Nokia Sanjay

A Report On Study the consumer choice of Nokia handset over competitive mobile phone brand By Anjana Chorasia Neha Sharma Rahul Gupta Sanjay Saraogi Prof K.K.Morya 1| Page

A Report On Study the consumer choice of Nokia handset over competitive mobile phone brand For Prof K.K.Morya (Faculty IBS Jaipur) By Anjana Chorasia Neha Sharma Rahul Gupta Sanjay Saraogi 19th January 2009 2| Page

The Report forms a part of our course curriculum in Business Research Method. Our Respected Business Research Method Faculty Prof K.K.Morya has authorized the making of the Report

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Acknowledgement It is always said that we need “two hands to clap” and it

proved exactly the same. One hand was ours and other was provided by none other than Prof. K.K.Morya who served us as a mentor and also lifted our moral up in the whole process. He took pain and helped us in overcoming the hurdles of our path. Along with sir we would like to give our special thanks to individuals who devoted their precious time on our live project and helped us by filling the questionnaire. In short words this project is one among those which will live in our hearts, as the project made us come across various facts of life and may be time will also fail in diluting it from us as all together

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Table of content Contents Executive Summary.........................................................................................................5 Project title-................................................................................................................5 Name of the company-...................................................................................................5 Objective.................................................................................................................6 Methodology followed..................................................................................................6 List of illustration............................................................................................................6 Introduction...................................................................................................................8 Purpose of the report.....................................................................................................8 Methods of collecting data and their sources-.......................................................................8 Background information................................................................................................8 HISTORY OF NOKIA.................................................................................................9 Pie chart study..............................................................................................................12 Mobile brand.........................................................................................................12 Price Range..............................................................................................................12 Service Centre......................................................................................................14 Battery..................................................................................................................15 Price.......................................................................................................................16 Accessories...........................................................................................................16 Features................................................................................................................17 Descriptive Analysis.................................................................................................18 Correlation Matrix....................................................................................................20 KMO and Bartlett’s test............................................................................................22 Communality.........................................................................................................22 Communality estimates;..................................................................................23 Total variance explained..........................................................................................23 Component matrix...................................................................................................24 5| Page

Component transformation matrix...........................................................................27

Executive Summary Project titleTo study the customer choice of Nokia handset over competitive mobile brand.

Name of the companyNOKIA

Objective To conduct a survey this will show us the result of the consumer preference of mobile handset especially regarding Nokia To use SPSS and applying factor analysis to find out the mean, correlation matrix, inverse of correlation matrix, KMO and Bartletts test, communalities, total variance explained, component matrix, rotated component matrix and component transformation. Apart from this the scope includes the study of pie chart.

Methodology followed • • • • • •

Preparation of questioner. Making people to fill the questioner Filling of data in SPSS Factor analysis on SPSS Preparation of graph Preparation of report

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List of illustration S.N 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

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Item Chart 1 Chart 2 Chart 3 Chart 4 Chart 5 Chart 6 Chart 7 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8

Page No 12 13 14 15 16 17 18 19 21 22 23 24 26 26 27

Introduction Purpose of the report To Study the consumer choice of Nokia handset over competitive mobile phone brand.

Methods of collecting data and their sourcesPreparation of questioner and making people to fill and the working on SPSS Sources of collecting data – • • • • • •

Classmates Family Relatives Market Road Restaurant

Background information After deciding upon the topic we prepared the questioner and it was filled by the people and then factor analysis was done with the help of SPSS software

