Study on the Scope of Virtual Digital Stores in Rural India with respect to aapkishop.com

 

Dibyendu Bikash Datta¹*, Shashanka Shankar Boruah²

¹Associate Professor, Department of Fashion Management Studies, National Institute of Fashion Technology (Ministry of Textiles, Govt. of India), Kolkata, India

² Post Graduate Alumni, Department of Fashion Management Studies,

National Institute of Fashion Technology (Ministry of Textiles, Govt. of India), Kolkata, India

 

ABSTRACT:

aapkishop.com is a rural focused e-commerce company which was founded with the sole aim to become a conduit between rural and urban India and narrow the gap that has been created over the years. It combines both online and offline shopping experiences for rural people through its digital stores. The main emphasis is to study the scope of Virtual Digital Store in Rural India. In a country as vast as ours, with such widespread geography, the problem multiplies manifold and it is with this challenge that aapkishop.com took place. Engineered in such a way that it will equally empower the rural masses and bring them at par with urban population. It is the only e-commerce company in India which shakes hands with its customer at each stage of order lifecycle. aapkishop Digital Store is a virtual shop set up in a district town with zero product inventories but equipped digitally to take orders from any common man. The store is manned by a dedicated store-in-charge who helps people to shop online. E-commerce is on its growth stage and the model of virtual digital stores would be an excellent one for tapping the rural market which is untouched in terms of e-commerce.

 

 

 

INTRODUCTION:

aapkishop is a unique blend of online and offline shopping experience that enables customers to not only order products online, but also visit the online physical shops in rural India and place orders. The online physical shops have been designed keeping in mind the needs of rural India. aapkishop's model focus on personalized approach for customer satisfaction and grievances resolution. They help merchants bring visibility to their brands/products through our online physical stores.

 

To understand what a virtual digital store is we need to first understand what exactly is meant by virtual. Virtual means which is not physically existing as such but made by software to appear to do so from the point of view of the program or the user.

 

Digital store means stores which are digitally equipped. These stores have all the digital equipments (laptop, tablets, etc) which set the stage for a common person to shop online. These stores have zero inventories. The store is also managed by a dedicated store-in- charge who is appointed from their regional area in order to breach the regional language gap and help the rural people shop online. The digital store is a 3-in-1 solution where a customer can place an order online, take delivery of products when they arrive and also address their concerns directly to the store keeper without going through the pain of calling a call center. Many of them do not have online payment mechanism so they can also pay in cash to the store in-charge. The power of access which is felt by people in Urban India has reached Rural after lot of hard work and perseverance. It needs lots of personal interactions to gain trust and confidence for people to shop virtually. The digital store serves as a place for enquiry, delivery and after sales. It provides a human touch to the virtual shopping and is a trust point and a catalyst to help people in these geographies to shop online. Virtual digital stores in rural areas seemed to be very fascinating and available research reports provides an insight to the increasing figures of internet penetration and literacy rates in rural India.

 

1.      LITERATURE REVIEW:

According to Turban et al. (2006), e-tailing is defined as retailing conducted online, over the internet. Wang (2002) has provided a broad definition of e-tailing by defining it as the selling of goods and services to the consumer market via the internet. Zeithaml (2002) has defined that the success of e-tailing depends on the efficient web site design, effective shopping and prompt delivery. The other e-store services are delivery on real time, return and replacement process, period of filling out online orders form, speed of response time to e-customers queries. Rabinovich (2004) have identified the challenges of e-tailing industry. This challenge begins with the response time of the web-server; moves to the amount of time the customer must wait until the order ships, and also includes the time the shipping process takes. Ratchford et al. (2001) has said that through Internet, consumers can gather information about merchandise and they compare a product across suppliers at a low cost. Khan and Mahapatra (2009) remarked that technology plays a vital role in improving the quality of services provided by the business units. Rao (1999), E-commerce offers increased market activity for retailers in the form of growing market access and information and decreased operating and procurement costs. Chandra (2012), expressed that consumers are getting smarter in using e-tailers (and online search engines and agents) for convenience and comparison- shopping. Guttman (1998) describes several unique elements make online shopping different from the traditional in-store retail model. Besides offering convenience and expanded product variety, the online model also makes it easy for consumers to access and compare data from multiple sources. Meeker (1997), retailers might cry foul, but the new shopping paradigm they have to face is that as premium customers begin to accept the e-tail alternative in larger numbers. Liang et al. (2004) divided e-tailing into three categories: (1) pure online players, (2) click-and-mortar retailers, and (3) retailers who replace the physical store with a wholly-online operation.

