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Nottingham University Business Schoo l

The role of logis tics in e-commerce

Joseph George

MSc Operations Management


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The role of logi sti cs in e-commerce

by

Joseph George

2008

A Dissertat ion presented in part cons iderat ion for the degree of

MSc Operations Management.

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ABSTRA CT

Click.

Shopping is now as easy as the press of a button from the

comfort of ones home. The Internet has revolutionized the way

business is conducted. When the online shopping business

started growing, many prophesied that it was the end of the road

for many of the intermediaries, who were dominant in traditional

supply chains, as more and more suppliers and manufacturers

were likely to prefer selling direct to the customer in order to

reduce delivery time, costs and compete in the online market.

This meant that most of the business for logistics service

providers would be mainly limited to the last mile of the online

shopping order cycle. However, this has not been the case and

the logistics service providers dealing with internet shopping

have witnessed tremendous growth in business. The focus of this

report is to review the role of logistics service providers or 3PL in

online/internet shopping and to study the importance of their

services in internet shopping or e-tailing. The importance aspectwill be analysed through statistical analysis based on online

customer feedback and by trying to forge links, if any, between

customer loyalty to an online supplier and logistics related

activities like order processing, warehousing and delivery.

Another aim of th is research is to determine the leve l of

importance of logistics for different product types. Furthermore,

qualitative analysis of data obtained from primary and secondary

interviews will be used to conclude whether logistics providers

have been positively or negatively affected by the advent of

internet shopping.

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ACK NOWL EDGEMENTS

This dissertation would not have been possible without the

support of a number of people. I would like to take this

opportunity to thank my supervisor, Dr. Ram Ramanathan for his

assistance and moral support whenever I needed it. I would have

been helpless without his guidance, especially since I was

having a lot of time constraints and had to complete my

dissertation in the middle of a lot of other external issues. He

understood all my problems and encouraged me at every stage,

which helped me complete my study on time. I thank my parents

and my sister for their love, patience and understanding and for

supporting me in every way throughout this course and

especially during my dissertation period. I also thank all my

relatives who prayed for me ; and last but not least, I would like

to thank some of my dear friends for all their assistance.

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TABLE OF CONTENTS

1. INTRODUCTION ....................................................................... 8

2. L ITERATURE REVIEW ............................................................12

2.1 What is e-commerce? ........................................................12

2.2 E-commerce business models...........................................17

2.3 Order fulfilment / logistics in B2C e-commerce ................22

2.4 Customer service and loyalty ...........................................37

2.5 Customer expectations for different product categories ...41

3. RESEARCH METHODOLOGY ................................................45

3.1 Importance of logistics and different product types ..........45

3.1.2 Data collection from eBay .........................................48

3.2 Logistics services and customer loyalty ...........................54

3.2.2 Data collection from BizRate ....................................57

3.3 Impact of internet shopping on logistics providers ...........64

4. ANA LYSIS AND RESULTS .....................................................66

4.1 Analysis for data from eBay to test propositions 1,2,3 . ...66

4.2. Analysis for data from BizRate to test propositions 4,5. . .73

4.2.1 Further analysis for proposition 4 and 5 ...................77

4.3 Analysis of qualitative data from interviews......................80

4.3.1 Company A................................................................81

4.3.2 Company B................................................................834.3.3 Home Delivery Network Ltd. .....................................85

5. SUMMARY AND MANAGERIAL IMPLICATIONS ...................86

6. CONCLUSION .........................................................................93

REFERENCES.. 95

APPENDICES. 100

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LIST OF TABLES

Table 1- Feedback category description..52

Table 2- BizRate quality ratings.57

Table 3- Actual frequencies, high price/low price..67

Table 4- Expected frequencies, high pr ice/low price.67

Table 5- Calculated values, high pr ice/low price67

Table 6- Actual frequencies, low/high ambiguity69

Table 7- Expected frequencies, low/high ambiguity..69

Table 8- Calculated values, low/high ambiguity..69

Table 9- Actual frequencies, low/high/medium risk71

Table 10- Expected frequencies, low/high/medium risk71

Table 11- Calculated values, low/high/medium risk..71

Table 12- Descriptive statistics of the variables involved73

Table 13- Relative contribution towards customer loyalty75

Table 14- Relative contribution towards overall rating..77

Table 15- Order of importance of the independent variables

based on canonical correlat ion analysis..80

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LIST OF FIGURES

Figure 1- Typical e-commerce model.17

Figure 2- Simple supply chain model.18

Figure 3- Supply chain with dis- intermediation20

Figure 4- Supply chain with re- intermediation.20

Figure 5- Distribution methods in B2C e-commerce..30

Figure 6- Selecting the right e-fulfilment strategy..31

Figure 7- Relative comparison of order fulfilment costs32

Figure 8- Model visualising the difference in importance of

logistics for different product types.47

Figure 9- The different logistics and non-logistics relate(others)

parameters involved in determining customer loyalty and overall

rating56

Figure 10- Model in Figure 9. shown using BizRate ratings

parameters.56

Figure 11- Impact of B2C e-commerce on logistics service

providers.65

Figure 12- Scree plot..78

Figure 13- Importance of logistics for different product types.88

Figure 14- Relative importance of BizRate variables in

determining customer loyalty and overall rating.91

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CHAPTER ONE

INTRODUCTION

In todays fast moving world, with competition and customer

expectations constantly on the rise, businesses have to strive

harder to achieve their targets and revenue growths. This applies

to businesses in the virtual world or online world as well.

Suppliers, manufacturers and shops selling products and

services over the internet are under continuous pressure to

streamline their processes in order to achieve cost reductions

and gain their share of the market pie.

One way in which internet based businesses have tried to reduce

costs is by attempting the elimination of intermediaries in the

supply chain extending from them to the customers or in other

words, by going direct. This was initially seen as a bad sign for

the intermediaries like distributors and indirectly, logistics

service providers because the logistics providers business

revenues were likely to be restricted to last-mile (final delivery to

customer) delivery which was a market segment already filled

with niche players like Royal Mail in the UK. However, time has

painted a different picture. Logistics providers have witnessed

tremendous growth in revenues over the past few years with

business from internet shopping contributing significantly.

The main aims of this report is three-fold.

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1) To review and emphasise the role and importance of

logistics services in internet shopping, and to also

determine whether the level of importance depends on

product type or category.

2) To add to existing literature and provide readers with an

understanding of customer loyalty and overall rating

(which is useful in measuring customer satisfaction) of an

online store in internet shopping from a logistics aspect,

by trying to empirically forge links between the logistics

services component of the internet shopping process and

customer loyalty.

3) To confirm whether internet shopping has been a boon or

a bane to logistics service providers.

There are various reasons for having selected this topic.

Information was easily available from the internet and from

research and journal articles. Another reason was that though

this topic was based on existing research, it showed tremendous

potential to discover new avenues for future research. More

importantly, this topic involves a lot of data collection and

statistical analysis which aids in developing analytical skills.

The report is divided into different chapters as follows. Chapter 2

is the literature review part which is further divided into five

sections. The first section deals with e-commerce in general

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while the second section discusses the different business models

in e-commerce. The third section explains in detail the role of

logistics in order fulfilment, based on existing literature review.

Hence, this section partly contributes to the first aim of this

report by reviewing the different roles and importance of logistics

in internet shopping. The fourth section of the second chapter

reviews existing literature on customer loyalty and satisfaction in

online shopping and this partly contributes to the second aim of

this research and forms the background for the analysis carried

out later on in this report to explore the links between logistics

and customer loyalty. The final section discusses the variation in

customer expectations for different product categories which

forms the basis for trying to verify whether the importance of

logistics varies for different product types. Chapter 3 is the

research methodology section which explains the reasoning

behind the research propositions that are put forward and also

explains the data collection process from BizRate and eBay.

