nurturing excellence listed in : google scholar, ebsco ... · nurturing excellence volume 8 issue 2...

Nurturing Excellence

Volume 8 Issue 2 July - December 2017

Listed in :

Google Scholar, EBSCO Discovery, indianjournals.com & CNKI Scholar(China National Knowledge Infrastructure Scholar)

Impact of Promotional Strategies on Consumer Buying Behaviour for Apparels

Pooja Kunwar, Hrudanand Misra

An Enquiry into India's Export Market and Product Diversification

Rashmi Taneja, Rakesh Mohan Joshi

Measuring Destination Brand Personality of Jaipur as a Destination Brand

Kirti Singh Dahiya, D.K Batra

Boys Vs Girls: Pestering Strategies of Children in India

Asha Chauhan, Ravindra

Virtual Teams Vs Face to Face Teams: A Comparative Study on Performance Indices

Shikha Matta

A Comparative Analysis of South Indian Bank's Performance Post Implementation of Information Technology: An Empirical Analysis

Titto Varghese, Sneju Sajan

The Impact of Marketing Mix Strategy on Hospital's Performance Measured by Patient's Satisfaction (An Empirical Study on Santokba Durlabhji Memorial Hospital, Jaipur)

Ankita Jain, Varsha Choudhary

ISSN (PRINT): 0976-8629 ISSN (ONLINE): 2349-9826www.iitmipujournal.org

IITM Journal of Management and IT

Vision

Mission

Editorial Board

Editorial Team

(Interdisciplinary Tenets) is a Bi-Annual Research Publication of Institute of Information Technology and Management (NAAC Accredited & ISO Certified) which is instituted and governed by Mata Leelawati Sikshan Sansthan, New Delhi. It is a peer reviewed journal with an objective to disseminate experiences, ideas, case studies of professionals in Management and Interdisciplinary areas to propagate better understandings. Its focus is on empirical, applied research and reflections that are relevant to Management professionals with academic standards and rigor within purview.

The views expressed in the Journal are those of authors. The editor, editorial board, editorial advisory board and the institute disclaim the responsibility and liability for any statement of facts, opinion, and originality of contents as well as violation of any copyright by the authors.

No part of this publication may be reproduced in any form without the written consent of the publisher.

The Institute aims to be a Centre of Excellence promoting Value Based Quality Education in the emerging areas of advanced professional studies in Information Technology & Management.

The Institute endeavours to contribute towards meeting the growing demands for competent and trained Information Technology Professionals, Software Engineers and the World Class Managers determined to achieve excellence.

PatronShri J C SharmaChairman

Chief EditorProf (Dr) Rachita RanaDirector

EditorProf (Dr) Sheela BhragavaProfessor

Dr Renu ChoudharyAssociate Professor

Dr Kirti DahiyaAssistant Professor

Dr Neha GahlawatAssistant Professor

Advisory Board

Prof (Dr) Krishna S. DhirDean, College of Business and Economics University of Hawaii at Hilo 200 W. Kawili Street Hilo, Hawaii 96720-4091, USA

Prof (Dr) Madhu Vij

Professor, Faculty of Management Studies, University of Delhi

Prof (Dr) Sanjeev Mittal Dean & Professor, University School of Management StudiesGuru Gobind Singh Indraprastha University, New Delhi

Prof (Dr) B S Nagi

Ex-Director, Research Council of Social Development, New Delhi

Prof (Dr) V A EshwarProfessor and Managing Director, Saai Consultants, New Delhi

Prof (Dr) P D KaushikPro-Vice Chancellor, Teerthanker University, Moradabad

Dr Anupam Narula Associate Professor, Marketing, FORE School of Management, New Delhi

Dr Indu Uprety,

Associate Professor, School of Management, Gautam Buddha University, Noida

Dr P K Singh Professor, Course Director, Master of Banking & Insurance University College of Commerce & Management Studies, Mohan Lal Sukhadia University, Udaipur

Prof (Dr) Som DeoFormer Dean, Faculty of Commerce University of Rajasthan, Jaipur.

IITM Journal of Management and ITVolume 8 Issue 2 July-December 2017

C O N T E N T S

Research Papers & Articles

Page No.

● Impact of Promotional Strategies on Consumer Buying Behaviour for Apparels 3-19

- Pooja Kunwar, Hrudanand Misra

● An Enquiry into India’s Export Market and Product Diversification 20-28

- Rashmi Taneja, Rakesh Mohan Joshi

● Measuring Destination Brand Personality of Jaipur as a Destination Brand 29-37

- Kirti Singh Dahiya, D.K. Batra

● Boys Vs Girls: Pestering Strategies of Children in India 38-44

- Asha Chauhan, Ravindra

● Virtual Teams Vs Face to Face Teams: A Comparative Study on 45-55

Performance Indices

- Shikha Matta

● A Comparative Analysis of South Indian Bank’s Performance 56-69

Post Implementation of Information Technology: An Empirical Analysis

- Titto Varghese, Sneju Sajan

● The Impact of Marketing Mix Strategy on Hospital’s Performance Measured 70-78

by Patient’s Satisfaction

(An Empirical Study on Santokba Durlabhji Memorial Hospital, Jaipur)

- Ankita Jain, Varsha Choudhary

ISSN (PRINT) : 0976-8629 www.iitmipujournal.orgISSN (ONLINE) : 2349-9826 Rs. 300/- (Single Copy)

Listed in :Google Scholar, EBSCO Discovery, indianjournals.com & CNKI Scholar

(China National Knowledge Infrastructure Scholar)

Impact of Promotional Strategies on ConsumerBuying Behaviour for Apparels

Pooja Kunwar*Hrudanand Misra**

Abstract

One of the basic necessities of human being is apparel. This basic necessity to cover the body hasleaped to fashion and lifestyles with growing advancement in human civilization. Apparel is a highlysymbolic product category due to its high visibility. The Apparel Industry reflects people’s lifestyles andshows their social and economic status (Bhanot S, 2013). Consumer behavior is associated with theculture and economy of a country. There is a need to research on the buying behavior of consumers inorganized retail market, as they play the pivotal role to bring the success of any business.

Consumers’ buying behavior has drawn a considerable attention of the earlier researchers. Its impacton designing marketing strategies also became the part of a sizeable number of studies. Recurrentchange in lifestyle also dictates a continuous variability in the fashion preference of consumers givingorganized retailers a wide scope of opportunity to appear with newer fashion apparels. Various factorslike increasing family income, changing lifestyle, development of information technology, working womenand increasing promotional strategies have led to changing consumer behavior. In view of the immenseand colorful growth of apparel segment at such rapid pace, the proposed study is intended to keepconfined only to the organized apparel retailing. The study aims to assess the impact of promotionalstrategies on consumer buying behavior for apparels bought from organized retail stores. It will help inunderstanding the promotional strategies, which influence the consumer decision making process.

The study was conducted in 4 major cities of Gujarat which are Ahmedabad, Surat, Vadodara andRajkot. The study will also make a contribution towards a comprehensive understanding of the Indianapparel retail market.

Keywords: Promotional Strategies, Consumer Buying Behavior, Retail, Apparels

IntroductionConsumer behavior research is the scientific study ofthe processes consumers use to select, secure, use anddispose of products and services that satisfy their needs.Knowledge of consumer behavior directly affectsmarketing strategy (Anderson et al. 2005). This isbecause of the marketing concept, i.e., the idea thatfirms exist to satisfy customer needs (Winer, 2000).Firms can satisfy those needs only to the extent thatthey understand their customers. For this reason,marketing strategies must incorporate knowledge of

Pooja Kunwar*Research Associate, Gujarat TechnologicalUniversity, Ahmedabad

Hrudanand Misra**Director, Technical Campus, TMES Institute ofManagement & Computer Studies, Surat

consumer behavior into every facet of a strategicmarketing plan (Solomon, 2002). The human behavioris complex, full of controversies and contradictionsand comes as no surprise to marketing academiciansas well as practioners. There is a widespread recognitionthat consumer behavior is the key to contemporarymarketing success (Hawkins et al., 2003). Consumerbehavior has been legitimized in marketing for itprovides the conceptual framework and strategicthinking for carrying out successful segmentation ofmarkets (Schiffman and Kanuk 2000).

Introduction - Retail IndustryIndia is one of the important retail markets for globalretailers. Other than the fact that it one of the biggesteconomies of the world, its demographics also workin its favor. India’s retail sector has been undergoingstructural changes for the last two decades. Shopping

4 IITM Journal of Management and IT

malls, lined with specialty retailers, started dotting theretail markets of the country’s top cities during mid-1990s. Since then, the ‘mall culture’ gradually pervadedthe population, especially in the metros and mini-metros, heralding the beginning of the modern retailmovement in India (Knight-Frank 2016).

Khare (2011) in a research on “Mall shopping behaviorof Indian small town consumers”, studied thatconsumers’ gender and age play an important role indetermining their attitude towards shopping in malls.The influence of mall attributes such as décor, layout,services, variety of stores, and entertainment facilitiesmust be considered while planning malls in smallercities as they have an effect on consumers’ buyingbehaviour. The mall shopping behaviour ofmetropolitan city shoppers should not be generalizedwith that of shoppers in smaller cities.

Rajagopal (2008) in a research on “Point-of-Salespromotions and buying stimulation in retail stores”indicated that point-of-sales promotional programmeshave become the principal tool of retailing in Mexicoin order to acquire new customers and retain loyalcustomers. The study found that loyal customers areattracted to the store brands during the promotionaloffers while new shoppers are price sensitive and are

attracted by the in-store ambience and salespromotions and volume discounts.

Apparel Retail Sector in IndiaAccording to the recent report of Textiles, Governmentof Gujarat (2017), the Indian apparel market wasvalued at US$ 41 billion in 2013, and it is set to reachUS$ 64 billion and US$ 102 billion in 2018 and 2023respectively. Growth in the Indian apparel segment isbolstered through robust growth in the organizedapparel segment and entry of major global players suchas H&M, Zara and Marks & Spencer into the domesticmarket. “India is the world’s second largest exporterof textiles and clothing” (UN Comtrade data June2014).

The Indian apparel retail is the fastest growing sectoramong other sectors of Indian market and is expectedto sustain its growth in the coming years. Accordingto the Indian Apparel Market Current Status andFuture Outlook Report presented in the Indian TextileSummit, 2012, organized retail in India is estimatedto grow five times to 150 bn by 2020. Also, amongvarious segments in retail industry, the appareldominated Indian organized retail, with 35 percentshare (Sahni H, 2012).

Source: Indian Apparel Market: Current Status and Future Outlook (Indian Textile Summit, Mumbai,27th September , 2012)

The Organized Retail market stood at Rs.96, 500 crorein 2008. The industry has grown at a CAGR of 36percent between 2004 and 2008. This growth was

mainly driven by changing lifestyles, rising disposableincomes, favorable demographics, and easy creditavailability, etc (NSDC, nd).

Figure 1.1: Share of Various Segments in the Retail Industry (%)

Volume 8, Issue 2 • July-December 2017 5

The apparel retail industry of India derives its strengthfrom the high degree of vertical integration of theindustry. It is estimated that the US $45 billion Indianfashion apparel market will grow at a CAGR of 10 percent to touch US $122 billion by 2025. The promisinggrowth opportunities of fashion retail in India comewith its own set of issues and challenges. The mostdaunting challenges for fashion retail in the countryinclude rapidly changing customer demands,infrastructural bottlenecks, a complex tax regime,inherent heterogeneity of the market, supply sidelimitations and shortage of skilled manpower. Withpopulation more than 1.25 billion, India fosters varietycultures, geographies and regional trends whichinherently promote heterogeneity of Indian retailmarket. This forces apparel retailers to search for localoptima in their business strategy. To add thecomplexity, consumer groups in these heterogeneousmarkets are evolving continuously owing to increasingexposure to fashion trends and rising income level(Amit and Chhillar ; 2016).

Prominent Indian Retailers: Chopra (2011) studiedthe prominent Indian retailers. These are Future Groupwith subbrands Pantaloons, Central, Big Bazaar, PlanetSports, Home Town, e-Zone, Aadhaar, FutureGenerali, Future Mediap, Blue Foods, SpaghettiKitchen, Noodles Bar, The Spoon etc., K. RahejaGroup with: Shopper’s Stop, HomeStop, Desi Cafe,Hypercity, Inorbit, Brio etc., Reliance Retail withReliance Fresh, Reliance Digital, Reliance Mart,

Reliance Timeout, Reliance Trends, Reliance Jewels,Reliance Autozone, Reliance Wellness etc., Tata Groupwith Trent, Westside, Star Bazaar, Landmark, FashionYatra, Croma, Titan, Tanishq etc., RPG Group withSpencer’s Retail, Aditya Birla Group’s Trinethra, more,more. MEGASTORE, Vishal Group’s VishalMegamart etc.

Literature ReviewPromotion is a tool that is used by the retailers to inviteconsumers to purchase more. Various promotionalstrategies are used by the retailers to attract thecustomers to increase their sales. “The impact of salespromotion on consumer buying behavior has beenwidely stated in many researches and studies, they haveshown that there are a lot of factors can effect consumerbuying behavior, either to buy or not” (Nagar, 2009;Smelser and Baltes, 2001).

Sanad (2016) in his paper aims to make acomprehensive review of factors affecting purchaserdecision towards apparel and textile products. Researchstudies concerned with factors having impact onmarketing of textile products including apparel andfashion products were reviewed. These factors includedifferent cultural, social, personal, psychological andenvironmental aspects

Sahney (2016) in a study, “A study on consumer buyingbehaviour towards branded retail outlets in India”, triesto find out the preference of the customer towardsorganized retail sector and how customer behaves at

Source: India Retail Report and IMa CS analysis28,000


the time of product purchase. The study shows thatsome of the specific elements like product information,customer involvement, atmosphere, customerattributions and choices play important roles duringvarious stages of the customer decision process. Socustomer are now showing preference for shoppingmalls, enable them to shop variety of products underone roof with shopping experience in term of ambienceand entertainment.

Sheek Meeran, Ranjitham (2016) ascertained thebranded apparel most preferred by respondents andto examine customer’s perception towards retailgarments showrooms and factor they considered tochoose a particular retail garments showroom for theirshopping in Tirunelveli Hub. The study reveals thatRaymond, Peter England, and John player remains thetop three branded apparels preferred by therespondents. It is clear that most of the shoppers onbranded apparel were highly influenced by the factorssuch as durability, reference groups, wider choice ofcolour and design, attractiveness, price range andcelebrity endorser.

Rajiv et al. (2002) in a research on “Asymmetric storepositioning and promotional advertising strategies:Theory and evidence” examined the role ofpromotional strategies for stores that vary in theirquality positioning in competing for customer. Thekey analysis done by the researchers showed that astores quality positioning impacts its frequency ofpromotional advertising and the depth of the discountthat it offers during “sale”. Specifically, relative to thelow-service store, the high service store offers advertisedsales more frequently but with shallower discounts.

Banerjee (2009), in a study on “Effect of productcategory on promotional choice: comparative studyof discounts and freebies” suggested that promotiontype influences the rate of increase in market demandand is product category dependent. Consumers try torationalize the promotion that is offered by theproduct. Hence promotion type can influence the rateof increase in market demand. The right combinationof product and promotion can be more effective inenhancing sale. It would be prudent to decide on thepromotion type after taking in to consideration theproduct category, its features and the target segment.

Promotional offerings, which can be readily convertedinto monetary terms are more preferred to freebies butin the long-run, they can affect the overall value of theproduct. Retailer discount is preferred over advertiseddiscount but has a negative perception. Hedonicfreebies are least preferred; but have a higher perceivedvalue.

Jane Lu Hsu and Roxy Hsien-Chen Mo (2009) in aresearch on “Consumer responses to incompleteinformation in print apparel advertising”, concludedthat consumers who thought missing information inprint apparel advertising to be important tended tofind missing information from other sources likemedia, word of-mouth, salespersons, and in stores.Information search behaviour positively influencedpurchasing intentions. Consumers with higher levelsof involvement tended to pay more attention tomissing information and were more likely to searchinformation.

Aggrawal (2010) in his thesis on, “Impact of ConsumerPerception on Buying Behavior in Apparel RetailSector, with special reference to selected Indian cities”found that nearly 95 percent of the respondentsbelieved that advertising is an effective medium ofcreating positive perception for the brand amongconsumers. So he recommended that retail apparelcompanies should not definitely forget that goodadvertising is necessary both for good brand image aswell as for customer consciousness and awareness. Also,nearly 93 percent of the respondents agreed that salespromotion does effect their positive perception towardsthe brand in India. Hence, it was concluded that retailapparel companies should invest heavily on salespromotion techniques, especially the free goods andgifts and visual merchandising.

Special discounts and promotions increase customers’interest toward the store (Grewal D, Krishnan R, BakerJ, Borin N 1998). Chavadi and Shilpa S. Koktanur(2010), tried to find out the various factors drivingcustomers towards shopping malls and consumerbuying response for promotional tools. They foundfour major factors that drive the customers towardsthe shopping malls. Those factors are product mix,ambience, services and promotional strategies.Customers consider fast billing, parking facility andlong hours of operations as prime services.


Table 1.1: Literature : Key Findings

Sr.No.

Author/s Year Key Findings

1 Mughal Aurangzeb, Asif Mehmood,Ammar Mohi-ud-deen and BilalAhmad

2014 The survey found that there was an insignificantrelationship between coupons and buying behavior.On the other hand the buy-one-get-one free,Physical surrounding has a significant relationshipwith the purchasing behavior.

2 Ghafran Ashraf 2014 The study confirmed that consumers buyingbehavior and sales promotion can be motivatedthrough various kinds of elements, includingpromotion techniques such as free samples, pricediscounts, social surroundings and physicalsurrounding.

3 Aggrawal Amit 2010 Nearly 93 percent of the respondents agreed thatsales promotion does affect their positive perceptiontowards the brand in India.

4 Chavadi and Shilpa S. Koktanur 2010 The study found four major factors that drive thecustomers towards the shopping malls which wereproduct mix, ambience, services and promotionalstrategies

5 Banerjee Subhojit 2009 Promotional offerings which can be readilyconverted into monetary terms are more preferredto freebies but in the long-run, they can affect theoverall value of the product. Retailer discount ispreferred over advertised discount but has a negativeperception.

6 Das and Kumar 2009 The study shows that promotion plays a limitedrole on consumers buying behavior where only smallpercentage of people are attracted to such salespromotion and wait for it.

7 Rajagopal 2008 The study found that loyal customers are attractedto the store brands during the promotional offerswhile new shoppers are price sensitive and areattracted by the in-store ambience of salespromotions and volume discounts.

8 Huddleston, Whipple, and VanAuken 2004 The research identified promotions, location,variety and service as key loyalty factors that affectstore patronage by customers.


Sonia (2008) conducted a study on customers’perception towards mega marts in Ludhiana. Theauthor highlighted that customers preferred aparticular mega mart due to its convenience in termsof space, product range, billing system, multiplechoice, etc., and location at an easy approach andsafety. She concluded that customers preferred cashdiscount offers, followed by free gifts and financingfacility. Quality and discount were the most importantfactors in influencing customers’ decision to purchase.

According to Ashraf (2014), their study confirmed thatconsumers buying behavior and sales promotion canbe motivated through various kinds of elements,including promotion techniques such as free samples,price discounts, social surroundings and physicalsurrounding.

The given table 1.1 shows major research work doneby different authors and the key findings of their work.

Research Methodology:The present research has been conducted in the fourcities of the state of Gujarat.The research design ofthe present study is descriptive. According to Malhotraand Dash (2009), descriptive research design describethe characteristics of relevant group, is moreappropriate in estimating the percentage of units in aspecified population showing certain behavior,determining the perception of product characteristics,degree of association between various marketingvariables and making specific predictions. Descriptiveresearch is characterized by the prior formulation ofspecific hypotheses.

Table 1.2: Methodology of the Study at a Glance

Particulars Consumer Survey

Research Design Descriptive research design

Target Population Organized apparel retail sector consumers of Gujarat State

Selected cities Ahmedabad, Surat, Vadodara and Rajkot

Sampling Unit Individuals – apparel consumers

Sampling technique Convenience Sampling

Sample Size 600 Respondents

Citywise sample size 150 per city

Data Collection Method Mall Intercept Method

Data Collection Instrument Structured Questionnaire

A close-ended structured tool was designed tointerview the consumers. Based on the objective ofthe research, the questionnaire was designed. A five-point Likert-type scale ranging from ranging from leastpreferred or strongly disagree to most preferred orstrongly agree was used.

Promotional strategies are used by the retailers to inviteconsumers to purchase more. Sales promotions consistof a huge variety of temporary planned promotiontools which plan is generating a preferred responsefrom the consumer (Gilbert and Jackaria, 2002).Different types of promotion tools and promotionstrategies are utilized by the marketers so that they canknow consumer first choice and boost their sales.

H1: There is Significant Impact of DemographicVariables on the Promotional Strategies for BuyingApparels from Organized Retail Outlets.Kotler (2005) discovered that promotions have becomea critical factor in the product marketing mix whichconsists of the specific blend of advertising, personalselling, sales promotion, public relations and directmarketing tools that the company uses to pursue itsadvertising and marketing objective.

The factor - wise break-up of hypothesis 1 in relationto the demographic variables are as follows:

Independent Variable:- GenderH1.1 - There is statistically significant impact of gender


on celebrity endorsement for buying apparels fromretail outlets.

H1.2 - There is statistically significant impact of genderon number of end of season sales for buying apparelsfrom retail outlets.

H1.3 - There is statistically significant impact of genderon product knowledge of festive promotions (Diwali,Christmas etc) for buying apparels from retail outlets.

H1.4 - There is statistically significant impact of genderon friendliness of Loyalty card programme for buyingapparels from retail outlets.

Independent Variable:- Age GroupH1.5 - There is statistically significant impact of agegroup on celebrity endorsement for buying apparelsfrom retail outlets.

H1.6 - There is statistically significant impact of agegroup on number of end of season sales for buyingapparels from retail outlets.

H1.7 - There is statistically significant impact of agegroup on product knowledge of festive promotions(Diwali, Christmas etc) for buying apparels from retailoutlets.

H1.8 - There is statistically significant impact of agegroup on friendliness of Loyalty card programme forbuying apparels from retail outlets.

Independent Variable:- EducationH1.9 - There is statistically significant impact ofeducation on celebrity endorsement for buyingapparels from retail outlets.

H1.10 - There is statistically significant impact ofeducation on number of end of season sales for buyingapparels from retail outlets.

