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Vol 1. no 1. NOVEMBER
ISSN 1849-8558
2015
Journal of International Business Research and Marketing
1
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Journal of International Business Research and MarketingISSN 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-keting analyzes the complex relationships between numerous business activi-ty fields, o�ering a variety of business solutions, theoretical contributions and recommendations for practice fitting for the actual business setting.
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CONTENTCost-volume-profit Analysis and Decision Making in the Manufacturing Industries of NigeriaJ.C.Ihemeje, Ge� Okereafor, Bashir M. Ogungbangbe
Impact of Intellectual Capital on Financial Performance of Banks in TanzaniaJaneth N. Isanzu
University-industry Partnership as a Key Strategy for Innovative Sustainable Eco-nomic GrowthEkaterina Panarina
Importance of Customer Relationship Management in Customer Loyalty(Studies at O�set in East Java, Indonesia)Chamdan Purnama
The Role of Purchase Tendencies Data in the Transformation of Foreign-made Pro-ducts Consumption in ChinaCamilo I. Koch R.
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24
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35
Journal of International Business Research and Marketing
7
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 Okereafor
b, Bashir M. Ogungbangbe
c
aCollege of Management Sciences, Michael OkparaUniversityof Agriculture, Umudike
bCollege of Management Sciences,MichaelOkpara University of Agriculture, Umudike cCollege 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
AB ST R ACT
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
Journal of International Business Research and Marketing
8
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.
Journal of International Business Research and Marketing
9
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;
Co
st a
nd
Rev
enu
e (N
aira
)
Y axis
Fixed cost
Variable cost Profit region
Loss region
Break-even point
X axis Activity level (units)
Activity level (units) Revenue Range
Co
st a
nd
Rev
enu
e (N
aira
)
Loss region
Bre
ak –
ev
en p
oin
t
Profit region
Total Revenue
Variable expenses
Total Fixed cost
Journal of International Business Research and Marketing
10
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
Journal of International Business Research and Marketing
11
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.
Journal of International Business Research and Marketing
12
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.
Journal of International Business Research and Marketing
13
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)(∑Y
2)
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.
Journal of International Business Research and Marketing
14
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 R
2
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.
Journal of International Business Research and Marketing
15
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.
Journal of International Business Research and Marketing
16
Journal of International Business Research and Marketing
Volume 1, Issue 1, November, 2015 journal homepage: www.researchleap.com
Impact of Intellectual Capital on Financial Performance of Banks in Tanzania
Janeth N. Isanzua
aSchool 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 VAIC
TM 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.
AB ST R ACT
2015 Research Leap/Inovatus Services Ltd.
All rights reserved.
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
Journal of International Business Research and Marketing
17
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.
Journal of International Business Research and Marketing
18
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
Journal of International Business Research and Marketing
19
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
and financial performance of banks
H3: There is significant positive relationship between the SCE
and financial performance of banks
H4: There is significant positive relationship between the CEE
and financial performance of banks
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)
Journal of International Business Research and Marketing
20
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.
Journal of International Business Research and Marketing
21
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
a. Dependent Variable: ROA
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%.
Journal of International Business Research and Marketing
22
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
a. Dependent Variable: ROA
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
H2: There is a significant positive relationship between the HCE
and financial performance of banks
H3: There is significant positive relationship between the SCE
and financial performance of banks
H4: There is significant positive relationship between the CEE
and financial performance of banks
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
a. Dependent Variable: ROA
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
H2: There is a significant positive relationship between the HCE
and financial performance of banks.
H3: There is significant positive relationship between the SCE
and financial performance of banks
H4: There is significant positive relationship between the CEE
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” (VAIC
TM) 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.
Journal of International Business Research and Marketing
23
References and notes
1. Ahangar, R. G. (2011). The relationship between intellectual capital
and financial performance: an empirical investigation in an Iranian company. African Journal of Business Management, 5(1), 88-95.
2. Alipour, M.(2012) The effect of intellectual capital on firm
performance: an investigation of iran insurance companies, Measuring Business Excelent,16(1),53-66.
http://dx.doi.org/10.1108/13683041211204671
3. Bontis, N. 2001. Assessing knowledge assets: a review of the models used to measure intellectual capital. International Journal of
Management Review, 3(1), 41-60. http://dx.doi.org/10.1111/1468-
2370.00053 4. Chan, K. (2009). Impact of Intellectual Capital on organizational
performance. The Learning Organization, 16(1), 4-21.
http://dx.doi.org/10.1108/09696470910927641 5. Bontis, N. (2001). Assessing knowledge assets: a review of the models
used to measure intellectual capital, International Journal of
Management Reviews, 3(1),41-60 http://dx.doi.org/10.1111/1468-2370.00053
6. Cabrita, M. and Vaz, J. 2006. Intellectual capital and value creation:
Evidence from the Portuguese Banking Industry. The Electronic Journal of Knowledge Management, 4(1), 11-20.
7. Edvinsson, L. (2003), Corporate Longitude: What You Need To Know
To Navigate The Knowledge Economy, Financial Times Prentice Hall, Pearson Education, Inc., Upper.
8. Edvinsson, L. & Malone, M. (1997). Intellectual Capital: Realizing Your Company's True Value by Finding Its Hidden Brainpower.
New York: HarperCollins. PMCid:PMC1564650
9. Firer, S. & Williams, S.M. (2003). Intellectual capital and traditional measures of corporate performance. Journal of Intellectual Capital, 4
(3), 348-60. http://dx.doi.org/10.1108/14691930310487806
10. Goh, P.C. (2005). Intellectual capital performance of commercial banks in Malaysia, Journal of Intellectual Capital, 6 (3), 385-96.
http://dx.doi.org/10.1108/14691930510611120
11. Itami, H. (1987). Mobilizing Invisible Assets. Boston: Harvard University Press
12. Iswati, S., and Muslich A. The Influence of Intellectual Capital to
Financial Performance at Insurance Companies in Jakarta Stock Exchange (JSE). Proceedings of the 13th Asia Pacific Management
Conference, Melbourne, Australia, 2007, 1393-1399.
13. Jalilian, O., Hassani, S. R., Ghanbari, M., Jalilian,H.R. &Moradi, M. (2013). The Relationship between Intellectual Capital and Financial
and Non-Financial Performance in West Cement Company in
Kermanshah, Journal of Basic and Applied Scientific Research,3(6), 427-432
14. Johnson, K. (1999), Making loyalty program more rewarding, Direct
Mark, 61 (11), 24-27. 15. Joshi, M., Cahill, D., & Sidhu, J. (2010). Intellectual capital
performance in the banking sector: An assessment of Australian
owned banks. Journal of Human Resource Costing & Accounting, 14(2), 151–170. http://dx.doi.org/10.1108/14013381011062649
16. Joshi, M., Cahill, D., Sidhu, J., and Kansal, M. (2013). Intellectual
capital and financial performance: an evaluation of the Australian financial sector. Journal of Intellectual capital, 14(2), 264-285.
http://dx.doi.org/10.1108/14691931311323887
17. Kamal, M. H. M., Mat, R. C., Rahim, N. A., Husin, N., and Ismail, I. (2012). Intellectual capital and firm performance of commercial
banks in Malaysia. Asian Economic and Financial Review, 2(4),
577–590 18. Kamath, G. B. (2007). The intellectual capital performance of Indian
banking sector. Journal of Intellectual Capital, 8(1), 96–123
http://dx.doi.org/10.1108/14691930710715088 19. Latif, M., Malik, M. S., & Aslam, S. (2012). Intellectual capital
efficiency and corporate performance in developing countries: A
comparison between Islamic and conventional banks of Pakistan. Interdisciplinary Journal of Contemporary Research in Business,
4(1), 405–420.
20. Lipunga A. M. (2014). A Longitudinal Assessment of Intellectual Capital of Companies Listed on Malawi Stock Exchange. European
Journal of Business and Management, 6(9), 27–35.
21. Lipunga A. M. (2014). A Longitudinal Assessment of Intellectual Capital of Companies Listed on Malawi Stock Exchange. European
Journal of Business and Management, 6(9), 27–35.
22. Mavridis, D. G. (2004). The intellectual capital performance of the Japanese banking sector. Journal of Intellectual Capital, 5(1), 92–
115. http://dx.doi.org/10.1108/14691930410512941
24. Mondal, A., & Ghosh, S. K. (2012). Intellectual capital and financial performance of Indian banks. Journal of Intellectual Capital, 13(4),
515–530. http://dx.doi.org/10.1108/14691931211276115
25. Muhammad, N. M. N., and Ismail, M. K. A. (2009). Intellectual Capital Efficiency and Firm's Performance: Study on Malaysian
Financial Sectors. International Journal of Economics and Finance,
1(2), 206–212 http://dx.doi.org/10.5539/ijef.v1n2p206 26. Phusavat, K., Comepa, N., Sitko-Lutek, A., &Ooi, K. (2011).
Interrelationships between intellectual capital and performance:
Empirical examination. Industrial Management & Data Systems, 111(6), 810–829. http://dx.doi.org/10.1108/02635571111144928
27. Pulic, A. (2004) "Intellectual capital – does it create or destroy
value?", Measuring Business Excellence,8(1)62-8 http://dx.doi.org/10.1108/13683040410524757
28. Pulic, A. (2000). VAICe – an accounting tool for IC management.
Retrieved from www. measuring-ip.at/Papers/ham99txt.html 29. Pulic, A. (1998). Measuring the performance of intellectual potential
in knowledge economy www.measuring-ip.at/OPapers/Pulic/
Vaictxt/vaictxt.html 30. Riahi-Belkaoui, A. (2003). Intellectual capital and firm performance
of us multinational firms, Journal of Intellectual Capital, 4 (2), 215-
26. http://dx.doi.org/10.1108/14691930310472839 31. Sharabati, A.A., Jawad, S.N. and Bontis, N. (2010). Intellectual
capital and business performance in the pharmaceutical sector of
Jordan. Management Decision, 48(1), 105-31. http://dx.doi.org/10.1108/00251741011014481
32. Shiu, H. (2006). The application of the value added intellectual
coefficient to measure corporate performance: evidence from technological firms. International Journal of Management, 23(2),
356-65.
33. Stewart, T.A. (1997). Intellectual Capital: The New Wealth of Organizations Doubleday/Currency. New York: NY.
34. Sveiby, Karl-Erik (1998), Measuring Intangibles and Intellectual
Capital–An Emerging First Standard, Internet version. 35. Tan, H.P., Plowman, D. and Hancock, P. (2007). Intellectual capital
and financial return of companies. Journal of Intellectual Capital, 8
(1), 76-95. http://dx.doi.org/10.1108/14691930710715079 36. Ting, I.W & Lean, H.H. (2009). Intellectual capital performance of
financial institutions in Malaysia. Journal of Intellectual Capital, 10
(4), 588-99. http://dx.doi.org/10.1108/14691930910996661 37. Tovstiga, G and Tulugurova, E. (2007). Intellectual capital practices
and performance in Russian enterprises. Journal of Intellectual
Capital, 8 (4), 695-707 http://dx.doi.org/10.1108/14691930710830846
38. Tsen, Shu-Hsiao and Hu, Hsiang-ling (2010). A Study of the
organizational competitiveness and intellectual capital indicators of international tourist hotels, Human Resource Management Student
Newspaper, 10 (1), 79-104
39. Wang, W., and Chang, C. (2005). Intellectual capital and performance in causal models Evidence from the information
technology industry in Taiwan. Journal of Intellectual Capital, 6(2), 222–236. http://dx.doi.org/10.1108/14691930510592816
40. Williams, S.M. (2001). Is intellectual capital performance and
disclosure practices related Journal of Intellectual Capital, 2 (3), 192-203. http://dx.doi.org/10.1108/14691930110399932
41. Yalama, A. and Coskun, M. (2007). Intellectual capital performance
of quoted banks on the Istanbul stock exchange market. Journal of Intellectual Capital, 8 (2), 256-71.
http://dx.doi.org/10.1108/14691930710742835
42. Zehri, C., Abdelbaki, A. &Bouabddellah, N. (2012). How Intellectual Capital affects firm'sperformance? Australian Journal of Business
and Management Research, 2 (8), 24-31
Journal of International Business Research and Marketing
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Journal of International Business Research and Marketing
Volume 1, Issue 1, November, 2015 journal homepage: www.researchleap.com
University-industry Partnership as a Key Strategy for Innovative Sustainable
Economic Growth
Ekaterina Panarinaa
aPerm 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
AB ST R ACT
2015 Research Leap/Inovatus Services Ltd.
All rights reserved.
