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Customers' Perception towards the Fast MovingConsumer Goods in Rural Market: An Analysis

DATASET · SEPTEMBER 2013

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2 AUTHORS, INCLUDING:

Dr. Surinder Singh Kundu

Chaudhary Ranbir Singh University (JIND)

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International Journal of Techno-Management Research, Vol. 01, Issue 02, September 2013 ISSN: 2321-3744

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Customers’ Perception towards the Fast Moving Consumer Goods in Rural Market:

An Analysis

Dr. Surinder Singh Kundu

Assistant Prof. & Incharge, Department Of Commerce, Chaudhary Devi Lal University, Sirsa

Abstract

A consumer sets a frame of references in his/her mind to choose or purchase a product or service of

same or different brands or producers. Keeping in view the frame of references the present paper is an

attempt to study the factors affecting the purchase decision of consumers towards purchase of the Fast

Moving Consumer Goods (FMCGs) and to recommend the policies which may be adopted by the

advertisers to enhance awareness among the rural buyers. The study used primary data collected from a

sample of 1000 rural consumers from the 40 villages of 04 districts of Haryana state with the help of a

well-structured questionnaire by following „Foot-in-Door Strategy‟ (FIDS). And found that rural buyers

perceived that TV commercials followed by print advertisements and word of mouth plays a significant

role for taking the decision to purchase these FMCGs. Further, they consider their own experience,

display at shops; incentive schemes for the purchase of these FMCGs, whereas they do not fully agree

that advice of beautician influences their decision towards the purchase of these FMCGs. On the other

hand, rural buyers perceived that social factors are no so strong that those may influence their decision

to purchase these FMCGs. Hence, it may be recommended that the producers or marketers should frame

ethical advertising strategies keeping in mind that rural people are fond of electronic and print mediaadvertisements.

Key Words: Frame of References, Foot-in-Door Strategy, FMCGs.

Introduction

As we know, consumers have different frame of references formed out of the information gathered from

their experience. They try to fit the goods and services in these frames of references. If they do not fit

they reject the things. Many of these reference points are subconscious because they are deeply imbedded

in the subconscious mind. It is important to have knowledge of assumptions and beliefs held by

consumers. Some consumers may use price as an index of quality. They may declare a product or service

as cheap if it sells at a price substantially below the level at which competitive brands are selling.

Consumers make a number of assumptions regarding products, services and producers often without fact,

e.g. the beer in pastel colour bottles is thought to be lighter and beer in the coloured bottles is considered

stronger. Similarly, natural fabrics e.g. wool are considered better that synthetic; and the products


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produced in one country are considered superior than produced in other country. Attributes and beliefs

are closely related to attitude but harder to change than attitudes. Many times, they are obtained from

previous generations and are implanted in an early age of person. People above certain group affiliations

and their assumptions and beliefs are drawn from affiliations as in the case of a social class (Sumathi, et

al., 2003).

REVIEW OF LITERATURE

Sukato and Elsey (2009) examined the phenomena of male consumer behaviour in buying skin care

products in Thailand. In order to cope with the research purpose, Fishbein and Ajzen‟s theory of

reasoned action model is employed as a theoretical framework and modified by adding the self-image

construct. The results of the study confirm that beliefs, self-image, normative influences, and attitudes

have impacts on purchase intention and purchase behaviour in buying skin care products among

Bangkok male consumers and therefore, the modified theory of reasoned action is appropriate to explain

male consumer behaviour in the purchase of specific cosmetic product in the Thai setting.

Vani, et al. (2010) examined consumer behaviour in Bangalore city regarding toothpaste bands and

found that consumer awareness of toothpaste is less in Bangalore city. Oral care market offers huge

potential as penetration and per capita consumption of oral care product is very low in India. Further, the

study found that brand image, advertising and offer play an important role in purchasing toothpaste,

sometimes based on the offer the consumer compare with competitor product and select the best one.

Product attribute also analyzed by the consumer for deciding a brand. Switching of one product to other

company product is mainly based on advertisement, brand name, packaging availability and price rise.

Sridhar and Mishra (2010) analyzed that the rationale and method for studying product adaptation in

rural markets and concluded that the findings of the study are contrary to the general understanding that

rural is perceived very differently and hence operationalized differently by different organization.

However, the results show that contingency theory holds true in case of product adaptation in rural

markets also. With the increase in executives‟ representation of rurality, product adaptation degree also

increased.Gautam and Gangal (2011) analyzed the factors responsible of the boom in rural marketing, consumers‟

preference for FMCG products based on 4 „A‟s (i.e. Awareness, Affordability, Adoptability and

Availability) by employing convenient sampling method for administering the questionnaires using

Liker Scale to total 200 respondents of HUL & ITC in rural areas of Agra district from January 2011 to