H1.11 - There is statistically significant impact ofeducation on product knowledge of festive promotions(Diwali, Christmas etc) for buying apparels from retailoutlets.

H1.12 - There is statistically significant impact ofeducation on friendliness of Loyalty card programmefor buying apparels from retail outlets.

Independent Variable:- OccupationH1.13 - There is statistically significant impact of

occupation on celebrity endorsement for buyingapparels from retail outlets.

H1.14 - There is statistically significant impact ofoccupation on number of end of season sales for buyingapparels from retail outlets.

H1.15 - There is statistically significant impact ofoccupation on product knowledge of festivepromotions (Diwali, Christmas etc) for buyingapparels from retail outlets.

H1.16 - There is statistically significant impact ofoccupation on friendliness of Loyalty card programmefor buying apparels from retail outlets.

Independent Variable:- Annual Family IncomeH1.17 - There is statistically significant impact ofannual family income on celebrity endorsement forbuying apparels from retail outlets.

H1.18 - There is statistically significant impact ofannual family income on number of end of seasonsales for buying apparels from retail outlets.

H1.19 - There is statistically significant impact ofannual family income on product knowledge of festivepromotions (Diwali, Christmas etc) for buyingapparels from retail outlets.

H1.20 - There is statistically significant impact ofannual family income on friendliness of Loyalty cardprogramme for buying apparels from retail outlets.

Data collected from 600 consumers was tabulated andanalysed. The results of the study has been presentedinto two sections, viz., (1) Demographic profile of theconsumers & (2) Impact of promotional strategiesaffecting the consumer buying behavior for apparels

Demographic Profile of the ConsumersIn this section, an attempt is being made to study thedemographic profile of 600 respondents interviewedby gender, age, education, occupation and by annualfamily income. Such an analysis helps in understandingthe socio-economic background characteristics of therespondents and helps in category-wise comparison.

Gender Wise Distribution of the RespondentsTotal 600 consumers were interviewed, 150 from each


selected city. TABLE 1.3 shows that out of total sample,328 (54.7 percent) were males and 272 (45.3 percent)were females. The male-female percentages of thepresent study are close to that of urban population ofGujarat as per Census 2011. Out of total 25745083population in urban Gujarat, 53.2 percent are malesand 46.8 percent are females (Census Info India 2011).

Age Group wise Distribution of theRespondentsIn the questionnaire, the age-group was divided into

five categories, viz., below 18 years, 18-24 years, 25-34 years, 35-44 years and 45-60 years. These age- groupcategorization was done considering changeableapparel choice within a gap of nine years. TABLE 1.4shows that most of the consumers interviewedbelonged to the age group 35-44 years (66.2 percent).Other consumers belonged to the age groups 18-24years (21.7 percent), below 18 years (8.5 percent), 25-34 years (3.5 percent) and only one (0.2 percent)belonged to the age group of 45-60 years. The averageage of the respondents was 33.3.

Table 1.3: Gender Wise Distributions of the Respondents

Gender Number Percent

Male 328 54.7

Female 272 45.3

Number of respondents 600 100.0

TABLE 1.4 Age group wise Distribution of the Respondents

Age group Number Percent

Below 18 years 51 8.5

18-24 years 130 21.7

25-34 years 21 3.5

35-44 years 397 66.2

45-60 years 1 0.2

Average age 33.3


Educational Profiles of the RespondentsLiteracy is considered as one of the main parameter ofhuman development. The categorization of theeducational background in the questionnaire was doneas up to class 12, Graduation, Post-graduation and

Others. As expected, majority of the respondents (83.5percent) were either graduates (44.2 percent) or postgraduates (39.3 percent), since the data was collected inupcoming malls of the cities. This was followed by thosestudied up to class 12 (13.2 percent) (TABLE 1.5).

Table 1.5: Educational Profiles of the Respondents

Education Number PercentUp to 12 79 13.2

Graduation 265 44.2

Post-graduation 236 39.3

Others (technical, professional course) 20 3.3



Occupational Profile of the RespondentsLike education, occupation and family income are thetwo important deciding demographic indicators forpurchasing apparels. TABLE 1.6 shows highestpercentage of the respondents was students (28.3percent), 27.7 percent were housewives orunemployed, 20.7 percent were engaged in business,18.8 percent of the respondents were engaged in privateservice, 2.7 percent of the respondents weregovernment employees and 1.8 percent of therespondents were self-employed or freelancers.

Annual Family Income of RespondentsIn order to identify the consumers on the basis ofpurchasing affordability, they were categorized on thebasis of annual family income. Income is a vital factor

which Indian consumers have been found to besensitive about while shopping for apparels. But toget correct information about family income in quickmarket survey is perplexing. TABLE 1.7 shows thatthe annual family income of highest percentage of therespondents was below Rs.1,20,000/- (27.2 percent),followed by 23.3 percent of the respondent’s familyincome per annum was Rs.2,40,001/- toRs.4,80,000/-, 20.8 percent informed their annualfamily income asRs. 4,80,001/- to Rs. 6,00,000/-, 15.7percent belonged to the income category ofRs.1,20,001/- to Rs.2,40,000/- and 13.0 percent ofthe consumers interviewed belonged to the annualfamily income category of above Rs.6,00,000/-. Themedian annual family income of the respondents wasreported to be Rs. 2,40,001/- to Rs.4,80,000/-.

Table 1.6: Occupational Profiles of the Respondents

Occupation Number Percent

Private Service 113 18.8

Government Service 16 2.7

Student 170 28.3

Professional (self employed or freelancer) 11 1.8

Business 124 20.7

Others (Housewife, unemployed) 166 27.7


Table 1.7: Annual Family Incomes of Respondents

Family income per annum (in Rs) Number Percent

Below 1,20,000 163 27.2

1,20,001 to 2,40,000 94 15.7

2,40,001 to 4,80,000 140 23.3

4,80,001 to 6,00,000 125 20.8

Above 6,00,000 78 13.0

Median income per annum 2,40,001 to 4,80,000


One Way ANOVA to Assess Impact ofDemographic Factors on Promotional FactorsAffecting Buying Behaviour: A one way betweengroup Analysis of Variance (ANOVA) was conducted

to explore the impact of independent variables,demographic factors namely gender, age, education,occupation, and annual family income on dependentvariables, promotional factors namely, celebrity


endorsement, end of season sales, festive promotions(Diwali, Christmas etc) and loyalty card program.

Output of ANOVA of Gender and 4 Promotionalfactors: Promotion is the various promotionaltechniques adopted by the retail stores to attract andpersuade customers. Many previous research studiesindicated that there is a significant positive influenceof promotional techniques on consumer buyingbehavior (Balanga Gurunathan K and M.Krishnakumar, 2013). In this study, promotionalfactors are measured by the indicators namely,Celebrity Endorsement, End of Season Sales, FestivePromotions (Diwali, Christmas etc) and Loyalty CardProgramme. TABLE 1.8 shows the relationshipbetween gender and four factors of promotionalstrategies. The table shows the significance value lessthan 0.05 for celebrity endorsement (0.000). Thisimplies that gender has a statistically significant impactonly on celebrity endorsement of promotional

strategies for buying apparels from the retail outlets,thus, accepting hypothesis 1.1.

Output of ANOVA of Age Group and 4PromotionalFactors: TABLE 1.9 shows the relationship betweenage group and four factors of salesmanship andcourtesy. The table shows the significance value lessthan 0.05 for celebrity endorsement (0.000) and endof season sales (0.001). This implies that age grouphas a statistically significant impact on celebrityendorsement and end of season sales variables ofpromotional factors for buying apparels from the retailoutlets. Hence, hypothesis 1.5 and 1.6 has beenaccepted.

Output of ANOVA of Education and 4 Promotionalfactors: TABLE 1.10 shows the relationship betweeneducation and four factors of promotional factors.Except Festive Promotions (Diwali, Christmas etc),the table shows the significance value less than 0.05for three promotional factors. This implies that

Table 1.8: Output of ANOVA of Gender and 4Promotional factors

Independent variable: HypothesisGender Sum of Mean Accepted/Dependent variables: Squares df Square F Sig. RejectedQ5.1 Celebrity Between 15.590 1 15.590 13.465 *.000 AcceptedEndorsement Groups

Within 692.384 598 1.158 Groups

Total 707.973 599

Q5.2 End of Between .279 1 .279 .287 .593 RejectedSeason Sales Groups

Within 581.981 598 .973Groups

Total 582.260 599

Q5.3 Festive Between .107 1 .107 .127 .722 RejectedPromotions Groups

(Diwali, Within 502.691 598 .841 Christmas etc) Groups

Total 502.798 599

Q5.4 Loyalty Between 1.354 1 1.354 1.115 .292 Rejectedcard Groups

Programme Within 726.245 598 1.214Groups

Total 727.598 599

*Significant at 5% level of significance


education has a statistically significant impact on thepromotional factors of Celebrity Endorsement (0.015),End of Season Sales (0.000) and Loyalty cardProgramme (0.14) for buying apparels from the retailoutlets. Hence, hypothesis 1.9, 1.10 and 1.12 has beenaccepted.

Output of ANOVA of Occupation and 4Promotional factors: Table 1.11 shows therelationship between occupation and four promotionalfactors. The table shows the significance value less than0.05 for all the four promotional factors. This impliesthat occupation has a statistically significant impacton all the four promotional variables for buyingapparels from the retail outlets. Hence, hypothesis1.13, 1.14, 1.15 and 1.16 have been accepted.

Output of ANOVA of Annual family income and4 Promotional factors: Table 1.12 shows therelationship between annual family income and fourpromotional factors. Except Loyalty Card programme,

the table indicates the significance value less than 0.05for all three promotional factors. This implies thatannual family income has a statistically significantimpact on celebrity endorsement, end of season salesand festive promotions (Diwali, Christmas etc.) (each0.000) for buying apparels from the retail outlets.Hence, hypothesis 1.17, 1.18 and 1.19 under theindependent variable annual family income has beenaccepted.

The summary of break-up hypothesis under hypothesis4, which have been accepted is shown in TABLE 1.13.

Findings of the Study:Demographic Factors Influencing ConsumerBuying Behaviour

� Gender influence: Overall, majority of theconsumers visit the apparel retail stores, purchasecasual wears (78.0 percent). More females preferto purchase casual wear (80.1 percent) as

Table 1.9: Output of ANOVA of Age group and 4 Promotional factors

Independent variable: HypothesisAge group Sum of Mean Accepted/Dependent variables: Squares df Square F Sig. RejectedQ5.1 Celebrity Between 35.582 4 8.895 7.872 *.000 AcceptedEndorsement Groups

Within 672.392 595 1.130 Groups

Total 707.973 599

Q5.2 End of Between 18.768 4 4.692 4.954 *.001 AcceptedSeason Sales Groups

Within 563.492 595 .947Groups

Total 582.260 599

Q5.3 Festive Between 4.536 4 1.134 1.354 .249 RejectedPromotions Groups

(Diwali, Within 498.263 595 .837Christmas etc) Groups

Total 502.798 599

Q5.4 Loyalty Between 8.576 4 2.144 1.774 .132 Rejectedcard Programme Groups

Within 719.022 595 1.208Groups

Total 727.598 599



compared to that of the males (76.2 percent).Males were inclined towards purchasing formalwear more (39.9 percent) than females (26.1percent). This may be due to that, most of themales, who are working need the formal wear tomaintain the office decorum.

� Influence of Age group: Youngsters aged below18 years and 18-24 years prefer to buy formal wear(43.1 percent and 50.0 percent respectively) morethan the older age groups. This may be due tothat youngsters need formal wear for going toeducational institutions, to face interviews etc.

� Influence of Education: It is pertinent in the presentstudy that higher educated consumers like postgraduates (38.1 percent) and others (technical,professional course) (65.0 percent), who were ineither lucrative services or perusing professionalcourses like management, mass communication etc.

prefer to purchase formal wear more than those whowere graduates (29.1 percent) and educational levelupto 12 (27.8 percent).

� Influence of Occupation: More than half of theconsumers who were engaged in services, bothprivate (57.5 percent) and government (50.0)prefer to purchase formal wear followed by student(35.3 percent). Since the service class consumersand also the students need to maintain the officeand institutional decorum, their need influencethem to purchase formal wear besides casual forregular wear.

� Influence of Family income: The present studyshows that the family income has influence in theconsumer buying behavior for apparels. Thepurchaser of formal wear was found highest amongthe respondents whose annual family income wasas high as Rs. 4,80,001/- to 6,00,000/- (52.8

Table 1.10: Output of ANOVA of Education and 4 Promotional factors

Independent variable: HypothesisEducation Sum of Mean Accepted/Dependent variables: Squares df Square F Sig. RejectedQ5.1 Celebrity Between 12.238 3 4.079 3.495 *.015 AcceptedEndorsement Groups

Within 695.735 596 1.167 Groups

Total 707.973 599


Within 563.173 596 .945 Groups

Total 582.260 599

Q5.3 Festive Between 3.296 3 1.099 1.311 .270 RejectedPromotions Groups


Total 502.798 599

Q5.4 Loyalty Between 12.801 3 4.267 3.558 *.014 Acceptedcard Programme Groups

Within 714.797 596 1.199 Groups

Total 727.598 599



percent) and that of casual wear was highest amongthose whose annual family income was as low asRs1,20,001/- to 2,40,000/- (86.2 percent).

Pearson Chi-Square test revealed that the demographicfactors such as gender, age, education and occupationof consumers show significant influences in theconsumer buying behaviour for apparels.

Impact of Demographic Factors on PromotionalFactors Affecting Buying Behavior� Gender has a statistically significant impact only

on celebrity endorsement of promotional strategiesfor buying apparels from the retail outlets.

� Age group has a statistically significant impact oncelebrity endorsement and end of season salesvariables of promotional factors for buyingapparels from the retail outlets.

� Education has a statistically significant impact onthe promotional factors of celebrity endorsement,

Table 1.11: Output of ANOVA of Occupation and 4 Promotional factors

Independent variable: HypothesisOccupation Sum of Mean Accepted/Dependent variables: Squares df Square F Sig. RejectedQ5.1 Celebrity Between 59.632 5 11.926 10.927 *.000 AcceptedEndorsement Groups

Within 648.342 594 1.091 Groups

Total 707.973 599



Total 582.260 599

Q5.3 Festive Between 21.310 5 4.262 5.258 *.000 AcceptedPromotions Groups


Total 502.798 599

Q5.4 Loyalty Between 20.460 5 4.092 3.437 *.005 Acceptedcard Programme Groups

Within 707.138 594 1.190 Groups

Total 727.598 599


end of season sales and loyalty card programmefor buying apparels from the retail outlets.

� Occupation has a statistically significant impacton all the four promotional variables for buyingapparels from the retail outlets.

� Annual family income has a statistically significantimpact on celebrity endorsement, end of seasonsales and festive promotions (Diwali, Christmasetc.) for buying apparels from the retail outlets.

� Various promotional schemes influencing decisionmaking while buying apparel from organized retailstores were ranked and, the highest rank was givenfor Lucky Draw Scheme (6.6), followed byContest (6.4) and Free Coupons (6.0).

� It seems that the respondents in the present studywere not much interested on price off scheme, asthey ranked it lowest (3.8) for influencing decisionmaking while buying apparels.


Limitations of the StudyThe proposed study covered only major four cities ofGujarat State only and the conclusions may not begeneralized for all areas. Also, the study findings arestrictly based on the responses given by respondentswho were approached in malls.

ConclusionConsumers’ buying behaviour has drawn aconsiderable attention of the earlier researchers. But,while reviewing the literature on buyer behaviour aswell as retail marketing, few studies has been foundavailable that analyses how consumer behaviourfunctions in retail market. Considering the importanceof the study in India, it was proposed to conduct thestudy of consumer behaviour towards organisedapparel retail industries in the state of Gujarat havingheterogeneous population in the selected cities namely,Ahmedabad, Surat, Vadodara and Rajkot. The present

Table 1.12: Output of ANOVA of annual family income and 4 Promotional factors

Independent variable: HypothesisAnnual family income Sum of Mean Accepted/Dependent variables: Squares df Square F Sig. RejectedQ5.1 Celebrity Between 27.086 4 6.772 5.917 *.000 AcceptedEndorsement Groups

Within 680.887 595 1.144 Groups

Total 707.973 599



Total 582.260 599

Q5.3 Festive Between 42.763 4 10.691 13.827 *.000 AcceptedPromotions Groups


Total 502.798 599

Q5.4 Loyalty Between 10.645 4 2.661 2.209 .067 Rejectedcard Programme Groups

Within 716.953 595 1.205 Groups

Total 727.598 599


research was conducted with the objectives to studythe demographic factors influencing the consumerbuying behaviour for apparels, to identify the factorsinfluencing the consumer behaviour in selecting retailoutlet and to study the impact of promotionalstrategies affecting the consumer buying behaviour.

In the present study, 600 consumers selected on thebasis of convenience sampling method from variousorganized apparel retail shopping Malls of the fourcities of Gujarat were interviewed personally usingstructured questionnaire. Out of total consumersinterviewed, 54.7 percent were males and 45.3 percentwere females. Average age of the respondents was 33.3and most of them were graduates (44.2 percent). Thehighest percentage of the respondents was students(28.3 percent) and the median family income perannum was Rs.2,40,001/- to Rs.4,80,000/-. Theanalyses of the finding were done keeping in mind theobjectives of the study.


Table 1.13: Summary of Accepted Hypothesis under Hypothesis 4

Hypothesis accepted

Independent variable: GENDER

H1.1 - There is statistically significant impact of gender on celebrity endorsement for buying apparels fromretail outlets.

Independent variable: AGE GROUP

H1.5 - There is statistically significant impact of age group on celebrity endorsement for buying apparels fromretail outlets.

H1.6 - There is statistically significant impact of age group on number of end of season sales for buyingapparels from retail outlets.

Independent variable: EDUCATION

H1.9 - There is statistically significant impact of education on celebrity endorsement for buying apparels fromretail outlets.

H1.10 - There is statistically significant impact of education on number of end of season sales for buyingapparels from retail outlets.

H1.12 - There is statistically significant impact of education on friendliness of Loyalty card programme forbuying apparels from retail outlets.

Independent variable: OCCUPATION

H1.13 - There is statistically significant impact of occupation on celebrity endorsement for buying apparelsfrom retail outlets.

H1.14 - There is statistically significant impact of occupation on number of end of season sales for buyingapparels from retail outlets.

H1.15 - There is statistically significant impact of occupation on product knowledge of festive promotions(Diwali, Christmas etc) for buying apparels from retail outlets.

H1.16 - There is statistically significant impact of occupation on friendliness of Loyalty card programme forbuying apparels from retail outlets.

Independent variable: ANNUAL FAMILY INCOME

H1.17 - There is statistically significant impact of annual family income on celebrity endorsement for buyingapparels from retail outlets.

H1.18 - There is statistically significant impact of annual family income on number of end of season sales forbuying apparels from retail outlets.

H1.19 - There is statistically significant impact of annual family income on product knowledge of festivepromotions (Diwali, Christmas etc.) for buying apparels from retail outlets.

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An Enquiry into India’s Export Market andProduct Diversification

Rashmi Taneja*Rakesh Mohan Joshi**

Abstract

India’s exports during the post economic reform period witnessed an upward growth, until the globalrecession erupted in 2008. Annual export growth rate was registered at 29 % in 2007-08, but declinedto 13.6% in 2008-09 and turned negative at (-) 3.5% in 2009-10. Export market and export productconcentration is considered one of the major causes for significant decline in India’s exports resultingfrom slowdown in advanced countries viz. USA and EU. Therefore, the Government of India has put agreat emphasize on export diversification strategy to restrain, revive and strengthen its exports. Fewmomentous changes are broadly observed in terms of direction and composition of India’s exports, butno extensive analysis has been done so far in this context. The paper is aimed at undertaking an indepth analysis of measuring and analyzing diversification of markets and products of India’s exports.The analysis shows that the country has been relatively more successful in enhancing exports to thedifferent markets as compared to enhancing variation in its export products.

Keywords: Global Recession, Export Market Concentration, Export Product Concentration, ExportDiversification Strategy

IntroductionIndia’s exports have witnessed an upward and steadygrowth after the economic reforms of 1991. India’sexports registered at US$18 billion in 1991-92, roseby 9.6% (CAGR) to US$45 billion in 2000-01 andby 17% (CAGR) to US$163 in 2007-08. However,the global recession of 2008 jolted this upward risingtrend. Annual export growth rate which was registeredat 29% in 2007-08 declined to 13.6% in 2008-09and turned negative at (-) 3.5% in 2009-10(Table -1). Export market concentration is consideredas one of the major causes for significant decline in

Rashmi Taneja*Associate Professor, JIMS,PhD. Research Scholar, IIFT, New Delhi

Rakesh Mohan Joshi**Professor, Indian Institute of Foreign Trade,New Delhi

India’s exports resulting from slowdown in advancedeconomies viz. USA and EU1.

The Government of India has put a great emphasizeon export diversification strategy wherein exportersare encouraged to diversify their export markets andexport products to restrain, revive and strengthen theirexports. The government has extended various policymeasures viz. Focus Market Scheme (FMS), MarketLinked Focus Product Scheme (MLFPS), FocusProduct Scheme (FPS) etc. to support the exportersfor diversifying into new export markets2 and newexport products. Few momentous changes are broadlyobserved in terms of direction and composition ofIndia’s exports, however, no extensive analysis has beendone so far in this context. At this juncture, the paperis aimed at undertaking an in-depth analysis ofmeasuring and analyzing diversification of markets andproducts of India’s exports. The study enables us toassess the influence of export diversification measures

1 America and Europe together comprised of around 40% share in India’s total exports in FY2008, Ministry of Commerce and Industry, GOI2 In the Foreign Trade Policy 2015-2020 all these schemes have been merged into a single scheme, namely Merchandise Export from India Scheme(MEIS). MEIS, with product and market focused incentives for 4914 tariff lines, is a major export promotion scheme. Rewards under MEIS arepayable as a percentage of realized FOB value of exports, by way of the MEIS duty credit scrip which can be transferred or used for payment of a numberof duties including the basic custom duty. Furthermore, the Government has recently announced tariff revision for export of various products. Thecurrent revision has introduced 110 new tariff lines and increased rates or country coverage or both, for 2228 existing tariff lines.


undertaken by the government during the post globalcrisis period.

