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Go Back       IAR Journal of Business Management | IAR J Bus Mng, 2020; 1(2): | Volume:1 Issue:2 ( Aug. 30, 2020 ) : 58-65.
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Corporate Governance and Disclosure of Human Capital Accounting In Quoted Firms in Nigeria


Article History

Received: 25.06.2020; Revision: 24. 07.2020; Accepted: 12. 08.2020; Published: 15. 08.2020

Author Details

Onipede Raphael Oluwafemi

Authors Affiliations

Department of Accountancy. Federal Polytechnic, Ado Ekiti. Nigeria


Abstract: The study investigated the impacts of corporate governance on human capital accounting disclosures among some quoted firms in Nigeria. The study used secondary data on the following variables; Human capital disclosure, Composition of Independence Directors in Company, Board size, Size of Audit Committee in Company, Frequency of Audit Committee Meetings in Company and Managerial Shares Ownership. The study applied panel data as the method of analysis. The result shows that corporate governance characteristics have combination of positive and negative relationship with human capital disclosure among quoted firms in Nigeria. Corporate governance characteristics such as board composition, board size and managerial shares ownership are the most important factors of corporate governance that have significant impact on human capital disclosure.

Keywords: Corporate Governance, Human Capital Disclosure, Quoted Companies.


INTRODUCTION

Corporate Governance has been recognised as a mechanism for attaining maximum efficiency as well as sustainability, productivity and profitability (Anup & Cooper, 2017). Good corporate governance enhances the firms’ ability to attract talented employees, adopts advanced technological infrastructures and maintain good relationships with suppliers and other stakeholders (Al-Sartawi, 2017; Mousa & Desoky, 2012). The responsibility of Corporate Governance involves creating, developing and leveraging human Capital embedded in the people, structures and process of the firm.. Human capital or Intellectual capital can be introduced as the accumulated pool of knowledge regarding recourses and users of these resources which includes physical and intangible assets, management style, internal and external communication lines, human skills and abilities in adding values or solving problems and technological infrastructure. Good corporate governance characteristics such as board size, board composition, audit committee etc promotes fully disclosure of human capital information in annual reports and play an important role in reducing the agency problem by representing managements' transparency and accountability in conducting a business (Al-Sartawi, 2016). Moreover, disclosing information about human capital like employee know-how, educational qualification, entrepreneurial spirit and innovations, work environment, employee compensation plan, etc. reflects the ability of the firms in managing their assets to create long-term competitive advantage (Ranani & Bijani, 2014) and as well by increasing the percentage of knowledge-based investments (Abdalmuttaleb, & Al-Sartawi, 2018).


Despite the number of theories proposed for accounting and financial reporting recognition of firms’ investment in human capital, coupled with several researchers who have developed models on the measurement of human capital, none of these approaches has been accepted by the international accounting bodies. Research in corporate governance characteristics and that of human capital disclosures has come a long way and is still evolving and in modest terms it can be regarded as one of the most debatable areas of corporate reporting. Due to its value creation potential, prior researchers have made several attempts to quantify the human capital disclosure of firms using different techniques such as; present value of future earnings applying discounted wage flows method (Lev & Schwartz, 1971), acquisition cost


(Brummet, 1968), replacement cost (Flamholtz, 1973), opportunity cost, market value, discounted earnings level, economic value, and group value model (Grove, 1977) amongst others. The difficulty of accurately determining the monetary value of human capital is cited as a major reason for the lack of progress in recognising it in traditional accounting statements and hence the need to report in other forms such as discretionary disclosures in the annual reports (Rana & Maheshwari, 2005). This challenge can be faced by the corporate governance in the knowledge era through getting best out of its intellectual assets and view corporate knowledge as being one of the most sustainable sources of competitive advantage in business.


