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[86] MALL MOTIVATIONS IN INDIA ALIGNING DEMOGRAPHICS FOR SEGMENTATIONS Ritu Srivastava, Assistant Professor, School of Management, Gautam Buddha University, Greater Noida, U.P., Abstract Purpose: Mall Management is a new discipline in India where Consumer Motivations and Market Segmentation are concerns of the Marketing strategy that Mall Managers must understand essentially to craft success. This study addresses these concerns with the objectives of identifying the primary reasons/ motivations for Indian customers to visit malls and analysing the influence of demographic variables of Age, Education, Income and Gender on the identified customer motivations to visit malls that could affect mall patronage and segmentation. Research Design and Methodology: The study is a cross sectional survey executed in the National Capital region (NCR) of New Delhi, India. The data was collected from more than 600 respondents through a “Mall Intercept Survey” from 27 malls. Factor Analysis was used to identify the Shopping Motivations. Multivariate Analysis of Variance was done to analyse whether the Select Demographic variables create a variation in the Shopping Motivations. Findings: People in India visit malls in India for Diversion/Browsing, Economic Reasons, Shopping Environment, Refreshments and Snacking, Services and Social Experience. Of the select demographic factors only Age shows a significant effect on shopping motivations in malls in India, individually. However interactions in various combinations are relevant for different shopping motivations. Within each demographic variable different level behave differently with respect to each shopping motivational factor. This finding is important for mall planners and retailers when planning their market segmentation strategy. Value: The paper will help mall managers/retailers in India design their marketing strategies better with respect to shopper motivations and demographic influences. Keywords:-India, Retailing, Malls, Shoppers, Demographic Variables, Market Segmentation

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MALL MOTIVATIONS IN INDIA

ALIGNING DEMOGRAPHICS FOR SEGMENTATIONS

Ritu Srivastava,

Assistant Professor, School of Management,

Gautam Buddha University, Greater Noida, U.P.,

Abstract

Purpose: Mall Management is a new discipline in India where Consumer Motivations and Market Segmentation are concerns of the Marketing strategy that Mall Managers must understand essentially to craft success. This study addresses these concerns with the objectives of identifying the primary reasons/ motivations for Indian customers to visit malls and analysing the influence of demographic variables of Age, Education, Income and Gender on the identified customer motivations to visit malls that could affect mall patronage and segmentation.

Research Design and Methodology: The study is a cross sectional survey executed in the National Capital region (NCR) of New Delhi, India. The data was collected from more than 600 respondents through a “Mall Intercept Survey” from 27 malls. Factor Analysis was used to identify the Shopping Motivations. Multivariate Analysis of Variance was done to analyse whether the Select Demographic variables create a variation in the Shopping Motivations.

Findings: People in India visit malls in India for Diversion/Browsing, Economic Reasons, Shopping Environment, Refreshments and Snacking, Services and Social Experience. Of the select demographic factors only Age shows a significant effect on shopping motivations in malls in India, individually. However interactions in various combinations are relevant for different shopping motivations. Within each demographic variable different level behave differently with respect to each shopping motivational factor. This finding is important for mall planners and retailers when planning their market segmentation strategy.

Value: The paper will help mall managers/retailers in India design their marketing strategies better with respect to shopper motivations and demographic influences.

Keywords:-India, Retailing, Malls, Shoppers, Demographic Variables, Market Segmentation

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1. INTRODUCTION

Malls in India mirror the present stage of Indian consumerism and Organized Retail life cycle. The Indian retail industry is in its’ growth phase with the gradual phasing of the Foreign Direct Investment (FDI) restrictions, since 1996 to 2012. While the industry itself is as old as business in India, it has basically existed in unorganized form. Even today organized retail accounts for only 7% of India’s approximately $435 billion market that is expected to grow at 20% by 2020 (A.T. Kearney, 2011). The regulatory changes coupled with the lifestyle changes have changed the business models wherein the customers have become more demanding and competition intense.

To garner success firms must have very clear understanding of fundamental and critical strategic business decisions such as;

Who is our customer? Why does he come to us? How are we going to serve them, i.e.,

why should he come to us?

