Nexus between intellectual capital and financial performance: An investigation of Chinese manufacturing industry

Xu, Jian; Liu, Feng Article Nexus between intellectual capital and financial performance: An investigation of Chinese manufacturing industry Journal of Business Economics and Management (JBEM) Provided in Cooperation with: Vilnius Gediminas Technical University (VILNIUS TECH) Suggested Citation: Xu, Jian; Liu, Feng (2021) : Nexus between intellectual capital and financial performance: An investigation of Chinese manufacturing industry, Journal of Business Economics and Management (JBEM), ISSN 2029-4433, Vilnius Gediminas Technical University, Vilnius, Vol. 22, Iss. 1, pp. 217-235, https://doi.org/10.3846/jbem.2020.13888 This Version is available at: https://hdl.handle.net/10419/317470 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Published by Vilnius Gediminas Technical University *Corresponding author. E-mail: liufen[email protected]u.cn This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Journal of Business Economics and Management ISSN 1611-1699 / eISSN 2029-4433 2021 Volume 22 Issue 1: 217–235 https://doi.org/10.3846/jbem.2020.13888 NEXUS BETWEEN INTELLECTUAL CAPITAL AND FINANCIAL PERFORMANCE: AN INVESTIGATION OF CHINESE MANUFACTURING INDUSTRY Jian XU 1, Feng LIU 2* 1School of Management, Qingdao Agricultural University, Qingdao, China 2Business School, Shandong University, Weihai, China Received 11 April 2020; accepted 29 October 2020 Abstract. How to manage financial performance through the utilization of intellectual capital (IC) is an important issue in the knowledge economy. The objective of this study is to investigate the impact of IC on financial performance for manufacturing listed companies in the Chinese context. Financial performance is measured from two distinct aspects: (1) firm profitability, measured through earnings before interest, taxes, depreciation and amortization (EBITDA), net profit margin (NPM), and gross profit margin (GPM), and (2) corporate return, measured through return on investment (ROI), return on assets (ROA), and return on equity (ROE). The results show a positive relationship between NPM, GPM, ROI, ROA, ROE, and IC (measured through the market-to-book ratio). In addition, the more intangible-intensive manufacturing listed companies exhibit better financial performance. The study provides evidence that higher investment in IC can improve value creation in the emerging economies. Keywords: intellectual capital, financial performance, firm profitability, corporate return, marketto-book ratio, manufacturing listed companies. JEL Classification: O34, M41, M44. Introduction In the knowledge economy, experts and scholars have reached a consensus that intellectual capital (IC) can drive competitiveness and sustainability (Guthrie & Petty, 2000; Marr etal., 2003; Sonnier etal., 2009; Siboni etal., 2013; Crema & Verbano, 2016; Xu & Wang, 2018; Tonial etal., 2019; Gross-Golacka etal., 2020). IC is commonly considered as intangible assets that a firm possesses (Kennedy, 1998; Sullivan, 2000; Anghel, 2008). What’s more, IC investment has a pivotal role in firms that seek to obtain sustainable competitive advantage (Bhasin, 2011; Xu & Wang, 2018; Hermawan etal., 2020). The IC-financial performance relationship is the main focus that attracts most researchers (Inkinen, 2015; Jordão & de Almeida, 2017; 218 J. Xu, F. Liu. Nexus between intellectual capital and financial performance: an investigation of... Xu & Wang, 2018; Xu & Liu, 2020). However, it is a challenge for researchers to accurately recognize, measure, and assess the value of organizational knowledge (Abdi etal., 2018). In addition, more attention should be paid to this challenge in a context where a firm’s value relies on IC rather than conventional resources (e.g. land, capital, and labor) (Bontis etal., 1999). Measuring the role of IC is still a problem that needs further research, especially in emerging economies (Tseng & Goo, 2005; Sharma & Dharni, 2017; Dzenopoljac etal., 2017; Ferramosca & Ghio, 2018; Xu & Li, 2019; Xu & Liu, 2020). China is currently in the process of economic transition, and manufacturing industry is undergoing industrial structure upgrade and energy-consuming equipment replacement with the implementation of the “MadeinChina2025” strategy, which aims to turn “Made in China” into “Create in China” (Xu & Sim, 2017; Jin etal., 2018). However, one of the persistent problems in corporate governance is how to maintain long-term sustainable financial performance, which is one of the most difficult tasks