because of lack of adequately qualified outside directors in sufficient numbers and second, the directors outside kinship may not gel with the internal directors leading to slow decision-making, resulting into inferior performance. Companies Act 2013 also specifies minimum and maximum number of directors, indicating that increasing the number of directors may not have a monotonically increasing effect on firm performance. This leads to following hypotheses:

H3.

Board size is negatively related to firm performance.

H3a.

Relation between board size and firm performance is different for smaller companies in comparison to larger companies.

H3b.

Relation between board size and firm performance is different for smaller board companies in comparison to larger board companies.

Board of directors achieve monitoring through board meetings; hence, number of board meetings is a good proxy for the monitoring effects of directors (Vafeas, 1999). Vafeas (1999) demonstrated that boards meet more often during periods of turmoil, and that a board meeting more often shows improved financial performance. A board that meets more often should be able to devote more time to issues such as earnings management. A board that seldom meets may not focus on these issues and may perhaps only approve the management decisions. Lipton and Lorsch (1992) suggested that the greater frequency of meetings is likely to result in a superior performance. Hence, the following hypothesis is presented:

H4.

Number of board meeting is positively related to firm performance.

Monitoring function and resource providing function of board would be established through board meetings. As indicated in various prior studies (Jackling and Johl, 2009; Minichilli et al., 2009; Forbes and Milliken, 1999), the effectiveness of the board and subsequent firm performance depends on number of times the meeting happens and the “quality of meetings”. When the directors attend at least 75 per cent of the meetings, it leads to an enhanced firm valuation (Brown and Caylor, 2004). Board activity has been found to positively impact firm value (Brick and Chidambaran, 2010). In the present study, internal busyness of directors has been measured in terms of average participation of board meetings, which is in line with earlier studies done in the Indian context (Mishra and Mohanty, 2014). This leads to our next hypothesis:

H5.

Directors’ internal busyness is positively related to firm performance.

Busyness hypothesis has been propounded to reflect the number of positions that directors accept on different company boards (Ferris et al., 2003). In this paper, external busyness of directors has been measured in terms of average number of directorship and committee positions held in other companies by the directors of the company. Studies have found that directors with multiple appointments have a positive impact on firm performance (Harris and Shimizu, 2004; Ferris et al., 2003). This is based on the presumption that they have networks and corporations and would benefit by accessing these resources (Booth and Deli, 1996).

In this regard, some studies (Fich and Shivdasani, 2006) view that a large number of appointments make directors over committed, which leads to a compromise over monitoring function affecting firm value adversely. In the studies done in the Indian context, it has been pointed out that multiple directorship is also because of supply constraint in directors market owing to lack of industrial leadership and adequacy of experience (Jackling and Johl, 2009). Family control of business groups leads to directorship position held under kinship and social ties (Khanna and Rivkin, 2001). Hence, outside directorship may not have a positive association with firm value, and this leads to our last hypothesis:

H6.

Directors’ external busyness is negatively related to firm performance.

5. Data, model and methodology

5.1 Data

Data for analysis has been obtained from the prowess database of Centre for Monitoring of Indian Economy (CMIE). The data starting set is CNX 500 companies which accounts for about 95.77 per cent of the free float market capitalization of the stocks listed on NSE as on 31 March 2015. Data for above companies have been selected for five financial years from 2010 to 2014 (ending on 31st March of respective year). Banks and financial companies (78 out of 500) were excluded from our sample because of their different accounting structure, which makes it difficult to calculate the financial ratios used in study and previous authors’ example in this type of analysis (Yatim et al., 2006; Yammeesri and Kanthi Herath, 2010; Jackling and Johl, 2009; Black et al., 2010; Mustapha and Ahmad, 2011; Di Vito and Bozec, 2012; Kumar and Singh, 2013). Further, we deleted companies that did not have full data set for all variables under study for relevant five years. Hence, we were left with 391 companies with five-year data, resulting in 1955 data points. For some of the missing data, for example, data on number of board meetings, data on CEO duality for some companies for some years, we extracted data directly from the annual reports of respective companies.

5.2 Model and variables

To test the effect of board structure on firm performance we propose following models:

Tobins Q = f (percentage of independent directors, CEO duality, board size, number of board meetings, internal busyness of directors, external busyness of directors as director in other companies,external busyness of directors as committee members in othercompanies,firm size, firm age, leverage, sales growth)+eit
ROA = f (percentage of independent directors, CEO duality, board size, number of board meetings,internal busyness of directors, external busyness of directors as director in other companies, external busyness of directors as committee members in othercompanies, firm size, firm age, leverage, sales growth)+eit
where i and i represent the firm and periods, respectively, and eit is the error term.

Researchers have used different parameters to measure firm performance, namely, market-based and accounting-based. Tobin’s Q as a performance measure is commonly used as a dependent variable (Perfect and Wiles, 1994; Agrawal and Knoeber, 1996; Loderer and Peyer, 2002; Reddy et al., 2008; Kumar and Singh, 2013). Tobin’s Q ratio, which is a marked-based performance measure, is calculated as the market value of common stock and preferred stock plus book value of debt divided by the book value of assets.

ROA has been used as an accounting-based performance measure by different studies, namely, Demsetz and Villalonga (2001), Fich and Shivdasani (2006) and Thomsen et al. (2006). ROA has been defined as the after tax net operating income divided by the total operating assets (Copeland et al., 2000). Net operating income is computed as the operating earnings before income and taxes, before extra-ordinary items and prior adjustment. Prowess database of CMIE has an item called PBDITA (profit before depreciation interest and tax) prior to extra-ordinary items. It has been used as a proxy for net operating income.

