Determinants and consequences of board size: conditional indirect effects

Muhammad Ali (QUT Business School, Queensland University of Technology, Brisbane, Australia)

Corporate Governance

ISSN: 1472-0701

Publication date: 5 February 2018

Abstract

Purpose

Board size is an important dimension of corporate governance. The purpose of this study is to propose and test indirect effects of organization size on organizational performance via board size, in the context of industry.

Design/methodology/approach

The study’s predictions were tested in 288 medium and large organizations listed on the Australian Securities Exchange using archival data.

Findings

The findings of this study suggest the following: organization size is positively associated with board size and this relationship is stronger in manufacturing organizations; board size is positively associated with performance and this relationship is conditional on industry; and organization size has an indirect effect on performance via board size, and this indirect effect is also conditional on industry.

Research limitations/implications

The results provide some support for the resource dependency theory, agency theory and contingency theory.

Practical implications

The findings suggest that directors should take into account the effects of board size and industry to provide a more precise assessment of the board’s performance.

Originality/value

It predicts and tests the pioneering moderating effect of industry (manufacturing vs services) on the organization size–board size, board size–organizational performance and organization size–board size–organizational performance relationships.

Keywords

Citation

Ali, M. (2018), "Determinants and consequences of board size: conditional indirect effects", Corporate Governance, Vol. 18 No. 1, pp. 165-184. https://doi.org/10.1108/CG-01-2016-0011

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Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


Introduction

Corporate governance refers to “the framework of rules, relationships, systems and processes within and by which authority is exercised and controlled in corporations” (Owen, 2003, p. 33). The relationships involve board of directors, management, shareholders and other stakeholders (Department of Social Services, 2010). A company’s board of directors is an important player in the corporate governance framework, being primarily responsible for safeguarding the interests of the company and its shareholders (Australian Institute of Company Directors, 2013). Good corporate governance cannot be overemphasized for a number of reasons, including the fact that companies compete with each other for limited financial resources such as investors’ capital (Corporate Governance Council, 2014). Both the push factors (e.g. stricter corporate regulations like the US Sarbanes Oxley Act 2002) and pull factors (e.g. the recent global financial crisis and corporate collapses such as HIH insurance group and Enron) have further highlighted the importance of good corporate governance (Kimber et al., 2005). The Australian Securities Exchange (ASX) has put forward 8 overarching principles and 28 specific recommendations that cover a wide range of corporate governance rules, relationships, systems and processes. The second principle involves structuring boards to add value, including the dimensions of size, composition and skills (Australian Securities Exchange, 2012).

Given the significance of board size as a dimension of corporate governance, a body of literature has investigated the determinants and consequences of board size, with more interest shown in the latter (see meta analysis Dalton et al., 1999). The determinants studied include organization size, organization age, growth opportunities, growth, diversification and firm complexity (Boone et al., 2007; Coles et al., 2008; Lehn et al., 2009; Linck et al., 2008). The consequences studied include corporate reputation (Musteen et al., 2010), capital structure (Abor, 2007) and environmental reporting (Rao et al., 2012), but predominantly focus on organizational performance using measures such as accounting- and market-based financial performance measures (Adams et al., 2010; Adams and Mehran, 2012; Kim et al., 2012). This body of literature provides conflicting findings: positive impact on performance (Adams and Mehran, 2012; Aggarwal et al., 2012; Dalton et al., 1999; Dwivedi and Jain, 2005; Ilhan Nas et al., 2016; Kalsie and Shrivastav, 2016; Kathuria and Dash, 1999; Kiel and Nicholson, 2003; Kim et al., 2012; Larmou and Vafeas, 2010; Mishra and Mohanty, 2014; Saibaba and Ansari, 2012; Sahu and Manna, 2013; Tanna et al., 2011; Upadhyay 2008); negative impact on performance (Adams et al., 2010; Afrifa and Tauringana, 2015; Bai, 2013; Bennedsen et al., 2008; Cheng, 2008; Cheng et al., 2008; Chintrakarn et al., 2017; Conyon and Peck, 1998; Garg, 2007; Guest, 2009; Kumar and Singh, 2013; Liang et al., 2013; Mak and Kusnadi, 2005; McIntyre et al., 2007; Mohapatra, 2017; Morekwa Nyamongo and Temesgen, 2013; Nguyen et al., 2016; Pathan and Faff, 2013; Pathan et al., 2007; Shakir, 2008; Ujunwa, 2012); U-shaped impact on performance (Coles et al., 2008); inverted U-shaped impact on performance (Hartarska and Nadolnyak, 2012; Titova and Titova, 2016; Xie and Fukumoto, 2013); more complex curvilinear impact on performance (Mayur and Saravanan, 2017); and non-significant impact on performance (Beiner et al., 2004; Darko et al., 2016; Rodriguez-Fernandez et al., 2014; Farhan et al., 2017; Yammeesri and Kanthi Herath, 2010). Inconclusive findings have encouraged tests of moderating effects on the board size–organizational performance relationship (Johns, 2006). Some of the moderating variables studied are: CEO duality (Elsayed, 2011), organization size (O’Connell and Cramer, 2010), complexity/advising needs (Coles et al., 2008), for-profit/not-for-profit status (Bai, 2013), organizational form (Adams et al., 2010), politically connected directors (Menozzi et al., 2012), pre-global financial crisis/post-global financial crisis and market power (Pathan and Faff, 2013). Moreover, scholars suggest testing sophisticated models (Murphy and McIntyre, 2007), especially when the past findings have been inconsistent (van Knippenberg et al., 2011).

