Intellectual Capital and Firm Performance: Evidence from Technology Sector in Malaysia

The aim of the paper is to examine the impact of intellectual capital on firm performance of technology firms listed on the main market of Bursa Malaysia. The study covers a period of seven years from 2013 to 2019 and usable data was drawn from 32 firms, providing 224 observations for the analysis. Intellectual capital is proxy by Modified Value Added Intellectual Coefficient (MVAIC), human capital efficiency (HCE), structural capital efficiency (SCE), relational capital efficiency (RCE) and capital employed efficiency (CEE), while performance is proxy by return on asset (ROA). The empirical findings reveal a positive and significant association between MVAIC and ROA but a mixed relationships between the efficiency of MVAIC components and ROA. Two components, HCE and CEE, are positive and significantly associated with ROA. SCE is significant but negatively associated with ROA. While, RCE is insignificantly associated with ROA. There are some limitations associated with the study. The research outcome is specific to technology sector; therefore, the findings cannot be generalised to other industries. Further, the analysis uses MVAIC model and the model does not cover innovation capital and process capital, thus it may omit other aspects of intellectual capital. Some practical implications from the findings are to achieve higher future profitability, technology firms should not only manage physical capital effectively but also improve employee, internal processes and networking efficiently.


Introduction
Global transition to knowledge-based economy during the third industrial revolution in 1990s has created a new economic model that account 'knowledge' as factor input in the firm production process in addition to capital and labour (Harris, 2011). Accordingly, the knowledgebased economy has raise the importance of intellectual capital or knowledge assets as the firm's key resources in gaining and sustaining competitive advantage (Martin-de-Castro, Delgado-Verde, Lopez-Saez and Navas-Lopez, 2011) through various value creations that requires human intellects (Lerro, Linzalone, and Schiuma, 2014). The critical role of intellectual capital for the firm in balancing the innovation and exploitation activities is further emphasised in the present fourth industrial revolution era (Mahmood and Mubarik, 2020). Going forward, intellectual capital is the key assets for the firm survival.
Intellectual capital is a broad concept which is often divided into various components and the widely accepted components are human, structural and relational capital (Ting, Ren, Chen and Kweh, 2020). Human capital is described as the abilities, knowledge and experience that when employees leave, they take with them. At the same time, the knowledge that stays within the organisation is known as structural capital. Relational capital is characterised as all resources linked to the company's external relationships (Starovic and Marr, 2003). Intellectual capital is perceived as an intangible, reusable and manageable asset that can be used to build sustainable resources, accessible at the micro-level comprising individuals and organisations and the macrolevel including cities, regions, and countries (Matos, Vairinhos, Selig and Edvinsson, 2019). For both society and organisations, intellectual capital is critical. In the context of an organization, it could be a source of competitive business advantage and stimulating innovation that leads to the generation of wealth. In an atmosphere of constant change, the ability to react effectively to unexpected events and consistently generate value is precisely the aim and province, the function and rationale of intellectual capital. Intellectual capital's unique focus is to assist build businesses that are prepared for strategic surprises and change (Rastogi, 2003).
The literature of intellectual capital has revealed the significance of intellectual capital on firm performance, and it has captured significant attention from scholars over the last three decades. Past studies have covered a wide range of industries and scholars often classify them into the knowledge-intensive sector (e.g. banks, finance, and technology) and capital-intensive sector (e.g. oil and gas, textile). Of the firms in the knowledge-intensive sector, the widely investigated are financial institutions involving banks and finance, but very few studies concentrated on technology firms (Vishnu and Gupta, 2014;Dženopoljac, Janoševic and Bontis, 2016). Generally, prior studies reported consistent empirical findings on the impact of intellectual capital on firm performance involving financial institutions, suggesting the positive association enhances firm performance. However, concerning technology firms, the results were inconclusive.
Based on the literature review undertaken, it is discovered that minimal empirical studies have endeavoured to clarify on intellectual capital and its association with firm performance of technology firms in Malaysia using Modified Value-Added Intellectual Coefficient model (Nimtrakoon, 2015). Thus, the findings of this paper aim to fill that void. The primary objective of the study is to examine the impact of intellectual capital on firm performance of technology firms in Malaysia. The specific objectives are to examine the impact of intellectual capital on firm performance using MVAIC model and to investigate the separate effect of human capital efficiency (HCE), structural capital efficiency (SCE), relational capital efficiency (RCE) and capital employed efficiency (CEE) on firm performance.
The paper is structured as follows. Section 2 provides literature review and research hypotheses. Section 3 presents methodology of the study. Section 4 provides analysis and discussion on the findings. Section 5 presents conclusion of the study.