2. RESEARCH OBJECTIVES

To answer the third question a retailer has to answer the first two. This study germinates at this point with a focus on the Mall Customers addressing following objectives;

What are the primary reasons / motivations for Indian customers to visit malls?

Do demographic variables of Age, Education, Income and Gender create a variation in the customer motivations to visit malls?

3. RESEARCH OUTCOME

The study would throw light on;

The principle reasons for which people visit malls in India;

What is the direction of the trend, hedonic or utilitarian?

The effect of select Demographic factors on Retail Market Segmentation strategy

4. SHOPPING MALLS

The study builds on the significant distinction in shopping benefits that is utilitarian versus hedonic values. Utilitarian values involve satisfying basic physiological needs and assuring the security of satisfying purchase performance whereas hedonic values involve fun, gratification and pleasure. Utilitarianism involves an attempt to reduce needs arising from states of deprivation, whereas hedonism involves pleasure seeking instead of pain avoidance. Hedonism is a prominent feature of the consumer culture and results in an endless and ultimately unfulfilling quest for novelty, primarily through consumption (Campbell, 1987; Tse, Belk and Zhou, 1989; Dholakia, 1999; Solomon, 2002; Arnold and Reynolds, 2003; Rintamäki et.al, 2006). An insight of these two values is given below.

4.1 Utilitarian Benefits

Consumers may be attracted to a particular shopping centre because of the existence of special store that appeals to them (Nevin and Houston, 1980; Bleomer and Ruyter, 1998; Koo, 2003). To illustrate; anchor stores, consisting of a mix of mass merchandise anchors and department store anchors, help draw customers to a shopping centre. Non anchors also sometimes have a high customer drawing power (Andersen, 1985; Ibrahim and Galven, 2007). The smaller specialty stores typically found in the malls include bookshops, music stores, gift shops, apparel and shoe stores etc. to satisfy the consumer desire for comparison shopping.

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Competitors generally locate their stores in close proximity in these shopping centres. Mall attractiveness also increases with agglomeration of diverse retailers (Kimball, 1991; Brown, 1992; Balaz, 1995; Yavas, 2001; Nicholls et al 2002, Ismail, 2007). To reduce the time and cost of shopping, consumers may sometimes by pass closer stores to visit agglomeration stores which are farther away in order to shop for different types of goods on the same trip (Ghosh, 1986). This phenomenon of multipurpose shopping can offer “one stop for all needs”, to more cost and time efficient functional shoppers.

4.2 Hedonic Benefits

Since an increasing number of consumers use convenience as a primary basis for making purchase decisions, retailers generally tend to focus on consumer purchase patterns and to ignore the emotional benefits that can be provided during shopping activities (Babin et al, 1994). Shopping may be done for emotional and experiential reasons (Babin et al, 1994; Tauber 1972, Westbrook and Black 1985; Jones, 1999; Arnold and Reynolds, 2003; Lotz et al., 2010). The mall offers experiences that are consumable and mall managers do build on this trend by organizing special events (Christman, 1988).

Traditionally malls have offered patrons the advantage of climate comfort and freedom from the noise and traffic that characterize other shopping venues. Some shoppers may be interested in “seeing new items and learning about new trends”; others may go shopping in their leisure time (Tauber, 1972; Hirschman and HollBrook, 1982; Dholakia, 1999, Arnold and Reynolds, 2003; Howard, 2007). Malls have become places that provide opportunities for social experience outside home (Richardson, 1993). Many people enjoy the pleasant, park like atmosphere (Balazs, 1994). The regional shopping mall offers maximum opportunity for

browsing and browsers are often considered to be essential to the success of such an institution (Bloch and Richins, 1983; Jarboe and McDaniel, 1987; Bloch et al., 1989; Lombart, 2004; Lombart and Labbe´-Pinlon, 2007). Browsers may make unplanned purchases because of in-store promotions and exposure to new products. They also gather information in advance of future purchasing (Bloch and Richins, 1983; Bloch, Ridgway and Sherell, 1989). The browsers are also effective word of mouth advertisers, peer influencers and trend setters especially for socially visible products (Jarboe and Mc Daniel, 1987; Nsairi, 2012).

