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Journal of International Business
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ISSN 1849-8558 (Print)
Journal of International Business Research and Marketing covers both
traditional fields of business administration along with a cross-functional,
multidisciplinary research that reflects the complex character of business
research and marketing issues. Articles that analyze the development of
novel perspectives or exploring new research domains are of specific interest
of the journal. Recognizing the complex relationships between the many
areas of business activity, Journal of International Business Research and Mar-
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Journal of International Business Research and Marketing is primarily published
for executives, researchers and scholars alike, aiding in the application of empiri-
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include Risk Analysis, Organizational Efficiency, Marketing Strategy, Data Analysis
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AA

CONTENT
Cost-volume-proﬁt Analysis and Decision Making in the Manufacturing Industries of
Nigeria
J.C.Ihemeje, Geff Okereafor, Bashir M. Ogungbangbe
Impact of Intellectual Capital on Financial Performance of Banks in Tanzania
Janeth N. Isanzu
University-industry Partnership as a Key Strategy for Innovative Sustainable Eco-
nomic Growth
Ekaterina Panarina
Importance of Customer Relationship Management in Customer Loyalty
(Studies at Offset in East Java, Indonesia)
Chamdan Purnama
The Role of Purchase Tendencies Data in the Transformation of Foreign-made Pro-
ducts Consumption in China
Camilo I. Koch R.
7
16
24
28
35

Journal of International Business Research and Marketing7
Journal of International Business Research and Marketing
Volume 1, Issue 1, November, 2015
journal homepage: www.researchleap.com
Cost-volume-profit Analysis and Decision Making in the Manufacturing Industries of
Nigeria
J.C.Ihemejea
, Geff Okereaforb
, Bashir M. Ogungbangbec
a
College of Management Sciences, Michael OkparaUniversityof Agriculture, Umudike
b
College of Management Sciences,MichaelOkpara University of Agriculture, Umudike
c
College of Management Sciences,MichaelOkpara University of Agriculture, Umudike
1. Introduction
Cost- volume- profit analysis according to Glautieret al
(2001) is the systematic examination of the inter-relationship
between selling prices, sales and production volume, cost,
expenses and profits. The above definition explains cost-volume-
profit analysis to be a commonly used tool providing
management with useful information for decision making. Cost-
volume-profit analysis will also be employed on making vital and
reasonable decision when a firm is faced with managerial
problems which have cost volume and profit implications. Such
problems are in the areas of profit planning, product planning,
make or buy decision, expansion or contraction product line,
utilization of productive capacity in a period of economic boom
or depression.
More especially cost -volume-profit analysis is used by
managers to plan and control more effectively and also to
concentrate on the relationship among revenues, cost, volume
changes, taxes and profit. It is also known as break-even analysis.
Finally this study is aimed at examining the effect of cost-
volume-profit analysis on decision making process of some
selected manufacturing industries in Nigeria.
The major problem encountered by manufacturing industries
when cost-volume-profit analysis stands as a basis for decision
making is managerial inefficiency and this includes ignorance of
this concept ie inability of the management to employ it in their
decision making and also not knowing the importance of cost-
volume-profit analysis. Manufacturing industries are not relevant
in their decision making process. Most manufacturing industries
in Nigeria do not determine the extent to which cost-volume-
profit analysis affect their various decisions. Manufacturing
industries is faced with the problem of how to make use of the
available scare resources in order to achieve the objective of
profit maximization. Another major problem manufacturing
industries in Nigeria face, is when the application of cost-
volume-profit analysis techniques are meant to apply, they don’t
apply it in their enhancement of managerial efficiency of
manufacturing industries. To what extent is cost- volume-profit
analysis considered relevant in the decision making process of
manufacturing industries? To what extent does the application of
cost-volume profit analysis technique in decision making process
enhance managerial efficiency of manufacturing industries? To
what extent does cost-volume-profit analysis affect the various
decisions of manufacturing industries? To what extent does each
of the identified approaches to cost volume profit analysis is
being adopted in manufacturing industries? What is the decision
making opportunities of the selected industries based on their re-
order level and economic order quantity?
2. Conceptual Framework
Adenji (2008) states that cost-volume-profit analysis are
predetermined costs, target costs or carefully pre planned costs
which management endeavors to achieve with a view to
establishing or attaining maximum efficiency in the production
process. According to him, cost-volume-profit analysis is cost
plans relating to a single cost unit. Because cost-volume-
profitanalysis purports to be what cost should be, any deviation
represents a measure of performance. The predetermined costs
are known as cost-volume-profit analysis and the difference
between the cost-volume-profit analysis and actual costs are
ABSTRACT
2015 Research Leap/Inovatus Services Ltd.
All rights reserved.
This study determined the effect of cost-volume profit analysis in the decision making of
manufacturing industries. The study combined both survey research and longitudinal research
design. Both primary and secondary data were used for collection. They were analyzed using
regression and correlation techniques. The results revealed that the sales value of a product and
the quantity of the product manufactured has a positive effect on profit made on the product,
also that there is a significant relationship between the cost of production and profit. The re-
order and economic order quantity were also determined as a base for assessing decision making
opportunities. Based on the result, the researcher recommends that manufacturing industries
should always adopt cost-volume profit analysis in their decision making.
Keywords:
Cost Volume-Profit Analysis
Decision making
Manufacturing industries

known as a variance. Drury (2000) defines cost-volume-profit
analysis as predetermined cost; they are cost that should be
marred under efficient operating conditions. The cost-volume-
profit analysis may be determined on a number of bases. The
main uses of cost-volume-profit analysis are in performance
measurement, control, stock valuation and in the establishment of
selling prices. Cost-volume-profit analysis is a target cost which
should be attained. The buildup of cost-volume-profit analysis is
based on sound technical and engineering studies, knowing the
production methods and layouts, work studies and work
measurement, materials specification and wage and material
price projections. A cost-volume-profit analysis is not an average
of previous costs. They are likely to contain the results of past
inefficiencies and mistakes. Furthermore, changes in methods,
technology and costs make comparison with the past of doubtful
value for control purposes. In order to assist the decision making
of manufacturing industries in cost-volume-profit analysis
control, the cost-volume-profit analysis system must first of all
indicate what is attainable by efficient performance and then
highlight any area where attainable efficiency is not being
achieved. The definition of cost-volume-profit analysis as per the
institute of chartered accountants official terminology is “a
predetermined calculation of how much cost should be under
specific working conditions in manufacturing industries. It is
built up from an assessment of the value of cost element and
correlates technical specifications and the quantification of
materials, labor and other costs to prices and/or wages expected
to apply during the period which the cost-volume-profit analysis
is expected to be used.
Cost- volume- profit analysis, according to Glautier et al
(2001), is the systematic examination of the inter-relationship
between selling prices, sales and production volume, cost,
expenses and profits. The above definition explains cost-volume-
profit analysis to be a commonly used tool providing
management with useful information for decision making. Cost-
volume-profit analysis will also be employed on making vita and
reasonable decision when a firm is faced with managerial
problems which have cost volume and profit implications. Cost-
volume- profit analysis according to Hilton R.W (2002:230) is a
mathematical representation of the economics of producing a
product. The relationship between a products revenue and cost
function expressed within the cost-volume-profit analysis are
used to evaluate the financial implication of a wide range of
strategic and operational decisions.
According to Garrison et al (2003) cost-volume-profit
analysis is a study of inter-relationship between the following
factors: princes of products, volume or level of activity, per-unit
variable cost, total fixed cost, mix of products sold. Also state
further the cost-volume-profit analysis is a key factor in many
decisions including choice of products lines, pricing of product,
marketing strategies and utilization of productive facilities
Principles and Assumption of Cost-Volume-Profit Analysis
Underlying the operation of cost-volume-profit analysis is a
principle which states that “at the lowest level of activity cost
exceed income but as activity increases income rises faster than
cost and eventually the two amount are equal, after which income
exceed cost until diminishing returns bring cost above income
once again.This principle describe cost-volume-profit analysis
with curvilinear. Cost and revenue curves which thought
theoretically sound lack practicability. Accountant found the
need to bring in addition information relating to cost behavior
and sales policy this was to ensure that practical model be
develop out of this principles.
The followings are the underlying assumptions of cost-volume-
profit analysis according to Horngen et al (2006)
 The behavior and revenues is linear.
 Selling price is constant.
 All cost can be divided in to their fixed and variable
element.
 Total fixed cost remains constant.
 Total variable cost is proportional to volume.
 Volume is the only drive of cost.
 Prices of production inputs (eg materials) are constant.
Methods of Cost-Volume-Profit Analysis
There are two main approaches used in analysis cost-volume-
profit.
Inter-relations. They include:
 The Graphical Approach
 The Algebraic Approach
 The Net Income Equation
 The Contribution Margin Equation
 The Margin Of Safety Equation
 The Contribution Margin Ratio
The Graphical Approach
The cost-volume-profit graph can be very useful because it
highlighted cost-volume-profit relationship over wide range of
activity and give managers a perspective that can be obtained in
on other way. Such graph is referred to as preparing a break even
chart. This is correct to the extent that breakeven point is clearly
shown on the graph. Garrison et al (2003).
Steps in Preparing Cost-Volume-Profit Graph
This involves three steps:
Draw a line parallel to the volume axis to represent total fixed
expenses; choose some volume of sales and plot the point
representing total sales amount at the activity level you have
selected; again choose some volume of sales and plot the point
representing total sales amount at the activity level you have
selected. The anticipated profit or loss at any given level sales is
measured by the vertical distance between the total revenue and
the total expenses line cross Garrison et al (2003) (figure 1).
Some managers prefer an alternative format to the cost-volume-
profit graph as illustrated in figure 2.
The Profit Graph
This is another approach to cost-volume-profit graph. It is
sometime preferred by some managers because it focuses more
directly on how profit change with changes in volume. It has the
added advantage of being easier to interpret than the traditional
approach. It have the disadvantage of not showing as clearly how
cost are affected by changes on the levels of sales.
Steps in constructing profit graph
Locate total fixed expenses on the vertical axis, assuming o
level of activity. This point would be in the “loss area”, equal to
the total fixed expenses expected for the period. Plot a point
representing expected profit or loss at any chosen level of sales.
After this point plotted draw a line through it back to the point o
vertical axis representing the total fixed expenses.

