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: