A study on consumer buying behavior towards ready to eat food

A study on consumer buying behavior toward ready to eat food

A study on consumer buying behavior toward ready to eat food

Maryam Sameer 10108015Raana Kanwal 10108023Komal Rehman 10108030

Submitted to:Mr. Mauhamad Abid AwanGIFT University , Gujranwala

AcknowledgmentAll praises and thanks to Almighty Allah. The Lord and Creator of this universe by whose power and glory all good things are accomplished. He is also the most merciful, who best owed on us the potential, ability and an opportunity to work on this project. Apart from the efforts of us, the success of any project depends largely on the encouragement and guidelines of many others. I take this opportunity to express my gratitude to the people who have been instrumental in the successful completion of this project. I would like to show my greatest appreciation to Sir Muhammd Abid Awan we cant say thank you enough for her tremendous support and help. Without his encouragement and guidance this project would not have materialized. We are grateful to our respected teacher Sir Mauhammad Abid Awan who has guided us in each and every step of this project. Indeed, without his kind guidance we may not be able to even start this project. May ALLAH give him the reward, which he deserves. We also grateful to all those members who are related to this section.

List of AppendicesAppendix Name of Appendix Page No A Questionnaire 50

List of TablesTable Table Name Page NoTable no 1 Demographic AnalysisTable no 2 ReliabilityTable no 3 Exploratory Factor AnalysisTable no 4 Descriptive AnalysisTable no 5 CorrelationTable no 6 Regression AnalysisTable no 7 Two way independent sample t-test

List of FiguresFigure Name of Figure Page No 1 Framework 14

List of AbbreviationsDV: Dependent VariableIV: Independent VariableCBB: Consumer Buying BehaviorFP: Food PreferenceCO: ConvenienceSC: Social ClassCD: Consumer DemographicPK: Product Knowledge

A study on consumer buying behavior toward ready to eat food Abstract The purpose of conducting this research is to provide a detailed study on consumer buying behavior in ready to eat food. This study is based on identifying the factors leading to purchase of the ready to eat food. This research will help to provide a guidance to the business working in this sector. It will enable them to understand consumers buying behavior for ready to eat food.A sample of 150 respondents were taken from GIFT University and Satellite Town Market. These respondents were given structured questionnaire. The statistical tool of reliability, exploratory factor analysis, correlation, regression analysis, independent sample t-test were used for checking the results. These results were interpreted for final analysis of consumer buying behavior lead by various factors. This study will contribute to the present body of literature and give an insight to the business working in this sector of food industry so that they can better understand the consumer preferences for their food.

Key Words: Consumer buying behavior, ready to eat food, statistical tool.Paper Type: Research Paper

1 Introduction

1.1 Rationale of study Food is one of the basic necessities of humans. Man has used various resources to fulfill this need since the growth of this earth. Every individual has to satisfy their physiological needs like shelter, hunger, and thirst for their survival. (Meenambekai and Selvarajan, 2012). Changing lifestyle has given a different meaning to the need of food. A wide variety of food is available in the market for consumers. Food consumption choices are made on the basis of different factors such as life style, culture, preference, quality and price. In modern century the changing lifestyle has also modified the need and wants of people. Due to this change in the need of food, latest technology has transformed the food into different forms. In ancient times preparation of food takes a time. But in current time preparation of food is much easier, less time consuming and convenient. To fulfill this need market is offering a wide variety of ready to eat foods. Consumption includes social features like social identity, group influences, social cultures, shopping for family and feeling influences from social norms. ( Miller, 1998; Ryan, 1982).Consumers are being shifting towards cultural food from restaurants and for them preparing these dishes is time consuming and difficult for this reason ready to eat foods playing an important role to fulfill this gap.1.2 Problem StatementWith the increase in population, food industry has a growing demand and it is due to changing lifestyle of people and other socio-demographic factors. With the passage of time consumer food preference are also changing and it has become the modern trend in people. Ready to eat foods have previously worked on microbiological assessments (Bae HJ. Park HJ,2007; Park Sy,Choi Jw,Yeon jh,2005). Past study show that demand for ready to eat food has increased due to change in consumer lifestyle and socio-demographic characteristics.(Verbeke w. lopezGP, 2005 ;Buckley M.Cowan c,2007). Although ready to eat food has a growing demand but there is less information available regarding people shifting from traditional home made foods to ready to eat foods and their factors affecting the decision of choosing ready to eat food as food over traditional home made foods. The main purpose of conducting this research is to study consumer food consumption pattern and their behavior towards ready to eat food. To find the factors that affects their purchase decision towards ready to eat food. The problem of this research is to study consumer purchase behavior, consumer demographic, food preferences, shift from traditional food to ready to eat food, consumer lifestyle, brand preferences.