Figure 1: Cost-Volume-Profit Graph (Traditional Approach)
Source: Garrison et al (2003)
Figure 2: Cost-Volume-Profit Graph (Modern Approach)
Source: Garrison et al (2003)
Figure 3: Break-even point
Source: Garrison (2003)
Note: The break-even point is where the profit line crosses the
break-even line.
The Algebraic Approach
The issues involved on this approach are the putting of marginal
income statement format in formula, the incorporation of the
contribution concept into the marginal costing income statement
formula and the mathematical arrangement re-arrangement and
evaluation of some of the basic cost –volume-profit factors.(unit
selling price, unit variable cost’ fixed cost’ sales volume). The
marginal income statement employs the marginal costing
technique where too much attention may be given to variable
costs at the expense of disregarding fixed costs; in the long run
fixed cost must be recovered.
The formulae and ratios that constitute then algebraic approach
include the following;
CostandRevenue(Naira)
Y axis
Fixed cost
Variable cost
Profit region
Loss region
Break-even point
X axis
Activity level (units)
Activity level (units) Revenue Range
CostandRevenue(Naira)
Loss region
Break–evenpoint
Profit region
Total Revenue
Variable expenses
Total Fixed cost

 The net income ratio
 The contribution margin equation
 The variable cost ratio
 The contribution margin ratio
 The tax adjusted ratio
The Net Income Equation
This is a form of marginal costing statement used in processing
cost-volume-profit data. Marginal costing differentiates between
fixed costs and variable cost. In decision making, marginal
costing is used simply because fixed cost is considered as a sunk
cost or historical cost which is incurred whether profit is made or
not.
The formula is stated thus;
NI=S- Vc – Fc
This can be regarded as;
S= Vc + Fc +_NI
Where:
S = sales
Vc = variable cost
NI = Net income
At breake-even point, the equation changes because at that point,
net income is zero, (no profit or loss).
Therefore
s = ___F____
S – V
The net income includes the break-even point, margin of safety
and profit and loss at a given level of activity and it is computed
thus:
IN = Sn – Vn – Fn
Required quality to be produced and sold to obtain a target
income; in order to compute the quality required to be
manufactured and sold to obtain a target income this equation
must be used:
Q = FC + NI
CM
Where: CM = S – V. Garrison (2003)
The Contribution Margin Equation
Contribution margin is the amount by which revenue exceed the
variable cost of producing that revenue. Contribution margin per
unit is the different between selling price and variable cost per
unit. Horngren et al (2006). Contribution margin is very
important in decision making and it states that the planner ought
to think in terms of contribution margin rather than in terms of
absolute profit. It should be noted that each additional unit sold
of a particular product contributes to a margin towards profit. The
contribution margin equation could be stated thus
Cm = S - V
Where:
CM= contribution margin
S= sales
V= variable cost
In contribution margin approach break-even point is calculated as
FC
CM
Sales unit to earn a desired profit to be
FC + Target profit
CM
The Margin of Safety Equation
Margin of represents the difference between break-even point
and budgeted activity level. It indicate how much sales may
decrease before a company will suffer a loss. Adeniji (2004). The
formula for calculating margin of safety is:
a. Most (unit) = Budgeted unit – Break –even Point (unit).
b. Most (sales volume) = Budgeted sales – Break-even
point
(Sales volume)
The Contribution Margin Ratio
This is the ratio of contribution to a particular sale value is
describe as contribution margin ration. Also referred to as profit-
volume ratio. It is designed to measure the level of contribution
derivable from a specific amount of sales. It will be determined
as follows.
a. CMR (unit) = Selling price – Variable cost per unit
Selling price
b. CMR (Total) = Total sales – Total variable cost
Total sales
c. CMR = fixed cost + profit
Contribution + variable cost
Note: - This occurs where selling price is completely omitted.
d. CMR = change in profit
Changes in sales volume
Operating Leverage
Operating leverage refers to the extent to which an organization
uses fixed cost in its cost structure. According to Horngenet el
(2006) operating leverage describes the effect that fixed cost have
on changes operating income as changes occur in units sold and
hence in contributed margin. Operating leverage is a measure of
how sensitive net operating income is to percentage changes in
sales. Operating leverage act as multiplier. If operating leverage
is high, a small percentage increase in sales can produce a much
larger percentage in net operating income Garrison et el (2003) .
Organizations with a high proportion of fixed cost in their cost
structures have high operating leverage.
The degree of operating leverage is given level of sales is
computer by following formula;
Degree of operating leverage = contribution margin
Net operating income

Uses of Cost-Volume Profit Analysis
Besides providing management with general information on the
cost-volume-profit relationship of their firms , accountant can be
also use it to provide management with useful information
necessary for selling, certain planning, control and special
decision problems . The decision areas where this analysis is
include:- profit planning budgetary control, control, product
replacement, pricing decision, selecting of distribution channels,
setting volume, sensitive retain on investment target, entry into
foreign marking performance measurement. (Meigs and Meigs
,1996)
Profit Planning: A firm first decides its sales, cost and activity
beforecomputing the profit that will emerge, but it profit
planning, the firm first decides what profit it wants and then
considers the sales, cost and activity required to produce that
profit. The items under consideration on profit planning are cost-
volume-profit variables. Garrison et al (2003). Here to conduct
the basic cost-volume-profit analysis (graphical or algebraic)
using a forecast or planned economic structure of the firm as data
source and then examining how planned profit will change if
fixed cost, variable cost and sales volume are varied.
Figure 4: Cost-Volume-Profit Chart (Profit Planning Graph)
This will enable management know if the inherent economic
structure of the firm and what direction changes are required. It is
appropriate to present profit planning in cost-volume-profit
analysis in charts, the sample of such chart is shown below.
This chart merely shows a single line that cuts the activity line at
break-even point where the firm is neither making profit nor loss.
The profit planning cost-volume-profit analysis also involves the
use of equation determine the minimum amount that industries
need to achieve its cash dividend payout target for the year.
The equation is given as
Revenue required to meet the dividend payment
F + PAD (1 – d)
CMR
Where
F = Fixed cost
PAD = Profit after divided
d = dividend
CMR = contribution margin
The revenue gotten shows whether the firm will be able to pay
the dividend or not, where its gets the revenue targeted, then it
can pay such dividend.
Product Mix Decision: The selection of which products to
products, which to abandon, and which to postpone is one of the
most critical decision confronting a firm’s management. The
products selected from the product mix decision determine the
revenue, profit and cash flow of firm’s operations. Perhaps
equally important, the products selected determine on part the
firm’s competitive position vis-à-vis its competitive position
from the products selected currently provide the funds required to
develop and produce products in the future.
Cost-volume-profit analysis is used to measure the economics
characteristics of manufacturing a proposed product. Based on
accounting data, the cost-volume-profit analysis is used to
determine the sales quantity needed to break-even as well as the
sales quantity required to earn a desired profit margin. Manager
then compare a product’s expected sales with the sales quantities
required to break-even and earn a target profit margin to
determine whether the product should be produced.
Budgetary Control: Budgetary control is the establishment of a
budget relating to the responsibility of the executives and to the
requirement of the policy and the continuous comparison of
actual with budgeted result. J. O. Kalu (lecture note book pg
11).Budgetary control takes off from where budget planning
stops and aspirations continued in budget are achieved.
Budgetary control is concerned with use of budget to control a
firm’s operational activity either to secure by individual action
the objective of policy or to provide a basis for its revision.
Cost-volume-profit analysis can be used in area of budgetary
control to compare budgeted sales, volume, cost and profit with
actual. The analysis of the variance is being computed only for
cost-volume-profit. The process of comparing actual result with
planned results and reporting budgetary control sets or control
framework which helps expenditure to be kept within agreed
limits. Deviations are also noted so that corrective measure can
be taken provided with a given data, one can compute the break-
even point, margin of safety and p/v ration for the budgeted and
actual revenue. This helps management to know when it is
deviated from its target point, it causes and how to take
corrective measures.
Pricing Decision: Pricing decision are strategic decision that
affect the quality produced and sold, and therefore the cost and
revenues. To make these decisions, managers need to understand
cost behavior patterns and cost drivers, they can then evaluate the
value chain and over a products life cycle to achieve
profitability.(Horngren et al 2006).
According to Horngren et al (2006) the major influence on
pricing decision are customers competitors and cost. Customers
influence price through the effect on the demand for a product or
services, based on factors such as the features of a product and its
quality. Competitors influence pricing decision due to the fact
that no business operates in a vaccum but in an environment with
many competitors, the company uses knowledge of their rivals
technology, plant capacity and operating policies to estimates its
competitor’s cost. A valuable information to set its own price.
Cost also influences pricing decision because they affect supply.
The lower the cost of producing a product, the greater the quality
of product the company is willing to supply and managers who
understand the cost of producing their companies products set
prices that make the products attractive to customers while
maximizing their companies operating income. In using cost-
volume-profit analysis in this area, it is necessary to examine the
cost of products produced and the planned profit before making
the pricing decision.

