ABSTRACT

The objective of this study is to determine if there is a meaningful relationship between the VAIC™ (Value Added Intellectual Coefficient) values and MV/BV (Market Value/Book Value) ratios of Turkish Banks, of which stocks are exchanged at the Istanbul Stock Market (IMKB).

The paper begins with the introduction of the concepts ‘intellectual capital’, ‘company performance’, and ‘VAIC™’. Then, the association between VAIC™ values and MV/BV ratios of twelve banks in Turkey is empirically explored for the years 1998 and 2001.

According to the data of these four years, there has been no meaningful relationship between the dependent variable MV/BV and the independent variables VACA (Value Added Capital Coefficient) STVA (Structural Capital Value Added Coefficient), and VAHU (Value Added Human Capital Coefficient), the three sub-components VAIC™ consists of.

Keywords: Commercial Banks,  Intellectual Capital, Performance, Value, Turkey.

First published in Journal of Administrative Sciences (YBD), Vol. 4, No. 1, 2006, pp. 207-226.

INTRODUCTION

n today’s global marketplace, hardly anybody would dispute the decisive role of information and skilled workers in producing goods and services effectively and efficiently. Consistently increasing progress of information has taken the knowledge-based workforce into a superior position.

Modern companies base their operations upon information and relevant technologies. Therefore, so as to evaluate performances of them, new valuation techniques are continued to be sought.

It is commonly agreed that there is a lack of appropriate method of valuation, particularly in monitoring and managing intangible assets.

After the long-lasting hegemony of the classical factors of production, many authors are now to define the term “Intellectual Capital” that is actually not novel, but has not been openly discussed until the last decade. While modern managers acknowledge the importance of fixed assets and financial assets, they tend to be uncertain about the importance of intellectual capital and about utilizing it efficiently and adequately.

Today, knowledge has become the key resource, which will require to be continually acquired and up-dated. It wouldn’t be wrong to contend that the society has turned out to be an information society in which the main economic resource is information. In this new information society and in its economy, information and skilled workers– in other words intellectual capital will determine the competitive edge of the firms.

The conventional indicators of business success serve to heavily accentuate physical and/or tangible capital. The valuation techniques, such as ROI (return on investment), ROS (return on sales), and EPS (earnings per share), are inadequate to measure the business success of companies, implementing intellectual capital intensely. While it is quite burdensome to formulate the information aspect in traditional accounting, financial performance can be measured easily and monitored, though it represents only the tip of the iceberg.

Whereas conventional companies base their operations on physical and financial capital, modern companies rely on information. Information is more or less inherent in workers to transform it into value. Intellectual capital studies may be divided into two main views. While the first view focuses on determining, managing, and creating intellectual capital, the latter view concentrates upon appropriately measuring it.[1]

Researchers, such as Edvinsson, Malone[2], Sveiby[3], and Stewart[4], maintain that traditional accounting is inapplicable to modern companies for it cannot appropriately measure and indicate their natural dynamics. However, only using intellectual brainpower intensely in the production process can now increase the value of commodities. To accomplish this, a company should and may rely on its skilled workers.

Conventional companies’ objective was to increase production, and everything was contingent upon production. Modern companies’ objective is, however, to produce commodities by using more information as much as possible. Today, business success rests upon the ability and efficiency of companies to utilize information. The value-based management approach pushes the managers so as to maximize the economic value of the assets by using them efficiently[5].

INTELLECTUAL CAPITAL AND COMPANY PERFORMANCE

Hitherto, no consensus has been reached for a general definition of intellectual capital. According to Stewart[6], "Intellectual capital is the sum of everything everybody in your company knows that gives you a competitive edge in the marketplace". For Brooking[7], intellectual capital is the sum of intangible assets by which companies operate, while Edvinsson[8] considers intellectual capital as the information that can be transformed into value. Klein and Prusak[9] define intellectual capital as a tool that is carefully formulated, captured, and leveraged to obtain a greater-valued asset.