1.3 Aim of StudyThe main aim of this study is to study the consumer buying behavior towards ready to eat food. In fulfilling this aim we are studying the factors that affect the consumer buying behavior towards these products and what factors are affecting the most. 1.4 Research Objective To understand the factors associated with the purchase decision of ready to eat food products. To estimate whether some of those factors are more influential on consumption pattern of ready to eat food over traditional home made food. 1.5 Research QuestionWhat Factors are affecting the consumer decision of buying ready to eat food over traditional home made food?Why consumers are preferring ready to eat food over traditional home made food?1.6 Delimitations of StudyThe delimitations of a study are those individuality that boundary the range of that were made throughout the improvement of the proposal. We formulate boundary on variables. We have 11 variables but we cannot use all variables for the reason that following variables are take away important rather than selected variables. Consumer demandProduct involvementBrand preference Consumption pattern2 Literature ReviewAs food is one of the basic necessities of human beings .With the changing lifestyle, cultural shifts and other socio-demographic factors affects on consumer purchasing power in food product category. Due to changing lifestyle Ready to eat food products is also gain much interest in consumer choice. Ready to eat food are those that are ready for consumption and have no culinary skills require for their preparation. There are various product categories in Ready to eat including packed foods, fully prepared and convenience foods, frozen and desserts and some dried foods etc. These all food product categories fulfilled the need of today consumer. The increasing consumer preference towards ready to eat food is mainly due to some factors including busy lifestyle, convenience lack of time and increasing work pressure. Consumer food preference and non preference for convenience stores, discount marts and brands reflect the consumer developing attitude towards a particular product (Moye and Kincade ,1999).Due to work life pressure, lack of time, entrance of woman in work force and limited culinary skills have resulted in consumer search for convenience factor in food category (Euromonitor International, 2008). Due to this consumer preference is increasing towards Ready to eat food.Because the changing lifestyle of people and entrance of woman in workforce largely attribute to changing consumer choice of ready to eat food. Women are real owner at home that is prepared food for its children at home. Now due less availability of time its choice moves to purchasing convenience food. Consumers purchase decision in food choice is a complex process and it is affected by level of income, lifestyle, consumer taste, consumption pattern and others environmental factors. Consumer food preference depends on its taste, perception, product knowledge and these things base on consumer consumption pattern and demographic factors (Rees, 1992).Consumer product knowledge and its beliefs lead to develop consumer buying behavior (Verdurmeet, 2001). Social class can be defined as group of people that share common attitudes, beliefs, values, education also communication styles but the members of one social class differ from other social class members (Williams, 2002).Social classes are classified on the basis of income level of people of class. Every social class has its own preferences for food and lifestyle pattern. When it comes to the marketing of a product marketers need to consider the social class differences in building their strategy (Yakup, Mcahit and Reyhan, 2011).Media plays an important role in informing the consumers about a particular product. The right kind of information should be travelled to the consumers through the right kind of medium. The marketers should also understand the language differences among social classes and communicate to their target consumers. When an appropriate kind of information is obtained by the consumer they will help them build their buying behavior regarding their purchases.Consumer consumption pattern affected by many socio-demographic factors and this thing determine the consumer purchase behavior of Ready to eat food (Roux, 2000; Roslow, 2000; Turrell, 2002; Choo, 2004; Rao, 2005; Krystallis and Chryssohoidis, 2005; Batte, 2007; Goyal and Singh, 2007; Bukenya and Wright,2007).Consumer perceived attributes about a particular product is affected by consumer choice in food and this impact on buying behavior (Batra and Sinha,2000; Kupiec and Revell, 2001).Consumer buying behavior in food choice depends on many demographic factors like age, income education level, awareness about a particular food product (Rao, 2000; Shetty, 2002; Deshingkar, 2003; Vepa,2004; KPMG, 2005; Kaushik, 2005; Kaur and Singh, 2007; Pingali, 2007).Several factors that are influencing buying behavior of consumers includes cultural changes, psychological needs, and consumer internal satisfaction (Shaw, 1993; Brokaw and Lakshman, 1995; Asp, 1999; Roux, 2000; Roslow, 2000;Roininen, 2001; Choo, 2004; Ling, 2004; Ahlgren, 2004; Goyal and Singh, 2007; Nagla, 2007).These factors positively impact on attitude toward ready to eat food. A number of research has been made on how gender differences affect the consumer purchase decision throughout the world whereas in most of the cases women play a significant role in making all the purchase decision.