HUMAN RESOURCE DISCLOSURE: THE CURRENT PRACTICE AND ITS ASSOCIATION WITH CORPORATE CHARACTERISTICS IN MALAYSIA

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1 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, ( Economic Planning Unit, 2002) and a Knowledge-Based Economy Master Plan which was launched in 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. COPY RIGHT 2012 Institute of Interdisciplinary Business Research 68

2 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 serviceoriented 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 valueadded 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 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 COPY RIGHT 2012 Institute of Interdisciplinary Business Research 69

3 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. COPY RIGHT 2012 Institute of Interdisciplinary Business Research 70

4 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 ( 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 COPY RIGHT 2012 Institute of Interdisciplinary Business Research 71

5 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 COPY RIGHT 2012 Institute of Interdisciplinary Business Research 72

6 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 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. COPY RIGHT 2012 Institute of Interdisciplinary Business Research 73

7 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). COPY RIGHT 2012 Institute of Interdisciplinary Business Research 74

8 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 2 For a disclosure of 21 to 40 words 3 For a disclosure of 41 to 60 words 4 For a disclosure of 61 to 80 5 For a disclosure of 81 to For a disclosure of 101 to For a disclosure of 121 to 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, ,000, ,000, ,000, ,000, ,000, ,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 COPY RIGHT 2012 Institute of Interdisciplinary Business Research 75

9 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, 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 COPY RIGHT 2012 Institute of Interdisciplinary Business Research 76

10 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 Human resource disclosure in mission statement Type of industry Listing status Profit after taxation Human resource disclosure in annual report 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. COPY RIGHT 2012 Institute of Interdisciplinary Business Research 77

11 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 is fair agreement iii is good agreement iv is excellent agreement Table 3: Results of Cohen s Kappa compute by SPSS: Variables Cohen s Kappa Market capitalization for the organizations Human resource disclosure in mission statement Type of industry Listing status Profit after taxation Human resource disclosure in annual report The analysis (Table 3), shows that all the variables fall in the range of 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. COPY RIGHT 2012 Institute of Interdisciplinary Business Research 78

12 Table 4: Frequencies of human resource terms used in annual report for Malaysian organizations Employee Human Labor People Recruit Staff Workers Workforce N Valid Missing Mean Median Mode 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 Consumer Finance Hotel Industrial Product Others** Mining Plantation Properties Technology Trading & Services Total Disclosure Ranking 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 COPY RIGHT 2012 Institute of Interdisciplinary Business Research 79

13 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 Consumer Product CONS Finance Accounting FIN Hotel HOT Industrial Product IND Mining MIN Plantation PLA Properties PROP Trading & Services TRA Technology TECH Others** OTH Total Overall Mean 43 **Details of organizations classified under Others COPY RIGHT 2012 Institute of Interdisciplinary Business Research 80

14 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 Annual report human resource disclosure (ARHRD) Independent variables Size of the organization (SO) Type of Industry (TI) Listing Status (LS) HR disclosure in mission statement (MSD) Financial performance (FP) Measurement The level of human resource disclosure in annual reports Measured by market capitalization Coded by industry classification in BURSA Coded by local or foreign listing Coded by YES/NO human resource disclosure in mission statement 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. COPY RIGHT 2012 Institute of Interdisciplinary Business Research 81

15 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 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 Minimum Maximum Mean Standard Skewness Kurtosis size (N) Deviation SO (Log) TI LS MSD FP 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 Sig. (2-tailed) - TI Pearson Correlation Sig. (2-tailed) SO Pearson Correlation.228(**) Sig. (2-tailed) LS Pearson Correlation.127(**) -.123(**).094(*) Sig. (2-tailed) FP Pearson Correlation.205(**) (**) Sig. (2-tailed) MSD Pearson Correlation -.494(**) (*) -.168(**) -.144(**) Sig. (2-tailed) ** 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 COPY RIGHT 2012 Institute of Interdisciplinary Business Research 82

16 distribution with skewness of 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 Sig. (2-tailed) - TI Correlation Coefficient.089(*) Sig. (2-tailed) SO Correlation Coefficient.154(**) Sig. (2-tailed) LS Correlation Coefficient.130(**) -.110(*) Sig. (2-tailed) FP Correlation Coefficient.180(**) Sig. (2-tailed) MSD Correlation Coefficient -.473(**) (*) -.168(**) -.116(**) Sig. (2-tailed) ** 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 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 with a corresponding p- value of significant of which 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 size of the organizations. iii. The correlation between human resource disclosure and the organizations listing status is with a corresponding p- value of significant of 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 with a corresponding p- value of significant of which 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 financial performance. v. The correlation between human resource disclosure and mission statement of human resource disclosure is with a corresponding p- value of significant of based on 522 samples. There is a significant negative relationship between the human resource disclosure in the annual report and the mission statement disclosure. COPY RIGHT 2012 Institute of Interdisciplinary Business Research 83

17 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 > 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 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. COPY RIGHT 2012 Institute of Interdisciplinary Business Research 84

18 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 H1 H2 H3 H4 H5 Relationship Hypothesized Actual Level of human resource disclosure and the size Positive Statistically of the organizations significantly positive Level of human resource disclosure and the type Positive Statistically of industry significantly positive Level of human resource disclosure and the Positive Statistically foreign listing status significantly positive Level of human resource disclosure and the Positive Statistically disclosure of human resource emphasis in mission significantly statement Negative Level of human resource disclosure and the financial performance Positive Statistically significantly positive Table 12 is the model summary of the analysis. The model summary table display R, R 2, Adjusted R 2 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) 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= The R 2 is the coefficient of determination of is to measures the proportion of the total variation of human resource disclosure. According to Field (2005), values of R 2 below 0.2 are considered COPY RIGHT 2012 Institute of Interdisciplinary Business Research 85

19 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 R 2 modifies the value of R 2 in an attempt to better estimate the true population value. Finally, the standard error of the estimate of 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 Mode l Sum of Squares df Mean Square F Sig. 1 Regression (a) Residual Total 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 SO TI LS MSD FP a e = Annual report human resource disclosure- The level of human resource disclosure in annual reports = Size of the organization- Measured by market capitalization = Type of Industry- Coded by industry classification in BURSA = Listing Status- Coded by local or foreign listing = HR disclosure in mission statement- Coded by YES/NO human resource disclosure in mission statement = Financial performance- Measured by profit after taxation = Intercept = error term COPY RIGHT 2012 Institute of Interdisciplinary Business Research 86

20 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 = SO TI LS + ( ) MSD FP + 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 B Std. Error Beta t Sig. (pvalue) 1 (Constant) Capitalization of organization Type of Industry Listing Status Profit After Tax HR disclosure in Mission Statement 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 On the other hand, type of industry and listing status, is not significant, since the p-value is and 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 COPY RIGHT 2012 Institute of Interdisciplinary Business Research 87

21 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). COPY RIGHT 2012 Institute of Interdisciplinary Business Research 88