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\begin{document}

\title{The Mediating Effect of Intellectual Capital on Capital Structure and
Financial Performance of Listed Banks in Nigeria}


\author[ ]{Kabiru Saidu}

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\date{}

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\textbf{By}

Sani Kabiru Saidu

Department of Business Administration

Federal University, Birnin Kebbi

sani.kabiru@fubk.edu.ng

Khalid Haladu Bello

Department of Accountancy

Kano State Polytechnic-Kano

khalidfagge@gmail.com

\textbf{Abstract}

The main focus of this research is to investigate the mediating link
between intellectual capital, capital Structure and financial
performance in banks listed on the Nigerian Stock Exchange. The study
used documented data drawn from the annual reports and accounts of the
sampled banks as well as NSE data for the period 2011- 2015. Structural
Equation Modelling (SEM) is applied to find the association between
latent constructs and to find their impact of financial performance of
listed Nigerian Banks. This study empirically shows that the capital
structure through intellectual capital efficiency has a significant
relationship with financial performance measures and weak link with
leverage level of the sampled banks as a whole. This empirical study
gives a new insight into intellectual capital, capital structure for
companies to enhance their financial performance through proper
investment in intellectual capital while maintaining focus on the level
of corporate leverage as it declines with IC efficiency. This study is
the first of its own kind that measures mediating impact of IC
efficiency relationship between capital structures and financial in the
Nigerian banking industry using the adopted methodology and analysis.
Further studies using alternate methodology and analysis while
maintaining the key study's structure would further validate the current
outcome is suggested in order to further strengthen the discourse.

\textbf{Keywords} : Capital Structure, Financial Performance,
Intellectual Capital, Nigerian Banks

\textbf{INTRODUCTION}

Post industrial revolution era brought about the dominance of four
factor input in the production process owing to the fact liquid wealth,
properties and capital were prominent components. Gradually, human input
begun to take the driver's sit due principally to the advent of
knowledge economy. As such, intellectual capital has today become the
major and indispensable component of the modern knowledge economy
(Pulic, 2004).

In addition to the intellectual capital, other elements namely;
information and communication technology and innovation have now a key
lead in the ever dynamic knowledge economy which transcends conventional
traditional niche and ever dynamic environment. The significance of this
emerging capital brought human assets into pivotal component of
strategic corporate decisions of organizations.

Intellectual capital despite its significance and prominence in
corporate performance surfers a great deal of setback in the empirical
realm largely due to the fact that it can't be objectively measured and
compared across firms because of its intangible nature, relative
scarcity, and its enveloped into other forms of assets Lev(2011).
Intellectual Capital as highlighted earlier surfers a pitfall in terms
of measurement inconsistencies methods as often used by corporate
managers and other stakeholders to avail them the assessment of the
level of intellectual capital of firms. As a result of the absence of
standardized procedure in practice and research, it is therefore
cumbersome to effectively measure and compare intellectual capital
across similar firms for decision making at strategic level of
governance.

One method used to overcome this difficulty is the Value Added
Intellectual Coefficient (VAIC) model (Pulic 1998; 2000). Other methods
used other than the VAIC model include various schools of thoughts in
modelling IC as described by chen at el (2009) and Roos et al. (2005)
are Direct Intellectual Capital Methods (DICM), Return on Assets Methods
(ROAM) and Scorecard Method. The VAIC model applies the efficiency
concept by way of measuring the extent of output produced over input
entered. Previous studies have used the VAIC model to measure IC and
examined the impact of IC on market performance of firms in different
countries. However, the results are at best mixed. Recently the VAIC
model has been subject to critical review (Iazzolino \& Laise 2013;
Stahle, Stahle \& Aho 2011) both in regard to the conceptual basis for
the model and the mixed empirical findings. This study provides further
empirical evidence on the model especially as used to mediate between
Capital Structure (CS) and Financial Performance (FP). The present study
examines the relationship between IC and market performance of
multinational firms engaged in R\&D. This sample is specially chosen to
show the effect of reliance on IC in R\&D firms as perceived by
investors.

Fundamentally, the purpose of this paper is to present the extended VAIC
model by Makki and Lodhi (2008) with some other relevant variables by
relating IC with capital structure to creating corporate value
represented by financial performance of listed banks in the Nigerian
Stock Market 2010 to 2015. The other main objective of this study is to
fill the gap in the field of intellectual capital by using IC as the
mediator, use of structural equation model for impact measurement and
other methodological alterations.

\textbf{LITERATURE REVIEW}

IC refers to the knowledge that is embedded in employees as well as in
the structure of the firm (Edvinsson \& Malone 1997). The knowledge or
know-how depending on its productivity represents the value human
capital provides to the value generating process. According to the
Shranel (2012), IC has three distinct feature, namely: future economic
value, lacks physical substance, and can, to some extent, be owned and
traded by firms.

