A Study on Factors Influencing the Shopping Intention and Shopping Habits of Consumers towards Organized Retailing in Bangalore

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1 A Study on Factors Influencing the Shopping Intention and Shopping Habits of Consumers towards Organized Retailing in Bangalore Dr.Janardhan T.G Associate professor, Dept. of commence, MES College, Bangalore Abstract: The present study has been undertaken to study the shopping intention of consumers to purchase from organised retail outlets and shopping habits of consumers at organised retail outlets in Bangalore city. The study used both primary and secondary data. Primary data were collected from the sample respondents by administered questionnaire. The respondents were customers to organised retail outlets in various zones of Bangalore city. The research findings revealed that the positive shopping habit once adhered to by the consumers for shopping products in the organised retail outlets; it will become the regular pattern in substantial part of the life of the consumers. But creating a favourable habit towards organised retail stores is a mammoth task on the part of the retailer. In order to appeal to all classes of society organised retail stores have to identify different lifestyles and socio economic strata of consumers and respond to their respective requirements and shopping pattern. The research area is confined to Bangalore city. Therefore, author suggests for conducting longitudinal study covering respondents from other states in India to further validate the findings. Keywords: Shopping Intention, Shopping Habits, Organized Retail outlets 1. THEORETICAL BACKGROUND: India is the second fastest growing economy in the world. It is third largest economy in the world in terms of GDP and fourth largest economy in terms of Purchasing Power Parity. The retail sector structure in India may be divided into two parts (i) Unorganised retailing (ii) Organised retailing. Unorganised retailing is those which are operating under traditional formats of low cost retailing and normally not registered for sales tax, income tax, etc. Organised retailing is those which are operating their retailing activities taking license from the Government and registered for sales tax, income tax, etc. These include the corporate backed supermarkets, hypermarkets and retail chains, and privately owned large retail business houses like Tata Group, Reliance Retail Group, and RPG Group etc. The growth factors of organised retail in India are: Increase in per capita income which in turn increases the household consumption, Demographic changes and improvements in the standard of living, change in patterns of consumption and availability of low-cost consumer credit, Improvements in infrastructure and enhanced availability of retail space and Entry to various sources of financing. The organised retailing is luring consumers with incredible offers in terms of discounts, schemes, low prices, wide range of goods, pleasant shopping experiences. Hence the concept of shopping has changed in the minds of consumers. It is no more a chore which has to be done but it is an activity which is looked forward to. JIGNASA International Journal of Commerce & Management - JANUARY

2 Knowledge about consumers shopping habits: what products and brands they buy, where, when and how often they shop and how much they spend, enables retailers make quantified decision to offer proper product mix, design target advertising and coupon campaigns and provide customer-centric amenities to meet proactively the consumers needs and desire. 2. REVIEW OF LITERATURE: Mathew Joseph, Nirupam Soundararajan, Manisha Gupta and Sangamitra Sahu through their study Impact of Organised retailing on unorganised sector proved that consumers have definitely gained from organised retailing on multiple counts, overall consumer spending has increased with the entry of organised retail and the lower income consumers saved more when compared to higher income groups. They concluded that organised retailing is relatively more beneficial to the less well of consumers. 1 Sengupta and Noopur Agarwal estimated the organised retail pie to comprise clothing, textiles and fashion accessories -40%, food and grocery 19%, durable 13%, footwear 9%, Jewellly and watches, homedocor 7%, books,music and gifts 3%, beauty products 2% and others 7%. 2 Vinod Kumar Bishnoi, Bharati and Nidhi Gupta investigated the shopping behaviour dimensions of consumers who visited organised stores for food and grocery items in the areas of National capital region namely, Gurgaon, Faridabad, Noida and Delhi and measured the significance of demographic variables on shopping behaviour. They found out that behaviour dimensions signify the quality consciousness of the consumer for products as they consider that stores provide better quality products and also compare the quality of the brands while purchasing. The one way ANOVA indicated the significant difference between the consumers of different demographic profile and their shopping behaviour dimension. They concluded that consumers belonging to different age groups differ significantly on quality consciousness dimensions and value for money and considered that store brands were reasonably priced. 3 Shelja J. Kuruvilla, Nishank Joshi and Nidhi shah had explored mall-shopping habits in India and attempted to identify and contrast possible differences between genders using a sample of 2721 mall consumers across seven cities. While the findings of the study suggest that in India, there are significant differences in shopping behaviour that can be ascribed to gender, there are fundamental questions about stereotyping of shopping as a feminine activity. To do this, discriminant analysis was made in the study to analyse whether shopping orientation and mall-shopping attitudes can discriminate between male and female shoppers. 4 Muhammad Ali, Kashif and Iqbal investigated the relationship between independent variables: shopping lifestyle of consumers, fashion involvement of consumers, pre-decision stage and post-decision stage on consumer purchase behaviour with the attitudinal and behavioural aspects of impulse buying behaviour. The study was an attempt to explore the association that exists between the variables involved. The study revealed that the behaviour of the consumers had strong association with impulse buying. Further the study revealed that JIGNASA International Journal of Commerce & Management - JANUARY

