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Editorial Board A Refereed Monthly International Journal of Management
Prof. B. P. Sharma
(Editor in Chief)
Prof. Mahima Birla
(Additional Editor in Chief)
Dr. Khushbu Agarwal
(Editor)
Ms. Asha Galundia
(Circulation Manager)

 Editorial Team

Dr. Devendra Shrimali
Dr. Dharmesh Motwani
 

What Prompts a Customer to Search and Shop Online – A Study of Punjab

 

Savneet Kaur

Research Scholar, University School of Business,

Chandigarh University, Gharuan

 

Abstract:

Shopping has gone to a new dimension with the presence of stores selling products and services through internet. E-commerce and online shopping has expanded like a forest fire in last few years. Most of the traditional retailers have become e-tailers along with traditional brick and mortar business houses. There are several factors which motivate the consumers to shift from the physical stores to online stores. Since, likes and preferences varies from person to person, it’s not necessary that same factors affect the consumers buying behaviour in the same manner. What needs to be answered is - how the behaviour and expectations of the consumers will change when they shift from traditional physical markets to virtual markets.  What’s more challenging is to compare the two different sets of consumers with regards to their preferences towards online shopping. The present study was conducted among the rural and urban consumers of Punjab. Two cities and two villages falling under the districts of the selected cities were selected as per the convenience of the researcher for the purpose of the study. A hypothesis was tested to understand the impact of motivating factors on the online purchase behaviour of the rural and urban consumers in Punjab. The collected data was analyzed using KMO factor analysis, Pearson’s correlation and stepwise multiple regression analysis. The results indicated that rural consumers in Punjab have better online behaviour as compared to their urban counterparts i.e. rural consumers are more satisfied with their online purchases.

 

Key Words: Online Purchase behaviour, E-tailers, Online shopping, Electronic Commerce, Consumer Expectations.

 

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 understanding 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.

Constant inides (2004) in the study “Influencing the online consumer’s behavior: the Web experience” analyzed the factors affecting the online consumers behavior and examined how e-marketers can influence the outcome on the basis of customers virtual experience. The authors reviewed 48 academic papers selected from a large pool of articles on consumer behavior in online environments. This study identified the main constituents of the online experience or web experience as: the functionality of the website including its usability and interactivity, the psychological elements like communicating trust and credibility of the online vendor and the content elements including the aesthetic aspects of the online presentation and the marketing mix. It was suggested that the study is a good starting point for further research in the direction of developing a comprehensive theory on the online buying behavior.

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.

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.

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.

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 & 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.

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.

Jain et al (2014) carried out a study on online shopping behaviour of consumers in Delhi. A sample of 160 respondents pursuing higher education and professionals employed with various institutions was collected from March 2014 to May 2014. Data was collected using closed ended questionnaire with five point likert scale responses. The collected data was analyzed with the help of SPSS software. The results revealed four important factors viz. perceived risk, perceived enjoyment, perceived ease of use and perceived usefulness to be affecting the online shopping behavior of consumers in Delhi. It was highlighted that there is a significant relationship between perceived risk and attitude toward online shopping. Perceived Risk is the most significant factor that may affect online shopping behavior of consumers in Delhi. Perceived risk indicates the lack of trust among consumers and many other reasons like that of chance of being cheated, inferior quality of products, non returnable policy etc. Scholars recommended that consumers’ intention to shop online with special reference to product categories and brands can be further studied.

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.

Kaur and Pathak (2015) in their study “E-Payment System on E-Commerce in India” reviewed the e-payment systems and see which one most suitable. The study is based on both primary and secondary data. Questionnaires were used to get the data primary data from a sample of 200 respondents and were analysed using graphical and percentage method. Secondary data was collected from various sources including websites, newspapers, various published and unpublished article about pre-primary education etc. The study revealed that it is quite difficult to suggest one best payment system and that some systems are quite similar and differ only in some minor details. Various strengths of e-payment system were found to be quality customer service, greater reach, time saving, customer loyalty, easy access to information, 24 hours access, reduce paper work, no need to carry cash, easy online applications etc. The study suggested future direction of research to formulate a system with similar features that supports person to person settlement as well.

Park et al (2015) through their study “The Effect of Online Social Network Characteristics on Consumer Purchasing Intention of Social Deals” aims to understand how the characteristics of online social network structure can impact consumer purchase intention through network involvement. The study concluded that tie strength, network density, network centrality and homophily will increase both social networking service (SNS) users affective involvement and cognitive involvement to the online social network, both of which will increase their purchase intention of the recommended deals by their friends in SNS. The data was collected from a sample of 211 SNS users in South Korea and factor analysis was employed on the collected data. The study suggested that factors like individual’s personality, cost, perceived risk, innovativeness and social commerce sites shopping experience should be considered in the further study.

