Mobile Phone Service PRovider

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Mobile Phone Service PRovider Uses And PRoblem

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Mobile Phone Service Provider- Uses and Problem

AbstractIn India there is an increasing convergence in perception about institutional structure, instrument and communication strategy in selecting a particular service. The Governments dream of providing urban service to rural areas will come true only if certain hindrances are removed. The education level of most of the cellular phone user is at least the matriculation. Income level of most of the cellular phone user is higher than the national per capita income. The cellular phone user show significant variation in respect of age, education, income, occupation, and residence. This study focuses on problems faced by the consumer and service expected from mobile service provider.

KEY WORDS : User, Service, Problem, Instrumentation, Communication

Introduction India is competing for global leadership, and within a few decades the effort will become reality. Indian technological developments have largely been insulated from episode of global technological inconsistencies, and over the year, India has built resilience to shocks and now less vulnerable to output volatility. Technological level and education standard are steadily growing, and almost all the state governments and central government are heading toward a breakthrough in the field of communication technology. This will make the way for advanced consumer satisfaction in all the fields. There is an increase convergence in perception, institutional structure, choice of instrument and communication strategy in telecom policy making. The governments dream of providing urban service to rural areas will come true only if certain hindrances are removed. This study is done during the month of March and April, 2009 in urban city of Tamil Nadu to understand the consumer preference for cellular preference for cellular phone services and problem they are facing. In depth study was undertaken to analyze preference pattern, problem of cellular phone services.

Objectives

To know how much people are aware about the mobile service they are using. To know which age people is maximum using mobile phone. To know how brand ambassador is affecting the peoples choice of selecting mobile phone service. To know which channel is better for sales and advertisement. To know what are the basic problem peoples are facing in mobile service

Literature Review

In India cellular phone service provider are facing high competition. There are ten service provider who are providing service in India. Due to this they have to different every time to maintain their customer loyalty. Roni Peleg(2003)- Major problem people are facing is connectivity problem. Mobile is very important part of peoples life, and they are so much depend on it for their daily routine and some other wok like consulting Physician. Eric Ford in his research (2005) the major problem service provider are facing is arrangement of towers to get maximum profit. Andrews, Edmund. L (The New York Times, 2006) Customer is more concerning about new service like TV in mobile phone, and many providers are also thinking about it. So to maintain their customer loyalty service provider should focus on better connectivity, with availability of recharge coupon, more advertisement, good scheme and should accurate in the choice of brand ambassador. Andrews, Edmund L (2006) The Federal Communications Commission is expected to rule soon on whether to allow Fleet Call Inc to provide a new form of mobile telephone service. The ruling, should it be in favor of the company, will have far reaching effects on the cellular industry. Currently, regulations only allow two cellular companies to operate in a single city. The ruling would allow private radio service companies that cater to taxi fleets and delivery services, for example, to provide mobile telephone services to individuals. The FCC is said to be in favor of the scheme as this would open up the market to greater competition. The new services may have some drawbacks when compared to regular cellular systems and may turn out to be no cheaper, but critics of the current system claim that the competition factor alone should reduce market prices. Fleet Call would initially set up networks in only six major cities. Sunitha, N.R. Ambedkar (2008) everyday cellphone service providers are growing like mushrooms after rain providing reliable facilities for customers to meet their budget. In view of the growing demand, service providers require the services of various local agents all over the world. The service providers must delegate the power to these agents to execute the services and monitor their performance. If found efficient the agents can continue to operate else they may need to be revoked. S.A. Pandya ,Rajput (2008) Mobile networks reuse frequency bands based on a color map to increase the capacity of the network. A handoff should occur when a mobile unit moves from the influence of one base station with weaker signal into another's that has stronger signal. Handoff behavior of all units is an important factor in quality of service of a mobile phone service. Handoff decisions, also called mobility decisions, are made by mobile phone based on the observed power from base stations. Premature, delayed or exceedingly

sensitive decisions are considered poor decisions. Excessive poor decisions result in degradation of service quality in otherwise a healthy mobile system.

Research MethodologyThe study done in this research is of descriptive type where the 300 peoples were given the questionnaires to fill and then analyzing the results of the various responses given by the people with regard to the use and problem people are facing. Various methods have been used in this study to find out the relationship between different variables. CHI SQUARE TESTS, ANOVA ONE WAY, KRUSKAL-WALLIS H-TEST, CORRELATION ANALYSIS has been used to find out these relationships between different variables according to the various responses given by the respondents.

