march, 2004 volume - i - pdfs.semanticscholar.org · the ximb journal of management is a forum for...

MARCH, 2004 VOLUME - I

EDITOR : NINA JACOB,PROFESSOR, XAVIER INSTITUTE OFMANAGEMENT, BHUBANESWAR, INDIA

EDITORIAL BOARD : SHARON SUTHERLAND,PROFESSOR, SCHOOL OF PUBLIC STUDIES,QUEEN’S UNIVERSITY, CANADA

PAULINE GOODWIN,PUBLISHING DIRECTOR,KOGAN-PAGE PUBLISHING HOUSE, LONDON,UNITED KINGDOM

BRAIN O’CONNOR LEGGETT,PROFESSOR, IESE BUSINESS SCHOOL,BARCELONA, SPAIN

DANIEL OTTO ABERER,PROFESSOR, KS GRADUTE BUSINESS SCHOOL,KS KADERSCHULE. ST. GALLEN. SWITZERLAND

PRADIP KHANDWALLA,FORMER LARSEN & TOUBRO CHAIR PROFESSOR,INDIAN INSTITUTE OF MANAGEMENT,AHMEDABAD, INDIA

EDITORIAL

“The owl of Minerva,” Hegel wrote in his Philosophy of Right “only takesflight at dusk.” The number of interpretations given this aphorism is legionand should be known to all scholars of management. The aphorism alsoconnotes that a seeker of wisdom is circumspect about what he/she presentsas the truth. A sagacious owl is the harbinger of accuracy, and validity, andbrings, a deeper understanding of phenomena.

The XIMB Journal of Management is a forum for seekers of managerialwisdom, to document and throw light on management phenomena. Thered thread that connects all lead articles is empirical research. In otherwords, what is championed is a relentless quest for data to either prove orrefute a stated proposition.

This Journal attempts to distinguish itself, by encouraging a philosophicalcommitment to knowledge-seeking. The knowledge-seeking process itselfcan assume any shape or proportion. There are available today a multitudeof methodological approaches for researching management phenomena.Let a thousand different flowers bloom.

The Journal ultimately hopes for a community of practice, where researchshared becomes research doubled. The focus is on an incremental build-upof facts. Empirical evidence can be both suggestive and conclusive. Theformer type is more common and constitutes the lifeblood of researchendeavours. This is the type more likely to be found in this Journal.

The field of management is an eclectic and copious one. However, thisfield is still evolving. There is therefore scope for a consolidation of researchefforts, followed by the field’s continued development as a science. TheXIMB Journal of Management is founded on the premise that managementresearch begets more management research, as management paradigmskeep getting re-defined. This is an intrinsic property of the field ofmanagement. And all those owls who sagely hoot that the ‘only constant inthe world of management is change’ may like to chronicle, describe, andexplain this phenomenon.

And so may we contribute as much to the existing body of research inmanagement, as we take from it.

LEAD ARTICLESEXPLORING CUSTOMER PREFERENCE FOR LIFE INSURANCE IN INDIA- FACTOR ANALYSIS METHODSathya Swaroop Debasish 7

AN ALTERNATIVE PREDICTING MODEL FOR

CORPORATE MERGERS & ACQUISITIONS

P.K. Panigrahi 16

SEX DIFFERENCES AND SIMILARITIES ON THE FACETS OF INDIVIDUALISM ANDCOLLECTIVISM: A STUDY ON M.B.A. STUDENTSG.Suryanarayana Reddy & P.Govinda Reddy 26

DOES INVOLVEMENT ACT DIRECTLY ON ADVERTISING ATTITUDE FORMATION ? :CONCEPTUAL ISSUES AND EMPIRICAL INVESTIGATION IN THE INDIAN CONTEXTHimadri Roy Chudhuri 33

NOTES AND COMMENTS

BUILDING A LIVE BRAND FROM WITHINMukta Kamplikar & Shambhavi Sharma 43

IS THE HYSTERIA ON JOB MIGRATION TO DEVELOPING COUNTRIES MISPLACED? 51Anurag Dhanwantri

SIGNIFICANCE OF FLEXIBLE NPD STRATEGIES FOR MARKETING SUCCESSSaji K.B. 54

BOOK REVIEWWHY SMART EXECUTIVES FAIL AND WHAT YOU CAN LEARN 63FROM THEIR MISTAKESV. J. Rao

CONTENTS

VVVVVILAKSHAN

XIMB JOURNAL

OF MANAGEMENT

LEAD ARTICLES

6

VILAKSHAN - XIMB JOURNAL OF MANAGEMENT

VOL. 1 NO. 1, MARCH 2004

7



EXPLORING CUSTOMER PREFERENCEFOR LIFE INSURANCE IN INDIA- FACTOR ANALYSIS METHOD

Sathya Swaroop Debasish !

Abstract

With privatization of Insurance in India, the insurance industry is slowlybecoming cluttered with numerous private joint ventures trying to woothe Indian consumers with well –designed products and benefits. Thispaper makes an attempt at identifying the key factors responsible forcustomer preference for Life insurance products in India. Using thetechnique of Factor Analysis, this study identifies five major factors:Risk-Return Factor, Promotional Factor, Service Quality Factor, ConsumerExpectation Factor and Core Product Factor in the order of preference.A prudent product design, by adding the features expected by investorsand spelt out in this research will make the new Life Insurance Productmore attractive for investors.

INTRODUCTION

Insurance is over Rs.400 billion businessin India, and together with bankingservices adds to about 7 percent ofIndia’s Gross domestic product (GDP). Afundamental characteristic of insuranceis the transfer of r isks from anindividual (the insured) to a group (theinsurer). The insurer then reimbursesthe insured for “covered” losses i.e.,those losses it pays for under the policyterms. Life Insurance is a contract forpayment of a sum of money to theperson assured (or failing him/her, tothe person entitled to receive the same)on the happening of the event insuredagainst. The Commission on InsuranceTerminology of the American Risk and

! FacultyNirma Instituute of Management,Ahmedabad.

Insurance Association has definedinsurance as follows:

“Insurance is the pooling offortuitous losses by transfer of suchrisk to insurers, who agree toindemnify insured for such losses, toprovide other pecuniary benefits ontheir occurrence, or to render servicesconnected with the risk”.

The Confederation of Indian Industry(CII) has projected the growth of lifeinsurance premium from Rs.350 billionat present to Rs.1400 billion by year2009. Today the Indian insurance sectorhas transformed into a buyer’s market,where the customer has the choice toselect from a variety of products,services and service providers.Generally, investors (customers) do notevaluate all possible product attributeswhile making a choice. Their preference

8



is guided by a set of “key attributes /factors” attached to the insurancescheme.Tracking these features for lifeinsurance products is the fundamentalobjective of the study. An attempt is alsomade to find out the important Life-Insurance product attributes that areessential in influencing the purchasedecision of investors.

INDIAN INSURANCE – AN OVERVIEW

Insurance is over one and one-halfcenturies old in India. The first generalinsurance company Title InsuranceCompany Ltd. was established in 1850.Life Insurance comes in its present formto India from the U.K. in 1880 with theestablishment of the Oriental LifeAssurance Company in Calcutta. TheIndian Life Assurance Companies Act1912 was the first statutory measure toregulate the life insurance business inIndia. By 1938, the Insurance marketwas buzzing with 176 companies–bothlife and non-life. In 1938, the earlierlegislation was consolidated andamended by the Insurance Act, 1938 withcomprehensive provisions for detailedand effective control over Insurance.Consequently, the life insurance industrywas nationalized under the Life

Insurance Corporation (LIC) of India in1972.

Table 1 shows that Life Insurance fundsconstitute approximately 10-12 percentof gross household savings in financialassets in India, and a little more than 1percent of gross domestic product.

While effecting reforms in the bankingsector and capital markets during the1990s the Government of India (GOI)also recognized the importance ofInsurance as an important part of theoverall financial system where it wasnecessary to undertake similar reformmeasures. In April 1993, the GOIappointed a Committee on Reforms inthe Insurance Sector (the MalhotraCommittee). The Committee, whichsubmitted its report in January 1994,recommended that the Insurancebusiness in India be opened up to privateplayers and laid down several guidelinesfor managing the transition.

Subsequently, in pursuant to theannouncement made by the UnionFinance Minister in his Budget Speechof 1998-99 the Insurance Regulatory &Development Authority (IRDA) Bill 1999was passed by both Houses of Parliament.On March 16, 1999, the Indian Cabinet

Table-1. : Life Insurance, Household Savings & GDP

Year Life Insurance As % OF Life Insurance As % OF Grosssavings in Financial Assets Domestic Product (GDP)

1980-81 7.6 0.71985-86 7.0 0.71990-91 9.5 1.01994-95 8.1 1.21996-97 10.3 1.22002-03 11.8 1.3

9



Approved an Insurance RegulatoryAuthority (IRA) Bill that was designed toliberalize the Insurance sector. The Actprovides for the establishment of astatutory IRDA to protect the Insuranceinterests policy holders and to regulate,promote and ensure orderly growth ofthe Insurance industry. The IRDA Act wasformed by an Act of Parliament on April19, 2000. The IRDA Act also seeks toamend the Life Insurance Act, 1956, theGeneral Insurance Business(Nationalization) Act 1972, and theconsequential provisions in theInsurance Act, 1938 with a view toseizing the exclusive privilege of LIC &GIC in the life and non-life businessesrespectively. With the regulators(e.g.,IRDA) giving licenses to manyprivate players several companies haveentered into the Insurance market andthe competition will be intense in future.(see Table-2)

According to Malhotra Committee Reportonly 22 % of the insurable population hasbeen tapped indicating low marketpenetration, and thus there is immensepotential of insurance business in India.Moreover, with new private jointventures coming into existence, thegoing will be tough, but surely rewardingfor the winners who can accurately judgeconsumer’s expectation and preferencein designing Life insurance schemes.Keeping all these facts in mind, ourstudy is relevant in the Indian Context.

SCOPE AND DESIGN OF THE STUDY

The study is explorative in nature andfocuses basically on primary data aboutcustomer preference for life insurancein the Northern Region of India. Thedata has been collected throughstructured questionnaire surveyconducted through 45 designated

Table-2: Joint Venture Insurance Business in India

Indian Partner Foreign Insurer SpecializationAditya Birla Group Sun Life, Canada Life InsuranceBajaj Auto Allianz Life & Non Life InsuranceDabur CGNU Life, UK Life InsuranceHDFC Standard Life, UK Life InsuranceICICI Prudential, UK Life InsuranceICICI Lombard, Canada Non – Life InsuranceIFFCO Tokio Marine, Japan Non – Life InsuranceKotak Mahindra Old Mutual,S. Africa Life InsuranceMax India New York Life, USA Life InsuranceNABARD GECapital Services, USA Life InsuranceSanmar Group AMP, Australia Life InsuranceSBI Cardiff, France Life InsuranceSundaram Royal & Sun Alliance Plc, UK Non – Life InsuranceTata Group AIG,USA Life & Non Life Insurance

10



insurance agents of various l ifeinsurance companies i.e., 12 agents ofLIC, 9 of ICICI prudential, 7 belongingeach to HDFC standard and ING Vyasa,and 10 agents of Allianz Bajaj. The studyis based on non-probability, convenientsampling held during the period coveringJuly 2002 to March 2003, and 600 filledresponses were obtained from customers(possessing life insurance schemes),across 5 states in North India. Table-3provides the details of the geographicsample distribution.

The opinion of 600 customers on 20variables/reasons for preference of lifeinsurance (second column of Table-4)were measured on a five-point scale(Likert Scale) ranging from “1” (Leastimportant) to “5” (most important)depending on the importance attachedto each reason. These variables havebeen derived from various earlierstudies conducted both in India andabroad. Some of these studies havebeen conducted by Singh (1979);Lehtinen and Lehtinen (1982, 1991);Lawson and Watt (1983); Gronroos(1984); Parasuraman, Zeithml and Berry(1985); Rao (1987); Lewis and Smith(1989); Kaptan and Sagane (1995);

Gavini and Athma (1997). In India aninvestor takes into account variousfactors while deciding about buying of aLife Insurance. These range of factorsbegin with investor perception, thepromised return and the attractivenessof the offer. So from informaldiscussions with Life-Insurance agentsand from references to earlier studies,all the relevant variables in the purchaseof a Life Insurance are included.

The data obtained for the study wasanalyzed by using “FACTOR ANALYSIS”for identification of the KEY FEATURES/FACTORS preferred by the respondentsin an Life Insurance Scheme. FactorAnalysis identifies common dimensionsof factors from the observed variables thathave a high correlation with the observedand seemingly unrelated variables but nocorrelation among the factors.

Principle Component Analysis is thecommonly used method for grouping thevariables under few unrelated factors.Variable with a factor loading of higherthan 0.5 are grouped under a factor. Afactor loading is the correlation betweenthe original variable with the specifiedfactor and is the key to understandingthe nature of that particular factor.

Table -3: Geographic Sample Distribution

State / Place No. of Insurance No. of Response % ResponseAgents Employed

Delhi 15 210 35Harayana 08 96 16Punjab 07 156 26Himachal Pradesh 05 48 08Uttar Pradesh 10 90 15Total 45 600 100%

11



In this study, Principal Componentanalysis has been used since theobjective is to summarize most of theoriginal information (variance) in aminimum number of factors forprediction purposes. Here the factorsare extracted in such a way that factoraxes are maintained at 90 degrees,meaning that each factor is independent

of all other factors. A factor is a linearcombination of original variables.Factors also represent the underlyingdimensions that summarize in accountfor the original set of observedvariables. An important concept in factoranalysis is the rotation of factors. Wehave used Varimax Rotation to simplifythe factor structure. Only the factors

Table-4: Rotated Factor Matrix (Loading Criteria > .50)

Var. ATTRIBUTES F1 F2 F3 F4 F5NO.V1 Awareness of the Product 0.6128V2 Service Behavior 0.6987V3 Advertisement 0.56895V4 Product Feature 0.6552V5 Safety of the Scheme 0.6058V6 Performance Guarantee .5868V7 Agent Recommendation 0.55896V8 Friends/Relatives 0.54892

SuggestionV9 Hassle-Free / Convenience 0.5986V10 Delivery Schedule 0.5685V11 Public Sector/Private 0.5926

sector OwnershipV12 Regular Income .5525V13 Technology 0.6589V14 Tax Benefits 0.5548V15 Transparency 0.6258V16 Assured Return .5278V17 Brand Name 0.52487V18 Maturity Amount to be .5258

ReceivedV19 Premium Amount to pay .5012V20 Extra Coverage (Bonus) .5987

Eigen Value 6.173 5.721 5.229 4.178 4.073Cumulative Variance 24.33 47.19 63.17 79.11 93.46

Note: F1, F2, F3, F4 and F5 are the five derived factors.

12



having latent roots (eigen values)greater than 1(unity) are considered. AnEigen value is the column sum of squaresfor a factor. It represents the amountof variance in data. We chose thosefactor loadings which were greater than0.45 (ignoring the signs) and loadedthem on the extracted. The final stepin factor analysis is naming the factors.This labeling is intuitively developed bythe factor analyst based upon theappropriateness for representing theunderlying dimensions of a particularfactor.

