project submitted to professor gautam bhattacharya

Acknowledgement

I am extremely thankful to our faculty Professor Gautam Bhattacharya , Professor of National Institute Of Technology (Durgapur) who have guided us throughout the project for analyzing the consumer behavioural pattern using Factor and Cluster Analysis as a Statistical tool and helped us in all possible ways to complete the project .

Executive Summary

Our research intends to provide an overview of the consumer behaviour and their tastes and preferences regarding the use of Dish TV and Set Top Box in Indian Context. Although the launch of the DTH service and set top box has an overwhelming impact backed with a phenomenal response in the Western countries, the Indian market is yet to flourish, as compared to the First world Countries. Yet the Indian market has a huge prospect . This report should provide an insight about the consumer choices and their approach about the DTH and set top box market in India.

INTRODUCTION

Background Of The Research

The direct home to home (DTH) TV sector in India continues to expand rapidly, attracting large investments from new and existing players over past few years and is showing no signs of slowing down due to economic recession. The DTH market which began commercial operations in 2003 has been able to acquire over 12 million subscribers during the past six years. As per “ Indian DTH market forecast to 2012”, the number of DTH subscribers are forecasted to grow at a CAGR of around 25% during 2009-2012. Currently , the Indian DTH market is being served by the five private players , Dish TV, Tata Sky, Sun Direct, Big TV and Airtel Digital TV. Given the superior quality of DTH services in terms of viewing and interactivity, the number of DTH subscribers has been soaring. However, the current number of DTH subscribers constitutes only a meager population of the total number of TV households, indicating vast future growth potential.

This research provides extensive research and rational analysis about the set top box market in India. India is an important and growing market for set top box. By 2012 India’s pay TV market is expected to reach 90 million subscribers. Though imports constitute a major share of set top boxes, due to Government’s incentives to encourage Indian Companies to manufacture them, the demand for STB will increase considerably. It can be estimated in the range of greater than 1.0 billion US $ worth market for STB by 2012. The semi conductor Companies actively involved in selling STB devices in Indian market are ST Microelectronics, national Semiconductor, NXP and Broadcom. Tensilica is another fables firm with STB device offerings.

The STM 5107 from ST Microelectronics is fully developed at ST’s India design centre. National Semiconductor has sold geode processor based STB solutions to some key customers in India. Just few days back Bharti Airtel has selected Broad Com’s satellite Set Top Box system on a chip solutions to power its direct home to home TV service. Therefore we can say that India’s TV and consumer market is sure to present some pleasant surprise opportunities to the semi conductor and other electronic component Companies.

Need And Objective Of The Research

The primary purpose of the research is to gain an insight on taste and the consumer behaviour when it comes to an installation of Set Top Box. Through our research we have tried to locate the specific qualities which the consumer prefer while going for a DTH and set top box service. It can be said beyond any shadow of doubt that price, quality and durability are the three main aspects which influences a consumer while buying a product.

Amongst the 90 million expected subscribers in 2012 more than 50% buyers will belong to a mediocre income level. So the aim of the research is to provide an insight to the kind of service that can be provided at a minimum cost in order to capture the more of the mediocre market. For this purpose we have designed the questionaires in such a way so that it reflects the choices of the consumers not only in terms of the kind of service they look forward to but also taking the price factor into consideration as well. Moreover, the kind of research tool that we have utilized in order to generate the results will yield the highly correlated factors which reveals some specific consumer preferences. This in turn is helpful, in determining the quality of service that can be provided and it provides a scope for further improvement. As mentioned earlier repeatedly. Our

basic aim is to gain an insight of the consumer choices. It will also help in determining the prospective customers of set top box in India. Hence there are two basic objectives of this study.

a) Locate the section of population that may be a potential customers for this Set Top Box Service. b) Find out the causes (e.g. Socio-Economic, Psycological causes, habits etc.) for which customer’s interest about Set Top Box differs from individual to individual.

Key wordsFactor Analysis, orthogonal Transformation, Communalities, Eigen values, Correlation, Standard Deviation, Bartlett Test of Spherecity, Kaiser Meyer Olkin test, Variance, Principal Component analysis, Kolmogorov Test of Normality, Multi-collinearity, Factor loading, Five Point Likert Scale, Random Sampling, Primary data, Secondary Data.

Research Methodology

This section describes the design of the research methodology and aims to improve assurance that appropriate procedures were followed. This research seeks to examine the perception of the consumers about DTH service in India.

Type Of Research

The research design of the project is a type of descriptive research ; this is because the research looks to identify the factors underlying consumer’s perception about Set Top Box and DTH service in India. This research looks to identify the psyche that drives the consumers to react in a certain way while buying set top box or going for a DTH service. The descriptive research also known as statistical research, describes the data and characteristics about the population or the phenomenon being studied. Descriptive research answers the question who, what, where and how.

Sources And Tools Of Data Collection

Primary data: The data is gathered through a survey based research approach with the help of a questionnaire. The respondents lie within the age of 22-51 years . Questionaires have some pertinent advantage over some other types of surveys which is why it was selected as a research tool. They are economical, do not require as much effort as in verbal and telephone survey and often have standardized answer to make it simple to compile data. It is also easy for the respondents to file the questionnaire rather than think extensively and answer it in depth interview questions.

Secondary Data: These sources are books, articles, various web sites and research papers.

Sampling Techniques: Random sampling methods were used to collect the data as the questionaires are distributed. The targeted respondents ranged from 22-51 years of age . Simple Random sampling is used because each individual is choosen randomly and entirely by chance, such that each individual has the same probability of being choosen at any stage during the sampling process, and each subset of k- individuals has the same probability of being choosen for the sample as any other sub set of k individuals .

Sample Size: Regarding the sample size question, generally a sample of fewer than 50 observations would not factor analyze and preferably the sample size should be 100 and larger. For this reason we have taken 105 observations. As a general rule the minimum is to have at least five times as many observations as the number of variables to be analyzed, and the more acceptable sample size would have 10:1 ratio.

Scale used in Questionnaire: 15 different questions were asked in order to know the respondents perception regarding various aspects which come into play while they think about the DTH service. A FIVE POINT LIKERT SCALE was used and the respondents were asked to rate where -2 represents strongly agree and 2 represents strongly disagree.