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
Journal of International Business Research and Marketing
25
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%
Journal of International Business Research and Marketing
26
4. Center of Competence at Perm National Research Polytechnic University
National and local governments in many countries stimulate
their economies by forming ―Science Parks‖ or ―Technology
Centers‖ or what we call ―Centers of Competence‖ (CCs). Based
on a review of the relevant literature, for the purposes of the
present research we have developed the following definition of a
Center of Competence: an entrepreneurial, flexible, innovative
eco-structure that integrates knowledge produced by universities
with industry expertise, and utilizes the support of government
and local communities to create strategic synergies that boost
economic growth.
We propose the creation of a CC at Perm National Research
Polytechnic University. It would be the first CC in the Perm
region and would be designed as a hub of science (knowledge
produced by the university), industry (the major economic
sectors), local government (funding and financial support through
programs and grants), and society (the entrepreneurial
community). The Perm CC would support innovations from the
early stages of development to commercialization. Its mission
will be to accelerate the commercialization of discovery-driven
innovations from universities and to foster and accelerate the
exchange of ideas between researchers on campus, through better
access to informational, financial, technological, and human
resources.
Perm National Research Polytechnic University is well suited
for the design and implementation of a CC. In 2009 the
university received a status of a ―national research university,‖
one of only twenty-nine other universities in Russia to achieve
this status. Thus, Perm is an ideal location for an entrepreneurial
center blending technology, engineering, applied science, and
education. The center will become a catalyst for innovation
through the integration of resources, and it will focus on
launching innovative projects by utilizing state and regional
programs and promoting entrepreneurial activity. CC initiatives
will be focused on generating cross-disciplinary solutions,
creating interdisciplinary knowledge, and developing new
technologies and processes. We strongly support the
implementation of a CC at Perm National Research Polytechnic
University, as it will represent a significant step towards
economic development and successful competition in the region
and beyond. Innovation, science, and human capital will serve as
the cornerstones of the new innovative system designed to serve
social and economic needs.
5. Center of Competence as an ecosystem for innovation development
The Center of Competence (CC) will become a tool for
integrating knowledge, expertise, and supporting entrepreneurial
activity. Designed as a flexible system, and managed to ensure
competitive growth, the CC will assist with the implementation
of innovative strategies for creating competitive companies in the
Perm region.
The CC will help to pool the following components within an
integrated management system for innovation development:
business, government, academia, professional associations, and
the local community (figure 1); within the CC a flow of qualified
specialists, active entrepreneurs, creative youth, and government
agencies, together with science and education, will define the
innovative development of economic sectors.
Figure 1: The CC as an ecosystem for innovation
development
The Center of Competence will link industry and the
university as well as assess public/private resources for mutually
beneficial needs (e.g., facilitate tech transfer and startups;
administer industry contracts and out- reach efforts; provide
innovation services to internal and external
researchers/organizations; utilize industry retirees to promote
innovation and entrepreneurship; increase research funding and
seed capital opportunities; train and mentor start-ups and small
businesses; and facilitate collaboration between large companies
and recognized researchers). These efforts should intensify
technology transfer and commercialization, and attract venture
capital and other private investment resources, leading to the
creation of a vibrant technology and innovation-driven ecosystem
in the Perm region.
The objective of the CC as the core of communication
between these different elements is ensuring the integration of
knowledge and processes, and stimulating the emergence of an
innovative culture. The CC will help companies in the Perm
region strengthen their competitive edge, build dedicated teams
of specialists with new comprehensive competencies, and drive
the shift to an innovative management model.
The suggestions below provide examples of how we might
better position the CC to achieve the goals stated above:
1. Create an executive advisory board to advance the
reputation and capabilities of the center. Work with the advisory
board to identify potential cooperation with enterprises in the
region and to establish partnerships with those entities.
2. Motivate faculty members to lead research in the area
of their expertise with connections to market needs.
3. Pursue funding through the local and federal
governments to sponsor research initiatives of faculty and
graduate students.
4. Organize business plan competitions for university
students to build entrepreneurial skills and develop an innovative
culture. Create cross-disciplinary teams to compete such as
engineering, science, information systems, etc., in which
interdisciplinary student teams will be required to write business
plans focused on new technologies.
5. Develop a focused strategy that includes leading areas
of expertise for the university such as mechatronics,
nanotechnologies, aerospace, energy, and information systems
technology.
Journal of International Business Research and Marketing
27
Long-term collaborations made though the CC will give rise
to new technologies helping to transform industries while
modernizing the role of the university. However, collaboration is
not going be easy. As a rule, for most universities, partnering
with industry does not come naturally. Most Russian academics
are not engaged at all in collaborations with industry. When
Russian universities do form partnerships with industry, too often
the potential for synergy is thwarted by communication failures.
The most productive collaborations are strategic and long-
term; they are built around a shared research vision and may
continue for a decade or beyond, establishing deep professional
ties, trust, and shared benefits that work to bridge the cultural
divide between academia and industry. The collaboration
requires strong university leadership, faculty who understand
business, academics who have worked in industry, and making
industry university partnerships a clear priority.
The key recommendations for universities to foster
successful collaboration with industry are the following:
• Make industry-university partnerships a strategic priority
and communicate the message regularly to the entire
academic community.
• Create an advisory board of executives from selected
industry sectors and the highest level from the university
who will develop an understanding of the key scientific and
technological questions companies are seeking to answer.
As a first step, a joint steering group including senior
academics and company executives should be formed.
• Assess the core academic strengths of the university and the
core research competence of local companies to identify
promising opportunities for collaboration.
• Design incentives for university faculty and provide
resources to manage a cultural shift that puts a clear priority
on engaging with industry for mutual benefits.
• Encourage industry involvement. The university must
utilize people capable of building and managing
partnerships. Collaborations only work well when they are
managed by people who cross boundaries easily and who
have a deep understanding of the two cultures they need to
bridge.
• Create opportunities for academics, company researchers,
and executives with shared interests to come together and
develop a dialogue. For example, informal exchanges over
lectures or seminars can bring both sides together to spark
conversations and lead to new relationships.
• When a partnership has been launched, have an executive
board meet regularly to encourage strong two-way
communications between academics and senior company
officials. The chair should follow up regularly with
members to keep the dialogue flowing and encourage
impromptu feedback on the project from both sides at any
time.
• Develop two-way exchanges to build a substrate of
academics who understand industry. The university should
encourage professors to get internships in industry and
invite industry researchers to teach.
• Create long-term strategic partnerships that focus the
university's creativity and talent on future innovations that
can be taken to market by industry and deliver economic
benefits within five to ten years.
• Encourage diversity. Innovation works when there is
diversity. Invite to the projects individuals from different
disciplines to contribute to the whole process. Collaboration
of ideas, people, and places should be systematic.
Redefine the role of the research university as a source of
competence and problem solving for society.
Julio A. Pertuze, Edward S. Calder, Edward M. Greitzer and
William A. Lucas, in their ―Best Practices for Industry-
University Collaboration‖ (2010), propose a set of seven
guidelines that companies should follow to get the most out of
their research collaborations with universities. The guidelines
partly correlate with the key recommendations for universities
stated above: longer-term projects, continuing relationships,
assigning project managers who make the contract feel like a
partnership, and enabling these managers to invest the time and
effort to generate effective knowledge flows between the
university and the company.
6. Conclusion
In the end, we emphasize that bold, visionary partnerships
between industry and university are able to accelerate innovation
and help deliver solutions to pressing economic and social
challenges. Universities should collaborate with industry, and the
role of the research university should be redefined for the twenty-
first century as one that goes beyond teaching and public service
to tackling key social challenges and helping drive economic
growth. The university in the twenty-first century should be
viewed not just as a generator of ideas but also as a source of
knowledge and competence that can benefit society.
References and notes
1. Cohen, W.M., Nelson R. R., & J. Walsh P. (2002). Links and Impacts:
The Influence of Public Research on Industrial R&D. Management Science, 48(1), 1–23. http://dx.doi.org/10.1287/mnsc.48.1.1.14273
2. Colyvas, J., Crow M., Gelijns A., Mazzoleni R., Nelson R., Rosenberg
N., & B. Sampat. (2002). How Do University Inventions Get into Practice? Management Science, 48 (1), 61-72.
http://dx.doi.org/10.1287/mnsc.48.1.61.14272
3. World Economic Forum (2012). The Global Competitiveness Report 2012–2013: Full Data
4. Edition. Retrieved from
http://www.weforum.org/issues/globalcompetitiveness 5. INSEAD and the World Intellectual Property Organization (2012).The
Global Innovation Index 2012: Stronger Innovation Linkages for
Global Growth. 6. Hicks, D., Hamilton K. (1999). Real Numbers: Does University
Industry Collaboration Adversely Affect University Research?
Issues in Science and Technology Online. Retrieved from http://www.nap.edu/issues/15.4/realnumbers.htm
7. Making Industry-University Partnerships Work: Lessons from
Successful Collaborations. 2012. Science Business Innovation Board AISBL. http://www.sciencebusiness.net/assets/94fe6d15-5432-4cf9-
a656-633248e63541.pdf
8. Mowery, D.C., Nelson R., Sampat B., &Ziedonis A. (2003). The Ivory Tower and Industrial Innovation: University-Industry Technology
Transfer before and after the Bayh-Dole Act. Stanford: Stanford
University Press. 9. National Research University-Higher School of Economics, Moscow
(2012).Indicators of Innovation in the Russian Federation: Data
Book. 10. Pertuze, J. A., Calde E. S. r, Greitzer E. M., and William A.
Lucas.(2010). Best Practices for Industry-University Collaboration. Management Review, 51 (4), 83–90.
11. Ernst & Young (2013).Russia 2013: Shaping Russia's Future.
Retrieved from www.ey.com/attractiveness. 12. Santoro, M. D., Betts S.C. (2002). Making Industry-University
Partnership Work. Research-Technology Management, 45(3), 42–46.
