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A STUDY ON CONSUMER SWITCHING BEHAVIOR AMONG ONLINE

RETAILERS IN INDIA

BY

NISHANT CHAND

2014

A Dissertation presented in part consideration for the degree of

“Master of Business Administration Degree”

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ABSTRACT:

66% of consumers switched companies in at least one of ten industries due to poor service in the past year. 82% of

consumers felt their service provider could have done something to prevent switching. 55% say they’d have stayed

if the company had proactively contacted them, and 51% would have stayed had the company simply recognized

them and rewarded them for their business (Accenture Global Consumer Pulse Research, 2013).

In the booming of age of organizations expanding their operations globally and companies diversifying their services

and products, the only constant and critical aspect is - customer. One such booming nation is India where with

improvement and penetration of internet, E-commerce is emerging as one of the biggest industry. However as it is

still a new player, there is doubt of what Indian consumers seek in these online retailers to be loyal. The objective of

this research is to study the consumer switching behavior among Indian online retailers. It not only focuses on the

various factors which influence customers to skip from one e-retailer to another, but also why they choose to remain

loyal. It also gives a perception of Indian online consumers on how they see customer value and E-service quality.

Data for this research was collected via online survey questionnaire from 163 consumers in India. The results show

that Indian consumers perceive high standards towards E-service quality and privacy is incredibly important to them.

Most of the respondents were happy with their current “favorite” online retail but emphasized that they would make

a switch on few reasons. The one reason which is most crucial is ethical issues. The findings of the research also show

that price and promotion is least influential in order to choose another retailer.

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ACKNOWLEDGEMENTS:

The completion of MBA and this research study is one of best enlightening and enriching experience of my life. It

would have not been possible because of few people and I would l like to take this opportunity to thank them

from bottom of my heart.

Firstly my supervisor, Ms Anita Chakrabarty for her never ending support, thoughts and assistance for this

dissertation. Your guidance and was critical and is much appreciated.

To all my MBA batchmates, with whom I worked during this course. Memories of knowing and spending time with

you all would be cherished lifelong.

My ex-bosses, who helped and guided me in my professional career. I could use and relate to many work

experience moments during my MBA.

My dad, to whom I look up to everyday of my life. Thank you for instilling importance of education in our family.

Lastly my mom, for all that you have done for me. I could have never been here without your love, care, support

and encouragement.

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List of figures:

Figures: Page:

Figure 1.1 Global online sales from 2007-2012 .............................................................................................................................. 12

Figure 1.2 2013 Global retail e-commerce index ............................................................................................................................ 13

Figure 1.3 E-commerce evolution in India - The Two Waves ......................................................................................................... 19

Figure 1.4 Annual internet sales growth in 5 years ......................................................................................................................... 20

Figure 1.5 Top 10 online shopping websites in India ....................................................................................................................... 23

Figure 1.6 Evolution of E-commerce logistics ................................................................................................................................ 26

Figure 2.1 Comparison of physical and online stores .................................................................................................................... 36

Figure 2.2 Framework for Online consumer satisfaction ................................................................................................................ 39

Figure 2.3 Conceptual framework of E consequences of E-servqual ............................................................................................. 44

Figure 2.4 Framework of Consumer value for an E-commerce model ........................................................................................... 47

Figure 2.5 Framework for Online trust for E-commerce model ....................................................................................................... 52

Figure 2.6 Types of switching costs ................................................................................................................................................ 60

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List of TABLEs:

Tables: Page:

Table 2.0 Factors leading to customer switching for Online platform ........................................................................................ 56

Table 3.1 Research plan ........................................................................................................................................................... 66

Table 3.2 E-service quality variables with questions ................................................................................................................. 68

Table 3.3 Consumer value variables with questions .................................................................................................................. 69

Table 3.4 Online consumer trust and Online loyalty variables with questions ........................................................................... 69

Table 3.5 Consumer switching behavior variables with questions ............................................................................................. 70

Table 4.1 Demographic profile of the respondents .................................................................................................................... 75

Table 4.2 Descriptive statistics of statements in construct ........................................................................................................ 77

Table 4.3 Reliability analysis of E-service quality variables ....................................................................................................... 79

Table 4.4 Reliability analysis of Consumer Value variables ....................................................................................................... 80

Table 4.5 Reliability analysis of Online trust variables ................................................................................................................ 80

Table 4.6 Reliability analysis of Online loyalty variables............................................................................................................. 80

Table 4.7 Reliability analysis of Consumer switching variables ................................................................................................. 81

Table 4.8 Total variance explained for top 4 constructs ............................................................................................................ 82

Table 4.9 Rotated Component Matrix with Communalities and Respective Eigenvalues and Variance .................................... 84

Table 4.10 Reliability statistics ..................................................................................................................................................... 85

Table 4.11 Regression analysis on E-service quality and Online loyalty ...................................................................................... 86

Table 4.12 R and R Square scores for the model ....................................................................................................................... 86

Table 4.13 Regression analysis on Consumer value and Online loyalty ...................................................................................... 87

Table 4.14 R and R Square scores for the model ........................................................................................................................ 88

Table 4.15 Regression analysis on Online trust and Online loyalty .............................................................................................. 88

Table 4.16 R and R Square scores for the model ........................................................................................................................ 89

Table 4.17 Consumer switching behavior component ranking ..................................................................................................... 90

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TABLE OF CONTENTS:

Abstract… ............................................................................................................................................................................... 2

Acknowledgements ................................................................................................................................................................ 3

List of Figures ........................................................................................................................................................................ 4

List of Tables .......................................................................................................................................................................... 5

Table of Contents ................................................................................................................................................................... 6

CHAPTER 1 INTRODUCTION ........................................................................................................................... 9

1.0 Introduction .................................................................................................................................... 10

1.1 Evolution of internet and Online Shopping .................................................................................... 11

1.2 Changing attributes of Online Retailing ........................................................................................ 13

1.3 Consumer behavior towards Online Shopping ............................................................................. 15

1.4 Consumer switching behavior ...................................................................................................... 17

1.5 Online retailing in India ................................................................................................................. 18

1.6 Scope of business and major Indian players ................................................................................. 22

1.7 Future trends in Indian e-commerce landscape ........................................................................... 24

1.8 Challenges ahead ........................................................................................................................ 25

1.9 Research objective ....................................................................................................................... 28

1.10 Research question ....................................................................................................................... 29

1.11 Chapter’s outline ........................................................................................................................... 30

1.12 Conclusion…………..……………………………………………………..............................................31

CHAPTER 2 LITERATURE REVIEW ............................................................................................................... 32

2.0 Introduction………………………………….…………………………………………………………….33

2.1 Online retailing ........................................................................................................................... 34

2.2 Online retail - Having attributes of a physical store .................................................................... 36

2.3 Online shopping behavior ........................................................................................................... 37

2.4 Influencers of Online shopping behavior .................................................................................... 42

2.5 Customer switching behavior ..................................................................................................... 53

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2.6 Switching costs and switching behavior ..................................................................................... 58

2.7 Types of switching costs ............................................................................................................ 59

2.8 Summary of Literature Review ................................................................................................... 60

CHAPTER 3 RESEARCH METHODOLOGY ............................................................................................... 62

3.0 Introduction……….... ….……………………………………………………………………………… 63

3.1 Research method ..................................................................................................................... 64

3.2 Determining the research design .............................................................................................. 65

3.3 Identification of information and source .................................................................................... 65

3.4 Designing the survey instrument .............................................................................................. 65

3.5 Survey questionnaire design .................................................................................................... 66

3.6 Sampling size and Data collection ............................................................................................ 67

3.7 Research parameters and measures………………….……………………………………………..68

3.8 Conclusion…………………………………………………………………………………..…………..71

CHAPTER 4 ANALYSIS AND FINDINGS .................................................................................................... 72

4.0 Introduction…………….……………….……………………………………………………………….73

4.1 Demographic profile of respondents ......................................................................................... 74

4.2 Descriptive statistics of construct variables .............................................................................. 76

4.3 Reliability .................................................................................................................................. 79

4.4 Factor analysis ......................................................................................................................... 82

4.5 Regression analysis ................................................................................................................. 85

4.2 Descriptive statistics of construct variables .............................................................................. 76

4.3 Reliability .................................................................................................................................. 79

4.4 Factor analysis ......................................................................................................................... 82

4.5 Regression analysis ................................................................................................................. 85

4.6 Consumer switching behavior ranking model ........................................................................... 90

4.7 Conclusion ............................................................................................................................... 92

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CHAPTER 5 DISCUSSION AND CONCLUSION......................................................................................... 93

5.1 Introduction … ............................................................................................................................ 94

5.2 Indian online shopping behavior ............................................................................................... 95

5.3 Consumer demographic profile ................................................................................................ 95

5.4 Consumer perception towards E-service quality ...................................................................... 97

5.5 Consumer perception towards Consumer value ........................................................................ 98

5.6 Consumer perception towards Online loyalty ............................................................................ 99

5.7 Consumer perception towards Consumer switching behavior ................................................ .100

5.8 Key findings of the research .................................................................................................... 101

5.9 Managerial recommendations to e-retailers ............................................................................ 104

5.10 Closure .................................................................................................................................... 106

5.11 Limitations of the research ...................................................................................................... 107

5.12 Future research recommendations…………………………………………………………………..108

References … ..................................................................................................................................................................... 109

Appendices ........................................................................................................................................................................ 123

Appendix 1 Questionnaire Survey ......................................................................................................................... 123

Appendix 2 Demographic data analysis ................................................................................................................ 136

WORD COUNT: 21897 [Excluding Table of contents, References, Appendices, Abstract and Acknowledgments]

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CHAPTER I: INTRODUCTION

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CHAPTER 1 : INTRODUCTION:

1.0. Introduction:

The online retail industry was in its immaturity stage for most time in the previous decade. However, the last

few years have surprised everyone, with industry witnessing incredible growth of 150 percent, increasing from

USD 3.8 billion in 2009 to USD 12.6 billion in 2013 (PwC,2013). Out of the numerous business models which are

prevalent, consumer e-commerce is seen to have a stronger and wider impact on retail and has engaged

governments, entrepreneurs and investors.

India has also not been untouched. As India advances to become a consumption driven economy, a

transformative and enormous opportunity is presented by this consumer centric model. Over the past three

years, the e-commerce allied companies have turbo charged the online shopping growth engine by introduction

of innovative business models to capture wallet share and online time. Marketing concepts such as ‘by invite

only’, ‘Cyber Monday’, ‘Great online shopping festival’, ‘Flash sales’ have proved to be smashing hits in order to

promote online shopping (KPMG, 2013)

This chapter introduces the evolution of internet shopping over the years and how the attributes have changed

in time. It then discusses consumer shopping and switching behavior when shopping online. It then discusses

the scope of e-commerce in India, the future trends and challenges which lie ahead.

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1.1. Evolution of internet and Online Shopping:

The magnitude at which internet has expanded itself, many aspects of the business practices have been

transformed throughout the globe (Zwass, 1996). It has lead to the creation of a novel blue ocean business

platform facilitating consumers buying and making transactions online, and after a while an era of E-commerce.

Terms like E-commerce or internet commerce have been refined over the years to explain the process in which

electronic transactions simplify exchange and payment for goods and services among consumers, private and

public organizations, businesses and consumers. OECD (Organization for Economic Co-operation and

Development) defines E-commerce as a process of ordering goods and services by the use of internet platform,

but the payment as well as final product or service delivery is conducted online or offline.

E-commerce had created an offline marketplace which made global businesses easier, accessible, cost efficient

and simple as all transactions were regulated and communicated over the internet (Haque and Khatibi, 2005).

Despite the global economic crisis in 2008, research conducted suggests that Business-to-consumer (B2C) online

retail has achieved massive growth and scope. Figure 1.1 illustrates on how the online retail sales have rocketed

to a $500 plus industry in just five years, and growing. The growth rate of 17% can be seen as spiking faster than

most of the other industries.

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Figure 1.1 : Global online sales from 2007-2012 ( Source from AT Kearney, 2013)

In the meantime, as online retail is termed as a key revenue generating unit in developed nations, a global

research from (MasterCard, 2009) shows a significant growth in developing nations of Asia and Africa.

Figure 1.2 shows on how research from Euromonitor sees immense potential growth in online retail market of

“Established and growing” and “Next Generation” in the next few years. Even though some of developing

countries like India, China, Thailand etc have low internet penetration and significant infrastructure constraints,

results show that consumption of online shopping has increased over the years and still thriving.

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Figure 1.2 : 2013 Global retail e-commerce index ( Source from AT Kearney, 2013)

1.2. Changing attributes of Online Retailing:

As discussed in above sections, online retailing has become more prevalent and consumers have

accepted it faster and in greater number than expected in past the past decade. However, within this

context, the characteristics of e-retailers and customers have evolved rapidly in recent years. The

following paragraphs key changes in customers as well as online retailers in recent past.

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Consumer transformation: Customers in both developing and developed and countries are found to do a

detailed research before buying anything online and looking for things like product description, shipping

options, return policy etc. Therefore they are seen to become more cynical. They tend to gather more

information from physical stores and through peers & friends by social media. For example, research shows that

half the French customers research products in physical store before going online and three fourth of Brazilian

customers have product discussions on social network (Vazquez, 2009). More consumers are using comparison

shopping engines (CSE’s) to collect product information from a list of retailers on a single page in order to

compare the best overall product (Chylinski, 2010).

Retailer’s transition: Online retailers are becoming more creative and skillful and they understand that in order

to attract and retain intelligent consumers, they must create industry lead value proposition, variety and

competitive price. Online websites have evolved into product encyclopedias presenting impeccable

information, user reviews and details about the products in every category to the consumers (Yannopoulos,

2011). Customers also have the option to contact the manufacturers, fashion advisors, publishers etc. Retailers

encourage customers to write post purchase feedback but with different intentions. For example many Chinese

e-retailers ask customers to leave a positive feedback for a promo coupon whereas online giant Amazon,

investigates reviews for product flaws (Mckinsey, 2013).

Dominant product categories of online retail: Though consumers have different taste and preference for clothes

and electronics, yet these two categories dominate over other product categories globally. They sell well

because they have clear product specifications which can communicated easily. Customers can also read user

reviews and compare with other online retailers. Some of the websites offer trying out the apparels on virtual

models in order to facility buying (Parvinen, 2014).

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Level of competition: The online retail platform has grown to be quite competitive over the years. The

competition can be easily seen in market fragmentation as in every top 30 markets, 50 retailers account for

about 80 per cent sales (Morganosky, 1997). Amazon, world’s biggest online retailer is a leader in 9 markets,

showcasing an aggressive global expansion strategy and it’s USP of gathering consumer followings. Many

retailers with global dominance are now partnering with e-commerce channels and logistics companies in order

to sell their products to international consumers (Colla, 2012).

The above changing trends significantly indicate on how consumer perception towards e-commerce has

evolved. We can certainly claim that this change offers them a larger and wider platform of choices, and thus

more options to choose and switch. The successive section would discuss consumer purchasing and switching

behavior.

1.3. Consumer behavior towards Online Shopping:

Existing research suggests that growth of online retailing looks optimistic, and there is a rapid increase in

numbers of customers worldwide (Ernst and Young, 2001). Product categories of apparel, electronics and

grocery are among the top selling on most online platforms. As per (UCLA Centre Communication Policy, 2001),

online retailing has become third most favorite internet activity after emailing and web browsing. As the internet

penetration and scope increases worldwide, it hints towards the massive sales and opportunity ahead. The

interactive nature of web browsing and internet has improved over the past to offer many opportunities to

upsurge the efficiency of online shopping behavior of customers. Some of them are detailed product

information and prices which enable direct comparisons reducing customers search costs and time (Alba et al,

1997)..

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Online shopping behavior or online buying behavior can be defined as the process of buying products and

services via internet. Liang and Lai (2000) state that there are five steps involved in this process compared to

traditional shopping. In a standard online shopping process, when individual recognizes a need for an item, they

search for need related information on the internet. However most of the times, they are attracted by

information presented about the product linked to their need. They then compare and evaluate the options,

and choose the one which best fits their need. In the end, a transaction is made and a post sales service

conducted. Online shopping attitude can be termed as psychological state of customer in making a purchase

decision on the internet (Davis et al, 1992). Consumer purchase decisions are influenced by cyberspace

appearance such as product info, images, videos, reviews etc and not on actual experience

A framework developed by Laudon and Traver (2009), comparing offline decision making and online customer

decision suggested that when customer want to purchase a product, they would evaluate the brand and the

features of the product or services. While some products can be easily bought and shipped over the internet

e.g. books, software, some were hard to decide through online channel e.g. jewellery.