Whilst some researchers, such as Edvinsson, Malone and Roos, classify intellectual capital into two groups, as human capital and structural capital, other researchers, such as Bontis and Stewart, classify intellectual capital under the titles of human capital, structural capital, and customer capital.[10]

How is intellectual capital measured? This discussion is first started by L. Edvinsson who advocated that intellectual capital should be exhibited in the companies’ annual reports. Measuring the performance of a company is one of the most rigorous fields of strategic management.[11]

Strassman draws attention to the attempts that are made to measure and value intellectual capital in the last two decades, whereas these attempts had insoluble difficulties in pricing intangible assets. With the help of this problem, researchers realized that the value of intellectual assets is exhibited not in their costs but in their usage. Strassman argues that it is now widely understood that there exists no relationship between the cost of acquiring information and the potentially value-adding ability of information.

In the new economy as was in the old economy, it is important to understand performance of a firm. According to Garbi, however, it seems difficult to formulate a method measuring the future performances of electronic companies.[12]

Financial performance indicators consisting of previous terms inform about previous performance. Non-financial indicators, however, as being different from financial indicators, gives important information about the present value of company as well as the value-adding potential of a company.[13]

For about several centuries, conventional accounting equation has helped financial managers to perform operations. But now time is ripe for a new equation in which human ability plays a pivotal role.[14]

According to Kalafut and Low, despite often being neglected, intangible assets are the main determinants of the performance of a company. Therefore, investors, who are aware of the effects of intangible assets on the performance of a company, monitor these assets in their analyses to estimate the yields of these assets, and attempt to formulate unconventional methods for measuring intangible assets.[15] Measuring intellectual capital is one of the most attractive fields in information management. However, further international research in this field is needed.[16]

In the new economy, because in measuring performance, financial measures seem to be restrictive, there is a need for non-financial measurements, and research in this field is being performed.[17] Skandia model of Leif Edvinsson consists of finance, customers, process innovation and development, and human capital. Index of intellectual capital was first formulated by G. Roos and friends and used by Skandia in 1997.[18] “Balanced Scorecard” of Kaplan and Norton is a system that monitors the critical effects in valuation process, and embraces the financial and non-financial measures.[19]

Annie Brooking developed the Technological Broker Model to measure intellectual capital. Brooking defines intellectual capital as a mix of four components, i.e. market value, human-centered assets, intellectual ownership assets and structural assets. So as to form an indicator of intellectual capital, Brooking directed 20 questions to the organization.[20]

Karl-Erik Sveiby believes that difficulties in measuring intangible assets can be overcome. Sveiby offers a conceptual framework, focusing on three kinds of intangible assets, i.e. external structure (trademarks and relations with customers and suppliers), internal structure (organizational management, legal structure, software, and research and development), and individual efficiency (education and experience)[21].

Thus, Sveiby provides an informational outlook instead of conventional accounting approaches. In this perspective, Sveiby discusses the measurement techniques of intangible assets using non-financial measures as well as financial measures, representing shareholder’s value and financial success as a whole.[22]

CORPORATE VALUE CREATION EFFICIENCY METHOD

Ante Pulic contends that VAIC^TM method performs well in measuring and monitoring the value-adding potential of a company, a sector, or a national economy and can be used in valuation of business performance as a modern tool. VAIC^TM is quite easy to be calculated and does not give rise to additional managerial costs.[23]

VAIC^TM method assumes that company is a dynamic and ever-changing system, and a company’s workers are viewed as the primary asset for success. VAIC^TM method is based upon physical, financial and intellectual capital. This method measures the performance of both physical and intellectual capital in value-adding process. The coefficient of VAIC^TM is the efficiency of all resources and exhibits the value-adding ability of a company or an economy. The larger the coefficient, the more efficiently used physical, financial and intellectual capital turn out to be.

VAIC^TM numerically shows that total efficiency of physical, financial and intellectual capitals in value-adding process. Pulic’s methodology focuses on value-adding, value-adders, and value-adding procedures. VAIC^TM considers the entire company as a dynamic system.

CE (Capital Employed) can be briefly described as the company’s financial and physical assets.

