LF1566306

1

E-commerce Strategy: An empirical
investigation of Dynamic Pricing in the Airline
Industry

Roberto Leumann
BIEMF
2014
1566306

Empirical Thesis
Tutor: Ferdinando Pennarola

3

Acknowledgements

I take this opportunity to thank my parents for the moral support they provided
during my college years.
I am also grateful to Prof. Ferdinando Pennarola for the assistance offered during
the drafting of the thesis and Prof. Barbara Chizzolini for the precious help she gave
me in the Statistical Analysis of the paper.
In addition, I want to express my sincere gratitude to all my family and friends, and
in particular to my friend Nico Klaas for having helped me developing the idea
behind this paper.

5

Table of Contents

Abstract ........................................................................................................ 6
1. Introduction and Research objectives ..................................................... 7
2. Related Literature .................................................................................... 8
3. The Airline Industry: E-commerce and Dynamic Pricing ....................... 10
3.1 E-commerce and the Airline Industry ....................................................................... 10
3.2 Dynamic Pricing and the Airline Industry .................................................................. 12
3.3 Evolving E-commerce and Dynamic Airline Pricing: A Brief Background of Browser
Cookies ....................................................................................................................... 15
4. Methodology ........................................................................................... 17
4.1 Collection Specifics ................................................................................................. 18
4.2 SPSS Analysis ......................................................................................................... 22
5. Results .................................................................................................... 24
5.1 Preliminary Examination .......................................................................................... 24
5.2 Cookie Analysis ...................................................................................................... 26
5.3 Correlation Analysis ................................................................................................ 32
6. Conclusions and Recommendations ....................................................... 33
Appendix A – Cookie-NoCookie price distribution in No-Frills Carriers. .... 36
Appendix B – Ethical, Social and Legal Consequences of Dynamic Pricing
with Browser Cookies ................................................................................. 38
References .................................................................................................. 40

6

Abstract

Technological breakthroughs are strengthening the entanglement between retailers
and customers online.
As digital instruments evolve, firms that operate online are modifying their E-
commerce strategies in order to adapt themselves to a market that changes on a
continuous base.
Whenever users access the web or perform a research online, they leave some
traces, which can be used by firms to enrich the profile of their customers. The
ability of firms is to make full use of these tools and use them to create an
advantage, especially if these firms are able to price the goods they sell dynamically.
In this paper we analyze how Airlines, a perfect example of sector in which dynamic
pricing practices are performed, are introducing the data gathered through the latest
virtual platforms in their online pricing model.
In particular we study the impact of browser cookies on the degree of price
discrimination implemented by various carriers.

7

1. Introduction and Research objectives

Information Communication Technology (ICTs) has generated major changes in the
way competitive schemes are organized in the market providing firms with powerful
strategic and tactical tools.
The implementation of ITCs has evolved along three main dimensions. Intranets
ensured speedy flow of information within the company, Extranets guaranteed real-
time communication with associates and supported B2B relationships and the
Internet, most importantly, allowed companies to interact and communicate with the
customers.
More specifically, within the framework of internet-based interfaces, E-commerce
platforms reshaped the way many businesses not only related with their customers,
but also gained precious data about them.
In the airline industry, one of the most competitive markets since the deregulation
wave of the late 70s, E-commerce advancements have had a particularly wide range
of strategic implications.
Given the nature of the service provided, Airlines apply a specific type of pricing
model called Dynamic.
Some features of Dynamic pricing models, only conceivable in theory, have become
commercially feasible only with the employment of state-of-the-Art E-commerce
technologies.

The relationship between E-commerce tool and Dynamic Pricing is the core interest
of this paper, whose aim is to study the outcome price offered by Carriers on their
Airline e-commerce platforms over time.
The analysis of these prices will shed a light on the way new online tools, more
specifically the use of Browser Cookies, allowed Airlines to enhance the degree of
discrimination applied in their pricing model.

8

Focus is set primarily on the current1
online approach of Scheduled and no-frills (also
known as “Low cost”) carriers in the European Market.
Although the empirical investigation mainly concentrates on the impact of cookies on
the dynamic pricing strategy, the data gathered will be used to compare the
outcome price distribution of the selected Airlines2
.

2. Related Literature

Most of the existing literature either concentrates on Dynamic Pricing (mainly with a
semi-pure quantitative approach) or on E-commerce strategies.

Fredrik Wallenberg, in his paper “A Study of Airline Pricing” proposed a general
airline pricing model based on three factors:
• The origin/destination pair.
• Three advanced purchase periods, depending on the number of days
preceding the departure date.
• Whether or not there is a Saturday stayover.
Wallenberg tested the model against 49,000 airfare transactions, creating a
theoretical model with sound results.
Integrating Wallenberger’s factors with those made possible today by the use of
cutting-edge E-commerce technologies is another objective of this paper.

1
Research
conducted
in
the
period
April-‐‑May
2014.

2
Schedule
Airlines:
British
Airways
and
Lufthansa;
No-‐‑frill
airlines:
Easyjet
and
Ryanair.

9

“Dynamic pricing — A future airline business model” by Burger B. and Matthias F.,
describes the feasibility of a Dynamic approach in the airline pricing model using a
“basic dynamic pricing prototype model”. Even though the paper, written in 2004,
could only in part prove the validity of the hypotheses stated, the results appear to
be quite realistic when compared with the practices implemented nowadays by
Airline Carriers.

The journal article “eAirlines: strategic and tactical use of ICTs in the airline
industry” by Dimitrios Buhalis provides a detailed history of e-Commerce in the
Airline Industry highlighting the importance of virtual channels for both B2B and B2C
relationships. However, having been written in 2003, the research does not include
the use of online cookies as a mean of market segmentation enhancement.

Ultimately, the Article “Price Discrimination in E-Commerce? An Examination of
Dynamic Pricing in Name-Your-Own-Price Markets “ published on MIS Quartely by
Hinz, O., Hann, I.H and Spann, M., even though targeted at a different market3

contains interesting facts about the way Dynamic Pricing is implemented and the
shortcomings of price discrimination with respect to firm image.
Additionally, Hinz, O., Hann, I.H and Spann, M.’s work briefly deals with the matter
of browser cookies, which makes it ideal for the empirical investigation presented
here.

3
In
their
paper,
Hinz,
O.,
Hann,
I.H
and
Spann,
M.
study
the
NYOB
markets.

10

3. The Airline Industry: E-commerce and Dynamic
Pricing

3.1 E-commerce and the Airline Industry

E-commerce – short for Electronic Commerce, is broadly defined as “conducting
business online”4
.
In common sense, e-commerce deals with the sales aspects of the more general
context of e-business.
However, E-commerce provides the capability of performing several operations
within a single online interface, introducing an electronic marketplace where buyers
and sellers meet, submit bids, keep track of the orders and get to the end of the
transaction electronically.