FACTOR ANALYSIS AND FINDINGS:

The 20 variables (Table-9.2) used for thefactor analysis were coded using thefive-point Likert Scale. Table 4 providesthe varimax rotated factor loadings

against the 20 variables measuringpreference level for life insurance. Thiswas obtained in 7 iterations throughSPSS (Version 10).

Factor analysis using Varimax rotationfinds five derived factors, each havingeigen value greater than unity. In therotated factor matrix, those variableswhich had factor loading of above 0.50(ignoring the signs) are grouped undertheir respective derived factors. Thus,the 20 variables (reasons for satisfaction/ dissatisfaction) were then loaded onthe five factors.

Factor-I (F1) has an eigen value of 6.173and explains 24.33% of total variance.The eigen values of the second factor,third factor, fourth and fifth factor are5.721,5.229,4.178 and 4.073

Table-5: Factors of Customer Preference for Life InsuranceF1 F2 F3 F4 F5

Core Product Promotional Consumer Service Risk-ReturnFactor Factor Expectation Quality Factor Factor

FactorAwareness of Advertisement Safety of the Service Performancethe Product (4.12) Scheme (4.13) Behavior Guarantee

(3.95) (3.77) (4.31)

Product Feature Agent Hassle-Free/ Technology Regular Income(3.42) Recommendation Convenience (2.42) (4.59)

(4.42) (3.82)

Public /Private Friends/ Delivery Transparency Assured Returnsector Relatives Schedule (2.77) (4.33) (4.49)

Ownership Suggestion(3.73) (2.32)

Brand Name Tax Benefits Extra (Bonus) Maturity Amount(3.11) (3.26) Coverage (2.17) to be Received

(3.99)

Premium Amountto pay (3.73)

N.B. : The figures in parenthesis represent the average scores for the variablesunder each Factor that determine preference level for Life Insurance

13



respectively. The total varianceaccounted for by all the five factors was93.46% which is quite high, and thisestablishes the validity of the study.Naming the factors has been done on thebasis of the size of factor loading of thevariables. Greater a factor loading fora variable, greater are the chances ofthe factor being named after thisspecified variable.

Table 5 depicts the variables under eachof the five derived factors. The firstfactor (F1) identified with productfeatures of life insurance are Awarenessof the Product, Product Feature andPublic Sector/Private sector Ownership.These variables have been groupedunder Factor-I, and termed as CoreProduct Factor. These are the core partof a Life Insurance product, which arecommon expectation of any customerwhile making a purchase decision.

The second factor is designated asPromotional Factor, on the basis of theloaded variables. The data set of theFactor–II (F2) loading indicates variablesl ike Advertisement, AgentRecommendation, Friends/RelativesSuggestion and Brand name. Promotionof the Insurance products expressed indetails is an influential variable in thepurchase decision of the investor making

it the most tangible component, visibleto the investor in the offering. Factor-III (F3) shows Safety of the Scheme,Hassle-Free / Convenience, DeliverySchedule and Tax Benefits. Thiscomponent of customer preference istermed as Consumer expectation Factor.An investor’s service expectationstatement should be the vision for theorganization to aspire for. Factor-IV(F4) includes attributes such as ServiceBehavior, Technology, Transparency andExtra Coverage (Bonus). Factor-IV istermed as Service Quality Factor. FactorV clearly indicates the combination offive variables about customerspreference namely PerformanceGuarantee, Regular Income, MaturityAmount to be Received and PremiumAmount to pay. This factor is named asRisk-Return Factor. In order to find outwhich of the above given factor rank themost satisfying / most dissatisfying,the factor-wise average score (from the5 point scale) were calculated. Theaverage scores and ranks were obtainedfor the five derived factors and arepresented in Table 6.

Risk-Return Factor (F5) ranks the higheston the l ist (4.51), followed byPromotional Factor (4.22), ServiceQuality Factor (3.95), ConsumerExpectation Factor (3.71) and lastly Core

Table-6: Ranking of Factors Representing Customer Preference for Life Insurance

Factor Average Score RankRisk-Return Factor 4.51 1Promotional Factor 4.22 2Service Quality Factor 3.95 3Consumer Expectation Factor 3.71 4Core Product Factor 3.69 5

14



Product Factor (3.69). Thus, while Risk-Return factor and Promotional factor areconsidered to have highest (score ofabove 4.00) customer preference in lifeinsurance, the remaining are those withmoderate preference level among thecustomers (in the range 3.00 – 3.99).

Out of a total of 20 variables studied,customers with Life insurance have highpreference (above 4.0) in case of only 7variables namely, Advertisement (4.12),Agent Recommendation (4.42), Safety ofthe Scheme (4.13), Transparency (4.33),Performance Guarantee (4.31), RegularIncome (4.59) and Assured Return(4.49).

10 variables have moderate preferencelevel (3.0 to 3.99) among thesecustomers. And the remaining threevariables with slight preference level(2.0-3.99) Friends/Relatives Suggestion(2.32), Delivery Schedule (2.77) andExtra Coverage i.e. Bonus (2.17). Noneof the variables were found to havelowest preference level (averages scoreof below 2.0) for customers with Lifeinsurance in North India.

CONCLUDING REMARKS

The Indian Insurance sector in today’sscenario has transformed into a buyer’smarket, where the customer has thechoice to select from a variety ofproducts, services and serviceproviders. More and more customers arenow identifying newer dimensionattached to life insurance to match theirlife-cycle needs. Given uncertainty aboutlife’s duration and about increasing

costs and responsibilities, consumerswould definitely opt for a life Insurancepolicy, but which one will depend on thecompetitive edge of the Life insurancecompanies as measured on the aboveFive factors.

The present study looks at customerlevels in a Life Insurance product. Thiskind of customer orientation isnecessary in a market like India, wherethe market in turning competitive dueto large number of players with variedfinancial musicale and expertise ofreinvestment. The small investorspurchase behavior does not have a highlevel of coherence due to the influenceof different purchase factors. Thebuying intent of a Life Insurance productby a small investor can be due to multiplereasons depending upon customers riskreturn trade off. Due to the reductionin the bank interest rates & high degreeof volatility in Indian Stock Market,investors are looking for an alternativefor their small time as well as long timeinvestment which will provide them ahigher return & also safety to theirinvestment. The Stock market is alsopassing through a recession due tointerest parity with bank instruments.Thus Life insurance offers the bestalternative to small investors in India.A prudent Product design, by adding thefeature expected by investors and speltout in this research will make the newLife Insurance Product more attractivefor investors. The factor identified inthe study provide key information inputsregarding investor’s preference andpriorities that will guide future LifeInsurance Product Managers.

15



1. Agarwal, V.K., “InsuredExpectations in a LiberalizedInsurance Market”, CharteredSecretary, August 2001.

2. Bhattacharya, Anabil, “Indian Bank’sEntry into insurance sector”, Journalof Insurance Institute of India,Vol.XXV, (July-Dec. 2000).

3. Gronros, C. (1984a), “A ServiceQuality Model and its MarketingImplications”, European Journal ofMarketing, Volume 18, November 4,p. 36-44.

4. Gupta, N.D., “Insurance- A BoomingProfessional Opportunity”, TheChartered Accountant, Vol.51, No.12,p.1195-1200.

5. Lehtinen, U. and Lehtinen, J.R.(1982), Service Quality : A Study ofQuality Dimensions, Working Paper,Service Management Institute,Helsinki, Finland.

6. Lewis, B. and Smith, A.M. (1984),“Customer Care in Financial ServiceOrganisations”, International Journalof Bank Marketing, Volume 7, Number5, p. 13-22.

7. Mukhi, M.D. “Is Life Insurance a goodinvestment?”, Yogakshema (LICMonthly Magazine), June 2001.

8. Verma, Vinay,”New Trends in ProductDesign- An Overview of LifeInsurance Products”, The InsuranceTimes, June 2003, P.16-21.

9. Walia, Harsh, “Challenges faced byInsurance Industry in India”, TheInsurance Times, June 2003, P.22-28.

10. Purugami, (The Journal of LIC,Eastern Zone), April-June 2000.

11. Galaxy (The LIC Magazine), April-June 2000.

12. Economic Survey 2002-2003.

REFERENCES

16



AN ALTERNATIVE PREDICTING MODEL FORCORPORATE MERGERS & ACQUISITIONS

P.K. Panigrahi !!!!!

Abstract

During the last couple of years, a significant growth in corporate mergersand acquisitions, has been observed. Corporations adopt mergers as astrategy for various reasons such as expansion or growth, stability,survival etc. It is often very difficult to predict which companies aresuitable for mergers and acquisitions. Mergers and acquisitions takeplace for a multitude of reasons. It is difficult to identify all the variables.Traditional statistical techniques such as logistic regression are usedfor prediction purposes.

In this paper, a new technique “Artificial Neural Network (ANN)” hasbeen proposed for predicting domestic corporate mergers & acquisitions.The predictors considered, are based on various popular hypothesesrelated to mergers and acquisitions. ANNs are one of the fast-growingparadigms for “learning” systems with a wide variety of potentialapplications in business. The explanatory and predictive capabilities ofthe artificial neural network have been discussed and also comparedwith corresponding traditional methods considering Indian corporatemergers and acquisitions. It is found that the proposed techniqueprovides better results than traditional methods and is also applicablewhere traditional methods are not appropriate and suitable. The proposedtechnique has been explained with an illustration. It is also observedthat even if ANNs have several advantages over statistical methods andprovide a good mathematical fit of the data, the solution would notnecessarily lead to a good prediction model because of variouslimitations.

1. INTRODUCTION

Mergers and acquisit ions (M&A)designate a very close form of corporatealliance, namely a full merger. It is notalways easy to distinguish a merger froman acquisition. The technical differenceis the following - In the case of anacquisit ion, one of the merging

companies (A) plays a dominant role andacquires the other (B). An enlargedcompany A is created as a result. Thetake-over may take place with or withoutthe consent of B’s management. Thelatter action is called a hostile take-over.The acquisition may be paid for in cashor by means of the acquiring entity’sown shares. In the case of a merger,two (or more) companies (in this case,A and B) decide to merge into a new

! Faculty, Loyola Institute of BusinessAdministration, Chennai

17



company (C), whereby the new entityis fully made up of the two existingcompanies. Sometimes, under closerscrutiny, mergers turn out to beacquisitions in disguise. This is thecase when one of the merging partnersis dominant and accordingly assumes amore powerful position in the newlyfounded company. Mergers are highlycomplex transactions involving not onlythe “mere” integrat ion of twocompanies, but also the decision of twotheoretically equal-sized partners,which are often radically different, tooperate as a single entity.

The exploitation of synergy is a popularreason for M&A activity. The variousreasons of M & A are (a) Opportunity forgrowth (b) Need for faster growth (c)Access to capital and brand (d) Acquirenew customers (e) Need to enhance skillsets (f) Diversification (g) Widen theportfolio of addressable market (h)Gaining complementary strengths (i)Meet end-to-end solution needs, (k)Avoiding excessive competition, (l)Disinvestments and (m) Fosteringinnovation etc.

Mergers and Acquisitions (M&As) haveemerged as a natural process ofbusiness restructuring throughout theworld (Bhoi, 2000). The process of M&Asspans geographical boundaries: cross-border M&As, mostly by transnationalcorporations (TNCs), have assumed asignificant proportion. For the Indianindustry, market driven M&As areessentially a phenomenon of the late1990s. The early M&As in India werearranged either by government agenciesor by financial institutions within theframework of a regulated regime.However, since 1991, Indian industries

have been increasingly exposed to bothdomestic and international competitionand competitiveness has become animperative for survival. Hence, in recenttimes, companies have startedrestructuring their operations around theircore business activities through M&As.

There is an acute data deficiency withrespect to M & As in India. Security &Exchange Board of India (SEBI) iscurrently maintaining a limited databaseon M & As relating to the companiesregistered on Indian stock exchanges.This, however, provides a partial pictureof M & As in India. Also the database ismaintained by private agencies. Againthe information is partial, not reliableand also not available for public use ona regular basis. The Centre forMonitoring Indian Economy (CMIE) is theonly agency, which has been publishingdata on M & As in India on a regular basissince Jan 1997. One of the limitationsof CMIE is that it maintains informationbased on announcements of M & As, notactual execution of the deals. Thenumber of mergers and acquisitionstaken place in India is provided in table-1.1 and table 1.2.

Here the number of open offers weremostly in industries like software,cement, chemical & pharmaceuticals.Takeovers or acquisitions are thedominant feature of M & As. Despite theslowdown in the economy, there was amarginal pick up in the quantity.

2. MODELING PREDICTIONS OFCORPORATE MERGERS &ACQUISITIONS

The purpose behind being able to predicttargets for mergers is to make abnormal

18



returns by investing in predicted firms.In the past, statistical merger predictionmodels (Dietrich and Sorensen, 1984)were used to classify possible targets formergers with an accuracy of 60% to 90%.Although the accuracy is very high, themodels have sampling biases due to themanner in which the holdout sample isselected in order to test the predictiveability of the model. In these studies arandom sample of target and non-targetfirms is considered in which the numberof non-target firms selected is far lessthan the number of non-target firms thatexist in reality. Palepu (1986) corrected thisbias, used logistic regression and showedthat 45% of the targets and non-targets inholdout sample can be predicted.

In this paper, an attempt has been madeto compare the predictive and

explanatory capabilities of neuralnetworks and logistic regressions. Tamand Kiang (1990) observe that neuralnetworks outperform logistic regressionin a bankruptcy prediction task. Ourresults indicate that neural networksmodel is better than logistic regressionin many aspects. Logistic regressionmodel, one of the best statistical modelsfor prediction is studied and thencompared with the neural networkmodel.

In our study, only domestic mergers andacquisitions are considered. Both thetargets and acquirers are Indian. Thepredictive ability of the binary logisticmodel is tested using the modelestimation sample (Year 2000) and theholdout sample (Year 2001). The dataobtained from 2000 were used to train

Table: 1.1: No. of Mergers and Acquisitions in India (CMIE)

Sl. No Year Mergers Other Total No of openAcquisitions offers

1 1999-2000 205 1201 1495 89

2 2000-2001 294 1107 1477 76

3 2001-2002 294 952 1344 98

Source: Centre for Monitoring Indian Economy (CMIE).

Table: 1.2 Table: Substantial Acquisitions of Shares & Takeovers in India (SEBI)

Sl.No Period From Period To Numbers1 01-04-1999 31-03-2000 75

2 01-04-2000 31-03-2001 77

3 01-04-2001 31-03-2002 81

4 01-04-2002 31-03-2003 88

5 01-04-2003 12-09-2003 29*

* Till 12th September 2003

Source: Security & Exchange Board of India (SEBI)

19



the network and build the logisticregression model. 46 targets wereidentified and 46 non-targets wererandomly identified from the pool of 1690non-targets. Data gathered from 2001were used then to test & verify theneural network and binary logisticregression model developed usingestimation sample. In 2001, a total of85 targets and 1000 non-targets wereidentified and selected. The targetswere identified from different sourceswhere full information is available.