The trend of human capital researches in developing economies like Nigeria has lagged behind considerably in terms of volume and coverage. Most of the studies on human capital disclosure such as Enyi and Adebawojo (2014), Enofe, Mgbame, Otuya and Ovie (2013), Olajide, Olugbenga, Lateef and Ajayi, (2018), Oyewo, Faboyede and Fakile (2014) and Ekundayo and Odhigu (2016) were mostly on its determinants using disclosures index. However, one weakness with studies using disclosure index is that the authors arbitrarily select a number of items disclosed in annual reports without any anchoring framework. However, this study addresses this limitation by adopting a much comprehensive perspective. Following Vithana (2014), this study proposes a methodology that reflects firm value creation through human capital investment using a framework developed based on the balanced scorecard (BSC) (Kaplan & Norton, 2001). No prior study in the Nigeria, to the best of the researcher’s knowledge, has employed a balance score card (BSC) approach in formulating the human capital disclosure index and this is an incremental and unique contribution of the study.


Consequently the main objective of the study is to investigate the impact of corporate governance on human capital disclosure among the quoted companies in Nigeria. The rest of the paper is divided into literature review, methodology, results and discussions, conclusions.


LITERATURE REVIEW

There some recent studies around corporate governance and human capital disclosure. These studies are discussed as follows;

Vandana, Amargeet and Rasgmi (2017) examine intellectual capital disclosure by the Indian corporate sector.The objective of the study critically examined the extent of disclosure of intellectual capital (IC) items, made by major Indian corporates indexed in fifty (50), main index of National Stock Exchange (NSE). The study discloses that a higher percentage of service sector companies have a high disclosure level as compared to industrial or other sector companies. The data reveal that company size and independence of the board (indicated by percentage independent directors) are positively associated and significant determinants of disclosure. The company size plays a more important role in influencing disclosure levels in industrial sector companies as compared to service sector. Companies with higher leverage and higher government ownership are likely to have lower disclosure indices. Intellectual capital disclosure (ICD) is not influenced by profitability of firms.


Abdalmuttaleb and Al-Sartawi (2018) examine corporate governance and intellectual capital: Evidence from gulf cooperation council countries. The empirical study of the current research depends on a sample which includes all the listed companies in the GCC Bourses for the year 2015. However, the required data for calculating ICLand CGLwere gathered from 274 companies out of 285companies listed under the financial sector. A regression model was developed to measure the relationship between the variables. The results showed that the total level of intellectual capital was 73% and the level of corporate governance applied by the GCC firms was 78%. Moreover, the findings indicate that there is a weak negative relationship between CGL and ICL.


Tulung (2018) conducts the influence of corporate governance on the intellectual capital disclosure of Indonesian private banks. The variables examined in the study include composition of independent commissioners, competence of audit committee and risk oversight committee. The samples are taken using purposive sampling, considering particular criteria.62 banks were selected and taken as research samples. The data are analysed using multiple linear regression analysis method. The result of a partial test shows that the composition of independent commissioners has a positive and significant influence on the intellectual capital disclosure; the competence of audit committee has a positive and significant influence on the intellectual capital disclosure; and the competence of risk oversight committee does not influence the intellectual capital disclosure.


Hasmanezan andNajihah (2019) investigate the relationship between Corporate Governance (CG) mechanisms and Intellectual Capital (IC) efficiency following the revision of the Malaysian Code of Corporate Governance (MCCG) in 2012. A final sample of 150 large companies was chosen from the companies listed on the main board of Bursa Malaysia for 2014.The multiple regression analysis reveals that board size and frequency of audit committee meetings have a significant and positive association with IC efficiency, but no evidence existed for an association between board composition and role duality on IC efficiency. The result of this study could be useful for regulators and policy makers, particularly to the Securities Commission Malaysia, to further revise and strengthen its MCCG.


Olajide, Olugbenga, Lateef and Ajayi (2018) examine the impact of human resource accounting disclosure on financial performance of selected listed firms in Nigeria. The population of the study comprises 188 manufacturing and non-manufacturing firms in the Nigerian Stock Exchange annual report between the period of 2011 – 2015 out of which 20 were selected. The data collected are analysed using descriptive statistics, correlation and regression and the study reveals that there is a positive co-efficient value of 0.565 between the independent and dependent variables.