5. RESEARCHING THE MALL SHOPPER IN INDIA

While malls have been an interest of research for more than forty years, country specific research related to Mall Management in India is only about a decade old. The Indian researchers have been working on consumer visit to malls with respect to shopping style orientations (Sinha, 2003), shopper boredom which encourages out-shopping (Roy and Masih, 2007; Singh and Bose, 2008), sustainability of shopping malls in India (Srivastava, 2008), in context of Gen Y (Hemalatha, Jagannathan and Ravichandran, 2009), shopper attributes and retail format (Prasad and Aryasri, 2010), and consumer decision making styles (Reji Kumar, Sudharani and Harisunder 2010).Whereas there is an emergence of research in this field, it is still a new area and research done is in isolation.

6. RESEARCH DESIGN AND METHODOLOGY

The study has been designed as a cross sectional descriptive study executed in the National Capital region (NCR) of New Delhi, India. India has approximately 200 malls (Singh, Bose, Sahay, 2010) of which almost one third are in the NCR. Delhi NCR and Mumbai pioneered mall development and contribute

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nearly 79 per cent of available mall space (Taneja, 2007).The NCR region was divided into nine zones, East, North, West, South Delhi, Gurgaon, Faridabad, Noida, Greater Noida and Ghaziabad. The data was collected from more than 600 respondents through a “Mall Intercept Survey”, by trained MBA student enumerators who were specializing in Retail Management. Altogether twenty seven malls, three from each zone, across nine zones of NCR of New Delhi were randomly selected for the survey. The survey instrument was a questionnaire that was prepared in consultation from industry and academic literature in the domain.

7. FACTOR ANALYSIS

The data is collected in the form of score on seven point Likert type scale through individual interview using a questionnaire from more than 600 hundred selected respondents. The collected data from the questionnaires were filter out for missing values, duplication and other anomalies, finally 532 data points were used for the analysis. Based on the value of the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO = 0.836) and high chi-square value of the Bartlett’s test of Sphericity (Chi-sq = 3507 with degree of freedom = 531), which shows the degree of common variance among the variables is quite high, Factor analysis technique is undertaken to explain the covariance relationships among the motivational reasons for Mall Shoppers (Table 1).

Table 1: KMO and Barlett’s Test of Sphericity

Kaiser-Meyer-Olkin Measure of

Sampling Adequacy

0.836

Bartlett's Test of Sphericity

Chi- Square (approximate) 3507

Degree of freedom 231

Level of significance 0.000

This in turn, describes the covariance relationship in terms of few underlying, but unobservable random quantities called Factors. However, the factor analysis is guided by the following argument that motivational reasons can be grouped by their correlations. That is, all motivational reasons within a particular group are highly correlated among themselves but have relatively smaller correlations with motivational reasons in a different group. In consequence, it is conceivable that each group of motivational reasons represents a single unobserved random variable named as factor, which is responsible for the observed correlation among the motivational reasons. Mathematically factor model of p motivational reasons and m common factors is represented in matrix notation as;

matrixdiagonaltheiswhereCOVCOV

CovEE

and

factorjtheonitheofloadingl

factorcommonjthF

factorspecifici

ithofscoremean

where

ththij

j

thi

i

pxmxpxmpxpx

ψψεIF

0εF0ε0F

εFLμX

,)(,)(

,),(,)(,)(

reasons almotivation

,

,

,reasons almotivation

,

)1()1()()1()1(

[1]

The factor loading lij are estimated by the popular estimation procedure, principal component method. In order to summarize the information contained in the original p variables, m number of factors is extracted.