The predictors considered in this paperare based on various popular hypothesesrelated to mergers and acquisitions.(T.K.Sen, Virgina Tech, R.Oliver et.al,1995). Whatever models we select, itspredictive abil ity wil l mainly beinfluenced by the selection ofappropriate independent variables.Palepu hypothesises that a low performermanagement is most l ikely to beacquired. Variables related to theperformance of management of thecompany are returns, return on equity& sales/asset turnover. Palepu alsoindicates that companies can beacquired under two conditions: a)companies with high growth and lowresources b) companies with low growthand high resources. The variables suchas sales growth, liquidity, & leverage,which reflect the above phenomenon, areconsidered. A dummy variable is used torecode “high” or “low” values. Acommon hypothesis in mergers &acquisitions is that companies with lownet book value of assets i.e. smallercompanies are most likely to be acquired.Here the appropriate variable isconsidered to capture the ‘size’ factor.Another hypothesis that firms with lowmarket-to-book ratios or low price-

earnings ratios are likely takeovertargets is also considered for findingappropriate variables. Dietrich andSorensen (1984) hypothesise that acompany with very low investmentopportunities, which leads to highdividend payouts is a potential candidateto be acquired. Here variable ‘dividendpayouts’ is considered for analysis.

Based on above hypotheses, 11 variables(V1 to V11) are considered for furtheranalysis as follows:

V1 Excess Returns (ER) = Actual return –market model based returnHere actual return is calculated as[closing price of year end – openingprice year beginning] * 100 /(Opening price of year beginning).The market model based returns iscalculated based on BSE SENSEX.

V2 Return on Equity (ROE) = Incomebefore extraordinary items (i.e.Profit after tax) / (preference capital+ paid-up equity capital + reserves &surpluses)

V3 Sales/Asset Turnover (SAT) = (GrossSales – Excise Duty)/ Total assets

V4 Sales Growth (GRO) = (Net sales1 –Net sales0)/ Net sales0

V5 Liquidity (LQ) = ((Cash & bankbalances+ Marketable securities orinvestments) – Current liabilities &provisions) / Total assets

V6 Leverage (LV) = Long term debt/(Preferred equity + common equity)Here long-term debt includes all long-term (> 12 Months) borrowings +debentures/ bonds + fixed deposits.

V7 If Low sales growth, high liquidity,and low leverage = 1 ORHigh sales growth, low liquidity and

20



high leverage = 1GRV = 1 elseGRV = 0

V8 Net Book Value of assets (NBV) = (Networth / Number of shares i.e.common equity)Here net worth = Preferred Equity +Common Equity + Reserves &Surpluses

V9 Market to Book Ratios (MBR) = Stockprice / (f irm’s book equity/outstanding shares)

V10 Price Earning Ratios (PE) = (Stockprice/ Earnings per share)

Here earnings per share = PAT/Number of equity shares

V11 Dividends Payout (DP) = (Dividendsper share/ Earnings per share)

The actual data are collected fromPROWESS database, press releases &company annual reports. Stock priceinformation is collected from BSESENSEX/ NSE SENSEX. The variables areaveraged for 2-3 years depending on theavailability of data. The above variablesare tested in both logistic as well asneural network techniques.

3. BINARY LOGISTIC REGRESSION &FINDINGS

Predicting whether an event will or willnot occur, as well as identifying thevariables useful in making theprediction, is important in mostacademic disciplines and in the “realworld”. Binary logistic regression, a non-linear model, is one of the predictionstechniques with few assumptions and thedependent variable is a binary or dummyvariable. Very few assumptions arerequired in this model in comparison to

other similar dependence techniquessuch as discriminant analysis. In logisticmodel, the probability of occurring of anevent is estimated directly.

The probability of an event occurring =ez / (1 + e-z)

Where Z is the linear combination Z = B0+ B1X1 + B2X2+ …. +BpXp

And p is the number of independentvariables.

The probability of the event notoccurring is estimated as

Prob(no event) = 1 – Prob(event)

Logistic regression on the SPSS was run.The response variable in the logisticmodel was a binary variable with thevalue 1 for target companies and 0 fornon-target companies. In logisticregression, the parameters of the modelare estimated using the maximum-likelihood method where the coefficientsthat make our observed results mostl ikely are selected. Some of thepredictors are dummy variables andothers are interval/ratio type ofvariables. In this study direct method isused to include all the variables in thelogistic model. The coefficients alongwith their hypotheses testing, goodnessof fit of the model, classification tableand histogram of estimated probabilitiesetc. are derived and analyzed.

Simple multiple regression module of theSPSS is f irst run to detect anymulticollinerity among the predictors.The collinearity statistics such asTolerance and VIF are observed. It isfound that returns and sales assetturnover are correlated significantly.Similarly the GRV variable is correlatedto LQ, LV & GRO. This is obvious as

21



some variables are derived from others.The multicollinearity factor is removedby dropping variables SAT, LQ, LV & GRV.It is also confirmed that predictivepower of the model is not affected whenthe above variables are dropped. Alsowhen these variables are entered inlogistic model, they are found to beinsignificant. After the removal of thevariables, which cause multicollinearity,direct method is used to include rest ofthe variables in the logistic model. Theparameter estimates for the logisticregression model derived is depicted inTable: 3.1:

The overall model is good as the scorestatistic is significant at less than 5%of error (i.e. 0.003). –2LL, one of theindicators of goodness of fit of themodel is found to be very low. The -2

It is observed from the table that theonly variables that are statisticallysignificant in prediction are ER, GRO &MBR. The Wald statistics of thesevariables are significant at 5% level.Hence we reject the null hypotheses thatcoefficients of ER, GRO & MBR are 0.The B coefficient can be interpreted ina different way by using eB, the oddsratios. The odds ratio for a variable tellschange in odds for a case when the valueof that variable increases by 1. It isfound that the relative importance of thevariables in decreasing order is: ER, MBR& GRO respectively. Of course there isno guarantee that this will hold true fordifferent samples or for different timeperiods. The results may differ.

One of the ways to assess how well themodel fits is to compare our prediction

Table: 3.1 Variables in the Equation

B S.E Wald Sig.ER 4.66 1.69 7.603235 0.023

ROE -2.23 1.12 3.964365 0.635

GRO 3.26 0.998 10.67024 0.002

NBV -1.02 0.962 1.124217 0.582

MBR - 1.34 0.568 5.565612 0.013

PE -0.012 0.855 0.000197 0.483

DP -1.09 1.56 0.488207 0.669

Constant 0.062 4.689 0.000175 0.986

Log likelihood (-2LL) is 70.253 when onlyconstant is included in the model &48.109 for the entire model. This lowvalue of –2LL indicates that the abilityof the model to predict is good. The Cox& Snell R Square is 0.341 and NagelkerkeR Square is 0.465. This explains theproportion of “variation” in the logisticregression model.

to the observed outcomes. Table 3.2 &table 3.3 below compare the observedand predicted group memberships whencases with a predicted probability of 0.5or greater are classified as havingpositive nodes. In order to determinehow well the binary logistic modelperforms in classifying the learning

22



sample into targets and non-targets, aprobability cut-off is determined. Thecut-off in this study is arbitrarily chosenas 0.5.

It is found that in the case of estimationsample, 58.69% is correctly identified astargets and 89.13% is identified as non-targets. The overall accuracy is 73.92%.In the case of holdout sample, out of

4. ARTIFICIAL NEURAL NETWORKMODEL & FINDINGS

ANN is an emerging flexible tool in thearea of data modeling, particularlyuseful if knowledge about the inherentcomplex relationship among the dataelements is not available or notassumed. It uncovers cause and effectrelationships and identifies different

1085 sampling units, 85 are targets andthe rest are non-targets. The modelcorrectly classifies 31 out of 85 targetsand 688 out of 1000 non-targets with anoverall performance 66.27%, less thanthe estimation sample. The overallaccuracy is reduced but without anysampling bias. The model predicts non-targets better and misclassifies targetsas non-targets very high.

Table: 3.2 Classification Matrix for Estimation Sample (2000): Binary LogisticModel

PredictedTarget Non-Target

Observed Target 27/46 (58.69%) 19/46 (41.31%)

Non-Target 5 / 46(10.87%) 41/46 (89.13%)

Overall CorrectClassificationPercentage 73.92%

Table: 3.3 Classification Matrix for Holdout Sample (2001): Binary Logistic Model

PredictedTarget Non-Target

Observed Target 31/ 85 (36.47%) 54/ 85 (63.53%)

Non-Target 312 / 1000 (31.20%) 688/ 1000 (68.80%)

Overall CorrectClassificationPercentage 66.27%

patterns, which are difficult to detectby other tools. That is why in manypractical situations it outperformstraditional statistical methods andanalytical tools. ANN performance is atleast as good as classical statisticalmodeling, and better on most problems.It quickly studies the structure ofdataset, teaches itself the patterns ofdata and develops models throughlearning rather than programming. These

23



can build models when more conventionalapproaches fail. ANNs are flexible in achanging environment. Although ANNsmay take some time to learn a suddenchange, they are excellent at adaptingto constantly changing information.

Neural networks are non-linear. For manyyears, linear modeling has been thecommonly used technique in mostmodeling domains, since linear modelshad well-known optimization strategies.Where the linear approximation was notvalid (which was frequently the case)the models suffered accordingly. Logisticregression is based on the assumptionthat the classes are normally distributedwith equal covariance matrices. Wherethese assumptions are untrue (and it isquite common for them to be untrue)neural network provides more accuratepredictions. Neural networks also keepin check the dimensionality problem.ANNs are data driven. No predeterminedshape of the data or solution isassumed. NNs learn the solution that fitsbest directly from the data itself. Ratherthan fitting straight lines, or evenmultiple straight lines to the data, theNN algorithm fits a smooth curve basedon the shape of the data itself. Withthis approach, it’s possible to fit theunderlying relationship in a very powerfulmanner. The NN finds the non-linearsolution by representing a smooth curvein those parts of the graph where thebasic direction of the underlyingrelationship is changing.

The network is trained to learn from thetraining data sets. A portion of thetraining data set is kept to cross verifythe network’s performance. This set iscalled verification data set. It avoidsover learning or over fitting. Anotherportion of the data set known as “test

data set” is used to check whether thecross verification error is artifactual.

There are various types of learning suchas supervised, reinforcement andunsupervised. The common ANNarchitectures are Multi-Layer Perceptron(MLP), Radial Basis Function Networks(RBFN), and Kohonen Networks.

In our study, a custom neural networkmodel is designed using commercialpackage “STATISTICA Neural Networks”.ST Neural Networks support variouscomplex options for networks.Intelligent solver option of the softwareconfigures and designs the networkbased upon the current data set. It helpsthe users in avoiding networkcomplexity. It is not easy to determinethe number of hidden layers and thenumber of nodes required in the hiddenlayers that would provide the best neuralnetwork model. Also the advancedmodule of the software guides a serioususer in designing a customized network.In our study, 92 samples (46 targets and46 non targets) were taken forestimation sample & 1085 data sets (85targets and 1000 non-targets) forholdout. The Intelligent problem solveris used to create the network.

As the network training algorithms areiterative, training is repeated a numberof times until a satisfactory solution isfound. In our study, 35 networks aretested and the best 10 are retainedtaking account of diversity. These 10networks selected are good in someparameters and bad in others. One hasto engage in trade-off to select theappropriate network for interpretation.Here the input variables are selectedautomatically. Considering theparameters such as various types of

24



errors (training, verification & test),number of nodes in inputs, outputs andhidden layers as well as the performanceof the network, it is found that MLP 5-2-4-1:1 is most appropriate in our study.In predicting more targets accurately, itmisclassifies many more non-targets totargets. This factor is also consideredwhile choosing the network. Thestructure MLP 5-2-4-1:1 means it is aMultilayer Perceptron, 5 input variables,that are pre-processed and passed to anetwork with three layers, two inputunits in the first layer, 4 hidden units inthe second layer, one output unit in thethird layer; and that the output is post-processed into a single output variable.The variables selected in the abovenetwork are DP, ER, MBR, GROW & SAT.It includes those three variables, whichare selected in the logistic regression.It is found that the networks perform wellwhen the number of predictors are reduced.

The training, verification, and test errorare very low and close to each other(0.002926, 0.001217 & 0.000719respectively). It indicates that thenetwork has not over learned andgeneralizes well. It has been trainedproperly. Since the number of data setis low, sufficient number ofpresentations of the observations isobtained by using randomized orderingsof the training data. The classificationmatrix is provided in table 4.1.

It is found that the neural network modelcorrectly classifies 41 out of 46 targetsof estimation sample and 775 out of1000 non-targets. The overall accuracyis 91.31% for estimation sample and74.65% for holdout sample. This is animprovement over the logistic model.Also the standard deviation ratio isfound to be 0.2304 on verification setof the neural network compared to0.2365 of logistic regression.

5. CONCLUSION

ANN is better than logistic regression inmany aspects while predicting domesticM & As. ANN is not free from limitations.One of the limitations is its inability toexplain the relative importance of itsinputs. It requires sufficiently largenumber of data sets to train, verify andtest the network. On the other hand, inIndia, the availability of data related tomergers and acquisitions is a problem.It is worth mentioning here thatalthough the overall prediction accuracyrate for the neural network is higherthan that of the binary logistic model,this does not necessarily suggest thatby using the neural network, abnormalreturns from the stock market may beobtained. The neural network predictsonly 35 out of 85 (41.18%) targets, whichis very low in general.

Table: 4.1 Classification Matrix: Artificial Neural Network Model

Artificial Neural Network ModelTargets Non Targets Overall

Estimation Sample (2000) 41/46= 89.13% 43/ 46 = 93.48% 91.31%Holdout Sample (2001) 35/85 = 41.18% 775/1000 = 77.5% 74.65%

25



REFERENCES

[1] Bhoi. B.K (2000), Mergers andAcquisit ions: An IndianExperience, Report of Departmentof Economic Analysis and Policy ofthe Bank, India

[2] Dietrich, J.K. and Sorensen, E.(1984), An application of Logitanalysis to prediction of mergertargets, Journal of BusinessResearch, No. 12, 393-402.

[3] Palepu, K.G. (1986), Predictingtakeover targets, amethodological and empirical

analysis, Journal of Accountingand Economics, No. 8, 3-35.

[4] Tam, K.Y. and Kiang, M. (1990),Predicting bank failures: A neuralnetwork approach, AppliedArtificial Intelligence, No. 4, 265-82.

[5] T.K.Sen, Virgina Tech, R. Oliveret.al edited by Apostolos-PaulRefenes (1995), PredictingCorporate Mergers, NeuralNetworks in the Capital Markets,324-340.

26



SEX DIFFERENCES AND SIMILARITIES ON THEFACETS OF INDIVIDUALISM AND COLLECTIVISM:

A STUDY ON M.B.A. STUDENTSG.Suryanarayana Reddy !

P.Govinda Reddy !!

