Extended Value Added Intellectual Coefficient in Manufacturing Companies: Technology Based Companies

International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 676 Extended Value Added Intellectual Coefficient in Manufacturing Companies: Technology Based Companies Hamidreza Jafaridehkordi 1 National University of Malaysia, UKM Bangi, Selangor, Malaysia Ruzita Abdul Rahim National University of Malaysia, UKM Bangi, Selangor, Malaysia Abstract The main purpose of this study is to empirically compare of intellectual capital (IC) and its efficiency among manufacturing companies with different level of technology using a sample of 135 Malaysian listed manufacturing companies during the 2006-2012 period. The manufacturing companies are classified into different sectors based on their products and services (Standard Industrial Classification (SIC) code) on OSIRIS databases. Then, they are categorized into one of the four groups: high, medium-high, medium-low, and low technology. The results of ANOVA test indicate that investment in IC and its components, and efficiency of IC and its components vary with degree of technology of the manufacturing companies. It also can be concluded that more investment in IC components does not necessarily lead to more efficiency of IC. Keywords: Intellectual Capital, Extended Value Added Intellectual Coefficient, Technology Based Companies Cite this article: Jafaridehkordi, H., & Abdul Rahim, R. (2015). Extended Value Added Intellectual Coefficient in Manufacturing Companies: Technology Based Companies. International Journal of Management, Accounting and Economics, 2(7), 676-706. 1 Corresponding author’s email: [email protected] International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 677 Introduction According to Organisation for Economic Co-operation and Development (OECD) (2006), nowadays many firms are investing in employee training, job training programs, research and development (R&D), customer relations, computer and administrative systems, and so on. In some countries, investment in such business activities and items that are often referred to IC is growing and is competing with investment in physical and financial capital. In the U.S for instance, Apple company had been converted into the most invaluable company in the history with a share market value of above USD600 billion that is directly resulted from investment in IC and revenue from apps and its network (Edvinsson, 2013). Similarly, Google and Microsoft are included among the successful companies which are investing low in fixed assets, but a significant amount of capital in IC (Ong, Yeoh, & Teh, 2011). Leadbeater 2000) reports that merely about 7 percent of share market value of Microsoft is accounted for by tangible assets, while the remaining (93%) is derived from intangible assets such as patents, brands, and R&D. Those world’s top companies are not unique cases of IC success stories. Figure.1 indicates the percentage of the market value of tangible and intangible assets of S&P500 companies in different periods of time. There is a clear trend showing the growing importance that these 500 large-capitalization American companies place on intangible assets. Figure.1 Components of market value (the U.S case) Source: Oceantomo (2013). Malaysia has embarked on becoming a knowledge-based economy (K-economy) as its main vehicle to transform into a developed country by 2020. A K-economy is an economy where the creation and exploitation of knowledge act as the main factor in the process of value creation(Goh, 2005). Developing the K-economy was the focal point of the 2002 Economy Master Plan (EMP 2002) which was aimed at creating competitive advantages 0% 20% 40% 60% 80% 100% 1975 1985 1995 2005 2010 1975 1985 1995 2005 2010 intangible assets 0.17 0.32 0.68 0.80 0.80 tangible assets 0.83 0.68 0.32 0.20 0.20 International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 678 among companies and communities. EMP 2002 was a plan summarizing the diverse strategies to speed up the transformation of Malaysia to the K-economy (Kim & Lee 2004). Without deemphasizing the importance of traditional physical and natural factors of production such as raw materials, labor, capital and entrepreneurship, the K-economy places intellectual capital (IC) as its nucleus. Emerged in the midst of the information age, the K-economy entrusts IC as the key driver of organizational performance, competitive advantage and value creation (Bontis, Keow & Richardson 2000; Mustapha & Abdullah 2004). Table 1 illustrates the position of Malaysia relative to some other countries in term of the Knowledge Economy Index (KEI) in 2000 and 2012 as reported by the World Bank (2013). The KEI ranking for Malaysia relative to the U.S is good evidence that Malaysian companies need to invest more on intangible capital in general and knowledge capital in more specific. Focusing on the Asian region alone, it is rather obvious that Malaysia is catching up but still lagging behind the other developed countries such as Japan