3 young people more often get attracted to products displayed on store shelves and has greater tendency of impulse buying behaviour. 5 Hotniar and Anacostia have identified individual determinants such as shopping intention, attitude towards retail outlet and shopping habits play an important role on consumer shopping decision. They opined that attitude towards retail outlet and shopping habit influences shopping intention. This implied that retailers should concentrate on strategies in building consumers positive attitude towards their retail so that consumers visit their retail in order to make purchase regularly. 6 Piyali Ghosh, Vibhuti Tripathi, Smit Saini, Swati Agarwall attempted to explore shopping and purchase behaviour pattern of consumers within organised retail outlets of Allahabad, a Tier II city in India. According to them, Retail industry in India is acknowledged as a sunshine sector and is driven by factors like strong income growth, changing lifestyles and favourable demographic patterns. Having cemented its presence in metros and Tier I cities, retailers are allured by opportunities in Tier II and III cities like low-cost real estate ad shifting consumption patterns of consumers who are graduating to affluence and lifestyle purchases. Variables identified for shopping orientation were treated with Factor Analysis: motivating factors for store selection and purchase patterns on each shopping trip have been analysed. 7 Mahesh, Balamurugan and Sureshkumar analysed the shopping intention and attitude of consumers towards retail stores in Chennai district. The study found out that there was significant difference among the gender, age of the respondents and factors of shopping intention. The study concluded that an understanding and knowledge of various factors that led to shopping intention and attitude of the consumer would help the retailers to climb the high ladder in the market. 8 Sangeetha and Rahul tried to identify the awareness about the organised retail outlets, explore the reasons for visiting the organised retail outlets and the reasons for the change in the consumer preference for organised outlets in Hisar City. Their study found out that, the change in consumer preference for organised retail outlets from unorganised retail shops, was due to extraordinary benefits such as EDPL and carry bag facility, special discount, local goods, purchase without the disturbance of the salesperson, having entertainment and gaming zone as well as convenient shopping time. 9 Gopalakrishnan and Varadaraj made an attempt to find out the reasons for store loyalty of customers for organised retail format and to find out the cause and extent of satisfaction of consumers in the organised food and grocery retail outlet in Coimbatore City. The study found out that no consumer was loyal to one store as the majority of the consumer s loyalty was shallow in nature. The study also found out that gender, marital status and family size of the respondents correlate with satisfaction level. 10 Venugopal and Santosh Ranganath have in their paper on Behavioural changes of consumers on Indian Organised Retailing presented the state of the Indian retail sector with influence of changes in the consumer behaviours at a moment in time when it is in great flux. They opined JIGNASA International Journal of Commerce & Management - JANUARY

