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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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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


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.


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


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


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.


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.


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.


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.


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.


16



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


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.


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


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


H3: There is significant positive relationship between the SCE


H4: There is significant positive relationship between the CEE


3. Research methodology

3.1 Sample and data collection

The sample of the present study consists of 31 banks and is

based on secondary data collected from annual report of the

mentioned banks .Banks were selected on the basis of availability

of information necessary for conducting the study and the

readiness of Annual Reports for the financial year 2010-2013.

Hence the applied sampling procedure could be defined as

convenience sampling. Data was collected from the annual

reports of the banks consistent with other related studies (Goh,

2005;Mavridis, 2005; Tan et al., 2007;Joshi et al., 2010; Joshi et

al., 2013;Lipunga,2014).

3.2. Variables and empirical models

Firm Performance = f (Intellectual Capital)

Or

FP it = β 0 + β 1 IC it + µ

Where,

FP = Firm performance

IC = Intellectual Capital

The regression model used

ROA= α + β1 VAIC+ ε (1)

ROA= α+β1HCE+ β2SCE+ β3CEE+ ε (2)

VAIC TM

Method

Although the measurement of intellectual capital is still a

debatable issue, numerous methods have been developed to

measure it. In this study, the Value Added Intellectual Capital

(VAICTM

) method, developed by Public (1997, 1998, 2001,

2002a, 2002b, 2004), was used.

VAICTM

method is formulated as follows:

Equation (1) formalizes the VAICTM

VAIC=HCE+SCE+CEE

where:

VAICTM

= value added intellectual coefficient for bank i,

CEE = capital employed efficiency coefficient for bank i,

HCE = human capital efficiency coefficient for bank i,

SCE = structural capital efficiency for company i.

The first step is calculating CEE, HCE and SCE. These three

components of VAIC are calculated as follows:

HCE = VA/ HC

SCE = SC/ VA

CEE = VA/ CE

Where

VA = Value added

HC = Human capital

SC = Structural capital

CE = Capital employed

The above variables of the model are calculated by following

procedure:

VA=OUTPUT-INPUT

Output it is the total income generated by the firm from all

products and services sold during the period t, and Input it

represents all the expenses incurred by the firm during the period

t except cost of labor, tax, interest, dividends and depreciation.

Although there are many ways to measure the performance of

intellectual capital such as market value asset turnover employee

productivity and Return on equity but for this study the ROA is

picked as compared to ROE the ROA variable does take financial

risk of banks into consideration.

Return on Asset (ROA)

Return on Asset is a profitability ratio that measures the

firm’s ability to generate profit using its asset. The greater the

ROA, a firm is more efficiency in using its assets. This is one of

the commonly used ratios to measure firm’s financial

performance, which is calculated by ROA

Return on Asset= Net Income /Total Asset

4. Findings and Discussion

The data collected has been analyzed using different statistical

tests. First of all descriptive statistics relating to the variables of

the study are presented. After that correlation analysis if provided

Human Capital Efficiency

(HCE)

Structural Capital Efficiency

(SCE)

Capital Employed Efficiency

(CEE)

Financial performance

(ROA)

Intellectual Capital

(VAIC)


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.


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


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%.


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


b. Predictors: (Constant), SCE, HCE, CEE

The table 8 above shows Human capital and capital employed

they are significant and positively with financial performance but

the structural capital is not significant and is negatively influence

with financial performance this may be because bank may fail to

utilize full their structural capital. That leads us to accept our

hypothesis H2and H4 and reject hypothesis H3







This can be summarized in table below:

Table 8: Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) -.037 .005 -6.873 .000

HCE .007 .002 .300 4.567 .000

CEE .796 .092 .586 8.614 .000

SCE -.001 .002 -.039 -.598 .551


The table 8 above shows Human capital and capital employed

they are significant and positive with the financial performance,

but the structural capital is not significant and is negatively

influenced with financial performance this may be due to bank

may fail to utilize fully their structural capital. That leads us to

accept our hypothesis H2and H4 and reject hypothesis H3


and financial performance of banks.





The results were summarized in table below

Table 9: Results summary

Model Hypothesis Relation Expected sign Results Accept/Reject

Financial

Performance 1

H1 VAIC/ROA + + Accept

Financial

performance 2

H2 HCE/ROA + + Accept

H3 SCE/ROA + - Reject

H4 CEE/ROA + + Accept

5. Conclusion

The present study attempted to investigate the relationship between intellectual capital (IC), and financial performance of the banks operating in Tanzania. The methodology adopted is the one of “Value Added Intellectual Coefficient” (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.


23


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24



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



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


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%


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.


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.


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.




28



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



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


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).


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


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.


Y1 = 32,125 + 0.189 X2

Description:X2 = Aspects process; Y1 = Customer loyalty


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.


Y1 = 32,125 + 0.201 X3

Description:X3 = Aspects of technology; Y = Customer

loyalty


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,


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


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


34

able to provide emotional value besides relations benefit to the

customer.


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35



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



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


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


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.


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


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


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


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.


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.


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.

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