2.      OBJECTIVE OF THE STUDY:

The main objective of the study is to draw attention towards the scope of virtual digital store in rural India. Other objectives of the study are as follows a) to know the rural market status in Indian economy (b) to identify the challenges for rural marketing and (c) to identify the market potential of rural India.

 

3.      RESEARCH METHODOLOGY:

The study was carried out using exploratory research design (Krishnaswamy et al., 2006) and both primary and secondary data were collected. The secondary data was collected from e-journals, research articles, marketing journal, and reference books. The primary data was collected by using the survey method with the help of structured questionnaire. The data was collected via one-to-one interaction with 200 respondents from different occupation, income, and age group. The data derived from questionnaire is analyzed using SPSS software.

 

4.      DATA ANALYSIS AND INTERPRETATION:

The basic findings related to demographic characteristics of customers are given in Table 1.

 

Table 1: Demographic details

 

 

 

4.1.  Reliability Analysis:

Reliability is the degree to which an instrument measures the same way each time it is used under the same condition with the same subjects. For determining reliability of the study Cronbach's alpha method was used. The alpha coefficient for the 20 items indicate a value of 0.871 (Table 2), suggesting that the items have relatively high internal consistency and are generally acceptable and reliable.

 

 

 

Table 2: Reliability statistics

Reliability Statistics
Cronbach's Alpha	Cronbach's Alpha Based on Standardized Items	N of Items
.870	.871	20

 

 

4.2.  Factor Analysis for Data Reduction:

Factor Analysis is a data reduction statistical technique that allows simplifying the correlational relationships between a numbers of continuous variables. Before moving further with factor analysis it is necessary to check whether the sample is sufficient for factor analysis and this is done by Kaiser-Meyer-Olkin (KMO) analysis test of sampling adequacy. It is the ratio of sum of the squared correlations for all variables in the analysis to the squared correlations of all variables plus the sum of the partial squared correlations for all variables. Small value of KMO indicates that factor analysis may not be appropriate for the data. Generally KMO test value greater than 0.6 is acceptable. The KMO value for the present study is .763 which is more than threshold value 0.6 (Kaiser and Rice,1974), therefore data is found to be sufficient for applying factor analysis on it.

 

Bartlett's Test of Sphericity found significant (p<0.05) with Chi-square 568.254. Bartlett's Test of Sphericity evaluates the null hypothesis that the correlation matrix is an identity matrix (all the values in the diagonal are 1 and all the off-diagonal values are zero) which would indicate no relationships among the variables, and thus no basis to proceed with factor analysis. A significant test result as shown in Table 3 allows us to proceed with the factor analysis.

 

Table 3: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		0.763
Bartlett's Test of Sphericity	Approx. Chi-Square	568.254
	Sig.	0.000

 

Factor Analysis was performed to extract underlying factors that plays a prominent role while shopping from a virtual digital store. Each of the variables loaded high on a single factor. One variable with a loading less than 0.5 was excluded. The results of the factor analysis with varimax rotation are outlined in Table 4 and Table 5. Factor Analysis has extracted four factors as the latent dimensions that were salient for the consumer's to shop from virtual digital stores. Four components were extracted because of their Eigen values being greater than 1. Together they account for 42.661% of the total variance. First factor explains 13.478% variance and consists of five variables each with factor loading of more than 0.6.

 

 

Table 4: Total Variance Explained

Extraction Method: Principal Component Analysis.

 

 

 

Variables under first factor are related to the attributes of the price. This factor can be labelled as 'Price attributes''. Second factor explains 11.11% variance and five variables exhibit high loading for it. All these variables are related to quality. This factor can be labelled as 'Quality'. Third factor explains 10.72% variance and five variables exhibit high loading for it. All these variables are related to service. This factor can be labelled as 'Service'. Third factor explains 7.34% variance and five variables exhibit high loading for it. All these variables are related to service. This factor can be labelled as 'Technology'. Table 6 contains the factors with variables and corresponding factor loading.

 

 

 

Table 5: Rotated Component Matrix^a

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 5 iterations

 

Table 6: Factors with Variables and corresponding factor Loading:

Factor:1: Price	Factor loading
The prices are value for money	0.693
Prices are different from that of its competitors	0.547
Prices are very difficult for competitors to copy it	0.643
Price is affordable	0.504
The difference in price is clearly visible	0.829
Factor: 2: Quality
I am satisfied with the quality	0.644
Quality are different from that of its competitors	0.696
Quality are very difficult for competitors to copy it	0.576
Quality is affordable	0.547
The difference in quality is clearly visible	0.705
Factor:3: Service
I am satisfied with the services provided to me	0.894
Services are different from that of its competitors	0.612
Services are very difficult for competitors to copy it	0.531
Service is affordable	0.692
The difference in service is clearly visible	0.565
Factor:4: Technology
Technology provides value to me	0.623
Technology are different from that of its competitors	0.511
Technology are very difficult for competitors to copy it	0.523
Technology is affordable	0.735
The difference in technology is clearly visible	0.843

4.3.  ANOVA:

ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes t-test to more than two groups. ANOVAs are helpful because they possess an advantage over a two-sample t-test. Doing multiple two-sample t-tests would result in an increased chance of committing a type I error. For this reason, ANOVAs are useful in comparing two, three or more means.

H₁:  There is no significant difference between gender and price of products.

H₂:  There is no significant difference between gender and quality of products.

H₃:  There is no significant difference between gender and service of products.

H₄:  There is no significant difference between gender and technology of products.

H₅:  There is no significant difference between marital status and price of products.

H₆:  There is no significant difference between marital status and quality of products.

H₇:  There is no significant difference between a marital status and service of products.

H₈:  There is no significant difference between marital status and technology of products.

H₉:  There is no significant difference between income and price of products.

H₁₀: There is no significant difference between income and quality of products.

H₁₁: There is no significant difference between income and service of products.

H₁₂: There is no significant difference between income and technology of products.

H₁₃: There is no significant difference between age and price of products.

H₁₄: There is no significant difference between age and quality of products.

H₁₅: There is no significant difference between age and service of products.

H₁₆: There is no significant difference between age and technology of products.

 

 

Table 7: Gender and Price, Quality, Service, Technology

 

 

 

For H₁:    Since, the value for alpha is .221 which is greater than .05, the null hypothesis H₁ is accepted. There is no significant difference between buying behaviour of people based on price for different gender.

For H₂:    Since, the value for alpha is .137 which is greater than .05, the null hypothesis H₂ is accepted. There is no significant difference between buying behavior of people based on quality for different gender.

For H₃:    Since, the value for alpha is .027 which is less than .05, the null hypothesis H₃ is rejected. There is significant difference between buying behavior of people based on services received for different gender.

For H₄:    Since, the value for alpha is .079 which is greater than .05, the null hypothesis H₄ is accepted. There is no significant difference between buying behavior of people based on services received for different gender.

 

 

Table 8: Marital Status and Price, Quality, Service, Technology

 

 

 

 

For H₅:    Since, the value for alpha is .609 which is greater than .05, the null hypothesis H₅ is accepted. There is no significant difference between buying behaviour of people based on price of the products for different marital status.

For H₆:    Since, the value for alpha is .015 which is less than .05, the null hypothesis H₆ is rejected. There is significant difference between buying behavior of people based on quality of the products for different marital status group.

For H₇:    Since, the value for alpha is .543 which is greater than .05, the null hypothesis H₇ is accepted. There is no significant difference between buying behavior of people based on services received by different marital status group.

