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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS

COPY RIGHT © 2012 Institute of Interdisciplinary Business Research 68

OCTOBER 2012

VOL 4, NO 6

HUMAN RESOURCE DISCLOSURE: THE CURRENT PRACTICE

AND ITS ASSOCIATION WITH CORPORATE CHARACTERISTICS

IN MALAYSIA

Lee Miin Huui, Swinburne University of Technology, Sarawak Campus, Malaysia

MSB Siddiq, Tun Abdul Razak University, Malaysia

Abstract

The purpose of this research paper is to identify the current practice of human resource

disclosures in annual report of Malaysian public organizations and to identify the correlations of

five different variables to the level of disclosure of human resource in annual report.Findings

show that the most common terms used by organizations in Malaysia to disclosure human

resource in the annual report are “Employee”, follow by “Staff’, “Labor”, “Human”, “People”,

“Workforce”, “Workers” and “Recruit. In the analysis on the level of disclosure, the results show

the overall extent of human resource disclosure was higher in labor intensive industries.

Keywords: HUMAN RESOURCE DISCLOSURE; CURRENT PRACTICE ; ASSOCIATION

; CORPORATE CHARACTERISTICS ; MALAYSIA

Introduction

Intense market competition and increasing interdependence, integration and interaction among

people and companies in disparate locations, have brought about a global climate change in the

way companies are managed and business strategies are executed. This change has gradually

transformed the traditional production or industrial-driven economy to a knowledge-based and

service-based intensive economy. The emergence of a variety of terms such as Information

Economy, New Economy, Knowledge-based Economy or the Knowledge Society (Michaela M.

S. 2004), reflects this dynamic economic transition.

Malaysia in the last three decades has transformed itself from a country that depended on

agricultural commodities and mining to an industry-based economy. Statistics from the

Department of Statistics showed that in 2005, the manufacturing and services industries

accounted for 32 percent and 57 percent of Malaysia’s GDP respectively. Malaysia also

embarked on a mission to develop a knowledge-based society as highlighted in its Third Outline

Perspective Plan , 2001- 2010 ( Economic Planning Unit, 2002) and a Knowledge-Based

Economy Master Plan which was launched in 2002. This plan consists of various strategies to

accelerate the transformation of Malaysia into a knowledge-based economy. Immediate to this

vibrant transformation, modern business companies in Malaysia will no longer solely rely on

investment in tangible asset but also in intangible asset such as human resource to create wealth

for shareholders.

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This phenomenon is supported by Vance (2001) who stressed that “the assets of companies have

become less tangible in line with the emergence of the new digitized knowledge” and “service-

oriented economy and a major portion of a company’s investment is in the more abstract form of

intangible, knowledge based assets”. Companies like Microsoft and Google and other service,

knowledge-based companies rely heavily on their intangible assets (employees) to create value-

added services and wealth. “You do not buy Microsoft because of its software factories; the

organization does not own any. You buy its ability to write codes, set standards for personal

computing software, exploit the value of its name, and forge alliances with other companies”

(Stewart, 1998). This clearly shows that such companies cannot exist without their employees

and the performance of a company is dominantly and directly linked to their human resources in

which employees make up the core of a business institution.

Given the importance of human resources in a company, slogans like, “People are our Business”

and “People are our greatest asset” are significantly used in the business world. This has

consequently raised the issue of how the traditional accounting system could cater for the change

to reflect the business’ greatest asset.

Flamholtz (1985) highlighted the failure of traditional accounting to capture the important

information which determines decisions involving a company’s human resources. Expenditures

on employees encourage managers and shareholders to take a short-term perspective in decisions

regarding employees. Managers often lay off employees, freeze or cut pay, and cut training

program to enhance short-term profit (Downs, 1996). Since labor cost is usually substantial,

these cuts usually increase net income in the short term. Traditional accounting system has failed

to evolve in order to capture the value of intangible assets such as human resource (Edvinsson,

1997). In short, the traditional accounting concentrates mainly on tangible assets and historical,

transaction-based information, has so far not been able to keep pace with the changes towards a

knowledge driven economy.

The inadequate information disclosure of human resource in an annual report has misguided

companies and this has led to the companies’ failure to reflect some of their more valuable

intangible assets. This is reflected in the service-oriented industry such as computer software

companies, accounting firms, management consultancy firms, entertainment agencies, financial

services and higher education institutions, whose main operation is to provide intellectual

enlightenment for their respective clients. Human resource plays an important part since the

performance of these companies is the result of human actions. Therefore, to classify human

resource as a cost in such companies may not be appropriate. As human resource is more

important than the physical assets in this instance, the annual report could be more informative in

reflecting the value of human resource.

Given such shortcoming of traditional accounting, Human Resource Accounting emerged with

the aim to measure, develop and manage human capital in a company, but it has progressed very

slowly. The term Human Resource Accounting was used by Brummet et al for the first time in

1968. The concept of Human Resource Accounting developed rapidly since then, but at the end

of the 1970s the interest in the concept declined. In the 1980s, an intensive development took

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place with numerous experiments concentrating mainly on the theory of the concept. Revival of

the popularity of the concepts and renewed international interest in Human Resource Accounting

theory and practice occurred in the early 2000s because of the growing evidence of positive

correlation between quality of human resources and good organizational performance

(Flamholtz, Maria & Wei 2002).

According to John, Edward & Gary (2001), the non-acceptance of the concepts of recording

human resource is mainly due to the unawareness of the concept while the slow development of

the concept itself was greatly due to the absence of demonstrated usefulness and awareness in the

accounting field. The studies of Guthrie et al. (2000), Petty and Guthrie (2004); Flamholtz

(1999); Mouritsen (1998), Grojer and Johanson (1996) also showed that only a few companies

had utilized models and concepts in measuring and recording human resource in their respective

annual reports in spite of the general acceptance of significance of measurement of human

resource for companies.

