ojbm Open Journal of Business and Management 2329-3292 2329-3284 Scientific Research Publishing 10.4236/ojbm.2026.141019 ojbm-148675 Article Business Economics Women on Board-Company Performance and Diversity 0000-0001-5075-874X Scholer Finn 1 Department of Management, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark

The author declares no conflicts of interest regarding the publication of this paper.

 05 12 2025 12 2025 14 01 314 332 03 11 2025 06 01 2026 09 01 2026 © 2026 by the authors and Scientific Research Publishing Inc. 2026 This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/ ). https://doi.org/10.4236/ojbm.2026.141019

We investigate two-tier supervisory board characteristics and relations versus financial performance by using a unique sample of Danish non-financial listed companies. We investigate a positive association between corporate performance measures (ROA and Tobin’s Q) and board diversity using some structural board characteristics. Our main analyses are based on fixed effects regression analyses to evaluate the relationships between performance and board size, structure, independence, and other board characteristics. Interestingly, we find that although gender is not the only relevant diversity dimension, it is the one with singular largest impact on performance. In addition, the results show that the two-tier based supervisory board composition and diversity information is useful to improve our understanding of what drives performance, and it contributes to a better understanding of the circumstantial relationship between board structures and financial performance. The two-tier setting extends the traditional unitary board characteristics issues to relationships between three parties, namely the supervisory board, the management board, and the stockholders, which extends our knowledge on the traditional monitoring and controlling tasks performed by the supervisory board, and thus on the supervisory board’s contribution to corporate governance and performance.

 Board Independence Composition Characteristics Diversity Women 1. Introduction

The board of directors of a corporation is meant to perform the critical functions of monitoring and advising top management. It is a widespread belief that a greater level of board independence allows for more effective monitoring and that it improves company performance, and thus board independence has been a key issue in unitary Anglo-American governance settings where research has centred on both the number and function of Non-Executive Directors (NEDs), and whether it should be accepted if same person is simultaneously CEO and chairman of the board (the CEO-duality issue). Despite a broad debate in the literature as to whether board independence adds (any) value to companies, a definitive conclusion has not been made thus far, and there are still many practical ways to organise the governance structure.

The Danish two-tier board system shares important similarities with unitary board systems since a supervisory function, and a managerial function are recognised in both systems although the formal Danish governance regulation prescribes clear separation of management board and supervisory board controlling roles and duties. Consequently, the governance structure includes more interrelated parties which provides a unique opportunity for an empirical examination if certain board member characteristics such as age, experience, skills, and interrelations potentially correlate with superior performance.

According to [31] it is important to describe and understand what is meant by diversity on board and its implications for decision-making, and thus it becomes relevant to investigate the appointment of directors from different professional backgrounds, levels of independence, age, gender and ethnicity. And they find that introducing greater diversity into the board better balances the skills and attributes that are needed for quality decision-making. They also discuss the influence of the characteristics’ influence on board performance and the ability of individual directors to make effective contribution as board members, and their conclusions highlight the various mythologies associated with the value of board diversity.

[18] present a set of economic arguments in favour of a stronger presence of women on boards based on the idea that companies that select management without any prejudice can attract and retain talent from a wider pool of human capital than those companies that fail to select the most able candidates due to some sort of discrimination. But are there special female characteristics that should be part of a well-functioning board?

[31] suggest that although diversity is not defined solely in terms of gender, the focus in the literature has been on this issue. Despite the widespread conventional wisdom that female board members positively impact company performance, a thorough examination of the research to date reveals no consensus that female board members have either a positive or a negative effect on form performance.

According to [17] decision making improves with diversity since this leads to higher creativity, and thus a broader set of alternatives is considered, and consequently board decisions are better. [11] find that the culture of the board is what can affect how well diverse boards perform their duties and oversee their companies. As [17] remarques, much of the work on boardroom diversity has focused on gender diversity, and yet it is not just gender that brings diversity into the boardroom, since benefits from diversity in effective corporate boards can come from ethnic, nationality, functional or other types of diversity as well. Following this thinking we present evidence that board members’ age, independence, and experience are at least as important as gender when explaining company performance by board member characteristics.

The remainder of the paper is structured in the following way. In Section 2, we examine prior literature. In Section 3, we describe the methodology, the data collection, and the model development. In Section 4, we provide the analyses and results, and finally we conclude the paper in Section 5.

 2. Literature Review 2.1. Diversity and Board Composition

Among others, [8] and [5] have conducted comprehensive studies on the diverse roles and responsibilities of boards, highlighting numerous paradoxes inherent in governance structures and delineating the prerequisites for an effective board. As a female professional board member said in an interview regarding a potential new board position: “If you think my only value is the fact that I’m female, I can’t add value to your board”. Similarly on tokenism: “I think that there’s so much conversion now about adding more females to boards” and everyone feels like “okay, if we have ten board members, we should recruit three women”—but “I think we need to make sure they have the right skills” (all quotations from [11]).

In a comprehensive literature review of more than 400 published articles, working papers, and book chapters, [29] identified twenty theory-based studies about women on boards that apply a variety of frameworks at the individual, board, company and environmental level. Most of the academic literature on women in top management take the firm level perspective and use either the resource dependence or the agency theoretical framework.