for Chinese manufacturers. In its neighboring country, Xu and Wang (2018) concluded that IC is a contributor to sustainable growth of Korean manufacturing firms. Consequently, it is crucial to analyze how IC can contribute to financial performance in the context of this emerging market. This study designs an empirical study to evaluate the impact of IC on financial performance (i.e., firm profitability and corporate return) by using the data from Chinese manufacturing listed companies. This research contributes to the IC literature in three ways. Firstly, this study provides empirical evidence of IC management and performance improvement in the context of China. Although a large body of IC literature has been carried out in various developed nations such as the U.S., the UK, and Italy, little has been done in emerging markets. The IC-financial performance relationship in emerging economies is still worth further analysis. This study attempts to analyze the role of IC in manufacturing sector in China that is experiencing an economic transition. Secondly, this study develops a systematic and comprehensive measurement for ascertaining financial performance from two aspects: firm profitability and corporate return. Actually, there still has been a hot debate on the use of the best indicators to assess financial performance at the firm level, and this study employs some new measurements such as earnings before interest, taxes, depreciation and amortization (EBITDA) and return on investment (ROI), which helps to better understand the IC’s role in financial performance improvement. Finally, this study offers practical insights on how to maximize shareholders’ value and protect the interests of other stakeholders by improving corporate performance. For corporate managers who seek to improve the firm’s performance, they might not totally understand the IC-financial performance relationship. This paper will help them to effectively manage IC resources and achieve sustainable development. The rest of this paper is organized as follows. Section 1 reviews the literature and develops two relevant hypotheses, along with Section 2 discussing the research methodology. The empirical results are presented in Section 3, and these results are discussed in Section 4. Finally, the conclusions and further research development are given in last section. Journal of Business Economics and Management, 2021, 22(1): 217–235 219 1. Literature review and hypotheses development 1.1. IC definition and measurement Although knowledge management is believed to take a leading role in corporate governance by creating business value and achieving sustainable development, IC as an important concept has been hotly debated by academics (Rodov & Leliaert, 2002; Osinski etal., 2017; Tran & Vo, 2018). For example, Edvinsson and Sullivan (1996) simply defined IC as “knowledge that can be converted into value”. Stewart (1997) defined IC as “intellectual material (e.g., knowledge, information, intellectual property, and experience) that can be used to create wealth”. Marr and Moustaghfir (2005) stated that IC encompasses any valuable intangible resource obtained by experience and learning in the process of wealth generation. From the perspective of financial accounting, some researchers (Goebel, 2015; Dženopoljac etal., 2016; Forte etal., 2017; Jordão & de Almeida, 2017; Anghel etal., 2018) define IC in terms of its intangible asset nature, and consider that IC is the difference between a firm’s market value and its accounting value (Edvinsson & Malone, 1997; Sharabati etal., 2010; Wang, 2013; Krstić & Bonić, 2016; Anghel etal., 2018). The knowledge-intensive companies tend to have higher value in the market (Stewart, 1997). Most researchers (Chen etal., 2005; Goh, 2005; Wang, 2011; Lu etal., 2014; Vishnu & Gupta, 2014; Nimtrakoon, 2015; Tripathy etal., 2015; Ma etal., 2017; Urban & Joubert, 2017; Sardo & Serrasqueiro, 2018; Smriti & Das, 2018; Xu & Wang, 2018; Vidyarthi, 2019; Xu & Li, 2019) defined IC in relation to its components–human, structural, and relational capitals. They argued that IC includes the whole knowledge, abilities, and experience of human resource in line with its internal and external organizational structure. Human capital, a major capital, is employee’s experience and expertise that can improve organizational performance (Chen etal., 2005; Phusavat etal., 2011; Nimtrakoon, 2015; Dzenopoljac etal., 2017; Allameh, 2018; Xu & Wang, 2018). Structural capital incorporates things like corporate culture, digital asset, and information management (Chen etal., 