The possibility to increase competitive advantage through ICTs and E-commerce has
been grasped quite early by Airline Carriers, whose investments in this field started
in the mid-50s.
The cornerstone of ICT strategy in the Airline Industry is the SABRE Computer
Reservation System, used as a control system to generate flight plans, schedule
crews and track spare parts. At this point the technologic advancement only had
internal managerial implications allowing companies to cut organization costs.
After years of technological breakthroughs, the deregulation wave started in 1979
and the advent of the Internet, the SABRE system gave way to online interfaces,
initially used by online travel agencies and ticket outlets5
and eventually by Carriers
themselves, who started to sell tickets directly from their websites.

4
Definition
from
Investopedia.

5
Such
as
LastMinuteTravel.com,
Priceline.com
and
Flightserv.com.

11

These online interfaces incorporated a number of innovations useful not only for the
Carrier but also for the clients, such as6
:
§ Paperless tickets,
§ Proactive and  reactive approach to pricing strategy,
§ Commission capping and publication of net fares,
§ Financial incentives for self-booking online,
§ Auctions and online promotions,
§ Enhanced customer relationship,
§ Context-relevant advertising.
The online experience of e-Ticketing 7
is thus characterized by a 2-way B2C
experience where the Carriers have the chance to gather information about the
customers’ preferences with regards to ticket purchases.

This process of information gathering matched the main objectives of Carriers,
namely getting closer to customer and cutting costs.
Data collection from customers evolved together with the new virtual techniques.
While in the late 90s, Carriers were able to simply know and store data about price
and types of tickets purchased by clients (historical approach), the current
implementation of the latest tools allows a deeper knowledge of the customer’s
profile.
Two examples of these tools are the Internet Protocol (IP) based systems and
application of browser Cookies.
The former encapsulates data mainly about the geographic position of the user, the
latter, started to be used in very recent times, informs Carriers about previous
searches made by the user on the website.

6
Innovation
listed
by
Buhalis,
D.

7
Airline
tickets
bought/sold
online.

12

Aggregating the bulk of information available is a way to make the market price paid
by the single client closer to her Willingness-to-pay (WTP), which means engaging to
some degree in Dynamic pricing and Price Discrimination.

3.2 Dynamic Pricing and the Airline Industry

Dynamic pricing, also known as real-time pricing, is the practice of setting and
resetting the price of a product almost continuously according to some pre-defined
factors. This approach makes price flexible and volatile and allows firms to make
quick adjustments in response to market demand and customers’ preferences.

Dynamic pricing strategies are normally coupled with the sale of goods that are
subjects to some degree of decay or spoilage, namely perishable goods.
Among others, the main features shared by perishable products are:
• The quantity is fixed and reordering is not possible.
• There is a deadline for sales.
• The marginal cost of selling one or  more items is low.
Examples of perishable goods are food, electricity and, most importantly the seats
on a flight.

Given the capacity of the aircraft, Airline Carriers have a fixed and pre-specified
number of seats;; once the airplane takes off, any unsold seat becomes valueless;;
moreover, the marginal cost in the industry is known to be very low, “merely the
cost of a bag of peanuts and a can of soda”8
.
These characteristics make the Airline Industry particularly suitable for the
application of Dynamic Pricing strategies.
Before the advent of e-Commerce and the era of Internet consumption however,
revenue management could only proceed through static pricing, which resulted in a
less efficient strategy.

8
From
“Principle
of
Economics”
(2011)
by
Mankiw,
N.
G.

13

The existing literature9
has studied static pricing models assuming that, due to the
multiple-fare classes which used to10
characterize the composition of the seats on a
plane, demand for different classes appeared to have a sequential time of
appearance, with requests for the lowest classes coming first, followed by next
lowest, etc.
This assumption, partly verified by empirics, was (and actually still is) the cause of
several issues such as over-booking, cancellations and no-show. A Dynamic pricing
approach, apart from increasing marginal revenues, would also limit the impact of
these problems.

From an economic point of view, the practice of dividing customers in subsets
according to their common needs and priorities is referred to as Market
Segmentation.
In order to get involved with Dynamic pricing, the firm must primarily segment the
market in clusters with common preferences. Eventually, the same good is priced
differently among clusters according to their intrinsic characteristics.
In the case of Airlines, different passengers have different reasons to fly according
to their needs. In turn, needs of different individuals are associated with different
elasticity of demand.
The goal of Airlines is to SEGMENT potential passengers with respect to their
reasons/needs and only later CHARGE higher rates to those with lower elasticity.

A very basic form of segmentation consists in dividing clients between business
travellers and vacationers. While the formers have very little advance warning and
need to fly quickly to a specific location, vacationers usually take some time to plan
their vacations and are more flexible with regards to location. A better price is
applied to the second group, which shows higher elasticity.

9
Belobaba
(31987,
31989),
Brumelle
and
McGill
(31993),
Curry
(31990),
among
others,

have
tried
to
disentangle
the
static
pricing
inefficiency
problem.

10
Nowadays,
especially
in
the
case
of
Low-‐‑cost
carriers,
the
seats
are
organized
on
a

double-‐‑fare
structure
(mainly
Economy
and
Business).

14

Nevertheless, this way of grouping customers is quite basic since a larger variety of
customer profiles actually exist.

This is where E-commerce technologies come into play. Indeed as customers search
the web, they leave some “footprints” that can be used by Airlines to refine the way
they form clusters, thus making the discrimination more accurate.

15

3.3 Evolving E-commerce and Dynamic Airline Pricing: A Brief Background
of Browser Cookies

In the words of the Economist Paul Krugmann, Dynamic Pricing is merely a new
version of price discrimination, made commercially feasible by the current
technologies.
E-commerce platforms have become the bridge between the pure theory of Dynamic
Pricing and its real-world application.
The mechanism through which a dynamic model is applied starts by defining an
algorithm used to control the behavior of online pricing bots. These “software
agents” aggregate certain amounts of data provided, voluntarily and involuntarily, by
customers and produce a “customized” outcome price.

Among the types of information that the bots use to produce the outcome price, two
are particularly interesting because they can extract information about the
customers collecting their “virtual footprints”.

IP (Internet Protocol) number, as previously stated, contains information about the
users’ geographical location.
This type of information can be used to discriminate with respect to currency,
country’s purchasing power etc.
Since 200111
, Airlines IT Investments were channeled in the direction of IP based
systems with the consequence that this technique was already employed on E-
commerce platforms since 2006.