To date, research has been carried out by researchers and practitioners in Scandinavia, Australia,

Canada and Europe. However, in Asia, there is still a lack of research in this area. With the

emergence of knowledge-based innovation economy, whereby knowledge workers have become

an important resource for modern business firms; it is important to record such resource to reflect

the “true and fair view” of the companies’ financial position. Literature search points to the

limited research on Human resource disclosure in Malaysia. This research study was carried out

in Malaysia in an attempt to fill this gap by identifying the current human resource disclosure

practices for Malaysian companies in different industries.

Literature Review

Various research carried out substantiated the importance of human resource disclosure. Human

Resource Accounting provides quantitative information about the value of human assets and

other non financial human resource information has been proved to be useful in making

decisions internally and externally.

Toulson and Dewe P. (2004) revealed that measuring human resource is perceived as important

firstly because the measurement reflects the strategic and competitive importance of human

resources, and secondly, because in order to earn credibility of a company, human resource must

be expressed in financial terms.

John, Edward & Gary (2001) stated that this disclosure was first developed to help management

to make decision. The two most prominent classes of decision makers who are most likely to use

the accounting information are the investor in securities (external) and managers making

resource allocation decision within the firm (internal). The investors can benefit from human

resource data as they reflect the current state of business organization and their growth

possibilities. On the other hand, this can inform managers on the cost of specific personnel

behaviors, such as training and turnover, thus encouraging better assessment and development of

people.

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Failure to measure and report the value of human resource, however, may cause managers to

ignore the impact of their decision on employees. According to Managers may make decisions

which in effect liquidate a company human resource by suspending the human resource

investment in order to increase the short term profit while the organization will definitely suffer

in the long run.

James, Henry and Gertrude (1976) indicated a few benefits of recording human resource. As the

world is moving into the service economy, whereby humans have become the key element,

failure to measure their value and account for human cost will reduce the effectiveness of the

organization. Capitalizing human resource would assist in restoring individual relationships

between management and the employees in a complex organization. If a manager realizes there

are future benefits, he or she may spend more time developing employees.

While human resource disclosure internally helps the top management make decisions regarding

the adequacy of human resources, it has an impact on the decisions of the investors, clients and

potential staff of the organization. Without proper valuation and accounting of human resources,

the management might not be able to recognize the negative effects of certain programs, aimed at

improving profit in the short run. If not recognized on time, this might lead to the fall in

productivity levels, high turnover rate and low morale among existing employees

(http://www.galintranet, godrej.com/tmm/kzone/).

Ulrich, Geller, & Desouza, G (1984) called attention to the positive correlation between human

resource disclosure practices and business performance. It was supported by Yeung and Ulrich

(1990), confirming that the manner of alignment between human resource and business strategy

has an impact on organizational performance.

Research evidence by companies, and examined selected high performing companies, both

showed that financial outcomes of human resource investment has significant positive

correlation between an increase in the companies competence share and added value. Research

carried out in the United States and United Kingdom with selected listed companies showed

similar results which further confirmed that human resource measurement and reporting can lead

to improved profitability and competitiveness of a company.

In Malaysia, there is yet evidence of research particularly on Human resource disclosure. The

closest research related to Human resource disclosure is literature on general intellectual capital

disclosure and corporate social disclosure with limited published works. Studies conducted in the

research area of Intellectual Capital Disclosure include the recording of human capital, structural

capital and customer capital. Abdul and Fauziah (2007) conducted one of the first empirical tests

in Malaysia involving public listed companies in services and manufacturing in relation to

intellectual capital. The findings showed that there was no significant differences in the degree to

which companies from different industries (manufacturing and service industries), type (local

based or foreign affiliated) and size of the companies in the disclosure of intellectual capital

information in the annual reports.

Concerning corporate social disclosure which includes details of physical environment, energy,

human resources, product and community involvement matters, research studies were conducted

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by Pang (1982), Ho (1990), Mohamed Zain (1999), Shireenjit & Zuaini (1998) and Hasnah et al

(2004).

Pang (1982) found in his study that the highest disclosure of corporate social disclosure was

related to human resource theme followed by community involvement and environmental theme.

Ho (1990) found more or less similar themes: human resource, community involvement, product

improvement, energy and environment in his research study whereas Mohamed Zain (1999)

found in his study of 100 leading Malaysian companies that the highest disclosure was in the

human resource theme followed by product and community involvement.

Shireenjit & Zuaini (1998) studied the general level of corporate social responsibility disclosure

by corporations listed on the KLSE (now known as BURSA Malaysia). Their study examined

159 annual reports of companies in 1994 and the findings revealed that it was clear that the

overall social responsibility reporting status in Malaysia was extremely low (3.9%), and with

similar disclosure themes. From the study by Hasnah et al (2004) on Corporate Social disclosure,

the highest overall theme was the human resource theme and the industry that made the most

disclosure was the trading and service industry.

Research Objectives

The objective for this research paper is to identify the current practice of annual report human

resource disclosures of Malaysian public organizations; it was necessary to use the secondary

data; utilizing publicly available information from sources such as organizations’ annual report

to gather the information.

The aim of this research study was to provide a descriptive account of “what is” the current

Human Resource reporting practice in Malaysia and how the practice may or may not be related

to the following variables:

i) Size of the organization ( Market capitalization)

ii) Types of industry

iii) Listing status ( local or foreign listing)

iv) Mission statement

v) Financial performance

Research Methodology

This study is conducted by employing regression model to analyze the quantitative data using

SPSS and content analysis using word count technique.