Demsetz and Villalonga (2001) argue that although the numerator of Tobin’s Q partly reflects the value that investors assign to a company’s intangible assets, the denominator does not include the investment a company has in intangible assets, such as advertisement and research and development. These items are simply treated as expenses. To overcome this problem, some studies have used depreciated value of tangible assets. The accounting-based profit measure is criticised for being backward-looking and it estimates future events only partially in the form of depreciation and amortization. On the other hand, Tobin’s Q is greatly influenced by a wide range of unstable factors, such as investors’ psychology and market forecasts (Reddy et al., 2010). For this reason, we have used both the performance measures in this study.

Control variables used in the study are size of firm, age of firm, financial leverage used by firm and sales growth of firm.

Size of the company in terms of total assets is used as the first control variable. This is in line with earlier studies in Indian context such as Sarkar and Sarkar (2000), Kumar (2004), Black and Khanna (2007), Dharmapala and Khanna (2013), Balasubramanian et al. (2010), Kota and Tomar (2010) and Kumar and Singh (2012). In above-mentioned studies, it is hypothesized that size has a positive influence on performance of the firm because of various reasons such as diversification, economies of scale and access to cheaper sources of funds. In this study, we have used natural logarithm of total assets as Fsize.

Another control variable considered in various studies in Indian Context (Sarkar and Sarkar, 2000; Kumar, 2004; Kota and Tomar, 2010; Kumar and Singh, 2012) is age of firm, which is calculated by difference between the year of study and the year of incorporation. It is hypothesized that older firms are more efficient than younger firms because of the learning curve and survival bias effects. In this study, natural logarithm of the number of years since the incorporation is considered as Fage.

Financial leverage of the firm has also been used as control variables in several studies (Sarkar and Sarkar, 2000; Kumar, 2004; Ehikioya, 2009; Kota and Tomar, 2010; Kumar and Singh, 2012). It is calculated by dividing total liabilities with total stockholders’ equity. A high debt/equity ratio generally means that a company has been aggressive in financing its growth with debt. It is hypothesized that if a firm uses debt to finance increased operations, the firm could potentially generate more earnings than it would have without this outside financing.

Sales growth is used as control variable in the study and is calculated as total sales of the current year minus total sales in the previous year divided by total sales in the current year (Hermalin and Weisbach, 2012).

Independent variables used in the study are different parameters associated with board of directors. Variables used and their measures are indicated in Table I.

The data descriptive and Pearson correlations between variables have been presented in Table II. It indicates a significant relationship between dependent variables Tobin’s Q and ROA with most of the independent variables (board characteristics) and also with control variables. In general, it is indicated that performance measures are positively related to duality and negatively related to board independence, busyness of directors, firm size, leverage and sales growth. For every data set, we have run two regressions each with Tobin’s Q and ROA as dependent variable.

5.3 Methodology

When we are interested in analyzing the impact of variables that vary over time, fixed effects (FE) model is used. FE explores relationships between predictor and outcome variables within an entity (in present study, this entity is company). Each company may be having its own characteristics/culture that may or may not influence the predictor variables. While applying FE, it is assumed that something within the company may impact or bias the predictor or outcome variables and this is needed to be controlled. Hence, it is assumed that there is correlation between entity’s error term and predictor variables. FE is expected to remove the effect of these time-invariant characteristics to enable estimation of net effect of predictor on the outcome variable. In random effects (RE) model, variation across entities (companies) is assumed to be random and uncorrelated with the predictor or independent variables included in the model. This allows time invariant variables to play a role as explanatory variables.

For checking the applicability of suitable model for the study, we applied Hausman test in which null hypothesis is that RE model is appropriate and alternate hypothesis is that the FE model is appropriate. We obtain significant p-values for both the cases, i.e. when Tobin’s Q is the dependent variable and when ROA is the dependent variable. Hence, we rejected the null hypothesis and accepted the alternate hypothesis. Thus, between FE and RE models, the FE model is more appropriate for the study.

Next, to check appropriateness between FE and pooled regression model, we conducted Wald test in which null hypothesis is that pooled ordinary list square regression technique (OLS) is acceptable, i.e. the dummies (for companies) have value equal to zero, and alternate hypothesis is that FE is appropriate. We obtain non-significant p-values for both the cases, i.e. when Tobin’s Q is dependent variable and when ROA is dependent variable, thus failed to reject null hypothesis. Hence, between FE and pooled OLS, pooled OLS is more appropriate for the study.

The reason of the above result may be that in Indian environment, corporate governance and corporate performance are more affected by the general socio, economic and political factors than the company-specific factors. Hence, the intercept value in regression equation appears to be independent of the entity (i.e. company). Further, FE is taking away certain degree of freedom. Thus, pooled OLS regression may be considered more appropriate for the study.

6 Discussion and analysis

6.1 Data descriptive

Performance variable Tobin’s Q has mean (SD) 1.90 (2.06), another performance variable ROA has mean (SD) 0.15 (0.10). It reflects the fact that the accounting-based performance measure (ROA) has lesser variability than the market-based performance measure (Tobin’s Q). The size of firm in terms of total asset value varies between Rs 872m to Rs 3,677,440m with mean (SD) Rs 101,188m (267,110). The median value is Rs 30,199m. Board size measured by number of directors varies from 2 to 26 with mean (SD) 11.59 (3.56). Board independence measured by percentage of independent directors to the total number of directors varies from 0 to 100 with mean (SD) 46.78 (11.85). Leverage mean (SD) value is 0.22 (0.18), showing that Indian companies depend more on equity rather than debt.