More recently, Airlines are updating their pricing algorithm process making it process
Browser Cookies as well.
A Browser Cookie is a small piece of data sent by a website’s server and deposited in
the user’s hard disk via the browser.
Every time the user accesses the website again, the cookies are sent by the browser
back to the website in order to retrieve some useful data about previous research.

11
As
reported
by
Buhalis.

16

Even though they were introduced in order to ensure a smooth and user-friendly
surfing experience (cookies save, for instance, the items in a shopping cart or
Facebook’s ID and password), they also store several kinds of procedures done by
the user, thus permitting to read them and process them by any given server
(including Airlines platforms). As a wise man once said “Your past defines your
future”.

Discovering whether Pricing bots use these tools to update ticket prices according to
previous researches made by the users is thus one of the main objectives of this
paper.

17

4. Methodology

The empirical investigation presented here has been structured in order to
understand the way Airline carriers choose the prices to offer to their online clients.
The source of data is primary, because the prices have been collected by the
researcher directly from the Carriers’ online agency, namely the websites12
where
Airlines show their fares and conclude transactions.

First of all, as previously mentioned, the subject matter of the study is the European
market.
In order to make a more general evaluation, two Carriers for each type (Scheduled
and no-frills) have been selected.
Ryanair and Easyjet will represent No-Frills Carriers, while Lufthansa and British
Airways will proxy of the behavior of Scheduled Carriers.

After having selected two dates (outbound and inbound – 16th
and 18th
of May 2014)
and 3 routes, Milan-Barcelona, Milan-Berlin, Milan-London, the prices of these routes
have been collected for each of the four Airline Carriers over a period of 34 days
(from April 12th
to May the 15th
).

In order to check for the use of Cookies the prices are collected twice on two
different computer systems. In one, the browser cookies are daily deleted before
and after the price check, in the other the cookies are kept so to highlight if the
algorithm enshrined in the pricing bot of each company takes the cookies’
information into account.
A total of 1604 prices have been collected.

12
The
sources
are:

• http://www.ryanair.com

• http://www.easyjet.com

• http://www.britishairways.com/travel

• http://www.lufthansa.com

18

In order to reduce the “noise” of other variables that may interfere with the cookies
in the price determination process, some precautionary measures are taken:
• In order to exclude the impact of the IP (Internet Protocol) on the outcome
price, the researcher used the TOR (TheOnionRouter) Browser. This particular
browser, created to enable online anonymity and censorship resistance,
conceals the IP number of the terminal, making impossible for the pricing
bots to locate or trace Internet activity of the user.
Obviously, TOR would delete Browser Cookies by default, so the settings of
the browser have been changed on one of the two computers in order to
keep track of the Cookies.
• The prices have been checked everyday between 11am and 1pm. Moreover,
in order to exclude daily price fluctuations the Cookie/Non-cookie collection
has always been performed within a 20-25-minute timeframe.

4.1 Collection Specifics

The airline website is accessed everyday in the aforementioned timeframe (11am-
1pm). Every website normally has a research string requesting basic information
required to retrieve the flight (Outbound date and location, Inbound date and
location and number of people flying).
The following tables sum up the initial data given by the researcher to the pricing
bot. All Prices for each carrier are Economy Class 1-passenger prices13
.

13
Since
Lufthansa
further
distinguishes
Economy
Class
in
3
subgroups(Economy
Saver,

Economy
Basic
and
Economy
Flex),
the
researcher
has
chosen
the
“Economy
Basic”

ticket.

19

Table 1 - Milan-London
Carrier Departure Airport Arrival Airport Time departure
May 16th
Time departure
May 18th
Ryanair Malpensa Stanstead 17.15 13.50

Easyjet Malpensa Gatwick 16:30

13:05

British Linate Heathrow 19:10

12:05

Luft14
Linate Heathrow 12:55

13:30

Table 2 - Milan-Berlin
Carrier Departure Airport Arrival Airport Time departure
May 16th
Time departure
May 18th
Ryanair Malpensa Schönefeld 06.30

08.55

Easyjet Malpensa Schönefeld 10:40

13:50

British Linate Tegel 07:45

06:55

Luft15
Linate Tegel 06:40

07:10

Table 3 - Milan-Barcelona
Carrier Departure
Airport
Airport Time departure
May 16th
Time departure
May 18th
Ryanair Malpensa El Prat

08.35

17.35

Easyjet Malpensa El Prat 06:35

08:45

British Linate El Prat 13:2016
11:1017

Luft18
Linate El Prat 07:15

12:45

14
Flight
characteristics:
Milan-‐‑Frankfurt
(FF),
FF-‐‑London.

15
Flight
characteristics:
Milan-‐‑FF,
FF-‐‑Berlin.

16
Flight
characteristics:
Milan-‐‑London
Heathrow
(LH),
LH-‐‑Barcelona

17
Flight
characteristics:
Milan-‐‑Madrid,
Madrid-‐‑Barcelona;
the
second
flight
is
on-‐‑board

an

Iberia
(British
Airways’
Partner
Carrier)
aircraft.

18
Flight
characteristics:
Milan-‐‑FF,
FF-‐‑Barcelona.

20

Once collected from the website the data were organized in a Excel sheet.
The following table provides an example of spreadsheet organization. It refers to the first
day of data collection (find column description in footnote).
Boldfaced prices indicate the ones in which Cookies are kept.

Table 4
Price
May
16th19

Price
May
18th20

Destination
21

Carrier
22

Days to
Departure
23

Cookie
Dummy
24

Cookie-
NC
OUT25

Cookie-
NC IN26

%chang
e OUT27

%chang
e IN28

€ 124.00 € 137.00 London B -34 0
€ 215.00 € 269.00 Berlin B -34 0
€ 449.00 € 153.00 Barcelona B -34 0
€ 89.20 € 101.64 London E -34 0
€ 98.42 € 83.12 Berlin E -34 0
€ 80.06 € 70.37 Barcelona E -34 0
€ 140.88 € 166.78 London L -34 0
€ 217.67 € 149.60 Berlin L -34 0
€ 191.88 € 187.49 Barcelona L -34 0
€ 40.79 € 98.93 London R -34 0
€ 27.53 € 33.65 Berlin R -34 0
€ 82.61 € 82.61 Barcelona R -34 0
€ 124.00 € 137.00 London B -34 1 € 0.00 € 0.00 0.0000% 0.0000%
€ 215.00 € 269.00 Berlin B -34 1 € 0.00 € 0.00 0.0000% 0.0000%
€ 449.00 € 153.00 Barcelona B -34 1 € 0.00 € 0.00 0.0000% 0.0000%
€ 89.20 € 101.64 London E -34 1 € 0.00 € 0.00 0.0000% 0.0000%
€ 98.42 € 83.12 Berlin E -34 1 € 0.00 € 0.00 0.0000% 0.0000%
€ 80.06 € 70.37 Barcelona E -34 1 € 0.00 € 0.00 0.0000% 0.0000%
€ 140.88 € 166.78 London L -34 1 € 0.00 € 0.00 0.0000% 0.0000%
€ 217.67 € 149.60 Berlin L -34 1 € 0.00 € 0.00 0.0000% 0.0000%
€ 191.88 € 187.49 Barcelona L -34 1 € 0.00 € 0.00 0.0000% 0.0000%
€ 40.79 € 98.93 London R -34 1 € 0.00 € 0.00 0.0000% 0.0000%
€ 27.53 € 33.65 Berlin R -34 1 € 0.00 € 0.00 0.0000% 0.0000%

19
(1)Price
May
16th:
Price
of
the
Outbound
Route.