Literature ReviewThe existing literature exhibits that the role of exportdiversification has received considerable attention overthe last 50 years. Before that free trade was premisedon comparative advantage, specialization andinternational labor division inspired by classic tradetheories developed by Smith (1776) and Ricardo(1817). According to them each country has acomparative advantage in producing something, inexporting certain products and that specialization inthose export lines generates gains from trade. This viewhas been challenged by Presbish (1950) and Singer(1950) who argued that too much specialization in anarrow group of export products exposes a country toincreased instability in export earnings. This volatilitycan be mitigated through diversification by expandingproduction and trade of a variety of commodities withdifferent price trends, which can potentially help to

achieve some stability in economic performance.

The concept of diversification gained importance withthe modern theory of portfolio management developedby Nobel prize winner Professor Harry Markowitz whostressed that “Don’t put all your eggs in the samebasket” and inspired by modern portfolio selectiontheory which regarded diversification as a means ofreducing a country’s dependence on a particularproduct or a very restricted range of primary productsgenerally exported before processing.

Massell (1964) found that there is a significant positiverelationship between instability of export earnings andconcentration of exports. Across, cross sectionalanalysis conducted by Soutar (1977) determined thatgeographic concentration is one of the significantvariables in explaining the instability in 48 lessdeveloped countries from 1957 to 1969.

A link between export diversification, export growthand overall growth is also established by differentscholars (Vernon, 1966; Krugman, 1979).

Table-1: India’s exports FY 2001 - FY 2016

Years Exports (US $ billion) Annual % Change

2000-01 44.07 20.05

2001-02 43.8 (-) 0.56

2002-03 52.7 20.29

2003-04 63.8 21.1

2004-05 83.5 30.8

2005-06 103.1 23.41

2006-07 126.4 22.62

2007-08 163.1 29.05

2008-09 185.3 13.59

2009-10 178.7 (-) 3.53

2010-11 249.81 39.76

2011-12 305.96 22.48

2012-13 300.4 (-) 1.82

2013-14 314.4 4.66

2014-15 310.3 (-)1.29

2015-16 262 (-) 15.5

Source: Ministry of Commerce and Industry, GOI


Evenett and Venables (2002) showed that about onethird of the export growth of developing countriesbetween 1970 and 1997 were due to exports of oldgoods to new markets. Kahn and Cadot (2007)explained the robust hump shaped relationshipbetween export diversification and level of income.For the low and middle income countriesdiversification takes place mostly along the extensivemargin, whereas for income above the turning pointhigh income countries diversify along the intensivemargin and ultimately re-concentrate their exportstowards fewer products.

Shepherd (2008) stated that the trade growth ofdeveloping countries can take place through thecreation of trading relationship between with newpartners. Balza, Caballero, Pineda (2008) whilestudying the pattern at the level of destination marketof 10 Latin American countries, by implementing themethodology presented by Evenett and Venables(2002) shows that extension of the export markets isessential to enhance the export growth of bothtraditional and new products. Saikat and Anwesha(2008) determine that there is non-linear relationshipbetween export concentration and economic growthi.e. economic growth increases with diversification upto a critical level of export concentration, beyondwhich increasing specialization leads to highergrowth.

Agosin (2009) highlights that, the countries withdiversified export structures are able to recordconsistently higher export growth than countries whoseexports are largely confined to just a few products.Naude and Rossouw (2011) argues that contributionof export diversification to economic developmentdepends on the size of the economy. Smaller the sizeof the economy, better the impact of exportdiversification on its economic development and vice-a-versa. Subsequently a study by La (2011) computedthe relationship between market diversification andexport stability by applying new correlation adjusteddiversification indices and regression model based onSoutar (1977). The study concluded that there is anegative relationship between market diversificationand export instability i.e. higher the level of marketdiversification, higher will be the stability in exportsof an economy.

In the context of global economic imbalances and tradeflows pattern, several studies have been conducted, buta study by Bacchetta, Jansen et al (2009) is a pathbreaking as it explains the ability of geographicaldiversification to reduce the income volatility and toabsorb the country specific shocks of trading partners.Using a data set of 180 countries over a period of 1985-2004 the panel regression confirmed that exposure toexternal country specific shocks contributes to GDPvolatility of a country. The study also proved thatgeographical diversification helps to buffer shocks andthus reduces transmission of external volatility to theexporting economy. But, the relationship betweenexternal shocks and geographical diversification isnonlinear indication that the “beneficial” effects ofdiversification become smaller, as the country getsmore diversified.

Thus, most of the studies show that exportdiversification, both product and marketdiversification has a significant role in enhancingexport growth and overall growth of an economy. Thestudies also explain the ability of export diversificationin absorbing the external shocks and repercussions ofglobal economic imbalances. The Government ofIndia, in the light of global economic crisis hasundertaken several policy measures pertaining toexport diversification to restrain its declining exportgrowth and stability. Therefore, the study poses fewquestions whether these measures responded in termsof market diversification and product diversificationof India’s exports? Is this market diversification or theproduct diversification which responded better as aresult of consistent measures undertaken by thegovernment after the global crisis? The analysis wouldenable us to know the outcome of various policymeasures undertaken by the government to diversifyits exports to new markets and products.

Objectives of the StudyThe main objectives of the study are:

i. To analyze and compare the direction of India’sexports during the pre and post global economiccrisis

ii. To measure and analyze India’s export marketdiversification and export product diversificationduring the specified time period.


Research MethodologyThe study is based on the secondary data collectedfrom Ministry of Commerce and Industry (MOC&I).Further, few specialized indices viz. Regional HirshmanIndex (RHI) and Sectoral Hirshman Index (SHI) areapplied to measure and analyze the market and productdiversification of India’s exports. Time period FY2000-FY2015 is considered for the purpose of conductingthe captioned study.

Export Diversification Product Vis-À-VisMarket: Conceptual FrameworkExport diversification is variously defined as the changein the composition of a country’s existing product mixor export destinations (Ali, Alwang et al. 1999).Pacheco & Pierola (2008) defined the concept ofexport diversification in terms of intensive andextensive margin. The intensive margin of trade refersto the growth of exports in goods that are already beingexported i.e. “old products”. The extensive margin isdefined as the growth of exports in new categories i.e.“new products”. This traditional classification is wellsuited to discuss diversification issues from a productpoint of view, but it lacks a geographic dimension.This is why a twist is added to the traditional

definition. The twist simply consists of including thegeographic dimension in order to make distinguishbetween the product and geographic diversification.This implies that the intensive margin will consist of“Old Products” being exported to “Old Destinations”(OPOD). In the same way, the extensive margin willconsist of “Old Products” being exported to “NewDestinations” (OPND), “New Products to NewDestinations” (NPND), and “New Products to OldDestinations” (NPOD). To sum up, there are twodimensions to export diversification. Productdiversification is the sum of NPND and NPOD,whereas geographical diversification is the sum ofNPND and OPND. Figure 1 illustrates theclassification as per the above discussion.

Data AnalysisThe following section pertains to analyzing changesin India’s direction and composition of exports over aperiod of time.

India’s Direction of Exports: A Brief AnalysisDuring the past few years, India’s exports witnessedmomentous changes in terms of direction of exportmarkets. The share of developed regions (Europe andAmerica) which was approximately 50% in India’s

Figure - 1: Definitions


exports by the end of the first decade of economicreforms in FY2000 declined to 44% in FY2005 and36.5% in FY2010 (Table -2). Though the share ofthese regions has registered a marginal increase to37.2% in FY2015, it is still relatively less than theshare of other developing regions (Asia and Africa) inthe same year. The share of the fastest developing regionviz. Asia in India’s total exports witnessed a rise fromaround 37% in FY2000 to 48% in FY2005 and 52%in FY2010. The share of Asia in India’s exports hasreported a decline to around 50% in FY2015; but it isstill the highest amongst all regions. The region Africahas also registered a significant rise of share in India’sexports from 5% in FY2000 to around 7% in FY2005and FY2010 and 11% in FY2015.

The analysis shows that export diversification policymeasures undertaken and highly focused upon by theGovernment in recent times has produced positiveoutcomes in terms of shifting of its exports from thedeveloping regions to the developing regions.

Measurement of Market Diversification ofIndia’s ExportThe shift of India’s share from developed economies

to developing economies can be measured by using astandard measure of export market diversification i.e.Regional Hirshman Index (RHI) which is calculatedas below.

Regional Hirshman Index = RHI = sqrt [sum (xi /Xt)^2]

Where, RHI - Regional Hirshman Index

xi - Exports to country I

Xt - Total exports of the country

The highest possible value of market concentrationindex (RHI) is 1; this occurs when total exports aremade to only one market. The index would enable usto find whether India’s exports are diversifying intonew markets and to what extent. The Table 3 showsthat RHI for Europe and America have declined from0.067 and 0.061 in FY 2000 to 0.033 and 0.036 inFY2015 while, RHI for Asia has witnessed a significantrise from 0.14 in FY2000 to 0.24 in FY2015 and RHIfor Africa has increased from 0.002 in FY2000 to0.0112. This indicates that India’s concentrations ofexports in traditional markets have reduced anddiversified to the non-traditional markets viz. Asia andAfrica.

Table – 2 India’s Direction of Exports (Value in US$bn)

FY2000 FY2005 FY2010 FY2015

Exports Exports Exports Exports(US$ Share (US$ Share (US$ Share (US$ Share

Regions billions) (%) billions) (%) billions) (%) billions) (%)

1) Europe 10.23 25.94 19.67 23.55 38.52 21.55 56.30 18.14

2) America 9.62 24.70 16.79 20.1 26.87 15.03 59.05 19.03

Total 19.85 50.64 36.46 43.65 65.39 36.58 115.35 37.17

3) Asia 13.86 37.44 40.00 47.88 93.33 52.21 153.81 49.57

4) Africa 1.90 5.35 5.57 6.67 13.43 7.51 32.84 10.58

Total 15.76 42.79 45.57 54.55 106.76 59.72 186.65 60.15

5) CIS & 1.06 2.35 1.09 1.31 1.69 0.94 3.40 1.09Baltics

6) Unspecified 0.04 4.22 0.4 0.49 4.91 2.76 4.93 1.59Region

Total 36.71 100 83.53 100 178.75 100 310.34 100

Source: Ministry of Commerce and Industry


Overall, RHI of India’s exports has reported a rise from0.52 in FY2000 to 0.57 in FY2005 and 0.59 inFY2010, but it declined to 0.57 in FY2015. Thissuggests that India’s exports have been marginallydiversified to non-traditional markets in last few years.However, export market concentration coefficient(0.57) is still found to be very high and gives anindication to undertake further measures for enhancingexports to different non-traditional markets.

Measurement of Product Diversification ofIndia’s ExportsThe shift of India’s share from traditional to non-traditional products pertaining to different regions canbe measured by using a standard measure of exportproduct diversification i.e. Sectoral Hirshman Index

(SHI), which is calculated as below.

Sectoral Hirshman Index = SHI = sqrt [sum (xi /Xt)^2]Where, SHI –Sectoral Hirshman Indexxi - Exports of product iXt - Total exports of the country

The highest possible value of commodityconcentration index (SHI) is 1; this occurs when totalexports are comprised of only one commodity. Theindex enables us to find whether India’s exports arediversifying to new products and to what extent.

Overall, SHI of India’s exports has reported a rise fromaround 0.32 in FY2000 to 0.36 in FY2005, 0.41 inFY2010 and 0.40 in FY2014. This suggests that India’sproduct export concentration has increased contrary

Table-3: Export Market Diversification (Regional Hirschman Index)

Regions FY2000 FY2005 FY2010 FY2015

1) Europe 0.0673 0.055 0.046 0.033

2) America 0.0610 0.040 0.023 0.036

3) Asia 0.1402 0.229 0.273 0.246

4) Africa 0.0029 0.004 0.006 0.011

5) CIS & Baltics 0.0006 0.000 0.000 0.000

6) Unspecified Region 0.0018 0.000024 0.001 0.000

Sum of the squares of the 0.2737 0.3298 0.3481 0.3264share of regions

Regional Hirshman Index (RHI) 0.5231 0.5742 0.5900 0.5713

Source: Calculation based on the data, Ministry of Commerce and Industry, GOI

Figure-2: Regional Hirshman Index (RHI)


Table-4: Export Product Diversification–Sectoral Hirshman Index (SHI)

Exports of Top 10 % Share of Top 10 ExportYear Commodities (US$bn) Commodities in India’s Exports SHI

FY2000 20.7 56.3 0.317

FY2005 50.2 60.05 0.361

FY2010 114.3 63.9 0.408

FY2014* 200 63.5 0.403

Source : Compiled from Ministry of Commerce and Industry* FY2014 is taken instead of FY2015, because new commodity classification has been adopted since FY2015

to the expectations of its decline in the light ofundertaking various product diversification measuresby the Government.

Major Findings1. With regard to export market diversification, the

share of developed regions viz. America andEurope in India’s exports has reduced from onehalf to around one third, while share of developingregions, Asia and Africa has witnessed a gradualrise over the period. This indicates that India hasbeen successful in venturing into non-traditionalexport destinations, though the share of traditionalexport destinations in India’s total exports is stillhigh. This is also evident from the ConcentrationCoefficient Index i.e. RHI which has declined incase of Europe and America from around 0.060in FY2000 to 0.033 in FY2015, while, RHI in

case of Asia and Africa has witnessed a significantrise during the same period, indicate that India’sconcentration of exports in these markets havereduced and are tilted towards the other regionviz. Asia and Africa.

2. Overall, RHI of India’s exports has reported a risefrom 0.52 in FY2000 to 0.57 in FY2005 and 0.59in FY2010, but it declined to 0.57 in FY2015.This suggests that India’s exports have beenmarginally diversified to non-traditional marketsduring the last few years.

3. Measuring the shift of India’s share fromtraditional to non-traditional products, standardmeasure of export product diversification i.e.Sectoral Hirshman Index (SHI) measures andshows that SHI of India’s exports has reported arise from around 0.32 in FY2000 to 0.36 in

Figure-3: Sectoral Hirshman Index (SHI)

Source: Calculated on the basis of data, Ministry of Commerce and Industry, GOI


FY2005, 0.41 in FY2010 and 0.40 in FY2014.This indicates that contrary to expectations of adecline in product export concentration in thelight of various product diversification measuresundertaken by the government, product exportconcentration has gone up which is disappointingin the light of being highly ambitious of revivingand sustaining India’s export growth.

4. India has registered a rise in export productconcentration from 0.32 in FY2000 to 0.40 inFY2014, while export market concentration hasincreased from 0.52 to 0.57 during the sameperiod. This suggests that India has not been ableto enhance exports of varied products and to thedifferent markets, rather the country is stilldependent on few products and few markets forits exports which is not a desirable situation inthe present times.

5. The analysis also shows that though exportproduct and market concentration has increasedin FY2000, it has registered a decline whencompared with its position in FY2010. Exportmarket concentration reduced from 0.59 inFY2010 to 0.57 in FY2015; while export productconcentration has declined from 0.41 to 0.40. Thisshows that the country has been relatively moresuccessful in enhancing its exports to the differentmarkets as compared to enhancing variation inexport products. This is a sign of narrow productrange and inability of extending value chain of

the country, which needs to be achieved amid afrequent changing global dynamics.

Conclusion and SuggestionsThe analysis shows that the Central Government hasundertaken several measures for encouraging exportersto venture into new markets and to extend/diversifytheir export baskets; however a desired level of exportdiversification is still not achieved. In order to makeexport diversification strategy catalyst for revivingand enhancing India’s exports: i. It is imperative toidentify the factors which are detrimental to itseffective implementation in the economy ii.Exporters, especially the MSMEs exporters shouldbe made aware and encouraged to take the advantageof policy measures w.r.t export market and exportproduct diversification iii. Efforts should be made toenhance value chain of various products in whichIndia has the core competence viz. textiles, leather,engineering etc. iv. Exporters should be wellsupported with adequate funds at reasonable rates v.Measures should be taken to provide efficientinfrastructure and logistics facilities, advancedtechnology and updated market information to theexporters vi. Last but not the least, process for availinggovernment benefits should be made simpler andless time consuming. These measures may encouragethe exporters to undertake the decisions pertainingto diversification of their export baskets or/and exportmarkets which are considered being risky andchallenging.

References

1. Agosin R. Manuel (2007), Export Diversification and Growth in Emerging Economies, Cepel Review, 97.

2. Ridwan Ali; Jeffrey Alwang and Paul B. Siegel, (1991), Is Export Diversification the Best Way to AchieveExport Growth and Stability? A Look at Three African Countries, Policy Research Working Paper Series, No729, The World Bank.

3. Alberto Amurgo Pacheco and Martha DenissePierola(2008), Patterns of Export Diversification in DevelopingCountries: Intensive and Extensive Margins,Policy Research Working Paper 4473, The World Bank InternationalTrade Department.

4. Bacchetta Marc, Jansen Marian et al (2009), Exposure to External Shocks and the Exposure to ExternalShocks and the Geographical Diversification of Exports, WTO, Geneva.

5. Ben Shepherd(2008), Geographical Diversiûcation of Developing Country Exports, MPRA Paper No. 11267.

6. Evenett, S. J., and Venables, A. J. (2002), Export Growth in Developing Countries/ : Market Entry andBilateral Trade Flows, World Trade Institute, University of Bern, and CEPR, Memeo, pp. 1-43.


7. Joo La(2011), Correlations-Adjusted Export Market Diversification, Journal of East Asian Economic Integration,Vol. 15, No. 1.

8. Krugman, P. (1979), A Model of Innovation, Technology Transfer and the World Distribution ofIncome,Journal of Political Economy, 87: 253–66

9. Cadot, Olivier &Carrèère, Cééline& Strauss-Kahn, Vanessa (2007), Export Diversification: What’s Behindthe Hump?The Review of Economics and Statistics, 93, 590-605.

10. Lenin Balza, Maria Caballero, Leonardo Orgtega Y Jose Pineda(2008), Market Diversification and ExportGrowth in Latin America, Universidad Central de Venezuela, January 2008.

11. Massell F. Benton (1964), Exports, Capital Imports and Economic Growth, Kyklos, Volume 17, Issue 4,627-635.

12. Naude and Rossouw(2011), Export Diversiûcation and Economic Performance: Evidence from Brazil, China,India and South Africa, Econ Change Restruct, https://link.springer.com/article/10.1007%2Fs10644-010-9089-1.

13. Geoffrey N. Soutar (1977), Export Instability and Concentration in the less Developed Countries: A Cross-sectional Analysis, Journal of Development Economics, 4, (3), 279-297

14. Simon J Evenett and Anthony J Venables (2002), Export Growth in Developing Countries: Market Entryand Bilateral Trade Flows, Working Paper, University of St. Gallen, Berne.

15. Sharma S P and Taneja Rashmi(2015), India’s Exports Diversification : CrissCross Concerns, PHD ResearchBureau, PHDCCI.

16. Singer, H.W(1950),The distribution of Trade between Investing and Borrowing Countries,American EconomicReview, Vol. 40, May 1950, pp.531- 548.

17. Foreign Trade Policy 2015-2020, Directorate General of Foreign Trade, Government of India.

18. Vernon, R (1966), International Investment and International Trade in the Product Cycle,Quarterly Journalof Economics, 80:190–207.

Books

1. Presbish, R(1950),The Economic Development of Latin America and its Principal Problems,United Nations,New-York.

2. Ricardo (1817), Principles of Political Economy and Taxation ed. R.M. Hartwell, Penguin, Hammondsport,1971.

3. Salvatore Dominick, International Economics: Trade and Finance, 2014, 11th edition, Wiley Publications.

Measuring Destination Brand Personality of Jaipuras a Destination Brand

Kirti Singh Dahiya*D.K. Batra**

Abstract

The present research study is based on the application of the concept of brand personality to the touristdestination brand in Indian context. The study location is Jaipur, a popular Indian heritage city. Throughexploratory factor analysis and frequency distribution five personality traits have been discovered forJaipur’s brand personality as a tourist destination brand. The study is based on the domestic tourists’perception. The study concludes that the destination brand personality scale differs from one destinationto another because of the different characteristic and tourism offerings of the destination. However, theconcept of brand personality can be easily applied to destinations similar to the corporate brands.Every destination has its unique personality.

Keywords: Brand, Personality, Destination, Tourism, Tourist

IntroductionThe concept of destination brand has gainedmomentum since the book on destination brandingby Morgan, Pritchard and Pride was introduced in theyear 2002 (Morgan, Pritchard and Pride 2004). Sincethen, the concept has become a global phenomenonfor the different tourist destinations across the world.The globalization is the driving force behind thedevelopment of tourism brands.In the year 2002, thedestination brand for the Indian tourism industry‘Incredible India’ was launched as a response to theglobal competition. The campaign aimed at creatinga distinguished identity of India as a tourism brand tobeat the competition. Since then, the destinations aredeveloping themselves as a tourist destination brand.

The destination brands are emerging equivalently tothe corporate brands. The concept of brands andbranding has been widely applicable to the touristdestinations. The marketing campaign helps thedestination brand to develop an imagery in the mindof the targeted audience and brand is being personified.

Kirti Singh Dahiya*Assistant Professor,IITM, New Delhi

D.K. Batra**Professor,IMI, New Delhi

The concept is known as brand personality. Thus,brand personality can be defined as the “set of humancharacteristics associated with a brand” (Aaker1997).Keller (1993) regarded brand personality as a userimagery, a component of brand association that leadsto formation of brand image. In other words, brandpersonality is an important component of destinationbrand image.

Brand personality defines the different human traitsthat can be associated with a brand based on thedifferent products and services offered by the brands.For example, Coca Cola is characterized as cool. Interms of tourist destination, Rishikesh can becharacterized as adventurous because of white waterriver rafting.Every brand has different personality traits,which can be modified to suit the different needs ofthe customers (Kaplan, 2008). The knowledge aboutthe destination brand personality is important for thedestination marketer to know the brand perceptionamongst the tourists. Identification of destinationbrand personality traits helps the destination marketerto match the tourism supply with the tourist’spreference as tourists choose the destination, which isidentical to their personality.

Indian tourism industry is highly characterized by forts,palaces and monuments mainly the heritage tourism;as India is known as “Land of Maharajas”. Therefore,the present research study aims to identify the brand


personality traits of a popular Indian heritage city-ThePink City Jaipur.

