ASSESSMENT OF
OPERATIONAL PERFORMANCE OF PUBLIC SECTOR FUNDED INFRASTRUCTURE IN NIGERIA: THE
AIRPORTS PERSPECTIVE
Ikechukwu A. Diugwu
Federal University of Technology, Minna, Nigeria
E-mail: i.diugwu@futminna.edu.ng
Obioma R. Nwaogbe
Federal University of Technology, Minna, Nigeria
E-mail: obioma.nwaogbe@futminna.edu.ng
Victor Omoke
Federal University of Technology, Minna, Nigeria
E-mail: victor.omoke@futminna.edu.ng
Solomon T. Johnson
Federal University of Technology, Minna, Nigeria
E-mail: Solomon.johnson@st.futminna.edu.ng
Ashem E. Egila
Federal University of Technology, Minna, Nigeria
E-mail: egilashem@gmail.com
Submission: 18/04/2018
Revision: 02/05/2018
Accept: 14/05/2018
ABSTRACT
The study assessed the
performance of public sector funded infrastructure in Nigeria, with a special
focus on airports. It utilized secondary data obtained from the Federal
Airports Authority of Nigeria (FAAN), the Nigerian Civil Aviation Authority
(NCAA), and the National Bureau of Statistics (NBS) covering the period 2004 to
2016. A simple regression analyses of
the data were carried out using total number of employees as the predictor
variable and the total aircraft movement, total passenger movement, and total
cargo movement as the dependent variables. The results of the analyses show
that the p values calculated were < 0.05 alpha value, implying existence of
a statistical relationship among the dependent variables (aircraft movement,
passenger throughput, and cargo throughput) and independent variable (number of
employees). Furthermore, the time series graphs show fluctuations in growth of
the outputs (passenger throughput, aircraft movement and cargo throughput) for
the Nigerian air transport system at various periods. This study has shown that
there is a need for the government and stakeholders to take immediate actions
in tackling factors responsible for the decline and fluctuations in the air
transport industry.
Keywords: air transport; passenger
throughput; aircraft movement; operational performance; infrastructure
1. INTRODUCTION
“Measurement is the first step that leads to
control and eventually to improvement. If you can’t measure something, you
can’t understand it. If you can’t understand it, you can’t control it. If you
can’t control it, you can’t improve it.”
-
H. James Harrington
The
transport sector is noted to be critical for development; transport investment
promotes access and contributes to economic growth and quality of life, while
inadequate transport sector performance constrains development, aggravates the
conditions of the poor, harms the environment, ignores the changing needs of
users and exceeds the capacity of public finances (WORLD BANK, 1996).
There
are benefits derivable from adequate investment in the transport sector. For
instance, WORLD BANK (1996) projected an economic rate of return of about 22% on
transport projects upon completion, a lowered agricultural production costs due
to increased access to markets and credit, an indirect facilitation of the
development of the non-agricultural rural economy, increased labor market
efficiency and access to amenities, as well as domestic and international trade
through a more efficient movement of people and freight .
The importance of the transport
sector to economic development is further noted by Owen (1987), who argues that
mobility is about the most important contributor to economic and social
progress because the other components of a satisfactory life (ranging from food
and health to education and employment) rely on the availability of adequate
means of movement of people, goods and ideas.
This notion finds strong support in Easterly and Rebelo (1993), whose
work established that investment in public infrastructure in transport has a
significant and positive effect on economic growth.
There
are also country specific studies, for instance, Dharmawan (2012), that
highlight the relationship between provision of adequate (air) transport
infrastructure and economic performance. A similar relationship between
investment in transport infrastructure and economic growth is equally observed
in Nigeria. For instance, the study by Bosede et al. (2013) concluded that
investments in transport infrastructure contributed positively to growth, with
a strong and statistically significant impact. The outcome of this study has
necessitated arguments in favour of economic policies that would not only
improve the transport infrastructure, but also increase the investments in the
sector with a view to achieving a sustainable economic growth in Nigeria.
These
possible benefits notwithstanding, it has been observed that the contribution
of the transport sector to Nigeria’s GDP has been dwindling. This is in spite
of the enormous expenditure by the Government aimed at promoting viable
sustainable transportation system Consequently, studies, for instance by
Anfofum et al. (2015), have recommended a proactive and dynamic cohesive
transport policy that would be beneficial to all stakeholders as a solution.