Initially Shopping motivations of mall shoppers were evaluated through p = 31 original variables such as “I visit malls to get best deals”,

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or “I visit malls just to enjoy crowds” etc. but examining the value of communality, the part of the total variance explained by the common factors, greater than 0.5 (Table 2) only p=22 variables were iteratively retained in the analysis. In the absence of any prior information, the number of factors (m) is estimated through evaluation of following facts. Table 2: Variables Retained in the Analysis based on Extracted Communalities

VARIABLES UNDER

OBSERVATION INITIAL EXTRACTION

MEALS 1.000 0.589

FRIENDS 1.000 0.535

AVOID BOREDOM 1.000 0.672

FUN 1.000 0.651

FIND GOOD PRICES 1.000 0.681

BUY 1.000 0.560

COMPARISON SHOP 1.000 0.613

HASSLE FREE PARKING 1.000 0.661

CLEAN AND POSH ENV 1.000 0.528

GYM/SPA 1.000 0.535

CHANGE 1.000 0.585

OTHER SERVICES 1.000 0.635

HAIR AND BEAUTY 1.000 0.536

OUT OF HOUSE 1.000 0.576

MULTIPLE SHOPPING 1.000 0.532

BETTER STORE

PERSONNEL 1.000 0.590

CONVENIENCE SHOPPING 1.000 0.682

SNACK 1.000 0.562

MEAL AT FOOD COURT 1.000 0.657

SPEND TIME WITH

FAMILY 1.000 0.642

CHILD ENJOY 1.000 0.741

BEST DEALS 1.000 0.513

The Scree plot, which is nothing but a plot between number of eigen values and factor of motivational reasons in order of extraction, has distinct break point between the steep slope of factors and a gradual trailing off associated with rest of the factors. This gradual trailing off points indicated the possible number of factors and it is observed to be six in this analysis (Figure 1). At the same time the eigen values of these factors are greater than one whereas other factors has less than one, which again indicate the extracted number of factors should be six. Thus, six factors of motivational reasons are extracted, which explains 60.34 per cent of the total variability, were named as Diversion/Browsing, Economic Reasons, Shopping Environment, Refreshments and Snacking, Services and Social Experience (Table 3). To enhance the interpretation, these extracted factors were orthogonally rotated by varimax procedure with Kaiser Normalization.

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Table 3: Factor Influencing Motivational Reasons of Mall Shoppers

Factor

Factor Interpretation (% Variance Explained)

Loading

Variables Included in the Factors

F1 Diversion/ Browsing (13.16% )

0.794 Avoid Boredom

0.755 Fun

0.722 Out Of House

0.707 Change

0.619 Friends

F2 Economic Reasons (12.377% )

0.803 Find Good Prices

0.729 Comparison Shop

0.683 Buy

0.633 Best Deal

F3 Shopping 0.697 Better Store Personnel

Environment (10.521% )

0.665 Clean And Posh Environment

0.631 Conveniences Shopping

0.607 Hassel Free Parking

0.563 Multiple Shopping

F4

Refreshments and Snacking (8.876% )

0.751 Meals

0.678 Meal At Food Court

0.648 Snacks

F5 Services (8.441% )

0.782 Other Services

0.683 Gym/ Spa

0.628 Hair And Beauty

F6 Social Experience (6.967% )

0.821 Let My Child Enjoy

0.668 Spend Time With Family

7.1 Diversion/ Browsing

The first factor accounted for 13.16% variance. The items located in this factor with their factor loadings are; “Avoid Boredom” (0.794), “Fun” (0.755), “Out of House” (0.722), “Change" (0.707), and “Freak out with Friends” (0.619). Indian Consumers visit malls just because they may be getting bored or want to break free from a routine rather than for purchasing motivations. This is a form of hedonic motivation.

7.2 Economic Reasons

The items included in the second motivational factor are; “to find good price” (0.803), “to comparison shop” (0.729), “buying” (0.683), and for “for best deal” (0.633). This is a utilitarian function for which the customer visits the mall and focus on price related reasons.