The resource dependency theory, first put forward by Pfeffer and Salancik in 1978 views companies as operating in an open system and needing to exchange and acquire certain resources in order to service, making the companies dependent on external units in their environment, and it advocates that boards are important actors in delivering the resources that are crucial for a firm to succeed, since companies seek linkages with the most beneficial resources and also structure membership on the corporate board on this basis. [24] suggest that directors bring four benefits to organisations: advice and council, channels of information, access to resources, and legitimacy. [27] suggests that board diversity might facilitate access to resources, critical to the company, which indicates that diversity, relating to age, gender and nationality can have a positive impact on performance, since board diversity brings fresh perspectives to the board and thus contribute to more balanced decision-making. [9] find that management tasks could be positively influenced by group diversity through increased overall problem-solving capacity and that a more diverse board could for example benefit from a greater understanding of its customers or other stakeholders, since it is likely that the best performing management teams consist of members that represent a variety of characteristics in terms of experience, independence, working background, age, ethnicity, age and gender. [15] find that an unprejudiced selection of management enables companies to attract and retain talent from a broader pool of human capital and in that perspective, it would seem logic to presume that roughly half the number of board members were female. However, although an underrepresentation of women in top management could be regarded as discrimination, which is both unethical and suboptimal, it is unclear if, and how, gender diversity influences corporate performance.

The agency theory which describes the relationship between one party, the principal (e.g. shareholder), that delegates work to another, the agent (e.g. directors and managers) was made generally accepted by [20] who conceptualised that corporations are structured to minimise the agency cost, or the cost of getting agents to follow the directions and interests of the principals. The importance for board independence is rooted in the separation of ownership and control as introduced by [14] since this minimizes the agency problem, which according to [20] arises due to dispersed ownership that lacks strength compared to the management of the company. Despite this foundational work, formal economic theory on boards remains limited, but the intuitive understanding of the challenges facing boards has led to a proliferation of empirical studies filling this theoretical gap. Agency theory posits that individuals are self-interested and opportunistic rather than altruistic, and thus a board comprising several outside non-executive independent directors can independently monitor and advise managers and thereby promote shareholders’ interests ([6]) although independent directors often have limited knowledge about the industry and business they oversee, which can lead to a dependency on the CEO, who typically possesses comprehensive know-ledge. Ironically, the more independent directors there are on a board, the more reliant board becomes on management for information ([8]). Thus, the practical use of solely independent board members leads to a paradox where understanding the business becomes impossible for an independent person, contradicting the goal of having competent board members. The agency theory also supports the separation of the CEO and the board chairman roles to ensure that the chairman is distanced from day-to-day company operations, which probably enhance the board’s oversight functions, but as [4] suggest, it might cause lack of efficient decision making. Consequently, they find that the CEO should have a seat at the board table and participate in most board meetings, but the governance structure would be clearer if the CEO was not a board-member and instead only had a board consulting role. Regarding the relationship between performance and board independence, the findings of the meta-analytic study by [2] on 86 previous studies and the meta-analytic study by [12] on 31 previous studies are generally inconclusive since there was no causal relationship between board independence and company financial performance.

Conventional wisdom suggests that a bigger company would tend to have a larger board of directors as these companies are more complex and require more diverse expertise on board. However, there is consensus that smaller boards operate more efficiently (e.g. [16]), while [19] argues that larger boards could be less efficient and less effective due to coordination problems arising because of its size (see also [22]). Furthermore, evidence related to company value, as measured by Tobin’s Q and several accounting return-measures is provided by [13], and [10], who demonstrate that smaller boards are associated with higher firm value and better profitability, probably because smaller groups are more cohesive and hence can fulfil their monitoring role better.

 2.2. Board Characteristics and Company Performance

[5] and [9] reason, that a more diverse board is also a more independent board, and as [32] and [8] find, independence enables non-executive directors to bring a fresh perspective which is often welcome in more complex and competitive business surroundings. Although there do not seem to be a commonly agreed definition of what board diversity covers, [31] propose an intriguing theoretical definition of board diversity: “the concept of diversity relates to board composition and the varied combination of attributes and characteristics, and expertise contributed by individual board members in relation to board process and decision making”. According to the study by [31], there is no universal consensus on the benefits of greater boardroom diversity and it remains uncertain whether various forms of diversity—such as age, gender, ethnicity, culture, religion, professional background, experience, technical skills, expertise, and independence—have a definitive impact on company performance with respect to firm value, return on assets, and growth.

During the last decades an increased amount of attention and research has been devoted to the gender diversity aspect, and many researchers have explored the relationship between women in management and on boards of directors, and the economic performance of companies. Yet, there is still no clear consensus about whether the impact from women on board makes any difference, neither in general nor in specific circumstances on performance. In Table 1, we present a short overview of a few selected broader and quite recent empirical evidence on women on board versus company performance.

The evidence covers countries from all over the world, and in fact the studies are basically quite identical since 1) the output financial performance measures are most often Tobin’s Q (market value added) and ROA (accounting profitability performance); 2) the primary gender diversity measure is “proportion of women

Table 1. Some empirical studies on Women on board versus financial performance from all over the World.