4 that the Indian consumers have undergone a transformation but the transformation is only partial. Higher income, increased exposure and greater willingness to spend will spur the organised retail sector. 11 Manmath Raut and Saroj Kumar Dash have analysed various factors like Accessibility factors, satisfaction factors, sales workforce factors, Tangibility factors, promotional factors, Assortment factors, Trustworthiness factors and Surveying factors. These are the major factors that define consumer buying behaviour. Factor analysis is used to understand the significance of each factor from a sample of 485 customers using a questionnaire. The outcome of the research has major implications for the organised retailers. The result of the study clearly highlighted the need for retailers to be much more customer centric than they are today. 12 Amaresha and Dinakar analysed the consumer behaviour while purchasing FMCG in a retail outlet in Bangalore city. Their study tried to identify the consumer s reasons for choosing to purchase in fast moving consumer good in the organised retail outlet. The study concluded that the reasons for a consumer to go to organised retail outlets are price, products and location. The study also found out that there was significant increase in the expenditure on FMCG after the opening of many organised retail outlets in Bangalore and a significant relationship between organised retail outlets and age and consumer s average monthly income NEED FOR THE STUDY: Previous studies focussed on the attitude, preferences towards organised retailing, buying behaviour of consumers in retail shops and different formats of retail shops, etc. Only a very few studies have been carried out to analyse the intention, attitude towards organised retail stores and shopping habits of the consumers at the organised retail shops and shopping behaviour in organised retail outlets. Hence the present study has been undertaken to study the shopping intention of consumers to purchase from organised retail outlets, attitude of consumers towards organised outlets and shopping habits of consumers at organised retail outlets in Bangalore city. 4. STATEMENT OF THE PROBLEM: Indian organised retail sector is in a nascent stage, with few major players, compared to the 15 million kirana stores through which more than 90% of retailing happens in India. However, with the middle class Indian population and disposable income of the young population increasing, organised retailers were almost on the verge of a major breakthrough. New players are entering the retail sector with different formats and incumbents are continuously experimenting to attract customers but understanding the Indian customer remains a tough task as many of the retailers have failed to establish their formats and many are only moderately successful. The organised retailing is luring consumers with incredible offers in terms of discounts, schemes, low prices, wide range of goods, pleasant shopping experiences. Hence the concept of shopping has changed in the minds of consumers. It is no more a chore which has to be done but it is an activity which is looked forward to. JIGNASA International Journal of Commerce & Management - JANUARY

5 Knowing consumers shopping habits: what products and brands they buy, where, when and how often they shop and how much they spend, enables retailers make quantified decision to offer proper product mix, design target advertising and coupon campaigns and provide customer-centric amenities to meet proactively the consumers needs and desire. Customers award their life style stores with purchase, satisfaction, loyalty and word of mouth promotion. Shopping habits helps the retailer to understand the consumers, lifestyles and anticipate their needs and desires. With shopping habits in hand, retailer can identify the best customer, offer the right products, design, and more effective marketing campaigns and make the retail outlet more convenient. 5. OBJECTIVES OF THE STUDY: To analyze of the level of effect of factors of shopping intention on consumers in organized retail outlets To analyze the final cluster centers based on factors of shopping habit of consumers 5.1 HYPOTHESIS: H1: There is an association between demographic factors and clusters of organised retail shopping intention H2- There is an Association between Demographic Variables and Cluster Centres of Consumers of Organised Retail Outlets Based on Shopping Habits of Respondents. 6. METHODOLOGY: Sampling The study used non-random sampling method i.e convenient sampling method for collection of data. Sample size The study has used a sample size of 600 from Bangalore city. The respondents have been drawn from all the five zones of Bangalore city Eastern, Western, Northern, Southern and Central. The respondents were customers to organised retail outlets in various zones of Bangalore city. 6.1 DATA SOURCE: The study used both primary and secondary data. Primary data were collected from the sample respondents by administered questionnaire. Questionnaires were distributed to the respondents directly. 6.2 TOOLS OF ANALYSIS: Descriptive statistics such as percentages and average K-means Cluster Analysis Chi-square analysis JIGNASA International Journal of Commerce & Management - JANUARY

6 6.3 LIMITATIONS OF THE STUDY: The research area is confined to Bangalore city. Hence the generalization of the study may not hold good for the entire universe. 6.4 RESEARCH FINDINGS: Objective-1: To Analyse of the Level of Effect of Factors of Shopping Intention on Consumers in Organised Retail Outlets An attempt has been made to identify the level of effect of factors of Shopping Intention on Consumers in Organised Retail Outlets among the different groups of people. The data collected from the primary sources of information were arranged systematically and sequentially relevant to the analysis. In order to group the respondents in the various clusters based on the various factors and to identify the effective factor in each cluster, one of the Advance multivariate statistical technique cluster analysis, have applied. The grouped respondents in each cluster are segregated based on Ambience, Price and Discount, Product Display, Impact of Promotional Measures and Brand Image, Influencer, Status of Consumer &Product Usage and Tradition. Table 1- Hierarchical cluster method with agglomeration schedule -- to decide the number of clusters - Average Linkage (Between Groups) Agglomeration Schedule Stage Cluster Combined Stage Cluster First Appears Coefficients Cluster 1 Cluster 2 Cluster 1 Cluster 2 Next Stage JIGNASA International Journal of Commerce & Management - JANUARY