CE= Total assets – non interest bearing short term liabilities

In VAIC^TM methodology, while calculating CE, physical and non-financial assets are subtracted from total assets, because these assets are taken into consideration as intellectual assets, i.e. intellectual capital.

HC: As the Human Capital is not only one of the most important components of intellectual capital, it is also the ability source of intellectual capital. Stewart suggests that the workers in a company from bottom to top must be seen not as assets, but investment.

SC: Structural Capital is made up of patents, intellectual properties, databases, information technologies etc. of the company. It also consists of social relationships between the individuals in the company. Structural capital is a mean of transportation channel in the company.

VAHU: Shows the value created from each dollar invested on the workers in the company. VAHU shows the value creation efficiency of human capital in the company.

VAHU= VA / HC

STVA: Shows the efficiency of structural capital in value creation process in the company.

STVA= SC / VA

VAIC^TM= VAHU + STVA + VACA

OUTPUT: Is the income generated from the sales of products and services of the company.

INPUT: Is all the expenses and costs undertaken by the company, except the expenditures made for the workers of the company.

VA: OUTPUT – INPUT

VACA: Is the ratio between value created and the physical and financial capital total.

VACA= VA / CE

METHODOLOGY OF THE RESEARCH

In this research, the relationship between the dependent variable MV/BV and the independent variables of VAHU, STVA and VACA of 12 Turkish banks, of which shares are traded-off in IMKB (Istanbul Stock Exchange Market), are examined by using simple and multiple regression analyses.

The data of the study is gathered from the financial statements of the banks and of IMKB. The significance of determination coefficient (R²), which is found as the result of the analysis is tested with F test. To be able to determine whether or not R² is found in the regression analysis, the result of F test was used. In order to find whether there is a dependency between independent variables, firstly, the correlation coefficient ρ between free variables was calculated. Then, by using the suitability of v=n-2 freedom degree and Student (t) split, the comments have been made according to T table. Moreover, the significance of partial correlation coefficient, which is a proportional measure used to calculate the relationship between one of the independent variables and the dependent variable, isolating it from the effects of other variables, is examined by using F test. In order to find if there is a strong auto-correlation between standard error terms, Von-Neumann test has been used, as N<15.

QUESTIONS OF THE RESEARCH

1. Whether there is a correlation between the variables VAHU, STVA, VACA and the variable MV/BV

2. When STVA is constant, whether there is a correlation between variables VAHU and VACA and the variable MV/BV

3. When VACA is constant, whether there is a correlation between the variables STVA and VACA and the variable MV/BV

4. When VAHU is constant, whether there is a correlation between the variables STVA and VACA and the variable MV/BV

ANALYSING THE PHYSICAL, FINANCIAL AND INTELLECTUAL CAPITAL AND MV/BV OF THE BANKS IN QUESTION

The physical, financial and intellectual capital and MV/BV of banks used in the research are analyzed below. The money amounts in the tables and figures are presented in thousands of US dollars.

Table 1:

Findings for Akbank

Figure 1:

Findings for Akbank

As shown at Table 1, between 1998-2001 Akbank’s HC was increasing %11, its CE increased about %50. In 1998, 1 $ CE investment was creating 0,095$ VA, in 2000 0,056$ VA and in 2001 0,010$ VA it created. In 2001 in comparison with the previous year there was a significant decrease in VAIC^TM, there was some increase in MV/BV. In 1998 while each 1$ HC investment was creating 7,457$ VA, in 2001 1$, 1,111$ VA it created.

Table 2:

Findings for Finansbank

Figure 2:

Findings for Finansbank

Between 1998-2000 Finans Bank’s HC increased twice, while its SC was decreasing %5. While its CE was increasing %75, its VA increased %15. In 2000 VAIC^TM and MV/BV decreased at the same rate.

Table 3:

Findings for Garanti Bankası

Figure 3:

Findings for Garanti Bankası

Garanti Bank’s HC increased % 17 between 1998 and 2000. In the same period, while there was an increase of %30 in its CE, there was a decrease of %20 in its VA.