This study advances our knowledge of determinants and consequences of board size in the following ways. First, it predicts and tests a positive relationship between organization size and board size (Oba et al., 2014). Second, it predicts and tests a positive relationship between board size and organizational performance based on the resource dependency theory (Pfeffer, 1972) and agency theory (Eisenhard, 1989). Third, it predicts and tests the moderating effect of industry (manufacturing vs services) on the organization size–board size and board size–organizational performance relationships, based on contingency theory (Galbraith, 1973). Industry is an important determinant of various dimensions of corporate governance, including certain regulations (Lawrence and Stapledon, 1999). Thus, this study takes Zona et al.’s (2013) recommendation of focusing on contingent effects a step further by proposing and testing the conditional effects at two stages: organization size–board size and board size–organizational performance. Fourth, the paper presents and tests indirect effect of organization size on organizational performance via board size, and how this effect is conditional on industry (Figure 1). These predictions were tested in 288 medium and large companies listed on the ASX with a one-year time lag between organization size (Year 2011) and organizational performance (Year 2012). Past research has not tested these indirect and conditional effects. Thus, this study responds to Dalton and Dalton’s (2005) call for empirical evidence for best corporate governance practices by testing determinants and consequences of board size.

Theories and hypotheses development

Organization size and board size

Corporate governance practices and their effectiveness varies across firms of different sizes (Klapper and Love, 2003; O’Connell and Cramer, 2010). The responsibilities of a board of directors include: selecting a CEO; advising on the development and implementation of corporate strategic plans; monitoring top management’s running of the firm; regularly assessing the backgrounds, skills and abilities of its members as part of succession planning; and engaging with shareholders and other stakeholders (Business Roundtable, 2012). Advising and monitoring are the two most important functions (Business Roundtable, 2012; Linck et al., 2008; Sur, 2014).

Advising involves helping the organization’s top management team in establishing goals and developing strategies (Department of Social Services, 2010). As an organization grows, the complexities of its various functions increase exponentially. These large and complex organizations need a more hierarchical structure (Fama and Jensen, 1983). The increased complexities also require a larger board with members having expertise and knowledge in various areas (Coles et al., 2008; Linck et al., 2008; Ning et al., 2010). A large board can handle and process the huge amount and depth of information on the complex operations (Boone et al., 2007). Moreover, large organizations engage in sophisticated technologies, mergers and acquisitions (Lehn et al., 2009). A larger board thus helps to advise management in various areas. Furthermore, large organizations have several actors in their environment such as regulatory agencies and suppliers (Murphy and McIntyre, 2007). Therefore, large organizations need to have large boards to link with those environmental actors to obtain the necessary resources (Pfeffer and Salancik, 1978). Larger organizations also engage more in external contracting which requires additional board members with extensive industry networks (Pfeffer, 1972). Finally, large organizations play an important role in the society and, thus, require a larger board to connect with a broader stakeholder base (Pfeffer, 1972).

Monitoring is about overseeing shareholders’ interests by making sure that the top management team and other managers are implementing the strategies and leading the organization in the right direction to achieve set goals (Business Roundtable, 2012; Murphy and McIntyre, 2007). It also requires boards to ensure that management is refining the strategies and implementing them successfully (Neville, 2011). As organizations grow, the board’s monitoring role also becomes more complex and difficult because of the diversification, scale and scope of operations (Coles et al., 2008). The geographical dispersion, products/services diversification and the huge financial stakes involved require a large board to perform the monitoring role. Such organizations require boards to operate in the form of committees with specialized knowledge in different areas (Oba et al., 2014). Taking a team perspective of board of directors, it is suggested that the teams should be large enough to have a range of competencies (Hickman and Creighton-Zollar, 1998; Murphy and McIntyre, 2007). Thus, large organizations would need more members on their boards than the boards of small organizations which can operate as a whole (Boone et al., 2007). Moreover, large organizations operate in large information environments attracting close public and media scruitiny (John and Senbet, 1998; O’Connell and Cramer, 2010) and, thus, would need a larger board for a stricter monitoring function.

In sum, a larger organization needs a larger board. Empirical research supports the proposition that organization size is positively related to board size. For instance, Lehn, Patro and Zhao (2009) studied 81 organizations and found a positive link between organization size and board size. Similarly, Boone et al. (2007) focused on 1,019 listed companies over a period of 10 years and found a positive relationship between organization size and board size. Thus, it is proposed:

H1.

Organization size is positively associated with board size.