IC can be segregated into two types: human capital and structural
capital. Human capital (HC) refers to the knowledge, skills and
experience that reside in employees (Edvinsson \& Malone 1997; Roos,
Roos, Dragonetti \& Edvinsson 1997; Stewart 1997). Structural capital
(SC) refers to the knowledge that resides in company assets other than
employees such as systems, products, processes, patents, trademarks,
brand names, customer databases, collaborations and networks (Edvinsson
cannot be owned and controlled by firms, whereas SC can be owned and
even traded (Edvinsson \& Malone 1997).

It is of great significance for any organization to decide on the level
of debt and equity for running their business successfully. There is a
lot of work done on capital structure determination but this is
relatively new dimension to relate it with IC to check its impact on
value addition and valuation process of an organization. Modigliani and
Miller (1985) worked on capital structure determination but in a very
restricted environment. Services sector, specifically banks are selected
for this study to compare them in terms of their value created by their
IC efficiency, as banking sector is a knowledge-intensive sector.

Saint-Onge's, (1996) model developed in the early 1990s divides
intellectual capital into three parts: Human capital, Structural
capital; and Customer capital. Also Edvinsson (1997) agrees that
intellectual capital comprises human capital, structural capital and
customer capital. Bontis (2000) adjusts customer capital into relational
capital arguing that it not only the customer's contribution that
affects intellectual capital but the whole lot of relations with
customers, suppliers, shareholders and other partners. Tseng and Goo
(2005) categorized intellectual capital (IC) framework in terms of human
capital, organizational capital, innovation capital and relationship
capital. Therefore following from the above arguments, intellectual
capital is expressed mathematically as: IC = Human Capital (HC) +
Structural Capital (SC) + Relational Capital (RC).

The study is explained by resource based theory, epistemology and
knowledge based theories. While the resource based theory focused
largely on industrial revolution based firm set up with focus on
inventory and physical assets. The epistemology approach emphasized on
abstracted attributes similar to the knowledge based theory, but however
focuses more on intuitive knowledge such as faith, belief and intuition.

The knowledge based theory is rather more plausible in explaining this
study because the key premises of its arguments and assumptions are
situated around knowledge management and its indispensability in
managing modern service oriented firms. Based on certain premises
regarding the nature of knowledge and its role within the firm, the
theory explains the rationale for the firm, the delineation of its
boundaries, the nature of organizational capability, the distribution of
decision-making authority and the determinants of strategic alliances.
According to Barton (1996) and Grant (2000), the key elements of the
theory follows the path of previous efforts evidenced by extant studies
on the measurement and management of intellectual capital.

The NSE listed banks provides a potent case for examining the IC
efficiency in the corporate sector. Although similar efforts were put in
by extant studies like Ekwe (2013a, b). The study made valuable
contribution on IC studies in developing economy like Nigeria especially
in the banking sector. Though the findings were germane and succinct in
capturing and revealing the impact of IC on FP, it's focused was more on
direct relationship. The study equally used three year data analysed
through OLS regression.

\textbf{METHODOLOGY}

The study used the entire population of 16 deposit money banks. Data
were collected (2011- 2015) from the annual accounts and report of these
banks. Partial Least Square's (PLS) Structural Equation Model was used
for the analysis of the data collected. Using the PLS-SEM, two broad
techniques of data analysis were used to analyse the data generated for
the study. These are the analysis of the measurement model and
structural model.

SEM is used to find the simultaneous relationship between the variables
of this study. SEM technique allows researchers to examine series of
relationships simultaneously. It attempts to minimize between the sample
covariance and the reproduces covariance matrix of observed measures.
That's why it follows the assumptions of normality and independence.
Partial Least Square is as a non-parametric SEM technique described as
second generation multivariate analysis (Fornell, 1987). It is useful
when theoretical knowledge is scarce and theory confirmation is the
objective. It is quit suitable for this study because in this study the
authors' objective is to confirm the newly proposed relationship by
extended VAIC and capital structure with financial performance.