Data Analysis

The population of this study encompassed mostly students, the purpose to involve students included to get an idea about the perception of the youth about the Set Top Box in the Indian context. The result of surveying this population produced an effective sample size of respondents.

In the assessment of 21 variables, which assess consumer perceptions and choices about the entertainment media were measured in a five point likert scale in which respondents were asked to indicate their perceptions. The respondents were asked to fill a questionnaire mentioning the factors measuring their perception. In the research respondents were intercepted and asked to participate in a survey.

Prospective customer: The communication company has several of ideas about who are the potential customers for this service. They think that the highest potential is among television viewers who own video recorders and rent video films. They think that users of competitive services such as cable and DTH services might be potential customers. But the company believes that people who prefer to go out to the cinema or to the theatre are not good prospects and the customer must be urban.

Video owners: The first step is to find out how many owners of video recorders there are in the sample by running a frequency table, which is shown in Figure 1. The table shows that 105 questionnaire forms were entered. 64 respondents do not own video recorders, which represents 61% of the total respondents. 40 respondents own video recorders and are 38.1% of the respondents. 1 (or 0.9%) of the respondents did not answer the question, so no data was entered. Without including this 1 in the analysis of video ownership, the column labeled Valid Percent tells us that about 61.5% do not own video recorders and about 38.5% do(approximately one third of the population only).If management is right, and the sample does represent the population, 38.5% represents a very low prospects for the company.

Prospective customer surveys such as this one usually try to get some information about the respondents’ possible interest in the product or service.

. Figure-2 shows the tabulation of the Interest in Set Top Box question(Question No. 12). It shows that 65.7% of those surveyed expressed some interest.

Figure 1: Frequency table for the video recorder ownership

Frequency Percent Valid Percent Cumulative Percent

Valid No 64 61.0 61.5 61.5

Yes 40 38.1 38.5 100.0

Total 104 99.0 100.0

Missing System 1 1.0

Total 105 100.0

Figure 2: Frequency table for interest in Set Top Box

Frequency Percent

Valid No 25 23.8

May be/It depends 69 65.7

Don't know/No answer 11 10.5

Total 105 100.0

The next step is to cross-tabulate video ownership with the question about interest in Set Top Box, the results of which are shown in Figure 3. Amazingly the table shows (by reading the row percents) that the majority or 39.4% of non video owners expressed an interest. Reading the table percent in that cell shows that 66.3% of the population might be potential customers. It should give some additional enthusiasm to the company.

Figure 3: Cross-tabulation of video recorder ownership by interest in the Set Top Box

Interested in Set Top Box

TotalNo

May be/It

depends

Don't know/No

answer

video recorder ownership No Count 15 41 8 64

% of Total 14.4% 39.4% 7.7% 61.5%

Yes Count 10 28 2 40

% of Total 9.6% 26.9% 1.9% 38.5%

Total Count 25 69 10 104

% of Total 24.0% 66.3% 9.6% 100.0%

Experienced researchers learn how to read differences that are revealed in these tables, but they ask for statistical support as well. The Chi-square statistics for the table in Figure 3 are shown in Figure4. The fact that the significance level for the Pearson chi-square is reported as .450 tells us that the differences shown in the cells of the table aren’t representing real differences in the population from which the sample was drawn. So the researcher may not feel confident about reporting these results to management.

Figure 4: Chi-square statistics

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 1.596a 2 .450

Tables like that shown in Figure 3 are easy to read for experienced researchers, and an entire set of cell statistics and measures of association can be requested. However, this kind of output is not appropriate to show management. Therefore, we want to present the results in graphical form, which is shown in Figure 5. The bar chart shows more dramatically that a higher proportion of population who might be interested in Set Top Box , have no video recorder.

Figure 5: Bar chart for video recorder ownership and Set Top Box

Viewer frequency:

Management also thinks that potential customers for set top box can be found among frequent television viewers, regardless of whether they watch standard TV, cable, DTH or rent videos. Two frequency questions were asked in the survey: the average number of hours per day of television viewing and the average number of days per week. SPSS can multiply these variables to get some idea of the number of hours per week the respondent watches television.

To check management's idea about frequent viewers, we ask for the mean number of hours per week by the respondents' interest in Set Top Box, which is shown in

.

People who respond positively to the new service do watch television less than those who are not interested: 15 hours per week compared to 17 hours per week. It also doesn’t support the assumption of management about the potential customers.

One way of testing conclusions about differences is an analysis of variance test that helps support a conclusion that the difference in means of the three groups cannot be attributed only to the natural variability in the population. A researcher wants to be able to conclude that these groups come from sub-populations that actually have different viewing habits. The results of the analysis of variance test for the table in

is shown in Figure 6. Again, the reported significance value of nearly one gives the conclusion that there are no such differences in the population.

Figure 6: Analysis of variance statistics

Sum of Squares df Mean Square F Sig.

Between Groups 96.539 22 4.388 .605 .909

Within Groups 594.661 82 7.252

Total 691.200 104

A way to visualize the information in

is in a

box plot, as shown in Figure8.The boxes represent the middle 50% of the distribution (the 25th to the 75th percentiles), and the dark line shows the median. The lines (or “whiskers”) show the remaining distributions. However, points beyond 1.5 box lengths are called outliers, some of which shows up for the May be/It depends category (marked with the weekly viewing hour). We can see that there are no such differences in the distributions. The Non interested group shows more weekly television

watching, and the DK/NA group shows the least. Figure-7 shows the box plot of weekly viewing hours by interest in set top box.

Viewer frequency types:

We would like to create a categorical variable that records light, medium and heavy television viewers. First, we ask for a histogram of weekly viewing hours to look at the entire distribution. The histogram in Figure 7 shows that weekly viewing is not normally distributed (the line represents a normal distribution). It can be seen that most respondents to the survey view less than the mean of 15.69 hours and that the distribution is skewed to the right because of some people who view television more than 50 hours per week.