7.3 Shopping Environment

0

1

2

3

4

5

6

1 4 7 10 13 16 19 22

Eig

en

Va

lue

s

Number of Motivational Reasons of Mall Shoppers

Figure 1: Scree Plot between Eigen Values and

Motivational Reasons Of Mall Shoppers

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Service atmosphere and physical evidence are an established part of the marketing strategy. Consumers wish to shop in a pleasant atmosphere. This means that instead of following more traditional consumption related motives as was the cases in India, consumers now visit malls also because it presents an attractive décor and pleasant atmosphere. The results in Table 3 highlight these trends. Indian Consumers go to malls for “Better store personnel” (0.697), “Clean and Posh Environment” (0.665), “Conveniences Shopping” (0.631), “Hassel Free Parking” (0.607), “Multiple Shopping” (0.563). These have been captured in this motivational factor. This is a hedonic motive.

7.4 Refreshments and Snacking

An excellent restaurant or food court provides unique dining opportunity for which a consumer may visit a mall. Food consumption has become a major reason to visit such places. Items included in this factor were; “To Have a Meal at the Restaurant” (0.751), “To Have a Meal at the Food Court” (0.678), and “When I want to Snack” (0.648).

7.5 Services

The Indian customer has started availing and expecting services such as; “ATM/Banking” (0.782), “Gym / Spa” (0.683) and “Hair and Beauty” (0.628), in the malls. As consumers perceive their time increasingly limited and valuable they not only do multiple shopping for products but also look for services that are conveniently available in Malls. This is a utilitarian function that has been captured in the factor on Services where customer looks for accomplishing assorted tasks to save time and achieve efficiency. These services work as incentives for customers.

7.6 Social Experience

Social experience is considered as a hedonic motivation. (Richardson, 1993) has mentioned that malls may have become important locations for providing opportunities for social experience outside home such as meeting friends and watching people. Indian customer with the lifestyle changes takes his family and children out to malls for socializing and spending time with them; “Let My Child Enjoy” (0.821) and “Spend Time with Family” (0.668).

To summarize the shopping motivational factors of Economic Reasons, Refreshments and Snacking and Services reflect motivations of shoppers that are utilitarian in nature. In contrast the motivational factors of Diversion/Browsing, Shopping Environment and Social Experience reflect hedonic considerations. Together these six factors account for 60.34% variance where hedonic factors contribute to almost equal variance as utilitarian factors. This is a major cultural shift for a country like India, which focused more on utilitarian functions. It carries bearings for service marketers

8. A NOTE ON DEMOGRAPHIC VARIABLES

Demographic characteristics of the sample survey data including gender, age, education and income are represented in Table 4. A good understanding of its customers is the key to the success of a retail strategy. This involves understanding of the target customer’s needs and desires, shopping attitudes and behaviour, his lifestyles and demographics. The task of profiling the target customer begins with consumer demographics. Demographics are objective, quantifiable, easily identifiable and measurable population data. To begin the identification demographic profiling based on variables such as gender, age, education, population growth rate, life expectancy, literacy, education, language spoken, household size,

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marital status, income, occupation is typically done. These factors affect retail shopping and retailer’s actions (Bermans and Evans, 2010). Four established demographic factors that influence market segmentation strategies; Gender, Education, Age and Monthly Household Income are being considered in this study (Crask and Reynolds, 1978; Sampson and Tigert, 1992; Arnold, 1994; Fox et.al., 2004, Carpenter and Moore, 2006).

Table 4 Demographic Composition of the Sample Data

Demograp

hic factors N %

Demograph

ic factors N %

Age

18-25

years 227

42.

9

Edu

cati

on

> 12th

standar

d

36 6.8

25-30

years 135

25.

6

Gradua

tion

20

0

37.

8

30-35

years 99

18.

8

Post

Gradua

tion

19

6

37.

1

> 35

years 67

12.

7

Profess

ional

Qualific

ation

96 18.

3

Mo

nth

ly

Ho

use

ho

ld In

com

e

(IN

R)

<

10,00

0

54 10.

2

Ge

nd

er

Male 33

5

63.

5

10,00

0-120

22.

7 Female

19

3

36.

5

24,99

9

24,99

9-

49,99

9

158 29.

9

50,00

0-

1,00,0

00

96 18.

2

>

1,00,0

00

100 18.