Chapter 4 provides the detailed results of the various statistical

tests conducted on the data in order to verify the importance, if

any, of logistics in determining online customer loyalty and

satisfaction which in turn provides the reader with an

understanding of the role and importance of logistics in online

shopping. The results also help in verifying the differences, if any,

in the importance of logistics for different product types. The final

section of this chapter provides qualitative data obtained through

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primary and secondary interviews, which helps in determining the

impact of internet shopping on logistics service providers. This,

in turn, is the third aim of this research. Chapter 5 provides a

summary of the findings and Chapter 6 concludes this report.

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CHAPTER 2

LITERATURE REVIEW

2.1 What is e-commerc e?

E-commerce is about using computer networks (including the

internet) to conduct business. Buying, selling, exchanging

products, services and information are all a part of e-commerce.

Normally, a term known as e-business is used to give a broader

definition of e-commerce by viewing it from different perspectives

like e-learning, e-transactions within an organization,

electronically collaborating with customers, social networking etc.

According to McKay and Marshal l (2004), e-business is the use

of the internet to support commerce. Erlandsson and Linden

(1999) quoted Mr.Kevin Koym, president of Praxsys System

Development, E-commerce includes everything from learning

products online and electronic transactions to online customer

service and support. In this report, e-commerce will be referring

to online shopping and e-retailing or e-tailing or more popularly

B2C (Business-to-Consumer).

Electronic commerce can take different forms (Choi et al.,1997)

depending on the degree of digitization (varies from physical to

digital) of a) the product or service, b)the process (ordering,

payment ,fulfilment) and c) delivery method. This research is

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only concerned about the physical delivery methods (as opposed

to the digital delivery method, example- E-books) as that is

where logistics comes into play.

E-commerce can be classified into various categories, some of

which are B2B or Business-to-business where all the participants

are business or organizations, B2C where transactions take

place between a business and individual shoppers, C2B or

consumer-to-business, mobile commerce, intra-commerce, B2E

(employers), collaborative commerce etc.

B2B, which is much more complex than B2C, experiences the

strongest drive and growth among all the above categories

whereas the B2C market is still immature. From a research point

of view, it is much more difficult to obtain the required statistics

and data from participants in the B2B market due to the high

levels of strategic importance associated with it. Therefore, this

research is restricted to B2C e-commerce, which is experiencing

rapid growth these days.

The benefits and limitations of e-commerce as summarized by

Turban et al. (2008) is given below.

Benefits to organization: provides a global reach, cost reduction,

supply chain improvements by reducing inventory and delivery

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delays , business can be carried out 24/7, customization is

possible, sellers can specialize and make more money from

niche markets, efficiency in procurement, customer service can

be improved and electronic products (like mp3s) can be delivered

easily, new or novelty products can be introduced to the

customers faster, new business models like that of Google can

emerge and improved transparency as information related to

comparison of organizations, products etc are easily available.

Benefits to consumers: large variety of products and services

giving a lot of choice, cost savings due to high competition

between online businesses as entering an online market is easy,

hard-to-find items and out-of-date product spare parts is usually

available, information is easily available, opportunity to

participate in auctions, buy unique items and no local sales tax.

Limitations of e-commerce : high costs of technology, network

access limitations, bandwidth limitations, privacy issues, security

issues, lack of trust, many traditional manufacturers face channel

conflicts, customer loyalty is not easy to count on, difficulty in

replacing the feel factor as many customers would want to

touch and feel certain products before purchasing them,

international shipping, outsourcing too many of the support

processes can lead to the company losing control, resistance to

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change from the physical store model and cannot take advantage

of mass business in certain markets.

E-commerce has had its impact on operations management and

manufacturing in many ways. With the introduction of online tools

such as FAQs (Frequently Asked Questions) and configuring

products (e.g. Dell), generic activities have been shifted to

others in the supply chain and this has resulted in cost reduction.

E-commerce has facilitated the creation of hub-based supply

chains which is a notable improvement in supply chain design

and management as firms like logistics service providers can

connect an e-tailer (online retailer) with its suppliers and

customers. E-commerce has also brought about improvements in

manufacturing, like the ability to run multiple manufacturing

plants as though they were at a single location; made possible

through information sharing. E-commerce has played an

important role in changing the mindset from that of mass

production to Just-In-Time and build-to-order manufacturing for

which Dell is the most apt example.

De Koster (2001) classified the online companies into four

categories:

a) Product manufacturers such as Dell, b) Traditional retailers

such as Tesco, c) new internet companies without physical

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assets like eBay and d) new internet companies with physical

assets like Amazon.

Future of e-commerce: Many research companies like AMR

Research, Jupiter Media, Emarketer.com, Forrester, bizrate.com

etc have provided data about e-commerce. Online statistics from

various research companies provide different data because

measurements could be in different time periods or for different

product categories. In 1996, it was predicted that the B2C

industry would be $6.6 billion by 2000, up from $518 million in

1996. In 1998, B2C sales in the US alone accounted for 1% of

the total retail sales or $43billion. According to Forrester

Research (2006), online sales account for nearly 5% of the

Amer ican retai l market. The number of internet users worldwide

was estimated at 700 million by mid-2006(Mann, 2006).

According to Jupi ter Media (2006), by 2010, 71% of the online

users shop via the internet and nearly half of the total retail sales

will be influenced by the internet. It is also said that now nearly

85% of the worlds online population use the internet to shop.

According to Turban et al . (2008) , the best se ll ing categories on

the internet include travel, computer hardware and software,

consumer electronics, office supplies, sports and fitness goods,

books and music products, toys, health and beauty,

entertainment, apparel and clothing, jewellery, cars and other

services.

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2.2 E-commerce busi ness mod els

Brick-and-mortar retailers are the traditional retailers having

physical stores. Click-and-mortar retailers are brick-and-mortar

businesses having a transactional channel over the internet as

well. The term E-tailer can refer to either a click-and-mortar

retailer or a pure play online store (those not having a physical

store). Barua et al. (1999) proposed a 4 layer framework for

describing the internet economy the infrastructure level,

applications layer, the intermediary layer and the commerce

layer which includes companies that conduct business over the

internet.

E-commerce has facilitated the emergence of new business

models. A business model shows how a company can create

value and generate and sustain revenue growth. A typical e-

commerce system or business model is shown below in Figure 1.

Figure 1.Typical e-commerce model

Suppliers E-tailer Customers

B2CB2B

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This is very similar to a traditional supply chain with physical

goods moving downstream towards the customer from the

suppliers and information flow in both directions. The only

difference being that the store is not physical and is just an entity

in cyberspace where transactions processing takes place at

remote locations not known to customers and the products are

delivered to them at their doorsteps.

Turban et al. (2008) proposed three different supply chain

models for B2C supply chains. These are best suited to explain

the business models of brick-and-mortar retailers who are

entering the B2C e-commerce market to develop new channels to

generate revenues.

a) Simple supply chain It is the traditional model similar to the

model described above and shown in Figure 2.

b) Supply chain with dis-intermediation This supply chain

model is similar to the above one except that many

intermediaries are eliminated. Initially, this was one of the major

Figure 2 Simple supply chain model

concern of logistics providers because some producers decided

to deal with the customers directly instead of going through

Suppliers Distributors RetailersProducers Customer

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distributors and retailers like in the case of Dell.com. Revenues

were likely to get affected as more and more producers decided

to outsource fewer activities, the best example being

Amazon.com which takes care of the logist ics related activ ities

on its own except for the last mile delivery. Amazon experienced

this model to be beneficial because their logistics stream was

reduced, thereby ensuring better responsiveness and lower costs.