June 2011. The study found that skincare and fragrance have been found as the prime reasons for using

https://www.researchgate.net/publication/235277105_Executives_social_representation_of_rurality_and_product_adaptation_A_case_of_rural_markets_in_India?el=1_x_8&enrichId=rgreq-1ba1590a-658a-4de0-a9c6-159a5c110f4f&enrichSource=Y292ZXJQYWdlOzI1NjgzOTc2OTtBUzoxMDIwMzMxNjgyNzM0MTVAMTQwMTMzODAxMTg3Mw==https://www.researchgate.net/publication/235277105_Executives_social_representation_of_rurality_and_product_adaptation_A_case_of_rural_markets_in_India?el=1_x_8&enrichId=rgreq-1ba1590a-658a-4de0-a9c6-159a5c110f4f&enrichSource=Y292ZXJQYWdlOzI1NjgzOTc2OTtBUzoxMDIwMzMxNjgyNzM0MTVAMTQwMTMzODAxMTg3Mw==


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bathing soaps (personal wash) and consumers buy detergent due to its primary function for cleanliness

and few purchase it for its fragrance. The cleanliness followed by freshness has been the primary

motives to purchase toothpaste (oral care) and some consumers also purchase it for protection of gums

and whiteness value. The consumers purchase hair oil for hair care and good looks. The study also found

that the factors influencing the purchase decision of the respondents, consumers buying are influence the

most by the product factor due to design, quality, durability, made from safe environment and product

range but few respondents are not satisfied with the packaging, image and size of the product. Both the

companies are almost on the same platform regarding the factors of competitive price, shape, design,

Haats and mandis and message/languages/ presentation of advertisement. The consumers are showing

their dissatisfaction for malls and super markets, greater mobility, shop is conveniently situated, and

product display is attractive, value for price paid, cash discount and pricing policy. Lastly, the study

concluded that in parameters like, image, shape and size, packaging, durability, small size products, low

priced sample packets, price scheme, celebrity endorsement and use of transport like autos, camel carts,

HUL has an edge over ITC.

Vernekar and Wadhwa (2011) examined the consumer attitudes and perceptions towards eco-friendly

products in FMCG sector and their willingness to pay on green products. The study revealed that the

green products have substantial awareness among urban Indian customers and they are willing to pay

something more on green products. The majority of customers considered that package is most

important element of such products.

Chandrasekhar (2012) analyzed the consumer buying behaviour and brand loyalty in rural markets

regarding fast moving consumer goods and found that brand loyalty is more in Badangpet and Nadergul

region and less in Chintulla in soaps category. In hair oil category, branded products usage is more in

Badangpet and Nadergul villages and consumer prefer to purchase local brands in Chintulla village. It is

also found that Vatika and Navratan hair oils dominate in Badangpet, Parachute hair oil in Nadergul and

Gograda local brand and Dabur in Chintulla. In case of Biscuits category, consumers mostly buy in

loose, which are available in nearby shops like Salt biscuits, Osmania biscuits etc. Parle-G and Tiger are

mostly used brands in Badangpet. Tea is purchased in loose, which is available in local shops. The

popular brands Red Label, Three Roses and Gemini are used in Badangpet village. Further, the study

found that coffee consumption is very less or no consumption in Nadergul and Chintulla villages. In

case of washing powder, Nirma dominate all the three selected sample rural markets regions. In remote

area like Chintulla, Nirma sell Rs. 1 sachets. In washing soap category, Rin, 501, Nirma, XXX and Extra


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Local Brand dominates all the three selected rural markets. It is also concluded that Ponds, Chintol and

Santoor face powder dominated the market and Pond‟s has dominated the market in consumption in

Badangpet. In sum, the study also found that male members of the family are alone going to buy

consumer products and women are not interested in shopping and do not come out from their houses

frequently.

Jain and Sharma (2012) analyzed the brand awareness and customer preferences for FMCG products

in rural market of Garhwal region. The study found that average awareness of the respondents in the

rural market is approximately 75 per cent, 70 per cent, 72 per cent, 64 per cent and 73 per cent in case of

shampoo, washing powder, soap, tea, toothpaste respectively, which infers that people in the rural

market have on an average awareness about most of the products. In the shampoo category, the study

found that the respondents give 1st rank to Pantene and last rank to Chik; in case of washing powder, 1

st

rank to Surf Excel and last rank to Nirma; to soap category, 1 st rank to Dettol and last rank to Rexona; in

case of Tea, 1st rank to Tata tea and last rank to Maharani tea and in category of toothpaste, 1

st rank to

Colgate and last rank to Cibaca which infers that advertising and marketing activities have major

influences in choices of people in rural market. The study further found that among various factors like

quality, price, easy availability, family liking, advertisement, variety, credit attributes of brand

preference; the quality is the first preference in case of brand choices and rural people give least

preference to variety and credit attributes. It is also concluded that there is a positive impact of media on

brand preference of FMCG products among consumers.

Jayswal and Shah (2012) analyzed the effect of some selected FMCG product‟s television

advertisements with commonly used negative emotional appeals on cognitive message processing style

of Indian house wives. The study revealing different effect of different advertisement with negative

emotional appeals derived that advertising creative aspect has considered most important and it has been

truly said that “what you say is equally important to how you say.” Through varied hypothesis

developed by the researcher, the findings is negative emotional advertising appeal makes the cognitive

response positive, helps to form positive attitude and this increases the customer intention to buy the

brand. The different advertising themes have difference in their effect on individual perception.

