INTRODUCTION:
Online shopping is a form of electronic
commerce where the consumers use digital technology to purchase products or
services over the internet using web browser. Online shopping has seen a
tremendous growth in the recent past.
Companies have started using internet as a
medium of sales in order to cut down on their costs and providing product
information for generating more customers. On the other hand, consumers use
internet for comparing product prices and features, to generate product
information and compare after sales services provided by various e-tailers.
Thus, online shopping environment is playing an important role in the overall
relationship between marketers and their consumers (Koo et al 2008). It is
important for the marketers to study what a consumer sees, thinks, prefers and
buys so that they can update their marketing offers and achieve high level of
consumer acceptance and satisfaction. In other words, it is important for the
marketers to understand how their consumer behaves before and after purchase.
Studying the online purchase behaviour involves the under standing of consumer
online purchase process including trends, influence of online advertising,
reasons for preferring online purchases over going to a physical store, among
others. Moreover, the emergence of rural market as a feasible opportunity has
sparked a new interest among marketers to explore and understand their
customers (Lalitha Ramakrishna 2005). Consumers have different preferences and
expectations from a marketer or the product. There are many consumers who
prefer going to a brick and mortar shop over online shopping. In order to
understand the behavioral change in the purchase decision making of a consumer,
it is important to identify related factors which have an impact on the purchase
decision of consumers. A few studies have been conducted in this area but this
study aims to study the difference in behaviour of rural and urban consumers in
selected areas of Punjab with respect to the factors which motivate them to
make online purchases.
REVIEW OF LITERATURE:
Li et al (1999) conducted a study “The impact
of perceived channel utilities, shopping orientations and demographics on the
consumer’s online buying behaviour” to test a model of consumer online buying
behavior which stated that consumer online buying behavior is affected by
demographics, channel knowledge, perceived channel utilities and shopping
orientations. Data was collected using simple random sampling through an online
survey of 999 U.S. internet users. Collected data was analysed using factor
analysis, one way ANOVA, correlation analysis. Multiple regression was used to
see how the variables are combined to affect a consumer's online buying
behavior. The study concluded that education, convenience orientation,
experience orientation, channel knowledge, perceived distribution utility and
perceived accessibility are predictors of online buying status of Internet
users.
Hooda and Aggarwal (2012) in their research “Consumer
behaviour towards e-marketing: A study of Jaipur consumers” studied the
acceptance rate of e-marketing among the Jaipur consumers and its impact on
their purchase decision. The data was collected from a sample of 75 respondents
including business professionals, students and other educated people of urban
areas of Jaipur. SPSS and chi square test were used as analysis tool. It was
found that majority of respondents found shopping from shop easier, convenient
and preferable over online purchasing and that people were tradition bound and
had doubt in mindset due to security concern related to privacy of personal
information. The other major concern among people included authenticity of
product and services offered online.
Kanwal (2012) in her study “Consumer’s perception
towards online shopping- The case of Punjab” analyzed various reasons for
adoption and non-adoption of online shopping. Data for the study was collected
from 400 respondents in Punjab and was analyzed using factor analysis and
chi-square analysis. It was found that most of the consumers prefer to buy some
selected products online because they will get heavy discounts in comparison to
store purchases. The respondents felt that there are good websites available
which can be trusted for purchases. Consumers found it very convenient to shop
online as one has to just open a laptop or PC to shop rather than getting ready
and pass through rush hour traffics.
Prasad and Aryasri (2009) in their
study “Determinants of shopper behaviour in e-tailing: an empirical analysis”
collected primary data from a sample of 135 respondents from five leading
software companies in Hyderabad. The data was analyzed using statistical tools
like mean, standard deviation, multiple correlations, multiple regressions,
t-test and ANOVA. The results revealed that convenience, web store environment,
online store enjoyment and customer service are the factors that influence
customers to shop online.
Chen and Barnes (2007) conducted a
study “Initial trust and online buyer behaviour” with an aim to investigate how
online consumers develop their initial trust and purchase intentions. The
research was conducted in the context of Taiwanese online bookstores with a
sample of 103 under graduate and post graduate students. The collected data was
tested using regression and factor analysis. It was found that perceived
usefulness, perceived security, perceived privacy, perceived good reputation,
and willingness to customize are the important factors in online initial trust.
