The Role of E-Commerce in the Success of Low-cost Carriers

Abstract

This research aims to answer the question if e-commerce favoured in a special way the growth of low-cost carriers within the civil aviation market. After defining low-cost and traditional carriers’ business models, data on transported passengers were collected for three countries (Italy, Germany and Spain) and confronted with the number of e-consumers. Despite a significant correlation in all the three markets, only in Italy our hypothesis has been supported by Granger causality, and the regression analysis allows to forecast a future characterized by a growing dominance of LCCs. Although the definition of an econometric model will require further studies, the distinctive features of the Italian market might represent a starting point for future research on the complex relationship between e-commerce and air transport.

Full text

Scientific Annals of Economics and Business
65 (4), 2018, 407-425
DOI: 10.2478/saeb-2018-0029
The Role of e-Commerce in the Success of Low-cost Carriers
Marco Ginanneschi*, Pietro Piu**
Abstract
This research aims to answer the question if e-commerce favoured in a special way the growth of low-
cost carriers within the civil aviation market. After defining low-cost and traditional carriers’ business
models, data on transported passengers were collected for three countries (Italy, Germany and Spain)
and confronted with the number of e-consumers. Despite a significant correlation in all the three
markets, only in Italy our hypothesis has been supported by Granger causality, and the regression
analysis allows to forecast a future characterized by a growing dominance of LCCs. Although the
definition of an econometric model will require further studies, the distinctive features of the Italian
market might represent a starting point for future research on the complex relationship between e-
commerce and air transport.
Keywords: e-commerce; e-consumers; low-cost carrier; Ryanair; Granger-causality.
JEL classification: L10; L93; C53; C63.
1. INTRODUCTION
It has been observed that during the last decades few industries have undergone the
same turmoil as the European airline industry (Cento, 2008). The pace and the intensity of
this change depended on many factors. Institutional and political decisions displayed a
fundamental role, with the establishment in 1997 of the “European single aviation market”
which followed liberalization of the US market during the 1970s (Kassim and Stevens,
2010). But then, another very important push for change came from within the industry: in
1991 Ryanair converted into the first European low-cost carrier (LCC), following the
example of South West Airlines in America. A new business model “no frills” definitively
started just on time to intercept the booming demand for travel at a reasonable price which
came along with globalization and widespread economic growth.
* Finanza Futura Srl, Italy; e-mail: ginanneschi.marco@alice.it (corresponding author).
** Vismederi Srl, Italy; e-mail: piu@vismederi.com.

408
Ginanneschi, M., Piu, P.
Many industry experts and academicians sincerely believed that LCCs operated inside
a “new market segment”, made of potential customers who could not afford to buy tickets
from traditional companies (Dudas, 2010). In other words, the change introduced by LCC
model would not disrupt traditional carriers (Full Service Network Carrier or FSNC). After
all, there was not a single LCC model but many variants developed by a number of
imitators, and according to the former IATA President Giovanni Bisignani, “every airline is
now a lower-cost airline” (Smyth and Pearce, 2006), meaning that cost and tariff reductions
were a common factor for the whole industry.
From 2000 onward, however, LCCs started to have a significant weight on the whole
European passengers’ traffic. Their share grew from 19% in 2007 to 30% in 2016
(Eurocontrol, 2017), evidently eroding market shares from traditional carriers. At this time
the second wave of digital revolution took place, with internet connecting 3.48 billion of
people
1
and e-commerce by individuals
2
in the EU (27 countries) passing from 26% to 55%.
The research question we want to address in this article regards the possible role of e-
commerce in boosting especially LCCs’ market share, giving the latter a competitive
advantage towards traditional carriers.
In order to test this hypothesis, we will first summarize the distinctive characteristics of
the two business models, as portrayed in academic literature. Subsequently we will define
the perimeter of the research in terms of data and statistical methodology. Then we will
present the result of the analysis conducted on three countries (Italy, Spain and Germany)
for which a comparable set of data is available. Finally, we will try to draw some
preliminary conclusions.
2. LITERATURE REVIEW
Until recently, scholars kept believing alternatively that: 1) among LCCs there may be
successful companies but this does not imply their model to be successful (Button, 2012); 2)
a “coexistence” of the two business models is still possible (Alderighi et al., 2005; Acar and
Karabulak, 2015); 3) the low-cost model is reaching anyway maturity, and a stop to market
share increase is behind the corner (Bitzan and Peoples, 2016).
In any event, international authorities from ICAO to IATA and Eurocontrol started to
collect and monitor statistics on LCCs’ market share, drawing primary data from Member
States, in the belief that “the identification of LCCs in ... statistics is gaining importance”
and that the adoption of this business model is the discriminating factor for this
identification (ICAO, 2009).
For the purpose of our research, it is important to recall the fundamental characteristics
of LCCs compared to FSNCs and we will concentrate on these aspects to categorize airlines.
We are not excluding the existence of hybrid models but at the same time we observe that
LCCs are not hiding their nature, and on the contrary they advertise it.
We adopted the concept of business model as defined by Amit and Zott (2012)
3
and,
drawing from a series of studies on the subject (Cento, 2008; Reichmuth, 2008; Diaconu,
2012b, 2012a; Eller and Moreira, 2014; Miranda et al., 2016), we created a synthesis,
technologically updated, of LCC and FSNC characteristics
4
(Table no. 1).

