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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-
Value
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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
Purelogisticsoriented
Otherservicesrelated
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
Purelogisticsoriented
Otherservicesrelated
Productoriented
Lowambiguity 651 115 233High
ambiguity 301 174 489
Table 7.Expected frequencies, low/high ambiguity
Categories
Purelogisticsoriented
Otherservicesrelated
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