For H₈ :   Since, the value for alpha is .044 which is less than .05, the null hypothesis H₈ is rejected. There is significant difference between buying behavior of people based on Technology involved for different marital status group.

 

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Table 9: Income and Price, Quality, Service, Technology

 

 

 

 

 

For H₉:    Since, the value for alpha is .110 which is greater than .05, the null hypothesis H₉ is accepted. There is no significant difference between buying behaviour of people based on product features for different income group.

For H₁₀: Since, the value for alpha is .253 which is greater than .05, the null hypothesis H₁₀ is accepted. There is no significant difference between buying behavior of people based on quality for different income group.

For H₁₁:  Since, the value for alpha is .021 which is less than .05, the null hypothesis H₁₁ is rejected. There is significant difference between buying behavior of people based on services received for different income group.

For H₁₂:  Since, the value for alpha is .014 which is less than .05, the null hypothesis H₁₂ is rejected. There is significant difference between buying behavior of people based on services received for different income group.

 

 

 

Table 10: Age and Price, Quality, Service, Technology

 

 

 

 

For H₁₃: Since, the value for alpha is .908 which is greater than .05, the null hypothesis H₁₃ is accepted. There is no significant difference between buying behaviour of people based on price for different age group.

For H₁₄: Since, the value for alpha is .015 which is less than .05, the null hypothesis H₁₄ is rejected. There is significant difference between buying behavior of people based on quality for different age group.

For H₁₅: Since, the value for alpha is .114 which is greater than .05, the null hypothesis H₁₅ is accepted. There is no significant difference between buying behavior of people based on services received for different age group.

For H₁₆: Since, the value for alpha is .304 which is greater than .05, the null hypothesis H₁₆ is accepted. There is no significant difference between buying behavior of people based on services received for different age group.

 

4.4.  Conjoint Analysis:

Conjoint analysis is a market research tool for developing effective product design. Using conjoint analysis, the researcher can answer questions such as: What product attributes are important or unimportant to the consumers? What levels of product attributes are the most or least desirable in the consumer's mind? What is the market share of preference for leading competitors' products versus our existing or proposed product? The virtue of conjoint analysis is that it asks the respondent to make choices in the same fashion as the consumer presumably does- by trading off features, one against another. Conjoint uses full-profile (also known as full-concept) approach, where respondents rank, order or score a set of profiles, or cards, according to preference. Each profiles describe a complete product or service and consists of a different combination of factor levels for all factors (attributes) of interest.

 

 

 

 

Table 11: Utilities

 

 

 

The total utility of the 8 profiles are given as:

Total Utility for Profile 1: Utility (HQ) + Utility (HP) + Utility (GS) + Constant             =       5.965

Total Utility for Profile 2: Utility (HQ) + Utility (HP) + Utility (PS) + Constant             =       5.56

Total Utility for Profile 3: Utility (HQ) + Utility (LP) + Utility (GS) + Constant              =       3.63

Total Utility for Profile 4: Utility (HQ) + Utility (HP) + Utility (PS) + Constant             =       5.55

Total Utility for Profile 5: Utility (LQ) + Utility (HP) + Utility (GS) + Constant              =       5.775

Total Utility for Profile 6: Utility (LQ) + Utility (HP) + Utility (PS) + Constant              =       5.37

Total Utility for Profile 7: Utility (LQ) + Utility (LP) + Utility (GS) + Constant              =       3.44

Total Utility for Profile 8: Utility (LQ) + Utility (LP) + Utility (PS) + Constant               =       3.035

Total utility score has been sequenced as:

Profile 1, Profile 5, Profile 2, Profile 4, Profile 6, Profile 3, Profile 7, Profile 8,

[HQ: High Quality; LQ: Low Quality; HP: High Profit; LP: Low Profit; GS; Good Service; PS; Poor Service]

 

Table 12: Importance Values

 

The range of the utility values (highest to lowest) for each factor provides a measure of how important the factor was to overall preference. Price plays the most significant value with 57.456% and Service plays a moderate role with 23.288%.