Country Author and year Sample Financial performance measure Gender diversity measures Methodology Results (positive or negative effect)
USA [ 1 ] 1939 firms 1996-2003 Tobins Q; ROA Proportion of women on board OLS-regression- IV-test. Negative effect
47 countries [ 28 ] 3876 listed firms 2010 Tobins Q (market-based indicator); ROA (accounting- based indicator) Proportion of women on board Gen. Method of moments regression Postive effect
16 European countries [ 18 ] FT500 largest companiew 2010-2012. 922 obs. Tobins Q; ROA Proportion of women on board Simultan Equation Model No significant effect on Tobin’s Q. Positive effect on ROA
Visegrad countries [ 7 ] Listed 451 companies 2019-2021 ROE; ROA; Market-to-Book Value Proportion of women on board. Diversity very low. OLS regression No significant effect
Africa (9 countries) [ 23 ] 1166 obs 2007-2019 ROA Proportion of women on board. Diversity very low. OLS regression (and GMM) Negative effect
India [ 21 ] NIFTY50 listed 2014-2019.Transport and Logistics Industry. ROA Proportion of women on board. Diversity very low. OLS regression No significant effect
Australia [ 3 ] 2483 firms 2005-2016 ROA; Tobin’s Q; ROE; CFO/TA Proportion of women on board. Diversity very low. OLS regression No significant effect
Asia [ 30 ] 444 obs. 2017-2020 Net profit (Accounting based); ROA Proportion of women on board. Diversity very low. OLS regression Postive effect, but no significant correlation to ROA

on board”; and 3) ordinary least squares regression is the methodological starting point in an attempt to document that gender has an effect on performance. To figure out if gender affects performance researchers often use multiple linear regression models including many variables attempting to control for ideally all non-gender effects and thus leaving the rest as the answer since the causality effect is or should be isolated. However, as presented in alarmingly many of the studies there are no (marginal) positive effects measurable, and sometimes even a negative effect is present contradicting the hypothesis. Further, researchers have probably not been able to both convincingly handle multicollinearity issues, omitted variables, eventual fixed effects and causality versus correlation, and thus more sophisticated statistical analyses trying to find the hoped results seems to be used attempting to reach the expected results. One quite obvious reason for this lack of clear evidence could be that it is not possible to separate a gender effect since all people, men and women, apart from being characterised by gender they are also characterised by all other forms for diversity, like age, ethnicity, culture, religion, etc. and in this mix, gender as such might not be the magic “thing” that has the key-effect. For human beings it should be expected, inside and outside of board rooms, that everybody uses masculine and feminine characteristics, and acts as appropriate when seeking goals without taking into consideration if a specific action is more or less woman-like or man-like although women as such on average are more feminine than men, and vice versa.

Four Harvard researchers ([11]) interviewed 19 board directors, 15 women and four men who held seats on 47 corporate boards in the U.S. across a variety of industries, to learn whether and how corporate boards were benefiting from diversity, and this research illustrates the challenge since they found that diversity doesn’t guarantee a better-performing board and company; rather, the culture of the board is what can affect how well diverse boards perform from which the results can be seen in the previous studies we have highlighted in Table 1. [5] who questions whether generating wealth for shareholders is the sole or even prime role of boards of directors, common sense tells us that there is or must be a relationship between corporate governance and firm performance. Consequently, we have chosen to elaborate more directly on women on board by use of another approach trying to answer the following seemingly important and relevant questions: What is really needed? How should we compose a board? Do women on board ensure better firm performance? Should we have women on board? What board member characteristics should one look for when hiring new board members?

 3. Methodology 3.1. Dataset and Variables

The unique sample consists of different performance measures retrieved from the annual accounts for all listed Danish companies at the NASDAQ OMX Stock exchange in Copenhagen (CSE) between 2015 and 2020 having non-negative equity and several quantitative corporate governance measures for the very same companies. For instance, on 31 December 2016, the Danish stock market consisted of a total of 154 Danish listed companies, i.e. foreign companies were removed since these companies are likely to have other corporate governance structures1. Of these 154 companies, eight companies reported negative equity, which makes it difficult to calculate and interpret key financial profitability measures for which reason they were excluded. Further, all finance services companies, banks, and insurance companies, a total of 26 companies, were excluded since these companies are subject to additional regulation. This leaves us with a dataset consisting of 120 observations from 2016, which is also the total population this year. Same for all other years, which leaves us with a total of 509 pooled firm-year observations. The corporate governance data derives from information in the companies’ annual reports, and where needed, some additional information from the company websites published simultaneously with the annual report and it consists of a battery of observed and measured attributes related to the boards, i.e. structure, composition and other characteristics. The measured variables are heavily inspired by the governance literature, for which reason we expect them to adequately represent the underlying governance structures.

Different metrics concerning accounting and market performance measures, like the measuring ROA or Tobin’s Q can be used, and just as most previous studies, we choose ROA as indicator of corporate financial performance, calculated as the relation between accounting earnings and average total assets. The measure is preferable, since it is constructed by use of accounting book values that can be controlled by management and board through expense adjustments, investments, and financing decisions. Further we choose Tobin’s Q as indicator of the value adding competencies, since it measures companies’ enterprise value divided by companies’ total assets and thus reflects companies’ market capitalisation in relation to their accounting value.

We expect company value and performance to be a function of diversity as such and thus a function of several measures of which some are interrelated, and some are not. However, we might have omitted some variables since it is not unambiguous what diversity as such is in relation to performance apart from contribution to a well-functioning board that apparently takes the optimal and right decisions. The marginal contribution analysis provides helpful information when suggesting how one’s supervisory board should be composed. It is deliberately a partial analysis of all these characteristics, since individual persons and thus potential board member simultaneously can be attributed all these characteristics.

 3.2. Descriptive Statistics

In general, to ensure (almost) normality by avoiding too much skewness and kurtosis, the data are transformed using square roots or logarithms where appropriate. Further the data has been undertaken parallel shift to ensure non-negativity, which makes interpretation of results easier and more straight forward.

