“DISCRIMINATING FACTORS AND STRUCTURAL EQUATION MODEL FOR RURAL AND URBAN CONSUMERS’ FOR BUYING MOBILE PHONE IN VADODARA DISTRICT”

For 67th All India Commerce Conference

ON‘COMPETITIVE PRESSURE AND CUSTOMER SATISFACTION: BOON OR BANE’

OrganizedBy

Indian Commerce AssociationKIIT University, Bhubaneswar  

SubmittedBy

Professor (Dr.) Parimal H. Vyas Joint Professor of Management Studies, Department of Management Studies,

Faculty of Management Studies&

Professor of Commerce and Business Management, Department of Commerce and Business Management, Faculty of Commerce

The Maharaja Sayajirao University of Baroda, Vadodara [Gujarat] 39 0002e-mail:parimalvyas17@gmail.com (M) 09825237942

&Dr. Madhusudan N. Pandya

Assistant Professor, Department of Commerce and Business ManagementFaculty of Commerce

The Maharaja Sayajirao University of Baroda, Vadodara [Gujarat] 39 0002e-mail: mnpandya61@rediffmail.com, (M) 09898278567

Address for CommunicationProfessor (Dr.) Parimal H. Vyas

71, Sundaram SocietyBehind Vrajdham Temple

Near Mangaleshwer Mahadev Manjalpur

Vadodara (Gujarat) 390 011Contact Details:

Phone [R]: (0265) 2663343 [M] 09825237942

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“DISCRIMINATING FACTORS AND STRUCTURAL EQUATION MODEL FOR RURAL AND URBAN CONSUMERS’ FOR BUYING MOBILE PHONE IN VADODARA DISTRICT”

* Professor (Dr.) Parimal H. Vyas ** Dr. Madhusudan N. Pandya

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Key Terms: Consumer, Buying Behaviour, Mobile Phone__________________________________________________________________________________

ABSTRACTThe key reason behind manifold growth of usage of mobile phone in India can be attributed to the reduction in service charges and the cost of handsets. According to data released by the Telecom Regulatory Authority of India the number of telephone subscribers in India has reached to 922.04 million at the end of January, 2014, from 915.19 million at the end of December, 2013 thereby showing a monthly growth of 0.75 per cent. The share of urban subscribers has declined from 60.03 per cent to 59.86 per cent whereas share of rural subscribers has increased from 39.97 per cent to 40.14 per cent in the month of January, 2014. With this, the overall Tele-density in India has touched to 74.50 at the end of January, 2014 from 74.02 at the end of December, 2013 (Telecom Regulatory Authority of India; Press Release No. 13/2014). Further, according to PTI News, The Economic Times (23rd April, 2014) the number of telephone subscribers in India showed monthly growth of 1.08 per cent from 922.04 million at the end of January, 2014 to 931.95 million at the end of February, 2014. (Prachi Salve/Gregory Frank, (2013), PTI News, The Economic Times (23rd April, 2014). The key objective of the research study was to identify discriminating factors influencing buying of mobile phones in case of urban vis-à-vis rural customers that were conveniently selected from the Vadodara City and its surrounding villages. This research study is based on exploratory research design and required primary data were collected using structured-non disguised questionnaire supported with personal interviewing of the selected urban and rural customers. The researchers have compared and analysed the buying behaviour of urban as well as rural customers on selected criteria viz., price, quality, style, functions, and brand that acts as motivators for both rural and urban customers in buying of mobile phones. The researcher has offered results and put forward findings in the form of summary and conclusions which support and help in formulation and modifications of marketing strategies concerning motivational factors influencing buying of mobile phones.

___________________________________________________________________________* Professor (Dr.) Parimal H. Vyas is a Joint Professor of Management Studies, Department of Management Studies, Faculty of Management Studies & also Professor of Commerce and Business Management, Department of Commerce and Business Management, Faculty of Commerce, The Maharaja Sayajirao University of Baroda,Vadodara, Gujarat [E-mail: parimalvyas17@gmail.com] & ** Dr. Madhusudan Pandya is an Assistant Professor, Department of Commerce and Business Management, Faculty of Commerce, The M. S. University of Baroda, Vadodara [mnpandya61@rediffmail.com]__________________________________________________________________________________________

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DISCRIMINATING FACTORS AND STRUCTURAL EQUATION MODEL FOR RURAL AND URBAN CONSUMERS’ FOR BUYING MOBILE PHONE IN VADODARA DISTRICT”

* Professor (Dr.) Parimal H. Vyas ** Dr. Madhusudan N. Pandya

__________________________________________________________________________________

Key Terms: Consumer, Buying Behaviour, Mobile Phone__________________________________________________________________________________PROLOGUE:As per the Provisional Population Totals of Census 2011, the total population of India was 1210.2 million. Of this, the rural population stands at 833.1 million and the urban population 377.1 million. In absolute numbers, the rural population has increased by 90.47 million and the urban population by 91.00 million in the last decade (Census of India, 2011). Marketer needs to understand the composition of rural and urban population and the changes in this composition to formulate suitable marketing strategies. It has been observed in the recent past that the ownership and use of mobile phone has become necessity and has become part of life style in form of a fashion accessory which was considered to be a luxury in India till the late 1990s. Compared to other developing economies India is moving ahead at rapid speed and communication is the key to growth and especially widespread use of mobile-phone is one of the essential part of speedy communication around the globe. Various service providers in India have achieved different success in capturing growing subscription in telecommunication. It becomes clear from figure no. 01 that 89.01 per cent of the wireless subscriber market share is hold by Private operators in telecommunications where as BSNL and MTNL, the two PSU operators hold only 10.99 per cent market share. Table No. 01 provides clear idea about status of telecom subscription as on 31st January, 2014 in terms of monthly growth of Total Subscribers (0.75 %); Urban Subscribers (0.47 %) Rural Subscribers (1.17 %) and growth of Overall, rural and urban Teledensity. Considering 144 Broadband service providers, there are 56.90 Million Broadband subscribers in the country at the end of January, 2014. It becomes clear from Table No. 02 that the percentage change in broadband subscribers is attributed highest to Mobile devices users (Phones + Dongles) (4.2 %) than Wired and other broadband subscribers segment (Telecom Regulatory Authority of India; Press Release No. 13/2014). [Please Refer Appendix- Figure No. 01 and Table Number- 01 and 02].Since the 1980’s the major attraction for marketers to put efforts to market their products in the mass rural Indian market is due to the fact that 70 per cent of country’s population was unaddressed. In recent time marketers’ attraction to rural market has increased due to the additional money that comes into hands of rural consumers due to green revolution, rise in Agri-produce prices and MNREGA Spending and Budget allocation of 2013 further strengthens the rural spending. Such initiatives shift the rural consumer’s preferences towards Branded products and rural market constitutes an important segment of overall economy (Pawan Kumar and Neha Dangi, 2013). Such a population diversity and density force the marketer to adopt different marketing strategies. Marketing to rural consumers is not rural marketing but it is marketing to a rural mindset (http://coffeeanddonuts.co.in). The fundamental distinction is that rural consumers are different from their urban counterparts and the most well-known reasons are, confined exposure to product and services for rural consumers, lower levels of literacy in rural area and in addition there are differences in availability of occupation in rural areas which had a direct impact on level of income and flow of income and such a high level of inter-dependency affecting the dynamics of rural community behaviour (http://www.martrural.com).SPENDING PATTERNS OF URBAN, RURAL INDIAN HOUSEHOLDS:With increase in household incomes their expenditure patterns also changes in terms of expenditure on durables, health and education, and investment related spending. Households spending more depends on profiles of urban and rural consumers in terms of age of chief earners; their occupation, educational qualifications etc., which demonstrate urban-rural disparity in the ownership profile of consumer durables. According to National Sample Survey Organisation (NSSO) survey report the Median Monthly Per Capita Expenditure (MPCE) increased 33 per cent in rural areas and 34 per cent in urban areas between 2009-10 and 2011-12. In both rural and urban areas spending on food is reduced.