Data Collection Procedure

This phase of research utilizes secondary data, information available in the organizations’ annual

reports. Annual report is chosen because it is generally accepted as the most comprehensive

communication channel and has the potential to make information easily and routinely available

in a single document (Hooks et al. 2002). Annual report represents the concerns and interests of

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organization in a comprehensive and compact manner (Abeysekera and Guthrie, 2005). Annual

reports are highly useful sources of information, because manager of organizations commonly

signal what is important through this reporting mechanism. The annual report is also being

viewed as a communication device that allows organizations to connect with various external

and internal stakeholders (Guthrie and Petty, 2000). Annual reports are indeed regarded as one of

the most important documents in corporate reporting literature due to the high degree of

credibility that they lend to the information reported within them. In addition, they are used by a

number of stakeholders as the sole source of certain information due to their widespread

distribution and availability. In this study, the population is the annual report of organizations

listed on the BURSA main board in 2008.

Determinant of the Sample Size for Annual Report

Data collection from annual report is to collect secondary data. With the nature of secondary data

which is readily available, this study covered all possible data available for the organizations

listed on the Main Board of Bursa Malaysia. There were a total of 637 organizations listed on the

Main Board of Bursa Malaysia but not all annual reports of organizations were available for

analysis. Providing 20% of unavailable data, the target sample size would be approximately 510

and virtually there was a 100% response rate. The actual annual reports analyzed were 522.

Content Analysis

Content analysis is one of the most important research techniques in the social sciences

literature. It is considered to be an acceptable research method, especially for corporate social

reporting studies (Krippendorff, 2004). It is perhaps the fastest growing research technique in

quantitative research (Neuendorf, 2002).

Content Analysis is a method of codifying the text (or content) of a piece of writing into various

groups (or categories) depending on the selected criteria (Weber, 1985). Content analysis is a

form of qualitative evaluation, which is concerned with the understanding of multiple truths or

ways of knowing and being. Qualitative data takes into account various forms of human

communication and expression, not numbers.

Content analysis is a process of sorting and making sense of the data, especially written and

narrative data. The goal is not to generalize across a population, but rather to provide

understanding and explanation from the respondent’s perspective. Content analysis has been

conducted on annual reports by a number of researchers, as it is a good instrument to measure

comparative position and trends in reporting. (Neuendorf. K, 2002)

Stemler and Bebell (1998) conducted a content analysis of school mission statements to make

inferences about what do schools hold as their primary reasons for existence. Based on this

previous study, this method of analyzing the data can be applied to this research study in which

the objective is to analyze the information in the annual reports to determine the current practices

of recording human resource value in the annual reports.

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Further research by Guthrie et al. (2004) found that, content analysis the most appropriate

method to explain the extent of information disclosure in annual reports of companies. This is

carried out by quantifying the level of disclosure by means of a disclosure instrument which

comprises of a list of items that could appear in the organizations’ annual report. The frequency

of appearance of the item indicates the importance or significance of that particular item.

Content Analysis Procedure and Data Analysis

The following content analysis procedure was adopted from Kimberly A. (2002), as a guide to

analyze the data which were used for data analysis in this study.

Analysis was conducted by using the annual reports of companies listed on the main board of

BURSA (formerly known as Kuala Lumpur Stock Exchange (KLSE)) for the financial year

ending 2008 which downloaded either from the BURSA website or the organization websites.

The annual reports of targeted companies were converted into text file using software called E-

PDf to text.

The texts were being analyzed using software called Concordance. Concordance captures the

volume of Human Resource content by counting/analyzing pre-defined words. Words that are

mentioned most often are the words that reflect the greatest concerns and therefore draw useful

inference. In each annual report, sentences containing the following words were used to identify

the volume of human resource disclosure in the annual reports. The software Concordance tracks

the following pre-defined words:

Employees

Staff

Recruits

Human Capital

Workers

Human Resource

People

Workforce

Other required data were identified by going through individual annual report using the “Find”

function in Microsoft Word and Acrobat Reader. This process was conducted to gather data

which is necessary for the regression analysis to identify the correlation between the disclosure

and variables that had been identified - Market Capitalisation of the organisation, Listing Status,

Profit after Taxation, Human Resource disclosure in Mission Statement and the information of

types of industry was extracted from BURSA website by using the categories function on the

web page. All data collected (independent and dependent variables) were converted into code

(Table 1).

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Table 1: Coding for the Independent and Dependent Variables

Variable 1 Range of human resource disclosure ( number of words) related to Human Resource –Dependent Variable

Code

1 For a disclosure of 1 to 20 words



4 For a disclosure of 61 to 80




8 For a disclosure of more than 141 word and above

Variable 2 Market capitalization of organizations. (Size of the organization) –Independent Variable

Code

1 <101,000,000

2 101,000,000

3 201,000,000

4 301,000,000

5 401,000,000

6 501,000,000

7 601,000,000

8 701,000,000

Variable 3 Type of industry the organization is in. –Independent Variable

Code

1 Construction

2 Consumer

3 Finance

4 Hotel

5 Industrial Product

6 Mining

7 Plantation

8 Properties

9 Trading & Services

10 Technology

11 Others

Variable 4 Listing status of the organization –Independent Variable

Code

1 Local listing

2 Foreign listing

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Variable 5 Financial performance of the organization- Independent Variable

Code

1 Losses

2 Profit RM0 to RM100,000

3 Profit RM101,000 to RM200,000

4 Profit RM201,000 to RM300,000

5 Profit RM301,000 to RM400,000

6 Profit RM401,000 to RM500,000

7 Profit RM501,000 to RM600,000

8 Profit RM601,000 to RM700,000

9 Profit RM701,000 to RM800,000

10 Profit Above RM 800,000

Variable 6 Inclusion of Human Resource information in Mission Statement - Independent Variable

Code

1 Yes

2 No

The data (coded information) were processed in the SPSS analysis package. Analysis was

conducted on the correlations of the variables, after which, a regression model was developed.