Table 4:

Findings for İş Bankası

Figure 4:

Findings for İş Bankası

İş Bank’s HC increased about %25 between 1998 and 2000. Its SC decreased %15 and its VA increased %5. In 2000, while VAIC^TM was increasing, MV/BV decreased significantly.

Table 5:

Findings for Alternatif Bank

Figure 5:

Findings for Alternatif Bank

Between 1998 and 2001, Alternatif Bank’s HC 12 times, its SC 20 times, its CE 6 times and its VA 16 times increased. In 2000 and 2001 VAIC^TM and MV/BV decreased.

Table 6:

Findings for T.Kalkınma Bankası

Figure 6:

Findings for T.Kalkınma Bankası

The example of Türkiye Kalkınma Bankası exemplified the importance of HC in creating value. For Türkiye Kalkınma Bankası in 1998 VAHU was 1,173, MV/BV was 2,20; in 1999 VAHU increased up to 2,169 and MV/BV increased up to 18,23. That was a significant increase. In 2000, while VAHU increased up to 2,565. MV/BV decreased significantly.

Table 7:

Findings for Şekerbank

Figure 7:

Findings for Şekerbank

As seen on Table 7 and Figure 7, between 1998 and 2001 there was a parallel trend between VAIC^TM and MV/BV for Şekerbank.

Table 8:

Findings for Türkiye Sanayi Bankası

Figure 8:

Findings for Türkiye Sanayi Bankası

Moreover, the significance of degree of relationship between independent variable and each of independent variables was analyzed by considering the result of F-test, which is a ratio measure of partial correlation coefficients isolated from the effects of other variables.

Türkiye Sanayi Bank’s VAIC^TM decreased, its MV/BV increased in 1999. However, in 2000 the decrease in VAIC^TM followed by a decrease in MV/BV.

Table 9:

Findings for Türkiye Ekonomi Bankası

Figure 9:

Findings for Türkiye Ekonomi Bankası

For Türkiye Ekonomi Bankası VAIC^TM constantly decreased between 1998 and 2001. The decrease in 2001 was very significant. The two economic crises following each other with short periods that Turkey had, has been thought as the main reason for this result.

Table 10:

Findings for Tekstil Bank

Figure 10:

Findings for Tekstil Bank

As seen on Table 10 and Figure 10, Tekstilbank’s VAIC^TM decreased %39.5, and its MV/BV decreased %62. The increase and decrease in VAIC^TM between 1998 and 2001 heavily affected MV/BV.

Table 11:

Findings for Yapı Kredi Bankası

Figure11:

Findings for Yapı Kredi Bankası

In 1999, there was a significant decrease in VAIC^TM for Dışbank, there while was an increase in MV/BV. In 2000, there was an important increase in VAIC^TM, but an important decrease in MV/BV.

Table 12:

Findings for Dışbank

Figure 12:

Findings for Dışbank

EMPIRICAL RESULTS

As seen on Table 13, VAHU, STVA and VACA variables, which are the parameters of VAIC^TM, explains together %30 of MV/BV variance. According to the result of 2001, three variables explain %41.4 of MV/BV; while in 1999, explaining %2.2 of and in 2000 %6.6 of MV/BV. The adjusted R²= -1.344 means that the low numbers of observations (n=5) caused high determination coefficient for 2001. According to the total results of the years between 1999 and 2000, the %1.6 of MV/BV is explained by VAHU, STVA and VACA variables.

When the significance of determination coefficient (R²= 0.016), which is found according to the results of 1998-2000, is examined with F-test. The determination coefficient, which is equal to 0.016 does not show any significant correlation as F value is 0.002<0.187.

For Von-Neumann value 2.385 %1 significance level, which is found according to 1998’s data, when observation value is 11, parameter is 4 and d=, it is tested whether there is an auto-correlation or not and an autocorrelation has not been found as 0.7163<2385<39504. In similar way according to the total results of 1998-1999 and 1998-2001, among standard errors, no correlation has been found.

Tablo 13:

MV/BV=CONSTANT +B1 VAHU+B2 STVA+B3 VACA + i.

Table 14:

ANOVA Table.