Literature Review and Hypotheses
In the accounting and finance literature, research has been devoted to intellectual capital disclosure, measurement, and impacts to firm performance (Ashton, 2005;Kamath, 2008;Maditinos et al., 2011;Dumay, Guthrie and Rooney, 2020). This research specifically focuses on intellectual capital impacts to firm performance.
Past studies have covered a wide range of industries and scholars often classify them into the knowledge-intensive sector (e.g. banks, finance, and technology) and capital-intensive sector (e.g. oil and gas, textile). Further, the knowledge-intensive sector tends to invest more in intellectual capital than capital-intensive sector to gain competitive advantage (Vishnu et al., 2014;Nimtrakoon, 2015;Dzenopoljac et al., 2016;Xu and Li, 2019). Of the firms in the knowledge-intensive sector, the widely investigated are financial institutions involving banks and finance, but very few studies concentrated on technology firms (Vishnu et al., 2014;Dženopoljac et al., 2016). Generally, prior studies reported consistent empirical findings on the impact of intellectual capital on firm performance involving financial institutions, suggesting the positive association enhances firm performance. Al-Musali and Ismail (2016) studied the relationship between intellectual capital investment and firm performance of listed commercial banks in Gulf countries, and their study identified a positive association. Irsyahma and Nikmah (2017), drawing from the Indonesian banking sector, reported a positive association between intellectual capital and firm performance, implying banks with a higher degree of intellectual capital efficiency would demonstrate higher performance. Research by Tiwari and Vidyarthi (2018) exhibited a positive correlation between intellectual capital and firm performance in Indian public and private banks, suggesting banks with better intellectual capital efficiency usually have a better performance. A study by Tran and Vo (2018) on Thailand listed banks, Ousama, Hammami and Abdulkarim (2019) on Islamic banks of Gulf countries and Soewarno and Tjahjadi (2020) on Indonesian banks reported similar results, a positive and significant association between intellectual capital and firm performance.
However, concerning technology firms, there is little evidence and the results were inconclusive. Nimtrakoon (2015) conducted an empirical analysis of intellectual capital and firm performance of technology firms across five ASEAN countries and discovered a positive impact of intellectual capital on firm performance. The study suggested that intellectual capital may be an indicator of future firm performance. In contrast to the positive association, Dženopoljac et al. (2016) found no significant links between intellectual capital and firm performance in the technology sector of Serbia. Ting et al. (2020) revealed similar results in an analysis involving the relationship between intellectual capital and firm performance of technology firms in Taiwan. The empirical findings reported by Dženopoljac et al. (2016) and Ting et al. (2020) provide insights that stakeholders still perceive tangible assets as superior to intellectual capital in generating wealth for the firms. Closely related to this research is the study of intellectual capital impacts on technology-intensive firms in Malaysia which documented that many firms still depend on psychical capital efficiency compared to other intellectual capital components (Gan and Saleh, 2008). Based on the literature review undertaken, it is discovered that minimal empirical studies have endeavoured to clarify on intellectual capital and its association with firm performance of technology firms in Malaysian using MVAIC model.
In reference to the empirical evidence, the research framework and the corresponding hypotheses are drawn below.

H1
There is a positive and significant association between MVAIC and Profitability. H2 There is a positive association between MVAIC components and Profitability. a. There is a positive and significant association between HCE and higher Profitability. b. There is a positive and significant association between SCE and higher Profitability. c. There is a positive and significant association between RCE and higher Profitability d. There is a positive and significant association between CEE and higher Profitability.