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Spending on non-food products in rural areas increased from 46 per cent in the year 2010 to 51 per cent in the year 2011-2012 while it increased from 59 per cent to 61 per cent in urban areas. As given in figure number 02 the rural and urban consumers on an average spent Rs 48 and Rs 88 which is higher than Rs 35 and Rs 66 reported in the 2009-2010 NSSO survey report. Indian consumers’ expenditure follows a growing trend that can be noticed from figure no. 03 i.e. per month spending by rural consumers increased from Rs 772 to Rs 1,430 from 2007-08 to 2011-12 whereas urban consumer spend rose from Rs 1,472 to Rs 2,630 per month. Considering monthly per capita expenditure as given in table No. 03 out of the miscellaneous goods & services section, rural consumers spend the most out of their income on medical care (6.9 per cent) and the second most on ‘conveyance’ (4.8 per cent), or ‘transportation’. Urban consumers, on the other hand, spend the most on ‘conveyance’ (7.5 per cent), and the second-most on ‘consumer services’ (6.5 per cent), or services that exclude conveyance. Interestingly, 5.7 per cent of urban India’s miscellaneous expenditure budget is for education while only 3.1 per cent of rural India’s spending is for education (Prachi Salve/Gregory Frank, 2013). [Please Refer Appendix- Figure No. 02 and 03 ad Table No. 03].A Brief Review of Marketing of Mobile Phones and Related Services in India:According to Cyber Media Research India Monthly Mobile Handsets Market Review, CY 2013, February 2014 release, India recorded 247.2 million mobile handset shipments for CY (January-December) 2013. During the same period, 41.1 million smart phones were shipped in the country. As given in Table No 05 it becomes clear from a comparison of overall mobile handset shipments that for CY 2013 and 4Q 2013   similar rankings for the top three players i.e. Nokia, Samsung and Micromax is observed. Further, Considering the status of India’s shipments in Smart phones market, rise of 60.3 per cent was observed during 2nd Half of 2013 over 1st Half 2013 and 16.6 per cent overall contribution was observed during full year of 2013. Further, 65.8 per cent of the total smart phones shipped in the India were 3G smart phones during CY 2013. For local handset vendors in Indian market the CY 2013 was primarily the year of smart phones as on a year-on-year basis a marginal decline in feature phone shipments was observed and such a trend is likely continue with more vendors focusing on entry level smart phone offerings targeting consumer segment of smart phones. Considering ‘Tier One’ brands like Apple, Samsung, Nokia, Sony, HTC,LG and Blackberry served by nearly 70 vendors operated in the highly competitive India smart phones market in CY 2013 and captured close to 53 per cent of the total smart phones market followed by India brands capturing close to 43 per cent of total smart phone shipments. The remaining market of roughly 4 per cent smart phone shipments was captured by China brands. (CMR’s Monthly Mobile Handsets Market Review, CY 2013, February 2014 release). [Please Refer Appendix- Table No. 04 and 05].Future Growth Potential: According to the GSMA and A.T. Kearney report ‘The Mobile Economy 2013’ almost half the population of the earth now uses mobile communications and a billion mobile subscribers were added in the last 4 years to leave the total standing at 3.2 billion. There are still many adults and young people who would appreciate the social and economic benefits of mobile technology but are unable to access it, highlighting a huge opportunity for future growth and a challenge to all players in the industry. Given the strong growth path and pace of innovation it was projected that the next few years will see continued growth with a further 700 million subscribers expected to be added by 2017 and the 4 billion mark to be passed in 2018. This growth is reflected in mobile connections growth that has reached to almost 7 billion connections in 2012, due to the use of multiple devices or multiple SIMs by many consumers to access the best tariffs, and the market is expected to grow even more strongly over the next five years between 2012 and 2017 with 3 billion additional connections expected to be added at a growth rate of 7.6 per cent per annum (GSMA and A.T. Kearney report ‘The Mobile Economy 2013’).The major engines of mobile connection and subscriber growth are emerging markets in particular Asia Pacific will add nearly half of all new connections between 2012 and 2017 i.e.1.4 billion, and will remain at just under 50 per cent of both global connections and subscribers; Latin America and Africa combined will add 595 million new connections will remain at 20 per cent, and developed markets’ connection and subscriber growth is slowing and were forecasted to grow at just 1 per cent per annum between 2012 and 2017 in Europe and North America due to market maturity,but still total connections will grow faster than in emerging markets at 9 per cent and 10 per cent per annum respectively due to strong Mobile to Mobile connections growth by 2017 (ibid).

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Mobile continuously remains a vibrant and evolving industry as it constantly finding new ways to inter-connect the user’s world in spheres such as automotive, utilities, health and education, and new ways to manage financial transactions. Millions of people who cannot imagine life without the smart phone and the mobile broadband connection as to remain connected living have become a reality for people. The mobile internet is the heart of everybody as the innovation reaching beyond expectations. In the race to differentiating themselves in competitive market the mobile device manufacturers, including the operating system (OS) developers, are pouring innovation to make smart phones faster, lighter and more intuitive to use. In order to support the network demands of tomorrow at lower price the Network infrastructure vendors are responding to the demand for improving their efficiencies.Given this dynamism, it is no surprise that the mobile industry makes a substantial economic contribution, with mobile operators alone expected to contribute 1.4 per cent to global GDP in 2012 and their revenues expected to grow at a robust 2.3 per cent per annuam to reach US$1.1 trillion by 2017. When the rest of the mobile ecosystem is included, total revenues are forecast by A.T. Kearney to reach US$2 trillion in 2017, which represents an annual growth rate of 4.7 per cent (ibid).Extending support and protecting citizens too is also the activity of the mobile industry. Mobile phones have significant potential to change the lives of millions by empowering women; protecting the vulnerable; helping responses to natural and man-made disasters, But with any new technology comes new risks and the whole mobile ecosystem is collaborating to reduce risks to users such as handset theft, mobile fraud and breach of privacy (ibid).REVIEW OF LITERATURE:The choice of handset (Nokia and other than Nokia) depends on age and gender parameter of the respondent. In case of teenagers, peer group compliance was found to be the major influencing factor for the cell phone purchase (Macro Market Analysis & Consumer Research Organization, 2004). The influence of culture need to be the part of strategic decisions for marketer of mobile handset (Jiaqin Yang, Xihao He, and Xihao He, 2007). There is no significant difference of price and style consciousness between rural and urban consumers but there is significant difference of quality, functions and brand consciousness between rural and urban consumers for buying mobile phone (Chirag V. Erda, 2008). While price & features are the most influential factors affecting the purchase of a new mobile phone, its audibility, network accessibility, are also regarded as the most important in the choice of the mobile phones (Sheetal Singla, 2010). The important factor influencing choice of mobile services includes Brand, Convenience, Service, Economic aspects, and Technological Factor with one major limiting factor that there is poor awareness among the mobile phone buyers about advance feature provided in the mobile phone services (Dr. D.S. Chaubey et. al., 2011). The advertisement through different media, new added features and low maintenance cost truly influence the customers to purchase the mobile hand set (Dr Debadutta Das (2012). The call tariffs, network coverage and brand image are three major factor that induces the consumers to buy a particular mobile phone operator (Jegan, A. And Dr. S. Sudalaiyandi, 2012). The important preferred criteria for buying mobile phone in among consumers’ of Pakistan were value added facilities like camera, large screen, familiar brand and low price and services like SIM card of low rate, free talk time, call clarity at low call rates (Hassan Jawad Soomro, Dr. Ikhtiar Ali Ghumro, 2013). The motivational force that influences consumer’s purchase decision for a mobile phone was value given to price followed by mobile phone features as the most important variable amongst all (Mesay Sata, 2013). As urban markets are becoming more saturated and competitive Indian rural market is gaining more attention by marketers and especially Mobile phone market is not only growing but has changed the lives of rural people and stressed on the need for rigorous research work on rural buying behaviour In India (Erda, Chirag V., 2013). consumers are contentious about technology and system i.e. users of the Nokia, Samsung, Motorola and LG brands give importance to making use of cell phone of brand name carries SIM, screen type, memory capacity, camera resolution, connectivity / internet and talk time (Suvalaxmi Patra And Jayakrushna Panda, 2013). The price and properties were the most influential factors affecting the purchase of a new mobile phone (Jukka Pakola et. al., Accessed on 10/06/2014).