Moderating effect of industry on the organization size–board size relationship

Organizational contingency theory states that there is no one way of organizing which can be referred to as the best (Galbraith, 1973). The main argument of organizational contingency theory focuses on the fit between an organization’s structure and other situational attributes called contingencies (Donaldson, 2001). Organization size determines the board size because of the complexities involved in governing a large organization. However, industry adds to some of those complexities and may moderate the strength of the positive organization size–board size relationship (Murphy and McIntyre, 2007; Ning et al., 2007; Ning et al., 2010).

Manufacturing and services organizations diverge on a number of factors, such as the degree of direct involvement of customers in the production and delivery of products/services and the customization of products/services (Jiang, 2009; Schmenner, 1986). Manufacturing organizations also tend to have high levels of the following complexities: logistical, technological, organizational and environmental (Khurana, 2014). One of the most significant challenges facing manufacturing organizations is to deal with these complexities (Humphlett, 2014); a large board can help. For instance, a large board can bring high levels of social capital that can help manage the environmental complexities and provide valuable connections to procure materials, improving logistics (Pfeffer, 1972). Similarly, a large board can help tighten the monitoring of a manufacturing organization’s complex and diverse operations (Fama and Jensen, 1983). Moreover, the optimal board size is a trade-off between the costs and benefits associated with the size of the board which may vary across industries (Lehn et al., 2009; Ning et al., 2010). The costs of a large board include ‘free-riding’ and coordinating its decision-making process. The benefits include a variety of perspectives, additional information and insights. This paper argues that manufacturing organizations’ complexities allow them to benefit more from additional board members than services organizations. For instance, increasingly manufacturing organizations rely more on outsourcing than vertical integration (Sissons, 2011). A large board can provide valuable contacts with prospective efficient suppliers. Thus, the benefits of a large board outweigh its costs in a manufacturing organization, whereas in a services organization, the board size where the benefits offset the costs might be reached at a lower level.

In sum, organization size interacts with industry to determine the board size. Large manufacturing organizations tend to have larger boards than large services organizations. No prior research has investigated the moderating effect of industry on the organization size–board size relationship. However, Coles et al. (2008) found that complex organizations (large, diversified or those that rely heavily on debt financing) have larger boards. Similarly, Pearce and Zahra’s (1992) findings indicate that environmental uncertainties are associated with larger boards. Thus, it is proposed:

H2.

Industry moderates the strength of the positive relationship between organization size and board size such that the positive relationship is stronger in the manufacturing industry than in the services industry.

Board size and organizational performance

Resource dependency theory suggests that organizations depend on external actors in their environment for resources necessary for their survival and growth (Pfeffer, 1972). The level of dependency is conditional on the extent of the need for resources (e.g. specific raw material) and the number of sources of resources (i.e. single supplier or multiple suppliers) (Thompson, 1967). Organizations manage their environment to secure resources (e.g. their directors sit on the boards of supplier organizations called interlocking directorates) or reduce dependency on external actors (e.g. through vertical integration). The strategic nature of boards makes them suitable to connect with the environment (Pfeffer and Salancik, 1978). A large board can help the organization through board members’ connections with external actors in the environment such as suppliers and business customers (Nicholson and Kiel, 2004; Pfeffer, 1972). Through interlocking directorates, board members can provide valuable advice to management to secure important contracts. Large boards are more likely to have more interlocking directorates; thus, in comparison to small boards, large boards are likely to perform the advising role in a better way (Dalton et al., 1999).

Agency theory suggests that there is an inherent conflict of interest between the principal (shareholders) and agents (management) (Eisenhard, 1989). While shareholders want to maximize their returns, management may be more interested in their own gains. Therefore, board members need to play the important role of monitoring the behaviors of management leading to the attainment of objectives set in the strategic planning process. A large board is likely to perform the monitoring role in an effective manner as more directors will be involved in this process (Kiel and Nicholson, 2003). Boards can operate by forming various committees based on the expertise of the board members (Kiel and Blennerhasett, 1984). These committees can include governance committee, audit committee, nomination committee, remuneration committee, compliance committee and risk committee (Corporate Governance Council, 2014). Therefore, a large board with directors having a range of expertise would help perform the various complicated roles in a more efficient manner than a smaller board doing all the work as one group.

In sum, larger boards should be associated with superior performance. Empirical research supports this proposition. For instance, a meta-analysis of 131 studies found a significant positive relationship between board size and both accounting- and market-based financial performance (Dalton et al., 1999). Similarly, Kiel and Nicholson (2003) studied 348 large Australian publicly listed companies and found that board size was positively associated with Tobin’s Q. Thus, it is proposed:

H3.

Board size is positively associated with organizational performance.

Moderating effect of industry on the board size–organizational performance relationship

Compared to small boards, large boards perform the advising and monitoring roles in a more effective manner (see theoretical arguments leading to H3). These effective advising and monitoring functions should have a larger impact on organizational performance in manufacturing organizations than in services organizations. Manufacturing organizations develop strategies and restructure their organizations to introduce innovations, compared to services organizations that tend to do this less formally (Ettlie and Rosenthal, 2011). Proper advising from a large board in a manufacturing organization is especially important in these cases. Similarly, the benefits of tighter monitoring by a large board of a manufacturing organization outweigh the costs of such monitoring (Fama and Jensen, 1983); this happens in services organizations as well, but the net positive effect (benefits minus costs) of such tighter monitoring might be smaller in services organizations. Therefore, a manufacturing organization, because of its complexities, will benefit more from a large board than a services organization with a similar-sized board.