Analysis was conducted for the following multiple regression and correlation:

ARHRDs = a + MCx1 + TI x2 + LSx3 + MSDx4 + FPx5 + e

Where

ARHRD = Annual report human resource disclosure- The level of human resource

disclosure in annual reports

SO = Size of the organization- Measured by market capitalization

TI = Type of Industry- Coded by industry classification in BURSA

LS = Listing Status- Coded by local or foreign listing

MSD = HR disclosure in mission statement- Coded by YES/NO human resource

disclosure in mission statement

FP = Financial performance- Measured by profit after taxation

a = Intercept

e = error term

Testing of Data – Reliability test and Reproducibility test

Reliability test was carried out on data collected. The data collected from annual report for this

research will be tested for the reliability on Stability using Krippendorff’s alpha ().

Krippendorff’s alpha () is a reliability coefficient developed to measure the agreement between

observers, coders, judges, raters, or measuring instruments.

SPSS will be used to compute the Alpha to test the stability. Ten randomly selected samples for

content analyses were tested for stability. The coder will code the annual reports and samples

will be run through SPSS for the alpha to be computed and measured. .This procedure will be

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repeated by the same coder and the results will be computed and measured by using SPSS’s

alpha test. Finally, both sets of SPSS results will be compared to determine if there are any

discrepancies.

According to Krippendorff, (2004) an acceptable level of agreement which data have to be

rejected as too unreliable must be chosen. The choice of a cutoff point should reflect the

potential costs of drawing invalid conclusions from unreliable data. To assure that the data under

consideration are at least similarly interpretable by researchers, it is customary to require

.800. =0.8 means that 80 % of the units recorded are perfectly reliable while 20% are the results

of chance.

In this study, an alpha () .800 will be applied to be the cutoff point which is the acceptable

level of agreement. If is lower than 0.8 then the data would be rejected.

Following is the summary of the results of Alpha (Table calculated using SPSS for the six

variables (dependent and independent). The results show that all the variables analyzed were

consistent and reliable. The Alpha for all the variables is more than the cutoff point of 0.8 which

is with a maximum of 20% of unreliable data. Table 2 shows that the variable of human resource

disclosure in the mission statement and type of industry are perfect with the consistency and

repeatability. The rest of the variables are having 0.4% to 8.6% of unreliable data.

Table 2: Summary of the Results of Alpha

Variables Alpha

Market capitalization for the organizations 0.996

Human resource disclosure in mission statement 1.000

Type of industry 1.000

Listing status 0.942

Profit after taxation 0.914

Human resource disclosure in annual report 0.960

Reproducibility test was also conducted to test the consistency between the judgments of two of

more coder. In order to ensure that the content analysis is reliable, it should bring in the same or

acceptable level of similar results.

One of the more commonly used measures of inter-rater reliability is Cohen’s Kappa. The data

collected from annual report for this research will be tested for the reliability on reproducibility

using this measure computed by SPSS. One hundred organizations were randomly selected for

the test. The process of the content analysis was repeated after they were initially performed. The

process then will be repeated by the second coder and ensure that the same annual report is being

analyzed.

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Larger sample size were selected for this test because the analysis of the reliability using Cohen

is attributable to chance, as larger sample will give a more accurate result of the analysis. The

results to be measured using Cohen’s Kappa compute by SPSS.

Fleiss (1981) suggested that a Cohen’s Kappa results of:

i. Less than 0.40 indicate poor agreement

ii. 0.40 – 0.59 is fair agreement

iii. 0.60 – 0.74 is good agreement

iv. 0.75 -1.00 is excellent agreement

Table 3: Results of Cohen’s Kappa compute by SPSS:

Variables Cohen’s Kappa

Market capitalization for the organizations 0.717

Human resource disclosure in mission statement 0.853

Type of industry 1.000

Listing status 0.922

Profit after taxation 0.932

Human resource disclosure in annual report 0.947

The analysis (Table 3), shows that all the variables fall in the range of 0.717 – 1.000 of the

Cohen’s Kappa test, indicating a high degree of consistency and reliability.

Findings of the Study

This section summarize and report on the overall human resource disclosure practices of the

organizations by common terms used by organizations in the disclosure of human resource in

annual report, the results of the correlations of human resource disclosure in annual report with

several independent variables and the results of the hypothesis testing using multiples regression

analysis.

Terms used in Annual Report in relation to Human Resource Disclosure

This phase of research employs the SPSS statistical software for analyzing the data. A

descriptive analysis was employed in determining the common human resource terms being used

among the sample organizations, based on the attribute of the score of each of the terms used.

Analysis using mean, mode and median score ranking, revealed that the most common terms

used by organizations in Malaysia in disclosure of Human Resource in the annual report is

“Employee”, follow by “Staff’, “Labor”, “Human”, “People”, “Workforce”, “Workers” and

“Recruit”. Table 4 below, shows details of the analysis.

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Table 4: Frequencies of human resource terms used in annual report for Malaysian organizations

Employee Human Labor People Recruit Staff Workers Workforce

N Valid 522 522 522 522 522 522 522 522

Missing 0 0 0 0 0 0 0 0

Mean 4.04 1.57 1.68 1.42 1.04 2.28 1.16 1.18

Median 4.00 2.00 2.00 1.00 1.00 2.00 1.00 1.00

Mode 3 1 2 1 1 2 1 1

Table 5 below, shows the breakdown of the average disclosure of human resource information

base on the terms used and the industries in details.