Therefore,
there is a need to periodically assess a nation’s transport policy with a view
to making it more efficient and effective.
This entails an efficient use of the sector’s resources as well as a
proper maintenance of the assets, because “public transport systems that fall
into disrepair because they are economically or financially unsustainable, fail
to serve the needs of the poor, and often have environmentally damaging
consequences” (World Bank, 1996).
A
failure to periodically review a nation’s transport policy framework leads to a
possible stagnation in the expansion of transport physical infrastructure; because
a rapid growth in transport demand strains the transport capacity, leading to
capacity crisis, increase in congestion, and other challenges (UGBOAJA,
2013). Based on the observations by Pabedinskaitė
and Akstinaitė (2014), the development of a proactive and cohesive transport
policy in Nigeria can best be achieved through an assessment of the operational
efficiency and the quality of the services provided by the sectors and its
sub-sectors.
Transport
infrastructure connotes different thins to different people. Consequently, this study adopts the
description of transport infrastructure by Crockatt (2000) as encompassing air
infrastructure among others, as well as the definition of air transport infrastructure
as the facilities and oversight required to provide efficient and on-time air
transport services to the public (JUAN, 1995).
The
aviation industry has been chosen because of its strategic role in the attainment of sustainable
development. It has been observed that
improvement in the air transport infrastructure (encompassing airports and
air-traffic management), lowers transport costs, supports rapid economic growth
and increases personal mobility, thus plays a key role as a facilitator of, and
good complement to policies aimed at improving living standards and poverty
alleviation (OXFORD ECONOMIC FORECASTING, 2003).
Earlier
studies, for instance by Ishutkina and Hansman (2008), found a positive
relationship between air transport and economic development. Again, the study
by Baltaci et al. (2015) supports the view of airline transport as being an
important factor that positively affects economic growth such and that an
economic growth could be achieved by increasing the number of active airports
and traffic frequency.
It
is within the above context that this paper attempts to assess the operational
performance of airports in Nigeria. This, it is hoped, would lead to the (re) establishment
of clearly defined responsibilities and objectives of the various stakeholders
within the sector, improved control by administrators, management and
regulators, a strategic alignment of objectives, as well as a better
understanding of the business processes in the sector (KAYDOS, 1998).
2. LITERATURE REVIEW
In
view of the level of competition in global business environment, there is a
need for targeted cost-effective timely investment, as well as the development
and implementation of policies that would lead to successful economic
development (JUAN, 1995). To achieve this, management principles (e.g. total
quality management) are used to assess operational performance, that would form
the basis for the development of plan(s) capable of enhancing customer
satisfaction through continuous improvements in quality of products, services
and processes.
It
has been proven that the adoption of these management philosophies or
principles lead to improvements in various aspects of a business endeavor such
as competitiveness, the ability to satisfy the requirements of customers,
effectiveness, flexibility, and competitiveness of business organizations
(OAKLAND, 1995; OAKLAND, 2003; TERZIOVSKI, 2006). The application of total
quality management principles to services oriented organizations such as
airports has been discussed in Bon and Mustafa (2013).
According
to the United States Federal Aviation Administration, “an airport is defined in
the law as any area of land or water used or intended for landing or takeoff of
aircraft including appurtenant area used or intended for airport buildings,
facilities, as well as rights of way together with the buildings and
facilities.” An inference from Gillen and Lall (2001), is that an airport is a
complex and highly sophisticated system, made up of an air system comprising of
two major elements, the airside and landside.
The
basic functions of an airport are to provide access for aircraft to the
national airspace, permit easy interchange between aircrafts, and facilitate
the consolidation of traffic. To effectively deliver these functions, an
airport must have some basic infrastructure elements such as runway, taxiways,
aprons (airside infrastructure) and airport ground resources for passengers or
cargo. Airport infrastructure could be
seen as an aggregate of different services (airside, landside, security and
safety, as well as surface) offered in an airport (JUAN, 1995).