A large board should add more value in manufacturing organizations than in services organizations because of the inherent complexities involved in a manufacturing organization. Large boards can help deal with the complexities of logistics, technology, organization and environment in manufacturing organizations (Khurana, 2014). For instance, a large board comprising industry experts, former CEOs of competitors, customers or supplier organizations and interlocking directorates can help improve the complex supply chains which are vital in a manufacturing organization (Humphlett, 2014). Similarly, the interconnected, yet somewhat independent, and geographically dispersed operations based on various technologies add to the technological complexities in manufacturing organizations. These complex technologies, along with the increasingly more common global value chain and greater proportions of knowledge-intensive input (Sissons, 2011), require multiple directors with know-how of those technologies and the global value chain. Moreover, manufacturing organizations perceive greater changes in their environment than services organizations (Rant, 2007). A large board with a diverse set of experiences in dealing with environmental factors might help manufacturing organizations to deal with those environmental changes.

In sum, the strength of the positive board size–organizational performance relationship is contingent on the industry. The large boards in manufacturing organizations are associated with superior performance compared to large boards in services organizations. No prior research has tested the moderating effect of industry on the board size–organizational performance relationship. Di Pietra et al. (2008) found some industry differences regarding the impact of board size on share prices. It is proposed:

H4.

Industry moderates the positive relationship between board size and organizational performance such that the positive relationship is stronger in the manufacturing industry than in the services industry.

Indirect effects between organization size and performance

Organization size may have a positive impact on organizational performance. Large organizations benefit from economies of scale (Katrishen and Scordis, 1998). They may influence their environment and, thus, may be able to reduce their dependency on it (Starbuck, 1965). Some of that effect might be enacted through a larger board with greater levels of connections with the organizations in its environment. Based on the arguments presented for a positive relationship between organization size and board size (H1) and a positive relationship between board size and organizational performance (H3), it is proposed that there is an indirect relationship between organization size and organizational performance via board size. No prior research has tested this indirect relationship. It is proposed:

H5.

Board size mediates the positive relationship between organization size and organizational performance.

Conditional indirect effect between organization size and performance

Based on the preceding arguments regarding a positive indirect effect of organization size on organizational performance via board size (H5) and the moderating effects of industry on the organization size–board size relationship (H2) and the board size–organizational performance relationship (H4), the positive indirect effect of organization size on organizational performance (via board size) will be conditional on industry. The positive indirect effect will be stronger in the manufacturing industry than in the services industry. No prior research has tested such a conditional indirect relationship. It is proposed:

H6.

Industry moderates the indirect positive relationship between organization size and organizational performance via board size such that the positive indirect effects are stronger in the manufacturing industry than in the services industry.

Methods

This study used archival data to test the hypotheses with a one-year time lag between organization size (Year 2011) and organizational performance (Year 2012).

Sample and data collection

The population of this research comprises for-profit medium and large organizations across industries in Australia. The sample frame comprised 2,164 organizations listed on the ASX in 2011. A total of 446 organizations with over 100 employees were selected. Small organizations were excluded because public databases are less likely to have complete data on their boards. Missing data further reduced the sample size to 288 organizations. Data on organization size (obtained from the DatAnalysis database), board size (Osiris), organization age (Osiris), organization type in terms of holding/subsidiary or stand-alone (OneSource) and industry (ASX website) for Year 2011 were matched with data on operating revenue (Osiris) and net operating profit less adjusted taxes (DatAnalysis) for Year 2012.

The final sample of 288 organizations was diverse in terms of size and industry. The sample organizations ranged in size from 101 to 190,000 employees. They represented nine industry groups based on Standard Industrial Classification (SIC) codes; no organization belonged to the Public Administration category. The highly represented industry groups were: Transportation, Communications, Electric, Gas and Sanitary Services (24 per cent of the organizations); Mining (18 per cent); Services (16 per cent); Construction (15 per cent) and Finance, Insurance and Real Estate (12 per cent).

Australian context

Australian businesses operate in a highly regulated business environment. The recent corporate governance reforms include the Corporate Law Economic Reform Program (Audit Reform and Corporate Disclosure) Act 2004 and the ASX Corporate Governance Council’s Principles of Good Corporate Governance and Best Practice Recommendations (Australian Securities Exchange, 2012). The Australian Securities and Investment Commission is responsible for making sure that the directors are fulfilling their duties under the Corporation Act 2001 (Australian Securities Exchange, 2014; Lucy, 2006). This includes performing commercial duties (e.g. advising management to make sound business decisions for healthy shareholder returns) while making sure that all the legal duties (complying with various laws) are being fulfilled. A director can be held personally liable under 600 plus laws, including competition law and work health and safety laws (Australian Institute of Company Directors, 2013). The ASX plays a leadership role in improving corporate governance in Australia. The publicly listed organizations report to the ASX on an ‘if not, why not basis’, explaining why they decided not to adopt Principles of Good Corporate Governance and Best Practice Recommendations. The 8 overarching principles and 28 specific recommendations cover a wide range of corporate governance rules, relationships, systems and processes. Following the first principle of effective monitoring, the second principle involves structuring the board to add value, including the dimensions of size, composition and skills. The specific recommendations in this area include aiming for a board size appropriate for the scale of an organization’s operations. The Corporations Act 2001 requires that there needs to be at least three directors in a publicly listed company, two of whom should be based in Australia (Australian Institute of Company Directors, 2013; Redchip Lawyers, 2012).