Customer skills, website features, marketing communication stimuli, click-stream behavior and company’s

capabilities were main constituents in their framework. Customer skills or customer experience with online

shopping which means consumer knowledge about products, technology and competition affect the buying

decision of customer (Broekhuizen and Huizingh, 2009). A lot of retailers invest to improve their website

features and quality in order to influence customer perceptions of web environment and hence affecting their

decision making (Prasad and Aryasri, 2009). Click-stream behavior is another critical element in online purchase

decision making. It refers to online behavior of customers as they search for information through numerous

websites (many websites at the same time), then to a single website, to a single page, and finally to a decision

to buy (Laudon and Traver, 2009).

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All of the above factors lead to certain behavior of online purchasing as well as a sense to control their shopping

environment on online platform.

At last the key influencers of online buying decision are availability of products and services, convenience,

efficiency of cost and time and authenticity of information (Prasad and Aryasri, 2009).

1.4. Consumer switching behavior:

With internet users increasing to a quarter billion worldwide and estimated about 30 billion by 2016, growth in

online retail has been exponential. With this perceived growth, a concern for customer “churn” has developed.

It is a concern which parallels problems of customer switching behavior in online retail industry. Some of this

churn is discontinuance of online service, where consumers try a service but subsequently stop using service

category on the whole. For example, 10 million tried and started using internet by 2006 but stopped by 2008

(Kingsley, 2008). Some of this churn is “consumer switching behavior” where individuals continue to use the

service, but switch to another service provider.

A survey by consultants found that consumers are more likely to revisit and repurchase items from their favorite

“e-retail stores” than traditional physical retail stores and also that e-retail stores are “sticky” as they enjoy

more consumer loyalty (Reichheld and Schefter, 2000). On the whole, with e-shopping developments becoming

more prevalent, online retailers have the benefit of more consumer loyalty than their brick and mortar

competitors.

But the bigger question which has started reflecting on their business is how much customer loyalty persists

within the online retail community. The cost involved in locking up new customers means that loyalty has

become economic necessity and business differentiation among e-commerce retailers.

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While financially driven companies aim for efficient system resulting a drop in customer acquisition cost, “for

most, average consumer acquisition cost is greater than average lifetime value of consumer” ( Novak and

Hoffman, 2000). In addition to costs related to customer acquisition, retaining and winning back lost consumers,

a lot of other expensive activities develop like marketing expenditure to build value equity, brand equity and

relationship equity. This is the reason that a lot of focus has developed in order to study and investigate the

buying patterns and switching behavior of customers.

With this pattern, two key strategies have developed to increase behavioral form of loyalty. The first being

increasing customer satisfaction so that consumers see fewer gaps to switch to a competitor; the second being

to make it difficult for consumers to switch i.e. increasing switching barriers. Customer satisfaction and

switching barriers are seen as antecedents of customer loyalty (Dick, 1994).

Therefore in order to stay profitable, retailers have to ensure that their customers stay with them on online

channel of shopping. E-commerce vendors need to showcase integrated multi channel operations and value

packages which generate interest and offer product differentiation (Sinioukov, 2000). Also, to retain customers

and minimize switching to other retailers, some companies need to provide same shopping experience on both

online and offline platforms. By carefully synchronizing its channels, activities and operations, a retailer can offer

a superior service output which gives consumers fewer reasons and opportunities to switch. This would entail

comparison of multiple retail channel benefits and costs viewed by customers more holistically.

.1.5. Online retailing in India:

As per World Internet Stats, India finds itself as third largest among internet users in the world after China and

USA. It is surprising because it has a very low internet penetration of 8.5 %. The count for Indian internet users

has been rising at a CAGR of 35 per cent since 2007. As per Boston Consulting Group 2010 report, the figure of

100 million Indian internet users in 2010, the number would rise to 237 million by 2015 (Brown, 2001).

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Figure 1.4 : E-commerce evolution in India - The Two Waves ( Source Ernst & Young, 2013)

With the debut of internet in India in 1995, the first wave of e-commerce had also touched the country (Figure

1.4). Moreover, with liberalization of economy in 1991, many MNC’s were setting up their base in India brining

IT growth in the country (Ganguly, 1999). Although online businesses were beginning to develop by late 1990’s,

the infrastructure required to support the activity was not in place. Therefore the first wave of e-commerce was

met only by small online shopping consumers due to low internet penetration, slow broadband speed, low level

of acceptance of online retail and poor logistics.

With the introduction of Low Cost Carriers (LCC’s) in 2005, a second wave of e-commerce in India was predicted.

Travel started emerging as one of largest segment in online retail segments. Consumers were now depending

on internet websites and portals to search of travel information, bookings, payments etc. With a ripple effect,

the acceptance of online travel made Indian consumers comfortable towards the concept on shopping online,

thus leading to growth of online retail industry.

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India ranks as 16th in annual internet sales growth depicting its future growth and capacity in internet business

(Figure 1.5). This rapidly increasing internet penetration and scope is believed to directly impact Indian Online

shopping business and trends or e-tailing.

Figure 1.5 : Annual internet sales growth in 5 years (Source Cushman & Wakefield, 2013)

Some of the reasons which have resulted in high internet growth are:

Smaller urban areas are growing: Out of 120 million users in India, the bigger growth is seen from small towns

with a population of half a million. They have a combined usage of 60 per cent which is greater than eight metro

cities added together (Ling, 2010).

Increasing internet diffusion in lower SEC’s: India is classified into a number of Socio Economic Classification

(SEC) bases on per capita income. But the growth of internet has been seen more in SEC C at 25 per cent and 11

per cent in SEC D and E status. With the rise in literacy rate, these figures are projected to rise further.

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Increased usage of internet on mobile: India is believed to have more than 1.1 billion active mobile users as of

December 2013. Out of this, about 67 million access web via their mobile devices. This behavioral change has

been nourished by high cost of ownership by broadband companies.

Youth driving the change: About 75 per cent of active internet users are Indians within the age group of 21-35.

The major usage is towards social networking, emails, browsing, and downloading digital content

(Economist, 2012).

When India’s first online retail website, Fabmart.com (now Indiaplaza) had started operating in 1999, only a

small number of three million users shopped online and the market size was only 11 million USD (Economist,

2012). The same Indian market is seen as 8.8 billion USD online retail industry by 2016 and its growth rate is

projected to be fastest in Asia Pacific at 57 per cent. The online platform in India is burgeoning by offering

various options of movies, books, travel, hotel reservations, matrimonial services, gadgets, groceries etc. India

is a nerve center to 2217 import hubs, 3311 e-commerce hubs, 391 export hubs and 1267 rural hubs

(Ebay, 2011).

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1.6. Scope of business and major Indian players:

With introduction, implementation and revolution in the technology and internet, a new market has been

created for service providers as well as manufacturers. It has also handed over a new arena which involves

constant improving strategies and innovative marketing (Yulihasri, 2011). There have been numerous reasons

as to major shift of consumer buying patterns towards e-commerce. The ease of comparing products with

competitive items on basis of color, size, price, quality and shipping speed has been one of the biggest benefits

perceived. With evolution of online shopping in India, new terms became popular like web stores, virtual store,

online storefront, web-shop, e-shop, internet mall etc (Na Wang, 2008). With increasing sale of smartphones,

mobile commerce or m-commerce is also becoming popular by which consumers can simply place orders via

mobile application of websites. Also, the various coupons, discounts, deals like Black Friday & Big Billion Day

sale are fascinating Indian consumers to shop online.

Even the rising inflation in past few years has not affected the performance of leading online shopping portals

of India. In 2013, the capacity of online retail in India was $16 billion which was only $8.5 billion in 2012

(Ernst & Young, 2013). Earlier the Indian online shopping market was only limited to sale of mobile phones,

books and electronic gadgets, but in past two years products reflecting lifestyle, viz. apparels, health & beauty,

watches etc. have gained high popularity. Now the e-retailers are aiming to make categories like e-books,

jewellery, kitchen appliances more acceptable to consumers.

If we were to consider the update from (Ernst & Young, 2013), the number of online shopping websites have

reached 600 in 2013, which was mere 100 in 2012. This rising popularity of e-commerce websites is also

significant in addition to global online retail leaders like Amazon and Ebay. The following Table 1.1 gives a

glimpse to current top ten e-commerce websites in India.

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RANKING WEBSITES SERVICES OFFERED

1 Flipkart It is a mega online store which offers wide range of products including

clothes, books and electronics.

2 Ebay India It has unique business concept where a seller can sell the product

directly to buyer.

3 Snapdeal It is online marketing and shopping company which has

existence in more than 400 cities in India.

4 Jabong It has been a front runner in online shopping websites in India and

offer attractive discounts, promotional and deals for Indian

customers on many fashion, home décor and lifestyle variants.

5 Myntra It retails many famous national and international brands like Puma,

Adidas, John miller, Lotto and many more.

6 Tradus It offers wide range of wholesale and retail products online. Tradus.

com is an Auction and shopping company operate in many European

countries.

7 Junglee Junglee is an online website which provides electronics, lifestyle,

men & women apparel, accessories, movie CD/DVD, home décor

products etc.

8 Homeshop18 It is an online shopping website and retail distribution network

company.

9 Shopclues An online mega store recorded highest growth in year 2012 and

Alexa ranked 1000 in mid of August -13.

10 Yebhi It deals in many top national & international brands and products

such as footwear, fashion, accessories and jewellery.

Table 1.1 : Top 10 online shopping websites in India

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1.7. Future trends in Indian e-commerce landscape:

First few movers and focused will proper: With second wave of e-commerce hitting the Indian consumer market,

many young entrepreneurs have ventured into e-commerce. Across wide number of categories, there are

numerous retailers spending money on marketing to grab some portion of market share. The scenario is quite

similar to dot com bubble in US, where several companies tried to sell the same common concept to consumers,

with little or no differentiation (Chen, 2002). With past studies and examples, the e-commerce industry shows

that only a few early starters and companies offering differentiated services, products, marketing and customer

relationship management will emerge as long run winners. Flipkart and recently launched Amazon are seen as

big players in Indian market.

Acquisition cost and Customer lifetime contribution are key factors: Online retailers archive promotions,

discounts and free shipping under marketing budget which increases Customer Acquisition Cost (CAC). CAC in

India is currently about an average of USD 25 (Businessline, 2013). It is surprisingly higher than international

brand like Amazon which has CAC of USD 12. Ebay has even lower CAC of USD 4. Customer Lifetime Contribution

(CLTC) on the other hand is the Net present value of profit from consumer’s total purchases and ideally it should

be more than CAC for a profitable consumer. If CLTC is twice the cost of acquiring a new customer, with 1X

coming in first 12 months after it acquires, the company is doing very well (Bauer, 2003). A successful retailer

needs to invest in business analytics and study parameters like website performance, conversion, returning

traffic, customer service etc to create better customer acquisition strategies.

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Physical retailers will have an advantage: In the west, where brick-and–mortar retailers are seeing fierce

competition from digital retailers, the market scenario in India is likely to be different. With low internet

penetration, limited payment options and slow growing infrastructure, pure click retailers might face slow

growth and revenue. Digital platform can thus be an additional benefit to big physical stores who wish to take

their business online. These physical stores can leverage on their size, brand name, operations efficiency and

marketing strategies to penetrate the online platform better (Crisil, 2014).

Tier II and III cities should on business hit list: Towns with population of about 0.5 million have internet usage of

more than 60 per cent, more than eight metros put together. With growing economy, the disposable income is

also rising. A successful e-retailer needs to create a delivery and business network of about 3000-4000 pin codes

out of total 150,000 pin codes in India (Crisil, 2014)..

1.8. Challenges ahead:

Logistics: Logistics is a major key driver towards successful service fulfillment. It has been a key focus area for

digital retailers in an attempt to make Indian consumer comfortable to shop online. For quite few years, almost

all online retailers were dependent on third party logistics companies for delivery to customers. Now many have

started delivering the orders themselves. The number of courier companies has also been rising, with about

4500 covering all the pincodes of the country. The cost of logistics is high due to lack of good quality

infrastructure. With creation of its own logistics, the retailer can benefit by higher profits; competitive

advantage over local and new global entrants; lastly, it can open its delivery options to other retailers and charge

them e.g. Amazon encouraging small companies to use it marketplace platform and cloud services.

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Figure 1.7 : Evolution of E-commerce logistics (Source Jones Lang LaSelle, 2013)

Figure 1.7 shows how the e-commerce logistics has evolved from their initial introduction in 1970’s to 20h

century in India. Suppliers have changed their supply chain from delivering to shops to creating a full strategic

operational supply chain cycle.

Payment options: With Cash On Delivery (COD) being provided as a payment options by most Indian online

retailers, perception of shopping online has certainly increased. Earlier, many consumers felt uncomfortable

sharing their debit/credit card details over the internet which was a major hindrance for shopping online. Also

a major population in India did not have a debit or a credit card. COD allowed customers to make payment when

the items were delivered to them. Furthermore, even those who did possess a card, preferred to shop via COD.

About 60 per cent of Indian buyers who had a credit card, still preferred COD for most of their purchases (BCG

Report, 2013). More than 50 per cent of payment transactions were via COD (IBEF Report, 2013). But unlike

electronic payments, collecting cash manually is expensive, laborious and risky. The current need is to make

credit or debit card more popular as payment option.

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Short attention span by consumers: One of the major challenge and difference on shopping online is that

consumer’s attention span and patience is quite less. Customers can easily navigate to various websites in case

they do not find the product. The problem becomes worse in places where broadband speed is very slow. About

40 per cent would not wait for more than 3 seconds for a webpage to respond (Roland, 2013).

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1.9. Research objective:

As online shopping is gaining rapid interest in India, an investigation on the consumer switching behavior, online

trust, E-service quality and consumer value has therefore become a timely topic for research.

The main objectives of the research would be:

i. To identify key factors which influence Indian consumer switching behavior when shopping online.

ii. To determine crucial elements which influence online loyalty, consumer value and E-service quality perceptions

of Indian customers towards e-commerce companies.

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1.10. Research question:

The first and foremost reason which provoked an interest for this study was with growing economy, Foreign

direct investment by raising billions, more use of tablets & smart phones, the Indian E-commerce is growing at

an astounding rate. Though attracting new customers is a key strategy for major players, retaining many of them

by studying a switching behavior might give them larger loyalty and thus bigger profits. Hence, in order to retain

and attract consumers in a thriving competitive market, e-commerce store owners need to recognize on how

web-consumers perceive value and loyalty (Goldsmith, 2002). At the same time, understanding reasons and

patterns of customer exit can help them offer prolong and enriching shopping experience.

Therefore to meet the objectives of the research, I have developed three research questions. They are:

i. What are the factors which influence customer exiting behavior amongst Indian consumers?

ii. Which are the key variables which determine online loyalty for E-commerce companies by Indian online

shoppers?

iii. What are the factors which help Indian customers determine value and E-service quality of online retailers?

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1.11. Chapter’s outline:

Now that the groundwork for the research has been introduced, the remaining part of the research aims to

answer the objectives in the following structure:

Chapter 2 : “Literature Review” would cover the relevant literature, findings and previous studies associated

with online retailing, E-service quality, Consumer value, online loyalty & trust and switching behavior. It would

focus on various previous research frameworks. Lastly, a conceptual research model is discussed in this chapter.

Chapter 3: “Research Methodology” would bring out on how the research objective is planned to be responded.

It would illustrate research plan, design and action plan of data collection.

Chapter 4: “Data Analysis and Findings” would present an analysis of the result from the surveys and compare

them with earlier discussion of Literature Review. Also, it would highlight new and major findings linked to

research objective.

Chapter 5: “Discussion” would try and elaborate the major finding of the research and create a debate around

it.

Chapter 6: “Conclusion” would be the last chapter which would bring out key findings and conclude the research

with future research recommendations.

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1.12. Conclusion:

In this chapter we were able to have an overview of evolution of internet retailing in the world and how it has

grown in India in past few years. Not only has it evolved in number of shopping websites being offered, it has

seen sales in new product categories. A research which brought this out was conducted by Great Online

Shopping Festival (GOSF), which is supported by Google. As per the sales analysis of GOSF (2013), the partner

online websites saw 350 per cent growth in daily sales and 50 per cent of buyers were shopping for the first

time (Chaudhary, 2013). Also surprisingly was seen that highly valued items like five houses worth Rs 25 crore,

34 Nissan cars and over 200 Tata motor vehicles being bought in four days of online shopping festival (Sushma,

2013).