7 Cluster method: Average linkage between groups method (hierarchical clustering method) Distance method: Squared Euclidean Distance measure In the above agglomeration schedule from top to bottom (State 1 to 599) indicates the sequences in which cases get combined with other until all 600 cases are combined together in one cluster at the last state (stage 599). To identify the number of cluster, the co-efficient values (i.e., difference between rows) in column 4 is considered. The figures of co-efficient values are seen from the last row upwards to have lowest possible number of clusters for interpretation. The difference in the value of co-efficient from state 599 and stage 598 is ( ) indicating the 1 cluster. The procedure is continued till the difference between the 2 states gets reduced in order to identify the number of clusters. In the next state the difference between sate 598 and 597 is 0.71 ( ) which is low but again the difference between 597 and 596 is 3.35 ( ) indicating the increasing trend with more difference. But the difference between stages 596 and 595 is 2.43 ( ), stages 595 and 594 is 0.13 ( ) showing a little increase and decrease. So it is better to stop with the state 595 and 596 with the difference indicating a 3 cluster solution with maximum differences in the value of co-efficient. So finally it is decided to have 3 clusters from the agglomeration schedule. Table 2: Initial Cluster Centres Dimensions Initial Cluster Centres Ambience Price and Discount Product Display Impact of Promotional Measures & Brand Image Influencer Status of Consumer Product Usage and Tradition Table 2 shows the initial cluster formations for 7 variables selected with their mean scores. JIGNASA International Journal of Commerce & Management - JANUARY

8 Table - 3 Final Cluster Centres Final Cluster Centers Dimensions Mechanical Consumers Dynamic Consumers Casual Consumers Ambience Price and Discount Product Display Impact of Promotional Measures and Brand Image Influencer Status of Consumer Product Usage and Tradition Based on the factors of shopping intention, different types of clusters were formed. This would provide a basis for categorizing the consumers who visit the organised retail outlets and also useful for deciding the marketing strategies by the organised retail outlets. Table 3 contains the mean values for each variable in each cluster. The dimensions in each cluster were ranked based on the mean scores. The dimension in each cluster with the mean scores less than 3 were ranked as weak, the variables for which the mean values in each cluster was 3-4 were ranked at moderate level and the variables for which the mean values were more than 4 in each cluster was ranked strong in level to influence shopping intention. Table 4: Ranking of Final Cluster Centres Final Cluster Centers Dimensions Mechanical Consumers Dynamic Consumers Casual Consumers Ambience Moderate Strong Moderate Price and Discount Moderate Strong Moderate Product Display Moderate Strong Weak Impact of Promotional Measures and Brand Image Moderate Strong Weak Influencer Moderate Strong Weak Status of Consumer Moderate Strong Moderate Product Usage and Tradition Moderate Strong Weak So in cluster I all the variables Ambience, Price and Discount, Product Display, Impact of Promotional Measures and Brand Image, Influencer, Status of Consumer, Product Usage and Tradition are moderate, and in cluster 2, all the variables selected are strong whose mean values are more than 4. In cluster 3, the variables Product Display, Impact of Promotional Measures and Brand Image, Influencer, Product Usage and Tradition are weak, with the mean value less than 3. From the above table the variables in each cluster are identified for the three cluster segments. The variables in each cluster segment are identified based on the mean values in the final cluster centre table-5. The number of respondents in each cluster are also found and given in the following table. JIGNASA International Journal of Commerce & Management - JANUARY

9 Table 5: Number of Cases in Cluster Centres Cluster Number of Cases in each Cluster Total 600 Table No. 5 shows the number of respondents in each cluster out of the 600 respondents. Out of 600 respondents of the study, 278(46.33%) were identified as Mechanical consumers in I cluster, 187 respondents as Dynamic consumers (31.17 %) in II cluster and 135 respondents (22.5 %) were identified as Casual Consumers in Cluster III ASSOCIATION BETWEEN CLUSTERS OF SHOPPING INTENTION OF ORGANISED RETAIL OUTLET CONSUMERS AND THEIR DEMOGRAPHIC FACTORS H1: There is an association between demographic factors and clusters of organised retail shopping intention TABLE 6 - Association between Demographic Variables and Clusters of Consumers of Organised Retail Outlets Based on Shopping Intention of Respondents Cluster Number of Case Total Demographic variables Mechanical Dynamic Casual N % N % N % N Gender Male Female Age Above School level Arts/Science/Commerce Graduates Arts/Science/Commerce Educational Post Graduates Qualification Professional Graduates Professional Post Graduates Research Degrees Studying Home-maker Occupation Business Employees Professional Retired Up to 20, Family 20,001-40, Monthly 40,001-60, Income 60,001-80, Above 80, JIGNASA International Journal of Commerce & Management - JANUARY