Scientific Annals of Economics and Business, 2018, Volume 65, Issue 4, pp. 407-425
409
Table no. 1 – Major characteristics of LCC and FSNC
Characteristic
Low-cost carriers
Full Service Network Carriers
Aircraft utilization
high
Low utilization on short sectors
Aircraft
Single type
Multiple types
Sectors
Short, 500-1.000 km
(continental)
From ultra-short to long
(continental and intercontinental)
Network
Point to point
Hub and spoke (network connectivity)
Airports
Secondary or less congested
Focus on large airports
Classes
1
2-3
Catering
No meal or free drinks
In-flight catering
Turn around
20-30 minutes
1 hour on short sectors
Fares
Simple (minimum restrictions)
Complex (multiple restrictions)
Luggage
Hand luggage
Hold luggage up to 25kg
Distribution/sales/booking
Direct online, through website
travel agents, own ticket office, website
Ticketing
e-tickets or ticketless
Paper or electronic ticket
Check in
digitalized
At the desk
Even at first glance differences are such that the LCC model qualifies easily into
Markides (2006) definition of <business model innovation> (“To qualify as an innovation,
the new business model must enlarge the existing economic pie, either by attracting new
customers into the market or by encouraging existing customers to consume more”).
Experts agree that the LCCs’ business model implies cost cutting in respect to
traditional passengers’ transport (Smyth and Pearce, 2006; Atiqur et al., 2012; Huschelrath
and Muller, 2012; Bitzan and Peoples, 2016; Miranda et al., 2016), but are not unanimous in
the identification of the most relevant economic advantages. Alderighi et al. (2005) and
Eller and Moreira (2014) consider preponderant the choice of a network (point-to-point or
hub-and-spoke) or the route structure. Riwo-Abudho et al. (2013) underline the influence of
seven “Key Success Factors”, among which technology plays a horizontal role. According
to Campisi et al. (2010), the lower staff costs is the most relevant competitive advantage of
LCCs’ model although “sales and reservations” represent the second biggest cost difference
(about 13%-18%) between the two models.
Cost leadership is not the only force behind competitive advantage. Correctly, Button
(2012) has observed that “numerous large carriers that do not follow the low-cost path still
survive”. The last column of Table no. 1 helps identifying the advantage in terms of
“differentiation” (Porter, 1985) behind the FSNCs’ commercial offer. Using Porter’s
analytical framework (Porter, 1980), we could state, for example, that FSNCs can promise
the travellers not just a lift to a remote destination but the assurance that they will get to the
place they want (feature and technology), right in the middle of it (ease to use), under
whatever conditions (network), in the most comfortable way and enjoying the experience of
travelling (customer service).
The synthesis presented under Table no. 1 is not conclusive on which between the two
business models will succeed in terms of market share
5
. Actions and reactions of the two
groups of carriers are alternatively given by experts and academicians a chance of success
for the future. In this panorama, the authors of this paper asked themselves why so little
attention has been paid to the role of e-commerce. After all, this form of digitalized
distribution, which reduces the distance between the company and the customer, interacts
with almost all the characteristics of the air service (underlined in colour grey in the schema
under Table no. 1).

410
Ginanneschi, M., Piu, P.
We found two possible explanations for this lack of consideration. The first one is of
general order. No one has made a final statement yet of which economic activity benefitted
most and which suffered most the advent of e-commerce (Lieber and Syverson, 2012). The
question is complex given that we are speaking of a relatively young technology (younger
than the LCC model) with high impact on the number of companies (concentration) and the
number of employees (automation) within an industry. The second explanation is peculiar to
civil air transportation. In this sector it is commonly accepted that e-commerce represents a
horizontal innovation with an impact on all industry segments (Harteveldt, 2012; Babic et
al., 2017). Even recent SWOT analysis of Ryanair strategic position (for example Geller et
al., 2013) do not mention among its strengths neither ICT nor e-commerce.
We speculated though that e-commerce could make a difference in the success of one
of the two models. To measure this possible impact, in terms of competitive advantage, we
decided to confront two series of data: the number of transported passengers respectively by
LCCs and by FSNCs and the number of e-consumers over the longest possible span of time.
3. DATA AND METHODS
3.1 Research framework and data
Given the fact that the question of competitive advantage of one business model over
the other one is a debated one, we decided to measure the influence of one specific factor, e-
commerce alias online purchases, over the dynamic of air passengers. We are dealing with
two recent phenomena, e-commerce and the new scenario for the air industry after the low-
cost model introduction, and the lack of data has probably inhibited similar studies before.
Only one of the two variables has been surveyed through a harmonized system for a
sufficient long and statistically relevant period of time at the European level. Among the
existing Eurostat data collections, we took into consideration the evolution of the number of
e-consumers (consumers who made at least one purchase online during the last 12 months).
No Eurostat data are available however on the historical trend of low-cost passengers
(only total passengers transported and national passengers transported). Eurocontrol, an
intergovernmental organisation with 41 Members committed to the building of the Single
European Sky, keeps records of and makes forecast about air traffic and low-cost weight but
only in terms of flight movements
6
. The number of passengers flying low-cost is of crucial
importance for the purpose of our research and for its relative homogeneity and comparability
to the number of persons who bought online. Missing the data of low-cost passengers at the
European level, we searched at national level and found accessible or published data for Italy
(source: ENAC) and Germany (source: DLR) while the analysis for Spain (source: AENA)
has been limited to the number of passengers transported by Ryanair (the most important low
cost on this market) and Iberia (the former flagship and legacy company).
Further limits to the research analysis are discussed and specified country by country
in the Section Results.
Finally, only in one case (Italy), the Granger-causality test resulted in significant
connection between the number of e-consumers and the number of LCC passengers.
Accordingly, the formulation of a forecast by means of a three years lagged regression was
appropriate only for Italy.

Scientific Annals of Economics and Business, 2018, Volume 65, Issue 4, pp. 407-425
411
3.2 Statistical methods
We first associated the values of variable “number of passengers transported by low-
cost companies” to the “number of e-consumers” along the period 2005-2016. Index
numbers of both time series were built by taking the 2005 values as bases (= 100). The
Pearson product-moment correlation was used to measure whether and to what extent the
two variables were (linearly) associated. To address the issue whether the number of e -
consumers could help forecast the number of passengers of LCCs, the time series
underwent a Granger-causality test with a maximum numb er of lags equal to 3. The best
model order was evaluated through the Bayesian information criterion (BIC). Magnitude
and direction of the Granger-causality were calculated from the F-statistics (Seth, 2005).
Accordingly, a simple linear regression was then calculated to predict the number of
passengers based on the number of e-consumers. Robustness analysis of the parameter
estimation was provided by a two-stage procedure. Firstly, the observed normalized time
series were resampled one thousand times from a Gaussian-copula, such that the
generated bivariate data preserved the correlation structure measured between the original
time series. Subsequently, a cross-validation technique was applied over the resampled
time series. Specifically, the holdout method was used to randomly assign data points to a
training set (80 % of the copula-generated data points) or to a test set (20 % of the copula-
generated data points). Each bivariate data series, both in the training set and in the test
set, underwent a linear regression analysis. The mean of the root mean square (RMSE)
values calculated from the errors in each regression was evaluated, along with its 95%
confidence interval, as a measure of the performance of the regression model. The test set
performance compared to the training set performance indicated the robustness of the
hypothesized linear relationship between the two time series. Lastly, distributed lagged
regression was implemented to estimate forward prediction of the number of passengers
for a three-years period from 2017 to 2019 based on the e-consumers data observed in
2016. A comparison of the 2017 predictions with real data was also reported.
4. RESULTS
4.1 Overall outlook
The distribution by year and by country of the LCC and FSNC passengers along with
their ratio over the total number of passengers are presented in Table no. 2. The overall
variation measured the percentage of change between the data of 2016 and of 2005 for the
total number of passengers, the LCC passengers, and the FSNC passengers. All the
countries showed a positive overall increase in the total number of passengers and in the
LCC passengers. Instead, the overall variation of FSNC was positive only in Germany. The
data of passengers available for Spain are referred to the Ryan Air and Iberia numbers,
which in the analysis were considered by extension as proxy of the LCC passengers and the
FSNC passengers, respectively.