20
(2)Price
May
18th:
Price
of
the
Inbound
Route.

21
(3)Destination:
check
the
tables
in
the
previous
page
for
further
info.

22
(4)Airline
Carrier:
where
“B”
(=British
Airways),
“E”
(=Easyjet),
“L”
(=Lufthansa),
“R”(=Ryanair).

23
(5)Days
to
Departure:
indicate
the
number
of
days
before
the
16th
of
May
(day
of
the
outbound
flight

departure).

24
(6)Cookie
dummy:
when
the
value
of
this
column
is
“0”
(“zero”),
the
price
has
been
collected
from
the

browser
where
the
cookies
are
deleted;
the
cookies
have
been
kept
untouched,
when
the
value
is
“1”.

25
(7)Cookie-‐‑NC
OUT:
is
the
result
of
the
subtraction
of
the
price
WITH

cookie
and
the
price
WITHOUT

cookies
in
column
(1).

26
(8)Cookie-‐‑NC
IN:
is
the
result
of
the
subtraction
of
the
price
WITH

cookie
and
the
price
WITHOUT

cookies
in
column
(2).

27
(9)%change
IN:
Is
the
percentage
change
between
WITH
and
WITHOUT
cookie
prices
in
column
(1).

28
(10)%change
OUT:
Is
the
percentage
change
between
WITH
and
WITHOUT
cookie
prices
in
column

(2).
Column
9
and
10
are
obviously
0
since
data
refers
to
the
first
day
of
investigation.

21

€ 82.61 € 82.61 Barcelona R -34 1 € 0.00 € 0.00 0.0000% 0.0000%

22

4.2 SPSS Analysis

Two types of analysis have been conducted through SPSS.

In the first analysis, which aims at controlling if there is a significant variation
between Cookie-NoCookie fares, all prices gathered for each carrier (Outbound,
Inbound for all 3 destinations) have been aggregated along 6 variables:
• BritishNOCOOKIE: All the prices gathered from britishairways.com on the
terminal where cookies were deleted.
• BritishCOOKIE: All the prices gathered from britishairways.com on the
terminal where cookies were NOT deleted.
• LuftNOCOOKIE: All the prices gathered from Lufthansa.com on the terminal
where cookies were deleted.
• LuftCOOKIE: All the prices gathered from Lufthansa.com on the terminal
where cookies were NOT deleted.
• EasyNOCOOKIE: All the prices gathered from easyjet.com on the terminal
• EasyCOOKIE: All the prices gathered from easyjet.com on the terminal where
cookies were NOT deleted.
• RyanNOCOOKIE: All the prices gathered from ryanair.com on the terminal
• RyanCOOKIE: All the prices gathered from ryanair.com on the terminal where
cookies were NOT deleted.

A paired-sample T-test procedure is run where the means of groups are compared
2-by-2 (Cookie vs. NoCookie for each Carrier).
Substantially the sample mean of each “NOCOOKIE” group is compared with the
mean of the “COOKIE” group.

In the second analysis, which wants to compare the price pattern ACROSS Carriers
in order to detect if carriers take competitors’ behavior into account when computing

23

the outcome price, for each of the 3 destinations (Barcelona, Berlin, London) a 4x4
correlation matrix is created using the variables containing the fares of the carriers.
The 12 variables, created using only the NoCookie prices (in this analysis the impact
of cookies is no longer of interest), for each carrier, are:
• BritishBAR
• BritishBER
• BritishLON
• EasyBAR
• EasyBER
• EasyLON
• LuftBAR
• LuftBER
• LuftLON
• RyanBAR
• RyanBER
• RyanLON
Where “BAR” stands for Barcelona, “BER” for Berlin and “LON” for London.

24

5. Results

5.1 Preliminary Examination

An initial examination of the price trends reveals that the Scheduled carriers have a
price unquestionably higher than their No-frills opponents, with British Airways being
the most expensive one.
Even though substantially less expensive, No-frills prices are way more volatile than
Scheduled ones.
As we can see in Figure 1-3 (next page), while Lufthansa and British Airways often
experience periods of long plateaus where the price remains stable for 4-7 days,
Ryanair and Easyjet exhibit small fluctuations on a daily basis.

Overall, all four carriers charge higher fares as the departure day approach, which is
no surprise in 2014.
However, this phenomenon represents a shift from the “Last minute Practice” which
has been very common among airline revenue managers until 2004-2006. This
practice has been discredited by several scholars who pointed out that, in the
presence of rational customers, last minute practices are dangerous because they
might lead to an inevitable postponement of the purchase and a “a cycle of price
degradation that will eventually lead to [...] destroying the airlines" (Sviokla 2004).

In order to visualize the price distribution of the carriers, six graphs organized by
Destination (for instance, “Barcelona” indicates the Milan-Barcelona flight) are
presented in the next page.
The horizontal axis represents the variable “Days to departure” while the vertical
axis corresponds to the price in euro of the ticket.
Ryanair and Easyjet are also presented separately due to the fact that their fares are
way below those of the Scheduled carriers.

