• Author / Uploaded
• Vignesh Kg

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A REPORT

ON

UNDERSTANDING CONSUMER PERCEPTION ABOUT EDIBLE CUTLERY

By GROUP – 12

1|Page

Shipra Gupta

-

15BSPHH010955

Pallav Khedekar

-

15BSPHH011010

Saurabh Kumar Sabu

-

15BSPHH011013

Vignesh KG

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15BSPHH011075

Sadiya Shaik

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15BSPHH011212

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INTRODUCTION The advent of fuel based plastics has revolutionized the industrial world and there is no area of manufacturing which is untouched by plastics. Convenience and cost factors have pitch forked plastics as the most preferred material of choice till recently, a rethinking about its impact on environment and sustainability is slowly putting a brake on its continued use. While cheap petroleum fuels from which most plastics are derived was once justified to introduce them in place of traditional materials like glass and metals, this plea cannot hold any more since the cost of non-renewable fossil fuels have increased several folds over the last 3 decades.

Plastic is a necessary evil. The amount of plastic that is disposed of every year can circle the earth four times. Every day we come across plastic in various forms such as garbage and grocery bags, bottles, food containers, computer keyboards, plastic mouse, coffee cup lids and other such products. Though plastic products are very convenient to use, they play a harmful role in polluting the environment. Plastic as utensils Plastic utensils were introduced in the 1940s, but did not start being mass produced until the 1950’s with two main causes: 1) The introduction of polypropylene and 2) the massive expansion of families into the suburbs after World War II. Plastic utensils are typically made out of two types of plastics: polypropylene and polystyrene. Plastics are made from monomers and are produced from a process called polymerization. Monomers, single sequence molecules, such as ethylene and propylene are produced from natural gas and oil. Natural gas and oil, both fossil fuels, are hydrocarbons, or a series of molecules composed of carbon and hydrogen that are linked together in a repeating chain. The natural gas and oil are heated to the point where the constituent hydrocarbons are converted into the reactive monomers. The monomers then become polymers (or multiple monomer molecules linked together) and are then cooled into blocks of the respective plastic they are designed to be come, depending on the additives put into the liquefied substance when the monomer conversion process takes place. The number one ingredient in plastic is the hydrocarbon, which comes from oil or natural gas. Both of these materials, again fossil fuels, are typically found within the Earth’s crust. All fossil 3|Page

fuels are reflective of their name, as they are merely the remains of organic matter that existed millions of years ago. Living matter such as plants, animals, fungi; anything composed of cells, is compressed by the growing weight of the Earth’s crust, eventually changing from the solid form into a dense liquid, which is crude oil. The original intention of plastic utensils being “disposable,” the ultimate destination for plastic cutlery is the trashcan. Now, technically the plastic types that make up most plastic utensils, polypropylene and polystyrene, are recyclable but most recycling plants do not accept them because they are cumbersome to process and not cost effective per unit. Because of that, most plastic utensils follow a fate of either being placed in a landfill or incinerated. If the fate of being placed in the landfill is chosen for the utensil, it can be hermetically sealed for decades, receiving little opportunity to decompose, if at all. The molecular linkages in plastics is incredibly strong, and with the addition of some of the additives such as the protectors mentioned in the materials section, the breaking of the molecular bonds to actually decompose the fork takes an incredibly long time. Landfills are structured in a way that seals oxygen out from the material inside. Without oxygen, let alone UV rays from the sun along with wind and water erosion, the breakdown of trash, let alone a plastic utensil, is near impossible.

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The other potential option is incineration. As plastic is made from hydrocarbons, which is what fossil fuels are made of, it has the potential for releasing a good amount of energy by breaking the carbon bonds that were from the original crude oil. When heat is applied to the utensil and it combusts to release the energy stored in the carbon bonds, it also produces the products of carbon dioxide, water and non-toxic ash. Some of the other harmful effects to the environment caused by plastic are-

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  

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Chemicals added to plastics are absorbed by human bodies. Some of these compounds have been found to alter hormones or have other potential human health effects.

Plastic debris, laced with chemicals and often ingested by marine animals, can injure or poison wildlife. Floating plastic waste, which can survive for thousands of years in water, serves as mini transportation devices for invasive species, disrupting habitats. Plastic buried deep in landfills can leach harmful chemicals that spread into groundwater.

Around 4 percent of world oil production is used as a feedstock to make plastics, and a similar amount is consumed as energy in the process.

Although the plastic utensil might be the ultimate in convenience and affordability, the life cycle around it is quite complex and we may not be able to afford the ecological and social costs if we keep using them. We definitely need an alternative to this menace.