412
Ginanneschi, M., Piu, P.
Table no. 2 – Distributions and year-on-year variations of LCC and FSNC passengers (2005-2016)
Country
Year
Total
passengers
LCC
Passengers*
% LCC
Passengers
Year-on-year
variation (%)
FSNC
Passengers*
% FSNC
Passenger
Year-on-year
variation (%)
Germany
2005
164200000
31200000
19
133000000
81
2006
172900000
40300000
23,3
29,2
132600000
76,7
-0,3
2007
183575690
48290432
26,3
19,8
135285300
73,7
2
2008
189888350
53793481
28,3
11,4
136094900
71,7
0,6
2009
180780489
52107581
28,9
-3,1
128672900
71,2
-5,5
2010
190513700
65176600
34,2
25,1
125337100
65,8
-2,6
2011
199711000
64058000
32,1
-1,7
135653000
67,9
8,2
2012
201700000
64263000
31,9
0,3
137437000
68,1
1,3
2013
203180000
65573000
32,3
2
137607000
67,7
0,1
2014
209179000
66855000
32
2
142324000
68
3,4
2015
217143000
68519000
31,6
2,5
148624000
68,4
4,4
2016
224434000
75828000
33,8
10,7
148606000
66,2
0
Overall
variation (%)
36,7
143,0
11,7
Spain
2005
178986878
6410938
3,6
43111524
24,1
2006
191476011
7382523
3,9
15,2
42568831
22,2
-1,3
2007
209055003
11142132
5,3
50,9
40508364
19,4
-4,8
2008
202695768
14871089
7,3
33,5
33647618
16,6
-16,9
2009
186764003
19666616
10,5
32,2
30253630
16,2
-10,1
2010
192045959
26616567
13,9
35,3
28850192
15
-4,6
2011
203627283
34170177
16,8
28,4
24289628
11,9
-15,8
2012
193449137
35565648
18,4
4,1
19816175
10,2
-18,4
2013
186754594
32428398
17,4
-8,8
13243711
7,1
-33,2
2014
195226852
31697207
16,2
-2,3
13348357
6,8
0,8
2015
206865852
35155859
17
10,9
15020168
7,3
12,5
2016
229649422
39850773
17,4
13,4
16588273
7,2
10,4
Overall
variation (%)
28,3
521,6
-61,5
Italy
2005
112981108
20240940
17,9
208,9
92740168
82
-6,4
2006
122970312
28405320
23,1
40,3
94564992
77
2
2007
135308151
34870500
25,8
22,8
100437651
74
6,2
2008
132952402
43393983
32,6
24,4
89558419
67
-10,8
2009
129859539
46871169
36,1
8
82988370
64
-7,3
2010
138909695
50803188
36,6
8,4
88106507
63
6,2
2011
147946210
58123580
39,3
24
89822630
61
8,2
2012
146000783
60293876
41,3
3,7
85706907
59
-4,6
2013
143510334
57942340
40,4
-3,9
85567994
60
-0,2
2014
150243142
68831494
45,8
18,8
81411648
54
-4,9
2015
156965253
75943424
48,4
10,3
81021829
52
-0,5
2016
164368109
81287723
49,5
7
83080386
51
2,5
Overall
variation (%)
45,5
301,6
-10,4
Note: * The passengers’ data for Spain are proxy of LCC and FSNC.
Source: ENAC (Italy, www.enac.gov.it, Dati di traffico, 2005-2016); AENA (Spain, www.aena.es,
Estadísticas de tráfico aéreo); DLR (Germany, www.dlr.de, Low Cost Monitor, 2006-2017).

Scientific Annals of Economics and Business, 2018, Volume 65, Issue 4, pp. 407-425
413
Cross comparisons of the LCC passengers and of the FSNC passengers were enabled
by their year-on-year variations. In all the countries, damping and almost always positive
fluctuations characterized the time series of the LCC passengers. Instead, the variations in
the FSNC passengers were on average lower. For Spain, the FSNC decrease was particularly
dramatic, having suffered a loss in the absolute number of passengers of around 30 million
units in the period considered (Figure no. 1).
Note:. The panels show the year-on-year variation of the number of LCC and FSNC passengers over the period
2005-2016 for Germany (black circles), Spain (red squares), and Italy (blue triangles). The plots enable year-based
comparisons among countries of the variations in the numbers of passengers and help discern possible trends.
Source: data on the plots from Table no. 2, columns “Year-on-year variation (%)”.
Figure no. 1 – Year-on-year variation of the number of LCC and FSNC passengers
Steady growth of the number of e-consumers was observed in the three countries
(Table no. 3). Specifically, the average growth rate of the e-consumers in the period 2005-
2016 was around 4.8 % in Germany, 12.1 % in Spain, and 14.5 % in Italy. Different
inclinations to online purchasing as well as infrastructural lacks or digital divide rates may
explain the large differences in the e-consumers data.
Table no. 3 – Total number of e-consumers and their share over the population (2005-2016)
Population
e-consumers
% e-consumers
Year
Germany
Spain
Italy
Germany
Spain
Italy
Germany
Spain
Italy
2005
82500849
43296338
57874753
34650357
5195561
3472485
42
12
6
2006
82437995
44009971
58064214
40394618
6601496
5225779
49
15
9
2007
82314906
44784666
58223744
42803751
8061240
5822374
52
18
10
2008
82217837
45668939
58652875
43575454
8677098
6451816
53
19
11
2009
82002356
46239273
59000586
45921319
10172640
7080070
56
22
12
2010
81802257
46486619
59190143
49081354
11156790
8878521
60
24
15
2011
80222065
46667174
59364699
51342122
12600140
8904705
64
27
15
2012
80327900
46818219
59394207
52213135
14045470
10097015
65
30
17
2013
80523746
46727890
59685227
55561385
14952930
11937045
69
32
20
2014
80767463
46512199
60782668
56537224
17209510
13372187
70
37
22
2015
81197537
46449565
60795612
59274202
19508820
15806859
73
42
26
2016
82175684
46440099
60665551
60810006
20433640
17593010
74
44
29
Source: Eurostat (population; e-commerce statistics for individuals: at least one online purchase in the
previous 12 months). Available at <https://ec.europa.eu/eurostat/data/database> [1 January 2018]

