an analytical study to frame a service model on …international conference on management and...

International Conference on Management and Information Systems September 21-22, 2018

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An Analytical Study to Frame a Service Model on Indian Online Shopping

Dipa Mitra

[email protected]

Indian Institute of Social Welfare and Business Management

Online shopping is escalating towards excellence with ever-evolving trends and a rising number of online

shoppers every year. Present study has been constructed to audit the service on online shopping in India.

Further the study has been performed through a structured questionnaire to identify the most significant factor

in online shopping with respect to their perceived service and to identify their impact on the dependent variable

on the basis of the responses of 244 online shoppers from all four regions of India. Reliability Test, Principal

Component Analysis (PFA), Correlation, Multiple Regression, Data Envelopment Analysis (DEA), Simple

Mediation Analysis and Path Analysis are applied to analyse the data. From the findings a prescribed Causal

Model has been developed which may lay a foundation for future investigation on Global Service Excellence

with respect to Online Shopping. One probable step may be to enhance the scope of research by crossing

national boundary to evaluate and benchmark the perceived service from the e-shoppers all over the world and

frame a universal model on Global Online Shopping Service Excellence with the help of Bayesian Probabilistic

Network.

Key words: Online, Shopping, Analysis, Service, Excellence

1. Introduction India has been going through a digital revolution during the last five years, which is transforming the consumer shopping experience, at a different level. In addition to factors such as comparison of multiple products, online market places have a lot of schemes using multiple payment channels that provide additional cash backs and discounts. Growth of mobile internet users will be one of the key reasons for driving growth in this channel, covering more than 300 million users in India, in the next few years. With increasing network infrastructure development and usage of wireless technologies such as 3G and 4G, overall internet user base in India is expected to be 600 million in 2020, which accounts for 27% penetration. The past year has seen the rivalry between two online marketers Amazon and Flipkart further intensify. From funding to shopping extravaganzas, the e-commerce behemoths went all out to outdo each other. Amazon is speculated to set up physical shops, primarily to cater to grocery needs, but eventually will expand in appliances. So, even though traditional stores are trying to move online, the online retailers could be still eyeing the space of physical stores as even in 2020, we expect that 75-80% of the sales would be through the brick-and-mortar stores. Ecommerce giant Flipkart has been ranked as the top Indian e-tailors for consecutive two financial years. At present this online player has raised its fund up to $1.4 billion at a valuation of $11.6 billion and acquired eBay India operations. Further, the company is in final stages of acquiring Snapdeal. On the contrary its competitor Amazon India secured the second position for the second time. This Global company Amazon which is simultaneously enjoying business in nine other countries, currently concentrating more on Amazon pay that was earlier known as Amazon Online Distribution Services, and has invested around $10.45 million.

But these all are the statistical know how to understand online market. Now, what about the Indian shoppers perception towards online shopping? Research says the different levels of online buying intensity are not significantly related to the intensity of internet usage among the customers; the online marketers should identify the means to convert low and medium intensity online buyers into high intensity online buyers.

In this regard present study has been undertaken to identify the most significant factors and their impact on Service Excellence in Online Shopping. Then two selected e-commerce giants are compared on the basis of abovementioned important factors. The association of the dependent variable with the demographic is specified. The efficiency level of the two e-commerce giants ’ Service Excellence in Online Shopping on the basis of the aforementioned factors have been identified. Further the significant predictor (depicted from regression) of the

dependent variable to be identified and finally a Causal model on Service Excellence in Online Shopping is developed.

2. Literature Review The continuous growth of internet user in India provides a glowing prospect for online shopping providing a huge market. Kothari et al.5 (2016) conducted a study on the factors which customers keep in mind while doing online shopping. Customers would like to be assured to buy quality products at least possible prices. Deshmukh and Joseph Sanskrity (2016)3 researched to understand online shopping behaviour of consumers in India and found that the demographic profile of customers, products type to be purchased, online seller of the product, and