Data Analysis & Interpretation Correlation analysisRelationship between age of people and frequency of recharge of sim by them.

Correlations Pearson Correlation Sig. (2-tailed) N age rchrgsim age rchrgsim age rchrgsim age 1.000 .014 . .808 300 300 rchrgsim .014 1.000 .808 . 300 300

InferenceValue in this context is coming to be 0.014 which shows a low positive correlation between age and recharge sim by people. Author also found that there is a low positive correlation between the ratio of advertisement of a Mobile service provider and customer intention to switch for another service provider. Author also finds that there is a positive correlation between income of people and frequency of sim recharging by them. So more is the income more they will recharge.

Chi- square testAssociation between liking of scheme by respondent provided by mobile service provider and frequency of sim recharge by respondent. H0: There is no significant difference between schemes liking and frequency of sim recharge by customer.

Case Processing Summary Cases Missing N Percent 0 .0%

N rchrgsim * schmulik

Valid Percent 300 100.0%

Total N 300 Percent 100.0%

rchrgsim * schmulik Crosstabulation Count lwdaily 21 27 17 14 79 schmulik lwnyt freemsg 14 5 45 23 29 15 13 12 101 55 lwstd 11 32 10 12 65 Total 51 127 71 51 300

rchrgsim

daily 1-10 10-20 20-30

Total

Chi-Square Tests Asymp. Sig. (2-sided) 9 9 1 .113 .110 .478

Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases 14.266 14.359 .504 300a

df

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 9.35.

Directional Measures Value .022 .000 .040 .015 .016 Asymp. a Std. Error .021 .000 .039 .008 .009 Approx. T 1.018 .b

Nominal by Nominal

Lambda

Goodman and Kruskal tau

Symmetric rchrgsim Dependent schmulik Dependent rchrgsim Dependent schmulik Dependent

Approx. Sig. .309 .c

c

1.018

.309 .143 .103d d

a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. c. Cannot be computed because the asymptotic standard error equals zero. d. Based on chi-square approximation

Directional Measures Value .022 .000 .040 .015 .016 Asymp. a Std. Error .021 .000 .039 .008 .009 Approx. T 1.018 .b

Nominal by Nominal

Lambda

Goodman and Kruskal tau

Symmetric rchrgsim Dependent schmulik Dependent rchrgsim Dependent schmulik Dependent

Approx. Sig. .309 .c

c

1.018

.309 .143 .103d d

a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. c. Cannot be computed because the asymptotic standard error equals zero. d. Based on chi-square approximation

Symmetric Measures Value .218 .126 .213 300 Approx. Sig. .113 .113 .113

Nominal by Nominal

Phi Cramer's V Contingency Coefficient

N of Valid Cases

a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis.

Inference: From the Chi-Square test output table, a significance level of 0.113 has been achieved. This mean the Chi-square test is showing significant association between two variables at 88.7% confidence level. Thus we can say that at 88.7% confidence level, SCHEME LIKE MOST and FREQUENTLY RECHARGE OF SIM BY CUSTOMER.

From Contingency coefficient of 0.213 it can be inferred that association between dependent independent variable is not significant as it is more closer to zero than One From the lambda asymmetric value (with recharge sim dependent) of 0.000, it is not possible to predict relationship between scheme liking and recharge sim. Cramers V is coming to be 0 .126 which is less than 0.25. Therefore there is a low relationship between two variables. It is also found by author that there is significant difference between mobile service people are using and use of advertisement media by MSP. So use of media has equal effect on people choice of service provider. Author also found that there is no significant difference Income of people choice of using particular mobile service.