Journal of International Business Research and Marketing
28
Journal of International Business Research and Marketing
Volume 1, Issue 1, November, 2015 journal homepage: www.researchleap.com
Importance of Customer Relationship Management in Customer Loyalty (Brangkal
Offset of East Java, Indonesia)
ChamdanPurnamaa
aPresident School of Economics, Al-Anwar Mojokerto, Indonesia
1. Introduction
The business strategy is currently focused on the creation of a
trust or confidence of customers to the company. The customer
is a very valuable asset for the company.If the company loses
its customers, it will not only lose its profit, but also its
possible sales that may happen in the future will be in at risk.
Acquired loyal customers are the biggest advantage of
thecompany becausethe company can sell more goods or
services to those loyal customers who have already tried the
relevant goods or services of the company and formed some
knowledge on them. Besides, the company has spent a lot of
effort to establish a relationship with the customer.
The best way to build relationships with customers can be
realized by building a customer relationship management.
Customer relationship management is a type of management
that specifically discusses the theory about the handling the
relationship between the company and its customers with the
goal of increasing the company's value in the eyes of its
customers. Customer relationship management embraces all
aspects of dealing with prospective and current customers,
including the call center, sales force, marketing, technical
support and field service. As the wording implies, customer
relationship management is an activity aimed at obtaining a
relationship with the customer to be able to provide a
significant advantage for the company.
Research conducted in Taiwan for 58 hotels analyze
customer relationship management in the form of operational
processes by Edward (2010) states that the operational
processes both increase profitability and maximize customer
relationships and operational capabilities can accelerate the
process of customer ordering. Research conducted by Borle et
al (2010) showed that the customer community management
program can increase the number of customers the
companytargets.
Research in the US by Krasnikov (2009) conducted on
commercial bank ofthe US examines the impact of the
implementation of customer relationship management in two
company's performance, operational performance, cost
efficiency and their ability to generate profit (profit efficiency).
Results of the study found that implementation of customer
relationship management improve cost efficiency and increase
profits.
The results of the research conducted in Egypt by Battor
(2010) reinforces the view that developing good relationships
with customers can improve a company's ability to innovate.
Relationship with customers is one indicator of customer
relationship management.
Purnama (2014: 242) in his study conducted in Indonesian
small clothing industrystates that attitude, intelligence,
emotion, skills and knowledge of employees altogether can
influence the ability of the employee. Results of this study
prove that the skills and knowledge of employees affect the
ability of businesses in the works and will also increase
production.
Prasad (2008) examined the effect of relational marketing
attributes such as trust, commitment, communication, empathy,
and conflict on relationship quality and customer loyalty. The
study was conducted on 300 customers of retail companies in
AB ST R ACT
2015 Research Leap/Inovatus Services Ltd.
All rights reserved.
This study examines the importance of customer relationship management to increase
customer loyalty. Study uses two years’ data on 71customer (respondents)of Brangkal Offset
chosen on the basis of random samplingtechnique. Results of this investigation indicate that
important aspects of customer relationship management those are people, process and
technologyboth partially and simultaneously have an impact on the increase of customer loyalty.
Keywords:
Customer relationship management
Customer loyalty
Indonesia
Journal of International Business Research and Marketing
29
India. The research proves that the quality of relationships has
a significant effect on customer loyalty.
Few studies conducted in Taiwan, the US, India, Egypt and
Indonesia are related to customer relationship management and
customer loyalty relationship. Through customer relationship
managementcompanies can build closer relationships with
customers, and the company can learn the needs of customers
and provide a selection of products or services in accordance
with their request. As expressed by Kotler and Keller (2007:
189), customer relationship management is “the process of
managing detailed information about individual customers and
carefully managing all customer touch points in order to
maximize customer loyalty”. This study is quite the same with
other studies. The distinguishing feature of this study is that
previous studies looked at the performance in terms of costs,
the ability to innovate and company's profits and customer
relationships and they were carried on the example of retail,
food, hospitality and banks. This study has been carried out in
the example of an offset servicecompanyto measure its
performance in terms of customer loyalty.
At the core of customer relationship management, there is a
way to analyze customer behavior. From this analysis, the
company can finally be able to take ways how to serve
customers in a more personalized way so that customers
become loyal to the company. To be able to maintain a loyal
customer and in order not to lose them to a competitor, the
company should establishgood relationship with the customers
and should try toincrease company's value in the eyes of its
customers. This endeavor requires a precise and efficient
strategy from the company to know its customers betterto serve
them better. Customer relationship management is not a
new,recently invented conceptby consultant’s world. Customer
relationship management is a fundamental paradigm of how to
look at the customer and how to better satisfy customers
through a harmonious relationship and quality.
2. Literature Review
2.1. Customer Relationship Management
According to Widjaja (2008: 45),customer relationship
managementis a comprehensive approach to create, maintain,
and developrelationships with customers. According to Buchari
(2004: 271),customer relationship management is a process to
acquire, retain and grow the most profitable customers.
According to Frederick (2000: 2),customer relationship
management is the process of modifying consumer behavior
over time and learning from each interaction, change, taking
care of customers, and strengthening ties with them. According
to understanding of the customer relationship management by
Luke (2001:3), it is “an activity that involves the entire human
resources to retain existing customers; a strategy to cultivate
and maintain relationships with customers; an attempt to
determine the wants and needs of customers”.
Based on a variety of definition ofcustomer relationship
management from above, it can be concluded that customer
relationship management is a business strategy of the company
to establish relationships with customers and provide
satisfactory services for customers. Luke (2001:
116)dividesthree main components, namely: People, Process
and Technologyof customer relationship management into
2.1.1. People
In this case the employee has a very important role in the
sustainability of the implementation ofcustomer relationship
management, because they are implementing customer
relationship management as an activity or desire ne companies.
With the implementation of customer relationship management
has been a change in marketing paradigm, when previously, the
production becomes the main focus in the implementation of
customer relationship management, the customer is the main
focus. As for what needs to be addressed from a aspect people
is the enthusiasm, knowledge, skills, friendliness, and
responsiveness to the customer's own employees.
2.1.2. Process
Implementation of customer relationship management
changes the complete business processes that have beenin
place for a long time.It changes both business processes that
directly involve customers or not. On the whole, customer
relationship management business functionality is focused on
the customer. Customer relationship management process
includes:
1. Identification: Identification of customers and prospects
based on existing data, customers who are profitable, he lived
where and why he was favorable. Most companies only care
how big the benefits of its customers without knowing who are
the customers that have been profitable. There are a few things
to know about the customer such as: (a) Firm graphic: namely
information about customers or companies that do business
with us. Such as: address, business, zip code and so on. (b)
Demographic and psychographic: the information concerning
contact person (customers). (c) Info graphic: how to contact
Pearson wanted a way of interaction in obtaining information
about him.
2. Differentiation: Segment customers based on behavior,
demographics, and customer expectations.
3. Interaction: Make the best plan for interacting with
customers, and then create customer loyalty programs,
crossselling, and so on. The longer the interaction occurs, the
more know each other, the more reluctant customers moving to
competitors because customers will find it hard to start a new
relationship with a competitor. Interaction can be done by
email, telephone and fax, mail, and face to face.
4. Personalization: Products and loyalty programs tailored
to the wishes of customers who continuously. Using all the
information that has been obtained prior to making goods and
services in accordance with the wishes and needs of customers.
2.1.3. Technology
Technology has a role in customer relationship
management. Firstly, it is building a data base ofcustomers
ranging from the operating system up to the transaction.
Secondly, to analyze who the customer is themost good; he
bought what, how often. Third, implement the activities of
sales, marketing, and customer service by integrating different
communication channels (operational customer relationship
management).
Journal of International Business Research and Marketing
30
Customer loyalty has an important role in a company, the
longer the company maintains a loyal customer, the greater the
profit generated. This is the main reason for a company to
retain customers. When companies spend less in order to
obtain new customers, the company can also spend money to
improve the quality of products or services continuously. In
turn, it also can help make customers become more loyal.
Having a loyal customer is the ultimate goal of all
companies, but most companies do not realize that customer
loyalty is formed through the stages starting from looking for
potential customers to the Customer Advocate will bring
benefits to the company. Customer loyalty according to Griffin
(2005: 4) is: "Customer loyalty is defined purchasing buying
behavior nonrandom disclosed from time to time by some of
the decision-making unit". According toTjiptono (2000: 110)
says that: "Customer loyalty as a customer commitment to a
brand, the store, the supplier is based on a very positive attitude
and reflected in repeat purchases consistent." Meanwhile,
according to Widjaja (2008: 6), customer loyalty is attachment
to a brand, store, manufacturer, service provider, or other entity
based on a favorable attitude and a good response as repeat
purchases".
From the above definition, it can be concluded that the
more loyalty leads to behavior (behavior) compared with an
attitude (attitude) and a loyal consumer purchasing behavior
will exhibit behavior that is defined as the purchase of a regular
and behaviors throughout the show by the decision maker.
Loyal customer is an asset to the company and to determine
the company's loyal customers to be able to offer products or
services that can meet customer expectations and satisfy its
customers, when customers make a purchase action repeatedly
and regularly then the customer is a loyal customer. This is
reinforced by the statement of Griffin (2005: 31), which states
that the characteristics of loyal customers include:
Make purchases on a regular basis or regular.
Buying outside the line of products or services.
Recommend to others.
Not easily affected competitor product appeal.
3. Research Methods
This study is classified as explanatory research. Approach
to research using correlation design. Draft correlation is useful
to analyze the relationship between one variable to another
variable, or how a variable affects other variables. The study
population was Brangkal Offset customers since the last two
years as many as 250 customers. Usage sample technique is to
use simple random sampling. The size of the sample to be
studied using questionnaires as the data collection tool
according to population numbers mentioned above, which
amounted to 250 people, while the determination of the
number of samples is done by using the formula of Slovin
Umar (2002: 146). Based on the calculation of the number of
members of the sample in this study were 71 respondents. In
this study the analysis of the test data validity, reliability and
classical assumption of all items of questions (instruments) as
well as multiple regression analysis to see the effect by using
SPSS for Windows version 16.0.
Model equations to see the influence of variables customer
relationship management, which include: Aspects People,
process and Technology on customer loyalty are as follows:
Y1 = a + β1X1 + β2X2 + β3X3
Description:Y1 = Customer Loyalty, a = Constanta, X1 =
Aspects People, X2 = Aspects Process, X3 = Aspects
Technology, (β1, 2, 3, = Coefficient that describes the path of
the influence of the independent variables to the dependent
variable
4. Results
4.1. Test Results of Validity and Reliability
The test results question the validity of the entire item
(instrument) study of samples collected and processed using
SPSS analysis tools 16:00 prove that all items are valid
questions. The analysis showed that all items have a question
of correlation greater than 0.40 and have significant value
Pearson smaller than α (0.05). Thus, all the indicators used to
dig respondents on variables customer relationship
management which include: the aspect people, process and
technology and customer loyalty is valid.
While the reliability test results that have been performed
using SPSS 16.0 analysis tool of data that can be collected, it is
known that the value of Cronbach’s alpha all variables in this
study is greater than 0.70 so it can be said that the reliability is
acceptable even better. Thereby, it can be concluded that the
results of measurements that have been done are reliable for
further analysis.
4.2. Test Results of Regression Analysis
In this regression test in addition to looking for the
coefficient of determination can also be used to test the
hypothesis, testing the hypothesis with a simple regression and
t test with the rules t test >_ t table rejected significant
meaning, and if t test >_ t table Ho is accepted, it means
insignificant. Testing this hypothesis using regression 3
doubles, and regression testing and f test, with the rules f test
>_ f table Ho rejected significant meaning,and if f test >_ f
table Ho is accepted, it means insignificant.