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HISTORY OF NOKIA Nokia is a Finnish multinational communications corporation, headquartered in Keilaniemi Espoo, a city neighbouring Finland's capital Helsinki. Nokia is focused on wireless and wired telecommunications, with 112,262 employees in 120 countries, sales in more than 150 countries and global annual revenue of 51.1 billion euros and operating profit of 8.0 billion as of 2007.[t is the world's largest manufacturer of mobile telephones: its global device marketsharewas about 38% in Q3 of 2008, down from 39% in Q3 2007 and down from 40% sequentially Nokia produces mobile phones for every major market segment and protocol, including GSM CDMA, and W-CDMA (UMTS. Nokia's subsidiary Nokia Siemens Networks produces telecommunications network equipments, solutions and services. Nokia has sites for research and development manufacture and sales in many continents throughout the world. As of March 2008, Nokia had R&D centers in 10 countries and employed 30,415 people in research and development, representing approximately 27% of Nokia’s total workforce The Nokia Research Center, founded in 1986, is Nokia's industrial research unit of about 800 researchers, engineers and scientists. It has sites in seven countries: Finland, Denmark, Germany, China, Japan, United Kingdom and United States. Besides its NRCs, in 2001 Nokia founded (and owns) Indy– Nokia Institute of Technology, a R&D institute located in Brazil Nokia's production facilities are located at Espoo, Oulu and Salo Finland; Manaus, Brazil; Beijing, Dongguan and Suzhou, China; Fleet England; Komárom Hungary; Chennai India; Reynosa , Mexico; Jucu , Romania and Masan South Korea.Nokia's Design Department remains in Salo, Finland. Nokia plays a very large role in the economy of Finland: it is by far the largest Finnish company accounting for about a third of the market capitalization of the Helsinki Stock Exchange (OMX Helsinki) as of 2007; a unique situation for an industrialized country It is an important employer in Finland and several small companies have grown into large ones as Nokia's subcontractors Nokia increased Finland's GDP by more than 1.5% in 1999 alone. In 2004 Nokia's share of the Finland's GDP was 3.5% and accounted for almost a quarter of Finland's exports 9| Page

in 2003. In 2006, Nokia generated revenue that for the first time exceeded the state budget of Finland. Finns haveranked Nokia many times as the best Finnish brand and employer. The Nokia brand valued at $35.9 billion, is listed as the fifth most valuable global brand in Inter brand Business Weeks Best Global Brands list of 2008 (first non-US company) It is the number one brand in Asia (as of 2007) and Europe (as of 2008) the 23rd most admirable company worldwide in Fortune’s World's Most Admired Companies list of 2008 (tied with Exxon Mobil; second in Network Communications, fifth non-US company), and is the world's 88th largest company in Fortune Global 500 list of 2008, up from 119 of the previous year As of 2008, AMR Research ranks Nokia's global supply chain number two in the world .

Chairman Jorma Ollila, b. 1950 Chairman of the Board of Directors, Nokia Corporation.

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Georg Ehrnrooth, b. 1940 gupte b.1948

Dr. Bengt Holmström,b. 1949 b. 1947

Olli-Pekka Kallasvuob. 1953

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Lalita

Prof. Dr. Henning Kagermann,

Per Karlsson, b. 1955

Dame Marjorie Scardino, b. 1947

Risto Siilasmaa, b. 1966

Keijo Suila, b. 1945

Pie chart study Mobile brand

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Chart 1 Here we can see that maximum people prefer Nokia handset over other handset Price Range

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Chart 2 Here also we see that maximum people want to choose handset that lies in the range of Rs 2500 and Rs 6000 and we can also infer that people preferring low range phone is very less low range phone include phone costing less that Rs 2500.

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Service Centre

Chart 3

Here we can see that maximum area is covered by average and another opinion is good that also cover an area equal to average

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Battery

Chart4 Here we can that in case of battery most of the people said that the battery quality of Nokia is average and similarly there were lots of people who said that it is best and many also said that it is good and very less number of people said that it is bad and those who said that it is worst is very less in number.

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Price

Chart 5

Here also we can see that most of the people said that it is best and average and the number who said that it good is also quite large in number. Accessories

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Chart 6 Here we can infer that maximum people says that the accessories provided by Nokia is good where as a huge amount of people also says that it is average and many says it is best.

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Features

Chart 7 Here we can see that out of the total sample maximum people said that the features of Nokia handset is good and huge amount of people also said that it is good and also many said it is average.