10 Marital Status Size of the Family Unmarried Married Small Medium Large Total Table 6 reveals that out of the male respondents majority 42.61% are mechanical consumers while among the female respondents majority 48.65% are mechanical consumers. Out of the 278 mechanical consumers 64.75% are female respondents. With regard to age the respondents are more in the age group of years, followed by respondents in the age group of years % of the respondents in the age group of are mechanical consumers. Out of the 278 mechanical consumers 49.64% of the respondents are in the age group of years. Concerning Educational Qualification, majority of Professional graduates are mechanical consumers followed by Arts/science/Commerce Graduates accounting for 51.38% of the Graduates. Out of the 278 mechanical consumers 46.76% are Arts/Science/Commerce Graduates % of the home-makers are mechanical consumers. Among 278 mechanical consumers 30.22% of the respondents are employees Among respondents having family monthly income more than Rs.80,000 majority 50.67% are mechanical consumers. Among 278 mechanical consumers, 28.78% of them are have family income less than Rs.20,000 per month. Based on marital status, 46.65% of the married respondents are mechanical consumers. Among 278 mechanical consumers 60.07% of the consumers are married. As far as size of the family of the respondents is concerned, the maximum 57.14% belong to medium size family. Among the 278 mechanical consumers 83.81% belong to small size family. TABLE 7 - Chi-Square Test Results Chi-Square Tests Demographic variables Chi square Value df p Gender Age Educational Qualification Occupation Family Monthly Income Marital Status Size of the Family It is evident from Table 7 that there exists no association between demographic factors and clusters of organised retail shopping intention. JIGNASA International Journal of Commerce & Management - JANUARY

11 Objective-2: To analyse the final cluster centres based on factors of shopping habit of consumers Table 8: Hierarchical Cluster Method with Agglomeration Schedule -- To Decide the Number of Clusters - Average Linkage (Between Groups) Stage Cluster Combined Coefficients Stage Cluster First Appears Next Stage Cluster 1 Cluster 2 Cluster 1 Cluster In the above agglomeration schedule from top to bottom (State 1 to 599) indicates the sequences in which cases get combined with other until all 600 cases are combined together in one cluster at the last state (stage 599). To identify the number of cluster, the co-efficient JIGNASA International Journal of Commerce & Management - JANUARY

12 values (i.e., difference between rows) in column 4 is considered. The figures of co-efficient values are seen from the last row upwards to have lowest possible number of clusters for interpretation. The difference in the value of co-efficient from state 599 and stage 598 is 3.01 ( ) indicating the 1 cluster. The procedure is continued till the difference between the 2 states gets reduced in order to identify the number of clusters. In the next state the difference between sate 598 and 597 is 1.86 ( ), the in next stage 597 and 596 the difference is 1.39 ( ) which is low but again the difference between stages 596 and 595 is 1.87 ( ) indicating the increasing trend with more difference, stages 595 and 594 is 1.73 ( ) showing a little increase and decrease. So it is better to stop with the state 595 and 596 with the difference indicating a 3 cluster solution with maximum differences in the value of co-efficient. So finally it is decided to have 3 clusters from the agglomeration schedule. Table 9: Final Cluster Centres for Factors Of Shopping Habits Of Consumers Cluster Dimensions Wandering Loyal Consumers Need Based Impulsiveness & price conscience Awareness on utility and variety of products Store Loyalty Quality Consciousness Opinion Seeker Time Management Amusement Table 9 shows the initial cluster formations for 7 variables selected with their mean scores. Table 10: Ranking of the Final Cluster Centres Cluster Dimensions Wandering Loyal Consumers Need Based Impulsiveness & price conscience Moderate Moderate Strong Awareness on utility and variety of products Moderate Moderate Strong Store Loyalty Moderate Weak Strong Quality Consciousness Weak Weak Moderate Opinion Seeker Moderate Weak Strong Time Management Moderate Weak Strong Amusement Moderate Weak Strong Table-10 reveals that in cluster I all the variables are ranked as moderate except Quality Consciousness which is ranked as weak. In cluster 2, all the variables are ranked as weak except Impulsiveness and price conscience and Awareness on Utility and Variety of Product which are rank as moderate. In cluster 3, Impulsiveness and price conscience, Awareness on utility and variety of products, store loyalty, Opinion Seeker, Time Management, Amusement JIGNASA International Journal of Commerce & Management - JANUARY

