research report- olive oil as cooking medium
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STUDYING MARKET FOR OLIVE OIL AS A COOKING MEDIUM
INTRODUCTION
India—second largest populated country is gaining the world’s attention as the fastest growing
economy. With growing needs and market potential there is a huge demand for food and other
household products. Growing health consciousness and increased incomes have caused a shift in
eating habits of people which are still evolving.
India has been exposed to the usage of olive oil lately. It was used only for skin and hair usages
by a few. Inspired by the Western lifestyles, Indians too have now become health-conscious and
are looking forward to healthier food alternatives.
The main objective of the research is to study the scope of olive oil as a medium of cooking in
Indian kitchens.
India ranks fourth in consumption of vegetable oil in the world and is a leading importer of this
produce. But olive oil is not an explored area in this perspective.
Its business potential is yet to be explored. In this research we wish to explore the factors that are
important in determining the consumption of olive oil.
We plan to find out the causes for this scarce demand and how can we maximize on the untapped
potential.
LITERATURE REVIEW
Olive Oil consumption is growing in India, opening a new frontier for manufacturers and
exporters
By FMCG on 30 September 2010
This report turns our attention to the fact that Indian middle class is hungry for healthy food
alternatives and olive oil is poised to gain significant market share in the wellness food industry
in the country. It also provides precise information to customers about different olive oil brands
available in India and various macroeconomic factors viz. income, demographics and education
affecting buying behavior.
Entering the Olive Oil Market in India
By FMCG on 9 October 2010
Publisher: FoodBizIntel
This research paper is an expansion of FMCG’s previous paper published on 30 September 2010.
From this research paper, it can be concluded that olive oil is still little known in India and there
is a great business opportunity for those who are involved in its production and exports helping
them to understand the olive oil trade in India. This review gives an overview of India's
macroeconomic indicators, which help us understand the basic behavior of the Indian consumers.
This research paper also gives an idea to understand current and emerging models, food
distribution structure in India, edible oil market in India, government regulations, promotion of
olive oil, market data by the means of compound annual growth rate (CAGR) and its market
potential.
An Olive Oil Experiment in India
By Gita Narrayani, olive oil times on 3 July 2010
The experiment done proved that olive oil as cooking medium is not widely prevalent in India. It
is restricted to only those people who can afford the exorbitant price of the imported commodity.
This report also brought the health benefits of olive oil in to the light.
The 2009-2014 Outlook for Olive Oil in India
Author: Bharat Book Bureau
Category: Business RSS
This study is quite strategic in nature because it takes an aggregate and long term view. Also
using econometric study and multi-stage methodology, latent demand or potential industry
earnings (P.I.E) for olive oil is estimated in all the cities of India which are useful for knowing
distribution and sales force strategy.
An olive oil dream
By Smitha tripathy, Rediff.com on 29 November 2003
This research paper gives us an entrepreneurial idea of V.N.Dalmia, chairman of Dalmia group
and a member of Indian olive association to import olive oil in large quantities in India. Through
this research, we discovered that 10 million litres of olive oil is imported to India every year with
the market growing at a rate of 40% annually. However oil available in India is highly fruity in
flavor and has a distinct aroma cannot be used by Indians. This report concludes on a very
interesting note that, Leonardo oil is priced at Rs 270 per litre whereas the oil we import is priced
at Rs 560 per litre. Apart from these, regular cooking oil is priced at Rs 100 per litre. So how far
Dalmias would be successful in this business is a big question.
Plant Oil Market in Indi]
By Business Report 2010
This report provides an in-depth analysis of plant oil market in the country and concludes that
olive oil market occupies an important role in India and is booming with the passage of time.
The aim of this study was to enable making decisions of how to penetrate in the Indian market
and exploit commercial opportunities.
DATA AND METHODOLOGY
RESEARCH METHODOLOGY
We used both qualitative as well as quantitative research for our analysis.
Focus Group Study: First, we conducted a focus group of 8-10 students. The focus group was
asked to state the attributes, which, according to them are essential for buying olive oil and how
the general masses might be thinking about its usage. The data collected from the focus group
helped deciding the questions for the questionnaire. Depending on the response from the focus
group the questions were framed. Those variables which had been left out by the focus group
were included, making the questionnaire a holistic one, comprising all the essential points.
Survey: By using this method we gathered the information from a sample of people using a
Questionnaire.
Questionnaire: An exhaustive list of questions, including both open ended and closed-ended
questions were prepared and administered to various respondents.
SAMPLING
Target Population: Individuals who were the prospective customers of olive oil were the target
population.
Sample Size: The sample size taken was 150 individuals.