and other more develop countries such as Singapore. It is important to note that Malaysia‘s rank in term of its KEI has dropped in 2012 compared with 2000. Table.1 Countries ranking on the Knowledge Economy Index (KEI) Country/Economy 2012 Rank KEI 2012 2000 Rank Change from 2000 Sweden 1 9.43 1 0 Finland 2 9.33 8 6 Denmark 3 9.16 3 0 United States 12 8.77 4 -8 Taiwan, China 13 8.77 16 3 United Kingdom 14 8.76 12 -2 Japan 22 8.28 17 -5 Singapore 23 8.26 20 -3 Korea, Rep. 29 7.97 24 -5 Malaysia 48 6.1 45 -3 Thailand 66 5.21 60 -6 Indonesia 108 3.11 105 -3 India 110 3.06 104 -6 Source: http//:siteresources.worldbank.org K-economy places its focus on IC but Knowledge Economy Index (KEI) (Table 1) shows that Malaysian companies have not been investing enough on IC. If the K-economy is needed to transform Malaysia into a developed country status, then the nucleus of Keconomy (i.e. IC) needs to be given a renewed energy. This study proposes that this can be done more efficiently by targeting on the companies that can optimize the IC. Past studies (Hayton 2005; Sáenz, Aramburu & Rivera 2009; Tseng & James Goo 2005) show that high technology is the sector which requires innovation the most and IC is the main input to fuel innovation. Therefore, this study proposes to examine the role of IC in manufacturing companies of different level of technology. As one of the pioneers in the scope of defining, measuring and dealing with intellectual capital (IC), Edvinsson (1997:368) defines this concept as “the possession of knowledge, International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 679 applied experience, organizational technology, customer relationships and professional skills that provide a company (Skandia) with a competitive edge in the market”. Edvinsson (1997) believes that HC is the combined knowledge, skill of the firm's individual employees, culture and philosophy, and values of the firms. Edvinsson (1997) states that SC is organizational capability with the purpose of supporting the efficiency of the workforce and everything that are left in the company when the staff go home like trademarks, databases patents, hardware and software. Skandia (1994) and Edvinsson and Malone (1997) argue that SC can be subdivided into customer capital (CC) and organizational capital (OC). CC is association expanded with vital customers by acquisitions of information and knowledge concerning customers' tastes, required technology, new goods and services (Edvinsson, 1997). OC can be described as systems, equipments, and operational attitudes that speed up the stream of knowledge throughout the company. Edvinsson and Malone (1997) classify organizational capital (OC) further into innovation capital (InC) and process capital (PC). InC indicates the firm’s revolutionary capability, innovative success, and potential accumulation of new product and service (Wang, 2008).Process capital (PC) represents working processes, standardized methods or schemes that can raise and enhance workers’ efficiency and productivity. Figure. 2 display intellectual capital and its components. (Edvinsson & Malone, 1997) Figure.2 Skandia Navigator model: intellectual capital and its components Manufacturing companies can be divided into four groups based on the technology that they are using: high technology, medium-high technology, medium-low technology, and low technology (Czarnitzki & Thorwarth, 2012; Hatzichronoglou, 1997; Kim & Lee, 2004; Mendonça, 2009). The literature shows that high technology companies have more investment on R&D expenditures as part of the IC than low technology companies (Czarnitzki & Thorwarth, 2012). Higher technology companies rely more heavily on the quality of human capital and the other components of IC because they operate in a more dynamic environment which forces them to be consistently on the innovative and creative mode to remain competitive. Consequently, it is expected that high technology companies present more efficiency than their low technology counterparts in using the IC and its components. Intellectual capital Structural capital Customer capital Oganizational capital Process capital Innovation capital Human capital International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 680 Zéghal and Maaloul (2010) compare IC among high technology, traditional and services companies and their results indicate that IC and its component vary in these three groups. However, there is no research regarding the distinct IC and its efficiency among manufacturing companies with different levels of technology. Considering the important role of IC in developing the nation, investigating and comparing IC and its efficiency in manufacturing companies can help to identify the driving factors that the companies and government must emphasize on to realize the developed nation vision. Therefore, this research seeks to find credible answer(s) to this research questions: Is there a significant difference in intellectual capital and its efficiency among manufacturing companies with different