(Dholakia, 1999; Hawfield and Lyons, 1998).Product knowledge about a particular product helps consumer to develop their attitude toward a product. Product knowledge is the knowledge about any good, services and product. Particular knowledge of a product, its main features, and its use involve in effective buying (Kotler, 1990). Main features of a product that are linked with these products will help to build their attitude and consequently purchase these products. With the passage of time consumer food preference are also changing and it has become the modern trend in people. Ready to eat foods have previously worked on microbiological assessments (Bae HJ. Park HJ,2007; Park Sy,Choi Jw,Yeon jh,2005). Past study show that demand for ready to eat food has increased due to change in consumer lifestyle and socio-demographic characteristics (Verbeke w. lopezGP, 2005 ;Buckley M.Cowan c,2007).But choosing a ready to eat food over traditional home made food also look intense (Costaet, 2007). Although ready to eat food has a growing demand but there is less information available regarding people shifting from traditional home made food to ready to eat food and their factors affecting the decision of choosing ready to eat as food over traditional home made food.Food PreferenceFood preference defines as selection of food according to taste, culture, lifestyle and affordability in purchasing. Consumer food preference and non preference for convince stores, discount marts and brands reflect the consumer developing attitude towards buying of particular food product (Moye and Kincade ,1999). So it is determine that food preferences have positively impact on consumer buying behavior. Mostly people prefer food on the basis of numbers of factors including quality, packging, price, taste and some choose by cultural difference .some search convenience factor in food and prefer that food that are ready to consume. Since convince is working as a moderating variable in our framework. Convenience food are those that can be prepared by investing less time (Berry, 2002).Due to work life pressure, lack of time, entrance of woman in work force and limited culinary skills have resulted in consumer search for convince factor in food category (Euromonitor International, 2008).ConvenienceConsumer demographic play effective role in choosing a product category and it influence the consumer to buy a product of its choice. changes in consumer demographic of people and entrance of women in work force are the fundamental drivers of developing attitude towards buying .This has led to the entry of Ready to eat food category.(Sarathy,T.;Gopal,Shilpa). These factors positively impact on attitude toward ready to eat food. A number of research has been made on how gender differences affect the consumer purchase decision throughout the world whereas in most of the cases women play a significant role in making all the purchase decision.(Dholakia, 1999; Hawfield and Lyons, 1998).Social ClassSocial class can be defined as group of people that share common attitudes, beliefs, values, education also communication styles but the members of one social class differ from other social class members (Williams, 2002).Social classes are classified on the basis of income level of people of class. Every social class has its own preferences for food and lifestyle pattern. When it comes to the marketing of a product marketers need to consider the social class differences in building their strategy (Yakup, Mcahit and Reyhan, 2011).Media plays an important role in informing the consumers about a particular product. The right kind of information should be travelled to the consumers through the right kind of medium. The marketers should also understand the language differences among social classes and communicate to their target consumers. When an appropriate kind of information is obtained by the consumer they will help them build their buying behavior regarding their purchases. These purchases include buying the basic necessities among which the purchase of food items. These food items purchases are build on the basis of what kind of information is given to the consumer. Consumer DemographicConsumer demographic play effective role in choosing a product category and it influence the consumer to buy a product of its choice. changes in consumer demographic of people and entrance of women in work force are the fundamental drivers of developing attitude towards buying .This has led to the entry of Ready to eat food category.(Sarathy,T.;Gopal,Shilpa). These factors positively impact on attitude toward ready to eat food. A number of research has been made on how gender differences affect the consumer purchase decision throughout the world whereas in most of the cases women play a significant role in making all the purchase decision.(Dholakia, 1999; Hawfield and Lyons, 1998).Product KnowledgeProduct knowledge about a particular product helps consumer to develop their attitude toward a product. Product knowledge is the knowledge about any good, services and product. Particular knowledge of a product, its main features, and its use involve in effective buying (Kotler, 1990). Product knowledge about a particular product helps consumer to develop their buying behavior about ready to eat food. Main features that are linked with the product will help to build their behavior and consequently purchase that product. If a consumer has knowledge about different products he can easily differentiate in different products and this behavior leads to effective buying in choosing a product. Product knowledge is basically customer awareness of a particular product ( Brucks,1985).3 Theoretical/Conceptual Framework

