THE IMPACT OF INTELLECTUAL CAPITAL ON FIRM PERFORMANCE: A MODIFIED AND EXTENDED VAIC MODEL

Intellectual capital (IC) is generally understood as an important driver of firm competitiveness improvement and value generation in the knowledge economy. The manufacturing industry, the backbone of the South Korean economy, is coming under increasing international pressure. In order to increase the competitiveness of Korean industry, the main objective of this paper is to examine the impact of IC and its components on the performance of Korean manufacturing firms over the period 2013–2018. The modified and extended Value Added Intellectual Coefficient (VAIC) model was adopted to more accurately measure IC, and firm performance was systematically and comprehensively measured in three distinct parameters: profitability, productivity and market value. Our regression results show that physical capital was the most influential factor to firm performance; human capital was viewed as a performance enhancing measure; structural capital had no significant impact on firm performance; and innovation capital and relational capital hurt a firm’s profitability. It is also evident that the modified and extended VAIC model performs better than the original VAIC model proposed by Pulic (1998). This study extends the understanding of IC in achieving a competitive edge in the manufacturing sector, with IC representing a valuable platform for the sustainable development of the manufacturing sector in emerging Asian markets.


INTRODUCTION
In the knowledge economy, intellectual capital (IC) is considered a more important contributor to firms rather than tangible assets in firm competitiveness improvement and value generation (Ahangar, 2011;Hsu & Chang, 2011; St-Pierre & Audet, 2011; Jelínková & Jiřincová, 2015). Based on resource-based theory, the resources possessed by any organization are unique and inimitable (Marr et al., 2003). IC, a relatively new designation as a strategic resource, is related to securing Xu a competitive edge and superior performance by value generation (Marr et al., 2003;Clarke et al., 2011). Therefore, it is essential for firms to understand, identify, develop, and utilize IC efficiently, all of which can help firms gain competitive advantage.
The manufacturing industry, as a capital-and knowledge-intensive industry, is the backbone of a nation's economy (Behun et al., 2018). However, this industry has been facing great pressures from domestic and international markets (Herrmann et al., 2014). In 2014, the strategy Manufacturing Innovation 3.0 was released with the goal of improving the overall competitiveness of Korea's manufacturing industry (Xu & Sim, 2018). The manufacturing sector in South Korea mainly relies on the imports of high-tech production materials from Japan (Fukao et al., 2016). Recently, Japan and South Korea have become engaged in a trade restriction battle which will seriously hinder the sustainable development of Korea's manufacturing sector, especially electronic manufacturing firms. For this and many other reasons, Korean manufacturing firms should seek new ways to obtain a sustainable competitive advantage and improve firm competitiveness by using IC efficiently. In addition, the manufacturing sector has attracted the attention of many scholars in the IC literature (Phusavat et  This paper aims to modify and extend the original Value Added Intellectual Coefficient (VAIC) model by introducing two extra components. Our work investigates the effect of IC and its components on the performance of manufacturing firms in the context of South Korea. Firm performance measurement is divided into three categories: profitability, productivity and market value, with results showing that out of four other components, physical capital influences profitability and productivity positively and significantly as does human capital (HC), which also stimulates corporate profits. Innovation capital and relational capital (RC) both have negative impacts solely on a firm's profitability.
The contribution of this paper is presented in four aspects. First, this paper extends IC research by applying the modified and extended VAIC model to more accurately measure IC. This new model consists of two additional IC components, namely, innovation capital and RC. Second, the calculation of value added (VA) is altered based on the idea that proxies of IC components should be treated as investments instead of costs. Research and development (R&D) expenses as well as marketing and advertising expenses are added back into the VA calculation. Third, this paper adds the macroeconomic indicator to minimize external influences. Finally, this paper can provide insights into the IC-firm performance relationship in emerging Asian economies. Policymakers and corporate managers can use these findings as a starting point to better understand the significance of IC components and their impacts on firm performance, and thus to develop more effective strategies to efficiently manage IC resources to gain competitive advantage.

IC and its measurement
A relatively new concept, IC has been defined and classified by researchers in various ways. Edvinsson & Malone (1997) describe IC as encompassing all the experience and skills gained by employees and customer relations. One important definition has been proposed by Stewart (1997), who thought that IC comprises knowledge, information, intellectual property and experience that can generate wealth for the firm. The difference between the market value and book value of a firm has also been categorized in terms of IC (Maditinos et al., 2011).
Scholars have classified IC using various components. Brooking (1996) characterized IC as comprising HC, structural capital (SC), market capital, and knowledge property rights capital. Edvinsson & Malone (1997) classified IC into HC and SC, with HC, the core of IC, referring to the individual tacit knowledge sets, skills, abilities, experiences of employees in an organization (Bontis, 1998;Ngah & Ibrahim, 2009). HC is the kind of knowledge that is implicit by nature. Knowledge generated by the cooperation of employees or divisions is also included in the definition of HC (Swart, 2006). SC reflects an organization's capabilities, procedures, processes, patents, culture, etc. (Ahangar, 2011). Even if a firm does not possess HC, SC is still stored within the firm (Ngah & Ibrahim, 2009). Sveiby (1997) has divided IC into HC, SC and customer capital, with customer capital subsequently replaced with RC by some researchers. Stewart (1997) classified IC into HC, SC, and RC. RC contains the value and knowledge from corporate networks among customers, suppliers, distributors, competitors and all other related parties. Nevertheless, customer relations are viewed as the most important component of all (Bontis, 1998

IC and firm competitiveness and performance
The competitiveness of a company results from possessing valuable and inimitable resources, which enables the firm to achieve a favorable competitive position to maintain its market position and get superior performance. Therefore, firms need to identify, maintain, and develop IC resources.