414
Ginanneschi, M., Piu, P.
The three countries showed different patterns in the dynamics of the interaction
between the increasing number of e-consumers and the variation in the number of LCC or
FSNC passengers (Figure no. 2).
Note. Left column: scatter plots of the number (thousands) of e-consumers (abscissa) vs. the observed number
(thousands) of passengers in the major LCC (black dots) and FSNC (red dots) carriers in Germany (panel A), Spain
(panel C), and Italy (panel E). Right column: the panels display the scatter plots of the normalized values of e-
consumers vs. the normalized values of passengers in the major LCC (black dots) and FSNC (red dots) carriers in
Germany (panel B), Spain (panel D), and Italy (panel F). The two variables were rescaled by setting as base the
year 2005 (= 100). The dashed lines represent how the passenger growth would evolve if it were proportional to the
e-consumers growth rate. Deviations from those lines indicate unbalanced variation of the passengers.
Source: our elaboration from data reported in Tables no. 2 and no. 3.
Figure no. 2 – Passengers in LCC and FSNC vs. e-consumers: absolute and normalized data
Actually, the analysis of the absolute data revealed for Germany that both the
distribution of LCC related to e-consumers and the distribution of FSNC related to e-
consumers had positive trend. In addition, the curve of FSNC was always above the LCC
curve (Figure 2A). In Spain the two curves gave rise to a chiasmus (Figure 2C), with an
intersection point at year 2010 whence the number of LCC passengers began overcoming
the number of FSNC passengers. Like in Spain, in Italy the number of FSNC passengers
declined steadily with the increasing number of e-consumers, but at a slower pace than in

Scientific Annals of Economics and Business, 2018, Volume 65, Issue 4, pp. 407-425
415
Spain. Other things being equal, the number of LCC passengers are expected to exceed the
FSNC passengers by the year 2017 (Figure 2E). Data normalization gave further
information about the relationship between the growth of e-consumers and the variation of
passengers. With respect to the base value (2005 = 100), in all countries the variation of
FSNC passengers compared to the variation of e-consumers was, on average, either barely
above 100 like in Germany (102.7) (Figure 2B), or moderately below 100 like in Italy (94.6)
(Figure 2F), or severely below 100 like in Spain (56.8) (Figure 2D). These findings
evidenced the net loss of passengers for the FSNC in Spain and Italy over the period 2005-
2016. On the contrary, the variation of LCC passengers outperformed the variation of e-
consumers both in Germany and in Spain because their curves of the LCC were always
above the bisection (dashed line), which is the locus of the points where the ratio of the
variation of passengers over the variation of e-consumers equals 1. Instead, in Italy the LCC
curve was above but quite close to the bisection up to 2012, and whereupon the growth has
shifted below.
4.2 E-commerce and civil aviation in Italy
The time series
7
for the Italian passengers’ market (Table no. 2) shows at glance a
number of trends. First of all, we have to register an explosive growth of LCCs, which have
increased their passengers by 301.6 % and total market share, passed from 17.9 % to 49.5%.
Then we notice how the low-cost transport represents a driver for the growth of the total
number of passengers (+45.5 % during the period). Finally, in a market in slow expansion,
we take note that low-cost transport grew in part at the expenses of FSNCs, which lose 10.4
% of passengers during the 12 years period. Numbers show also that LCCs and FSNCs
reacted differently to the 2008-2009 crises, with the first ones increasing their market share
by 24% (2008) and by 8% (2009) and the second ones losing respectively the 10.8% and the
7.3% of passengers. The phenomenon can be explained with the drastic reduction of
disposable income coupled with an accrued price sensitivity of the Italian consumers.
Traffic data relative to the first 10 air carriers in 2015-2016 are reported in Table no. 4:
Table no. 4 – Distribution of passengers (major air carriers in the Italian market, 2015-2016)
Ranking
Carrier
Country
Nr. transported
passengers
Variation (%)
2016/2015
Market share
(%) 2016
2015
2016
1
Ryanair
Ireland
29706675
32615348
9.8
19.8
2
Alitalia
Italy
22987134
23106354
0.5
14.1
3
Easyjet
UK
14363022
14335531
-0.2
8.7
4
Vueling Airlines
Spain
5304079
5901919
11.3
3.6
5
Lufthansa
Germany
4336318
4287095
-1.1
2.6
6
Wizz Air
Hungary
3168232
3517535
11
2.1
7
British Airways
UK
3036624
3109075
2.4
1.9
8
Meridiana Fly
Italy
2803712
2627654
-6.3
1.6
9
Air France
France
2790046
2598309
-6.9
1.6
10
Air Berlin
Germany
1750422
1873891
7.1
1.1
Source: our elaboration from ENAC, Dati di traffico 2015 and Dati di traffico 2016 (www.enac.gov.it).

416
Ginanneschi, M., Piu, P.
Ryanair is market leader with more than 32 million passengers transported in 2016
(+9.8% over the previous year) and a share of 24.3% followed by the former flagship
company Alitalia, a traditional carrier, with a share of 17.2% and a stagnant number of
transported passengers since 1995 (Bergamini et al., 2010). Moreover, we observe how the
third and fourth most important carriers are LCCs and that 2016 has not been a favourable
year for both Lufthansa and Air France.
In order to evaluate the role of e-commerce in the Italian civil aviation market we have
reconstructed first the dynamic of online purchases made by Italians during the period.
Eurostat data was available only for the period from 2005 to 2016 (Table no. 3).
The number of passengers in the low-cost companies was significantly correlated to
the number of e-consumers, r = 0.96
8
, p < 0.0001 (Figures 2E-F). Likewise, also the
correlation between the number of FSNC passengers and e-consumers was significant, r = -
0.75, p = 0.005. The results of the Granger-causality test indicated: a) that a model order of
three lags was the most informative (BIC = 11.47) compared to the two lags order (BIC =
13.05), and b) that the variable number of e-consumers Granger-caused the variable number
of passengers in the low-cost companies (F = 24.5, p = 0.01), while the inverse direction of
causality was not significant (F = 0.65, p = 0.63).
Furthermore, a simple regression analysis was performed on the normalized data to
predict of the number of passengers in the low-cost companies (NP) based on the number of
e-consumers (E), which resulted in a significant regression equation, F (10,1) = 129.1, p <
0.0001. Precisely, the results of the regression (Table no. 5) indicated that NP = 64.566 +
0.704E. Therefore, NP increased by some 0.7 units for each incremental unit of E over the
period from 2005 to 2016. The predictor E also explained a significant portion of variance in
NP (R2 = 0.93).
Table no. 5 – Results of the linear model in observed normalized data
Predictor
Coefficient
SE
95% CI low
95% CI up
t stat
p value
Intercept
64.566
18.602
23.118
106.013
3.47
0.006
E
0.704
0.062
0.566
0.842
11.36
< 0.0001
Linear regression analysis was then replicated 800 times on copula-generated bivariate
data of size 12  2. In this training set the averaged results of the linear models (see Table
no. 6) yielded NP = 54.548 + 0.709E, which was consistent with the regression equation
calculated for the original normalized data, and produced an average R2 = 0.97 and RMSE
equal to 0.069 with a 95% confidence interval from 0.068 to 0.07.
Table no. 6 – Results of the linear model in the training set
Coefficient
SE
95% CI low
95% CI up
t stat
p value
Training
Intercept
54.548
13.483
24.506
84.590
4.046
0.002
E
0.709
0.039
0.621
0.798
17.849
< 0.0001
Test
Intercept
54.649
13.237
25.155
84.143
4.128
0.002
E
0.710
0.040
0.621
0.800
17.627
< 0.0001