levels of technology in Malaysia? Most studies on IC have only focused on comparing IC among different companies in sectors such as banking or financial sector (Pal & Soriya, 2012; Śledzik, 2012; Zeghal & Maaloul 2010). Therefore, the main purpose of this study is to empirically compare of intellectual capital (IC) and its efficiency among manufacturing companies with different level of technology. This is a paradox given the argument that high-technology companies are more dependent on intellectual capital (Nunes, Serrasqueiro, Mendes, & Sequeira, 2010;Porrini, 2004; Wang & Chang, 2005) than their low-technology counterparts because these are the companies that rely mostly on innovation for its competitiveness. One of the obstacles in examining IC empirically is the difficulty to quantify this variable, which could also explain why this item is not recorded explicitly in the financial statement. The difficulty to quantify IC is evident by the fact that the literature has not shown a commonly accepted definition and classification for IC (Pablos, 2004). To empirically test intellectual capital, this study adopts an extended version of the Pulic’s model (Pulic, 2000). Referred as Value Added Intellectual Coefficient (VAICTM), Pulic’s model is a composite index that disaggregates intellectual capital into two main components; human capital (HC) and structural capital (SC). The VAICTM model is proposed to measure the efficiency of intellectual capital in creating or adding value to the firms. The extent of acceptance of this model may be evidenced by a finding by Volkov (2012) who states that as of June 2012, VAICTM model of Pulic (2000) has been used in 46 researches and has been cited by 2373 researchers. This study takes a step further by adopting an extended version of the VAICTM model which is proposed by Nazari and Herremans (2007) (henceforth, eVAIC). This study proposes eVAIC to measure intellectual capital, which is introduced by Nazari and Herremans’s (2007) because it disaggregates structural capital further into customer capital (CC) and organizational capital (OC). More importantly, eVAIC further segregates organizational capital into process capital (PC) and innovation capital (InC). Literature review Numerous studies have attempted to examine intellectual capital (IC) and intellectual capital efficiency (ICE) in different sectors. Kujansivu and Lönnqvist (2007) evaluate the efficiency of IC as measured by using VAIC™ and the value of IC by using calculated intangible value (CIV) methods for 16 industries for 20,000 Finnish companies throughout the period of 2001-2003. The average IC is roughly half of the worth of tangible assets in these Finnish companies. The highest value of investment on IC is reported for the electronic industry (high technology) and the lowest value is seen in the International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 681 electricity, gas and water services, metal, and forest and construction sectors (low technology). Meanwhile, Ngwenya (2013) finds that among Zimbabwean companies, a lower level of IC is documented in information, communication and technology (ICT) companies (high technology) than other manufacturing companies. The highest value is reported for Agricultural sector and Ngwenya (2013) suggests that it could be because of small number of top managers is needed to create value in the Agriculture companies. Kujansivu and Lönnqvist (2007) argue that the competencies and stakeholder relationships are important factors in creating greater value for the electronics and chemical industries, while the value is mostly constructed on tangible assets in forestry and construction industries. The results of the study (Kujansivu & Lönnqvist 2007) show that the highest human capital efficiency (HCE), structural capital efficiency (SCE) and value added intellectual coefficient (VAIC) are related to commercial services (low technology) and the lowest for biochemical industries (high technology). Pal and Soriya (2012) compare VAIC in 105 pharmaceutical companies and 102 textile companies in India. They argue that the pharmaceutical company is typically considered as ‘an innovative and knowledge intensive sector’ while textile industry is considered as ‘the labor-intensive’ industry. Despite the differences in the orientation, their findings indicate that VAIC is not much different in the two sectors. They explain that utilization IC is efficient for both groups. Kamath (2007) analyzes the data from 98 commercial banks that are divided into four categories (state bank of India and its associates, national bank, foreign banks, and private sector domestic banks) for a five-year period from 2000 to 2004 in India. His results show that human capital (HC) and capital employed (CE) have significant positive effects on value added (VA), that is, increasing in HC and CE result in their efficiency. In addition, HCE is highest in foreign banks while capital employed efficiency (CEE) is highest in public sector banks whereas the overall VAIC is highest in foreign banks. Kamath (2007) argues that public banks employ a huge number of inefficient employees, which result in fewer added values. He also claims that the poor performance of private sector domestic banks is due to high infrastructure costs, high social obligations, enormous nonperforming assets, inappropriate allocation of resources and weak investment decisions. These findings are similar to the results of Fayez, Hameed and Ridha (2011) in 8 Kuwaiti commercials and non-commercial banks for the period of 1996–2006 where HCE is greater than CEE. Kweh, Chan and Ting (2013) examine the intellectual capital performance (ICP) of small sample of Malaysian public-listed software companies (25 companies in only one sector) in the Main market and ACE market in 2010. The results show that investment in human capital is more than structural and employed capital in their sample and HCE and CEE in the Main market companies are more than ACE market companies while SCE in ACE market companies is more than Main market companies. Overall, efficiency of IC in ACE-market companies is more than Main-market companies. Kweh et al. (2013) believe that managers of 80 per cent of software firms are inefficient in managing and transforming intellectual capital into tangible and intangible values because of the technical problem. Reviewing the literature regarding IC and its efficiency indicates that prior studies have not compared the investment and efficiency of IC and its components International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 682 at different levels of technology of the manufacturing companies and results for the various industries from previous studies shows mixed results. Hypotheses development In line with resource-based view (RBV), different companies own different packages of resources and capabilities, and some companies within similar industry may do specific activities better than the others because of their different resources (Wernerfelt, 1995; Barney, 1991; Dierickx & Cool, 1989; Wernerfelt, 2010). Therefore, it can be concluded that IC as a resource (and thus, its efficiency) varies among different companies in term of theory. Based on RBV, this study proposes that human capital should be of more importance to companies of higher technology than lower technology. Drawing from these arguments, this study hypothesizes that: H1: Intellectual capital investment varies with degree of technology of the manufacturing companies. H1a: Investment in components of intellectual capital varies with degree of technology of the manufacturing companies. H1b: Human capital is the most invested component of IC among high technology companies. The second hypothesis is proposed based on the arguments that higher technology companies are more efficient than their low technology counterparts in using the IC and its components. Higher technology companies rely more heavily on the quality of human capital and the other components of IC because they operate in a more dynamic environment which forces them to be consistently on the innovative and creative mode to remain competitive. Of all components of ICE, this study focuses on the roles of HCE which are expected to be leveraged most efficiently by companies of higher technology than those of lower technology. H2: Efficiency of intellectual capital varies with degree of technology of the manufacturing companies. H2a: Efficiency of components of intellectual capital vary with degree of technology of the manufacturing companies. H2b: Human capital is the most efficiently used component of IC among high technology companies. Research methodology This study selects its sample from manufacturing companies that are listed in Bursa Malaysia from 2006 to 2012. The sample manufacturing companies are classified into different sectors based on their products and services (or Standard Industrial Classification (SIC) code) in the OSIRIS databases. This study then follows researches of Czarnitzki and Thorwarth (2012), Mendonça (2009) and Hatzichronoglou (1997) to segregate manufacturing companies in different sectors into four groups of differing International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 683 technology intensity, that are: high technology, medium-high technology, medium-low technology, and low technology. The categorization of the sectors under each of the technology-based groups is as follows: i. High-technology industries: aerospace and defense, pharmaceuticals and biotechnology, technology hardware and equipment, mobile telecommunications, electricity, electronic and electrical equipment, and fixed line telecommunications, ii. Medium-high-technology industries: chemicals, automobiles and parts, health care equipment, industrial transportation, and oil equipment, iii. Medium-low-technology industries: general industrials, household goods, industrial metals and mining, leisure goods, construction and materials, and iv. Low-technology industries: beverages, food producers, forestry and paper, personal goods, and tobacco. In