Oyewo (2013) investigates human resource accounting practices of financial service and manufacturing firms in Nigeria. By content-analysing the financials of 12-selected companies, human resource accounting disclosure indices were derived. Study variables are subjected to statistical procedures such as ANOVA, T-test and Correlation. The study finds out that though human resource accounting disclosure practice index of banks is higher in comparison to manufacturing companies, the difference is not statistically significant. Also, there it is strong positive relationship between human resource accounting disclosure and company size. It was the findings of this research that there is a positive connection between the volume of financials and human resource reporting.

From the empirical review it is obvious that most of the recent studies are outside Nigeria. The few ones on Nigeria focused on impacts of human capoital resource on performance. In addition none of the studies used balance score card (BSC) approach which has been adjudged to be appropriate to measure human capital.


METHODOLOGY

Research Design

The research design is a combination of cross sectional and time series design. The study focuses on non financial companies quoted on the Nigerian Stock Exchange. The companies cut across those in the Brewery, Bottling, Agriculture, Beverages, Conglomerate, Hardware, Construction, Printing and oil and gas industries. The time period covered is eight (6) years which is (2012-2017). The choice of these periods becomes necessary following amendment of corporate governance in 2010. It is a panel data survey of firms.


Population and Sample Size

The population of this study consists of non financial firms quoted on the Nigerian stock exchange. A total of one hundred and fourteen (114) non-financial quoted firms on the Nigeria Stock Exchange (NSE) as at 31st December, 2017 (NSE Fact Book, 2017) constituted the population, while eighty-nine (89) firms formed the sample size in the study. The remaining firms of the population size had no updated annual report to 2017. The sample size was arrived at by adopting Yamane (1967) number estimation formulae. The formulae states that:

n = N/l+N(e)2

where:

n is the sample size;

N is the population of the study; and,

E is the error term of (0.05) 5%.

Computation

n = 114

1+114(0.05)2 = 88.72= 89 quoted firms.


Historical data were obtained from financial statements and accounts of sampled firms.

Model Specification

The model is on the resource agency theory that focuses on the role that director’s play in directing the decisions of the firm and this also extends to the corporate reporting posture of the firm. Based on the theory, our model is specified in explicit form as:

HCDit= β0+ β1 BOARDCOMPit + β2 BSIZEit + β3SACit + β4FACMit + β5MSOWNit + αit

Where;

HCD= Human capital disclosure

BOARDCOMP = Composition of Independence Directors in Company

BSIZE = Board size

SAC = Size of Audit Committee in Company

FACM = Frequency of Audit Committee Meetings in Company

MSOWN = Managerial Shares Ownership

β0 = Constant

β1 - β5 = Coefficient of the Explanatory Variables

αi = Error or Disturbance Terms of Company

Our apriori expectation is stated : >0, >0, >0, >0 and >0; meaning that:

β1>0;indicates that a unit increase in composition will lead to increase in human capital disclosure.

β2 >0;shows that a unit increase in board size of the company, will lead to increase in human capital disclosure.

>0 means that a unit increase in size of audit committee in company will lead to increase in human capital disclosure.

>0 increase in frequency of audit committee meetings in company will lead to increase in human capital disclosure.

>0 suggests that a unit increase in managerial shares ownership will lead to increase in human capital disclosure.


Operationalization of Variables

Variables

Notation and Measurement

Sources

Apriori Sign

HCD

HCD is measured using HC disclosure index Dichotomous variable sign is applied where qualitative information was disclosed a score of one (1) is assigned, otherwise 0 for non-disclosure of IC information. The IC score for the firm will be derived by calculating the ratio of actual sum of scores awarded to a firm to the maximum score. This score will be converted into a percentage form.