Abstract

The aim of this paper is to ascertain how far males and females keepup their traditional orientations towards the different facets ofindividualism and collectivism. To measure individualism andcollectivism, a 15 item questionnaire was prepared and data werecollected in March 2003 from 100 M.B.A. students of Madras University.The results indicate that males and females are converging on theirimportance scores for achievement orientation, distinctiveness ofprivate and public spaces, autonomy, collectivist conscientiousness, andin-group harmony. Males scored significantly more on the importanceratings of group orientation in comparison to females. However, bothsexes rated group orientation as least important of all facets.

INTRODUCTION

Individualism is defined as a situationin which people are concerned withthemselves and family members only,while collectivism is defined as asituation in which people feel theybelong to larger in-groups or collectiveswhich care for them in exchange forloyalty (Hofstede and Bond, 1984).Collectivism can also be defined as acluster of attitudes, beliefs andbehaviors toward a wide variety ofpeople. The difference betweenindividualism and collectivism can beexpressed by the range of social bondsand links people develop with others (Huiand Triandis, 1986). Individualistic

cultures emphasize promoting theindividual’s self interest, personalautonomy, privacy, self realization,individual initiative, independence andan understanding of personal identity.Collectivistic societies emphasize loyaltyto the group, emotional dependence ongroups, the belief that group decisionsare superior to individual decisions,interdependence and concern about theneeds and interests of others (Darwishand Hubler, 2003).

Sex differences in values, attitudes andbehaviors arise due to different genderroles, gender stereotypes and genderedsocial structures that influence selfconcept and self presentation (Konradet al. 2000). Males have beentraditionally associated with traits ofdominance, aggression, achievement,exhibition, endurance and autonomywhile females have been traditionally

! Department of Management Studies,University of Madras

!! Department of Anthropology,University of Madras

27



associated with the characteristics ofaffiliation, nurturance, succorrance,deference and abasement (Williams andBest, 1990). The differential nature ofgender roles, gender stereotypes andgendered social structures is attributedto (a) the division of labor betweenwomen and men and (b) the greater statusand power of men that emerges in thecontrol of women’s general behavior (Woodand Eagly 2002). The traditional valuesystems attributed to males and femalesmake males more individualistic and lesscollectivistic than females.

The aim of this paper is to investigatesex similarities and differences on thefacets of individualism and collectivism.

METHOD

To measure individualism and collectivism,15 items from Oyserman et al. (2002) aretaken and a questionnaire is prepared.Each item is given seven responses with

response one indicating “not at allcharacteristic of me” and response sevenindicating “completely characteristic ofme”. The questionnaire is administeredto 100 M.B.A. students belonging to theUniversity of Madras during March 2003.Factor analysis (principal components) withvarimax rotation is done on the datarelated to these 15 items to ascertaindifferent facets of individualism andcollectivism. For each facet, the Mean andS.Es. are computed for males (No.=62) andfemales (No.=38).

RESULTS AND DISCUSSION

Factor analysis with varimax rotation onthe 15 items has given rise to sixFactors. Table one shows these Factorswith items having significant Factorloadings (greater than 0.4).

The first Factor is named grouporientation. This Factor has significant

Table 1: Factors and items with significant loadings

Factor one – Group orientationItem Item FactorNo. Loadings 4 To me pleasure is spending time with others 0.813862 To understand who I am you must see me with 0.79514

members of my group10 Before making a decision I always consult others 0.691667 I am unique – different from others in many aspects -0.6289815 I would rather do a group task rather than an individual task 0.56119

Factor two – Achievement orientationItem Item FactorNo. Loadings5 It is important to me that I perform better than others in a task 0.809733 I take a great pride in accomplishing what none else can 0.74012

Accomplish13 I have respect for the authority figures with whom I interact 0.71742

28



Factor three – In-group harmonyItem Item FactorNo. loadings8 I make an effort to avoid disagreements with my 0.80752

group members11 How I behave depends on who I am with, where I am, or both 0.67461

Factor four – Collectivist conscientiousnessItem Item FactorNo. loadings6 I would help with in my means if a relative were in a 0.80746

financial difficulty12 I know my weaknesses and strengths 0.77310

Factor five – Distinctiveness of private and public spacesItem Item FactorNo. loadings9 I like my privacy 0.8369814 I always state my opinions very clearly 0.63697

Factor Six - AutonomyItem Item FactorNo. Loadings1 I tend to do my own thing 0.90428

loadings on items, “to me pleasure isspending time with others”, “tounderstand who I am you must see mewith members of my group”, “beforemaking a decision, I always consultothers” and “I would rather do a grouptask than an individual task”. All theseitems have positive loadings on thisFactor and indicate group orientedness.The item “I am unique, different fromothers in many respects” has negativeloadings on Factor one, as this itemrepresents a feature of individualismwhereas group orientedness is a featureof collectivism.

Factor two is named achievement. “It isimportant for me that I perform better thanothers in a task”, “I take great pride inaccomplishing what none else canaccomplish” and “I have respect for the

authority figures with whom I interact”have significant positive loadings on thisFactor. Achievement is an individualisticcharacteristic. In the North Americanculture the item “I have respect for theauthority figures with whom I interact” isconsidered as a collectivistic characteristic(Oyserman, 2002). Hofstede (1980)pointed out that power distance issignificantly high in India. In Indiansetting, superiors have considerable powerover their subordinates and they can makeor mar the achievement of theirsubordinates. Respecting authority figuresis an important skill achievement orientedsubordinates shall develop in societies likeIndia. That is the reason this item isconsidered as correlated to achievement.

Factor three is named in-group harmony.“I make an effort to avoiddisagreements with my group members”

29



and “How I behave depends on who I amwith, where I am or both”, havesignificant loadings on this Factor. Thesecond item emphasizes contextorientedness. Collectivists are contextoriented. They deal with in-groupmembers in a different way from out-group members. In-group harmony is acollectivist characteristic.

Factor four is called collectivistconscientiousness. “I would help within my means if a relative were infinancial difficulty” and “I know myweakness and strengths” havesignificant loadings on this Factor.Helping an in-group member is acollectivist tendency. Rendering suchhelp can be called conscientiousness.

Factor five is called clarity of privateand public spaces. “I like my privacy”and “I always state my opinions veryclearly” have significant loadings on thisFactor. Levin (1936) indicated thatpeople differ in their concepts of whatis private and what is public? Incollectivistic societies everything is

context oriented. In such societies,what is private or public depends on thecontext and with whom the collectivistsare dealing. As such there is nodefiniteness to the concepts of what isprivate and what is public? This Factormeasures Individualism.

Factor Six has only one item withsignificant loading. This Factor is calledautonomy, which represents anindividualistic characteristic.

Table two gives the mean importanceratings and standard errors of the sixFactors for both males and females.

The facets of individualism andcollectivism in order of importance formales and females are presented inTable three.

Both sexes have rated achievementorientation as most characteristic ofthem and group orientation and in-groupharmony as least characteristic of them.The difference between males andfemales is related to the ranking of

Table 2: Mean and Standard errors for different Factors

Factor Factor Males FemalesNo. Mean S.E. Mean S.E.F1 Group orientation 4.438 0.208 3.75 0.245

(Collectivism)F2 Achievement orientation 5.92 0.152 5.853 0.1959

(Individualism)F3 In group harmony 5.35 0.213 5.25 0.236

(Collectivism)F4 Collectivist conscientiousness 5.6 0.142 5.83 0.196

(Collectivism)F5 Distinctiveness of private and 5.765 0.178 5.725 0.241

Public spaces (Individualism)F6 Autonomy 5.79 0.153 5.71 0.168

(Individualism)

30



Table 3: Facets of Individualism and collectivism in order of importance amongmales and females

Males Females1. Achievement orientation 1. Achievement orientation2. Autonomy 2. Collectivist conscientiousness3. Distinctiveness of private and 3. Distinctiveness of private and

public spaces public spaces4. Collectivist conscientiousness 4. Autonomy5. In-group harmony 5. In-group harmony6. Group orientation 6. Group orientation

autonomy and collectivistconscientiousness. Males ratedautonomy as second most characteristicwhile females rated autonomy as fourthmost characteristic. In Indian family andschool circumstances males are givenmore independence and independencefor females is greatly curtailed. Whenpeople get used to independent action,they enjoy autonomy and find itimportant. On the contrary, when peopleare not used to independent action, theyconsider autonomy as less characteristicof them. This may be the reason forthe difference in the rankings. Thefemales have ranked collectivistconscientiousness as second mostcharacteristic. This is a collectivistcharacteristic. The items in this Factorare “I would help within my means if arelative were in financial difficulty” and“I know my weaknesses and strengths”.Only once in a while people come acrossa relative being in need of help. Thoughwe designated this Factor asrepresenting collectivism, it does notrepresent the core of collectivism. Thecore of collectivism is represented bygroup orientation and in-group harmony.Males and females have ranked these asleast characteristic. Based on theoverall rankings given to the facets of

individualism and collectivism, the sampleof females has shown more collectivistorientation than the sample of males.

Z tests are conducted to find out if meanratings for each of these facets differacross the genders. Signif icantdifference in the mean rating at 95%confidence level is found only on thefacet – group orientation with malesscoring significantly higher than females.Males and females scored alike on all otherfacets of individualism and collectivism.

Based on traditional roles andstereotypes, we expected males to scoremore on the facets of individualism andfemales to score more on the facets ofcollectivism. Our results point out thatmales and females have scored alike onfive of the facets of individualism andcollectivism. This is due to modernism.We have attributed the differentialnature of gender roles, stereotypes andsocial structures due to the division oflabor. Increasingly women have beentaking paid jobs outside the home. Theyare motivated to compete with others.This has been accepted in the family,educational institutions and otherplaces. Willinger (1993:108-130) foundthat over time undergraduate men inU.S.A. developed more liberal attitudes

31



toward women’s paid work and towardgender roles in the family. This is truein India also.

The other variable that causesdifferential roles, stereotypes and socialstructures across genders is status. Ina society, status may be achieved orascribed. When a status is achieved,status depends on the nature of work.As females are increasingly taking uppaid work outside homes, statusdifferentials between genders isnarrowing down. When status ispredominantly ascribed in a society,status attribution is independent of thenature of work. In urban areas andespecially in the college/universitycircumstances, people predominantlyattribute status to achievement.Modernity is narrowing down thetraditional differences across genders.

Yang (1988:67-85) reported that abouttwo thirds of individual modernity traitsreflected individualism. Bendix (1967)explained that traditional characteristicsthat do not come in the way of thedemands of modern urban living will stayand traditions that are not compatiblewith modern living are ignored andneglected. Triandis (1988:67-88)explained that changes in individualistand collectivist orientations may takeplace with changes in affluence andeducation. Sinha and Tripathi(1994:123-136) found that younger,highly educated and urban people tendedto be less collectivistic.

New approaches towards division oflabor between the sexes is the reasonthat on the most important five facetsof individualism and collectivism, thereis no significant difference betweenmales and females on their mean scores.

Our results indicate signif icantdifference between males and femaleswith males scoring higher than femaleson the least characteristic facet of grouporientation. Group orientation is acollectivist facet. Since grouporientation is least characteristic forboth the sexes, we expect the norms inthe social groups formed by therespondentsto be generally not bindingon the members. In the college/university socialization one can easilychange social groups based ondifferential needs. When norms areunclear and sanctions are unlikely to beimposed anticonformity is observedmore in collectivist cultures (Darwishand Hubler, 2003). Frager (1970) hasfound that Japanese subjects (morecollectivist) conformed less than U.S.subjects (more individualistic) insituations when norms are not enforced.This may be the reason that males haverated significantly higher on collectivistfacet of group orientation in comparisonto females.

CONCLUSION

Modernity is changing traditional genderroles. As females are increasingly takingup paid jobs outside the homes, theirroles are transforming from homemakerto breadwinner for the family. Eagly(1987) pointed out that sex differencesin social orientation may not be seen ifmales and females are engaged in sameroles. Modernity is also changing thegender stereotypes. Allan and Coltrane(1996) found that images of womenhave changed to become lessstereotypically feminine. Twenge (1997)pointed out that undergraduate womenin the early 1990s described themselvesas significantly more masculine than

32



their counterparts in the mid 1970s.Konrad et al. (2000) reported thatbetween 1970 and 1998, changes haveoccurred in gendered social structureswhich allowed more androgyny in the U.S.

Similar changes are taking place inurban India. As a result of thesechanges, females and males areconverging on their scores ofindividualism and collectivism.

REFERENCES

11. Oyserman, D., Coon, H.M. andKemmelmeier, M. 2002: RethinkingIndividualism and Collectivism: Evaluationof Theoretical Assumptions and MetaAnalysis. Psychological Bulletin, 128: 3-72.

12. Sinha, D. and Tripathi, R.C. 1994:Individualism in a Collectivist Culture: ACase of Coexistence of Opposites. InU.K.M., H.C.Triandis, S.C.Choi and G.Yoon(Eds.): Individualism and Collectivism:Theory, Method and Application, London:Sage.

13. Twenge, J.M. 1997: Changes in Masculineand Feminine Traits over Time: A MetaAnalysis. Sex Roles, 36: 305-325.

14. Triandis, H.C. 1988: Collectivism andDevelopment. In D.Sinha and H.S.R.Kao(Eds.): Social Value and Development:Asian Perspectives, New Delhi: Sage.

15. Williams, J.E. and Best, D.L. 1990: Sex andPsyche: Gender and Self Viewed Cross-Culturally. Thousand Oaks, CA: Sage.

16. Willinger, B. 1993: Resistance and Change:College Men’s Attitudes Toward Familyand Work in the 1980s. In J.C.Hood (Ed.):Men, Work and Family. Thousand Oaks,CA: Sage.

17. Wood Wendy and Eagly H. Alice 2002: ACross-Cultural Analysis of the Behavior ofWomen and Men: Implications for theOrigins of Sex Differences. PsychologicalBulletin, 128 (5): 699-727.

18. Yang, K.S. 1988: Will SocietyModernization Eventually Eliminate CrossCulture Psychological Differences? InM.H.Bond (Ed.): The Cross CultureChallenge to Social Psychology, NewburyPark, CA: Sage.

1. Allan,K. and Coltrane,S. 1996: GenderDisplay in Television Commercials: AComparative Study of TelevisionCommercials in the 1950s and 1980s. SexRoles, 35:185-203.

2. Bendix, R. 1967: Tradition and ModernityReconsidered. Comparative Study inSociety and History, 9: 292-346.

3. .Darwish E. Abdel-Fatah and Huber L.Gunter 2003: Individualism Vs. Collectivismin Different Cultures: A Cross CulturalStudy, Intercultural Education, 14 (1).

4. Eagly, A.H. 1987: Sex Differences in SocialBehavior: A Social Role Interpretation,Hillsdale, NJ: Erlbaum.

5. Frager, R. 1970: Conformity andAnticonformity in Japan. Journal ofPersonality and Social Psychology, 15:203-210.

6. Hofstede, G. 1980: Culture’sConsequences. Beverly Hills, CA: Sage.

7. Hofstede, G. and Bond, H.M. 1984:Hofstede’s Culture Dimensions: AnIndependent Validation Using Rockeach’sValue Survey. Journal of Cross CulturalPsychology, 15 (4): 417-433.

8. Hui, C.H. and Triandis, C.H. 1986:Individualism Vs. Collectivism. A Study ofCross Cultural Researches. Journal ofCross Cultural Psychology, 17 (2): 225-248.