Overall, electronic commerce in India still accounts for only small fraction of total sales, but looks to grow to a

considerable amount due to the factors of rapid urbanization, support of demographics, increasing adoption,

ease of payment modes, penetration of internet and technology, invitation to foreign direct investments and

customer centric innovative policies. While the online retailers view future opportunities as being first movers

and acquiring and retaining customers by various marketing strategies, they must understand the challenges

which stand in terms of logistics, difficult payment options, acquiring new customer or exit of existing customer.

The introductory chapter thus provides background and future of online retail in relation to growing Indian

market. It also presents various opportunities and challenges which can be a crucial service differentiator. In

the successive section we would lay the research objectives and questions followed by a breakdown of research

outline by chapters.

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CHAPTER II: LITERATURE REVIEW

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Chapter 2: Literature review:

2.0. Introduction:

The main objective of this chapter is to bring forward relevant literature focusing on dimensions of consumer

switching behavior, online shopping behavior, online consumer trust & loyalty, E-service quality and customer

value. The chapter showcases an overview on consumer exiting behavior and provides previous studies and

researches conducted in relation to above mentioned parameters.

The chapter starts with an introduction to online retailing and how it shares common attributes of offline stores

followed by literature on online purchasing behavior. Then the chapter brings about various influencers of

online purchasing behavior. Next, the chapter sets forth the consumer switching behavior model and discusses

various switching costs. The chapter then ends with a summary of the complete Literature Review.

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2.1. Online retailing:

The rapid rise in growing number of online shopping customers has brought forward numerous opportunities

and challenges for organizations (Bai et al, 2008).These growing numbers lead interest of many researchers to

investigate consumer’s perception and behavior, and their influence for the emerging new e-commerce

business models (Hackman et al, 2006; Janda et al, 2002; Liu et al, 2008). Srinivasan and Anderson (2003)

suggested using a service marketing framework which was initially created for offline businesses. They felt it

could also investigate the consumer purchasing behavior patterns of e-commerce business models.

In contrast, few researchers emphasize on the fact that few of the crucial fundamentals of online retailing

environment and behavior, were distinct to conventional offline or physical store context (Janda et al, 2002; Liu

et al, 2008). Regardless to these different approaches to various frameworks, most researchers admitted that

by offering anticipated quality and satisfaction, retailers can control the consumer behavior and limit their

switching habits (Janda et al, 2002; Bai et al, 2008). This also helps to create a long lasting loyalty and trust

platform offering them a distinctive competitive advantage.

It can now be universally accepted that internet’s scope, interactivity and power provides retailers potential and

opportunity to transform customer shopping experience (Wolfinbargers and Gilly, 2003; Evanschitzky et al.,

2004), and in doing so, also bolster their competitive position (Levenburg, 2005; Doherty and Ellis-Chadwick,

2009).

The magnitude of the internet to facilitate communication with consumers, provide information, collection of

market data, promotion of products and services and supporting online shopping experience, provides retailers

innovative, flexible and rich channel (Muylle and Basu, 2003).

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By doing so, internet has provided a mechanism to broaden target markets, extend product categories, reduce

costs, improve efficiency, enhance consumer relationship and deliver promotions and offers. But it was not until

last decade that internet touched shopping world. In few years it redefined the conventional world of retailing.

Retailing is defined as a list of business activities which add useful value to services and products sold to

customers for their use (Levy, 2006). On the other hand, Internet retailing, Electronic retailing, E-tailing or E-

commerce can termed as retail-ing business over the internet (Rosenbloom, 1999). Electronic retailing, which

involves online transaction and interaction between a retailer and a consumer, from the moment the consumer

browses retailer’s website to the point consumer’s order is fulfilled by the retailer, has swiftly emerged to

become an efficient and effective class of service operations (Field et al., 2004; Smith et al., 2007). It can also be

explained as, on one side, sellers sell products or provide services over their online platform i.e. website;

whereas on the other side customers purchase these products and services by accessing or browsing these

platforms by the use of internet. Non-digital products get delivered by linking supply chain and logistics whereas

digital products are delivered over the internet.

Ellis-Chadwick and Doherty (2006) divided the research on online retail in three categories. The first category

examines customer perception, psychology and focuses on online purchasing behavior of consumer. The second

studies company’s or retailer’s approach to retail management, business model and online inventory. The third

category researches technology perspective on how innovation in emerging IT sector can influence future

trends e.g. use of flash player to display products to be visually compelling.

But many researchers believe that much of the business model and strategies of online retail have been

developed from traditional stores. Many concepts which online retailers use have been developed by traditional

brick-and-mortar shopping methods. The same is presented in next section which focuses on how similar and

how different is online platform from conventional retail stores.

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2.2 Online retail - Having attributes of a physical store:

Electronic or online shopping incorporates some of the same features as “real” shopping which has lead to its

wide acceptance. Many of the attributes which consumers favor while shopping in department stores, are seen

while shopping online (Berry, 1969). Some of the researches have categorized physical store into areas of

function like store polices, merchandise selection, price and layout of a store. These areas are also considered

when designing a business model for an online platform. (Lindquist, 1974) further explains these attributes by

categorizing into four groups of service, promotion, merchandise and navigation. The variable of service

showcases general service process in a store and sales service for product returns. The variable of promotion

records marketing, advertising and features to attract customers e.g. “What’s new” section on a webpage. The

variable of merchandise measures product selection, quality, variety, pricing, guarantee and warranty. The

variable of navigation defines layout of the store and checking out process.

Figure 2.1 : Comparison of physical and online stores (Source Lohse and Spiller, 1999)

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Figure 2.1 shows comparison among online and offline stores. We can see how sales desk customer service in

physical store is replaced by aspects such as product page, search function and customer service on phone or

email in an online platform. The physical window shopping is showcased in the form of website homepage to

customers whereas checkout cashier’s role is played by online shopping cart and electronic payment page.

The above literature gives a perception on how online retailing is identical in some aspects of conventional

retailing and how some forms are modified for consumers, all of which serves as a variable for online shopping

behavior which is discussed elaborately in upcoming section.

2.3. Online shopping behavior:

Electronic commerce has emerged as one of the most distinctive characteristics of internet era. As per (UCLA,

2001), it is third most popular activity on the internet after email and web browsing. It even beats trends like

looking for entertainment and news on the internet, two most common activities people think what internet

users do. Online shopping behavior/Online buying behavior/Internet shopping behavior is referred to as process

of purchasing services or goods on the internet. The shopping process involves five steps similar to traditional

shopping process (Liang and Lai, 2000).

Online shopping is viewed complex in nature and can be subdivided into various processes such as customer

interaction, navigation, search for information, online transaction etc. It is highly unlikely that consumers

evaluate each sub-process individually and in detail during single transaction. Rather they would evaluate and

rate the overall shopping experience (Van Riel et al., 2001).

The Online shopping process starts when consumers feel the need for a product or service, go on the internet

and search for related product. However, most of the times potential consumers are engaged by product or

service information which they feel fits their need. They then compare various options, make an evaluation and

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choose the best which fits their budget. Finally, a payment is made for the product and after-sales service is

provided.

Donthu and Garcia (1999) said that online shoppers were innovative, variety seekers, less risk adverse and more

impulsive when compared to non-online shoppers. As they exhibited higher level of self-confidence, they were

found to have better knowledge and information about shopping online. In order to ensure that internet spreads

and propagates as retail channel, it is critical to realize customer shopping intentions and conduct in relation to

online buying practice. Johnson,Lohse and Bellman (2009) examined relationship between personal

characteristics, demographics and attitude in relation to internet shopping. They found that consumers who

have more wired lifestyle and have more time constraints, tend to shop more frequently i.e. people who used

internet as routine tool and/or deprived of personal time, preferred shopping via online retailers. Another study

on young Malaysian shoppers showed that young consumers were searching and browsing more for online

products and services & found internet shopping more convenient compared to traditional shopping (Sorce et

al., 2005).

Dholakia (1999) claimed that items which sold the most through online platform were usually of low risk and

had low cost convenience e.g. books, music etc. However with higher internet penetration, over the years

consumers have started shopping largely on other product categories. However products with low investment

are purchased frequently, have intangible value and high on differentiation are likely to be purchased more

through online retail than conventional shopping method.

Consumers also use both offline and online platforms in combination to make a purchase decision.

Francis (2004) found that online research also drove offline sales. Many consumers navigate through websites,

find product information, compare prices and in the end make the final purchase from a physical store. Morris

(2013) conducted a study on ‘More Consumers Prefer Online Shopping’ Shoppers increasingly want what’s

called a “seamless omnichannel experience,” meaning one in which retailers allow them to combine online and

brick and mortar browsing, shopping, ordering and returning in whatever combo they would like.

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Schiffman, and Long (2003) asserted in their research that “individual attitude does not influence one’s intention

or behavior by itself, instead that intention or behavior is a result of variety of attitudes which the customer has

about range of points relevant to situation at hand, in this case it would be internet shopping”. A study on

consumer attitude towards online shopping in New Zealand included several variables towards four main

categories; shopping experience, value of product or service, quality of service offered by e-retailer and risk

perceptions of e-commerce. It was found that regular and loyal shoppers were more satisfied by all four

variables compared to rare or trial buyers (Shergill and Chen, 2005).

The above two studies suggest that regular shoppers tend to be relatively more comfortable shopping online

and perceive risks as non-existing. Also, e-commerce is widely accepted by young age customer base with loyalty

and trust being developed by long term service and ethics.

Figure 2.2: Framework for online consumer satisfaction (Source Bigné and Ruiz, 2008)

Figure 2.2 shows a framework created by Bigne and Ruiz (2008) showing positive relationship between customer

satisfaction, trust and commitment towards retailer. They also presented that positive effect of effective

communication, privacy and user friendly features of the website developed trust and consumer satisfaction.

On the other hand, Chowdhury and Ahmad (2011) found in their study of Malaysian online shopping behavior

that four key variables which were trust, ability, benevolence and integrity, were directly positively influenced

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by each other. Though trust and ability emerged as major influencers of online shopping behavior, integrity

instigated customer exit. The study however does not take into account other major variables like consumer

switching, competition etc.

The source and nature of information and knowledge has been seen to influence online buying behavior (Bigné-

Alcañiz et al., 2008). The most critical feature of internet is that it regulates pre-buying stage as it facilitates

consumer to compare various options of the same item (Dickson, 2000). In the purchasing stage, assortment of

products, sale service and quality of information are the most important purchase decision factors for the

consumer (Koo et al., 2008). They decide the end product and the online retailer. The major influencers of online

shopping are - efficiency, availability of products, information, convenience and cost efficiency.

Post-purchase behavior comes into picture after the sale is made. Customers might have concerns or problems

relating to product or service and might need to return or exchange. Thus, two major dimensions of post-

purchase behavior are return and exchange services (Liang and Lai, 2002).

Few of the conclusions from above literature is that need for a product or service initiates the online purchasing

behavior where information plays the role of the catalyst. Online buyers seek diversity of options, comfort and

cost capacity.

Internet has also emerged as a tool for comparison of shopping as consumers often browse through various

websites, compare products, and maybe make a purchase online or offline. Also online consumers do more

research for products and prices compared to offline shoppers. Therefore, online shopping allows customers

freedom to visit “virtual shops” and make a purchase, or even perform “window-shopping” with confidence.

With the increasing size, more demand by youth and change in the behavior of youth towards shopping has

clearly indicated a huge market is available to the incumbents and existing performers. And at this stage it is

important to understand the buying behavior of Indian customers towards online shopping which is mandatory

for a great marketing strategy by the players in this industry.

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The size and growth rate of this industry was never like this before. And considering all this, the present study

has made an attempt to understand the online shopping behavior of Indian customers.

Content privacy, vendor profile, security of transactions, logistics timeframe and discounts are crucial factors

which influence electronic exchange (Rao, 1999). Donthu and Garcia (1999) presented convenience, risk

aversion, income, impulsiveness, age, attitude towards direct marketing and advertising and variety seeking

propensity as key elements to influence online buying behavior. Lukas and Maignan (1997) and Rowley (2000)

presented a research which conveyed that financial risks were cited as primary reason to stop shopping over

the internet and personal security had become dominant concern in both online relationship as well

transactions. Customer’s preference and willingness in order to adopt internet as his/her shopping medium is

positively associated with household size, income and innovativeness (Sultan and Henrichs, 2000). Perceived

risk is also seen to negatively affect the online purchasing behavior. Internet shopping experience is also affected

with privacy and security concerns. Therefore the above literature suggests that with online shopping still not

be in comfort zone of most shoppers in developing countries, it should be a prime strategy of online vendors to

assure customers of transaction security, managing website traffic, minimizing return hassle for better online

shopping experience. Apart from the above mentioned influencers of online shopping, few distinct have been

consistently mentioned.

The past literature and research have suggested that there are three main influencers which govern online

shopping behavior and are discussed in upcoming section.

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2.4. Influencers of Online shopping behavior:

(a) E-Service Quality:

The main goal for functioning of any business is to earn high and long term profits. While the production in

scope of conventional offline service quality is measured by comparison of consumer expectations with

company’s substantive service performance (Sasser, Olsen, and Wyckoff, 2001), components evaluating

electronic service quality were modified to adapt to electronic domain (Parasuraman et al., 2005). E-Service

quality is one of the crucial aspects to determine failure or success of an e-commerce has been developed from

traditional internet marketing. The concept of electronic service quality or E-SERVQUAL, also termed as E-service

quality in online retail, can be defined as customer’s evaluation of excellence and quality e-service offered on

virtual platform. A perceived service quality framework incorporates guaranteed tailored web services,

optimum performance of logistics (Doney and Cannon, 1997), and warranties provided by the online retailer

(Kim et al., 2004). In order to deliver superlative quality service over the internet, e-commerce organizations

need to understand consumer perceptions regarding how they evaluate the quality of their services.

Santos (2003) studied E-SERVQUAL and found that electronic service quality is assessment of extensive

consumer intuition and measurement of delivery of internet retailers on virtual marketplace. The importance

of e-service delivery is highly recognized in business world and also as to why consumers seek an increase of

these services is due to its ease of making comparison among various service providers in contrast to traditional

offline ways (Santos, 2003). Because comparing product prices, information and features becomes much easier

than traditional methods of shopping, E-service quality becomes a crucial factor for consumers (Santos, 2003).

Therefore it would be correct to mention that online consumers expect equal or higher level of E-service quality

compared to traditional shopping method.

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The literature of the above two studies shows that as for online retail stores, a well accessible and designed

website creates an expectation for consumer for the service they expect E-service quality (E-SERVQUAL) not

only provides a competitive edge over the competition but also boosts relationship between e-retailers and

customers.

Online consumers believe that when internet and technology potential is realized by e-retailers, ideal E-service

quality can be achieved (Yang, 2001). In the past many researchers have laid importance on detailed analysis of

links between E-SERVQUAL and its outcomes (Oliver and Rust, 2001), because the links are not direct and simple

(Brady et al., 2005). Few of the studies have probed in comparison of conventional service quality and outcomes

of trust (Sharma and Patterson, 1999), loyalty and consumer satisfaction (Cronin et al., 2000) but only in context

to physical retailing.

With improvement in technology, most internet based companies have modified their interaction with

consumers. Ample studies have been conducted which focus on evaluating and measuring online e-service

quality. Researchers have developed distinctive scales to evaluate E-SERVQUAL. E-service is also an interactive

information tool which provides firms a mechanism to differentiate their key strategies and gain competitive

advantage (Santos, 2003). Key notes from E-service quality literature, various dimensions of web experience

were used in relation to factors such as trust, satisfaction, convenience and loyalty (Rowley, 2006). A research

by Hitt and Chen (2002) concluded that Electronic service quality was negatively correlated to consumer

switching behavior and costs. The findings also showed that high levels of electronic service quality can increase

customer intentions like repurchase of products and services from website, site revisit and reduced switching

behavior.

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Figure 2.3 : Conceptual framework of E consequences of E-servqual Source (Keyoor ,2008)

Figure 2.3 further illustrates the consequences of E-servqual linkage between dimensions of E-SERVQUAL and

aspects of customer satisfaction and trust. All of the core variables of E-service quality when put together in

strategic business framework of online retail leads trust and customer satisfaction which in turn leads to long

term customer loyalty. Zeithaml et al. (2002) developed an assessment tool for measuring E-service quality

which consisted of following seven dimensions: contact, efficiency, privacy, compensation, reliability,

responsiveness, fulfillment and contact. They are divided into core service and recovery scale depending on

when the consumers contact to experience the e-service. While the core-service variables control the pre-

purchasing online shopping behavior, recovery scale variables come into effect after the sale, usually in cases of

service failure.

They are as follows:

Core Service Scale of E-SERVQUAL: (a) Fulfillment: Efficiency in service requirements, items in stock and

delivering orders on time. (b) Efficiency: Accessibility of website to consumers, finding the desired product and

information regarding minimum effort from consumers. (c) Privacy: Assurance that payment details are secure.