Scientific Annals of Economics and Business, 2018, Volume 65, Issue 4, pp. 407-425
417
The robustness of the linear relationship between NP and E was evaluated in an
independent test set of 200 replications of copula-generated bivariate data of order 12  2,
where the average RMSE was equal to 0.093 with a 95% confidence interval from 0.088 to
0.098. The regression equation of the test set was NP = 54.649 + 0.710E. Overall, the
cross-validation showed that the regression coefficients were both significant in training and
test set. In addition, the results in validation were consistent with the estimates obtained
from the regression between the observed normalized data.
Distributed lagged regressions enabled the predictions of the variation of the number of
LCC passengers in the 3-years interval from 2017 to 2019 (Figure no. 3 and Table no. 7).
Note: Projections of the variation in the number of passengers of low-cost companies for years 2017, 2018, and
2019 based on variation in the number of e-consumers up to year 2016.
Figure no. 3 – Distributed lagged regressions
Table no. 7 – Expected LCC passengers in years 2017-2019 based on the number of e-consumers
Observed
Projections
2016
2017
2017
2018
2019
E-consumers
Normalized value
507
558
554
582
621
Absolute value
17593010
19388622
19242293
20214651
21570865
LCC Passengers
Normalized value
402
439
453
484
511
Absolute value
81287723
88820337
91696943
98001328
103470916
Note: Normalized values refer to the 2005 basis (= 100).
By comparing the projections for the year 2017 with the real data, an almost exact
prediction of the number of e-consumers was scored (- 0.75 %), while a slight
overestimation in the number of LCC passengers was found (+ 3.24 %).
The year-on-year increase of the number of passengers (Δ NP) were expected to grow
at a faster pace compared to the predicted year-on-year increases of the number of e-
consumers (Δ E) across the period 2017-2018, where the measured incremental ratios, i.e.,
the marginal propensity Δ NP / Δ E, were estimated greater than 1.A relative minimum
equal to 0.69 is expected in year 2019 (Figure no. 4).

418
Ginanneschi, M., Piu, P.
Note: The ratios of the year-on-year increase of the number of passengers (Δ NP) over the increase of the number
of e-consumers (Δ E) across the period 2017-2019 indicated that the variations in the number of LCC passengers
are expected to grow more than the expected variations in the number of e -consumers. Instead, a slight reduction of
the ratio is also predicted for the year 2019.
Figure no. 4 – Ratio of the year-on-year increases in the period 2017-2019
4.3 E-commerce and civil aviation in Germany
For the German market we found that the number of passengers in the low-cost
companies was significantly correlated to the number of e-consumers, r = 0.96, p < 0.0001.
The number of e-consumers was significantly correlated with the number of FSNC
passengers too, r = 0.70, p < 0.012. No significant causality was found between the e-
consumers and the LCC passengers, in fact neither the number of e-consumers Granger-
caused the number of LCC passengers (F = 2.7, p = 0.22), nor the inverse direction of
causality was significant (F = 0.35, p = 0.80). This finding suggested that the regression
analysis for the German data was not consistent.
On the contrary to what happened in Italy and in Spain (Table no. 2), in Germany
FSNCs succeeded in increasing the number of transported passengers by 11.7% during the
period 2005-2016 and in keeping LCCs’ passengers down to 32% (+/- 1) between 2010 and
2016, while in the other two countries this percentage approached or surpassed 50%
9
. Why
the German consumers, with the highest propensity to buy online (74% of the population
against 29% of e-consumers among the Italian population), accorded so much preference to
traditional carriers in relation to other countries can only be here object of speculation.
Per capita income could explain in part the different LCCs’ market penetration levels
in these countries (we already noticed how the LCC model at the start “created” a new
demand for cheap travels). In fact, a low per capita income could increase the number of
customers buying LCCs’ offers. On the contrary, FNSCs can benefit from a high per capita
income (Del Chiappa, 2013; Saleh, 2015)
10
.
However, as far as the German market is concerned, the most impacting factors acting
on the number of LCCs’ passengers are probably not strictly income-related and stay all on
the supply side of the market. From a literature review, we have identified the following:
a) market dominance (causing imperfect competition);
Imperfect competition can impair the functioning of the online market, for example by
reducing the options available to consumers or by making options more expensive.
Imperfect competition might result from excessive market power, such as dominance over

Scientific Annals of Economics and Business, 2018, Volume 65, Issue 4, pp. 407-425
419
routes and airports, from market control through subsidiaries or from alliances reducing
competition on hub to hub connections (ACI Europe, 2014; Ciliberto et al., 2016).
According to Domanico (2007), a common way incumbents can stop new entrants in the
market is through the use of the so called “grandfather’s rights” over slot allocation in
congested airports. Exactly to prevent such a case, EU Commission opened an investigation
on the Lufthansa – Air Berlin group projected acquisition of 2017 and accepted the deal
only under condition that 50% of slots at Düsseldorf airport were held by Lufthansa's
competitors (European Commission, 2017). In fact, new entrants (in this specific case,
Ryanair) might find difficult to expand their clientele if the most valuable slots in the largest
airports are not contestable (Fageda, 2014b). Under these conditions the e-commerce could
not be able to improve competition and LCCs’ offers may be artificially restricted.
b) a very efficient use of e-commerce by FSNCs;
This hypothesis is consistent with the wide diffusion of online purchases in Germany
(higher than in Italy or Spain) but less consistent with the historical trend of travel
distribution in this country (Ginanneschi, 2014). Doubts remain in particular on legacy
airlines’ website efficiency as e-commerce channels. A study on consumer perceptions
(Powell, 2011) highlighted an average performance of the Lufthansa’s website during 2001-
2009 while Lufthansa Group’s decision in 2015 to introduce a distribution cost charge for
bookings made via GDSs (global distribution systems) is difficult to read in terms of
strength/feebleness of the existing company website (Gnutzmann and Spiewanowski, 2016;
Bingemer, 2018). Of course, this decision assumes a different meaning and economic
weight in the context of Lufthansa’s new big data strategy, based on the acquisition and
anticipation of every possible customer need. Though already considered a school case
(“Amazon in the Air”), this new IT and global strategy adopted in 2014 (Chen et al., 2016)
will eventually produce results in the future but does not change the track record of a
distribution channel – the FSNC’s web site - which is still today of secondary importance,
despite all the efforts.
c) the power of brand;
Lufthansa Group, controlling also Austrian Airlines, Swiss International Airlines,
Brussels Airlines, Eurowings and several Aviation Services companies, with total revenue
EUR 31.66 billion and 109 million transported passengers in 2016 (half the size of the
German passengers market) declared himself “the world’s leading aviation group”
(Lufthansa, 2016). Undoubtedly Lufthansa owns also one of the strongest brands among
airlines in Europe and in the world. According to a recent study (Brand Finance, 2018), in
financial terms the Lufthansa’s brand is worth USD 2.9 billion. Calculating the share of
brand-driven revenues for Lufthansa could be a useful tool to assess to what extent this
brand increases the preference accorded by consumers to FSNCs. A commonly accepted
definition of brand is “a promise made by the company to its consumers regarding its offer
from a qualitative point of view” (Musetescu and Chira, 2015) and German nationals seem
to especially appreciate the fulfilment of this promise. According to an EU Commission’s
survey, the highest level of consumer satisfaction with air traffic is in Germany (BDL,
2016). Germany scores very high also in the Country Brand Index (n° 3 in the world for
2012-2014)
11
and the company brand Lufthansa is consequently reinforced (“Country of