Figure 7: Histogram of weekly television viewing hours

The distribution shown in Figure 7 looks like it might be divided into three groups. The first group seems to divide somewhere around 8 hours, and the second group seems to divide somewhere around 21 hours. To divide the sample into three equal parts, we can ask SPSS to give us the dividing values (33.3 and 66.7 percentile values) as shown in Figure 8..

Figure 8: Percentile values for weekly viewing hours

Observations

Valid 105

Missing 0

Percentiles

33.3 8

66.7 21

Using the variable recoding facility in SPSS, it is easy to divide whole sample into three viewer categories: light (up to 8 hours), medium (between 8 and 21hours), and heavy (over 21 hours). A pie chart of this new variable is shown in Figure 9.

Figure 9: Pie chart of television viewer type

A bar chart showing the viewer frequency type by the Set Top Box interest question is shown in Figure 10. The results seem to indicate that people who are heavy viewers are not interested in Set Top Box. However, if we look at the bars for medium & Low viewers, we can see that there really isn't much of a difference between them.

Figure 10: Bar chart of interest by television viewer type

To confirm our visual conclusion from the bar chart in Figure 10, we can produce the cross-tabulation shown in figure-13. The Pearson chi-square significance indicates that there is probably no real differences. The row percentages show that Heavy viewers seem to be different from medium and Light viewers, but these last two categories are not very different from each other.

Figure 11: Cross-tabulation of viewer type by interest in

Interest in Set Top Box Total

No May be/It

depends

Don't know/

No answer

Type

of

View

er

Light 7 28 3 38

18.4% 73.7% 7.9% 100.0%

Medium 11 28 7 46

23.9% 60.9% 15.2% 100.0%

Heavy 7 13 1 21

33.3% 61.9% 4.8% 100.0%

Total 25 69 11 105

23.8% 65.7% 10.5% 100.0%

Market research tables:We can report results to management using a compact table that contains a great deal of information such as that shown in Figure 12. For example, if we look across the rows for possible interest in Set Top Box, we can see a notable result that a small proportion of respondents are ‘video owners’ (32.1% of light viewers, 39.3% of medium viewers and 61.5% among heavy viewers). Continuing across, we can see a moralless same result for DTH subscribers (28.6% of light viewers, 7.1% of medium viewers and 23.1% among heavy viewers).

Every kind of viewers who express some interest in Set Top Box are cable subscribers (85.7% of light viewers, 92.9% of medium viewers and 92.3% among heavy viewers).This may be a target population for the management.

Chi-Square Tests

Valu

e

df Asymp.

Sig. (2-

sided)

Pearson

Chi-Square

3.72

5a

4 .445

Figure 12: Table of viewer type by Set Top Box interest and video, DTH Service and cable ownership

Type

of V

iew

er

Do you have video recorder ? Are any of your Tv connected to

a DTH service?

Are any of your television

Connected to a cable network?

No Yes No Yes No Yes

Coun

t

Row % Coun

t

Row

%

Coun

t

Row

%

Cou

nt

Row

%

Cou

nt

Row

%

Coun

t

Row

%

Inte

rest

in S

et T

op B

ox

No Light 5 71.4% 2 28.6% 7 100% 0 0% 1 14.3% 6 85.7%

Mediu

m

6 54.5% 5 45.5% 10 90.9% 1 9.1% 1 9.1% 10 90.9%

Heavy 4 57.1% 3 42.9% 5 71.4% 2 28.6% 2 28.6% 5 71.4%

May

be/It

depend

s

Light 19 67.9% 9 32.1% 20 71.4% 8 28.6% 4 14.3% 24 85.7%

Mediu

m

17 60.7% 11 39.3% 26 92.9% 2 7.1% 2 7.1% 26 92.9%

Heavy 5 38.5% 8 61.5% 10 76.9% 3 23.1% 1 7.7% 12 92.3%

Don't

know/

No

answer

Light 2 100% 0 .0% 1 50% 1 50% 0 0% 2 100%

Mediu

m

6 85.7% 1 14.3% 7 100% 0 0% 3 42.9% 4 57.1%

Heavy 0 .0% 1 100% 1 100% 0 0% 0 0% 1 100%

Factor Analysis

The general purpose of Factor Analytic techniques is to find a way to condense the information contained in a number of original variables into a smaller set of new, composite dimensions with a minimum loss of information. In summarizing the data, factor analysis derives underlying dimensions when interpreted and understood, describe the data in a much smaller number of concepts than the original individual.

There are a number of different varieties of factor analysis, the discussion here is limited to Principal Axis Factor Analysis and the factor solutions in which the common factors are uncorrelated to each other. It is also assumed that the observed variables are standardized ( mean zero, standards deviation of one) and the factor analysis is based on the correlation matrix of the observed variables.

Why carry out a factor analysis? In the first place, if we can summarize a multitude of measurements with a smaller number of factors without loosing too much information, we have some economy of description, which is one of the goals of scientific investigation. Secondly, in order to achieve the economy of interpretation the factor analysis group those variables having high correlation. If the data is highly inconsistent, it becomes difficult for a researcher to interpret the results. Factor Analytics methods seek to provide a solution to this difficulty in interpretation by condensing the number of variables without loosing too much information, and yet providing a meaningful result.

A descriptive statistical analysis of our data set clearly indicates that there exists multicollinearity among the variables i.e. the correlation is fairly high among the variables.

Inspection of the correlation matrix reveals that the variables are significantly correlated at .01 level and .05 level which provides an adequate basis for proceeding to an empirical examination of adequacy of factor analysis. Tabulating, the number of significant correlations per variable finds a range from .231 to .376 .Although no limits are placed on what is high or what is too low, variables that have no significant correlations may not be a part of any factor and if the variables have high correlations may be a part of many factors. Secondly the standard deviations are fairly high as well, (refer to the table). It is also possible that factor analysis will allow us to test theories involving variables which are hard to measure directly. This study demonstrates the role of a Principal component analysis in conducting an analysis to determine the factors underlying consumer perceptions about DTH services and installation of set top boxes. A set of 21 items measuring consumer perceptions are constructed .