For further analysis, the data are divided in different subsets. As the median value comes at Rs 30199m, companies with asset value of greater than and equal to Rs 30,199m has been termed as large companies and those with asset value less than Rs 30,199m has been termed as small companies. Median value of board size (Bsize) is 11, hence companies with Bsize less than 11 have been termed as small board companies and those with more than and equal to 11 have been termed as large board companies. We have run the regression models first for complete data set and then for each of the data subsets mentioned above.

We have checked the significance of statistical difference between characteristics of small companies and large companies (results are presented in Appendix). It has been found that large companies have significantly bigger board size compared to smaller companies. Bigger board size for larger companies indicates requirement of more number of directors to oversee the increased business size. It is also found that the board of bigger companies meet more often as compared to the board of smaller companies. Duality, i.e. the CEO holding the position of chairman of board of directors, is more in case of smaller companies than the larger companies. It may be because with increasing size of company, the perceived good practice of separating the two roles is being adopted by the companies more and more. Firm performance is also found to be significantly different between larger and smaller companies, with smaller companies exhibiting better performance on both kinds of performance measures, namely, market-based measure (Tobin’s Q) and accounting-based measure (ROA).

It is observed that board meetings are happening more in case of bigger board size companies as compared to companies with smaller board. Duality, i.e. the CEO holding the position of chairman of board of directors, is more in case of smaller board companies than the larger board companies. Firm performance is also found to be significantly different between larger board and smaller board companies, with smaller board companies exhibiting better performance on market-based measure (Tobin’s Q) and larger board companies exhibiting slightly better performance on accounting-based measure based (ROA).

Regression results: pooled OLS regression results for complete data set and for subsets are presented next.

6.2 Results and analysis

From the regression result presented in Tables III-VII, value of collinearity statistics, i.e. tolerance and variance inflation factor (VIF), is within their acceptable limits (i.e. VIF < 10 and tolerance > 0.1), indicating the absence of multicollinearity problem. Values of Durbin Watson statistic for all regressions are close to 2, indicating absence of auto correlation, which is expected in case of panel data if error terms are related with previous years data.

For all data regression (Table III) and for small companies (Table IV), value of coefficient of board independence is negative but non-significant when Tobin’s Q is the dependent variable and positive and significant when ROA is the dependent variable. In case of large companies (Table V) and small board companies (Table VI), value of coefficient of board independence is positive but non-significant with Tobin’s Q as the dependent variable and positive and significant with ROA (0.001) as the dependent variable. For large board companies (Table VII), value of coefficient of board independence is negative but non-significant when Tobin’s Q is the dependent variable.

So, H1 is supported when performance measure is accounting-based. It indicates that although board independence is affecting the performance of the company, market does not attach value to it. This is giving credence to the theoretical aspect that independent boards do better monitoring function, leading to better firm performance.

For all data regression (Table III), coefficient of number of CEO duality is positive and significant for both the performance measures, Tobin’s Q (0.249) and ROA (0.013). In case of small companies (Table IV) and small board companies (Table VI), coefficient of CEO duality is positive and non-significant when Tobin’s Q is the dependent variable and positive and significant when ROA is the dependent variable. In case of large companies (Table V) it is reverse, i.e. coefficient of CEO duality is positive and significant (0.194) when Tobin’s Q is the dependent variable and positive and non-significant when ROA is the dependent variable. In case of large board companies (Table VII), coefficient of number of CEO duality is positive and significant for both the performance measures, Tobin’s Q (0.266) and ROA (0.010).

So, H2 is validated for all data and for large board companies. It indicates that for large board, it is desired that chief executive officer and chairman positions are separated for better firm performance.

From the regression result of all data presented in Table III, H3 is not validated as board size is having statistically significant positive coefficient (0.029) in regression equation with Tobin’s Q as the dependent variable and significant positive coefficient (0.004) in regression equation with ROA as the dependent variable. Further, it is indicated that board size impacts market-based performance measure (Tobin’s Q) more as compared to accounting-based performance measure (ROA). This is in line with results reported by Dwivedi and Jain (2005), who found a positive relationship between board size and Tobin’s Q; however, it contradicts the negative relationship observed by Kumar and Singh (2013).

When the regression is run for data subsets (Tables IV Table V), the value of coefficient of board size for smaller companies is negative but non-significant when Tobin’s Q is the dependent variable and positive and significant when ROA is the dependent variable. In case of large companies, value of coefficient of board size is positive and significant with both Tobin’s Q (0.048) and ROA (0.003) as dependent variables. Thus, H3a is not validated.

In case of small board companies (Table VI), the value of coefficient of board size is negative (−0.124) and significant when Tobin’s Q is the dependent variable and positive (0.012) and significant when ROA is the dependent variable. In case of large board companies (Table VII) values of coefficient of board size are positive and significant with both Tobin’s Q (0.041) and ROA (0.002) as dependent variables. So, H3b is supported when the dependent variable is Tobin’s Q and not validated when the dependent variable is ROA, i.e. companies with small board and companies with large board show different relationship between board size and performance when the performance measure is market-based, whereas they show similar relationship when the performance measure is accounting-based.