420
Ginanneschi, M., Piu, P.
origin effect”). Finally, as far as German consumers are concerned, there is a certain
evidence of “domestic country bias” to be considered (Evanschitzky et al., 2008).
Without pretending this to be an exhaustive list of supply-related factors impacting the
consumers’ choice and probably reducing the number of LCC passengers in Germany, while
waiting for more in-depth research on each item, we can though attempt an overall quality
assessment (Dumbrava and Iacob, 2013) through a probability impact matrix (Table no. 8).
Table no. 8 – Supply related factors impacting the number LCC Passengers in Germany
(probability impact matrix)
Factor
Impact
Probability
Significance
Market dominance
5
4
20
Efficient use of e-commerce
3
2
6
Brand power
2
4
8
Note: Rating 1-5 (Min.1, max 5).
In conclusion, if we cannot demonstrate via Granger-causality that in Germany the
number of e-consumers is determining the number of passengers it is probably because
supply-related factors contribute to keep lower the number of LCCs passengers compared to
Italy (or Spain).
4.4 E-commerce and civil aviation in Spain
In the analysis of the Spanish data we considered the passengers of Ryanair as a proxy
of LCC passengers, and the passengers of Iberia as a proxy of the FSNC passengers. This
assumption, forced by the lack of aggregated data, reduces the possibility of generalizing the
final result.
The number of e-consumers was significantly correlated both to the number of LCC
passengers (r = 0.92, p < 0.0001) and to the number of FSNC passengers (r = -0.93, p < 0.0001).
No significant connections were found between the e-consumers and the LCC passengers, in fact
neither the number of e-consumers Granger-caused the number of LCC passengers (F = 3.4, p =
0.10), nor the inverse direction of causality was significant (F = 0.89, p = 0.46). This finding
suggested that the regression analysis for the Spanish data was not consistent.
The case of Spain shows similarities with the Italian one but also differences. Among
the former, it must be mentioned the competition moved by low-cost airlines to FSNCs at
their operating bases/hubs, Madrid and Rome-FCO, and the consequent erosion of market
shares undergone by the ex flag carriers (Fageda, 2014b).
Among the latter, we noticed Iberia’s establishment in 2006 of a low-cost airline,
Clickair, which in 2008 merged with the rival Vueling, ending up under Iberia’s control. The
larger Vueling intercepted the new consumption trends and some of the passengers traffic lost
by Iberia. In 2016, with a share of 7% and 16.5 million transported passengers, Iberia was only
third in the Spanish market, after Vueling with 14% and Ryanair with 17%
12
.
Another difference lays on the size of the ex flag company. Iberia succeeded in
merging with British Airways into IAG (International Airlines Group) in 2011. Both these
operations, favouring concentration, could have resulted in new limits to perfect competition
(Fageda, 2014a; Dobruszkes, 2009), therefore reducing the possible causality relationship
between e-consumers and LCC’s passengers.

Scientific Annals of Economics and Business, 2018, Volume 65, Issue 4, pp. 407-425
421
4.5 Towards a synthesis
Considering only the supply-related factors, we tried to represent graphically the above
described differences in a scheme showing market forces’ influence over e-consumers
(Figure no. 5).
Note: C=Convenience; B=Brand Attractiveness; D=Market Dominance; e-C=e-consumers.
Figure no. 5 – Market forces’ influence over e-consumers choices
To summarise, in Italy convenience of the commercial offer, market dominance and
airline brand act all in favour of LCCs, in Germany the same factors support FSNCs while
in Spain these forces operate in different directions.
5. CONCLUSIONS
Despite a significant correlation in all the three markets taken into consideration (Italy,
Germany and Spain), no generalised and significant causality has been found between e-
consumers and LCC passengers. Our hypothesis that the first variable could act
independently in determining the value of the latter, with e-commerce giving a competitive
advantage especially to LCCs, has not been supported by widespread evidence. In practice it
is impossible to finalize a model without adding several other variables, which will require a
supplementary research effort.
But in one market, Italy, the initial hypothesis has been supported by Granger causality
and the regression analysis allows to forecast a future characterized by a growing dominance
of LCCs, whose share of the civil aviation market will attain 60% in a few years. In Italy
FSNCs could end up carrying out their activity in a niche.
Even when the Italian market were a class by itself, researchers could undertake new
investigations on the role of e-commerce actually starting from what makes it special. At
this stage we can suggest two distinctive features: 1) a large liberalized market; 2) no former
flag company in a dominant position.
Moreover, the Italian and Spanish market dynamics show how crucial the e-distribution
system can be for the leadership of cost. The number of passengers transported by Alitalia and
Iberia presented low positive or negative correlation to the increase of online purchases.