Problems of Cost-Volume-Profit Analysis
Regardless of the uses and the estimated benefit of cost-volume-
profit analysis to the management of a firm in various areas, there
are a lot of factors which affect the use and validity of cost-
volume-profit analysis labour specialization and standardization.
In other words manufacturing can be described as changing raw
materials into finished goods.
 Consumer goods
 Industrial goods
Consumer Goods: Consumer goods are goods that are ready for
consumption after its production. These goods are bought from
retail stores for personal, family or household use. They
differentiated on basis of durability. Durable goods are products
that have a long life such as furniture garden tools etc. Non –
durable goods are those that are quickly use up or worn out or
can become outdated such as food items, school supplies etc.
Consumer goods can also be grouped into sub-categories on the
basis of consumer buying habits. Convenience goods are items
that buyers want to buy with less amount of effort, that is as
conveniently as possible as possible. Most of these goods are low
value that are frequency purchased in small quantities eg candy
bars, soft drinks, newspapers Shopping goods are purchased only
after the buyers compares the product of more than one store or
looks at more than one assortment of goods before making a
deliberate buying decision. They are of higher value than
convenience goods they are infrequently and are durable. Price,
quality, style, colour are typical factors for buying them eg lawn
movers, bedding, camping equipment etc. Specialty goods are
items that are unique or unusual-at least in the mind of the buyer.
Buyers known what they want and are willing to exert
considerable effort to obtain it. Such goods include wedding
dresses, antiques, fine jewelries, electronics, automobiles
etc(Kalu et al 2004).
Industrial Goods: industrial goods are products that firms
purchase to make other products, which they later sell. Some are
used directly in the production of products for resale, and some
are used indirectly goods are classified on the basis of their use
and they include: Installations are major capital items that are
typically used directly in the production of goods, some
installations such as convey or systems, robotics equipment and
machine situations others like stamping machines large
commercial ovens are built to a standard design but can be
modified to meet individual requirement.
Raw Materials are products that are purchase on their raw state
for the purpose of processing them into consumer or industrial
goods e.g are iron, ore, crude oil, diamond, copper, wheat,
leathers, some are converted directly into another consumer
product while others are converted into an intermediate product
to be resold for use in another industry.
Accessory Equipment are capital goods that are less expensive
and have short life span eg hand tools, compacted desk
calculators, forklifts, typewriters etc. Fabricated parts are items
that are purchased to be placed in the final product without final
processing. Fabricated materials on the other hand require
additional processing before being placed in the end products. Eg
are batteries, sun roofs, spark plugs, steel, upholstery fabric etc
Industrial supplies are frequently purchased expense items. The
contribute directly to the production the production process. They
include computer paper light bulbs, lubrication oil, cleaning and
office supplies etc. Kaluet el (2004)
3. Theoretical Framework
Analysis of the interdependence of the cost-volume-profit
analysis is incorporated into the system of calculating the
variable costs. In fact, the system calculation within the variable
costs rests on a contribution theory of managing business
outcome and its methodology encompasses the successful
combination of costs and sales volume in order to optimize
financial results. The cost-volume-profit analysis is
operationalized through the critical break-even point of
profitability. Break-even point can be mathematically calculated
and graphically presented with certain conditions. For our further
analysis we consider more useful to graphically display the
break-even point. According to some, undoubtedly, great
authorities in the area of cost management, cost-volume-profit
analysis cannot be imagined without the following assumptions;
 Total costs can be divided into the fixed and variable
component, respecting the level of activity,
 Behavior of total revenue and total cost is linear in
relation to the volume of activities within the relevant
range,
 The selling price per unit, unit variable and total fixed
cost is known and unchanging.
 The analysis refers to a product, and if there is a wider
range of products, the implementation structure is
constant,
 Total costs and revenues are facing each other without
involving the time value of money,
 Changes in the level of revenues and costs should be
treated as the consequence of changes in the number of
products or services that are produced and sold.
Number of manufactured units of products (services) is
carriers of revenues and costs.
Figure 5: Cost-Volume-Profit Graph
In addition to these assumptions other can be made, such as:
stability of the general price level, unchanging labor productivity,
the overall synchronization between production and sales is
indisputable, and also the principle of reagibility costs (fixed and
variable).
The main purpose of Cost- volume- profit analysis and
profitability break-even point is to provide information to the
management in planning the target profit within the relevant
range of activities under conditions of short- term.