screening out the sample, companies are excluded if they report negative values of ICE and earnings or if they have missing data. The final composition of this sample is 30, 33, 31, and 35 of high, medium-high, medium-low, and low technology companies respectively. The sub-samples generate balanced panels of 210, 231, 217, and 245 yearcompany observations per variable for high, medium-high, medium-low and low technology companies, respectively. Data are sourced from DataStream and companies’ annual reports. This study adopts extended version of the VAICTM model which is proposed by Nazari and Herremans (2007) for measuring the intellectual capital(IC) and the intellectual capital efficiency (ICE). That is, VAICTM can be dissected into; e iiiii TM iCEESCEHCECEEICEVAIC  )( =HCE+ (CCE+OCE) +CEE =HCE+ (CCE +PCE +InCE) +CEE Where HCE = VA/HC, VA = OP + EC + D + A, OP = operating profit, EC = employee cost, D = depreciation, A = amortization, HC (human capital) = total salaries and wages for a company, SCE = SC/VA, SC (structural capital) = VA – HC, CEE = VA/CE, CE = book value of the net asset for a company. CCE = 𝐶𝐶 𝑉𝐴 Where, CCE = customer capital efficiency, International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 684 CC (customer capital) = marketing cost, OCE=SCE-CCE OCE = organizational capital efficiency, InCE=Inc 𝑉𝐴 Where, InCE = innovation capital efficiency, InC (innovation capital) = research and development expenditures PCE=OCE-InCE Where, PCE = process capital efficiency, To compare the means of more than two groups or more than two variables, this study will be using the analysis of variance or ANOVA tests. In order to use ANOVA efficiently, there are two significant conditions: normality and having similar variances of data (Bland & Altman, 1995). If non-normal distribution of the data is seen, the Kruskal Wallis test should be employed as a non-parametric statistic test to the hypotheses(Shirley, 1977). To compare the mean of one variable with another variable of one sample (high technology companies) in sub-hypotheses of the first and second hypotheses (H1b, H2b) this study will be using the one sample tests. Results and discussion Table.2 displays descriptive statistics after the outlier treatment with improved range of skewness and kurtosis than original data. Replacements are made to extreme values identified as univariate outliers in accordance with Tabachnik and Fidell (2007). After replacing univariate outliers, companies with multivariate outliers also were omitted (Tabachnik and Fidell 2007). Figures .3 and 4 show the relative positions of intellectual capital (IC) and its components, and efficiency of intellectual capital and its components in the manufacturing companies of different technology levels are plotted in to simplify comparison. As predicted, intellectual capital investment varies with degree of technology of the manufacturing companies. According to Figures.3 and 4, investment in IC and each of its elements is greater in medium-high technology companies than high technology companies, while the efficiency of IC and each of its elements is greater in high technology companies than medium-high technology companies. It can be deduced that more investment in IC and its components do not necessarily lead to more efficiency on IC and its components. Comparing the components of ICE, it can be seen that the HCE component is the dominant contributor of ICE, making up 84% (3.108/3.692), 82% (2.561/ 3.123), 81% (2.410/ 2.960), and 85% (2.399 / 2.813) of total ICE for high, medium-high, medium-low and low technology companies respectively. Aminiandehkordi, Ahmad, and Hamzeh (2014) report 80% of ICE comes from HCE for 110 companies listed on the ACE Market of Bursa Malaysia from 2009 to 2012. Thus, in International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 691 Modaraba Sector Of Pakistan . Australian Journal of Business and Management Research, 1(5), 8–16. Sáenz, J., Aramburu, N., & Rivera, O. (2009). Knowledge sharing and innovation performance: a comparison between high-tech and low-tech companies. Journal of Intellectual Capital, 10(1), 22–36. Shirley, E. (1977). A non-parametric equivalent of Williams’ test for contrasting increasing dose levels of a treatment. Biometrics, 33(2), 386–389. Skandia. (1994). Visualizing Intellectual Capital in Skandia, intellectual capital supplement. Śledzik, K. (2012). The Intellectual Capital Performance Of Polish Banks : An Application Of VAIC TM. University of Gdansk Faculty of Management Department of Banking ul. Retrieved from Electronic copy available at: http://ssrn.com/abstract=2175581[20Jun 2013] Tabachnik, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics. cALIFORNIA sTATE UNIVERSITY: Pearson. Tseng, C., & James Goo, Y. (2005). Intellectual capital and corporate value in an emerging economy: empirical study of Taiwanese manufacturers. R&D Management, 35(2), 187–201. Volkov, A. (2012). Value Added Intellectual Co-efficient ( VAIC TM ): A Selective Thematic-Bibliography. Journal of New Business Ideas & Trends, 10(1), 14–24. Wang, J.