The value of coefficient is positive and significant when ROA is the dependent variable for all cases, i.e. board size is positively related to accounting-based performance measure, and for market-based performance measure, it varies from significantly positive to insignificant to significantly negative under different cases. This is in line with results reported by Kamal Hassan and Saadi Halbouni (2013), who found that board size significantly influences accounting-based performance measure, while none of the governance variables significantly affect firms’ market performance. This may be because of the reason that for small companies, market might not attach positive value to the resource dependency theory of having more number of directors, leading to more contact to the outside world. For complete data and for large companies, market-based performance measure is positively associated with the board size, indicating that the resource linkage requirement is being fulfilled by the increased size of the board, thus validating that resource dependency theory.

For all data regression (Table III), coefficient of number of board meeting is positive and significant (0.1) when Tobin’s Q is the dependent variable and positive and non-significant when ROA is the dependent variable. In case of small companies (Table IV), coefficient of number of board meeting is positive and significant for both the performance measures of Tobin’s Q (0.158) and ROA (0.006), whereas in case of large companies (Table V) and small board companies (Table VI), it is non-significant in both the cases. In case of large board companies (Table VII), the result is similar to all data regression.

So, H4 is broadly validated in case of Tobin’s Q as performance measure, whereas it is only validated in case of small companies when performance measure is ROA. It indicates that number of board meetings send a positive signal to the market and is found to be value-creating.

For all data regression and data subsets, coefficient of internal busyness (IBUSY) is found to be statistically non-significant. So, H5 is not validated.

For all data (Table III), value of coefficient of external busyness of directors as directors in other companies (OBUSYD) is negative but non-significant when Tobin’s Q is the dependent variable and negative and significant when ROA is the dependent variable. For small companies (Table IV), value of coefficient of is positive but non-significant when Tobin’s Q is the dependent variable and negative and significant when ROA is the dependent variable. In case of large companies (Table V) and small board companies (Table VI), values of coefficient of OBUSYD are non-significant with both Tobin’s Q and ROA as dependent variables. For large board companies (Table VII), value of coefficient of board independence is non-significant when Tobin’s Q is the dependent variable and is negative and significant when ROA is the dependent variable.

So, H6 is supported when ROA is the dependent variable and external busyness of directors is measured in terms of number of directorship positions held in other companies.

For all data (Table III), small companies (Table IV) and small board companies (Table VI), values of coefficient of external busyness of directors as committee members in other companies (OBUSYC) are negative and significant when Tobin’s Q is the dependent variable and non-significant when ROA is the dependent variable. In case of large companies (Table V) and large board companies (Table VII), values of coefficient of OBUSYC are non-significant with both Tobin’s Q and ROA as dependent variables.

So, H6 is supported when Tobin’s Q is the dependent variable and external busyness of directors is measured in terms of number of committee positions held in other companies. Hence, external busyness of directors perceived by the market is the committee position held by directors in other companies. Thus, it is indicated that when directors held too many outside committee positions, they become overburdened and their involvement in board functions such as monitoring decreases, which leads to decreased firm performance. This is in line with reports by Sarkar and Sarkar (2009), who found that multiple directorships is negatively related to firm performance.

For all data regression (Table III), small companies (Table IV) and small board companies (Table VI), values of coefficient of firm size are negative and significant with both Tobin’s Q and ROA as dependent variables. This indicates that for bigger size firms, it is difficult to manage performance. In case of large companies (Table V), value of coefficient of firm size is non-significant with both Tobin’s Q and ROA as dependent variables. For large board companies (Table VII), value of coefficient of firm size is non-significant when Tobin’s Q is the dependent variable and is negative and significant when ROA is the dependent variable.

For all data regression (Table III), firm age is having significant negative coefficient with Tobin’s Q as the dependent variable and non-significant coefficient with ROA as the dependent variable. In case of small companies (Table IV) and small board companies (Table VI), value of coefficient of firm age is negative and significant when Tobin’s Q is the dependent variable and positive and significant when ROA is the dependent variable. Negative coefficient in case of regression with Tobin’s Q indicates that market is not attaching value to the cumulative learning compared to expected gain from agility of new firm. In case of large companies (Table V) and large board companies (Table VII), the value of the coefficient is non-significant for both ROA and Tobin’s Q as dependent variables.

Coefficients of leverage in regression equations is negative and significant for all data and for all the data subsets for both Tobin’ Q and ROA as dependent variables, indicating that for Indian companies, cost associated with debt is more compared to the value created by it. It may be because of the reason that for Indian companies, capital is available through internal resources easily compared to external source of capital, particularly debt.

Sales growth as control variable does not seem to impact any of the performance variables Tobin’s Q or ROA, as it is not having any of the coefficients significant in regression with either all data (Table III) or in regression with most of the data sets (Table V and Table VI). However, in case of data subsets for small companies (Table IV) and for large board companies (Table VII), values of coefficient of sales growth are significantly positive in the regression with both Tobin’s Q and ROA as dependent variables. This may be because smaller companies on growth path show firm value positively related to sales growth. In case of large boards, it may be explained in terms of more monitoring by the board, leading to further value-creation based on the background of previous growth.

7. Conclusions

This paper examines the hypothesized relationship between board structure and firm performance measured by Tobin’s Q and ROA for a sample of Indian firms listed at NSE. The results broadly indicate that corporate governance variables affect market-based performance measures (Tobin’s Q) more in comparison to accounting-based performance measure (ROA).