2005; Phusavat etal., 2011; Nimtrakoon, 2015; Dzenopoljac etal., 2017; Allameh, 2018; Xu & Wang, 2018). Relational capital deals with a company’s associations with customers, suppliers, and other stakeholders (Chen etal., 2005; Phusavat etal., 2011; Nimtrakoon, 2015; Dzenopoljac etal., 2017; Allameh, 2018; Xu & Wang, 2018). Although companies are not required to disclose IC information in the financial statements, it indeed has an impact on financial performance. Many methods of assessing IC have been put forward in the extent literature. They include the MTB, Tobin’s Q ratio (Stewart, 1997), Skandia Navigator (Edvinsson & Malone, 1997), the balanced scorecard (Kaplan & Norton, 1996), the Intangible Asset Monitor (Sveiby, 1997), economic value added, and the Value Added Intellectual Coefficient (VAIC) (Pulic, 2000). Scholars explained that there does not exist the best tool for IC assessment and measurement. Among them, some researchers (Forte etal., 2017; Jordão & de Almeida, 2017; Anghel etal., 2018) considered that the MTB is a good proxy for IC measurement, given that IC is the “hidden value” of a company. The MTB is “well established in the literature and, although broad, readily identifies those organizations doing a better job with their knowledge assets” (Bramhandkar etal., 2007). 220 J. Xu, F. Liu. Nexus between intellectual capital and financial performance: an investigation of... 1.2. IC and financial performance The vast majority of studies have confirmed that IC has a positive impact on financial performance. For instance, an early study by Chen, Cheng, and Hwang (2005) documented that IC is positively related to market value, financial performance measured by return on assets (ROA) and return on equity (ROE), growth in revenues, and employee productivity. In the study of Pal and Soriya (2012), involving Indian pharmaceutical and textile companies, the findings showed a positive relationship between IC and corporate return (ROA and ROE) but no relationship between IC and market value. Based on the data of companies in information technology (IT), manufacturing and real estate industries, Ma, Qiu, and Zhang (2017) found that human and structural capitals positively influence return on net assets of Chinese manufacturing companies. Taking 172 IT companies as a sample, Xu (2017) reported that IC and its components–organizational capital and relational capital–contribute significantly to business profitability (measured through profit margin). The findings of Sardo and Serrasqueiro (2018) revealed that IC efficiency in the current period positively affects corporate return (i.e., ROA) and growth opportunities of non-financial listed companies in 14 European countries. Xu and Wang (2018), collecting data of Korean manufacturing firms, concluded that IC is beneficial to the improvement of financial performance (measured by ROA, ROE, net profit margin, and gross profit margin) and sustainable growth. Vidyarthi (2019), using Data Envelopment Analysis (DEA) approach based on 38 listed Indian banks from 2004–2005 to 2015–2016, provided evidence that higher investment in IC can improve operating efficiency and value creation. Based on the survey of small and medium-sized enterprises (SMEs) in Pakistan, Khalique etal. (2020) argued that the overall IC has an effect on SMEs’ performance. However, Firer and Williams (2003) failed to find any significant relationship between IC components and profitability. Using the data from Brazilian real estate companies, Britto, Monetti, and Lima (2014) found that IC has a negative impact on market value. In the Chinese context, few studies have been done on the IC-financial performance relationship. Based on the data from the constituent companies of the Hang Seng Index of Hong Kong Stock Exchange, Chu, Chan, and Wu (2011) found that IC positively and significantly affects corporate profitability. Lu, Wang, and Kweh (2014) studied a sample of 34 Chinese life insurance companies during 2006–2010. Using the VAIC model, they argued that IC exerts a positive impact on firm operating efficiency. Li and Zhao (2018) estimated the relationship between IC (measured by human capital and organizational capital) and firm value, and found a strong association between organizational capital and firm value measured through ROA, ROE, growth in sales, and capital market return. Zhang etal. (2018) found that IC improves product innovation performance of manufacturers in China and India. The findings of Xu and Li (2019) also showed that IC improves firm performance in both high-tech and non-high-tech SMEs. Applying