Measures

Outcomes.

This study used the objective performance measures of ‘operating revenue’ (Sahu and Manna, 2013) and ‘net operating profit less adjusted taxes’ (NOPLAT). Operating revenue is a measure of income generated through the firm’s operations (effectiveness of operations), whereas NOPLAT is a measure of operating profit before interest but adjusted for taxes (efficiency of operations). The two measures can provide insight into how effectively and efficiently boards (indirectly) and management (directly) runs an organization’s operations.

Predictor.

Consistent with previous research, ‘organization size’ was operationalized as the total number of employees (Kim et al., 2012).

Mediator.

‘Board size’ was measured by the total of number of directors on the board of each organization (Aggarwal et al., 2012; Linck et al., 2008).

Moderator.

The nine SIC industry groups of the sample organizations were collapsed into manufacturing and services. ‘Agriculture, Forestry and Fishing’, ‘Mining’, ‘Construction’ and ‘Manufacturing’ made up the manufacturing category, while ‘Transportation, Communications, Electric, Gas and Sanitary Services’, ‘Wholesale Trade’, ‘Retail Trade’, ‘Finance, Insurance and Real Estate’ and ‘Services’ made up the services category (Ali et al., 2011). A dummy variable called ‘Industry type’ was created with ‘1’ representing manufacturing and ‘0’ representing services.

Controls.

The analyses controlled for the effects of ‘organization age’ and ‘organization type’ (holding/subsidiary vs stand-alone). Organization age may have an impact on performance as older firms tend to have more connections and exert some level of control over environmental factors. Complexity can also increase with organization age. Organization age was operationalized as the number of years since the organization was founded (Boone et al., 2007). Holding companies or subsidiaries, compared to stand-alone organizations, may benefit from combined financial resources. Holding companies may also have a different board structure because of the coordination needed across subsidiaries. As such, organization type was created with ‘0’ representing ‘Holding or subsidiary’ and ‘1’ representing ‘Stand-alone’.

Data analysis

This paper developed hypotheses from simple effects to interaction effects and then from simple indirect effects to conditional indirect effects. However, a significant simple effect is not a requirement for an interaction effect. Similarly, a direct effect between two variables is not a prerequisite for an indirect effect via a third variable, and a simple indirect effect is not a requirement for a conditional indirect effect (Parker et al., 2011). Hierarchical regression was used to test H1 to H4. The Process macro was used to test H5 and H6. This program is based on ordinary least squares regression and uses the bootstrap method for inference (Hayes, 2013). This study used 5,000 bootstrapped samples to estimate indirect effects, leading to confidence intervals for strong inferences (Preacher and Hayes, 2004, 2008).

Results

Table I presents the means, standard deviations and correlation coefficients for all variables. Multicollinearity does not seem to be an issue because of low to moderate correlations between the controls, predictor and moderator variables.

H1 proposed that organization size would be positively related to board size. To test H1, board size was regressed on organization size and control variables (see Model 1 columns in Table II). The results indicate that organization size had a significant positive effect on board size (β = 0.45, p < 0.001). These results were consistent with H1, leading to full support for this hypothesis. H2 proposed that industry would moderate the positive relationship between organization size and board size such that the positive relationship is stronger in the manufacturing industry than in the services industry. To test H2, the interaction term of organization size × industry was entered in Model 2 (see Model 2 columns in Table II). The interaction term had a significant effect on board size (β = 0.19, p < 0.01).

The interaction term for the manufacturing and services industries was further probed (Aiken and West, 1991). Figure 2 illustrates the relationships between organization size and board size for the two industries. The positive relationship was stronger for manufacturing organizations (b = 0.00012, p < 0.001) than for services organizations (b = 0.00006, p < 0.001). The direction (positive) and strength (stronger for manufacturing than services) were consistent with H2; thus, full support was found for H2.