H1 states that the board independence would be positively associated with firm performance. From the results, board independence is found positively and significantly related to accounting-based performance measure (ROA) and not related to the market-based performance measure (Tobin’s Q). This is in line with earlier studies (Kumar and Singh, 2012; Dey and Chauhan, 2009) that used Tobin’s Q as performance measure and found board independence not impacting firm performance significantly. Above finding is a result of the fact that the board independence in India is heavily influenced by the incumbent promoter owners, family owners or private individuals, who are actually responsible for the appointment of independent directors. These independent directors usually go with the management’s decision and are not so strong a force to do efficient monitoring.

The finding is also directionally similar to the findings of Jackling and Johl (2009), who found a positive association between firm performance and board independence. However, it contradicts with the findings of Jackling and Johl (2009), when the result is interpreted for different performance measures, namely, Tobin’s Q and ROA. Jackling and Johl (2009) found this relationship positive and significant in case of Tobin’s Q and not significant in case of ROA. The difference may be ascribed to difference in sample size (180 companies against 391 companies for the present study) and period of analysis (one year, 2005-06, for Jackling and Johl and five years, 2009-2014, for current study). The requirement of compliance to Clause 49 of listing agreement was made mandatory from 1 April 2005. So, the positive effect of corporate governance provisions on firm performance would have reflected in the performance of later years.

H2 states that CEO duality (i.e. role of CEO and chairman of the board of directors vested into one person) would be affecting firm performance negatively. Results indicate that the separation of the position of CEO and chairman of the board is creating value. This is in line with earlier studies in Indian context (Kota and Tomar, 2010; Jackling and Johl, 2009; Ghosh, 2006). This finding supports the importance of monitoring role of the board of directors. Separation of the two roles would avoid the conflict of interest, and consequently, the board of directors would be more efficient in its role of monitoring the behaviour of management including the CEO.

Regarding the relationship between board size and firm performance, different studies have contrasting views. Resource access theory predicted larger boards providing greater access to resources, thereby leading to superior performance; however, it also indicates that if the board size becomes too large, decision-making and working as a group towards strategic goal becomes difficult, leading to inferior performance. Because of difference in monitoring as well as resource requirements amongst different type of companies, board size and performance relationship is also contingent upon size of the company itself. This paper hypothesized that the board size is negatively related to firm performance, and the relation between board size and firm performance is different for different type of companies. Results of this study found that board size is positively and significantly related to ROA, adding credence to “resource access” theory. This finding may be due to the contextual aspect of Indian business environment, where directors are providing linkage to the external resources. We also observed a significant difference between board sizes of smaller and larger companies which may be due to increased monitoring requirements as the company size grows. In case of bigger companies, board meetings were found more frequent compared to smaller companies, confirming increased monitoring requirements.

Board meetings are platforms for discussing the performance and behaviour of management apart from deciding on the strategic directions for the company. Keeping this in view, this paper hypothesized that the number of board meetings is positively related to firm performance. Results indicate that the number of board meeting is positively and significantly related to market-based performance measure, i.e. Tobin’s Q. Hence, an increased number of board meetings is found to send a positive signal to market, thus creating value for the firm.

Board of directors perform their monitoring function through their participation in board meetings. This study has defined busyness of directors in terms of their participation in board meetings. So, it is hypothesized that the directors’ internal busyness is positively related to firm performance. However, results indicate that the performance is not significantly related to any of the performance measure in all regressions. This may be because of possible complex relationships between number of board meetings and performance in place of assumed linear relationship, thereby making it difficult to estimate. There may also be a possibility of lag effect in this relationship, i.e. the response of board of directors towards poor performance may yield result in the following year (Vafeas, 1999).

Resource dependency theory predicted that multiple positions held by directors would create more resource linkage for firms, leading to superior performance. However, on the contrary, it also predicts that directors with too many outside positions would reduce their effectiveness as far as the monitoring role is concerned. With this view, this paper hypothesized that the directors’ external busyness is negatively related to firm performance. The results indicate that the external busyness when measured in terms of number of directorship position held in other companies affects market based-performance measure negatively, and when measured in terms of number of committee positions held in other companies, it affects the accounting-based measure adversely. Thus, overall overburdened directors affect firm performance negatively. This result also indicates that busy directors may not have necessary reputation and networking contacts that are necessary to generate benefits to the company (Jackling and Johl, 2009).

Among control variables, both firm size and firm age are found to have a negative relationship with firm performance, indicating that newer firm with newer technologies and up to a certain optimal size are better able to manage performance. The negative relationship of leverage with firm performance indicates that for Indian firms, it is less costly to manage resources through internal sources than through the debt market. Sales growth has generally been found not related to firm performance.

This study has implications for investors, academicians and policy makers as the findings indicate impact of specific corporate governance variables on corporate financial performance. The study is important for both domestic and foreign investors as it gives an indication to the type of companies (from corporate governance point of view) in Indian context that may give better financial results. Findings also indicate that investors should look for companies with an optimal and diversified board. Relatively smaller companies based on newer technologies may also be chosen by investors for a better return. As an implication, this indicates that if foreign investors in developed economies come with their newer technologies and improved corporate governance practices, it may result into good corporate performance. The literature review done under the study suggests that governance reforms that encourage firms to adopt better governance practices reduce the likelihood of earnings management. The evidences from emerging market enhance our understanding of corporate governance in those economies.

