Clarifying the effect of intellectual capital on firm performance: The case of Vietnam

914 CLARIFYING THE EFFECT OF INTELLECTUAL CAPITAL ON FIRM PERFORMANCE: THE CASE OF VIETNAM Hoang Thi Hue hoanghue@neu.edu.vn Faculty of Human Resources Economics and Management, National Economics University, Hanoi, Vietnam. Vu Huy Hoang vuhuyhoangyesneu@gmail.com Audit K57, Institute of accounting – auditing, National Economics University, Hanoi, Vietnam. Nguyen Duong Hong Nhung nhungndh.yesneu@gmail.com Mathematical Financial 57, Faculty of Mathematical economics, National Economics University, Hanoi, Vietnam. Quach Hong Hanh hanhqh.yesneu@gmail.com Actuarial science K57, Faculty of Mathematical economics, National Economics University, Hanoi, Vietnam. Abstract: In a corporate world where true value is no longer determined by physical assets alone, managers and practitioners consider more about developing inner capital of the company. Recent studies around the world also suggest an impact of intellectual capital and on firm performance. However, there are a few studies around Vietnamese company. The purpose of this empirical study is to investigate the three elements of intellectual capital, i.e. human capital, structural capital, and relational capital. It also focuses on assessing the contribution of Intellectual capital to the performance of a firm. Data is collected for enterprise surveys from 2012 to 2015 released by General Statistics Office (GSO) and analysed using regression, the result shows that there are positive relationships between the intellectual capital and performance of company business. In particular, human capital and relational capital have a strong effect on performance of firm. Yet, structural capital is not important and seems to reduce the impact of intellectual capital to the performance of the business. Key: Intellectual capital, Performance, Value added, Viet Nam. 1. Introduction By the end of the twentieth century, the economy began to undergo certain changes that had a decisive influence on economic growth and development. In this stage, the competitive advantage of individual countries as well as of enterprises is reflected not only in the traditional resource of production, such as properties of land, labor and other 915 economic assets (Sullivan, 2000). According to resource-based view, a company's resources, especially intangible assets, have an important role in helping the company achieve high efficiency and sustainability (Eisenhardt and Schoonhoven, 1996). The importance of the company's intangible assets is increasing (Lev, 2001). Bontis (2002) suggest that the current trend is that firms focus less on tangible assets than on intangible assets when seeking a competitive advantage. Over the past two decades, intellectual capital (IC) has been adopted by most organizations around the world. Intellectual capital plays a fundamental role in modern organizations. It is part of the business foundation in the 21st century (Rudez and Mihalic, 2007). Intellectual capital, therefore, has been identified as one of the key drivers of corporate performance (Youndt et al., 2004). It can be said that measuring IC variables is not easy. Zambon (2004) asserts that no method is applied around the world to measure. However, quantitative studies have used the VAIC - value added method (Public, 1998) to measure the effect of IC. The topic of IC has been studied and evaluated by many scholars to provide a concept for improving the efficiency of enterprises (Augier and Teece, 2005). However, in Vietnam, IC is a new concept. Thus, this study uses the quantitative measure, value added intellectual coefficient (VAIC) developed by Pulic (1998) as a measure of IC efficiency. Data is collected for enterprise surveys from 2012 to 2015 released by General Statistics Office (GSO) and analysed using regression. The paper is organised as follows. The following section discusses the prior IC literature, focusing on the relationship between IC efficiency (VAIC) and firm performance. The hypotheses to be tested and the method used to test those hypothesized relationships are described in the next sections. The results are then outlined, discussed and some conclusions are offered. 