Sampling Units Selection: The sampling technique used was Probability Sampling. Under this
we have used Simple random sampling.
COST & TIME INVOLVED
The cost incurred was of printing the material and doing survey. To be more economical, we
used an online survey. This reduced the cost and also made it more fast and easy to calculate the
results.
DATA COLLECTION
Data used was both primary and secondary. Primary data was by means the of the survey
conducted. Secondary data was from other researches and studies or surveys conducted for
related topics.
METHODOLOGY
We have used Discriminant Analysis. The dependent variable was the buying behavior of people
i.e. whether they buy olive oil for cooking or not and the independent variables were availability,
taste, price, income of individuals and awareness of the health benefits olive oil among
individuals.
DISCRIMINANT ANALYSIS
Discriminant analysis is a technique for analyzing data when the criterion or dependent variable
is categorical and the predictors or independent variables are interval in nature. It gives us an
idea about how the different groups vary with respect to the chosen independent variables
(predictors). Taking buying behavior of people as the dependent variable we ran two group
Discriminant analysis for mean and uni-variate ANOVA using SPSS.
OUTPUT ANALYSIS
Analysis Case Processing Summary:
Analysis Case Processing Summary
Unweighted Cases N Percent
Valid 150 100.0
Exclude
d
Missing or out-of-range
group codes0 .0
At least one missing
discriminating variable0 .0
Both missing or out-of-
range group codes and at
least one missing
discriminating variable
0 .0
Total 0 .0
Total 150 100.0
This table summarizes the analysis dataset in terms of valid and excluded cases. The reasons
why SPSS might exclude an observation from the analysis are listed here, and the number ("N")
and percentage of cases falling into each category (valid or one of the exclusions) are presented.
In our case all of the observations in the dataset are valid.
Group statistics:
Group Statistics
2.67 .875 75 75.000
41.87 11.962 75 75.000
1.79 .793 75 75.000
1.79 .741 75 75.000
1.76 .675 75 75.000
2.55 .963 75 75.000
40.23 10.645 75 75.000
3.81 .865 75 75.000
3.55 .552 75 75.000
3.84 .823 75 75.000
2.61 .919 150 150.000
41.05 11.315 150 150.000
2.80 1.311 150 150.000
2.67 1.097 150 150.000
2.80 1.285 150 150.000
Price
Income
Taste
Awareness
Availablity
Price
Income
Taste
Awareness
Availablity
Price
Income
Taste
Awareness
Availablity
CB1.00
2.00
Total
Mean Std. Deviation Unweighted Weighted
Valid N (listwise)
This table presents the distribution of observations into the two group’s buyers(1) and lnon
buyers (2). We can see the number of observations falling into each of the 2 groups. In this
example, we are using the default weight of 1 for each observation in the dataset, so the weighted
number of observations in each group is equal to the unweighted number of observations in each
group.
Tests of equality of group means:
Tests of Equality of Group Means
.996 .638 1 148 .426
.995 .787 1 148 .377
.398 223.548 1 148 .000
.352 272.135 1 148 .000
.340 286.670 1 148 .000
Price
Income
Taste
Awareness
Availablity
Wilks'Lambda F df1 df2 Sig.
In the table ‘Tests of Equality of Group Means’ the results of uni-variate ANOVA’s, carried out
for each independent variable, are presented. In the above table the Sig.value is less than 0.05 for
taste awareness availability. This implies that all these predictors are significantly contributing to
the discriminant model in differentiating between groups. Hence, we interpret that all these
predictors are significantly differentiating between the buy and do not-buy categories of
consumer.
Wilks’ lambda:
It is 0.162. Wilks’ lambda whose value is closer to zero is preferred. Thus in the above case the
value is good.
Chi-square:
This is the Chi-square statistic testing that the canonical correlation of the given function is equal
to zero. In other words, the null hypothesis is that the function, and all functions that follow,
have no discriminating ability. This hypothesis is tested using this Chi-square statistic.
df:
This is the effect degrees of freedom for the given function. It is based on the number of groups
present in the categorical variable and the number of continuous Discriminant variables. The
Chi-square statistic is compared to a Chi-square distribution with the degrees of freedom stated
here.
Sig.:
This is the p-value associated with the Chi-square statistic of a given test. The null hypothesis
that a given function's canonical correlation and all smaller canonical correlations are equal to
zero is evaluated with regard to this p-value. For a given alpha level, such as 0.05, if the p-value
is less than alpha, the null hypothesis is rejected. If not, then we fail to reject the null hypothesis.