25

€ -‐‑
€ 200,00

€ 400,00

€ 600,00

€ 800,00

€ 1.000,00

€ 1.200,00

-‐‑34-‐‑31-‐‑28-‐‑25-‐‑22-‐‑19-‐‑16-‐‑13-‐‑10 -‐‑7 -‐‑4 -‐‑1
Fig.
1A
-‐‑ Barcelona
British Easyjet Lufthansa Ryanair
€ -‐‑
€ 200,00

€ 400,00

€ 600,00

€ 800,00

€ 1.000,00

€ 1.200,00

€ 1.400,00

-‐‑34-‐‑31-‐‑28-‐‑25-‐‑22-‐‑19-‐‑16-‐‑13-‐‑10 -‐‑7 -‐‑4 -‐‑1
Fig.
2A
-‐‑ Berlin
€ -‐‑
€ 100,00

€ 200,00

€ 300,00

€ 400,00

€ 500,00

€ 600,00

€ 700,00

-‐‑34 -‐‑31 -‐‑28 -‐‑25 -‐‑22 -‐‑19 -‐‑16 -‐‑13 -‐‑10 -‐‑7 -‐‑4 -‐‑1
Fig.
3A
-‐‑ London
€ -‐‑
€ 20,00

€ 40,00

€ 60,00

€ 80,00

€ 100,00

€ 120,00

€ 140,00

€ 160,00

-‐‑34 -‐‑31 -‐‑28 -‐‑25 -‐‑22 -‐‑19 -‐‑16 -‐‑13 -‐‑10 -‐‑7 -‐‑4 -‐‑1
Fig.
1B
-‐‑ Barcelona
Easyjet Ryanair
€ -‐‑
€ 20,00

€ 40,00

€ 60,00

€ 80,00

€ 100,00

€ 120,00

€ 140,00

€ 160,00

-‐‑34 -‐‑31 -‐‑28 -‐‑25 -‐‑22 -‐‑19 -‐‑16 -‐‑13 -‐‑10 -‐‑7 -‐‑4 -‐‑1
Fig.
2B
-‐‑ Berlin
Easyjet Ryanair
€ -‐‑
€ 20,00

€ 40,00

€ 60,00

€ 80,00

€ 100,00

€ 120,00

€ 140,00

€ 160,00

-‐‑34 -‐‑31 -‐‑28 -‐‑25 -‐‑22 -‐‑19 -‐‑16 -‐‑13 -‐‑10 -‐‑7 -‐‑4 -‐‑1
Fig.
3B
-‐‑ London
Easyjet Ryanair

26

5.2 Cookie Analysis

Coming to the core of the investigation, the cookie analysis compares the means of
two groups of data (for each carrier):
• The first containing prices WITH COOKIES
• The second containing prices WITHOUT COOKIES

The Analysis is based on the “Paired Sample T-test” since the research aims at
comparing means of two related groups where a different “treatment” has been
applied.
What is analyzed by SPSS is the difference between the COOKIE-NOCOOKIE means.
If this different is positive, the mean of the Cookie group results to be higher than
the mean of the NoCookie one, with Cookie prices being overall higher than the
NoCookie ones.

The results of table 2 and 3 are clear.
The difference between the sample means of the cookie and no-cookie subgroups is
positive for all the Carriers.
However, the results of No-frills carriers other than being way higher than the
Scheduled ones (1.22 and 1.02 vs. 0.71 and 0,78 – highlighted in the table), are also
the only ones being significant (2-tails 0.00 vs. 0.87 and 0.12 - highlighted in the
table).
This shows that both Ryanair and Easyjet significantly use the information contained
in the Cookie to increase the ticket price.

Table 5 - Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean

27

Pair 1 BritishCOOKIE 419.3993 195 269.66195 19.31089
BritishNOCOOKIE 418.6872 195 269.96225 19.33240
Pair 2 LuftCOOKIE 214.6504 199 86.11970 6.10486
LuftNOCOOKIE 213.8629 199 85.94224 6.09228
Pair 3 EasyCOOKIE 89.4143 204 24.67563 1.72764
EasyNOCOOKIE 88.1940 204 24.27706 1.69973
Pair 4 RyanCOOKIE 79.7714 204 32.65570 2.28636
RyanNOCOOKIE 78.7493 204 32.04974 2.24393

Table 6 - Paired Samples Test

Paired Differences
t Mean
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair 1 BritishCOOKIE -
BritishNOCOOKIE
.71210 63.89675 4.57574 -8.31248 9.73669 .156
Pair 2 LuftCOOKIE -
LuftNOCOOKIE
.78754 4.76236 .33760 .12179 1.45328 2.333
Pair 3 EasyCOOKIE -
EasyNOCOOKIE
1.22029 1.81788 .12728 .96934 1.47125 9.588
Pair 4 RyanCOOKIE -
RyanNOCOOKIE
1.02216 1.67651 .11738 .79072 1.25360 8.708

Table 7 - Paired Samples Test
df Sig. (2-tailed)
Pair 1 BritishCOOKIE - BritishNOCOOKIE 194 .876
Pair 2 LuftCOOKIE - LuftNOCOOKIE 198 .121
Pair 3 EasyCOOKIE - EasyNOCOOKIE 203 .000
Pair 4 RyanCOOKIE - RyanNOCOOKIE 203 .000

For what concerns the magnitude of the price increase, a closer look at the raw data
would be useful.

28

Even though the change in price is not registered everyday and also the direction of
the change is not always the same (in the majority of the cases the Cookie price is
higher than the No-cookie one, but in some days the Cookie price is lower than the
No-Cookie one), an interesting fact is that the MAGNITUDE of this change is always
constant.

Table 829
represents the data collected from the various websites on day 24 (10
days before departure) and it is exemplificative of the normal trend of the data
collection.
While British Airways (B) and Lufthansa (L) do not show any difference (Cookie –
NoCookie = 0), Easyjet (E) shows a 2% increase while Ryanair (R) increases by
1.96%.
These two percentages (2% and 1.96%) represent the constant magnitude of the
price change;; indeed, even when the cookie price is lower than the no-cookie one,
the decrease is always by a 2% and a 1.96%, respectively for Easyjet and Ryanair.
Table 8
Price 16th
may
Destinati
on
Carrie
r
Days to
departure
Cooki
e
Cookie-
NK OUT
%change
OUT
€ 494.00 London B -10 1 € 0.00 0.0000%
€ 711.00 Berlin B -10 1 € 0.00 0.0000%
€ 806.00 Barcelona B -10 1 € 0.00 0.0000%
€ 105.56 London E -10 1 € 2.07 2.0002%
€ 70.88 Berlin E -10 1 € 1.39 2.0003%
€ 53.54 Barcelona E -10 1 € 1.05 2.0004%
€ 215.89 London L -10 1 € 0.00 0.0000%
€ 217.67 Berlin L -10 1 € 0.00 0.0000%
€ 316.89 Barcelona L -10 1 € 0.00 0.0000%
€ 82.58 London R -10 1 € 1.59 1.9612%
€ 58.11 Berlin R -10 1 € 1.12 1.9613%
€ 69.32 Barcelona R -10 1 € 1.33 1.9615%
Going back to SPSS results (tables 5-7), there are some aspects of the behavior of
the Scheduled carriers (Ryanair and Lufthansa) which are not grasped by the Paired
samples T-Test but definitely need further examination.