Table 5: Analysis showing human resource terms used in different industries

Employee/s Human Labour People Recruit Staff Workers Workforce

Construction 4 2 2 2 1 2 1 1

Consumer 4 1 2 1 1 2 1 1

Finance 4 2 1 2 1 3 1 1

Hotel 4 1 2 1 1 2 1 1

Industrial Product 4 1 2 1 1 2 1 1

Others** 5 2 1 2 1 3 1 1

Mining 3 2 2 1 1 2 1 1

Plantation 4 1 2 1 1 2 1 1

Properties 4 2 1 1 1 2 1 1

Technology 5 2 2 1 1 2 1 1

Trading & Services 4 2 2 2 1 2 1 1

Total Disclosure 45 18 19 15 11 24 11 11

Ranking 1 4 3 5 6 2 6 6

Industry with Highest Degree of Human Resource Disclosure

Previous studies have found that industry type has a significant impact on the level of disclosure

practices in the annual report (Cowen et al., 1987).

For the purpose of this study, the relationship between industry type and the extent of human

resource disclosure was considered using the 11 industries specification groups as classified in

the BURSA listing:

1. Construction

2. Consumer

3. Finance

4. Hotel

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5. Industrial Product

6. Others**

7. Mining

8. Plantation

9. Properties

10. Technology

11. Trading & Services

According to Cowen, et al. (1987), the organizations tend to provide information which is in line

with the unique nature of their industry sector. For example: labor intensive industries are

expected to provide more information about their employees; chemical industries are likely to

provide more information on environment.

Table 6, demonstrates that the extent of the human resource disclosure by the sample

organizations varied according to their industry type and the mean disclosure of human resource

related information by sectors. From the data collected an overall mean of 43 was obtained.

The mean disclosure for construction, finance, technology, trading and service and organizations

classified under “others” – mainly infrastructure project organizations are above 43 of overall

mean. Organizations under sectors such as consumer, hotel, industrial product, mining,

plantation and properties have human resource disclosure lower than the mean of 43.

The results of this study show that the overall extent of human resource disclosure was higher in

labor intensive industries which are consistent with the previous studies of Cowen, et al. (1987).

Table 6: Industry Types, Coding and Average HR Disclosure in Annual Reports

Industry Type

Coding Number of

organizations

Total Human resource

disclosure Average

Disclosure Ranked

1 Construction CON 39 1902 49 4

2 Consumer Product CONS 74 2713 37 8

3 Finance Accounting FIN 33 1880 57 2

4 Hotel HOT 6 230 38 7

5 Industrial Product IND 126 4435 35 9

6 Mining MIN 2 43 22 11

7 Plantation PLA 35 1374 39 6

8 Properties PROP 69 2211 32 10

9 Trading & Services TRA 114 5708 50 3

10 Technology TECH 17 814 48 5

11 Others** OTH 7 492 70 1

Total 522 21802 477

Overall Mean 43

**Details of organizations classified under “Others”

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Business Description Number of Disclosure

Water Treatment 46

Telecommunication 51

Highway construction 38

Highway maintenance 185

Managing toll road concessions and manages portfolio of property and real estate investments 27

Telecommunication 73

Selling water and disposing waste water 72

Correlation Analysis

For this phase of the research, the dependent variable – human resource disclosure was measured

by assessing the level of human resource disclosure from 522 annual reports. It also summaries

descriptive statistic for dependent variable (human resource disclosure in annual report) and the

following independent variables:

Level of human resource disclosure in annual reports (RA)

Size of the organization (SO)

Type of Industry (TI)

Listing Status (LS)

Disclosure of HR information in mission statement (MSD)

Financial performance (FP)

Based on the hypothesis, the following dependent and independent variables were developed:

Table 7: Construct of the Dependent and Independent Variables

Dependent variable Measurement

Annual report human resource

disclosure (ARHRD)

The level of human resource disclosure in annual reports

Independent variables

Size of the organization (SO) Measured by market capitalization

Type of Industry (TI) Coded by industry classification in BURSA

Listing Status (LS) Coded by local or foreign listing

HR disclosure in mission statement

(MSD)

Coded by YES/NO human resource disclosure in

mission statement

Financial performance (FP) Measured by profit after taxation

Descriptive statistics of all the independent variables used in the test of the relationship between

ARHRD and organizations’ specific characteristics is being presented in Table 8 below.

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The main objective of describing the variables is to identify the distribution of the data. A range

values between 2 to -2 is normally used as an acceptable range for normality assumption

Norusis, (2004). The variables used to measure the size of organizations using market

capitalization is quite severely affected by skewed distribution with skewness of 3.254. The data

of the size of organizations’ data has been transformed into log form in order to gain normality

distribution of the data.

Table 8: Descriptive Statistic for all variables

Variables Sample

size (N)

Minimum Maximum Mean Standard

Deviation

Skewness Kurtosis

SO (Log) 522 5 10 8 0.496 2.055 2.181

TI 522 1 11 5.79 2.865 -0.181 -1.298

LS 522 1 2 1.13 0.335 1.810 2.029

MSD 522 1 2 1.87 0.116 -2.154 2.049

FP 522 5 9 7 0.767 1.766 2.115

The correlations presented and discussed below for the transformed variables provide insights

into the association between the variables.

Table 9: Pearson’s correlation matrix for all variables with sample size of 522

Variables ARHRD TI SO LS FP MSD

ARHRD Pearson Correlation

1.000

Sig. (2-tailed) -

TI Pearson Correlation

.070 1.000

Sig. (2-tailed) .109 -

SO Pearson Correlation

.228(**) -.015 1.000

Sig. (2-tailed) .000 .734 -

LS Pearson Correlation

.127(**) -.123(**) .094(*) 1.000

Sig. (2-tailed) .004 .005 .032 -

FP Pearson Correlation

.205(**) .005 .117(**) -.026 1.000

Sig. (2-tailed) .000 .914 .007 .560 -

MSD Pearson Correlation

-.494(**) -.048 -.090(*) -.168(**) -.144(**) 1.000

Sig. (2-tailed) .000 .273 .041 .000 .001 - ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed).