9. Konrad, M.A., J.Edgar Ritchie, Jr., PamelaLieb and Elizabeth Corrigall 2000: SexDifferences and Similarities in JobAttribute Preferences: A Meta Analysis.Psychological Bulletin, 126 (4): 593-641.

10. Levin, K. 1936: Ed. Principles ofTopological Psychology. New York:McGraw Hill Publications

33



DOES INVOLVEMENT ACT DIRECTLY ON ADVERTISINGATTITUDE FORMATION ? : CONCEPTUAL ISSUES ANDEMPIRICAL INVESTIGATION IN THE INDIAN CONTEXT

Himadri Roy Chudhuri !

Abstract

Involvement is an important variable that significantly mediate consumerbehaviour dynamics including information processing, attention etc.Involvement is a very well-studied construct in consumer behaviourliterature. Although several studies have demonstrated the influence ofinvolvement on selected consumer characteristics and behaviours, littleresearch has been devoted to examining the potential impact of involvementon advertising-related attitude formation and behavioural intentions. Thepresent study examined past studies and hypothesised that there is nodirect relationship among involvement and the variables in question. Then168 student subjects were exposed to an ad of a fictitious brand of shampooand asked for their post-exposure attitudes to the ad, the brand, and theirintention to purchase the new brand. Expectedly, involvement with shampoowas found not to be related to any of the parameters.

INTRODUCTION

It is of great importance for marketersand advertisers to know how specificconsumer attitudes are formed after anindividual is exposed to theadvertisements. It is a well-acceptedfact (e.g., Brown and Stayman, 1992;Homer, 1990; Shimp, 1981, Kanuk andSchiffman, 1988) that the brand attitudeand purchase intention are substantiallyinfluenced by consumers’ attitudestoward ads. From the perspective ofconsumer-information processingmechanism also an insight into thephenomenon of consumer advertisingevaluation leads to better creativestrategy and persuasion. Thus the

consumer and advertising researcherscontinue to explore the impact thatdifferences in consumer characteristicshave on their evaluations of advertisingand other ad-relevant variables (Cushingand Douglas-Tate, 1985)—— variableslike consumer expertise and knowledge,Involvement, personality characteristics,values, demographics, psychographics,etc are often studied for this purpose..

INVOLVEMENT REVIEW AND ADVERTISING

Involvement remains one of the mostresearched subjects in consumerliterature (for a review see Andrews etal., 1990). In spite of some itsdefinitional ambiguities (Poiesz and deBont, 1995) involvement can be broadlyconceptualised as associated with selfrelevance(Zaichowsky, 1985, Celsi andOlson, 1988) and is supposed to be a

! Faculty,National Institute of ManagementCalcutta

34



significant moderator of consumerbehaviour(Antil,1984). It can bementioned that, involvement, as aconstruct, is believed to be existing intwo different forms (Houston andRothschild, 1978 and Rothschild, 1979)—Situational involvement (SI) andEnduring involvement (EI). In SI,consumers may experience a temporaryinvolvement or concern with a productduring its purchase when there are highstakes associated with the purchaseoutcome. The greater the amount atstake, the higher will be the consumer’slevel of involvement. The involvementand its behavioural correlates such asinformation search and negativecognitive responses are directed at thepurchase act rather than at the productitself, and represent energy expendedto help insure a favourable purchaseoutcome. Once the purchase iscompleted and its immediate outcomeresolved the involvement is no longerrequired and rapidly wanes. On the otherhand, EI can occur even when a purchasegoal is not operative and is based noton risk but on the strength of theproduct’s relationship to individualneeds, values, or self-concept. Mostlyseen in enthusiasts and hobbyists butfor consumers as a whole, enduringinvolvement in case of most products islow(Bloch,1982).

WHY INVOLVEMENT IS TO BECONSIDERED ?

The interest in understanding the effectof involvement on communicationprocesses has been increasing in recentyears. There seems to be a generalunderstanding and agreement thatcommunication effects under conditions

of “high involvement” are differentfrom those under “lowinvolvement”.(Mitchell,1981). Andaccordingly, many researchers havestudied the interrelationships betweeninvolvement and communication effects.Some of the relevant researches are on— levels of processing and messageinterpretation (Celsi and Olson, 1988;Maheswaran and Meyers-Levy, 1990;Miniard et al., 1991; Mitchell, 1981),motivation to process information(Burnkrant and Sawyer, 1983; Bloch1982), type of processing (Mittal, 1988),behavioral intention (Swinyard, 1993).Other areas like interest in advertising(Mittal and Lee, 1989), differences inadvertising effectiveness of radio andtelevision (Buchholz and Smith, 1991),advertising appeal(Vaughn, 1989 ;Rossiter and Percy,1991) need specialmention in the given context.

There is also considerable research thatindicates that the type of evaluativeprocessing during exposure affectsattitude formation and change (e.g.,Greenwald 1968, Wright 1973, 1980)—hence, counter-arguments reduce thefavourableness of the attitudes that areformed while support argumentsenhance their favourableness. But afinding(Michell,1981) suggested thatonly counter-arguments etc cannotdefine all the differences in attitude andsome other mediators should be thoughtof . It is well-known (Fishbien, et. al,1980) that attitude formation andbehavioural intentions are closely linkedto various personal characteristics, likevalues, goals, knowledge, etc.Moreover, researchers (Brown andStayman, 1992; Lutz et al., 1983;Mackenzie and Lutz, 1989; Mackenzie etal., 1986) have shown consistent

35



positive correlations among various adrelated attitudinal and behaviouralintention(purchase intention or PI)constructs.

The above findings logically leads toanother relevant area of enquiry, whichis yet to be assessed i.e. the potentialeffect of involvement with importantadvertising related attitudinal andintention variables.

THEORETICAL BACKGROUND ANDHYPOTHESIS DEVELOPMENT

The two crit ical stages in anyinformation acquisition process areattention and processing. Processingremains a very key stage which iscomplex and affects the later stages inattitude formation. Here the individualsinterpret the information from theenvironment, make inferences andevaluate the information. Numeroustheories from cognitive psychologyindicate that how individuals processinformation affects their ability toretrieve the information at a later pointin time (Mitchell 1980). For instance,the depth of processing approachsuggests that the use of semanticprocessing and the amount ofelaboration that occurs duringprocessing enhances the ability toretrieve the processed information(e.g., Craik and Tulving 1975, Craik1979). Thus these two stages affect theformation and change of attitudes.

The consumer attitude formationthrough advertising occurs through twodistinct yet inter-related variablesnamely, attitude-toward-the-ad (AAD)and (Kanuk and Schiffmann,1992)a t t i tude - toward - the -b rand(AB) .

Consumers form various affectivefeelings and cognitive judgements as aresult of an exposure to an ad. Thesefeelings and judgements, in turn, affectthe individuals’ attitude-toward-the-adand beliefs. Finally, the consumerattitude-toward-the-ad influence hisattitude-toward-the-brand.

An advertisement, as a stimulus, is amajor factor that tends to affectattention and processing of theindividual while s/he is exposed to thestimulus. Again, ads combined withindividual goals, in turn, determine thelevel and direction of involvement(Mitchell,1981). Thus based on theintensity of the involvement theindividual will differentially process thead information and thereby formingattitude. Thus, under the “highinvolvement” condition, individualsdevote all their attention to theadvertisement and execute an in-depthprocessing strategy(Mitchell, 1981).Consequently, they will critically evaluatethe brand information in theadvertisement and will generally forman overall evaluation of the advertisedbrand during exposure to theadvertisement. This means that theirverbal thought processes duringexposure to the advertisement willcontain a large number of counter-arguments and support arguments.(Fig 1)

Under the “low involvement” condition,individuals will also execute a brandprocessing strategy; however, thisstrategy will be executed with a muchreduced attention level leading to aweak attitude formation. Thus ratherthan involve directly, involvementcreates an environment for betterprocessing of ad information, attitude

36



formation and finally behaviouralintention.

This leads to our prime hypothesis:

H1: Involvement is not directlyassociated with ad attitudeformation and behaviouralintention.

In addition, the present study will alsoreplicate the findings involving thepositive correlation among the threeconstructs namely: AAD, AB and PI in atypical Indian condition. However, thereis no requirement to study the influenceof involvement on PI as this is abehavioural intention and it followsattitude development (Fishbein, 1980).

METHODOLOGYSample

The sample consisted of 168postgraduate students at the NationalInstitute of Management Calcutta.Shampoo was selected from among a listof other FMCGs as a pretest indicatedthat these subjects were heavy users of

the said commodity. This follows Yi’s(1990) recommendation that subjectsshould have some interest in the productso that they will process information inthe ad. There were 72.3% male and 27.7%female subjects with a mean age of22.20 years.

Research Design

The product chosen for the study was ashampoo based on the results of a pre-test to determine products that thesample population used and purchased.A fictitious brand name of shampoo,“Actigel,” was created to be featuredin the advertisement. A one page, blackand white, print ads featuring this “new”brand of shampoo was produced similarto ads for other existing real brands. Thead included a black and white photographof a woman posing with long andbountiful hair.

Some common hair problems like bounceand manageability were highlighted.Both the ads feature straight forwardrational solutions (Maloney,1961 cited in

CONCEPTUAL MODEL

Goals

Involvement

Storage ofProcessed

Information

Attitudes

Stimulus Attention Processing

37



Kotler,1996) to these common hairproblems . A fictitious brand of shampoowas purposefully used in the ads toremove the effects of prior experienceand attitudes toward existing brands andtheir ads.

To make the ad appear to come from areal company, the subjects were given ashort description about the new brandand were explained the utility of thepresent study. The subjects then wereallowed to look at the advertisement attheir own pace, but no interactionbetween participants was allowed(Lowrey, 1998) and they were asked toread the ad as if they were seeing it ina magazine or a newspaper(Yi,1990).After they studied the ad the subjectswere asked to fill out a questionnaireconcerning the product and the ad

Instruments

To measure the constructs of interest,the questionnaire contained multiple-item scales used by other researchersthat have demonstrated adequatereliability and validity. The ten-itemscale, the Personal InvolvementInventory, described by Zaichkowsky(1987) measured product involvement.Attitude toward the brand was measuredby the three-item scale used by Muehlingand Laczniak (1988). The three-itemscale used by Gotlieb and Sarel (1988)measured purchase intentions. Attitudetoward the ad was measured by thethree-item scale used by Mackenzie andLutz, (1989). So far as the cognitiveresponse was concerned the subjectswere asked to write down any thoughtthat went through his/her mind whilereading the ad (Wansink, Ray, and Batra,1994). Cognitive responses were used

because they can mirror the actualthoughts that occur to people as theyevaluate a persuasive message(Wansink, et. al) and are importantindicators of attitude change(Greenwald, 1968 cited in Lowrey,1998).

ANALYSIS AND RESULTS

The scale-values were first subjected tonormal transformation using Likert’scategory-scale method(Goon, et al1990). This procedure was deemednecessary as normality is considered tobe the most fundamental assumption inany multivariate data analysis (Hair,et.al 1998).

Correlation Analysis

Pearson correlation was computedamong the scales. AAD, AB, and PIsignificantly correlated with eachother(r=.517, .523, .513 p<.01) as paststudies have shown (Lutz et al., 1983;Mackenzie et al., 1986). Positive(negative) attitudes generated by an adare related to positive(negative)attitudes toward the advertised brandand also influences intention to purchasethe advertised brand.

Involvement and the Other Parameters

Poor fit between involvement andattitude was revealed by thedetermination coefficients. The values(R2) were found to be .031, in case ofAAD and .082 incase of AB, F-valuebeing signif icant at 5% level ofconfidence in both the cases.

The possibility that higher level ofinvolvement might act as a moderatingvariable affecting AB and AAD was alsoexamined. For this the subjects were

38



divided into high and low involvementcategories along the median value(.015), thus having 84 subjects as highand the rest being low. An independenttwo-sample t-test was done in this caseto ascertain if there is any differencebetween the mean attitudinal measuresacross the involvement categories ——— the result remained negative (For AADt=-1.44, p>. 01 and for AB t=-. 562, p>.01)

Analysis of the cognitive responses wasdone with the help of two trained judgeswho separated between counter-arguments and support-arguments. Theinter-judge agreement was calculated tobe——. 746 that indicated a very goodagreement. A similar two-sample t-testdidn’t indicate any significantdifferences in the number of cognitiveresponses across high involvementgroup.

DISCUSSION

The purpose of the present study was toempirically validate the relationshipbetween product involvement and three adrelated attitudinal variables. It washypothesised and subsequently proved thatinvolvement is not directly related to thesevariables. However, attitude toward thead, attitude toward the brand, andpurchase intention were found to be allpositively correlated with each other. Asprior studies have shown, involvement wasunrelated to any of them.

The utility of the hypothesis and thesubsequent empirical proof indicatesthat researchers who test theeffectiveness of ads should have noworry about the effect of consumers’involvement level on the outcome oftheir copy tests. Thus consumers makejudgements of an ad’s appealindependently of how they are involvedwith the product.

REFERENCES

1. Andrews, J.Craig, Srinivas Durvasulaand Syed H. Akhter (1990), “Aframework for conceptualizing andmeasuring the involvement constructin advertising research,” Journal ofAdvertising, 19, 27-40

2. Antil, J.H. (1984): “Concept-ualization and Operationalization ofInvolvement,” Advances in ConsumerResearch, 11, pp203-209.

3. Bloch Peter H (1982): “InvolvementBeyond the Purchase Process:Conceptual Issues And EmpiricalInvestigation,” Advances inConsumer Research,9, 413-417

4. Brown, Steven P. and Stayman D. M.,(1992): “Antecedents andConsequences of Attitudes Towardthe Ad: A Meta-Analysis,” Journal ofConsumer Research, 19, 34-51.

5. Burnkrant, Robert E. and Allan G.Sawyer, (1983), “Effects ofinvolvement and for conceptualizingand measuring the involvementconstruct in advertising messagecontent of information-processingintensity,” in Information ProcessingResearch in Advertising, ed. RichardJ. Harris, Hillsdale, NJ: LawrenceErlbaum research,” Journal ofAdvertising, 19, 27-40

39



6. Celsi, Richard L. and Jerry C. Olson(1988), “The role of involvement inattention and comprehensionprocesses,” Journal of ConsumerResearch, 15, 210-24.

7. Craik, F. I. M. (1979), “Levels ofProcessing: Overview and ClosingConsents,” in L. S. Cermak and F. I.M. Craik, eds., Levels of Processingin Human Memory, Hillsdale, NJ:Lawrence Erlbaum Associates, 447-462.

8. Craik, F. I. M. and E. Tulving (1975),“Depth of Processing and theRetention of Words in EpisodicMemory,” Journal of ExperimentalPsychology: General, 104, 268-294.

9. Cushing, Peter and Douglas-Tate, M.,(1985): “The Effect of People/ProductRelationships on AdvertisingProcessing,” in Linda F. Alwitt andAndrew A. Mitchell (eds.), PsychologicalProcesses and Advertising Effects:Theory, Research, and Applications,Lawrence Erlbaum, Hillsdale, NJ., pp.241-259.