Prajapati and Thakor (2012) examined the competitive and innovative promotional tools used by

toothpaste companies in rural market and its impact on consumer buying behaviour in Gujarat. The

study found that rural consumers are more concerned about the quality, brand name of the oral care

products purchased by them. Further, it was also found that once the rural consumers found that certain


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brands are suitable to them, they do not change it easily due to influence of friends or social groups and

lack of availability of their usual brands. In toothpaste category, Colgate and Close-up are the most

favorite brands. Price, promotional schemes, color and availability of the product are more influencing

factor when they buy the toothpaste. Rural consumers are generally following the instructions of the

retailers for buying the toothpaste and also consider the promotional scheme when buy the toothpaste

and the prices off schemes are the most influencing scheme to them. When there are special discount

and dentist suggest them to purchase the toothpaste they definitely purchase it.

Ranu and Rishu (2012) analyzed the scope of Ingredient branding in creating sustainable differentiation

advantage for FMCG companies. The results of the study revealed that careful planning must be done

before entering into a relationship in order to maximize the benefits of any ingredient branding strategy.

Along with the costs involved in forming and maintaining the alliance, and the opportunity cost involved

for the partnering firm, the consumer‟s quality sensitivity and their ability to evaluate quality must also

be considered. Firms considering an ingredient branding strategy must also evaluate the customer‟s

perception toward each brand prior to the alliance. The perceived fit of the products as well as the

brands must be understood, and the level of customer familiarity with each brand must be gauged. This

will help marketers in developing a successful Ingredient branding strategy, which builds on the

strengths of the partnering brands and generates additional value for the consumer.

Mishra, et al. (2012) examined the major dimensions of consumers‟ perception about the benefits they

derive from different types of sales promotion schemes in durable goods and to build a framework

showing the valid relationships among all types of multiple consumer benefits of sales promotion in

consumer durables. This exploratory study is mainly based on field survey carried out in India. The

findings indicate that consumers perceive factors like savings, higher product quality, shopping

convenience categorized as utilitarian benefits and value expression, entertainment, exploration

categorized as hedonic benefits as primary reasons for taking advantage of various sales promotion

schemes.

Vaishnani (2012) examined and measured brand equity perception with reference to sales promotion

schemes for selected FMCG products and it is concluded that there is no significant difference between

of brand equity perception among gender as one of the demographic variables. Apart from it, it is

concluded that there is significant difference between brand equity perceptions among various

employment status. Adding to it, it is clear that self employed consumers compare to not employed


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consumers perceive sales promotion schemes less favorably. Furthermore, it is concluded that there is no

significant difference between brand equity perception and educational qualification.

Franco, et al. (2012) analyzed and focused on use of modern portfolio theory in price promotions,

which have become a key component in the marketing mix of stimulating sales, particularly in the

FMCG environment. The hypothesis of this study is that previous limitation of modern portfolio theory

in marketing can be overcome through use of brackets of price promotion. This is proven through study

of FMCG data and it is shown that price promotions can be optimized to improve return without

increased risk.

After, reviewing the existing literature it is observed that the above studies have considered different

factors a lot. However, these studies not focussed upon the purchase decision behaviour of rural areas.

Consequently, the present study entitled “Customer’ Perception towards the Fast Moving Consumer

Goods in Rural Market: An Analysis” may be conducted.

Methodology and Objectives

The present study is of exploratory, descriptive, pure and empirical in nature. The present research paper

attempts to identify the factors affecting the purchase decisions of customers towards the purchase of

FMCGs and to recommend the particular factors that should be considered most important for such type

of decisions. To achieve the said objectives, only twelfth question item of the questionnaire (Total 16

question items) was used. The study used primary data collected with the help of a well-structured

questionnaire by following „Foot-in-Door Strategy‟ (FIDS) (Malhotra, et al., 2010). Further, to analyze

and interpret the data frequency distribution, mean, mode, percentage for exploratory data analysis and

standard deviation (S. D.), correlation, F -test (ANOVA) and factor analysis were used for confirmatory

data analysis. In the light of the above mentioned objective the following hypothesis was that the rural

buyers do not significantly differ demographically towards factors influencing their purchasing decision

for fast moving consumer goods (H1).