the extended VAIC model, Xu, Chen, and Zhang (2020) pointed out that executive human capital positively influence sustainable growth for China’s high-tech agricultural listed companies. Therefore, the first hypothesis is stated as follows: Hypothesis 1 (H1). IC positively contributes to financial performance of Chinese manufacturing listed companies. According to Tseng and Goo (2005), tangible and intangible assets together constitute a firm’s value. Goebel (2015) found that IC (measured through MTB, Tobin’s q, and long-run Journal of Business Economics and Management, 2021, 22(1): 217–235 221 value to book) is positively related to leverage and motivational payments to employees. Forte etal. (2017), based on 140 Italian corporations during 2009–2013, also proved that IC (measured by MTB) is positively related with intangible assets and profitability. Jordão and de Almeida (2017) explored the relationship between IC and financial sustainability (measured by long-term corporate performance) of Brazilian companies. They concluded that the more intangible-intensive public companies have higher financial sustainability. On the contrary, Anghel etal. (2018) confirmed a significantly negative relationship between IC (measured through MTB) and ROA and ROE. Seo and Kim (2020) documented that intangible resources (human capital, advertising, and R&D) have a positive impact on Korean SMEs’ profitability and value. Therefore, the second hypothesis is stated as follows: Hypothesis 2 (H2). The intangible-intensive manufacturing listed companies have better financial performance than the less intangible-intensive manufacturing listed companies. Figure1 shows the conceptual framework. Figure 1. Conceptual framework 2. Research methodology 2.1. Sample selection This study selects manufacturing companies listed on the Shanghai and Shenzhen stock exchanges as the sample and collects data from the CSMAR database and the RESSET database, covering the 2012–2017 period. Table1 shows the procedure of sample selection. The initial sample includes 6528 firm-year observations. Then, companies with missing information on the MTB, profitability, and return are deleted. Finally, an unbalanced sample of 746 companies and 5166 firm-year observations are obtained. Table 1. Details of research sample Item Firm-year observation Manufacturing companies listed from 2012 to 2017 6528 Manufacturing companies with missing information 1362 Final observation 5166 222 J. Xu, F. Liu. Nexus between intellectual capital and financial performance: an investigation of... 2.2. Variables (1) Dependent variables. In this study, financial performance is measured from two distinct aspects: firm profitability and corporate return. Guided by Janošević, Dženopoljac, and Bontis (2013), Nimtrakoon (2015), Dzenopoljac etal. (2017), and Jordão and de Almeida (2017), firm profitability is evaluated by the chosen indicator: EBITDA, net profit margin (NPM), and gross profit margin (GPM). EBITDA is of particular interest for manufacturing companies that are subject to heavy depreciation charges of fixed assets. NPM and GPM are used to determine how wella company’s management is generatingprofits. The calculation formulas are as follows: NPM = Net income/Revenue; (1) GPM = (Revenue – cost of goods sold)/Revenue. (2) Corporate return is measured by the following performance indicators: ROI, ROA, and ROE (Chen etal., 2005; Wang & Chang, 2005; Wang, 2011; Vishnu & Gupta, 2014; Pucci etal., 2015; Tripathy etal., 2015; Jordão & de Almeida, 2017; Ginesti etal., 2018; Smriti & Das, 2018; Xu & Wang, 2018). First, ROI tries to directly measure the amount of return on a particular investment, which can be easily compared with returns from other investments. Second, ROA reflects how the firm utilizes total assets to generate revenue (Brealey etal., 2011). Third, ROE shows how much profit each dollar of common stockholders’ equity generates. The calculation formulas are as follows: ROI = Operating income/Investment; (3) ROA = Net income/Total assets; (4) ROE = Net income/Total equity. (5) (2) Independent variable. Guided by Sveiby (1997), Goebel (2015), Forte etal. (2017), Jordão and de Almeida (2017), and Anghel etal. (2018), the MTB is used to evaluate IC of Chinese manufacturing listed companies. Simply, an index (IC-index) is used to replace the MTB, indicating the level of intangibility for a company’s assets. If this index is greater than 1, it suggests that IC is possessed by the company. IC-index = MV/BV, (6) where MV – the average annual market value of the company × the number of its shares; BV – book value of the company from financial statements. (3) Control variables. Guided by previous literature (Pal & Soriya, 2012; Nimtrakoon, 2015; Ma etal., 2017; Anghel etal., 2018; Xu & Wang, 2018; Xu & Li, 2019; Xu & Liu, 2020), SIZE (measured by the natural logarithm of total assets) and LEV (measured by the ratio of total liabilities to total assets) are chosen as control variables. 