2. Literature review Intellectual capital (IC) Edvinsson and Malone (1997) defined intellectual capital as knowledge that could be transformed into values, intangible assets which are not clearly listed in the corporate balance sheet, yet having positive influence on the efficiency of the business, thus showing the relationship between employees, ideas and information. They divide intellectual capital into human capital and structural capital. Human capital is based on the knowledge created and stored by the organization's employees. While structural capital is based on the presence, empowerment, and infrastructure of human capital. Structural capital is divided into organizational capital (the knowledge that is created and stored in the organization's IT system and processes that accelerate the flow of knowledge through the organization) and customer capital (relationships that an organization has with its customers). The study by Reed et al. (2006) conclude that intellectual capital can be divided into human capital, organization capital and social capital. Although researchers may not agree to the exact classification and definition of intellectual capital, there is widespread agreement that it contains human capital (HC), 916 relations capital (RC) and structural capital (SC) (Edvinsson, 1997; Tayles et al., 2007). Therefore, our research will evaluate the capital (IC) based on human capital, relationship capital, and structural capital. Human capital (HC) Human capital is a key component of intellectual capital. The concept of human capital has been adequately and systematically mentioned since the 1960s. Human capital is defined as knowledge, skills, innovation and competence (Bontis et al., 2000). Edvinsson and Malone (1997) defined human capital as a combination of knowledge, skill, innovativeness and ability of company‘s individual employees to meet the task at hand. Human capital makes reference to the knowledge – explicit and tacit – that people possess, as well as their ability to generate it, which is useful for the mission of the organization (CIC, 2003). Relationship capital (RC) Relationships capital can be viewed as a perception of value that consumers hold when they engage with suppliers of (Petrash, 2001). In this way, we can see that the relationship capital is only obtained from the customer, which is the result of their perception of the company. Relationship capital is defined as the set of available resources of the organization and current relationships which are made through interactions between individuals or organizations (Kostova and Roth, 2003; Shipilov and Danis, 2006). Structural capital (SC) Structural capital is often used to refer to the processes and procedures that are created and stored in a company's technology system to accelerate the flow of knowledge through the organization (Carson et al., 2004). This definition differs from the approach given in strategic management literature (eg Gibson and Birkinshaw, 2004; Kang and Snell, 2009). Gibson and Birkinshaw (2004) put the structural capital into an imbalanced context by developing a set of systems and processes. It allows the ability of link systems and adaptations to maintain performance at the company level. Kang and Snell (2009) classify organizational capital into two alternative forms: mechanical and organic. The alternative forms of capital of an organization have different effects in integrating knowledge within a company. Performance According to Armstrong and Baron (2005), firm‘s performance is defined as the aggregate assessment of how many benefits an organization generates based on the amount of resources spent. Business performance is measured by the attainment of business objectives and the efficiency of the enterprise's use of resources to achieve the goals. For analysis and measurement, researchers evaluate organization performance through financial parameters such as profitability, market share, capital growth, etc. It is because of that financial results are one of firm‘s objectives and also a tool for management and decision-making, setting the next target within the enterprise. Hypothesis IC as a strategic resource that companies use to gain competitive advantage and create value for efficiency (Marr et al., 2003). Although experimental tests show mixed 917 results, after the findings of Chen et al., (2005) and Tan et al., (2007) and other theories based on resource-based theory, it can be hypothesized that there is a direct positive relationship between the efficiency of the IC and the performance of the firm. H1 (a). VAIC positively affects the performance of the company. Previous studies have found that different aspects of IC also have a significant impact on business performance (Chen et al., 2005; Chan, 2009). Hence, it can be assumed that the components of VAIC have a positive impact on the efficiency of the business: H1 (b). HCE positively affects the performance of the company. H1 (c). SCE positively affects the performance of the company. In addition, capital efficiency has been found to have a significant positive effect on efficiency (Chen et al., 2005). Therefore, this relationship is also hypothesized: H1 (d). CEE positively affects the performance of the company. IC or capital efficient use over a period of time may not affect performance until after some time. For example, new managers (HCs) may not add value until later become more experienced. New