Pooled within group matrices:
Pooled Within-Groups Matrices
1.000 .550 .082 .037 .064
.550 1.000 -.034 .087 .003
.082 -.034 1.000 -.034 .032
.037 .087 -.034 1.000 .065
.064 .003 .032 .065 1.000
Price
Income
Taste
Awareness
Availablity
CorrelationPrice Income Taste Awareness Availablity
Since all the values are below 0.8 which implies that there is no existence of multi-collinearly in
the pair of variables. It therefore indicates that both the variables in the particular pair do not
share a large amount of common shared variance and thus reflect different attributes. This
indicates that the predictors would make the model reliable.
Box's Test of Equality of Covariance Matrices
Log Determinants
Log Determinants
5 2.288
5 2.267
5 2.499
CB1.00
2.00
Pooled within-groups
RankLog
Determinant
The ranks and natural logarithms of determinantsprinted are those of the group covariance matrices.
Test Results
32.721
2.102
15
88192.421
.007
Box's M
Approx.
df1
df2
Sig.
F
Tests null hypothesis of equal population covariance matrices.
The significance value of 0.007 indicates that the data do not differ significantly from
multivariate normal. This means one can proceed with the analysis.
SUMMARY OF CANONICAL DISCRIMINANT FUNCTIONS
The discriminant function coefficients (un-standardized) are the multipliers of variables, when
the variables are in the original units of measurement.
Eigen value
For each discriminant function, the Eigen value is the ratio of between-group to within-group
sums of squares. Large Eigen values imply superior functions
Eigenvalues
5.155a 100.0 100.0 .915Function1
Eigenvalue % of Variance Cumulative %CanonicalCorrelation
First 1 canonical discriminant functions were used in theanalysis.
a.
An Eigenvalue indicates the proportion of variance explained (Between-groups sums of squares
divided by Within-groups sums of squares). A large eigenvalue is associated with a strong
function.
Wilks lambda
λ . Sometimes also called the U statistic, Wilks‘ λ for each predictor is the ratio of the within-
group sum of squares to the total sum of squares. Its value varies between 0 and 1. Large values
(near 1) indicate that group means do not seem to be different. Small values (near 0) indicate
that the group means seem to be different.
Wilks' Lambda
.162 264.410 5 .000Test of Function(s)1
Wilks'Lambda Chi-square df Sig.
Canonical correlation
Canonical correlation measures the extent of association between the discriminant scores and the
groups.
Canonical Discriminant Function Coefficients
Function
1
Price -.149
Income .001
Taste .669
Awareness .894
Availablity .753
(Constant) -6.008
Unstandardized coefficients
The canonical relation is a correlation between the discriminant scores and the levels of the
dependent Variable. A high correlation indicates a function that discriminates well. The present
correlation of 0.915 is extremely high (1.00 is perfect). The square of the canonical correlation of
0.915 is 0.837. Thus we can say that 83.7% of the variance in the model discriminating between
the buyers and non buyers of olive oil is due to the changes in the 5 predictors i.e., price, income,
taste, awareness and availability.
Standardized Discriminant Coefficient
The standardized discriminant function coefficients are the discriminant function coefficients and
are used as the multipliers when the variables have been standardized to a mean of 0 and a
variance of 1.
Standardized Canonical Discriminant Function Coefficients
Function
1
Price -.137
Income .009
Taste .555
Awareness .584
Availability .566
For taste, awareness and availability, the standardized discriminant function coefficient is
positive and high which implies higher the liking for taste greater the awareness and higher the
availability the more likely it is for people to buy olive oil for cooking.
Structure Matrix
Functi
on
1
Availability .613
Awareness .597
Taste .541
Income -.032
Price -.029
Pooled within-groups correlations between discriminating variables and standardized canonical
discriminant functions.
Variables ordered by absolute size of correlation within function.
The loading suggests that variable availability is the most important while variable price is the
least important in discriminating the buyers or non buyers of olive of oil.
Loadings(r) Squared discriminant loadings(r2)
0.613 0.3757
0.597 0.3654
0.541 0.2926
-0.032 0.0010
-0.029 0.0008
These squared discriminant loadings indicate the amount of variance that the discriminant scores
share with the 5 variables.
Group centroid
The centroid is the mean values for the discriminant scores for a particular group. There
are as many centroids as there are groups, as there is one for each group. The means for a
group on all the functions are the group centroids.
Functions at Group Centroids
CB
Function
1
1.00 -2.255
2.00 2.255
Unstandardized canonical discriminant functions evaluated at group means
One way to determine the degree of separation between the two groups is to compute the mean
discriminant score for either group.These means are called the group centroids.
Classification statistics
Sometimes also called confusion or prediction matrix, the classification matrix contains the
number of correctly classified and misclassified cases.