Both online initial trust and familiarity with online purchasing had a positive
impact on purchase intention.
Bhatt and Bhatt (2012) in their
research to study the factors influencing online shopping in Ahmedabad found
that ease to use, attractiveness of website, service quality of website and
website security are the dominant factors which influence consumer perceptions
regarding their online purchasing experiences. The study is a primary survey of
online shoppers which was conducted in Ahmedabad and consumer perceptions were
analyzed using factor analysis and Analysis of Variance (ANOVA) test.
Thakur and Srivastava (2013) conducted a study
"Customer usage intention of mobile commerce in India: an empirical
study" to investigate the factors influencing the adoption intention of
mobile commerce. A research model was developed for the
purpose of study which was empirically tested using second generation
statistical technique of SEM. It was found that adoption of mobile commerce
depend on perceived usefulness, perceived ease of use and social influence
whereas security risk and privacy risk are the factors deterring customers from
using mobile commerce.
Singh (2014) conducted research study titled “Factors
Influencing the Customer’s Purchase Decision for Various Telecom Services - The
Case of Select Districts of Punjab” to study customer preferences and the
satisfaction level towards various telecom services providers in Punjab. A
sample of 200 respondents was collected from three districts of Punjab covering
rural and urban parts. The data was collected using convenience sampling
technique and was analyzed with the help of chi-square and exploratory factor
analysis. The study concluded that there is a significant difference in the
satisfaction levels of rural and urban customers. Also the rural customers are
more satisfied then their urban counterparts. The researcher argued that rural
customers being less aware are less demanding from their mobile service
provider as compared to urban customers are more satisfied.
OBJECTIVES OF THE STUDY:
·
To find out the factors
motivating the rural and urban consumers to purchase online.
·
To compare the online
purchase behaviour of rural and urban consumers in Punjab.
·
To study the impact of
motivating factors on online purchase behavior of rural and urban consumers in
Punjab.
HYPOTHESIS:
H0: There is no significant impact of motivating factors
on online purchase behavior of rural and urban consumer in Punjab.
H1: There is significant impact of motivating factors on
online purchase behavior of rural and urban consumer in Punjab.
RESEARCH METHODOLOGY:
Descriptive research method has been
used to conduct the research and data was collected from the defined sample for
the purpose of the research.
Sample:
The data was collected from two
cities in Punjab i.e. Ludhiana and SAS Nagar and two villages falling under the
respective districts i.e. Khanpur and Bhelopur. These places have been selected
randomly while considering the convenience for the collection of the data.
Data Collection:
Both primary and secondary data was
collected for the purpose of research. Primary data was collected with the help
of a structured questionnaire having twenty seven questions related to the
factors which could affect the purchase decision of a consumer and seven
questions related to online purchase behaviour. These questions were framed on
a five point Likert scale (where 1=strongly disagree to 5=strongly agree). A
total of 300 questionnaires were sent and 250 useable questionnaires were
obtained from the people as a response. Out of these, 118 were from rural areas
and 132 from urban area. Secondary data for the research was collected from
sources like books, journals, publishes research papers, internet and
e-library.
Reliability and Validity:
Reliability check of the collected
data is very important before applying any statistical tool to the data.
Cronbach’s alpha has been used as a measure of reliability since it is the most
widely used method to check reliability of the data. The value of alpha varies
from 0 to 1 and satisfactory value is considered to be more than 0.6 for the
scale to be reliable (Malhotra, 2001; Cronbach, 1951). The measurement was done
repeatedly in order to understand the extent of the statistical tool to produce
consistent results. This is done by determining the association between the scores
obtained from different administration of the scales. If this association is
high, the scale yields consistent result and is said to be reliable.
Correlation between the extracted validates the reliability of the scales.
Table-1 and Table-2 shows the value of alpha and correlation values.
