Clarifying the effect of intellectual capital on firm performance: The case of Vietnam

CLARIFYING THE EFFECT OF INTELLECTUAL CAPITAL ON
FIRM PERFORMANCE: THE CASE OF VIETNAM
Hoang Thi Hue

Faculty of Human Resources Economics and Management,
National Economics University, Hanoi, Vietnam.
Vu Huy Hoang

Audit K57, Institute of accounting – auditing,
National Economics University, Hanoi, Vietnam.
Nguyen Duong Hong Nhung

Mathematical Financial 57, Faculty of Mathematical economics,
National Economics University, Hanoi, Vietnam.
Quach Hong Hanh

Actuarial science K57, Faculty of Mathematical economics,
National Economics University, Hanoi, Vietnam.
Abstract:
In a corporate world where true value is no longer determined by physical assets
alone, managers and practitioners consider more about developing inner capital of the
company. Recent studies around the world also suggest an impact of intellectual capital
and on firm performance. However, there are a few studies around Vietnamese company.
The purpose of this empirical study is to investigate the three elements of intellectual
capital, i.e. human capital, structural capital, and relational capital. It also focuses on
assessing the contribution of Intellectual capital to the performance of a firm. Data is
collected for enterprise surveys from 2012 to 2015 released by General Statistics Office
(GSO) and analysed using regression, the result shows that there are positive
relationships between the intellectual capital and performance of company business. In
particular, human capital and relational capital have a strong effect on performance of
firm. Yet, structural capital is not important and seems to reduce the impact of intellectual

capital to the performance of the business.
Key: Intellectual capital, Performance, Value added, Viet Nam.
1. Introduction
By the end of the twentieth century, the economy began to undergo certain changes
that had a decisive influence on economic growth and development. In this stage, the
competitive advantage of individual countries as well as of enterprises is reflected not only
in the traditional resource of production, such as properties of land, labor and other

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economic assets (Sullivan, 2000). According to resource-based view, a company's
resources, especially intangible assets, have an important role in helping the company
achieve high efficiency and sustainability (Eisenhardt and Schoonhoven, 1996). The
importance of the company's intangible assets is increasing (Lev, 2001). Bontis (2002)
suggest that the current trend is that firms focus less on tangible assets than on intangible
assets when seeking a competitive advantage.
Over the past two decades, intellectual capital (IC) has been adopted by most
organizations around the world. Intellectual capital plays a fundamental role in modern
organizations. It is part of the business foundation in the 21st century (Rudez and Mihalic,
2007). Intellectual capital, therefore, has been identified as one of the key drivers of
corporate performance (Youndt et al., 2004). It can be said that measuring IC variables is
not easy. Zambon (2004) asserts that no method is applied around the world to measure.
However, quantitative studies have used the VAIC - value added method (Public, 1998) to
measure the effect of IC.
The topic of IC has been studied and evaluated by many scholars to provide a
concept for improving the efficiency of enterprises (Augier and Teece, 2005). However, in
Vietnam, IC is a new concept. Thus, this study uses the quantitative measure, value added
intellectual coefficient (VAIC) developed by Pulic (1998) as a measure of IC efficiency.
Data is collected for enterprise surveys from 2012 to 2015 released by General Statistics

Office (GSO) and analysed using regression. The paper is organised as follows. The
following section discusses the prior IC literature, focusing on the relationship between IC
efficiency (VAIC) and firm performance. The hypotheses to be tested and the method used
to test those hypothesized relationships are described in the next sections. The results are
then outlined, discussed and some conclusions are offered.
2. Literature review
Intellectual capital (IC)
Edvinsson and Malone (1997) defined intellectual capital as knowledge that could
be transformed into values, intangible assets which are not clearly listed in the corporate
balance sheet, yet having positive influence on the efficiency of the business, thus showing
the relationship between employees, ideas and information. They divide intellectual capital
into human capital and structural capital. Human capital is based on the knowledge created
and stored by the organization's employees. While structural capital is based on the
presence, empowerment, and infrastructure of human capital. Structural capital is divided
into organizational capital (the knowledge that is created and stored in the organization's IT
system and processes that accelerate the flow of knowledge through the organization) and
customer capital (relationships that an organization has with its customers). The study by
Reed et al. (2006) conclude that intellectual capital can be divided into human capital,
organization capital and social capital.
Although researchers may not agree to the exact classification and definition of
intellectual capital, there is widespread agreement that it contains human capital (HC),