9

9. EFFECTS OF DEMOGRAPHIC FACTORS ON SHOPPER MOTIVATIONS

To examine whether the demographic factors of age, education, income and gender have an effect on different shopping motivations Multivariate Analysis of Variance (MANOVA) was conducted. Dependent variables consisted of six identified shopping motivational factors as mentioned in the preceding section; Diversion / Browsing, Shopping Environment, Economic Reasons, Refreshments and Snacking, Services and Social Experience. Categorical independent variables included Age with four levels (18-25 years, 25-30 years, 30-35 years and 35-50 years), Education with four levels ( Below class 12

th, Graduation, Post Graduation and

Professional Qualification.), Monthly Household Income with five levels ( less than INR 10000, 10000-24999, 25000-49999, 50000-100000 and more than 100000.) and Gender (Male and Female). The Wilks Lambda test results are displayed in Table no. 5.

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Table 5 Results Of Multivariate Analysis Of Variance for Select Demographic Factors on Shopping Motivations, Wilks Lambda (At 95% Confidence Interval)

Effect

Value F

Hypoth

esis df

Error

df

Signif

icanc

e

Gende

r .990 .750 6.000

468.00

0 .610

Month

ly

House

hold

Incom

e

.950 1.00

3 24.000

1.6340

00 .459

Educat

ion .953

1.27

2 18.000

1.3240

00 .197

Age .851

4.31

6 18.000

1.3240

00 .000

Results from MANOVA revealed

insignificant effect on the shopping motivational factors for malls because of education, monthly household income and gender. Only Age had a significant effect on them (p=0.000). In addition a two way interaction of gender and age on motivational factors was significant (p =0 .024) whereas all the other interactions were insignificant. These results led to further probing of the situation as household income and education along with gender do display significant differences in the consumer behaviour in markets, where malls are an application area.

To explore the results of MANOVA, the Tests of Between Subjects Effects were analyzed. The tests show effect of each demographic variable on six individual shopping motivational factors. The results are reported in Table no. 6 and summarized in following sections.

Table 6 Tests of between Subject Effects of Select Demographic Factors and Shopping Motivations at 95% Confidence Interval

Source

Depende

nt

Variable

Type

III Sum

of

Squar

es df

Mea

n

Squa

re F

Signific

ance

EDUCATI

ON

Diversion

/

Browisng

7.130 3 2.37

7

2.90

9 .034

AGE Diversion

/

Browisng

15.437 3 5.14

6

6.29

9 .000

Economic

Reasons 8.680 3

2.89

3

3.05

4 .028

Social

Experienc

e

25.501 3 8.50

0

10.0

07 .000

GENDER

*AGE

Refreshm

ents and

Snacking

9.492 3 3.16

4

3.31

5 .020

MONTHL

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YHOUSE

HOLDINC

OME *

AGE

Economic

Reasons 20.552 12

1.71

3

1.80

8 .044

GENDER

*

EDUCATI

ON

Economic

Reasons 9.321 3

3.10

7

3.28

0 .021

MONTHL

YHOUSE

HOLDINC

OME *

EDUCATI

ON

Diversion

/

Browisng 20.229 12

1.68

6

2.06

3 .018

Age showed a significant effect on Diversion / Browsing (p=.000), Economic Reasons (p=0.028) and Social Experience (p=0.000). Age was insignificant for Shopping Environment, Meals and Snacks consumption and Services. Gender and Monthly Household Income was insignificant for all. Education was significant for Diversion / Browsing (p=0.034). These were the individual results of demographic variables on shopping motivations. However, interaction of two variables on shopping motivations reflected a different pattern:-

Gender + Age were significant for Refreshment and Snacking (p=0.020);

Monthly Household Income + Age were significant for Economic Reasons (p=0.044);

Gender + Education were significant for Economic Reasons (p = 0.021) and

Monthly Household Income + Education were significant for Diversion / Browsing (p=0.018)

Economic Reasons is a utilitarian function whereas Diversion/Browsing is a hedonic one. Of the six motivational factors for mall visits these two accounts for 25% of variance (Table 3) 12-13% respectively. They are both thus important for mall managers/ store operators. Of these the hedonic one is even more important than the utilitarian one, since it marginally contributes more. Variables that affect the motivational factors are in different combinations for both of them;

For Diversion / Browsing the effective combination is Monthly Household Income + Gender + Education whereas,

For Economic Reasons the effective combination would be Monthly Household Income + Age +Gender + Education.