 

4.5.  Analysis of Sales Data

This study was to see the effect of promotional campaigns on buying behaviour of rural people residing in Jharsuguda and Warisaligunj.

In order to study the purchase pattern of our target customers, sales data was analysed in terms of two things- the Sales Value and the Order Quantity. Studying the Sales values only would be deceiving.

 

Table 13: Importance Values

 

 

Figure 12: Importance Values

 

The total sales generated from the month of August 2014 to April 2015 was Rs 12,13,720 and the total order quantity was 1148. The company since its inception ran few promotional campaigns. The company started online sales on 29th August 2014. People found the new concept interesting and therefore some of them made few purchases. A total of 25 orders were generated in 3 days in August. The month of September was the first month in which the company did not run any type of promotional campaign and 75 orders were receive in the entire month.

 

In the month of October, two promotional offers were applied. The first one was the "Sab Honge Mobilewale" deal wherein on registration, a person was given a Gift Voucher of Rupees 400 which could be used on purchase of any mobile phone from aapkishop.com. Along with this, keeping in mind the festive season, a Rupees 50 discount was being given on purchase of a silver coin. This month saw a rise in sales figures by 95.4% from the previous month and an increase by 187.14% in the number of mobile phones ordered from the previous month. 83% of the total sales of silver coins was seen in this month. The promotional offers continued during November, where the number of orders was very high.

 

In the month of December, the promotional offers continued with an additional discount of 5% on purchase of any mobile phone. This month saw the highest sales figures for the entire period with maximum number of orders for mobile phones. The sales figures for the mobile category contributed to 73% of the total sales of the month.

 

There were no promotional campaigns in the months of January and February and there is an evident dip in sales figures. A drop of 255% in sales figures is observed from the month of December to January. Apart from a drop in sales figures, a drop in order quantity is also seen (150%).Order quantity of mobile phones decreased from 62 in December to 10 in January. Sales figures saw a drop from Rs 206692 to Rs 29436.

 

A T-shirt offer called "T-Shirt Mania" was introduced during March and April. Here, people could purchase a T-Shirt which was marked at Rs 599 for Rs 299 and if they purchased 2 T-shirts, they would have to pay just Rs 399. During this campaign, a steady increase in the sale of T-shirts was observed. A total of 249 T-shirts were sold during the campaign which lasted for a month and a half where as total T-shirts sold between the period of August 2014 and March 14th 2015 was just 92. A total of 341 T-shirts were sold in the period of 9 months of which 73% were sold during the offer period of just 45 days. Mobile phone which was a fast moving category took a back seat during this period as well. Apart from the categories mentioned above, the other categories did not see any major drop or increase in any of the months. Therefore, it would be fair enough to conclude that the rural segment of population residing in the areas of Jharsuguda and Warisaligunj are strongly driven and influenced by promotions, price reduction being the strongest promotional campaign influencing purchase pattern. Studying the people awareness towards internet, their knowledge for Online transaction and trying to understand their view on virtual digital stores in rural areas of Jharsuguda (Orissa) and Warisaligunj (Bihar) which will give a better understanding of the market as well as give an idea of market scope of Virtual Digital Stores of aapkishop.

 

4.6.  Analysis of Questionnaire based survey from people who have visited the Virtual Digital Stores of aapkishop:

·         Around 89% respondents were aware of internet and 11% were still unaware of internet.

·         Around 63% respondents agreed that they have used internet in their lifetime whereas 37% respondents denied from using internet.

·         Out of the total respondents, 54% have used internet to serve entertainment, followed by social networking which is 46%. 44% respondents for communication and 37% for online services. Only 9% respondents have used internet to make an online transaction.

·         Around 89% respondents have never shopped online whereas only 11% respondents have shopped online at least once in their lifetime. Out of 89% respondents who have never shopped online, around 52.3% respondents said that they feel insecure while doing online transactions whereas 22.1% replied that they don't know how to make an internet transaction. 16.3% didn't have a debit/credit card to do an online transaction. 9.3% respondents didn't know where to spend online and remaining 3.5% has other reasons like they don't feel like doing online transaction.