Data Analysis:
For the identification of the motivating factors,
exploratory factor analysis was employed. Table 1 exhibits the factor analysis
for the variables of factors motivating the online purchase. The respondents
were asked to rate twenty seven variables on a five point Likert scale, ranging
from 1 = strongly disagrees to 5 = strongly agree. The factor loading are found
to be more than 0.5, we can say that the factors are efficiently explaining the
variance for all the variables. In the present concern the loading ranged from
.62 to .81. Items with factor loadings < 0.5 have been removed. The five
factors which were generated have Eigen values ranging from 1.59 to 7.17, since
all the values are greater than 1; hence these items are good enough to
contributing to the respective factors. All the factors cumulatively account
for the 87.615% of the total variance. The names assigned to the extracted
factor are personal benefits, website features, product information, promotional
features and social characteristics. Value of cronbach's alpha for motivating
factors came out to be 0.829. It means there is a high level of reliability for
our scale. Cronbach's alpha is the most common measure of internal reliability.
It is used when we have multiple Likert questions that form a scale and we wish
to determine if the scale is reliable. The details of extracted factors are
given below:
F1 Personal Benefits:
The first factor alone has explained 32.72% of the
total variance in the factor analysis. The Scale reliability alpha of
this factor is .831 and factor loading ranges from .68 to .81.
F2 Website features:
This factor has explained 13.76% of the total
variation in the factor analysis. The factor loading ranges from .67 to .79. It
covers 3.85 of the Eigen values and reliability value of alpha is .812.
F3 Product Information:
This factor has explained 13.09% of the total
variation in the factor analysis. The factor loading ranges from .66 to .78. It
covers 3.47 of the Eigen values and the scale reliability value of alpha is
.811.
F4 Promotional Features:
This factor has explained 8.09% of the total variation
in the factor analysis. The factor loading ranges from .64 to .76. It covers
2.09 of the Eigen values and .832 is the scale reliability value of alpha.
F5 Social Characteristics:
This factor has explained 5.93% of the total variation
in the factor analysis. The factor loading ranges from .62 to .72. It covers
1.59 of the Eigen values and .798 is the scale reliability value of alpha.
Table 2 validates the factors analysis results by
calculating “Correlation between summated scales or factors”. The score of the
correlation between the five factors was < .478, therefore they are
independent from each other. The factor analysis results were valid as the
correlation among the summated scales was low (< 0.5).
Online Purchase Behaviour of Rural and Urban Consumers
in Punjab – A Comparison:
Since the second objective of the study is to compare
the online purchase behaviour of rural and urban consumers in Punjab, two
sample t-test has been used. Two sample t-test also known as independent t-test
is an inferential statistical test that determines whether there is a
statistically significant difference between the means in two un-related
groups. Table 3 represents the results of independent sample t-test to find the
significant difference in the online purchase behavior amongst rural and urban
consumers in Punjab. There is a significant difference in means of rural (M =
25.83; SD = 4.26) and urban (M = 23.97; SD = 3.58) customers for online
purchase behavior as analyzed by t-value 3.742 which was found to be significant
at 0.01 level of significance (p=.001). On the basis of mean values, we can
conclude that rural consumers of Punjab have better online purchase behavior as
in comparison to urban consumers.
Impact of Motivating Factors on Online Purchase
Behaviour of rural and urban consumers in Punjab:
In order to study the impact of motivating factors on
online purchase behaviour of rural and urban consumers in Punjab, Pearson’s
correlation coefficient, r, was computed to assess the relationship
between independent variables viz. motivating factors and the dependent
variable - online purchase behaviour. Correlation value measures the strength
and direction of linear relationship between two variables. The value lies
between +1 to -1. -1 indicates a perfect negative correlation and +1 indicates
a perfect positive correlation. For examining the influence of motivating
factors on online purchase behaviour, step wise multiple regression has been
used, as it focuses on extracting the best combination of independent (predictor)
variables to predict the dependent (predicted) variable. In stepwise
regression, a regression model is fitted in which the choice of variables is
carried out by automatic procedure. Forward selection has been used here with
no predictors in the model and a variable is considered for addition in each
step. Thus, beginning by including the variable that is most significant in the
initial analysis, and continue adding variables until none of remaining
variables are "significant" when added to the model or P-value is
below some pre-set level. This way the dimensions of motivating factors having
significant impact on the online purchase behaviour will be extracted.
Table 4 exhibits the correlation between the online
purchase behavior and dimensions of motivating factors among rural and urban
consumers in Punjab. Online purchase behavior is positively correlated to
personal benefits, website features, product information, promotional features
and social characteristics with the correlation coefficients of 0.635, 0.302,
0.437, 0,412 and 0.280 respectively. The results are found to be significant at
1% level of significance. Thus, purchase behavior is highly correlated to
personal benefits dimension of motivating factors and moderately correlated to
rest of the dimensions.