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the characteristics of online shopping have significant influence on online shopping behaviour of Indian customers. Kanupriya et al.6 (2016) investigated, evaluated and understood the features of online shopping. This study emphasised on online consumer behaviour, providing online-marketers a framework for betterment. Deepali2 (2013) studied the prime factors that attract the customers towards online shopping and the features of the product they consider before making the purchase. Saha9 (2015) made a study into the various factors on which retail businesses are being affected and also the ways to revive themselves with those e-stores in their struggle for survival. This paper disclosed the effect upon the profitability of the small retailers due to increasing trend for online shopping. Gupta4 (2015)”opined that, consumer measure different options before purchasing and built up a conceptual model that evaluates consumer value perception for using the online shopping over the traditional shopping. Sharma et al.11 (2014) performed a study to understand the online buying behaviour of customers in India. It also studied about the scope of improving the online shopping website. Nagra Gagan et al.8.(2013) investigated that the online shopping has been vastly influenced by various demographic factors like age, gender, marital status, size of the family and income. Mathew7 (2015) analyse the changing fashion trends in the apparel segment with respect to Indian customers and their online shopping. In the early stages of online shopping consumers were hesitant to buy apparels online as it has many limitations. But now the market is able build confidence among the consumers to buy online. Shanti R., et al. 10(2015) conducted a research on students’ attitude and behaviour towards online shopping and their preference of products in online shopping. This enable the e-marketers to provide better opportunity by developing special marketing strategy in order to pull and convert potential customer by encouraging them in an efficient way to make a purchase . Bhatt 1(2014) focused on factors that online buyers from Indian origin keep in mind while shopping online. This research derived that usefulness, enjoyment and privacy are the dominant factors having significant influence on consumer perceptions in online purchasing. Upadhyay and Kaur13 (2018) opined that online shopping behaviour of Indian consumers and the Product acceptance among prospective buyers in India have sound relationship. Shivanesan12 (2017) analysed the problems faced by customers in online shopping with special reference to Kanyakumari District. People spent a majority of time buying the product specially the teenagers and college students; but to become inevitable among all age groups online shopping have to cover a long way. The study has revealed that most of the customers are attracted towards online shopping, but majority of the customers suffer due to too much delay. So the marketers should concentrate to speed up their delivery service transfer their one time customer towards loyal customers.

3. Methodology On the basis of the previous researches a descriptive research has been conducted with the help of primary data collected by personal interview and close ended structured questionnaire. After collecting data from 244 respondents through questionnaire survey from all four regions of India, CHI-SQUARE ANALYSIS is applied to check the association of the demographics with the dependant variable, RELIABILITY TEST has been done to check the internal consistency of the data, PRINCIPAL COMPONENT ANALYSIS is used to identify major factors, MULTIPLE REGRESSION ANALYSIS is performed on the major factors identified to investigate their influence level and to frame an equation on the basis of the same. DATA ENVELOPMENT ANALYSIS

(DEA) has been performed to find out the efficiency level of the two top online marketers operating in India.

SIMPLE MEDIATION ANALYSIS is applied to incorporate significant predictor of the dependent variable and

PATH ANALYSIS is applied to frame a structural model.

Analysis: & Discussion

Frequency Distribution with Descriptive Statistics

Figure 1 Demographic Profile of the Data

(Authors’’ Calculation)


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Out of 244 respondents, male and female respondents are in the ratio of 142:102; whereas the age group distribution amongst below 25: 26-40: 41-55: 56 and above is 44:104:56:40 , again the ratio between undergraduate: graduate: Post-graduate: others= 56:68:80:40 and the ratio between Service holder: Business professionals: Housewives: Students is 85:72:33:54;

Table 1 Chi-Square Analysis

Chi-Square analysis depicts that customer demographics like age and profession play significant association with their perception towards online service excellence whereas gender and education have no influence on the same.

The above table shows how the perception level varies from age group, younger customers tends to be more satisfied compared to the aged on their perception towards Service Excellence in Online Shopping. Again in terms profession, students are highly prone towards the same followed by housewives, service holders and business professionals. The result also reveals that these two demographics have significant association as the p values are 0.000 and 0.002 respectively on Service Excellence in Online Shopping. On the other hand though the graduate and post graduate students are highly inclined towards Service excellence in online shopping, but as a whole the result of the test shows that Gender and Education level don’t have significance association (p values are 0.214 and .804 respectively) with the dependent variable.

4. Reliability Test To start with the first phase, reliability for each dimensions has been checked, Cronbach’s Alpha value in each case varies from .706 to .913.

Principle Component Analysis (PFA)- From PFA of 22 traits, 3 factors are extracted explaining 83.508% of Service Excellence in Online Shopping

significantly. The most significant factor may be renamed as Product Variety (with coefficient .881, .821 and .765 for Variety in brand, Variety in colour and Variety in size respectively) followed by Delivery Speed (.831) and Trust (with coefficient .834 for COD facility).

Interpretaion of Data Envelop mental Analysis The analysis compares the relative efficiency of various organizations named as decision making units (DMU) such as bank branches, hospitals, vehicles, shops and other instances where units perform similar tasks. These units utilize similar resources, referred to as inputs, to generate similar outputs. For example, a shop has inputs of staff and floor space, and has outputs of sales volume and total revenue. However, there can be considerable differences in the way in which individual units combine inputs to produce


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outputs. In addition there may also be differences in potential among units caused by the technology they have available, their geographical location or catchment population. For present research, Flipkart and Amazon (mainly Amazon India) are considered as DMUs and their efficiency has been examined on the basis of the identified factors i.e., Product Variety, Delivery Speed and Trust. The results are as follows:

Table 2 FLIPKART vs. AMAZON with Respect to Product Variety

When it comes to PRODUCT VARIETY, according to the respondents from all four regions of India, Amazon is the most efficient one (1.0000), though the score of Flipkart in this particular case is also satisfactory well (0.80310) but not superior to its counterpart.