Literature Review:The universally acceptable scale on brand personalitywas proposed by J. Aaker in the year 1997. In herresearch study, Aaker validated and generalized thebrand personality scale, which is widely adopted andhas relevance till today. Five dimensions were identifiedthrough exploratory factor analysis, the dimensionsare Sincerity, Excitement, Competence, Sophisticationand Ruggedness. The first factor ‘Sincerity’ has fourfacets namely ‘down-to-earth’, ‘honest’, ‘wholesome’and ‘cheerful’. The second factor is ‘Excitement’, itcomprises of four facets, which are ‘Daring’, ‘Spirited’,‘Imaginative’ and ‘Up-to-Date’. The third factor is‘Competence’ with three facets. The facets are‘Reliable’, ‘Intelligent’ and ‘Successful’. The two facets‘Upper Class’ and ‘Charming’ constitutes the fourthfactor ‘Sophistication’. The fifth factor is ‘Ruggedness’represented by two facets, first is ‘Outdoorsy’ andsecond is ‘Tough’. Adopting this scale, numerousresearch studies were conducted in the area of brandpersonality including destination brands.

In the year 2006, Ekinci and Hosany conducted aresearch study in the field of destination brandpersonality following the research framework providedby Aaker (1997). The study attempted to revalidatethe previous scales of brand personality. Theydeveloped a three-dimensional scale for destinationbrand personality. The first factor obtained fromexploratory factor analysis was ‘Sincerity’ factorexplains the dependability and trustworthiness of thetourist destinations. The second factor ‘Excitement’is represented with personality traits such as daring,exciting, spirited and original. The third factor wasnamed as ‘Conviviality’ which represents thedestination personality traits such as family-oriented,charming and friendly. The two factors, ‘Sincerity’ and‘Excitement’ shared a similarity with the scaledeveloped by Aaker (1997).

Kaplan et al. (2008) conducted a research studyexamining the application of brand personality fortourism destinations. The study stated that the conceptof brand personality can be easily applied to thedestination brand as similar to conventional products.

Through exploratory factor analysis the study obtainedsix factors solution. The factors of brand personalityidentified for place branding are ‘Excitement’,‘Malignancy’, ‘Peacefulness’, ‘Competence’,‘Conservatism’ and ‘Ruggedness’. Out of six, four werecongruent with the earlier researches. However, thetwo new factors are significant contribution to theexisting literature. The new factors are Malignancy andConservatism.

‘Excitement’ is defined with the facets like passionate,outgoing, feminine and sympathetic. ‘Malignancy’facets were unreliable, arrogant and self-seeking.‘Peacefulness’ was defined as the calmness and serenityof the place. The facets of ‘Competence’ factors areauthoritarian and sophisticated. Conservatism wasdefined as religious and uneducated characteristics ofthe place. Last factor ‘Ruggedness’ was not definedproperly in the research study.

The research study stated that brand personality isimportant for place branding for the cities all aroundthe world. It was stated that brand personality dependson the tourism offerings of the place and it is differentfrom one destination to another destination.

Upadhyaya (2012) conducted a research study on theconcept of brand personality for Jaipur as a touristdestination and identified the factors of the destinationpersonality for Jaipur. The six factors were obtainedfrom exploratory factor analysis. The factors are‘Modern’, ‘Youthful’, ‘Ruggedness’, ‘Vibrant’,Sincerity’ and ‘Contemporary Trends’. It is difficultto generalize the results as the study is specific only toone city of India. The factors identified in the researchare limited to India only as the study was carried inIndia, Albeit, the study is very useful for the presentstudy as its study location is Jaipur, which is the studydestinations itself.

Bilim and Bilim (2014) undertook a research study toanalyze the association between brand personality andbrand image of the tourist destination. The studyclassified destination brand image as cognitive andaffective. Cognitive image defined as image perceptionof the tourist destination elements.Affectivedestination image was defined as tourist feelingstowards a tourist destination like pleasant-unpleasantand exciting-boring.


Table 1: Variable Extraction Table from Literature Review

Construct: Brand personality

Research Parameters Explanation to parameters

Aaker (1997) Sincerity Down-to-earthHonestWholesomeCheerful

Excitement DaringSpiritedImaginativeUp-to-date

Competence ReliableIntelligentSuccessful

Sophistication Upper classCharming

Ruggedness OutdoorsyTough

Ekinci and Sincerity ReliableHosany (2006) sincere

IntelligentSuccessful wholesome

Excitement ExcitingDaringOriginalSpirited

Conviviality FriendlyFamily orientedCharming

Unurlu and VibrancyKucukkancabas (2013) Sophistication

Competence -NA-ContemporarySincerity

Bilim and Bilim (2014) SincereIntelligentReliableSuccessful -NA-SecureWholesomeDown to earthExciting



OriginalUniqueSpiritedFriendlyFamily orientedCharming

Glinska and Kilon (2014) PeaceNeatness -NA-ConservatismOthers

Meer (2010) Thesis (Amsterdam)

Down to earthFamily-orientedSmall-townHonestSincereRealWholesomeOriginalCheerfulSentimental -NA-FriendlyDaringTrendyExcitingSpiritedCoolYoungImaginativeUniqueUp-to-dateContemporaryReliableHard workingSecureIntelligentTechnicalCorporateSuccessfulLeaderConfidentUpper classGlamorous



Good lookingCharmingFeminineSmoothOutdoorsyMasculineWesternToughRugged

Kaplan et al. (2008) ExcitementMalignancyPeacefulness -NA-CompetenceConservatismRuggedness

Upadhyaya(2012) SincerityExcitement -NA-CompetenceSophistication

The scales for personality and image were foundcompatible with each other however, they weredifferent in reality. The study location was Didim.Thestudy identified ten different brand personality traitsfor Didim as a tourist destination. The traits aresincerity, intelligent, reliable, successful, secure,wholesome, down to earth, exciting, original andunique.

Glinska and Kilon (2014) conducted a research studyto identify the personality traits of Poland as adestination brand based on the perception ofdestination marketer. The study followed the footstepsof Aaker (1997) and discovered three new factors inaddition. The factors are peace, neatness andconservatism. However, destination brand personalityconcept is more appropriate to tourists and the studydid not considered tourists’ perception in measuringdestination brand personality.

Meer (2010) conducted an extensive research studyon the application of the concept of brand personalityto the tourist destinations. Amsterdam was selected asa destination for study purpose. The research studyidentified different traits of brand personality withrespect to tourists’ destination.

Research Methodology:Research Objective:

To Know the Personality Traits of Jaipur as aDestination Brand

Research Design:

Research design was exploratory in nature as the studyexplored the different personality traitsof Jaipur as atourist destination brand through exploratory factoranalysis.

Sample Design and Procedures:

Judgemental sampling design was adopted to suitablyselect the respondents, who were on the heritagetourism trip of Jaipur. The sample unit consists of theindividual respondents at the tourist destination. Thedata intercept point was the outside area of themonuments in Jaipur. The total sample size for thestudy was 176 respondents. As per the conditions offactor analysis sample size should be five to seven timesof the item statements. 13 interval-scale itemstatements were employed in the questionnaire. Samplesize is quite high because the research study constitutesthe pilot study of the doctoral thesis.


Scales Adopted in the Research Study:

7-point interval scale was adopted and nominal scalewas also used to measure demographic profile of thestudy respondents.Nominal scale was also used tomeasure different personality traits of Jaipur.

Data Analysis and Presentation:

After data collection, it was manually entered into the

SPSS file. Then it was analyzed using SPSS 22.0.Exploratory factor analysis and frequency distributionwas the data analysis technique adopted. Exploratoryfactor analysis was conducted with principalcomponent analysis as a method of extraction andvarimax as rotation method. Reliability statistics wascalculated using Cronbach alpha value. Data ispresented with the help of tables and bar charts.

Table 2: Rotated Component Matrix

Component

Item statements 1 2 3

Serenity of the place -.200 .786 .165

Calm and clean environment .093 .761 .011

Place welcoming everyone .342 .596 .031

Exciting atmosphere of the destination .268 .608 .186

Nightlife of the city .692 .359 .023

Well maintained roads .602 .238 .225

Monuments cleanliness .471 .174 .556

Monuments beautiful architecture -.092 .106 .834

Cheating with the tourists -.636 .071 -.140

Basic amenities at the tourist destination -.501 .050 -.439

Tourist facilities in the city .589 .003 .175

New infrastructure developments .367 .154 .571

Tourism department sincerity .624 .254 -.082

The first factor comprises of four statements. Thefirst statement is ‘nightlife of the city’ with factorloading of 0.692, the second statement is ‘wellmaintained roads’ with the factor loading 0.602.The third statement is ‘tourism department sincerity’with factor loading of 0.624. The statements areshowing the responsibility of the tourismdepartment to provide good nightlife environment,better infrastructure in terms of roads as roads arethe major mode of transportation in Jaipur and thelast statement is representing the sincerity of thetourism department, thus the factor is named as‘Sincerity’.

The second factor is made up of four statements. The

first item statement is ‘serenity of the place’ with factorloading 0.786. It represents calmness at the touristdestination. The second statement is ‘calm and cleanenvironment’ with factor loading 0.761. The thirdstatement is ‘place welcome everyone’ representing thehospitality of the place. The factor loading for thisstatement was 0.596. The fourth item statement inthe second factor is ‘exciting atmosphere at thedestination’ with factor loading of 0.608. All thestatements are representing the serene and calm tourismenvironment at the destination. Therefore, the factoris named as ‘Calm’.

The third factor is constituting of three itemstatements. The first statement ‘monuments


cleanliness’ with factor loading of 0.556. The secondstatement is ‘monuments beautiful architecture’ is0.834. The third item statement is ‘new infrastructuredevelopment with factor loading of 0.571. The itemstatements are related with monuments beauty andcleanliness and infrastructure, therefore the factor isnamed as ‘stunning’.

The Kaiser-Meyer-Olkin Measure of SamplingAdequacywas 0.791, which shows sample size

adequacy to run the factor analysis. The Bartlett’s testof sphericity obtained was 552.080 with 78 degreesof freedom and p-value 0.000.

The three factors (personality traits) obtained fromexploratory factor analysis are ‘Sincerity’, ‘Calm’ and‘Stunning’.

The Cronbach alpha value for 13 interval itemstatements was 0.611. It shows the internal consistencyof the research instrument.

Exhibit-1: Personality Traits Based on Nominal Scale Presented through Frequency Count

Respondents were provided these personality traitswith the checkboxes. They were supposed to tick markthe trait, which they can link with Jaipur as adestination brand. The top two traits with highestnumber of frequencies are ‘Exciting’ and ‘Family-oriented’.

Thus, three factors from exploratory factor analysisand two factors from nominal scale analysis wereobtained. The factors are ‘Sincerity’, ‘Calm’,‘Stunning’, ‘Exciting’ and ‘Family-oriented’

Total sample size was 176. 135 of the studyrespondents were males and 41 of the respondents werefemales. 10 of the respondents were from the age groupof 16-20 years. 35 of the respondents were agedbetween 21-25 years. 49 of the study respondents werefrom the age group of 26-30 years. 25 of the studyrespondents were lying in the age group 31-35 yearsand 23 were from the age group of 36-40 years. 9 of

the respondents were from the age group of 41-45years. 6 of the respondents belonged to the age groupof 46-50 years. 5 of the respondents belonged to theage group of 51-55 years. 2 respondents were fromthe age group of 55-60 years. 1 of the respondent wasfrom the age group of 61-65 years, 1 respondent wasbelonging to the age group of 65-70 years. One of therespondents was senior citizen aged 72 years. 8 of therespondents have not mentioned their age.

61 respondents were travelling alone and 69 weretravelling with their families and 18 of the studyrespondents were travelling in group. 28 of the studyrespondents did not define their travelling pattern. 59of the study respondents were travelling for tourismpurpose and five were travelling because of the officialwork. 112 respondents have not mentioned theirtravelling purpose.

114 of the study respondents were graduates and 25 were


Table 3: Demographic Profile of the Study Respondents

Gender Travelling Purpose

Male 135 Tourism 59

Female 41 Official 5

Age Not defined 112

16-20 10 Education

21-25 35 School Level 23

26-30 49 Graduation 114

31-35 25 Post-Graduation 25

36-40 23 Doctorate 4

41-45 9 Post Doctorate 4

46-50 6 Not defined 6

51-55 5 Nationality

55-60 2 Indian 176

61-65 1

65-70 1

Above 70 1

Not defined 8

Travelling Pattern

Alone 61

Family 69

Group Travel 18

Not defined 28

post graduates. 4 of the respondents were doctorates and4 were post doctorates. 23 of the study respondents hadeducation up to school level. All the study respondentswere Indian nationals as the study was focused onmeasuring domestic tourists’ perception.

Conclusion:The study was oriented towards identification of thepersonality traits of Jaipur as a destination brand basedon the domestic tourists’ perception. Variables wereextracted from the literature review including theuniversal scale of brand personality given by J. Aakerin 1997. Questionnaire was formulated based on thevariables that were extracted from the literature reviewand the destination characteristics. Throughexploratory factor analysis three factor solutions wasdeveloped or in other words three personality traits

were identified. The identified traits are ‘Sincerity’,‘Calm’ and ‘Stunning’. Apart from factor analysis, oneof the questions was based on nominal scale wheredifferent personality traits were presented to therespondents. Checkbox was provided against everyoption as respondents were required to tick-mark thetraits which is congruent to Jaipur based on theirperception. The calculation was done using frequencydistribution. Two most desirable personality traits withhighest frequency count are ‘Exciting’ and ‘Family-oriented’. Therefore, comprehensively the researchdiscovered five personality traits for Jaipur as a touristdestination brand. The identified traits are ‘Sincerity’,‘Calm’, ‘Stunning’, and ‘Exciting’ and ‘Family-oriented’. The personality traits are different fromindividual to individual similarly the personality traitdiffers from destination to destination.


The first factor represents Jaipur as a ‘Sincere’ touristdestination, which is responsive towards the touristsneeds. Second factor is ‘Calm’ that represents serenityand tranquility at the various tourist destinationsmainly at the monuments in Jaipur. The third factor‘Stunning’ represents the exquisite architecture of themonuments including forts and palaces. The fourthfactor is ‘Exciting’ based on the tourist’s frequencycount. It represents that tourists were excited to visitthe heritage city of Jaipur and feel the rich Indianheritage in term of culture and monuments. The fifthfactors ‘Family oriented’ represents Jaipur as a familyholiday destination.

However, the identified personality traits are specificto the study location i.e. the Pink City Jaipur, whichis also one of the limitation of the research study.However, some of the findings congruent with thepast studies. The factor ‘Exciting’ is similar to thefindings of Aaker (1997), Ekinci and Hosany (2006),

Bilim and Bilim (2014), Meer (2010), Kaplan et al.(2008) and Upadhyaya (2012). The factor ‘Familyoriented’ is congruent with the studies of Ekinciand Hosany (2006), Bilim and Bilim (2014) andMeer (2010). First factor, ‘Sincerity’ is also congruentwith the brand personality scale developed byAaker (1997), Ekinci and Hosany (2006), UnurluandKucukkancabas (2013), Bilim and Bilim (2014),Meer (2010) and Upadhyaya(2012). The factor‘Calm’ is congruent with the research studiesof Kaplan et al. (2008) and Glinska and Kilon(2014).

The study concludes that brand personality conceptis applicable to tourist destination brands similar tothe products and services or in other words similar tothe corporate brands. Every destination has its uniquepersonality based on the tourism offerings of the place.Thus, brand personality is different from one place toanother.

References1. Aaker, J. L. (1997, August). Dimensions of Brand Personality. Journal of Marketing Research, XXXIV,

347-356.

2. Bilim, Y., &Bilim, B. (2014). Does a Destination have Personality? Athens Journal of tourism, 1(2),121-134.

3. Ekinci, Y., &Hosany, S. (2006). Destination Personality: An Application of Brand Personality to TourismDestinations. Journal of Travel Research, 45, 127-139. doi:10.1177/0047287506291603

4. Keller Kevin Lane (Jan. 1993), Conceptualizing, Measuring, and Managing Customer-Based Brand Equity,Journal of Marketing, Vol. 57, pp. 1-22

5. Kilon, E. G. (2014). Desirable Traits of the City Brand Personality in the Opinion of Managers for thePromotion of the City Government in Poland. Procedia - Social and Behavioral Sciences, 418-423.

6. MelikeDemirbag Kaplan Oznur Yurt BurcuGuneri Kemal Kurtulus, (2010), “Branding Places: ApplyingBrand Personality Concept to Cities”, European Journal of Marketing, Vol. 44 Issue 9/10 pp. 1286 – 1304

7. Meer, L. V. (2010, September). Communicating Destination Brand Personality. Rotterdam.

8. Morgan, N., Pritchard, A., & Pride, R. (2004). Destination Branding: Creating the Unique DestinationProposition (2nd ed.). Elsevier Butterworth Heinemann

9. Unurlu, C., & Celin Kucukkancabas. (2013). The Effect of Destination Personality Items on DestinationBrand Image. International Conference on Eurasian Economies, (pp. 83-88).

10. Upadhyaya Makarand (January - April 2012), Influence of Destination Image and Destination Personality:An Empirical Analysis, Journal of Marketing & Communication, Vol. 7 Issue 3 pp. 40-47

Boys Vs Girls: Pestering Strategies of Childrenin India

Asha Chauhan*Ravindra**

Abstract

This paper attempts to bring sensitive issue of mounting consumerism in children in India whose outcomeis Pester Power, which means the nagging ability of children to purchase the product they desire due tosome motive. In today’s techno savvy epoch, the urban children are relatively more coupled, conversant,and are more prospective to influence their parent’s decision. As disposable income is expanding inmany families, parents appear more eager to buy goods for their children as compared to past. Due toenlargement of Media, Internet and Television advertising, children are stuffed with supplementaryinformation and entertainment options which lead to have a strong impact on their parents. As children’s’culture are dominated by technology oriented entertainment and advertising as a result Children’s asinfluencer in decision-making is mushrooming worldwide. This paper accomplish the key drivers ofpester power and effect on parent’s decision. The effort is made through this paper to try and find outwhether there are any differences in the pestering strategies with respect to the gender of the child. Anextensive review of relevant literature is done for a clear understanding of the concept. Descriptiveresearch design is used for the study and a non probability sampling techniques are used. A sample of200 parents (100 parents of girl child and 100 parents of boy child) were interviewed with a closeended questionnaire.

Keywords: Pester Power, Consumerism, Buying Behavior, Decision making

IntroductionIndia has revolutionized a lot with the globalizationand altering economic activities. As a result, due tonuclear family size and more dual income, parents aremore willing to buy goods for their children if wecompare with the past trends (Dotson & Hyatt, 2005).Children are becoming more aware, rational, cleverand advanced to find their own approach. Thisgrowing awareness was developed during the 1990sthat children had enormous market potential, not asone market but as three – a current market spendingtheir own money in order to satisfy their own needsand wants, an influential market attracting a substantialamount of parental expenditure, and a future marketthat eventually will constitute all the customers for a

Asha Chauhan*Research Scholar, Department of Commerce,Indira Gandhi University, Rewari

Ravindra**Assistant Professor, Department of Commerce,Indira Gandhi University, Rewari

firm’s services (McNeal and Yeh, 2003). Thisdevelopment of knowledge and skills enforce childrento act as an active partner while deciding what to buy.Thus, Children are now extremely aware about thefeatures of the product they are trying to buy. Childrenacquire such skills and knowledge by interacting withtheir parents, peers, accessing internet and mediamay prompt to ‘pester power’, which can lead tounhappiness or conflict (Palan, 2001).

According to Director General of the AdvertisingAssociation, Pester power has been defined as ‘a snipingterm for children making requests of their parents’(Brown 2004). The foundation of the concept of pesterpower is children unparalleled command because oftheir ability to set up variety of strategies that can exertpressure on their parents while purchasing.Traditionally, parents used to buy all needy things fortheir children but now if we analyze the working modelof “Parents-Children’s” shopping, child will act as anindependent consumer. According to Mr PurnenduBose, Chief Operating Officer, Hungama TV, the factthat Indian cable and satellite homes are largely single


television homes explains the children’s power on thebuying decisions. The new generation of children hasgrown-up from being the power to key decisionsupport system of the families (McDermott, 2006).Nowadays parents are unable to spend time with them,so in order to compensate they are giving expensivegoods.

Child interaction with his/her parents play vital rolein a commercial context. For example, in order toattract customers a marketer can craft suchadvertisement which can encourage child to nag theirparents into buying something that is not healthy forthem, or they don’t need or their parents cannot afford’.Thus, Pester Powers can act as sales promotional tool.According to the American Centre for Science in thePublic Interest, pestering strategies undermine parentalauthority. Parents are forced to choose between being‘the bad guy’ by saying no to junk food or giving in toincessant demands.

Marketers are targeting children as they have influenceon parents buying decisions. They perceive pesterpower in broader sense, targeting children as a formof the tactic. Marketers take Pester Power in a positivemanner which seems contradictory. It is usually alliedwith manipulative marketing strategies that pushchildren for nagging. Marketer can exert two kinds ofinfluence; Kidfluence and Indirect influence.“Kidfluence” is the direct influence that kids have overfamily household purchases. Indirect influence meansthat the kids’ preferences are given consideration whenparents make a purchase decision. The major lashingforce behind this extensive change is the advertising.Advertising has transformed the way kids learn, reactand behave towards the environment.

In India the culture of pocket money has embarkedthe parents, especially the middle class people.According to The Indian Express Pocket money ofIndian kids has shot up by over 200 per cent over thepast decade. In a study, it was shown that in a yeararound Rs. 500 crore is the money given to childrenas pocket money. They are evolving as a group ofinformed customer and media is impacting their brandchoice, buying behaviour and buying patterns. Thissegment of ‘Children’ is now evolving as a majordemand creator for many market players. Thus, it is

always good from marketing perspective. They have asimple rule; they will buy whatever they will watchover the television. Today’s parents overburden theirchildren’s life with heavy dose of academics andextracurricular activities. They compel to surpass bothat school and extra activities, thus, in return childrennag their parents to fulfill their demands. As a result,pester power creates mental distress and family tensions(McDermott, 2006). Somehow capability of a childto nag their parents depends upon parent-childinteraction as his/her character gets molded by theactivities of their parents and their occupation.

There are many instances, wherein both the officegoing parents, who cannot really concern anything fortheir children, try to provide all the facilities andamenities that the children require. Due to paradigmshift in the behavior of the Indian parents, childrenare getting expensive goods to compensate not beingable to spend time with them. Not only parents but itis broadly seen that because of emotional affiliation,grandparents bend forward to care more for theirgrandchildren and try to satisfy their desires whichact as a catalyst for the increasing level of Pester Power.

