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