A stepwise multiple regression was
conducted to evaluate whether the dimensions of motivating factors were
necessary to predict online purchase beahviour. Table 5 shows the multiple
linear regression models summary and overall fit statistics for the dependent
variable online purchase behavior for rural consumers. The multiple correlation
coefficient of model 3 was .688, indicating approximately 47.3% of the variance
of the online purchase behaviour could be accounted for personal benefits, promotional
features and website features.
Table 6 represents the output for ANOVA
analysis. The F-ratio in
the ANOVA table
tests whether the overall regression model is a good fit for the data. The
table shows that the independent variables Motivating factors statistically
significantly predict the dependent variable online purchase behavior, for all the models (i.e., the regression
model is a good fit of the data).
Table7 presents the beta coefficients where
unstandardised beta coefficients indicate how much the dependent variable
varies with an independent variable when all other independent variables are
held constant; whereas, a standardized beta
coefficient compares the strength of the effect of each individual independent variable to the dependent
variable. For model 3, looking at the p-value of the t-test for
predictors, we can say that personal benefits (β = .514; p ≤ 0.01),
promotional features (β = .226; p ≤ 0.01) and website features
(β = -.135; p ≤ 0.01) contribute to the model.
While the other dimensions of motivating factors were
excluded from the regression model due to their non-significant contribution in
the model. Hence, we have seen that motivating factors have significant impact
on online purchase behavior of rural and urban consumer in Punjab.
FINDINGS OF THE STUDY:
The research work was undertaken to
understand the mindset of rural and urban areas in Punjab regarding the online
purchases and to find out the factors which affect their purchase behaviour.
The interaction with the both sets of consumers during data collection and
review of the available literature helped to find different aspects. Some of
the major findings of the study are mentioned below:
·
Personal benefits,
website features, promotional features, product information and social
characteristics are the major factors which influence the purchase decision of
the consumers while making online purchases.
·
The interaction with
the consumers also revealed that features like 24*7 shopping facility, easy
return policy, price and product comparison and cash on delivery attract
consumers to make online purchases. Previous studies by Sharma et al (2014)
and Bhatt and Bhatt (2012) have similar findings as ours.
·
The rural consumers of
Punjab have better online purchase behavior as compared to urban consumers.
This may be attributed to the fact that the rural consumers can buy those
product and services online which may not be available in their vicinity and
online medium provides them with an opportunity to choose a product from the
variety of available products after comparing their prices and features. A
previous study by Singh (2014) had similar findings that the rural customers
are more satisfied than urban consumers in Punjab.
·
Motivating factors have
significant impact on online purchase behavior of rural and urban consumer in
Punjab. Personal benefits, promotional features and website features are found
to be significant predictors of online purchase behaviour of the rural and
urban consumers in Punjab.
Thus, we reject the null hypothesis (H0)
that, there is no significant impact of motivating factors on online purchase
behavior of rural and urban consumer in Punjab; and accept alternate hypothesis
(H1) that, there is no significant impact of motivating factors on
online purchase behavior of rural and urban consumer in Punjab.
CONCLUSION:
The study was conducted with the purpose to understand
the online purchase behaviour of the urban and rural consumers from Punjab.
Both primary and secondary data helped to reach certain conclusions. It was
found that online purchasing habit of the consumers depend to a great extent on
the trust they place on that particular website, ease of using internet along
with the facilities provided by the e-tailers. The study also revealed that
online purchase behaviour of rural consumers differs from the online purchase
behaviour of urban consumers. It is suggested that e-tailers pay special
attention to what personal information they ask from the consumers and ensure
to provide them with ample product information. The present study only focused
on the factors influencing online purchasing and their impact on the online
purchase bahviour of the rural and urban consumers in Punjab. Thus, future
research can focus on other factors like socio-economic profile, purchase
perception, etc. that influence online purchasing. The study can be replicated
in some different geographical location. Impact of social media marketing on
both sets of consumer behaviour can also be studied as this is the latest tool
being used by the marketers to promote their products and websites.
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