Sampling Plan

The steps in the sampling design were as follows:

Target Population: Elements: Male and Female Rural Buyers; Sampling Unit: Rural Buyer; Extent:

Haryana State; and Time: May, 2011 to September, 2011.

https://www.researchgate.net/publication/240212799_Marketing_Research_An_Applied_Orientation?el=1_x_8&enrichId=rgreq-1ba1590a-658a-4de0-a9c6-159a5c110f4f&enrichSource=Y292ZXJQYWdlOzI1NjgzOTc2OTtBUzoxMDIwMzMxNjgyNzM0MTVAMTQwMTMzODAxMTg3Mw==https://www.researchgate.net/publication/240212799_Marketing_Research_An_Applied_Orientation?el=1_x_8&enrichId=rgreq-1ba1590a-658a-4de0-a9c6-159a5c110f4f&enrichSource=Y292ZXJQYWdlOzI1NjgzOTc2OTtBUzoxMDIwMzMxNjgyNzM0MTVAMTQwMTMzODAxMTg3Mw==


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Sampling Frame: Map of Haryana State, Sampling Technique: Multi-Stage Sampling and Sample Size:

1,000 (04 districts x10 villages per district x 25 respondents per village). To confirm the sample size of

1,000 was adequate, calculations for sample size(n) determination by proportion were made as follows,

using the maximum possible population variation (π=0.5). The precision of D in the present study was

±0.05 for a 95 percent confidence level (z=1.96). n=π(1- π)z2/D

2 , n=(0.5)(1-0.5)(1.96)

2/(0.05)

2=384.16

or 385 rounded to the next higher integer.

Therefore, the 1,000 sample size may be considered more than sufficient (Malhotra, et al., 2010).

In the present study, the sampling quota called for sampling rural buyers with 50:50 ratio to eliminate

distortion and due to a non-representative gender ratio (Table 1). Hence, gender was the control

dimension. In total, multi-stage sampling was used in the present study.

Data Preparation

After the data collection, the data was prepared for the further analysis (Kundu, et al., 2013). A data file

namely "UGC_F. No. 5-270(1)2009 (HRP)_MRP_Datawarehouse" was prepared which was viewed as

an n×m matrix of number and values, where n is the number of respondents or records and m is the

number of variables or fields. In the present research paper, the twelfth question item‟s data from the

said data warehouse was used for analysis and interpretation.

Results and Discussion

Table 2 gives a general understanding towards 36 factors affecting the purchase decision taken by rural

buyers towards FMCGs under study. The response on these factors were collected from rural buyers on

5-point Likert scale from 5 for strongly agree, 4 for agree, 3 for neither agree nor disagree, 2 for disagree

to 1 for strongly disagree continuum. The mean values of most of the variables are more than 3, which

gives the inference that rural buyers admit that the above said factors certainly affect their decision

regarding the purchase of all fast moving consumer goods.

To test the appropriateness of factor analysis technique the correlation between the variables is checked

and Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy statistic is also used for the same. The

correlation matrix is a lower triangle matrix showing the simple correlation, r , between all possible pairs

of variables included in the analysis. Being an identity matrix of population correlation matrix, all the

diagonal terms are 1, and all off-diagonal terms are 0. The test statistics for Sphercity is based on a Chi-

square transformation of the determinants of the correlation matrix. A large value of the test statistic

https://www.researchgate.net/publication/240212799_Marketing_Research_An_Applied_Orientation?el=1_x_8&enrichId=rgreq-1ba1590a-658a-4de0-a9c6-159a5c110f4f&enrichSource=Y292ZXJQYWdlOzI1NjgzOTc2OTtBUzoxMDIwMzMxNjgyNzM0MTVAMTQwMTMzODAxMTg3Mw==https://www.researchgate.net/publication/240212799_Marketing_Research_An_Applied_Orientation?el=1_x_8&enrichId=rgreq-1ba1590a-658a-4de0-a9c6-159a5c110f4f&enrichSource=Y292ZXJQYWdlOzI1NjgzOTc2OTtBUzoxMDIwMzMxNjgyNzM0MTVAMTQwMTMzODAxMTg3Mw==


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favours the rejection of the null hypothesis. Further, KMO compares the magnitude of the observed

correlation coefficients to the magnitude of partial correlation coefficients. Small the value of KMO

statistic indicate that the correlation between pairs of variables cannot be explained by other variables

and the factor analysis may not be appropriate. Generally, a value greater than 0.5 is desirable for the

test statistic. Here, it can be seen from Table 3 that the null hypothesis, that the population correlation

matrix is an identity matrix, is rejected by Bartlett's Test of Sphericity. The approximate Chi-square

statistic value is 22612.664 with 630 degree of freedom, which is significant at 0.05 level. The value of

KMO statistic (.774) is also large (>0.5). Thus, factor analysis may be considered as appropriate

techniques for analyzing the correlation matrix. The matrix constructed from the data obtained in form

of the responses of rural buyers‟ overall opinion about the factors influence their decision to purchase

various FMCGs.

Once, it has been determined that factor analysis is suitable for analyzing the data, an appropriate

method must be selected. The approach used to drive the weight or factors score coefficients. The two

basic approaches are principal component analysis (PCA) and Common factor analysis (CFA). In PCA,

the total variance in data is considered. The diagonal of the correlation matrix consists of unities and full

variance is brought into the factor analysis. PCA is recommended when the primary concern is to

determine the minimum number of factors that will account for maximum variance in the data for use in

subsequent multivariate analysis. Further, PCA may be carried out if the correlation for the variables

contains at least two correlations of 0.30 or greater. The correlation matrix of 36 variables which were

developed to know the overall opinion of rural buyers towards factors influence their decision to

purchase FMCGs under study and it is found there are high correlations between the variables; therefore,

it may be stated that factor analysis is appropriate. Further, it is found that 17 variables have the

correlations above 0.30. Therefore, PCA method is used for extraction of variable for the component or

factor concerned.