H3 proposed that board size would be positively associated with organizational performance. H4 predicted that this positive relationship would be stronger in the manufacturing organizations that in the services organizations. To test H3, operating revenue and NOPLAT were separately regressed on board size and the control variables (see Model 1 columns in Table III). The results indicate that board size had a significant positive effect on operating revenue (β = 0.19, p < 0.001) and NOPLAT (β = 0.18, p < 0.01); therefore, full support was found for H3. To test H4, the interaction term of board size × industry was entered in the second step of hierarchical regression. The results shown under Model 2 columns in Table III indicate that the interaction term had a significant effect on both operating revenue (β = 0.41, p < 0.001) and NOPLAT (β = 0.98, p < 0.001). The significant interaction terms were further investigated for manufacturing and services industries. Figure 3 illustrates the board size–operating revenue relationship for the two industries. The relationship between board size and operating revenue was positive for both industries and was stronger for the manufacturing industry (b = 1206.28, p < 0.001) compared to the services industry (b = 253.35, ns), as predicted by H4. However, the relationship was significant only for the manufacturing industry. Figure 4 demonstrates the board size–NOPLAT relationship for the manufacturing and services industries. The relationship between board size and NOPLAT was positive and significant for the manufacturing industry (b = 331.14, p < 0.001) but negative and non-significant for the services industry (b = −44.96, ns). The positive relationship in the manufacturing industry was consistent with H4. Thus, partial support was found for H4 for both performance measures.

H5 proposed that board size mediates the relationship between organization size and organizational performance. A simple mediation model in the Process macro was used to test the mediation effects for each of the performance measures (Hayes, 2013). The analyses controlled for the effects of organization age, organization type and industry. The results are presented in Table IV with detailed total effects, direct effects and indirect effects for each of the two performance measures. The results indicate that organization size had a significant positive indirect effect on both operating revenue via board size (B = 0.0447, LLCI 0.016, ULCI 0.088) and NOPLAT via board size (B = 0.0070, LLCI 0.001, ULCI 0.017). The 95 per cent bootstrap confidence intervals based on 5,000 samples did not include zero. The Sobel test (normal theory test) confirms that organization size had a significant indirect effect on operating revenue via board size (Effect 0.0447, p 0.0002) and NOPLAT via board size (Effect 0.0070, p 0.0092); thus, full support was found for H5.

H6 predicted that organization size has a conditional indirect positive effect on organizational performance via board size such that the positive indirect effect is stronger for the manufacturing industry than for the services industry. To test H6, a conditional indirect effect model in the Process macro was used (Hayes, 2013). Panel A of Table V shows that the board size × industry interaction term is significant for both operating revenue (B = 952.94, p < 0.001) and NOPLAT (B = 376.10, p < 0.001), suggesting that the indirect effects are conditional for both performance measures[1]. Panel B of Table V presents separate effects for the manufacturing and services industries. The results indicate that organization size had a significant positive indirect effect on operating revenue for the manufacturing industry (B = 0.149, LLCI 0.009, ULCI 0.364), but the positive effect was non-significant for the services industry (B = 0.015, LLCI −0.020, ULCI 0.064). Similarly, organization size had a significant positive indirect effect on NOPLAT for the manufacturing industry (Effect 0.041, LLCI 0.003, ULCI 0.099) but a non-significant negative effect for the services industry (Effect −0.003, LLCI −0.015, ULCI 0.002). The significant positive indirect effects on operating revenue and NOPLAT for the manufacturing industry were consistent with H6 leading to partial support.

Discussion

The main objectives of this study were to investigate whether:

  • organization size has a positive effect on board size which may vary across industries;

  • board size has a positive effect on organizational performance which may vary across industries; and

  • organization size has a positive indirect effect (via board size) on organizational performance which may vary across industries.

The results of this study provide evidence for these relationships.

Organization size and board size relationship

The findings of this study indicate that organization size is positively associated with board size. This result is consistent with past research which also found a positive relationship between organization size and board size (Boone et al., 2007; Coles et al., 2008; Lehn et al., 2009; Linck et al., 2008). This study’s findings indicate that the positive relationship between organization size and board size is stronger in manufacturing organizations than in services organizations. The results provide pioneering evidence that for a given organization size manufacturing organizations have larger boards than services organizations.

Board size and organizational performance relationship

This study provides evidence of a positive relationship between board size and organizational performance. The results are consistent with a body of literature (Adams and Mehran, 2012; Aggarwal et al., 2012; Dalton et al., 1999; Dwivedi and Jain, 2005; Ilhan Nas et al., 2016; Kalsie and Shrivastav, 2016; Kathuria and Dash, 1999; Kiel and Nicholson, 2003; Kim et al., 2012; Larmou and Vafeas, 2010; Mishra and Mohanty, 2014; Saibaba and Ansari, 2012; Sahu and Manna, 2013; Tanna et al., 2011; Upadhyay 2008). However, the findings are in contrast with literature that found a negative relationship between board size and organizational performance (Adams et al., 2010; Afrifa and Tauringana, 2015; Bai, 2013; Bennedsen et al., 2008; Cheng, 2008; Cheng et al., 2008; Chintrakarn et al., 2017; Conyon and Peck, 1998; Garg, 2007; Guest, 2009; Kumar and Singh, 2013; Liang et al., 2013; Mak and Kusnadi, 2005; McIntyre et al., 2007; Mohapatra, 2017; Morekwa Nyamongo and Temesgen, 2013; Nguyen et al., 2016; Pathan and Faff, 2013; Pathan et al., 2007; Shakir, 2008; Ujunwa, 2012). The current study’s findings strengthen the argument that large boards are effective and board downsizing does not add value (Uchida, 2011) and weaken the argument that the costs of a large board (e.g. free-riding and coordination) outweigh its benefits (e.g. a range of perspectives and knowledge) (Guest, 2009). The findings also weaken the arguments that large boards are risk-averse and, thus, show inferior performance or a lack of variance in performance (Nakano and Nguyen, 2012). The results also indicate significant moderating effects of industry on the board size–organizational performance relationship. Figures 3 and 4 illustrate a significant positive relationship between board size and organizational performance in manufacturing organizations. The similar effects across the two performance measures enhance the validity of these pioneering findings.