BOARDCOMP

Board Composition was measured as proportion independent directors on the board of directors during the year.

Souissi and Khlif(2012),

+

BSIZE

Board size was measured as total number of member that constitute the board.

Hasmanezan and Najihah (2019)

+

SAC

Audit Committee size was measured as total number of persons that constitute the committee during the year.

Obeua (2015)

+

FACM

Frequency of Audit Committee Meetings was measured as the number or frequency of meeting held during the year.

Aram (2015)

+

MSOWN

Managerial Shares Ownership was measured as proportion of shares held by executive directors during the year.

Souissi and Khlif(2012),

+

Source: Author’s compilation (2019)


METHOD OF DATA ANALYSIS

The effect of corporate governance on human capital disclosures was analysed using panel regression. The study used panel data regression as the technique for estimating the econometric models specified in this chapter. Panel data regression was chosen because of the multidimensional nature of the data which has both time or periodic dimension and also cross-sectional dimension. Unlike, pooled regressions, panel data regression had enormous benefits and hence its choice in this study. Firstly, panel data can take explicit account of individual-specific heterogeneity. Secondly, by combining data in two dimensions, panel data gives more data variation, less collinearity and more degrees of freedom. Thirdly, panel data regression is better suited than pooled regressions for studying the dynamics of change. Fourthly, it is better in detecting and measuring the effects which cannot be observed in either cross-section or time-series data. And finally, it can minimise the effects of aggregation bias, from aggregating firms into broad groups. The panel regression has both the fixed effects (FE) and the random effects (RE) estimation options. The major difference between them is the assumption made regarding the behaviour of the error term. The fixed effect model allows for correlation between the unobserved effect and the independent variable. On the other hand, the random effect does not allow for any correlation (Hausman, 1978). To determine the preferred estimation technique between the FE and RE, the hausman specification test was employed. The Hausman test was used to select between the fixed effect and random effect estimator in panel data analysis (Hausman, 1978; Wooldridge, 2000). The pooled OLS, random effects (RE) and fixed effects (FE) are estimated. To determine which model is better, an F-test for the FE model, the Breusch-Pagan Lagrange Multiplier (LM) test for RE and the Hausman test for both fixed and random models were conducted.


RESULTS AND DISCUSSION

This section of the paper presents the empirical results .interpret them and discuss the results as well. Inferences are later drawn from the results based on comparison with the findings of previous studies.


Analyses of Regression Results

Table 2: The Results of the Regression Analyses

VARIABLE

POOLED

Coefficient

t-stat

(PV)

RANDOM EFFECT

Coefficient

t-stat

(PV)

FIXED EFFECT

Coefficient

t-stat

(PV)

C

0.193494

3.985129

(0.0001)*

0.474054

7.921038

(0.0000)*

9.471325

9.283244

(0.0000)*

BOARDCOM

-0.136933

[-9.910802]

(0.0000)*

-.002293

-2.934391

(0.0017)*

-0.123647

-3.153802

(0.0000)*

BSIZE

0.137576

0.410555

(0.6814)

0.030457

3.665738*

(0.0000)

1.573930

2.254527

(0.0248)**

ACS

1.028208

14.30924

(0.0000)***

-0.001581

-0.153675

(0.8779)

-0.075930

-0.377734

(0.7059)

FACM

1.028208

10.23422

(0.0000)*

0.005567

0.530528

(0.5961)

0.353027

1.861002

(0.0637)*

MSOWN

0.136933

9.910802

(0.0000)

0.128847

2.888428

(0.0205)**

0.083699

4.363296

(0.0000)***

R-squared

0.568539

0.690137

0.754651

Adjusted R-squared

0.551576

0.569297

0.736692

S.E of regression

0.118450

0.097163

0.026736

F-statistic

7.676849

16.68286

31.05119

Prob(F-statistic)

0.000000

0.000000

0.000000

 Durbin-Watson stat

1.832353

1.831490

2.238990

Hausman


0.3306


Observations

532

532

532

Source: Researchers Computation (E-Views 8.0) 2019. (All variables are significant at the 5% level) (See appendix for result)

[ ] – t-statistics*** - 1% *** - 10%( ) – P-value ** - 5%



The results of the regression analysis presented in Table 4.5 above showed the pooled, random effect and fixed effect regression analysis. Below the coefficient values were the t-statistics while the probability values are in parenthesis respectively.