In this study, board independence is found to have a positive relationship with firm performance. Hence, agency theory has been supported by findings of this study. Next finding of the study, i.e. separate positions of CEO and chairperson of the board positively associated with firm performance, is contrary to the stewardship theory. Positive association between size of the board and firm performance indicates support for resource dependency theory. Hence, the study supports agency theory and resource dependency theory.

Few limitations of this study are in terms of methodology and possible omission of some variables. Concerns have been raised about the kind of relationship between board structure and firm performance in various theoretical and empirical studies. It has been indicated that board structure is endogenously determined by firm-specific factors such as scale economies, regulation and the stability of the environment in which they operate (Hermalin and Weisbach, 1991, 2001, Linck et al., 2008). Existence of reverse causality between board structure and firm performance has also been considered by some of the studies, for example, Adams and Mehran (2012). The existence of reverse causality could have been explored using a simultaneous equation framework.

In future, researchers may try to explore governance performance relationship using a wider set of data in terms of number of companies and number of years. Reverse causality may also be studied using simultaneous equation approach. Even study around introduction of major changes in corporate governance regime in India, namely, 2000 (introduction of Clause 49 in the listing agreement), 2004 (introduction of penal provisions in Clause 49), 2010 (recommendations of Murthy committee on audit committee and whistle blower policy) and 2014 (enforcement of new companies act with specific provisions on corporate governance), may give insights into impact of specific governance mechanism on corporate performance in Indian context.

Variables used in the model

Serial no. Variable name Description Measurement
Dependent variable
1 Tobin’s Q Market value of equity + book value of short-term and long-term debt divided by total assets (FA + INV + CA) Ratio
2 ROA Operating profit before depreciation and amortization divided by total assets Ratio; considered as ratio of PBDITA to total assets
Independent variable
3 Bsize Number of directors on the board of a firm Number
4 Bind % of outside directors of total number of directors %
5 Bmeet Number of board meetings in a year Number
6 Duality A binary variable; if chairman of the board is also CEO of the company, its value is 0, otherwise 1 Binary number
7 OBUSYD Average number of directorship/chairmanship positions in outside companies held by the directors of a company Number
8 OBUSYC Average number of committee positions in outside companies held by the directors of a company Number
9 IBUSY Average number of board meeting attended by the directors of a company Number
Control variable
10 Fsize Natural logarithm of total assets Number
11 Fage Natural logarithm of the number of years since the establishment Number
12 Lev Ratio of long-term debt to the total assets Ratio
13 Sgrowth Total sales of the current year minus total sales in the previous year divided by total sales in the current year %

Data descriptive and correlation coefficient between variables

Variables Statistical measures Pearson correlations
N Minimum Maximum Mean Median SD Tobin’s Q ROA Bind Duality Bsize Bmeet OBUSYD OBUSYC Fsize Fage IBUSY Lev Sgrowth
Tobin’s Q 1,955 0.18 28.98 1.9 1.22 2.06 1
ROA 1,955 −0.67 1.33 0.15 0.14 0.1 0.360** 1
Bind 1,955 0 100 46.78 46.15 11.85 −0.074** −0.009 1
Duality 1,955 0 1 0.58 1 0.49 0.099** 0.107** −0.107** 1
Bsize 1,955 2 26 11.59 11 3.56 −0.036 0.031 −0.094** −0.142** 1
Bmeet 1,955 1 14 4.66 4 1.11 0.016 −0.015 0.004 −0.100** 0.048* 1
OBUSYD 1,955 0 19.8 4.39 4 1.69 −0.055* −0.082** 0.154** 0.072** −0.097** −0.087** 1
OBUSYC 1,955 0 21.33 4.29 3.91 2.7 −0.066** −0.035 0.144** 0.090** −0.191** −0.085** 0.464** 1
Fsize 1,955 0 11 2.09 2.1 1.42 −0.206** −0.196** 0.023 −0.149** 0.495** 0.089** 0.053* 0.004** 1
Fage 1,955 6.77 15.12 10.38 10.32 1.39 −0.072** 0.064** 0.052* 0.012 0.142** 0.019 −0.083** −0.084** 0.119** 1
IBUSY 1,955 1.1 5.02 3.45 3.37 0.63 −0.059** −0.086** −0.017 −0.117** 0.036 0.114** −0.018 0.041** 0.263** 0.018 1
Lev 1,955 0 1.59 0.22 0.21 0.18 −0.334** −0.440** 0.128** −0.116** 0.028 0.081** 0.043 −0.008** 0.218** −0.060** 0.040** 1
Sgrowth 1,955 −1.91 3193.22 3.19 0.13 85.34 −0.001 −0.019 −0.008 −0.008 −0.021 0.005 0.032 0.014 0.021 −0.067 −0.016 0.029 1
Total assets 1,955 872.3 3,677,440 101,188.45 30,199 267,110.5
Notes:

**correlation is significant at the 0.01 level (two-tailed);

*correlation is significant at the 0.05 level (two-tailed)