 

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. Since the factor loadings of all the manifest variables exceed the threshold limit of 0.60, therefore all the statements have been included for the alanysis.

 

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 variables 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 I 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 0.60, 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 contribute 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. The results indicated that the online consumers are attracted to online shopping because of the convenience and freedom to take decisions attached to it. In order to convert prospects into consumers, web marketers must take note of the consumers needs on the basis of feedback provided to them.

 

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. It is important for online marketers to create user friendly websites by adding features in it which are attractive for consumers and are easy to use. As far as the web sites of the merchants were concerned consumer prefer these websites to be user friendly, convenient, informative and easy to navigate.

 

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. The results indicated that consumers place importance to the product information provided by the marketers on their websites. Hence to frame favorable perception, marketers must insure the consumers should not face any kind of hassle regarding the information required by them for making online purchase. Online marketers should be able to provide detailed information regarding the products being sold by them.

 

F4     Promotional Features

Factor fourth Promotional Features has been developed from the five variables. The results indicated that respondents demand facilities from the merchants in the form of rewards and discounts, cash back offers etc. All these elements have been considered as the predominant motivators for the consumers’ to purchase online. 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

The fifth factor has been labeled as Social Characteristics. The results indicated that the previous online experiences of self, friends, family and opinions discussed online have an impact on purchase decisions of the online consumers. 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 I: Factor analysis results for factors motivating to purchase online

 

Factor

Item

Rotated Factor Loading

Eigen Value

% of Variation

Factor Name and Cronbach’s Alpha

I

Online shopping saves time

0.81

7.17

32.72

Personal Benefits

 

.831

Freedom of choice

0.76

24*7 shopping facility

0.74

Easy return policy, easy/multiple payment options

0.73

Shopping can be customised as per individual needs

0.71

Emergence of nuclear family

0.69

Direct Communication with the seller

0.68

II

Latest information is provided online

0.79

3.85

13.76

Website features

 

.812

Online tracking of shipment

0.76

Shopping websites are attractive and clear

0.75

Easy registration facility on websites through promotional blogs

0.72

Page downloading speed of websites is good

0.71

Simple and easy to browse and find products

0.67

III

Facilitates comparison of similar products

0.78

3.47

13.09

Product Information

 

.811

Product reviews are available

0.75

Wide variety of products

0.73

Better quality products are available online

0.72

Detailed information about the product, features, quality and price is available

0.66

IV

Rewards and discounts

0.76

2.09

8.09

Promotional Features

 

.832

Cash back offers on debit/credit cards are attractive

0.74

Some websites provide free home delivery

0.72

Promotional offers sent on e-mail are attractive

0.68

Easy order cancellation and return facility

0.64

V

The opinions and experiences of your family and relatives affect your online purchase decision

0.72

1.59

5.93

Social Characteristics

 

.798

The opinions and experiences of your friends and colleagues affect your online purchase decision

0.67

The opinions and experiences discussed online affect your online purchase decision

0.64

Own past experience with website affect online purchase decision

0.62

 

Cumulative % of Variation

-

-

73.59

-

 

Cronbach’s Alpha

-

-

 

-

0.829

Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.876, Bartlett's Test of Sphericity (Approx. Chi-Square = 4114.56, Df = 498, Sig = .0001**)

 

Table II 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).

 

Table II: Correlation between Extracted Factors / Summated Scales

Correlations

 

Personal Benefits

Website Features

Product Information

Promotional Features

Social Characteristics

Personal Benefits

1

.327**

.478**

.417**

.372**

Website Features

 

1

.428**

.448**

.208*

Product Information

   

1

.448**

.358**

Promotional Features

     

1

.230**

Social Characteristics

       

1

* Correlation is significant at 0.05 level (2-tailed). ** Correlation is significant at 0.01 level (2-tailed).

 

Table III represents the results of independent sample t-test to find the significant difference in the motivating factors for online purchase between rural and urban customers in Punjab. According to the table, the online purchase behaviour of rural consumers’ is most affected by personal benefits, followed by social characteristics, website features, product information and promotional features; similarly, the online purchase behaviour of urban consumers’ is most affected by personal benefits, followed by website features, social characteristics, product information and promotional features. There is a non-significant difference in means of rural and urban customers for factors personal benefits and social characteristics, which means that the personal benefits and social characteristics motivate both the urban and rural consumers in the same manner with respect to online purchases. Whereas, there is a significant difference in the means of motivating factors viz. website features, product information and promotional features, between rural and urban customers analyzed by t-value 10.558 (Rural – M = 18.77; SD = 2.53; Urban – M = 22.33; SD = 2.63), 3.141 (Rural – M = 18.01; SD = 3.24; Urban – 19.47; SD = 3.78) and 8.350 (Rural – M = 16.94; SD = 2.05; Urban – M = 19.25; SD = 2.17) respectively, which was found to be significant at 0.01 level of significance.