Convenience

Food Preference

+vet

Social ClassConsumer Buying Behavior

+vet

Consumer Demographic

+vet

Product Knowledge

HypothesisH1: Food preference positively enhances consumer buying behavior.H2: Social class significantly impacts consumer buying behavior.H3: Consumer demographics significantly influences consumer buying behavior.H4: Increase in product knowledge has a significant impact on consumer buying behavior.

4 HypothesisFood preferences of consumers has a positve impact on consumer buying behavior. Convince has significantly moderating the relation between food preference and consumer buying behavior. Several studies show that consumer preferring convenience food due to a number of reasons such as limited for food preparation, increased stress, insufficient food preparing skills, and societal values are some of the (Candel, 2001; Scholderer and Grunert, 2005;Mahon , 2006; Brunner , 2010).H1: Food preference positively enhances consumer buying behaviorSocial class of consumers signifcantly impacts consumer buying behavior.The purchase decision are greatly influenced by social class. Consumer belonging to elite class opts for buying good quality and expensive product, whereas consumer from middle class build their preferences lead by their social class. Social class social class also plays an important role in changing consumption pattern of people and this thing developing the consumer buying behavior. (Rich and Jain, 1968)H2: Social class significantly impacts consumer buying behavior.Consumer demographic is an important factor in developing consumer buying behavior. A number of research has been made on how gender differences affect the consumer purchase decision throughout the world whereas in most of the cases women play a significant role in making all the purchase decision.(Dholakia, 1999; Hawfield and Lyons, 1998). H3: Consumer Demographics significantly influences consumer buying behavior.Consumer purchase ready to eat food on the basis of particular product knowledge. Main features of a product that are linked with these products will help to build their attitude and consequently purchase these products. Product knowledge is basically customer awareness of a particular product ( Brucks,1985).The message content in advertisement helps consumer to build their product knowledge about a product.This product knowledge leads to consumer buying behavior.H4: Increase in product knowledge has a significant impact on consumer buying behavior5 Research Methodology 5.1 Sample Selection ( Size and Techniques)Although ready to eat food has a growing demand but there is less information available regarding people shifting from traditional home made food to ready to eat food and their factors affecting the decision of choosing ready to eat as food over traditional home made food. To collect the data for our research we have selected targeted students GIFT University students and local market customers from where ready to eat food products are available. The sample size that will be used in our research is 150. In order to carry our research we have decided to use the non-probability sampling technique from which the convenience sampling technique will be used. We have selected this technique as it will be easier for us to collect the data. Also our research topic is such that the data on it can be collected by using this technique i.e. convenience sampling technique. The reason for selecting the convenience sampling technique is that these types of techniques do not require much cost and are less time consuming. Also the results obtained in from this technique gives different views of people.5.2 Population FramePopulation frame in a research is represents all the elements about which the researcher wants collect the data relevant to its topic. The population is divided into two groups one group includes GIFT University students and other groups includes consumers from the market where ready to eat products are available. The reason for selecting such population is as we are studying consumer buying behavior so by studying the responses from the GIFT University students will show buying behavior of young consumers and the responses from local market consumers give us varying views of consumers from variety of consumers from different age group with different income level. The result from these two populations will help us to analyze the consumer buying behavior towards ready to eat food in different age group, different locality, and customers with varying income level.5.3 Unit of AnalysisThe unit of analysis used in our study is individual. Since we are study the consumer buying behavior towards ready to eat food. Consumer buying behavior is a concept that varies from person to person. It is possible to get varying results when this concept is studied. Every consumer has different food preference i.e. it will be suitable to use the individual unit of analysis.5.4 Type of StudyThe type of research that we have conducted i.e. study type is explanatory research. As an explanatory research is aimed to describe the reasons behind why a particular phenomenon occurs. Our research objectives are such that they we will be describing the consumers buying behavior toward ready to eat food. These objectives will be explaining what factors are affecting consumer buying behavior towards ready to eat food and which factors are more influential.5.5 Time HorizonOur study is cross-sectional as we will be conducting it for a short period of time and not for a longer period of time. As our research is based on studying the consumer buying behavior that keeps changing with the passage of time.5.6 Researchers StrengthWe are undertaking research on Consumer Buying Behavior towards Ready to Eat food. We are acknowledging our abilities. We are three group members and doing BBA. 5.7 Instrument development/selectionA research questionnaire which is made for a research can be of three types it can either be self-developed in which all the questions are developed by the researcher. Adapted type in which the questions for the questionnaire are taken from relevant articles and changes are made according to the researchers preference. Adopted type includes in which all the questions are taken from relevant articles and are written in the questionnaire without making any changes.The questionnaire for our research data collection is made by adopting the questions from the relevant article.5.8 Proposed Data Collection ProceduresThe data for research can be collected either through questionnaire or interviews. In our research the data from our target population is collected by distributing structured questionnaire. This questionnaire included questions related to the variables as mentioned in the framework. Each variable included 5 questions. Likert scale has been used for answering the questions given in the questionnaire. Next to each question there was given 5 options ranging from 1 to 5. 1 for strongly disagree and 5 for strongly agree. The response rate for these questions were answered on these options.

5.9 Proposed Data Analysis TechniquesTo reach the final results and to justify our research we use various statistical tool for analysis of the data. The respondents responses are entered into the SPSS for final data analysis. There are number of statistical tools that we can use but it varies from research to research and depends on the researches requirement. The statistical tools that we have used in our research includes four steps of exploratory data analysis which includes finding the outlier, finding the missing value, check out of range value and checking the normality of the data. To check the reliability of the data we have performed the reliability tests. An exploratory factor analysis is used. In order to find the relationship between two variables Correlation analysis is used which showed us how different variables are related with each other and it showed the strength of their relationship. Descriptive analysis has also been used. Regression analysis is used to check the impact on the dependent variable due to a change in the independent variable. The regression analysis has also showed us whether our stated hypothesis are accepted or not. To check the means between two independent groups we have used two way independent sample t-test5.10 Proposed Data Analysis SoftwareThere are different software for data analysis that includes SPSS, LISERAL, AMOS etc. Among these we have used SPSS. After the data that was collected through questionnaire it was be computed and analyzed by using the Statistical Package for Social Sciences (SPSS) version 19 .The statistical tools which we have selected were used through this software giving us the findings of our collected data.