4. Empirical Framework
Cost-volume-profit analysis is management tools that would
be employed in making plausible decisions which have cost-
volume (level of activity) and profit implications. There is no
doubt that if management do not sufficiently apply cost-volume-
profit analysis in their decision making process, it will result to
substandard decisions low performance and profitability. The
purpose of this study was to discover if the application of cost-
volume-profit analysis techniques has any effect on profitability,
to explore the relationship between cost-volume-profit analysis
and the profitability of manufacturing industries and also to
determine whether cost-volume-profit analysis techniques
principles are being adopted and practiced in Nigerian
manufacturing industries. Underlying the operation of cost-
volume-profit analysis is principles which state that, at the lowest
level of activity cost exceed income but as activity increase
income rises faster than cost and eventually the two amount are
equal, after which income exceed cost unit diminishing returns
bring cost above income once again. This principle describe cost-
volume-profit analysis with curvilinear. Cost and revenue curves
which though theoretically sound lack practicability. The study
combined both survey research and longitudinal research design.
Determine whether cost-volume-profit analysis techniques
principles are being adopted and practiced in Nigerian
manufacturing industries. Underlying the operation of cost-
volume-profit analysis is principles which state that, at the lowest
level of activity cost exceed income but as activity increase
income rises faster than cost and eventually the two amount are
equal, after which income exceed cost unit diminishing returns
bring cost above income once again. This principle describe cost-
volume-profit analysis with curvilinear. Cost and revenue curves
which though theoretically sound lack practicability. The study
combined both survey research and longitudinal research design.
5. Methodology
The simple linear module has to do with the causal relationship
between two variables one dependent and the other independent
which related with a linear function. The formula is represented
thus
Y = α + βx
Where; x = the dependent variable; Y = the independent variable;
α = the point where the regression line or equation crosses y –
axis; β = the slope of the regression line.
This technique was used to test the reliability of data in Ho1 and
Ho2.
Decision rule: if T cal > T tab we reject the null hypothesis but if
T cal < T tab, we accept the null hypothesis.
This technique measures the degree of relationship existing
between variable. The correlation co-efficient(r) lies between 1
and -1 (-1<R<1).
The formula is given as
rxy= n∑xy - ∑ x ∑Y
(n∑x2) – (∑×)2
(n∑ Y2) – (∑Y)2
Or rxy= ∑xy
(∑x2
)(∑Y2
)
T – calculated r = n - 2
1 – r2
Where r = coefficient of correlation
n = number of years
x = dependent variable
y = independent variable
This technique was used to rest the reliability of data in Ho2. The
decision rule is to rejected Ho if T cal> T tab and accept Ho if
cal< T tab.
6. Data Analysis
The R value of .856(85.6%) is shown to be significant at 5%
level (table 1), implying the existence of a strong positive
relationship between sales value of bottled and sachet water will
invariably increase the profit made on them. The coefficient of
determination (R2
) indicates that about 73.2 change in the profit
made on bottled and sachet water are attributable change in the
sales value of bottled and sachet water. The F-ration 27.380 is
significant at 5% probability level and highlights the
appropriateness of the model specification. With t-value of 5.233
being significant at 5% level. The researcher therefore rejects the
null hypothesis concludes that sales values of bottled and sachet
water significantly affect the profit made on them.
Table 1: Regression analysis result on the effect of sales value of
a product on profit made on the product
Variable Profit of Bottled water and Sachet
water
co-efficient P- value
Constant 817248.3 658902.2
t 1.240
Sales value of
bottled water
andsachet water
.146 0.028
t 5.233 ***
R .856 ***
R2
.732
f.ratio 27.380
Note***
= significant at 5% level
Values in parenthesis are standard errors
Source: Extracted from appendix B
Testing for relationship between cost of production and profit
made.
HO: There is no significant relationship between cost of
production and profit made by manufacturing industries.
In testing this hypothesis, correlation analysis was employed and
test results were extracted from appendix C.
From appendix C the correlation co-efficient of .884***
is
significant at 0.01 level, this indicates the existence of positive
high association between cost of production of bottled and sachet
water and profit made on them. The researcher therefore reject
null hypothesis and concludes that there is a significant
relationship between cost o productions on bottled and sachet
water and profit made on them.
Testing for the effect of the quantity of a product manufactured
and profit made on product.

HO: The quantity of a product manufactured does not
significantly after profit made on the product.
In testing this hypothesis, regression analysis was employed and
test results were extracted Appendix D
Table 2: Regression analysis result on the effect of sales value of
a product on profit made on the product
Variable Profit of Bottled water and Sachet
water
co-efficient
Constant 1354238 Constant
t 1.735 t
Quantity produced
of bottled and
sachet water
8.089
Quantity produced
of bottled and
sachet water
t 3.692 ***
t
R 759 ***
R
R2
.577 R2
f.ratio 13.630 f.ratio
Note***
= significant at 5% level
Values in parenthesis are standard errors
Source: Extracted from appendix B
The R value of .759(75.9%) is shown to be significant at 5%
level, implying the existence of a strong positive relationship
between the quantity of bottled and sachet water manufactured
and profit made on them.
Change in the quantity of bottled and sachet water manufactured
will equally change the profit made on them .the co-efficient of
determination (R2
) indicated that above 57.7%increases in profit
of a bottled and sachet water are attributable to change in the
quantity manufactured of bottled and sachet water.
The f-ratio of 13.360 is significant at 5% probability level and
highlight appropriateness of the model specification. With t-
values of 3.692 been significant at 5% level. The researcher
concluded that the quantity manufactured of bottled and sachet
water significantly affect the product made on them, thereby
rejecting HO.
7. Conclusions and Recommendations
Based on the research conducted in this study, it has been
observed that cost-volume-profit analysis is a veritable tool in the
decision making process of manufacturing industries most
especially in a competitive environment like ours. It was also
observed that cost-volume-profit analysis has a very large effect
on decision made by the management of manufacturing
industries in Nigeria. In the course of this study the researcher
examined the effect of cost-volume-profit analysis on kechis
water (a division of Ulovr international Resources), and Big
Chief Fast Food industries limit Umuahia and the following
findings were made.
1. The study revealed that cost-volume-profit analysis is
considered to a large extent in the decision making process of
manufacturing industries and hence affect the various decisions
made by manufacturing industries. It was also found these
manufacturing industries adopt both graphical and algebraic
approaches to cost-volume- profit analysis.
2. The study further revealed that the application of cost-volume-
profit analysis techniques in decision making process to a very
large extent enhance managerial efficiency of manufacturing
industries. In addition it was revealed that the benefits derived
from the application of cost-volume-profit analysis include:
efficient cost control, high productive capacity and increase in
profitability.
3. The study also revealed that the sale value of a product and the
quantity of a product manufactured has an effect o the profit
made on the product and there is a relationship between the cost
of production and profit made by manufacturing industries.
Finally the re-order level and economic order quantity of the
selected manufacturing industries were determined.
9. Conclusion
In this research study, the researcher has attempted to examine
critically the effect of cost-volume-profit analysis on the decision
making process of manufacturing industries in Nigeria. We
discover from the study that the management of manufacturing
industries in Nigeria have not adequately and successful applied
the technique of cost-volume-profit analysis in their industries
and this has lead to this technique not having its full effect in the
decision making process of manufacturing industries. Deductive
from the study finding is that some management and staff of
these manufacturing industries are ignorant of the concept of
cost-volume-profit analysis and hence do not apply it. This
research study has also made findings that cost-volume-profit
analysis is a commonly used tool providing management with
useful information for decision making and it will also be
employed in making vital and reasonable decision when a firm
(especially manufacturing firm) faced with managerial problems
which have cost, volume and product implication.
Recommendations
In the light of our finding in this study, some recommendations
been made, they include:
 Each of these element; cost, volume and profit should be
taken cognizance in the process of making managerial
decisions. They should not be treated in the isolation this
is because plausible decisions are unrealizable by
employing any of the elements in isolation but rather be
analyzed in a form called cost-volume-profit analysis.
 The management of manufacturing industries and other
users of cost-volume-profit analysis should determine the
best approach to cost-volume-profit analysis (whether
graphical or algebraic) to adopt.
 Manufacturing industries should present previous years’
cost-volume-profit result in a trend analysis and this
should be used for comparison with present and with
other industries performance.
 In order to enhance managerial efficiency in
manufacturing Industries, cost-volume-profit analysis
technique should be applied in their decision making
process.
 The benefit of efficient cost-control, high productive
capacity and increase in profitability will only be derived
if there should be adequate application of cost-volume-
profit analysis.
 In order to maximize profit, manufacturing industries
should endeavor to increase the quantity of output
produce and also increase sales volume which will then
increase sales value.
 Manufacturing industries should endeavor to embrace the
consultancy service offered by research and consultancy
unit of most university and higher institution in Nigeria.
This will make decision maker to update their knowledge
in strategic decision making.

 Manufacturing industries should employ experts with
requisite knowledge of the concept and application of
management accounting principles and techniques.
 Manufacturing industries should in addition to cost-
volume-profit analysis employ other managerial tools like
activity based costing, inventory/ stock control, linear
programming etc. in their decision making process.
References and notes
1. Adenji, AAdenji, (2004). An insight into Management Accounting.
Value Analysis Consult Bariya, Shomulu, Lagos.
2. Durry, Colin (2008). Management and Cost Accounting. Booking
Power Publishers London.
3. Garrinson, R. H. and Norren, E. W. (2005). Management
Accounting McGraw – Hid Irwin.
4. Glautier, M. W. E and Underdown B. (2001). Accounting Theory
and Practice. Pearson Education Limited. Harlow England.
5. Hilton, R. W (2002). Management Accounting Creating Value in a
Dynamic: Business Environment. McGraw Hill Irwin.
6. Horngern, T. C, Datar, S. M and George, F (2006). Cost Accounting:
A Managerial Emphasis Pearson Education Incorporation Upper
7. Kalu, J. O and Mbanasor. J. A. (2004). Fundamentals of Business
Management. Toni Publishers Aba.
8. Kaplan, R. S and Atkinson, A. A. (1998). Advanced Management
Accounting. Prentice Had Upper Saddle River, New Jersey. Lucey,
Terry (2002). Costing. TJ international PadstowCornwacl.
9. Meigs, R. F and meigs, M. A (1996). Accounting: The Basis for
Business Decisions. McGraw-Hill New York.