This has led to a reduction in price which in turn has increased

profit margins and sales for Amazon.com. Similarly, businesses

dealing with digital products could eventually eliminate all

intermediaries.

Mahadevan (2000) mentions that in spite of dis-intermediation,

new forms of intermediation like infomediaries have emerged to

add value to the logistics stream of internet businesses.

Infomediaries provide an essential service by enabling customers

to get all the information they need from a single point. Figure 3.

shows a supply chain with dis-intermediation.

c) Supply chain with re-intermediation In this model, traditional

intermediaries performs new services, providing added value in

an online transaction process. Thus, for an intermediary, B2C e-

commerce provides new market and new ways to generate

revenues. Apparently this is what has ultimately transpired and

this model seems to be the most apt in terms of reality. This is

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what research proposition 6 in the research methodology section

of this report aims to find out whether B2C e-commerce has

had a positive or negative impact on logistics service providers,

from a business/revenue point of view. Figure 4.gives an idea

about this supply chain model

Figure 3.Supply chain with dis-intermediation

.

Figure 4.Supply chain with re-intermediation

Suppliers Distributors Retailers Customer Producers

Intermediary

Intermediary

Intermediary

Suppliers Distributors Retailers Customer Producers

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Yousept and Li (2004) studied the business models in the online

supermarket industry and their paper discusses three models for

fulfilment in online supermarkets which can very well be

considered as platforms for other models. The first model is in-

store picking suitable for online supermarkets which already

have their own physical stores. The second model is based on

reducing sorting and picking cost by building a dedicated picking

centre to serve online customers covering a wide geographical

area; however this model requires a very high initial investment.

The third model is a cross or hybrid of the above two models

brought about by incorporating local distribution centres into the

traditional supply chain. This model can significantly bring down

costs by reducing picking for online orders.

Mahadevan (2000) proposed a three dimensional framework for

defining a business model which can be applied to the emerging

B2C e-commerce market structure and he also listed factors to

guide organizations in deciding which business model is best

suited to their needs. The three dimensions of his proposed

framework comprise value streams, revenue streams and

logistical streams. The latter consists of dis-intermediation,

infomediaries which have been explained earlier. He suggested

that the internet economy has enabled organizations to generate

revenues from new streams which were not possible in a

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traditional brick-and-mortar economy and he discussed a few

such revenue streams , some of which are

1) Increase in margins by a reduction in transaction costs

and through dis-intermediation.

2) Revenue from online communities by building a buyer and

supplier community and thereby getting access to

information.

3) Advertising internet giants have emerged thanks to the

help of revenues from advertisements and banners , the

best examples being Google, Yahoo and MSN.

2.3 Order fu l f i lment / logis t ics in B2C e-commerce

Supply chain management aims at ensuring that the right product

is at the right place at the right time in the required quantity,

quality and form and reducing the cost required to do so.

According to the Counci l of Logist ics Management , Logist ics is

that part of the supply chain process that plans, implements and

controls the efficient, effective flow and storage of goods,

services and related information from the point of origin to the

point of consumption in order to meet the customers

requirements. McKinnon (1989) stated that logistics assists an

organizations activities by integrating all the sub-systems

together and by improving the material and information flow. E-

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commerce logistics or e-Logistics is nothing but logistics in

relation to business conducted over the internet like in B2C e-

commerce.

The internet has driven e-commerce and one of the key

advantages is that a retailer is allowed to offer goods to

customers anywhere around the world at anytime. This

eliminates the need for a physical shopping trip and is in itself

the biggest challenge for e-tailers managing the delivery to

customers. There are a number of reasons as to why people

shop online, some of which are easy internet access, money

savings by participating in auctions (online purchasing is

conducted either through an online catalogue or through

auctions) and purchasing used items, time savings and

availability of special products. On the same note, it is

interesting to understand the reasons as to why some people

have inhibitions when it comes to internet shopping. The most

commonly cited reasons are online payment security issues,

delivery issues and concerns regarding returning goods if not

satisfied.

The internet has provided supply chains with plenty of

opportunities for increasing customer satisfaction while

minimizing costs (Lancioni et al.,2000) , some of which are the

introduction of online vendor catalogs, ability to track shipments,

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24/7 service and international trading. Their research revealed

that the most popular use of the internet for supply chain

management is in transportation. This gives a picture of the other

side of the coin where e-commerce has had a positive impact on

the logistics industry, be it for those in the B2C e-commerce

industry or those in the traditional markets, as the internet has

made a constant monitoring of supply chain activities possible

which in turn can result in higher efficiency or lower costs.

According to Turban et al . (2008), the most important suppor t

services in e-commerce are order fulfilment and logistics,

technology, infrastructure, payments and security. Fulfilment and

customer order delivery are the most complicated parts of B2C e-

commerce (Vitarek and Manrodt, 2006). Yankelovich (2000)

stated that 89% of online shoppers rate on-time delivery as a

very important factor, second only to privacy. The Boston

Consulting Group (2001) reported that the absence of a good

return mechanism was the second most important reason why

people do not prefer online shopping. Pyke et al.(2001)

described five processes which defines e-fulfilment (order

fulfilment in e-commerce) , the five being order capture, order

processing, pick and pack, ship and after-sales service and

returns handling.

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According to UPS, current trends in logist ics, brought about by

the growth of e-commerce, is an increase in the number of

smaller packages, demand for more frequent deliveries, more

international transport and the time factor. This impacts logistics

providers because wherever the movement of physical goods is

involved, there is a scope for business for logistics service

providers. There is a conflict between achieving both low cost

and customer service through quick and accurate delivery and

these two are the key attractions of online shopping. Online

retailers will have to give importance to customer preferences if

they are to survive in the long run but at the same time, they

cannot neglect the needs for cost minimization.

In traditional supply chains, a large amount of items are moved

to a few destinations like retail outlets whereas in e-commerce

logistics or e-logistics, a small number of parcels are sent to a

large number of destinations (customer homes). Moreover, B2C

e-commerce is a pull based system where a product moves

downstream to the customer only when there is an order placed.

There has been an exponential increment in the volume of small

shipments which has placed a demand on increasing existing

infrastructure. On the other hand, in traditional systems, the

product is pushed downstream to the retail stores based on

forecasted demand in spite of whether there is a real demand or

not and hence it is a push based system. Demand uncertainty is

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a huge challenge in e-logistics. Economies of scale can hardly

be achieved by e-tailers unlike in the traditional channels. The

Forrester effect or the Bullwhip effect plays an importance role in

e-logistics as it is very likely that e-tailers will hold excess

inventory based on the demand forecast and the producers or

manufacturers will add a buffer to that figure and produce an

even larger amount of inventory. Inventory management plays an

important role as the customers expect the product to be

delivered immediately after they place the order within a span of

a couple of days, which produces the need to have sufficient

inventory in stock whereas as far as e-tailers and producers are

concerned, their aim would be to reduce the inventory in stock

and still be able to process and deliver a customer order without

any delay. An important difference between e-tailing and retailing

is that there are infinite product categories or SKUs (Stock

Keeping Units) in e-tailing. It is quite evident that the power is

with the buyers as the sellers strive to meet expectations. There

is a much greater opportunity for customer self-service and a

large number of online customers which is an important aspect of

e-commerce which imposes new restrictions and requirements on

logistics providers. Traditional logistics systems tend to be

improper for the logistics demands of e-commerce because it

involves much more packaging and transport. All this explains

the importance of logistics services in e-commerce and the

difficult challenges faced by them.