Table 3 FLIPKART vs. AMAZON with Respect to Delivery Speed

In case of DELIVERY SPEED, again Amazon is the most efficient one (1.0000) compared to its competitor. The score of Flipkart (0. 72002) in this particular field is not bad, but according to the Indian respondents the product delivery speed of this online marketer is not so fast as that of Amazon.

Table 4 FLIPKART vs. AMAZON with Respect to Trust

When it comes to TRUST, the scenario is totally reversed. Here, Flipkart is the most efficient one (1.0000). May be due to COD facility, Flipkart wins the trust of the Indian online shoppers. On the other hand, only in this particular area the score of Amazon (0.70310), the most accepted Global Brand, is lagging far behind according to the perception of the respondents.

Pearson Correlation To understand the relation between the independent variables, Pearson Correlation has been performed. According to this analysis, the correlations between Product Variety & Delivery Speed, Delivery Speed & Trust , Trust & Product Variety are .79, .31 and .83 respectively.

Multiple Regression Analysis

To investigate the influence level of the aforesaid factors identified by PFA, Multiple Regression Analysis was run. The regression equation and the associated values are as follows


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Figure 2 Multiple Regression Equation for Regression Model

Hence from the results of the above multiple regression and correlation analysis, the Regression Model may

be drawn as follows

Figure 3 Multiple Regression Model

In the above model the correlation scores of the independents variables are shown in small yellow boxes and

regression weights i.e., the influence levels of the independent variables on dependent are portrayed in light green boxes.

But, this is not the final model. Interestingly, from the personal interview a few factors emerged as the predictor between all other factors and Service Excellence in Online Shopping.; to check their influence level, multiple regression analysis is performed. The result reveals only one relation may be established, as other models are robust and the only relation is given below:

Figure 4 Multiple Regression Equation (No.1) for Path Analysis

From above result Reasonable Price emerged as the only significant predictor or as the mediator between all

other factors and Service Excellence in Online Shopping; now to check their influence level of the other factors on this mediator another set of multiple regressions is run.

Figure 5 Multiple Regression Equation (No.2) for Path Analysis


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Again to test the accuracy of the above result, Simple Mediation Analysis has been applied as follows

Simple Mediation Analysis Simple Mediation Analysis is applied incorporating Reasonable Price as the only significant predictor; it’s the mediator between all other factors and Service Excellence in Online Shopping.

The pre-requisite conditions before carrying out Mediation are checked (Meyers et al., 2015) from the regression:

• All 3 factors are significantly able to predict Service Excellence in Online Shopping and the Mediator in isolation.

• The Mediator is able to significantly predict the dependent variable in the mediation models. In the first case, Product Variety is independent variable whereas in second and third case Delivery Speed and

Trust are independent variables respectively and Reasonable Price is mediating variable.

Result of Sobel Test and Aroian Test

Figure 6 Sobel Test and Aroian Test Output

Thus, it may be stated that for all three variables- Product Variety, Delivery Speed and Trust -the indirect

effect of the three dimensions through Reasonable Price is statistically significant.

Path Analysis The next logical step is to form a Path Analysis using the variables concerned. For, the purpose, two sets of Multiple Regression Equations are carried out. With the help of the findings of those two equations following path diagram may be drawn.

Figure 7 Final Service Excellence Model on Indian Online Shopping

The Reasonable Price accounted for 55% of the variance in Service Excellence in Online Shopping (The regression weights are represented by small red boxes). The standardized beta coefficients associated with Reasonable Price to Service Excellence in Online Shopping is 0.493. (The standardized beta coefficients are represented by small blue boxes). Product Variety, Delivery Speed and Trust together accounted for 70% of the variance in Reasonable Price. Each predictor was associated with a statistically significant coefficient. The standardized beta coefficients associated with, Product Variety, Delivery Speed and Trust are 0.380, 0.127 and 0.359 respectively. The correlations between Product Variety, & Delivery Speed, Delivery Speed & Trust, Trust & Product Variety are. 0.31, 0.22 and 0.17 respectively (Correlations are represented by small orange boxes). Therefore, our general conclusion from the analysis is that Product Variety, Delivery Speed and Trust indirectly influence Service Excellence in Online Shopping through the mediated effect of Reasonable Price.