As Pearson correlation should only be used when all variables are normally distributed. The

initial analysis of skewness for market capitalization is quite severely affected by skewed

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distribution with skewness of 3.254. Spearman’s rho correlations were computed to compare

with the Pearson’s correlations to ensure reliability of the research.

Table 10 below presents Spearman’s rho correlation analyses between variables and no

significant differences from Pearson’s correlations were identified in terms of direction, level of

significant or extent of correlation.

Table 10: Spearman’s rho correlation matrix for all variables with sample size of 522

Variables ARHRD TI SO LS FP MSD

ARHRD Correlation Coefficient

1.000

Sig. (2-tailed) -

TI Correlation Coefficient

.089(*) 1.000

Sig. (2-tailed) .041 -

SO Correlation Coefficient

.154(**) -.015 1.000

Sig. (2-tailed) .000 .731 -

LS Correlation Coefficient

.130(**) -.110(*) .050 1.000

Sig. (2-tailed) .003 .012 .256 -

FP Correlation Coefficient

.180(**) .019 .079 -.035 1.000

Sig. (2-tailed) .000 .667 .072 .425 -

MSD Correlation Coefficient

-.473(**) -.058 -.104(*) -.168(**) -.116(**) 1.000

Sig. (2-tailed) .000 .182 .018 .000 .008 . ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed).

The correlations table indicates that:

i. The correlation between human resource disclosure and type of industry is 0.089 with a

corresponding p-value of significant of 0.041which is less than 0.05 based on 522 samples.

There is a significant positive relationship between the human resource disclosure in the

annual report and the type of industry.

ii. The correlation between human resource disclosure and size of the organizations is 0.154 with

a corresponding p- value of significant of 0.000 which is less than 0.05 based on 522 samples.


annual report and the size of the organizations.

iii. The correlation between human resource disclosure and the organizations’ listing status is

0.134 with a corresponding p- value of significant of 0.003 based on 522 samples. There is a

significant positive relationship between the human resource disclosure in the annual report

and the organizations listing status.

iv. The correlation between human resource disclosure and financial performance is 0.180 with a

corresponding p- value of significant of 0.000 which is less than 0.05 based on 522 samples.


annual report and the financial performance.

v. The correlation between human resource disclosure and mission statement of human resource

disclosure is -0.474 with a corresponding p- value of significant of 0.000 based on 522

samples. There is a significant negative relationship between the human resource disclosure

in the annual report and the mission statement disclosure.

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Multiple Linear Regression

Multiple regression analysis is a statistical technique that can be used to analyze the relationship

between a single dependent variable and several independent variables. The valid sample size for

the multiple regression analysis was 522.

For the purpose of this study, a series of multiple linear regression analysis was conducted using

SPSS to test the hypotheses as below:

H1: Large organizations with various stakeholder groups are more likely to provide Human

Resource Disclosure in their Annual reports.

H2: Organizations with high level of employees’ concentration (service industry) are more likely

or willing to provide Human Resource Disclosure compared to organizations with low-level

employees’ concentration (manufacturing industry and others).

H3: Organizations, which are foreign listed organizations, are more likely to provide Human

Resource Disclosure compared to those organizations, which are not.

H4: Organizations with staff development and staff welfare in their vision and mission statement

are more likely to provide Human Resource Disclosure as compared to those that do not consider

such development and welfare.

H5: Organizations with better financial performance are more likely to provide Human Resource

Disclosure in their Annual report.

There are several potential problems associated with the generalization of the multiple regression

analysis which must be considered before employing the regression models. The assumptions

and potential problems underlying multiple regression models are discussed below:

i. One of the key issues which must be considered before employing a multiple linear

regression analysis is multicollinearity. Multicollinearity exists when two or more of the

independent variables used in a regression are correlated. One simple way to detect the

multicollinearity in regression is to calculate the coefficient of correlation between each pair of

independent variables-the presence of high correlations is an indication of substantial

collinearity. According to Field (2005), a commonly accepted threshold for a potential

multicollinearity problem is when the correlation coefficient is >0.800.

Table 9: Pearson’s correlation matrix and Table 10: Spearman’s rho correlation matrix there was

no cause for concern regarding multicollinearity as both of the matrix show correlation

coefficient of independent variables which is below the threshold of 0.800.

ii. The other fundamental assumption in multiple regression analysis is the normality of data.

Table 8, shows the Kurtosis which is slightly above 2 for most of the variable suggest that the

normality assumption is nearly satisfied.

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The following section discusses in detail the results to the regression models examined in this

study.

Table 9 and Table 10 – the Pearson correlation table and Spearman’s rho correlation table

display the correlations for all the variables included in this study. Each independent variable is

correlated low to moderately high to the dependent variable except HR disclosure in Mission

Statement with a negative correlation. The correlations table shows that the correlations between

the independent variables and dependent variable are significant (as most of the variables have p

value <0.05). The independent variables are having low correlations with each other; this would

allow them to make relatively unique contributions in predicting the dependent variable.

From the analysis of Table 9 and Table 10, a summary has been produced to show the

hypothesized and actual relationship of variables. Table 11 highlights the hypothesized and the

actual relationships for the key variables based on the hypotheses, the correlations indicate

whether or not a relationship between the variable exist.

Table 11: Hypothesized and Actual Relationship

Relationship Hypothesized Actual

H1 Level of human resource disclosure and the size of the organizations

Positive Statistically significantly positive

H2 Level of human resource disclosure and the type of industry


H3 Level of human resource disclosure and the foreign listing status


H4 Level of human resource disclosure and the disclosure of human resource emphasis in mission statement

Positive Statistically significantly Negative

H5 Level of human resource disclosure and the financial performance


Table 12 is the model summary of the analysis. The model summary table display R, R

2,

Adjusted R2 and standard error of estimate. The value of R, R

2, and Adjusted R

2 measured the

degree to which the human resource disclosure was predicted to the five independent variables.