Variables
For dependent variable, firm performance is measured in three aspects, namely, profitability, productivity, and market value. ROA The modified and extended VAIC model in this current study is applied by adding two IC components, innovation capital and RC. R&D expenses are chosen as a proxy for innovation capital, and marketing and advertising expenses are adopted to proxy for RC. Pulic (1998) calculated VA by adding labor costs and depreciation and amortization back to operating profit. He argued that spending on employees should be treated as investment rather than costs due to its long-term benefits to the firm. Based on this point of view, R&D expenses and marketing and advertising expenses should also be added back. Therefore, the VA calculation in the new model is modified as follows: VA m =Operating profit+Depreciation+Amortization+Employees' salaries and wages+ Marketing and advertising expenses+R&D expenses

Models
Model 1 and 2 are used to determine IC components' impact on firm profitability.  (7) Model 3 is utilized to examine the impact of IC components on firm productivity.  (9) where β stands for the presumed parameters and ε denotes the measurement error term.  To test for multi-collinearity, an analysis of the variance inflation factor (VIF) was conducted.

RESULTS AND DISCUSSION
Individual VIF values greater than 10 indicate a multi-collinearity problem (Neter et al., 1989). It was observed that all VIF values vary between 1.004 and 3.096, which suggests that multicollinearity is not a serious concern in the current study. The correlation analysis results in Table  3  Before the regression analysis, the Hausman test was applied to determine whether to use the fixed effect model or random effect model. In Table 4, p values of models (1)-(4) are significantly lower than 0.05, the fixed effect model was employed in this study. Prob > χ 2 = 0.000 Table 5 shows the regression results of the original VAIC model, with model (2) having the highest explanatory power. The results show that, while HCE and SCE have positive impacts on profitability (i.e. ROA and ROE), they are not significant predictors of ATO and MB. CEE was found to be the significant predictor of firm performance, except the dependent variable MB.
Tab The results of a regression analysis for the modified and extended VAIC model are shown in Table 6, in which all models were found to be significant. Compared to the original VAIC model, entering RDEm and RCEm caused a slight increase in adjusted R2 values in the four models. According to the results of models (1) and (2), where ROA and ROE are the dependent variables, CEEm is the most influential contributor to firm profitability, which supports the study of Phusavat et al. (2011) of the Thai manufacturing industry. This means that the higher the CEE of a manufacturing firm is, the more profitable the firm can be.
The results in models (1) and (2) show that HCEm is positively related to ROA and ROE at the 1% level. The findings suggest that spending on employees should be treated as investment, and firms should effectively use these human resources to create more wealth. Conversely, Firer & Williams (2003) argued that spending on employees is treated as expenditure by firms. Of the control variables, firm size (Size) was positively significant with ROA and MB, and negatively significant with ATO. Lev had a positive impact on firm productivity and a negative impact on firm profitability. GDP had significant impacts on the performance measurements.

CONCLUSION
This study investigates whether IC and its components affect firm performance in manufacturing sector in South Korea. It also compares the regression results of the original VAIC method with the results of the modified and extended VAIC method. Based on our results, the modified and extended VAIC model performs better than the original VAIC model. The findings contribute to the IC literature, suggesting that IC is a key driver in creating value in manufacturing firms in the Korean context. In addition, CEEm contributes most to performance of the Korean manufacturing firms. HCEm positively influences firm profitability measured by ROA and ROE, while RDEm and RCEm have negative impacts. SCEm was observed to have no significant impact on firm performance.
This study can help managers review the performance of manufacturing firms through the management of their IC resources to sustain business operation over time. This study uses the modi-fied and extended VAIC method to more specifically investigate how the performance of Korean manufacturing firms might be improved through IC. The study reveals that the modified and extended VAIC model can be employed with confidence to accurately measure IC. This study also fills a gap by using data from South Korea, an emerging economy where the manufacturing sector has been undergoing industrial transformation.
Managers in the Korean manufacturing sector should place great emphasis on the role of physical and financial capital. They need to invest more in their HC through continuous learning and training. The insignificant impacts of SC reveal that manufacturing firms should devote attention to developing SC by maintaining a clear knowledge strategy, implementing information systems and tools, constructing an innovative organizational culture along with related steps. Meanwhile, manufacturing firms should construct technological innovation networks to improve their technology innovation capabilities through various initiatives. In addition, good social network relationships with their customers as well as suppliers should be established to build the corporate image.
This paper has some limitations. First, the lagged effect of IC components on firm performance was not taken into consideration. Prior studies (e.g. Tran & Vo, 2018; Xu & Wang, 2019b) have shown that IC components have a several-year lagged effect on firm performance. Second, other industries should be included to compare these with the manufacturing industry. Further, the results of this paper could also be compared with studies from other countries or regions.