Test results of regression statistical analysis tools SPSS
version 16.0, to examine the effect of customer relationship
management which include: (X1) People, (X2) Process and
(X3) Technology on customer loyalty (Y1) like the following
table:
Table 1: Result of ANOVA; the influence of Aspects
People, Process and Technology to Customer Loyalty
Model Summary
Model R R Square Adjusted R
Square
Std. Error
of the
Estimate
1 .0878a .772 .762 1.83985
Journal of International Business Research and Marketing
31
Table 2: Test Result Coefficient the influence of Aspects
People, Process and Technology to Customer Loyalty
ANOVAb
Model Sum of
Squares
df Mean
Square
F Sig.
1 Regression 766.780 3 255.593 75.507 .000a
Residual 226.797 67 3.385
Total 993.577 70
a.Predictors: (Constant), X3, X1,X2
bDependent Variable: Y1
Table 3: Test Result of influence between the aspect
people of customer loyalty
Coefficientsa
Model Unstandardized
Coefficients
Standardi-
zed Coeffi-
cients
t Sig.
B Std.
Error
Beta
1 (Constant) 32.125 3.003 10.697 .000
X1 .303 .049 .454 6.153 .000
X2 .189 .058 .258 3.389 .001
X3 .201 .042 .345 4.846 .000
aDependent Variable: Y1
Test Result of influence between the aspect people of
customer loyalty
Table 3 describes the level of influence between the
variables of the aspect people (X1) on customer loyalty (Y)
which is calculated by the coefficient of determination (R2)
was 0.772. This indicates a strong influence on the aspect
people of customer loyalty. While significant levels (measured
of probability) gives the figure of 0.000. Because the
probability is much below 0.05, then the influence of the aspect
people of customer loyalty significantly. Based on table 3 of
the ANOVA test or t test, was obtained t test was 26.121
withbecause the probability of a significant level of 0.000 is
much smaller than 0.05, so the regression model can beused to
predict customer loyalty.
Table 3 illustrates that the regression equation as follows:
Y1 = 32,125 + 0.303 X1
Description:X1 = Aspect people; Y1 = Customer loyalty
Constants of 32,125 states that if there is no increase in the
value of the variable aspect of people (X1), then the value of
customer loyalty (Y1) is 32,125. Regression coefficient for
0.303menyatakan that each additional score or the value of the
aspect people will provide increased customer loyalty by
0.303.
Based on the calculation of the value, it can be said that
Ho is rejected. Because t test > t table = 6.153 > 1.670. Seen in
the sig column in the table is 0.000 or probability values far
below 0.05. Because t test > t table or 6.153> 1.670, then Ho is
rejected it means a significant regression coefficient or the
aspect people significant direct effect on customer loyalty.
Test results of influence between the aspect processes to
customer loyalty
Based on the table 1 that the influence of the variable
aspect of the process (X2) on customer loyalty (Y1) is
calculated with a coefficient of determination (R2) was 0.772.
This shows the strong influence of the aspects of the process to
customer loyalty. While significant levels (measured by
probability) give the figure 0,001. Because the probability is
much below 0.05, then the influence of the aspect of the
process towards a significant customer loyalty. Based on table
3 of the ANOVA test or t test, was obtained t count is 3.369 to
0.001 due to a significant level of probability (0.000) is much
smaller than 0.05, so the regression model can be used to
predict customer loyalty.
Table 3 illustrates that the regression equation as follows:
Y1 = 32,125 + 0.189 X2
Description:X2 = Aspects process; Y1 = Customer loyalty
Constants of 32,125 states that if there is no increase in the
value of the variable aspect of the process (X2) the value of
customer loyalty (Y) is 32,125. Regression coefficient of 0.189
states that each additional score or value aspects of the process
will provide increased customer loyalty by 0.189.
Based on the calculation of the value t test > t table or
3.369> 1.670, and 0.001 sig value or probability is much below
0.05, then Ho is rejected means regression coefficient
significant or significant influence over the process aspect.
Test results of influence between the aspects technological
of customer loyalty
Based on the table 1 that the influence between the
variables of the technological aspects (X3) on customer loyalty
(Y1) is calculated with a coefficient of determination (R2) was
0.772. This shows the strong influence of the technological
aspects of customer loyalty. While significant levels (measured
by probability) give the figure of 0.000. Because the
probability is much below 0.05, then the influence of the
technologicalaspects of customer loyalty significantly. Based
on table 3 of the ANOVA test or t test, was obtained t count is
4.846 to 0.001 due to a significant level of probability (0.000)
is much smaller than 0.05, so the regression model can be used
to predict customer loyalty.
Table 4 illustrates that the regression equation as follows:
Y1 = 32,125 + 0.201 X3
Description:X3 = Aspects of technology; Y = Customer
loyalty
Constants of 32,125 states that if there is no increase in the
value of the variable aspect of technology (X3) then the value
of customer loyalty (Y1) is 32,125. A regression coefficient of
0.201 states that each additional score or value aspects of the
process will provide increased customer loyalty by 0,201.
Based on the calculation of the value t test > t table or
4.846> 1.670, and sig 0,000 or probability is much below 0.05,
Journal of International Business Research and Marketing
32
then Ho is rejected means regression coefficient significant or
technological aspects significant influence.
Test results of influence between the aspects people,
process and technology of customer loyalty
Based on the table 1 that the influence between the
variables of the aspect people (X1), aspects of the process (X2)
and technological aspects (X3) on customer loyalty (Y1) is
calculated with a coefficient of determination (R2) was 0.772.
This suggests a strong influence on the aspect people, process
and technology aspects of the customer loyalty. While
significant levels (measured by probability) give the figure of
0.000. Because the probability is much below 0.05, then the
effect of jointly between the aspect people, the aspect of the
process and technology aspects of the customer loyalty
significantly.
According to the table 2 of the ANOVA test or f test, was
obtained f test was 75.507 with a significant level of 0.000 for
the probability (0.000) is much smaller than 0.05, which means
the aspect people, aspects of the process and technology
aspects have a significant effect on customer loyalty.
From Table 3 illustrates that the regression equation as
follows:
Y1 = 32,125 + 0.303 X1 + 0.182 X2 + 0.201 X3
Description:X1 = Aspect people; X2 = Aspects process; X3
= Aspects of technology; Y1 = Customer loyalty
Constants of 32.125 states that if there is no increase in the
value of the variable aspect people (X1), aspects of the process
(X2) and technological aspects (X3), then the value of
customer loyalty (Y) is 32,125. Regression coefficient for the
aspect people of 0,303menyatakan that each additional score or
the value of the aspect people will provide increased customer
loyalty by 0.303. While the regression coefficient for the
aspects process of 0.189 states that each additional score or
value aspects of the process will provide increased customer
loyalty by 0.189. While the regression coefficient for the
aspects technological of 0.201 states that each additional score
or value aspects of technology will provide improved customer
loyalty amounted to 0.201. When viewed from three aspects,
aspects people, aspects of the process and technology aspects
that most influence on customer loyalty is the aspects people of
the coefficient for 0,303 second sequence is technological
aspects with a coefficient of 0.201 and the third is the aspect of
the process with a coefficient of 0.189.
5. Discussion
In this section we will discuss the research findings are
described in the previous section. The discussion be based on
empirical findings and theories and previous research relevant
to the research conducted. This discussion is intended to
explain the relationship between the independent variables and
the dependent variable. Based on the test using SPSS 16.0 for
Windows through regression analysis, the results of testing the
validity and reliability of the research instrument produces
valid and reliable instrument. Results of this study to answer
that very good regression analysis model to explain the effect
of customer relationship management. That include: aspect
People, process and technology on customer loyalty in the
Brangkal Offset.
By analyzing the effect of customer relationship
management. That include: aspect people, process and
technology on customer loyalty is expected to be able to gain
an understanding of the process through customer relationship
management that management. That include: Aspect people,
process and technology by management and its effect on
customer loyalty in the Brangkal offset. In this research linking
the four variables proposed in the conceptual model. Four of
these variables include: the independent variables people,
process and technology and the dependent variable customer
loyalty. The indicators of the four variables were identified, all
eligible both validity and reliability. In this study discovered
the influence of variables management. That customer
relationship include: Aspect people, process and technology on
customer loyalty in Brangkal Offset as test results of the
regression analysis model. An explanation of the effect of
customer relationship management include aspect people,
process and technology on customer loyalty in Brangkal Offset
is as follows:
Effect of the aspect people of customer loyalty
Through regression analysis found that customer loyalty is
influenced by aspect people. Based on the above test results
obtained that all the indicators used as a measure of the
variable in explaining aspect people, namely: enthusiastic,
knowledge, skills, friendliness and responsiveness of
employees together can be used as a measurement variable
aspect people.
It can be concluded that the results of testing by regression
analysis through SPSS 16.0 shows that the aspect people that
include indicators of enthusiasm, knowledge, skills,
friendliness and responsiveness to spur employees to improve
customer satisfaction and therefore contributes to customer
loyalty. These findings Sedana with Battor (2010) that
developed a close relationship with the customers improve the
company's ability to innovate. Thereby increasing customer
satisfaction and ultimately have an impact on customer loyalty.
These findings are also in accordance with what is presented in
Tjiptono (2003: 95) the creation of customer satisfaction can
provide several benefits, including the relationship between
companies and consumers to be harmonious, provide a good
foundation for repeat purchases and create customer loyalty
and provide recommendations by word of mouth (word-of-
mouth) that benefit enterprises. At the consumers are basically
all the same, regardless of the money they spend, regardless of
the products they buy, they have the right to get the best
service. If they are disappointed, they will not hesitate to vilify
companies in front of many people. Oneindicator of the level
of customer satisfaction is a 'repeat order'. If consumers are
satisfied when buying a product for the first time, they will
come again to buy it at a greater amount than the first order.
Effect of the process aspect of customer loyalty
Based on the results of the regression analysis found that
customer loyalty is influenced by aspects of the process. Based
on the above test results obtained that all the indicators used as
a measure in explaining aspects of the process variables,
namely: identification, differentiation, interaction and
personalization together capable of being used as a
Journal of International Business Research and Marketing
33
measurement variable aspects of the process. It can be
concluded that the test results and regression analysis through
SPSS 16.0 indicates that aspects of the process including
identification, differentiation, interaction and personalization
can be a positive influence on customer loyalty.
These findings show that the results are in line with what is
mentioned by Edward (2010) which states that the process of
analysis, operations, and the ability of both analytical
capabilities, increase profitability, maximize customer
relationships, operational capability, cut in the booking process
can reduce costs so as to increase sales. In terms of processes
and procedures, companies should define more clearly the
target market targeted and procedures in more detail in serving
consumers. It is important that employees who deal directly
with consumers to have clear rules about how to serve their
customers. Moreover, one thing that is how companies connect
between customer satisfaction with employee performance.
That is not only a slogan and jargon, but the customer service
process into a system that must be exercised by all employees.
Effect on the technological aspect of customer loyalty
Based on regression analysis found that customer loyalty is
influenced by technological aspects. Based on the above test
results obtained that all the indicators used as a measure of the
variable in explaining technological aspects, namely: speed,
confidence and satisfaction of use of technology together can
be used as measurement variable technological aspects. It can
be concluded that the test results and regression analysis
through SPSS 16.0 indicates that the aspect of technologies
that includes speed, confidence and satisfaction of the use of
technology can be a positive effect on customer loyalty.