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Various solutions and some innovations have been brought about

by logistics service providers to tackle some of these challenges.

a) Use of Warehouse Management Systems (WMS), which

are software solutions to manage warehouses often linked

the e-tailers website and information systems ; similar to

the system deployed by Amazon.com

b) Build-to-order production systems, similar to Dells

production system which reduces inventory and is based

upon supplier information sharing.

c) Logistics postponement, using information to delay final

assembly of components until required and also directing

the final destination of goods only when the required

information regarding demand or an order has been

received.

d) VMI or Vendor Managed Inventory, here the

vendor/producer is in charge of maintaining inventory at

the required levels either with them or with the e-tailer or

any geographic location which can easily facilitate a quick

delivery as soon as the order is placed.

e) RFID or Radio Frequency IDentification, the introduction of

RFID tagging of products and pallets has enabled

continuous tracking possible no matter where and when.

f) Automated warehouses, with very less manual labour

required. Fully automated warehouses use robots for the

pick-up process.

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g) Merge-in transit, components for a product may come from

2 or more different locations and are combined and

shipped to the customer location while in-transit. HP is

one of the companies that have adopted this strategy.

h) Rolling warehouse , it is a logistics method in which

products on the delivery truck are not pre-assigned to a

destination but a decision about the quantity to be

unloaded at each destination is made during unloading

using the latest information available (Knaack , 2001).

i) Lee and Whang (2001) proposed leveraged shipments;

shipments are planned based on a combination of size or

value of the order and geographic location.

j) Pool ing transport in frastructure among e- ta ilers just as is

done by some companies in the traditional retail model, a

good example being the US automaker Saturn which uses

the pooling concept to take care of after-sales service and

spare parts inventory.

Delivery in B2C e-commerce: Figure 5.shows three methods for

the delivery process based on a classification by Turban et

al.(2008). The first (direct to customer) and second (traditional

delivery strategy) have been explained earlier and is self

explanatory. It is worth mentioning that the stores in the second

delivery method function as collection points for customers to

collect and return goods. The third option is a new distribution

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strategy by using e-fulfilment centres. This model can be

developed depending on the volume of e-commerce in the future.

It tends to be the most efficient model due to the presence of

dedicated and specially designed warehouses for online

customers. This would entail building new warehouses and

possibly automating it as manual order picking is expensive.

Kamarainen et al. (2001) have described the different order

picking systems available. When it comes to unattended delivery,

a reception box (refrigerated box) or a delivery box (insulated

box) which is installed at the customers site can be used.

In all the three strategies, transport arrangements can either be

undertaken by the retailer/supplier or a third party. Some

retailers manage customer delivery on their own while others

outsource it to a logistics specialist. The result in a way is that

express parcels and courier services are getting most of the

business. A third option chosen by e-tailers is drop-shipping,

where an e-tailer on receipt of the order, sends it to the supplier

or manufacturer who then takes responsibility for the rest of the

order fulfilment process including delivery to the customer. A

limitation of this option is that returning goods is a hassle as the

e-tailer does not deal with the logistics aspect directly.

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Figure 5. Distribution methods in B2C e-commerce

Suppliers

Lee and Whang (2001) proposed two core concepts for making

e-fulfilment efficient and also stated that these two concepts

were linked to five e-fulfilment strategies. The first concept

relates to using information more efficiently and there are two e-

fulfilment strategies based on this concept logistics

postponement (mentioned earlier) and dematerialization

( replacing physical flows with information flows, eg :a music cd

converted into digital format like mp3). The second concept is

about leveraging existing resources or in other words, making

the best use of existing infrastructure and the three strategies

related to this concept are resource exchange (resource pooling

which may be facilitated by logistics service providers),

leveraged shipments (consolidating shipments on the existing

Customers

Regionaldistribution

centres

Stores

Order pickingcentres

Van centres

1 2 3

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Figure 7. Relative comparison of order fulfilment costs

Source: Pyke et al. (2000)

increase of efficiency at each level. Levels 1 and 2 are the least

efficient, operating as a push based system, with excess

inventory and each progressive level aims to reduce total costs,

inventory, improve cycle time and customer satisfaction with the

improved use of information and collaboration with internal or

external logistics providers.

Delfmann et al. (2002) argued that the impact of e-commerce on

logistics service providers could be differentiated into two

categories the rise of e-marketplaces, which has more to do

with B2B and the upstream part of the supply chain, and dis-

intermediation, which is related to the downstream part of the

supply chain or the B2C sector. They explain that the fact that

every stage in the supply chain adds costs (handling, shipping

etc) is the reason for dis-intermediation. However, the fact that

intermediaries add value helps in promoting the importance of

logistics (Gurau et al., 2001) thereby providing great

opportunities for logistics providers.

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Rabinovich and Knemeyer (2006) suggested that the term

logistics service providers or LSPs should not be confused with

third-party logistics as LSPs not only provide logistics services

but also enable Internet sellers to leverage their networks of

relationships in order to fulfil their customer orders more

effectively. In their study, in which 200 e-tailers participated,

nearly 30% of those who dealt with LSPs disclosed that they had

no formal procedure for establishing contractual relationships

with LSPs. Their study also showed that many sellers got into

relationships with LSPs for quick-fix solutions without

considering the long-term impact and the benefits that could be

reaped and many selected a LSP based on who quoted the

lowest, without giving much thought regarding how important a

LSP is in the order fulfilment process. Bayles (2001) stated that,

most companies, barring a few large ones, simply outsource

logistics instead of a Joint Venture or partnership as this enables

them to change the logistics provider whenever they desire. This

may not prove to be a good strategy in the long run.

Rabinovich and Knemeyer (2006) classified LSPs into six

categories , based on the services offered , the first two being

buyer-focussed, the next two being supplier-focussed and the

final two are delivery-focussed. The services in the six different

categories are a) order returns, processing and exchange, b)

order payment and inquiry , c) inventory control and order

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are stocked in bins which have a red light which turns on

automatically when an order is placed and resets when an item is

picked and this light guides the pickers to the correct location.

The picked items are placed on a crate in a conveyer belt and

bar code scanners placed at different points identifies and tracks

the product until the crate arrives at a central location in the

warehouse where the bar code and the order numbers are

matched before being diverted to the packing section where the

boxes are packed, weighed and despatched to one of the many

truck bays. Each warehouse can deliver 200,000 or more pieces

per day and to increase efficiency, small orders are combined

into a single shipment. Amazon.com operates a separate

warehouse to manage returned and exchanged goods. This

reveals the kind of work carried out by major e-tailers in the

background to fulfil even the smallest of orders; which in turn

helps in understanding the importance of order fulfilment as a

vital e-commerce support service.

As ev ident from the above reviews, a lo t of research linking

logistics to e-commerce has been published. This report will add

to that by considering a different view; whether logistics service

providers have benefited or suffered due to the growing demands

and different business models of e-commerce.

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considerable revenues and to ensure profitability, the value of

customer loyalty or e-loyalty has to be fully appreciated.

Loyal shoppers will always return to purchase more and will be

ready to pay higher prices if needed and thus a strong link

between loyalty and profitability is visible (Rabinovich and

Knemeyer, 2006).