·         Around 58% respondents usually shop from town whereas remaining 42% respondents said they prefer to shop from town.

·         Around 41.2% respondents agreed that they would like to purchase mobiles if they have some extra money to spend in. It was followed by footwear with 29.4% , Apparels with 11.8% and Others with 5.9%.

·         Around 72% respondents agreed that they are eager to make an online transaction whereas still 28% were skeptical to make an online transaction.

·         56% respondents were aware or had some knowledge about the top 3 online e- commerce player in Indian Ecommerce Market whereas still 44% didn't have any clue about them.

·         Out of the total respondents, 88% respondent agreed that they would like to shop from the top online sites in India.

·         Around 81% respondents are affected by the promotional schemes whereas remaining 19% doesn't have any impact on the choice by the promotional schemes.

·         63% respondents said that their selection of products is affected by the discount policy whereas remaining 37% said promotional policy affects their selection.

·         Around 66% respondents felt that there is not much price difference in online and brick and motor products whereas remaining 34% agreed that there is some price difference in the products available in both the retail format.

·         58% respondent said that they would like to make a pre-purchase visit to the store while 42% would like to visit the store at the time of purchase only.

·         Reason of coming to the store is mentioned below (Table 14)

 

 

Table 14: Reason of coming to the Store of aapkishop                                                                      (in %)

 

4.7.  Analysis of Questionnaire based survey from people who have purchased from the Virtual Digital Stores of aapkishop.                           (in %)

	Strongly disagree	Disagree	Neutral	Agree	Strongly agree
1.       The prices are value for money	7	11	27	39	16
2.       I am satisfied with the quality	14	16	14	45	11
3.       I am satisfied with the services provided to me	3	12	15	56	14
4.       Technology provides value to me	8	5	16	32	39
5.       Prices are different from that of its competitors	14	22	38	20	6
6.       Quality are different from that of its competitor	6	11	36	32	15
7.       Services are different from that of its competitor	6	11	37	37	9
8.       Quality is very difficult for competitors to copy it	6	11	37	37	9
9.       Technology are very difficult for competitors to copy	6	11	11	56	16
10.    Price is affordable	4	16	11	63	6
11.    Quality is affordable	5	12	48	32	3
12.    Technology is affordable	4	6	21	57	15
13.    Prices are very difficult for competitors to copy it	2	33	23	31	11
14.    Services are very difficult for competitors to copy it	8	16	33	38	5
15.    Technology are different from that of its competitors	5	5	7	19	64
16.    The difference in price is clearly visible	10	24	23	29	14
17.    The difference in quality is clearly visible	13	35	19	22	11
18.    The difference in service is clearly visible	6	13	21	46	14
19.    The difference in technology is clearly visible	4	6	19	53	18
20.    To identify the most influential factor to buy a product from aapkishop	Not al all important	Fairly important	Important	Quite important	Strongly important
a)       Advertisement	6	17	38	33	6
b)       Word of mouth	5	16	34	27	18
c)       Store display	3	35	35	12	15
d)       Family/friends/relatives	16	28	22	17	17
e)       To experience	11	16	26	35	12
f)        Sense of security	5	8	41	42	4
g)       Wide range of products	8	17	41	11	23
h)       Convinced by the store manager	2	17	58	17	6
i)        Sales and discounts	4	5	39	29	23

 

5.      Key Findings and Recommendations:

Secondary data sources showed that entertainment was the main reason why people in rural area used internet. The primary research also reflects the same. People are still hesitant in making online transactions owing to the risks involved in it. Therefore PAS serves as a desirable option which almost serves the purpose of Cash in Delivery.

 

Most off the respondent desired to make online purchases but were unable to do so mainly because they do not trust the internet transactions and they do not have the knowhow to purchase on the internet.

 

Most of them purchase products from the local markets. These local markets and stores are competitors to aapkishop in terms of the product offering and price point but aapkishop stands out in regard to the service provided.