The Name of the 21 variables are

1) In enjoy going to cinema.2) I like a wide range of choice in the video shop.3) My leisure activities are mainly out of the home.4) I would rather watch a TV than go out for a video.5) The TV is always on in my household.6) The choice of films on TV is limited.7) I do not agree with having to pay TV licenses.8) Satellite and cable offer wider choices of films.9) I am tele addict.10) Satellite dishes are a blot on the environment.11) I often forget to return a rented video film.12) I rarely have time to sit down and watch TV.

13) I prefer films over weekly serials.14) Commercial channels offer enough choices.15) I get annoyed if the video I want is out.16) Ads in the middle of the programme annoy me.17) I prefer the cinema over watching videos.18) Satellite and cable need more film selections.19) Videos are an excellent form of entertainment.20) I don’t mind paying more for TV programmes.21) TV is a last resort in entertainment.

Interpreting The Result From SPSS

Preliminary Analysis

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .622

Bartlett's Test of Sphericity Approx. Chi-Square 461.260

df 210

Sig. .000

Table-1 shows several very important parts of the output. The Kaiser Meyer Olkin measure of sampling adequacy and Barlett Test Of Spherecity. The KMO statistic varies between 0 and 1. A value of 0 indicates that the sum of the partial correlations are large relative to the sum of the correlations indicating diffusion in the pattern of correlations( hence, factor analysis is appropriate). A value close to 1 indicates that patterns of correlations are relatively compact and so factor analysis should yield distinct and reliable factors. Kaiser (1974) recommended accepting values greater than 0.5 as acceptable (value below this should either lead to collection of more data or rethink which variable to include). Furthermore values .80 or above are considered meritorious, .70or above mediocre, and below .50 is unacceptable. For this data the value is .622, which falls into the range of being satisfactory, so we should be confident that factor analysis is appropriate for this data. Bartlett’s measure tests the null hypothesis that the original correlation matrix is an identity matrix. For factor Analysis to be appropriate we need some relationships between the variables and if the original matrix is an identity matrix then all the correlation coefficients would be zero. Therefore, we want this test to be significant (i.e. having a significant value of less than .05). here, the significance level is .000 which indicates that the R- matrix is not an identity matrix, therefore, there are some relationship between the variables we hope to include in the analysis. For this data Bartlett Test is highly significant and therefore the analysis is appropriate.

Communalities Analysis

Communalities

Initial Extraction

Q.6.01 1.000 .474

Q.6.02 1.000 .653

Q.6.03 1.000 .673

Q.6.04 1.000 .633

Q.6.05 1.000 .467

Q.6.06 1.000 .575

Q.6.07 1.000 .480

Q.6.08 1.000 .675

Q.6.09 1.000 .759

Q.6.10 1.000 .665

Q.6.11 1.000 .620

Q.6.12 1.000 .530

Q.6.13 1.000 .583

Q.6.14 1.000 .634

Q.6.15 1.000 .598

Q.6.16 1.000 .529

Q.6.17 1.000 .669

Q.6.18 1.000 .656

Q.6.19 1.000 .713

Q.6.20 1.000 .589

Q.6.21 1.000 .691

Extraction Method: Principal

Component Analysis.

Table-2 shows communalities before and after extraction. Principal Component analysis works on the assumption that all variance are common; therefore before extraction the communalities are all 1. The communalities in the column extraction reflect the common variance in the data structure. So for example, we can say that 47.4% of variance associated with question 1 is shared or common variance. Communalities basically indicates the amount of variance explained in a variable that is accounted for by the factors taken together. The size of the communality is an useful index for assessing the, how much variance is accounted for by the factor solution. Higher the communalities values it indicates that a larger amount of variance in the variable has been extracted by the factor solution and vice versa. Although no statistical guidelines indicate exactly what is large or small, practical considerations dictate a lower level of .50 in the analysis. After the extraction some of the factors are discarded and some information is lost. In our analysis the variables which are retained for for further analysis are very well explained and infact none of the variables are dropped after

extraction. For e.g. question no 9 shows an explained variance of .759 which is fairly high and all the other variables have communality values of more than .50 except question no 1, 5 and 7. However considering the relevance of these variables we retain them in our data set. Therefore as we proceed with further analysis it will yield a more meaningful result.

Factor Extraction How do we decide on the number of factors to extract? The factor analysis methods are interested in the best linear combination of variables- best in the sense that the particular combination of original variables accounts for more of the variance in the data as a whole than any other linear combination of variables. Therefore, the first factor may be viewed as the single best summary of linear relationships exhibited in the data. The second best linear combination of the variables, subject to the constraint that it is orthogonal to the first factor. To be orthogonal to the first factor,the second factor must be derived from the variance remaining after the first factor is extracted. Thus, the second factor may be defined as the linear combination of variables that accounts for the most variance that is still unexplained for the after the effect of first factor being removed from the data. The process continues extracting factors. accounting for smaller and smaller amounts of variance until all the variance is explained. For example, the Principal Component analysis method actually extracts n factors, where n is the number of variables in the analysis.

So the question is how many factors to extract or retain? An exact qualitative basis for deciding the number of factors to extract has not been developed. However, the most commonly used criteria for the number of factors to extract is the Latent Root Criterion.

Latent Root CriterionThe rationale of the latent root criterion is that any individual factor should account for the variance of atleast single variable if it is to be retained for interpretation. With p.c.a. each variable contributes a value of 1 to the total eigen value. Thus, only the factors having latent root greater than 1 are considered significant. i.e. all factors with latent root less than 1 are disregarded.

Table-3 lists the eigen values associated with each linear component before extraction, after extraction and after rotation. Before extraction SPSS has identified 21 linear components within the data set. The eigen values associated with each factor represents the variance explained by the particular linear component and SPSs also displays the eigen values in terms of the percentage of variance explained. So factor -1 explains 15.174% of the total variance and factor -2 explains 10.445% of the total variance and so on. It should be noted that the first factor explained relatively large amount of variance whereas subsequent factors explain only small amounts of variance. SPSS then extracts all factors with eigen values greater than 1, which leaves us with 7 factors. The eigen values associated with these factors are again displayed( and the percentage of variance explained) in the columns labeled extraction sums of squared loadings. The values in this part of the table are

the same as the values before extraction, except that the values for the discarded factors are ignored.( hence the table is blank after the seventh factor). In the final part of the table (labeled rotation sums of squared loadings), rotation are displayed. Rotation has the effect of optimizing the factor structure and one consequence for these data is that the relative importance of these seven factors are equalized. This is because factor rotation simplifies the factor structure. Therefore, a rotational method should be applied to achieve simpler and theoretically more meaningful factor solutions. In most cases rotation of the factor improves the interpretation by reducing some of the ambiguities that often accompany initial unrotated factor solutions.