Impact of Intellectual Capital on Financial Performance of Banks in Tanzania
Janeth N. Isanzua
a
School of Management, Wuhan University of Technology, Wuhan, P.R.China, 430070
1. Introduction
The 21st
century is more dominated by knowledge economy,
many firms are shifting from using physical capital and embrace
intellectual capital, as more and more firms are trying to find
better ways to use their resources efficiently in order to sustain in
the dynamic changing business environment, hence there is a
drastic move by many firms from production era to knowledge
era and from production labor to knowledge worker (Lipunga,
2014). It is no secret that the organization that continues to invest
in new skill and technology will continue to be successful. Thus
being said intangible assets especially Knowledge are gaining
prominence than ever before as a matter of survival and of
achieving competitive advantage for the firm to compete
strategically (Latif et al. ,2012).In today’s fast moving economy
with the rapid growth of knowledge and technology innovation,
the growth of organization has changed to cope with the
changing environment. With amounting competitions in the
global economy intellectual capital has become the main
ingredient and vital for the organization to sustain the
competitive world in which they operate and to create more
values. Thus it can be put as an established fact by (Bontis, 2001)
that intellectual capital has become the critical driver for
sustainability.
While the grounded framework of intellectual capital have
been in place and Intellectual capital being studied in many
countries to give their firms competitive advantage over rivals
still, there is still a gap in understanding if to invest and use
intellectual capital is viewed as a critical asset. Therefore there is
a need to measure intellectual capital of the firm and its impact
on financial performance, in order to create more awareness.
Furthermore, many studies have focused the research of
intellectual capital in the developed world, there have been very
few studies that have used emerging developing worlds
especially in Sub-Saharan Africa as a case for evaluating the
implications of intellectual capital for specific industries like
banks (Kamath, 2007). This has created a gap that needs to be
addressed because, with rapidly changing environment filled with
innovation, information and technology, firms [both in developed
and developing economies] are increasingly threatened with
global competition (Muhammad and Ismail, 2009), which is
making intellectual capital more important to all of them for
sustainability and competitive advantages. Thus being stated
there is still a need to promote more studies in developing
countries.
This study uses the bank sector to find the impact of
intellectual capital and financial performance since the bank is
one of the high knowledge-intensive sector and, therefore it
provides a rich environment for the research and the availability
of the reliable data from the audited annual reports of banks. The
study uses VAICTM
model to analyze if the intellectual capital
has an impact on financial performance of Tanzanian banks.
2. Literature review
2.1 Intellectual capital definition
Intellectual capital although is the critical value driver for the
firm to succeed in the fiercely competitive world; it still has
many issues remain to be clear regarding its definition. Up to
now the definition of intellectual capital is not uniform among
different sectors.
ABSTRACT
Since the financial sector reforms took place in the last two decades, Banks in Tanzania
have continued to play the major role in reshaping the economy of the nation. With the
emergence of knowledge based economy many firm have changed their way of doing business
instead of relying more on physical capital they have shifted to intellectual capital. This is no
exception for the banks operating in developing counties Tanzania included. Many studies have
been done in the area of intellectual capital and its contribution to the value of the firm. This
study sets out to extend the evidence by investigating the intellectual capital of banks operating
in Tanzania for the period of four years from 2010 to 2013. Annual reports, especially the profit
and loss accounts and balance sheets of the selected banks have been used to obtain the data.
The study uses Value Added Intellectual Capital model (VAICTM
) in determining intellectual
capital and its three major components like Human Capital Efficiency (HCE) Structural capital
efficiency (SCE) and Capital Employed Efficiency (CEE). The results revealed that Intellectual
capital has a positive relationship with financial performance of banks operating in Tanzania and
also when the VAICTM
was divided into its three components it was discovered that the financial
performance is positively related to Human capital efficiency and Capital employed efficiency
but is negatively related to Structural capital efficiency.
Keywords:
Intellectual Capital
Banks
Value Added Intellectual Capital (VAICTM)
financial performance

Itami (1987) was the early contributor of intellectual capital
definition sees as intangible asset that comprises of technology,
customer loyalty, brand name loyalty, and goodwill etc. Stewart
(1997) also contributed to the definition of intellectual capital by
defining as a concept that involves human capital, structural
capital and customer capital. He further defines human capital as
the package which includes of innovations, knowledge,
experiences, and learning capabilities; structural capital as the
existing knowledge which can be found within the organization it
can be collected, tested, organized, integrated, and the important
part can be available for distribution; customer capital is the
relationships a firm establish when doing business includes
customer ,suppliers, it has mainly to do with satisfaction
retention, and loyalty. At the same time, Edvinsson and Malone
(1997) defined intellectual capital as the sum of human,
structural, and customer capitals.
On the other study Sveiby (1998), divided the components of
intellectual capital into three parts individual competence,
internal structure and external structure, with the individual
competence the this includes employees capability it involves
experience knowledge and social interactions; internal structure
includes computer programs, patents , concepts, patterns,
designs; external structure being the relations with customer,
suppliers and shareholders, which involves the brand, reputation,
loyalty and trademarks.
Johnson (1999) tries to define as intellect, or wisdom, as the
combination of human capital, structural capital and relationship
capital, where human capital means the idea capital (i.e., the
human skills ,knowledge, team work and talents) combined with
leadership capital (i.e., problem solving and creativity );
structural capital means the innovation capital (i.e., patents,
trademarks, technology, copyrights knowledge database, designs
) combined with process capital (i.e., work procedures and trade
secrets); relationship capital means the sum of relationships with
customers, suppliers, shareholders and other group in the network
society.
In a simplified definition, Edvinsson (2003) expressed
intellectual capital as what helps any company to be sustainable
and have competitive advantage in the future as well as an
indicator of whether that company will be maximizing value. It is
impossible for a company to gain momentum for reforms unless
it invests in intangible assets ( Tsen and Hu, 2010). Meanwhile,
Cabrita and Vaz (2006) simply stated that intellectual capital is a
matter of creating and supporting connectivity between all sets of
expertise, experience and competences inside and outside the
organization.
The latest definition of intellectual capital Mondal and Ghosh
(2012) described intellectual capital as “intangible assets or
intangible business factors of the company, which have a
significant impact on its performance and overall business
success, although they are not explicitly listed in the balance
sheet (if so, then under the term goodwill).”
There are many researchers who divided the intellectual
capital into three main components of human capital, structural
capital and relation capital Edvinsson and Malone (1997); Kaplan
and Norton,(1992) Sveiby,(1997); human capital is the personal
combined, knowledge, technologies, and experiences of
employees are linked with company capabilities, that includes the
creativity and innovation to enhance value creation. The
structural capital, is a supportive infrastructure that assist human
capital to perform well, it is an important link between human
capital and relational capital. customer capital, they refer to the
relational value between people and firm, it includes customer
satisfaction, retention, durability, reputation and the financial
soundness of suppliers, government, investors and business
network and other stakeholders including competitors
2.2 Intellectual capital and firm performance
There have been prior studies around the world which show
the intellectual; capita; and firm performance. Among these
studies Goh (2005) investigated the intellectual capital of
Malaysian commercial banks based on VAIC™ model and found
that there is significant relationship between VAIC™
performance and Human Capital Efficiency (HCE) and also the
study shows that HCE has relatively larger contribution in
measuring VAIC™ performance as compared to SCE and CEE.
Same findings are revealed by Joshi et al (2010) also in the same
manner the empirical results examined while exploring the
Intellectual Capital and banks performance of Australian owned
Banks for the period of 2005-2007 through VAIC™ model. They
showed same findings that. Human Capital Efficiency (HCE) is
positive and significant to VAIC the evidence also indicate
Human Capital has higher explanatory power to enhance the IC
performance of Australian banks as compared to other
determinant of VAIC™.
Studying the relationship of intellectual capital to firm
performance, in recently study Joshi et al., (2013) investigated
relationship between intellectual capital and their components
and financial performance in Australia context for the time of
2006-2008. The results show human capital efficiency, capital
utilized efficiency and structural efficiency were all important,
but they differ in utilization. It was found that intellectual capital
was critical in connection with human efficiency and worth
expansion of Australian banks. Human capital efficiency is
higher than capital utilized efficiency and structural efficiency on
Australian claimed banks.
In other study Mention and Bontis (2013) performed a study
using data from 200 banks from Belgium and Luxembourg the
empirical results confirms that human capital was both a direct
and an indirect impact on business performance. Structural and
relational capitals were found to be strong and positively related
to business performance; however results failed to establish
significant impact on relationship. Similar results were found by
Mohiuddin et al. (2006) in the study of 17 sampled commercial
banks operating in Bangladesh for the period from 2002 to 2004.
In another study Mavridis (2004) found that Japanese banks with
the greatest performance were those who were most efficient in
the use of their Human capital, whereas efficiency in physical
assets utilization was less important. Yolama and Coskun (2007)
conducted a study on the effect of intellectual capital profitability
of Turkish banks and found out the VAICTM
model could be used
as a benchmark for level of intellectual efficiency.
In other study, Jalilian, et .al (2013) examined a case study to
investigate the impact of intellectual capital on the financial and
non-financial performance of West Cement Company of
Kermanshah, Iran. The variable integrated were intellectual
capital as measured by human capital, structural capital and
relational capital, organizational learning capability and firm
performance; which were measured through financial and non-
financial performance. The study found an inter-relation between
all three components of intellectual capital. And they also had a
direct correlation with organizational learning capability,
financial and non-financial performance.