(d) Reliability: Technical functionality and availability of website.

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Recovery Service Scale of E-SERVQUAL: (a) Contact: The need of consumers to speak and interact with “living”

agent and not a “robot”, whenever they contact customer service. (b) Responsiveness: It involves assurance by

e-retailers to consumers to handle warranties, returns and data as promised before purchase decision is made.

(c) Compensation: It involves refunds made back to consumers and how return shipping is handled by the

retailer.

Therefore it can be concluded that efficient and exceptional E-service quality is a critical factor for online

vendors as it is one the factors that would not only enable them to attract more online consumers but also

prevent customer switching. The variables of Core-service scale of E-SERVQUAL would further be discussed in

Research Methodology chapter.

(b) Consumer value:

From a strategic perspective, this change has provided opportunities for retailers to improve business efficiency

and increase market reach by using internet platform and offer better service value to their customers.

(Zeithaml, 1988) defined perceived customer value as consumer’s appraisal of the benefits received for a

product or service in comparison to the cost incurred for those benefits. These benefits include objective,

subjective, quantitative and qualitative attributes.

From the viewpoint of marketing, creating consumer value means ensuring consumer needs are met and

increasing consumer satisfaction (Porter, 1985). The concept of consumer value management is extensively

used by market oriented companies to differentiate their selling points from competition (Heiman, 1996).

Adding to it Woodruff (1997), states that consumer value is “perceived preference for evaluation of product

traits, performance of those traits and consequences resulting from usage which help achieving consumer goal

and purpose”.

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With the help of above literature, we can define consumer value as net benefits a consumer obtains from a

store or a product. And in terms of e-commerce, if the company has presence on internet by selling products or

providing information, then consumer expects different value and service compared to offline retail. Consumer

value has received extensive research in marketing segments in the past two decades e.g. Parasuraman et

al.1985, Zeithaml 1988, Dodds et al. 1991, Holbrook 1999, Chen & Dubinsky, 2003). Not only it plays a key role

in consumer choice prediction, but also influences long term loyalty. If retailers offer optimum value to their

customers, they create enormous competitive advantage (Chen and Dubinsky, 2003). Despite its critical

importance in offline environment, consumer value has been less studied in online retail context. Online

consumer value is different from offline counterpart in many aspects. In e-commerce settings, not only the

product but even the e-store and internet channel affect value quotient for consumers (Keeney, 1999).

There have been two key streams of the research; product value and shopping value. Product value is referred

as to what a customer gets on paying for a product or price/quality tradeoff whereas shopping value is defined

as store‘s shopping experience evaluations (Babin et al.,1994).

While we can derive some similarities, there are key differences between two. Firstly shopping value of retail

mentions on how items are provided to consumer via store’s operations e.g. efficient service, lower pricing.

Therefore two retail stores selling the same item could be offering different value to consumers. Secondly,

certain share of product value, e.g. price is determined by operations of the store, which gives the perception

of overlapping of shopping and product value (Chen and Dubinsky, 2003).

We can therefore derive that product value is critical to consumer’s decision for product choice whereas

shopping value is critical to patronage online shopping behavior. Also consumer value is used as underlying

concept which encompasses both product and shopping value.

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Figure 2.4: Framework of Consumer value for an E-commerce model Source (Lohse,2000)

The framework shown in Figure 2.4 presets major factors which influence perceived consumer value in B-to-C

E-commerce scenario. It focuses on perceived core value as core variable and other antecedents and mediating

factors. The framework also justifies two scenarios. Firstly, consumers spend too much effort and time on pre-

purchase evaluation and internet is an ideal medium for this activity. Secondly, perceived consumer value also

influences purchase intentions of individuals. Also a number of literatures reveal four major aspects which are

involved in pre-purchase shopping phase which have powerful influence on customer’s value perception as well

as buying intentions. They are price, emotional experience, perceived risk and perceived quality. Each of these

factors can either negatively or positively provoke perceived consumer value. For e.g. highly perceived product

quality can benefit company sales whereas, low or bad quality can act as a cost and reduce customer’s value

perception.

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Customers use price as an indicator of quality as it shows a belief that demand and supply forces lead to a

universal ordering of items on price scale. It means that there is positive relationship between product quality

and price. Emotional experience is defined as customer’s attitude or emotional state which is aroused by pre-

purchase online shopping. Consumer behavior is a function of both, the environment and the person (Wilkie,

1994). Customer perception is a process of sensing, selection, interpretation of stimuli from physical world to

mental world of a person.

Perceived product quality in terms of online shopping can be explained as a situation when customers have no

intrinsic attributes for products to develop opinion about product quality in pre-purchase shopping stage (i.e.

customers not having previous experience with the product). Online retail only provides visual showcasing of

product categories. This lack of demonstration, further promotes lower level of tangibility perception among

the customers.

Perceived risk is seen as customer’s perception of uncertainty, fear and consequence of using a product or

service. The three risks associated with online shopping are privacy, financial and performance. Broydrick (1998)

claims that eliminating risk is one of the ways to enhance perceived consume value. For e.g. in online retail,

word of mouth is crucial source to reduce risk perception as its independent nature shows true features of a

product (Bansal & Voyer, 2000). Fornell (2001) found that perceived risk, perceived quality and price not only

effect perceived consumer value but also influence consumer switching behavior by controlling satisfaction

levels. He claimed that consumer value is positively related to customer satisfaction. High consumer value

heightens customer loyalty, reduces consumer service failure sensitivity, enhances brand name and prevents

customer churn or exit. A few key observations can be drawn from the above mentioned literature. Firstly, if

consumers are to give up traditional retail stores, they must be offered value added features and benefits which

are not available in conventional marketplace. It would offer them additional customer value. Secondly, as both

shopping and product value make consumer value, both online store operation effects as well as product effects

affect online shopping behavior. Thirdly optimum consumer value not only provokes online shopping behavior

but also reduces customer exit.

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(c) Online consumer trust and loyalty:

Creating loyalty among online customers or retention of existing consumers has become a necessity for e-

retailers. In order to attract new customers, online retailers incur at least 20-40% more than it costs serving

them in traditional market space (Reichheld and Schefter, 2000). To reduce these costs and earn profit, online

retailers must push customer loyalty which would mean convincing consumers to return to their websites to

purchase again and again. For e.g. in the e-book selling platform, it takes about a year of repeated purchases to

cover the initial cost of attracting the customer. For grocery and apparel categories, the figure is also a year. But

it electronics the average is about four years to break even. Given these timeframes, pushing for more customer

loyalty becomes an economic necessity (Zhu. Z., 2004).

As a behavioral notion, consumer loyalty is defined as consumer intentions to do more business with seller and

recommend him to other consumers (Zeithaml et al., 1996). (Heskett et al, 1994) suggested that one of the ways

to increase consumer loyalty is by offering superior e-service quality. As quality is something consumers typically

demand and value, by offering high quality service they would be willing to return and do more business with

the e-retailer. On the other hand, consumers experiencing low service value are more inclined to defect, switch

or exit to other retailers (Reichheld and Schefter 2000).

Customer loyalty increases revenue and growth in many ways. Firstly by increasing customer loyalty by 5 per

cent, revenues can increase from 30 to 85 per cent (Chow and Reed, 1997; Heskett et al. 1994). Considering the

type of industry, the revenue is even higher for online retailing industry (Reichheld and Schefter, 2000). Secondly

consumers are willing to pay a higher price and also stand a better chance for “service recovery”. They are also

easier to satisfy as retailer knows their expectations (Heskett et al. 1994; Reichheld and Sasser 1990; Zeithaml

et al. 1996). The success stories of many online retailers can be linked to their high inclination to customer

loyalty. For example, Amazon.com’s success is highly linked to customer loyalty as 66 per cent of their purchases

are made by returning consumers (The Economist, 2000). Loyal customers also reduce advertising and

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marketing expenses, as they speak about their experiences to other consumers which acts as word of mouth.

For example, EBay highly capitalized on this aspect by attracting consumers through a similar referral system.

Another key antecedent of consumer loyalty is degree of trust which consumers have in the retailer (Reichheld

and Schefter, 2000). Trust can be defined as willingness to make oneself susceptible towards actions taken by

trusted party upon feelings of assurance and confidence (Gefen, 2000). Trust is of imperative importance when

it is nearly impossible to fully regulate business agreement or when it is necessary to depend on third party on

not to take unfair advantage or engage in opportunistic behavior (Young, 2001).

Consumer trust on online retailer is significant because there is minimum guarantee on online platform that the

retailer with refrain from opportunistic, undesirable, unethical behaviors, such as distribution of personal data,

unfair pricing, purchase activity without permission, inaccurate information presentation and unauthorized use

of payment methods (Kollock, 2002). Given these risks, consumers who do not trust the retailer would be least

inclined to do business or return for repeated purchases (Jarvenpaa and Tractinsky, 1999). Therefore trust can

be considered as compelling and imperative constituent of a long term business interaction. One of ways to

build trust is through process of transference, by which consumers start trusting “unknown others” only because

“unknown others” are trusted by individuals they trust (Doney and Cannon, 1997).

Trust is also a major precedent for consumers who are willing to do business with e-commerce marketplace

vendor (Jarvenpaa and Tractinsky, 1999). Consumer trust becomes important on online retailing platforms

because of the reason that the “invisible” seller provides little guarantee that on online environment he/she

would refrain from opportunistic, undesirable and unethical behaviors. There is always scope for presentation

of wrong information, unfair prices, distribution of personal data, unauthorized use of debit and credit cards

and monitor purchase activities (Naquin, 2003).

Given these perceived risks, a segment of population who do not accept and trust online platforms would be

less inclined to shop online or even return back for re-shopping.

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Trust plays a central role in major repeated buying decision because of two possible reasons. The first being that

it is a method to reduce social complexity (Gefen, 2000), and secondly reduction in risk of doing business with

the seller (Jarvenpaa and Tractinsky, 1999). As a mechanism for social complexity reduction, trust ensures

overpowering social complexities are reduced on interaction with other people. (Luhmann, 1979) tells us that

trust is important because individuals are motivated to understand the social environment they live in and how

their behavior is perceived by others and actions they take.

Based upon above literature we can conclude that trust plays a central role in increasing long term loyalty. Not

only it serves as a social complexity reduction tool, but also reduces perceived risk of doing business with the

online retailer.

As previously mentioned, internet also links up offline and online retail space. Many consumers like to compare

products over the internet and then make the purchase on physical retail stores. This in a way also invokes

customer exit behavior. Nearly all the product categories have also been used in study of consumer switching

behavior. It is due to low level of trust and loyalty, that consumers merely “use” online retailer’s platform to

make better purchase decision (Mayer, 2003).

“High touch” product categories like health & grooming, clothing, consumer electronics were bought more in

traditional physical stores whereas “low touch” categories like software, books, and airline tickets were favored

by online services as they required quick delivery (Levin & Heath, 2003).

There also have been studies investigating consumer switching behavior in relation to online loyalty using

combination of three channels (internet, brick and mortar and catalogs) for both shopping and information. It

was found that compared to customers who buy through single platform, multi-channel shoppers have greater

trust, lower perception for risk and deeper relationship with retailer which further motivates them to spend

more with retailer (Kumar & Venkatesan’s, 2005).

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Figure 2.5: Framework of Online trust for e-commerce platform Source (Gefen,2000)

Figure 2.5 shows a framework developed by Gefen (2000) for online trust for e-commerce platform. It revolves

around three key variables of reputation, habit and satisfaction which build online trust. The reputation

variables includes parameters of membership, recommendation and repurchase intention. The habit variable

incorporates consistency of website and product and delivery speed. The satisfaction comprises of quality and

price.

With presentation of above literature we can conclude that consumer satisfaction significantly contributes to

online loyalty. It also adds up to retailer’s revenue as loyal consumers recommend the retailer to other

customers by word of mouth which also reduces marketing expenses. Habit, satisfaction and reputation are key

determinants of online trust and loyalty.

Online

trust

Reputation

Satisfaction

Habit

Consistency of website

Product delivery &

speed

Price

Membership

Recommendation to

others

Repurchase intention

Quality

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2.5. Customer switching behavior:

Business organizations globally, in dynamic changing marketplace are becoming increasingly consumer-

oriented, realizing gravity of keeping consumers in long term relation and seeking their loyalty. Online retailing

has not been much different. And though consumer retention is critical, it is equally necessary to examine and

explore factors which can cause consumers to switch or exit from their services. It is simply because in order to

“preserve” consumers, it is crucial for companies to understand why they switch (Colgate, 2001).

The era of offline and online retailing is marked with a number of events which have restructured the industry

over the years. Among these are arrival of new concepts such as superstore, discount store and new technology

like Point-of-Sale (POS terminals) (Rauh & Shafton, 2001). Therefore today both online and offline retailing is

predominantly about choices; a “needy” consumer has a choice of shopping channels of catalogs, traditional

stores and the internet. And even in their individual categories, there is large number of choices due to fierce

competition.

By attracting customers via marketing methods like promotions and discounts across numerous channels, online

retailers generate more sales and gain more revenue per customer than they would from different channels

different customer approach (Hoover, 2001). Various online retailing platforms created on multi-dimensions,

influence buying decisions and channel preference by a variety of factors (Hyde, 2003). The past research

suggests that customers have been choosing those channels which they perceive as most effective, efficient and

satisfactory (Kim & Kang, 1997).

Customer switching behavior is described as customer exit or defection (Hirschman, 1970; Stewart, 1994).

According to Bolton and Bronkhurst (2001) consumer switching behavior shows the decision which a consumer

makes to stop using a particular product or service or patronizing the firm’s services completely. Reichheld

(1996) and Keaveney & Parthasarathy (2001) researched and found that consumer switching behavior reduces

company’s profits and earnings.

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Added dividends are also lost because start up investment on customer (e.g. advertising, promotion costs) are

wasted and additional costs add up to obtain a new customer (Colgate, Steward & Kinsella, 1996; Fornell &

Wernerfelt, 1987). As per Reichheld & Sasser’s (1990) study, consumer exit is seen having a larger influence to

impact unit costs, revenue, market share and factors affecting competitive advantage. Consumer switching

brings negative word of mouth marketing which can downslide online retailer’s brand and reputation (Diane,

2003).

The value which consumers seek the most is usually composed of four resources; space, money, effort and time

(Seth & Sisodia, 1997). The time has evolved in business context where a single marketing strategy was effective

for all target segments as consumers expect tailor-made strategies as per their needs (Pitta, 1995). In the

current scenario, it is critical for e-retailers to know who their consumers are and why they are choosing one

channel or one e-retailer over another. (Crawford, 2005) says that ambiguity is that it is relatively easy for online

retailers to know their customers compared to brick and mortar outlets.

We can therefore conclude from above literature that customers now drive the entire marketing process and

demand more customized or tailor made services from the retailer. We can also claim that customer would be

willing to switch retailers as well as channels upon their attitude and belief towards each retailers, as well as

social norms and perceived behavioral control.

A model was created by (Keaveney, 1995) which mentioned eight incidents which lead to consumer switching

behavior. These are attraction by competitors, inconvenience, failure of core-services, pricing, ethical issues,

failure of service encounters, involuntary switching and employee reaction towards service failure.

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These incidents can further be defined as:

Attraction by competitors: This category mentions customers who switch due to “negative” experience from an

online retailer to a “positive” e-retailer. Negative experiences can be defined as situations which drive a

consumer away from one retailer to another e.g. if a customer shops from a retailer who promises One Day

Delivery and if the package is delayed, the experience becomes negative (Diane, 2003).

Inconvenience: It means aspects of location, operating hours and waiting time involved in transaction or delivery

which causes customer to feel inconvenience. Customers may feel that they have to spend considerable effort

and time.

Failure of core-services: This section discusses service mistakes such as wrong order, billing errors etc.

Pricing: This category mentions increase in price, high prices, unfair and deceptive pricing.

Ethical Issues: This category mentions unsafe warehouses, dishonest behavior and conflict of interest.

Failure of service encounters: This category has the attributes of impolite, uncaring and unknowledge staff.

Involuntary switching: This category discusses the reason of switching as change of location for consumer or

retailer or maybe merger or alliance of the retailer.

Employee reaction to service failure: This category describes level of employee reaction to service failure or

negative response.

Dahui and Glenn (2007) further developed on Keaveney’s switching model and produced factors leading to

customer switching behavior on online platform (Table 2.0). In their research they were able to specifically

converge the exit behavior for online platform. Thus their study is optimum for this research analysis.