These findings show that the results are in line with what
was mentioned by Prasad (2008) examined the effect of
relational marketing attributes such as trust, commitment,
communication that can affect customer loyalty. With
customer relationship management applications, we can do the
sales and service via the web so the chance of global sales
without the need to provide a special effort to support the sales
and service. Internet enables remote communication and global
nature, so that the company and its customers or potential
customers can interact without being limited by time and place.
Through the utilization of the Internet, a company can reach
consumers on a global scale with limited capital, and conduct
business activities such as promotion, product introduction,
product description, product price to sales transactions that can
be more indulgent products consumers.
In addition the company can perform an analysis of the
customer based on specific criteria such as the analysis can be
generated very diverse based on the data of incoming
information, in the form of questions, complaints, or
suggestions customers often helps the company to improve its
products and services. Can display a warning or reminder as
say happy birthday earlier than the partner or customer contacts
so as to make the customer flattered, this is one example of the
usefulness warming or reminder on the customer
relationshipmanagement system. Not limited only to pamper
the customer, warning, or a reminder can also be used to
remind the customer on certain events, such as due date
product / service specific, it will help facilitate business
activities so that the company can build a relationship that is
directly with the consumer (direct marketing).
Effect of the aspects people, process and technology to the
Customer loyalty
Based on regression analysis found that customer loyalty is
influenced by aspect people, Based on the above test results
obtained that all the indicators used as a measure of the
variable in explaining aspect people, aspects of the process and
technology aspects together can be used as a measurement
variable. It can be concluded that the test results and regression
analysis through SPSS 16.0 shows that the aspect people,
aspects of the process and technology aspects together
influence on customer loyalty.
These findings are in line with Kotler and Keller (2007:
189), Customer relationship management is "the process of
managing detailed information about individual customers and
carefully managing all customer touch points in order to
maximize customer loyalty". Customer relationship
management applications provide information can increase
revenues and profits. Customer relationship management can
do sales and service via the web so the chance of global sales
without the need to provide a special effort to support the sales
and service. Additionally customer relationship management
enables companies to leverage information from all points of
contact with customers, whether it is via web, call center, or
through enabling sales staff or service at a cheaper cost.
Automation of sales and service processes can reduce the risk
of decline in the quality of services and reduce the burden on
cash flow. The use of web technology and a call center, for
example, will reduce red tape and costs as well as
administrative processes that may arise. With sales capability
via the web, then the barriers of time, geography, until the
availability of data sources can be ruled out to accelerate the
sale of such products. This is done by Brangkal Offset
Mojokerto in increasing sales and improving customer loyalty.
6. Conclusion
From the results of research and discussion of the influence
of customer relationship management to customer loyalty can
conclude that customer relationship management is needed by
the company on the grounds: First, it is a change of Production
Driven Company into shape Customer Driven Company. These
changes occur because the customer who will decide which
companies will be chosen based on the company's ability to
meet the needs of its customers. Second, the customer
determines a business organization that must be managed.
Without customers a business may not be able to walk.
Therefore, the customer must be managed properly so that
customers be satisfied, and be loyal to the company. Third, the
customer has a need or preference which different from one
another. Customer relationship management to know that
different customers representing the value of the company are
also different. Thus, the purpose of customer relationship
management is the customer knows best and believes the
company will increase the understanding of their needs as
individuals, to meet their expectations, and make their lives
changed. Fourth, the cost to acquire customers 6 to 7 times
more than the cost to maintain it, meaning that each customer
has its own uniqueness, they have the desire, the demand,
which different capabilities and characteristics, so knowing
who the customer are an obligation. While the last / fifth,
customer relationship management is not just serving, with the
database, the company should be able to serve its customers
better. A good customer relationship management should be
Journal of International Business Research and Marketing
34
able to provide emotional value besides relations benefit to the
customer.
References and notes
1. Bettor, Moustafa, 2010. The impact of customer relationship
management capability on innovation and performance
advantages, Tanta University, Egypt Journal of Marketing
Management Vol. 26, Nos. 9-10, August, 842-857.
http://dx.doi.org/10.1080/02672570903498843
2. Borle, Sharad; Singh, Siddharth S.; Jain, Dipak C. 2008, Customer
Lifetime Value Measurement, Management Science, Vol.54 No.
21, pp. 100-112 http://dx.doi.org/10.1287/mnsc.1070.0746
3. Buchari Alma 2004, Marketing Management and Marketing
Services, Bandung: Alfa beta.
4. Edward CS, 2010. The impact of customer relationship
management through implementation of information systems,
Department of Travel Management, National Kaohsiung
University of Hospitality and Tourism, Taiwan, Republic of China
Total Quality Management Vol. 21, No. 11, November,
1085¬1102
5. Frederick Herzberg 2000, Organizational Behavior, Tenth Edition,
New Jew Jersey: Prentice Hall Griffin, J, 2005, Customer Loyalty,
Lexington Books, New York.
6. Prasad JS and AR Aryasri, 2008. Versus Relationship Marketing
Relationship Quality and Customer Loyalty In Food Retailing,
Pranjanavol 11, No 2,
7. Philip Kotler & Kevin Lane Keller (2007). Marketing
Management. 13th edition. Pearson, Prentice Hall
8. Krasnikov Alexander, Satish Jayachandran, and V. Kumar, 2009.
The Impact of Customer Relationship Management
Implementation on Cost and Profit Efficiencies: Evidence from
the US Commercial Banking Industry, Journal of Marketing Vol.
73 (November), 61-76
9. Luke, Paul Ade, 2001 Seminar Papers: Customer and Partner
Relationship Management, Telematic Research Group.
10. Philip Kotler and Kevin Lane Keller, Interpreting Benjamin
Molan, 2007, Marketing Management, Twelfth Edition, Volume
1, PT. Index.
11. Purnama Chamdan and RahmahMihamida, (2014) Empowering
Small Industry in Improving Business Success, Lap Lambert
Academic Publishing, Saarbrucken Germany
12. Sugiyono 2008, Statistics for Business Research, Bandung:
Alfabeta.
13. Tjiptono Fandy, 2000, Marketing Strategy, Yogyakarta: Andi
Offset.
14. Umar, Husein, 2002, the Marketing Research and Consumer
Behavior, Jakarta: GramediaPustakaUtama.
15. Usmara, A, 2003, the New Strategy Marketing Management,
Amoro Book, Yogyakarta
16. Widjaja Amin Tunggal 2008, Customer Relationship Management
(Concept and Case), Jakarta: Harvarindo
Journal of International Business Research and Marketing
35
Journal of International Business Research and Marketing
Volume 1, Issue 1, November, 2015 journal homepage: www.researchleap.com
The Role of Purchase Tendencies Data in the Transformation of Foreign-Made
Products Consumption in China
CamiloI.Koch R.a
aSchool of Management, Wuhan University of Technology, Wuhan, P.R., 430070, China
1. Introduction
The investigation focuses on discovering the essential
elements that influence Chinese consumers' purchase willingness
of foreign-made products from particular countries available in
the online marketplace, in concern whether the foreignness
condition of a product makes them more or less preferable.
Taobao Online marketplace (―淘宝网” in simplified Chinese
and ―digging for treasure‖ in English) from the People's Republic
of China has been selected as study context to collect Chinese
consumers' purchasing market statistics throughout a year-period.
Subsequently, the investigation' analyses identified three main
relevant effects responsible for Chinese consumers' purchase
willingness towards foreign-made products: (a) country-of-
origin; (b) stereotypes; (c) ethnocentrism. The investigation
reveals Chinese consumers' demand for imported but locally-
customized products is hastily increasing, and it may have direct
implications for multinational corporations' product development
and pricing strategies. Furthermore, the variability in preferences
is associated to individual countries of origin; Chinese consumers
stereotype particular countries, and local producers utilize this
condition to market goods. The development of homemade
duplicated versions generates extensive lists of similar deceitful
products and a confusion state in the consumers' minds ruled by
misperception; influencing Chinese consumers' preferences to
purchase ―disguised homemade versions‖ of products instead of
foreign-made ones due to pricing and capabilities to satisfy local
customers' needs.
Country-of-origin enable consumers to make the purchasing
decision process quicker; utilizing it at times when no other
material cues are available upon which consumers can rely on
shaping opinions to make decisions; in the Chinese market it is
used as main part of the marketing strategy of foreign-made
products sold online. Utilizing country-of-origin as a positive
association creates and reinforce on Chinese consumers a
positive attitude, perception, evaluation, and preference towards
foreign-made products.
2. Literature Summary
Perceptions that consumers hold of products from a particular
country, as well as the feelings towards the people of that
country, contribute to shaping the concept of country stereotype
or geographic origin (Papadopoulos and Heslop, 1993).
Stereotypes are a critical variable in multinational corporations’
development for foreign markets, principally the ones with
unique characteristics from the home-market. Particular studies
propose that products from countries considered culturally
similar to the home country, in contrast to the culturally different,
are preferred (Nagashima, 1970; Crawford and Lamb, 1981;
Wang and Lamb, 1983; Papadopoulos, 1990; Heslop, 1998).
Geographic origin provides an emotional cue for product
quality judgments, but, in addition, has affective and normative
connotation (Verlegh and van Ittersum, 2001). For the basis of
this investigation, a comprehensive review and analysis of the
primary literature on country-of-origin was carried on. Preceding
studies are utilized to develop a hypothetical structure for
AB ST R ACT
2015 Research Leap/Inovatus Services Ltd.
All rights reserved.
This paper introduces the concept of consumption data in the Chinese online marketplace
including the examination of various purchase tendencies' statistics discovered on product
descriptions associated with relevant keywords. The distinction between consumption data and
consumer trends in the Chinese online market is unrelated through acquisitions and time.
Literature introduces the concepts of purchase willingness, stereotyping, and consumer
ethnocentrism; interpreting them regarding consumer tendencies in later sections. Consumption
has grown during the last years in China; demonstrated by the examination of purchase numbers
effected by local consumers through several online platforms. Findings confirm significant
consumer preferences patterns for products correspondent to the most popular categories in the
online marketplace. Based on the discoveries, corporations are provided with recommendations
for utilizing consumer tendencies data as a strategic instrument to improve production based on
local customers' needs, preferences, and trends.
Keywords:
Country-of-Origin
Chinese Consumers
Consumption Tendencies
Product Customization
Journal of International Business Research and Marketing
36
modeling the investigation of the study subject; outlined by those
major factors that have been reported to persuade and influence
the effects in the willingness to purchase process.
2.1. Country-of-Origin
The definition of country-of-origin or made-in label distinctly
describes a relevant process able to influence consumers’
purchase willingness. It corresponds to the location where the
headquarters of the company marketing the product are
(Johansson, Douglas, and Nonaka, 1985). It can mean
manufactured-in (Cattin, 1982), engineered-in, designed-in
(Chao, 1993; Ahmed, d’Astous and d’Almeida, 1995),
assembled-in (Ahmed, 1995), and often ―wanting to look like it
was made-in‖ (Papadopoulos, 1993). Additionally, consumers
hold defined awareness of a country influencing their purchase
willingness (Nagashima, 1970; Bilkey and Nes, 1982; Roth and
Romeo, 1992; Chao, 2005) that can be affected by several
causes; mainly by mass communication and personal experience.
It influences consumers to make fast decisions in two possible
circumstances: when product attributes involve complexity or
when there is a lack of comprehensive information available
(Granzin& Olsen, 1998).