-C. (2008). Investigating market value and intellectual capital for S&P 500. Journal of Intellectual Capital, 9(4), 546–563. Wang, W.-Y., & Chang, C. (2005). Intellectual capital and performance in causal models: Evidence from the information technology industry in Taiwan. Journal of Intellectual Capital, 6(2), 222–236. Wernerfelt, B. (1995). The Resource-Based View of the Firm: Ten Years After. Strategic Management Journal, 16(3), 171–174. Wernerfelt, B. (2010). The Use of Resources in Resource Acquisition. Journal of Management, 37(5), 1369–1373. World bank. (2013). Retrieved from http://data.worldbank.org/indicator Zéghal, D., & Maaloul, A. (2010). Analysing value added as an indicator of intellectual capital and its consequences on company performance. Journal of Intellectual Capital, 11(1), 39–60. International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 692 Table.2 Descriptive statistics Level of technology HC SC IC CC OC PC InC High Mean 21492981 26100681 47593662 9587424 16513257 14353352 2312543 Min 199000 523000 1331000 90000 58000 24000 20000 Max 92183000 108679000 173280000 53172000 95223000 65573000 12349000 S. Deviation 26482 26577 50057 13689 17537 16347 3198 Skewness 1.473 1.121 1.205 2.044 1.521 1.473 1.901 Kurtosis 0.856 0.138 0.252 3.453 2.255 1.368 2.777 Medium-high Mean 36049550 53407957 89457506 13581424 39826532 35379251 4443654 Min 215000 265000 713000 35000 149000 12000 17000 Max 155828000 233457000 382416000 65615000 209782000 195945000 22201000 S. Deviation 39983 62836 101155 17257 51249 48287 5954 Skewness 1.715 1.588 1.669 1.743 1.813 1.87 1.552 Kurtosis 2.369 1.64 2.055 2.387 2.668 2.767 1.346 Medium-low Mean 17157083 27505258 44662341 2648129 24582880 23962779 612267 Min 115000 230000 345000 25000 28000 6000 0 Max 78301000 153844000 216869000 13046000 129588000 128474000 2388000 S. Deviation 18995 36548 54445 3740 33505 33332 577 Skewness 1.735 1.92 1.816 1.746 1.897 1.893 0.601 Kurtosis 2.608 3.107 2.741 1.806 2.857 2.837 -0.75 Low Mean 24136441 35904135 60040576 6753053 29151082 28714514 439016 Min 1143000 166000 3367000 100000 25000 25000 0 Max 98560000 163534000 236395000 25708000 148027000 148027000 3038000 International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 693 S. Deviation 25176 42003 65921 6969 38168 37890 969 Skewness 1.843 1.684 1.729 1.395 1.749 1.758 2.108 Kurtosis 2.462 1.711 1.851 1.193 1.922 1.933 2.776 Level of technology HCE SCE ICE CCE OCE PCE InCE High Mean 3.108 0.583 3.692 0.181 0.402 0.328 0.079 Min 1.402 0.287 1.689 0.008 0.019 0.007 0.001 Max 8.119 0.877 8.996 0.462 0.851 0.778 0.4 S. Deviation 1.816 0.179 1.981 0.11 0.215 0.195 0.098 Skewness 1.206 0.109 1.115 0.35 0.19 0.243 1.787 Kurtosis 0.251 -1.234 0.052 -0.671 -0.92 -0.912 2.472 Medium-high Mean 2.561 0.562 3.123 0.157 0.405 0.345 0.068 Min 1.1 0.091 1.191 0.003 0.013 0.002 0.001 Max 4.634 0.784 5.418 0.465 0.759 0.735 0.293 S. Deviation 0.826 0.158 0.975 0.116 0.195 0.2 0.073 Skewness 0.304 -0.983 0.108 0.773 -0.337 -0.136 1.379 Kurtosis -0.582 0.332 -0.632 -0.213 -0.943 -1.037 1.073 Medium-low Mean 2.41 0.549 2.96 0.081 0.467 0.424 0.044 Min 1.117 0.104 1.221 0.001 0.007 0.007 0 Max 4.08 0.757 4.835 0.302 0.754 0.753 0.292 S. Deviation 0.7048 0.147 0.834 0.073 0.171 0.194 0.064 Skewness 0.311 -0.988 0.074 1.281 -0.68 -0.547 1.965 Kurtosis -0.41 0.397 -0.461 1.051 -0.187 -0.655 3.554 low Mean 2.399 0.541 2.813 0.155 0.386 0.378 0.009 Min 1.009 0.009 1.019 0.001 0.001 0.001 0 Max 3.821 0.738 4.408 0.552 0.719 0.698 0.088 International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 694 Note: HC is human capital = sum of total salaries and wages. SC is structural capital = VA-HC. VA is value added = operating profit + employee cost + depreciation + amortization. IC is intellectual capital= HC + SC. CC is customer capital = sum of total marketing cost. OC is organizational capital= SC - CC. PC is process capital = OC - InC. InC is innovation capital = sum of total research and development expenditure (R&D). HCE is human capital efficiency = VA HC. SCE is structural capital efficiency = SC VA. ICE is intellectual capital efficiency = HCE +SCE. CCE is customer capital efficiency = CC VA . OCE is organizational capital efficiency = SCE - CCE. PCE is process capital efficiency = OCE - InCE. InCE is innovation capital efficiency = R&𝐷 VA . MBVA is ratio of market value to book value of assets = market value assets book value of assets . Market value asset = book value of debt + market value equity. Market value equity = number share outstanding* share closing price. Book value of assets = book value of debt + book value of equity. MBVE is ratio of market value to book value of equity = market value equity book value of equity . GPPEMVA is ratio of gross plant, property and equipment to market value assets = gross plant,property and equipment market value assets . Gross plant, property and equipment =cost of plant, property and equipment - accumulated depreciation of plant, property and equipment. DMVA