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CUSTOMER

SWITCHING

BEHAVIOR

FACTORS EXAMPLE OF FACTORS

Pricing

High prices

Rapid increasing prices

Unfair pricing

Deceptive pricing

Inconvenience

Location/Time

Wait time for complaints

Wait time for service

Core service failure

Service errors

Billing errors

Service catastrophe

Service encounter failure

Uncaring

Impolite

Unresponsive

Unknowledgeable

Response to service failure

Negative response

No response

Reluctant response

Competition

Found better service

Ethical issues

Cheat

Hard sell

Unsafe

Conflict of interest

Involuntary switching

Customer migration

Service provider closes

Table 2.0 : Factors leading to customer switching for online platform (Dahui and Glenn, 2007)

Consumer switching or channel switching can further lead to conflict among channels as customers can get

service or information from another channel (e.g. physical store) and conduct business with another e-retailer

(e.g. online store). It is an opportunity cost faced by today’s retailers (Moore, 1996).

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Internet provides services for products with prevalent search characteristics, low buying frequency and rapidly

changing technology; whereas cross channel customer retention is more vivid for items which are bought

infrequently (Baal & Dach’s, 2005). Their research showcases that product features did influence consumers

who would seek information and knowledge online but conducted their business offline.

It therefore confirms that at any stage of shopping process, customers will choose the retailer or the channel as

per its relevance to reach the satisfaction or goal they pursue while shopping. And if two retailers or channels

are similar and provide same utility or service, consumers tend to switch easily.

Adding to this, (Bala & Mahajan, 2005) said that customer’s usage of a channel or channels in the shopping

experience should be examined either as final outcome (i.e. purchasing product or information browsing) or the

process of using the channel, as various channels provide different opportunities to the customers. Most of the

times the final outcome was attaining the economic goals the consumers desired. (Bendoly & Venkataramanan,

2005) too investigated consumer switching behavior and found out that more anticipation of integration among

company’s various channels eventually lead to customer loyalty. However if customers find that certain

products would take longer to deliver (out of stock or delayed shipping), alternate competitors or channels

appear appealing to them in present as well as long run. Online shoppers tend to have different value for

different product category, with customers being less interested in using multiple retailers when buying

frequently purchases items, e.g. grocery items. Most customers appreciate the option of buying products online,

getting it delivered and being able to return the items back to the retailer via mail (Burke’s, 2002). The consumer

switching behavior can also be explained with various shopping orientations like credibility of product

information and demographics (age, gender, location) which are also different for both offline and online

platforms. Interestingly multi-channel offline shoppers consider use of internet and family/ friends reference as

critical purchasing decision compared to online shoppers (Choi & Park, 2006). Demographics such as gender,

age, income, family composition etc have major impact on customer perception of online and physical shopping

benefits.

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(Gupta & Walter, 2004) linked the theory of utility maximization with consumer switching behavior stating that

utility derived from e-commerce needs to be greater than utility provided from traditional physical stores in

order to influence consumer to switch to online platforms. The study also showed that risks (payment option,

invisible seller) had negative influence on switching behavior, wide variety (products, prices and information)

had positive influence and effort (evaluation, comparison) did not seem to have major influence on customer’s

intentions to switch to online retailers.

We can therefore summarize that “having consumers, not merely acquiring consumers” determine long run

productivity, profits and image of a firm. In terms of consumers, relationship management, service quality and

overall satisfaction can improve consumer intentions to stay with the retailer. Though a new and growing

concept, E-commerce has to yet to achieve delightful and stimulating benefits related to shopping experience.

2.6. Switching costs and switching behavior:

A consumer’s loyalty is determined not only by costs arising from dealing with the retailer, but also those costs

from switching to other retailer (Lee and Cunningham, 2001). Fornell (1992) added that consumer loyalty is

created by both consumer satisfaction as well as switching costs.

Dick and Basu (1994) however described switching costs in terms of monetary, time and psychological costs.

They could also include perceived risk which can be explained as consumer’s perceptions of adverse

consequences and uncertainty in buying a service or a product. On the other hand, Fornell (1992) summarized

switching costs into learning costs, emotional costs, search costs, loyal consumer discounts and transaction

costs.

Porter (1980) implied that switching costs are only incurred “onetime” in comparison to ongoing costs which

grow once the products and services are purchased repeatedly due to long term relationship. It can also be

defined as onetime cost which consumers associate to switch from one retailer to another. Though switching

costs are associated with switching process, it does not need to be incurred immediately on switching.

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Therefore this shows that in today’s fierce competition, consumers have to face a tradeoff between benefits

received and the costs incurred. For “high” switching costs, even unhappy consumers may remain “loyal” as the

alternative retailer would require consumers to invest money, time and effort. On the other hand, satisfied

consumers can be easily disloyal if low switching costs make the exit or defection easier.

2.7. Types of switching costs:

Switching costs can further be subdivided and categorized into three types as suggested by (Thomas, 2003).

They are as discussed below and shown in Figure 2.5:

Procedural switching costs: These comprise of learning, economic risks, setup and evaluation costs. These costs

mainly involve expenditure on effort and time.

Financial switching costs: They consist of benefit and financial loss costs. These costs result in loss of financially

quantifiable resources.

Relationship switching costs: These costs result in loss of brand and personnel relationship. It involves emotional

stress due to breaking of alliances.

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Figure 2.5 : Types of switching costs Source: (Thomas A, 2003)

2.8. Summary of Literature Review:

In today’s world, retailing as an industry has diversified itself into more than one medium of business. Customers

shop across all three channels (brick & mortar, online retailers and catalogs) depending on their personal choices

to make successful purchase decision. But one channel which has emerged as attractive and growing platform

is e-commerce.

As for the retailers, in order to respond to multi-dimensional mind of customers, it would mean offering retail

mix in multiple formats with various types of shopping appeals (Hyde, 2003). The scale of anticipating customer

demands and fulfilling the desired preference is huge. The challenges faced on online platform are fewer

returns, lower out-of-stock items, increasing loyalty, less markdowns, increasing conversion rates, fewer risks,

higher average size of order, aligning products to correct market segment and effective aggregate spending of

customer (Salmon, 2005).

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There is no doubt that taking care of consumer preference on online as well as other channel is a complex

endeavor, which when regulated by a firm decides it success or failure on long run (Crawford, 2005). With

evolution of technology, online retailers are developing strategic opportunities to gain market size, grow and

leverage on key variables of marketing, advertising, logistics, distribution networks, brand equity etc. With this,

the need to understand the reason and scale of customer switching behavior has developed.

Most online retailers realize that the market size is limited with increasing competition. There are many new

online retail companies launching in developing countries and old ones are capitalizing by mergers and

acquisition.

The need to increase consumer loyalty and capture existing business is very critical for long run success.

However even the aspect of understanding a customer and then regulating consumer migration is becoming

challenging (Goersch, 2002).

The above presented literature demonstrates a structured insight into online purchasing behavior and customer

switching behavior. The various areas of literature also incorporate concepts of online loyalty, consumer value

and E-service quality. Information plays a critical role in online shopping behavior as shoppers seek comfort,

options, and cost efficiency. Effective e-service quality helps not only in creating long term loyalty but also

prevents customer migration. Price, risk and quality help determine consumer value for shoppers whereas habit,

reputation and satisfaction create online trust & loyalty for the retailer. On a whole, most of the above variables

regulate customer switching behavior.

All the areas include various models which would later lead to further analysis and discussion in the research.

Various variables would be constructed out of these models and incorporated into an online questionnaire in

the upcoming chapter, Research Methodology.

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CHAPTER III:

RESEARCH METHODOLOGY

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CHAPTER 3: RESEARCH METHODOLOGY:

3.0. Introduction:

The development of designing the dissertation includes many correlated decisions. The critical decisions were

to select appropriate research tactics and approach, making sure all dimensions fit together to achieve desired

objectives. Though many researches and studies have been aimed at studying various antecedents of consumer

switching behavior, few discuss customer exit specifically and those focusing precisely online platforms are very

less. A lot of probing and exploration was found to study predecessors of customer defection. Moreover, almost

none of the literature concentrated on Indian online retail which is growing at a substantial rate. This chapter

reviews research design and methodologies used in this research to collect and measure the required data.

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3.1. Research method:

Research design process:

Marketing research has be termed as “ identification of objective, collection of data, analysis, dissemination and

using information to improve decision making related to problem solution and opportunities” (Malhotra, 2010).

The aim of this research is to determine various factors and patterns which influence switching behavior among

consumers. In order to accomplish this, an efficient design process is essential to conduct the market research

effectively. For this research, we are using seven steps of marketing research proposed by (Burns and Bush,

2000).

The seven steps would be applied in the following pattern:

Step 1: Establishing the research objective

Step 2: Determining the research design

Step 3: Identification of information and source

Step 4: Designing survey instrument

Step 5: Designing how to sample design process

Step 6: Data collection

Step 7: Analysis of data

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3.2. Determining the research design:

The two main categories of research design are conclusive and exploratory. Conclusive research is further

divided into the categories of casual and descriptive. For this research, casual research design is chosen to study

cause-and-effect relationship between perception of online customers and their behavior to shop online

(Malhotra, 2010). Casual research approach is quantitative in nature which is structured and requires

representative and large data.

Quantitative method also refers to study where data is collected from large groups to make constructive

conclusions with well defined measuring tool. The two goals of quantitative approach are; (1) validating

relationship within behavior and market factors, and (2) testing different hypothesis (Monash University, 2011).

3.3. Identification of information and source:

Usually information collection can be accomplished by primary and secondary sources. Primary source is data

originating from researcher for addressing specific goal in research problem, while secondary data is collection

of information for any other purpose other than problem statement (Malhotra, 2010).

The five approaches for primary data collection are: focus groups, behavioral data, surveys, experiments and

observations. The three approaches to collect secondary data are: journal articles e.g. Journal of marketing,

government statistics and economic research reports by consulting groups like McKinsey. For this research, both

primary and secondary data has been put to use. Primary data was achieved by survey questionnaire while facts

from secondary data have been highlighted in Chapter 1 & 2.

3.4. Designing the survey instrument:

A survey method is tool used to derive responses from sampling population segment. The design and flow of

survey instrument is crucial in order to derive maximum results. A well crafted survey instrument leads us to

obtain factual data information required for the research. The questionnaire had 44 questions which were

divided into five sections with answers on a scale from one to seven (see Appendix 1). The extensive details of

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the questionnaire are discussed later. The survey conducted, followed the research plan as shown in Figure 3.1.

The sampling units for this survey were Indian consumers who preferred shopping online.

Research

Approaches

Research

instruments

Contact

methods

Sampling plan

Survey

Observation

Experiment

Questionnaire

Mechanical

instruments

E-Mail

questionnaire

Personal

interview

Telephone

interview

Online

interview

Sampling plan

Sampling unit

Sampling

procedure

Table 3.1 : Research plan (Source Kotler et al, 2006, pp.105)

3.5. Survey questionnaire design:

The survey’s first section starts with 8 questions targeted to obtain respondents background information. The

questions were designed so as to reduce the possibility that respondents would misrepresent the questions.

The second section of the survey, measures the outcome of the various variables. Every variable has three or

more measurement questions to precisely determine value of each variable. Following this 37 questions were

created. With these 37 scaled questions, participating respondents were able to rate their thoughts about

consumer value, E-service quality, online loyalty and consumer switching behavior. The questions of this section

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were crafted to be compact and simple so as to facilitate accurate responses. As measuring perceptions is

subjective, interval scaled questions have been used to quantify them (Burns and Bush, 2000). The range of

these measurements was from 1 to 5 ranging from Strongly Disagree to Strongly Agree. A large range scale is

recommended as it in measures questions more precisely (Trochim, 2006).

After the sample questionnaire was drafted, it was found that an average respondent took 10-12 minutes to

complete the questionnaire. The survey questionnaire was friendly, compact, in flow and easier to complete.

The final questionnaire was later sent to targeted respondents.

3.6. Sampling size and Data collection:

This research has availed internet based survey called www.surveymonkey.com which provides the service of

creating, customizing and assisting in data collection of the questionnaires.

After the final questionnaire was drafted, an email explain the purpose of participation together with a URL link

https://www.surveymonkey.com/s/RPQBFZ7 was emailed to three groups of users to reduce biasness in the

answers. The first group comprised of author’s Facebook friends living in India. The second group was LinkedIn

professional contacts. The third group was ex-colleagues from previous organizations.

Maximum participation was encouraged from these three groups as they were part of prevalent and active

culture of Online shopping in India. The target was to collect minimum of 120 responses. The emails with survey

link were sent on August 15th 2014. By August 25th, there were about 174 respondents after which the survey

was closed. As three groups were targeted, it facilitated in high and quick response rate. Out of 174 return

responses, about 11 were incomplete. These 11 were eliminated due to their lack of data in analyzing data. The

remaining 163 surveys were later used in analysis of the data. Figure 3.2 shows the snapshot of the website.

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The advantage of using the online questionnaire was that with web based interface it saved a lot of time and

effort in creating the survey. Secondly it was low on cost and large sample population could be targeted. Thirdly

it could easily import data collection to excel format for analysis.

3.7. Research parameters and Measures:

Table 3.2 shows the E-service quality variables which link to the questions asked to the respondents. The

variables of E-service quality have been developed from Zeithaml’s (2002) framework. They have been discussed

in detail in Literature Review chapter. The detailed questions can be refereed in Appendix 1. A multiple answer

scale from 1-“Strongly Agree” to 5-“Strongly Disagree” has been used to measure Indian consumer’s perception

of E-service quality towards online retailers. The higher the points, more the agreement to the statement.

E-Service quality variables Survey questions

1 Efficiency Q9, Q10

2 Fulfillment Q11, Q12

3 Privacy Q13

4 Responsible Q14, Q15

Table 3.2: E-service quality variables with questions

Table 3.3 shows the Consumer Value variables and the questions asked to measure the same. The variables of

Consumer Value have been developed from Lohse’s (2000) framework. They have been discussed in detail in

Literature Review chapter. The detailed questions can be refereed in Appendix 1. A multiple answer scale from

1-“Strongly Agree” to 5-“Strongly Disagree” has been used to measure Indian consumer’s perception of

consumer value towards online retailers. The higher the points, more the agreement to the statement.

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Consumer value variables Survey questions

1 Price Q16, Q17

2 Emotional experience Q18, Q19

3 Perceived product quality Q20

4 Perceived risk Q21, Q22

Table 3.3: Consumer value variables with questions

Table 3.4 shows the Online consumer trust and Online loyalty variables and the questions asked to measure the

same. The variables of Online trust and Online loyalty have been developed from Gefen’s (2000) framework.

They have been discussed in detail in Literature Review chapter. The detailed questions can be refereed in

Appendix 1. A multiple answer scale from 1-“Strongly Agree” to 5-“Strongly Disagree” has been used to measure

Indian consumer’s perception of online consumer trust towards online retailers. The higher the points, more

the agreement to the statement.

Online consumer trust and loyalty

variables

Survey questions

1 Reputation Q23, Q24

2 Habit Q25

3 Satisfaction Q26

Table 3.4: Online consumer trust and Online loyalty variables with questions

Table 3.5 shows the Consumer switching behavior variables and the questions asked to measure the same. The

variables of Consumer switching behavior have been developed from Keaveney’s (1995) framework. They have

been discussed in detail in Literature Review chapter. The detailed questions can be refereed in Appendix 1.

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A multiple answer scale from 1-“Strongly Agree” to 5-“Strongly Disagree” has been used to measure Indian

consumer’s perception of Consumer switching behavior towards online retailers. The higher the points, more

the agreement to the statement.

Consumer switching behavior

variables

Survey questions

1 Attraction by competition Q27, Q28

2 Inconvenience Q29, Q30, Q31

3 Failure of core services Q32, Q33, Q34

4 Pricing Q35, Q36, Q37

5 Ethical issues Q38, Q39

6 Failure of service encounters Q40, Q41

7 Involuntary switching Q42, Q43

8 Employee reaction to service failure Q44, Q45

Table 3.5: Consumer switching behavior variables with questions

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3.8. Conclusion

Overall, the study engaged in quantitative research approach to achieve greater understanding and insight of

the research topic. Furthermore, quantitative research approach is relevant for this topic as there is very less

research available for consumer switching behavior for online retailers in India. Therefore this research

approach would allow this study to measure and weigh various variables for consumer value, E-service quality,

online loyalty and customer exit. It would allow the researcher to probe respondent’s feedback to gather an

understanding to fulfill research objectives. The next chapter brings out the results, analysis and findings of the

results.