29
For
simplicity
table
8
doesn’t
contain
(like
table
4)
the
inbound
and
NoCookie
values.

29

Table 7 shows that the difference between the Scheduled Airlines pairs is not
significant, thus it’s not possible to state that Scheduled Carriers make full use of
Browser Cookies.
Nevertheless, a closer look to the raw data gives useful insights about the more
subtle strategy implemented by British airways and Lufthansa.

For what concerns British Airways, there is no difference between the Cookie and
NoCookie prices with one exception only.
On day 2 (32 days before departure), precisely after two days of cookie collection,
the price obtained when cookies are kept untouched jumps by almost 10% with
respect to the no cookie price (the Cookie price is €12.19, €29.90, and €44.16
HIGHER than the no-cookie price in the 3 destination selected).

Table 9
Price 16th
may
Destinat
ion
Carrie
r
Days to
Departure
Cooki
e
Cookie-NK
OUT
%change
OUT
€ 136.19 London B -32 1 € 12.19 9.8343%
€ 333.90 Berlin B -32 1 € 29.90 9.8343%
€ 493.16 Barcelon
a
B -32 1 € 44.16 9.8343%
€ 87.20 London E -32 1 € 2.07 2.0002%
€ 98.28 Berlin E -32 1 € 1.39 2.0002%
€ 60.68 Barcelon
a
E -32 1 € 1.05 -2.0002%
€ 140.88 London L -32 1 € - 0.0000%
€ 217.67 Berlin L -32 1 € - 0.0000%
€ 191.88 Barcelon
a
L -32 1 € - 0.0000%
€ 40.79 London R -32 1 € 1.59 1.9650%
€ 27.53 Berlin R -32 1 € 1.12 1.9650%
€ 82.61 Barcelon
a
R -32 1 € 1.33 1.9650%

With respect to Lufthansa instead, the only day in which a non-zero difference
between Cookie and NoCookie Price is registered on day 5 (29 days before
departure) where a 13.45% increase is detected.

30

Table 10
Price 16th
may
Destinati
on
Carrie
r
Days to
Departure
Cooki
e
Cookie-
NK OUT
%change
OUT
€ 124.00 London B -29 1 € - 0.0000%
€ 355.00 Berlin B -29 1 € - 0.0000%
€ 567.00 Barcelona B -29 1 € - 0.0000%
€ 78.02 London E -29 1 € - 0.0000%
€ 94.34 Berlin E -29 1 € - 0.0000%
€ 80.06 Barcelona E -29 1 € - 0.0000%
€ 244.94 London L -29 1 € 29.05 13.4542%
€ 246.96 Berlin L -29 1 € 29.29 13.4545%
€ 212.67 Barcelona L -29 1 € 25.22 13.4549%
€ 40.79 London R -29 1 € 0.80 2.0005%
€ 33.65 Berlin R -29 1 € 0.66 2.0006%
€ 82.61 Barcelona R -29 1 € 1.62 2.0002%

The individual results for British Airways and Lufthansa are summarized in figure 4-6
(next page), where the lines in blue represent the outcome price in the browser
where cookies were kept, while the red lines epitomize the No-Cookie price.
It is clear that the lines are tangent all days but day 2 and 5 for British and
Lufthansa, respectively.

In case the reader wants to compare the price distribution of the scheduled airlines
(presented below) with that of the No-frills carriers, please refer to the Appendix A.

31

€ 380,00

€ 480,00

€ 580,00

€ 680,00

€ 780,00

€ 880,00

€ 980,00

€ 1.080,00

€ 1.180,00

-‐‑34 -‐‑31 -‐‑28 -‐‑25 -‐‑22 -‐‑19 -‐‑16 -‐‑13 -‐‑10 -‐‑7 -‐‑4 -‐‑1
Fig.
4A
-‐‑ British
Airways,

Milan
-‐‑ Barcelona
Cookie NoCookie
€ -‐‑
€ 200,00

€ 400,00

€ 600,00

€ 800,00

€ 1.000,00

€ 1.200,00

€ 1.400,00

-‐‑34 -‐‑31 -‐‑28 -‐‑25 -‐‑22 -‐‑19 -‐‑16 -‐‑13 -‐‑10 -‐‑7 -‐‑4 -‐‑1
Fig.
5A
-‐‑ British
Airways,

Milan
-‐‑ Berlin
Cookie NoCookie
€ 100,00

€ 150,00

€ 200,00

€ 250,00

€ 300,00

€ 350,00

€ 400,00

€ 450,00

€ 500,00

€ 550,00

€ 600,00

-‐‑34 -‐‑31 -‐‑28 -‐‑25 -‐‑22 -‐‑19 -‐‑16 -‐‑13 -‐‑10 -‐‑7 -‐‑4 -‐‑1
Fig.
6A
-‐‑ British
Airways,

Milan
-‐‑ London
Cookie NoCookie
€ 150,00

€ 200,00

€ 250,00

€ 300,00

€ 350,00

€ 400,00

€ 450,00

€ 500,00

-‐‑34
-‐‑32
-‐‑30
-‐‑28
-‐‑26
-‐‑24
-‐‑22
-‐‑20
-‐‑18
-‐‑16
-‐‑14
-‐‑12
-‐‑10
-‐‑8
-‐‑6
-‐‑4
-‐‑2
Fig.
4B
-‐‑ Lufthansa,

Milan
-‐‑ Barcelona
Cookie NoCookie
€ 150,00

€ 200,00

€ 250,00

€ 300,00

€ 350,00

€ 400,00
-‐‑34
-‐‑32
-‐‑30
-‐‑28
-‐‑26
-‐‑24
-‐‑22
-‐‑20
-‐‑18
-‐‑16
-‐‑14
-‐‑12
-‐‑10
-‐‑8
-‐‑6
-‐‑4
-‐‑2
Fig.
5B
-‐‑ Lufthansa,

Milan
-‐‑ Berlin
Cookie NoCookie
€ 100,00

€ 150,00

€ 200,00

€ 250,00

€ 300,00

€ 350,00

€ 400,00

€ 450,00

€ 500,00

€ 550,00

-‐‑34
-‐‑32
-‐‑30
-‐‑28
-‐‑26
-‐‑24
-‐‑22
-‐‑20
-‐‑18
-‐‑16
-‐‑14
-‐‑12
-‐‑10
-‐‑8
-‐‑6
-‐‑4
-‐‑2
Fig.
6B
-‐‑ Lufthansa,

Milan
-‐‑ London
Cookie NoCookie

32

5.3 Correlation Analysis

The aim of the correlation analysis on the prices is to understand if Airline Carriers
take competitors’ price into account when computing the outcome price, that is, if
“competitor price” is a factor of the Airline’s price algorithm.
For each of the three destinations, a correlation matrix is extracted using both the
outbound and inbound NoCookie (we are no longer interested in the cookies) prices.