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RESEARCH METHODOLOGY:Research methodology mainly consists of following.The researcher has used Exploratory research design to determine discriminating factors that have influenced purchase decision of mobile phone by rural and urban consumers who were conveniently drawn by applying non-probability sampling design on the basis of convenience sampling method for the collection of the required primary data. Data were collected from total 360 respondents in which 180 respondents were form rural area and another 180 were from urban area. In this research study required primary data were collected using structured-non disguised questionnaire supported with personal interviewing of the selected urban and rural customers. By using survey research approach data were collected from the representative sampling units from Baroda city and its surrounding villages who are the users of mobile phones. The researcher has made an attempt to put forward the results and findings based on use of descriptive statistics, discrimination analysis and also offered Structural Equation Model using SPSS and AMOS software. The researchers have also offered the implications in formulation and modifications of marketing strategies concerning underlying incentives that have influenced buyer’s decision of buying mobile phones.Objectives of the Research Study:The objectives of the research study was (i) to examine the information sources considered by selected rural and urban consumers while buying a mobile phone; (ii) to examine the who played an important role in making buying decisions made by selected rural and urban consumers and (iii) to identify discrimination factors which affected rural and urban consumers buying decision of mobile phone as well as to measure their satisfaction/dissatisfaction form their mobile phone.Reliability of the Structured Non-disguised Questionnaire:Reliability test was applied to determine how strongly the opinion of rural and urban consumers were related to each other, and also to compare its composite score. The Cronbach’s Alpha score of 0.601 as shown in Table Number 06 showed internal reliability of the scale and reflected the degree of cohesiveness among the given below items (Naresh K. Malhotra, 2007 and Jum C. Nunnally, 1981). [Please Refer Appendix- Table No. 06].DATA ANALYSIS AND INTERPRETATION:As the present research study is based on primary data from selected respondents from Baroda city and its surrounding villages, the researcher has used frequency distribution, mean, and median values for analysing data as well as the z test was put to use to test the significant differences in mean values of the rural and urban customers for selected items (Price, Quality, Style, Functions and Brand name of the Mobile Phone). Results & Findings of the Research Study:An attempt has been made to offer results received based on data analysis and use of SPSS 15.0. as considering the Demographic Profile of Respondents given in table number 07 out of total 180 respondents 69 per cent of the respondents were male and 32 per cent were female in urban area and 85 per cent were male and 15 per cent were female in rural area; 68 percent were below 30 years and 32 per cent were above 30 years in urban area whereas in rural area 64 per cent were below 30 years and 36 per cent were above 30 years. In urban area 53 per cent were under graduate and 47 per cent were above graduate whereas in rural area 72 were under graduate and 28 per cent were graduate and more than graduate. So far as occupation of selected respondents is concerned in urban area 48 per cent were student, 24 per cent were from service, 12 were from business, 11 per cent were from profession and another 5 per cent from other occupations, where as in rural area 28 per cent were from agriculture, 23 per cent were from service, 18 per cent were from business, 16 per cent were students and 15 per cent were engaged in other occupation. Considering monthly family income 68 per cent were earning more than 10,000 and 32 per cent earning lesser than 10,000 in urbanized area, whereas 82 per cent were earning less than 10,000 and only 18 per cent were earning more than 10,000 in rural area. [Please Refer Appendix- Table No. 07]. Mobile Phone Used by Respondents:The use of handset by urban respondents Nokia remained at 1st preferred with 54 per cent purchased it, followed by Samsung as 2nd preferred with 22 per cent of urban respondents and Sony and Blackberry would be 3rd preferred with 4 per cent, and balance 16 per cent purchased other mobile phones.

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In case of rural respondents for first two preference same results was obtained i.e. Nokia would be 1 st

preferred with 52 per cent purchased, Samsung would be 2nd preferred with 23 per cent purchased and Motorola would be 3rd preferred with 8 per cent purchased, and balance 17 per cent purchased other mobile phones. [Please Refer Appendix- Table No. 08].Sources of Information Used by Respondents:The preference showed by rural consumers for sources of information used for collecting information about mobile purchase is for three major sources i.e. 24 per cent of respondents showed preference for Newspapers followed by 22 per cent for Mobile Phone Retailer and 21 per cent for Television. Whereas, the preference showed by urban consumers relates to two major sources i.e. 46 per cent of respondents showed preference for Mobile Phone Retailer, followed by 24 per cent for Friends, and 11 per cent for internet [Please Refer Appendix- Table No. 09].Influence on Buying Decisions of Selected Respondents:The major influence in buying decision of rural consumer was related with Self decisions (45 per cent) followed by influence of Family members (35 per cent); Friends (12 per cent) and mobile phone retailers (8 per cent). In case of urban consumers the major influence in buying decision was related with Self decisions (55 per cent) followed by influence of Family members (28 per cent); Friends (12 per cent) and mobile phone retailers (5 per cent). [Please Refer Appendix- Table No. 10].Motivating Factors in Buying Decisions:Analysis showed that price is important consideration for 92 per cent and less important for 8 per cent of rural consumers whereas in case of urban consumers price is important consideration for 72 per cent and less important for 28 per cent. 90 per cent of urban consumers and 76 per cent of rural consumers give importance to quality of products whereas quality is less important for 10 per cent of urban and 24 per cent of rural consumers. So far as style or look of the mobile phone is concerned it is important for 62 per cent of urban consumers and 49 per cent of rural consumers, whereas it is less important for 18 per cent of urban consumers and 51 per cent of rural consumers. About 75 per cent of urban consumers and 67 per cent of rural consumers give importance to functions and brand name of mobile phones whereas functions and brand name is less important for 25 per cent of urban and 33 per cent of rural consumers. [Please Refer Appendix- Table No. 11].Considering overall satisfaction from performance of their mobile phones, 96 per cent of rural consumers and 83 per cent of urban consumers have expressed their satisfaction whereas 17 percent of urban and 4 percent of rural consumers expressed their dissatisfaction [Please Refer Appendix- Table No. 12].DISCRIMINANT ANALYSIS:Discriminant Analysis is used to investigate the attributes/variables that are responsible for differentiating the groups on the basis of the observed value as reported by the respondents. Linear combination of attributes which contribute most to group separation known as canonical discriminant functions (equations) are and Membership in mutually exclusive groups identified by Discriminant Analysis. Discriminant Analysis that involves the determination of a linear equation which is likely to be used in determining regression equation and the form of the equation or function is given as follows.D = v1 X1 + v2 X2 + v3 X3 = ........vi Xi + a (Where D = discriminate function; v = the discriminant coefficient or weight for that variable; X = respondent’s score for that variable; a = a constant; i = the number of predictor variables).Following are the results obtained by the researcher after running Discriminant Analysis through the use of SPSS 15.0 software.In order to predict a respondent’s group membership we examined first whether there are any significant differences between groups on each of the independent variables considering group means and ANOVA results. It becomes clear that there exists mean differences between Price scores and Quality scores described in Table No. 13 namely ‘Group Statistics’ and these may be good discriminators as the separations are large. ‘Tests of Equality of Group Means’ Table No. 14 provides strong statistical evidence of significant differences between means of price and quality producing very high value F’s (Price = 67.467 and Quality = 50.331). It is not worthwhile to proceed with further analysis if there are no significant group differences observed [Please Refer Appendix- Table No. 13 and 14].