Data and Methodology
The secondary data used in this empirical study were collected from the published annual reports of technology firms listed in the main board of Bursa Malaysia over the seven-year period from 2013 to 2019. As of 31 August 2020, a total of 40 firms were listed, however usable data was drawn from 32 firms, providing 224 observations for the analysis. MVAIC model was employed to measure intellectual capital efficiency. The following table provides the description of the model.  (2019) The dependent variable, firm performance is viewed from profitability perspective which is proxy by return on asset (ROA). ROA is computed as net income over total assets. It indicates the ability of a firm in utilizing total assets and shows the profitability of a firm. To test the hypotheses of the study the following regression models (1) and (2) have been formulated. In addition to the key human capital variables, the regressors included the βnZit which is a set of firm controlled variables documented in empirical evidence including size (SIZE) and leverage (LEV). Firm size is measured as the natural logarithm of total assets and leverage is measured by total debt to total assets. While εit is the standard regression error terms. Model 1: ROAit = β0 + β1MVAICit + βnZit + εit (1) Model 2: ROAit = β0 + β1HCEit + β2SCEit + β3RCEit + β4CEEit + βnZit + εit (2)

Findings and Analysis a. Descriptive Analysis
The descriptive analysis of technology firms is provided in table 2. The mean value of profitability of technology sector as indicated by ROA which stood at 0.0427 is sound, suggesting that the sample firms were able to generate profit. The value of standard deviation for each of the four components of intellectual capital as presented in Table 2 indicated small deviation from mean value (e.g. mean of HCE 2.6930, Std dev 3.5604). The value of standard deviation tests the consistency of the data. It indicates how far the data from each other is similar or different. As a rule of thumb, the lower the standard deviation value, the higher the consistency is. Meanwhile, the value of standard deviation as presented in Table 2 for CEE across technology sector recorded small deviation from their mean values. It shows a high consistency of the treatment in physical capital across firms in the technology sector.
Most variables are highly skewed (less than -1 or greater than +1) as indicated in the table 2 (Bulmer, 1979). Despite the skewness values, these variables are not transformed into a natural logarithm function (a method chosen to mitigate normality problem) due to the data being in percentage. The kurtosis values as presented in table 2 indicated all variables, HCE, SCE, RCE, CEE, MVAIC, ROA, SIZE, except leverage have kurtosis problem where the kurtosis values more than three indicating a leptokurtic distribution characterized by higher and sharper central peak with tails longer and fatter due to the kurtosis problem (Akinlawon, Asiribo and Adebanji, 2010). For leverage, the kurtosis value is three indicating normal distribution. The kurtosis problem in the data may not create an obstacle to produce quality and reliable statistics as this is expected in a research with financial time series. Akinlawon et al. (2010) argued that studies with financial time series often exhibited leptokurtosis value greater than 3. A leptokurtic situation may happen in both, the unconditional distribution and conditional distribution of daily asset returns (Akinlawon et al., 2010). In addition, when the sample size is large that is the number of observation is greater than 30, a variable with statistically significant skewness and kurtosis can be considered as a variable with normal distribution (Tabachnik and Fidell, 2007;Hair, Black, Babin and Anderson, 2010). Since the sample size is large (n=224) normal distribution of data can be considered in this study.

Notes: Number of observation is 224.
A comparison of MVAIC components as depicted in The mean of the four components are presented below in order to evaluate the intellectual capital performance of technology firms using the MVAIC model:

b. Pearson Correlation Analysis
The Pearson correlation analysis of the technology sector is provided in the following table. Table  4 shows that the correlation value indicated significant positive associations between only several pairs of variables. As expected, MVAIC has significant positive correlation with firm's financial performance, ROA. It implies that firms with greater intellectual capital efficiency have higher financial performance. Specifically, MVAIC is significantly and positively related to ROA (r=0.4031, p<0.0001), indicating strong relationship between value efficiency and financial performance. Regarding the components of MVAIC, HCE, SCE and CEE exhibited significant positive correlations with ROA, except RCE. CEE (r=0.6811, p<0.0001) have the strongest correlation with ROA. HCE (r=0.3878, p<0.0001) and SCE (r=0.0393, p<0.0001) also implies significant positive correlations, but weakly correlated with ROA. Meanwhile, RCE have no correlation with ROA. It is noted that MVAIC has significant positive relationships with the components of intellectual capital except RCE. MVAIC has the strongest association with HCE (r=0.9826, p<0.0001), followed by its relationship with SCE (r=0.3353, p<0.0001) but weakly correlated with physical capital (r=0.1236, p<0.0001). One of the control variables, size, demonstrated weak correlation with profitability, ROA at correlation values of 0.2079 (p<0.0001). Size and leverage recorded significant positive correlation among themselves, but it is a weak association (r=0.2903, p<0.0001). The pairwise correlation between MVAIC and HCE is very strong suggesting multicollinearity problem (Gujarati, Porter and Gunasekar, 2017). Therefore, to mitigate the problem the two explanatory variables are separated into model 1 and model 2 in the study.
Notes: Number of observation is 224. The sign ***, **, * denotes the correlation significant level at 0.01, 0.05 and 0.1 respectively. Table 5 presents the results of the two regression models in this study, applying the control variables of firm size and leverage. In model 1, the value of R 2 of 0.2107 indicated that 21.07 percent of possible variations in ROA is explained by MVAIC and the reliability is examined through its F-value of 68.61 (p<0.0000) which is found to be statistically significant. MVAIC is positively and significantly associated with ROA with the coefficients of 0.  The two control variables in the study, firm size and leverage, document different results. Firm size in both models are insignificantly associated with ROA indicating that firm size, big or small, does not influence profitability. For leverage, both models exhibit similar pattern of relationship that is it affects ROA negatively. The higher the profit level, the lower the level of leverage indicating pattern of financing in line with pecking-order theory and empirical evidence.