In the study involving different financial sectors, Muhammad
and Ismail (2009) examined the impact of intellectual capital
efficiency on the performance of financial sector firm of
Malaysia( i.e., banking, insurance and brokerage firms). By using
VAICTM
to measure intellectual capital efficiency and ROA
along with profitability to measure performance, the study found
a strong and positive impact of intellectual capital efficiency on
the financial performance of the financial sector of Malaysia.
Moreover, it was also found that within financial sector banking
in Malaysia relies more heavily on the intellectual capital
efficiency, which was followed by insurance and brokerage
firms.
Zehri, et.al(2012) investigated a study in Tunisia to measure
the intellectual capital and financial performance. The study used
VAIC model to measure intellectual capital efficiency while
performance of the organization was measure in three ways
financial performance (return on assets), economic performance
(operating margin) and market performance (Market to book
ratio).The results of the study trace a direct impact on the
financial and economic performance of the company. However
the direct relationship between intellectual capital and market
performance was not established.
Ahangar (2011) examined intellectual capital and firm
performance in Iranian corporate sector. The study used VAICTM
model to measure intellectual capital efficiency and used
profitability, sales growth, and employee productivity as
performance proxies. The study indicated that human capital is
most important component of intellectual capital and all three
dimensions as proposed by VAICTM
are significant explanatory
variables for profitability as measured by return on asset (ROA).
Kamal et al. (2012) on another hand using 18 commercial
banks in Malaysia investigated the relationship between the level
of intellectual capital efficiency regarding human capital, capital
employed and structural capital with the commercial banks
performance ,the study combined traditional accounting that
comprised return on assets(ROA) and return on equity(ROE).
The overall results discovered the relationship between
intellectual capitals and performance of banks. Additionally, the
results revealed significance impact of intellectual capital
variables namely capital employed efficiency, human capital
efficiency towards bank performance. Thus, the study concluded
that intellectual capital matters and should be linked to firm
productivity.
Ting and Lean (2009) furthermore in Malaysia conducted the
study on the financial sector to investigate the relationship
between intellectual capital and financial performance for the
period 1999 to 2007. They also used VAIC TM
the results
confirmed that Intellectual capital and return on assets are
positively related. The result concluded that the three components
of intellectual capital had positive influence on profitability.
Tan et al. (2007) using data from 150 publicly listed
companies in Singapore conducted a similar kind of study to
assess the relationship between the intellectual capital of firms
and their financial performance. They used VAIC TM
methodology The results proved that intellectual capital and firm
performance were positively associated in particular, intellectual
capital was found to be correlated to future company
performance, and the rate of growth of a company’s intellectual
capital was positively associated to the performance. However it
was discovered the contribution of intellectual capital to
company performance differs by industry.
Chan (2009) using a sample of all companies listed on Hang
Seng stock exchange for the period 2001 to 2005, investigated
the relationship between the efficiency of the Intellectual Capital
of these companies and integrating its components (human and
structural) with measures used for firm performance: market
valuation, return on assets, and return on equity and productivity
measurement. The results confirmed that only structural capital
has a significant and positive relationship with profitability
measures (ROA and ROE).
Phusavat et al., (2011) targeted manufacturing firms in
Thailand conducted a study on the effects of intellectual capital
and integrated it components (e.g. human capital, structural
capital, and innovation capital) and performance using VAICTM
.
The study provides empirical evidence that intellectual capital
has positively and significantly affects a manufacturing firm’s
performance, having direct impacts on the all four performance
indicators under study, i.e. return on equity, return on assets,
revenue growth, and employee productivity.
On another perspective, some used to measure the
interrelationship between intellectual capital elements. Empirical
evidence indicates the existence of interrelationships. For
instance, Maditinos et al. (2009) found out the relationship
between structural capital and business performance using data
from Athens Stock Exchange (ASE) and the companies operating
in service and non service industries the case involved four
components of intellectual capital namely human capital,
customer capital, structural capital and innovation capital and
their relationship with business however is more stronger in non-
service industries. Furthermore it was revealed that human capital
was important and positively associated to customer capital;
customer capital had an influence on structural capital and
innovation capital had an important and positive relationship to
structural capital.
In addition to the interrelations, literature documented the
relative dominance of human capital in influencing other
intellectual capital components and the overall value added
intellectual coefficient. For instance, Wang and Chang (2005)
found that even though human capital did not have a direct
impact on business performance, but it had on the other
intellectual capital elements, which in turn affected performance.
Furthermore, a study done by Joshi et al., (2010) revealed that
VAICTM
has a significant relation with human costs and that all
Australian owned banks had relatively higher human capital
efficiency than capital employed efficiency and structural capital
efficiency.
The finding of these studies still yield mixed results for
example firer and Williams(2003) studied the intellectual capital
of South Africans the results only supported intellectual capital
and capital employed further more he examined the relationship
between IC and traditional measures of firm performance (ROA,
ROE) and failed to find any relationship, The opposite research
result also, studied by Iswati (2007) show that no inﬂuence
between intellectual capital to bank’s performance in Jakarta
Stock Exchange.
The studies highlighted above were mostly related to the
developing economies which show still there is a need to study
intellectual capital and financial performance of banks in other
countries, especially in African local context. The studies show

the concepts using various definitions of intellectual capital
methods, and proxies of performance. Most of the studies
indicated towards a direct impact of various dimensions of
intellectual capital on internal as well as market performance of
the firms.
2.3 Proposed Model and Hypothesis
The model for the study can be presented based on the review of
literature on intellectual capital and performance of banks the
framework is shown below.
Figure 1: Proposed model
This study proposed the following hypothesis
H1: There is a significant positive relationship between the VAIC
and financial performance of banks
H2: There is a significant positive relationship between the HCE
H3: There is significant positive relationship between the SCE
H4: There is significant positive relationship between the CEE
3. Research methodology
3.1 Sample and data collection
The sample of the present study consists of 31 banks and is
based on secondary data collected from annual report of the
mentioned banks .Banks were selected on the basis of availability
of information necessary for conducting the study and the
readiness of Annual Reports for the financial year 2010-2013.
Hence the applied sampling procedure could be defined as
convenience sampling. Data was collected from the annual
reports of the banks consistent with other related studies (Goh,
2005;Mavridis, 2005; Tan et al., 2007;Joshi et al., 2010; Joshi et
al., 2013;Lipunga,2014).
3.2. Variables and empirical models
Firm Performance = f (Intellectual Capital)
Or
FP it = β 0 + β 1 IC it + µ
Where,
FP = Firm performance
IC = Intellectual Capital
The regression model used
ROA= α + β1 VAIC+ ε (1)
ROA= α+β1HCE+ β2SCE+ β3CEE+ ε (2)
VAIC TM
Method
Although the measurement of intellectual capital is still a
debatable issue, numerous methods have been developed to
measure it. In this study, the Value Added Intellectual Capital
(VAICTM
) method, developed by Public (1997, 1998, 2001,
2002a, 2002b, 2004), was used.
VAICTM
method is formulated as follows:
Equation (1) formalizes the VAICTM
VAIC=HCE+SCE+CEE
where:
VAICTM
= value added intellectual coefficient for bank i,
CEE = capital employed efficiency coefficient for bank i,
HCE = human capital efficiency coefficient for bank i,
SCE = structural capital efficiency for company i.
The first step is calculating CEE, HCE and SCE. These three
components of VAIC are calculated as follows:
HCE = VA/ HC
SCE = SC/ VA
CEE = VA/ CE
Where
VA = Value added
HC = Human capital
SC = Structural capital
CE = Capital employed
The above variables of the model are calculated by following
procedure:
VA=OUTPUT-INPUT
Output it is the total income generated by the firm from all
products and services sold during the period t, and Input it
represents all the expenses incurred by the firm during the period
t except cost of labor, tax, interest, dividends and depreciation.
Although there are many ways to measure the performance of
intellectual capital such as market value asset turnover employee
productivity and Return on equity but for this study the ROA is
picked as compared to ROE the ROA variable does take financial
risk of banks into consideration.
Return on Asset (ROA)
Return on Asset is a profitability ratio that measures the
firm’s ability to generate profit using its asset. The greater the
ROA, a firm is more efficiency in using its assets. This is one of
the commonly used ratios to measure firm’s financial
performance, which is calculated by ROA
Return on Asset= Net Income /Total Asset
4. Findings and Discussion
The data collected has been analyzed using different statistical
tests. First of all descriptive statistics relating to the variables of
the study are presented. After that correlation analysis if provided
Human Capital Efficiency
(HCE)
Structural Capital Efficiency
(SCE)
Capital Employed Efficiency
(CEE)
Financial performance
(ROA)
Intellectual Capital
(VAIC)