13 are ranked as strong while Quality consciousness was ranked as moderate. From the above table the variables in each cluster are identified for the three cluster segments. Table 11:Number of Cases in Cluster Centres Cluster Number of Cases in each Cluster Percentage Total Table 11 a reveals that in there are 269 Loyal Consumers in Cluster 1aggregating to 44.83% of the total respondent, 106 Wandering consumers in Cluster 2 constituting 17.67% of the total respondents, 225 respondents are termed as Need based consumers and included in Cluster 3 thus accounting for 37.50% of the total respondents. ASSOCIATION OF DEMOGRAPHIC FACTORS AND CLUSTERS OF CONSUMERS BASED ON SHOPPING HABITS OF CONSUMERS OF ORGANISED RETAIL OUTLETS H2- There is an Association between Demographic Variables and Cluster Centres of Consumers of Organised Retail Outlets Based on Shopping Habits of Respondents TABLE 12 - Association between Demographic Variables and Cluster Centres of Consumers of Organised Retail Outlets Based on Shopping Habits of Respondents Cluster Number of Case Total Demographic variables Loyal Wandering Need Based N % N % N % N Gender Male Female Age Above School level Arts/Science/Commerce Graduates Educational Arts/Science/Commerce Post Qualification Graduates Professional Graduates Professional Post Graduates Research Degrees Studying Home-maker Occupation Business Employees Professional Retired Family Up to 20, Monthly 20,001-40, JIGNASA International Journal of Commerce & Management - JANUARY

14 Income 40,001-60, ,001-80, Above 80, Marital Status Unmarried Married Size of the Family Small Medium Large Total Table 12 presents the cluster number of cases based on the demographic variables of the respondents. Table 12 reveals that out of the male respondents majority 43.48% are loyal consumers while among the female respondents majority 45.68% are loyal consumers. Out of the 269 loyal consumers 62.82% are female respondents. With regard to age the respondents are more in the age group of years, followed by respondents in the age group of years % of the respondents in the age group of are loyal consumers. Out of the 269 loyal consumers 45.72% of the respondents are in the age group of years. Concerning Educational Qualification, respondents are more in the group of Arts / Science / Commerce Graduates accounting for 47.83% of the Graduates. Out of the 269 loyal consumers 44.98% are Arts / Science / Commerce Graduates % of the retired persons are loyal consumers. Among 269 mechanical consumers 28.25% of the respondents are employees Among respondents having family monthly income less than Rs.20,000 majority 50.00% are Loyal consumers. Among 269 Loyal consumers, 30.48% of them have family income less than Rs.20,000 per month. Based on marital status, 45.45% of the unmarried respondents are loyal consumers. Among 269 Loyal consumers 59.10% of the consumers are married. As far as size of the family of the respondents is concerned, the maximum 50.00% belong to large size family. Among the 269 loyal consumers 87.36% belong to small size family. TABLE 13 - Chi Square Test Results Demographic variables Chi square Value df p Gender Age Educational Qualification Occupation Family Monthly Income Marital Status Size of the Family JIGNASA International Journal of Commerce & Management - JANUARY

15 Table 13 reveals that there exists no association between demographic factors and clusters of consumers based on shopping habits of organised retail outlet consumers as the p values are more than 0.05 level. It is therefore inferred from the above analyses that determinants like shopping intention, attitude towards organised retail outlets and shopping habits play important role in consumers shopping decisions. Attitude towards organised retail outlets and shopping habit will influence shopping intentions. This implies that the organised retailers should concentrate on strategies in building consumers positive attitude towards their retail outlet so that consumers visit their retail in order to make purchase regularly. There is a tendency for consumers to visit all format in making convenience goods purchase. But organised retail sector is growing rapidly and consumers are shifting to go for shopping in organised retail stores. Thus, understanding of shoppers habits is the key to success for the retailers. Marketers will have to understand the components of shopping behaviour- shopping intention, attitude towards retail formats and shopping habits which will help them to tap the consumer in a better way. 6.5 DISCUSSION: All Human beings are consumers. People buy products according to their needs, preferences and buying power. The goods purchased can be consumable goods, durable goods, speciality goods or industrial goods. What a person buys, how he buys, where and when he buys, how much quantity he buys depends on perception, self-concept, social and cultural background and age and family cycle, attitudes, beliefs, values, motivation, personality, social class and many other factors that are both internal and external. While buying, one considers whether to buy or not to buy and from which source or seller to buy. In some societies there is a lot of affluence and these societies can afford to buy in greater quantities and at shorter intervals. In poor societies, the consumer can barely meet his barest needs. The marketers, therefore, must try to understand the needs of different consumers and their different behaviours which require an in-depth study of their internal and external environment, they should formulate their plans for marketing. The study of shopping intention and shopping habits of consumers is critical to understanding the motivation and decision strategies employed by consumers. The combination of beliefs, attitudes, habits and behaviors influence how a consumer reacts to products or services sold by a retailer or marketer. Marketers develop relative, compelling marketing messages using the combination of information based on the shopping intentions, attitude and habits of the consumers, and ultimately influence consumer behavior. Shopping behavior of the consumers are influenced by both internal and external factors. It depends internally on the intention to buy goods from a particular format which is assumed to be the organized retail outlet, his attitude towards organized retail outlet, shopping habits of the consumers as well as the external factors such as retail store format and the atmosphere in the format. JIGNASA International Journal of Commerce & Management - JANUARY