Since we analyse the variables’ relations to ROA and Tobin’s Q, it is important that the measures fluctuate across time and across companies. In Table 2, we present descriptive statistics for our dependent variables, ROA and Tobin’s Q, and it seems that the results reflect enough variation in the performance suggesting that an analysis focusing on what might contribute to the variation in the panel data is relevant.

Table 2. Summary statistics and correlations.

Variables Mean/Average Standard deviation 10 percentile Median 90 percentile
ROA 0.0388 0.1952 −0.0821 0.0481 0.1750
Tobin’s Q 2.4717 5.3074 0.7810 1.2245 4.6430
Women Share 0.1517 0.1600 0.0000 0.1667 0.3333
SB Independence 0.7430 0.2576 0.4000 0.7500 1.0000
SB Average Age (log) 4.0479 0.0855 3.9551 4.0604 4.1384
SB Average Experience (log) 1.7855 0.5413 1.0986 1.7918 2.4849
Board Expertise (log) 1.8249 0.6111 1.0296 1.8246 2.5096
SB Size (log) 1.5914 0.2992 1.0986 1.6095 1.9459
Total Assets (log) 14.1471 2.1985 11.4663 14.0223 16.8755
CEO Employed (log) 1.5858 0.9419 0.0000 1.6094 2.7726
Man. Board Connection 0.5549 0.3611 0.0000 0.6000 1.0000
Employee Representation Share 0.2220 0.2641 0.0000 0.0000 0.3750
Variables ROA Tobin’s Q Women Share SB Indepen - dence SB Average Age (log) SB Average Experie - nce Board Expertise (log) SB Size (log) Total Assets (log) CEO Employed (log) Man. Board Connec - tion
Tobin’s Q −0.1243***
Women Share 0.0929** 0.1047**
SB Independence −0.1442*** 0.1006** 0.2786***
SB Average Age (log) 0.0636 0.0232 −0.0307 −0.0748*
SB Average Experience (log) 0.1399*** −0.0333 −0.0519 −0.3301*** 0.5176***
Board Expertise (log) −0.0454 −0.1602*** −0.3059*** −0.1014** 0.0342 0.1146***
SB Size (log) 0.1902*** 0.0571 0.2123*** 0.0461 0.0652 0.0414 −0.3155***
Total Assets (log) 0.3954*** −0.4960 0.3961*** 0.0294 0.1819*** 0.0489 −0.1909*** 0.5947***
CEO Employed (log) 0.0990** −0.0329 0.0257 −0.1401*** 0.2349*** 0.3259*** 0.1331*** 0.0352 0.1612***
Man.Board Connection −0.0363 0.0320 −0.1028** −0.1233*** −0.0713 0.6450 −0.0258 −0.0656 −0.2257*** −0.7482***
Employee Representation Share 0.2551*** 0.0433 0.2484*** 0.0077 0.0724 −0.0098 −0.3432*** 0.2401*** 0.4036*** −0.0098 −0.0609

The sample consists of an unbalanced panel of 509 Danish firm-year observations from 154 firms for the period 2015 and 2020 having non-negative equity. Tobin’s Q is the ratio of the firms market enterprise value to its book value of assets. ROA is net income before extraordinary items divided by book assets. Women share is share of supervisory board being women. SB independence is share of supervisory board considered independent— as disclosed in annual report. SB Average Age is the supervisory board members’ average age, log. SB Average Experience is the average number of years members of supervisory board have been present in this board, log. Board Expertise is average number of other supervisory board positions, i.e. expertise gained from holding supervisory board positions in other firms. SB Size is supervisory board size, log. Size is company size measured by the value of total assets in balance sheet, log. CEO Employed is number of years the CEO has been employed as CEO in the company’s management board. Man.Board Connection is the share of the supervisory board appointed before the CEO was appointed by the supervisory board. Employee Representaton Share is the number of employee representatives divided by supervisory board size (excl. employee representatives). Asterisks indicate correlation significance at 0.01(***), 0.05(**), and 0.10(*) levels.

While most previous studies tend to favour the traditional multiple OLS regression method (with many variables and potentially multicollinearity problems) for examining hypothesised relationships between specific board characteristics and various performance variables we conduct similar multiples studies as well for comparability although our main focus is on the different board characteristics’ marginal contribution, where relevant.

For the proportion of women on board, the mean of 0.1517 reflects that one in six and a half board members is female and the 90-percentile of 0.3333 reflects that only 10 percent of the companies in the data have a relatively high proportion which in this context is one in three board members is female. For employee board representation an average of 0.2220 covers the fact that more than half of the companies do not have employees on board although the possibility is mandated by Danish Company Act2.

Since several of the size measures appears to follow a log-normal-like right-skewed distribution we use the log function to normalise the data. Thus, the average log-size of board at 1.5914 reflects an average of 4.9 persons on the boards.

The board characteristics show different dimensions—some are expected to show high correlations, for instance between age and experience showing one of the largest correlations (0.5176) which is only surpassed by the correlation (0.5947) between supervisory board size and company size (total assets). Similarly in accordance with [31], one would expect that women share and supervisory board size would correlate, since they see a tendency that women in boards leads to proportional increase in number of members in those boards—but our results show a correlation of 0.2123 which is significant, but it does not seem to back the tendency up in Denmark where size of supervisory board on average actually is just about five.