10. Fishbein, Martin(1980): “ AnOverview of the Attitude Construct,”in G B Hafer(ed), A Lookback, ALookback Ahead, Chicago AmericanMarketing Association, 8.

11. Goon, A.M., Gupta M.K., DasguptaS(1990): “Fundamentals ofStatistics,”, Vol I, 311-312, WorldPress: Calcutta

12. Gotlieb, Jerry B., and Sarel, D.,(1988): “Comparative AdvertisingEffectiveness: The Role ofInvolvement and Source Credibility,”Journal of Advertising, 20, 38-45.

13. Hair, J.F., Anderson R.E., TathamR.L., Black W.C. (1998): “Multivariate Data Analysis,” 70,Prentice Hall International: NewJersey

14. Homer, Pamela M., (1990): “TheMediating Role of Attitude Towardthe Ad Some Additional Evidence,”Journal of Marketing Research, 27,78- 86.

15. Houston, Michael J. and Michael L.Rothschild (1978), “Conceptual andmethodological perspectives oninvolvement,” in Research frontiersin marketing: dialogues anddirections, ed. S. Jain, Chicago:American Marketing Association,184-187.

16. Kotler, P (1996): “MarketingManagement-An Asian Perspective,”798, Prentice Hall: Singapore.

17. Lowrey, T.M. (1998): “The Effects ofSyntactic Complexity on AdvertisingPersuasiveness,” Journal ofConsumer Psychology, 7(2), 187-206.

18. Lutz, Richard J., Mackenzie, S. B.,and Belch, G. E., (1983): “AttitudeToward the Ad as a Mediator ofAdvertising Effectiveness:Determents and Consequences,” inAdvances in Consumer Research, 10,532-539.

19. Mackenzie, Scott B., and Lutz, R. J.,(1989): “An Empirical Examination ofthe Structural Antecedents ofAttitude Toward the Ad in anAdvertising Pre-testing Context,”Journal of Marketing, 53, 48- 65.

20. Mackenzie, Scott B., Lutz, R. J., andBelch, G. E., (1986): “The Role of

40



Attitude toward the Ad as a Mediatorof Advertising Effectiveness: A Test ofCompeting Explanations,” Journal ofMarketing Research, 23, 130-143.

21. Maheswaran, Durairaj and JoanMeyers-Levy (1990), “The influenceof message. framing and issueinvolvement,” Journal of MarketingResearch, 27, 361-367

22. Miniard, Paul W., Sunil Bhatla, KennethR. Lord, Peter R. Dickson and H. RaoUnnava (1991), “Picture-basedpersuasion processes and themoderating role of involvement,”Journal of Consumer Research, 18, 92-107.

23. Mitchell, Andrew A(1981): “TheDimensions Of AdvertisingInvolvement,” Advances inConsumer Research,8, 25-30

24. Mittal, Banwari and Myung-Soo Lee(1989), “A causal model of consumerinvolvement,” Journal of EconomicPsychology, 10, 363-389

25. Muehling, Darrel D. and Laczniak, R.N., (1988): “Advertising’sImmediate and Delayed Influence onBrand Attitudes: ConsiderationsAcross Message InvolvementLevels,” Journal of Advertising, 17,23-34.

26. Poiesz B.C., Cees, de Bont,(1995):“Do We Need Involvement toUnderstand Consumer Behaviour?”

Advances in Consumer ResearchVolume 22, 448-452

27. Rothschild, Michael L. (1979),“Advertising Strategies for High andLow Involvement Situations,” inAttitude Research Plays for HighStakes, (eds.) J. Maloney ant B.Si lverman, Chicago: AmericanMarketing Association,74-93.

28. Schiffman L and Kanuk L. L. (1997)“Consumer Behaviour,” PHI, 273.

29. Shimp, Terence A(1981) : “AttitudeToward the Ad as a Mediator ofConsumer Brand Choice,” Journal ofAdvertising, 10: 9-15.

30. Swinyard, William R. (1993), “Theeffects of mood, involvement, andquality of store experience onshopping intentions,” Journal ofConsumer Research, 20 (September),271-280.

31. Wansink Brian,. Ray M. L., BatraR,(1994): “Increasing CognitiveResponse Sensitivity,” Journal ofAdvertising, 23, 65-75

32. Yi, Youjae, (1990): “Cognitive andAffective Priming Effects of theContext for Print Advertisements,”Journal of Advertising, 19 40-48.

33. Zaichkowsky, J. L (1987): “ThePersonal Involvement Inventory:Reduction, Revision, and Applicationto Advertising,” Working Paper,Simon Fraser University.

41



VVVVVILAKSHAN

XIMB JOURNAL

OF MANAGEMENT

NOTES AND COMMENTS

42



43



BUILDING A LIVE BRAND FROM WITHINMukta Kamplikar !

Shambhavi Sharma !!

Abstract

In this paper the authors emphasize the People component of the serviceorganization. This component can contribute to service quality andcustomer satisfaction and finally to build the organizational brand.Organizations have to realize that the brand is simply the outwardmanifestation of the organization’s business strategy. The brand is theorganization - and brand values must match the service organization’soperational performance.

The highlights of this paper are:

" Significance of people in the organization" Magnifying the relationship of employee and customer satisfaction" Internal Branding (buy-in and commitment of the organization’s

own employees)" Employees as Brand Ambassadors

Building the organizational brand through people is building theorganization’s image for the future and it is the customer who is thefuture of a service organization.

to the customer when he walks into thefirm or calls their call center. Withlimitations of intangibility, perishability,heterogeneity service organizations facea challenge of creating a LIVE brandbecause the People P is an extremelyimportant P in marketing of services.

A LIVE brand is a very interestingconcept. On the one hand, serviceorganizations recognize the value of apowerful brand and are aware of the needto build their brand. Many serviceorganizations tend to focus most of theirbrand building efforts on externalcommunications such as advertising,

! Faculty, National Institute of BankManagement, Pune

!! Academic Associate, National Institute ofBank Management, Pune

As markets become more competitiveand customers become more demanding,service organizations have to securelong term relationships with consumersthat will enhance their business growth.The difference between an averagebrand and a great brand is that the greatbrands not only capture the customersimagination with their advertising – theydeliver on the promise communicated

44



public relations, mailers and other kindsof sales promotion activities - withoutconsidering the crucial role played by theiremployees in the brand.

The relationship between organizationalvalues (which are generally HR driven)and brand values (which are generallycommunicated externally to customersand are driven by the marketingdepartment) needs to be understoodvery carefully because it’s a prettyblurred area. It accounts for much of theskepticism with which consumers viewbrands and the messages and values theyare trying to portray. A rational and anaware customer justifiably believes thatthe “ad shimmer” provided by externalcommunications will not be matched bythe actual performance of the servicefirm. This belief arises from a simplecomparison of the message sent outthrough advertising and promotions andthe actual service encounter that thecustomer experiences. (Fig 1)

THE INTERNAL CUSTOMERS OF ASERVICE ORGANIZATION:

Service employees are the Service, theorganization in the customer’s eyes andservice marketers. The core business forany service firm is replicable bycompetitors but the only thing notreproducible is the people who make upthe service organization’s personalityand create that competitive edge overothers. Employees whether they are intoback-stage or front-stage activities ofa firm personify the service firm in thecustomer’s eyes.

The brand is a component of everythinga service organization does. Although it

is nurtured and managed by themarketing department, the brand isrepresented by the organization as awhole. That’s why a strong brandrequires that everyone in theorganization has a completeunderstanding of, and ability to expressall that the brand promises and also thebrand values.

The bottom line is that adopting a branddriven approach to business, bothinternally and externally, fosters the sortof customer loyalty that ultimatelytranslates into increased profitability -and a sustainable competitiveadvantage. A strong brand identitysustained by its people is theorganization’s most importantcompetitive advantage.

Successful brands have employees that‘live’ the brand values in their dailytransactions. Others have employeesthat are vaguely aware of the messagethe organization is trying to enforce andthe image it is trying to build.

For the brand to come to life withcustomers (and create a bond based onvalues that are backed up byperformance), it needs to form thebackbone of the organizational culture.Brand values need to become theinvisible glue of the organization, andin so doing project a culture of oneness- providing direction and clarifyingexpectations for customers, employeesand the organization.

Customers must be met with clear,consistent and co-ordinatedcommunications and service and thecustomer must experience a service thatmatches the advertisement they saw. Aclear understanding of the brand values

45



from all employees translates into theability to make strategic decisions as towhat is ‘on-brand’ and what is ‘off-brand’. These sorts of decisions arerequired on a daily basis from allemployees, and the ability to make themconfidently translates directly intogreater company performance, driven byincreased employee satisfaction.

THE RELATIONSHIP BETWEENEMPLOYEE SATISFACTION ANDORGANIZATIONAL PERFORMANCE:

There is concrete evidence that satisfiedemployees make for satisfied customers.Through their research with customersand employees in 28 different bankbranches, Benjamin Schneider and DavidBowen have shown that both a climatefor service and a climate for employeewell-being are highly correlated withoverall customer perceptions of servicequality. That is, both service climateand human resource managementexperiences that employees have withintheir organizations are reflected in howcustomers experience the service.

In similar vein, Sears found customersatisfaction to be strongly related toemployee turnover. In stores with thehighest customer satisfaction, employeeturnover was 54 percent, whereas instores with the lowest customersatisfaction, turnover was 83 per cent.Studies by Ryder Truck demonstratedthat when the company put pressure onemployees through certain negativehuman resource practices, employeesreacted with low motivation anddissatisfaction. Ultimately there is aconnection between employee tensionlevels, poorer quality service, andnegative customer reactions. Huselid

(1995) used two scales – one to measureemployee skills and organizationalstructure and the second to measureemployee motivation. The first scaleincluded a broad range of practicesintended to enhance employeeknowledge, skill and abilities. Thesecond scale measured how welldesigned the appraisal systems were andhow well they were l inked tocompensation and merit decisions in thecorporation. He found that in a sampleof 3,452 firms representing all kinds ofindustries, one standard deviationincrease in management practices wasassociated with increases in sales,market value and profits. Huselid andBecker (1997) found that one standarddeviation improvement in HR systemindex was associated with an increasein shareholder wealth of $ 41,000 peremployee. Delery and Doty (1996) in astudy of nearly 200 banks found thatdifferences in HR practices accountedfor large differences in financialperformance. Pfeiffer (1998) presentsthe following rationale to explain thisrelationship:

1. Performance increases becausepeople work harder.

2. People put in effort and show greatercommitment if they have greatercontrol over their environment, seetheir effort as related tocompensation and pressure activatedfrom self-managed teams.

3. Training, job rotation and such otherpractices help people to work smarteralso. High commitment to work alsosaves direct and indirect costs oflabor.

4. Training, multiskilled, self-managedand motivated employees save on a

46



variety of administrative costsincluding the cost of management.

Khandwalla after the review of severalcase studies and experiences in Indiaconcluded, ‘Western and Third Worldstudies of organizational excellenceindicate five major keys toorganizational excellence: mission,vision of excellence, core values, styleof management, goals, policies andchoices of domain, managementsystems and structure and theorganizational renewal processes.(Khandwalla,1992:69)

These evidences are sufficient toindicate that good people managementand HR practices matter. A good HRpractice can lend competitive advantageto the organization if other things areequal. It must be noted that employeesatisfaction rests heavily on the creation(and delivery) of performanceobjectives, true career advancementchannels and rewards systems.

A satisfied and motivated employee canLIVE the brand and also be a BrandAmbassador. The variables determiningemployee satisfaction in a serviceorganization can be intrinsic andextrinsic variables. The intrinsicvariables can be employee morale,motivation, commitment, employeeknowledge, skill and abilities and theextrinsic variables can be organizationalculture, management support andorganizational commitment.

If we just magnify one of the verticesof the Classical Services marketingTriangle and link it to the understandingabout the relationship betweenemployee and customer satisfaction wecan probably get the importance of what

is called as INNER Branding (Figure 2,3and 4). Unless a service organizationensures employee buy in and commit noprogram will endure.

Creating a LIVE Brand and ensuring thateach service employee LIVES the brandneeds efforts.

TURNING EMPLOYEES INTO BRANDAMBASSADORS:

When employees are brand advocatesthey create brand differentiation and soservice organizations should embraceinternal branding for many reasons. Itleads to increased organization loyaltyand job longevity. It enables employeesto better serve their customers becausethey understand the brand promise: itencourages employees who belief in thebrand to work harder and better.Therefore, it is important to:

" Align business and brand strategy:

Brand strategy must promoteloyalty-based relationships bydefining a relevant, differentiatedand credible value proposition.This is achieved by ensuring theservice quality that is consistentwith the value proposition andbrand promise. There is a need tomake new employees aware of thebrand promise and values as soonas they join.

" Give employees something tobelieve in:

Internal branding is about creationof human meaning. Employeesmust fully belief in the brandshigher vision and meaning goingbeyond the service being sold. Theemployees can then become self-

47



actualized and so the brand mustbe appealing not only to theorganization but also to theindividuals within it.

" Hire Brand Ambassadors:

Brand advocate employees musteither be found or created fromwithin. The organization mustensure that the customer and theemployee’s perception of the brandare aligned.

" Empower Brand Ambassadors:

The understanding andempowerment an employee canoffer goes a long way towardscreating a stronger brand. When aservice organization has aninternal branding plan theemployees relate better with thebrand, with the customers, andwith each other. They are morelikely to experience the brand in away that is consistent with thebrand’s public face and thecustomer experience.

" Garner Buy-in and support frommanagement:

Management support andleadership drives consistent brandbehavior. All managers mustdemonstrate that internal brandalignment is a priority through theircommitment to brand goals, valuesand behaviors through theexecution of the brand promisethrough every action (process),both internally and externally.

" Create employee responsibility andaccountability:

This often involves changes toprocess incentives, training andmanagement style as much ascommunication. Employees mustbe rewarded for responsibly andloyally living the brand.

" Cultivate Employee Participation:

There is a need to educate theemployees on the importance ofthe branding process. They needto understand how the brand hasbeen built.

" Design continuous performancemeasurement and feedback:

It is important that a coherentbrand evaluation program withobjective growth measures andcelebrations of achievement forsustaining the momentum ofinternal brand alignment isimplemented. It creates anemployee feedback loop to assesscustomer acceptance of andsatisfaction with the brandpromise.

" Energize the “force within” daily:

A brand becomes a successful brandwhen employees live its values.Aligning the service organization,its operations and the culturearound the brand values is vital.The brand stands for a relationshipthat an organization has with itsemployees as much as it representsthe relationship that it has with itscustomers.

" Nurture a two-way street betweenemployees and marketing:

Internal branding must be adialogue. Employees are a great

48



source of information of the brand.It is important to use theirperceptions, experiences andinteractions with customers forresearch and to strengthen thebrand message. The brandmessage can be refined andimproved when it is reflective ofemployee’s attitude andexperiences. Employees need tohear the same message theorganization displays to its externalcustomers and must not onlyunderstand but be empowered touphold it.