Regression results with all data

Variables and statistical measures Tobin’s Q ROA
Coefficients t value Collinearity statistics Coefficients t value Collinearity statistics
Tolerance VIF Tolerance VIF
(Constant) 5.229 11.362** 0.227 10.835**
Bind −0.001 −0.281 0.925 1.082 0.001 3.429** 0.925 1.082
Duality 0.249 2.75** 0.927 1.079 0.013 3.149** 0.927 1.079
Bsize 0.029 2.012* 0.686 1.457 0.004 5.262** 0.686 1.457
Bmeet 0.1 2.515* 0.964 1.038 0.003 1.486 0.964 1.038
IBUSY −0.006 −0.237 0.897 1.115 −0.002 −1.386 0.897 1.115
OBUSYD −0.006 −0.304 0.762 1.312 −0.002 −2.45* 0.762 1.312
OBUSYC −0.09 −2.545* 0.746 1.341 0 0.110 0.746 1.341
Fsize −0.218 −5.63** 0.637 1.571 −0.011 −6.318** 0.637 1.571
Fage −0.282 −4.019** 0.95 1.053 0.005 1.546 0.95 1.053
Lev −3.405 −13.929** 0.912 1.097 −0.217 −19.522** 0.912 1.097
Sgrowth 0 0.345 0.992 1.008 3.045E-06 0.132 0.992 1.008
R 0.386 0.478
R2 0.149 0.229
Adjusted R2 0.144 0.224
F 30.862** 52.399**
Durbin–Watson statistic 1.901 2.052
Notes:
*

significant at 5% level;

**

significant at 1% level

Regression results for small companies

Variables and statistical measures Tobin’s Q ROA
Coefficients t value Collinearity statistics Coefficients t value Collinearity statistics
Tolerance VIF Tolerance VIF
(Constant) 9.41 8.520** 0.233 5.581**
Bind −0.010 −1.540 0.921 1.086 0.001 2.651** 0.921 1.086
Duality 0.278 1.760 0.942 1.062 0.022 3.678** 0.942 1.062
Bsize −0.002 −0.061 0.836 1.196 0.005 4.551** 0.836 1.196
Bmeet 0.158 2.136* 0.966 1.035 0.006 2.004* 0.966 1.035
IBUSY −0.003 −0.049 0.921 1.085 −0.003 −1.444 0.921 1.085
OBUSYD 0.026 0.797 0.738 1.355 −0.003 −2.758** 0.738 1.355
OBUSYC −0.168 −2.991** 0.732 1.367 0.001 0.260 0.732 1.367
Fsize −0.620 −5.419** 0.810 1.235 −0.019 −4.462** 0.810 1.235
Fage −0.338 −2.678** 0.915 1.093 0.016 3.434** 0.915 1.093
Lev −3.079 −7.063** 0.928 1.077 −0.225 −13.672** 0.928 1.077
Sgrowth 0.849 3.200** 0.98 1.02 0.039 3.904** 0.980 1.020
R 0.374 0.499
R2 0.140 0.249
Adjusted R2 0.130 0.240
F 14.237** 29.015**
Durbin–Watson statistic 1.762 1.980
Notes:
*

significant at 5% level;

**

significant at 1% level

Regression results for large companies

Variables and statistical measures Tobin’s Q ROA
Coefficients t value Collinearity statistics Coefficients t value Collinearity statistics
Tolerance VIF Tolerance VIF
(Constant) 2.711 4.411** 0.166 4.189**
Bind 0.006 1.688 0.905 1.105 0.001 2.398* 0.905 1.105
Duality 0.194 2.208* 0.919 1.088 0.004 0.649 0.919 1.088
Bsize 0.048 3.672** 0.714 1.400 0.003 3.198** 0.714 1.400
Bmeet 0.049 1.356 0.964 1.037 0.000 0.091 0.964 1.037
IBUSY −0.033 −1.421 0.86 1.163 −0.002 −1.218 0.86 1.163
OBUSYD −0.009 −0.492 0.76 1.315 0.000 −0.43 0.760 1.315
OBUSYC 0.029 0.775 0.739 1.353 0.001 0.341 0.739 1.353
Fsize −0.089 −1.787 0.726 1.377 −0.002 −0.766 0.726 1.377
Fage −0.086 −1.282 0.940 1.064 −0.002 −0.387 0.940 1.064
Lev −3.513 −14.973** 0.952 1.051 −0.203 −13.465** 0.952 1.051
Sgrowth 0 0.768 0.988 1.012 0.000 −0.105 0.988 1.012
R 0.466 0.424
R2 0.217 0.180
Adjusted R2 0.208 0.170
F 24.318** 19.222**
Durbin–Watson statistic 1.928 1.955
Notes:
*

significant at 5% level;

**

significant at 1% level

Regression results for small board companies

Variables and statistical measures Tobin’s Q ROA
Coefficients t value Collinearity statistics Coefficients t value Collinearity statistics
Tolerance VIF Tolerance VIF
(Constant) 9.758 10.852** 0.153 3.836**
Bind 0.006 0.926 0.953 1.049 0.001 2.585* 0.953 1.049
Duality 0.317 2.019 0.936 1.069 0.019 2.671** 0.936 1.069
Bsize −0.124 −2.194* 0.915 1.093 0.012 4.708** 0.915 1.093
Bmeet 0.06 0.850 0.977 1.024 0.003 1.048 0.977 1.024
IBUSY −0.074 −1.623 0.925 1.081 −0.002 −0.833 0.925 1.081
OBUSYD 0.000 0.007 0.771 1.297 −0.003 −1.894 0.771 1.297
OBUSYC −0.198 −3.827** 0.765 1.308 0.000 −0.185 0.765 1.308
Fsize −0.431 −6.376** 0.849 1.178 −0.016 −5.402** 0.849 1.178
Fage −0.555 −4.519** 0.922 1.085 0.017 3.127** 0.922 1.085
Lev −2.795 −6.698** 0.869 1.151 −0.202 −10.904** 0.869 1.151
Sgrowth 0.000 0.345 0.980 1.021 0.000 0.313 0.980 1.021
R 0.436 0.503
R2 0.190 0.253
Adjusted R2 0.179 0.242
F 17.277** 24.915**
Notes:
*

significant at 5% level;