6 Data Analysis6.1 Exploratory Data AnalysisWhen a research is made it is considered that the data used for research does not include any abnormalities. To make this sure an exploratory data analysis is a technique which is used to check whether the collected data for the research is normal and is perfect for applying the statistical tools for analyzing the results. Findings of any study cannot be accurate when the data for study is normal and has been passed through exploratory data analysis(EDA). An exploratory data analysis is used when 1- There are not any outlier, non-normal distribution, missing values , out of range values .2- EDA can also be used to check whether assumptions for statistics planned for our study are fulfilled or not.Exploratory Data Analysis consists of four steps:To check for missing valuesTo remove outliersTo find out of range valuesTo check the normality of dataMissing ValueIn this step we find if there are any missing values in our data sheet of SPSS. If there are any missing values found in the data then they are replaced by taking an average of that column in which value is missing. In our study there was no missing value. Generally these missing values are due to two reasons if the respondent has left any question while filing the questionnaire or we have missed any value while entering the data into SPSS.OutliersOutliers in a data are those value which are the extreme level or showing any abnormality. If the general trend of an item in the questionnaire is 4 and 5 then there are some questionnaires that have 1 or 2 value. These are counted as outlier. An outlier should be removed from the data otherwise they will affect the normality of our data. In our data we have replaced these outliers by taking an average of the column that shows outlier.Out of Range ValueOut of range values are those that do not come into our given set of values. If a study has values on liker scale are from 1 to 5 but if we have accidently entered a value which is 13 then this will taken as an out of range value. In our data there were not any out of range values .These can be replaced with by taking an average of that column.Normality of DataA data is said to be normal if the value for both Skewness and Kurtosis are from +1 to -1. Also the bell shape curve on the histogram of these value tells us the how much the data is skewed from the mean. The bell shape curve is positioned in the center then the data is said to have normality in it. 6.1 Demographic AnalysisTable 1 Demographic AnalysisVariable PercentageVariable Percentage

Gender Male Female

Age Below 30 20-30 40-50 Above 50

Work Experience None Less than 5 years 5-10 years 10-15 years Above 15 years

4654

21.365.38.05.3

67.322.73.32.74.0

Income None 15000- 30000 30000-40000 40000 & Above

Profession Student Employee Housewife

Marital Status Single Married50.722.76.720.0

72.015.312.7

7029.3

Interpretation:The demographic analysis for our data in this study is calculated by taking demographic items. The total no of respondents in our study was 150. These demographic items include gender, age , income , work experience, profession, marital status of the respondents for the data survey. In demographic analysis we have calculated the percentage or frequency of for these items. The percentage result for gender gave result for Male 46 % and 54%. The percentage result for income is for None its value is 50.7%, 15000-30000 is 22.7% , for 30000-40000 is 6.7% , for 40000 & Above is 20.0%. The percentage value for age is for Below 30 is 21.3% , for 20-30 is 65.3%, for 40-50 is 8.0 % and for Above 50 is 5.3% .The percentage value for work experience None is 67.3% , for less than 5 years 22.7 , for 5-10 years is 3.3% , for 10-15 years is 2.7% and for Above 15 years is 4.0% . The percentage value for Profession is Student is 72.0% , for Employee is 15.3% and for Housewife is 12.7%. The percentage value for Marital Status for single is 70% and for Married is 29.3% .

6.2 ReliabilityReliability refers to the uniformity of an evaluation. A test is calculated reliable if we find the matching result again and again. For example, if a test is calculated to compute a feature, then each moment the test is administered to an area under discussion, the results should be around the same. Unfortunately, it is unfeasible to analyze reliability exactly, but it can be predictable in a number of unusual ways. Reliability test was useful to check out the internal consistency with items of a testing variable. Cronbachs Alpha is well-known test for reliability. The value of Cronbachs alpha should > 0.6 according to (Nulley, 1968) which is considered as sound (good). The alpha value of all the constructs is normal. In the above the table as you can see that the Cronbachs Alpha value of all variables are above 0.6. As per rule of the data is reliable when the value of Cronbachs Alpha should be above 0.6. So the above table shows that all values are above 0.6 so the data is reliable.Serial no.VariablesCronbachs alphaNo. of items

1Food Preference.6557

2Convenience.6296

3Social Class.6008

4 5Consumer Demographics Product Knowledge.614.70866

Table No 2Reliability

InterpretationWhen reliability for all the variables in our framework was checked the following results were obtained which are given in table no 2. The reliability for food preference (FP) had Cronbach alpha value .655 . This is also fulfillng the assumption for Cronbach alpha which is greater than 0.6. The reliability for convenience(CO) had Cronbach alpha value .629 . This is fulfillng the assumption which is greater than 0.6. The reliability for social class (SC) had Cronbach alpha value .600. This is fulfillng the assumption of Cronbach alpha which is greater than 0.6. The reliability for consumer demographics (CD) had Cronbach alpha value .614 . This is fulfillng the assumption of Cronbach alpha which is greater than 0.6. The reliability for product knowledge (PK) had Cronbach alpha value .708. This is fulfillng the assumption which is greater than 0.6.Hence we can say that the reliability for all our variables is correct because they have an accurate value that fulfill the assumption of Cronabach alpha.

6.3 Exploratory Factor AnalysisFactor AnalysisIn factor analysis we are measuring whether all the questions of a particular variable are explaining that variable properly. In other words it tells us that how much these questions/items are linked to each other. The other purpose of factor analysis is data reduction. This factor analysis is done through exploratory factor analysis. In studies where exploratory factor analysis, the data in these studies are collected through questionnaire which consists of questions/items explaining a particular variable. The exploratory factor analysis also explain whether these items are linked to each other or not.