is ratio of depreciation of property, plant and equipment to market value assets = depreciation of property,plant and equipment market value assets . ROA is return on assets = earnings before interest and tax book value of assets . LEV is financial leverage = book value of total debt book value of assets . FF is financial flexibility = cash and cash equivalents book value of the net asset . DP is dividend payout ratio = dividend paid net income . R&DS is ratio of R&D expenditures to net sales and calculates = R&𝐷 𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑠 total net sales .Total net sales = value of goods sold - discounts and returns. SIZ is company size = 𝑙𝑜𝑔10 of total assets. S. Deviation 0.6563 0.1627 0.848 0.125 0.191 0.186 0.02 Skewness -0.311 -1.438 -0.3 1.035 -0.345 -0.376 2.449 Kurtosis -0.657 1.448 -0.945 0.621 -0.908 -0.887 4.872 International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 695 Table.3 Results of Levene’s test Test of Homogeneity of Variances Variable Levene Statistic df1 df2 P-value Variable Levene Statistic df1 df2 P-value HC 25.266 3 899 0.000 HCE 110.933 3 899 0.000 SC 31.934 3 899 0.000 SCE 6.650 3 899 0.000 IC 27.763 3 899 0.000 ICE 90.039 3 899 0.000 CC 74.023 3 899 0.000 CCE 19.757 3 899 0.000 InC 185.479 3 899 0.000 InCE 71.472 3 899 0.000 PC 32.284 3 899 0.000 PCE .704 3 899 0.550 OC 34.009 3 899 0.000 OCE 5.991 3 899 0.000 Note: HC is human capital = sum of total salaries and wages. SC is structural capital = VA-HC. VA is value added = operating profit + employee cost + depreciation + amortization. IC is intellectual capital= HC + SC. CC is customer capital = sum of total marketing cost. InC is innovation capital = sum of total research and development expenditure (R&D). PC is process capital = OC - InC. OC is organizational capital= SC - CC. HCE is human capital efficiency=VA HC. HC is human capital = sum of total salaries and wages. VA is value added = operating profit + employee cost + depreciation + amortization. SCE is structural capital efficiency = SC VA. SC is structural capital = VA-HC. ICE is intellectual capital efficiency = HCE +SCE. CCE is customer capital efficiency = CC VA . CC is customer capital = sum of total marketing cost. InCE is innovation capital efficiency = R&𝐷 VA . PCE is process capital efficiency = OCE - InCE. OCE is organizational capital efficiency = SCE - CCE. International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 696 Table.4 ANOVA results variable Sum of Squares df Mean Square F P-value variable Sum of Squares df Mean Square F P-value HC Between Groups 44306344707 3 14768781569 17.778 0.000 HCE Between Groups 72.263 3 24.088 20.454 0.000 Within Groups 746848079102 899 830754259 Within Groups 1058.69 899 1.178 Total 791154423809 902 Total 1130.953 902 SC Between Groups 106235597886 3 35411865962 17.938 0.000 SCE Between Groups 0.223 3 0.074 2.847 0.037 Within Groups 1774757172447 899 1974145909 Within Groups 23.504 899 0.026 Total 1880992770334 902 Total 23.727 902 IC Between Groups 282960365743 3 94320121914 18.523 0.000 ICE Between Groups 97.231 3 32.41 21.356 0.000 Within Groups 4577707034741 899 5091998926 Within Groups 1364.369 899 1.518 Total 4860667400484 902 Total 1461.6 902 CC Between Groups 14294850207 3 4764950069 34.96 0.000 CCE Between Groups 1.219 3 0.406 34.58 0.000 Within Groups 122532767124 899 136298962 Within Groups 10.565 899 0.012 Total 136827617332 902 Total 11.784 902 OC Between Groups 62854580382 3 20951526794 14.874 0.000 OCE Between Groups 0.857 3 0.286 7.651 0.000 Within Groups 1266310971179 899 1408577276 Within Groups 33.549 899 0.037 International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 697 variable Sum of Squares df Mean Square F P-value variable Sum of Squares df Mean Square F P-value Total 1329165551561 902 Total 34.406 902 InE Between Groups 2411634962 3 803878321 68.222 0.000 InCE Between Groups 0.673 3 0.224 47.827 0.000 Within Groups 10593175991 899 11783288 Within Groups 4.216 899 0.005 Total 13004810953 902 Total 4.889 902 PC Between Groups 51508888894 3 17169629631 13.054 0.000 PCE Between Groups 1.146 3 0.382 10.187 0.000 Within Groups 1182405671522 899 1315245463 Within Groups 33.708 899 0.037 Total 1233914560416 902 Total 34.854 902 International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 698 Table .5 Results of one-sample test Variable Hypothesized mean of t p-value Mean Difference Variable Hypothesized mean of t p-value Mean Difference HC SC -2.521 0.006 -4607.7 HCE SCE 20.151 0.000 2.525 HC CC 6.515 0.000 11905.6 HCE CCE 23.359 0.000 2.927 HC OC 2.725 0.003 4979.7 HCE OCE 21.596 0.000 2.706 HC PC 3.907 0.000 7139.6 HCE PCE 22.186 0.000 2.780 HC InC 10.496 0.000 19180.4 HCE InCE 24.173 0.000 3.029 InC SC - 107.800 0.000 -23788.1 InCE SCE -74.325 0.000 -.504 InC CC -32.967 0.000 -7274.9 InCE CCE -15.056 0.000 -.102 InC OC -64.353 0.000 -14200.7 InCE OCE -47.639 0.000 -.323 InC PC -54.565 0.000 -12040.8 InCE PCE -36.729 0.000 -.249 InC HC -86.919 0.000 -19180.4 InCE HCE - 446.648 0.000 -3.029 Note: HC is human