Country-of-origin corresponds in the literature as a crucial
signal that might be used by marketers to persuade, influence, or
modify consumers’ judgment of a brand, product, or service.
Several researchers have formerly studied its consequences on
consumer perceptions, evaluations, and purchase willingness of
products; arguing that country-of-origin of products is not more
than one of the several cues available to consumers. Researchers
also discuss that country-of-origin can not necessarily lead to a
competitive advantage establishing prices, particularly due to the
emergence of hybrid products (Han, 1988; Chao, 1993, 2005)
where each country justify and explain their prices with their
quality arguments. Purchase willingness’ most significant
influence corresponds to consumers’ knowledge of a product’s
country of origin (Bilkey and Nes, 1982).
2.2. Stereotype
Stereotyping is a universal concept. The term was formerly
used by Lippmann in 1922 referring to ―pictures in our heads‖
that we use to apprehend the world (Seiter 1986). Darling and
Kraft (1977), proposed that additional variables such as
experience or reputation might also remain considered when
examining the impact of made-in labels. Cattin’s findings (1982)
supported that consumers sharing similar cultural values tend to
be similar in their evaluations of made-in levels and to
stereotype; the fewer information purchasers know about a firm,
brand, or product, the greater the impact of the country-of-origin
and the more significant stereotypes remain considered for
decisions. Maheswaran’s results (1994) indicated that when
attribute information was unambiguous, both expert and novice
consumers used country-of-origin differently in evaluations;
varying in the processing of common information. Country-of-
Origin stereotypes remain profoundly influenced by
ethnocentrism (Hooley, 1988; Lee, 1992; Stolman, 1992).
The first researcher to conduct country-of-origin studies was
Reierson in 1966; investigating whether or not preconceived
notions consumers have about foreign products are national
stereotypes rather than opinions about products. Study
respondents assessed products the made-in USA higher
indicating evidence of stereotyping.
Schooler in 1965 first examined country-of-origin bias as
influencing product evaluation. Nagashima in 1970 found that
Japanese consumers assessed products made-in-Germany higher,
followed by UK, US, Japan, and France. Nagashima in 1977
reported that images of Japanese, German, and French products
had improved, and UK image had deteriorated, suggesting that
national stereotypes change over time. Gaedeke in 1973 extended
the idea of national stereotyping to products from developing
countries. Gaedeke investigated the opinion of US consumers
towards the quality of imported goods and categories made-in
various developing countries including the USA; US products
meant rated first. Gaedeke concluded that country-of-origin
information did not affect opinions about the quality of branded
products.
2.3. Consumer Ethnocentrism
Consumer Ethnocentrism corresponds to the belief held by
consumers about the appropriateness of purchasing foreign-made
products. Sharma (1995) noted that consumer ethnocentrism
might result in an overestimation of the attributes and overall
quality of home-made products and an underestimation of the
quality of foreign-made products in countries where there is
unfamiliarity with foreign goods and brands (Ettenson, 1988;
Phau and Prednergast, 2000). Consumer’s ethnocentrism and its
consequences in developing countries remain uncertain due to its
early stage of research, but generalities apply; the more
ethnocentric a nation is, the less favorable their consumers’
attitudes are toward imported products (Pan and Lindquist,
1999).
Consumer Ethnocentrism is the belief held by consumers
about the appropriateness of purchasing foreign-made products.
Sharma (1995) noted that consumer ethnocentrism might result in
an overestimation of the characteristics and overall quality of
homemade products and an underestimation of the quality of
foreign-made goods. Consumer ethnocentric tendencies have
become a critical variable influencing consumer attitudes toward
brands (Netemeyes, Durvasula, and Lichtenstein, 1991; Sharma
and Shimp, 1992). Actual country-of-origin research has
demonstrated a tendency of consumers to prefer homemade
products (Han, 1988; Hong and Wyer, 1989; Papadopoulos,
1990). Ethnocentrism has been found to impact consumers’
evaluations of product attributes and purchasing willingness
(Yaprak and Baughn, 1991). Homemade or domestic goods
remain preferred over foreign-made ones in countries where: (a)
consumers have a strong sense of patriotism (Reierson, 1966;
Nagashima, 1970; Baumgartner and Jolibert, 1978); (b) the
domestic economy is threatened by foreign-made goods (Heslop
and Papadopoulos, 1993); (c) there is availability of product
serviceability (Han and Terpstra, 1988); and (d) there is
unfamiliarity with foreign goods and brands (Ettenson, 1988;
Phau and Prednergast, 2000).
3. Data and Methodology
3.1. Data
Data collection was structured as twenty keywords in
simplified Chinese, queried from Taobao online marketplace (i.e.
country names and product names) every Monday and Friday
throughout a year-period time. The query envisioned to study, as
the main objective, the assessment of Chinese online consumers’
beliefs and awareness about country stereotypes and the products
made in those countries. The investigation gathered primary data
for analysis. Principal data classes, for instance ―product
category,‖ ―price,‖ ―purchased quantity,‖ ―product origin,‖ and
―product name,‖ granted the creation of a ―country profile‖ and a
―product profile.‖ After filtering the data, three main variables
Journal of International Business Research and Marketing
37
were studied: ―price,‖ ―quantity,‖ and ―country of origin.‖ The following diagram represents the data filtering criteria.
Figure 1: Simplified schema of first and second query
The first query (figure 1) returned results that were filtered; those
countries with more than fifteen dissimilar products were
considered as ―relevant countries;‖ countries with the highest
relevancy were chosen for the study purposes and were
subsequently queried during a year-period time. In parallel, an
online survey was carried out to acquire the ―ten most known
countries‖ by Chinese consumers (three times: beginning,
middle, and end of the year period); and lastly, variations
between results were evaluated and categorized for meet the
investigation purposes.The first query returned results that were
filtered; those countries with more than fifteen dissimilar
products were considered as ―relevant countries;‖ countries with
the highest relevancy were chosen for the study purposes and
were subsequently queried during a year-period time.
In parallel, an online survey was carried out to acquire the ―ten
most known countries‖ by Chinese consumers (three times:
beginning, middle, and end of the year period); and lastly,
variations between results were evaluated and categorized for
meet the investigation purposes.
Subsequently, to query the selected relevant keywords,
countries with high popularity mean to be sorted by relevance,
results filtered, and the ―top three products‖ from each of them
categorized and analyzed regarding country-of-origin, prices, and
classes; the product with the highest relevancy signified further
consideration.
Figure 2: Simplified schema of keywords search
Results suggestions are taken into consideration and the
concept of product acceptance is, consequently, the result of the
purchase analysis. The experiment was designed to examine the
influence of culture, principally represented by naming strategy
and pricing strategy in the Chinese consumer purchase process,
regarding diverse products’ countries of origin. It considered
observed market data aiming to answer five relevant questions:
(a) what is the role of purchase tendencies data in the future of
product consumption? (b) which countries enjoy higher levels of
purchase willingness by Chinese consumers in the Chinese online
market? (c) are Chinese consumers aware of the respective
country of origin of the most relevant products present in the
online marketplace? (d) which are the well-known products from
the countries with high relevancy? (e) what is the role of the
homemade products and local duplicates regarding the foreign-
made versions of those products?
The central outcome of the experiment consisted in ―survey‖ and
―experiment‖ country ranks. Survey ranks are expressed as the
position of the country awareness obtained from the survey of
one thousand people through an online platform. Oppositely,
experiment ranks are expressed as the position of the country
awareness obtained from the observed market data composed by
twenty countries, three products from each country, and a total of
ninety-six entries corresponding to ninety-six queries. The
following table resumes the observed market data filtered
acquired.
Journal of International Business Research and Marketing
38
Table 1: List of countries sorted by units of products purchased by Chinese consumers
Country of
Origin
Experimen
t Rank
Survey
Rank Brand Name
Product
Category
Product
Origin
Units
Purchase
d
Average
Price
(RMB)
Korea, South 7 3 Nature
Republic* Skin Care China
617509 20
Switzerland 17 17 Binger* Watch China 278941 258
Thailand 8 11 Fibroin* Skin Care China 265991 12
Netherlands 10 12 Friso* Milk China 164018 547
Belgium 16 16 Lila* Cookies China 124347 56
Greece 18 18 Agric* Olive Oil Greece 95328 60
New Zealand 4 10 Baihuami Honey China 83402 85
Germany 6 6 Jiayunsi Candies China 79281 34
Singapore 15 8 Koka* Noodles Singapore 49495 31
Spain 14 14 Layier* Wine Spain 49113 103
Russia 19 19 Kpokaht* Candies China 39774 40
Chile 20 20 Shanghaipudong Cherries Chile 38716 202
U. Kingdom 2 2 Vkweiku Clothes China 30802 59
France 11 9 Guyennoise* Wine France 30117 230
Japan 9 15 Buniben* Cookies Japan 28657 36
United States 1 1 Yishiming Candies China 17221 75
Sweden 13 13 Lelo* Skin Care Sweden 13211 627
Australia 3 4 Botany Bay* Wine Australia 10132 80
Italy 12 7 Hanchang Pasta China 6294 172
Canada 5 5 Baby D Drops* Baby
Care Canada
4405 134
Note: including brand names, categories, countries of origin, and average prices. The examined sample consisted in three countries
from the American continent, five countries from Asia, ten countries from Europe, and two countries from Oceania; sixteen of them
corresponded to developed nations, and four corresponded to developing nations. *English named brands; corresponding to seventy
percent of the total.
3.2. Methodology
After reviewing all previous findings from literature, the study
collects data of Chinese sellers’ marketing approaches of foreign-
made products in the Chinese online marketplace including:
naming strategy, pricing strategy, and precedence strategy. The
study compares literature findings with observed market statistics
intending to validate the general stereotyping phenomena for the
particular case of Chinese consumers in the Chinese online
marketplace intending to discover consumption patterns. The
primary method of data collection consisted of keywords search
performed on the marketplace website; with the purpose of
sampling as much country diversity as possible among statistics
thus, a demonstrative view could be extracted from source.A list
of all countries of the world was utilized to perform search
queries, and countries without relevant results were unobserved;
countries with at least fifteen different results were considered.
One of the fundamental reasons and major aiming of utilizing
observed market data instead of questionnaires as research
methodology was to apply big-statistics to uncover and explore
realism, representativeness, and to skip assumptions (Patton,
1990). Market data collection was carried out with a customized
application in charge of performing queries during a year period
of time with certain recurrences to ensure an illustrative sample
of data and patterns. Besides the market data collection, an online
questionnaire was conducted with one thousand respondents
three times along during the data collection period; the primary
objective of the questionnaire was to seize a rank of the known
countries that Chinese consumers keep in mind.
