“DISCRIMINATING FACTORS AND STRUCTURAL EQUATION MODEL FOR RURAL
AND URBAN CONSUMERS’ FOR BUYING MOBILE PHONE IN VADODARA DISTRICT”
For
67 All India Commerce Conference
th
ON
‘COMPETITIVE PRESSURE AND CUSTOMER SATISFACTION: BOON OR BANE’
Organized
By
Indian Commerce Association
KIIT University, Bhubaneswar
Submitted
By
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 0002
e-mail:[email protected] (M) 09825237942
&
Dr. Madhusudan N. Pandya
Assistant Professor, Department of Commerce and Business Management
Faculty of Commerce
The Maharaja Sayajirao University of Baroda, Vadodara [Gujarat] 39 0002
e-mail: [email protected], (M) 09898278567
Address for Communication
Professor (Dr.) Parimal H. Vyas
71, Sundaram Society
Behind Vrajdham Temple
Near Mangaleshwer Mahadev
Manjalpur
Vadodara (Gujarat) 390 011
Contact Details:
Phone [R]: (0265) 2663343
[M] 09825237942
1
“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
__________________________________________________________________________________
ABSTRACT
The 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: [email protected]] & **
Dr. Madhusudan Pandya is an Assistant Professor, Department of Commerce and Business Management,
Faculty of Commerce, The M. S. University of Baroda, Vadodara [[email protected]]
__________________________________________________________________________________________
2
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 mobilephone 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 wellknown 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 interdependency 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.
3
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 (JanuaryDecember) 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).
4
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).
5
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.
6
In case of rural respondents for first two preference same results was obtained i.e. Nokia would be 1st
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].
7
‘The Pooled Within-Group Matrices’ also supports use of these Independent Variables as intercorrelations 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 varianceco-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 Crossvalidated group. This overall predictive accuracy of the discriminant function is called the ‘hit ratio’.
8
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.
9
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 grow
around the world and in particular in developing countries where it can have a profound socioeconomic 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.
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The
Economic
Times
(23rd
April,
2014).
Retrieved
from
http://articles.economictimes.indiatimes.com/ 2014-04-23 /news/49347725_1_subscriber-base-7-lakhnew-customers-one-crore.
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[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-ay-o-y-growth-of-11-6-over-70-million-mobile-handsets-shipped-in-4q-2013-alone.
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11
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APPENDIX
Figure 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
100
88
90
80
66
70
60
50
39
37
40
30
19
20
48
Rural
Urban
35
26
23
21
49
44
10
0
2004-2005 2005-2006 2006-2007 2007-2008 2009-2010 2011-2012
Source: NSSO (Prachi Salve/Gregory Frank, 2013).
Figure No. 03: Rural Vs Urban Expenditure per Month (in Rs.): 2004-2005 to 2011-2012
3000
2630
2500
1984
2000
1500
1105
1000
579
1171
625
1472
1312
1430
1054
695
772
500
0
Source: NSSO (Prachi Salve/Gregory Frank, 2013).
13
Rural
Urban
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, 2014
Particulars
Wireless
Wire line
Wireless +
Wire line
Total Subscribers (Million)
893.31
28.72
922.04
Total Net Monthly Addition (Million)
7.02
-0.17
6.85
Monthly Growth
0.79 %
-0.59 %
0.75 %
Urban Subscribers (Million)
529.30
22.66
551.96
Urban Subscribers Net Monthly Addition (Million)
2.67
-0.11
2.56
Monthly Growth
0.51 %
-0.48 %
0.47 %
Rural Subscribers (Million)
364.01
6.06
370.08
Rural Subscribers Net Monthly Addition (Million)
4.35
-0.06
4.29
Monthly Growth
1.21 %
-0.99 %
1.17 %
Overall Teledensity
72.18
2.32
74.50
Urban Teledensity
139.42
5.97
145.39
Rural Teledensity
42.43
0.71
43.13
Share 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.