Cheung and Lee (2005) proposed a research framework for

customer satisfaction with internet shopping from a service

quality perspective. They argued that customer satisfaction

depends on information quality, system quality and service

quality. Service quality comprises of timeliness of order delivery,

accuracy of order delivery, condition of the products,

responsiveness and flexibility when it comes to billing and

delivery options. Jun et al. (2003) identified six online service

quality dimensions as perceived by online customers ; prompt

response, ease of use, attentiveness, access, security and

credibility, out of which the first three had a significant impact on

overall service quality and customer satisfaction. Physical

distribution services quality is a part of logistics service quality

(Rabinovich and Bailey, 2004). Mentzer et al. (2001) suggests

that there are three quality aspects existing in physical

distribution services quality timeliness of delivery, reliability

and inventory availability.

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Three components can be identified from online customers

response to their online shopping experience (Chen and Chang,

2003). The first component is interactivity and refers to website

and system features. Transaction parameters and fulfilment are

the other two components. A significant amount of research has

been carried out linking customer loyalty and website design

features (Dadzie et al., 2005). Websites can influence last mile

supply chain efficiency through different learning rates (Kull et al.,

2007). Weinberg(2000) suggested that a website should not only

be appealing from the appearance and functionality point of view ,

but should also have a fast loading time as online shoppers are

known for low tolerance (Chen and Chang, 2003). Website

design and its features and their impact on customer loyalty and

satisfaction is not a part of the objectives of this research and

will not be described further.

The need to improve logistics services to consumers is greater

than ever before and the level of logistics service expectations in

an online environment is higher than the expectations of

consumers in traditional retailing (Dadzie et al., 2005). A linkage

between logistics service attributes and customer loyalty has

been forged in B2B environments but this is not the case when it

comes to B2C where such a link is yet to be completely verified

(Dadzie at al, 2005). The study by Dadzie et al. (2005) was able

to verify that as customer responsiveness quality increases, the

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level of customer loyalty towards that online retailer increases.

Semeijn and Riel (2005) derived a positive relationship between

overall customer satisfaction and customer loyalty and stated

that the relationship is much stronger in an online environment

that an offline one. Heim and Sinha (2001) were able to

statistically associate customer loyalty with three order

procurement variables (product information, price and website

navigation) and with three order fulfilment variables (product

availability, timeliness of delivery and ease of return) in their

study of data from 52 electronic food retailers obtained from

BizRate.

This research aims to add more to those findings by trying to

empirically verify the existence of a link between logistics service

attributes and customer loyalty and overall satisfaction (overall

rating) by using the data available from BizRate.

Before proceeding to the next sections, it would be interesting to

review the service profit chain theory proposed by Heskett et al.

(1994). The service profit chain is about how employee

satisfaction and productivity, service value and quality, customer

satisfaction and loyalty; and revenue growth and profitability are

linked with significant relationships between them. The key idea

is that employee satisfaction, loyalty and productivity affects the

value of services offered to the customer which in turn has an

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impact on customer satisfaction and loyalty ,which can directly

affect revenue growth and profitability. In other words, a chain

linking these parameters can be visualised. This report has

already stated that service quality impacts customer loyalty

which in turn can have a say when it comes to profits. The

employee satisfaction, loyalty and productivity aspect of the

service profit chain has not been considered in this literature

review as it is beyond the scope of this research. Moreover, a

study by Silvestro and Cross (2000) showed correlations

between profit, customer loyalty and satisfaction, service value

and quality, output quality and productivity, but they could not

find any correlations between employee satisfaction and

profitability .Hence, the validity of the service profit chain, as far

as the parameters concerning employees are concerned , is

arguably debatable.

2.5 Customer expectat ions for di f ferent product categories

Customer service and quality is determined by the level of

customer expectations. E-tailers can benefit by coupling logistics

services quality with online market expectations (Rabinovich and

Bailey, 2004). Customer expectations of the order fulfilment

processes varies across different product types convenience

goods (groceries, home suppliers) , shopping goods(clothes and

apparel) and speciality goods like electronics, such that there is

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He conducted a study of customer feedback and risk using eBay

ratings and his research and analysis methodology was a key

guide in the process followed in this report. He classified the

eBay products into four categories high-price high-ambiguity,

low-price high-ambiguity, high-price low-ambiguity and low-price

low-ambiguity. Low-ambiguity merely suggests that the buyer

knows for certain what the product and its characteristics are and

the seller does not matter from a product point of view whereas

high-ambiguity products are those where the buyer does not

know for certain whether the characteristics of the same product

offered by different sellers are the same or not. Finchs (2007)

research aimed at positioning risk as a function of product price

and ambiguity and he concluded transaction risk was highest for

products in the high-price high-ambiguity category whereas risk

was lowest for products in the low-price low-ambiguity category.

The amount of risk for the other two product categories is

between these two levels of risk and can be considered as

medium risk. In short, high risk products can be defined as high

price-high ambiguity products and low risk products are the low

price-low ambiguity products

Before proceeding to the research methodology section, a word

on risk in internet shopping to conclude the literature review

section.

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Finchs (2007) work highlighted two main risks, the first being the

risk that the seller may cheat the buyer (vendor risk) and the

second is the risk that the product will fall below expectations

(product risk). He went on to suggest that service-oriented

dimensions of quality are more important at low levels of risk

whereas product-oriented dimensions are more important at

higher risk levels. Another classification of risk into three types

(Lim, 2003) includes a third type of risk named technology risk

which is associated with the technology involved in online

shopping. Lee and Turban (2001) proposed a model to explain

trust in e-commerce. They categorized trust into three trust in

internet merchants, trust in business and regulatory

environments and trust in internet as a shopping channel. The

latter is what interests us and it refers to reliability and payment

and logistics security. Trust is important in e-commerce because

it can offset the inhibitions caused due to the various risks

involved.

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CHAPTER 3

RESEARCH METHODOLOGY

The main aims of this study is to try and examine the importance

of logistics to B2C e-commerce. The overall research can be split

into two parts a quantitative part based on the analysis of data

available from websites such as BizRate (www.bizrate.com) and

eBay (www. ebay.co.uk) , and a qualitative part based on primary

and secondary interviews conducted with senior staff of a couple

of leading logistics service providers in order to find out whether

internet shopping has positively or negatively impacted them.

The quantitative part is again divided into two sections, the first

which analyses data from eBay to identify if there is any

difference in the importance of logistics for different product

types and the second section which uses data from BizRate to

study the importance of logistics in determining customer loyalty.

3.1 Importance of logist ics and di f f erent product types

In section 2.5, it was observed that customer expectations

depend on the product type. Moreover, the study by Finch (2007),

which is mentioned in the same section, showed that customers

perception of risk varies for products depending on their price

and level of ambiguity. This in turn was reflected in the customer

feedback which emphasised on either service or product oriented

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attributes depending on the type of product and the risk levels

involved. Similar reasoning is used to proceed with this research.

Finchs (2007) research looked at a broader view services and

product oriented feedback whereas this research is going to

narrow down the view to split services into different categories in

order to determine the importance of the logistics component of

services. Customer expectations are likely to be more for higher

priced products. If a customer is purchasing a cheap book and a

high end laptop online, it is much more likely that he/she will be

more concerned about the on-time delivery, customer service,

packaging, order tracking and other logistics related aspects of

the laptop than of the book. A similar reasoning can be applied

to the product type classification based on ambiguity. However,

in this case, it is likely that the importance of logistics does not

differ for high or low ambiguity products. Let us consider the

case of somebody buying two products with the same price but

different levels of ambiguity, say a coin( high ambiguity) and a

book(low ambiguity). The feedback characteristics are quite

unlikely to state a higher importance for logistics services when

purchasing the coin as opposed to the book or vice versa. An

important finding of Finchs (2007) research was that risk in

online shopping is dependent on price and ambiguity levels. It

would be interesting to consider the importance of logistics

services for different levels of risks. It can be argued that product

oriented characteristics are more important for high risk products

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as the consumer is more likely to give more importance to the

product as such, as in the case of an antique piece auctioned

over the internet, whereas if the consumer purchases a low risk

product such as a magazine, he/she is likely to be more

concerned about a prompt delivery rather than other attributes.