and in the end regression analysis is provided in order to
establish relationships between the variables.
Descriptive statistics in the study are used to compare the means
and standard deviation of the variables which are being
considered in the study . The variables considered in the study
are return on assets (ROA), and value added intellectual capital
coefficient (VAIC) and its components
Table 1: Descriptive Statistics for studies variables
N Minimum Maximum Mean Std. Deviation
ROA 117 -.25 .23 .0116 .04093
HCE 117 -1.6778 13.6373 2.058312 1.7019372
CEE 117 -.1419 .1058 .043591 .0301394
SCE 117 -1.5669 11.8036 .636440 1.5866086
VAIC 117 -1.1704 14.6063 2.738343 2.2109172
Table 1 above provides descriptive statistics of the variables
considered in the study of banks operating in Tanzania. The
minimum of the first dependent variable i.e. ROA is -.25 along
with a maximum of .23. The mean and standard deviations of the
variable are .0116 and .04093 respectively. The minimum and
maximum for HCE, on the other hand are -1.6778 and 13.6373
respectively and mean for the variable is 2 .0583 along with a
standard deviation 1.7019.The next variable of the study is CEE
which has minimum of -.1419 and maximum of .1058 along with
a mean of .0435 and standard deviation of .03013 SCE has a
minimum of -1.5669 and a maximum of 11.80. The mean of the
variable on the other hand is .6364 and a standard deviation of
1.5866 VIAC is the last variable has a minimum of -1.1704 and
maximum of 14.6063 The mean average for this variable is
2.7383 and with a standard deviation of 2.21.To conclude it
shows HCE has the highest mean among all the components of
VAICTM
. The mean of SCE and the one for CEE respectively, the
CEE has the lowest mean among all the variables.
Table 2: Correlations Matrix of banks
ROA HCE CEE SCE VAIC
ROA
Pearson Correlation 1 .477**
.685**
-.228*
.213*
Sig. (2-tailed) .000 .000 .014 .021
N 117 117 117 117 117
HCE
Pearson Correlation .477**
1 .295**
-.098 .703**
Sig. (2-tailed) .000 .001 .292 .000
N 117 117 117 117 117
CEE
Pearson Correlation .685**
.295**
1 -.271**
.046
Sig. (2-tailed) .000 .001 .003 .622
N 117 117 117 117 117
SCE
Pearson Correlation -.228*
-.098 -.271**
1 .638**
Sig. (2-tailed) .014 .292 .003 .000
N 117 117 117 117 117
VAIC
Pearson Correlation .213*
.703**
.046 .638**
1
Sig. (2-tailed) .021 .000 .622 .000
N 117 117 117 117 117
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
The table 2 above, shows that ROA and HCE have moderate
positive relation .So the ROA and HCE have correlation of 0.477
and are significant to each other. ROA and CEE also keep
competitive strong correlation of 0.685 and are significant for
both of them. The correlation between Structural Capital
Efficiency (SCE) and ROA is -0.228 which is weak and negative.
These two variables are also significant in relation to them. The
correlation between ROA and VAIC is also positive and
significant but weak at 0.213.This is lower compared to Human
capital efficiency and capital employed efficiency.
The result describes that the CEE and HCE values are more
significant to ROA than Structural Capital Employed Efficiency
(SCE) and on the other hand SCE and HCE are more significant
to VAICTM
of Banks in operating in Tanzania. Regression
analysis in the study is the final step of analysis which provides
the estimation of the variables by considering performance
related variables dependent variables and VAIC as independent
variable.

Table 3: Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .213a
.046 .037 .04016
a. Predictors: (Constant), VAIC
Table3 above provides model summary for the regression
estimates relating to the model 1 which sought to establish the
impact of VAIC on return on assets (ROA) for banks operating in
Tanzania The R square of the model is .213 which is quite low as
it associates only 21% explanation of variation in ROA with
VAIC. The adjusted R square of the model on the other hand is
4.6%. along with a standard error of .0401.This show the model
has no good explanatory power.
Table 4 provides the ANOVA results of the model 1 which
considers ROA as dependent variable and VAIC as independent
variable. The F statistics of the model is 5.484 which is quite low
and indicates that model is not a good fit.
Table 4: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .009 1 .009 5.484 .021b
Residual .185 115 .002
Total .194 116
a. Dependent Variable: ROA
b. Predictors: (Constant), VAIC
Table 5 above provides the regression coefficient of the
regression model 1 which assumes ROA dependent variable and
VAIC as independent variable. The beta coefficient of VAIC is
found to be .004 along with a t statistics of 2.342 which confirms
that VAIC has a positive and significant impact on return on
assets of banks in Tanzania. That leads us to accept our first
hypothesis H1 There is a significant positive relationship
between the VAIC and financial performance of banks.
The results of the present study are in confirmation with the other
studies by Chen et al. (2005), Tan et al. (2007), Ting and Lean
(2009), Sharabatiet al. (2010) in which it is clearly revealed that
there was a significant positive relationship between VAIC and
ROA.
Table 5: Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) .001 .006 .128 .898
VAIC .004 .002 .213 2.342 .021
Table 6 on provides the model summary of the model 2 which
estimates the impact of VAIC components on Return on Asset. R
square for the model is .744% which indicates that independent
variable i.e. VAIC components ie (CEE, SCE, HCE) causes
almost 74% variation in the dependent variable i.e. Return on
Asset. The adjusted R square and standard error of the model are
.554 and 2.7702 respectively.
Table 6: Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change F Change df1 df2
Sig. F
Change
2 .744a
.554 .542 2.7702 .554 46.746 3 113 .000
a. Predictors: (Constant), CEE, SCE, HCE
Table 7 provides the ANOVA results of the model 2. The F
statistics of the model 2 is found to be 46.74 which indicate that
model is a good fit at the significance level of 5%.

Table 7: ANOVAa
Model Sum of Squares df Mean Square F Sig.
2
Regression .108 3 .036 46.746 .000b
Residual .087 113 .001
Total .194 116
b. Predictors: (Constant), SCE, HCE, CEE
The table 8 above shows Human capital and capital employed
they are significant and positively with financial performance but
the structural capital is not significant and is negatively influence
with financial performance this may be because bank may fail to
utilize full their structural capital. That leads us to accept our
hypothesis H2and H4 and reject hypothesis H3
This can be summarized in table below:
Table 8: Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -.037 .005 -6.873 .000
HCE .007 .002 .300 4.567 .000
CEE .796 .092 .586 8.614 .000
SCE -.001 .002 -.039 -.598 .551
The table 8 above shows Human capital and capital employed
they are significant and positive with the financial performance,
but the structural capital is not significant and is negatively
influenced with financial performance this may be due to bank
may fail to utilize fully their structural capital. That leads us to
accept our hypothesis H2and H4 and reject hypothesis H3
and financial performance of banks.
The results were summarized in table below
Table 9: Results summary
Model Hypothesis Relation Expected sign Results Accept/Reject
Financial
Performance 1
H1 VAIC/ROA + + Accept
Financial
performance 2
H2 HCE/ROA + + Accept
H3 SCE/ROA + - Reject
H4 CEE/ROA + + Accept
5. Conclusion
The present study attempted to investigate the relationship
between intellectual capital (IC), and financial performance of
the banks operating in Tanzania. The methodology adopted is the
one of “Value Added Intellectual Coefficient” (VAICTM
) and its
components described into HCE SCE and CEE that has been
previously utilized by similar studies (Chen et al., 2005; Firer and
Williams, 2003; Williams, 2001) . Despite the fact that
Intellectual Capital is increasingly recognized as an important
strategic asset for sustainable competitive advantage, the results
of the present study fail to support such a claim in all the when
the components are tested separately. Empirical results failed to
support one of the proposed, Hypothesis three. Only verifying the
relationship between Human capital efficiency and capital
employed efficiency. The finding shows there is still higher
emphasis on physical asset than intellectual capital.
The results reveals the banks can get benefit by investing in
more intellectual capital, as it shows the value added and
Intellectual capital components were able to increase firm
profitability. Investing in human capital is essential to achieve
banks goals. The capital employed is found as the most important
variable it shows the use of physical and financial assets must be
effective and efficiency. The banks should put greater efforts in
investing in Structural capital by being more innovative with
high technology and supportive infrastructures.