systems (SC) and new plant and equipment (CE) can having problems on the operation, the efficiency will decrease until it actually works again. Thus, Chen et al., (2005), and Tan et al., (2007) hypothesized that VAIC and its components over a period of time would positively affect performance in the following period: H2 (a). VAIC of last year positively impacted the performance of the company this year. H2 (b). CEE of last year before had a positive impact on the performance of the company this year. However, human capital has a certain lag when creating growth for business efficiency. Clarke (2011) confirmed that last year's HCE had a greater impact on the current year's business performance than the current HCE. However, he also proposed a longer time series evaluation: H2 (c). HCE of two years before positively impacted the company's operations this year. H2 (d). SCE of two years before had a positive impact on the performance of the company this year. H2 (e). VAIC of two years before positively impacted the performance of the company this year 3. Methodology Sample This study uses the annual survey data of the General Statistics Office (GSO), consists of firm from 2012 to 2015. Due to missing data on selected variables, the final sample for analysis consists of firm-year observations depending on the particular variable 918 concerned. After processing to retrieve the information needed for the study, the sample used included more than 500 thousand observations described in the following table 1: Table 1: Sample distribution by industry Firms 2012 2013 2014 2015 Agriculture and Forestry 951 985 951 828 Seafood 33 41 25 33 Mining industry 289 252 216 211 Processing industry 19,467 17,700 19,035 18,328 Production and distribution of electricity, gas and gas 410 287 411 382 Build 18,255 16,908 16,939 18,345 Commercial 58,848 54,036 53,699 56,725 Hotels, restaurants 3,104 3,101 3,394 4,066 Transportation, warehousing 11,212 10,408 10,745 12,802 Other services 31,087 27,805 27,379 30,131 Total 111,729 143,712 131,617 132,874 549,824 Measurement of variable Measurement of IC One of the most common method to calculate IC performance is the VAIC model developed by Pulic (1998). This model measures the added value of any business along with the individual contribution of each type of asset (HC, SC, and CE) to value creation. VAIC is an index-based measure that uses financial reported data. It calculates asset value and IC efficiency - which is very useful for managerial decision-making. Calculating VAIC To calculate VAIC, the ability of a company to generate value-added (VA) for all stakeholders must first be calculated. In the simplest form, VA is the difference between output and input. The results show net revenue and input contain all costs incurred in earning revenue except for labor costs as a value-creating entity (Tan et al., 2007). This VA is also defined as the net value generated by the firms in the year (Chen et al., 2005) and can be expressed as: VA = S - B = NI + T + DP + I + W Where: S is the net revenue (output); B is the material and service purchased or the cost of the goods sold (input); NI is net income after tax; T is the tax; DP is depreciation; I is the cost; and W is the salary and salary of the employee. The above equation VA is called the ―Positive Value-Added‖ approach (Riahi-Belkaoui, 2003) and is the method used in this study. 919 Human Capital Effect (HCE). Human capital (HC) includes skills, experience, productivity, knowledge and suitability of employees in the workplace. In the VAIC model, HC levels are defined as wages over time (Pulic, 1998). In particular, higher wages for more skilled workers add value to the company than lower-wage employees. HCE for a dollar spent on human capital or how many employees will generate VA and calculated as follows: HCE = VA/HC where HC is the total salaries and wages. If wage is low and VA is high, the company is using HC effectively. If the VA is low-wage-related, HC's are not used effectively and the HCE will be low. Higher HCE results from the efficient use of HC to increase value through operating profit. Structural Capital Effect (SCE). Structuring (SC) includes components such as strategy, organizational networks, patents and brand names. Pulic (1998) calculated SC as follows: SC = VA – HC. Thus, VA is affected by the effect of HC and SC. SC depends on HC, and larger HCs will be transformed into improved internal structures (Nazari and Herremans, 2007). HC and SC are inversely related (Tan et al., 2008). With this equation, SC decreases as HC increases, which is logically incompatible with SC's theoretical definition. To correct this problem, Pulic (1998) calculates SCE as follows: SCE = SC/VA. SCE is the SC dollar of the company and for every dollar of value added, and when the HCE increases, SCE increases. If