The value of the standard error of the estimate measures the degree to which human resource

disclosure was not predicted from the five variables.

Table 12: Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .544(a) .296 .290 .603

a Predictors: (Constant), HR disclosure in Mission Statement, Type of Industry, Capitalization of organization, Profit After Tax, Listing Status

The model summary table show that the multiple correlation coefficient of R=0.544. The R2 is

the coefficient of determination of 0.296 is to measures the proportion of the total variation of

human resource disclosure. According to Field (2005), values of R2

below 0.2 are considered

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weak, between 0.2 and 0.4, moderate and above 0.4, strong. In this study, the variable of human

resource disclosure in mission statement, type of industry, capitalization of organization, profit

after tax and listing status accounted for approximately 30% of the dependent variable of

disclosure of human resource in annual report which is considered as moderate correlation.

Adjusted R2

modifies the value of R2

in an attempt to better estimate the true population value.

Finally, the standard error of the estimate of 0.603 indicates the degree of which the independent

variables were unable to predict scores on the dependent variable of 0.60 points on average

which is quite a low estimation.

The next table, Table 13 ANOVA, test whether the regression model, with all of the independent

variables included, significantly predicts the disclosure of human resource disclosure in the

annual report. In ANOVA table, a p-value less than or equal to 0.05 indicates that the regression

model, with all the independent variables included, significantly predict the dependent variable

in a study.

The p-value of 0.000, which is less than 0.05 conclude that the regression equation with the five

independent variables significantly predicts the disclosure of human resource in annual report.

Table 13: Analysis on ANOVA

Model

Sum of Squares df

Mean Square F Sig.

1 Regression 79.109 5 15.822 43.487 .000(a)

Residual 187.736 516 .364

Total 266.845 521

a Predictors: (Constant), HR disclosure in Mission Statement, Type of Industry, Capitalization of organization, Profit After Tax, Listing Status b Dependent Variable: Annual Report HR disclosure

Table 14 -Analysis of coefficients provides the necessary values to construct a regression

equation and to test each of the independent variable for significance.

For the purpose of this study, the following regression equation has been developed:

ARHRDs = a + SOx1 + TI x2 + LSx3 + MSDx4 + FPx5 + e

Where

ARHRD = Annual report human resource disclosure- The level of human resource

disclosure in annual reports

SO = Size of the organization- Measured by market capitalization

TI = Type of Industry- Coded by industry classification in BURSA

LS = Listing Status- Coded by local or foreign listing

MSD = HR disclosure in mission statement- Coded by YES/NO human resource

disclosure in mission statement

FP = Financial performance- Measured by profit after taxation

a = Intercept

e = error term

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The values in the regression equation have been analyzed in Table 13: Analysis of Coefficients.

Substituting these values into regression equation formula produces the following equation for

predicting the human resource disclosure in annual report scores:

ARHRDs = 2.413 + 0.155SO + 0.014TI + 0.095LS + (- 0.947) MSD + 0.073FP + e

This is the regression equation based on the score of 522 organizations. Given an individual’s

score on the five independent variables, a predicted human resource disclosure score can be

calculated for individual organization.

Table 14: Analysis of Coefficients

Model Unstandardized

Coefficients Standardized Coefficients

t Sig. (p-value) B

Std. Error Beta

1 (Constant) 2.413 .238 10.140 .000

Capitalization of organization

.155 .034 .170 4.552 .000

Type of Industry .014 .009 .056 1.499 .134

Listing Status .095 .081 .045 1.176 .240

Profit After Tax .073 .023 .121 3.214 .001

HR disclosure in Mission Statement

-.947 .080 -.451 -11.865 .000

a Dependent Variable: Annual Report HR disclosure

Table 14 also shows the analysis of the independent variables for significance. The p-value for

capitalization of organization, profit after tax and human resource disclosure in mission

statement are significant since the p-value is less than 0.05. On the other hand, type of industry

and listing status, is not significant, since the p-value is 0.134 and 0.240 respectively.

Limitation of Study

This study is subject to several limitations. First, the sole use of the annual reports from the

companies listed on BURSA main board in the study has the potential to affect the

generalisability of the findings. Second, this study is that it does not examine the models of

measuring human resource value in the annual report. While the investigation into the level of

human resource disclosure in annual report many enhance the understanding of the motivation

behind such disclosure, exploring what model would be used or had been used would enhance

the research literature on the disclosure of human resource in the annual report on future

research. Third limitation is that the literature review might not be extensive enough. Despite the

considerable number of articles covered in the literature review, there is still articles on the title,

which could not be recovered due to the followings reasons such as the difference in the use of

key words by researchers from that used for the literature search, the availability of the relevant

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articles in the database and the failure to include some the articles due to the fact that only the

abstract rather than full text was available and which, the researcher felt, might not give the full

understanding of the topics.

There are also two limitations common to the research methodology adopted in the study: One

relating to content analysis and the other relating to multiple regression analysis.

Firstly, researchers cannot easily ascertain the importance or the quality of the disclosure using

content analysis. Content analysis is a purely descriptive tool. It describes what is there, but may

not reveal the underlying motives for the observed pattern ('what' but not 'why'). Content analysis

is based on the notion that the quality of disclosure is high if the quantity of disclosure is high.

This is based on the proposition that if an item is important to an organisation, the organisation

will consider disclosing greater amount of information regarding that particular item. However,

this does not equate with the consideration of the quality of such information, albeit that the item

may be important to the organization.