01
02
03
Segment
Wired Subscribers
Mobile devices users
(Phones + Dongles)
Other Wireless (Wi-Fi, Wi-Max,
Point-to-Point Radio & VSAT)
Total
Broadband subscribers
(in millions)
December-2013 January-2014
14.54
14.55
Percentage
Change
0.08
40.26
41.95
4.20
0.39
0.40
1.66
55.20
56.90
3.09
Source: ibid.
14
Table No. 03: Growing percentages of Miscellaneous Goods & Services in Median Monthly Per
Capita Expenditure (MPCE) over time
Items
Education
Medical Care
Entertainment
Toilet Articles
Other Household consumables
Consumer Services excluding Conveyance
Conveyance
Minor Durable type goods
Rent
Taxes and Cesses
Miscellaneous Goods and Services (Including
Education and Medical care)
Source: NSSO (Prachi Salve/Gregory Frank, 2013).
20042005
2.7
6.6
0.6
2.7
23
3.8
3.8
0.2
0.5
0.2
23.4
Rural
20092010
2.9
5.7
0.9
2.5
2.2
4.8
4.0
0.3
0.5
0.2
24.0
20112012
3.1
6.9
1.1
2.4
2.2
4.5
4.8
0.3
0.5
0. 3
26.1
20042005
5.0
5.2
1.9
2.6
2.2
7.0
6.5
0.2
5.6
0.8
37.0
Urban
20092010
5.2
5.0
1.8
2.5
2.0
7.1
6.5
0.2
6.6
0.9
37.8
20112012
5.7
5.5
1.8
2.4
2.0
6.5
7.5
0.4
7.0
0.9
39.7
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
11.6
Feature Phones
206.5
206.1
-0.2
Smart Phones
15.1
41.1
172.2
Source: 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
Share – Overall
Rank Overall
Share – Overall
CY 2013
(Percentage of
CY 2013
(Percentage of
Unit Shipments)
Unit Shipments)
CY 2013
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 Score
Sr. No.
01
02
03
04
05
Cronbach’s Alpha Coefficient
Grouped Indicator Items
Price of the Mobile
Quality of the Mobile
Style of the Mobile
Functions of the Mobile
Brand Name of the Mobile
0.601
15
Table Number: 07: Profile of Selected Urban and Rural Respondents
Sr.
No.
01
City or Rural Area
(Number and Percentages of Selected
Respondents)
Total
Urban Area
Rural Area
124 (68.9)
153 (85.0)
277 (76.9)
56 (31.1)
27 (15.0)
83 (23.1)
55 (30.6)
36 (20.0)
91 (25.3)
67 (37.2)
79 (43.9)
146 (40.6)
29 (16.1)
29 (16.1)
58 (16.1)
15 (8.3)
24 (13.3)
39 (10.8)
14 (7.8)
12 (6.7)
26 (7.2)
96 (53.3)
129 (71.7)
225 (62.5)
46 (25.6)
41 (22.8)
87 (24.2)
29 (16.1)
7 (3.9)
36 (10.0)
7 (3.9)
0 (0.0)
7 (1.9)
2 (1.1)
3 (1.7)
5 (1.4)
87 (48.3)
29 (16.1)
116 (32.2)
44 (24.4)
42 (23.3)
86 (23.9)
22 (12.2)
32 (17.8)
54 (15.0)
20 (11.1)
0 (0.0)
20 (5.6)
1 (0.6)
50 (27.8)
51 (14.2)
2 (1.1)
12 (6.7)
14 (3.9)
4 (2.2)
0 (0.0)
4 (1.1)
0 (0.0)
15 (8.3)
15 (4.2)
15 (8.3)
82 (45.6)
97 (26.9)
42 (23.3)
65 (36.1)
107 (29.7)
41 (22.8)
21 (11.7)
62 (17.2)
82 (45.6)
12 (6.7)
94 (26.1)
Selected Background Variables of Selected
Respondents
Gender
02
Age Group
03
Educational
Qualification
04
Occupation
05
Monthly
Family
Income
Males
Females
Below 20
21 to 30
31 to 40
41 to 50
Over 50
Under Graduate
Graduate
Post-Graduate
Professional Qualification
Ph. D.