The extraction communalities for each variable which is the amount of variance a variable shares with

all the other variables being considered. It is also the proportion of variance explained by the common

factors.

In the Table 4, five factors have been extracted on the basis of prior knowledge to describe the

relationships among variable in a best way. Further, the scree plot associated with this analysis is given

in Exhibit 1. From the scree plot, a distinct break occurs at five factors. Finally, from the cumulative

percentage of variance accounted for, it can be seen that five account for 50.835 per cent of the variance,


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contributed by first component is 17.480 followed by second (9.914 per cent), third (8.257 per cent),

fourth (7.740 per cent) and fifth (7.444 per cent) of total variance.

The rotation was made by the most commonly used method i. e. varimax procedure. This is an

orthogonal method of rotation that minimizes the number of variables with high loadings on a factor,

thereby enhancing the interpretability of the factors. Interpretation is facilitated by identifying the

variables that have large loadings on the same factor. That factor can be interpreted in terms of the

variables that load high on it. For the purpose of interpretation, each factor was composed of variables

that loaded 0.30 or higher on that factor. In case, where variables loaded 0.30 or above on two factors,

each variable was assigned to the factor where it had the highest loading. The maximum of each row

(ignoring the sign) indicates the respective variable belongs to the respective component (Table 5).

After interpretation of the factors, Table 6 enlists the rating of factors on the basis of their importance

and also depicts the results through ANOVA. It depicts that factor 3 is at the top by which rural buyers

perceived that TV commercials ( =4.20) followed by print advertisements ( =4.18) and word of mouth

( =.95) plays a significant role for taking the decision to purchase these FMCGs. Further, they consider

their own experience, display at shop, incentive schemes for the purchase of these FMCGs. Whereas,

they do not fully agree that advice of beautician influence their decision towards the purchase of these

FMCGs ( ==3.07). On the other hand, rural buyers perceived that social factors are no so strong that

those may influence their decision to purchase these FMCGs (factor 5, =3.084).

As far as F-statistics (ANOVA) is concerned, Table 6 shows that rural buyers significantly differ

education, gender, income and occupation-wise towards cultural and psychological factors; age,

education marital, income, occupation and district-wise towards social factors and marital status wise

they differ towards demographic factors which may influence their purchasing decision of fast moving

consumer goods at 0.01 significance level with respective degrees of freedom of demographic

characteristics of the rural buyers by rejecting null hypothesis.

Concluding Remarks

In total, it is found that rural buyers perceived that TV commercials followed by print advertisements

and word of mouth plays a significant role for taking the decision to purchase these FMCGs. Further,

they consider their own experience, display at shops; incentive schemes for the purchase of these

FMCGs, whereas they do not fully agree that advice of beautician influences their decision towards the

purchase of these FMCGs. On the other hand, rural buyers perceived that social factors are no so strong


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that those may influence their decision to purchase these FMCGs. Therefore, it may be recommended

that the producers or marketers should frame ethical advertising strategies keeping in mind that rural

people are fond of electronic and print media advertisements.

References1. Chandrasekhar, B. V. N. G. (2012), “Consumer buying behaviour and brand loyalty in rural

markets: FMCG”, IOSR Journal of Business and Management, Vol. 3, Issue 2, pp. 50-67.2. Franco-Laverde, J., Littlewood, A., Ellis, C., Schraner, I., and Varua, M-E, (2012), “FMCG

portfolio budget allocation to price promotions using modern portfolio theory”, International

Review of Business Research Papers, Vol. 8, No. 5, pp. 16-30.3. Gautam, N., and Gangal, V. K. (2011), “Consumers‟ preference for FMCG products in rural

India: a comparative study of HUL & ITC”, APOTHEOSIS: Tirpude‟s National Journal of

Business Research, Vol. 2, Issue 1, pp. 115-123.

4. Jain, A., and Sharma, M. (2012), “Brand awareness and customer preferences for FMCG products in rural market: an empirical study on the rural market of Garhwal region”, VSRD

International Journal of Business & Management Research, Vol. 2, No. 8, pp. 434-443.5. Jayswal, M., and Shah, K. (2012), “A study of effect of negative emotional appeals on cognitive

message processing style of Indian house wives with specific focus on FMCG product‟stelevision advertisements”, Asian Journal of Research in Business Economics and Management,

Vol. 2, Issue 3, pp. 58-72.

6. Kundu, Surinder Singh and Kumar, Vinod (2013). Modes of Advertisements Used by RuralPopulace: An Empirical Study, In Kundu, Subash C., Punia, Bijender K., Shabnam Saxena,

Suresh K. Mittal and Anjali Gupta (Eds.), Researches in Business and Management – Academic

and Professional Perspective, Delhi: Wisdom Publications, p. 404.