Airside
services include the airfield, gates, jet-ways, all facilities associated with
the movement of aircraft, as well as all facilities beyond the passenger security
areas such as runways, taxiways, aprons whereas the landside services encompass all facilities associated
with the movement of passengers and baggage to or from aircraft, facilities
devoted to service passengers into and inside the terminal areas such as
passenger services, food and beverage concessions, duty free, car parking
(JUAN, 1995; JUAN, 2001).
Furthermore,
security and safety services include facilities that are associated with the
provision of police, security, customs, immigration, fire and rescue, while
surface access refers to road and rail (JUAN, 1995; JUAN, 2001). Among the
concerns of airport industry is how these services are performed; as such,
routine measurement of productivity becomes important.
Earlier
studies established a positive relation between availability of infrastructure
and level of economic performance. With specific reference to the transport
sector, a study on the implication of improvement in transport infrastructure
and economic growth by Bosede et al. (2013) recommended an increase in the
budgetary allocation to the transport sector as a way of improving
infrastructure availability. Hence, there is a need to ensure that allocated funds are
judiciously spent.
Many
studies (JARŽEMSKIENĖ, 2012; ZHANG et al., 2012; TSENG et al., 2008) have been
carried out on the operational performance of airports with a view to assessing
their efficiency, productivity, rate of development, as well as capacity
utilization. For instance, a study by Pius et al. (2017a) assessed the
operational performance of Murtala Muhammed International Airport (MMIA)
terminal in Lagos and established a statistically significant relationship
between aircraft movement and variables such as total cost, total assets,
wages, and number of employees of the airport.
Another
study by Wanke et al. (2016) on the productive efficiency of Nigerian airports
using Fuzzy-DEA identified airport fixed costs (capacity cost) as an efficiency
driver and suggested the adoption of an airport efficiency improvement policy
based on keeping capacity (fixed) costs under control.
Airport
performance assessment is carried out for reasons such as attraction of
investments, reduction of operational cost, improvement of efficiency,
monitoring of safety and environmental impact among others (DOGANIS,
2005). Most airports aim to maximize the
movement of aircrafts, while increasing efficiency level in their operations
processes as a way of achieving sustainable competitive edge over their competitors
in the sector.
Stephens
and Ukpere (2011) note that air transport is relatively expensive when compared
with other modes of transport like road, rail and water transportation, and
competent and motivated human resources are needed for effective and efficient
utilization of physical infrastructure within the airports. Therefore, the availability of human
resources and its capabilities becomes crucial in any performance assessment.
To
this end, some studies, for instance, Sutia et al. (2013) have been carried out
to investigate the influence of influence of investment in human capital on
airport performance. The provision of
airport infrastructure is capital intensive and requires long gestation periods
to generate returns and breakeven.
Indeed,
existing literature has suggested that airports that are effectively regulated
and subjected to good performance controls would invariably have efficient
terminals capable of earning higher profits and/or further investments (NWAOGBE
et al., 2017a; OGWUDE et al., 2018; PIUS et al., 2017b; NWAOGBE et al., 2017b).
Infarct,
a study by Ahmad and Schroeder (2003) empirically showed that as human resource
management system deviates from the ideal-type human resource management
system, the plant’s performance decreases, and this relationship is
statistically significant.
A
study of the operational efficiency of major airports in the United States
carried out by Sarkis (2000) was based on four resource input measures
including airport operational costs, number of airport employees, gates and
runways, and five output measures including operational revenue, passenger
flow, commercial and general aviation movement, and total cargo transportation.
3. RESEARCH METHODOLOGY
3.1.
Study
Area
This study covers air
transportation in Nigeria for both domestic travel and international travel.
Nigeria is a country occupying 923,768.64 square Kilometres, situated on the
West Coast of Africa and lies on latitudes 4° North of the Equator and
latitudes 3° and 14° on the East of the Greenwich Meridian. It shares boundaries
with The Republics of Benin and Niger in the West, Cameroon in the East, Niger
and Chad in the North and the Gulf of Guinea in the South (NIGERIAN NATIONAL PETROLEUM CORPORATION, 2016).