The quantitative variables of the study are expressed in terms of latent
and exogenous constructs as appeared on the table below:

\textbf{Table 1}

\textbf{Variables Definition and Measurement}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Variables} & \textbf{Quantitative Measures}\tabularnewline
\midrule
\endhead
\textbf{Intellectual Capital} &\tabularnewline
VAIC & CEE+HEE+SCE\tabularnewline
Staff Remuneration & Total Compensation\tabularnewline
Executive Compensation & Amount of Management
Compensation\tabularnewline
\textbf{Capital Structure} &\tabularnewline
DTE & Debt to Equity\tabularnewline
Debt Ratio & Total Debt/Total Assets\tabularnewline
\textbf{Financial Performance} &\tabularnewline
Profitability & Earnings Before Interest and Tax (EBIT)\tabularnewline
Returns on Assets & EBIT/Total Assets\tabularnewline
Earnings Per Share & EBIT/ No of Shares Outstanding\tabularnewline
\bottomrule
\end{longtable}

\textbf{Proposed Structural Model}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/image1/image1}
\caption{{Couldn't find a caption, edit here to supply one.%
}}
\end{center}
\end{figure}

\textbf{RESULTS AND DISCUSSION}

\textbf{Validation}

Under the measurement model, two types of validity are relevant-
convergent validity and discriminant validity. It refers to the extent
to which multiple items measure the same concept are in agreement. As
posited by Hair et al. (2010), factor loadings, composite reliability
and average variance were used in assessing convergence validity. The
standard measures for these elements are Loadings (\textgreater{} 0.7),
CR (\textgreater{} 0.7) and AVE (\textgreater{} 0.5). If the measures
are above the cut-off values, then the measures are said to have
adequate convergent validity.

Discriminant validity, on the other hand, is the degree to which items
differentiated among the study construct or measure distinct concepts/
variables by examining the correlations between the measures of a
possible overlapping construct. Items should load strongly in their
construct in the model, and the mean variance shared between each
construct, and its measure should be greater than the variance shared
between the construct and other constructs (Fornell \& Larcker, 1981,
Campeau et al., 1999)

\textbf{Table 2: Convergent Validity Test}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Constructs} & \textbf{Items} & \textbf{Loadings} & \textbf{AVE}
& \textbf{CR}\tabularnewline
\midrule
\endhead
\textbf{Intellec. Cap} & HCE & 0.976 & 0.793 & 0.834\tabularnewline
& SCE & 0.915 & &\tabularnewline
& RCE & 0.942 & &\tabularnewline
\textbf{Capital Str} & DTE & 0.929 & 0.751 & 0.744\tabularnewline
& DR & 0.273 & &\tabularnewline
\textbf{Fin. Perf} & EBIT & 0.942 & 0.854 & 0.521\tabularnewline
& ROA & 0.688 & &\tabularnewline
& EPS & 0.768 & &\tabularnewline
\bottomrule
\end{longtable}

Sourced : Computed from Data using SmartPLS

AVE= Average Variance Extracted

CR= Composite Reliability

This shows that if the figures arrived at are above the cut-off values,
then measures have sufficient convergent validity. Based on the result
of table 2, all but debt ratio are valid. Despite the failure of a
single measure, the overall performance of the loading is at 90\%. In
terms of the AVE, the reported result is valid at 0.793, 0.751 and 0.854
are all above the cut-off value of 0.7. Also, the CR sufficient satisfy
the validity condition above 0.5.

\textbf{Structural Model Analysis}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/image2/image2}
\caption{{Couldn't find a caption, edit here to supply one.%
}}
\end{center}
\end{figure}

A normal view of the result of the combined coefficient of determination
IC and FP at 22\% may appear relatively smaller than other studies, it
should, however, be noted that only three of the five VAIC proxies were
used in measuring the impact. IC predicts the behaviour of CS at
cumulative level of 19\% which is marginally lower than IC-FP
relationship.

The clearer picture of the subsisting relationships between the
predictors and dependent variables would be seen in the subsequent
discussions where the t-values generated could be used in testing the
hypothesis and estimated the predictive relevance of the model.

\textbf{T-Value for Latent Construct}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
~Variables & \textbf{Original Sample (O)} & \textbf{Sample Mean (M)} &
\textbf{Standard Deviation (STDEV)} & \textbf{T Statistics
(\textbar{}O/STDEV\textbar{})} & \textbf{Standard Values}\tabularnewline
\midrule
\endhead
\textbf{Intellectual Capital -\textgreater{} Capital Structure} & -0.432
& -0.353 & 0.359 & 1.209 & 0.227\tabularnewline
\textbf{Intellectual Capital -\textgreater{} Financial Performance} &
0.465 & 0.396 & 0.313 & 1.488 & 0.037*\tabularnewline
\bottomrule
\end{longtable}

Source: Determined by the Researcher using Smartpls 3

\begin{quote}
*5\% level of Significance
\end{quote}

The result of the t-values for the predictors against the dependent
variable stood at 1.209 and 1.488 for IC-CS and IC-FP respectively. As
such, further analysis revealed that only IC is statistically
significant to FP p\textless{} 0.5 at 0.037 and insignificant for IC-CS
0.227.