2.3. Methodology Four levels of analysis are carried out in this study. This study starts the analysis with descriptive statistics. The second level of analysis, Spearman’s correlation test, is conducted to Journal of Business Economics and Management, 2021, 22(1): 217–235 223 measure the intensity of the relationship between the dependent and independent variables used (Cohen, 1988). Next, Kruskal-Wallis’ test is to analyze the quartiles, and graphics are used to check IC contribution to financial performance of manufacturing listed companies. To ensure the reliability and internal validity, the results from one analysis are compared with those from the others to examine the research hypotheses (Jick, 1979). Finally, regression analysis, the fourth level of analysis, is carried out to examine the impact of IC on financial performance by using Models (1)–(6). This study uses EBI (measured by natural logarithm of EBITDA) when the EBITDA indicator is used as a proxy of financial performance in Model (1) (Eqs (7)–(12)). Because the EBITDA indicator may have negative values, the observations are less than those in other measurement indicators. Table2 shows the definition of variables used in this study. In summary, H1 is tested using four levels of analysis, and H2 is tested by the first three levels. EBIi,t = β0 + β1IC-indexi,t + β2SIZEi,t + β3LEVi,t + ei,t; (7) NPMi,t = β0 + β1IC-indexi,t + β2SIZEi,t + β3LEVi,t + ei,t; (8) GPMi,t = β0 + β1IC-indexi,t + β2SIZEi,t + β3LEVi,t + ei,t; (9) ROIi,t = β0 + β1IC-indexi,t + β2SIZEi,t + β3LEVi,t + ei,t; (10) ROAi,t = β0 + β1IC-indexi,t + β2SIZEi,t + β3LEVi,t + ei,t; (11) ROEi,t = β0 + β1IC-indexi,t + β2SIZEi,t + β3LEVi,t + ei,t. (12) Table 2. Variable definition Variable Definition EBI Natural logarithm of EBITDA NPM Net income/Revenue GPM (Revenue – cost of goods sold)/Revenue ROI Operating income/Investment ROA Net income/Total assets ROE Net income/Total equity IC-index Market-to-book ratio SIZE Natural logarithm of total assets LEV Total liabilities/Total assets 3. Results 3.1. Descriptive statistics Table3 presents the descriptive statistics of financial performance with regard to firm profitability and corporate return. The full sample is divided into two groups: group 1 and group2. In group 1, the value of IC-index is greater than 1, while its value is less than 1 in group 2. On the basis of the average in both two groups, it shows a significant variability of corporate 224 J. Xu, F. Liu. Nexus between intellectual capital and financial performance: an investigation of... profitability and return, especially EBITDA, NPM and ROE. The values of the EBITDA indicator are collected in millions. More emphatically, except the EBITDA indicator, companies that possess IC in group 1 yield greater profitability as well as greater return than the other companies. Unexpectedly, as for the EBITDA indicator, companies in group 1 have much lesser cash flow than those in group 2, inconsistent with Jordão and de Almeida (2017). The t-test shows that there are significant differences between group 1 and group 2 in terms of EBITDA, NPM, GPM, ROA, and ROE. Table 3. Descriptive analysis Measure Variable Group NMean Min Max S. D. p-value (Difference t-statistics) Firm profitability EBITDA (million yuan) 1 3840 545.987 –4354.625 39806.100 1480.775 0.000 2 1326 1420.876 –5208.216 63241.164 4259.135 Total 5166 770.552 –5208.216 63241.164 2535.632 – NPM 1 3840 0.079 –7.171 5.566 0.239 0.000 2 1326 0.010 –8.911 0.483 0.262 Total 5166 0.061 –8.911 5.566 0.247 – GPM 1 3840 0.295 –0.482 0.929 0.171 0.000 2 1326 0.165 –0.180 0.632 0.097 Total 5166 0.261 –0.482 0.929 0.166 – Corporate return ROI 1 3840 0.075 –0.746 3.417 0.103 0.511 2 1326 0.054 –0.563 14.720 0.413 Total 5166 0.070 –0.746 14.720 0.227 – ROA 1 3840 0.052 –0.399 0.402 0.062 0.000 2 1326 0.014 –0.463 0.244 0.041 Total 5166 0.042 –0.463 0.402 0.060 – ROE 1 3840 0.076 –1.868 1.467 0.122 0.066 2 1326 0.019 –7.220 0.743 0.251 Total 5166 0.062 –7.220 1.467 0.167 – 3.2. Correlation test The results of correlation test are shown in Table4, including the sample of 746 manufacturing listed companies and total 5166 observations. 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