c. Regression Analysis
These results are broadly in line with prior research findings (e.g. Nimtrakoon, 2015;Al-Musali et al., 2016;Ozkan et al., 2017;Ousama et al., 2019;Xu et al., 2019), confirming firms with greater level of intellectual capital will exhibit higher profitability. Regarding the four components of MVAIC, CEE and HCE, are the most influential value drivers according to their association with ROA. The findings suggest that physical capital (proxy by CEE) is still relevant in generating firms' profitability and has long been associated with value creation process (Nimtrakoon, 2015). In addition, the findings also imply that human capital, has been acknowledged as an effective source of wealth creation. It could be attributed to the fact that human capital is vested in employees rather than the firm (Edvinsson, 1997;Nimtrakoon, 2015). Among MVAIC components, SCE and RCE seem to be the least influential value drivers.

Conclusion
This study is one of few studies focusing on intellectual capital utilisation in Malaysia technology sector. The empirical findings reveal a positive and significant association between MVAIC and profitability, suggesting that intellectual capital affects profitability of technology firms. However, a mixed relationships between the efficiency of MVAIC components and profitability are noted. A separate analysis into the components of MVAIC revealed that two components, HCE and CEE, are found to be positive and significantly associated with ROA. SCE is significant but negatively associated with ROA. While, RCE is insignificantly associated with ROA. Even though, both HCE and CEE are positive and significantly correlated with ROA, but CEE recorded higher efficiency level. As Malaysia is still a developing country and the technology sector is still developing, the findings of the study suggest that profitability is primarily driven by CEE which is in line with the study of Chowdhury, Rana, Akter and Hoque (2018); Ozkan et al. (2017). Therefore, the conclusion that can be drawn from the findings of the study is that both, physical capital and intellectual capital affect profitability, but physical capital is the primary determinant of profitability and the utilisation of intellectual capital is still at a low level.
The findings of the current study may offer several practical contributions. First, managers of technology firms should increase their firms' recognition of intellectual capital utilization in enhancing their profitability through gaining a better understanding of intellectual capital and put greater effort on its management. This is especially so as Malaysia is currently operating in knowledge-based economy, where firms' reliance on intellectual capital as a source of competitive advantage is greatly enhanced. Second, regarding the low-HCE, firms may revise their employees' financial and non-financial rewards schemes and employee-related programs such as training to enhance employee capability, attitude and satisfaction. Third, to enhance structural capital, the firm may consider re-designing and creating a conducive organizational culture, management control system and a strong information technology system to support internal processes and operations. Fourth, to strengthen relational capital, the firms may establish networking with valuable partners such as suppliers and customers.
There are some limitations associated with the study. The current research is limited to listed technology firms in the main board of Bursa Malaysia, therefore, the findings cannot be generalised to other industries. Further, the analysis uses MVAIC models and the model does not cover innovation capital and process capital, thus it may omit other aspects of intellectual capital. Therefore, future research may consider investigating the impact of intellectual capital on profitability using other sector as a research setting. To examine other aspect of intellectual capital by adopting different measurement model such as Integrated Intellectual Capital Model.