7. Malhotra, Naresh K. and Dash, Satyabhusan. Marketing Research-An Applied Orientation. 6thed. (New Delhi: Pearson Education, Inc. 2010), pp. 591-601.

8.

Mishra, U. S., Das, J. R., Mishra, B. B. and Mishra, P. (2012), “Perceived benefit analysis ofsales promotion: a case of consumer durables”, International Research Journal of Finance andEconomics, Issue 98, pp. 145-154.

9. Prajapati, S. and Thakor, M. (2012), “Competitive and innovative promotional tools used by

toothpaste com panies for rural market and its impact on consumer buying behavior in Gujarat”,International Referred Research Journal, Vol. 3, Issue 3(2), pp. 82-86.

10. Ranu, G., and Rishu, R. (2012), “Ingredient branding: a differentiation strategy for FMCG

companies”, Asian Journal of Management Research, Vol. 2, Issue 2, pp. 761-768.

11. Sridhar, G., and Mishra, D. (2010), “Executives social representation of rurality and productadaptation: a case of rural markets in India”, APJML, Vol. 23, No. 3, pp. 285-303.

12. Srinivastava, T N and Rego, Sahilaja. Business Research Methodology. 1st ed., New Delhi: Tata

McGraw Hill Education Private Limited. 2011, pp. 14.66-14.86.13. Sukato, N., and Elsey, B. (2009), “A model of male consumer behaviour in buying skin care

products in Thailand”, ABAC Journal, Vol. 29, No. 1, pp. 39-52.

14. Sumathi, S. and Saravanavel, P. (2003). Marketing Research & Consumer Behaviour. New

Delhi: Vikas Publishing House Pvt. Ltd., p. 322.15. Vaishnani, H. B. (2012), “An empirical study of measuring brand equi ty perception with

reference to sales promotion schemes for selected FMCG products”, Golden Research Thoughts,

Vol. 1, Issue 7, pp. 1-4.


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16. Vani, G., Babu, M. G., and Panchanathan, N. (2010), “Toothpaste brands: a study of consumer

behaviour in Bangalore city”, Journal of Economics and Behavioral Studies, Vol. 1, No. 1, pp.

27-39.17. Vernekar, S. S., and Wadhwa, P. (2011), “An empirical study of consumers attitudes and

perception regarding eco-friendly FMCG products, with special reference to Delhi and NCR

region”, Opinion, Vol. 1, No. 1, pp. 64-74.


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Exhibit 1: Scree Plot.

Source: Primary (Data processed through PASW 18.0).

Table 1: Demographic Profile of Rural Buyers

Demographic

Basis

Gender Total

Male Female

Age (Years) 10-20 117 132 249

20-30 126 125 251

30-40 98 98 196

40-50 99 93 192

50 and Above 60 52 112

Total 500 500 1000

E d u c a t i o n

Illiterate 16 54 70

Below Matric 68 146 214

Matric 184 161 345

10+2 107 65 172

Graduate 75 49 124

Post-Graduate 28 15 43Any Other 22 10 32

Total 500 500 1000

Marital Status Bachelor 199 182 381

Married 301 318 619

Total 500 500 1000

Income (Rs.) Less Than 5,000 22 10 32

5,000-10,000 35 37 72

10,000-15,000 90 83 173

15,000-20,000 160 189 349

20,000-25,000 110 120 230

More Than 25,000 83 61 144

Total 500 500 1000

O c c u p a t i o n

Student 174 175 349

Housewife 3 277 280

Businessman 54 2 56Employee (Private Sector) 67 15 82

Employee (Public Sector) 100 26 126

Farmer/Labourer 82 1 83

Any Other 20 4 24

Total 500 500 1000



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Table 2: Descriptive Statistics for Factor influencing the purchase decision of rural buyers towards FMCGs

Sr. No. Factor Mean Std.