KMO and Bartlett TestKMO and Bartlett TestThese test have two assumptions which are : KMO value should be greater than 0.6 Bartlett test should be significant i.e. less than 0.05The KMO value is measuring the variables sampling capability. Bartlett test value should be less than 0.05 which is explaining the correctness of our data. When these two assumptions of KMO and Bartlett test are fulfilled then it mean that our exploratory factor analysis has correct and the items are explaining a particular variable. The right items chosen to explain a variable are selected properly. If our values for exploratory factor analysis are lying within the specified range then we can successfully run the rest of test on our data. If these values are insignificant for Bartlett test or are lying inside the specified range for KMO value than it mean that the items chosen to explain the variable are not accurate.Variance ExplainedVariable explained shows all the factors extractable from the analysis along with fixed number of factors, factor extract in one.

Table 3 Exploratory Factor AnalysisS No.

Consumer Buying Behavior Food PreferenceConvenience Social ClassConsumer DemographicProduct Knowledge

1.687.481.771 - -.658

2.377.484.709.453.644.729

3.574.426.604.676.502.662

4.595.609.633.640.663.652

5.385.717 -.721 .774.518

6.463.678.514 .611 .795.604

7.573.593 - .481 - -

8.545 - - .547 - -

KMO

Bartlett

Variance Explained.702

.000

28.571%.778

.000

33.467%.708

.000

36.579%.759

.000

.33.101%.660

.000

38.984%

.706

.000

41.015%

InterpretationOur propose framework consists of 6 variables in which there are 4 independent variable, 1 moderator variable and 1 dependent variable. In order to collect the data we developed a questionnaire which consisted of items/questions that are explaining all of these variables. To check whether these items are explaining these variables effectively or not we have performed the exploratory factor analysis on our data using SPSS v.19 .When an exploratory factor analysis was run for our data so the following results were obtained which are given in table no 3. The result for our dependent variable Consumer Buying Behavior (CBB) were obtained it showed that the KMO value for CBB was .702 which is fulfilling the assumption for KMO value i.e. greater than 0.6. The result for Bartlett test for CBB is .000 which is showing the results are significant are fulfilling the assumption Bartlett test. The Bartlett test value tells us that common variance among variables is quite high. The variance explained for CBB is 28.571% which shows that variance is explained as a whole because of CBB questions/items. The results for Food Preference (FP) were obtained it showed that the KMO value for FP was .778 which is fulfilling the assumption for KMO value i.e. greater than 0.6. The result for Bartlett test for CBB is .000 which is showing the results are significant are fulfilling the assumption of Bartlett test. The Bartlett test value tells us that common variance among variables is quite high. The variance explained for FP is 33.467% which shows that variance is explained as a whole because of FP questions/items. The results for moderating variable Convenience (CO) were obtained it showed that the KMO value for CO was .708 which is fulfilling the assumption for KMO value i.e. greater than 0.6. The result for Bartlett test for CO is .000 which is showing the results are significant are fulfilling the assumption Bartlett test. The Bartlett test value tells us that common variance among variables is quite high. The variance explained for CO is 36.579% which shows that variance is explained as a whole because of CO questions/items. Results for Social Class (SC) were obtained it showed that the KMO value for SC was .759 which is fulfilling the assumption for KMO value i.e. greater than 0.6. The result for Bartlett test for SC is .000 which is showing the results are significant are fulfilling the assumption Bartlett test. The Bartlett test value tells us that common variance among variables is quite high. The variance explained for SC is 33.101% which shows that variance is explained as a whole because of SC questions/items. The variance explained tells us 33.101% items are explaining the variable while the remaining are not significant. The results for Consumer Demographics (CD) were obtained it showed that the KMO value for CD is .660 which is fulfilling the assumption for KMO value i.e. greater than 0.6. The result for Bartlett test for CBB is .000 which is showing the results are significant are fulfilling the assumption Bartlett test. The Bartlett test value tells us that common variance among variables is quite high. The variance explained for CD is 38.984% which shows that variance is explained as a whole because of these questions/items in CD. The results for Product Knowledge (PK) were obtained it showed that the KMO value for PK is .706 which is fulfilling the assumption for KMO value i.e. greater than 0.6. The result for Bartlett test for PK is .000 which is showing the results are significant are fulfilling the assumption Bartlett test. The Bartlett test value tells us that common variance among variables is quite high. The variance explained for is 41.015% which shows that variance is explained as a whole because of PK questions/items. The remaining items are not significant.