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‘The Pooled Within-Group Matrices’ also supports use of these Independent Variables as inter-correlations between them found low. The ANOVA is based on the assumption that for each group the variances were equivalent but in Discriminant Analysis the basic assumption is that the variance-co-variance matrices are equivalent. The null hypothesis that the covariance matrices do not differ between groups is understood by Box’s M tests. The researcher expects non-significant result from the Box’s M test so that the null hypothesis of groups does not differ and can be retained. The log determinants should be equal if the assumption of equality to be held true. When this assumption is tested by Box’s M, we are looking for a non-significant M to show similarity and lack of significant differences. In our research the log determinants does not appear similar and Box’s M is 73.985 with F 4.859 which is significant at p < 0.000 (Tables No. 16 and 17). It indicates that the data differ significantly and null hypothesis is not accepted that the covariance matrices differ between groups. [Please Refer Appendix- Table No. 15, 16 and 17].An eigenvalue indicates the proportion of variance which is sums of squares Between-groups divided by sums of squares within-groups. If the outcome is in the form of large eigenvalue it is considered as associated with a strong function but in our example it is only 0.406. The correlation between the discriminant scores and the levels of the dependent variable is known as canonical relation and high correlation indicates a function that discriminates well. The present correlation of 0.538 is not extremely high. The most perfect indicator of correlation is 1.00 [Please Refer Appendix- Table No. 18].Wilks’ lambda indicates the significance of the discriminant function that is the ratio of within-groups sums of squares to the total sums of squares. It is the proportion of the total variance in the discriminant scores not explained by differences among groups. The Table No. 19 indicates a highly significant function (p < .000) and provides idea that the proportion of total variability not explained i.e. it is 71.1 per cent unexplained by differences among groups [Please Refer Appendix- Table No. 19].Table No. 20 provides an index of the importance of each predictor and the sign indicates the direction of the relationship. Price score (0.817) was the strongest predictor while Quality (-0.682) with –ve sign was next in importance as a predictor. With large coefficients these two variables i.e. Price and Quality stand out as those that strongly predict allocation to the Urban and or Rural group. [Please Refer Appendix- Table No. 20].The structure matrix table No. 21 shows the correlations of each variable with each discriminate function and it is another way of indicating the relative importance of the predictors and it can be observed that the same pattern (as given in table no. 08) holds as 0.681 for Price and -0.588 for Quality. [Please Refer Appendix- Table No. 21].Table No. 22 indicates the unstandardized scores concerning the independent variables called as ‘Canonical Discriminant Function Coefficients’. It is the list of coefficients of the unstandardized discriminant equation. It operates just like a regression equation and in our example we derive equation as follows.D = (0.903 x Respondent’s Score for Price) + (-0.827 x Respondent’s Score for Quality) +

(0.082 x Respondent’s Score for Style) + (-0.132 x Respondent’s Score for Functions) + (-0.050 x Respondent’s Score for Brand Name) + 0.010 [Please Refer Appendix- Table No. 22].

The Table No. 23 namely ‘Functions at Group Centroids’ interprets discriminant analysis results and describe each group in terms of its profile, considering the group means of the predictor variables are called centroids. In our example, Urban area have a mean of -0.636 while Rural area produce a mean of 0.636, it means the two scores are equal in absolute value but have opposite signs. It indicates the average discriminant score for respondents in the two groups when the variable means rather than individual values for each respondent are entered into the discriminant equation [Please Refer Appendix- Table No. 23].As given in Table No. 24 a simple summary of number and percent of respondents classified correctly and incorrectly’ is provided. The original classifications produces a poorer outcome than the cross validated set of data which are considered as more honest presentation of the power of the discriminant function. The classification results revealed that 75 per cent of respondents were classified correctly into ‘Urban’ or ‘Rural’ groups in original category and 72.8 per cent in Cross-validated group. This overall predictive accuracy of the discriminant function is called the ‘hit ratio’.