Living the brand is an ‘executivechallenge’. It is important to create astrong brand from within the serviceorganization. But this will only happenif the organization’s employees actuallybelieve in the brand, its values and itscommitment to its values, especiallywhen it comes to the internal functioningof the service firm, the actualprocesses, the actual performanceonstage and offstage.

And this is where the make or breakchallenge lies for decision-makers. Inorder for service organizations to be

Figure 1

GAP 4

SERVICE DELIVERYEXTERNAL COMMUNICATIONTO THE CUSTOMER

49



50



competitive, executives have to live thebrand values too

The figure shows brand communicationthrough service delivery. Externalcommunication about the brand is now

strengthened because employees nowknow what exactly the brand promisesand they l ive the brand valuesthemselves and therefore relate to theorganization better and hence jobinvolvement is higher.

REFERENCES:

1. Rastogi, P.N. – KnowledgeManagement And IntellectualCapital As A Paradigm Of ValueCreation (AIMS – Sept’03- pg.89)

2. Schuler,R S & Jackson S (1999) -Strategic Human ResourceManagement.

3. Rohmetra.N.(1998)- HumanResource Development inCommercial Banks in India

4. Rao,T.V (1990) - The HRDMissionary.

5. Managing Human Resources-Bohlander, Snell and Sherman(2001)- South western collegepublishing.

6. The Learning Organisation- IBMconsulting group- UniversitiesPress-(2000).

7. Performance Management-Breakthroughs in achieving strategy

through people- Weiss B.T, Hartle F,(2000) – St. Lucie Press

8. Future of HRD- Rao,T.V.(2003)-Macmillan India Ltd.

9. Translating strategy into action-The Balanced Scorecard-Kaplan.S.R, Nortan. P.D,(1996)-Harvard Business School Press.

10. Services Marketing – ZeithamlValerie and Bitner Mary 1996

11. Turn Employees into BrandAmbassadors – Jacob Ricks , ABABank Marketing(July 2003) McGrawHill

12. Services Marketing – LovelockChristopher 1999

13. Advertising Management – Batra,Myers ,Aaker (2002) Prentice HallInternational

51



Is the hysteria on job migration to developingcountries misplaced?

Anurag Dhanwantri !

A lot has been said and written about jobmigration from the US to India in therecent past. Quite a lot of what has beenwritten is negative, US media raising thespecter of job losses and Indiancompanies worried about the backlash.Since I am straddling both the boats, anXIMB alum and currently a student atDarden Graduate School of BusinessAdministration, I decided to see if I couldconjure up a win-win version of thisdebate. While it is possible to write reamsof papers citing all sorts of economicstudies et all, I will attempt to prove asimple proposition – US citizens will bericher.

China will displace US as the largesteconomy?

Goldman Sachs came out with a veryinteresting study recently projecting GDPsof various countries over the next half acentury. Goldman Sachs economistspredict that favorable demographics,improving regulatory environment andcost competitiveness will drive China andIndia to the No. 1 and No. 3 slots in theGDP size sweepstakes by 2050 AD. Justto put figures in perspective, US is by farthe largest economy currently with a GDPalmost equal to those of the next sevenlargest economies put together. Do thesenumbers not prove what the critics of job

migration in the US have been saying allalong? No. It is my contention that youare looking at the wrong numbers. Let metake that back. You are looking at the rightnumbers but form the wrong perspective.

GDP per capita is the right measure ofwealth

The standard of living of any country isdetermined by GDP per capita and not byGDP per se. Goldman Sachs, in the studythat I quoted above, also projected theper capita GDP in US over the next half acentury. The trend is unmistakable – theper capita GDP in US will rise steadilyduring the coming decades, at rates verysimilar to those in the recent decades, andeven in 2050 AD US citizens will beamongst the richest in the world. So, ifyou are worried about the standard ofliving of average US citizen in future onaccount of outsourcing, worry not. US willnot lose its pre-eminence in the GDPsweepstakes because its citizens arebecoming relatively poorer, but becausedeveloping countries like China and Indiahave much larger populations. Even as thestandard of living in these countries willcontinue to be lower than in US the actualsize of their economies will become verylarge.

Goldman Sachs report

I can hear some murmurs of dissent alongthe lines of ‘can one really believe

!#Doctoral Student, Darden Business School, USA

52



Goldman Sachs’. Let me reassure you thatGoldman Sachs validated the output inmultiple ways. Let me quote from adiscussion I had with Economics professorat Darden, Peter Rodriguez “Even if theauthors prove to be wrong in theircalculations, I believe in theirpredictions. The economic world will bequite different when the BRICs (Brazil,Russia, India and China) are the largesteconomies, but none will be poorer. True, the US, Japan and other Westernpowers will be smaller, but their peoplewill have higher living standards – madericher in part by the incredible growthin the BRICs. And, imagine how many inthe BRICs will be lifted out of povertyand into the middle class.” The morecurious amongst you will be wonderinghow this works. How can one outsourceand still get richer?

How does outsourcing create value?

Let me quote from a recent seminal studyby McKinsey, on off shoring of businessprocessing jobs to India. In the studyMcKinsey contends that “….off shoring,far from being bad for the UnitedStates, creates net value for theeconomy. It directly recaptures 67 centsof every dollar of spending that goesabroad and indirectly might capture anadditional 45 to 47 cents—producing anet gain of 12 to 14 cents for everydollar of costs moved offshore.”

There are four key drivers of McKinsey’sestimates:" Cost savings. For every dollar of

spending on business services thatmoves offshore, US companies save 58cents, mainly in wages.

" New revenues. Indian companies that

provide offshore services need goodsand services themselves, and often,buy these from US companies.McKinsey estimates that for every dollarof corporate spending that movesoffshore, suppliers of offshore servicesbuy an additional five cents worth ofgoods and services in the US.

" Repatriated earnings. Many Indianoffshore service providers are in factUS companies that repatriate earnings.Such companies generate 30% of therevenues of the Indian offshoreindustry. Thus an additional four centsof every dollar spent on off shoringcreates value for the US.

" Redeployed labor. Beyond the directbenefits to the US, off shoring canindirectly benefit the economy: capitalsavings can be invested to create newjobs, for which labor will be available.Indeed, this is exactly what hashappened over the past two decadesas manufacturing jobs movedoffshore.

Conclusion:

Having so far run the bulk of my argumenton quoting others, let me finish on apersonal note. Why did I pick job migrationas the topic? I have a feeling that thechanges in the world economiccomposition will happen sooner than later.As a result the extremely dynamicbusiness world B-school students will bestepping out into, whether in India or inUS, will make us confront outsourcing asone of the biggest threat/opportunity nomatter what profession we choose.

US MBAs working on Wall Street will becalled upon to value companies with robust

53



outsourcing strategies vis-à-vis the oneswho do not have a implementable plan.US MBAs working as consultants may wellbe helping their clients draw up the bestoutsourcing strategy and those working inGeneral Management may well have todecide geographic locations for futureresource allocations. Indian MBAs willmostly be on the other side of theequation, making sure that theircompanies have the order winning criteriato bag outsourcing mandates.

I choose this topic to highlight the factthat outsourcing is not a win-lose game.It can be a win-win game for all concerned.So that when we do confront outsourcinglets take it as an opportunity and not athreat. For US MBAs when we do make anoutsourcing decision let us be fully securein our conscience that we have made adecision in the interest of not onlywhatever corporation we are working forbut also for the future economic prosperityof US citizens.

54



SIGNIFICANCE OF FLEXIBLE NPD STRATEGIESFOR MARKETING SUCCESS

Saji K.B.!

Abstract

The ever-changing global market has provided a challenge for marketersto get along with their new product development processes. Thetechnologies that are required to make a product should be capable ofchanging radically even as the product is under the process ofdevelopment. Companies want breakthrough products, but not all areadept at making them. Traditionally, product development teams believethat a high percentage of sales come from products that did not exist afew years ago. Unfortunately, the development teams at many companiesdon’t deliver the goods that the market wants. Ideally, the developmentteam should always get to know customer expectations to the last detail,which calls for a flexible product development process that would enablethem to design the product in the best possible way. Failure to do so mayresult in being outperformed by competitors in the market. This paperestablishes the significance of flexible new product development strategiesfor achieving success in marketing.

1.0 INTRODUCTION

The intense global competition hasresulted in companies developing a largenumber of products. Some of theseproducts go well with customers, whilesome do not, resulting in productfailures. The existing products of thecompanies are vulnerable to changingneeds and tastes, new technologies,shortened product life cycles, andincreased domestic and foreigncompetition. In order to counter thesefailures companies try to come up withnew products. New products here referto “product improvements andmodifications, or new products that the

firm develops through its own researchand development efforts”(Saji, 1997).

In the early seventies, Philips marketedits first practical videocassette recorderthereby gaining a three-year lead on itsJapanese counterparts. But this lead wascompletely wiped out in the eighties byJapanese manufacturers with theirproduct development capabilities bylaunching products that are superior andfar better in terms of technology andcost compared to Philips. But, developingnew products is inde ed a costly and riskyaffair. Several studies have reported thefailure rate to be very high with regardto new products. It is estimated thatnearly 75 per cent of all new productsfail at the launch of the product itself(Biyalogorsky, 1998).

! Faculty,Amrita Institute of Management,Coimbatore

55



2.0 REASONS FOR NEW PRODUCTFAILURES

One of the major reasons for newproduct failures is that companies arenot creating products that customerswant, but waste their resources on the“faster, better, cheaper” theme (Saji,1997; Munro, et. al., 1999). Forexample, recently Texas Instrumentshave lost nearly US$ 660 million beforewithdrawing from the home PC market;and RCA lost nearly US$ 575 million on itsill-fated videodisc players (Munro, 2001).

Another important reason for the newproduct failure is that the mangersoverestimate demand because of theirego involvement with new products,setting off a chain of events that resultsin actual or relative product failure(Schultz, 1999). Considerable anecdotaland some empirical evidence (e.g.,Schultz and Braun, 1997) shows that the“pet projects” do cause managers tolose sight of “reality,” where reality isdefined as what consumers really want.The remedy for this situation may be an“outside view” (Kahnemann and Lovallo,1993) that provides a check onmanagers’ wishful thinking. Thoughthere could be many other reasons fornew product fai lures, the mostsignificant ones are attributed tochanging customer expectations, cost,and technology availability.

An updated edition of Manufacturing intothe Late 1990s (DTI, 1993) stresses thevital need to plan for faster new productintroductions, and in the past few yearsspeed in many business activities hasbeen counseled as a nostrum for success(Stalk and Hout, 1990). In this rush, newproduct development (NPD) has featuredsignificantly (Smith and Reinertsen,

1991; Wheelwright and Clark, 1992). Butmost studies fail to make explicit theessential point that speed is irrelevant.What is important is that potentialcustomers should be presented withattractive products as soon as they arereceptive to these new offerings. Thisview is supported by Mathur (1991) whoargues that the primary focus ofcompetitive strategy should be onbusiness outputs – that is a customer/market orientation. For example, in theNPD domain it is not sufficient to developnew products in record time. They shouldalso be marketed soon. As Fred Forsyth,VP for World Wide Manufacturing at AppleCorporation said – “The ability to launchnew products quickly, with simultaneousworldwide introduction of localizedversions – that is the goal.” (Forsyth,1990).

3.0 THE RATIONALE FOR FLEXIBLE NPD

In a study of successful product launchesin the consumer electronics industry,Madique and Zirger (1990) haveidentified factors that lead to newproduct success. The various factorsidentified by them include: deeperunderstanding of customer needs, higherperformance-to-cost ratio, earlierproduct introduction ahead ofcompetition, effective developmentrelated cross-functional teamwork,extent of funds spent on announcing andlaunching the new product, and the topmanagement support to the NPD process.While looking at these factors, one canrealise that there is a need to deploy andadopt a flexible NPD process in firms.

Through this paper, the major reasonsfor f lexible NPD (includingcommercialization) are examined under

56



the following headings – the imperativeof shorter product life cycles (PLCs); theopportunity for cost reductions; gaininga price premium; more frequent/fresherinnovations; better product quality;greater product-line variety; andimproved market feedback.

3.1 THE IMPERATIVE OF SHORTER PLCS

The life cycle of products, especially thatof high-tech products has been gettingprogressively shorter. Goldstein (1989)found that for analytical instruments,the product life cycle (PLC) has shrunkfrom ten to four years, and Hewlett-Packard introduced 23 models in eightyears. Getting added to this dimensionis the real issue of shorteningtechnology life cycle (Saji, 2002). Assuch, effective commercialization isessential if firms are to reap the fullcash benefits of these products beforeobsolescence sets in (Goldman, 1982);and it has been shown that to maximizethe returns (ROI) to the innovator,aggressive marketing should be employedto facilitate market penetration (andhence market share) in as short a periodas possible (Gilman, 1982).

3.2 THE OPPORTUNITY FOR COSTREDUCTIONS

Better product design at NorthernTelecom reduced by a quarter the numberof days’ worth of stock required becauseproduction and distribution werespeeded up (The Economist, 1988); andStalk (1988) has calculated that a 25percent reduction in the time to producea good or service can cut costs by 20percent. A systems view of total costsalso suggests that it can pay to spend

more on NPD to ensure timely launch.Using economic modelling techniquesMcKinsey and Co. (Reinertsen, 1983)demonstrated that being 50 percent overNPD budget and on time can lead to a 4percent reduction in profits. However,being on budget and six months late tolaunch can lead to a 33 percent reductionin profits! The price of late delivery canbe lower sales and increased unit costs.The author of this oft-cited McKinseyreport also added that “Speed issometimes secondary”.

3.3 GAINING A PRICE PREMIUM

Customers will be ready to pay apremium for instant gratification – forexample, the recent success of cheapervehicle insurance products, mobilephone service providers, etc. Similarly,prompt delivery remains an importantmeans by which brands can bedifferentiated. In Japan, the NationalBicycle Industrial Company buildscustomized bicycles on an assembly line.With over 11 million variations, fittedto customers’ measurements, apersonalized bike can be delivered intwo weeks time! The price premium hereis 10 percent. Likewise, distributors andindustrial customers will pay for speedbecause it allows them to save moneyby adopting just-in-time (JIT) inventoryreduction techniques.

3.4 MORE FREQUENT INNOVATIONS

Techniques such as JIT, which speedproduction and distribution, alsofacilitate NPD. Typically, Japanese firmshave engaged in more frequent,incremental innovation (sometimescalled “product-churning”). Rather than

57



struggling through complete product linechanges every two to seven years, theyengage in a constant renewal process.Further, “breakthroughs” are achievedmore effectively in smaller easilymanaged steps rather than in greatponderous leaps – involving complex,difficult to manage structures (Nevenset al., 1990; Nayak, 1991; Saji, 2002).Constantly renewed products alsoseem fresher to the consumer and canbe adapted more easily to changingdemand.