It has nine (9) international
airports, the major ones being Murtala Muhammad Airport, Lagos, Nnamdi Azikwe
Airport, Abuja and Malam Aminu Kano Airport, Kano. The Murtala Muhammed Airport
is the hub and very busy, accounting for about 80% of the total air
transportation operation services in Nigeria. The Federal Airport Authority,
Nigeria Civil Aviation Authority and the Nigeria Airspace Management Agency,
have more than 24 local airports, some international airports also function as
local airport.
Figure
1: Map showing some airports in Nigeria
3.2.
Formulation
of Hypothesis
The following
hypothesis are proposed to facilitate data collected to arrive at a specified
conclusion, they are:
- H1:
There is a significant relationship between number of employee and aircraft
throughput.
- H2: There is
a significant relationship between number of employee and passenger
throughput.
- H3: There is a significant relationship between number of
employee and cargo throughput.
3.3.
Choice
of Method
A determination of whether there is a
significant linear relationship between the specified independent variable X,
and a dependent variable Y as indicated in the proposed hypothesis, would focus
on the slope of a regression line. Thus the decision to use regression analysis
for this study. In situations where there
is only one independent or explanatory variable (as in this case), Salvatore and Reagle (2002), observe that a
simple regression analysis is carried out.
The suitability of regression analysis for this study is shown variously
in Aczel and Sounderpandian (2009), Rencher
(2003), Weiss (2012).
3.4.
Source
of Data and Analysis Software
This study utilised secondary data
(2004-2016) from the Federal Airports Authority of
Nigeria (FAAN), Nigerian Civil Aviation Authority (NCAA), and the National
Bureau of Statistics (NBS). Statistical Package for the Social Sciences
(SPSS) was used for the linear regression analysis, while Minitab Statistical
Software was for was used for data presentation, graphical of time series. Table 1 shows the data used in the analysis.
Table 1: Cumulative Data of Nigeria
air transport operation Statistical Data
|
YEAR |
TOTAL PASSENGER MOVEMENT (MILLION) |
TOTAL CARGO MOVEMENT (KG) |
TOTALNUMBER OF EMPLOYEES (THOUSAND) |
|
2004 |
8,157,152 |
99,411,126 |
25,004 |
|
2005 |
8,310,515 |
77,286,904 |
24,689 |
|
2006 |
8,208,195 |
93,248,313 |
24,375 |
|
2007 |
8,409,944 |
94,523,909 |
23,246 |
|
2008 |
8,723,864 |
99,831,668 |
23,748 |
|
2009 |
12,526,464 |
166,782,990 |
23,432 |
|
2010 |
13,981,677 |
180,836,476 |
23,117 |
|
2011 |
14,889,820 |
175,809,524 |
22,803 |
|
2012 |
14,116,790 |
182,804,328 |
20,177 |
|
2013 |
15,274,833 |
198,443,781 |
21,321 |
|
2014 |
15,722,423 |
201,208,117 |
21,453 |
|
2015 |
15,092,478 |
211,324,605 |
22,112 |
|
2016 |
14,562,191 |
165,568,809 |
22,301 |
3.5.
Development
of Regression Model and EquationIntroduction
The simple linear regression, is
a type of regression in which a single numerical independent variable, X, is
used to predict the numerical dependent variable Y. It is observed from sources such as (RENCHER, 2003; WEISS, 2012; ANDERSON et al., 2015; TABACHNICK;
FIDELL, 2013; ACZEL; SOUNDERPANDIAN, 2009) that a general simple
regression model, assuming a linear relationship between the dependent variable
and the independent variable, takes the form:
(1)
Where,
Berenson et al. (2012)
note that the portion of a simple regression equation is a
straight line, with the slope of the line (
), respresenting the
expected change in Y per unit change
in X, and the intercept (
), representing the mean value
of Y when X equals 0. The overlying
assumptions used here have ben explained in great details (ANDERSON et al., 2015; KELLER, 2014; RENCHER, 2003).
The error term, , is assumed to be normally
distributed with a mean of zero and standard deviation (σ) and is independent
of the error terms associated with all other observations associated with all
other observations. Again, the probability of the error distribution is normal,
while the randomness in the dependent variable (
) comes from the error term (
).
A
total number of seven variables were selected and used for this study. The dependent (response) variables are total
aircraft movement (TAM), total passenger movement (TPM), and total cargo
movement (TCM), while the independent (predictor) variable is total number of
employees (TNE).