Interpretation of Component MatrixThe component matrix table tells us that how much each item/question is individually explaining a particular variable. If a variable has 5 items so the component matrix shows value for each item. These values are the rotated loading scores of a variable.The values less than 0.4 are suppressed as taking them small coefficient.The loading score for our first variable consumer buying behavior(CBB). The CBB1 loading score is .687 which mean that 68.7% of CBB is explained through CBB1.The loading score for CBB2 is .377 which means CBB is individually explained 37.7% through CBB2. The loading score for CBB3 is .574 which means CBB is individually explained 57.4% through CBB3. The loading score for CBB4 is .595 which means CBB is individually explained 59.5% through CBB4. The loading score for CBB5 is .385 which means CBB is individually explained 38.5% through CBB5. The loading score for CBB6 is .463 which means CBB is individually explained 46.3% through CBB6. The loading score for CBB7 is .573 which means CBB is individually explained 57.3% through CBB7. The loading score for CBB8 is .545which means CBB is individually explained 54.5% through CBB8.The loading score for the 2nd variable Food Preference (FP) has 7 items. The loading score FP1 is .481 which shows that FP1 explains FP individually through 48.1%. The loading score FP2 is .482 which shows that FP2 explains FP individually through 48.2%. The loading score FP3 is .426 which shows that FP3 explains FP individually through 42.6%. The loading score FP4 is .609 which shows that FP4 explains FP individually by 60.9%. The loading score FP5 is .717 which shows that FP5 explains FP individually through 71.7%. The loading score FP6 is .678which shows that FP6 explains FP individually through 67.8%. The loading score FP7 is .593 which shows that FP7 explains FP individually through 59.3%.The loading score for Convenience (CO) have 6 items. The loading score for CO1 is .771 which is explaining CO 77.1% individually. The loading score for CO2 is .709 which is explaining CO 70.9% individually. The loading score for CO3 is .604 which is explaining CO 60.4% individually. The loading score for CO4 is .633 which is explaining CO 63.3% individually. The loading score for CO6 is .260 which is explaining CO .514% individually.The component matrix table for Social Class (SC) showed loading score of 8 items. For SC2 is loading score is .453 it means that item individually explain 45.3% of that variable. For SC3 is loading score is .676 it means that item individually explain 67.6% of that variable. For SC4 is loading score is .640 it means that item individually explain 64.0% of that variable. For SC5 is loading score is .721 it means that item individually explain 72.1% of that variable. For SC6 is loading score is .611 it means that item individually explain 61.1% of that variable. For SC7is loading score is .481 it means that item individually explain 48.1% of that variable. For SC8 is loading score is .547 it means that item individually explain 54.7 % of that variable. For Consumer Demographics (CD) the loading score. The loading score for CD2 is .644 which means it is 64.4% individually explaining CD. The loading score for CD3 is .502 which means it is 50.2% individually explaining CD. The loading score for CD4 is .663 which means it is 66.3% individually explaining CD. The loading score for CD5 is .774 which means it is 77.4% individually explaining CD. The loading score for CD6 is .795 which means it is 79.5% individually explaining CD. The loading score last variable Product Knowledge (PK) is for PK1 is .658 which mean 65.8% PK is individually explained through this . The loading score for is for PK2 is .729 which mean 72.9% PK is individually explained through this . The loading score for is for PK3 is .662 which mean 66.2% PK is individually explained through this The loading score for is for PK4 is .652 which mean 65.2% PK is individually explained through this. The loading score for is for PK5 is .518 which mean 51.8% PK is individually explained through this . The loading score for is for PK6 is .604 which mean 60.4% PK is individually explained through this .6.4 Descriptive AnalysisA descriptive analysis is used for check the minimum value, maximum value, mean , standard deviation, skewness and kurtosis value for each of our variable in our data.Minimum Value and Maximum ValueThe minimum value in a descriptive analysis tells us the minimum value that exist in our data. The maximum value tells us the maximum value that appears in our data. Knowing the minimum and maximum value helps us to explain our data properly.Menthe mean value for our whole data for a single variable is found through by taking an average for that data. The advantage of calculating the mean of our data is helpful while knowing that what value on the most appears in our data.SkewnessIn order to check the normality of our data we use it skewness value. Basically skewness mean that how much the data is spread over normal distribution. The value for skewness should be between +1 and -1 for data to be normal.KurtosisKurtosis value tells us that how much our data is close to the mean value. The range for kurtosis is between +1 to -1.If the value lies within this then the data is normal.Table 4 Descriptive Analysis(N=150)VariablesNMinimumMaximumMeanS.DSkewnessKurtosis