Indirect effect and conditional indirect effect

The findings also provide pioneering evidence for an indirect effect of organization size on operating revenue and NOPLAT via board size. These findings support the arguments that large organizations show superior performance and part of this effect occurs through a larger board. Moreover, this study also provides pioneering evidence that the indirect effect of organization size on organizational performance via board size is conditional on industry. The positive indirect effects were significant in the manufacturing industry but not in the services industry.

Theoretical and research implications

This study has several theoretical and research implications. The significant organization size–board size relationship provides indirect support for the theoretical arguments presented in this paper that large organizations have large boards for complex advising and monitoring functions (Boone et al., 2007; Linck et al., 2008). The findings pertaining to a positive relationship between board size and organizational performance provide some support for the resource dependency theory (Pfeffer, 1972) and agency theory (Eisenhard, 1989). The current study focused on the two ends of this continuum: board size and organizational performance. Direct support for the resource dependency theory would include the intervening processes such as directors’ social and business networks and how they help secure important resources or manage the environment. Similarly, direct support for agency theory would require demonstrating how a large board can be effective in monitoring a firm’s management. Similarly, the results pertaining to the moderating effects of industry support contingency theory (Galbraith, 1973). The arguments put forward for the moderating effects of industry on the organization size–board size and board size–performance relationships in this study can help refine theories. These refined theories may predict differences across industries. A qualitative study focusing on how boards function differently in the two industries could provide valuable insights into the theoretical framework. Moreover, the conditional indirect effects model presented in this research may generate a stream of research investigating similar conditional indirect effects models between organization size and performance incorporating additional mediators (e.g. top management team) and moderators (e.g. organization life cycle stage, interlocking directorates, corporate regulations). The research in this direction can also benefit from including second stage parallel mediators between board size and performance such as advising and monitoring.

Practical implications

The findings of this study have a number of practical and policy implications. The results suggest that nomination committees should consider organization size to determine the optimal size of its board. While a growing organization would need a larger board, the findings of this study indicate that these requirements vary across industries. Thus, this study provides some empirical evidence for the ASX Corporate Governance Council’s second principle regarding the size of the board appropriate for its effective functioning (Australian Securities Exchange, 2012). Not all best governance practices are supported by empirical evidence (Dalton and Dalton, 2005) and organizations that follow more ASX Corporate Governance Council’s principles outperform their counterparts (Brown and Gørgens, 2009). Moreover, the findings suggest that large boards outperform small boards and that effect is conditional on industry. As part of the process where a board maintains a skills matrix of its members and aims to improve it, the board should consider how each new member adds value to the current skills set and their relevance to the industry (Corporate Governance Council, 2014). Similarly, in following the recommendation regarding periodically evaluating the performance of a board (Corporate Governance Council, 2014), directors should take into account the effect of board size and industry. Considering these will provide a more precise assessment of the board’s performance in a particular industry. Focusing on performance is one of the features of good corporate governance (Department of Social Services, 2010). A policy implication of these results is that Australia should continue to introduce industry-specific regulations (Lawrence and Stapledon, 1999) and make the ASX Corporate Governance Council’s principles more industry-based (Australian Securities Exchange, 2012). The conditional indirect effects model of this research provides a more comprehensive analysis of the determinants and effects of board size, enabling directors to see the bigger picture and the connections between various elements.

Limitations

This study has certain limitations. First, it focuses on the two ends (organization size and organizational performance) of a continuum, with one mediating variable (board size), but does not directly study the other mediating processes. These processes include a greater need for resources and improved ability to manage the environment. The large-scale quantitative design of this study did not allow the inclusion of these mediating processes. While the results provide some insights into the associations between the studied variables, causal relationships cannot be established even though the reverse causal relationship can be overruled. Past research found that higher (lower) performance does not lead to a larger (smaller) board (Belkhir, 2009). Second, the highly regulated Australian business environment along with a smaller mean size of the Australian boards (Kiel and Nicholson, 2003; Setia-Atmaja, 2008) need to be taken into account before the results are generalized to other economies.