422
Ginanneschi, M., Piu, P.
Undeniably the e-commerce, through the comparison of prices and services made available to
the consumer, reveals possible inefficiencies, privileges of position by a given carrier as well
as badly communicated attempts to differentiate the offer. The case of Lufthansa in Germany
is instead an example of product differentiation and of successful branding.
In conclusion, collected data do not allow asserting that LCCs, which adopted e-
commerce as its only propagation medium, are always in the best position to exploit this
technological innovation. However, if you are a traditional carrier operating in a perfectly
competitive market, it is best if you take seriously this eventuality.
References
Acar, A. Z., and Karabulak, S., 2015. Competition between Full Service Network Carriers and Low-
cost Carriers in Turkish Airline Market. Procedia: Social and Behavioral Sciences, 207, 642-
651. http://dx.doi.org/10.1016/j.sbspro.2015.10.134
ACI Europe, 2014. Competition in the European Aviation sector. Analysis Paper.
https://goo.gl/VgH38B.
Alderighi, M., Cento, A., Nijkamp, P., and Rietveld, P., 2005. Network competition - The coexistence
of hub-and-spoke and point-to-point systems. Journal of Air Transport Management, 11(5), 328-
334. http://dx.doi.org/10.1016/j.jairtraman.2005.07.006
Amit, R., and Zott, C., 2012. Creating Value Through Business Model Innovation. MIT Sloan
Management Review, 53, 41-49. http://dx.doi.org/10.2139/ssrn.1701660
Atiqur, M., Sarker, R., Hossan, C. G., and Zaman, L., 2012. Sustainability and Growth of Low Cost
Airlines: An Industry Analysis in Global Perspective. American Journal of Business and
Management, 1(3), 162-171. http://dx.doi.org/10.11634/216796061706141
Babic, R. S., Tatalovic, M., and Bajic, J., 2017. Air transport competition challenges. IJTTE.
International Journal for Traffic and Transport Engineering, 7(2), 144-163.
http://dx.doi.org/10.7708/ijtte.2017.7(2).01
BDL, 2016. Wie zufrieden sind die EU-Bürger mit Flugreisen? BDL Veröffentlichungen, 5.
https://www.bdl.aero/de/publikation/.
Bergamini, E., Gitto, S., and Mancuso, P., 2010. Restructuring the Alitalia business model. Journal of
Air Transport Management, 16(1), 16-19. http://dx.doi.org/10.1016/j.jairtraman.2009.05.003
Bingemer, S., 2018. Back to the future with IATA NDC? Critical turning points in the history of
airline distribution. Journal of Tourism Futures, 4(3), 205-217. http://dx.doi.org/10.1108/JTF-
05-2018-0032
Bitzan, J., and Peoples, J., 2016. A comparative analysis of cost change for low-cost, full-service, and
other carriers in the US airline industry. Research in Transportation Economics, 56, 25-41.
http://dx.doi.org/10.1016/j.retrec.2016.07.003
Brand Finance, 2018. Airlines 50 2018. The annual report on the most valuable airline brands.
https://goo.gl/NYEDKV.
Button, K., 2012. Low-cost airlines: A failed business model? Transportation Journal, 51(2), 197-219.
http://dx.doi.org/10.5325/transportationj.51.2.0197
Campisi, D., Costa, R., and Mancuso, P., 2010. The Effects of Low-cost Airlines Growth in Italy.
Modern Economy, 1, 59-67. http://dx.doi.org/10.4236/me.2010.12006
Cento, A., 2008. The Airline Industry: Challenges in the 21st Century. http://dx.doi.org/10.1007/978-
3-7908-2088-1
Chen, M. H., Schutz, R., Kazman, R., and Matthes, F., 2016. Amazon in the air: Innovating with big
data at Lufthansa. Paper presented at the 49th Hawaii Int. Conf. Syst. Sci. (HICSS).
http://dx.doi.org/10.1109/HICSS.2016.631

Scientific Annals of Economics and Business, 2018, Volume 65, Issue 4, pp. 407-425
423
Ciliberto, F., Murry, C., and Tamer, E., 2016. Market Structure and Competition in Airline Markets.
SSRN working paper. http://dx.doi.org/10.2139/ssrn.2777820
Del Chiappa, G., 2013. Internet versus travel agencies: The perception of different groups of Italian
online buyer. Journal of Vacation Marketing, 19(1), 55-66.
http://dx.doi.org/10.1177/1356766712466613
Diaconu, L., 2012a. The Development of the Low-Cost Carriers’ Business Models. Southwest Airlines
Case Study. Scientific Annals of Economics and Business, 59(1), 231-238.
http://dx.doi.org/10.2478/v10316-012-0016-7
Diaconu, L., 2012b. The Evolution of the European Low-cost Airlines' Business Models. Ryanair Case
Study. Procedia: Social and Behavioral Sciences, 62, 342-346.
http://dx.doi.org/10.1016/j.sbspro.2012.09.054
Dobruszkes, F., 2009. Does liberalisation of air transport imply increasing competition? Lessons from
the European case. Transport Policy, 16(1), 29-39.
http://dx.doi.org/10.1016/j.tranpol.2009.02.007
Domanico, F., 2007. The European airline industry: Law and economics of low cost carriers.
European Journal of Law and Economics, 23(3), 199-221. http://dx.doi.org/10.1007/s10657-
007-9016-8
Dudas, G., 2010. Low-cost Airlines in Europe: Network Structures After the Enlargement of the
European Union. Geographica Pannonica, 14(2), 49-58.
http://dx.doi.org/10.5937/GeoPan1002049D
Dumbrava, V., and Iacob, S., 2013. Using Probability – Impact Matrix in Analysis and Risk
Assessment Projects. Journal of Knowledge Management, Economics and Information
Technology, 3(6-7), 76–96. http://dx.doi.org/10.1039/b815978e
Eller, R. D. A. G., and Moreira, M., 2014. The main cost-related factors in airlines management.
Journal of Transport Literature, 8(1), 8-23. http://dx.doi.org/10.1590/S2238-
10312014000100002
Eurocontrol, 2017. The rapid rise of low-cost carriers. from http://www.eurocontrol.int/news/rapid-
rise-low-cost-carriers
European Commission, 2017. Mergers: Commission approves acquisition by Lufthansa of Air Berlin
subsidiary LGW, subject to conditions. Press release. from http://europa.eu/rapid/press-
release_IP-17-5402_en.htm
Evanschitzky, H., Wangenheim, F., Woisetschlager, D., and Blut, M., 2008. Consumer ethnocentrism
in the German market. International Marketing Review, 25(1), 7-32.
http://dx.doi.org/10.1108/02651330810851863
Fageda, X., 2014a. An empirical assessment of the merger between Iberia, Clickair and Vueling.
Cuadernos Económicos del ICE, 88, 77-95. https://goo.gl/dq4J3M.
Fageda, X., 2014b. What Hurts the Dominant Airline at Hub Airports? Transportation Research Part
E, Logistics and Transportation Review, 70(1), 177-189.
http://dx.doi.org/10.1016/j.tre.2014.07.002
Geller, D., Folan, B., and Sahain, B., 2013. Ryanair Strategy Report. Bridges Consulting. from
https://goo.gl/rNtunP
Ginanneschi, M., 2014. Research note: The growth of Internet purchases and the travel trade. Tourism
Economics, 20(5), 1105-1115. http://dx.doi.org/10.5367/te.2013.0337
Gnutzmann, H., and Spiewanowski, P., 2016. Did the Lufthansa group GDS surcharge stimulate direct
online sales? A causal analysis. e-Review of Tourism Research, 7, 1-16. https://goo.gl/wM8exh.
Harteveldt, H. H., 2012. The future of airline distribution: A look ahead to 2017. Cambridge: IATA.
Huschelrath, K., and Muller, K., 2012. Low Cost Carriers and the Evolutionof the Domestic U.S.
Airline Industry. Competition and Regulation in Network Industries, 13(2), 133-159.
http://dx.doi.org/10.1177/178359171201300202
ICAO, 2009. Definition and identification of low-cost carriers. Working Paper. Retrieved from
https://goo.gl/vceiV6