16 Shopping intention refers to creation of inclination in the minds of consumers to purchase a product in a particular retail format. Such an intention can be created spontaneously or nonspontaneously. 6.6 SUGGESTIONS: The organised retailers should periodically survey the interesting patterns of consumer behaviour and consider that behaviour pattern in their decision making processes if they hope to maintain or improve their competitive positioning. The customers visiting organised retail outlets are status and quality conscious while deciding on the store to purchase from and the brands to purchase. They always look for the benefits of shopping in a store over traditional retail outlets in terms of self-selection, variety, comparison of brands. They love to spend time in shopping and prefer to visit the outlets along with family and friends; in all they seek complete entertainment while shopping. In order to taste success, the organised retailer should equip himself with all the above anticipations of the consumers. If the food and grocery organised retailers want to attract consumers who are more likely to buy well-known brands they should improve the elements that constitute their image factor, especially the brands they carry and the quality of the merchandise they offer. The consumers care about the brands carried by the grocery retailers as well as the quality that these offerings represent to them. In addition, it is important that organised retailers offer services and convenience to the consumers because their perceptions of these aspects significantly influence their their overall satisfaction. As large number of respondents have expressed that they do window shopping, organised retailers should improve the window display of goods at their premises. This may increase the amount of impulse purchases. Since majority of the respondents preferred to shop on Sundays and holidays, organised retail outlets should be kept opened on Sundays and general holidays and the organised retail outlets can arrange for special events, programmes, offers for giving delight in shopping at the outlets to the consumers and their families. Nowadays majority of the consumers live as nuclear family and they prefer to take their children with them when they go for shopping in organised retail outlets. To attract and entertain the children special game centres, children care centres may be established in the premises of the organised retail outlets. Organised retail outlets should take every effort provide fresh stock of items for their customers. Organised retail outlets should improve their display of goods so as to have an aesthetic appeal to the customers. Implications of the study: JIGNASA International Journal of Commerce & Management - JANUARY

17 The positive shopping habit once adhered to by the consumers for shopping products in the organised retail outlets; it will become the regular pattern in substantial part of the life of the consumers. But creating a favourable habit towards organised retail stores is a mammoth task on the part of the retailer. In order to appeal to all classes of society organised retail stores have to identify different lifestyles and socio economic strata of consumers and respond to their respective requirements and shopping pattern. 6.7 END NOTES: An attempt has been made to identify the level of effect of factors of Shopping Intention on Consumers in Organised Retail Outlets among the different groups of people. The data collected from the primary sources of information were arranged systematically and sequentially relevant to the analysis. In order to group the respondents in the various clusters based on the various factors and to identify the effective factor in each cluster, one of the Advance multivariate statistical technique cluster analysis, have applied. The grouped respondents in each cluster are segregated based on Ambience, Price and Discount, Product Display, Impact of Promotional Measures and Brand Image, Influencer, Status of Consumer &Product Usage and Tradition. 6.8 CLUSTER ANALYSIS Cluster analysis is a multivariate statistical technique which groups person/objects/occasions into unknown number of groups such that the members of each group are having similar characteristics / attributes (Donald. R. Cooper. 2003). The primary objective of cluster analysis is to define the structure of the data by placing the most similar observations into groups. Various groups to be determined in cluster analysis is not pre-defined as in discriminate analysis. This analysis is ideally suited to segmentation applications in marketing research like factor selection, understanding buyer behaviour, market segmentation etc. The method of clustering may be either hierarchical or non-hierarchical or both. The performance is much superior when the results from the hierarchical are used along with nonhierarchical. Thus hierarchical and non-hierarchical techniques should be viewed as complementary clustering techniques rather than as competing techniques (Sharma, 1996). In this study, both hierarchical and non-hierarchical clustering techniques have been used. Cluster analysis is typically applied to data recorded on interval scale or continuous scaled variables. This analysis is applied to a large set of data which may consist of many variables. Cluster analysis determines internal homogeneity. i.e., the similarity existing among the respondents or items and external heterogeneity i.e., the difference existing between different groups of respondents or items. This analysis helps in grouping the objects or persons based on the variables considered. JIGNASA International Journal of Commerce & Management - JANUARY