Classification Processing Summary
Processed 150
Excluded Missing or out-of-
range group codes0
At least one missing
discriminating
variable
0
Used in Output 150
Classification results
Classification Resultsb,c
72 3 75
3 72 75
96.0 4.0 100.0
4.0 96.0 100.0
72 3 75
3 72 75
96.0 4.0 100.0
4.0 96.0 100.0
CB1.00
2.00
1.00
2.00
1.00
2.00
1.00
2.00
Count
%
Count
%
Original
Cross-validateda
1.00 2.00
Predicted GroupMembership
Total
Cross validation is done only for those cases in the analysis. Incross validation, each case is classified by the functions derivedfrom all cases other than that case.
a.
96.0% of original grouped cases correctly classified.b.
96.0% of cross-validated grouped cases correctly classified.c.
CONTRIBUTION
This study was conducted with a view of understanding certain variables affecting the olive oil
consumption in Indian homes. Though it just unveils only the tip of the iceberg yet is significant
in its purview. The subject of study itself was challenging. As far as the topic is concerned, all
efforts have been made to explore it. Not much research has been done in this area, therefore, our
study had its own share of exploration and struggle. With the scanty information and literature
available, we have tried to tap it to its maximum. But it has also given us the opportunity to
understand, interpret and infer newer aspects that were untouched till date.
This research has been prepared with a business as well as educational perspective. It can be of
use for qualitative and quantitative inferences. It gives concrete and useful interpretations.
Moreover, it solves the researcher’s purpose by answering relevantly to the questions it was
intended to answer.
It has fostered learning to all the members of the team.
LIMITATIONS
“Any study claiming to be perfect surely is imperfect”. Every research has its own limitations
which creates more scope for improvement.
The subject of this study was very challenging and primarily, the limited literature available has
hampered the course of study. Furthermore, the data collection and sampling errors though
unintended might also be responsible for the fact the inferences cannot be generalized over a
very large group of people.
REFERENCES
1. Malhotra, Naresh K., 2007. Marketing research-an applied orientation. 5th ed., New
Delhi: Prentice Hall of India Private Limited.
2. Churchill, Jr., Gilbert A., Iacobucci, Dawn & Israel, D., 2009. Marketing research- a
south asian perspective. Delhi: Cengage Learning India Private Limited.
3. FMCGREsearch.com, 2010, Olive Oil consumption is growing in India, opening a new
frontier for manufacturers and exporters [Online] (Updated 30 September 2010)
Available at: www.prlog.org/10967526- olive - oil -consumption-is-growing-in- india -
opening-new-frontier-for-manufacturers-and-exporters.html [Accessed 10 December
2010].
4. FoodBizIntel, 2010. Entering the Olive Oil Market in India [Online] (Updated 9 October
2010) Available at: http://fmcgresearch.com/catalog/product_info.php?products_id=84
[Accessed 10 December 2010].
5. Gita Narrayani, 2010. An Olive Oil Experiment in India [Online] (Updated 3 July 2010)
Available at: http://www.oliveoiltimes.com/olive-oil-business/asia/olive-oil-india/3863
[Accessed 10 December 2010].
6. Bharat Book Bureau, 2009, The 2009-2014 Outlook for Olive Oil in India [Online]
Available at: http://www.bharatbook.com/detail.asp?id=129831&rt=The-2009-2014-
Outlook-for-Olive-Oil-in-India.html [Accessed 10 December 2010].
7. Smitha Tripathy, 2003, An Olive Oil dream [Online] (Updated 29 November 2003)
Available at: http://www.rediff.com/money/2003/nov/29spec3.htm [Accessed 10
December 2010].
8. Merchant Research & Consulting, Ltd, 2010, Plant Oil Market in India: Business Report
2010 [Online] (Updated August 2010) Available at:
http://www.reportlinker.com/p0182647/Plant-Oil-Market-in-India-Business-Report.html
[Accessed 10 December 2010].
ANNEXURE
QUESTIONNAIRE
1. Have you ever used olive oil?
a) Yes
b) No
2. For what purposes you use olive oil?
a) For cooking
b) For hair
c) For skin
d) For medicinal uses
e) Others (if specify)
3. How often do you use olive oil for cooking?
Very Frequently Never
1 2 3 4 5
4. Do you think olive oil is expensive?
Very Expensive Not Expensive
1 2 3 4 5
5. Is the taste of food cooked in olive oil different?
Very tasty Not tasty
1 2 3 4 5
6. Is olive oil easily available to you?
Very easily available Not available
1 2 3 4 5
7. Is olive oil healthy?
Very healthy Unhealthy
1 2 3 4 5
Name:
Salary:
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