The
TAM describes the total number of aircraft arriving or departing the airports
(including international and domestic traffic from both commercial and private
airlines). An aircraft that arrives once and departs once is recorded as two
movements.
The
TPM is the number of passengers arriving or departing over a period of one
year, excluding those passengers who are just transiting from the airport. A
passenger that made a round trip journey is counted as two origination and
Destination (O&D) passenger.
The
TCM describes the annual record of cargo loaded or unloaded at the airport. The
TNE is the total number of staff (strategic, tactical, and the operational
level), working at the airport at a given time.
3.6.
The
Coefficient of Determination () and Adjusted Coefficient of Determination
According to Keller (2014), there are several models
that can be used to assess regression models. This study shall use coefficient
of determination (),
T-test and the F-test analyses shall be used as a measure of goodness of fit and
significance. The coefficient of
determination (
)
measures the proportion of the variation in the dependent variable that is
explained by the combination of the independent variables in the regression
model (ACZEL; SOUNDERPANDIAN, 2009; SALVATORE; REAGLE, 2002). The coefficient of determination can be
determined using equation (4) below (ACZEL; SOUNDERPANDIAN, 2009; NEWBOLD et
al., 2013);
(2)
But,
(3)
Making RSS the subject in equation (3), substituting
into equation (3) above and solving, we have;
(4)
R-square always lies between 0 and 1, with literature
suggesting that a value of close to 0 implies that the estimated
regression equation explains none of the variation in Y, while an
close to 1 implies that all points lie on the
regression line (SALVATORE; REAGLE, 2002; ACZEL; SOUNDERPANDIAN, 2009; WEISS,
2012).
The coefficient of determination ()
measures the rate of variation in the dependent variable, explained by the
independent variable. The coefficient has a result between zero and one (0 and
1), with a value of (1) demonstrating a great fit. The values are changed to
percentage, to find out the strength of relationship compared with the significance
level between the variables. The
decision rule here states that if
, the
relationship is strong; But if
,
then relationship is weak.
In order to establish if a significant relationship
exists between the dependent variable and the independent variable(s), the
F-test would be used to test for overall significance. Where an overall
significance exists, a t-test, which tests for individual significance, would
be used to ascertain if each of the individual independent variables is
significant (ANDERSON et al., 2015; ACZEL; SOUNDERPANDIAN, 2009).
The implication of rejecting H0 is that
there is sufficient evidence, statistically, to conclude that one or more of
the parameters is not equal to zero and that the overall relationship between
the dependent variable and the independent variable is significant However, if
H0 cannot be rejected, then there is no sufficient evidence to
conclude that a significant relationship is present.
Recall from equation (1) above that the regression
model proposed for this study is . Therefore, the hypotheses for the F-Test
involve the parameters of the regression model, thus:
(5)
The decision rule for F-Test for overall significance
is to reject H0 if or
is based on an F distribution having p degrees
of freedom in the numerator and n-p-1 degrees of freedom in the denominator.
(6)
where MSR is mean square due to regression and MSE is
mean square due to error.
Rejecting H0 signifies that a sufficient
statistical evidence to conclude that one or more of the parameters chosen is
not equal to zero exists.
The decision rule for t-Test for individual
significance is to reject H0: if ; or
; or
if
There
are however observations that the values of r-squared are affected by the
inclusion of unnecessary independent variable. In this situation, it is
recommended that a modified version of r-squared, the adjusted r-squared. which
has been adjusted for the number of predictors in the model be used. The
adjusted r-squared can only increase if the new term improves the model more
than would be expected by chance, and decreases when a predictor improves the
model by less than expected by chance (MINITAB BLOG EDITOR, 2013)
4. DATA ANALYSIS AND DISCUSSION
4.1.
Descriptive
Statistics
Table 2 shows the descriptive
statistics of the data used. The values
of the mean and standard deviation show that the data are original.