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Urban area respondents’ were classified with slightly better accuracy with77.8 per cent than Rural area with72.2 per cent in original group and Urban area respondents’ were classified with 74.4 per cent than Rural area with 71.1per cent in Cross-validated group [Please Refer Appendix- Table No. 24].STRUCTURAL EQUATION MODEL OF RELATIONSHIP BETWEEN SELECTED VARIABLES AND OVERALL SATISFACTION:In figure No. 04 a simple regression model is presented where one observed variable, the overall satisfaction with performance of Mobile Phone is predicted as a linear combination of the other five observed variables, viz., Price, Quality, Style, Functions and Brand Name of Mobile Phone. There are some other variables (other than selected five variables) that also assumed to have an effect on overall satisfaction with performance of Mobile Phone for which the model assumes ‘1’ as standardized regression weights. Each single-headed arrow represents a regression weight. The value shown against two sided arrows (0.36, 0.32, 0.34, 0.31, 0.76, 0.7, 0.41, 0.09, 0.24, 0.13and 0.01 is the correlation between five observed variables, Price, Quality, Style, Functions and Brand Name of Mobile Phone. The values shown with single sided arrow (0.22, -0.15, 0.02, -0.01and 0.03) are standardized regression weights. The value 0.07 is the squared multiple correlation of overall satisfaction with performance of Mobile Phone and five variables. It means the selected respondents overall satisfaction considering five observed variables, viz., Price, Quality, Style, Functions and Brand Name of Mobile Phone is influenced by Price (0.22) followed by Quality (-0.15), and for other three factors Brand Name, Style and functions the very low standardized regression weights was observed i.e. 0.03, 0.02 and -0.01 respectively. It also suggests that respondent’s overall satisfaction with performance of Mobile Phone is predicted mainly by price and quality of the mobile phone offered by the marketer. People prefer quality in the mobile phone at a given price [Please Refer Appendix- Figure No. 04]. DISCUSSION S AND IMPLICATIONS: This paper presents a comparative study investigating the influence of price, quality, style, functions and brand name of Mobile Phone on consumer purchasing behaviour of rural and urban mobile phone users of Vadodara city and selected villages. A structured non-disguised questionnaire was administered and survey was conducted to collect the data which then are used to analyse the data and then results were offered with the implication and conclusions which clearly provide idea about factors having strongest impact on mobile phone consumers. The discriminant analysis showed that considering result of ‘Standardized Canonical Discriminant Function Coefficients’ the Price with 0.817 score was the strongest predictor while Quality with -0.682 score with –ve sign was next in importance as a predictor. With large coefficients these two variables i.e. Price and Quality stand out as those that strongly predict allocation to the Urban and or Rural group. The Structural Equation Model also support the findings of discriminant analysis that price and quality of mobile phones are important factors with standardized regression weights of 0.22 for price and 0.15 for quality. It means 1 unit change in price leads to 0.22 change in overall satisfaction of the consumer form performance of mobile phone and 1 unit change in quality leads to 0.15 change in overall satisfaction of the consumer form performance of mobile phone.The result indicated that among the influences of five factors tested, the price and quality has the strongest impact on mobile phone consumers of rural and urban area of Vadodara. This has the implication to those domestic and multinational mobile phone manufacturers’ marketing practices that those firms should continuously consider the influence of combination of these price and quality to target mobile phone consumers in their future promotional efforts. Considering competition in the marketing of mobile phones the price is a major factor of the attraction to the buyers and many of the manufacturing company attempts to offer the mobile handsets at low price but in such cases mobiles were found to be of poor quality, the materials used in those mobile phones are of lower grade. It results in to difficulty for the marketer to attract customers for such mobile phones and therefore the mobile manufacturer company should make an attempt to know about those factors which influence the buying decisions of consumers. In such competitive marketing environment and the marketing condition all the mobile manufacturing company need to consider the situation and provide the higher quality of mobile sets at lower price which can be helpful to attract the poor people of rural area as well as attract the non-users in urban area.

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Considering other factors being equal (i.e., no significant differences), the marketing efforts with the appropriate application of the price and quality combination will of certainly helpful to the firms to better sell their products or services.The study showed that people of rural area are more price conscious compared to urban consumers due to the limitations of income and occupational opportunities (supported by the facts that 28 per cent of selected respondents in rural areas belongs to agriculture and 82 per cent of selected respondents earn less than 10,000 in a month). Quality consciousness is high among the urban consumers compared to rural consumers and therefore the mobile phone needs to be deigned differently for rural consumers considering their high price consciousness and relatively less quality consciousness for mobile handsets. Consumers buy the branded mobile phones taking in to consideration the assurance about its functions and consistent performance. By considering the kind of difference in terms of importance given by urban and rural segments the marketers of mobile phones can make alterations related with functions of handsets and will be able to make the brand more popular among different segments of users of mobile phones.The research study highlighted the fact that the buyers perception about price, quality, functions and brand name of mobile phone is not same, it really compel the marketers of mobile phone to not only understand the requirements of urban and rural consumers based on their demographic profile but also to formulate different marketing mix strategies for them. Though the perception about style or look of the mobile phone found similar but marketer should not forget the fact that difference exists between urban and rural consumers, and from time to time such preference may change, which helps in designing more suitable models for different segments of the society.Considering today’s marketing practices adopted by the marketers in using combination of media and the kind of favourable behaviour expected by the marketer from consumers, marketer need to understand difference might have been influenced by electronic media or any other media or by the prolonged attitude developed by different categories of consumers. It is very difficult to give or assign a one to one co-relation and induce any final judgment between marketing efforts and its influence on consumers as consumer behaviour and marketing orientation are the results of several factors i.e. market is changing, brands are also changing and technology is moving very fast, but human mind and attitude do not change so fast. As such Consumer Behaviour needs to be assessed from time to time.Further, The research study revealed that due to availability of variety of sources of information consumers are not only able to take self-decisions for buying mobile phone of their choice but at the same time the opinion of family members and friends also play an important role. While developing communication programme the marketer need to address the self-concept of persons to support their self-decisions as well as to incorporate the effect of social relations in making choice. Dissatisfaction among the small number of consumers will definitely have adverse impact on future market of the product considering the chances of influencing buying decisions of others through word of mouth. Continuous monitoring of dissatisfaction by marketer of different mobile phone will aid in adding the features which minimize the dissatisfaction and retain their customers for their replacement demand for handsets.This study besides studying brand selection by the rural and urban consumers of Vadodara will also help to understand the overall purchase behaviour of this segment of consumers. The results of this study will provide insight and information for administrators, practitioners, and researchers about the behaviour of consumers towards various mobile brands and services particularly in Vadodara.CONCLUDING REMARKS:It has been observed that use of mobile phone has become a life style and its usage continues to growaround the world and in particular in developing countries where it can have a profound socio-economic effect. The cost of mobile technology must remain low in order to be able to reach the poorest whose lives the mobile phone has the most potential to change, and the mobile phone operators and the rest of the mobile ecosystem have worked to deliver this via the development of low-cost handsets and micro-top-up pricing models, but this effort is negated if government increases the cost of ownership via heavy taxes on mobile use or on the investment in mobile infrastructure. The most important consideration about rural market is that no matter how good the service provided to them and having a qualitative cell phone, there are still places where cell phone would not work or will cut out.