Figure 3: The figure abridges the multinational corporations approach to the Chinese online marketplace and the decision making
process of Chinese consumers when purchasing
Journal of International Business Research and Marketing
39
Note: the simplified schema underlines product similarity as key factor in the purchase willingness of foreign-made products and
propose a supplementary process of product customization as purchase willingness enhancer for future strategies
Several studies have investigated consumers' purchase
willingness of products from particular countries; considered as
valuable guides for this research. Roth and Romeo (1992)
established that ―desire to acquire a product from a given country
will be high when the country-image is also an important
characteristic of the product category.‖ Johansson (1985)
proposed that ―previous experience with a particular country and
or product category may influence the country of origin effect.‖
Moreover, Han (1989) recognized ―the role of ethnocentrism in
consumers’ willingness to purchase.‖
Figure 4: Simplified schema of the relationship between country-image, purchasing willingness, and local product customization
Note: data analyses demonstrated the existence of duplicated products, with principle based on product similarity endeavoring a
significant early stage of local product customization
4. Findings and Analyses
Using the observed market data collected from the Chinese
online marketplace; the investigation assesses the obtainability,
consumer purchasing decisions, and consumption patterns of
foreign-made and homemade products, revealing strong
stereotyping tendencies; Chinese consumers utilize country-of-
origin to categorize and purchase foreign-made products and
homemade products alike. Country-of-Origin is employed in the
way of stereotypes by Chinese consumers to simplify the
decision-making process by providing a shortcut (Askegaard and
Ger, 1998) when choices can produce misperception. China
imported over $1 trillion worth of goods and services in 2014,
duplicating 2013, and the first half of 2015 have duplicated 2014
(Chinese Ministry of Statistics Bureau); the lives of Chinese
consumers are connected to international markets more intensely
than ever before through online marketplaces. The examined
observed data consisted in a total of twenty countries marked as
relevant; choosing the tree products with the highest foreignness
from each country for further analyzes. The following table
resumes both ranks, contrasted with the various Human
Development Index (HDI), the Human Development Index rank,
country development statuses, and the corresponding continent.
4.1. Summary of Findings
Summary of findings present the research discoveries of the
market observed data analyzed regarding the foreign-made
products available in the Chinese online marketplace and the
correspondence among the variables comprised. The
correspondence between countries of origin, prices, and purchase
quantities firstly summarized as a ―halo construct attitude‖ from
Chinese consumers towards foreign-made products due to the
high correspondence between the survey results and the
experiment results.
Table 2: List of countries ranked by survey and experiment within the Human Developed Index (HDI), developing status, and
belonging continent
Considered
Countries
Survey
Rank
Experimen
t Rank
HDI
Rank
HDI
(2014)
Development
Status Continent
Australia 3 4 2 0.933 Developed Oceania
Belgium 16 16 21 0.881 Developed Europe
Canada 5 5 8 0.902 Developed N. America
Chile 20 20 41 0.822 Developed S. America
France 11 9 20 0.884 Developed Europe
Germany 6 6 6 0.911 Developed Europe
Greece 18 18 29 0.853 Developing Europe
Italy 12 7 26 0.872 Developed Europe
Japan 9 15 17 0.890 Developed Asia
Korea, South* 7 3 15 0.891 Developed Asia
Netherlands 10 12 4 0.915 Developed Europe
Journal of International Business Research and Marketing
40
New Zealand 4 10 7 0.910 Developed Oceania
Russia 19 19 57 0.778 Developed Asia
Singapore 15 8 9 0.901 Developing Asia
Spain 14 14 27 0.869 Developed Europe
Sweden 13 13 12 0.898 Developed Europe
Switzerland 17 17 3 0.917 Developed Europe
Thailand* 8 11 89 0.722 Developing Asia
U. Kingdom 2 2 14 0.892 Developed Europe
United States* 1 1 5 0.914 Developing N. America
Note: Three points of interest are highlighted: South Korea signified the most sold product, Thailand shown the cheapest
product existing, and United States represented the top of mind country*
Examining market observed data allowed the analyzes and
understanding of the premises for each country of origin;
revealing the countries from where Chinese consumers were
more aware and held higher purchase willingness levels during
the defined period of the experiment. Study findings explain
positive stereotyping from Chinese consumers towards eighteen
countries and neutral stereotyping towards two; subsequently
presented by country, product, and price.
4.2. Analyses and Implications
Purchase willingness corresponds to the inclination to pay for
a product; willingness to purchase provides the threshold of
entering the market, which is the previous step before purchasing
(Soler, 2004). The next table presents the countries with high
purchase willingness and the ones with low purchase willingness.
Products with the highest amount of sells, together with the least
quantity of sells, concentrate each, two high prices and two low
prices; situating average rates in both different situations. Results
of the amount and price situation demonstrate that products
concentrating sold large numbers are altogether made-in-China.
Figure 5: Resume of homemade and foreign-made preferred product categories by Chinese consumers in the online marketplace
Pricing strictly relates to the product category: low price for skin care, accumulating the majority of sells with forty-three percent.
Oppositely, high priced imported skin care products concentrate zero point eight percent of sells. Watches, particularly advertised as
foreign-made but homemade, are a product directly associated with conspicuous consumption or consumers’ desire to provide
prominently visible evidence of their ability to afford luxury goods (Piron, 2000), situating Switzerland in the second place of products
sold. Milk is directly associated with baby care category, situating Netherlands in the favorite position with homemade baby powder
formula. Wine and pasta are two available product categories in the Chinese marketplace, with high adoption of products from Spain,
France, Australia, and Italy.
Table 3: List of countries and the respective product category stereotyped, their country of origin, units of products purchased by
consumers, and the product average price
Country of
Origin
Product or
Category
Product
Origin
Units
Sold
Average
Price
Country of
Origin
Product
or
Category
Product
Origin
Units
Sold
Average
Price
Korea, S.* Skin Care China* 617509 20 Russia Candies China 39774 40
Switzerland Watch China* 278941 258* Chile Cherries Chile 38716 202
Thailand* Skin Care China* 265991 12 U. Kingdom Clothes China 30802 59
Netherlands Milk China* 164018 547* France Wine France 30117 230
Belgium Cookies China* 124347 56 Japan Cookies Japan 28657 36
Greece Olive Oil Greece 95328 60 U. States Candies China 17221 75
New Zealand Honey China* 83402 85 Sweden Skin Care Sweden 13211 627*
Germany Candies China 79281 34 Australia Wine* Australia 10132 80
Singapore Noodles Singapore 49495 31 Italy Pasta China* 6294 172
Spain Wine Spain 49113
103 Canada* Baby
Care
Canada 4405 134
Journal of International Business Research and Marketing
41
Amidst consumer’s survey done first, answers matched between
study and experiment in ninety percent of the countries. The
results from this table demonstrate that Chinese consumers hold
certain stereotypes from countries —in some cases, without have
purchased a foreign-made product from any of the affected
countries, validating the previous research describing that
Chinese consumers tend to perceive imported products as
superior to domestic (Wang, 2000). The next table displays the
counterparts between survey and experiment.
Table 4: Categories matching: consumer’s stereotypes (from survey) from different countries of origin highly coincide with the
products available in the Chinese online marketplace (experiment)
Country of
Origin
Category
in Survey
Category
in
Experimen
t
Country of
Origin
Category in
Survey
Category in
Experiment
Korea, South Skin Care Skin Care Russia Chocolate Candies
Switzerland Watch Watch Chile Cherries Cherries
Thailand Skin Care Skin Care U. Kingdom Clothes Clothes
Netherlands Milk Milk France Wine Wine
Belgium Chocolate Cookies Japan Candies Cookies
Greece Oil Olive Oil United States Candies Candies
New Zealand Honey Honey Sweden Skin Care Skin Care
Germany Candies Candies Australia Wine Wine
Singapore Snack Noodles Italy Pasta Pasta
Spain Wine Wine Canada Baby Care Baby Care
The overview of conspicuous consumption (Piron, 2000) is
essential to understand Chinese consumers’ behavior when
preferring homemade products. Driven by a desire to impress
others with their ability to pay exceptionally high prices for
prestige products (Yang, 1981; Wong and Ahuvia, 1998),
conspicuous consumers are inspired by the group rather than the
monetary or physiological usefulness of products (Mason, 1981).
Chinese consumers with resilient conspicuous consumption may
have higher intentions to purchase foreign-made products from
developed countries. Oppositely, ethnocentric Chinese
consumers may have higher aims to acquire homemade products.
Conspicuous consumption counteracts ethnocentrism (Ger,
1993).
Five countries dominate the foreign-made products market in
the online marketplace having no significant similarities with the
homemade products available. Suggested is that, the more
important the country is, the higher the level of willingness to
purchase consumers will have. Contrary, the more geographically
far is the country, the less relevance it holds. Significantly,
Chinese consumers think about the United States as the important
country, but do not purchase products from United States.
Chinese consumers have most favorable beliefs and purchase
willingness in products from (in descending order) South Korea,
Switzerland, Thailand, Netherlands, and Germany. From the top
three countries with more sold products, South Korea, and
Thailand holds higher Purchase Willingness due to their cultural
similarities and geographical proximity. Chinese consumers have
a preference for categories relevant to their cultural background;
skin care with precedence from (in descending order) South
Korea, Thailand, and Germany. Baby care (milk powder) (in
descending order) with precedence from Netherlands, Germany,
and Canada. Moreover, food with precedence from (in
descending order) Germany, Singapore, and Sweden; prices and
Chinese consumers’ preferences are positively implicated.
Switzerland has the highest price and most sold products while
Thailand has the lowest price and most marketed products.
Products from Sweden held the highest rate (RMB627, skin care
category) and products from Thailand, held the lowest price by
(RMB12, skin care category).
5. Conclusions and Future Research
Globalization offers challenges and opportunities for
international traders, and new policies provide Chinese
consumers more foreign-made product choices than ever before.
Nonetheless, Chinese consumer attitudes toward products made-
in foreign countries have not been of interest to consumer
behavior researchers at all. Commonly, consumers have a general
preference for domestic over foreign goods, particularly when
they lack information about the product (Bilkey and Nes, 1982;
Wall and Heslop, 1986, 1989). With the purpose of risk-
decreasing bias concerning products made-in developing
countries and a nationalistic bias against foreign-made products
(Bilkey and Nes, 1982). The tendency of Chinese consumers to
be ethnocentric represents their beliefs about the suitability and
ethical rightfulness of purchasing foreign-made products (Shimp
and Sharma, 1987); preferring homemade goods because of the
beliefs that products from their country are the best (Klein,
1998). Furthermore, consumers in developed countries tend to
perceive domestic products as being of higher quality than
imported goods (Morganosky and Lazarde, 1987; Damanpour,
1993; Eliott and Cameron, 1994). Although the opposite is exact
for consumers in developing countries (Bow and Ford, 1993;
Sklair, 1994; Wang, 2000). Chinese consumers have high
purchase willingness levels toward products, which come from
developed nations, even when products are homemade versions
branded as foreign-made products as discovered. Previous studies
have suggested a complete correspondence between the
evaluations of domestic products and a country’s level of
economic development (Gaedeke, 1973; Wang and Lamb, 1983).
The findings of this study have revealed several implications for
marketing experts and global corporation strategists.
Journal of International Business Research and Marketing
42
5.1. Conclusions
Consumers’ evaluation of the quality of homemade and
foreign-made products will influence their purchase preferences,
and the impact of ethnocentrism on purchase willingness will be
different between purchasers from developing and developed
countries, particularly when the products stand related to
conspicuous consumption and developing countries as China.
Consumers in developing countries frequently regard foreign-
made products as status symbols (Mason, 1981; Ger, 1993;
Alden, 1999; Batra, 2000). Perceived product quality and
significant benefits that Chinese consumers acquire from foreign-
made products neutralize the influence of their ethnocentrism. It
is noteworthy that fifty-five percent of the countries studied were
part of the marketing strategy of homemade products. With
particular emphasis on the detail that the products marketed using
South Korea, Switzerland, Thailand, Netherlands, and Belgium
(accumulating seventy-two percent of all the studied countries’
sells), were homemade products conforming the top five
countries in the studied list.