3.5 BETTER PRODUCT QUALITY

The fastest way to do a job is to do itproperly the first time. In a majorcomparison of European and Japaneseautomobile suppliers, McKinseyconsultants (Rommel et al., 1994) foundthat the highest quality manufacturerswere faster in both manufacturingthroughput and delivery time at loweroverall cost, although these firmsactually devoted more resources to boththeir R&D and quality functions. Thislends support to the proposition that thebest firms, by significantly reducingdefects in the NPD and manufacturingprocesses, were able to achieve a timeadvantage. Additionally, PIMS evidence(Buzzell and Gale, 1987) demonstratesthat quality often commands a premiumprice. Quality also works by increasingdemand, leading to greater market shareand a consequent higher ROI.

3.6 GREATER PRODUCT-LINE VARIETY

When companies learn to make thingsquickly this often reduces the cost ofvariety because set-up times arereduced. Firms with greater product line

variety (i.e. broader product lines)generally earn a higher ROI. Hence, firmsneed to master the organizational andtechnological requirements of flexiblemanufacturing. Japanese firms likeToyota have the ability to inculcatechanges as late as in thecommercialisation stage, which is citedto be one of the major reasons for theirsuccess. By way of an example, Toyota(Japan) can make and deliver acustomer-specified model within tendays of the order!

3.7 IMPROVED MARKET FEEDBACK

The more frequent the new productintroductions, the better will be themarket feedback (Saji, 1997; Saji,2002). Additionally, the shorter the leadtimes between order and delivery, thegreater the chance that the firm will beable to tailor its production/inventoriesto changes in demand at the market.Avoiding the crises of surprise can improvemorale and allow managers to focus oncreating new products instead of shoringup past mistakes (Munro, 1997).

4.0 THE NPD MYOPIA

The previous section has indicated themerits of doing things faster inaccordance with the changing customervoice by going for a flexible NPD process,although no specific reference to rapidlaunch has been made. This is becausethe existing literature makes no directreference to this feature of NPD. Perhapsit is implicit, but unless there isprecision in our management vocabularywe wil l end up restricting ouropportunities for effective action. Thisoversight is apparent in two otherwise

58



invaluable guides to faster NPD –“Managing for Speed to Avoid ProductObsolescence” (Cordero, 1991), and “ASurvey of Major Approaches forAccelerating New Product Development”(Millson et al., 1992). The focus of thesestudies is internal and in both NPD endsat the factory gates. Even the standardNPD model (Booz, Allen and Hamilton,1982) encompasses a ‘commercialization’stage, and by implication this must beconcerned with the introduction of thenew product to its market. Self-evidentapparently, but even Nevens et al.(1990) make no mention of the productlaunch. In other words, we have anexample of an input mentality, ratherthan a customer-oriented outputperspective.

5.0 THE PROBLEMS OF SPEED

Although “getting things done soonerrather than later” is a generally valuableskill, it should be recorded that thereare potential problems associated withspeedier NPD/commercialization.Crawford (1992) highlights the hiddencosts of speeded-up developmentthrough various dimensions, viz. anoveremphasis on incrementalinnovation, NPD team creationdisrupting other organizationalactivities, project teams becomingdysfunctional, and the dangers of over-curtailment or even missed NPD stages.

Crawford’s criticism is directed atmisconceived NPD strategies and poorlyexecuted tactics rather than the principle

of time compression. Thisinterpretation is borne out by the re-evaluation of Japanese time-basedcompetition (“the dark side of time”)undertaken by Stalk and Webber (1993).They emphasize the dangers of a blindpursuit of efficiency at the expense ofeffectiveness. Doing things faster for itsown sake can lead to classic myopia. But,if used correctly, time-based flexible NPDcan help erode the boundary between theexternal and internal environment,matching the external needs & wantswith internal capabilities & skills.

6.0 CONCLUSION

The value of flexible NPD is only as goodas the quality of the process it uses togenerate information about theinteraction between technical choicesand market requirements. Unliketraditional development projects, whichrely on periodic bursts of input on users’needs, projects in turbulent businessenvironments require continualfeedback. To acquire and use thisinformation, the development processmust be flexible enough to sense thecustomer needs and wants, to testalternative solutions, and to integratethe knowledge gained from both marketand technologies while arriving at acoherent product. Organizations thathave adopted flexible NPD processeshave begun to transform the veryindustries that forced them to adopt it.They have implemented NPD strategiesthat the companies cl inging totraditional approaches cannot follow.

59



REFERENCES

10. Kahnemann and Lovallo (1993);“Timid Choices and BoldForecasts”; Management Science,Vol.39, 17-31.

11. Madique, M. A.; and Zirger, B. J.(1984); “A Study of Success andFailure in Product Innovation: TheCase of the U.S. ElectronicsIndustry”; IEEE Transactions onEngineering Management, Vol.31,No. 4, pp.192-203.

12. Mathur, S.S. (1991); TalkingStraight About CompetitiveStrategy, City University BusinessSchool Working Paper No. 124.

13. Munro, H. (2001); “New ProductDevelopment and CompetitiveIntell igence”, In: ManagingFrontiers in CompetitiveIntelligence; Craig Fleisher & DavidBlenkhorn (Eds.), Quorum Books,Greenwood Publishing Group.

14. Munro, H.; Noori, H.; and G.Deszca (1997); “Root Causes ToNew Product Failures: LookingInward in a TelecommunicationsFirm”; Proceedings of DecisionsSciences Institute International,Sydney, Australia.

15. Munro, H.; Noori, H.; and G.Deszca (1999); “DevelopingBreakthrough Products: Challenges& Opportunities For MarketAssessment”; Journal of OperationsManagement, Vol.17, pp. 613-630.

16. Nayak, P.R.(1991); Managing RapidTechnological Development, A.D.Little, London.

1. Buzzell, R.D.; and Gale, B.T. (1987);The PIMS Principles: LinkingStrategy to Performance, The FreePress, New York.

2. Biyalogorsky, E.; Boulding, W.; andStaelin, R. (1998): “Stuck in thePast: Why Managers Stick with NewProduct Failures?,” MSI Paper, No.98-130.

3. Crawford, M.C. (1992); “TheHidden Costs of AcceleratedProduct Development”; Journal ofProduct Innovation Management,Vol.9, pp.188-199.

4. Department of Trade and Industry(DTI) (1993); Manufacturing intothe Late 1990s, HMSO, London.

5. The Economist (1988), “Time IsMoney”; The Economist, October 8,pp.90-95.

6. Forsyth, F. (1990); “The fruits offlexibility”; Financial Times,August 17.

7. Gilman, J.J.(1982); “Penetrationrates and their Effect on Value”;Research Management, Vol. 25,No. 3, pp. 34-39.

8. Goldman, A. (1982) ; “ShortProduct Life Cycles: Implicationsfor the Marketing Activities ofSmall High-Technology Companies”;R&D Management, Vol.12, No.2, pp.81-89.

9. Goldstein, G. (1989);“IntegratingProduct and Process Design”;Mechanical Engineering, April, pp.48-50.

60



17. Nevens, T.M., Summe, G.L. andUttal, B. (1990); “CommercializingTechnology: What the BestCompanies Do?,” Harvard BusinessReview, May-June, pp.154-163.

18. Reinertsen, R.G. (1983);“Whodunit? The Search for theNew-Product Killers,” ElectronicBusiness, July 9, pp.62-66.

19. Rommel, G., Kempis, R.D., andKaas, H.W. (1994); “Does QualityPay?”; The McKinsey Quarterly,Vol.1, pp. 51-63.

20. Saji, K.B. (1997); “Quality FunctionDeployment: A Valuable MarketingTool to Integrate the Voice of theCustomer”; ManagementResearcher, Vol.4, No.3, pp.53-58.

21. Saji, K.B. (2002); “Issues InManaging Global R&D”, Sankalpathe Journal for ManagementDevelopment and Application,Vol.10, No.1, pp.92-99.

22. Schultz, R. L. (1999), “The Role ofEgo in Product Failure”; workingpaper, University of Iowa, January.

23. Schultz, Randall L. and Kathryn A.Braun (1997), “The OverreachEffect on New Product Decisions”;working paper, University of Iowa,December.

24. Smith, P.G.; and Reinertsen, R.G.(1991); Developing Products in Halfthe Time, Chapman and Hall,London.

25. Stalk, G. Jr. (1988); “Time - thenext Source of CompetitiveAdvantage,” Harvard BusinessReview, July-August, pp. 41-51.

26. Stalk, G. Jr.; and Hout, T.M.(1990);Competing Against Time, The FreePress, New York.

27. Stalk, G. Jr.; and Webber, A.M.(1993); “Japan’s Dark Side”;Harvard Business Review, July-August, pp.93-102.

28. Wheelwright, S.C.; and Clark, K.B.(1992); Revolutionizing ProductDevelopment, The Free Press,New York.

61



VVVVVILAKSHAN

XIMB JOURNAL

OF MANAGEMENT

BOOK REVIEW

62



63



WHY SMART EXECUTIVES FAIL ANDWHAT YOU CAN LEARN FROM THEIR MISTAKES

Author Sydney FinkelsteinPublisher – Penguin Group

Book Review by V. J. Rao !

I am sure that this book will start a trendof books on “Analysing and Learningfrom Failures” – coming as it does inthe wake of so many failures of so manywell-known companies in the recentpast.

In fact, if there is one lesson that canbe learnt from this book, it is that “Smart companies have failed onlybecause smart executives in thosecompanies have failed – and Why havesmart executives failed? This book goeson to explain some of the reasons forthe failure of smart executives basedon the largest research projectundertaken devoted to analysing thecauses of leadership failures.

Sydney Finkelstein, a Professor at theTuck’s School of Business at Dartmouthand his team have spent six years inidentifying well-known companies thathave failed and in analysing the causesof failure of these companies.

This research has led them to believethat while a wide range of companieshave failed across the world, the reasonsfor the same are not only few but quitesimilar. The research includes study ofarticles, reports, meetings with

analysts, and interviews with presentand past employees of several of thesecompanies. The companies studiedinclude Motorola, Rubbermaid, Saatchi& Saatchi, Enron, Sony, Tyco and severalothers. Some of these companies areMalcolm Baldrige Award WinningCompanies.

The book is divided into three parts:

Part I: Documents some of themistakes that companies havemade

Part II: Looks at the causes of failure

Part III: Looks at the learnings fromthese failures

The author has tried to create highlightearly warning signals so that thecompanies could recognise early signsof failure as they go ahead.

The book begins by noting some of theassumptions cited for executivefailures.

These are:

! The executives were stupid

! The executives could not have knownwhat was coming

! It was a failure to execute!#Director, Tata Management Training Centre, Pune

64



! The executives weren’t trying hardenough

! The executives lacked leadershipability

! The company lacked necessaryresources

! The executives were simply a bunchof crooks

He then argues that none of the abovecould be singled out for reasons ofcompanies failures because some of theexecutives leading these companieswere outstanding professionals withextremely good track records. Theresearch concludes that there are otherreasons for leadership failures.

Some of the corporate mistakes that heanalyses are divided into fourcategories:

Category I: New Business Breakdowns :General Magic, Iridium and SamsungMotors – What went wrong when thesecompanies ventured into new businesses

Category II: Innovation & Change:Johnson & Johnson Stent Business,Rubbermaid - What mistakes thesecompanies made when they undertook“Change Projects”

Category III: How some Mergers &Acquisit ions did not l ive uptoexpectations: Sony, Saatchi & Saatchi

Category IV: Strategy gone bad: WangLaboratories, Snow Brand Milk – Howineffective strategies led to failures

Causes of Failure:

The research study has concluded thefollowing as some of the primary causesof failure:

! Brilliantly fulfilling the wrong vision– General Motors & LTCM

! Delusions of a dream company -Motorola

! Not being sensitive enough to signalsfrom various constituencies – NASA

! There is a fair amount of detail interms of each of these and there areseveral examples in each of these.

The author has also listed down what hecalls “Seven habits of spectacularlyunsuccessful people” basically listingdown the personal qualities of leaderswho preside over major failures. Theseseven are:

! They see themselves and theircompanies as dominating theirenvironment

! They identify so completely with thecompany that there are no clearboundaries between their personaland company interests.

! They think they have all the answers

! They ruthlessly eliminate anybodywho is not 100% with them.

! They are consummate companyspokepersons, obsessed with theirown company image

! They underestimate major obstacles

! They stubbornly continue to rely onwhat worked for them in the past

! The author has then gone on to liston some questions that we could askwhen looking for early warning signs.

The book contains detailed analysis withrelevant examples on what has causedthese companies and their leaders to

65



fai l. The research seems to beexhaustive. However, there does notseem to be new revelations in terms of“Why Companies Fail”. This is asincere effort to state, analyse and tosome extent predict and like the authorhimself says in the end, “There is nosilver bullet” and at the end of the dayit all boils down to people.

Harry Stonecipher, the Chief ExecutiveOfficer of Boeing said in one of hismeetings with the Top Management “ourproblem is us”. That essentially seemsto sum up the message of the book.

In the end the book reminds us that it ishuman capabilities and frailties thatdecide the future of companies.

COVER DESIGN : RACHIT HALDIA,POST GRADUATAE PROGRAMME IN RURAL MANAGEMENTSTUDENT, XIMB, BATCH OF 2003-2005

TITLE : POST GRADUATAE PROGRAMME IN RURAL MANAGEMENTSTUDENTS, XIMB, BATCH OF 2003-2005

Potential Contributors : Please contact Dr. Nina Jacobfor further details at [email protected]

Editorial Disclaimer : (1) Views expressed by contributors are not necessarily viewsespoused by the Xavier Institute of Management,Bhubaneswar, (XIMB), India

(2) Reasonable efforts are made by the XIMB Journal ofManagement to ensure that papers published do not containplagiarized material. The ultimate onus for ensuring that noplagiarism has been resorted to however rests with theauthors of published pieces.

Published by : XAVIER INSTITUTE OF MANAGEMENTBhubaneswar, India (XIMB)

Publisher : Director, XIMB

Printed at : LIPEESCANBhubaneswar, Ph. - (0674) 2/3588578

Subscriptions:

Per copy 1 year 2 years 3 yearsRs. 75 Rs. 150 Rs. 300 Rs. 400

N.B.: All payments are to be made by drafts drawn in favour of XIMB, Bhubaneswar

EDITOR : NINA JACOB,PROFESSOR, XAVIER INSTITUTE OFMANAGEMENT, BHUBANESWAR, INDIA

EDITORIAL BOARD : SHARON SUTHERLAND,PROFESSOR, SCHOOL OF PUBLIC STUDIES,QUEEN’S UNIVERSITY, CANADA

PAULINE GOODWIN,PUBLISHING DIRECTOR,KOGAN-PAGE PUBLISHING HOUSE, LONDON,UNITED KINGDOM

BRAIN O’CONNOR LEGGETT,PROFESSOR, IESE BUSINESS SCHOOL,BARCELONA, SPAIN

DANIEL OTTO ABERER,PROFESSOR, KS GRADUTE BUSINESS SCHOOL,KS KADERSCHULE. ST. GALLEN. SWITZERLAND

PRADIP KHANDWALLA,FORMER LARSEN & TOUBRO CHAIR PROFESSOR,INDIAN INSTITUTE OF MANAGEMENT,AHMEDABAD, INDIA

PUBLISHED BYXAVIER INSTITUTE OF MANAGEMENT, BHUBANESWAR, INDIA (XIMB)