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5. Conclusion This research lays a foundation for Service Excellence in Indian Online Shopping. Using Chi-square test association of the dependent variable with the demographic is specified.. Three most significant factors Product

Variety, Delivery Speed & Trust and their influencing levels on the dependent variable have been identified by principal component analysis and multiple regression analysis respectively. The correlations between Product

Variety, & Delivery Speed, Delivery Speed & Trust, Trust & Product Variety have been represented by Pearson Correlation. Data Envelopment Analysis showed the efficiency level of the two top e-Commerce giants’ Service

Excellence on the basis of the aforementioned factors. Simple mediation is applied incorporating Reasonable

Price as the only significant predictor (depicted from regression) of Service Excellence in Online Shopping. Path Analysis typically involves weaving together the measured variables in an empirically meaningful way to explain the variance of the outcome variable. (Meyers et al. 2015). In this Causal Modeling of Path Analysis, Product Variety, Delivery Speed & Trust are taken as exogenous variables whereas Reasonable Price and Service Excellence in Online Shopping are the Endogenous variables; wherein Service Excellence in Online

Shopping is also the final Outcome variable. Thus this study may facilitate the E-commerce giants to realize their current position in Indian consumers’

mind; this causal model on Service Excellence in Online Shopping may act as a pathway to comprehend the importance of the identified factors so that the marketers would try to strengthen them, and may focus on their limitation. Moreover, this may facilitate the policy makers to fine-tune their strategies accordingly. Further, present study may be extended crossing the national borders; and there’s a possible scope of this research that it may be expanded to the Global arena to establish a Global Service Excellence Model on Online

Shopping with the help of scenario and causal analysis using Bayesian Probabilistic Network.

6. Selected References 1. Bhatt (2014) “Consumer Attitude towards Online Shopping in Selected Regions of Gujarat”, Journal of

Marketing Management June 2014, Vol. 2, No. 2, pp. 29-56 ISSN: 2333-6080 (Print), 2333-6099 (Online) 2. .Deepali (2013) “Study on growth of Online Shopping in India” International Journal of Computer Science

and Mobile Computing”, Vol. 2, Issue. 6, June 2013, pg.65 – 68 3. Deshmukh G.K, Joseph Sanskrity (2016) “Online Shopping In India: An Enquiry of Consumers World”,

IOSR Journal of Business and Management (IOSR-JBM) e-ISSN: 2278-487X, p-ISSN: 2319-7668. Volume 18, Issue 1 .Ver. III (Jan. 2016), Pg28

4. Gupta (2015) “Comparative Study of Online and Offline Shopping: A Case Study of Rourkela in Odisha”, ethesis.nitrkl.ac.in/6738/1/Comparative__Gupta_2015.pdf

5. Kothari et al.(2016) “A Study on Customers Attitude towards Online Shopping in India and its Impact: With Special Reference to Solapur City”, International Journal of Advance research , Ideas and Innovations in Technology, ISSN: 2454-132X Impact factor: 4.295 (Volume2, Issue6) Available online at: www.ijariit.com

6. Kanupriya et al. (2016) “A Study Of Behaviour Of Consumer Towards Online Shopping”, Orbit-Biz-Dictum Volume 1, Issue 1, January-June 2016.

7. Mathew Binoy(2015) “A Study on Changing Trends in Online Shopping of Indian Consumers in Apparel Segment”, International Journal of Applied Research 2015; 1(9): 207-214

8. Nagra Gagan (2013) “An study of Factors Affecting on Online Shopping Behavior of Consumers”, International Journal of Scientific and Research Publications, Volume 3, Issue 6, June 2013 1 ISSN 2250-3153

9. Saha (2015) “A Study on “The impact of online shopping upon retail trade Business”, IOSR Journal of Business and Management (IOSR-JBM) e-ISSN: 2278-487X, p-ISSN: 2319-7668. PP 74-78 www.iosrjournals.

10. Shanti R (2015) “Consumers’ Perception on Online Shopping”, Journal of Marketing and Consumer Research www.iiste.org ISSN 2422-8451 An International Peer-reviewed Journal Vol.13, 2015

11. Sharma et al. (2014) “Understanding Online Shopping Behaviour of Indian Shoppers”, IJMBS Vol. 4, Issue 3, Spl- 1 July - Sept 2014

12. Shivanesan R(2017) “A Study on Problems Faced by Customers in Online Shopping with Special Reference to Kanyakumari District”, International Journal of Research in Management & Business Studies (IJRMBS 2017) Vol. 4 Issue 3 (SPL 1) Jul. - Sept. 2017

13. Upadhyay Kaur(2018) “Analysis of Online Shopping Behavior of Customer in Kota City”, International Journal in Multidisciplinary and Academic Research (SSIJMAR) Vol. 2, No. 1, January-February (ISSN 2278 – 5973)

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