10

Companies should divert their attention to rural areas to cater to the rural market as Indian market has still not reached to its saturation level, but it has to still make inroads in rural areas. Government should make an attempt to provide the companies secured environment so that the marketer get attracted to invest in rural India to serve some of the village requirements in order to provide better buying experience to rural consumers. Companies need to formulate integrated marketing strategies and action plans in such a way that they are able to get favourable consumer’s response. Content providers among the marketers of mobile phones are harnessing new hardware and software innovations leads to the growth of communities through delivering their own innovative services and products to the consumer over mobile access. One can say that mobile phone is proving to be a lifeline that allows the children and elderly to keep in touch with their families and to aid them in an emergency.The increased adoption of smart phones particularly by the young has brought many benefits and opened up access to new services and products. The advent of the Smartphone, combined with the widespread deployment of mobile broadband networks, has led to an explosion of mobile data services. The mobile industry is a strong supporter of an open Internet, but the flexibility to manage traffic and innovate on the network and in customer propositions is required to keep it open and effective.REFERENCES:[01] Telecom Regulatory Authority of India - Press Release No. 13/2014, 12th March, 2014. Retrieved from www.Trai.Gov.In.[02] Prachi Salve/Gregory Frank, (2013). Rich-Poor Gap Widens In Urban & Rural India. July 6, 2013, PTI News, The Economic Times (23rd April, 2014). Retrieved from http://articles.economictimes.indiatimes.com/ 2014-04-23 /news/49347725_1_subscriber-base-7-lakh-new-customers-one-crore.[04] Census of India (2011). Provisional Population Totals’; Paper 2, Volume 1 of 2011, Rural Urban Distribution India Series 1. Retrieved from http://censusindia.gov.in/2011-prov-results/paper2 /data files/India/ Executive%20summary%20final.pdf.[05] Pawan Kumar and Neha Dangi (2013). Rural Marketing in India: Challenges and Opportunities. International Journal of Management and Social Sciences Research (IJMSSR); Volume 2, No. 8, August 2013.Pp. 93-100. (ISSN: 2319-4421).[06] http://coffeeanddonuts.co.in/files/Bite%20Reality%20-.[07] http://www.martrural.com/Market%20Research%20in%20Rural%20India.pdf.[08] CMR’s Monthly Mobile Handsets Market Review, CY 2013, February 2014 release. Retrieved from http://cmrindia.com/more-than-247-million-mobile-handsets-shipped-in-india-during-cy-2013-a-y-o-y-growth-of-11-6-over-70-million-mobile-handsets-shipped-in-4q-2013-alone.[09] GSMA and A.T. Kearney report ‘The Mobile Economy 2013’. Retrieved from www.atkearney.com /documents/ 10192 /760890/The_Mobile_Economy_2013.pdf/6ac11770-5a26-4fef-80bd-870ab83222f0.[10] Macro- Market Analysis & Consumer Research Organization (2004). A Report on Study of Mobile Phone Usage among the Teenagers and Youth in Mumbai. April-May, 2004.[11] Jiaqin Yang, Xihao He, and Xihao He (2007). Social reference group influence on mobile phone purchasing behaviour: a cross-nation comparative study. Int. J. Mobile Communications, Vol. 5, No. 3, 2007. PP. 319 – 338.[12] Chirag V. Erda (2008). A Comparative Study on Buying Behaviour of Rural and Urban Consumer on Mobile Phone in Jamnagar District. Conference Proceedings of Conference on Marketing to Rural Consumers – Understanding and tapping the rural market potential, IIMK; 3, 4, 5 April 2008, PP. 79-92. Retrieved from http://dspace.iimk.ac.in/handle/2259/426. [13] Sheetal Singla (2010). Mobile Phone Usage Patterns among Indian consumer – An Exploratory Study. Asian Journal of Management Research; 2010. Pp. – 594-599.[14] Dr. D.S. Chaubey, Dr. S.M. Tariq Zafar and S.M. Hasan (2011). Mobile Phone Users’ Behaviour and Service Uses: A Study of Consumers of Uttrakhand State. Annals of Management Research, Volume 1, Number 1, September – October 2011, Pp. 5 – 21. [15] Dr Debadutta Das (2012). An Empirical Study of Factors Influencing Buying Behaviour of Youth Consumers towards Mobile Hand Sets: A Case Study in Coastal Distracts of Odisha. Asian Journal of Research in Business Economics and Management; Vol.2 Issue 4, April 2012, ISSN 2249 7307, Pp. 68 – 82.

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[16] Jegan, A. And Dr. S. Sudalaiyandi (2012). Consumer Behaviour towards Mobile Phone Services in Kovilpatti, Thoothukudi District (India) - A Case Study. International Journal of Current Research, Vol. 4, Issue, 04, April, 2012. Pp.261-265.[ ] Hassan Jawad Soomro, Dr. Ikhtiar Ali Ghumro, (2013). An Analysis of Consumer Behaviour in Mobile Phone Market in Sindh. European Scientific Journal November 2013 edition vol.9. PP. 505-513, [No.31 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431].[17] Mesay Sata (2013). Factors Affecting Consumer Buying Behaviour of Mobile Phone Devices. Mediterranean Journal of Social Sciences, MCSER Publishing, Rome-Italy, Vol. 4 No 12. PP. 103 – 12 [ISSN 2039-2117 (online); ISSN 2039-9340 (print)].[18] Erda, Chirag V. (2013). Review And Classification of Literature on Rural Consumers' Buying Behaviour for Mobile Phone in India/ International Journal of Research in Computer Application & Mana; Academic Journal, August, 2013, Vol. 3 Issue 8. [19] Suvalaxmi Patra and Jayakrushna Panda (2013). A Study on Consumer Behaviour in Telecom Service Provider. VSRD International Journal of Business and Management Research, Vol. III Issue XI November 2013. Pp. 453 – 458 [e-ISSN: 2231-248x, P-ISSN: 2319-2194].[20] Jukka Pakola, Marjukka Pietilä, Rauli Svento, and Heikki Karjaluoto; (Accessed on 10/06/2014). An investigation of consumer behaviour in mobile phone markets in Finland. Submission to 32nd EMAC conference, Track: New Technologies and E-Marketing. Retrieved from http: //www.economicswebinstitute. org/essays/mobilefin.pdf).[21] Naresh K. Malhotra (2007). Marketing Research an Applied Orientation. Pearson Prentice Hall, Fifth Edition, 2007. P - 315. [22] Jum C. Nunnally (1981). Psychometric Theory. Tata McGraw-Hill Publishing Ltd. New Delhi, 1981.

APPENDIXFigure No. 01: Service Provider wise Market Share as on 31st January, 2014.

Source: Telecom Regulatory Authority of India; Press Release No. 13/2014.

12

Figure No. 02: Rural Vs Urban Expenditure per Day (in Rs.): 2004-2005 to 2011-2012

2004-2005

2005-2006

2006-2007

2007-2008

2009-2010

2011-2012

0

10

20

30

40

50

60

70

80

90

100

19 21 23 2635

48

37 3944

49

66

88

RuralUrban

Source: NSSO (Prachi Salve/Gregory Frank, 2013).

Figure No. 03: Rural Vs Urban Expenditure per Month (in Rs.): 2004-2005 to 2011-2012

2004-2005

2005-2006

2006-2007

2007-2008

2009-2010

2011-2012

0

500

1000

1500

2000

2500

3000

579 625 695 7721054

14301105 1171

13121472

1984

2630

RuralUrban

Source: NSSO (Prachi Salve/Gregory Frank, 2013).

13

Figure No.: 04: SEM of Relationship between Selected Variables and Overall Satisfaction Experienced by Total Rural and Urban Buyers of Mobile Phone

Table No. 01: Highlights on Telecom Subscription Data as on 31st January, 2014Particulars Wireless Wire line Wireless +

Wire lineTotal Subscribers (Million) 893.31 28.72 922.04Total Net Monthly Addition (Million) 7.02 -0.17 6.85Monthly Growth 0.79 % -0.59 % 0.75 %Urban Subscribers (Million) 529.30 22.66 551.96Urban Subscribers Net Monthly Addition (Million) 2.67 -0.11 2.56Monthly Growth 0.51 % -0.48 % 0.47 %Rural Subscribers (Million) 364.01 6.06 370.08Rural Subscribers Net Monthly Addition (Million) 4.35 -0.06 4.29Monthly Growth 1.21 % -0.99 % 1.17 %Overall Teledensity 72.18 2.32 74.50Urban Teledensity 139.42 5.97 145.39Rural Teledensity 42.43 0.71 43.13Share of Urban Subscribers 59.25 % 78.89 % 59.86 %Share of Rural Subscribers 40.75 % 21.11 % 40.14 %

Source: Telecom Regulatory Authority of India; Press Release No. 13/2014.

Table No. 02: Broadband Subscribers (≥ 512 Kbps download) Data as on 31st January, 2014

Sl.No. Segment

Broadband subscribers(in millions)

Percentage Change

December-2013 January-2014

01 Wired Subscribers 14.54 14.55 0.0802 Mobile devices users

(Phones + Dongles) 40.26 41.95 4.20

03 Other Wireless (Wi-Fi, Wi-Max, Point-to-Point Radio & VSAT) 0.39 0.40 1.66

Total 55.20 56.90 3.09Source: ibid.