CBB15015.0037.0027.46004.67986-.055-.131

FP15011.0035.00 25.40004.51099-.226.380

CO1508.0029.0020.84003.84617-.458.603

SC 1504.0020.0014.44002.83194-.058-.280

CD1506.0020.0014.39332.92615-.333-.120

PK1507.0030.0020.90004.37005-.301-.088

InterpretationWe have performed the descriptive analysis for our data. In which the following results were obtained. The minimum value for CBB is 15 and the maximum value for this 37. The mean value is 27.4600 whereas the value for Skewness is -.055 and for Kurtosis is -.131. Which mean that our data for CBB is normal. The minimum value for FP is 11 and the maximum value for this 35. The mean value is 25.4000 whereas the value for Skewness is -.266 and for Kurtosis is -.380 which mean that our data for FP is normal. The minimum value for CO is 8 and the maximum value for this 29. The mean value is 20.8400 whereas the value for Skewness is -.458 and for Kurtosis is 603. Which mean that our data for CO is normal. The minimum value for SC is 4 and the maximum value for this 20. The mean value is 14.4400 whereas the value for Skewness is -.058 and for Kurtosis is -.280. The minimum value for CD is 6 and the maximum value for this 20. The mean value is 14.3933 whereas the value for Skewness is -.333 and for Kurtosis is -.120. Which mean that our data for CD is normal. The minimum value for PK is 7 and the maximum value for this 30. The mean value is 20.9000 whereas the value for Skewness is -.301 and for Kurtosis is -.088. Which mean that our data for CD Is normal. The S.D value for CBB,FP,CO,SC,CD and PK are 4.67986 , 4.51099, 3.861617, 2.83194, 2.92615 and 4.37005 respectively.6.5 CorrelationCorrelation is a statistical technique that measures the interdependence between two variables. Interdependence means that two variables are both dependent on each other. When two variables move in the same direction i.e. if one variable is increasing the other variable will also increase. These variables are positively correlated. If one variable is increasing and the other variable is decreasing then these variables are said to be negatively correlated. If change in one variable results no change into the other variable then these variables seem to have zero correlation. Correlation coefficient measures the strength of the relationship between two variables.

Table 5CBBFPCOSCCDPK

CBB1-----

FP.581**1----

CO.561**.560**1---

SC.466**.572**.524**1--

CD-.139-.105-.132-.1351-

PK.463**.536**.576**.654**.1331

Note: ** Correlation is significant at 0.01 (1%) level( 2-tailed) *Correlation is significant at 0.05 (5%) level (2-tailed).InterpretationThe r value for FP &FP , CO&CO, SC&SC, CD&CD ,PK&PK is 1 respectively. This correlation can be explained in our research as FP and CO has correlation of r =.560 and the +ve sign shows that they are strongly correlated. In other words an increase in FP results into an increase in CO. The relationship between FP and SC is r=.572 and the +ve sign shows there FP and SC are positively correlated. There is a strong relationship between FP and SC. An increase in one variable results into an increase of the other variable. The relationship between FP and CD has correlation value of r=.-.105 and the -ve sign shows that FP and CD are negatively correlated. If FP is increasing then CD will decrease. For FP and PK the value of r=.536 and the positive sign shows that there exists a strong positive relationship between FP and PK. In other words both the variables will move in the same ratio. The relationship with SC and CO r=.542 and the +ve sign shows that there is strong positive relation between SC and CO. the relation between CD and CO r= -132, negative sign shows that there is weak relationship between two variables CD and CO. It means increase in CD decrease in CO. the relationship between PK and CO r=.576 +ve sign is showing that these two variable have strong relation if PK will increase CO will also increase. Relationship between CD and SC r=-.135. They have weakly negative relationship between the two variables. If CD increases SC decreases due to weak relationship. Relationship between PK and SC is .645 +ve sign shows that they have strong positive relation between PK and SC. Increase in PK also increase in SC. The relationship between PK and CD r=.133 they have weakly positive relation.6.6 Regression AnalysisInterpretation of R-SquareRegression analysis is a statistical technique which is used to analyze the change in one variable caused by the change in the other variable. In a regression analysis there are two variables one is a predictor or independent variable and the other variable is criterion or dependent variable. A regression analysis are of two types i.e. linear regression model and multiple regression model. A multiple regression model includes a dependent variable and more than two dependent variable. In our case we have used multiple regression model because there is one dependent variable consumer buying behavior and 4 independent variable food preference, social class , consumer demographics and product knowledge and a moderator variable convenience. The value of R-square means that the variation in the dependent variable can be explained by regression which means that 38% variation in consumer buying behavior can be explained by regression. In other words R-square which is also called as coefficient of determination tells us that how well the independent variable explains the dependent variable i.e. it tells about the model fitness. It is also concluded that 38% value of R-square means that the independent variable in the model are explaining the dependent variable and the remaining 62% shows that there were more variables in the model. The value of adjusted R-square is often used to summarize the fit as it takes into account the number of variables in the model. The general form for a multiple regression equation is as:Y= a++++.bixi

When this regression equation is made using the values for our framework it is given as below: CBB= 10.624++ -+

Interpretation of ConstantThe value of constant tells that if all the independent variables i.e. food preference, convenience, social class, consumer demographics and product knowledge are kept equal to zero then the value of dependent variable which is consumer buying behavior. The value for constant is 10.624Interpretation of SlopeThe value of b explains the change caused in the dependent variable by a 1 unit change in independent variable. Interpretation of p-valueIn order to fulfill the condition for the acceptance of hypothesis there is an assumption which tells that if that the significance level should be less that 0.05 i.e. p< 0.05 which means that the data value is significant. If the p-value is greater than 0.05 then we will reject null the hypothesis or the value is insignificant and will accept the alternative hypothesis. If p-value is less than 0.05 then we will accept the null hypothesis and reject alternative hypothesis.

Table 6 Regression analysis summary (N=150) Variable B S.E t p-value Hypothesis

Constant 10.624 2.443 4.349 .000 FP .444 .086 5.183 .000 Supported SC .109 .088 1.244 .126 Not Supported CD -.099 .113 -.878 .381 Not Supported PK .161 .096 1.681 .095 Not Supported

Note=R2=.380 f(4, 145)=22.211 P.V