Student
Service
Business
Profession
Agriculture
House Wife
Retired
Religious Activity
Up to Rs. 5,000
Rs. 5,001 to 10,000
Rs. 10,001 to 20,000
Above 20,000
Table Number: 08: Table Showing Mobile Phones of Different Brand Used by Respondents
City or Village
Sr. No.
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
Name of the Brand
Nokia
Motorola
Reliance LG
Sony
Samsung
TATA LG
LG
Max
I Phone
Ideas
Videocon
Blackberry
Micromax
HTC
China
Total
Urban Area
97 (53.9)
3 (1.7)
2 (1.1)
8 (4.4)
40 (22.2)
3 (1.7)
5 (2.8)
5 (2.8)
1 (0.6)
2 (1.1)
2 (1.1)
8 (4.4)
3 (1.7)
1 (0.6)
0 (0.0)
180 (100.0)
16
Rural Area
(Number and Percentages)
93 (51.7)
14 (7.8)
3 (1.7)
7 (3.9)
41 (22.8)
1 (0.6)
4 (2.2)
5 (2.8)
0 (0.0)
2 (1.1)
1 (0.6)
1 (0.6)
2 (1.1)
0 (0.0)
6 (3.3)
180 (100.0)
Total
190 (52.8)
17 (4.7)
5 (1.4)
15 (4.2)
81 (22.5)
4 (1.1)
9 (2.5)
10 (2.8)
1 (0.3)
4 (1.1)
3 (0.8)
9 (2.5)
5 (1.4)
1 (0.3)
6 (1.7)
360 (100.0)
Table Number: 09: Table Showing Sources of Information Used by Respondents for Buying
Mobile Phones:
Sr. No.
01
02
03
04
05
06
07
Name of the Information Sources
Used by respondents
City or Village
Total
Urban Area
Rural Area
(Number and Percentages)
12 (6.7)
43 (23.9)
15 (8.3)
37 (20.6)
19 (10.6)
20 (11.1)
83 (46.1)
40 (22.2)
2 (1.1)
8 (4.4)
6 (3.3)
16 (8.9)
43 (23.9)
16 (8.9)
180 (100.0)
180 (100.0)
News Paper
TV
Internet
Mobile Phone Retailer
Magazines
Radio
Friends
Total
55 (15.3)
52 (14.4)
39 (10.8)
123 (34.2)
10 (10.0)
22 (6.1)
59 (16.4)
360 (100.0)
Table Number: 10: Table Showing the Influencer in Making Purchase Decision of Mobile
Phone
Sr. No.
01
02
03
04
City or Village
Influencer in Making Buying
Decision
Urban Area
Self-Decision
Family Members
Friends
Mobile Phone Retailer
Total
98 (54.4)
51 (28.3)
22 (12.2)
9 (5.0)
180 (100.0)
Rural Area
(Number and Percentages)
81 (45.0)
63 (35.0)
21 (11.7)
15 (8.3)
180 (100.0)
Total
179 (49.7)
114 (31.7)
43 (11.9)
24 (6.7)
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)
Price
Quality
Style
Functions
Brand
ENI
NI
SIM
IM
EIM
ENI
NI
SIM
IM
EIM
ENI
NI
SIM
IM
EIM
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)
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)
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)
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)
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
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Table Number: 12: Table Showing the Overall Satisfaction with Performance of Mobile Phone
Selected Criteria
Overall Satisfaction with
Performance of Mobile
Phone
City or Village
Total
Urban Area
Rural Area
(Number and Percentages)
24 (6.7)
0 (0.0)
24 (6.7)
1 (0.3)
0 (0.3)
1 (0.3)
4 (1.1)
7 (1.9)
11 (3.1)
132 (36.7)
40 (11.1)
172 (47.8)
19 (5.3)
133 (36.9)
152 (42.2)
180 (50.0)
180 (50.0)
360 (100.0)
Satisfaction/
Dissatisfaction
Highly Dis-satisfied
Dis-satisfied
Somewhat Satisfied
Satisfied
Highly Satisfied
Total
Table No. 13: Group Statistics of Urban and Rural Respondents for Selected Criteria
City or Village
Criteria
Mean
Std. Deviation
Unweighted
Weighted
Urban Area
Price is Important in Choosing Mobile
1.099
3.93
Quality is Important in Choosing
0.780
4.53
Mobile
Style is Important in Choosing Mobile
3.72
1.144
Functions of the Mobile
4.17
1.067
Brand Name in Choosing Mobile
4.06
1.061
Rural Area
Price is Important in Choosing Mobile
0.656
4.71
Quality is Important in Choosing
0.867
3.91
Mobile
Style is Important in Choosing Mobile
3.68
1.054
Functions of the Mobile
3.84
1.037
Brand 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
Wilks'
Criteria
Lambda
F
df1
df2
Sig.