Table 2: Descriptive statistics of the
data
|
TOTAL AIR MOVEMENT (THOUSAND) |
TOTAL
CARGO MOVEMENT (KILOGRAMS) |
TOTAL NUMBER OF EMPLOYMENT (THOUSAND) |
TOTAL
PASSENGER MOVEMENT (MILLION) |
|
|
Mean |
224274.5385 |
149775426.9 |
22906 |
12152026.62 |
|
Median |
225922 |
166782990 |
23117 |
13981677 |
|
Maximum |
275827 |
211324605 |
25004 |
15722423 |
|
Minimum |
177627 |
77286904 |
20177 |
8157152 |
|
Std. Dev. |
33207.94558 |
48826191.47 |
1413.10415 |
3212267.529 |
|
Skewness |
-0.058259227 |
-0.302168401 |
-0.28379064 |
-0.326573458 |
|
Kurtosis |
1.581045181 |
1.421353454 |
2.29792036 |
1.269282607 |
|
|
|
|
|
|
|
Jarque-Bera |
1.097963386 |
1.547730107 |
0.44149318 |
1.853574445 |
|
Probability |
0.577537622 |
0.461226952 |
0.80191987 |
0.395823362 |
|
|
|
|
|
|
|
Sum |
2915569 |
1947080550 |
297778 |
157976346 |
|
Sum Sq. Dev. |
13233211799.23 |
28607963684695100.00 |
23962360.00 |
123823952136685.00 |
|
|
|
|
|
|
|
Observations |
13 |
13 |
13 |
13 |
Table 2 shows that the skewness (S) values of total
aircraft movement, total cargo movement, total number of employees, and total
passenger movement range between -0.328 and -0.058. This result suggests that
the data used are approximately symmetric around the mean because the values
are between -0.5 and +0.5 (BULMER, 1979), and that distributions have long left
tails because of the negative signs (IHS GLOBAL INC., 2015).
Again, the values of mean and median of the data
confirms that the distributions are left-skewed, because the mean is usually
less than the median in a skewed-left distribution (NEWBOLD et al., 2013). Furthermore, the Kurtosis (K) values for total
aircraft movement, total cargo movement, total number of employees, and total
passenger movement range between 1.269 and 2.297, leading to the conclusion
that the distributions are platykurtic as the kurtosis values are less than
three (K<3) (IHS GLOBAL INC., 2015; RENCHER, 2003).
The Jarque-Bera (JB) test was used to test the
hypothesis that the samples are drawn from a normal distribution. The
probabilities associated with the Jarque-Bera are all above 0.05 (p>0.05).
Therefore, we
do not have enough statistical evidence to reject the null hypothesis that the
data is normally distributed. Thus, we conclude that it is likely that the data
follows a normal distribution.
4.2.
Analysis
of Aircraft Movement
Figure 2 shows the trend of aircraft
movement between 2004 and 2016. It is observed that the Nigeria aviation
industry has been facing challenges from years back following the continuous
fluctuations as seen in the graph.
Figure
2: Aircraft Movement Between 2004 and 2016
The graph shows a
steady decline in aircraft movement between 2004 and 2007. This decline in
aircraft movement could be explained by the exit of Nigeria’s carrier, the
Nigeria Airways, from the market in 2004. The lowest dip occurred in 2007,
which was the year that the concession of terminal two of the Murtala Mohammed
Airport (MMA2) was granted to Bi-Courtney Aviation Service Limited (BASL) by
the Government of the Federal Republic of Nigeria. An upward trend in aircraft
movement is noticeable again between 2008 and 2011.
An explanation for
this is that the concession of terminal two of the Murtala Mohammed Airport to
Bi-Courtney Aviation Service Limited (BASL) boosted aircraft movement in the
country. However, between year 2011 and 2012
there was also a slight decrease in aircraft movement, with a rebound again in
2013, which represents the peak in aircraft movement. The decline from 2013 to 2016 is due to
recession and inflation in the country.
In order to test H1, which states that there is a significant relationship
between number of employee and aircraft throughput, a simple regression analysis was used to
test for the level of significance and correlation existing between the number
of employees (the independent variable) and aircraft movement (the dependent
variable). The scatterplot diagram (Figure 3) of variables shows negative association between total air movement
and total number of passengers.
Figure
3: Scatter Plot of Total Air Movement Vs
Total Number of Employees
The analysis of variance is shown in Table
3, while Table 4 contains the model summary.