ANOVA Variance between income of the Respondents and maximum recharge by them available in market Ho: There is no significance difference between income of people and maximum recharge by them.Descriptives 95% Confidence Interval for Mean Lower Upper Bound Bound 1.87 2.07 1.65 1.34 1.64 . 1.84 2.24 1.83 2.76 . 2.01

N maxrchrg income student self employement 4 Total 216 37 36 10 1 300

Mean 1.97 1.95 1.58 2.20 1.00 1.92

Std. Deviation Std. Error .75 5.10E-02 .88 .73 .79 . .77 .14 .12 .25 . 4.47E-02

Minimum Maximum 1 4 1 1 1 1 1 3 3 3 1 4

Descriptives 95% Confidence Interval for Mean Lower Upper Bound Bound 1.87 2.07 1.65 1.34 1.64 . 1.84 2.24 1.83 2.76 . 2.01

N maxrchrg income student self employement 4 Total 216 37 36 10 1 300

Mean 1.97 1.95 1.58 2.20 1.00 1.92

Std. Deviation .75 .88 .73 .79 . .77

Std. Error 5.10E-02 .14 .12 .25 . 4.47E-02

Minimum 1 1 1 1 1 1

Maximum 4 3 3 3 1 4

ANOVA Sum of Squares maxrchrg Between Groups Within Groups Total 6.222 173.015 179.237 df 4 295 299 Mean Square 1.555 .586 F 2.652 Sig. .033

Inference: From F probability valve in the ANOVA table is less than 0.05. Hence we conclude that there is no significant relationship between income of people and maximum recharge by them. Author also found that there is no significance difference between incomes of people and frequency of sim recharge by them. So more is the income of people more they will do recharge. Author also found that there is no significance difference between education of people and choice of brand ambassador. Hence choice of brand ambassador by people is depend on their education.

Kruskal Wallis Test Association between between the brand association used by different mobile service provider and its impact on people while choosing mobile service provider Ho: Irrespective of different brand ambassador using by service provider it has equal impact on peoples selection of mobile service provider.Ranks brndambs crckt othrsport film model Total N 100 68 89 43 300 Mean Rank 164.95 133.57 142.01 161.24

mobservi

Test Statisticsa,b Chi-Square df Asymp. Sig. mobservi 7.560 3 .056

a. Kruskal Wallis Test b. Grouping Variable: brndambs

Inference: The value is coming to be .056 which is more than .05. Therefore null hypothesis is accepted. Hence all ambassadors have equal impact on peoples selection of mobile service provider.

Author also found that Irrespective of presence of other service provider if recharge is easily available customer will continue the use of particular service. So if service provider wants that customer should continue the use of service they should make it sure that recharge coupon are available all the time.

Discriminant AnalysisAnalysis Case Processing Summary Unweighted Cases Valid Excl Missing or uded out-of-range group codes At least one missing discriminating variable Both missing or out-of-range group codes and at least one missing discriminating variable Total Total N 300 0 Percent 100.0 .0

0

.0

0

.0

0 300

.0 100.0

Group Statistics Valid N (listwise) Unweighted Weighted 51 51.000 51 51.000 127 127.000 127 127.000 71 71.000 71 71.000 51 51.000 51 51.000 300 300.000 300 300.000

rchrgsim daily 1-10 10-20 20-30 Total

age income age income age income age income age income

Analysis 1 Summary of Canonical Discriminant Functions

Eigenvalues Function 1 2 Eigenvalue .011a .003a % of Variance 80.1 19.9 Cumulative % 80.1 100.0 Canonical Correlation .102 .051

a. First 2 canonical discriminant functions were used in the analysis.

Wilks' Lambda Test of Function(s) 1 through 2 2 Wilks' Lambda .987 .997 Chi-square 3.870 .771 df 6 2 Sig. .694 .680

Standardized Canonical Discriminant Function Coefficients Function age income 1 1.347 -.638 2 -.455 1.271

Structure Matrix Function 1 age income .894* .320 2 .449 .947*

Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. *. Largest absolute correlation between each variable and any discriminant function

Canonical Discriminant Function Coefficients Function 1 1.716 -.743 -2.865

age income (Constant)

2 -.579 1.480 -.849

Unstandardized coefficients

Functions at Group Centroids Function rchrgsim daily 1-10 10-20 20-30 1 .179 -.102 1.504E-02 5.353E-02 2 -6.79E-02 -2.89E-02 4.959E-02 7.090E-02

Unstandardized canonical discriminant functions evaluated at group means

Classification StatisticsClassification Processing Summary Processed Excluded 300 Missing or out-of-range group codes At least one missing discriminating variable 0

0

Used in Output

300

Prior Probabilities for Groups Cases Used in Analysis Unweighted Weighted 51 51.000 127 127.000 71 71.000...

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