Total Variance Explained

Com

pone

nt

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation Sums of Squared Loadings

Total % of

Variance

Cumulativ

e %

Total % of

Variance

Cumulativ

e %

Total % of

Variance

Cumulativ

e %

1 3.186 15.174 15.174 3.186 15.174 15.174 2.505 11.930 11.930

2 2.193 10.445 25.618 2.193 10.445 25.618 1.882 8.960 20.890

3 1.911 9.101 34.719 1.911 9.101 34.719 1.778 8.469 29.359

4 1.718 8.182 42.901 1.718 8.182 42.901 1.724 8.208 37.568

5 1.448 6.897 49.798 1.448 6.897 49.798 1.707 8.127 45.695

6 1.317 6.270 56.068 1.317 6.270 56.068 1.687 8.034 53.729

7 1.092 5.201 61.269 1.092 5.201 61.269 1.583 7.540 61.269

8 .986 4.696 65.965

9 .903 4.300 70.265

10 .797 3.797 74.062

11 .778 3.705 77.767

12 .759 3.612 81.379

13 .623 2.966 84.346

14 .541 2.574 86.920

15 .499 2.376 89.296

16 .469 2.232 91.528

17 .453 2.156 93.685

18 .401 1.910 95.595

19 .344 1.637 97.232

20 .316 1.507 98.739

21 .265 1.261 100.000

Extraction Method: Principal Component Analysis.

The scree test is derived by plotting the latent roots against the number of factors in their order of extraction, and the shape of the resulting curve is used to evaluate the cutoff point. In figure-1

The first 21 variables are extracted in the study. Starting with the first factor, the plot slopes steeply downward initially and slowly becomes an approximate horizontal line. The point at which the curve begins to straighten out is considered to indicate the maximum number of factors to extract. Another rule of thumb is to plot all the eigen values in their descending order. The plot looks like the side of a mountain and the scree refers to the debris fallen from a mountain and lying at its base.

So the scree test proposes to stop analysis at the point where the mountain end, and the debris( error) begin. In this instance, the point coincides with eigen value criterion, i.e. the line becomes horizontal after the extraction of the 7th factor is over.

Rotated vs Unrotated Factor Matrix Examine the factor matrix loadings for the unrotated factor matrix

Factor loadings, in either the unrotated or rotated factor matrices, represents the degree of association of each variable with each factor. The loadings take on a key role in interpretation of the factors, particularly if they are used in ways that requires characterization as to the substantive , meaning of the factors. The objective of the factor analysis in these instances is to maximize the association of each variable with the single factor, many times through rotation of the factor matrix.

In our research we will first explain the unrotated factor matrix and determine wheather the use of rotated solution is necessary.

Table-4 represents the unrotated component analysis factor matrix. To begin the analysis, let us explain the numbers included in the table. Seven columns of numbers are shown. It indicates the result of the seven factors that are extracted.(i.e. factor loading of each variable on each of the factor) As expected the factor solution extracts the factors in order of their importance, with factor -1 accounting for the most variance, factor-2 slightly less and so on.

Having defined the various elements of the unrotated factor matrix, let us examine the factor loading patterns. As discussed earlier, the factor loadings allow for the description of each factor and structure in the set of variables. As anticipated, the first factor accounts for the largest amount of variance in the table-3. The second factor is somewhat of a general factor with all of the variables having high loadings,(high loadings are defined as greater than.40). The third factor has only two loadings .The fourth and sixth factor has only 1 loading . a notable point is that although the seventh factor accounts for 5.201% of variance as per table-3, none of the variables are loaded in this factor which is kind of unusual. Based on this factor loading pattern with a relatively large number of loadings on factor -1 and 2 and no loadings in factor -7, interpretation becomes difficult and theoretically less meaningful. Therefore we can proceed to rotate the factor matrix to redistribute the variance from earlier factors to later factors. Rotation should result in a simpler and theoretically more meaningful factor pattern. However, before proceeding with the rotation process, we must examine the communalities to see whether any variables have communalities so low that they should be eliminated.

Component

1 2 3 4 5 6 7

Q.6.01

Q.6.02

Q.6.03 .593

Q.6.04 .652

Q.6.05 .514

Q.6.06

Q.6.07 .565

Q.6.08 .770

Q.6.09 .614

Q.6.10 .588

Q.6.11 .501

Q.6.12 -.565

Q.6.13 .511

Q.6.14 .626

Q.6.15

Q.6.16

Q.6.17 .621

Q.6.18 .614

Q.6.19 .534 .503

Q.6.20

Q.6.21

Extraction Method: Principal Component Analysis.

a. 7 components extracted.

Assess The Communalities Of The Variables In The unrotated Factor MatrixThe row sum squared factor loadings, known as communalities, shows the amount of variance in a variable that is accounted for by the 7 factors taken together. The communalities in table-2 are shown in the third column of the table. For e.g the communality figure of .467 for variable(1) indicates that it has less in common with the other variables included in the analysis than does variable(9) which has a communality figure of .759.Both the variables, still share more than 40% of their variance with the 7 factors. Therefore we can conclude that all of the communalities are sufficiently high to proceed with the rotation of the factor matrix.

Applying An Orthogonal (Varimax) Rotation

Given the unrotated factor matrix did not have a completely clean set of factor loadings, a rotation technique can be applied to hopefully improve the interpretation. In this case the VARIMAX rotation is used and its impact on the overall factor solution and factor loadings are described in the next section.

The varimax rotated component analysis factor matrix is shown in table-5. Please note that the total amount of variance extracted is the same in the rotated solution as it was in the unrotated one.