From the result of
the analysis of variance (ANOVA) presented in Table 4, the model significance
is F(1,11)=18.973,P = 0.001. We can conclude that the regression model
statistically significantly predicts the outcome variable as the sig. (-value) of 0.001 is less than
alpha (0.05). We therefore accept the hypothesis that there is a significant
relationship between the total number of employee and total aircraft
movement.
Table 3: ANOVAa Table
|
Source |
DF |
Sum of Squares |
Mean Square |
F |
Sig. |
|
Regression |
1 |
8376648545/121 |
8376648545.121 |
18.973 |
0.001b |
|
Residual
Error |
11 |
4856563254.110 |
441505750.374 |
|
|
|
Total |
12 |
13233211799.231 |
|
|
|
a. Dependent Variable: TOTAL AIRCRAFT
MOVEMENT
b. Predictors: (Constant), TOTAL NUMBER OF EMPLOYEES
From Table 3,
Adjusted R Square value of 0.600 signifies the 60% of the variance in total
aircraft throughput (response variable) can be explained by the number of
employees (the predictor or explanatory varaible). An R value of 0.796
indicates a high degree of correlation between the dependent variable (Y) and the independent variable or
explanatory variable (X).
Table 4: Model Summary
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
1 |
.796a |
.633 |
.600 |
21012.03823 |
|
a. Predictors: (Constant), TOTAL NUMBER OF
EMPLOYEES |
||||
Again, since we are
able to get a significant value as the coefficient of the equation (β), it
gives a relationship between the dependent and independent variables. This
implies that the higher aircraft movement operations in terms of take-off and
landing, the higher aircraft traffic services, thereby increases airport
operational performance (PIUS et al., 2017).
Table 5: Regression Coefficientsa
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
652546.467 |
98494.995 |
|
6.625 |
.000 |
|
TOTAL
NUMBER OF EMPLOYEES |
-18.697 |
4.292 |
-.796 |
-4.356 |
.001 |
|
|
a.
Dependent Variable: TOTAL AIRCRAFT MOVEMENT |
||||||
From
Table 5, we predict total aircraft movement from total number of employees as
follows:
.
The
negative 18.697 shows that for every variable of aircraft movement predicted
there will be a negative increase in the number of employee by 18.697. The
p-value (< 0.05) next to the total number of employee implies that this
variable is significant in explaining total aircraft movement.
4.3.
Analysis
of Passenger Movement
Figure 4 is the time series graph of
passenger throughput in Nigeria from 2004 to 2016, showing a distinct
fluctuation in passenger throughput in the country. It could be seen from the
graph that there was limited movement between 2004 and 2008, after which
passenger throughput began to increase sharply from the year up to 2011 with a
passenger throughput of 14,889,820.
This is perhaps as a
result of the concession arrangement in some of the airports. Although there was a decline in 2012, another
increase was seen immediately after this up to 2014, with up to 15,722,423
total number of passengers moved. There was another decline in passenger
throughput up to 2016, perhaps, caused by the recession in the country, which
affected various airline industries, and the impact of high exchange rate on
the price of aviation fuel.
Figure
4: Passenger Throughput (2004 to 2016)
A scatter plot of total
passenger movement and total number of employees (Figure 5) shows a negative
association between the two variables.
Figure
5: Total Passenger Movement vs Total
Number of Employees
The analysis of
variance is summarised in Table 6, while Table 7 is the regression model
summary. From the result of the analysis of variance (ANOVA) presented in Table
6, the model significance is F(1,11) = 21.132, P=0.001. Hence, it can be
concluded that the regression model statistically significantly predicts the
outcome variable as the sig. (-value) of 0.001 is less than
alpha (0.05). Therefore, there is enough statistcial evidence to accept the
hypothesis that there is a significant relationship between the total number of
employee and total passenger movement.
Table 6: ANOVA
|
|||||||
|
Model |
Sum of
Squares |
df |
Mean
Square |
F |
Sig. |
||
|
1 |
Regression |
81433995095030.40 |
1 |
81433995095030.40 |
21.132 |
.001b |
|
|
Residual |
42389957041654.60 |
11 |
3853632458332.24 |
||||
|
Total |
123823952136685.00 |
12 |
|||||
|
a.