Even though logistics services like quick delivery will be very

important for a high risk product, the relative magnitude or

frequency of customer expectations regarding logistics services

is likely to be more for a low risk product as the customers main

expectations would be convenience and prompt delivery unlike in

the case of a high risk product where product oriented

characteristics will be more important to the customer. Based on

the research propositions devised above, a conceptual model

can be visualised as shown in Figure 8 below.

Figure 8.Model visualising the difference in logistics importance fordifferent product types.

High risk

pr od uct s

Mediumrisk

pr od uct s

Low risk

pr od uct s

Lower Higher

No re lat io n bet we enthe level ofambiguity andimportance oflogistics

Low pricepr od uct s

Importance of logistics services

High pricepr od uct s

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The three propositions related to the reasoning above are stated

below.

Proposit ion 1: The frequency of logistics related feedback will

be higher for high price products.

Proposit ion 2: There is no correlation between the importance

of logistics and level of ambiguity of the product ,as the

frequency of logistics related customer feedback is likely to be

the same for the low and high ambiguity products

Proposit ion 3: The frequency of logistics related feedback will

show that here is a higher importance for logistics related

activities for low risk products as opposed to high risk products.

3.1.2 Data collect ion fro m eBay

An understanding of eBay is required to fu lly comprehend the

reason why data was collected from this website and why the

author feels that this data is valid for evaluating some of the

research propositions put forward. eBay was founded in 1995

and is well known as the worlds best auction engine or

marketplace. eBay has a presence in 37 markets with a total

customer base of 233 million. The author collected data from

eBays UK website which is the UKs largest on-line market with

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more than 14 million active users and more than 10 million items

on sale at any given time. A significant number of users use

eBay as their primary or secondary source of income (eBay

Worldwide, 2008).

At the end of a transact ion on eBay, the buyer rates the se ller

with feedback which could be positive, negative or neutral. The

feedback is not only based on the product characteristics but in

fact stresses more on the service attributes of the entire

transaction as is evident from the customer feedback data

available on eBay. Such feedback is collected to interpret the

customers/buyers satisfaction level. Each feedback is read and

converted into a quantitative form by classifying it into one of 6

categories and then by adding 1 to that respective category.

Similarly each feedback is read, interpreted and classified into

one of the categories and the respective category is incremented

by 1 each time. This is the simplest explanation of the eBay data

collection and interpretation process. The six categories are 1)

delivery speed /timeliness, 2) delivery speed/ timeliness and

other service factors like sorting, picking, packaging,

communication, order tracking etc , 3) other service factors (all

excluding delivery speed/timeliness , 4) product-only , 5)

product and service related and 6) non specific. The first three

categories together form the service-only category. It has been

sub-divided into three separate sections in order to gauge the

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relative importance of the different logistics activities. Moreover,

from a customers viewpoint, delivery speed is arguably the most

important contribution of logistics service providers in internet

shopping. This view can be verified by sub-dividing the service-

only category.

Based on similar lines as the study by Finch (2007) which is

mentioned in section 2.5 of this report, 1000 positive feedback

ratings were collected for each of the four product categories

(therefore, 4000 in all) and the ratings were classified into one of

the earlier mentioned six categories (Appendix B.). For each of

the product categories, it was ensured that the sellers selected

were active with significant activity levels in the past few months.

This could be confirmed by the number of customer feedbacks

within the past 90 days. Only the latest 25-100 ratings were

considered for each seller. Low price was defined as products

below the 100 mark and high price products were those costing

above 200. In the low price-low ambiguity category, products

such as DVDs, comics, video games and accessories and

computing accessories were considered. In the high-price low-

ambiguity category, laptops, digital cameras and other

electronics were considered. In the high ambiguity category,

products such as antiques (wooden and oriental), paintings, art,

pottery and coins were considered. Based on the price range of

the high-ambiguity products sold, sellers were classified into one

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Table 1.Feedback category description

Service-onlyFeedbackexamples

Deliveryspeed

Speedand

otherservice

Otherservice

Product-only

Serviceandproduct

Notspecific

1)Super fastdelivery, productas described.

2)Brilliantservice, highlyrecommended.

3)Good ebayer!

4)Perfect

transaction.

5) Super fastdelivery andexcellent item.

6)Great productandcommunication.

7)Quick delivery,wouldrecommend.

8) Item asdescribed andquick delivery.

9) GREATebayer. Thankyou!

10)Brillant, wowvery impressed.Very quicklyrec'd. Not a mark

or scratch.

11)Great item!

12)Fast and goodpacking

1

1

1

1

1

1

1

1

1

1

1

1

Total 1 1 2 1 5 2

of service related comments) , b) other services related (third

sub-category of the service related category and c) product

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oriented .The data collected was tabulated and statistical tests

were conducted to draw a conclusion regarding the three

propositions. Hypothesis testing was done by stating a null (Ho)

and alternative hypothesis (HA), such that they are mutually

exclusive, for each of the three propositions and based on the

results of the analysis, the null hypothesis was rejected or

accepted. A Type 1 error occurs when the null hypothesis is

rejected when it is true. A Type 2 error refers to accepting the

null hypothesis when the alternative hypothesis is true. The

probability of producing a Type 1 error is known as significance

level, denoted by , and for this analysis a value of 0.05 wasconsidered throughout. Computed from the data collected, the

test statistic used in this analysis was chi-square. Chi-square

analysis was carried out and the expected frequencies and the p-

values were calculated using Minitab. The p-value is the

probability of observing a value as extreme as the test statistic.

The results were interpreted using the p-value approach and as

per this approach, the null hypothesis is rejected if the p-value is

less than the significance level. The Marascuilo procedure

(Levine et al., 2007) was used to confirm and justify the decision.

The Marascuilo procedure enables comparison between pairs of

groups. The observed differences ( - ) among the pairs are

computed followed by the critical range

(

xp

yp

y

yy

x

xx

U

n

pp

n

pp )1()1(2 +

) where is obtained from the tables2U

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based on the value of p and degrees of freedom. If x and y are

the observed frequencies, then px = x /(x+y) and = y /(x+y) .

As per the Marascui lo procedure, a pa ir is cons idered to be

significantly different if the absolute difference in the sample

proportions is greater than its critical range. This process was

repeated for all the three research propositions related to data

from eBay and conclusions based on the results can be drawn.

yp

3.2 Logist ics services and custom er loyalty

Section 2.4 of this report describes the importance of ensuring

customer loyalty in online businesses and also mentions that the

level of logistics related customer expectations is higher than in

traditional businesses. It is worth repeating an important point

(mentioned in section 2.4) at this juncture; a linkage between

customer loyalty and logistics service attributes, in a B2B

environment , has been forged by many researchers whereas a

similar link in a B2C environment has not been completely

verified , as research in this area has been limited. This will be

analysed using data from BizRate. One of the rating parameters

is would shop here again ; which is considered as the loyalty to

an e-store or the customer loyalty variable. Ratings of an e-store

on BizRate contains a column called overall rating. Customer

satisfaction depends on customer expectations being met, which

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in turn will increase customer loyalty to an e-store which will tend

to give a higher overall rating to an e-store. Therefore, just as

there is a relation between logistics services and customer

loyalty, there has to be a link with the overall rating too, denoting

customer satisfaction, which is to be verified. In section 2.4 of

this report, which deals with customer service and customer

loyalty, a number of parameters /variables which are related to

loyalty are highlighted ; product availability, timeliness of order

delivery, product condition, ease of navigation, security, product

information, price, website design, shipping options and

customer service/support being some of them. These can be

linked to the overall rating of an e-store in addition to customer

loyalty. Also, it is possible to group these parameters into two

logistics related and others (non-logistics related). A model

based on this is depicted in Figure 9. Using the BizRate rating

parameters or variables in table 2 which is in section 3.2.2, the

next section in this report, the parameters in Figure 9 can be

replaced by the equivalent or almost equivalent BizRate

parameters. This is shown in Figure 10 and will be useful to

visualise the analysis conducted to verify the importance of

logistics. This leads us to 4th and 5th research propositions.