At this point it is required that the effect of different layers of select individual demographic factors may also be explored where results are significant, which has been done through pair wise comparisons in the following section.

10. PAIRWISE COMPARISONS

10.1 Age

On examination of pair wise comparisons of four age levels for each motivational factor it was observed each age group category behaved differently with respect to others. Age group 18-25 years (‘young adults’) displayed a significant variance to age group 25-30 years (“growing adults1”, p=0.012) and 30-35 years (“growing adults 2”,p=0.000) for Diversion / Browsing; Age group 25-30 years showed a difference to age group 35-50 years (p=0.008). Age group 35-50 years (“mature adults”) showed a significant

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difference to all other age group levels for Diversion / Browsing.

It is therefore clear that Age is creating significant difference and different age levels are behaving differently for this motivational factor and accordingly customer behaviour and customer characteristics across age levels must be studied by a retailer.

Table 7 Results of Pairwise Comparisons for Age across Demographic Variables And Shopping Motivations At 95% Confidence Interval

Depende

nt

Variable

(I)

AGE

(J)

AGE

Mean

Differ

ence

(I-J)

Std

.

Err

or

Signific

ance

95%

Confidenc

e Interval

for

Difference

Low

er

Bou

nd

Upp

er

Bou

nd

Diversion

/Browsing

18-

25

25-

30 .410

.16

3 .012 .090 .731

35-

50 1.100

.27

8 .000 .553

1.64

6

25-

30

35-

50 .689

.29

2 .019 .116

1.26

3

30-

35

35-

50 1.059

.39

8 .008 .276

1.84

2

Economic

Reasons

18-

25

30-

35 -.718 .34

9 .040

-

1.40

4

-

.031

35-

50 -.729 .30

0 .015

-

1.31

8

-

.140

Socializing

Experienc

e

18-

25

25-

30 -.367

.16

6 .028 -.694

-

.040

30-

35 -

1.268

.33

1 .000

-

1.91

8

-

.618

35-

50 -

1.202

.28

4 .000

-

1.75

9

-

.644

25-

30

30-

35 -.901 .35

4 .011

-

1.59

8

-

.205

35-

50 -.835 .29

8 .005

-

1.42

0

-

.250

For Economic Reasons age group 18-25

years behave differently in terms of their shopping behaviour to that of 30-35 years (p =0.040)and 35-50 years (p=0.015); Age group 25-30 years behaves differently than age group 35-50 years (p=0.08). This means that “young adults” behave differently than “growing adults 2” and “mature adults”, whereas similar to “growing adults 1”. “Growing adults 1” behave differently than “growing adults 2”.

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For Socialising Experience- Age group 18-25 years showed a significant difference from age groups 25-30 years (p=0.028), 30-35 years (p=0.000) and 35-50 years (p=0.000); age group 25-30 years shows a significant difference with age group 18-25 years, 35-50 years (p=.011) and 35-50 years (p=0.005); age group 30-35 years shows a significant difference to age group 18-25 years and 30-35 years but insignificant difference with age group 35-50 years.

10.2 Education

Education showed significant variance only for the motivational factor Diversion / Browsing in Test of Between Subject Effects. Within the four levels of education for Diversion / Browsing there was a significant difference (p= 0.004) in the behaviour of graduates and post graduates. Thus while class 12

th or below category and

professional categories did not display any significant difference these two categories showed difference and it may be viewed that people with education level of graduation and below behave differently from people with education at post graduate level or above. This is important for retailers.

Table 8 Results of Pairwise Comparisons for Education across Demographic Variables And Shopping Motivations At 95% Confidence Interval

Depen

dent

Variabl

e

(I)

EDUC

ATION

(J)

EDUCATI

ON

Mea

n

Differ

ence

Std.