Rotated Component Matrixa

Component

1 2 3 4 5 6 7

Q.6.01 .643

Q.6.02 .603

Q.6.03 .601

Q.6.04 .699

Q.6.05 .504

Q.6.06 .704

Q.6.07 .664

Q.6.08 .650

Q.6.09 .627

Q.6.10 -.551

Q.6.11 .512

Q.6.12 -.516

Q.6.13 .662

Q.6.14 .696

Q.6.15 -.721

Q.6.16 .643

Q.6.17 .742

Q.6.18 .696

Q.6.19 .736

Q.6.20 .630

Q.6.21 .709

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 13 iterations.

Also, the communalities for each variable do not change when a rotation is applied.Still, two differences emerge. First, the variance is redistributed so that he factor loading pattern and the percentage of variance for each of the factors are slightly different. Specifically, in the varimax rotated factor solution the first factor accounts for 11.930% of variance compared to 15.174% in the unrotated solution. Likewise, the other factors also changed, the largest change being the factor 5, 6 and 7 . Initially the variance explained by these factors were 6.897% 6.270% and 5.201%. with the application of orthogonal transformation the amount of variance explained by each of these factors increased to 8.127%, 8.034% and 7.540% respectively. Thus, the explanatory powers shifted to a more even distribution because of rotation. Secondly,the interpretation of the factor matrix is simplified as now we have now two considerably high factor loadings in factor-7.

Findings Of The Study

Factor -1, seems to capture the consumers perception about the performance of the electronic media when it is a question of offering wide range of choices to the consumers. It also reveals the consumers preference of watching TV over video. Hence this factor can be named as CONSUMERS PERCEPTION ABOUT THE PERFORMANCE OF COMMERCIAL CHANNELS.

Factor-2,is a representation of the consumer’s perception about videos and it also indicates the consumers perception about paying more money for watching variety of programmes. So this factor can be termed as consumer’s own PERCEPTION OF ENTERTAINMENT.

Factor-1...a) i would rather

watch T.V. than go out for a

video..b)The t.V. is always on in

my household.c) satellite cable

offers wide choices of

films..d)commercial channels offer enough

choices...e) adds in the middle of the programme

annoy me.

Factor-2..a) Videos are an

excellent form of entertainment....

.b) I dont mind paying more for Tv programmes

Factor-3..a) I like a wide rabge of choice in video shop....b) The

choice of films is limited......c) satellite and

cable need more film selections.

Factor-4 a) I enjoy going to

the cinema.. b) I do not agree with having to

pay TV license...c)

satellite dishes are a blot to the

environment.

Factor-5 ..a) My liesure activities are mainly out of home..b) I often

forget to return a rented video

film.. c) TV is the last resort in

entertainment...

Factor-6 a) I am a tele addict...b) I rarely have time to sit down and watch TV.c) i prefer cinema over watching videos

factor-7..a) i prefer videos over weekly

serials...b) I get annoyed if the video I want is

out..

Factor-3,mainly reflects the consumers preference pattern about various films that are shown in various Tv channels and it also some what indicates their own perceptions about the range or choices of films that they want to watch. So this factor can be termed as consumer’s views about VARIETY OF FILMS SHOWN IN TV CHANNELS.

Factor-4, represents consumers choices about going to cinema and also reveals their thinkings about the satellite and cable channels. So this can be named as consumer’s views about CINEMA AND SATELLITE DISHES.

Factor-6 and 7,describes how the consumers reveals their choices of watching TV over videos .So this can be termed as consumer’s choice of TV VS VIDEO.

Cluster AnalysisNow using the same data we shall try to perform a cluster analysis and check the results to provider an insight about the set top box market in India. Cluster analysis is a group of multivariate techniques whose primary purpose is to group objects based on the charactaristics they possess. Ranging form the derivation of the taxonomies in biology for grouping all living organisms , to psychological classifications based on personality and other personal traits, to segmentation analyses of markets, cluster analysis has always had a strong tradition of grouping individuals. Now using the three factors that we have generated with the factor analyses we can classify the respondents according to their viewing preferences. which is a better method than using ownership or subscriber information. One way to classify the respondents is to use cluster analysis to see how the respondents cluster into groups according to their responses to these three factors. The results of a four-cluster solution tabulated by the factors is shown in figure 21. The four cluster solution is chosen among the possibilities because it clearly shows that there are four groups in the sample: Cluster 1 has highest mean score for the video factor, Cluster 2 has the highest mean score for the films/theatre factor, Cluster 3 has highest mean for DTH and Cable factor, and Cluster 4 has all positive mean for all factors . It can then be interpreted that Cluster 1 are people who enjoy completely video kinds of entertainment , Cluster 2 are flims/theatre fans, Cluster 3 are DTH and Cable fans, and Cluster 4 are people who enjoy all types of entertainment.

Four respondent clusters by viewer factors

Cluster DTH and cable Video Films/ Theatre

1 -.37314 .16609 -.68988

2 .10955 -1.15380 .45372

3 2.71745 -2.29718 -3.29140

4 .41151 .81078 .82031

Cluster 1 48.000

2 26.000

3 1.000

4 30.000

Valid 105.000

Missing .000

From figure 22 we can say that 48(45.71%) respondents are belongs to cluster 1; 26 respondents

(24.76%) are belongs to cluster 2; cluster 3 consists only 1 respondent (.95%) and cluster 4 consists

30 respondents(28.57%)

To see if there are differences among these four clusters with regard to their interest in Set Top Box, we can cross-tabulate the cluster variable by Set Top Box interest as shown in 23. We can see that those who are satisfied with DTH and CABLE fan are also not interested in SET TOP BOX (100% not interested). This result shows the danger in using DTH and CABLE ownership as measure of interest.

Cross-tabulation of cluster groups by interest in the SET TOP BOX service

Set Top Box interest

No May be/It depends

Don't know/No

answer

Cluster Number of Case Video Fan 12 33 3

25.0% 68.8% 6.3%

Cinema and Theatre Fan 6 15 5

23.1% 57.7% 19.2%

DTH and Cable Fan 1 0 0

100.0% .0% .0%

All Entertainment Fan 6 21 3

20.0% 70.0% 10.0%

The surprise for management is that those who are interested in cinema and Theatre are possibly interested in SET TOP BOX(57.7%%) and are undecided (19.2%). Similarly who are interested in video are possibly interested in the new service (68.8%) and are undecided (6.3%).Again all entertainment fans are possibly interested for service(70%) and undecided(10%). If this more in-depth analysis had not been done, decision-makers at SET TOP BOX might have missed this undecided possible market. The final conclusion is that all entertainment fans are the group most interested in SET TOP BOX (70%). SET TOP BOX management had previously thought that this was a secondary market.