18 STEPS INVOLVED IN CLUSTER ANALYSIS Six steps are basic to the application of most cluster studies. Selection of the sample to be clustered The sample may be persons (buyers, medical patients, employees etc.) inventory, products and other objects. Generally large, set of data are more suitable for cluster analysis. Definition of the variables The variables on which to measure the objects, events or people (ex. financial status, market segment characteristics, symptom classes, productivity attributes, various factor, and product competition definition) have to be decided. The set of variables selected should describe the similarity between objects in terms that are relevant to the marketing research problem. The variables should be selected based on the past research theory or a consideration of the hypothesis being tested. Selecting a distance or similarity measure The computation of similarities among the entities is very essential order to group similar objects together. Similarities can be measured either through correlation or distance measures. The most common approach is to measure similarity in terms of distance between pairs of objects. Various distance measures are used in the cluster studies. Selecting a clustering procedure Clustering procedures can be hierarchical, non-hierarchical or application of both the methods for better formation of clusters. Hierarchical clustering is characterized by the development of a hierarchy or tree like structure. Hierarchical methods can be agglomerative or divisive. Under this method agglomerative methods are commonly used in marketing research. Agglomerative clustering starts with each object in the separate cluster. Clusters are formed by grouping objects into bigger and bigger clusters. This process is continued until all objects are member of a simple cluster. Agglomerative method includes linkage methods, error sum of squares or variance methods and Centroid methods. Under the linkage method single linkage, average linkage and composite linkages are available. Selection of mutually exclusive clusters The number of clusters is being selected, based on the Dendrogram and the agglomeration schedule. The agglomeration schedules show all possible solutions from, cluster 1 to (n-1) clusters; where n are the numbers of respondents / cases. Going up from the bottom of the agglomeration schedule, the difference in the values of co-efficient column is considered to decide the number of clusters. The decision of selecting the number of clusters is based on the volume of difference in the value of co-efficient column one by one from the bottom of the agglomeration schedule. When the difference in the values of co-efficient starts decreasing compared to the previous one, the number of clusters is stopped with that point. JIGNASA International Journal of Commerce & Management - JANUARY

19 Identifying the clustering variables and Assessing the validity of clustering The clustering variables in each cluster can be identified with help of a final cluster centres table. The final clusters centres show that mean values for variables which help in the selection of the variables in each cluster. Case listing cluster membership shows the list of cases coming in each cluster. The ANOVA table shows the F vales and variables which are statistically significant. Step: 1 The sample selected for cluster analysis include the people who were all respondents viewing the factors. The researcher has taken the sample of 600 respondents form the various demographic characteristics. Step: 2 The most important part in the clustering problem is selecting the variables in which the clustering is based. The researcher have selected the various factors which includes Ambience, Price and Discount, Product Display, Impact of Promotional Measures and Brand Image, Influencer, Status of Consumer, Product Usage and Tradition. Step: 3 To compute the similarities among the cases /entitles either through correlations, distances measures and other techniques. Among various distances measures available squared Euclidean distance measure is adopted to compute the similarity between two cases in this study. Step: 4 In the clustering procedure hierarchical clustering method have been adopted for the I st stage in this method. Agglomerative methods have been used with average linkage between groups method. As the agglomeration schedule for 600 is very large, the values from last 35 cases are given in the table Step - 5 After deciding the number of clusters as 3, non-hierarchical k-means (quick clustering) clustering method has been used to find out the factor effective in each cluster. The output initial cluster centres, final cluster centres and ANOVA tables are interpreted to decide the variables in each cluster. JIGNASA International Journal of Commerce & Management - JANUARY

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