Literature ReviewIn the past there have been copious studies exploringthe concept and effect of Pester Power on parent’sbuying behavior. Pester Power is defined as the naggingability of children to purchase the product they desiredue to some motive. The concept of ‘Pester Power’ isdebatably one of the most knocking, perceptive, andcontroversial facet in present marketing practice.According to Director General of the AdvertisingAssociation, Pester Power has been defined by the as ‘apejorative term for children making requests of theirparents’ (Brown 2004). Due to change incontemporary socio-cultural trends regarding workingparents, lack of time, nuclear families it is glimpsedthat many children are spending less time with theirparents than their complement in the 1970s and 1980s.Therefore, it can be argued that parental influencepreserve one of the major factor in transiting childreninto consumers, along with relative to say, peers and/or media (Dotson & Hyatt, 2005). In US, childrenare more inclined by the peer activities and most ofcharacteristics get molded only with the peers, while


in contrast in India, children’s behavior is influencedmajorly by the activities done at home or the professionand occupation of the parents. Moschis and Churchill’s(1978) gave a model which is the most widely usedmodel of consumer socialization (Dotson & Hyatt,2000). Consumer socialization is the process by whichchildren attain skills, knowledge and attitudespertinent to their functioning in the market place. Inmodel they conferred five key variables, whichinfluence how individuals develop as consumers. Thesefive variables comprises of socialization agents, learningprocesses, social structural variables, age/life cycle stage,and learning properties. The literature has explorednumerous ways in which children can fuel purchaserequests to parents. As per a study conducted by Palan(2001), he identifies that there are four approaches:informing strategies in which children ask or tellparents regarding products, negative strategies whichis Pester Power or nagging ability of children,persuasion strategies in which children rather thannagging try to persuade their parents to buy a particularproduct, and reasoning strategies in which childrennotify parents about value-for-money offers. Thomson(2003) scrutinize the process of decision-making forfamily purchases and found that children have differentpriorities from parents but that, in common with otherfamily members, involvement in communicationincreased their influence on the final purchase.Essentially children learn the consumption purchasingpattern primarily from their parents. Children observethe parental behavior in purchase decisions over theyears and gradually they acquire the relevant consumerskills from their parents (Filiatrault and Ritchie, 1980).The family is a tool in teaching young people coherentaspects of consumption, which also include basicconsumer needs. Parents always try to teach theirchildren to be more rational in their life. Panwar (2006)have done study in the Indian context he stated thatfor new age children in India, television act asinfluential as a parent or a teacher do. This fact is verywell understood by the marketers today. It manifestedthat the growth of influence of media on children, hasincreasingly large number of advertisements aredirected at this potential target audience due to whichchildren are nagging or making requests to parents. Injoint families in India, it was examined that childrenare civilized by their parents to behave as coherent

thinking consumers in society. Though, with thegrowing influence of the west culture, there is anincrease in nuclear families, dual income families areon the mount and there has been a discrete shift in thefamily composition structure and the decision makinghas become more uncensored ( Kaur and Singh, 2006).Thus, children enjoy greater judgment, not only inmaking routine consumption decisions for the familybut also in pestering their parents to buy other productsdesired by them (Singh, 2006). A research byWackman’s (1972) explored that mothers of childrenaged between 5 and 12 years found that children whorequested products more often were more likely to havetheir requests granted. The researcher also suggestedthat mothers were more likely to agree to purchaserequests as the children grew older (i.e. aged 11 to 12years). Thus, maternal perception about older childrenwas more competent and informed consumers thanyounger children (five to seven years).

Purpose of the Research:1. To get aware about Pester Power.

2. To know about the key drivers of Pester Power.

3. To identify whether there is any significantdifference in the pestering strategies with respectto the gender of the child.

Research Methodology:This study is restricted to the area of Delhi region only.The study being undertaken is descriptive andexploratory in nature. It describes in detail the state ofaffairs of pester power in India. The present study hasconcern the children between the age of 8-16 years.The convenience sampling technique has been adoptedin order to draw analysis of difference in pesteringstrategies with respect to gender. The study wasundertaken by considering few product categories likegames, toys, chocolates, mobile phone and sweets thatexplore the discretionary preference to nag their parentsto purchase them.

SPSS (version 17) software has been used foreconometric analysis of data. In order to investigatedifferences in pestering strategies of children withrespect to gender in Delhi, certain hypotheses havebeen formulated. In order to know differentpestering strategies with respect to gender, it is very


important to know difference in the pestering powerof girls and boys across a range of products whichinfluences the family decision making. A sample of200 parents (100 parents of Girl child and 100 Parentsof Boys) were interviewed with a close endedquestionnaire.

Pester Power in India

Due to augmentation of media and growing nuclearfamily, children have become more independent,logical and confident than their ancestors and arebecoming more knowledgeable and mature in theirown right much earlier than before. Thus, Childrennow act as an important target market. They not onlyact just as purchasers but also act as influencers tochange decision.

In India, kid power get big drive because of the arrivalof niche channels like the Cartoon Network, Hungamaand Toonami. As a result child request for various playitems, snack food or even clothing often influence thefinal decision making at home. Pester Power has beenincorporated in the marketing campaigns to reach thetarget audience. The amount of influence exerted bythe children varies with different product categoriesand also with the different stages in the decisionmaking process.

With the increase in disposable income and mediagrowth, rural India has become an emerging up-and-coming market. According to some estimates, in 2005,there were more than 120 million tween (childrenbetween 8-12 years of age). Among them around 45million live in urban areas who have the power ofdetermining or influencing the whopping Rs. 20,000crore worth purchasing decisions on food, mobilephones, apparel, cars and FMCGs.

Drivers of Pester Power

1. Greater exposure to kids

Nowadays, Children are surviving in techno savvyenvironment as a result they are getting lot of exposureof internet, media etc, through which continuouslykeep on learning various notion. Effects of the massmedia have been found to be extensive and potentiallyharmful. They try to act in similar fashion that theylearnt from various exposures. This exhibits a specificaggressive behavior in them.

2. More working women

Evolution of education for women in India haschanged their life. Majority of the educated womenare working to support their family and groundsconsequently more dual income families. Workingwomen attempts to increase standard of living andmeeting desires on time. Children are smart enoughtoday that by seeing more dual income, their demandalso become twofold.

3. Rise in the number of single parent households

Indian society has undergone a extensive change interms of the structure and environment of the familyunit. There are many myths that single parents andtheir children have to face violence, disgrace and socialproblems. But the truth is they can be as healthy andemotionally secure as those from traditional families.Being a single parent can gear up to be financiallyautonomous, because there is no one to answer exceptyourself for the manner in which you spend yourmoney. Children may feel stressed out and alone, tobalance it they may nag to come out of the situation.Research proved that children in single parenthouseholds make their first purchases almost a yearearlier than their two-parent household counterparts.

4. Grand parents’ increasing role in bringing up4. children

With the tremendous increase in number of nuclearfamilies where both partners are working, parents feelthat grandparent’s presence will be interference in risingof the kids. Grandparents often bridge the gap betweenparents and their children. Every time parents do notentertain every demand of their children but childrenare smart enough to take the advantage fromgrandparents. Grandparents’ are the fastest growingincome sources for the children. Thus, they shift theirnagging tactics to grandparents for their own benefit.

5. Delayed parenthood

In this competitive era, it become first preference forcouples to have more financial security, secure careersbefore focusing their energy on children so that theycan buy needy and desire things for themselves andfor future child.

The hypothesis formulated for the purpose ofresearch is:


H1a: There is a significant difference in pesteringstrategy of girls and boys with respect to playingGame.

H1b: There is a significant difference in pesteringstrategies of girls and boys with respect to buyChocolate.

H1c: There is a significant difference in pestering

strategies of girls and boys with respect to use MobilePhone.

H1d: There is a significant difference in pesteringstrategies of girls and boys with respect to buy FastFood.

H1e: There is a significant difference in pesteringstrategy of girls and boys with respect to buy Sweets.

Table 1: Summary of Results

Nagging in relation to t-test p-value Boys Girls Grand Mean

Game .471* 0.02 3.59 3.32 3.45

Chocolate .433*** 0.000 3.40 3.90 3.65

Mobile Phone .251*** 0.000 3.91 3.48 3.69

Fast Food .426** 0.01 3.64 3.84 3.79

Sweets .352*** 0.000 3.37 3.89 3.63

Summary of ResultAbove table shows that for pestering related to playgames, t-value (.471) was found significant. Theinspection of mean score revealed that Boys (mean =3.59) nag more than girls (mean= 3.32). For pesteringrelated to buy chocolates, t-value (.433) was foundextremely significant as girls (mean = 3.90) are foundto nag more than boys (mean = 3.40) whereas for usingmobile phone, boys (mean = 3.91) nag more than girls(means = 3.48). For pestering related to buy fast food,t-value (.426) was found significant, the inspection ofmean score revealed that girls (mean = 3.84) are foundto nag more than boys (mean = 3.64) whereas for

buying sweets girls (mean=3.89) nag more than boys(means = 3.37). Thus, alternate hypothesis are acceptedi.e. there is a significant difference between girls andboys pestering strategies vary in relation to variety ofitems.

Results reveal that gender of the child play significantrole for different pestering strategies. Due to dynamicenvironment, children become more coupled,conversant, and are more prospective to influence theirparent’s decision. As a result we can infer that childrenhaving different need for different products anddifferent pestering appeal to their parents to influencetheir decision.

Fig. 1: Pestering Strategies Adopted by Children to Nag their Parents


Generally boys are more rational and girls are moreemotional in their behavior to take decision. Abovefigure proved this fact, which shows majority of boysused rational persuasion followed by emotional appeal,integrating appeal, upward and coalition tacticswhereas majority of girls use emotional appeal followedby rational persuasion, ingretating appeal, upwardappeal and coalition tactics.

ConclusionTo conclude, this study diffuse parent-child conflict,which arise from children’s purchase requests due toconsumer socialization process in which children takeinterest to influence their parent’s buying decisions.They come to know about the products from friends,family, and that are available in the marketplace. Afteranalysis we come to know that almost all parents agreethat their child nag them to fulfill their demands. Intoday’s technology savvy dynamic environment, newideas and innovations are emerging almost daily,technology is growing and children wants to learn anduse them. As India is male dominated country, boysare having more leverage than girls to get desired

products. Parent’s income slab decides their purchasingability, which grounds children to nag them. Thisresearch have proved that with the increase in consumerincome slab from lower income to middle class andfrom middle class to high class the pester power alsoincreases. Research also proved that gender and pesterpower of the child are independent. Outside India wecan see that children to get their desire product don’tmind to work part time or use their pocket moneybut in India if parents refuse to entertain child demandthen children seems not interested to work part timeor use their pocket money. Instead of pocket money,children look for other pestering strategies likeemotional appeal, promising good marks and naggingtheir grandparent. Survey also proved that incomparison to girls, boys try to get desire productsfrom grandparents. Indian children culture isdominated by technology, entertainment andadvertising as they have influence in parents decision-making, which is mushrooming worldwide. Thus, itwould give a clearer understanding that why marketersare targeting children either as active purchasers or aspassive influencers while selling consumer products.

References

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Virtual Teams Vs Face to Face Teams:A Comparative Study on Performance Indices

Shikha Matta*

Abstract

At present, the physical part of the work has shifted to virtual ways of doing work. These has been agrowing emphasis on boundary-less ways of work environments that operate in 24x7 cycle and indifferent time zones. Unlike, traditional Face to Face (FTF) teams, Virtual Teams (VTs) are more complicatedas they transcend the boundaries of time, space and culture. The study aims to find out, which type ofteam is more performance oriented: VT or FTF Team? To answer this question, the present study utilizedan experimental design to measure the Performance Outcomes in VTs and FTF Teams, for which a totalof 10 teams were formed. The subjects were same in both the team types FTF and VTs. Results indicatedthat VTs are equally performance oriented as FTF teams or even better than FTF teams.

Keywords: Dispersed Teams, e-Teams, Face to Face teams, Traditional Teams, Virtual Teams

IntroductionTeam has been conceptualized as one of the mostimportant organizational forms (Townsend et al.,1998).

Team has been referred to as a small group of membersin which they have common purpose, interdependentroles and complementary skills to accomplish a task(Yukl, 2006). Cohen and Bailey (1997) defined teamas “a collection of individuals who are interdependentin their tasks, who share responsibility of theiroutcomes, who see themselves and who are seen byothers as an intact social entity embedded in one ormore larger social systems, and who manage theirrelationship across organizational boundaries” (p.241).

Lately, a new and novel type of team has generatedinterests of both organizations and scholars. Increasing24x7 competition (Algesheimer et al., 2011),globalization, advances in Information Technology,cooperation and collaboration among organizations,and a move towards flatter organizational structuresand knowledge work cultures have provided ways tothe new form of team, generally referred as ‘VirtualTeams’ (Townsend et al., 1998). This novel type ofteam has stimulated interests of both members of theorganizations and researchers. In the early 1990’s the

Shikha Matta*Assistant Professor, Institute of InformationTechnology & Management

concept of VTs had taken some shape and place. Sincethen VTs are increasingly being adopted by companiesall around the globe, as a form of organizing work,driven by the increase in globalization, internationaltrade, and fast communications networks/technologies(Duarte and Cunha, 2015).

The term “virtual” specifies distributed and dispersedwork that is primarily based on electroniccommunication tools (Hertel et al., 2005). VTs areconsidered as independent groups of individuals thatwork across time, space, and organizational boundarieswith communication tools that are heavily dependenton advanced information technologies (Driskell et al.,2003; Thompson and Coovert, 2003).

Defining Virtual TeamsSome authors defined VTs with respect to geographicaldistance and communication technologies. Forinstance, Ale-Ebrahim et al. (2009) defined VTs as“small temporary groups of geographically,organizationally and/or time dispersed knowledgeworkers who coordinate their work predominantlywith electronic information and communicationtechnologies in order to accomplish one or moreorganization tasks” (p.2655).

Prior to 2000, Kristof et al. (1995) defined VT as “aself-managed knowledge work teams, with distributedexpertise that forms and disbands to address a specificorganizational goal” (p.230). Suzanne (1998) defined


such teams on the basis of continuity and described asa group of dispersed knowledgeable and skilled workerswho concentrate on a goal temporarily or on on-goingbasis. In recent times, Wadsworth and Blanchard(2015) noted that VTs exist on a continuum, whereinthere are pure VTs, moderate VTs, and FTF teams.Moderate VTs are somewhat virtual in the sense thatteam members primarily interact FTF, but also spendtime working with each other through informationand communication technologies.

According to Henry and Hartzler (1997) VTs havethe following characteristics:

a) Members are distributed across national orinternational geographies and work at distancefrom same or different locations,

b) The team size is usually less than 20,

c) Team members accomplish tasks, solve problems,and reach at decisions jointly and collaboratively,

d) Members are held accountable for theircoordinated team results.

Hertel et al. (2005) maintained that VTs have at-leasttwo persons who coordinate and collaborate to achievecommon goals through electronic communicationmedia (such as, e-mail, fax, phone, video conference,etc) wherein at least one of the team members worksat a different location, organization, or at a differenttime zone.

Face-to-Face versus Virtual TeamsBell and Kozlowski (2002) differentiated VTs and FTFteams on the basis of distance and communicationmedia. The researchers claimed that FTF teams workin close physical proximity and work under the sameroof. On the other hand, they claimed that VTs arephysically separated and tend to work by relying onCMC. Interestingly, a hybrid form of teams is alsoemerging where a FTF team is often joined virtuallyby other member(s) who might be physically away butinteract with the remaining FTF team. The increasingpopularity of ‘Work-From-Home’ often integratessuch VT members with a FTF team in the office.Table I shows the distinguishing characteristics of VTsand traditional FTF teams.

Table I: Distinguishing Characteristics of Virtual and Traditional teams

VTs All Teams Traditional Teams

� Geographically dispersed � Multiple individuals � FTF

� Communication through � Task interdependence � Communication primarily in

technology � Shared goals person

� Organizational setting

(Source: Horvath and Tobin, 2001)

According to Duarte and Snyder (2006) VTs can bemore complicated in contrast to FTF teams becausethey transcend time, distance, and organizationalboundaries that use CMC systems to collaborate.

Sproull and Faraj (1997) pointed three aspects in whichelectronic communities are superior to those of FTFcommunication: First, physical location is irrelevantin electronic communication. Second, the invisibilityissue in electronic communities is reduced by the useof video chats such as Skype. Third, logistical and socialcosts to participate in electronic communities are lower.

According to Hiltz and Wellman (1997) unlike FTF,virtual communities tend to be larger, more dispersed

in space and time, more densely knit, have memberswith more heterogeneous social characteristics, suchas, lifecycle stage, gender, ethnicity, and socio economicstatus, but with more homogeneous attitudes.

VTs are more task-oriented initially. Over the periodof time, VTs appear to lessen their task focus(Chidambaram and Bostrom, 1993; Walther,1995).VTs also report weaker relational links to teammates in comparison to traditional teams(McDonough et al., 2001; Warkentin et al., 1997).

Review of LiteratureSome researchers acknowledge that working in VTscan have a favourable impact on effectiveness (e.g.


Maynard et al., 2012), quality of project, performance(e.g. Altschuller and Benbunan-Fich, 2010), decisionmaking (e.g. Pridmore and Phillips-Wren, 2011), ideageneration (e.g. Alnuaimi et al., 2010), satisfaction(Huang et al., 2010), organizational commitment(Horwitz et al., 2006), while some other researcherskeep suggesting that working virtually negatively effectseffectiveness (Schweitzer and Duxbury, 2010).

The effectiveness can be measured from ratings ofoutside (Cummings and Haas, 2012) and by objectivemeasures of team performance (Rapp et al., 2010).

Some actual task based performance indices are alsoreported by a few studies in virtual and FTF contexts.For instance, actual task performance (e.g. Hambleyet al., 2007; Staples and Zhao, 2006), productivity(Andres, 2002) have been used in some studies.Surprisingly, no comparative study of effectiveness onthe basis of performance Indices are spotted yet inIndia.

Researchers have reported mixed results forperformance in VTs and FTF teams ranging from nodifference (e.g. Straus and McGrath, 1994) to a widedifference where VTs were found to be superior tothat of FTF teams (e.g. Staples and Zhao, 2006).

Some researchers also compared performance on the

basis of complexity of task, an idea generation task oran intellective task (Straus and McGrath, 1994), realworld task (Nicholson et al., 2007), businesssimulation task such as case involving marketingstrategies (Montoya-Weiss et al., 2001), case involvingchoosing the most likely suspect in a murder mystery(Warkentin et al., 1997), and consensus seeking desertsurvival task (Staples and Zhao, 2006). No such studyof comparative performance has been found in India.Based on the review of prior researches, the researcherthen framed the question.

Research QuestionWhich type of team is more performance oriented:VT or FTF Team?

Objective and HypothesesBased on the theory building and research to date, theresearcher tries to answer the research question byunderstanding the Performance Outcomes in termsof Performance Indices such as Average IndividualError Score (AIES) & Team Error Score (TES) andTeam Process Indices such as Team Functioning (TF),Team Effectiveness (TE), and Team Synergy (TS) inVTs as compared to FTF teams. Therefore, theobjective and its related Hypotheses of the presentstudy are as follows:

Objective: To assess the differences between Performance Indices in FTF and VTs

H1: AIES in FTF Team is no different from VT

H2: TES is higher in VT than FTF Team

H3: TF is higher in VT than FTF Team

H4: TE is higher in VT than FTF Team

H5: TS is higher in VT than FTF Team

MethodologyParticipants and Sampling Technique

To assess the differences between the PerformanceIndices in FTF and VTs on Correlated Groups- 50students pursuing a 2-year full-time MBA wereemployed from a leading business school, wherein 82%were males and 100% belonged to the age group of21-30. The demographic profile of the students isshown in Table II.

As the intent of the present research study, itwas decided that “purposive sampling” would bethe right approach to the sampling of the studyelements for the present design. Purposive samplingis essentially a non- probability sampling method thatis characterized by the use of judgement and adeliberate effort to obtain representatives samples byassuming presumable typical areas or groups in thesample.


Intervention ProcedureThe researcher formed 10 teams of 50 students (5students in a team). These 50 students were thenrandomized in two types of teams: a) FTF teams b)VTs. In this Correlated Group experiment, theresearcher controlled the experiment about teamcontext and learning effects as randomization was donein allotment of team members to teams.

The communication in VTs was again mediatedthrough synchronous chat systems using smart phones,laptops or desktops whichever was available. However,the communication in FTF teams was in person only.

The researcher utilized two different consensus seekingtasks (Desert Survival and Bushfire Experiment withthe same subjects. Bushfire Survival scenario was usedin VTs while Desert Survival Task was used in FTFcontexts.

Utilization of Consensus Seeking Scenario-Bushfire Survival TaskThe present study utilized Bushfire consensus seekingtask that requires interpersonal processes such asdiscussions, negotiations, to arrive at a solution. It alsohad an expert solution available that measuresIndividual and Team Performance Indices such asAIES, TES, TF, TE, TS.

Bushfire Survival Task (see human synergistic website1)was so chosen because the subjects were less familiar

with this scenario. Generally, in this task, participantsview a short movie that places them into a bushfirescenario in a forest of Australia with the rest of theirteam mates. Participants are then challenged to rank12 potentially useful items in order of relativeimportance to their survival. Solutions are firstdeveloped by the individuals and then in groups. Scoreswere generated by comparing individual answers withthe team answers to those provided by the experts.

The bushfire survival situation scenario takesapproximately 2 hours including scoring anddebriefing. Bushfire Survival task is an interdependentcollective task that requires teams to solve a problemthat has a correct answer (i.e. an expert answer). Thistask have the aspects of a judgment/decision makingsince a team member cannot prove the correctness ofhis/her answer and has to persuade teammates, andhas aspects of negotiation/cognitive conflict task. Teammates have to discuss and resolve differing opinionsregarding survival strategies and the ranking of theitems.

Desert Survival ScenarioA consensus seeking decision making task thatrequires interaction and communication and thathave an expert solution available (to create a measureof Individual and Team Performance Indices suchas AIES, TES, TF, TE, TS) was used for the presentstudy.

Table II: Demographic Characteristics of Sample

Demographics Frequency Approx.Percentage

n=50 studentsCorrelated groups

GENDERMale 9 18%

Female 41 82%Unreported

AGELess than 20 0

21-30 50 100%Above 30 0

Unreported 0

1 http://humansynergistics.com/docs/default-source/product-info-sheets/bushfire-product-info-sheet.pdf?Sfvrsn 2


Desert Survival Task reported in Pareek & Rao (1985)was chosen for the present research as it presents ascenario that few people have relevant experience in.In this task, participants first read a short documentthat places them into an airplane crash scenario in adesert with the rest of their team mates. The taskchallenges the participants to rank 15 items they mightneed for the survival. By having team memberscomplete the exercise individually and then as a team,the difference between the individual solutions andthe team’s solution can be identified with the help ofexpert ranks.