 

Table III: Comparison of motivating factors for online purchase between rural and urban consumers in Punjab

Region

N

Mean

Std. Deviation

t-value

p-value

Personal Benefits

Rural

104

26.1635

3.68706

1.288

.199

Urban

135

26.8222

4.08833

Website Features

Rural

104

18.7692

2.53224

10.558

.001**

Urban

135

22.3333

2.62877

Product Information

Rural

104

18.0096

3.23661

3.141

.002**

Urban

135

19.4667

3.78292

Promotional Features

Rural

104

16.9423

2.05188

8.350

.001**

Urban

135

19.2519

2.17076

Social Characteristics

Rural

104

22.1154

3.83471

.817

.415

Urban

135

21.6593

4.59098

(* significant at 5% level of significance, ** significant at 1% level of significance)

 

Hence, on the basis of mean values, we can conclude that website features, product information and promotional features motivate urban customers more for online purchase as compared to the rural customers.

 

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 IV 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.

 

Table IV: Comparison of online purchase behavior among rural and urban consumers in Punjab.

Region

N

Mean

Std. Deviation

t-value

p-value

Purchase Behavior

Rural

118

25.8288

4.25783

3.742

.001**

Urban

132

23.9739

3.58472

(* significant at 5% level of significance, ** significant at 1% level of significance)

 

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 V 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.

 

Table V: Correlation between Online Purchase Behavior and Dimensions of Motivating Factors among Rural and Urban Consumers in Punjab

 

Correlations (r)

Motivating Factors

Purchase Behaviour

Purchase Behaviour

1

Personal Benefits

.635**

Website Features

.302**

Product Information

.437**

Promotional Features

.412**

Social Characteristics

.280**

**Correlation is significant at the 0.01 level (2-tailed)

 

A stepwise multiple regression was conducted to evaluate whether the dimensions of motivating factors were necessary to predict online purchase beahviour. Table VI 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 VI: Regression analysis of motivating factors and purchase barriers on online purchase behavior for rural consumers

 

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.637

.405

.403

3.13053

2

.675

.456

.451

3.00106

3

.688

.473

.467

2.95937

 

Table VII 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).

 

Table VII: ANOVA analysis of motivating factors and purchase barriers on online purchase behavior for rural consumers.

Model

Sum of Squares

Df

Mean Square

F

Sig.

1

Regression

1238.863

1

1238.863

203.034

.000**

Residual

1513.237

248

6.102

 

 

Total

2752.100

249

 

 

 

2

Regression

1392.161

2

696.080

126.426

.000**

Residual

1359.939

247

5.506

 

 

Total

2752.100

249

 

 

 

3

Regression

1484.882

3

494.961

96.085

.000**

Residual

1267.218

246

5.151

 

 

Total

2752.100

249

 

 

 

 

Table VIII 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 1, on consulting the p-value of the t-test for predictors, we can say that personal benefits (β = .635; p ≤ 0.01) contribute to the model. In model 2, we can see the effect of personal benefits and promotional features dimension of motivating factors. For model 2, on the basis of p-value of the t-test for predictors, we can say that personal benefits (β = .506; p ≤ 0.01) and promotional features (β = .260; p ≤ 0.01) both contribute to the model. 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.

 

Table VIII: Coefficients analysis of motivating factors and purchase barriers on online purchase behavior for rural consumers

 

Models

Unstandardized Coefficients

Standardized Coefficients

t-test

Sig.

B

Std. Error

Beta

Model 1

         

(Constant)

10.979

1.160

 

9.466

.000**

Personal Benefits

.577

.044

.637

13.004

.000**

Model 2

         

(Constant)

7.874

1.288

 

6.115

.000**

Personal Benefits

.459

.049

.506

9.326

.000**

Promotional Features

.356

.074

.260

4.781

.000**

Model 3

         

(Constant)

10.228

2.127

 

4.807

.000**

Personal Benefits

.443

.049

.489

9.008

.000**

Promotional Features

.249

.080

.182

3.135

.002**

Website Features

.204

.072

.135

2.830

.005**

             

 

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 online purchase behaviour of rural consumers’ is most affected by personal benefits, followed by social characteristics, website features, product information and promotional features; similarly, the online purchase behaviour of urban consumers’ is most affected by personal benefits, followed by website features, social characteristics, product information and promotional features
  • 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 help e-marketers to match their customers’ desires with their offers. 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. The results of this study can help the government to understand consumer behavior in order to develop policies to protect consumer interest and control the online market.

 

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