Price is Important in
0.841
1
358
67.467
0.000
Choosing Mobile
Quality is Important in
0.877
1
358
50.331
0.000
Choosing Mobile
Style is Important in
1.000
0.113
1
358
0.737
Choosing Mobile
Functions of the Mobile
0.975
9.041
1
358
0.003
Brand Name in Choosing
0.988
4.351
1
358
0.038
Mobile
Table No. 15: Pooled Within-Groups Matrices Shows Correlations between Selected Criteria
Criteria
Price
Quality
Style
Functions Brand Name
Correlation Price
1.000
0.173
0.149
0.173
0.130
Quality
0.173
1.000
0.254
0.383
0.292
Style
0.149
0.254
1.000
0.339
0.319
Functions
0.173
0.383
0.339
1.000
0.352
Brand Name
0.130
0.292
0.319
0.352
1.000
Table No. 16: Log Determinants
City or Village
Rank
Log Determinant
Urban Area
5
-.457
Rural Area
5
-.985
Pooled within-groups
5
-.514
The ranks and natural logarithms of determinants printed are those of the group covariance matrices.
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Table No. 17: Test Results of Box's M Test of Equality of Covariance Matrices
Box's M
F
Approx.
df1
df2
Sig.
Tests null hypothesis of equal population covariance matrices.
Table No. 18: Eigen values - proportion of variance explained
73.985
4.859
15
516028.737
0.000
Canonical
Function
Eigen value
% of Variance
Cumulative %
Correlation
1
100.0
100.0
0.406 (a)
0.538
First 1 canonical discriminant functions were used in the analysis.
Table No. 19: Wilks' Lambda Scores - of the total variance in the discriminant scores
Test of Function(s)
Wilks' Lambda
Chi-square
df
Sig.
1
121.239
5
0.711
0.000
Table No. 20: Standardized Canonical Discriminant Function Coefficients
Function
Criteria
1
Price is Important in Choosing Mobile
0.817
Quality is Important in Choosing Mobile
-0.682
Style is Important in Choosing Mobile
0.090
Functions of the Mobile
-0.138
Brand Name in Choosing Mobile
-0.060
Table No. 21: Structure Matrix - correlations of each variable with each discriminate function
Criteria
Function 1
Price is Important in Choosing Mobile
0.681
Quality is Important in Choosing Mobile
-0.588
Functions of the Mobile
-0.249
Brand Name in Choosing Mobile
-0.173
Style is Important in Choosing Mobile
-0.028
Pooled 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 Coefficients
Criteria
Function 1
Price is Important in Choosing Mobile
0.903
Quality is Important in Choosing Mobile
-0.827
Style is Important in Choosing Mobile
0.082
Functions of the Mobile
-0.132
Brand Name in Choosing Mobile
-0.050
(Constant)
0.010
Unstandardized coefficients
Table No. 23: Functions at Group Centroids
City or Village
Function 1
Urban Area
-0.636
Rural Area
0.636
Unstandardized canonical discriminant functions evaluated at group means
Table No. 24: Classification Results
City or Village
Predicted Group Membership
Total
Urban Area
Rural Area
Original
Count
Urban Area
140
40
180
Rural Area
50
130
180
%
Urban Area
22.2
100.0
77.8
Rural Area
27.8
100.0
72.2
Cross-validated (a)
Count
Urban Area
134
46
180
Rural Area
52
128
180
%
Urban Area
25.6
100.0
74.4
Rural Area
28.9
100.0
71.1
19
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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