Figures

Research framework

Figure 1

Research framework

Organization size and board size relationship

Figure 2

Organization size and board size relationship

Moderating effect of industry on the board size–operating revenue relationship

Figure 3

Moderating effect of industry on the board size–operating revenue relationship

Moderating effect of industry on the board size-net operating profit less adjusted taxes relationship

Figure 4

Moderating effect of industry on the board size-net operating profit less adjusted taxes relationship

Means, standard deviations and correlationsa

Variable Mean SD 1 2 3 4 5 6
Controls
1. Organization age 36.95 33.75
2. Organization type (0 = holding/subsidiary; 1 = stand-alone) 0.10 0.30 −0.07
Predictor
3. Organization size 4,719.37 14,694.76 0.27** −0.09
Mediator
4. Board size 6.44 2.40 0.28** −0.11 0.50**
Moderator
5. Industry (0 = services; 1 = manufacturing) 0.42 0.50 0.09 0.10 −0.05 −0.10
Outcomes
6. Operating revenue (millions) 2,360.46 7,791.66 0.32** −0.09 0.75** 0.53** −0.02
7. NOPLAT (millions) 199.84 1,295.20 0.18** −0.05 0.36** 0.31** 0.07 0.74**
Notes:
a

two-tailed;

*p < 0.05;

**p < 0.01

Regression results for organization size and board size (H1 and H2)

Variable Board size
Model 1 Model 2
β p β p
Controls
Organization age 0.168 0.001 0.164 0.002
Organization type −0.055 0.282 −0.044 0.379
Predictor
Organization size 0.449 0.000 0.366 0.000
Control/Moderator
Industry −0.082 0.105 −0.138 0.010
Interaction term
Organization size × Industry 0.189 0.001
Model Summary
R2 0.29 0.31
F 28.603*** 25.787***
ΔR2 0.29 0.02
ΔF 28.603*** 10.631**
Notes:

n = 288; Standardized regression coefficients are reported;

*p < 0.05;

**p < 0.01.

***p < 0.001

Regression results for board size and organizational performance (H3 and H4)

Variable Operating revenue NOPLAT
Model 1 Model 2 Model 1 Model 2
β p β p β p β p
Controls
Organization size 0.630 0.000 0.629 0.000 0.256 0.000 0.254 0.000
Organization age 0.092 0.022 0.105 0.008 0.054 0.350 0.083 0.125
Organization type −0.005 0.897 −0.002 0.967 −0.012 0.833 −0.004 0.944
Predictor
Board size 0.188 0.000 0.078 0.137 0.178 0.006 −0.083 0.249
Control/Moderator
Industry 0.024 0.633 −0.361 0.001 0.099 0.076 −0.817 0.000
Interaction term
Board size × Industry 0.413 0.000 0.981 0.000
Model Summary
R2 0.60 0.62 0.16 0.28
F 84.620*** 76.313*** 11.097*** 17.773***
ΔR2 0.60 0.02 0.16 0.12
ΔF 84.620*** 14.508*** 11.097*** 42.908***
Notes:

n = 288; Standardized regression coefficients are reported;

*p < 0.05;

**p < 0.01;

***p < 0.001

Results for indirect effect (H5)

Variable Operating revenue NOPLAT
B t p B t p
Total effect
Total effect of organization size on outcome (c) 0.379 17.670 0.000 0.030 5.848 0.000
controlling for organization age, organization type
and industry type
Direct effect
Direct effect of organization size on outcome (c′) 0.334 14.284 0.000 0.023 4.019 0.000
controlling for organization age, organization type
and industry type
Indirect effect
controlling for organization age, organization type
and industry type
Indirect effect of organization size on outcome via
Effect LLCI ULCI Effect LLCI ULCI
Board size 0.0447 0.0157 0.0881 0.0070 0.0008 0.0174
Effect Z p Effect Z p
Normal theory test for indirect effect 0.0447 3.7594 0.0002 0.0070 2.6047 0.0092
Notes:

n = 288; Unstandardized regression coefficients are reported; Bootstrap sample size = 5,000 bias corrected; LL = lower limit, CI = confidence interval, UL = upper limit, Level of confidence 95%

Results for conditional indirect effect (H6)

Variable Operating revenue NOPLAT
B t p B t p
Panel A: Interaction effect
Constant −1,816.45 −1.61 0.110 189.45 0.73 0.466
Organization age 24.19 2.67 0.008 3.20 1.54 0.125
Organization type −41.04 −0.04 0.967 −15.78 −0.07 0.944
Organization size 0.33 14.60 0.000 0.02 4.27 0.000
Board size 253.35 1.49 0.137 −44.96 −1.15 0.249
Industry −5,689.77 −3.35 0.001 −2,318.19 −5.48 0.000
Board size × Industry 952.938 3.81 0.000 376.10 6.55 0.000
Panel B: Conditional indirect effect via board size
Effect LLCI ULCI Effect LLCI ULCI
Manufacturing 0.149 0.009 0.364 0.041 0.003 0.099
Services 0.015 −0.020 0.064 −0.003 −0.015 0.002
Notes:

n = 288; Unstandardized regression coefficients are reported; Bootstrap sample size = 5,000 bias corrected; LL = lower limit, CI = confidence interval, UL = upper limit, Level of confidence 95%

Note

1.

Incorrect inferences may be drawn because of possible multicollinearity among predictor, moderator and control variables (Becker 2005). The analyses reported in Tables II-V were repeated without control variables. In the absence of control variables, the results were similar, with all the significant terms in the reported results remained significant

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Corresponding author

Muhammad Ali can be contacted at: m3.ali@qut.edu.au

About the author

Muhammad Ali is Senior Lecturer at the QUT Business School, Queensland University of Technology, Brisbane, Australia.