424
Ginanneschi, M., Piu, P.
Kassim, H., and Stevens, H., 2010. Air transport and the European Union: Europeanization and its
limits. London: Palgrave Macmillan. http://dx.doi.org/10.1057/9780230245389
Lieber, E., and Syverson, C., 2012. Online versus Offline Competition. In M. Peitz and J. Waldfogel
(Eds.), The Oxford Handbook of the Digital Economy. New York: Oxford University Press.
http://dx.doi.org/10.1093/oxfordhb/9780195397840.001.0001
Lufthansa, 2016. Annual Report 2016. https://goo.gl/uygiFC.
Markides, C., 2006. Disruptive innovation: In need of better theory. Journal of Product Innovation
Management, 23(1), 19-25. http://dx.doi.org/10.1111/j.1540-5885.2005.00177.x
Miranda, M., Baltazar, M., and Silva, J., 2016. Airlines Performance and Efficiency Evaluation using
a MCDA Methodology. The Case for Low Cost Carriers vs Legacy Carriers. Open Engineering,
6(1), 389-396. http://dx.doi.org/10.1515/eng-2016-0059
Musetescu, A., and Chira, R., 2015. How to create a strong brand. Knowledge Horizons Economics,
7(2), 119-122.
Porter, M. E., 1980. Competitive Strategies: Techniques for Analyzing industries and Competitors.
New York: Free Press. http://dx.doi.org/10.1002/jsc.540
Porter, M. E., 1985. Competitive advantage: creating and sustaining superior performance. New
York: Free Press. http://dx.doi.org/10.1182/blood-2005-11-4354
Powell, C. M., 2011. Consumer Perceptions of Airline Web Sites: A Three Wave Longitudinal
Analysis. Journal of Aviation Management and Education. https://goo.gl/gu9AdS.
Reichmuth, J., 2008. Analyses of the European air transport market: Airline business models. Air
Transport and Airport Research. https://goo.gl/eVrgbB.
Riwo-Abudho, M., Njanja, L. W., and Ochieng, I., 2013. Key Success Factors in Airlines:
Overcoming the Challenges. European Journal of Business and Management, 5, 84-88.
https://goo.gl/MHvJNu.
Saleh, M., 2015. Assessing the consumer propensity of online shopping: a demographic perspective.
Journal of American Academy of Business, 21(2), 186-193.
Seth, A. K., 2005. Causal connectivity of evolved neural networks during behavior. Network (Bristol,
England), 16(1), 35-54. http://dx.doi.org/10.1080/09548980500238756
Smyth, M., and Pearce, B., 2006. Airline Cost Performance: An analysis of the cost base of leading
network. IATA Economics Briefing, 5. https://goo.gl/tukexk.
Notes
1
2016, source: ITU. http://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx
2
At least one online purchase in the previous 12 months. Source: Eurostat.
3
“a bundle of specific activities - an activity system - conducted to satisfy the perceived needs of the
market”.
4
We did not include among the characteristics the “ancillary services”, which have an economic
relevance in LCCs’ balance sheet, because we considered them external to the core activity of
passengers transport (here under consideration). We also excluded the “staff” component because its
lower cost for LCCs is partly due to the business model, which implies a limited use of staff (“no
service” or “no frills” model) and because some FSNCs (the ex “flag airlines”) suffer from the
“legacy” of high staff cost.
5
However, Porter (1980) warned that differentiation “often requires a perception of exclusivity, which
is incompatible with high market share”.
6
https://ext.eurocontrol.int/lexicon/index.php/Aircraft_movement. Also: https://www.eurocontrol.int/
sites/default/files/article/attachments/eurocontrol-glossary-for-flight-statistics-and-forecasts.pdf
7
Data on transported passengers reported yearly by ENAC (Ente Nazionale per l’Aviazione Civile),
have two unique properties: 1) They are collected according to the same standard since 2004 (13
observations available); 2) They are divided in LCC and FSNC passengers. These data have two
limits. The first one is inherent specifically to the Italian system of data collection. Conventionally
passengers are considered the sum of national and international passengers. While the latter are

Scientific Annals of Economics and Business, 2018, Volume 65, Issue 4, pp. 407-425
425
included in the figure if they arrived in an Italian airport or departed from an Italian airport, the first
ones are counted twice, at departure and at arrival, because both airports are on Italian territory.
Eurostat publishes only aggregated data (low cost detail unavailable) with no double counting. Being a
standard and a number measured without variations since publication of official statistics by ENAC,
this limit has been considered a minor one for the purpose of our research. A second limit is common
to the whole European (and international) system of data collection. Passengers’ data are not divided
into residents and foreigner citizens (who could be especially numerous on international flights).
8
In order to verify the possible influence on our analyses of the inclusion of foreigners in the number
of transported passengers in Italy, we reconstructed from yearly publications by ENAC the number of
the sole national passengers LCC transported 2009-2016 and measured again the correlation which
resulted in a r= 0,928117019. We deducted a substantial solidity of our first measurement.
9
In Spain LCC’s overcame FSNC’s passengers (as a % of international arrivals) in 2009. Source:
Instituto de Estudios Turísticos, Informe Turismo, tráfico aéreo y compañías aéreas de bajo coste.
2009. http://estadisticas.tourspain.es/
10
A partial correlation coefficient between number of passengers and e-consumers, controlling for the
real GDP per capita show lower but still significant correlations for all the countries (Italy = 0.88, p=
0.0003; Germany = 0.71, p = 0.01; Spain = 0.90, p = 0.0001).
11
The country’s brand has been defined as “an umbrella identity that is incorporating and offering a
meaning to brands orientated to specific objectives, such as promotion of direct foreign investment,
tourism, exports and even as political influence” (Musetescu and Chira, 2015).
12
Source: AENA (www.aena.es).
Copyright
This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.