The Indices in survival scenario are calculated as:

� AIES= Average of Individual Member Error Scoresin a Team

� TES= Sum of the deviations of team answer as awhole from the experts answers

� TF= {Highest Individual Error Score (HIES)-TES};

� TE= (AIES-TES); and

� TS= {Lower Individual Error Score (LIES)-TES}

All these Indices become a proxy measure of the group’sability to perform as a team. For the purpose of thepresent research these are called Performance Indices.

Research Findings and AnalysisDifferent Performance Indices such as AIES, TES, TF,TE, and TS were compared between FTF and VT. Tocompare the mean scores on Correlated Groups PairedSample t test was used.

Hypothesis 1 examined the differences in the AIES inFTF and VT. The paired sample t test analysis revealedthat there is a significant difference in the AIES inFTF (mean score= 4.39) and VT (mean score= 3.74)at p=.010, two tailed as shown in Table III. Therefore,this result leads to the rejection of null Hypothesis I,which states that AIES in FTF is no different fromVT.

Hypothesis 2 examined the differences in the TES inFTF and VT. The analysis revealed that there is nosignificant difference in the TES in FTF (mean score=3.51) and VT (mean score= 3.59) at p=0.412 (onetailed) as shown in Table III. Therefore, this result leads

to the rejection of Hypothesis 2, which states that TESis higher in VT than FTF Team.

Hypothesis 3 examined the differences in the TF inFTF and VT. The analysis revealed that there is nosignificant difference in the TF in FTF (mean score=0.89) and VT (mean score= 1.37) at p=0.168 (onetailed) as shown in Table III. Therefore, this result leadsto the rejection of Hypothesis 3, which states that TFis higher in VT than FTF Team.

Hypothesis 4 examined the differences in the TE inFTF and VT. The analysis revealed that there is asignificant difference in the TE in FTF (mean score=1.91) and VT (mean score= 0.14) at p=0.0025 (onetailed) as shown in Table III. Clearly, team effectivenessin FTF team is higher than in VT. Therefore, this resultleads to the rejection of Hypothesis 4, which statesthat TE is higher in VT than FTF Team.

Hypothesis 5 examined the differences in the TS inFTF and VT. The analysis revealed that there is nosignificant difference in the TS in FTF (mean score= -0.66) and VT (mean score= -0.97) at p=0.269 (onetailed) as shown in Table III. Therefore, this result leadsto the rejection of Hypothesis 5, which states that TSis higher in VT than FTF Team.

DiscussionThe researcher’s a priori expectation was that the AIESwould be same in FTF team and VT. Surprisingly,this was not the case. It was observed that AIES wasfound to be more in FTF team than VT which impliesthat members in VT performed better individually.Therefore, this result provided no support toHypothesis 1.

Could it be due to the Hawthorne Effect (Wickstromand Bendix, 2000)? Hawthorne Effect signifies thatwhenever a participant is informed that they are goingto be observed under an experimental condition, theybecome more alert and try to perform well. It is possiblethat for virtual contexts, students became more alertand attentive for their performance in a novel teamcontext than their familiar FTF context, therebyleading to high performance in such teams. Moreover,conceptually Hawthorne effect also applies to aControl Group. However, the present study does notsupport Hawthorne effect in the context of FTF group


(control group) because FTF group did notoutperformed or done equally well as compared tovirtual teams.

Also, this observation does not support the notion putforward by Duarte and Cunha (2015) that ComputerMediated Communication (CMC) hampers theirindividual performance levels in VTs due to thereduced informal communication that establishes lowlevel of interpersonal contact and enhances workersisolation, leading to increased levels of stress, burnout,and depression. It should be noted that our VTs werespecifically constructed for one-time use. These aredifferent from permanent VTs where pathologicaleffect of isolation from members could impairperformance.

The researcher’s a priori expectation was that the TES

would be same in FTF team and VT. As per theobservations there were no significant differences inTES in both the team types. It signifies that teamperformance was equally good in both the types ofteams. This provided no support to Hypothesis 2..

The other observations based on other Indices suchas: TF, and TS, implied that there were no significantdifferences in these Team Performance Indices.However, there was a significant difference in TE dueto AIES-TES where AIES is higher in FTF teams. Allthese observations led no support to Hypotheses 3-5.

The possible reasons for such types of PerformanceIndices individually and in groups of both the teamtypes could be the role of group affect, perceivedcommunication and verification, and leader emergenceacross teams. The observations based Team

Table III: Paired Sample t-Test for Performance Indices in Face-to-Face and Virtual Teams

N = 50, FTF = 10 teams: VTs = 10, Correlated Groups

FTF Team VT

Mean SD Mean SD

AIES 4.39 0.58 3.74 0.40

TES 3.51 0.94 3.59 1.14

TF 0.89 0.65 1.37 1.37

TE 1.91 0.93 0.14 1.00

TS -0.66 0.83 -0.97 1.10

Std 95% confidenceMean SD Error interval of the t df Significance Hypothesis Result

Mean difference

Lower Upper

AIES 0.65 0.63 0.20 0.20 1.10 3.26 9 0.010 H1 Not(2-tailed) Supported

TES -0.08 1.08 0.34 -0.85 0.70 -0.23 9 0.412 H2 Not(1-tailed) Supported

TF -0.48 1.50 0.47 -1.55 0.59 -1.02 9 0.168 H3 Not(1-tailed) Supported

TE 1.77 1.52 0.48 0.68 2.86 3.69 9 0.0025 H4 Not(1-tailed) Supported

TS 0.31 1.51 0.48 -0.77 1.39 0.64 9 0.269 H5 Not(1-tailed) Supported


Performance Indices implied that VT is equally usefulor not useful to that of FTF teams in accomplishingthe tasks. VTs function, perform, and synergise asequally as FTF teams owing to some of the followingreasons:

a) opportunities for leader emergence on shared basis:

b) communication identification and verification,

c) group affect.

Wickham and Walther (2009) were of the view thatVTs can often be created without the leader, whereinteam members take the responsibility to fill in the roleof a leader. Therefore, the present study possiblysupport the emergence of leaders on shared basis.

In addition to leader emergence, identitycommunication, and identity verification play a veryimportant role in teams, especially in VTs. Identitycommunication comprises of methods and techniquesa person uses to convey self- identities in teams. Onthe other hand, identity verification is the process ofbringing others to confirm one’s identity (Wilson etal., 2015). Prior work in FTF settings observed thatidentity communication and verification may produceindividual and group benefits such as: creativity(Cheng et al., 2008); career growth (Ibarra et al., 2005),motivation to perform (Dutton et al., 2010),satisfaction, meaning, and self-worth (Thatcher andGreer, 2008). Studies on VTs are less in terms ofidentity communication and verification. However,in a notable exception, Ma and Agarwal (2007)examined the influence of virtual co-presence (feelingof togetherness with others in a VT environment),persistent labelling (use of ID or label for a long timein virtual environment), self-presentation (process tocommunicate one’s identity over VTs), and deepprofiling (sharing personal and social identityinformation in virtual mode) on identity verification.Wilson et al. (2015) proposed in their paper of VTsthat perceived identity communication andverification positively influences VT performance. Thepresent study results support the possibility ofcommunication identification and verification in VTs.

The other possible factor that could impact theindividual and group outcome is the group effect(combination of positive and negative emotions at

collective level impacts individual and groupperformances) as pointed by Barsade and Gibson(2012). The present work presumes the effect of groupeffect on team performance through its results.

Similar levels of Performance Indices obtained in thepresent study do not support the claim made by Duarteand Cunha (2015) that VT communication act as abarrier to group level performance as well due todecreased informal communication. It can be also saidthat at group level, there is possibility of a greatercommunication identification and verification withother group members owing to efficient ways of usingtechnologies in VTs. As our VTs were created forspecial purpose and were temporary, the alienationsometimes observed in physically dispersed membersmight not at all be noticeable. On the other hand, theVT members were found to be comfortable as FTFowing to widespread usage of CMC in their daily lives.Smartphone using generation is truly smart!

The present study contradicts the results of Hambleyet al. (2007) work as they also found that superiortask performance was achieved in rich media than thosecommunicating in less rich media. Again, thedifference with this study is that they used FTF,videoconference, and chat systems while the presentstudy only used FTF and VT (using instant messagingsystem allowing instant uploading of audio/video too).

As far as team performance, TF, and TS are concerned,the present study had same outcomes in both the teamtypes. Therefore, the study is in contrast to all previousstudies such as Andres (2002) and Staples and Jhao(2006). Andres (2002) found that team productivityand quality were more in FTF than in video conferenceconditions. Staples and Jhao (2006) observedperformance were superior in virtual than FTF teams.

With these observations we could simply put that VTsare as effective to FTF teams or even better than FTFteams. Could these be due to effective decision making,open and clear communication, clarity of goals,defined roles and responsibilities, mutual trust, conflictmanagement, and coordinated efforts over virtualnetworks? These potent mechanisms have not beentried and tested particularly in the present study andtherefore can be explored in future studies.


ImplicationsThe results of the present study have some practicalimplications. The findings possibly suggest that usingmore rich and synchronous ways of doing work virtuallyenhances performance. For example, WhatsApp allowsits users to have group chats real time, conversations overcalls, instant uploading of audio, video, or images in arelatively inexpensive and convenient, yet effective,manner. Moreover, the findings suggest the potentialadvantageous role that information technology may playin enriching performance. As technology has become akey tool in the research and practice in the field ofOrganizational Behaviour, this study results helpcontribute to the growing case for the use of thistechnology in measuring team outcomes which adds toacademic implication.

Given that we are now the part of the virtual world,the need for technological training can be expected toincrease which would help build confidence in usingtechnologies within the organizations. With this, itcan be proposed that such technology trainings willescalate the productivity in real life and its use willcontinue to gain in popularity in training anddevelopment efforts in today’s and future workplaces.Therefore, appropriate technology training programcan be more confidently designed to cater to the newthinking and new approaches that are needed for thedynamic environment facing today’s workplaces.

Limitations of the Present Study andSuggestions for Future ResearchesThe present study has several limitations and in viewof these a few avenues for future researches have beensuggested.

First, the findings of this study should be used withcaution as the limited non-probability sample has beenused in the present experiment pertaining to NationalCapital Region of India. Further researches are requiredto be conducted across regions, across samples andacross cultures to allow generalizations. Second, thedrawback is that the researcher was unable to createVTs over dispersed geographic locations and observethe effects of temporal distance on PerformanceIndices. Future researches may create virtual teamsacross geographies. Third, the present experimentalstudy simulating the VTs was conducted in classroom

laboratory settings and with students as sample, andnot in the actual VT work settings. Future experimentsmay be conducted in real organizational settings withactual workforce, on real life problem solving tasksand assignments to synthesize the effect of realdispersed VTs, FTF teams, and or amalgamated teams.Fourth, the present study used only FTF and chatsystems primarily WhatsApp to conduct theexperiment. It will be important to investigate theimpact of using different media, which have the aspectsof media richness and social presence such as VideoConferencing that is more realistic with VTs in appliedsettings. Fifth, in the present study, the experimentercould not control the Hawthorne Effect. The futureresearches could exercise greater control to mitigatethese effects. Sixth, future researches may also pursueto assess more comprehensive comparisons by ensuringdifferent lengths of training, types of interface, differenttasks based on complexities, and types of technologythat are media rich or enable social presence. Futureresearch should pay attention to developing furtherunderstanding of how the extra visual and audioinformation, flash animations provided by a virtualworld may influence team outcomes.

ConclusionGlobalization, advanced technologies, and 24x7competitions have enabled the organizations transcendthe boundaries of traditional FTF teams to VTs. Thesenew kinds of distributed work teams operate in avirtual team environment in a 24 hour cycle and tapthe strength of diversity in terms of skills, experience,knowledge, and expertise, all around the globe. UnlikeFTF teams, VTs are bigger, faster, and better. The trendof these teams that are supported by electronic or CMCsystem is not likely to go away.

The literature seems to be lacking a thoroughunderstanding of the mechanisms in virtual teams. Thepresent study contributes to the growing case of virtualteams and information technologies in the field ofOrganizational Behaviour. Also, the comparison ofvirtual and FTF teams are worthy of additional studieson the basis of objective and subjective performanceoutcomes. The findings of the present research supportthe notion that VT performs, functions, or synergizesequally or even better as compared to FTF teams bothat individual and at team level.


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A Comparative Analysis of South Indian Bank’sPerformance Post Implementation of InformationTechnology: An Empirical Analysis

Titto Varghese*Sneju Sajan**

Abstract

Technological advancement has created a competitive environment in view of diversified banking products.The study unleashes the performance of South Indian Bank’s against the backdrop of technologyadoption in the banking industry. It is evident from the study that the bank showed significant improvementin all the CAMEL parameters after the IT adoption. Even though the Net Interest Income margin of thefirm decreased during the initial post adoption period; in the long run, it will definitely reap the benefitsdue to the economies of scale margin. Even though liquidity ratios were above the ideal values, a veryhigh value is an indication of lower amount of advances. A balanced approach would be, to bringliquidity margin near to ideal, which would improve the total business, along with an increase in incomefrom other sources and reduction in operating expenses to maintain profitability.

Keywords: Capital Adequacy, Asset Quality, Management efficiency, Earning Capacity, Liquidity.

IntroductionBanking environment has become highly competitivetoday. To be able to survive and grow in the changingmarket environment banks are going for the latesttechnologies, which is being perceived as an ‘enablingresource’ that can help in developing learner and moreflexible structure that can respond quickly to thedynamics of a fast changing market scenario. It is alsoviewed as an instrument of cost reduction and effectivecommunication with people and institutionsassociated with the banking business.

Information Technology enables sophisticated productdevelopment, better market infrastructure,implementation of reliable techniques for control ofrisks and helps the financial intermediaries to reachgeographically distant and diversified markets. Internethas significantly influenced delivery channels of thebanks. Internet has emerged as an important mediumfor delivery of banking products and services.

Titto Varghese*Associate Professor, SAINTGITS Institute ofManagement, Kottayam- Kerala

Sneju Sajan**Student MBA, SAINTGITS Institute ofManagement, Kottayam- Kerala

IT is increasingly moving from a back office functionto a prime assistant in increasing the value of a bankover time. IT does so by maximizing banks of pro-active measures such as strengthening andstandardizing banks infrastructure in respect ofsecurity, communication and networking, achievinginter branch connectivity, moving towards Real TimeGross Settlement (RTGS) environment the forecastingof liquidity by building real time databases, use ofMagnetic Ink Character Recognition and Imagingtechnology for cheque clearing to name a few. Indianbanks are going for the retail banking in a big way

The banking system is slowly shifting from thetraditional banking towards relationship banking.Traditionally, the relationship between the bank andits customers has been on a one-to-one level via thebranch network. This was put into operation withclearing and decision-making responsibilitiesconcentrated at the individual branch level. The headoffice had responsibility for the overall clearingnetwork, the size of the branch network and thetraining of staff in the branch network. The bankmonitored the organization’s performance and set thedecision making parameters, but the informationavailable to both branch staff and their customers waslimited to one geographical location.


Significance of the StudyNationalized banks had been computerizing theiroperations from 2005-06 onwards in a phased mannerbringing all branches under CBS by 2009-10 (RBIreport, 2004). So analyzing the direction of deviationsin the financial performance indicator after 2010(considered as post IT Adoption period) is of greatimportance. The management of the company isresponsible for taking decisions and formulating plansand policies for the future. They, therefore, always needto evaluate their performance and effectiveness of theiraction to realize the company’s level of achievementin the past for forecasting its future course of action.Keeping the above importance of services providedby SIB bank and its financial position, this study wasconducted on the topic “South Indian Bank’sPerformance Post Implementation of InformationTechnology: An Empirical Analysis”

Company ProfileSouth Indian Bank Limited (SIB) (BSE:532218, NSE:SOUTH BANK) is a private bank headquartered atThrissur city in Kerala, India. South Indian Bank has831 branches, 4 service branches, 33 ext. counters and20 regional offices spread across more than 26 statesand 3 union territories in India. It has set up 1269ATMs and Bulk Note Acceptor / Cash DepositMachines all over India. One of the earliest banks insouth India, “South Indian Bank” came into beingduring the Swadeshi movement. The establishmentof the bank was the fulfillment of the dreams of a groupof enterprising men who joined together at Thrissur,a major town (now knows as the Cultural Capital ofKerala), in the erstwhile State of Cochin.

Literature ReviewKetan Mulchandani, Kalyani Mulchandani (2016)analyzed and compared the financial performance ofselected listed gold loan non-banking financialcompanies in India. Results revealed that MuthootFinance Ltd. outperformed as compared toManappuram Finance Ltd. Muthoot Finance Ltd. hadtop ranking in Capital Adequacy Segment, AssetsQuality Segment, Management Efficiency Segment,Earnings Segment and Liquidity Segment. It impliedthat it is well capitalize and have greater capability

absorb negative shocks. Muthoot Finance also hadlowest nonperforming assets and its impact waspositive on profitability and margins. The managementof the Muthoot Finance is very efficient in terms ofmanaging lower total expenses / total revenue ratio,lower cost of funds and higher assets undermanagement per branch. Earnings of Muthoot ascompared to Manappuram are higher, which impliesthat efficient use of assets, higher returns onshareholder’s funds, higher profit per employee andgreater returns on capital employed Muthoot is havingsufficient funds to manage the short term liquidityrequirements.

Karri et al. (2015) conducted a study in which theobjective of this study is to analyze the FinancialPosition and Performance of the Bank of Baroda andPunjab National Bank in India based on their financialcharacteristics. This study attempts to measure therelative performance of Indian banks. For this study,they have used public sector banks. From theCAMELS’ analysis it clears that there is no significancedifference between the Bank of Baroda and PunjabNational Bank’s financial performance but weconclude that the Punjab National Bank performanceis slightly less compared with Bank of Baroda.

Karri, Meghani and Mishra (2015) conducted a studyto analyze the financial performance of public sectorbanks in India. Period of the study was 5 years from2010-2014. Bank of Baroda (BOB) and PunjabNational Bank (PNB) were considered as sample sizefor the study. CAMEL model and t-test applied fordata analysis purpose. Results revealed that out of 14ratios used in the CAMEL model the average figuresof Bank Of Baroda is the best for (6 ratios) followedby Punjab National Bank (5 ratios). Thus it isestablished that Bank of Baroda is the best bank in theselected public sector banks.

Muhmad and Hashim (2015) study evaluated theperformance of selected banks operating in Malaysia.Results from this study suggested three contributingfactors for better performance of banking institutionsin Malaysia, namely capital adequacy, asset quality,earnings quality and liquidity. It was suggested thatMalaysian banks must improve interest expenses toenhance their management competency. They need


to continuously monitor the health and profitabilityof bank borrowers to decrease the risk of non-performing loan. In addition, banks must take stepsto improve employee productivity by controllingpersonnel expenses and operating profit. Althoughmanagement competency was not significant andrejected the hypothesis, there is the possibility that theratio used is not suitable for the banking situation inMalaysia for the period of study.

Ifeacho and Ngalawa (2014) investigated the impactof bank-specific variables and selected macro economicvariables on the South African banking sector for theperiod 1994-2011 using the capital adequacy, assetquality, management, earnings, and liquidity(CAMEL) model of bank performance evaluation. Thestudy employs data in annual frequency from SouthAfrica’s four largest banks, namely, ABSA, FirstNational Bank, Ned bank, and Standard Bank. Thesebanks account for over 70% of South Africa’s bankingassets. Using Return on Assets (ROA) and Return onEquity (ROE) as measures of bank performance, thestudy finds that all bank-specific variables arestatistically significant determinants of bankperformance. Specifically, the study shows that assetquality, management quality, and liquidity have apositive effect on both measures of bank performance,which is consistent with a priori theoreticalexpectations. Capital Adequacy, however, exhibits asurprising significant negative relationship with ROA,while its relationship with ROE is significant andpositive as expected. Except for interest rates (in theROA model), unemployment rate (in the ROA model),and the rate of inflation (in the ROE model), the restof the macroeconomic variables are statisticallyinsignificant. The study reveals that bank performanceis positively related to interest rates and negativelyrelated to unemployment rates and interest rates

Rahman and Masngut (2014) uses CAMEL (CapitalAdequacy, Asset Quality, Management Quality,Earnings Efficiency, and Liquidity) ratings system,with the addition of Shari’ah Compliance Ratio(CAMELS) in order to detect the financial distress ofIslamic banks in Malaysia. It was found that all Islamicbanks have higher ETA ratios which portray a goodperformance of Capital Adequacy and are less likelyto face financial distress. As for Asset Quality, all

Islamic banks did not have the possibility to facefinancial distress as they are able to handle their non-performing loans throughout the years. Meanwhile formanagement quality, all Islamic banks show lowerratios in paying salaries to their employee. Earningefficiency for all Islamic banks show better performanceand will be less likely to face financial distress in termsof Return on Assets but not for Return of Equity.Liquidity indicates that the Islamic banks have largenumber of loans but they have sufficient liquid assetsin order to cover their liabilities and commitments.Lastly for Shariah Compliance, Islamic banks havecomplied with all rules and regulations that have beenregulated by Bank Negara Malaysia’s Shari’ah AdvisoryCouncil.

Wang et al. (2011) develop a performance efficiencyvalue by using Data Envelopment Analysis (DEA) tointegrate five perspectives of CAMEL (CapitalAdequacy, Asset Quality, Management, Earnings,Liquidity), which is used by the Federal DepositInsurance Corporation to evaluate bankingperformance. In addition, they utilize a tiered DEAto categorize banks into four groups. CAMEL variablesare manifested more strongly in highly efficient groupswhen compared with inefficient groups. The banksthat appear to have a better financial ratio performancein the five perspectives of CAMEL form the efficiencyfrontier. It implies that CAMEL financial ratios andnon-parametric techniques can be used as acomplement to each other for the evaluation of bankperformance. Furthermore, they utilize a tiered dataenvelope analysis to form efficiency groups. ThroughCAMEL performance, the analysis is able todistinguish between efficient and inefficient banks. Inaddition, the study develops and tests the tradeoffs inTier 1 efficiency bank performance to explain theperformance difference across five perspectives ofCAMEL in 35 Tier 1 efficiency banks and analyzehow an inefficient bank should improve in CAMELcomponents.