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Descriptive Analysis

Table 1 Mobile brand- Mean of 1.47 signifies that out of the total sample, maximum people preferred Nokia Handset where as the standard deviation is 0.502 . By analyzing the mean and standard deviation we can see that nearly 60% people preferred Nokia and standard deviation is .502 that means there are chances the people can shift to other brands. Price range- Mean of 2.22 signifies that out of the total sample size o 100, maximum people said that they purchase Phone within the price range of Rs 2500 – 6000. The standard deviation is .675 which means that even this will not effect and the preference will remain the same that is the price range of Rs 2500 – 6000.

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Goodwill- Mean of 4.02 means that out of the total sample selected the mean signifies that the goodwill of Nokia is good and they also prefer Nokia because of the goodwill and the standard deviation is 1.054 which means that their preference changes a lot in case of goodwill. Quality- Mean of 3.90 signifies that out of the total sample size most of the people say that the quality is more than average where as the standard deviation is .959 which means most of them also says that the quality is good there is a lot of variation in their preference and the weight age of quality is not much important it is Average. Features- Mean of 3.77 signifies that out of total sample size most of the people say that features of Nokia is good and the effect of features is important to some extend while choosing any mobile brand and we can see that the standard deviation is 1.053 which means that there is wide variation in case of this parameter that is features. Service Centre- Mean of 3.39 signifies that people had rated the service centre of Nokia as Average which means that it is neither good nor bad and their preference while choosing any Nokia handset is not based on the service centre available the standard deviation in case of service centre is 1.109 which means apart from people who says that the service centre of Nokia is average there are also many people who says that it is good and some also says that it is bad. Battery- Mean of 3.70 in case of battery signifies that people has rated the battery quality of Nokia as average and while choosing any Nokia handset battery is not as important feature that effect 21 | P a g e

their purchase decision. Here we can also see that the standard deviation is 1.109 that means there decision varies a lot. Accessories- Mean of 3.47 signifies that accessories do not effect much while purchasing any Nokia Handset and according to people the accessories provided by them is not best neither it is worst, they have rated it as good. The standard deviation is 1.176 which mean the variation is much high. Price- Mean of 3.96, which is near to 4 signifies that people say that the price of Nokia Hand set is average whereas the standard deviation of .974 signifies that out of the total sample there are lots of people who says that the price of Nokia hand set is Good and price is a very important factor that people take into consideration while selecting any Nokia handset.

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Correlation Matrix

Table 2 Here we can see that the correlation matrix between mobile brand and other factors like price range, quality, goodwill, etc have negative correlation but in case of correlation between mobile brand and price is positive with .080 which is also insignificance. Correlation between price range with mobile brand and price is negative whereas it is positive in other case like goodwill, quality, features, service centre, battery and accessories. The maximum is in case of price range and features that is .527 Correlation between goodwill with mobile brand and price is negative whereas it is positive in other case like quality, features, service centre, battery and accessories. The maximum is in case of goodwill and quality that is .722 23 | P a g e

Correlation between quality and mobile brand is negative whereas is maximum in other case like goodwill, features, service centre, battery, etc and the maximum is in case of goodwill that is .722 Correlation of features is maximum in case of accessories that are .594 because in most case features are mostly added when we add new accessories. Here we can see that the correlation between features with other parameter is less that .5 with all parameter that means the correlation is very less with features. The correlation between service centre and other parameter is also very less that is less than .5 that means the effect of service centre do not affect or is not affected by other parameter The correlation of battery is maximum with goodwill that is .504 and with other variables it is less than .5 and in case of mobile brand is it is negative. So we can say that there is some effect of battery on other parameter but to some extend it is related to goodwill of the brand. The correlation of accessories is near to.5 in case of quality that is .499 and is more than .5 that is .594 with its features and in rest of the cases it is less than .5 The correlation of price with is less than .5 in most of the case that means that the price of the mobile brand does not affect other parameter but it is the only parameter that effect the mobile brand selection.