\textbf{CONCLUSION}

The pivot research issue of this paper was to investigate the structural
links and resulting impacts of CS measures on IC efficiency (VAIC) and
IC efficiency on the firm's financial performance. The study was
situated to conclude that CS -- IC -- Financial Performance relationship
through empirical investigation. The path coefficient values (\selectlanguage{greek}β\selectlanguage{english}) confirm
the premise that there is a high negative correlation between leveraged
CS measures, IC efficiency though at statistically insignificant level.
Also, a positive link was found between IC efficiency and corporate
Financial Performance of listed Banks

This paper concludes that banks with high level of IC efficiency can
generate greater corporate values like profitability, returns on
investment and Earnings per share while reducing the negative impact
associated with high leverage in their capital structure. The latter
conclusion is consistent with work of Aslam et al (2014), Ekwe (2013)
and Mankiw, et al (1992). IC to CS concurred with the work of Yang and
Lin, (2009); Badinger and Tondl (2005), Chen et al., (2005);
Radhakrishnan, (2003).

Given the fact that the study is the first of its kind to use these
dimensions, method of analyses and VAIC adjusted model in Nigeria, the
outcome should be cautiously used until further contributions to the
discourse are made. This study is a single sub-sector and country study,
its further expansion and in terms industry, data period and
country-economic status may may create new dynamics in future study.

\textbf{References}

Arif, A., Islam, A., Zijl, T., (2015). Intellectual Capital and Market
Performance; A case Study of Multi-National R \& D Firms in the US. The
Journal of Developing Areas, Vol. 50, No. 5

Aslam, S., Makki, M., Nawaz, H., Latif, M., (2014), The Mediating role
of Intellectual Capital Efficiency, Capital Structure and Financial
Performance of Quoted Banks in Pakistan. Research Journal of Finance and
Accounting. Vol 5. No. 17

Barton-Leanard, D., (1996). Wellsprings of Knowledge , Prentice Hall,
London

Bontis, N., Corssan, M. and Hulland, J. (2002) Managing an
organizational learning system by aligning stocks and flows, Journal of
Management Studies, 39, 437-769

Chen, M.C., Chen, S. J., Hwang, Y. (2005). An empirical investigation of
the relationship between intellectual capital and firm's market value
and financial performance Journal of Intellectual Capital, 6(2), 159176.

Edvinsson, L. \& Malone, M. S. (1997), Intellectual capital: Realizing
your company's true value by finding its hidden brainpower, Harper
Collins, New York, NY.

Ekwe, M., (2013a). The Effect of Intellectual Capital and Growth in
Revenue of Deposit Money Banks in Nigeria. Research Journal of Finance
and Accounting. Vol 4. No 11

Ekwe, M., (2013b). The relationship between Intellectual Capital on
Employee Productivity of Banks in Developing Economies: The Nigerian
Experience. Research Journal of Finance and Accounting. Vol 4. No 12

Fornell, C. (1987). A second generation of multivariate analysis:
Classification of methods and implications for marketing research, M.J.
Houston (Ed.). Review of Marketing American Marketing Association,
Chicago

Grant, M.N., (2000), Prospering in Dynamically-Competitive Environments:
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Iazzolino, G. \& Laise, D. (2013), 'Value Added Intellectual Coefficient
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Makki, M.A.M. and Lodhi, S.A. (2008) Towards Extending the VAIC Model:
Capturing Intellectual Capital Components, IUB Journal of Social
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Ismail \& Karem, 2011)Nerdrum, L., \& Erikson, T. (2001) Intellectual
capital: a human capital perspective. Journal of Intellectual Capital,
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Pulic, A. (1998) Measuring the performance of intellectual potential in
knowledge economy Paper presented in 1998 at the 2nd McMaster World
Congress on Measuring and Managing Intellectual Capital by the Austrian
Team for Intellectual Potential.

Pulic, A. (2000) VAICTM -- An accounting tool for IC management,
International Journal of technology management, 20 (5-8), 702-714

Roos, J., Roos, G., Dragonetti, N. C., and Edvinsson, L. (1997)
Intellectual capital: navigating in the new business landscape.
Macmillan Publishers, London, UK

Stahle, P., Stahle, S. \& Aho, S. (2011), 'Value Added Intellectual
Coefficient (VAIC): A critical analysis', Journal of Intellectual
Capital, Vol. 12, No. 4, pp. 531-551. Stewart, T. A. (1997),
Intellectual capital: The new wealth of organizations

Saint-Onge, H., (1996). Tacit knowledge: the key to the strategic
alignment of intellectual capital. Strategic Leadership, 24(2): 10-14

Tseng, C., Goo, Y.J. (2005). Intellectual capital and corporate value in
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