CAMELS ModelCAMELS stand for capital adequacy, asset quality,management, earnings quality liquidity and sensitivityto market risk. It is considered as the best availablemethod for evaluating bank performance and health


of the bank since it considers all areas of bankingoperations. The Uniform Financial Institution Ratingsystem, commonly referred to the acronym CAMELrating, was adopted by the Federal Financial InstitutionExamination Council on November 13 1979, and thenadopted by the National Credit Union Administrationin October 1987. It has proven to be an effectiveinternal supervisory tool for evaluating the soundnessof a financial firm, on the basis of identifying thoseinstitutions requiring special attention or concern.(The United States Uniform Financial InstitutionsRating System 1997, APA).

Barr et al. (2002 p.19) states that “CAMEL rating hasbecome a concise and indispensable tool for examinersand regulators”. This rating ensures a bank’s healthyconditions by reviewing different aspects of a bank

based on variety of information sources such asfinancial statement, funding sources, macroeconomicdata, budget and cash flow. Nevertheless, Hirtle andLopez (1999, p. 4) stress that the bank’s CAMEL ratingis highly confidential, and only exposed to the bank’ssenior management for the purpose of projecting thebusiness strategies, and to appropriate supervisory staff.Its rating is never made publicly available, even on alagged basis. CAMEL is an acronym for fivecomponents of bank safety and soundness.

C-Capital Adequacy

A-Asset Quality

M-Management Efficiency

E-Earnings Capacity

L-Liquidity

Table 1: Bank Rating as per CAMEL Model

Rating Capital(CAR) Assets Management Earnings Liquidity

Strong 9% & Above 10% & Below Higher the 1.25% & Above 55% & Abovebest ratio

Satisfactory 8% & Above 11%-15% 0.75% & Above 60% & Above

Fair 7% & Above 16%-30% 0.40% & Above 65% & Above

Marginal 5% & Above 31%-40% 0.15% & Above 70% & Above

Unsatisfactory Below 5% 41% & Higher Below 0% 71% & Above

Source: AIA’s Annual Report 2010

ObjectivesTo Compare South Indian Bank’s performance duringpre-post IT Adoption period using CAMEL model.

Hypothesesa. H1

(1a, 1b, 1c): There is a significant difference

in Capital Adequacy between pre- and post-ITAdoption

b. H2 (2a, 2b, 2c): There is a significant difference

in Asset Quality between pre- and post-ITAdoption

c. H3 (3a, 3b, 3c): There is a significant differencein Management efficiency between pre- and post-IT Adoption.

d. H4 (4a, 4b, 4c, 4d): There is a significant

difference in Earning Capacity between pre- andpost-IT Adoption

e. H5 (5a, 5b, 5c): There is a significant difference

in Liquidity between pre- and post-IT Adoption.

f. H16: There exists a positive relation of Capital

Adequacy, Asset Quality and liquidity on financialperformance.

Methodology The financial performance of the South Indian Bankwas analyzed with the pre and post-performance dueto the introduction of information technology. Thestudy focused on analyzing, comparing andinterpreting the financial strength and weakness byconsidering 2010 as base year by comparing 6 yearsbefore and after the IT adoption. 2004 to 2009 isconsidered as pre IT adoption period and 2011 to 2016is considered as post IT adoption period. The statisticaltools applied were arithmetic mean, average annual


growth rate using geometric mean, standard deviationand student t-test, multiple linear regressions (DurbinWatson). CAMEL ratings were used an indicator toanalyze the performance of the bank.

Multiple Linear Regression ModelsMultiple linear regression analysis is a technique for

modeling the linear relationship between two or morevariables. It is one of the most widely used of allstatistical methods. In banking and finance literature(Kutner, Nachtsheim & Peter, 2004), regressionanalysis is a very common method used to find thedeterminants of bank performance.

Where,

Dependent Variable,

ROAi = Return on assess

Independent Variable,

DERi = Debt Equity Ratio

EPEi = Earnings per Employee

ATAi = Total Advances to Total Asset Ratio

CRi = Current Ratio

Data AnalysisCapital Adequacy

� Capital adequacy ratio =

(Tier 1 capital + Tier 2 capital) x 100

Risk weighted assets� Debt-Equity Ratio (DER)=Total Debt/Total

Equity� Proprietary ratio = Shareholders fund / Total

Tangible Assets*100

Table 2: Statement showing Capital Adequacy Ratios (` in Millions)

Year CAR (%) DER (Times) ProprietaryRatio (%)

2004 8.14 0.60 4.27

2005 8.49 0.35 4.80

2006 10.07 0.31 5.92

2007 9.55 0.28 5.30

2008 11.37 0.16 6.79

2009 14.48 0.32 6.40

2010 11.48 0.22 5.82

2011 10.43 0.16 5.63

2012 10.11 0.27 5.37

2013 13.49 0.43 6.04

2014 16.84 0.81 6.13

2015 15.58 0.62 6.07

2016 14.99 0.60 6.08

AM 10.35 0.34 5.58

PRE AAGR 2.14% 0.001% 0.01%

SD 2.33 0.24 0.97

AM 13.57% 0.48 5.89

POST AAGR 1.56% 0.003% 0.01%

SD 2.78 1.25 0.31

Pre-Post, t-value 1.58 -2.53 2.41

Source: Annual Reports of SIB from 2004 to 2016


Capital Adequacy Ratio (CAR): CAR ratio positionof the bank showing an increasing trend during theperiod of study. It was observed that the highestvalue of 16.84% was observed in 2014 and the leastof 8.14% in 2004.The statistical analysis shows thatIT adoption has resulted in an increase in averagevalue from 10.35% to 13.57%. The AAGR calculatedwith the help of geometric mean showed a lowervalue in the post IT adoption period. In both preand post adoption period the standard deviation wasfound to be very low. The student’s t-value of 1.58was below the table value of 1.96 which shows thatnull hypothesis was accepted i.e. no significantdifference in CAR between pre and post IT adoptionperiod.

Debt-Equity Ratio (DER): DER ratio position ofthe bank showed a fluctuating trend during the periodof study. It was examined that the highest value of0.81times was observed in 2014 and the least of0.16 times in 2008 & 2011.The statistical analysisshows that IT adoption has resulted in an increasein average value from 0.34 times to 0.48 times, whichshows a positive sign for the bank (ideal value 0.50:1).The AAGR calculated with the help of geometricmean showed a lower value in the post IT adoptionperiod. Ratio values showed a high degree of deviationin the post adoption period due to adaptability tonew system. The student’s t-value of -2.53 was abovethe table value of 1.96 which shows that nullhypothesis was rejected i.e. significant difference inDER between pre and post IT adoption period wasobserved.

Proprietary Ratio (PR): PR ratio position of the bankalso showed a fluctuating trend during the period ofstudy. It was examined that the highest value of 6.13%was observed in 2014 and the least of 4.27% in2004.The statistical analysis shows that IT adoptionhas resulted in an increase in average value from 5.58%to 5.89%. The AAGR calculated with the help ofgeometric mean showed a lower value in the post ITadoption period. Post IT adoption period showed alower level of variability with lower degree of standarddeviation. The student’s t-value of 2.41 was above thetable value of 1. 96 which shows that null hypothesiswas rejected i.e. significant difference in PR betweenpre and post IT adoption period was observed.

Asset Quality� Total Investments to Total Assets Ratio (IAR) =

Total Investments /Total Assets*100

� Allowances to Loan Loss Ratio = Total Allowances/Total Loans & Advances *100

� Provision for Loan Loss Ratio = Provisions &Contingencies / Loans & Advances *100

Investment to Assets Ratio (IAR): IAR ratio positionof the bank showing a decreasing trend during theperiod of study. It was examined that the highest valueof 43.81% was observed in 2004 and the least of45.34% in 2004. The statistical analysis shows thatIT adoption has resulted in decrease in average valuefrom 30.48% to 24.77%. The AAGR calculated withthe help of geometric mean showed a negative valueof -1.23% and -0.94%. The post adoption periodshowed a lower level of variability in terms of standarddeviation

The student’s t-value of 0.94 was below the table valueof 1.96 which shows that null hypothesis was acceptedi.e. no significant difference in IAR between pre andpost IT adoption period.

Advances to Assets Ratio (ATA): ATA ratio positionof the bank showing an increasing trend during theperiod of study. It was examined that the highest valueof 67.68% was observed in 2012 and the least of 8.14%in 2004.The statistical analysis shows that IT adoptionhas resulted in an increase in average value from56.35% to 64.91%. The AAGR calculated with thehelp of geometric mean showed a lower value in thepost IT adoption period. The post adoption periodATA showed a lower level of variability in terms ofstandard deviation of 0.92.

The student’s t-value of 0.92 was below the table valueof 1.96 which shows that null hypothesis was acceptedi.e. no significant difference in ATA between pre andpost IT adoption period.

Provision to Loan loss Ratio (PLL): PLL ratioposition of the bank showing an increasing trendduring the period of study. It was examined that thehighest value of 73.55% was observed in 2015 andthe least of 12.23% in 2011.The statistical analysisshows that IT adoption has resulted in an increase inaverage value from 21.09% to 38.38%. The AAGR


Table 3: Statement showing Asset Quality Ratios (` in Millions)

Year IAR (%) Advances to total Provision for Loanassets ratio (%) Loss Ratio (%)

2004 42.81 45.35 21.73

2005 33.06 56.61 21.54

2006 25.30 58.83 21.70

2007 25.12 58.00 22.05

2008 26.75 61.17 20.52

2009 29.81 58.14 19.00

2010 28.02 62.02 17.77

2011 27.19 62.60 12.23

2012 23.28 67.78 20.23

2013 25.15 64.08 33.78

2014 26.09 66.06 53.16

2015 23.81 63.51 73.55

2016 23.12 65.42 36.72

AM 30.48 56.35 21.09

PRE AAGR -1.23% 0.90% 0.27%

SD 6.76 5.59 1.15

AM 24.77 64.91 38.28

POST AAGR -0.94% 0.56% 0.00%

SD 1.65 1.89 22.33

Pre-Post, t-value 0.94 0.92 -0.54


calculated with the help of geometric mean showed aminimal value in the post IT adoption period. Inboth pre and post adoption period the standarddeviation was found to be very low.

The student’s t-value of -0.54 was below the tablevalue of 1.96 which shows that null hypothesis wasaccepted i.e. no significant difference in PLL betweenpre and post IT adoption period.

Management Efficiency� Earnings per Employee = EBIT / No. of

Employees

� Business per Employee = Total Business / No. of

Employees

Earnings per Employee (EPE): EPE ratio positionof the bank showing an increasing trend during theperiod of study. It was examined that the highest valueof 0.80 was observed in 2013 and the least of 0.10 in2005 & 2006.The statistical analysis shows that ITadoption has resulted in an increase in average valuefrom 0.25 to 0.58. The increase in EPE was due toincrease in net income of the bank during the post ITadoption period. The AAGR calculated with the helpof geometric mean showed a lower negative value inthe post IT adoption period. The growth rate washigher in the pre adoption period in comparison tothe post period. The absolute variation was found tobe lower during the period of the study.


Table 4: Statement Management Efficiency Ratios (` in Millions)

Year PAT (`) Total Business (`) No. of EPE BPEEmployee

2004 843.3 124,768.50 4216 0.20 29.59

2005 87 138,575.70 870 0.10 159.28

2006 509 159,488.80 5090 0.10 31.33

2007 1,041.20 201,581.30 3470 0.30 58.09

2008 1,516.20 256,098.70 3790 0.40 67.57

2009 1,947.50 299,402.50 4868 0.40 61.50

2010 2,337.60 388,483.20 4675 0.50 83.10

2011 2,925.60 502,659.20 5851 0.50 85.91

2012 4,016.60 638,707.70 5738 0.70 111.31

2013 5,022.70 761,699.70 6278 0.80 121.33

2014 5,075.00 838,299.00 7250 0.70 115.63

2015 3,072.00 894,861.50 7680 0.40 116.52

2016 3,332.70 970,832.40 8332 0.40 116.52

AM 990.7 196652.58 3717.74 0.25 67.90

PRE AAGR 7.86 3.30% 0.93 6.00 1.12%

SD 672.99 69335.52 1525.30 0.14 47.52

AM 3907.43 767843.25 6854.89 0.58 111.20

POST AAGR 0.83 2.40% 2.54 -1.2 1.14%

SD 960.33 172822.77 1058.80 0.17 12.79

Pre-Post, t-value -1.09 5.53 1.09 -2.98 0.40


The student’s t-value of -2.98 was above the tablevalue of 1.96 which shows that null hypothesiswas rejected i.e. significant difference in EPE betweenpre and post IT adoption period. T-value among pre-post of PAT showed no significant difference in itvalues.

Business Per Employee (BPE): BPE ratio positionof the bank showing an increasing/fluctuating trendduring the period of study. It was examined that thehighest value of 159.28 was observed in 2005 and theleast of 29.59 in 2004.The statistical analysis showsthat IT adoption has resulted in an increase in averagevalue from 67.90 to 111.20. The increase in EPE was

due to increase in total business of the bank duringthe post IT adoption period with an AAGR of 2.40%.The AAGR of BPE calculated with the help ofgeometric mean showed a lower positive value in thepost IT adoption period.. The absolute variation interms of standard deviation was found to be very lowin the comparison to the pre IT adoption period

The student’s t-value of 0.40 was below the table valueof 1.96 which shows that null hypothesis was rejectedi.e. no significant difference in BPE between pre andpost IT adoption period. T-value among pre-post oftotal business showed significant difference with greatervalue.


Table 5: Statement showing Earnings Capacity (` in Millions)

Year ROE (%) ROA (%) CIR (%) NIIM (%)

2004 21.36 0.91 72.46 2.24

2005 1.91 0.09 77.40 2.79

2006 7.94 0.47 77.00 2.97

2007 14.38 0.76 74.32 2.76

2008 13.06 0.89 80.37 2.25

2009 14.93 0.96 79.91 2.62

2010 15.74 0.92 78.67 2.28

2011 15.84 0.89 78.11 2.48

2012 18.51 0.99 81.15 2.59

2013 16.71 1.01 80.67 2.63

2014 15.06 0.92 81.84 2.59

2015 8.55 0.52 83.42 2.47

2016 8.67 0.53 83.50 2.56

AM 12.26% 0.68% 76.91 2.61%

PRE AAGR 5.73% 5.45% 0.35% 0.23%

SD 6.64 0.34 3.09 0.30

AM 13.89% 0.81% 81.45 2.55%

POST AAGR 6.53% -0.49% 0.15% 0.53%

SD 4.25 0.23 2.01 0.06

Pre-Post, t-value -1.01 -2.09 3.50 30.56


Earning Quality� Return on Equity = Net Profit after Interest and

Tax / Shareholder’s equity * 100

� Return on Assets = Net Interest Income / Totalassets * 100

� Cost to Income Ratio = Operating Expense / Netinterest & Noninterest income * 100

� Net interest income margin = net interest income/Average earning assets * 100

Return on Equity (ROE): ROE ratio position of thebank showed a fluctuating trend during the period ofstudy. It was observed that the highest value of 16.71%was observed in 2013 and the least of 1.91% in 2005.

The statistical analysis shows that IT adoptionhas resulted in an increase in average value from12.26% to 13.89%. The AAGR calculated withthe help of geometric mean showed a lower value inthe post IT adoption period. The relative variation ofthe ROE was very low during the post adoptionperiod.

The student’s t-value of -1.01 was below the table valueof 1.96 which shows that null hypothesis was acceptedi.e. no significant difference in ROE between pre andpost IT adoption period.

Return on Assets (ROA): ROA ratio position of thebank showed a fluctuating trend during the periodwith decreasing values during the last three years. It


was observed that the highest value of 1.01% wasobserved in 2013 and the least of 0.09% in 2005.Thestatistical analysis shows that IT adoption has resultedin an increase in average value from 0.68% to 81%.The AAGR calculated with the help of geometric meanshowed that the rate of increase was very low negativeduring the post IT adoption period. In both pre andpost adoption period the standard deviation was foundto be very low.

The student’s t-value of -2.09 was above the table valueof 1.96 which shows that null hypothesis was rejectedi.e. significant difference in ROA between pre and postIT adoption period was observed.

Cost to Income Ratio (CIR): CIR ratio position ofthe bank showed an increasing trend during the periodwith decreasing values during the last three years. Itwas observed that the highest value of 83.50% wasobserved in 2016 and the least of 72.46% in 2004.The statistical analysis shows that IT adoption hasresulted in an increase in average value from 76.91%to 81.45%. The AAGR calculated with the help ofgeometric mean showed that it decreased during thepost IT adoption period. In both pre and postadoption period the standard deviation of the ratiowas found to be constant.

The student’s t-value of 3.50 was above the table valueof 1. 96 which show that null hypothesis was rejectedi.e. significant difference in PR between pre and postCIR adoption period was observed.

Net Interest Income Margin (NIIM): NIIM ratioposition of the bank was between 2 to 3 percent duringthe period of study. It was examined that the highestvalue of 2.79% was observed in 2005 and the least of2.24% in 2004. The statistical analysis shows that ITadoption has resulted in a decrease in average valuefrom 2.61% to 2.55%. The AAGR calculated withthe help of geometric mean showed a higher growthrate in the post IT adoption period. In post adoptionperiod the standard deviation was found to be verylow.

The student’s t-value of 30.56 was above the table valueof 1.96 which shows that null hypothesis was rejectedi.e. significant difference in NIIM between pre andpost IT adoption period was observed.

LiquidityCurrent Ratio = Current Assets / Current Liabilities

Absolute Liquid Ratio = Cash or Cash Equivalents/Current Liabilities

CDR = Customer Deposits / Total Assets

Current Ratio (CR): (Table 6) CR ratio positionof the bank showed a fluctuating trend duringthe period of study. It was observed that thehighest value of 6.61 times was observed in 2016and the least of 2.22 times in 2005.The statisticalanalysis shows that IT adoption has resulted inan increase in average value from 3.52 times to4.88 times, which shows a positive sign for thebank (ideal value - 1.33 times). The AAGRcalculated with the help of geometric meanshowed a higher rate of increase in comparison tothe pre adoption period. Ratio values showed ahigher level of deviation among the Current ratiovalues.

The student’s t-value of 0.92 was below the table valueof 1.96 which shows that null hypothesis was acceptedi.e. no significant difference in Current ratio betweenpre and post IT adoption period.

Absolute Liquid Ratio (ALR): ALR ratio position ofthe bank showed a fluctuating trend during the periodof study. The highest value of the ratio was 0.90 timesand the least value was 0.88 times. AAGR during thepre as well as post adoption period was uniform andnegligible. The absolute variability among the ratiovalues was also very low.

The student’s t-value of -0.58 was below the tablevalue of 1.96 which shows that null hypothesis wasaccepted i.e. no significant difference in customerdeposit ratio between pre and post IT adoptionperiod.

Customer Deposit Ratio (CDR): CDR ratio positionof the bank showed a value which is near 0.90 timesduring the period of study. It was examined that thehighest value of 0.81times was observed in 2014 andthe least of 0.16 times in 2008 & 2011.The statisticalanalysis shows that IT adoption has resulted in anincrease in average value from 0.30 times to 0.53 times,


Table 6: Statement showing Liquidity (` in Millions)

Year Current Ratio Absolute Liquid Ratio Customer deposit to(Times) (Times) total assets ratio (times)

2004 2.79 1.90 0.89

2005 2.22 1.57 0.90

2006 3.79 2.82 0.88

2007 4.42 3.63 0.90

2008 3.23 2.54 0.89

2009 4.65 3.60 0.89

2010 3.82 2.63 0.90

2011 4.47 3.29 0.91

2012 3.38 2.41 0.90

2013 4.93 3.91 0.89

2014 3.48 2.46 0.86

2015 6.41 3.27 0.88

2016 6.61 4.05 0.88

AM 3.52 2.68 0.89

PRE AAGR 1.11% 7.11% 0.10%

SD 0.95 0.85 0.01

AM 4.88 3.23 0.89

POST AAGR 3.83% 5.46% 0.60%

SD 1.40 0.70 0.02

Pre-Post, t-value 0.92 0.79 -0.58


which shows a positive sign for the bank (ideal value0.50:1). The AAGR calculated with the help ofgeometric mean showed a lower value in the post ITadoption period. Ratio values showed a high degreeof deviation in the post adoption period due

adaptability to new system.The student’s t-value of -1.01 was below the table valueof 1.96 which shows that null hypothesis was acceptedi.e. no significant difference in ROE between pre and

post IT adoption period.

Multiple Regression AnalysisTable 7: Model Summaryb

Model R R Square Adjusted Std. Error of Durbin-R Square the Estimate Watson

1 .923a .851 .777 .12980 2.170

a. Predictors: (Constant), CDR, EPE, ATA, DERb. Dependent Variable: ROA


From Table 7 the Durbin-Watson statistic was 2.170it means that there was no serial correlation betweenindependent variables and NIIM.

Looking at regression from table 7 and 8, we find thatthe explanatory power of the whole second regressionmodel is about 77.7%, where at the same time, the F-stat is 0.002 and is less than 5%, which is significant.

Table 8: ANOVAa

Model Sum of d.f Mean Square F Sig.Squares

Regression .772 4 .193 11.448 .002b

1 Residual .135 8 .017

Total .906 12

a. Dependent Variable: ROA

b. Predictors: (Constant), CDR, EPE, ATA, DER

Table 9: Coefficientsa

Model Unstandardized Standardized t Sig.Coefficients Coefficients

B Std. Error Beta

(Constant) 12.175 5.115 2.380 .045

DER -.915 .340 -.675 -2.688 .028

1 EPE 1.663 .250 1.334 6.646 .000

ATA -.042 .010 -.880 -4.202 .003

CDR -10.339 5.345 -.486 -1.935 .089

a. Dependent Variable: ROA

It is evident from the model that DER, EPE and ATAis significant in explaining earning capacity of totalassets of South Indian Bank during the entire periodof study.

Thus, we can predict the average ROA (Earningsindicator) with about 77% explanatory power by the

following model at 5% level of significance:

Findings of the StudyCapital Adequacy Position (C): Considering thecapital adequacy parameters CAR, DER and PR, itwas observed that CAR showed a continuousincreasing trend and a higher average value in the postperiod. DER and PR showed a fluctuating trend withhigher average value in the post IT adoption period.Testing of sub hypotheses pertaining to first hypothesisshowed that both Debt-Equity Ratio and Proprietaryshowed significant fluctuations in its value during thepost and pre IT adoption period. This is an indicationthat IT implementation has resulted in significantimprovement in the capital adequacy position

considering the period under study.