Pooled Regression:

The pooled regression result reported an adjusted R-squared (R2) value of 0.1685, which signified that 57% of the systematic variation in the dependent variable (human capital information disclosure, HCD) was accounted for by the explanatory variables of board composition (BOARDCOM), board size (BSIZE), audit committee size (ACS), frequency of audit committee meeting FACM), and managerial shares ownership, while about 43% were unaccounted for, hence captured by the error terms. After adjusting the degree of freedom, adjusted coefficient of determination indicated 0.5516, meaning that approximately 55% of the changes were explained by the independent variables which are the corporate governance characteristics examined. The F-statistic of 7.6768 and the associated probability value of 0.000000 showed a significant linear relationship between the dependent variable and the explanatory variables. The explanatory variables were all positive, with the exception of board composition which was negative. The variables, board composition, audit committee size, audit committee meeting and managerial share ownership were all statistically significant at the 5% level apart from board size that was statistically insignificant.


Random Effect Model:

The result of the random effect model which reported a coefficient of determination (R-squared) value of 0.6901 with human capital disclosure, implied that about 69% of the systematic variation in the dependent variable (human capital disclosure) was accounted for by the explanatory. Again, after adjusting the degree of freedom, the adjusted coefficient of determination now stood at a value of 0.5692, suggesting that 57% of the systematic variations in the dependent variables were explained by corporate governance characteristics (Board composition, board size, audit committee size, frequency of audit committee meeting and managerial shares ownership) The F-statistic of 16.8287 with associated probability value of 0.0000 is greater than the standard error of regression with a value of 0.0972, implied that there a significant linear relationship between the dependent and the explanatory variables. Corporate governance variables board size, audit committee meeting, and managerial share ownership were positively related while board composition and audit committee size were negatively related with human capital disclosure. Also the random effect panel least square regression indicated that board composition, board size and managerial ownership were statistically significant, while audit committee size and audit committee meetings were statistically insignificant with human capital disclosure (HCD) at 5% significance level.


Fixed Effect Model:

It is deduced from the fixed effect panel least square regression that the coefficient of determination (R2) with a value of 0.7547 with human capital disclosure, indicated that over75% of the changes in the dependent variable (human capital disclosure) was explained by the corporate governance characteristics, while 25% were unexplained hence captured by the error term. On adjusting the degree of freedom, the adjusted R-squared showed a value of 0.7367, implying that about 74% of the systematic variations were explanined by the corporate governance characteristics while 26% were captured by error terms. The F-statistic of 31.8543 showed that overall result is statistically significant. It also revealed that board composition, board size, and managerial shares ownership were statistically significant at 0.05 (5%) significance level.


The respective regression results in Table 3 showed that a robust linear relationship exists between the variables; hence outcomes are suitable for prediction and fundamental decision making.

Table 3: Hausman Test

Correlated Random Effects - Hausman Test


Equation: Untitled



Test cross-section random effects


Test Summary

Chi-Sq. Statistic

Chi-Sq. d.f.

Prob. 











Cross-section random

5.757295

5

0.3306






Table 3 above shows outcome of Hausman test conducted whether to employ random or fixed effects panel least square regression. The Hausman test with probability value of 0.3306 which is statistically insignificant implies that random effect panel least square regression is appropriate and not fixed effects for the purpose of testing the hypotheses. Hence, we proceed to test of hypotheses.