Secondly, multiple regression analysis does not allow the researchers to consider the causal

relationship between the dependent and the independent variables, as the focus is on associations

rather than the cause and effect relationship. For example, there was a significant association

between the financial performance and the degree of human resource disclosure in the annual

report but this study does not attempt to conclude that high financial performance directly

influences the level of human resource disclosure in the annual report for any individual

organization. It does, however, ascertain that there is a relationship between the two variables

and that for the sake of analyses; companies with high financial performance do have high

human resource disclosure in their annual report.

Conclusions and Future Research

The study examined the current practice of recording human resource among the companies in

Malaysia, in particular by considering the terms used in recording human resource information in

the annual reports and the level of human resource disclosure for different industries.

Findings from the content analysis shows that the most common terms used by companies in

Malaysia in disclosure of human resource in the annual report is “Employee”, followed by

“Staff’, “Labor”, “Human”, “People”, “Workforce”, “Workers” and “Recruit.

In the analysis on the level of disclosure, calculation of average content disclosure was

conducted to identify the level of disclosure for different industries. The mean disclosure for

construction, finance, technology, trading and service and companies classified under “others”

(mainly infrastructure project companies) were above 43 of overall mean. Companies under

sectors such as consumer, hotel, industrial product, mining, plantation and properties had human

resource disclosure lower than the mean of 43. The results of this study show that the overall

extent of human resource disclosure was higher in labor intensive industries which are consistent

with the previous studies of Cowen, et al. (1987).

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The study findings also indicate that the level of human resource disclosure as a dependent

variable has a significant positive relationship with the independent variables of size of the

companies, type of industry, listing status of the companies, financial performance, and a

significant negative relationship with the mission statement disclosure.

As discussed, five hypotheses were developed in order to examine the relationship between the

levels of human resource disclosure in annual report in relation to the selected corporate specific

independent variables, namely the size of the companies, the type of industry, the listing status of

the companies, mission statement disclosure and the financial performance of the companies.

Hypothesis 1, firm size was found to be significantly associated with the level of human resource

disclosure in annual report. This finding is inline with most of the previous empirical studies

examining voluntary disclosure practices (Singhvi and Desai1971). Further investigation on

multiples regression analysis, firm size was statistically positively related to the disclosure and

hence H1 was supported.

Hypothesis 2, type of industry was found to be significantly associated with the voluntary

disclosure of information in the annual report (Cowen et al. 1987). In this study, hypothesis were

made for the level of disclosure of the human resource in the annual report .The results reveal

that there was a statistically positive relationship between industry type and the disclosure.

Analysis shows that companies with high level of employees’ concentration (service industry)

are providing more human resource disclosure. Based on multiples regression analysis, H2 was

supported.

Hypothesis 3 was concerned with correlation of the listing status of the companies with the

independent variable of the level of human resource disclosure in the annual report. It was

hypothesized that companies with foreign listings would have more incentive to voluntarily

disclose information in the annual report (Haniffa and Cooke 2005). The findings in this study

show that there is a statistical positive relationship between the listing status of the companies

and the level of the human resource disclosure, whereby companies which are foreign listing

companies provide more human resource information in the annual report. Based on the multiple

regression analysis, there was a significantly positive association between listing status and the

hypotheses H3 was supported.

Hypothesis 4 was concerned with the disclosure of human resource in the mission statement. It

was proposed that companies with an emphasis on human resource in their mission statement

were more likely to have higher level of human resource disclosure in the annual report. In order

to achieve the mission of the companies, the management is geared towards the recording of the

value of human resource. The findings show that there was a significant negative association

between mission statement disclosures with the level of human resource disclosure. In summary,

based on the multiple regression analysis, H4 was not supported.

Hypothesis 5 was related to the financial performance in relation to the level of human resource

disclosure. The investors generally perceive the absence of any extra information provided in the

annual report as an indication of not being transparent and reliable in their reporting. This

provides average or better performing firms with an adverse selection incentive to disclosure

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(Verrecchia, 1983). Prior empirical evidence shows that better performing companies have

greater incentive to disclose more information in their annual reports and are more aware of

various disclosure of information in the annual report so as to avoid misperceptions by the users

for hiding some unfavorable information (Skininner, 1994). The findings on the association

between the level of human resource disclosure and the financial performance show a significant

positive relationship. Based on the multiples regression analysis, H5a was supported.

Future research could examine the human resource disclosure practices of a larger number of

samples for the annual report analysis; this could include the companies listed on second board

and companies which are not listed in the stock exchange. While the current study was

concerned with the disclosure practices of the companies in the main board, due to the

proposition that these companies are the ones whose details are more in demand, other studies

may want to consider whether there is indeed a significant difference in the disclosure practices

of the main board companies and those companies which are not listed in the stock exchange.

The second research opportunity relates to investigating other forms of external reporting on the

qualitative measure of human resource disclosure. While the use of annual report as main

external reporting medium is well documented and supported, and therefore accepted as the most

relevant type of corporate documents in the literature, there are alternative means of

disseminating corporate information to various stakeholders. Other medium include press

releases, quarterly or half yearly reports, special report, corporate website and others. The

utilization of other medium to analyze the qualitative measure of human resource disclosure

should be explored in future studies.

The third avenue of further research relate to the quantitative measures of human resource value

in the annual report. It is essential that human resource be measured and valued in financial term

if it is to be accommodated within the accounting profession’s existing financially driven

framework. It is very clear that human resource value will be denied as equal to other assets in

financial statement unless emphasis is placed on some form of financial quantification.

A fourth direction of future research could involve examining the impact of recording human

resource in annual report for companies with such a procedure in place. While most of the users

aware that human resource disclosure is important to a company, it is equally important to know

how the procedure would impact the operation, decision making of a company.

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