The matrices contain Pearson Correlation Coefficients (“ϱ” - value between -1 and
+1 inclusive, where 0 means “no correlation).

Table 11, 12 and 13 (next page) contain the output of the correlation analysis.
When ϱ >0.6 the value has been highlighted in yellow. In addition the Ryanair-
Easyjet correlation coefficient has been highlighted in blue because it’s the only one
significantly higher than 0.6 in all three matrices (for all destination).

Table 11 – Milan/Barcelona Correlations
BritishBAR EasyBAR LuftBAR RyanBAR
BritishBAR Pearson Correlation 1 .100 .528**
.541**

Sig. (2-tailed) .574 .002 .001
N 34 34 32 34
EasyBAR Pearson Correlation .100 1 -.022 .642**

Sig. (2-tailed) .574 .905 .000
N 34 34 32 34
LuftBAR Pearson Correlation .528**
-.022 1 .278
Sig. (2-tailed) .002 .905 .123
N 32 32 32 32
RyanBAR Pearson Correlation .541**
.642**
.278 1
Sig. (2-tailed) .001 .000 .123
N 34 34 32 34

Table 12 – Milan/Berlin Correlations
BritishBER EasyBER LuftBER RyanBER
BritishBER Pearson Correlation 1 .456**
.089 .323

33

Sig. (2-tailed) .007 .619 .000
N 34 34 34 34
EasyBER Pearson Correlation .456**
1 -.182 .867**

Sig. (2-tailed) .007 .304 .062
N 34 34 34 34
LuftBER Pearson Correlation .089 -.182 1 .082
Sig. (2-tailed) .619 .304 .646
N 34 34 34 34
RyanBER Pearson Correlation .323 .867**
.082 1
Sig. (2-tailed) .000 .062 .646
N 34 34 34 34

Table 13 – Milan/London Correlations
BritishLON EasyLON LuftLON RyanLON
BritishLON Pearson Correlation 1 .863**
.611**
.674**

Sig. (2-tailed) .000 .000 .000
N 31 31 31 31
EasyLON Pearson Correlation .863**
1 .702**
.803**

Sig. (2-tailed) .000 .000 .000
N 31 34 34 34
LuftLON Pearson Correlation .611**
.702**
1 .758**

Sig. (2-tailed) .000 .000 .000
N 31 34 34 34
RyanLON Pearson Correlation .674**
.803**
.758**
1
Sig. (2-tailed) .000 .000 .000
N 31 34 34 34

**. Correlation is significant at the 0.01 level (2-tailed).

The analysis shows that the No-Frill carriers mutually influence their prices in all
three destinations, while some degree of correlation across ALL carriers is detected
only in the Milan-London route, probably the busiest and most competitive one.
6. Conclusions and Recommendations

34

All in all, the SPSS analyses have revealed some important characteristic of the
strategic behavior of the different Carriers.
The scope of the results presented in this paper is however limited due to the
sample size used in the SPSS analysis and the short timeframe of collection. A
research longer than 34 days could probably produce a wider and more precise
range of insights.

Recalling Wallenberg’s airline pricing model, the three factors proposed there can be
integrated with at least two new ones: Previous Customer’s Researches and
Competitors’ prices.

Previous Customer’s research varies according to the cookies accrued on the
customer’s browser.
Even though the investigation revealed that all studied Airlines make use of the
cookies in some way, the strategy that each Carrier implements is different and a
pattern of similarity can be detected with respect to the type of Carrier:
• No-frills (Ryanair and Easyjet) seem to be using cookie information on a
regular (daily30
) basis, with the same magnitude (1.96% and 2%) and overall
slight positive direction (cookies make the outcome price increase). The
rationale behind this strategy is that Carriers, knowing that the customer is
checking a given route, increases the fare for that route accordingly.
• Scheduled carriers (British Airways and Lufthansa) use the cookie information
in a more subtle and hidden way.
Even though the rationale is probably the same (making the price higher for
the customers who seem to be more interested in a given route), the strategy
differs. The price increase is way sharper (9.8% and 13.1%, respectively for
BA and L) and applied only one time, some days after the cookie information
is detected (after 2 and 5 days, respectively for BA and L). This kind of
approach is possibly implemented to make customers think that the ticket
price is rising fast encouraging them to make the purchase as quickly as
possible.

30
Refer
to
Appendix
A
to
see
the
regularity
of
No-‐‑frills
price
change.

35

The factor Competitor’s price has been introduced given that the daily “price
catalog” is available for everyone (including competitors and the researcher) online.
Airlines can therefore adapt their pricing model considering competitors’ behavior.

Given the correlation analysis conducted here, two remarks have to be made:
• No-frills take competitors’ fare into account more often than scheduled
carriers. For all of the destinations studied, the Pearson coefficient between
Easyjet and Ryanair’s prices is above 0.6 and significant. Given that the price
No-Frills offer is way lower than their Scheduled rivals, the competition
between firms is way more intense (data reported in tables 11-13).
• Some routes in which a multitude of Carriers happen to compete (due to the
higher customers’ demand), requires all Carriers to check competitors’ fare
and modify their strategy accordingly. This is what happens in the route
Milan-London (Table 13).

In conclusion, the high variability of Ticket fares shows how Dynamic Pricing
strategies evolved and improved as technologies advances.
Browser Cookies Strategies consists in just another step forward with respect to the
way price discrimination is implemented on the market. Even though price
discrimination is economically feasible and efficient from a theoretical point of view,
the real life implementation of this practices poses social, ethical and legal concerns
(discussed more rigorously in Appendix B).

36

Appendix A – Cookie-NoCookie price distribution in No-
Frills Carriers.
€ 40,00

€ 50,00

€ 60,00

€ 70,00

€ 80,00

€ 90,00

€ 100,00

€ 110,00

-‐‑34 -‐‑32 -‐‑30 -‐‑28 -‐‑26 -‐‑24 -‐‑22 -‐‑20 -‐‑18 -‐‑16 -‐‑14 -‐‑12 -‐‑10 -‐‑8 -‐‑6 -‐‑4 -‐‑2
EasyJet,
Milan
-‐‑ Barcelona
Cookie
NoCookie
€ 60,00

€ 70,00

€ 80,00

€ 90,00

€ 100,00

€ 110,00

€ 120,00

€ 130,00

€ 140,00

€ 150,00

€ 160,00

-‐‑34 -‐‑32 -‐‑30 -‐‑28 -‐‑26 -‐‑24 -‐‑22 -‐‑20 -‐‑18 -‐‑16 -‐‑14 -‐‑12 -‐‑10 -‐‑8 -‐‑6 -‐‑4 -‐‑2
EasyJet,
Milan
-‐‑ Berlin
Cookie
NoCookie
€ 70,00