Consumers are believed to make decisions about the quality
of products by a process of acquisition, evaluation, and
integration of informational stimuli or signals, which can be
inherent or extraneous (Rao and Monroe, 1989). When essential
signals cannot be easily assessed ( a particular case of the online
marketplace; consumers cannot touch or taste before purchase),
consumers make greater reliance on extrinsic cues. This is
particularly certain for low-involvement products since the cost
of evaluating introduce signals that may significantly outweigh
the benefit (Zeithaml, 1988). External cues, particularly price and
brand names, were discovered as being critical factors used in the
evaluation of foreign-made products by Chinese consumers; it is
entirely consistent with the literature assessment carried out. Top
sold products share common characteristics in their prices,
names, and countries of origin. These shared characteristics act as
the basis for future development of an integrative theory
regarding how Chinese consumers use country-image
information in forming stereotypes and in the purchase
willingness of foreign-made products.
Figure 6: Key factors for integrative theory: stereotyping data tendencies from Chinese consumers is proposed to be utilized to estimate
manufacture and produce customized products on-demand
Evidence supports consumers’ demonstration of willingness
to acquire at premium prices for manufactured goods from
developed countries (Wang and Lamb, 1983; Hulland, Todino,
and Lecraw, 1996). Such as skin care products from Sweden,
milk from Netherlands, cherries from Chile, wine from France
and Spain, and baby care products from Canada. These foreign-
made products represent forty-five percent of the entire countries
from the studied list. Consumers demonstrated unintentional
inclination for homemade products (Bilkey and Nes, 1982; Hong
and Wyer, 1989; Samiee, 1994), particularly when homemade
products do not have better quality or price (Gaedeke, 1973;
Darling and Kraft, 1977; Wall, 1986). In this case, sixty-five
percent of products from the studied list of countries were
homemade versions of products that utilized country-of-origin
for marketing purposes, such as skin care products from South
Korea and Thailand, cookies from Belgium, honey from New
Zealand, clothes from the United Kingdom, and pasta from Italy.
Chinese consumers prefer foreign brand names and
international product names (see table 3.1), but at the same time,
they do choose homemade products over foreign-made ones for
certain product categories (particularly low involvement
products) deliberately and not deliberately; with high confidence
concerning quality. Regarding their understanding of brands
origin, Chinese consumers’ ethnocentrism and stereotyping can
be pondered as substantial keys able to guide multinational
corporations’ efforts on how to produce and sell in the Chinese
online and offline marketplace, and how to utilize data tendencies
for transform production of foreign-made products. Marketing
specialists, subsequently, cannot treat country-of-origin as a self-
contained general marketing communicational plan. Furthermore,
experts need to deliberate the effects and interconnectedness of
other beliefs and purchase intentions influences of Chinese
consumers.
This study assesses the relevance of extrinsic cues (price,
name, origin, and brand) in Chinese consumer purchasing
decisions since end customers became involved in the research
and indirectly assess the relevance of intrinsic cues. The results
imply that Chinese consumers consider country-image as a high
overall high-level signal when purchasing. It seems clear that in
particular product categories, the name of individual countries
has become inextricably associated with a perception of ―best
quality‖ for specific products from that country or products that
market under the label of made-in that country.
Journal of International Business Research and Marketing
43
Figure 7: The interaction among effects is showed in the next figure; it explains how Chinese consumers choose if a foreign-made
product and a homemade product is eligible to be purchased (in both cases country-of-origin is utilized for marketing)
5.2. Future Research
The impact of country-image on Chinese consumers’ attitudes
and particularly on Chinese consumers’ willingness to purchase
foreign-made products is evident; country-image enables
consumers to make the purchase decision process quicker.
Country-of-origin is in times when no other tangible cues are
available upon which purchasers can rely on forming attitudes to
make decisions, but in the Chinese market (online as examined),
it is utilized as the primary part of the marketing strategy of every
foreign-made product sold. When selling foreign-made products
in the Chinese market, using country-image as an active
association with the country-of-origin creates on Chinese
consumers a positive evaluation and preference towards the most
foreign-made product.Turns relevant to understand the role and
importance of country-image, therefore, country-of-origin on
Chinese consumers’ purchase willingness. It is significant due to
the improvements that can be effected on the strategies of
multinational corporations when entering the Chinese market for
craft competitive advantages with the intention of transforming
Chinese consumers’ consumption over other multinationals and
local Chinese companies.
The present investigation acts as a first step in the
understanding of how corporations can utilize the analyzes of
consumption tendencies to manufacture in a customized way. It
is plausible as next step to examine the aspects of how product-
country images affect consumers’ attitudes regarding particular
products, considering its implications and intend to reconcile the
lack of cultural understanding from multinational corporations.
Future research should study the characteristics of products and
enable predictions patterns for product design and strategies
development, contributing to consumption transformation.
5.3. Questions
Research questions are a way to explore the problems that
arise through the development of the investigation and they are
relevant to suggest investigation continuity. The following
questions are the most relevant for the development of future
investigation founded on this basis research on Country-of-
Origin and Chinese consumers’ purchase willingness: (a) Can
purchase willingness be managed and manipulated? (b) Are
beliefs of country-of-origin reversible or irreversible? (c) Does
the product duplicates stimulate the purchase willingness of
products from particular countries? The role of tendency data is
unblemished, it can assure consumption transformation; it is turn
of multinational and local corporations to understand the
importance of statistics when developing new products and
strategies.
Acknowledgments
This investigation was conducted due to the curiosity of the
author about the Chinese consumer behaviors and the genuine
interest observed in Chinese consumers’ attitudes toward foreign-
made products and foreign societies and individuals: Chinese
consumers tend to identify foreign-made products as superior to
domestic (Wang, 2000). The emphasis on social reputation as
part of Chinese cultural values makes the environment idyllic to
examine the effects of conspicuous consumption based on the
country of origin of products.
References
1. Bilkey, Warren J. and Nes, Erik, Country-of-Origin Effects on Product
Evaluations (March 1982). Journal of International Business Studies,
Vol. 13, Issue 1, pp. 89-100, 1982. http://dx.doi.org/10.1057/palgrave.jibs.8490539
2. C. Min Han (1994), "Assessing the Roles of Cognitions, Country of
Origin, Consumer Patriotism, and Familiarity in Consumer Attitudes Toward Foreign Brands", in AP - Asia Pacific Advances in
Consumer Research Volume 1, eds. Joseph A. Cote and SiewMeng
Leong, Provo, UT: Association for Consumer Research, Pages: 103-108.
3. Cattin, Philippe J., Alain J. P. Jolibert& Colleen Lohnes, 1982. A
cross-cultural study of "made in" concepts. Journal of International Business Studies, 13 (3): 131-41.
http://dx.doi.org/10.1057/palgrave.jibs.8490564
4. Cordell, V.V. (1993), "Interaction effects of country of origin with branding, price and perceived performance risk", Journal of
International Consumer Marketing, Vol. 5 No. 2, pp. 5-18.
http://dx.doi.org/10.1300/J046v05n02_02 5. Erickson, Gary M., Johny K. Johansson & Paul Chao. 1984. Image
variables in multi-attribute product evaluations: Country-of-origin
effects. Journal of Consumer Research, 11 (September): 694-99. http://dx.doi.org/10.1086/209005
6. Francis Piron, (2000) "Consumers' perceptions of the country‐of‐origin effect on purchasing intentions of (in)conspicuous products", Journal
of Consumer Marketing, Vol. 17 Iss: 4, pp.308 – 321. http://dx.doi.org/10.1108/07363760010335330
7. Gaedeke, Ralph (1973), "Consumer Attitudes Towards Products 'Made
In' Developing Countries," Journal of Re ailing, 49 (Summer), 13-24.
8. Han, C.M. (1989), "Country image: halo or summary construct?",
Journal of Marketing Research, Vol. 26 No. 2, pp. 222-9. http://dx.doi.org/10.2307/3172608
9. Hong, S.T. and Wyer, R.S. (1989), "Effects of country-of-origin and
product attribute information on product evaluation: an information processing perspective", Journal of Consumer Research, Vol. 16,
September, pp. 175-87. http://dx.doi.org/10.1086/209206
10. Keith, Dinnie (2003). Country-of-Origin 1965-2004: A Literature Review. Journal of Customer Behavior.
11. Klein, J. G., Ettenson, R., & Morris, M. D. 1998. The Animosity
Model of Foreign Product Purchase: An Empirical Test in the People's Republic of China. Journal of Marketing, 62(1): 89-100.
http://dx.doi.org/10.2307/1251805
12. Laroche, M., Papadopoulos, N., Heslop, L. and Bergeron, J. (2002), "Effects of sub-cultural differences on country and product
evaluations", Journal of Consumer Behavior, Vol. 2 No. 3, pp. 232-
47. http://dx.doi.org/10.1002/cb.104 13. Laroche, M., Papadopoulos, N., Heslop, L.A. and Mourali, M.
(2005), "The influence of country image structure on consumer
evaluation of foreign products", International Marketing Review, Vol. 22 No. 1, pp. 96-115.
http://dx.doi.org/10.1108/02651330510581190
Journal of International Business Research and Marketing
44
14. Loureiro, M.L., Umberger, W.J., 2003. Estimating consumer willingness to pay for country-of-origin labeling. Journal of
Agricultural and Resource Economics 28, 287–301.
15. Nagashima (1970). "Comparison of Japanese and US attitudes toward foreign products". Journal of Marketing, Vol. 31, No. 1. pp. 68–74.
http://dx.doi.org/10.2307/1250298
16. Narver, J.C. &Sleiter, S.F. The Effect of a Marker Orientation on Business Profitability, Journal of Marketing, 2002,10(2):20~35
17. Papadopoulos, N. (1993), "What product and country images are and
are not", in Papadopoulos, N. and Heslop, L.A. (Eds), ProductCountry Image: Impact and Role in International Marketing,
International Business Press, New York, NY, pp. 3-38.
18. Peterson, R.A. and Jolibert, A. (1995), "A meta analysis of countryof-origin effects", Journal of International Business Studies, Vol. 26
No. 4, pp. 883-900. http://dx.doi.org/10.1057/palgrave.jibs.8490824
19. Roth, Martin S. & Jean B. Romeo, 1992. Matching product category and country image perceptions: A framework for managing
countryof-origin effects. Journal of International Business Studies,
23 (3): 477-97. http://dx.doi.org/10.1057/palgrave.jibs.8490276 20. Shimp, T. A. & Sharma, S. 1987. Consumer Ethnocentrism:
Construction and Validation of the CETSCALE. Journal of
Marketing Research, 24(3): 280-289. http://dx.doi.org/10.2307/3151638
21. Ying Wang (2000), "Chinese Luxury Consumers: Motivation,
Attitude and Behavior", Journal of Promotion Management, 17:345–359, 2011.
22. Zhang, Yong. 1996. Chinese consumers' evaluation of foreign
products: the influence of culture, product types and product presentation format. European Journal of Marketing, 30 (12), 50-68.
http://dx.doi.org/10.1108/03090569610153309
23. Zhou, Lianxi and Michael K. Hui., 2003. Symbolic value of foreign products in the People's Republic of China. Journal of International
Marketing, 11 (2), 36-58
http://dx.doi.org/10.1509/jimk.11.2.36.20163