Asset Quality (C): Considering the Asset Qualityparameters IAR, ATA and PLL, it was observed thatIAR showed a continuous decreasing trend and a loweraverage value in the post period. Whereas the ATAand PLL showed an increasing trend with higheraverage value in post period. This is an indication thatthe bank was focusing more on its primary businessof advances and deposits. Testing of sub hypothesespertaining to first hypothesis showed that both all theasset quality ratios showed no significant fluctuationsin its value during the post and pre IT adoptionperiod. Overall it can be observed that bank started


focusing more on its primary business after forced ITadoption.

Management Efficiency (M): Consider themanagement performance indicators, both earningsand business per employee showed improved averagevalues in the post IT adoption period. This increasewas due to the increase in total business and net incomeof the business. Total business of the bank showedsignificant difference in its value during the post ITadoption period. Hypothesis testing showed onlysignificant difference in the EPE value of the bankduring the period of study.

Earning Capacity (E): Considering the earningcapacity indicators ROE, ROA, CIR and NIIM, onlyNIIM showed decreased average value during the postadoption period. The decrease in NIIM is due to theincreased operating expenses associated withimplementation of new IT enabled platform fortransactions. The testing of sub hypotheses showssignificant difference in value between pre-post valuesfor ROA, CIR and NIIM. This is an indication thatthe earnings capacity showing improvements in itsvalues which will be reflected through the concept ofeconomies of scale.

Liquidity (L): Among the liquidity indicators, CurrentRatio, Absolute Liquid Ratio and Customer DepositRatio, Current and Absolute liquid ratio showedimprovement in its mean value in the post adoptionperiod. Even though these ratios were above the ideal

values, a very high value is an indication of loweramount of advances. This in turn will affect the bankin term of lower amount of business resulting in lowerprofitability. The banks must try to reduce the blockedcash so that more amount can be released to thecustomers, which will generate operating income.

Multiple Linear Regression Model: It is evident fromthe model that Total Investment to Asset Ratio,Allowances to loans & Advances and Current ratio issignificant in explain the Net Interest Income marginof South Indian Bank during the entire period of study.

ConclusionCAMEL rating system has been introduced to assessthe performance of the banks. This system evaluatedvarious parameters, such as Capital Adequacy, AssetQuality, Management, Earnings Quality, andLiquidity. By analyzing the 13 years data, it is foundthat SIB is making healthy improvement in theirCAMEL indicators. The liquidity position of the bankneeds to be improved as it can have an immediateimpact on its functioning if left unwarranted. Bankmust ensure that unused cash balances must beconverted into business so that decrease in Net InterestIncome Margin can be revamped. Thus SIB needs tocatch upon the efficient management of liquidityparameters. Earlier people used to talk about theforeign banks, nationalized banks and private banks,but, today it is all about the quality and servicesprovided by the competitors.

References1. AIA Annual Report. (2010). AIA’s CAMEL Approach for Bank Analysis. Retrieved from

http://media.corporateir.net/Media_Files/IROL/23/238804/AIA_2010Annual_Report_End_Final.pdf

2. Barr, Richard S. et al. (2002). Evaluating the Productive Efficiency and Performance of U.S. CommercialBanks. Engineering Management, 28(8), p.19.

3. Christopher, Ifeacho., & Harold, Ngalawa. (2014). Performance of the South African Banking Sector since1994. The Journal of Applied Business Research, 30 (4), 1183-1196.

4. Hari Krishna Karri., Kishore, Meghani., & Bharti, M. M. (2015). A Comparative Study on FinancialPerformance of Public Sector Banks in India: An Analysis on Camel Model. Arabian Journal of Business andManagement Review (Oman Chapter), 4(8), 18-34.

5. Hirtle, Beverly J., & Lopez, Jose A. (1999). Supervisory Information and the Frequency of Bank Examination.FRBNC Economic Review, p. 4.

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7. Ketan, Mulchandani., & Kalyani, Mulchandani. (2016). A Study of Financial Performance: A ComparativeAnalysis of Muthoot Finance Ltd. and Manappuram Finance Ltd. The International Journal of Business &Management, 4(2), 288-295.

8. Kutner., M., Nachtsheim, C. J., & Peter, J. (2004). Applied Linear Regression Models. McGraw Hill/IrwinSeries, 4th Edition.

9. Rashidah Abdul Rahman, & Mazni Yanti Masngut (2014). The Use of CAMELS in Detecting. FinancialDistress of Islamic Banks in Malaysia. The Journal of Applied Business Research, 30 (2). 65-72.

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Published Reports1. Annual Reports of South Indian Bank from 2004 to 2016.

2. RBI reports, 2004 & 2012.

The Impact of Marketing Mix Strategy on Hospital’sPerformance Measured by Patient’s Satisfaction(An Empirical Study on Santokba Durlabhji Memorial Hospital, Jaipur)

Ankita Jain*Varsha Choudhary**

Abstract

This research aims to study the impact of marketing mix strategy on patient satisfaction in santokbadurlabhji memorial hospital, Jaipur. This research consists of the independent variables represented bymarketing mix strategy components (namely health service, pricing, distribution, promotion, physicalevidence, process, and personal strategies) and dependent variable which represented by patientsatisfaction. In order to explore the relationship between independent and dependent variables, thequantitative method was used to collect primary data through a questionnaire, which was administeredin the selected hospital’s patients. All Patients’ of the SDMH were targeted in this research. The researchpopulation of this research consists of 250 patients. The research sample in this research also consistsof the total population accounted 250 patients. The researcher retrieves 190 valid research questionnaires.A purposive sampling strategy was used to choose the participants in this research.

Keywords: Marketing Mix Strategy, Patient Satisfaction, Hospitals

IntroductionThe marketing mix strategy is considered one of thecore concepts of marketing theory (Ziethaml andBitner, 2000). In recent years, the popular version ofthis concept, that of McCarthy (1964) relating tothe 4Ps: (product, price, place and promotion), hasincreasingly come under attack with the result thatdiverse marketing mix strategies have been putforward for different marketing contexts. The termmarketing mix refers to a set of tools available to anorganization to shape the nature of its offer tocustomers (Palmer, 2001). Kotler (2000: P15) definesthe marketing mix as “the set of marketing tools thatthe firm uses to pursue its marketing objectives in thetarget market”.

A number of researchers (Booms and Bitners, 1981;Lovelock, 2001, Ahmad, 2007) have previously arguedthat the traditional 4Ps of the marketing mix model

Ankita Jain*Associate ProfessorThe IIS University, Jaipur

Varsha Choudhary**Assistant ProfessorBiyani Group of Colleges, Jaipur

are inadequate for either the marketing of goods orfor services marketing. Services are different fromproducts, because of their characteristics; intangibility,inseparability, heterogeneity, and perishability. Earlierwork of Booms and Bitner (1981) extend marketingmix for services from 4Ps to 7Ps adding three elementsto the traditional model: people, physical evidence andprocesses. Customer satisfaction ranks high on the listof strategic priorities concerned with the achievementof long-term objectives (Day and Wensley, 1988).Customer satisfaction (Day and Wensley, 1988) reflectsthe effectiveness of the hospital in delivering value toits patients and other customers.

Patient satisfaction, a crucial piece in the puzzle ofperformance assessment, merits consideration as aperformance measure appropriate for small hospitals.Patient perceptions of quality of care are increasinglycentral in conceptual and operational models ofperformance measurement (Lied and Kazandjian,1999). In other words, customer satisfaction relates tothe patient and his family, and includes variousdimensions ranging from the “hotel” service aspects(such as food or parking services) to medical aspectssuch as morbidity, use of a range of antibiotics ornursing services.


We develop a conceptual framework aimed torecognize marketing mix strategy componentsinfluence patient satisfaction in SDMH. Themanuscript is organized as follows: Initial discussionbegins with a deeper look at the marketing mix strategyof a health organization. The purpose of the currentresearch is to explore the impact of services marketingmix strategy components hospital’s performance based

on patient satisfaction in SDMH.

Objectives of the Research1. To define the components of the marketing mix

strategy of the private sector hospital in SDMH.2. To determine the elements, which constitute the

hospital performance measured by patientsatisfaction of the private sector hospitals inSDMH.

Table-1: Review of Literature

Study Reference Objective Sampling Method/Sampling Size/

Data Source

Data AnalysisMethod

Findings

“T.DheepaN.GayathriP.Karthikeyan,(2015)”

• To know how tomeasure thepatient’s satis-faction and morecritical of thequality of servicethey experience

• To identifypatient’ssatisfactiontowards variousdimensions thatinfluence thequality of servicein thegovernmenthospitals in thewestern districtsof Tamil Nadu

• SM: Multistagesampling method

• SS: 286Respondents werechosen for thestudy

• DS: Selfcompletedquestionnaire

• Percentageanalysis

• Factor analysis

• Multipleregressions.

• It was noticed thatpatient’s weredisappointed andannoyed. Andgovernmenthospitals need toimprove on theirperformance.

“Zahra Khamda,Nazanin Pilevari(2013)”

• To measureservice providers’perceptions andpreferencestowards quality ofhealthcare services

• To present amodel for rankingservice qualityamong fourIranian hospitalwards

• SM: Randomsampling

• SS: Health careservice providerswere chosen fromdifferent areas ofIran.

• DS: 20-item scalequestionnaire

• PreferenceRankingOrganizationMethod

Research findingsconclude that asharper way todemystify gradesof service of anyorganization ifdone according toa ranking processwould be moreworth.



Data Source

Data AnalysisMethod

Findings

“Fethi Calisir,Cigdem AltinGumussoy, AyseElvanBayraktarogluand Burcu Kaya,(2012)”

• To evaluate theeffect of servicequalitydimensions oncustomersatisfaction.

• To understandthe usage ofmodifiedSERVQUALmodel

• SM: RandomSampling method

• SS: 292 Patientsfrom differenthospital types ofturkey

• DS: Survey itemwere adopted forquestionnaire

• Regressionanalysis

• Cronbach’salpha,”

“The effect ofSERVQUALdimensions oncustomersatisfaction andreturn intention”was undergonethoroughly foreach type ofhospital.Conclusionspointed out thatempathy was thedecidingingredientregardingcustomersatisfaction for allhospital typesshowing thatcustomers desireand welcome acustomer-focusedservice concept.

“S.SHARMILA,DR.JAYASREEKRISHNAN,(2013)”

• To present ananalysis of theliterature examineobjectiveinformationconcerning thesubject of patientsatisfaction, as itapplies to thecurrent medicalpractices.

• SM: SimpleRandomSampling

• SS: 320respondents werechosen for thestudy

• DS:Questionnaire

• Structuralequationmodeling (SEM)

Findings onceagain proved thatin privatehospitals doctorsare sincerelyconcerned aboutthe patients,doctors andnurses work morefaithfully there,and privatehospitals areleaving no stoneunturned in orderto providecomfort to theirpatients.



Data Source

Data AnalysisMethod

Findings

“Dr. MamtaBrahmbhatt, Dr.Narayan Baser,Prof. NisargJoshi (2011)”

• To explore theconcept of servicequality in a healthcare setting

• SM: Convenientsampling method

• SS: 246 patientswere chosen forthe study

• DS: StructuredQuestionnaire

• Mean score

• Comparativeanalysis

“Johan de Jagerand Therese duPlooy, (2011)”

• To study the in-patients and out-patientsexpectations,perceptions andsatisfactionrelated to services.

• SM: Randomsampling method

• SS: 448 Patientswere chosen fromprovincial hospitalin Gauteng, SouthAfrica.

• DS: PersonalInterview method

• Kolmogorov-Smirnov Testmethod

• Kruskall Wallistest”

The findingspointed out thatpatients fromstem to stern wishultimate level ofresponse, but onlybirdfeed of it isbeing given,resulting in failureon hospital partand dissatisfactionon customer part.

The resultsrevealed that thecustomers’perceptions didnot exceed theirexpectations, asthey weredissatisfied withthe level ofhealthcare servicesrendered by bothGovernment andprivate sectorhospitals.

3. To determine the effects of the marketing mixstrategy components on the hospital performancemeasured by patient satisfaction of the privatesector hospital SDMH.

Hypothesis of the Research� Service marketing mix strategy components have

a positive and significant effect on the hospitalperformance measured by patient satisfaction inSDMH.

Research MethodologyThe current study is based on primary and secondarydata both for the collection of primary data thequestionnaire was developed. The universe of the study

was the people who were the patients of SDMH Jaipur,Rajasthan.

Data Collection Method: - Questionnaire

Sample Size: - The size of respondents is 250 out ofwhich 190 were valid

Research Design: - Exploratory and Descriptiveresearch design

Interpretation:Table 3 shows the correlation matrix, which presentsthe value of the Pearson correlation coefficient betweenevery pair of variables, the 1-tailed significance of eachcorrelation and the number of cases contribution toeach correlation (N=190).


Data Analysis and InterpretationTable-2: Marketing Mix Strategy and Hospital Performance Measured by Patient Satisfaction (HPMPS)

Pearson HPMPS Health Price Distribution Promotion Physical Process PersonalCorrelation Service Strategy Strategy Strategy Evidence Strategy Strategy

Strategy

HPMPS 1.000 0.520 0.061 0.074 0.300 0.389 0.391 0.217

Health 0.520 1.000 0.316 0.034 0.286 0.452 0.306 0.254ServiceStrategy

Price 0.061 0.316 1.000 0.236 0.272 0.183 0.328 0.220Strategy

Distribution 0.074 0.034 0.236 1.000 0.235 0.117 0.032 0.040Strategy

Promotion 0.300 0.286 0.272 0.235 1.000 0.220 0.361 0.313Strategy

Physical 0.389 0.452 0.183 0.117 0.220 1.000 0.495 0.338Evidence

Process 0.391 0.306 0.328 0.032 0.361 0.495 1.000 0.351Strategy

Personal 0.217 0.254 0.220 0.040 0.313 0.338 0.351 1.000Strategy

Interpretation:With regard to the relationships among predictors, andthe outcome as shown in Table-2, (5) out of (7)marketing mix strategy components had a significantpositive correlation with the hospital performancemeasured by patient satisfaction that shows theinfluence of the marketing mix strategy componentson hospital performance measured by patientsatisfaction . Between the other predictor variables“marketing mix strategy components”, and theoutcome factor “hospital performance measured bypatient satisfaction” Pearson correlation results rangedfrom (0.520–0.217) with the correlation of all 5positive marketing mix strategy items being significant

(p<0.05). The only two capability found not to showa significant positive correlation is the distributionstrategy (r =0.07, p=0.191), pricing strategy (r=0.061,p=0.161) However, among all the predictors, healthservice strategy correlates best with the hospitalperformance measured by patient satisfaction in thatit has highest positive correlation with it, which is alsosignificant: (r =0.520, p<0.05). Therefore, it is likelythat this variable will best predict and/ or explain thevariance. The results of the analysis have demonstratedthat the multiple regression model (table 3), whichconsists of the marketing mix strategy components hassignificantly improved our ability to explain theoutcome variable.

Y = B0 + B1X1 + B2X2 + B3X3 + B4X4 +B5X5+B6X6+B7X7 + E

Y = 0.654+0.346X1 + 0.032X2 + 0.045X3 + 0.172X4 +0.179X5+0.184X6+0.142X7 + E

Where:Y= the predicted value on the hospitals performanceB0= the Y intercept, the value of Y when all Xs arezero

X1= Health service strategyX2=Pricing strategyX3=Distribution strategyX4=Promotion strategy


Table-3: Marketing Mix Strategy and Hospital Performance Measured by Patient Satisfaction (HPMPS)

Sig. HPMPS Service Price Distribution Promotion Physical Process Personal(1-tailed) Strategy Strategy Strategy Strategy Evidence Strategy Strategy

HPMPS - 0.000 0.161 0.191 0.000 0.000 0.000 0.005

Health 0.000 - 0.000 0.345 0.000 0.000 0.000 0.001ServiceStrategy

Price 0.161 0.000 - 0.002 0.001 0.014 0.000 0.004Strategy

Distribution 0.191 0.345 0.002 - 0.002 0.082 0.354 0.318Strategy

Promotion 0.000 0.000 0.001 0.002 - 0.004 0.000 0.000Strategy

Physical 0.000 0.000 0.014 0.082 0.004 - 0.000 0.000Evidence

Process 0.000 0.000 0.000 0.354 0.000 0.000 - 0.000Strategy

Personal 0.005 0.001 0.004 0.318 0.000 0.000 0.000 -Strategy

HPMPS 190 190 190 190 190 190 190 190

Health 190 190 190 190 190 190 190 190ServiceStrategy

Price 190 190 190 190 190 190 190 190Strategy

Distribution 190 190 190 190 190 190 190 190Strategy

Promotion 190 190 190 190 190 190 190 190Strategy

Physical 190 190 190 190 190 190 190 190Evidence

Process 190 190 190 190 190 190 190 190Strategy

Personal 190 190 190 190 190 190 190 190Strategy

X5=Physical evidence strategy

X6=Process strategy

X7=Personal strategy

B= the various coefficients assigned to the IVs duringthe regression

E = an error term.

Interpretation:These coefficients as shown in table 4 are referred toas B values, which indicate the individualcontribution of each predictor to the model. Byreplacing the B values into the above equation, themodel becomes defined. In this way, the B valuesinform the relationship among the hospital


performance measured by patient satisfaction andthe influences of the marketing mix strategy. If thevalue is positive, this indicates a positive relationshipbetween the predictor and the outcome, whereas anegative coefficient represents a negative relationship.Viewing the B value under the first column, healthservice strategy has the highest positive relationshipwith the outcome variable hospital performancemeasured by patient satisfaction (B=0.346).Nonsimilarly, pricing strategy (B=0.032), whiledistribution strategy has no significance (B= 0.045).Whereas the other four components (promotion,physical evidence, process, and personal strategies)are significantly related to the hospital performancemeasured by patient satisfaction (P-value=0.172,0.179, 0.184, 0.142) respectively.

Conclusion� Health Service Strategy: - It is found that the

majority of SDMH provides a comprehensiverange of health and medical service classes tofacilitate the diverse needs and wants their targetmarket. Developing and introducing new healthservices is applied in SDMH. The importance ofintroducing and developing new health services istwofold. First it is a competitive tool for thehospital’s growth and continuations, and forenabling the hospital to meet needs and wants for

the largest possible market. Second, in light of theupdated medical technology worldwide, it helpshospitals to gain opportunities that lead toincreased market share and penetrate new markets.The research data indicates that patient services isa fundamental factor in a health service strategyand a crucial part of the marketing strategy, whereasthe SDMH focus on customers’ (patients)confidential cases.

� Pricing Strategy: - The quantitative data analysisin SDMH indicated that there are disparate pricingstrategies are frequently adopted within thehospitals. These strategies involve pricing based ongovernment regulations, and the varying costs,which the SDMH incur. The pricing policy basedon competition in the Jeddah health market andprice discrimination according to market segmentwas utilized by SDMH.

� Distribution Strategy: - It is found that themajority of SDMH provide an hourly serviceavailability to match the non-programmedemergency and accident cases. The research dataindicates that SDMH have no branches in differentprovinces and cities in Jaipur. This may be due toa high cost of establishment or/ the concentrationpolicy in one branch. As such, most of SDMH donot have a mobile clinic.

Table-4: Coefficient of the Multiple Regression Model/Hospital Performance Measured by Patient Satisfaction

Model Unstandardized Standardized CoefficientCoefficient

B Std. Error Beta T Sig.

Constant 0.654 0.456 - 1.435 0.154

Health Service Strategy 0.346 0.077 0.360 4.494 0.000

Price Strategy 0.032 0.058 0.124 1.583 0.0116

Distribution Strategy 0.045 0.086 0.086 0.994 0.322

Promotion Strategy 0.172 0.079 0.167 2.167 0.032

Physical Evidence 0.179 0.038 0.151 2.067 0.041

Process Strategy 0.184 0.099 0.158 1.867 0.042

Personal Strategy 0.142 0.080 0.040 0.524 0.031

Dependent Variable: R2 =0.731 Adjusted R2F F=11.720patient satisfaction =0.743 =11.720 P<0.05


� Promotion Strategy: - The qualitative data analysissuggests that the most prominent method ofpromotion is by “word-of-mouth” communicationwhere an existing patient recommends thehospital services to other customers in similar ordifferent cases of illness. The word-of-mouthcommunication, personal selling and customerpersonal contact, and public relation, and publicityfor promoting health services were used by SDMH.The rationale behind using word-of-mouthcommunication in promoting health services isthat the health service has unique complexcharacteristics especially the aspect of intangibility.Medical and administrative staff believes that thegreatest means of promoting health service is byword-of-mouth. Furthermore, promoting healthservices is more problematic compared with otherservices or products. The rationale underlying useof public relations and publicity (free medical days)to enhance the hospitals image in promoting theirhealth service is that hospitals need to build trustand improve the reputation of their health services.The low use of other methods of promotion(advertising) remains a matter of debate among thehealth services in Jaipur.

� Physical Evidence Strategy: - The research dataindicates that customer service is a fundamentalobjective in designing the physical evidence strategyof SDMH by which it can create a customer-friendly atmosphere and comfortable access to thehealth services. Therefore, the customers ofhospitals face an altogether different psychologicalsituation compared to customers of other serviceorganizations, which need additional effort to help

them reduce the degree of anxiety experienced byconcentrating on the physical evidence atmospherefacilities.

� Health Process Strategy: - The research datareveals that the health/medical services deliveryprocess strategy is the most sensitive and criticalactivity that SDMH, as with any hospital aroundthe world concentrates upon to deliver their serviceson time. Most medical cases do not accept anydelay in treatment. SDMH also recognizedsatisfaction among their customers duringdelivering health services for two reasons: first, thesocial responsibilities, and second the greatcompetition extent in the health care market.

� Personal Strategy: - The data indicates thatSDMH are generally improving their personalability to perform their service role and to maintaina competitive level. They further concentrate ontheir staff ’s appearance because of the extremecontact occurring between staff and hospitalpatients. Serving customers in hospitals are criticalactivities that may earn customer satisfaction- orapprobation, so excellent standards are essentialwithin such an environment.

Limitations of the Study� This research has been conducted in a single service

industry, the health service industry in Jaipur,exclusively in the Santokba Durlabhji MemorialHospital, which implies that the generalisabilityof the research results are limited to the SDMH inJaipur business environment context, and cannotbe generalized to other health services marketseither in developed or developing countries.

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15. Thompson, Bruce, Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications,American Psychology Association, Washington DC, 2004.

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