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relations capital (RC) and structural capital (SC) (Edvinsson, 1997; Tayles et al., 2007).
Therefore, our research will evaluate the capital (IC) based on human capital, relationship
capital, and structural capital.
Human capital (HC)
Human capital is a key component of intellectual capital. The concept of human capital

has been adequately and systematically mentioned since the 1960s. Human capital is defined
as knowledge, skills, innovation and competence (Bontis et al., 2000). Edvinsson and Malone
(1997) defined human capital as a combination of knowledge, skill, innovativeness and ability
of company‘s individual employees to meet the task at hand. Human capital makes reference
to the knowledge – explicit and tacit – that people possess, as well as their ability to generate it,
which is useful for the mission of the organization (CIC, 2003).
Relationship capital (RC)
Relationships capital can be viewed as a perception of value that consumers hold
when they engage with suppliers of (Petrash, 2001). In this way, we can see that the
relationship capital is only obtained from the customer, which is the result of their
perception of the company. Relationship capital is defined as the set of available resources
of the organization and current relationships which are made through interactions between
individuals or organizations (Kostova and Roth, 2003; Shipilov and Danis, 2006).
Structural capital (SC)
Structural capital is often used to refer to the processes and procedures that are created
and stored in a company's technology system to accelerate the flow of knowledge through the
organization (Carson et al., 2004). This definition differs from the approach given in strategic
management literature (eg Gibson and Birkinshaw, 2004; Kang and Snell, 2009). Gibson and
Birkinshaw (2004) put the structural capital into an imbalanced context by developing a set of
systems and processes. It allows the ability of link systems and adaptations to maintain
performance at the company level. Kang and Snell (2009) classify organizational capital into
two alternative forms: mechanical and organic. The alternative forms of capital of an
organization have different effects in integrating knowledge within a company.
Performance
According to Armstrong and Baron (2005), firm‘s performance is defined as the
aggregate assessment of how many benefits an organization generates based on the amount
of resources spent. Business performance is measured by the attainment of business
objectives and the efficiency of the enterprise's use of resources to achieve the goals. For
analysis and measurement, researchers evaluate organization performance through
financial parameters such as profitability, market share, capital growth, etc. It is because of

that financial results are one of firm‘s objectives and also a tool for management and
decision-making, setting the next target within the enterprise.
Hypothesis
IC as a strategic resource that companies use to gain competitive advantage and
create value for efficiency (Marr et al., 2003). Although experimental tests show mixed

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results, after the findings of Chen et al., (2005) and Tan et al., (2007) and other theories
based on resource-based theory, it can be hypothesized that there is a direct positive
relationship between the efficiency of the IC and the performance of the firm.
H1 (a). VAIC positively affects the performance of the company.
Previous studies have found that different aspects of IC also have a significant
impact on business performance (Chen et al., 2005; Chan, 2009). Hence, it can be assumed
that the components of VAIC have a positive impact on the efficiency of the business:
H1 (b). HCE positively affects the performance of the company.
H1 (c). SCE positively affects the performance of the company.
In addition, capital efficiency has been found to have a significant positive effect on
efficiency (Chen et al., 2005).
Therefore, this relationship is also hypothesized:
H1 (d). CEE positively affects the performance of the company.
IC or capital efficient use over a period of time may not affect performance until
after some time. For example, new managers (HCs) may not add value until later become
more experienced. New systems (SC) and new plant and equipment (CE) can having
problems on the operation, the efficiency will decrease until it actually works again. Thus,
Chen et al., (2005), and Tan et al., (2007) hypothesized that VAIC and its components over
a period of time would positively affect performance in the following period:
H2 (a). VAIC of last year positively impacted the performance of the company this year.
H2 (b). CEE of last year before had a positive impact on the performance of the

company this year.
However, human capital has a certain lag when creating growth for business
efficiency. Clarke (2011) confirmed that last year's HCE had a greater impact on the
current year's business performance than the current HCE. However, he also proposed a
longer time series evaluation:
H2 (c). HCE of two years before positively impacted the company's operations this year.
H2 (d). SCE of two years before had a positive impact on the performance of the
company this year.
H2 (e). VAIC of two years before positively impacted the performance of the
company this year
3. Methodology
Sample
This study uses the annual survey data of the General Statistics Office (GSO),
consists of firm from 2012 to 2015. Due to missing data on selected variables, the final
sample for analysis consists of firm-year observations depending on the particular variable