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CHAPTER IV:

ANALYSIS AND FINDINGS

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CHAPTER 4: ANALYSIS AND FINDINGS:

4.0. Introduction:

This chapter focuses on findings derived from data analysis on E-service quality, consumer value, online

consumer loyalty and customer switching behavior. The findings of the survey would be presented and findings

would be interpreted. Out of 174 surveys returned, only 163 were used for the analysis as 11 were eliminated

as they were incomplete.

The data collected from questionnaires was transferred and analysis derived from Statistical Package for Social

Sciences (SPSS) software version. The chapter is divided into following two sections:

The first section analyzes relationship between E-service quality, consumer value and online trust &

loyalty. With help of statistical tools, we try to identify if E-service quality, consumer value and online

trust variables lead to Online loyalty.

The second section of this chapter identifies key reasons which are ranked as per their statistical values

leading to consumer switching behavior.

The chapter proceeds firstly explaining the demographic profile the respondents derived from the survey

results. We then move to descriptive statistics and reliability scale. The last part to study the relationship is

principal component and correlation analysis. In the last section, to study consumer switching behavior, we

have ranked the scores of switching reasons as percentages with the help of SPSS.

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4.1. Demographic profile of respondents:

The demographic profile, frequency of internet shopping and favorite product category of 163 respondents is shown in

Table 4.1. The respondent’s distribution in gender is dominated by 95 males to 68 females. The response has been mostly

from the age group of 23-30 years (37.4%) and 31-40 years (20.9%). The age group of 18-22 years also has significance

presence (19%). Therefore we can state that internet shopping is widely accepted in India from age group of 18-40 years.

This finding is quite accurate to an earlier research. Even though having low incomes, millennials, consumer aged between

18 to 36, remain critical age demographic for online commerce as they spend the most (Business insider, 2014). More than

half the respondents have higher level of education in the form of degree/diploma (53.4%), while 40.5% have a

postgraduate/Masters degree. In terms of occupation, (49.7%) respondents are employed in private sector. Surprisingly

the next close category is people who are self employed (26.4%).

Major income holders are in the category of USD1000-3000 (38%) followed by <USD 1000 (27.6%). The next major income

cluster is USD3000-5000 (19%). After the survey it was found that most respondents purchased items once a month (36.8%)

via online retailers. Even buying items once every 3 months was a major trend (33.1%). Only (9.6%) purchased items online

every 6 months or more.

In terms of product category, data showed that a number of choices were picked by respondents. But

Clothing/Accessories/Shoes were a major choice (31.9%) followed by Books (15.3%) and Consumer electronics (13.5%).

Data derived from respondent’s demographic details shows that online shopping in India is mainly spread in the age group

of 18-40 years. Major online shoppers are individuals working in Private sectors with a monthly income of USD1000-3000

who bought the items once every month. The most popular favorite category being Clothing & Accessories. Among the

favorite online retailers, Flipkart lead the survey with (43.6%) followed by largest online retailer of the world, Amazon.in

(41.7%).

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Demography Variables Frequency Percentage

Total 100 %

Gender

Male Female

95 68

58.3% 41.7%

Age

Below 18 years 18-22 years 23-30 years 31-40 years 41-50 years 51-60 years Above 60 years

9

32 61 34 10 9 8

5.5%

19.6% 37.4% 20.9% 6.1% 5.5% 4.9%

Education

High school and below Diploma/Degree holder Masters & above

10 87 66

6.1%

53.4% 40.5%

Occupation

Government sector Private sector Student Self employed Retired Not employed

13 81 21 43 4 1

8%

49.7% 12.9% 26.4% 2.5% .1%

Monthly income

< USD 1000 USD1000-3000 USD3000-5000 USD5000-7000 USD7000-9000 >USD 9000

45 62 31 11 6 8

27.6% 38% 19% 6.7% 3.7% 4.9%

How frequently you shop online

Everyday Once every week Once every month Once every 3 months Once every 6 months Once a year

3

30 60 54 13 3

1.8%

18.4% 36.8% 33.1%

8% 1.8%

Which product category you purchase the most

Books Clothing/Accessories/Shoes Videos/DVD’s/Games/Music Airline tickets Consumer electronics Event tickets Cosmetics/Nutrition Suppliers Computer hardware Computer software Tours/Hotel reservations Groceries Sporting goods Gifts/Flowers Others

25 52 14 15 22 1 8 6 5 3 7 1 1 3

15.3% 31.9% 8.6% 9.2%

13.5% 0.6% 4.9% 3.7% 3.1% 1.8% 4.3% 0.6% 0.6% 1.8%

Which is your favorite online retailer

Flipkart Amazon.in Myntra Ebay

71 68 16 8

43.6% 41.7% 9.8% 4.9%

Table 4.1: Demographic profile of the respondents

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4.2. Descriptive statistics of construct variables:

This section brings out the descriptive statistics of various variables as per the Likert scale system. The Likert

statements were converted and recoded into same of these variables to reduce and analyze data. The constructs

have been framed together in relation to the statements asked in the survey. Table 4.2 below summarizes

descriptive statistics for the variables used for the Likert scale. The 4 main constructs are as follows:

E-service quality with 7 statements ES1 to ES7 [Question 9 - Question 15]

Consumer value with 7 statements CV1 to CV7 [Question 16 - Question 22]

Online trust and loyalty with 4 statements OTL1- OTL4 [Question 23 - Question 26]

Consumer switching behavior with 19 statements CSB1 – CSB19 [Question 27- Question 45]

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Construct Variable Mean Std. deviation

E-Service Quality

ES1

ES2

ES3

ES4

ES5

ES6

ES7

3.97

4.05

3.77

3.60

2.86

3.70

3.64

0.819

0.967

0.831

0.954

1.190

1.025

0.941

Consumer Value

CV1

CV2

CV3

CV4

CV5

CV6

CV7

2.70

3.39

3.23

4.07

3.23

3.44

3.95

0.894

1.139

1.136

0.953

0.768

0.853

0.961

Online trust & loyalty

OTL1

OTL2

OTL3

OTL4

3.60

3.71

3.67

3.22

0.684

1.159

0.963

1.160

Consumer switching

behavior

CSB1

CSB2

CSB3

CSB4

CSB5

CSB6

CSB7

CSB8

CSB9

CSB10

CSB11

CSB12

CSB13

CSB14

CSB15

CSB16

CSB17

CSB18

CSB19

3.80

4.09

3.64

3.56

3.85

3.87

3.99

3.55

3.52

3.39

3.72

3.51

3.71

3.36

3.32

3.16

3.08

3.74

3.44

0.937

0.974

0.960

1.006

0.891

1.069

1.048

1.055

1.178

1.130

1.189

1.244

1.100

0.864

0.765

0.968

0.657

1.023

0.754

Table 4.2: Descriptive Statistics of Statements in Construct

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As an overview of the descriptive analysis, the mean of all the statements is closer to 4 (Agree) and not closer

to 3 (Neither agree nor disagree) which shows that respondents agree with all the variables. The standard

deviation for all the constructs was divided between 0.60 to 1.20. Standard deviation helps us in determining

on how closely the data is clustered around the mean and low scores below 1.50 are considered ideal and

reliable (Field, 2009). However for some of the statements which did stood out in terms of agreeing to the

variables were:

E-Service Quality: With high mean scores of 3.9 and 4.0, most respondents agree to the presented variables

that they find their online website fast, are able to make swift transaction, receive orders on time and updated

tracking on their shipments. In terms of trust however, respondents do not trust electronic retailers with their

personal information.

Consumer Value: Most respondents feel that online shopping is more convenient than traditional shopping and

find other consumer recommendations helpful in making purchase decisions. It is proved with relatively high

score means of 4.07 and 3.95. In terms of price, respondents convey that a higher price for an item does not

ensure a superior quality.

Online trust & loyalty: With most of the scores for mean values above 3 and closer to 4, respondents feel loyal

to their current online retailers. They often recommend it to their friends & relatives and have often chosen the

e-retailer as their first shopping preference.

Consumer switching behavior: The mean scores which stood out under this construct measured at 4.09 and

3.99 supporting that maximum respondents agree that they would switch their online retailer if they experience

bad service and if shipping delivery takes longer time than promised date. Surprisingly unethical behavior was

also highlighted as a switching reason along with better promotions offered.

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4.3. Reliability:

After the descriptive statistics, reliability analysis was initiated in order to derive the value of Cronbach’s alpha.

The aim of this analysis process was to determine consistency level of data collected from respondents. It is an

important step before starting further analysis.

Cronbach alpha has been widely used in studies involving attitude instruments which use Likert scale. Cronbach

alpha’s coefficient value of 0.7 or more is considered optimum to validate the consistency of data. Although

Nunnally (1978) recommended the value of 0.7 and above is a strong indicator of the consistency, Bowlin (1997)

states that an alpha values above 0.5 are also acceptable. Sample size also seems to affect the consistency of

the data. A bigger sample size would have higher alpha value while the smaller segment shows a smaller value

(Cronbach, 1951). The sample size used for data analysis is 163 which is small, therefore Cronbach alpha value

of 0.5 is acceptable for further analysis. We can find Cronbach alpha values of various constructs below:

E-Service quality:

Construct Variables Number of items Cronbach alpha

E-service quality

ES1

ES2

ES3

ES4

ES5

ES6

ES7

7

0.681

Table 4.3: Reliability analysis of E-service quality variables

The Cronbach alpha value derived from E-service quality is 0.681. As mentioned earlier, as a smaller sample

is used, values of 0.5 and above are also acceptable.

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Consumer value:

Construct Variables Number of items Cronbach alpha

Consumer value

CV1

CV2

CV3

CV4

CV5

CV6

CV7

7

0.586

Table 4.4: Reliability analysis of Consumer value variables

The Cronbach alpha value derived from Consumer value is 0.586. As mentioned earlier, as a smaller sample

is used, values of 0.5 and above are also acceptable.

Online trust:

Construct Variables Number of items Cronbach alpha

Online trust

OT1

OT2

2

0.850

Table 4.5: Reliability analysis of Online trust variables

The Cronbach alpha value derived from Online trust is 0.850. As alpha value of 0.7 and above is highly

desirable, it does strongly prove the consistency of data.

Online loyalty:

Construct Variables Number of items Cronbach alpha

Online loyalty

OL1

OL2

2

0.841

Table 4.6: Reliability analysis of Online loyalty variables

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The Cronbach alpha value derived from Online loyalty is 0.841. As alpha value of 0.7 and above is highly

desirable, it does strongly prove the consistency of data.

Consumer switching behavior:

Construct Variables Number of items Cronbach alpha

Consumer

switching behavior

CSB1

CSB2

CSB3

CSB4

CSB5

CSB6

CSB6

CSB7

CSB8

CSB9

CSB10

CSB11

CSB12

CSB13

CSB14

CSB15

CSB16

CSB17

CSB18

CSB19

19

0.874

Table 4.7: Reliability analysis of Consumer switching behavior variables

As studying consumer switching behavior is one of the main reasons behind this study, we were able to achieve the

highest Cronbach alpha value of 0.874 for this construct. It thus shows strong internal consistency or reliability of the

scale.

Another set of reliability set would be performed after Factor Analysis test to validate the scores and sample before

the regression analysis.

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E-service quality, consumer value and Online trust & loyalty relationship model:

4.4. Factor analysis:

The next step in data analysis testing involves statistical method known as Principal Component Analysis (PCA). It is

widely accepted as variable reduction process which magnifies the total variance accounted for into smaller number

of groups known as components. The components are sum total of variables and main objective of PCA is maximum

variance extraction with least components (Field, 2009). In this section, the main objective is to reduce 18

independent variables of E-Service quality, Consumer value, Online trust and Online loyalty to its principal

components to simplify structure for better data interpretation and analysis.

Principal component analysis is managed through a series of processed. As per Field (2009), not all variables are

needed to be retained in analysis. Therefore it is sensible to extract and retain statement which having an Eigen value

of greater than 1. The idea behind this is that Eigen values measure amount of variation regulated by a peculiar

variable and that when Eigen value is more than 1, it shows substantial amount of variation.

Component Total % of Variance Cumulative %

1 4.872 35.063 35.063

2 2.915 11.534 46.597

3 1.746 9.477 56.074

4 1.544 8.874 64.948

Table 4.6: Total variance explained for top 4 components

The results depicted in above Table 4.6 show that 4 components with Eigen value more than 1 were extracted as

they controlled 64.94 % of data variance. It should be noted that first component accounts for 35.6% of variance in

total data. Kaiser-Meyer-Olkin (KMO), Bartlett’s Test of sphericity and Significance were also conducted for all the

variables of E-service quality, consumer value and online trust and loyalty.

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After 4 components were identified, the next process of data reduction involved correlation matrix analysis among

various variables within every construct. The correlation coefficient value of each matrix shows stability of

relationship among two variables. As per Guilford (1956) suggestion, correlation coefficient values can be interpreted

as follows:

For values < 0.2 = Almost no relationship

For values 0.20 - 0.40 = Definite but limited relationship

For values 0.40 - 0.70 = Substantial relationship

For values 0.70 - 0.90 = Distinct relationship

For values 0.90 - 1.00 = Very dependable relationship

For the process of factor loading as well as cross loading, paradigm offered by Davis (1995) was adopted which helped

in order to identify and interpret various factors. Each item for the factors should ideally load 0.50 greater on one

factor and 0.35 or lower onto other factor. Various factor loading for each factor are shown in Table 4.7.

Based on the obtained results, Efficiency, Privacy, Responsible and Fulfilment are loaded onto Factor 1 [E-Service

quality] ; Price, Emotional experience, Perceive product quality and Perceived risk are loaded onto Factor 2

[Consumer value]; Current loyalty and Recommendation are loaded onto Factor 3 [Online trust]; Preference and Long

term business are loaded onto Factor 4 [Online loyalty]. All commonalities derived are above 0.50 which indicates

that large volume of variance in a variable has been derived from factor solution. Also all the variables seem to fall

on to their specific factor group.

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Table 4.7: Rotated Component Matrix with Communalities and Respective Eigenvalues and Variance

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Reliability analysis:

FACTOR CRONBACH’S ALPHA

CRONBACH’S ALPHA

BASED ON

STANDARDISED ITEMS

NUMBER OF ITEMS

E-Service Quality 0.710 0.714 7

Consumer value 0.762 0.766 7

Online trust 0.696 0.699 2

Online loyalty 0.675 0.681 2

Table 4.8: Reliability statistics

After the factor analysis was conducted, results in Table 4.8 show reliability analysis results. It was to ensure to

validate the consistency of data before moving to regression analysis. The results of E-service quality and

consumer value were 0.710 and 0.762 as desired above 0.70. Whereas cronbach alpha value of Online trust and

loyalty were 0.696 and 0.675, which was still acceptable from a minimum value of 0.50.

4.5. Regression analysis:

Regression analysis is termed as statistical tool which aims to assess the relationship between two or more

independent variables and a dependent variable (Field, 2009). It is the end step in analysis process to validate

the conceptual model, to study the relationship between E-service quality, consumer value, Online trust and

Online loyalty.

To recap, the dependent variable is Online loyalty (OL) wheras independent variables are E-service quality (ES),

Consumer value (CV) and Online trust (OT). The independent variables of E-service quality, Consumer value and

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Online trust were broken down to item level, in order to test the relation with overall Online loyalty intention.

It also studies contribution each item makes in order to provoke online loyalty.

Table 4.9: Regression analysis on E-service quality and Online loyalty

As shown in above Table 4.9, the constructs on ES1, ES3, ES5 and ES7 are seen to drivers for Online loyalty.

Efficiency, privacy and responsibility are seen as key elements of E-service quality which provoke online loyalty,

but fulfillment had the highest influence on loyalty. The value of Significance was also found in desired range of

0.000 to 0.005. Other variables do not seem to have a significant relationship with Online loyalty

Table 4.10: R and R Square scores for the model

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Value of R and RSquare are shown in above Table 4.10. R is multiple correlations coefficient showing the

relationship strength between dependent and independent variable. R Square (R2) is square value of R showing

how well the model fits the data. For better accuracy of predictions, higher R2 value is desirable.

The R value derived from the study was 0.788 while the value of R2 was 0.620. This shows model having a strong

linear correlations, thus validating the model. The R2 value illustrates that 62% of variance in the model can be

explained whereas 38% cannot be explained.

Below figures of beta and t-value prove high correlation of relationship.:

Efficiency [Beta value of .229 and t-value of 2.192]

Fulfillment [Beta value of .269 and t-value of 2.692]

Privacy [Beta value of .221 and t-value of 2.365]

Responsible [Beta value of .287 and t-value of 2.235]

Table 4.11: Regression analysis on Consumer value and Online loyalty

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In terms of Consumer value items, most of the independent variables are seen to have a significant impact on

driving online loyalty i.e. CV2, CV3, CV5 and CV7. Price, emotional experience, perceived product quality and

perceived risk are seen as key motivators of online loyalty for Indian online consumers. The value of Significance

was also found in desired range of 0.000 to 0.005. The Beta and t value for price and perceived risk was derived

as negative which suggests that higher perceived risk and price would have negative effect on online loyalty.