Proposit ion 4: There is a relation between logistics service

attributes and customer loyalty.

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Proposit ion 5: There is a relation between logistics service

attributes and overall rating of an e-store.

Figure 9.The different logistics and non-logistics related (others)parameters involved in determining customer loyalty and overall rating

Customer loyaltyProductavailability

Figu re 10.Model in figure 9. shown using BizRate ratings parameters

Price/clarity ofpr icin g

Website design

Productinformation

Ease ofnavigation

Product

selection

Logistics

Customer loyalty

Overall storerating

Others

On-time delivery

Shippingcharges/options

Order tracking

Productavailability

Customer support

Product metexpectations

Customer support and product met expectations can be

included as both logistics and othersrelated variables,

explained in section 3.2.2.

Ease ofnavigation

Security

Privacy

Productinformation

Product price

Logistics

Orderdelivery/time-liness

OthersProductcondition

Shippingoptions

Customerservice

Overall storerating

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3.2.2 Data coll ectio n fro m BizRate

It is essential to understand what BizRate does before

proceeding further. BizRate is an on-line service founded in 1996

aimed at helping on-line shoppers select the right store for their

needs. It is basically a comparison service with virtually unlimited

information about all the stores on the internet. They claim that

over a million online shoppers visit their website daily. Complete

information about BizRates data collection and rating process is

described on its website (BizRate Ratings, 2008 ). BizRate rates

a store only after it has received a significant number of

customer reviews (minimum of 20 reviews in the past 90 days).

According to BizRate, The number of customer reviews is the

total number of individual consumer reviews that BizRate has

collected for a particular store. The more people who have

reviewed a store, the more statistically reliable the ratings are.

There are 16 quality ratings associated with each store 8 of

which are collected at the checkout or in other words ,at the

end of the on-line transaction and the remaining 8 are related to

after delivery or after the order has been fulfilled. BizRate

explains the 16 ratings as described in Table 2.

Table 2. BizRate Quality Ratings

Source Rating Explanation

at checkoutEase of findingwhat you are

looking for

How easily were you able to f ind theproduct your were looking for

at checkout Selection ofproducts

Types of products available

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at checkoutClarity of product

informationHow clear and understandable was

the product information

at checkoutPrices relative to

other on-linemerchants

Prices relative to other web sites

at checkoutOverall look and

design of siteOverall look and design of the site

at checkout Shipping charges Shipping charges

at checkoutVariety of

shipping optionsDesired shipping options were

available

at checkoutCharges statedclearly before

order submission

Total purchase amount (includingshipping/handling charges) displayed

before order submission

after deliveryAvai labi li ty of

product you

wanted

Product was in stock at time of

expected delivery

after delivery Order tracking Ability to track orders until delivered

a fter del ivery On- time del ivery Product a rr ived when expected

after deliveryProduct metexpectations

Correct product was delivered and itworked as described/depicted

after delivery Customer supportAvai labi li ty /Ease of contac ti ng ,courtesy & knowledge of staff,

resolution of issue

after deliveryWould shop here

again

Likelihood to buy again from this

storeafter delivery Overall rating Overall experience with this purchase

after deliveryLikelihood torecommend

How likely are you to recommend thismerchant

Source : www.bizrate.com

Out of these 16, only 15 ratings were available for each store

and hence, only these 15 ratings were collected, the missing one

being the rating in the last row (likelihood to recommend) in

Table 2. The maximum possible rating or score for each quality

rating is 10. Would shop here again is taken as the customer

loyalty variable as it is the only component among the different

variables that implies loyalty.

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The aim is to find links if any, between the importance of

logistics service attributes and overall rating and customer

loyalty, both of which are part of the 15 quality ratings available

for each store. Out of these 15, 5 are purely logistics related.

These 5 are shipping charges, shipping options, product

availability (inventory management), order tracking and on-time

delivery. Out of the remaining 10, it can be argued that two more

are partially related to logistics. Product met expectations can

mean two things product characteristics were as expected

(product-related attribute) and the right product was picked,

packed and delivered (logistics-related attribute), this being one

of the major challenges faced by logistics companies when it

comes to internet shopping. Similarly, one can argue that

logistics services is an important dimension of the customer

support quality rating, be it in order tracking or product return or

some others. Hence, these two quality ratings can be considered

as logistics-related in a way, due to the strong influence of

logistics and this is why they are included in the logistics column

in Figure 9 and 10.

The initial aim was to collect data for 200 stores for 5 of the

biggest product categories (computers and software, home and

garden, electronics, clothing and accessories, sports equipment

and outdoor gear) in BizRate, giving ratings of 1000 stores in all.

However, when the data collection process was underway, a few

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observations were made which led to a change in the plan and it

was decided that ratings of about 150-200 stores would suffice

(Appendix C). The reasons for th is change are as follows :

a) The same stores were listed in dif ferent categories . In

other words, store listings had repetitions because if a

store sold electronics and clothing products, then it was

listed in both these categories. This would lead to

duplication of data and hence it was decided to collect data

for individual stores rather than stores for different

categories.

b) A majority of the stores had ratings between 8 and 10 for

all the factors. So there was no point in collecting a lot of

store ratings which were in the same range because

statistically, the data would not be a lot of help. The onus

was on collecting data which was spread over a wider

range. It was observed that very few stores had ratings of

less than 7. The maximum possible number of stores in

this range was identified and included in the data collection

process. It is important to recall that BizRate rates a store

only if it has a significant number of reviews over a period

of time and hence, a large number of the stores listed on

BizRate were yet to be rated.

c) Each store rating is based on data from between 20 to

1000 customer reviews and therefore even if data was

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collected from a smaller number of stores, it is significant

enough.

d) Similar research with data from BizRate had been carried

out in the past with data from as few as 52 stores (Heim

and Sinha, 2001).

Regression analysis is conducted on the store ratings data with

customer loyalty and overall rating as the dependent or output

variables while the remaining 13 ratings as the independent

variables for research propositions 4 and 5 respectively. The

statistically significant independent variables can be identified

depending on the p-values and with further analysis using the

regression sum of squares, these can be ranked based on their

relative contribution towards the dependent variable.

Furthermore, as earlier stated, BizRate collects customer

reviews in two stages at checkout and after delivery. In other

words, it can be stated that customer satisfaction with the overall

order cycle has two dimensions, order procurement and order

fulfilment (Heim and Sinha, 2001 and Thirumalai and Sinha,

2005). This can be empirically verified by conducting a factor

analysis. Such an analysis would assist in confirming the validity

of the data collected from BizRate which in turn would aid in

confirming the validity of the analysis and results.