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University-industry Partnership as a Key Strategy for Innovative Sustainable
Economic Growth
Ekaterina Panarinaa
a
Perm National Research Polytechnic University, Komsomolsky Avenue, 29, 614099, Perm, Russia
1. Introduction
Innovation is increasingly becoming the foundation of the
world's leading economies, economies in which long-term
prosperity and development depend on technologically based
intellectual products. These new products make possible the
creation of companies that can foster long-term sustainable
economic growth—in short, new economic perspectives to
create, harness, and leverage technology-based intellectual
capital. Russia's potential for growth is recognized by the World
Economic Forum's (WEF) Global Competitiveness Report 2013;
however, the report also acknowledges that the country is
currently falling behind India, China, and Brazil (BRICS
countries) in terms of competitiveness.
Russian large and expanding consumer market, a solid
telecommunications infrastructure, and abundant natural
resources are being central to Russia's competitiveness. However,
underdeveloped institutions, stifled competition, declining
quality of education, underdeveloped financial markets, and low
levels of business sophistication are the country’s key
competitive challenges. The lack of sufficient funding and a
supportive environment for startups has translated into a shortage
of new ventures.
When building a comprehensive innovation system, Russia
should focus on upgrading technological capabilities through
higher public expenditures on research and development (R&D).
This would enable the country to access its innovative potential,
which to a large extent is based on strong R&D capacities and an
innovative environment.
2. University-Industry Partnership as a key strategy for
innovative sustainable economic growth
Fostering collaborative university-industry partnerships to
enhance commercialization efforts has emerged as a critical
imperative to sustaining global competition. As shown by
countries such as the United States, innovation and business
competitiveness are greatly enhanced through the activities of
research universities. US universities through their research and
the products of their research have assumed a vital role in
growing vibrant economies (Cohen, Nelson, and Walsh 2002
Rosenberg and Nelson 1994; Mowery and Nelson 2003).
The success of high-technology regional clusters in the United
States such as Silicon Valley in California and Route 128 in the
Boston area have connected a large number of companies and
major research universities (in California, the University of
California at Berkeley, Stanford University, and the University of
California at San Francisco; in Boston, Harvard University and
MIT). Many new firms in these regions have been created
through efforts to commercialize technologies developed at
regional universities.
To build a knowledge-based economy, Russia needs to
similarly integrate business elements into its education system,
with the plan being to drive innovation by strengthening links
between higher education, research, and business practices. In
2012, Russian president Vladimir Putin announced in a formal
address that Russia’s universities must be revamped to become
key players in the economy of the country. As a long-term
ABSTRACT
The intensified global competition for factors that drive the competitiveness of entrepreneurial
ecosystems forces policymakers to seek new models of economic growth. The current Russian
model, based on the exportation of natural resources, has become increasingly obsolete. Today,
to achieve growth targets, Russia must move from the redistribution of mineral resources to
intensify innovation activity and develop technology-intensive products. Universities and
industry are two partners of the entrepreneurial ecosystem that can connect to merge the
discovery-driven culture of universities with the innovation-driven environment.
.
Keywords:
Innovation
Competitiveness
Partnership
Centers of Competence
Innovative environment

strategy, higher education has to become a strategic asset that
links with industry to strengthen the national economy by
enhancing and accelerating technology-transfer initiatives.
In this paper we propose for the establishment of stronger ties
between education and industry when Russian universities create
what are known as Centers of Competence. These centers can be
used to promote innovation and business competitiveness in the
Russian economy. World-class research universities are at the
forefront of creating such partnerships (Making Industry-
University Partnerships Work 2012), and it is these partnerships
that result in a broad range of beneficial activities that provide
regional and national economic outcomes. As
partners,educational institutions and industry can invest in
technological advancement, plan strategically, and greatly affect
the competitiveness of local and regional economies. Therefore,
Russian universities should go beyond the traditional funding of
discrete academic research projects and establish long-term
strategic partner ships with industry to improve innovation in
Russia.
Centers of Competence (CCs) will link innovative
technologies developed by research universities with industry
partners in an effort to target relevant market needs. Government
agencies will also be a key component of these endeavors with
supportive policy, as for example grants, reduced taxes, etc.
Coupled with government support and outside investment
these collaborations can help to solve pressing social and
economic challenges. The CC will be a hub for leaders in
science, education, business, and government where R&D
projects will be transformed into marketable high-tech products
and services. The CC will help create regional innovation clusters
and eventually lead to the advancement of the country's
competitive position and economic growth.
3. Russia’s innovative initiatives of economic growth
Positive notable changes to Russia’s innovation policy in
recent years have been accrued at the center of the government’s
agenda. The new government strategy ―Innovative Russia 2020‖
foresees large increases in funding for research,
commercialization, and innovation infrastructure. The strategy
implies an increase of the share of innovatively active companies
from the current 9.3% to 40–50% by 2020, as well as growth of
Russia's share of the global high-technologies market from the
current 0.3% to 2%. Under these plans, by 2020 the number of
patents registered by Russian companies in the European Union,
the United States, and Japan is expected to reach about three
thousand. Total budgetary funding on innovations in the next ten
years is estimated at approximately $530 billion, which includes
expenses on education, science, and a number of other fields.
However, on a global scale, these numbers are still low. In
2013, the United States, China, Japan, and Europe (excluding
Russia) accounted for about 80% of the total $1.6 trillion
invested in R&D around the world. For instance, in 2013, the
amount that Russia spent on R&D as a percentage of GDP was a
mere 1.5%; the percentage of total exports that were innovative
products, works, and services was 3.8%; and only 9% of Russian
organizations were involved in innovative activities. Despite the
existing potential in the sphere of human capital and research
activities, the level of innovation in Russia is very low. The
United States remains the world’s largest R&D investor with a
projected spending of $465 billion in 2014. At the same time in
2013, for the first time, China accounted for the largest number
of patents filed throughout the world.
In April 2012 the government adopted a list of innovative
territorial clusters (mostly in the central area of Moscow and St.
Petersburg) that would receive public support until 2018. The
first establishment of an innovation cluster is noteworthy: the
Skolkovo, which is an innovation hub built near Moscow to
provide researchers, entrepreneurs, and investors with a platform
to focus efforts on IT, energy efficiency, biomedicine, space, and
nuclear technologies. However, unfortunately, these initiatives so
far have had only a limited impact on enabling sustainable
economic growth in the country. Respondents who participated
in Ernst & Young's attractiveness survey Russia 2013: Shaping
Russia's Future suggest that a shift to a more collaborative
approach would help to improve Russia's innovation and
technological capacity (table 1). Their top recommendations are
as follows:
- Facilitate R&D collaborations between foreign and
local companies. A number of these partnerships have been
forged in the recent past, for example, Alcatel-Lucent signed an
R&D pact with SC Rostechnologii, Russia’s largest high-
technology corporation, to accelerate the deployment of
advanced long-term evolution or 4G mobile services, new
network systems, and groundbreaking trans- mission
technologies.
- Strengthen links between universities ad industry.
Encouraging collaboration between industry and academia would
help to improve Russia's innovation climate. This would
strengthen the foundation of entrepreneurship and innovation
Table 1: Measures Most Needed to Improve Russia’s Technology and Innovation Capacity (Source: Russia attractiveness
survey (total respondents: 206), 2013, Ernst & Young.)
Measure Percentage of respondents who named the
measure a top-three priority
Facilitate R&D partnerships between foreign investors and local companies
Focus on collaborations between universities and industry
Increase incentives for companies to invest in R&D and innovative technologies
Establish policies that support the development of emerging technologies
Support and facilitate the establishment of high-tech projects and techno parks
Develop a culture of innovation and creativity
Increase government support for the commercialization of innovative projects
Focus on public-private partnerships in technology
Develop joint research programs
Support the development of industrial parks and industrial zones
Can't say
25%
19%
17%
16%
14%
14%
14%
13%
11%
10%
18%

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