Error Sig

95%

Confidence

Interval for

Difference

(I-J) Lowe

r

Boun

d

Uppe

r

Boun

d

Diversi

on /

Browsi

ng

GRAD

UATIO

N

POSTGRA

DUATION -.359 .123

.00

4 -.601 -.116

10.3 Monthly Household Income

Pair wise comparison for five income levels revealed significant differences only for factor Economic Reasons between Monthly Household Income of less than INR 10000 and 25000- 49999 (p= 0.035) and 50000-Rs. 100000 (p = 0.012). Rest there also there is no significant difference.

Table 9 Results of Pairwise Comparisons for Monthly Household Income across Demographic Variables and Shopping Motivations At 95% Confidence Interval

Depe

ndent

Varia

ble

(I)

MONTH

LYHOUS

E-

HOLDIN

COME

(J)

MONT

HLYHO

USEHO

LDINCO

ME

Me

an

Dif

fer

enc

e

(I-

J)

Std

.

Err

or Sig

95%

Confidence

Interval for

Differencea

Lowe

r

Boun

d

Upper

Bound

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Econo

mic

Reaso

ns

LESSTHA

N10000

25000-

49999

.76

7

.36

2

.03

5 .056 1.479

50000-

100000

.97

1

.38

7

.01

2 .212 1.731

MORET

HAN10

0000

.67

2

.37

5

.07

4 -.064 1.409

Gender displayed no significant variance at all for any of the motivational factors.

The results of MANOVA reveal that individually only Age causes significant variance in Shopping Motivations. However, even Age is significant only for three shopping motivations, whereas Education is significant for one shopping motivation. The other variables of Gender and Monthly Household income do not create any significant variance at all on the shopping motivations individually but in various interactions and combinations that are different for utilitarian and hedonic motivations. Further pair wise comparisons reveal that different layers within each demographic variable behave differently with respect to each other in context of various shopping motivations. The summary and implications of these results are presented in the following section on Conclusion.

11. CONCLUSION

These points emerge from the study;

a) The primary reason for which people visit malls in India are Diversion/Browsing, Economic Reasons, Shopping Environment, Refreshments and Snacking, Services and Social Experience.

This is a reflection of a shift in the trend where people not only visit for utilitarian functions but also go for pleasure.

b) Of the select demographic factors that form the most fundamental bases for market segmentation strategy only Age shows a significant effect on shopping motivations in malls in India, individually.

However interactions in various combinations as highlighted in the Tests of Between Subject effects are relevant for different shopping motivations.

This is a critical finding for store operators and mall planners as store operators will have to match their product assortments and its nature to the kind of shopping motivation and choose its placement in a mall, whereas the mall planners will have to keep a balanced tenancy and select store operators accordingly.

c) Pair wise comparisons reveal how different levels of individual select demographic factors behave with respect to each shopping motivational factor. This helps in choosing a focus for the target market.

The above findings raise concerns that may have very deep implications for retail strategy

a) Demographics are typically the first step for identification of market segments. If they are clear enough they may form market segments that are measurable, actionable, sustainable and accessible but with all these characteristics also their nuances need to be understood.

b) Focus and Differentiation are still not evident in the Indian Malls. Since Organized Retail is just picking up, Indian industry may just be witnessing the growth while experimenting with business models, whereas now it must act to achieve focus and differentiate since different

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layers within the same parameter behave differently in malls.

c) Since Age individually shows influence over Hedonic Shopping Motivations like Diversion / Browsing and Social Experience, Mall planners may work on the probabilities to mall becoming social destinations. The marketing strategies will have to be craft accordingly, so that purchases are induced.

12. IMPLICATIONS FOR FUTURE RESEARCH:

This research study establishes a need for working on marketing segmentation variables for Indian retail industry.

The role of demographics; individual variables and combinations of variables for crafting more customer centric strategies may be explored.

Research needs to be done on exploring the possibilities of Indian malls being the new age social destination.

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