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OBJECTIVES Plastic contains chemical complexes, several of which are neuro toxic and carcinogenic. These leach into food. In fact, even the so called food grade cutlery is that, where this leaching is within permissible levels of 60 Parts per Million (PPM). When you know that the substances that leach can cause cancer and impact your nervous system, why should you allow even one part per million. This is why we made these edible and safe products. The cutlery is made of flours of jowar (sorghum) blended with rice and wheat. They contain NO chemicals/preservatives/fat/plasticizes, emulsifiers, artificial color or milk products. In fact they contain nothing that is not a plant product (except salt, which we add for taste).

They are made gluten free and with many other grains that are easily available in other countries. They are simply baked in high temperatures. This would be a "Make-In-India" initiative with the aim to protect environment which is already up and running in small scale and expanding slowly.

EXPLORATORY STUDIES Further the findings and data collection will be done through questionnaire followed by interviews. This would help to spread the awareness about the product among the target customers. This would help to create a definite conclusion and insights into a given problem.

Responses may not be statistically measureable, but they will give you richer quality information that can lead to the discovery of new initiatives or problems that should be addressed.

VARIABLES For this research the variables that are being used are one dependent variable and independent variable. Dependent variable The dependent variable for this product is “sales”. Sales are affected by many independent variables such as price, availability, quality, appearance of the product.

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Independent variable The independent variables would be price, availability, quality, appearance. These variables if they doesn’t meet the customer expectations would affect the dependent variable ‘sales’. As of now the cost of the spoon is Rs. 2 but as the production picks up it can come down to Rs. 1. As the shelf life is 18 months if the stock is available at the right time then it would not affect the sales. Quality and appearance should meet the expectations of customers in order to boost the sales.

SAMPLING The sampling technique which we are going to use is random sampling. In this project we need a sample of about 80-90 customers from whom the responses are recorded in a questionnaire form. After the data collection is done then we would further process with analysis through regression.

RESEARCH TOOL Data analysis would be done through SPSS software in which we perform factor analysis. Factor analysis is a method of data reduction. The purpose of factor analysis is to analyze the pattern of responses as a way of getting at the underlying factors. Factor analysis also allows you to use the weighted item responses to create what are called factor scores. These represent a single score for each person on the factor. Factor analysis is a technique that requires a large sample size. Factor analysis is based on the correlation matrix of the variables involved, and correlations usually need a large sample size before they stabilize. Basic terminology Factor loadings: Commonality is that the sq. of standardized outer loading of associate item. Analogous to Pearson's r, the square issue loading is that the % of variance in this indicator variable explained by the issue. To urge the % of variance all told the variables accounted for by every issue, add the total of the square issue loadings for that issue (column) and divide by the amount of variables. (Note the amount of variables equals the total of their variances because the variance of a homogenous variable is one.) This can be an equivalent as dividing the factor's eigenvalue by the amount of variables. 7|Page

Interpreting factor loadings: By one rule of thumb in substantiating correlational analysis, loadings ought to be .7 or higher to substantiate that freelance variables known a priori are depicted by a selected issue, on the explanation that the .7 level corresponds to concerning 1/2 the variance within the indicator being explained by the issue. However, the .7 customary could be a high one and real-life information would not meet this criterion, that is why some researchers, significantly for alpha functions, can use a lower level adore .4 for the central issue and .25 for alternative factors. In any event, issue loadings should be taken within the lightweight of theory, not by arbitrary cutoff levels.

Communality: The total of the square issue loadings for all factors for a given variable (row) is that the variance in this variable accounted for by all the factors, and this can be known as the communality. The communality measures the % of variance in an exceedingly given variable explained by all the factors put together. Spurious solutions: If the communality exceeds one.0, there's a spurious resolution, which can replicate too little a sample or the scientist has too several or too few factors. Uniqueness of a variable: The individuality is that the variability of a variable minus its communality. Eigenvalues: The eigenvalue for a given issue measures the variance all told the variables that is accounted for by that issue. The quantitative relation of eigenvalues is that the quantitative relation of informative importance of the factors with reference to the variables. If an element encompasses a low eigenvalue, then it's contributory very little to the reason of variances within the variables and should be unnoticed as redundant with a lot of necessary factors. Eigenvalues live the quantity of variation within the total sample accounted for by every issue.

Extraction sums of square loadings: Initial eigenvalues and eigenvalues once extraction (listed by SPSS as "Extraction Sums of square Loadings") are an equivalent for PCA extraction, except for alternative extraction ways, eigenvalues once extraction are going to be below their initial counterparts. SPSS conjointly prints "Rotation Sums of square Loadings" and even for PCA, these eigenvalues can disagree from initial and extraction eigenvalues, although their total are going to be an equivalent.

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Factor scores: To cypher the factor score for a given case for a given issue, one takes the case's standardized score on every variable, multiplies by the corresponding loadings of the variable for the given issue, and sums these product.