The analysis is shown in above Table 4.11. Below figures of beta and t-value prove high correlation of

relationship.

Price [Beta value of -.841 and t-value of -.952]

Emotional experience [Beta value of .219 and t-value of 2.292]

Perceived product quality [Beta value of .267 and t-value of 2.775]

Perceived risk [Beta value of -.233 and t-value of -.635]

Table 4.12: R and R Square scores for the model

Value of R and RSquare for consumer value model are shown in above Table 4.12. The R value derived from the

study was 0.716 while the value of R2 was 0.512. This shows model having a strong linear correlations, thus

validating the model. The R2 value illustrates that 51.2% of variance in the model can be explained whereas

48.8% cannot be explained.

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Table 4.13: Regression analysis on Online trust and Online loyalty

In terms of Online trust, both the independent variables are seen to have a significant impact on driving online

loyalty i.e. OT1 and OT2. Current loyalty and recommendation are seen as key motivators of online loyalty for

Indian online consumers. In other words, customers who are currently loyal to their e-retailers recommend their

sellers to their friends and family and also have earned their future loyalty. The value of Significance was also

found in desired range of 0.000 to 0.005. Other independent variables do not seem to have a significant

relationship with Online loyalty The analysis is shown in above Table 4.13. Below figures of beta and t-value

prove high correlation of relationship.

Current loyalty [Beta value of .266 and t-value of 2.223]

Recommendation [Beta value of .281 and t-value of 2.268]

Table 4.14: R and R Square scores for the model

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Value of R and RSquare for consumer value model are shown in above Table 4.13. The R value derived from the

study was 0.866 while the value of R2 was 0.749. This shows model having a strong linear correlations, thus

validating the model. The R2 value illustrates that 74.9% of variance in the model can be explained whereas

25.1% cannot be explained.

4.6: Consumer switching behavior ranking model:

After the positive relationship was derived among E-service quality, consumer value and Online trust & loyalty,

the next step involved finding key reasons resulting in consumer switching behavior. It was decided to follow

the ranking tool of SPSS after the data was validated. Ranking data provides us information about order of data

received, is much simpler to understand and gives much information in just a single glance (Grant, 2007).

We had already derived the mean and std. deviation of all variables used in Consumer switching behavior in

descriptive statistics analysis. It was noted that all variables had mean values between 3 and 4.

The next step involved ranking of the customer exit reasons based upon the responses from the respondents.

We used the Frequency function in SPSS model

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Table 4.15: Consumer switching behavior component ranking

The above Table 4.15 illustrates various variables ranking based on their Principal component analysis scores.

The KMO adequacy (0.849) and Bartlett’s test of sphercity (0.000) were found to be as per requirements for the

used variables. The respondent’s score were then ranked according to their impact on total scores. Ethical

issues was highest ranked reason for customer exit with 31.93 % weightage. Inconvenience was second favorite

switching reason for Indian online consumers (23.24%). Respondents were also willing to choose another e-

retailer incase the employees failed to react to service failure complaints..

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4.7. Conclusion

In this chapter, findings and results of the research were presented. The findings of the research were examined

in the sections of online trust & loyalty, consumer value, E-service quality and customer switching behavior.

The study showed that most Indian online shoppers were loyal to their current online e-retailer and thought of

it as their primary shopping preference. Online shopping was seen as more convenient in comparison to

traditional shopping and recommendations from other shoppers helped in making purchase decisions. E-

shoppers found the websites swift and safe, whereas the orders were received on time. Adding to it, most

shoppers agreed that their switching decisions would depend on bad service, unethical behavior, bad customer

service and better promotions.

The results on customer exit also showed that respondents were most likely to discuss their bad experiences

with their friends and family in event of a displeasing shopping experience than with the e-retailer. Next chapter

brings a detail and elaborative discussion of the key findings.

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CHAPTER V:

DISCUSSION AND

CONCLUSION

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CHAPTER 5: DISCUSSION AND CONCLUSION:

5.1: Introduction:

One of the key objectives of this research was to understand the consumer switching behavior among Indian

online retailers alongwith with their perception about consumer value, E-service quality and online loyalty. From

our data analysis, we were able to achieve intentions of this research. We were able to establish affirmative

linkage between E-service quality, Consumer value and Online loyalty and at the same time gather key reasons

for customer exit on Indian online platform.

An overall study helped us to establish a linkage between E-service quality, Consumer value and loyalty and how

they lead towards intention to purchase online or online purchasing behavior. The study also makes an effort

to derive key reasons which encourage consumer switching reasons for Indian consumer when shopping online.

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5.2. Indian online shopping consumer’s behavior:

In the previous chapter, we had analyzed and discussed data findings using regression analysis in following

sequence. Firstly after confirming correct data analysis from the survey results, the next step was to investigate

relationship perception of Indian consumer towards various variables of E-service quality, consumer value,

online trust and loyalty. Next we ranked key components of customer exit reasons in relation to Indian online

shopping platform. Now I the upcoming section, we would gain a better insight for online shopping behavior in

India context.

5.3. Consumer demographic profile:

Frequency distribution analysis was used in the research to describe demographic profile of the survey

respondents. The detailed results can be refereed from Table 4.1.

From the results it can be confirmed that 163 respondent results were fairly divided between male and females.

It also shows that most online shoppers in India are young people from age group of 18-40 years. However the

age group which preferred online shopping the most was 23-30 years (37.4%).

The people who prefer online shopping are also well educated. The results show that more than 53 % of

respondents have a degree or diploma while about 41 % have a Master’s degree. Nearly half of the respondents

who shop online work in private sector. A key finding was that about 27% online shoppers are self-employed

or own a business. It is usually a rare scenario in a country like India where entrepreneurship is still less

encouraged. But it does liaise with recent research which states that while US has 23 million small businesses,

India has 48 million (Inc Corp, 2015). That is double of an enormous economy like US, but then again major

drawbacks like financing, red tape are pointed out.

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Also, an increasing population of these small corporates lay emphasis on functioning the businesses on internet

as it not only expands their trade and presence geographically, it makes operation, service and feedback

efficient and effective.

Individuals who frequently shop online enjoy a monthly income of USD1000-3000 (38%). About 27% of the

respondents earn a monthly income of <USD1000 which is middle income for Indians in cities and urban centers.

It shows that a major population of middle and high income highly consider shopping online despite

geographical demographics, as shown in previous literature. This theory is further strengthened as about 37%

respondents purchase items atleast once a month. Some also preferred buying items weekly or every three

months. Very few respondents preferred shopping in every 6 months or more.

The favorite product category for most respondents was Clothing/Accessories/Shoes (32%). As mentioned in

Introduction and Literature review chapters, Apparels continues to be favorite category for most online

websites around the globe. Flipkart ranked as favorite online retailer for most respondents with Amazon not

being far behind.

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5.4. Consumer perception towards E-service quality:

The results derived from data analysis clearly illustrate that Indian online consumers seek a high level of E-

service quality from online retailers. Out of the four variables used to define the E-service quality component,

“privacy” was found to be most critical from Indian online customer perspective. About 66 % of the respondents

strongly lay emphasis on private data protection and at the same time trust their existing online retailer with

their personal information. This statement does prove the earlier literature review by (Harn et al, 2006) which

mentioned that privacy was one of the key reasons which refrained consumers to shop online.

The concern can be understood as in recent past there have been numerous payment and personal information

fraud cases in India. About 50,000 customers file litigation with their credit card compaies every month, while

2 out of 10 orders are cancelled by retailer as the payment offered is illegitimate. Thus by ensuring industry

standards for privacy, online retailers can surely gain confidence of Indian online customers.

Besides marking privacy as the most important aspect, the second key variable was “efficiency”. Most of the

respondents wanted the website to be fast and efficient. They also stressed that completing a transaction

swiftly is also crucial. As most of the times, transactions for online shopping are done virtually, creating

numerous payment options, high quality website and user friendly functions can help achieve consumer

perceptions. Another key feature would be to ensure “Cash on Delivery” as a payment option as most Indian

consumers like to pay on delivery.

“Fulfillment” was third favorite aspect for Indian consumers and about 71% respondents claimed that they did

receive an updated tracking number for all their orders. About 61 % felt that they did receive their orders on

time. Surprisingly “responsibility” was the least favorite and critical for Indian customers.

Most of them felt that the customer service assisted them with their queried and issues. About 68% of

respondents were also happy with warranties they received on most the items from their online retailers.

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5.5. Consumer perception towards Consumer value:

The results derived from data analysis showcase perception of Indian online shoppers about consumer value

they expect. Out of the four variables used to measure the consumer value component, “Perceived risk”

emerged as the most important key to seek value from the services. About 61% of respondents felt that there

were products which they felt were risky buying online. This statement holds true for categories like expensive

consumer electronics and groceries. These products are still preferred to be bought by offline channels.

However the scenario is rapidly changing as most respondents also felt that they feel safe to buy items when

reading recommendations by other customers.

The second major variable was “perceived product quality”. It actually stands true as some of literature review

mention “purchase phobia”, consumers see risk in buying items which they have not seen physically. “Price”

ranked third in measuring consumer value. About 37% felt that a higher price does not ensure high quality for

a product and about 54% were ready to sacrifice quality of an item for the price offered. The last favorite variable

was “emotional experience” but the results were unique. About 50% of the respondents felt that they were

emotionally aroused whenever shopping online and 79% felt that online shopping is more convenient that

offline shopping.

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5.6. Consumer perception towards Online loyalty:

The results derived from data analysis bring out perception of Indian online shoppers about online trust and

loyalty they seek. Out of the two variables used for measuring Online trust component, “reputation” was the

most popular. About 68% of the respondents felt that they were loyal toward the online retailer they were

using. 65% believed that they recommend the same online retailer to their friends and relatives. This type of

reputation is very critical is today’s business world as word of mouth can be best marketing.

“Habit” was second favorite variable used to measure online loyalty. 68% of the respondents confirmed that

the online retailer was always their first preference whenever deciding to buy online. 42% were also willing to

do their business with same online retailer over next twelve months while 26% were not sure. This clearly speaks

about “satisfaction” they get from using their existing online retailer. It also gives a framework on how existing

online retailers can develop strategies to maximize their customer loyalty.

In the end a successful regression analysis proves a positive relationship between E-service quality, consumer

value, online trust and online loyalty. Most of the variables used for the constructs influenced long term online

loyalty for Indian online consumers.

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5.7. Customer perception towards consumer switching behavior:

The results derived from data analysis throw light on understanding of Indian consumers towards consumer

switching behavior. It tells us major variables that they might consider on making decision to switch from one

online retailer to another. We had used 19 variables which were broken into 8 constructs. Based on the response

of the respondents to these constructs, a ranking was conducted to highlight the prime customer exit reasons.

The most critical variable was “ethical issues”. It was quite a surprise for me as I had expected it to be least

favorite before the survey. About 32% of respondents felt that they would switch their existing online retailer if

it does not treat its employees properly and were ready to make a switch because of any dishonest or unethical

behavior which is brought in the media. This clearly supports many previous literature review which stress on

display of ethical standards by organizations. While the business is highly ethical in developed economies and

concept of whistle blowing highly regarded, India finds itself on a poor position. As per a survey, only 12 % of

foreign companies found regulatory mechanism and legal framework in India as “good”, 15% described the

situation as “comfortable” while 94% found red tape and lobbying as serious concern (FICCI, 2011).

The second key reason for switching the retailer would be “inconvenience”. This justifies relation to earlier

variable of “efficiency” of E-service quality. 23 % of the respondents emphasized that they would buy from

another online retailer if the website is slow and payment page takes too much time to load. The third critical

aspect of changing the online retailer was “employee reaction to service failure”. 15% confirmed that customer

service being slow or non-responsive would make them stop using the current online retailer. They also see the

importance of customer service appreciating their feedback.

“Involuntary switching” was the fourth deciding factor on switching the online retailer however the responses

were quite equally distributed. 10% felt that they would not change the online retailer even if it was taken over

by another company. The reason was for the change of the e-retailer was that the services would no longer be

same. Also the respondents were willing to change their online retailer if they moved from their location within

the country.

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“Failure of core services” and “Failure of service encounters” were next two variables on the ranking of consumer

switching behavior. 6% of the respondents felt their order fulfillment was key decision to make a switch. Factors

like billing error, wrong order and item ordered not meeting product description emerged as popular reasons

to seek out a new online retailer. 5% of online shoppers wanted “intelligent” customer service and would seek

empathy in events of service failure.

The two variables which ranked last were another surprise of the research. They were “Attraction by

competitors” and “Pricing”. 4% of respondents believed in automatic switch to another online retailer due to a

bad service and if offered better promotions and discounts. This speaks to importance of marketing strategies

on online platforms. Those online shoppers were also ready to switch to another online retailer if offered a

lower price or if the current online retailer refuses price match (retailer not ready to sell the item at lowest

available price in the market). 3% respondents confirmed that they would take deceptive pricing (price dropping

on item ordered after you place the order) very seriously.

5.8. Key findings of the research study:

All the variables which were used in determining E-service quality, consumer value, online trust & loyalty and

Consumer switching behavior were found to successfully relate to each individual dimension. Positive

relationship between E-service quality, consumer value and Online loyalty was derived.

Indian shoppers who are inclined towards online retail can be associated as young age, well-educated and

middle as well high income segment. Clothing/Accessories, books and consumer electronics are favorite product

categories. Flipkart emerged as the favorite online shopping option.

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Indian online consumers have shown to have a high standard for E-service quality performance. Protection of

privacy was the most important concern for online shoppers as about 66% agree to its importance.

Most of the online shoppers did perceive risk when buying items online. However this risk was not equally

distributed across all product categories. It was highly regarded in consumer electronics and health & beauty.

About 68% of Indian online shoppers were happy with the services of their online retailer and intended to do a

major share of their business over next twelve months with the same e-retailer.

Surprisingly, the consumer switching behavior was found to be highly sensitive to ethical issues pertaining to

online retailers least sensitive to price offered on items.

By concentrating on various identified factors, the emerging corporates and e-retailers can streamline their

marketing strategies in most favorable ways. It could help the convert their potential consumers to active

consumers.

By improving post-sales services, timely delivery of goods, offering better packaging, provision for variety of

payment options can further spike the demand for services and products through websites.

The market segments like grocery, housewares, toys etc are untapped and must be targeted by marketers

through e-tail penetration. The knowledge and awareness for these categories needs to increase.

Further, the strategists could also focus on working in liaise with government to increase internet penetration

and understand future scope of internet retail by FDI etc.

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Consumer exit which results from more than a single incident seems to be prevalent in Indian online retail

industry. It is easier for customers to switch between various service providers where the relationship is simple.

For example, if a garage does not service a car as per customer expectations, it is easier for customers to look

for a new garage. In the same way online consumers can easily shift their business.

One final finding would be management reaction on realizing customer decision to switch the e-retailer. It seems

few online consumers like to address their inconvenience to the e-retailers. If e-retailers could encourage

consumers to air their opinions, retailers would be placed in a better situation to address their concerns. It

would result not only in higher retention rates but also more loyal pool of consumers. And even if unhappy

consumers are adamant to switch, the e-retailers would have acquired the valuable feedback and data. This

feedback could be used by management in order to structure new policies which would aim to minimize future

levels of customer exit.

In conclusion, the study conducted successfully meets the predefined research objective. The understanding

of perception of Indian consumers for consumer value and E-service quality can be considered essential in

developing online trust and reducing consumer switching. The model can assist e-retailers to drive their business

in profitable direction by ensuring that customer desired service levels are met. They can also use the findings

of customer switching behavior as signs of potential consumer defection and plan strategies to overcome the

issue.

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5.9. Managerial recommendations to e-retailers:

Based upon the earlier literature, responses from survey and data analysis, we can develop few critical

recommendation which can further assist in Indian online e-commerce growth.

Firstly, for any e-commerce retailer, website is the most critical and crucial aspect. There seems a massive need

for adapting tools to support development of consumer-oriented websites. Different organizational

departments, especially marketing need to determine which functions or features would support and serve

consumers better. After reviewing the performance of their current website, they need to seek major

weaknesses and strengths in their website in order to support Indian online consumers in their purchase

decision process.