€ 80,00

€ 90,00

€ 100,00

€ 110,00

€ 120,00

€ 130,00

€ 140,00

-‐‑34 -‐‑32 -‐‑30 -‐‑28 -‐‑26 -‐‑24 -‐‑22 -‐‑20 -‐‑18 -‐‑16 -‐‑14 -‐‑12 -‐‑10 -‐‑8 -‐‑6 -‐‑4 -‐‑2
EasyJet,
Milan
-‐‑ London
Cookie
NoCookie

37

€ 20,00

€ 30,00

€ 40,00

€ 50,00

€ 60,00

€ 70,00

€ 80,00

€ 90,00

€ 100,00

€ 110,00

-‐‑34 -‐‑32 -‐‑30 -‐‑28 -‐‑26 -‐‑24 -‐‑22 -‐‑20 -‐‑18 -‐‑16 -‐‑14 -‐‑12 -‐‑10 -‐‑8 -‐‑6 -‐‑4 -‐‑2
Ryanair,
Milan
-‐‑ Berlin
Cookie
NoCookie
€ 20,00

€ 40,00

€ 60,00

€ 80,00

€ 100,00

€ 120,00

€ 140,00

€ 160,00

-‐‑34 -‐‑32 -‐‑30 -‐‑28 -‐‑26 -‐‑24 -‐‑22 -‐‑20 -‐‑18 -‐‑16 -‐‑14 -‐‑12 -‐‑10 -‐‑8 -‐‑6 -‐‑4 -‐‑2
Ryanair,
Milan
-‐‑ London
Cookie
NoCookie
€ 40,00

€ 60,00

€ 80,00

€ 100,00

€ 120,00

€ 140,00

€ 160,00

-‐‑34 -‐‑32 -‐‑30 -‐‑28 -‐‑26 -‐‑24 -‐‑22 -‐‑20 -‐‑18 -‐‑16 -‐‑14 -‐‑12 -‐‑10 -‐‑8 -‐‑6 -‐‑4 -‐‑2
Ryanair,
Milan
-‐‑ Barcelona
Cookie
NoCookie

38

Appendix B – Ethical, Social and Legal Consequences of
Dynamic Pricing with Browser Cookies

Dynamic Pricing is a form of first degree price discrimination, a practice considered
economically efficient due to the reduction of the overall dead-weight loss.
From a theoretical point of view, these practices allow producers to capture part of
the consumer’s surplus, with the result that the total efficiency is increased, but a
redistribution of resources is implemented to the consumer’s detriment.

In real life, the application of dynamic pricing strategies are taking off, albeit slowly
due to producers’ fears of possible consumer negative response to these practices
on ground of unfairness.
For instance, some kind of dynamic pricing strategy applied by Amazon using the
purchasing history of customers was discovered by a customer who saw the price of
a DVD dropping from $26.24 to $22.74 when logging into Amazon from a different
account.
The adverse customer reaction to this event and the bad publicity connected to the
episode, forced Amazon to publicly apologize and refund the customers who had to
pay higher prices (Ramasastry, 2005)
This shows that the average consumer perceives Dynamic Pricing as a TOTALLY
UNFAIR practice.

With respect to this in 2009, the Directive 2009/136/Ce has entered into force. This
directive aims at protecting unaware users from the unrestricted utilization of
Cookies by third parties.
With respect to information stored by websites on the users equipment legitimately
(Cookies) “it is therefore of paramount importance that users be provided with clear
and comprehensive information when engaging in any activity which could result in
such storage or gaining of access. The methods of providing information and
offering the right to refuse should be as user-friendly as possible” (Directive
2009/136/Ce).

39

The comprehensive information cited above is practically realized by disclaimers,
which inform the user more or less clearly, that their data is being utilized.
Fig. 7 and 8 present some examples taken from the Airline websites used during the
investigation with Ryanair being the clearest one (pop-up available on every page).
Fig. 7 - Ryanair

Fig. 8 – British Airways

40

References

Barry C. Smith, Dirk P. Günther, B. Venkateshwara Rao, Richard M. Ratlife,
(2001). E-Commerce and Operations Research in Airline Planning, Marketing, and
Distribution. Interfaces 31(2), 37-55.
Buhalis, D. (2004). eAirlines: strategic and tactical use of ICTs in the airline
industry. Information & Management. 41 (1), 805–825.

Burger, B. & Fuchs, M.. (2004). Dynamic pricing — A future airline business
model. Journal of Revenue and Pricing Management. 4 (2), 39-53.

BusinessDictionary.com - Online Business Dictionary. (2014). [ONLINE]
Available at: http://www.businessdictionary.com. [Last accessed 23 May 2014].

Chin, C. L. (2004). Airline Pricing Model. Available:
http://cs.brown.edu/people/clc/airfaresim/model.pdf . [Last accessed 15th May
2014].

Elmaghraby, W. and Keskinocak, P. (2002). ‘Dynamic pricing: Research
overview, current practices and future Directions’, Technical Report, Logistics
Institute, Georgia Tech and The Logistic Institute – Asia Pacific, National University
of Singapore.
European parliament and of the council. (2009). Directive 2009/136/EC.
Available: http://eur-
lex.europa.eu/legalcontent/EN/TXT/PDF/?uri=CELEX:32009L0136&from=EN. Last
accessed 20th May 2014.

Fredrik Wallenberg. (2000). A Study of Airline Pricing. School of Information
Management & Systems, University of California at Berkeley. 1 (1), 1-33.

41

Massimo Fubini. (2014). Cookie: avviso ai naviganti. Available:
http://www.lavoce.info/cookies-avviso-ai-naviganti/. Last accessed 6th May 2014.

Hinz, O., Hann, I.H, Spann, M. (2011). Price Discrimination in E-Commerce? An
Examination of Dynamic Pricing in Name-Your-Own-Price Markets". MIS Quarterly,
Vol. 35(1), 81-96.

Investopedia - Educating the world about finance (2014). Available at:
http://www.investopedia.com [Accessed 20 May 2014].

Matodzi, A. & Warden S.C. (2005). The effect of e-commerce technology on the
airline industry. ICT Research Forum. 2 (1), 1-17.

Porter, M. (2001). Strategy and the Internet. Harvard Business Review. 103D, pp.
63–78.

Preston McAfee R.;; Te Velde V.. (2007). Dynamic pricing in the airline industry.
Economics and Information systems. 1 (3), 527-569.
Sviokla, John (2003). “Value Poaching,” Across the Board, 40 (2) 11–12.

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