Further, women share of supervisory board and average age is negatively correlated, although insignificant. There exists correlation between average age and experience and expertise, but not into working in supervisory boards as such. And there seems to be circumstantial correlation between company size measured by total assets and average age based on the indirect assumption that since older companies are bigger and tend to have bigger boards, these boards could also be expected to have relatively more older members that have participated in the companies’ development.

In general and following former literature we would expect positive correlation between the different board characteristics and performance measured by ROA, since more wisdom based on size of supervisory board, higher age, more experience and expertise would probably affect performance positively. Similarly for management experience and ability to navigate businesses (CEO employed), supervisory board members’ independence3 and management board connection, which focus on potential (informal) bindings between supervisory board members and CEO if the CEO is hired by the present board versus performance. The last board characteristic variable measures the share of the board already present when the CEO was appointed, and the crucial issue is whether CEO could have found or at least recommended new board members and thus they may only be formally independent.

Concerning Tobin’s Q, the correlations to the different board characteristics variables seem generally smaller pairwise than the ROA indicating that diversity does not drive the Tobin’s Q performance measure in Denmark.

 4. Analyses

Since the general impact of gender on diversity is not clear in the literature, we follow earlier studies and estimate a simple multiple model of performance including fraction of women on board, independence, size of board and company. We transpose the data for skewness and kurtosis to get close to normal distributed data in Table 3 ensuring the OLS-assumptions are not violated.

In the left half of Table 3 we present our panel data results from ROA as performance measure using both pooled 5-years data normal OLS-regression in the first column, and using company fixed effects regression in second column. Our pooled results indicate negative but unsignificant effect from gender diversity on ROA, but when we address potential admitted variables problems by adding fixed company effects the coefficient becomes statistically significant at one percent level at a minimal loss in R2. Eventual reverse causality is also a concern for the performance regressions since it could be the case that primarily well performing companies find it opportune to include women on supervisory board. To address this concern, we use fraction of women on board as dependant variable, and re-estimate using company fixed effects but the results in the third column show that there is rather a tendency of including women in board when performance is bad. Maybe because of realising that something profound should be done to recreate former profitability?

In the right half of Table 3 corresponding results are presented for Tobin’s Q as performance variable, which shows quite less clear relations and partly opposite estimates compared to the ROA results.

As such, our results are comparable to the studies in the review Table 1 where it is difficult to draw clear and certain conclusions. Our results follow [1] and we have exactly same difficulties in explaining as them.

Using the Table 3 results as starting point, we compare in Table 4 the diversity effect from fraction of women on board with diversity effects from other board characteristics, that could be expected to influence the ROA performance measure.

Table 3. Performance and gender diversity.

Variables Return On Assets Tobin’s Q
Pool OLS Regression Fixed Effects Regression Reverse Causality and Fixed Effects Regression Pool OLS Regression Fixed Effects Regression Reverse Causality and Fixed Effects Regression
Women Share −0.0333 −0.1817 n.a. 0.1088 0.0658 n.a.
t-value ( p -value) −0.68 (0.497) −2.82 (0.005) 2.64 (0.009) 1.17 (0.244)
SB Independence −0.1698 −0.0654 0.2406 0.0571 −0.0276 0.2538
t-value ( p -value) −3.46 (0.001) −1.17 (0.242) 5.69 (0.000) 1.38 (0.168) −0.56 (0.573) 5.98 (0.000)
SB Size (log) −0.0733 0.1728 0.0731 0.1206 −0.3071 0.0711
t-value ( p -value) −1.28 (0.201) 2.09 (0.038) 1.11 (0.266) 2.50 (0.013) −4.23 (0.000) 1.06 (0.292)
Total Assets (log) 0.5780 0.3667 0.1272 −0.1839 0.2163 0.0754
t-value ( p -value) −8.40 (0.000) 1.77 (0.077) 0.78 (0.438) −3.17 (0.002) 1.19 (0.234) 0.46 (0.647)
ROA n.a. n.a. −0.1105 n.a. n.a. n.a.
t-value ( p -value) −2.82 (0.005)
Tobin’s Q n.a. n.a. n.a. n.a. n.a. 0.0542
t-value ( p -value) 1.17 (0.244)
F-value ( p -value) 28.40 (0.000) 6.10 (0.000) 11.07 (0.000) 4.84 (0.000) 4.73 (0.001) 9.26 (0.000)
R 2 0.1839 0.1383 0.1094 0.0370 0.0233 0.1716
Corr( u i , X b ) n.a. −0.0268 0.0883 n.a. −0.4105 0.1524

The sample consists of an unbalanced panel of 509 Danish firm-year observations from 154 firms for the period 2015 and 2020 having non-negative equity. Tobin’s Q is the ratio of the firms market enterprise value to its book value of assets. ROA is net income before extraordinary items divided by book assets. Women share is share of supervisory board being women. SB independence is share of supervisory board cinsidered independent—as disclosed in annual report. SB Size is supervisory board size, log. Size is company size measured by the value of total assets in balance sheet, log. In first and forth column we perform ordinary least squares regression, while we use fixed effects regression in other columns. Exact F-, t-, and p-values are shown.

We start by presenting the basis estimation in the first column, which is similar to the company fixed effects regression in Table 3 for ROA. Compared to the results for the other board characteristics shown in the other columns of Table 4, it seems that gender is the most important diversity dimension since F-value here is 6.10 which expresses that this model is statistically most precise here. However, the part of the variation explained, R2, is larger for average age, CEO-connection, and employee representation, which indicates that these variables’ explanatory value is slightly larger. Finally, when we include all our board characteristics in one model the result is a poorer model, measured by the F-value, while especially the variation explained, R2, is significantly smaller than when the diversity characteristics were included individually. Further, measured by the R2 values, the average age characteristic seems preferable, but the coefficient is not significant, which demonstrates a strong interaction between the different board characteristics.