Secondly, just creation of a flashy website isn’t enough. It needs to establish link between website content and

organization’s performance. Both informational as well as transactional functions of website are critical for

company’s performance. And this is what Indian online retail companies lack when compared to global e-

retailing giants. Lack of trust keep Indian at bay from going to Indian online platform. As online retailing is still a

new development, people are cautious of their investment. Apart from having industry standard transactional

and information privacy, retailers need to be more bold and creative. Therefore the best way can be present

their website starting with its product and information features and end it with company’s financial

performance. Creating a concrete link with financial performance can be crucial in Indian e-commerce debates.

It would immensely earn the respect and trust of Indian buyers.

Thirdly, from point of view of the management, as the technology evolves constantly and rapidly, it is critical to

imbibe a clear objective in adaption of utilization/application of e-commerce technology. It should be kept in

mind that technology by Indian e-retailers should keep in mind the Indian online consumer.

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With our findings, it was quite clear that consumers demand “more user-friendly online platforms” and their

demands are directly linked to “lack of integrated information management system” and “shortage of in-house

technical expertise”. Perhaps the Indian consumers believe that affordable integrated information system as

well as user friendly platforms can provide greater profits/savings which would exceed e-commerce technology

investment cost.

Fourthly, the success of any business depends on its employees. And when the industry is still new it is

imperative that employees are trained and coached as per future trends. Online retailing is no different and

training of employees should be considered as a future investment as it is their response which determines

consumer rebuying and retention. Continuous training could provide Indian e-commerce with new technology,

knowledge and skills for meeting its future demands. Training is not only imperative in terms of technology,

logistics, information etc which the organization uses, but the vision, mission and culture of the company should

be taught as well.

Lastly, ensuring minimal or no customer exit largely depends on consumer repurchase intention, and repurchase

intention is largely dependent on consumer complaint behavior as well as service recovery. We need to keep in

mind that as Indian e-commerce is still young, a lot of factors are still missing e.g. infrastructure, logistics etc

Therefore the Indian e-retailers need to be well prepared for handling lot of angry, unhappy and dissatisfied

consumers. But it is a good news in disguise. As research shows that customer who have had negative

experience, lodged a complaint and were satisfied with service recovery, have higher rebuying intentions than

compared to customer who not even had a reason to complain as well as customers who did not criticize their

bad experience (silent complainer). Therefore not only Indian e-commerce needs to raise the current

benchmark for complaint handling, they need to develop strategies to ensure silent complaints raise their voice.

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5.10. Closure:

To bring this research project to a closure, the final chapters would provide a concluding summary, focus on few

limitations and also provide some future research recommendations.

Online retail has created a new market opportunity in global market scenario and studies have shown the

enormous potential scope in developing nation of India. The largest domestic e-retailer of India, Flipkart and

bigger global giants like Amazon & E-bay are broadening their supply chain networks and building billion dollar

logistics to boost infrastructure. Also they are keenly attracting consumers by shrinking delivery time by offering

same day delivery or in some cases as short as eleven hours. As per a report by Amazon, at current growth rate,

India is on road to become fastest country to touch $1 billion in gross sales.

Even with this E-retail boom, Indian consumers are cautious in choosing the best online retailer and making

purchase decisions. There have been numerous studies on studying online shopping behavior around the world,

but very few have explored Indian consumers. India being a developing nation with relatively poor infrastructure

and low internet penetration, there still lies a pool of challenges in ensuring that customers and companies

make the best out of online shopping platform.

In this research, an effort was made to find out the key reasons for consumer switching behavior among online

retailers in India. Also, the dimensions of E-service quality, consumer value and online trust and loyalty were

used to determine online purchasing behavior of Indian customers. The research was developed ad initiated

with reference to four major literature frameworks i.e. (Parasuraman, 1988) for E-service quality, (Lohse, 2000)

for consumer value, (Zeithaml et al. 1996), (Schefter, 2000) and (Kumar & Venkatesan’s, 2005) for online trust

and loyalty and (Keaveney , 1995) for consumer switching behavior.

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A quantitative approach was taken to carry out this research. The data was collected from a sample size of 163

respondents from India via online surveys. After the data was collected, reliability analysis, descriptive statistical

analysis and exploratory factor analysis was conducted by using SPSS analysis software. The results of this

analysis were found to be quite decisive and dependable.

5.11. Limitations of the research:

This research has been an attempt to provide insight on consumer switching behavior among online retailers in

India, their purchasing behavior and perception of consumer value and online loyalty. However there are few

constraints which could have affected research quality and analysis.

Firstly, as the concept of consumer switching among online retail is relatively new, a major challenge writing the

dissertation was difficulty accessing relevant literature. Therefore the paper includes significant articles which

are little old and a number of non-scholarly online articles.

Second limitation was the size of sampling. The total response for SPSS data analysis was 163. The final analysis

could be held more credible and concrete if the sampling size would have been more.

Thirdly respondents tend to lack the patience to fill up a questionnaire with about 50 statements. There are

chances that some of the statements answered missed respondents serious and honest attention.

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5.12. Future research recommendations:

This research was an intention as the first step in order to gain an understanding of consumer switching behavior

as well as E-service quality and consumer value perception of Indian online consumers. However as highlighted

by many researchers, consumer switching, at times can be difficult to interpret. I would like that further in depth

research should be performed and few of the imperative investigations could be:

Studying the consumer switching behavior among various individual product categories listed on online

websites.

Including direct or telephonic interviews with some e-retail management for data gathering to understand their

perspective on online consumers, future growth and obstacles.

Using the model of Theory of Planned Behavior (TPB) or Technology Acceptance Model (TAM) to replicate the

same study in terms of purchasing behavior for online retail model.

Re-examine the proposed model with higher scale of measurement (increase the survey count or include

personal interviews) in order to achieve higher level of accuracy.

Perform the testing on more South East Asian countries like Singapore, Malaysia, China etc. and compare the

reasons for consumer switching in order to develop a pattern.

Further studies can be conducted on customer’s relational behavior, commitment, differential effect of loyalty,

information satisfaction on buying behavior between transaction –oriented consumers and relationship-

oriented consumers on online shopping platform.

Lastly, keeping in mind the dynamic characteristics of virtual marketplace, it is critical for research attention to

be focused on different ways technology delivers new forms of communication and how virtual interaction and

experience affect the needs, attitude, perception and buying behavior of online consumers.

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APPENDICES:

Appendix 1: Questionnaire Survey

Hello,

Greetings Of The Day,

I am conducting this survey as a part of my MBA dissertation. The purpose of this research is to study the consumer

switching behavior of online retailers (Identifying reasons as to why consumers would likely choose one online retailer

over the other).

Since you are important as a consumer, I am requesting that you participate in my research. This survey will take

approximately 10 -15 minutes of your time.

I would greatly appreciate your time and effort for completing the survey. Please click o the below link to participate in

the survey:

https://www.surveymonkey.com/s/RPQBFZ7

Please be assured that the data collected is strictly for academic purpose and your response would be confidential and

anonymous.

Feel free to contact me at nishantchand@yahoo.com for any queries that you may have.

Thank you and looking forward to receive your valuable feedback before 24th August.

Regards,

Nishant Chand

Nottingham University Business School

MBA 2013-14

124 | P a g e

h

INTRODUCTION:

The E-commerce industry was in its infancy for the larger part of the previous decade. However, in last

three years, the industry has witnessed an incredible growth of 150 percent, increasing from USD 3.8 billion

in 2009 to USD 9.5 billion in 2012.

A number of business models for E-commerce have evolved and are in varying stages of maturity. Of the

various business models that are prevalent, consumer E-commerce is perceived to have a wider and stronger

impact on retail or direct consumer and has engaged entrepreneurs, VCs/ PEs and others. It is expected to

grow by 38 percent in the year 2016.

The E-commerce and allied companies have also turbo­charged the E-commerce growth engine by

introducing innovative business models, by offering convenient payment options and by introducing

technological innovations and customer friendly policies to capture online time and wallet share. Concepts

such as flash sales, ‘by invite only’ sales, “Cyber Monday’ or the ‘Great Online Shopping Festival’ have been

smashing hits in the past and such innovations will continue to play an important role to promote online

shopping.

Although many factors support the growth of E-commerce, the fledging industry does face the trend of

“customer switching/customer exit” behavior which involves consumers moving from one online retailer to

another for certain reasons. This impacts revenue and long term customer loyalty which firms seek.

This study is an intention to seek answers to such questions as to why consumers make this switch.

125 | P a g e

CCCCCS

*1. Gender

Male

Female

*2. Age

Below 18 years

18 ­22 years

23­30 years

31­40 years

41­50 years

51­60 years

Above 60 years

*3. Education level

High school and below

Degree/Diploma holder

Masters & above

*4. Occupation

Government sector

Private sector

Student

Self­employed

Retired

Not employed

*5. Monthly income

＜USD 1000

USD 1000­3000

USD 3000­5000

USD 5000­7000

USD 7000­9000

>USD9000

sumer switching behavior among Indian online retailers

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*6. How frequently do you shop online ?

Everyday

Once every week

Once every month

Once every 3 months

Once every 6 months

Once a year

*7. Which product do you purchase the most when shopping from online websites?

Books

Clothing/Accessories/Shoes

Videos/DVD's/Games/Music

Airline Tickets

Consumer electronics

Cosmetics/Nutrition Suppliers

Computer Hardware

Computer Software

Tours/Hotel Reservations

Groceries

Sporting Goods

Gifts/flowers

Others

* 8. Which is your favorite online retailer?

Flipkart

Amazon.in

Myntra

Ebay

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E­SERVICE QUALITY:

This section asks questions on E­Service Quality. Please consider your favorite online retailer for product category of books and

travel. As following sentences describe your service quality expectation from online shopping experience, what is your degree of

agreement: (1­ Strongly disagree, 7 – Strongly agree, The higher the points, the more you agree)

*8. Efficiency:

I find the website very fast and efficient.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*10. Efficiency:

I can complete the transaction very swiftly.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*11. Fulfillment:

I receive all my orders on time.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*12. Fulfillment:

I receive an updated tracking number for all my shipments.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree 1 2 3 4 5

*13. Privacy:

I trust the online retailer with all my personal information.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

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*14. Responsible:

I am satisfied with warranty on the items that I receive.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*15. Responsible:

I feel the customer service is responsive and solves every issue.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

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CONSUMER VALUE:

This section asks questions on Consumer value. Please consider your favorite online retailer for product category

of books and travel. As following sentences describe expectation of value from online shopping experience, what

is your degree of agreement: (1­ Strongly disagree, 7 – Strongly agree. The higher the points, the more you agree)

*16. Price:

I feel that a higher price ensures better quality for a product.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*17. Price:

I might sacrifice quality of an item for a difference in price.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*18. Emotional experience:

I feel emotionally aroused when shopping online.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree 1 2 3 4 5

*19. Emotional experience:

I feel online shopping is more convenient than traditional shopping.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree 1 2 3 4 5

*20. Perceived product quality:

I might not buy a product which I have not seen physically.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

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*21. Perceived risk:

I still feel it is risky to buy some products online than offline.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*22. Perceived risk:

I feel safe buying an item when I read reviews or hear other customer recommendations.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

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ONLINE CONSUMER TRUST AND LOYALTY:

This section asks questions on trust and loyalty. Please consider your favorite online retailer for product category

of books and travel. As following sentences describe your state of loyalty for the online retailer, what is your degree

of agreement: (1­ Strongly disagree, 7 – Strongly agree. The higher the points, the more you agree)

*23. I currently feel loyal to this online retailer.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*24. I always recommend this online retailer to my friends and relatives.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*25. I always consider this online retailer as my first preference.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*26. I intend to do all my business with this online retailer for next twelve months.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

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CONSUMER SWITCHING BEHAVIOR:

The sets of questions below describe the instances which lead to switching from the current online retailer. Indicate

your degree of agreement with the sentences below (1­Strongly disagree, 7­ Strongly agree). The higher the points,

the more you agree.

*27. Attraction by competition:

I would switch to another online retailer if I experience a bad service.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*28. Attraction by competition:

I would switch to another online retailer if offered better promotions/discounts.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*29. Inconvenience:

I would switch to another online retailer if this website is slow.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*30. Inconvenience:

I would switch to another retailer if the payment page takes too much time to load.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*31. Inconvenience:

I would switch to another online retailer if the delivery takes longer than promised time.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

133 | P a g e

*32. Failure of core­services:

I would switch to another online retailer if I receive a wrong order.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*33. Failure of core­services:

I would switch to another online retailer if the product description does not match the

item ordered.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*34. Failure of core­services:

I would make a switch if I notice a billing error.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*35. Pricing:

I would switch to another e ­retailer if I see a less price for the same item.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*36. Pricing:

I would switch if this online retailer does not price match. (Retailer is not ready to sell

the item at lowest available price in the market)

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

134 | P a g e

*37. Pricing:

I would switch to another e ­ retailer due to deceptive/unfair pricing. (You notice that

many a times prices change soon after you place the order)

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*38. Ethical issues:

I would switch to another online retailer if this e ­retailer does not treat its

employees properly.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*39. Ethical issues:

I would switch to another online retailer due to dishonest behavior?(Conflict of interest

or unethical practice comes in media.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*40. Failure of service encounters:

I would switch to another e ­retailer if the customer service representative does not

have knowledge/information.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*41. Failure of service encounters:

I would switch to another online retailer if the customer service is automated. (You feel

that personalization and empathy is missing).

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

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*42. Involuntary switching:

I would switch to another online retailer if the company gets taken over by another

retail company.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*43. Involuntary switching:

I would switch to another online retailer if I change my location?(Within the country)

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*44. Employee reaction to service failure:

I would switch to another online retailer if the customer service is slow or non ­responsive. Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

*45. Employee reaction to service failure:

I would switch to another online retailer if they do not encourage feedback.

Strongly Disagree Disagree Neither Disagree Nor Agree Agree Strongly Agree

1 2 3 4 5

136 | P a g e

Appendix 2: Demographic data analysis

Gender

Frequency Percent Valid

Percent

Cumulative

Percent

Valid

1 95 58.3 58.3 58.3

2 68 41.7 41.7 100.0

Total 163 100.0 100.0

Age

Frequency Percent Valid

Percent

Cumulative

Percent

Valid

1 9 5.5 5.5 5.5

2 32 19.6 19.6 25.2

3 61 37.4 37.4 62.6

4 34 20.9 20.9 83.4

5 10 6.1 6.1 89.6

6 9 5.5 5.5 95.1

7 8 4.9 4.9 100.0

Total 163 100.0 100.0

Education

Frequency Percent Valid

Percent

Cumulative

Percent

Valid

1 10 6.1 6.1 6.1

2 87 53.4 53.4 59.5

3 66 40.5 40.5 100.0

Total 163 100.0 100.0

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Occupation

Frequency Percent Valid

Percent

Cumulative

Percent

Valid

1 13 8.0 8.0 8.0

2 81 49.7 49.7 57.7

3 21 12.9 12.9 70.6

4 43 26.4 26.4 96.9

5 4 2.5 2.5 99.4

6 1 .6 .6 100.0

Total 163 100.0 100.0

Income

Frequency Percent Valid

Percent

Cumulative

Percent

Valid

1 45 27.6 27.6 27.6

2 62 38.0 38.0 65.6

3 31 19.0 19.0 84.7

4 11 6.7 6.7 91.4

5 6 3.7 3.7 95.1

6 8 4.9 4.9 100.0

Total 163 100.0 100.0

How frequently you shop online

Frequency Percent Valid

Percent

Cumulative

Percent

Valid

1 3 1.8 1.8 1.8

2 30 18.4 18.4 20.2

3 60 36.8 36.8 57.1

4 54 33.1 33.1 90.2

5 13 8.0 8.0 98.2

6 3 1.8 1.8 100.0

Total 163 100.0 100.0

138 | P a g e

Which product category you purchase the most

Frequency Percent Valid

Percent

Cumulative

Percent

V

a

l

i

d

1 25 15.3 15.3 15.3

2 52 31.9 31.9 47.2

3 14 8.6 8.6 55.8

4 15 9.2 9.2 65.0

5 22 13.5 13.5 78.5

6 1 .6 .6 79.1

7 8 4.9 4.9 84.0

8 6 3.7 3.7 87.7

9 5 3.1 3.1 90.8

10 3 1.8 1.8 92.6

11 7 4.3 4.3 96.9

12 1 .6 .6 97.5

13 1 .6 .6 98.2

14 3 1.8 1.8 100.0

Total 163 100.0 100.0

Favorite online retailer

Frequency Percent Valid

Percent

Cumulative

Percent

V

a

l

i

d

1 71 43.6 43.6 43.6

2 68 41.7 41.7 85.3

3 16 9.8 9.8 95.1

4

Total

8

163

4.9

100.0

4.9

100.0 100.0