Deviation

N

1 Print advertisements 4.18 .927 1000

2 T.V. commercials 4.20 .872 1000

3 Word of mouth 3.95 .966 1000

4 Salespeople 3.72 1.097 1000

5 Incentive schemes 3.46 1.183 1000

6 Own experience 3.36 1.181 1000

7 Advice of beautician 3.07 1.135 1000

8 Display at shop 3.27 1.138 1000

9 Occasion 3.11 1.183 1000

10 Reference Group 2.98 1.207 1000

11 Trial 3.01 1.224 1000

12 Availability of the product 3.08 1.194 1000

13 Non-availability of the expected brand 3.00 1.222 1000

14 Income 3.19 1.186 1000

15 Family 3.28 1.213 1000

16 Locality 3.33 1.198 1000

17 Age 3.35 1.232 1000

18 Social class/status 3.29 1.185 1000

19 Culture 3.25 1.209 1000

20 Climate/Environment 3.19 1.196 100021 Advice of Doctor 3.14 1.215 1000

22 Price of the Product 3.21 1.181 1000

23 Packaging of the product 3.20 1.196 1000

24 Labelling of the product 3.20 1.219 1000

25 Quality of the product 3.25 1.224 1000

26 Taste Change 3.16 1.202 1000

27 Defective Products 3.11 1.265 1000

28 Out dated 3.09 1.267 1000

29 New Ingredient 3.11 1.221 1000

30 Brand Loyalty 3.18 1.182 1000

31 Life of the product 3.20 1.211 1000

32 To follow the Celebrity 3.16 1.217 1000

33 Personality and Lifestyle 3.28 1.198 1000

34 Attitude and behaviour 3.29 1.187 1000

35 Need and Motives 3.24 1.203 100036 Quantity of the product 3.25 1.224 1000


Table 3: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .774

Bartlett's Test of Sphericity Approx. Chi-Square 22612.664

Df 630

Sig. .000



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Table 4: Total Variance Explained

ComponentInitial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %

1 6.293 17.480 17.480 6.293 17.480 17.480 4.453 12.368 12.368

2 3.569 9.914 27.395 3.569 9.914 27.395 4.094 11.371 23.739

3 2.972 8.257 35.651 2.972 8.257 35.651 3.652 10.145 33.885

4 2.786 7.740 43.391 2.786 7.740 43.391 3.120 8.666 42.550

5 2.680 7.444 50.835 2.680 7.444 50.835 2.982 8.284 50.835Extraction Method: Principal Component Analysis.


Table 5: Factors influencing the Purchase Decision of Rural Buyers towards FMCGS

Factor Factor interpretation

(% of variance explained)

Loading Variables included in the factor

F1 Cultural and Psychological

Factors (17.480)

.734 34 Attitude and behaviour

.710 33 Personality and Lifestyle

.688 35 Need and Motives

.658 36 Quantity of the product

.610 32 To follow the Celebrity

.488 22 Price of the Product

.488 21 Advice of Doctor

.478 20 Climate/Environment

.447 31 Life of the product

.440 19 Culture

F2 Social Factors (9.914) .760 28 Out dated

.716 27 Defective Products

.686 11 Trial

.659 29 New Ingredient

.607 12 Availability of the product

.596 10 Reference Group

.523 13 Non-availability of the expected brand

.513 26 Taste Change

.474 30 Brand Loyalty

.417 9 Occasion

F3 Psychographic and Promotional

Factors (8.257)

.757 4 Salespeople

.737 5 Incentive schemes

.707 3 Word of mouth

.657 6 Own experience

.533 2 T.V. commercials

.487 7 Advice of beautician

.411 8 Display at shop

.311 1 Print advertisements

F4 Demographic Factors (7.740) .793 16 Locality

.785 15 Family

.672 17 Age

.647 14 Income

.404 18 Social class/status

F5 Self-concepts (7.444) .776 24 Labelling of the product

.698 25 Quality of the product

.658 23 Packaging of the product



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Table 6: Factors influencing the Overall Opinion of Rural Buyers towards the Advertisement of Tooth Brush

Factor Mean

of

Variable

Inferential Statistics

Age

(df=4, 995)

Education

(df=6, 993)

Gender

(df=1, 998)

Marital Status

(df=1, 998)

Income

(df=5, 994)

Occupation

(df=6, 993)

Districts

(df=3, 996)

F Sig. F Sig. F Sig. F Sig. F Sig. F Sig. F Sig.

Cultural and Psychological

Factors

3.221 (3) 2.147 .073 4.504 .000* .052 .820 .935 .334 4.663 .000* 4.663 .000* 2.266 .079

34

Attitude and

behaviour

3.29 1.829 .121 4.018 .001* .006 .936 1.544 .214 1.367 .234 .867 .518 1.575 .194

33

Personality and

Lifestyle

3.28 1.828 .121 3.629 .001* .178 .673 1.749 .186 1.938 .086 1.616 .139 3.016 .029

35 Need and Motives 3.24 1.012 .400 2.880 .009* .249 .618 2.011 .156 4.046 .001* 1.153 .330 3.065 .027

36

Quantity of the

product

3.25 1.731 .141 3.509 .002* .683 .409 2.099 .148 3.348 .005* 2.021 .060 3.710 .011

32

To follow the

Celebrity

3.16 1.643 .161 3.868 .001* .649 .421 .340 .560 3.013 .010 2.510 .020 6.136 .000*

22 Price of the Product 3.21 1.869 .114 2.943 .007* .413 .521 .019 .890 2.897 .013 .805 .566 1.810 .144

21 Advice of Doctor 3.14 1.453 .215 2.743 .012 .244 .621 .247 .619 4.550 .000* .691 .657 12.414 .000*

20 Climate/Environment 3.19 2.832 .024 2.747 .012 .338 .561 .130 .719 3.578 .003* 1.331 .240 2.529 .056

31 Life of the product 3.2 1.075 .368 2.139 .047 .011 .917 1.962 .162 4.886 .000* 1.237 .284 3.609 .013

19 Culture 3.25 1.488 .204 3.024 .006* 1.449 .229 .764 .382 3.957 .001* 2.289 .034 3.547 .014

Social Factors (F2) 3.084 (5) 5.166 .000* 8.668 .000* .028 .867 9.855 .002* 7.887 .000* 7.887 .000* 33.061 .000*