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KMO and Bartlett’s test

Table 3

Communality The communalities, below, measure the percent of variance in a given variable explained by all the factors. That is, the communality is the squared multiple correlation for the variable using the factors as predictors. Communality for a variable is the sum of squared factor loadings for that variable (row), and thus is the percent of variance in a given variable explained by all the factors. For full orthogonal PCA, the communality will be 1.0 and all of the variance in the variables will be explained by all of the factors, which will be as many as there are variables. In the communalities chart, SPSS labels this column the "initial" communalities. The "extracted" communalities is the percent of variance in a given variable explained by the factors which are extracted, which will usually be fewer than all the possible factors, resulting in coefficients less than 1.0.

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Table 4 The proportion of a variable's variance explained by a factor structure. A variable's commonality must be estimated prior to performing a factor analysis. Communality does not have to be estimated prior to performing a principal component analysis. Communality is denoted by h2. Communality estimates; Estimates of the proportion of common variance in a variable. Prior communality estimates are those which are estimated prior to the factor analysis. Common methods of prior communality estimation are to use (1) an independent reliability estimate, (2) the squared multiple correlation between each variable and the other variables, (3) the highest off-diagonal correlation for each variable, or (4) iterate by performing a sequence of factor analyses using the final communality estimates from one analysis as prior communality estimates for the next analysis. Final communality estimates are the sum of squared loadings for a variable in an orthogonal factor matrix.

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The extraction is maximum in case of communalities is with goodwill that is .817 and with features it is .787 and apart from it is with price range that is .761 Total variance explained The "Total Variance Explained" table below shows the eigenvalues, which are the proportion of total variance in all the variables which is accounted for by that factor. A factor's eigenvalue may be computed as the sum of its squared factor loadings for all the variables. A factor's eigenvalue divided by the number of variables (which equals the sum of variances because the variance of a standardized variable equals 1) is the percent of variance in all the variables which it explains. The ratio of eigenvalues is the ratio of explanatory importance of the factors with respect to the variables. If a factor has a low eigenvalue, then it is contributing little to the explanation of variances in the variables and may be ignored as redundant with more important factors. The table shows 18 factors, one for each variable. However, only the first six are extracted for analysis because, under the Extraction options, SPSS was told to extract only factors with eigen values of 1.0 or higher.

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Table 5 In the particular study we have taken 9 variables and after proper calculation SPSS have recognized three factors. The total of these the factors are 3.589, 1.363 and 1.263 with% of variance as39.979, 15.150 and 14.030 and the total adds up to 69.169 that is more than .5 which signifies that the total sample size taken is appropriate it is neither less so the sample size taken is good in the sense that there will be no difficulties regarding this case. Component matrix The "Component Matrix," below, gives the factor loadings. This is the central output for factor analysis. The factor loadings, also called component loadings in PCA, are the correlation coefficients between the variables (rows) and factors (columns). Factor loadings are the basis for imputing a label to the different factors. 28 | P a g e

Loadings above .6 are usually considered "high" and those below .4 are "low." Note that the phone variables were coded so that high values correspondent to disliking that type of phone. Therefore a positive loading corresponds to disliking that type of phone, and a negative loading to liking. The first table below gives the unrotated solution and the second the rotated solution. Normally the rotated solution will be significantly easier to interpret (indeed, often the researcher will not ask for the unrotated matrix, but we requested it here for instructional purposes). Looking at the rotated matrix, the first factor has high loadings from six music variables: classical, classical(3), opera, Nokia handset, and had moderate loading on big bands. Because these six music items sort on the same factor, this is a justification for combining these items in a scale which might be called "general handset appreciation scale." Naming the factor is a matter of subjectivity and dispute in many cases.

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Table 6

Table 7 In this component matrix table all the parameter score has been estimated with all the factor component and from each component the parameter with the maximum score is selected to form a factor in each case. In our study goodwill, quality and accessories with a score of .791, .843 and .745 respectively have been added in the first component where as in the second component we have mobile brand and price with a score of .617 and .660 and in case of component 3 we have service centre, features, price.

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Component transformation matrix The "Component Transformation Matrix" below indicates the correlation of the factors before and after rotation.

Table 8 Here we can see that we have 3 component in total and the correlation of the factor 1 is .756, .665 and -.036. Correlation of factor 2 is -.300, -.383 and .873 Correlation of factor 3 is .595, -.641 and -.036

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