Table 4. Performance (ROA) and diversity.

Fixed Effects Regression (base) Fixed Effects Regression (SB Average Age) Fixed Effects Regression (SB Average Experience and Expertise) Fixed Effects Regression (CEO Employed and Connection) Fixed Effects Regression (SB Employee Share) Fixed Effects Regression (All Diversity Variables)
Women Share −0.1817 n.a. n.a. n.a. n.a. −0.2156
t-value ( p -value) −2.82 (0.005) −3.26 (0.001)
SB Independence −0.0654 −0.1115 −0.1167 −0.1394 −0.1122 −0.0841
t-value ( p -value) −1.17 (0.242) −2.07 (0.039) −2.17 (0.031) −2.46 (0.014) −2.08 (0.038) −1.45 (0.148)
SB Average Age (log) n.a. −0.0033 n.a. n.a. n.a. 0.0377
t-value ( p -value) −0.06 (0.953) 0.57 (0.570)
SB Average Experience (log) n.a. n.a. −0.0481 n.a. n.a. −0.0568
t-value ( p -value) −0.80 (0.422) −0.77 (0.442)
Board Expertise (log) n.a. n.a. −0.1380 n.a. n.a. −0.1568
t-value ( p -value) −2.18 (0.030) −2.46 (0.014)
SB Size (log) 0.1728 0.1624 0.1398 0.1457 0.1396 0.1119
t-value ( p -value) 2.09 (0.038) 1.93 (0.054) 1.67 (0.096) 1.75 (0.081) 1.50 (0.135) 1.22 (0.225)
Total Assets (log) 0.3667 0.3515 0.3760 0.3334 0.3620 0.3862
t-value ( p -value) 1.77 (0.077) 1.68 (0.093) 1.81 (0.072) 1.61 (0.109) 1.73 (0.085) 1.87 (0.062)
CEO Employed (log) n.a. n.a. n.a. 0.0693 n.a. 0.1436
t-value ( p -value) 0.86 (0.392) 1.70 (0.089)
Man.Board Connection n.a. n.a. n.a. −0.0855 n.a. −0.0406
t-value ( p -value) −1.04 (0.300) −0.47 (0.637)
Employee Representation Share n.a. n.a. n.a. n.a. −0.1073 −0.0949
t-value ( p -value) −0.56 (0.576) −0.50 (0.618)
F-value ( p -value) 6.10 (0.000) 4.02 (0.003) 4.45 (0.001) 4.49 (0.001) 4.10 (0.003) 4.14 (0.000)
R 2 0.1383 0.1795 0.1279 0.1503 0.1470 0.0543
Corr( u i , X b ) −0.0268 0.0047 −0.0901 −0.0438 −0.0038 −0.1544

The sample consists of an unbalanced panel of 509 Danish firm-year observations from 154 firms for the period 2015 and 2020 having non-negative equity. ROA is net income before extraordinary items divided by book assets. Women share is share of supervisory board being women. SB independence is share of supervisory board cinsidered independent—as disclosed in annual report. SB Average Age is the supervisory board members’ average age, log. SB Average Experience is the average number of years members of supervisory board have been present in this board, log. Board Expertise is average number of other supervisory board positions, i.e. expertise gained from holding supervisory board positions in other firms. SB Size is supervisory board size, log. Size is company size measured by the value of total assets in balance sheet, log. CEO Employed is number of years the CEO has been employed as CEO in the company’s management board. Man.Board Connection is the share of the supervisory board appointed before the CEO was appointed by the supervisory board. Employee Representaton Share is the number of employee representatives divided by supervisory board size (excl. employee representatives). In first column repeat results from Table 3 while other columns shows results for different sets of diversity measures—we use fixed effects regression in all columns. Exact F-, t-, and p-values are shown.

Using the Table 3 results again as starting point, we compare in Table 5 the diversity effect from fraction of women on board with diversity effects from other board characteristics, that could be expected to influence the Tobin’s Q performance measure.

Table 5. Performance (Tobin’s Q) and diversity.