capital = sum of total salaries and wages. InC is innovation capital = sum of total research and development expenditure (R&D). HCE is human capital efficiency =VA HC. HC is human capital = sum of total salaries and wages. VA is value added = operating profit + employee cost + depreciation + amortization. InCE is innovation capital efficiency = R&𝐷 VA . P-value of onetailed reported. The critical t with 209 degrees of freedom, α=0.05 and one-tailed is 1.65. The sample is high technology manufacturing companies. International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 699 Appendix A: Non-parametric test Null hypothesis Chi-Square df P-value Decision The distribution of HC is the same across categories of groups 50.439 3 0.000 Reject the null hypothesis The distribution of SC is the same across categories of groups 36.251 3 0.000 Reject the null hypothesis The distribution of IC is the same across categories of groups 44.984 3 0.000 Reject the null hypothesis The distribution of CC is the same across categories of groups 119.198 3 0.000 Reject the null hypothesis The distribution of OC is the same across categories of groups 20.975 3 0.000 Reject the null hypothesis The distribution of InC is the same across categories of groups 301.347 3 0.000 Reject the null hypothesis The distribution of HCE is the same across categories of groups 4.690 3 0.046 Reject the null hypothesis The distribution of SCE is the same across categories of groups 3.737 3 0.031 Reject the null hypothesis The distribution of ICE is the same across categories of groups 11.979 3 0.007 Reject the null hypothesis The distribution of CCE is the same across categories of groups 107.415 3 0.000 Reject the null hypothesis The distribution of OCE is the same across categories of groups 23.118 3 0.000 Reject the null hypothesis The distribution of InCE is the same across categories of groups 318.227 3 0.000 Reject the null hypothesis The distribution of PCE is the same across categories of groups 31.365 3 0.000 Reject the null hypothesis HC is human capital and calculates through sum of total salaries and wages. SC is structural capital and calculates through [VA-HC]. IC is intellectual capital and computes by sum of HC and SC.CC is customer capital and calculates through sum of total marketing cost. OC is organizational capital and calculates through [SC-CC]. InC is innovation capital and calculates through sum of total research and development expenditure (R&D). PC is process capital and computes by [OC-InC]. HCE is human capital efficiency and calculates through value added (VA) over human capital (HC). HC is human capital and calculates through sum of total salaries and wages. VA is [operating profit+ employee cost+ depreciation +amortization]. SCE is structural capital efficiency and calculates through SC /VA. SC is structural capital and calculates through [VA-HC]. ICE is intellectual capital efficiency and computes by sum of HCE and SCE. CCE is customer capital efficiency and computes by CC /VA. CC is customer capital and calculates through sum of total marketing cost. OCE is organizational capital efficiency and calculates through [SCE-CCE]. InCE is innovation capital efficiency and computes by R&D/VA. PCE is process capital efficiency and computes by [OCE-InCE]. International Journal of Management, Accounting and Economics Vol. 2, No. 7, July, 2015 ISSN 2383-2126 (Online) © IJMAE, All Rights Reserved www.ijmae.com 700 Appendix B. Results of multiple comparisons Dependent Variable Mean Difference (IJ) Pvalue 95% Confidence Interval Dependent Variable Mean Difference (IJ) Pvalue 95% Confidence Interval Lower Bound Upper Bound Lower Bound Upper Bound HC 1 2 -14556.569* .000 -23025.81 -6087.33 OC 1 2 -23313.275* .000 -32804.32 -13822.23 3 4335.898 .280 -1579.52 10251.32 3 -8069.623* .011 -14889.30 -1249.94 4 -2643.460 .859 -9077.90 3790.98 4 -12637.824* .000 -19840.48 -5435.17 2 1 14556.569* .000 6087.33 23025.81 2 1 23313.275* .000 13822.23 32804.32 3 18892.467* .000 11138.20 26646.73 3 15243.652* .001 4488.94 25998.37 4 11913.109* .001 3758.35 20067.87 4 10675.451 .062 -324.38 21675.28 3 1 -4335.898 .280 -10251.32 1579.52 3 1 8069.623* .011 1249.94 14889.30 2 -18892.467* .000 -26646.73 -11138.20 2 -15243.652* .001 -25998.37 -4488.94 4 -6979.358* .005 -12427.24 -1531.48 4 -4568.201 .676 -13379.57 4243.16 4 1 2643.460 .859 -3790.98 9077.90 4 1 12637.824* .000 5435.17 19840.48 2 -11913.109* .001 -20067.87 -3758.35 2 -10675.451 .062 -21675.28 324.38 3 6979.358* .005 1531.48 12427.24 3 4568.201 .676 -4243.16 13379.57 SC 1 2 -27307.276* .000 -39282.05 -15332.50 InC 1 2 -2131.111* .000 -3320.70 -941.52 3 -1404.577 .998 -9563.04 6753.88 3 1700.276* .000 1105.31 2295.24 4 -9803.454* .016 -18395.79 -1211.12 4 1873.527* .000 1265.53 2481.52 2 1 27307.276* .000 15332.50 39282.05 2 1 2131.111* .000 941.52 3320.70 3 25902.699* .000 13149.24 38656.16 3 3831.386* .000 2786.66 4876.11 4 17503.822* .003 4470.97 30536.67 4 4004.637* .000 2952.44 5056.84 3 1 1404.577 .998 -6753.88 9563.04 3 1 -1700.276* .000 -2295.24 -1105.31