Marketing Decisions For Locating prospectsEven after gaining an understanding of who are the best prospects for SET TOP BOX, management must also make a number of marketing decision. One important decision is how to promote SET TOP BOX to potential subscribers.

To decide where to advertise the service, SET TOP BOX management needs to know what kinds of television programmes prospective customers watch. The first step is to tabulate the four clusters by the questions about favourite programmes, as shown in Figure 23. There are actually three questions in the SET TOP BOX survey about first, second and third preferred television programmes. The table in figure 24 shows how all three can be treated as a multiple-response variable so that they

can be combined into a single variable .In the table in 24, we see that all entertainment fans who we have found might be interested in set top box prefer serials, films and musical program dramas in that order. Cinema viewers who also might be good prospects for set top box also prefer serials , films and music. Video fans who might be good prospect for set top box also prefer sports, quiz show, films and music.

Favourite television programmes by cluster groups

Cluster Number of Case

Video Fan

Cinema and

Theatre Fan DTH and Cable Fan

All Entertaintment

Fan

What are your favourite types

of television programmes?

Third

News 8 4 0 5

47.1% 23.5% .0% 29.4%

Serials 1 2 0 3

16.7% 33.3% .0% 50.0%

Films 11 7 1 9

39.3% 25.0% 3.6% 32.1%

Music programmes 9 9 0 8

34.6% 34.6% .0% 30.8%

Quiz Shows 8 1 0 3

66.7% 8.3% .0% 25.0%

Sports 10 3 0 2

66.7% 20.0% .0% 13.3%

Limitation Of The Study

1) The first limitation is due to geographical constraints the sample size was restricted to Kolkata city, where covering larger geographical area would have given better results.

2) The next limitation was the time availability to conduct the research was limited. Hence the findings of the research could not be evaluated in a detailed manner.

3) The third limitation is the sample size. Assize of 105 was considered for the research which cannot be generalized to a larger population very effectively.

The responses of the respondents may have been biased to some extent due to their busy schedule or some other reasons. Secondly majority of the respondents have the age ranging from22-30 years , hence it can reveal similar results.

Scope Of Further Research

The limitations discussed above suggest several areas for future research.

Firstly, similar research could be conducted for diverse geographical area. This could increase credibility and generalization in the findings.

Secondly, further research should be carried on to examine the impact of the demographics on the perception of the consumers about films and entertainment.

Thirdly, similar researches should be conducted separately for different product categories to examine whether the perception of consumer regarding choices of films, watching TV, and videos have different patterns in other context.

Annexure

Installation of Set top Box by Cable operator

1. How many television sets are there in your home? [Circle code]

No TVs 0

1 TV 1

2 TVs 2

3 TVs 3

4 TVs 4

If no TV, go to Q6. Otherwise, continue.

2. Do you have a video recorder?

Yes 1

No 0

3.(a) Are any of your televisions connected to a DTH Service?

Yes 1

No 0

3(b) DTH Service provided by

(1) Tata Sky (2) Reliance Big (3) Sun (4) Dish (5) Fun (6) Others

4. Are any of your televisions connected to a cable network?

Yes 1

No 0

5. What are your favourite types of television programs? [Show card #1]

News 1

Serials 2

Films 3

Music Programmes 4

Quiz Shows 5

Sports 6

First Second Third

6. The next question lists several statements about your views on various subjects. Please rate each question according to whether you agree or disagree with the statement using the following scale. [Show scale]

Strongly Strongly

Disagree Disagree Neutral Agree Agree

1 2 3 4 5

6.01 I enjoy going to the cinema. 1 2 3 4 5

6.02 I like a wide range of choice in the video shop. 1 2 3 4 5

6.03 My leisure activities are mainly out of the home. 1 2 3 4 5

6.04 I would rather watch TV than go out for a video. 1 2 3 4 5

6.05 The TV is always on in my household. 1 2 3 4 5

6.06 The choice of films on TV is limited. 1 2 3 4 5

6.07 I do not agree with having to pay TV licenses. 1 2 3 4 5

6.08 Satellite and cable offer wider choices of films. 1 2 3 4 5

6.09 I am a Tele-addict. 1 2 3 4 5

6.10 Satellite dishes are a blot on the environment. 1 2 3 4 5

6.11 I often forget to return a rented video film. 1 2 3 4 5

6.12 I rarely have time to sit down and watch TV. 1 2 3 4 5

6.13 I prefer films over weekly serials. 1 2 3 4 5

6.14 Commercial channels offer enough choices. 1 2 3 4 5

6.15 I get annoyed if the video I want is out. 1 2 3 4 5

6.16 Ads in the middle of programme annoy me. 1 2 3 4 5

6.17 I prefer the cinema over watching videos. 1 2 3 4 5

6.18 Satellite and cable need more film selections. 1 2 3 4 5

6.19 Videos are an excellent form of entertainment. 1 2 3 4 5

6.20 I don't mind paying for more TV programmes. 1 2 3 4 5

6.21 TV is a last resort in entertainment. 1 2 3 4 5

For the next two questions, your viewing should include all viewing of television programmes and personal video recordings of television programmes. You should exclude viewing of pre-recorded video tapes.

7. How many days per week do you watch television?

1 day or less 1

2 days 2

3 days 3

4 days 4

5 days 5

6 days 6

7 days 7

8. How many hours per day do you watch television?

Up to 1 hour 1

Up to 2 hours 2

Up to 3 hours 3

Up to 4 hours 4

Up to 5 hours 5

Up to 6 hours 6

Up to 7 hours 7

8 or more hours 8

9. How often do you rent videos for home viewing?

Never 0

Less than once a week 1

Once a week 2

Twice a week 3

More than twice a week 4

10. Do you buy video films? [If necessary, use these codes]

Yes 1 Don’t know 7

No 0 Refused to answer 8

11. How often do you visit a cinema?

Never 0

Less than once a month 1

Once or twice a month 2

More than twice a month 3

12. There is a new product soon to be available called Set top Box

This revolutionary product allows you to watch a recent film of your choice by selecting the number on a special remote control connected via your telephone. Do you think you would be interested in subscribing to this service?