Fixed Effects Regression (base) Fixed Effects Regression (SB Average Age) Fixed Effects Regression (SB Average Experience and Expertise) Fixed Effects Regression (CEO Employed and Connection) Fixed Effects Regression (SB Employee Share) Fixed Effects Regression (All Diversity Variables)
Women Share 0.0066 n.a. n.a. n.a. n.a. 0.0493
t-value ( p -value) 1.17 (0.244) 0.85 (0.397)
SB Independence −0.0276 −0.0124 −0.0125 0.0218 −0.0131 0.0057
t-value ( p -value) −0.56 (0.573) −0.27 (0.791) −0.226 (0.791) 0.44 (0.657) −0.28 (0.780) 0.11 (0.910)
SB Average Age (log) n.a. −0.0461 n.a. n.a. n.a. −0.0437
t-value ( p -value) −0.96 (0.338) −0.75 (0.453)
SB Average Experience (log) n.a. n.a. −0.0196 n.a. n.a. −0.0479
t-value ( p -value) −0.38 (0.707) −0.74 (0.461)
Board Expertise (log) n.a. n.a. 0.0943 n.a. n.a. 0.0847
t-value ( p -value) 1.70 (0.089) 1.51 (0.132)
SB Size (log) −0.3071 −0.3104 −0.2969 −0.3099 −0.3603 −0.3678
t-value ( p -value) −4.23 (0.000) −4.25 (0.000) −4.05 (0.000) −4.29 (0.000) −4.45 (0.000) −4.55 (0.000)
Total Assets (log) 0.2163 0.2316 0.2203 0.2373 0.2492 0.2766
t-value ( p -value) 1.19 (0.234) 1.28 (0.203) 1.21 (0.226) 1.32 (0.188) 1.37 (0.171) 1.53 (0.128)
CEO Employed (log) n.a. n.a. n.a. 0.2133 n.a. 0.2059
t-value ( p -value) 3.04 (0.003) 2.78 (0.006)
Man.Board Connection n.a. n.a. n.a. 0.1791 n.a. 0.1929
t-value ( p -value) 2.51 (0.013) 2.55 (0.011)
Employee Representation Share n.a. n.a. n.a. n.a. −0.2618 −0.2383
t-value ( p -value) −1.58 (0.116) −1.42 (0.155)
F-value ( p -value) 4.73 (0.001) 4.61 (0.001) 4.10 (0.001) 5.48 (0.000) 5.02 (0.001) 3.44 (0.000)
R 2 0.0233 0.0362 0.0512 0.0341 0.0250 0.0329
Corr( u i , X b ) −0.4105 −0.4346 −0.4865 −0.4435 −0.5619 −0.5938

The sample consists of an unbalanced panel of 509 Danish firm-year observations from 154 firms for the period 2015 and 2020 having non-negative equity. Tobin’s Q is the ratio of the firms market enterprise value to its book value of assets. Women share is share of supervisory board being women. SB independence is share of supervisory board cinsidered independent—as disclosed in annual report. SB Average Age is the supervisory board members’ average age, log. SB Average Experience is the average number of years members of supervisory board have been present in this board, log. Board Expertise is average number of other supervisory board positions, i.e. expertise gained from holding supervisory board positions in other firms. SB Size is supervisory board size, log. Size is company size measured by the value of total assets in balance sheet, log. CEO Employed is number of years the CEO has been employed as CEO in the company’s management board. Man.Board Connection is the share of the supervisory board appointed before the CEO was appointed by the supervisory board. Employee Representaton Share is the number of employee representatives divided by supervisory board size (excl. employee representatives). In first column repeat results from Table 3 while other columns shows results for different sets of diversity measures—we use fixed effects regression in all columns. Exact F-, t-, and p-values are shown.

We start again by presenting the basis estimation in the first column, which is similar to the company fixed effects regression in the right part of Table 3 for Tobin’s Q. Compared to the results for the other board characteristics shown in the other columns of Table 5, it seems that gender is not the most important since F-value here is 4.73 and thus other characteristics contribute more to statistically precision here. Both CEO-connection and employee representation shows better models, but in contrast to the non-significant employee representation variable, the CEO-connection’s two included variables are positive and significant at same time. However, the part of the variation explained, R2, is quite smaller for all the characteristics in Table 5 compared to those observed for the ROA-estimation. Similarly to the Tobin’s Q estimations, when we include all our board characteristics in one model the result is a relatively poorer model, measured by both the F-value and the variation explained, R2. Further, unlike the literature, both the board independence characteristic and the company size (total assets) have small insignificant t-values. In fact, the small impact that the board independence characteristic shows, probably reflect the management characteristics’ correspondence with the supervisory board and these relations’ importance for value creation (as measured by Tobin’s Q).

Following the quite dispersed results in Table 4 and Table 5 we look at marginal contribution from each of our board characteristics as a means to guide in search for board excellence. In Table 6 we present all these simple pairwise analyses using company fixed effect regressions on both ROA and Tobin’s Q in left respectively right part of Table 6.

The multiple regressions in Table 4 showed women share as the most influential diversity dimension measure for ROA. This is confirmed in the marginal regressions on ROA in Table 6 where women share is the board characteristic showing the highest F-value. Concerning Tobin’s Q, the women share is unsignificant, but it shows a positive coefficient and the model’s F-value indicate that gender is more important than the board characteristics independence, age, employee representation and company size explaining the Tobin’s Q performance. Further for Tobin’s Q performance, the supervisory board size followed by management connectedness show significant and most important results, probably revealing that supervisory board size matters, since larger boards are likely to entail more knowledge than smaller and that the connection to management board and CEO is crucial for company success measured by Tobin’s Q performance.

In the left part of Table 6 the individual analyses of board characteristics against the ROA performance measure are shown. Several of the characteristics show significant relations to ROA, although the sign might not be as expected. Both women share, independence, average age, and employee representation have negative impact on ROA, which as a marginal contribution contradict our expectation that diversity, independence and employee involvement would contribute positively to the company performance.

In the right part of Table 6 the individual analyses of board characteristics against the Tobin’s Q performance measure are shown, and although the main part of the characteristics does not show significant relations to Tobin’s Q, the coefficients’ signs are in accordance with our expectations. For instance, the negative signs for supervisory board size and company size are perfectly in accordance with the [13] and [10] results. The positive relation between the CEO-connection variables and company performance discussed earlier is also reflected here as they show clearly significant results.

Table 6. Performance and diversity marginally.

Return On Assets Tobin’s Q
Fixed Effects Regression Coefficient t-value ( p -value) F-value ( p -value) Corr( u i , X b ) Coefficient t-value ( p -value) F-value ( p -value) Corr( u i , X b )
Women Share p -value −0.2004 −3.210.001 10.320.0014 −0.3060 0.0543 0.990.324 0.970.3244 0.0471
SB Independence p -value −0.1354 −2.520.012 6.350.0122 0.0323 0.0103 0.220.827 0.05 0.8267 0.1064
SB Average Age (log) p -value −0.0069 −0.120.090 0.020.9018 −0.0801 −0.0263 −0.540.590 0.290.5896 −0.0653
SB Average Experience (log) p -value −0.0363 −0.600.546 3.130.0449 −0.1487 0.0029 0.060.955 1.930.1460 −0.3238
Board Expertise (log) p -value −0.1490 −2.320.021 0.1090 1.940.053
SB Size (log) p -value 0.2297 2.900.004 8.420.0039 −0.0129 −0.2729 −3.990.000 15.950.0001 −0.3967
Total Assets (log) p -value 0.5233 2.630.009 6.930.0088 −0.0155 −0.0188 −0.110.914 0.010.9142 0.0367
CEO Employed (log) p -value 0.1165 1.440.151 2.670.0704 −0.0336 0.1890 2.690.008 3.860.0219 −0.1812
Man.Board Connection p -value −0.0226 −0.280.776 0.1649 2.390.017
Employee Representation Share p -value −0.2992 −1.550.122 2.410.1217 −0.5241 0.0690 0.460.649 0.210.6493 −0.0507

The sample consists of an unbalanced panel of 509 Danish firm-year observations from 154 firms for the period 2015 and 2020 having non-negative equity. ROA is net income before extraordinary items divided by book assets. Tobin’s Q is the ratio of the firms market enterprise value to its book value of assets. Women share is share of supervisory board being women. SB independence is share of supervisory board cinsidered independent—as disclosed in annual report. SB Average Age is the supervisory board members’ average age, log. SB Average Experience is the average number of years members of supervisory board have been present in this board, log. Board Expertise is average number of other supervisory board positions, i.e. expertise gained from holding supervisory board positions in other firms. SB Size is supervisory board size, log. Size is company size measured by the value of total assets in balance sheet, log. CEO Employed is number of years the CEO has been employed as CEO in the company’s management board. Man.Board Connection is the share of the supervisory board appointed before the CEO was appointed by the supervisory board. Employee Representaton Share is the number of employee representatives divided by supervisory board size (excl. employee representatives). Each line represent our diversity measures and for each we perform fixed effects regressions on first ROA and second Tobin’s Q. Exact F-, t-, and p-values are shown.

Based on the Table 6 results we must conclude that overall women share does not have the expected positive importance for companies’ financial performance. And neither has the other board characteristics variables, but they do not show as strong a relationship as women share does. However, supervisory board size matters, and the larger the size, the larger the probability that board is diverse, if “un-cloned” members are sought and hired for the boards.

 5. Conclusion

In general, women share is not the only important diversity characteristic to look for when choosing how the supervisory board should be constituted. On the other hand, there is a significant effect suggesting that one might be better off when discriminating in favour of women when seeking board members—at the very least compared to new board members’ age and expertise from other boards. Further it can be concluded that size matters, which follows the general tendency in empirical financial performance evaluation studies.

The objective of this study was to find out if diversity represents relevant board characteristics in supervisory board in relation to company performance, and although the significance for our diversity measures do not vary much, it seems that women share of supervisory board contributes with more impact than other characteristics.

It is crucial to recognize the contextual nature of our found relationships, which is also the study’s limitation. Overall, our study suggests that selected board characteristics are linked to corporate financial performance represented by diverse proxy measures. In contrast, establishing clear links between supervisory board characteristics, shareholders, and corporate financial performance proved challenging, and although the different measured board characteristics implies underlying diversity qualifications that contribute to boards’ decision-making, our findings suggest that within the Danish two-tier system, the overall board tasks are probably clearer separated between the two boards-oriented measures management board and supervisory board than unitary boards. However, it must be remembered that the measures focus on different dimensions, but all on same real board members, i.e. their gender (binary variable), their age (continuous variable), their experience (years—continuous variable), and their independence (share being unrelated).

NOTES

1In the Danish two-tier model, the roles are distinctly separated for a management board and a supervisory board. According to [26], the management board holds responsibility for daily company operations, and there is strong emphasis on the independence of the management board, free from interference by both the supervisory board and shareholders in management decisions. Within the management board, including the CEO, members are considered equals and share collective responsibility for all management decisions. The supervisory board oversees the management board and appoints its members, but it cannot interfere core management functions of the management board, including decisions or actions of fundamental significance to the company’s assets, financial status, or profitability. In this governance structure, it is anticipated that the composition of the supervisory board, including characteristics like age, experience, expertise, independence, gender, culture, and skills, will influence performance by fulfilling monitoring and advisory roles. Additionally, maintaining a limited connection between the management board and the supervisory board is crucial to preserve independence between the two boards.

2According to the Danish Companies Act (Chapter 8, 2012), a company’s employees can decide that they want employee representatives on the supervisory board when the company has employed at least 35 employees on average in the last 3 years. This right comprehends the ability of electing representatives corresponding to half of the other board members—i.e. the right to elect at least one third of the total number of board members, but at least two people.

3According to [25] the supervisory board members are requested to state whether they should be considered independent. And in general, “at least half the members should be independent”—see eventually point 3.2.1 in .

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