917


concerned. After processing to retrieve the information needed for the study, the sample
used included more than 500 thousand observations described in the following table 1:
Table 1: Sample distribution by industry
Firms

2012

2013

2014

2015

951

985

951

828

33

41

25

33

289

252

216

211

19,467

17,700

19,035

18,328

410

287

411

382

Build

18,255

16,908

16,939

18,345

Commercial

58,848

54,036

53,699

56,725

3,104

3,101

3,394

4,066

Transportation, warehousing

11,212

10,408

10,745

12,802

Other services

31,087

27,805

27,379

30,131

111,729

143,712

131,617

132,874

Agriculture and Forestry
Seafood
Mining industry
Processing industry
Production and distribution of
electricity, gas and gas

Hotels, restaurants

Total

549,824
Measurement of variable
Measurement of IC
One of the most common method to calculate IC performance is the VAIC model
developed by Pulic (1998). This model measures the added value of any business along
with the individual contribution of each type of asset (HC, SC, and CE) to value creation.
VAIC is an index-based measure that uses financial reported data. It calculates asset value
and IC efficiency - which is very useful for managerial decision-making.
Calculating VAIC
To calculate VAIC, the ability of a company to generate value-added (VA) for all
stakeholders must first be calculated. In the simplest form, VA is the difference between

output and input. The results show net revenue and input contain all costs incurred in
earning revenue except for labor costs as a value-creating entity (Tan et al., 2007). This
VA is also defined as the net value generated by the firms in the year (Chen et al., 2005)
and can be expressed as:
VA = S - B = NI + T + DP + I + W
Where: S is the net revenue (output); B is the material and service purchased or the
cost of the goods sold (input); NI is net income after tax; T is the tax; DP is depreciation; I
is the cost; and W is the salary and salary of the employee. The above equation VA is
called the ―Positive Value-Added‖ approach (Riahi-Belkaoui, 2003) and is the method
used in this study.

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Human Capital Effect (HCE).
Human capital (HC) includes skills, experience, productivity, knowledge and
suitability of employees in the workplace. In the VAIC model, HC levels are defined as
wages over time (Pulic, 1998). In particular, higher wages for more skilled workers add
value to the company than lower-wage employees. HCE for a dollar spent on human
capital or how many employees will generate VA and calculated as follows:
HCE = VA/HC where HC is the total salaries and wages.
If wage is low and VA is high, the company is using HC effectively. If the VA is
low-wage-related, HC's are not used effectively and the HCE will be low. Higher HCE
results from the efficient use of HC to increase value through operating profit.
Structural Capital Effect (SCE).
Structuring (SC) includes components such as strategy, organizational networks,
patents and brand names. Pulic (1998) calculated SC as follows:
SC = VA – HC.
Thus, VA is affected by the effect of HC and SC. SC depends on HC, and larger
HCs will be transformed into improved internal structures (Nazari and Herremans, 2007).

HC and SC are inversely related (Tan et al., 2008). With this equation, SC decreases as HC
increases, which is logically incompatible with SC's theoretical definition. To correct this
problem, Pulic (1998) calculates SCE as follows:
SCE = SC/VA.
SCE is the SC dollar of the company and for every dollar of value added, and when
the HCE increases, SCE increases. If the counting methods for both HCE and SCE are
calculated in VA above the numerator, this generates a logical inconsistency (Pulic, 1998).
Capital Use Efficiency (CEE).
CEE includes the efficiency that SCE and HCE do not capture. Pulic (1998) argues
that IC cannot create value itself, and therefore it must be combined with the use of
physical and financial capital (CE).
Therefore, CE is calculated as total assets minus intangible assets and CEE is
determined as follows:
CEE = VA/CE where CE is the total book value of the company.
The CEE says that VA is made up of a dollar spent on capital (CE).
Value Added Intelligence (VAIC). VAIC measure by:
VAIC = HCE + SCE + CEE.
Measurement of Performance
Firer and Williams (2003), based on previous research on corporate performance,
have adopted a measure of corporate performance in terms of financial variables related to

919


profitability. The yield and valuation parameters of the market are: ROA, ATO and MB.
These variables are defined as follows: ROA: net profit margin (dividend preference of the
company is negligible, or not included) divided by the book value of total assets in the
yearend financial statement. ATO: The ratio of total revenue to the total book value of
assets reported in the financial statements at the end of the year. MB: market capitalization
rate (stock market price) on book value of net assets.