14

Table No. 03: Growing percentages of Miscellaneous Goods & Services in Median Monthly Per Capita Expenditure (MPCE) over time

Items Rural Urban2004-2005

2009- 2010

2011- 2012

2004-2005

2009- 2010

2011- 2012

Education 2.7 2.9 3.1 5.0 5.2 5.7Medical Care 6.6 5.7 6.9 5.2 5.0 5.5Entertainment 0.6 0.9 1.1 1.9 1.8 1.8Toilet Articles 2.7 2.5 2.4 2.6 2.5 2.4Other Household consumables 23 2.2 2.2 2.2 2.0 2.0Consumer Services excluding Conveyance 3.8 4.8 4.5 7.0 7.1 6.5Conveyance 3.8 4.0 4.8 6.5 6.5 7.5Minor Durable type goods 0.2 0.3 0.3 0.2 0.2 0.4Rent 0.5 0.5 0.5 5.6 6.6 7.0Taxes and Cesses 0.2 0.2 0. 3 0.8 0.9 0.9Miscellaneous Goods and Services (Including Education and Medical care)

23.4 24.0 26.1 37.0 37.8 39.7

Source: NSSO (Prachi Salve/Gregory Frank, 2013).

Table No. 04: India Mobile Handsets Market: CY 2013 versus CY 2012 (in terms of unit’s shipments)Form Factor Shipments (CY 2012) Shipments (2013) Year –on _ Year

Growth, CY2013 over CY 2013(in Percentage)

Mobile Handsets 221.6 247.2 11.6Feature Phones 206.5 206.1 -0.2Smart Phones 15.1 41.1 172.2Source: CMR’s Monthly Mobile Handsets Market Review, CY 2013, February 2014 release

Table No. 05: India Mobile Handsets Market: Leading Players - CY 2013 and 4th Quarter 2013 (Percentage of unit shipments)Player Rank Overall

CY 2013Share – Overall (Percentage of

Unit Shipments) CY 2013

Rank Overall CY 2013

Share – Overall (Percentage of

Unit Shipments) CY 2013

Nokia 1 18.9 % 1 16.6 %Samsung 2 13.8 % 2 15.6 %Micromax 3 10.3 % 3 11.6 %Source: CMR’s Monthly Mobile Handsets Market Review, CY 2013, February 2014 release

Table Number: 06: Table Showing Summary of Indicators and Reliability Alpha ScoreSr. No. Grouped Indicator Items Cronbach’s Alpha Coefficient

01 Price of the Mobile

0.60102 Quality of the Mobile03 Style of the Mobile04 Functions of the Mobile05 Brand Name of the Mobile

15

Table Number: 07: Profile of Selected Urban and Rural Respondents

Sr. No.

Selected Background Variables of Selected Respondents

City or Rural Area(Number and Percentages of Selected

Respondents)Urban Area Rural Area Total

01 Gender Males 124 (68.9) 153 (85.0) 277 (76.9)Females 56 (31.1) 27 (15.0) 83 (23.1)

02

Age Group

Below 20 55 (30.6) 36 (20.0) 91 (25.3)21 to 30 67 (37.2) 79 (43.9) 146 (40.6)31 to 40 29 (16.1) 29 (16.1) 58 (16.1)41 to 50 15 (8.3) 24 (13.3) 39 (10.8)Over 50 14 (7.8) 12 (6.7) 26 (7.2)

03

Educational Qualification

Under Graduate 96 (53.3) 129 (71.7) 225 (62.5)Graduate 46 (25.6) 41 (22.8) 87 (24.2)Post-Graduate 29 (16.1) 7 (3.9) 36 (10.0)Professional Qualification 7 (3.9) 0 (0.0) 7 (1.9)Ph. D. 2 (1.1) 3 (1.7) 5 (1.4)

04

Occupation

Student 87 (48.3) 29 (16.1) 116 (32.2)Service 44 (24.4) 42 (23.3) 86 (23.9)Business 22 (12.2) 32 (17.8) 54 (15.0)Profession 20 (11.1) 0 (0.0) 20 (5.6)Agriculture 1 (0.6) 50 (27.8) 51 (14.2)House Wife 2 (1.1) 12 (6.7) 14 (3.9)Retired 4 (2.2) 0 (0.0) 4 (1.1) Religious Activity 0 (0.0) 15 (8.3) 15 (4.2)

05Monthly Family Income

Up to Rs. 5,000 15 (8.3) 82 (45.6) 97 (26.9)Rs. 5,001 to 10,000 42 (23.3) 65 (36.1) 107 (29.7)Rs. 10,001 to 20,000 41 (22.8) 21 (11.7) 62 (17.2)Above 20,000 82 (45.6) 12 (6.7) 94 (26.1)

Table Number: 08: Table Showing Mobile Phones of Different Brand Used by Respondents

Sr. No. Name of the BrandCity or Village Total

Urban Area Rural Area(Number and Percentages)

01 Nokia 97 (53.9) 93 (51.7) 190 (52.8)02 Motorola 3 (1.7) 14 (7.8) 17 (4.7)03 Reliance LG 2 (1.1) 3 (1.7) 5 (1.4)04 Sony 8 (4.4) 7 (3.9) 15 (4.2)05 Samsung 40 (22.2) 41 (22.8) 81 (22.5)06 TATA LG 3 (1.7) 1 (0.6) 4 (1.1)07 LG 5 (2.8) 4 (2.2) 9 (2.5)08 Max 5 (2.8) 5 (2.8) 10 (2.8)09 I Phone 1 (0.6) 0 (0.0) 1 (0.3)10 Ideas 2 (1.1) 2 (1.1) 4 (1.1)11 Videocon 2 (1.1) 1 (0.6) 3 (0.8)12 Blackberry 8 (4.4) 1 (0.6) 9 (2.5)13 Micromax 3 (1.7) 2 (1.1) 5 (1.4)14 HTC 1 (0.6) 0 (0.0) 1 (0.3)15 China 0 (0.0) 6 (3.3) 6 (1.7)

Total 180 (100.0) 180 (100.0) 360 (100.0)

16

Table Number: 09: Table Showing Sources of Information Used by Respondents for Buying Mobile Phones:

Sr. No. Name of the Information Sources Used by respondents

City or Village TotalUrban Area Rural Area

(Number and Percentages)01 News Paper 12 (6.7) 43 (23.9) 55 (15.3)02 TV 15 (8.3) 37 (20.6) 52 (14.4)03 Internet 19 (10.6) 20 (11.1) 39 (10.8)04 Mobile Phone Retailer 83 (46.1) 40 (22.2) 123 (34.2)05 Magazines 2 (1.1) 8 (4.4) 10 (10.0)06 Radio 6 (3.3) 16 (8.9) 22 (6.1)07 Friends 43 (23.9) 16 (8.9) 59 (16.4)

Total 180 (100.0) 180 (100.0) 360 (100.0)

Table Number: 10: Table Showing the Influencer in Making Purchase Decision of Mobile Phone

Sr. No. Influencer in Making Buying Decision

City or VillageTotal

Urban Area Rural Area(Number and Percentages)