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Factor analysis helps in identifying underlying variables or

factors which can explain the correlations pattern of a set of

variables. This is used for data reduction to identify a small

number of factors (unobserved variables) which can explain the

variance in the observed variables. There are two important uses

of factor analysis. In exploratory analysis the relationships

between various variables are analysed without determining the

extent to which the results fit a particular model. Confirmatory

factor analysis compares the solution obtained against a

hypothetical one.

The initial step is to prepare a correlation matrix because if there

are no significant correlations between the variables, there is not

much point in conducting the factor analysis. There is no clear

answer as to how many factors should be considered after

conducting the factor analysis. One of the common rules is to

consider only those with eigen values over 1. Another rule is to

plot all the eigen values in their decreasing order. This is known

as the scree test. The scree test suggests stopping the analysis

(to find the number of factors) at the point the base of the slope

or graph starts. The extracted factors can be better interpreted

through rotation which maximizes the loading of the variables on

one of the factors and minimizes the loading of the variables on

all the remaining extracted factors. The factor loadings are the

correlation coefficients between the variables and the factors.

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In addition to regression analysis, canonical correlation analysis

can be carried out due to its several advantages over the former

(Hairet al., 1998), the most important being the ability to specify

more than one dependent variable at a time. As a result, analysis

can be done on a model where both customer loyalty and overall

rating are dependent variables at the same time, instead of

having to run two separate models for each variable as in the

case of the regression analysis. In other words, proposition 4

and 5 can be studied at the same time. The canonical analysis is

carried out using a statistics software named Systat.

In this study, the number of dependent variables is 2 and the

number of independent variables is 13. Hence, the maximum

number of canonical functions that can be derived is 2 (smaller

of the two numbers,2 and 13). The canonical functions are

interpreted using canonical loadings (similar to factor loadings in

factor analysis). According to Mai and Ness (1999), Canonical

loadings measure the correlation of each variable in the function

with the linear combination of variables in the set. Based on the

estimated canonical loadings, the order of importance of each of

the independent variables with respect to the dependent

variables (customer loyalty and overall rating) can be determined

by considering the strongest and weakest correlations.

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3.3 Impact of internet shopping on lo gist i cs provi ders

There is a doubt in many minds, regarding whether internet

shopping has benefited logistics service providers or not. Section

2.2 discusses dis-intermediation which could possibly affect

logistics in a negative manner but section 2.3 discusses the

various roles and importance of logistics in an online business

scenario and also explains the innovations brought about in the

field of logistics to meet the challenges posed. Also, it is

generally seen that the revenues of logistics providers are on the

rise. This part of the study was based on qualitative data

obtained by interviewing senior staff of leading logistics

providers. A part of the data was obtained from secondary

interviews or recent interviews conducted by leading publications.

The generic list of interview questions prepared for this part of

the research is available in Appendix A. The questions were

prepared keeping in mind the need to clear certain doubts

questions 1,2 and 3 - to know if the challenges faced by logistics

companies are in line with the differences in the e-commerce

business model compared to the traditional business model

(faster delivery expectation, large number of small packages etc),

questions 4 and 5 - to confirm the impact of e-commerce on

logistics based on the revenue/business growth of logistics

service providers over the last few years, questions 6 and 7- to

verify the role of logistics providers and to confirm that there is a

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value add involved and not just last-mile delivery, question 8- to

find out if there is any difference in managing different product

types and question 9 and 10 - to know more about what is in

store in the future. This helps in formulating the sixth and final

research proposition depicted in figure 11.

Figu re 11. Impact of B2C e-commerce on logistics service providers

Increase inrevenues /

business oflogistics

provider s

Growth of B2Ce-commerce

Proposit ion 6 : Logistics service providers have only been

positively affected by the advent of B2C commerce / internet

shopping (from a business/revenue perspective)

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CHAPTER 4

ANA LYSIS AND RESULTS

This section contains the detailed results of the statistical tests

conducted to study the propositions put forward in the earlier

section.

4.1 Analysis for data from eBay to test pr oposit ions 1,2,3 .

This section tests propositions 1, 2 and 3 using data from eBay,

as explained in section 3.1.2. These propositions aim to verify

the difference, if any, in the importance of logistics for different

product types classified based on price, ambiguity and risk.

Proposi t io n 1: The frequency of log ist ics re la ted feedback wi l l be

h igher fo r h igh p r ice p roducts .

Null hypothesis, Ho = Customer feedback characteristics is

independent of the price of the product.

Al ternative hypothesis, HA = Customer feedback characteristics

is not independent of the price of the product.

Table 3 shows the data collected while Table 4 and Table 5 are

the values calculated using MiniTab.

As per the P-Value approach, the null hypothesis is rejected if P-

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(0.05) and hence the action to be taken as per statistical rules

is Do not reject Ho .

Table 3.Ac tual frequencies , high price/ low pri ce

Categories

Purelogisticsoriented

Otherservicesrelated

Productoriented

Low Price 444 139 336

High Price 508 150 386

Table 4.Expected frequencies, high price/low price

Categories



Productoriented

Low Price 445.69 135.30 338.01

High Price 506.31 153.70 383.99

Table 5. Calculated values, high price/low price

Chi- square 0.225

Degrees ofFreedom 2

P-Value 0.894

A Type -2 error re fers to accept ing Ho when HA is true. There is a

high uncertainty associated with making a Type-2 error and

hence if the probability of making a Type-2 has not been

determined, then Do no reject Ho means either Ho or HA can

be true.

However, from the observed data one can notice that logistics

oriented feedback has a higher frequency for high price products

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(508>444). The Marascuilo procedure shown below can be used

to conclude whether this observation is justifiable.

Comparison of logistics only feedback for low and high price

products:

Actual frequencies being compared : 444 & 508

Critical range calculations for p-value < (0.05) : 0.055

Absolute va lues of proportion di fferences : 0.067

Since the absolute value of the proportion differences is greater

than the critical range (0.0672 > 0.055) , the difference in

logistics related feedback for high and low price products is

statistically significant (508 >444) and it can be interpreted that

logistics activities are more important for high price products.

Proposi t i on 2 : There is no corre lat ion between the impor tance of

log ist ics and level o f ambigui ty o f the product ,as the f requency of

log ist ics re la ted customer feedback is l ike ly to be the same for the low

and h igh ambigu i ty p roducts

Similar to the earlier proposition, first the null and alternative

hypothesis is stated. Null hypothesis, Ho = Customer feedback

characteristics is independent of the level of ambiguity of the

product.

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Al ternative hypothesis, HA = Customer feedback characteristics

is not independent of the level of ambiguity of the product.

Table 6 shows the data collected while Table 7 and Table 8 are

the values calculated using MiniTab.

Table 6.Ac tual frequencies , low/high ambiguity

Categories



Productoriented

Lowambiguity 651 115 233High

ambiguity 301 174 489

Table 7.Expected frequencies, low/high ambiguity

Categories



Productoriented

Lowambiguity 484.49 147.08 367.44High

ambiguity 467.51 141.92 354.56

Table 8.Calculated values, low/high ambiguity

Chi- square 230.941Degrees ofFreedom 2

P-Value 0.000

As per the p-value approach, Reject Ho if P-Value


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Also, from the observed data one can notice that log is tics

oriented feedback has a higher frequency for low ambiguity

products (651>301). The Marascuilo procedure can be used to

conclude whether this observation is justifiable.

Comparison of logistics only feedback for low and high ambiguity

products:

Actual frequencies being compared : 651 & 301

Critical range calculations for p-value < (0.05) : 0.051

Absolute va lues of proportion di fferences : 0.368

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