Maybe/It depends 1

No 0

Don't know/No answer 9

If no or don’t know, go to Q14 (page 4). Otherwise, continue.

13. Please consider the following four features of the Set top Box

service:

Price range

The number of new films each month

The release date of the films

Inclusion of Indian films

There are three price choices: Under Rs. 80 , Rs.120 and Rs. 140 per film..

There are three quantity choices: Up to 5 films per month, from 5 to 10, and over 10.

There are also three release date choices; The same time as the theatre release in India , one month following the theatre release, and the same time as the video release.

And there are two final choices: Including Indian films or not.

Please rank the combinations on the nine cards I'm giving you by returning them to meet with your first choice on top, your second choice next, and so forth.

Card 1

Card 2

Card 3

Card 4

Card 5

Card 6

Card 7

Card 8

Card 9

14. What is the year of your birth?

15. What is your working status?

Full time 1

Part time 2

Self-employed 3

Home 4

Unemployed 5

Retired 6

16. Sex of respondent:

Female 0

Male 1

.

.

Appendix

Questionaire

Installation of Set top Box by Cable operator

1. How many television sets are there in your home? [Circle code]

No TVs 0

1 TV 1

2 TVs 2

3 TVs 3

4 TVs 4

If no TV, go to Q6. Otherwise, continue.

2. Do you have a video recorder?

Yes 1

No 0

3.(a) Are any of your televisions connected to a DTH Service?

Yes 1

No 0

3(b) DTH Service provided by

(1) Tata Sky (2) Reliance Big (3) Sun (4) Dish (5) Fun (6) Others

4. Are any of your televisions connected to a cable network?

Yes 1

No 0

5. What are your favourite types of television programs? [Show card #1]

News 1

Serials 2

Films 3

Music Programmes 4

Quiz Shows 5

Sports 6

First Second Third

6. The next question lists several statements about your views on various subjects. Please rate each question according to whether you agree or disagree with the statement using the following scale. [Show scale]

Strongly Strongly

Disagree Disagree Neutral Agree Agree

1 2 3 4 5

6.01 I enjoy going to the cinema. 1 2 3 4 5

6.02 I like a wide range of choice in the video shop. 1 2 3 4 5

6.03 My leisure activities are mainly out of the home. 1 2 3 4 5

6.04 I would rather watch TV than go out for a video. 1 2 3 4 5

6.05 The TV is always on in my household. 1 2 3 4 5

6.06 The choice of films on TV is limited. 1 2 3 4 5

6.07 I do not agree with having to pay TV licenses. 1 2 3 4 5

6.08 Satellite and cable offer wider choices of films. 1 2 3 4 5

6.09 I am a Tele-addict. 1 2 3 4 5

6.10 Satellite dishes are a blot on the environment. 1 2 3 4 5

6.11 I often forget to return a rented video film. 1 2 3 4 5

6.12 I rarely have time to sit down and watch TV. 1 2 3 4 5

6.13 I prefer films over weekly serials. 1 2 3 4 5

6.14 Commercial channels offer enough choices. 1 2 3 4 5

6.15 I get annoyed if the video I want is out. 1 2 3 4 5

6.16 Ads in the middle of programme annoy me. 1 2 3 4 5

6.17 I prefer the cinema over watching videos. 1 2 3 4 5

6.18 Satellite and cable need more film selections. 1 2 3 4 5

6.19 Videos are an excellent form of entertainment. 1 2 3 4 5

6.20 I don't mind paying for more TV programmes. 1 2 3 4 5

6.21 TV is a last resort in entertainment. 1 2 3 4 5

For the next two questions, your viewing should include all viewing of television programmes and personal video recordings of television programmes. You should exclude viewing of pre-recorded video tapes.

7. How many days per week do you watch television?

1 day or less 1

2 days 2

3 days 3

4 days 4

5 days 5

6 days 6

7 days 7

8. How many hours per day do you watch television?

Up to 1 hour 1

Up to 2 hours 2

Up to 3 hours 3

Up to 4 hours 4

Up to 5 hours 5

Up to 6 hours 6

Up to 7 hours 7

8 or more hours 8

9. How often do you rent videos for home viewing?

Never 0

Less than once a week 1

Once a week 2

Twice a week 3

More than twice a week 4

10. Do you buy video films? [If necessary, use these codes]

Yes 1 Don’t know 7

No 0 Refused to answer 8

11. How often do you visit a cinema?

Never 0

Less than once a month 1

Once or twice a month 2

More than twice a month 3

12. There is a new product soon to be available called Set top Box

This revolutionary product allows you to watch a recent film of your choice by selecting the number on a special remote control connected via your telephone. Do you think you would be interested in subscribing to this service?

Maybe/It depends 1

No 0

Don't know/No answer 9

If no or don’t know, go to Q14 (page 4). Otherwise, continue.

13. Please consider the following four features of the Set top Box

service:

Price range

The number of new films each month

The release date of the films

Inclusion of Indian films

There are three price choices: Under Rs. 80 , Rs.120 and Rs. 140 per film..

There are three quantity choices: Up to 5 films per month, from 5 to 10, and over 10.

There are also three release date choices; The same time as the theatre release in India , one month following the theatre release, and the same time as the video release.

And there are two final choices: Including Indian films or not.

Please rank the combinations on the nine cards I'm giving you by returning them to meet with your first choice on top, your second choice next, and so forth.

Card 1

Card 2

Card 3

Card 4

Card 5

Card 6

Card 7

Card 8

Card 9

14. What is the year of your birth?

15. What is your working status?

Full time 1

Part time 2

Self-employed 3

Home 4

Unemployed 5

Retired 6

16. Sex of respondent:

Female 0

Male 1

.