01 Self-Decision 98 (54.4) 81 (45.0) 179 (49.7)02 Family Members 51 (28.3) 63 (35.0) 114 (31.7)03 Friends 22 (12.2) 21 (11.7) 43 (11.9)04 Mobile Phone Retailer 9 (5.0) 15 (8.3) 24 (6.7)

Total 180 (100.0) 180 (100.0) 360 (100.0)Table No. 11 Table Showing Factors Motivating Respondents for Buying Mobile Phone

Selected Criteria Rural Area Urban Area Total

(Number and Percentages)ENI NI SIM IM

EIM

ENI NI SIM IM

EIM

ENI NI SIM IM EIM

Price 0 3 11 21 145 11 5 34 66 64 11 8 45 87 209(0.0) (1.7) (6.1)

(11.7)

(80.6)

(6.1)

(2.8)

(18.9)

(36.7)

(35.6)

(3.1) (2.2)

(12.5)

(24.2)

(58.1)

Quality 0 16 28 92 44 1 4 14 41 120 1 20 42 133 164(0.0) (8.9)

(15.6)

(51.1)

(24.4)

(0.6)

(2.2) (7.8)

(22.8)

(66.7)

(0.3) (5.6)

(11.7)

(36.9)

(45.6)

Style 1 20 71 31 57 10 15 44 57 54 11 35 115 88 111(0.6)

(11.1)

(39.4)

(17.2)

(31.7)

(5.6)

(8.3)

(24.4)

(31.7)

(30.0)

(3.1) (9.7)

(31.9)

(24.4)

(30.8)

Functions 0 27 32 64 57 6 9 26 46 93 6 36 58 110 150(0.0)

(15.0)

(17.8)

(35.6)

(31.7)

(3.3)

(5.0)

(14.4)

(25.6)

(51.7)

(1.7)

(10.0)

(16.1)

(30.6)

(41.7)

Brand 15 19 27 46 73 7 8 30 58 77 22 27 57 104 150(8.3)

(10.6)

(15.0)

(25.6)

(40.6)

(3.9)

(4.4)

(16.7)

(32.2)

(42.8)

(6.1) (7.5) (5.8)

(28.9)

(41.7)

ENI= Extremely Not Important; NI= Not Important; SIM= Some What Important; IM = Important; EIM= Extremely Important

17

Table Number: 12: Table Showing the Overall Satisfaction with Performance of Mobile Phone

Selected Criteria Satisfaction/ Dissatisfaction

City or Village TotalUrban Area Rural Area

(Number and Percentages)

Overall Satisfaction with Performance of Mobile Phone

Highly Dis-satisfied 24 (6.7) 0 (0.0) 24 (6.7)Dis-satisfied 1 (0.3) 0 (0.3) 1 (0.3)Somewhat Satisfied 4 (1.1) 7 (1.9) 11 (3.1)Satisfied 132 (36.7) 40 (11.1) 172 (47.8)Highly Satisfied 19 (5.3) 133 (36.9) 152 (42.2)

Total 180 (50.0) 180 (50.0) 360 (100.0)Table No. 13: Group Statistics of Urban and Rural Respondents for Selected Criteria

City or Village Criteria Mean Std. DeviationUnweighted Weighted

Urban Area Price is Important in Choosing Mobile 3.93 1.099Quality is Important in Choosing Mobile 4.53 0.780

Style is Important in Choosing Mobile 3.72 1.144Functions of the Mobile 4.17 1.067Brand Name in Choosing Mobile 4.06 1.061

Rural Area Price is Important in Choosing Mobile 4.71 0.656Quality is Important in Choosing Mobile 3.91 0.867

Style is Important in Choosing Mobile 3.68 1.054Functions of the Mobile 3.84 1.037Brand Name in Choosing Mobile 3.79 1.302

Table No. 14: Tests of Equality of Group Means of Urban and Rural Respondents for Selected Criteria

CriteriaWilks'

Lambda F df1 df2 Sig.Price is Important in Choosing Mobile 0.841 67.467 1 358 0.000

Quality is Important in Choosing Mobile 0.877 50.331 1 358 0.000

Style is Important in Choosing Mobile 1.000 0.113 1 358 0.737

Functions of the Mobile 0.975 9.041 1 358 0.003Brand Name in Choosing Mobile 0.988 4.351 1 358 0.038

Table No. 15: Pooled Within-Groups Matrices Shows Correlations between Selected Criteria

Criteria Price Quality Style FunctionsBrand Name

Correlation

Price 1.000 0.173 0.149 0.173 0.130

Quality 0.173 1.000 0.254 0.383 0.292Style 0.149 0.254 1.000 0.339 0.319Functions 0.173 0.383 0.339 1.000 0.352Brand Name 0.130 0.292 0.319 0.352 1.000

Table No. 16: Log DeterminantsCity or Village Rank Log DeterminantUrban Area 5 -.457Rural Area 5 -.985Pooled within-groups 5 -.514

18

The ranks and natural logarithms of determinants printed are those of the group covariance matrices.

Table No. 17: Test Results of Box's M Test of Equality of Covariance MatricesBox's M 73.985F Approx. 4.859

df1 15df2 516028.737Sig. 0.000

Tests null hypothesis of equal population covariance matrices.Table No. 18: Eigen values - proportion of variance explained

Function Eigen value % of Variance Cumulative %Canonical Correlation

1 0.406 (a) 100.0 100.0 0.538First 1 canonical discriminant functions were used in the analysis.

Table No. 19: Wilks' Lambda Scores - of the total variance in the discriminant scoresTest of Function(s) Wilks' Lambda Chi-square df Sig.

1 0.711 121.239 5 0.000Table No. 20: Standardized Canonical Discriminant Function Coefficients

CriteriaFunction

1Price is Important in Choosing Mobile 0.817Quality is Important in Choosing Mobile -0.682Style is Important in Choosing Mobile 0.090Functions of the Mobile -0.138Brand Name in Choosing Mobile -0.060Table No. 21: Structure Matrix - correlations of each variable with each discriminate function Criteria Function 1Price is Important in Choosing Mobile 0.681Quality is Important in Choosing Mobile -0.588Functions of the Mobile -0.249Brand Name in Choosing Mobile -0.173Style is Important in Choosing Mobile -0.028Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions - Variables ordered by absolute size of correlation within function.

Table No. 22: Canonical Discriminant Function CoefficientsCriteria Function 1Price is Important in Choosing Mobile 0.903Quality is Important in Choosing Mobile -0.827Style is Important in Choosing Mobile 0.082Functions of the Mobile -0.132Brand Name in Choosing Mobile -0.050(Constant) 0.010Unstandardized coefficients

Table No. 23: Functions at Group Centroids City or Village Function 1Urban Area -0.636Rural Area 0.636Unstandardized canonical discriminant functions evaluated at group means

Table No. 24: Classification ResultsCity or Village Predicted Group Membership Total

Urban Area Rural AreaOriginal Count Urban Area 140 40 180

Rural Area 50 130 180% Urban Area 77.8 22.2 100.0

Rural Area 27.8 72.2 100.0Cross-validated (a) Count Urban Area 134 46 180

Rural Area 52 128 180

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% Urban Area 74.4 25.6 100.0Rural Area 28.9 71.1 100.0

A Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case.B 75.0% of original grouped cases correctly classified.C 72.8% of cross-validated grouped cases correctly classified.*************************************************************************************

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