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QUESTIONNAIRE FORM Understanding Consumer Perception about Edible Cutlery Hi Everyone, We use almost millions of plastic spoons and fork everyday and this new concept of edible cutlery has been introduced in India, where the forks/spoon used to eat your meals can be eaten once you are done. This survey is focussed on understanding the consumer perception about introducing this concept in the campus and replace the plastic or steel cutlery with edible ones.

* Required

 Name *

 Age *

 Which type of cutlery do you use most for day to day usage ? Mark only one oval. Plastic Stainless Steel Which cutlery do you use more as per your knowledge? Mark only one oval. Fork Spoon How many times in a day do you use Cutlery? Mark only one oval.

Less than 2 times Three times Four times More than Four times Which of the following factors would you consider while buying cutlery ? * Check all that apply. Price Quality Re-usability Appearance Size Colour Brand 7. According to you, To Which group of people or Entities do you think this product will be more suitable ? Check all that apply. Inside Family School going children Graduate Students Working Professionals Restaurants/ Canteen/ Cafe / Fast-Food Chains All of the above 8. Which flavor would you prefer for your edible cutlery? Mark only one oval. Chocolate Vanilla Strawberry 9. If Plastic or Stainless Steel cutlery ( Spoon / Fork ) is replaced by edible ones like shown above in the picture, Would it make a difference in your daily routine? * Mark only one oval. Strongly Agree Agree

Neither agree nor disagree Disagree Strongly Disagree

10. How likely are you to buy this product if you found it reduces waste and is nutritious but bit expensive than the plastic spoons considering this is an edible product? Mark only one oval. Extremely Likely

Very likely Moderately Likely

Slightly Likely Not at all likely

FACTOR ANALYSIS OUTPUT USING SPSS AND ITS INTERPRETATION

1. CORRELATION MATRIX Correlation Matrixa X2 Type of cutler y used on a daily basis

X3 Mostly preferre d cutley

X4 Frequenc y of usage per day

X5 Evaluatio n based on some factors

X6 Suitabl e entities

X7 Flavour preferre d

X8 Replacin g plastic with edible cutlery

X9 Likely to purchas e

1

-0.124

0.07

0.071

0.056

-0.108

0.161

-0.031

X3 Mostly preferred cutley

0.124

1

0.239

0.153

0.098

-0.011

-0.04

0.046

X4 Frequenc y of usage per day

0.07

0.239

1

0.173

0.086

0.036

0.168

0.013

X5 Evaluatio n based on some factors

0.071

0.153

0.173

1

0.311

0.039

0.112

-0.124

X6 Suitable entities

0.056

0.098

0.086

0.311

1

-0.17

-0.002

-0.031

X7 Flavour preferred

0.108

-0.011

0.036

0.039

-0.17

1

0.104

0.165

X8 Replacing plastic with edible cutlery

0.161

-0.04

0.168

0.112

-0.002

0.104

1

0.346

0.031

0.046

0.013

-0.124

-0.031

0.165

0.346

1

0.109

0.245

0.242

0.292

0.142

0.055

0.38

X2 - Type of cutlery used on a daily basis

Correlatio n

Sig. (1tailed)

X9 Likely to purchase X2 - Type of cutlery used on a daily basis

X3 Mostly preferred cutley

0.109

X4 Frequenc y of usage per day

0.245

0.008

X5 Evaluatio n based on some factors

0.242

0.065

0.043

X6 Suitable entities

0.292

0.167

0.199

0.001

X7 Flavour preferred

0.142

0.456

0.363

0.351

0.045

X8 Replacing plastic with edible cutlery

0.055

0.347

0.047

0.133

0.492

0.153

X9 Likely to purchase

0.38

0.326

0.449

0.11

0.378

0.051

0.008

0.065

0.167

0.456

0.347

0.326

0.043

0.199

0.363

0.047

0.449

0.001

0.351

0.133

0.11

0.045

0.492

0.378

0.153

0.051

0

0

a. Determinant = .566

INTERPRETATION The correlation matrix is an array of numbers that give correlation coefficients between a single variable and every other variable in the sample. If you observe the table you will notice that the correlation value along the diagonals are 1 and the reason for this is that the correlation coefficient between a variable and itself is 1. The determinant of the matrix is 0.566 mentioned at the foot of the table. As per the table if any pair of variable have values less than 0.5 then it is advisable to conduct the analysis again by removing those variables as this will give us a better understanding. In the above table we can notice that the diagonal matrix is 1 which is highlighted in yellow and the highest value of correlation is 0.492 for Variables X6 – Suitable entitles and X8 – Replacing plastic with edible cutlery.

2. KMO AND BARTLETT’S TEST Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity

Approx. Chi-Square

.488 54.355

Df

28

Sig.