According to Pham Thi Thanh Huong (2017), ROE and ROA indicators accurately
reflect the efficiency of using financial resources of enterprises and not much impact of
changes in the stock market. Another study on enterprise efficiency by Li-Chang Hsu and
Chao-Hung Wang (2010) used ROA as a measure and definition for enterprise efficiency.
Therefore, this study uses ROA to measures performance. ROA defined as: Return
on assets (ROA): Profit before tax/Average total assets.
Control variables
To minimise the impact of other variables that may explain observed relationships
with firm performance, two control variables (leverage and industry) are included within
the regression models:
- Leverage: A company with high debt ratios will usually focus on fulfilling the
requirements of creditors. This is not consistent with the point of view of making a profit from
VA and VAIC. As with previous studies (Shiu, 2006; Chan, 2009), we assume that leverage is a
control variable. Leverage is calculated as: Leverage = Total debt/Total assets.
- Industry: Kujansivu (2007) note that the effects of IC vary from one industry to
another. Chen et al. (2005) and Tan et al. (2007) divides the VAIC regression models and
regression models into groups, and finds significant differences in the interpretation of
strength across disciplines. Similarly, Firer and Williams (2003) suggest that the industry is
controlled in this study through a dummy control variable. This counterfeit symbolizes the
impact of different industries under the VSIC 2007 industry category.
Empirical models
The three hypotheses to be empirically tested are reflected in the following three
equations relating VAIC (Model 1) and components of VAIC (Model 2) to firm
performance.
Perfit = β0 + β1 VAICit + β2 VAICit-1 + β3Control variablesit + ɛit (Model 1)
Perfit = β0 + β1HCEit + β2SCEit + β3CEEit + β4HCEit-1 + β5SCEit-1 + β6CEEit-1 +
β7Control variablesit + ɛit (Model 2)
Where:
Perf is Return on assets (ROA); VAIC is Value added intellectual coefficient; HCE
is Human capital efficiency; SCE is Structural capital efficiency; CEE is Capital employed

efficiency; β0 = Constant; i = firm; t = year (between 2012 and 2015).
Control variables: LEV = Leverage; industry.

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4. Result
Descriptive statistics
Research from the survey data sets and using the variables: Human Capital
Efficiency (HCE), Efficiency, Capital Efficiency (CEE), Capital Efficiency (CEE), Net
Asset Return on Total Assets (ROA).
Table 2: Descriptive statistics for selected variables
Variable

Obs

Mean

Std. Dev.

Min

Max

vaic

546,924

353.2564

165011.2

-1018.45

109000000

vaic_lag1

296,202

-4.787787

237614.6

-48600000

109000000

vaic_lag2

150,498

-309.2309

127304.9

-46800000

11500000

hce

546,924

0.0355678

1.077847

0.00

359.04

hce_lag2

216,734

0.0262839

0.2832389

-0.31

46.27

cee

549,824

430.2587

164575.5

-0.06

109000000

cee_lag1

297,670

85.83999

237025.7

-48600000

109000000

sce

549,824

-78.89301

75.73992

-1018.47

1.00

sce_lag2

217,821

-34015.5

8746385

-3360000000

6801

ROA

549,330

0.0179176

6.931913

-157.84

4914.19

In which, X_lag1 is a delayed X variable for a period, X_lag2 is a delayed X
variable for two periods (X = vaic, hce, cee, sce)
Regression analysis and hypothesis testing:
Table 3: Regression results
ROA
Independent variables

Coefficients t-statistic

model 1

Panel A
(VAIC, VAIC_lag1,
VAIC_lag2, leve, branch,
ROA)

const

0.14349

1.88

VAIC

0.002825

36.56**

VAIC_lag1

-1.85E-06

-0.41

VAIC_lag2

-8.17E-10

-0.02

Adjusted R2
Obs

0.005
148704

T

Panel B
(HCE, HCE_lag2, CEE,
CEE_lag1, SCE, SCE_lag2,
leve, branch, ROA)

5

Model 2
Const

-0.163

-0.77

HCE

3.273287

460.56**

HCE_lag2

0.690122

25.84**

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CEE

0.006815

38.25**

CEE_lag1

3.17E-09

0.09

SCE

-0.00036

-5.88**

-1.21E-11

-0.03

SCE_lag2
Adjusted R2
Obs
T

0.191
215371
5

* indicates significant at the 5 percent level; ** indicates significant at the 1
percent level
Multiple regression results: direct relationship with current year performance
Table 3 (Panels A and B) shows the results of regression coefficients for all
independent variables VAIC and its components, using ROA as the dependent variable.
Model 1 at Panel A presents the results for VAIC while Model 2 at Panel B presents the
results for VAIC components.
In Model 1 of Table 3, VAIC coefficient is positive and significant at the 1 percent
level across all performance measures. Adjusted R2 is 0,005 for ROA. This result indicates
that there is a relationship between overall IC and company performance, thus contributing
to the hypothesis H1 (a): VAIC positively affects the performance of the company. In the
Vietnamese environment, VAIC has little impact on performance. This result is consistent
with prior studies by Clarke (2011), accordingly, in Australia, VAIC is positively related to
firm performance at the 1 percent level.
In Model 2 of Table 3, the coefficients of both HCE and CEE are significant at the 1
percent level and positively related to performance (ROA). H1(b) and H1(d) are supported.
This result is consistent with previous studies by Chen et al., (2005) and Ting and Lean
(2009). Accordingly, the use of physical, financial and human resources effectively is

important for businesses to generate higher profits. However, these results are not
consistent with companies in South Africa (Firer and Williams, 2003), Taiwan (Shiu,
2006) and Hong Kong (Chan, 2009), where there is a significant negative relationship
between HCE with performance. SCE has the opposite effect and low impact on
performance, this finding is generally consistent with previous VAIC studies. Chen et al.
(2005), showing small, negative and negligible SCE efficiency with ROA. Ting and Lean
(2009) and Shiu (2006) also found a negligible and negative relationship between SCE and
profitability. H1(c) is not supported.
Remarkably, in all models, adjustment R2 was increased significantly with the
components of VAIC (Table 3). R2 increase from 0.005 in Model 1 to 0,191 in Model 2, these
results are consistent with Chen et al. (2005). In general, these results suggest that VAIC
components have greater explanatory power when they are incorporated into individual VAIC
indicators. SCE is the only VAIC component that shows opposition, thus indicating the most

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important way to improve company performance is through HCE and CEE. SCE appears to
detract from the predictive power of the other two components in the VAIC index.
Multiple regression results: direct relationship with following year performance
There is no significant relationship between VAIC of the last or two-year time
period, therefore, H2(a) and H2(e) are rejected. Two-year period SCE (SCEt-2) is
negatively yet insignificantly related to ROA. This is consistent to study of Martin (2014).
Last year, CCE (CCEt-2) is positively but insignificantly related to ROA. This can be
understood that in terms of Vietnam in general, the constituents of IC have no lag effect.
However, two-year period HCE (HCEt-2) is positively and significantly related to
ROA at 1 percent level. This generally support for H2(b). Coefficient is 0.69 for ROA.
This result is consistent with previous studies (for example, Chen et al. (2005)) which is
that it takes time when to investing in human resource. However, our study has
investigated further time series to discovered that investing in human resource will bring

benefit to company many years after that.
5. Discussion and conclusion
The paper reviews a number of theories of intellectual capital in business in general,
including human capital, structural capital, and relational capital. It also provides evidence
for the relationship between intellectual capital and business performance. The paper
examines intellectual capital through the VAIC model (Pulic, 1998) while assessing the
relationship between intellectual capital and firm performance. In addition, using the
estimation method with panel data has overcome the problem of lack of variables in model
estimation, thus yielding better results than OLS when evaluating this relationship.
Using quantitative method, the article has shown that there are positive relationships
between the intellectual capital and performance of company business. In particular,
human capital (HC) and physical and financial capital (expressed in CEE) have a strong
effect on performance of firm. Yet, structural capital is not important and seems to reduce
the impact of intellectual capital to the performance of the business. Besides, the article
shows that human capital is important in the current year and also has implications for
future performance. Especially in the context of Vietnam, it can be seen that the impact of
human capital is rather slow (2 years) compared to CEE financial capital (1 year). SCE
seems to take longer to influence the performance of the company.
While it is possible to see the growing importance of intellectual capital to the
company, more evidence and research is needed to evaluate this relationship and develop a
framework for intellectual capital. We suggest that future studies seek out more deeply the
dynamic nature of this relationship to provide more evidence from different parts of the
world. Secondly, the VAIC model itself needs to be rigorously verified its reliability when
used to measure IC performance. One of the limitations of the VAIC model is that it does
not separate the relationship, thus, future studies can build and add to this importance in
the IC component of the VAIC model. In addition, in order to supplement and further
develop this research, more sophisticated statistical techniques (such as non-linear

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regression and quantile regression) can be used and can be investigated in a more efficient
manner over time.
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