**

significant at 1% level

Regression results for large board companies

Variables and statistical measures Tobin’s Q ROA
Coefficients t value Collinearity statistics Coefficients t value Collinearity statistics
Tolerance VIF Tolerance VIF
(Constant) 2.335 4.015** 0.252 9.032**
Bind −0.007 −1.666 0.897 1.115 0.000 2.273* 0.897 1.115
Duality 0.266 2.543* 0.902 1.108 0.010 1.992* 0.902 1.108
Bsize 0.041 2.082* 0.733 1.364 0.002 1.730 0.733 1.364
Bmeet 0.161 3.608** 0.941 1.063 0.002 0.785 0.941 1.063
IBUSY −0.027 −0.837 0.796 1.256 0.000 −0.171 0.796 1.256
OBUSYD −0.001 −0.050 0.717 1.395 −0.002 −2.278* 0.717 1.395
OBUSYC 0.054 1.136 0.724 1.382 0.003 1.274 0.724 1.382
Fsize −0.077 −1.711 0.647 1.546 −0.008 −3.795** 0.647 1.546
Fage 0.004 0.048 0.942 1.061 −0.004 −0.999 0.942 1.061
Lev −3.835 −13.422** 0.919 1.088 −0.225 −16.420** 0.919 1.088
Sgrowth 0.524 2.968** 0.965 1.037 0.020 2.415* 0.965 1.037
R 0.424 0.490
R2 0.179 0.240
Adjusted R2 0.171 0.232
F 22.254** 32.122**
Durbin–Watson statistic 1.976 1.873
Notes:
*

significant at 5% level;

**

significant at 1% level

Independent samples test for small and large companies

Variables Total assets N Mean SD Mean difference F value
Bsize ≥30,199.00 978 12.810 3.832 2.446** 72.854
<30,199.00 977 10.370 2.763
TobinsQ ≥30,199.00 978 1.574 1.478 −0.651** 44.502
<30,199.00 977 2.225 2.467
ROA ≥30,199.00 978 0.133 0.093 −0.040* 5.042
<30,199.00 977 0.173 0.100
Bmeet ≥30,199.00 978 4.750 1.189 0.182* 4.623
<30,199.00 977 4.570 1.015
Duality ≥30,199.00 978 0.530 0.500 −0.111** 70.762
<30,199.00 977 0.640 0.481
Notes
*

significant at 5% level;

**

significant at 1% level

Independent samples test for small and large board

Variables Bsize N Mean SD Mean difference F value
TobinsQ ≥11 1,132 1.826 1.822 −0.176** 11.663
<11 823 2.002 2.342
ROA ≥11 1,132 0.154 0.091 0.002* 6.122
<11 823 0.151 0.108
Bmeet ≥11 1,132 4.670 1.140 0.019 0.605
<11 823 4.650 1.065
Duality ≥11 1,132 0.560 0.497 −0.056** 24.99
<11 823 0.610 0.487
Notes:
*

significant at 5% level;

**

significant at 1% level

Appendix

Table AI

Table AII

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Further reading

Copeland, T.E., Koller, T. and Murrin, J. (1991), Valuation: Measuring and Managing the Value of Companies, John Wiley & Sons, NJ.

Haat, M.H.C., Rahman, R.A. and Mahenthiran, S. (2008), “Corporate governance, transparency and performance of Malaysian companies”, Managerial Auditing Journal, Vol. 23 No. 8, pp. 744-778.

Li, H., Meng, L., Wang, Q. and Zhou, L.A. (2008), “Political connections, financing and firm performance: evidence from Chinese private firms”, Journal of Development Economics, Vol. 87 No. 2, pp. 283-299.

Veronica, S. and Bachtiar, Y.S. (2005), “Corporate governance, information asymmetry, and earnings management”, Jurnal Akuntansi Dan Keuangan Indonesia, Vol. 2 No. 1, pp. 77-106.

Supplementary materials

JIBR_10_1.pdf (12.9 MB)

Corresponding author

Rakesh Kumar Mishra can be contacted at: mishrarakeshk@indianoil.in

About the authors

Rakesh Kumar Mishra is pursuing Doctoral Programme of Indian Institute of Foreign Trade, New Delhi, in the area of business strategy. The author is holding PGDM from Indian Institute of Management Lucknow in Finance and Strategy and MTech from Indian Institute of Technology Kharagpur in Industrial Engineering and Management. He also holds a BE degree in Mechanical Engineering. He has more than 18 years of experience in the areas of human resource management, project management, design and engineering and operation and maintenance of petroleum pipelines. The author has been working with Indian Oil Corporation Limited, a top rank company from India in Fortune 500 list. He has got papers published in the area of business strategy in peer reviewed journals.

Dr Sheeba Kapil is an Associate Professor at Indian Institute of Foreign Trade, New Delhi, since July 2007. She handles courses on corporate finance, mergers and acquisition, business valuation and investment analysis. She has teaching experience of around 20 years along with several international and national publications of research papers and books. She has conducted several Management development programmes (MDPs) for corporate sector and Faculty development programmes (FDPs).