the counting methods for both HCE and SCE are calculated in VA above the numerator, this generates a logical inconsistency (Pulic, 1998). Capital Use Efficiency (CEE). CEE includes the efficiency that SCE and HCE do not capture. Pulic (1998) argues that IC cannot create value itself, and therefore it must be combined with the use of physical and financial capital (CE). Therefore, CE is calculated as total assets minus intangible assets and CEE is determined as follows: CEE = VA/CE where CE is the total book value of the company. The CEE says that VA is made up of a dollar spent on capital (CE). Value Added Intelligence (VAIC). VAIC measure by: VAIC = HCE + SCE + CEE. Measurement of Performance Firer and Williams (2003), based on previous research on corporate performance, have adopted a measure of corporate performance in terms of financial variables related to 920 profitability. The yield and valuation parameters of the market are: ROA, ATO and MB. These variables are defined as follows: ROA: net profit margin (dividend preference of the company is negligible, or not included) divided by the book value of total assets in the yearend financial statement. ATO: The ratio of total revenue to the total book value of assets reported in the financial statements at the end of the year. MB: market capitalization rate (stock market price) on book value of net assets. According to Pham Thi Thanh Huong (2017), ROE and ROA indicators accurately reflect the efficiency of using financial resources of enterprises and not much impact of changes in the stock market. Another study on enterprise efficiency by Li-Chang Hsu and Chao-Hung Wang (2010) used ROA as a measure and definition for enterprise efficiency. Therefore, this study uses ROA to measures performance. ROA defined as: Return on assets (ROA): Profit before tax/Average total assets. Control variables To minimise the impact of other variables that may explain observed relationships with firm performance, two control variables (leverage and industry) are included within the regression models: - Leverage: A company with high debt ratios will usually focus on fulfilling the requirements of creditors. This is not consistent with the point of view of making a profit from VA and VAIC. As with previous studies (Shiu, 2006; Chan, 2009), we assume that leverage is a control variable. Leverage is calculated as: Leverage = Total debt/Total assets. - Industry: Kujansivu (2007) note that the effects of IC vary from one industry to another. Chen et al. (2005) and Tan et al. (2007) divides the VAIC regression models and regression models into groups, and finds significant differences in the interpretation of strength across disciplines. Similarly, Firer and Williams (2003) suggest that the industry is controlled in this study through a dummy control variable. This counterfeit symbolizes the impact of different industries under the VSIC 2007 industry category. Empirical models The three hypotheses to be empirically tested are reflected in the following three equations relating VAIC (Model 1) and components of VAIC (Model 2) to firm performance. Perfit = β0 + β1 VAICit + β2 VAICit-1 + β3Control variablesit + ɛit (Model 1) Perfit = β0 + β1HCEit + β2SCEit + β3CEEit + β4HCEit-1 + β5SCEit-1 + β6CEEit-1 + β7Control variablesit + ɛit (Model 2) Where: Perf is Return on assets (ROA); VAIC is Value added intellectual coefficient; HCE is Human capital efficiency; SCE is Structural capital efficiency; CEE is Capital employed efficiency; β0 = Constant; i = firm; t = year (between 2012 and 2015). Control variables: LEV = Leverage; industry. 921 4. Result Descriptive statistics Research from the survey data sets and using the variables: Human Capital Efficiency (HCE), Efficiency, Capital Efficiency (CEE), Capital Efficiency (CEE), Net Asset Return on Total Assets (ROA). Table 2: Descriptive statistics for selected variables Variable Obs Mean Std. Dev. Min Max vaic 546,924 353.2564 165011.2 -1018.45 109000000 vaic_lag1 296,202 -4.787787 237614.6 -48600000 109000000 vaic_lag2 150,498 -309.2309 127304.9 -46800000 11500000 hce 546,924 0.0355678 1.077847 0.00 359.04 hce_lag2 216,734 0.0262839 0.2832389 -0.31 46.27 cee 549,824 430.2587 164575.5 -0.06 109000000 cee_lag1 297,670 85.83999 237025.7 -48600000 109000000 sce 549,824 -78.89301 75.73992 -1018.47 1.00 sce_lag2 217,821 -34015.5 8746385 -3360000000 6801 ROA 549,330 0.0179176 6.931913 -157.84 4914.19 In which, X_lag1 is a delayed X variable for a period, X_lag2 is a delayed X variable for two periods (X = vaic, hce, cee, sce) Regression analysis and hypothesis testing: Table 3: Regression results ROA Independent variables Coefficients t-statistic Panel A (VAIC, VAIC_lag1, VAIC_lag2, leve, branch, ROA) model 1 const 0.14349 1.88 VAIC 0.002825 36.56** VAIC_lag1 -1.85E-06 -0.41 VAIC_lag2 -8.17E-10 -0.02 Adjusted R 2