.002

INTERPRETATION The KMO measures the sampling adequacy of the data set which in other words determines if the responses given are adequate or not and according to Kaiser the value should be close to 0.5 in order to have a satisfactory analysis which in this case works good as the value is 0.488. Hence this data can be accepted. And based on the Barlett’s test of Sphericity value, the significance value is less than 0.05 which implies that the correlation matrix is an identity matrix and we can accept the null hypothesis.

3. COMMUNALITIES Communalities Initial X2 - Type of cutlery used on a daily

Extraction

1.000

.617

X3 - Mostly preferred cutley

1.000

.585

X4 - Frequency of usage per day

1.000

.424

X5 - Evaluation based on some factors

1.000

.512

X6 - Suitable entities

1.000

.468

X7 - Flavour preferred

1.000

.403

X8 - Replacing plastic with edible

1.000

.700

1.000

.560

basis

cutlery X9 - Likely to purchase Extraction Method: Principal Component Analysis.

INTERPRETATION This table tells us about the level of variance. In general the extraction value greater than 0.5 should be considered for further analysis and in this case 70 % of the variance is in “ Replacing the Plastic” and 61.7% of variance in “ Type of Cutlery used on a daily Basis “ is accounted for.

4. TOTAL VARIANCE EXPLAINED Total Variance Explained

Compon

Initial Eigenvalues

Extraction Sums of Squared

Rotation Sums of Squared

Loadings

Loadings

ent Total

% of

Cumulativ

Variance

e%

Total

% of

Cumulativ

Variance

e%

Total

% of

Cumulativ

Variance

e%

1

1.580

19.755

19.755

1.580

19.755

19.755

1.572

19.647

19.647

2

1.474

18.427

38.182

1.474

18.427

38.182

1.477

18.468

38.115

3

1.216

15.194

53.376

1.216

15.194

53.376

1.221

15.261

53.376

4

.947

11.839

65.215

5

.938

11.728

76.943

6

.697

8.710

85.653

7

.655

8.186

93.838

8

.493

6.162

100.000

Extraction Method: Principal Component Analysis.

INTERPRETATION This table reflects the number of extracted factors whose sum should be equal to number of items which are subjected to factor analysis. The Eigen value table has been divided into three categories i.e., Initial Eigen Values, Extracted Sums of Squared Loading and Rotation of sums of squared Loadings. For analysis and interpretation purpose we are only concerned with Extracted sums of squared Loadings. Here the % of Variance recorded, the first factor accounts for is 19.755% of variance, the second 18.427 % of variance and third 17.013 % and all the other factors are not significant.

5. SCREE PLOT

INTERPRETATION The scree plot is the representation of Eigenvalues against all the factors and this helps us in determining the number of factors to retain. The point of interest is the region where the curve starts to flatten out and in this case if you observe the Eigenvalues of factors 4,5,6,7 and 8 are less than one and the flattening of the curve is starting between 1 & 2 itself. Hence we can retain four factors.

6. COMPONENT MATRIX

Component Matrixa Component 1 X2 - Type of cutlery used on

2

3

.221

.031

.753

X3 - Mostly preferred cutley

.463

-.072

-.605

X4 - Frequency of usage per

.613

.135

-.175

.690

-.192

.007

.568

-.353

.144

-.048

.523

-.356

.370

.678

.321

.067

.745

-.029

a daily basis

day X5 - Evaluation based on some factors X6 - Suitable entities X7 - Flavour preferred X8 - Replacing plastic with edible cutlery X9 - Likely to purchase

Extraction Method: Principal Component Analysis. a. 3 components extracted.

INTERPRETATION The component matrix shows the loading of the 9 variables on the three factors extracted. The higher the absolute value of the loading, the more the factor contributes to the variable. The values which are greater than 0.5 will be more significant in this case.

7. ROTATED COMPONENT MATRIX

Rotated Component Matrixa Component 1 X2 - Type of cutlery used on

2

3

.122

.047

.775

X3 - Mostly preferred cutley

.536

.034

-.545

X4 - Frequency of usage per

.593

.251

-.098

.708

-.062

.089

.603

-.247

.208

-.103

.516

-.355

.197

.724

.370

-.070

.745

-.014

a daily basis

day X5 - Evaluation based on some factors X6 - Suitable entities X7 - Flavour preferred X8 - Replacing plastic with edible cutlery X9 - Likely to purchase

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 5 iterations.

INTERPRETATION The concept of Rotated Component matrix is to reduce the number of factors on which the variables have high loadings. The rotation does not change the data but helps us interpret the data in a better way. In the above table we can observe that the X2-“type of cutlery” used as the most loaded factors. These can be used as variables for further analysis.