DISCUSSIONS

The study showed that board composition has a significant negative effect on human capital disclosure. This implied that board composition is a strong influencing factor of human capital disclosure. The finding is in tandem with the apriori expectation, indicating that a unit increase in board composition could lead to increase in human capital disclosure. The finding is consistent with Abdul Rahman and Ali (2006), Osama and Noguer (2007), and, Park and Shin (2003) who revealed that board composition do not have positive relationship with human capital disclosure. Sonda et al., (2003) argued that that increasing the level of board composition can have some positive relationship with human capital disclosure.


The study observed that board size has significant influence on human capital disclosure, suggesting that the result is statistically significant. This implied that board size is a critical factor enhancing human capital disclosure. The result showed coefficient value of 0.030257, meaning that a unit increase in board size by 3% could bring about corresponding increase in human capital disclosure. The result is in line with the apriori expectation. The result indicated that board size is a strong influencing factor of human capital disclosure. The finding is consistent with Abdul Rahman& Ali (2006), Chin et al., (2006) and Rahman and Ali (2007) who revealed that positive relationship exist between the size of the board and human capital disclosures. However, Peasnell et al., (2005) and Xie et al., (2003) argued aginst that a negative relationship exists between board size and human capital disclosures.


The study indicated that audit committee size has no significant influence on human capital disclosure. Also the result revealed that it has negative relationship with human capital disclosure. The finding was against the apriori expectation, as such a unit increase in audit committee size does not lead to a unit increase in human capital disclosure among quoted companies. By implication, audit committee size is a weak influencing factor of human capital disclosure. The find argued against Mohammad (2017) who found a significant positive effect with audit committee size and human capital disclosure.


The hypothesis tested showed that frequency of audit committee meeting has no significant influence on human capital disclosure. The result implied that frequency of audit committee meeting is a weak enhancing factor of human capital disclosure. The finding corroborated with Bhattacharjee, Chakraborty and Bhattacharjee (2017) who showed that no significant influence with frequency of board meeting, and human capital disclosure. It however differs from Hassan and Yaacob (2017), who argued that frequency of audit committee meetings has a significant and positive association with human capital disclosure.


The independent variable of managerial share ownership result revealed that it has significant influence on human capital disclosure. The result also showed that managerial ownership has a positive relationship with human capital disclosure. This is in alignment with the apriori expectation meaning that a unit increase in managerial share ownership could lead to a unit increase human capital disclosure. By implication, managerial share ownership is a strong influencing factor and has positive relationship with human capital disclosure. The finding corroborated with Rouf and Harun (2011) and Vu (2012) who showed that managerial shares ownership in firms can influence and positively related with level of human capital disclosures. But Elmans (2012) established that no significant association with managerial ownership and human capital information disclosures in annual reports.


CONCLUSIONS AND RECOMMENDATIONS

The study is on the relationship between corporate governance and human capital disclosure. The issue of corporate governance continues to attract researcher’s attention because of incessant corporate failures around the world with Nigeria inclusive. Corporate governance is instituted in Nigerian firms by policy makers like the Central Bank of Nigeria (CBN), Security and Exchange Commission (SEC) etc to promote accountability, transparency, credibility and integrity in the firm. Corporate governance disclosure in annual reports of firms in Nigeria can be mandatory or voluntary. Agency, stakeholder, stewardship and political theories have showed the importance of corporate governance in relation to human capital disclosure in corporate reports.


This study has showed that corporate governance characteristics in terms of board composition, board size, audit committee size, frequency of audit committee meetings and managerial shares ownership have implications with human capital disclosure. Following the various reviews and outcomes of analysis, interpretation and hypotheses tested, the study found that board composition, audit committee size and frequency of audit committee meetings have no significant influence and they have negative relationship with human capital disclosure. These suggested that board composition, audit committee size and frequency of audit committee meetings were weak determining factors of human capital disclosure. On the other hand, role duality and managerial shares ownership have significant influence and positively related with human capital disclosure, suggesting that they are strong enhancing factors of human capital disclosure. The outcomes were in alignment with previous studies. In conclusion, corporate governance characteristics have combination of positive and negative relationship with human capital disclosure among quoted firms in Nigeria.