28 Out dated 3.09 1.488 .204 5.230 .000* .010 .921 4.665 .031 3.095 .009* 2.069 .054 19.692 .000*

27 Defective Products 3.11 2.597 .035 5.760 .000* .141 .708 5.634 .018 2.509 .029 2.576 .018 20.896 .000*

11 Trial 3.01 3.367 .010 4.488 .000* 1.805 .179 6.331 .012 5.865 .000* 4.669 .000* 18.468 .000*

29 New Ingredient 3.12 3.455 .008* 3.631 .001* .017 .897 4.159 .042 2.539 .027 1.007 .419 8.374 .000*

12

Availability of the

product

3.08 3.298 .011 3.617 .001* .006 .937 4.693 .031 4.822 .000* 3.280 .003* 6.870 .000*

10 Reference Group 2.98 4.333 .002* 4.899 .000* .001 .979 6.966 .008* 4.557 .000* 5.047 .000* 12.293 .000*

13

Non-availability of

the expected brand

3.00 1.971 .097 3.545 .002* 1.610 .205 1.908 .167 3.730 .002* 1.541 .162 13.794 .000*

26 Taste Change 3.16 1.621 .167 3.614 .001* 1.108 .293 5.080 .024 4.996 .000* 1.457 .190 17.302 .000*

30 Brand Loyalty 3.18 2.048 .086 4.435 .000* .232 .630 3.291 .070 3.392 .005* 1.656 .129 5.561 .001*

9 Occasion 3.11 3.551 .007* 1.999 .063 .412 .521 2.981 .085 2.368 .038 2.663 .014 5.679 .001*

Psychographic and

Promotional Factors (F3)

3.650 (1) 2.085 .081 2.207 .040 .348 .555 1.108 .293 .864 .505 .864 .505 1.142 .331

4 Salespeople 3.72 .769 .546 1.153 .329 .748 .387 1.294 .256 2.427 .034 1.442 .196 .494 .687

5 Incentive schemes 3.46 1.263 .283 3.414 .002* .086 .769 .718 .397 .462 .805 .501 .808 .989 .397

3 Word of mouth 3.95 1.863 .115 2.814 .010 .274 .601 4.534 .033 .955 .445 3.172 .004* .658 .578

6 Own experience 3.36 3.105 .015 2.591 .017 .645 .422 5.431 .020 1.127 .344 2.871 .009* 3.658 .012

2 TV commercials 4.20 3.385 .009* 4.194 .000* 4.752 .029 7.000 .008* 2.252 .047 3.624 .001* 2.786 .040

7 Advice of beautician 3.07 2.335 .054 2.265 .035 .447 .504 3.288 .070 .672 .645 1.312 .249 12.510 .000*

8 Display at shop 3.27 6.034 .000* 2.199 .041 .000 1.000 9.155 .003* 3.664 .003* 3.239 .004* 2.820 .038

1 Print advertisements 4.18 2.059 .084 2.765 .011 2.573 .109 2.799 .095 1.643 .146 3.222 .004* 16.899 .000*

Demographic Factors (F4) 3.288 (2) 1.797 .127 2.014 .061 .548 .459 8.588 .003* .771 .570 .771 .570 3.217 .022

16 Locality 3.33 2.297 .057 1.077 .374 1.476 .225 8.133 .004* .599 .701 1.980 .066 2.289 .077

15 Family 3.28 2.692 .030 1.277 .265 .495 .482 10.450 .001* 2.224 .050 2.455 .023 1.347 .258

17 Age 3.35 2.348 .053 1.464 .187 1.277 .259 4.867 .028 1.964 .082 2.737 .012 4.268 .005*

14 Income 3.19 1.281 .275 1.528 .166 2.829 .093 4.088 .043 2.094 .064 2.975 .007* 3.698 .012

18 Social class/status 3.29 2.155 .072 2.395 .026 . 206 .650 2.843 .092 1.602 .157 1.901 .078 2.594 .051

Self-concepts (F5) 3.217 (4) 1.434 .221 .859 .525 .001 .972 1.148 .284 1.844 .102 1.844 .102 2.956 .032

24

Labelling of the

product

3.2 2.484 .042 1.490 .178 . 454 .500 2.408 .121 3.081 .009* 1.164 .324 4.820 .002*

25

Quality of the

product

3.25 2.337 .054 2.602 .017 .818 .366 6.531 .011 1.648 .145 2.396 .026 5.322 .001*

23

Packaging of the

product

3.2 3.626 .006 4.052 .001* .308 .579 .754 .386 2.650 .022 .880 .509 4.414 .004*

Note: Value in the parenthesis shows rank, *significant at .01 level.


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