 

Though most of the people said discount policy was one of the important factors affecting their purchase, sales figures showed that promotional policies were more important.

 

It is recommended that promotional policy should be incorporated and conveyed to the customers in order to see a steady rise in sales figures.

 

Most of the store visitors fall under the age group of 15-30 which means the main customers are youth. The website does not look user friendly to the youth. Better colors and product variety is what the youth is searching for aapkishop.

 

The family income of most respondents was between Rs10,000-Rs30,000. This should be kept in mind while planning the product mix.

 

6.      LIMITATIONS OF THE STUDY:

This study has attempted to delve into some of the important issues. However it is admitted that there are some limitations in the conduct of this study. The study is restricted only to selected areas because quantitative information was very difficult to obtain. 

 

7.      CONCLUSION:

The rural consumers are classified into: the affluent group, the middle class and the poor based on their economic status. The rural youth today are playing a far more significant role in influencing the purchase decisions. They may not be the end customers but often are the people who influence the purchase of high value products and they decide on which brands to choose. The biggest challenge today is to develop a scalable model of influencing the rural consumers' mind over a large period of time and keep it going. The following are the problems of rural retail marketing in India:

The development of appropriate communication systems to rural market may cost up to six times as much as reaching an urban market through established media, need rural communication facilities.

 

The problems of physical distribution and channel management adversely affect the service as well as the cost aspect. The existent market structure consists of primary rural market and retail sales outlet. The structure involves stock points in feeder towns to service these retail outlets at the village levels. But it becomes difficult maintaining the required service level in the delivery of the product at retail level.

 

Rural consumers are cautious in buying and decisions are slow and delayed. They like to give a trial and only after being personally satisfied, do they buy the product.

 

8.      REFERENCES:

1.        Bowen, J. T., and Chen, S. L. (2001). The relationship between customer loyalty and customer satisfaction. Int. journal of contemporary hospitality management, 13(5), 213-217.

2.        Chandra, P. S., and Sunitha, G. (2012). E-tailing-the mantra of modern retailer's success. Researchers World, 3(2), 42.

3.        Guttman, R. H., Moukas, A. G., and Maes, P. (1998). Agent-mediated electronic commerce: a survey. The knowledge engineering review, 13(02), 147-159.

4.        Wang, F., Head, M., and Archer, N. (2002). E-tailing: An analysis of web impacts on the retail market. Journal of business strategies, 19(1), 73.

5.        Rabinovich, E. (2004). Internet retailing intermediation: a multilevel analysis of inventory liquidity and fulfillment guarantees. Journal of business logistics, 25(2), 139-169.

6.        Rao, B. (1999). The Internet and the revolution in distribution: a cross-industry examination. Technology in Society, 21(3), 287-306.

7.        Ratchford, B. T., and Debabrata T. M. S. L. (2001). A model of consumer choice of the Internet as an information source. Int. Journal of electronic commerce, 5(3), 7-21.

8.        Turban, E., King, D., Viehland, D., and Lee, J. (2006). A managerial perspective. Electronic commerce.

9.        Kaiser, H. F., and Rice, J. (1974). Little Jiffy, Mark IV. Educational and psychological measurement.

10.     Khan, M. S., and Mahapatra, S. S. (2008). Service quality evaluation in internet banking: an empirical study in India. Int. Journal of Indian culture and business management, 2(1), 30-46.

11.     Krishnaswamy, K. N., Sivakumar, A. I., and Mathirajan, M. (2009). Management research methodology: integration of principles, methods and techniques. Pearson Education India.

12.     Liang, T. P., Lin, C. Y., and Chen, D. N. (2004). Effects of electronic commerce models and industrial characteristics on firm performance. Industrial Management and Data Systems, 104(7), 538-545.

13.     Meeker, M., and Morgan Stanley and Co. (1997). The internet advertising report. New York, N.Y: Harper Business.

14.     Zeithaml, V. A. (2002). Service excellence in electronic channels. Managing Service Quality: An International Journal, 12(3), 135-139

 

 

Received on 30.01.2017                                  Accepted on 21.02.2017        

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