Recommendations

Having examined findings and conclusion, the study put forward the following policy recommendations:

  1. Board size of companies should consists of persons of integrity and transparent characters with professional qualifications and experience in information disclosures especially voluntary aspect like human capital disclosures. The board composition should be persons of accountable, integrity and transparent characters capable of monitoring and controlling management and promote disclosure The composition should be combination of experts in different human endeavour capable of handling issues of human capital disclosure so as not to use it as avenue for earnings management practices by management.

  2. Board of quoted firms in Nigeria should not be too large so that it will not create unnecessary bottleneck in term of crucial decision making. Also, the composition should not be too small for easy influence by executive directors or management. The size of the company should and complexity of the company should determine the number of persons or members that should constitute the board of the company.

  3. Executive directors irrespective of the ownership in the firm should ensure that they implement policies that can encourage information disclosure so as to build confidence and trust in minds of stakeholders. Management should promote spirit of good corporate governance and avoid information disclosure that could be detrimental to the firm.

  4. Audit committee size should be based on the size of the firm. The audit committee should be persons of different backgrounds, professional and accounting and finance educational qualifications. The board should be persons with years of experience in corporate report interpretation and forecasting.


Audit committee meeting members should be regularly at least monthly, and this can help in checking any form of financial statement frauds. It will also help to further strengthen corporate governance in companies.


REFERENCES

  1. Abdalmuttaleb, M.A., & Al-Sartawi, M. (2018). Corporate governance and intellectual capital: Evidence from Gulf Cooperation Council Countries. Academy of Accounting and Financial Studies Journal, 22(1), 1-12

  2. Abeysekera, I. (2010). The influence of board size on intellectual capital disclosure by Kenyan listed firms. Journal of Intellectual Capital, 11 (4), 504-518.

  3. Abeysekera, I., & Guthrie, J. (2004). Human capital reporting in a developing nation,
    British Accounting Review 36 (3), 251–268.

  4. Abeysekera, I., & Guthrie, J. (2005). An empirical investigation of annual reporting trends of intellectual capital in Sri Lanka. Critical Perspectives on Accounting, 16(3). 151-163.

  5. Aram, J. M. (2015). Human capital disclosures: Evidence from Kurdistan. European Journal of Accounting Auditing and Finance Research, 3(3), 23-35

  6. Ekundayo, O .U., & Odhigu, F. (2016). Determinants of human capital accounting in Nigeria. Igbinedion University Journal of Accounting, 1,103-117,

  7. Hasmanezan, H., & Najihah, M.Y. (2019). Corporate governance mechanisms and intellectual capital efficiency: Evidence from Malaysia. International Journal of Economics and Finance, 11(1), 2019.

  8. Olajide, O.P., Olugbenga, A.F., Lateef, A. O., & Ajayi, O. M. (2018). An empirical study of human resource accounting disclosure on financial performance of selected listed firms in Nigeria. Journal of Accounting and Management, 8 (2), 70-82

  9. Oyewo, B.M. (2013). Comparative analyses of human resource accounting disclosure practices in Nigerian financial service and manufacturing companies. IOSR Journal Of Humanities And Social Science, 16(1), 20-26.

  10. Tulung, J.E. (2018). The influence of corporate governance on the intellectual capital disclosure: A study on Indonesian private banks. Banks and Bank Systems, 13(4), 1-3.

  11. Vandana, M., Amargeet, K.M., & Rasgmi., P. (2017). Intellectual capital disclosure by the Indian corporate sector.International Management Institute, 27, 1-16.

  12. Vergauwen, P., &Alem, F. (2005). Annual report IC disclosures in The Netherlands, France and Germany. Journal of Intellectual Capital, 6(1), 89-104.


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