Dependent Variable: TOTAL PASSENGER MOVEMENT |
|||||||
|
b. Predictors:
(Constant), TOTAL NUMBER OF EMPLOYEES |
|||||||
The adjusted R
Square value of 0.627 implies that about 62.7% of the variance in total
passenger throughput is explained by the number of employees. An R value of
0.811 indicates a high degree of correlation between the dependent variable (Y) and the independent variable or
explanatory variable (X).
Table 7: Model Summary
|
Model |
R |
R
Square |
Adjusted
R Square |
Std.
Error of the Estimate |
|
1 |
.811a |
.658 |
.627 |
1963067.105 |
|
a.
Predictors: (Constant), TOTAL NUMBER OF EMPLOYEES |
||||
Table 8: Regression Coefficient
|
Coefficientsa |
||||||
|
Model |
|
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
|
|
|
|
B |
Std. Error |
Beta |
|
|
|
1 |
(Constant) |
54378745.095 |
9201976.630 |
|
5.909 |
.000 |
|
|
TOTAL
NUMBER OF EMPLOYEES |
-1843.478 |
401.024 |
-.811 |
-4.597 |
.001 |
a. Dependent Variable: TOTAL PASSENGER MOVEMENT
Using
the result shown in Table 8, we can
predict total passenger movement from total number of employees as follows:
The implication of
the above is that for every variable of passenger movement predicted, there
will be a negative increase in the number of employee by a figure of about
1843. The p-value (< 0.05) obtained for the total number of employee implies
that this variable is significant in explaining total passenger movement.
4.4.
Analysis
of Cargo Movement
The
time series graph of annual cargo throughput of Nigeria air transport operation
is shown in Figure 6. The graph shows
continuous fluctuation of cargo throughput, with the highest decline in cargo
carried occurring in 2005, before it started increasing continuously again up
to 2015 when it peaked.
The
positive catalyst to this gradual increase was the airport concession
arrangement that came into being from 2006.
Cargo movement began to experience a decline again from year 2015 till
date, due to economic recession in the country.
Figure
6: Cargo Throughput (2004 to 2016)
A
scatter plot of total cargo movement and total number of employees (Figure 7)
shows a negative association between two variables.
Figure
7: Scatter plot of Total Cargo Movement Vs Total Number of Employees
The analysis of variance is presented in Table 9, while Table 10 is the
regression model summary is shown. The analysis of variance (ANOVA) shown in
Table 10 shows the existence of a significant relationship between cargo
movement and number of employees.
The ANOVA result
shown in Table 9, gives a model significance of F(1,11) = 20.234, P=0.001.
Thus, the regression model statistically significantly predicts the outcome
variable as the sig. (-value) of 0.001 is less than
alpha (0.05). This is enough statistcial evidence for us to accept the
hypothesis that a significant relationship exists between the total number of
employee and total passenger movement.
Table 9: ANOVA
|
ANOVAa |
||||||
|
Model |
|
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
1 |
Regression |
18532852220026400.00 |
1 |
18532852220026400.00 |
20.234 |
.001b |
|
|
Residual |
10075111464668700.00 |
11 |
915919224060791.00 |
|
|
|
|
Total |
28607963684695100.00 |
12 |
|
|
|
|
a. Dependent
Variable: TOTAL CARGO MOVEMENT |
||||||
|
b. Predictors:
(Constant), TOTAL NUMBER OF EMPLOYEES |
||||||
The
adjusted R Square value of 0.616 implies that 61.6% of the variance in the
response variable (total cargo throughput) can be explained by the predictor
variable (the total number of employees). An R value of 0.805 indicates a high
degree of correlation between the dependent variable (Y) and the independent variable or explanatory variable (X).
Table 10: Model Summary
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
1 |
.805a |
.648 |
.616 |
30264157.42 |
a. Predictors: (Constant), TOTAL
NUMBER OF EMPLOYEES
Table 11: Regression Coefficient
|
Coefficientsa |
||||||
|
Model |
Unstandardized
Coefficients |
Standardized
Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
786799131.662 |
141864773.028 |
|
5.546 |
.000 |
|
TOTAL NUMBER OF
EMPLOYEES |
-27810.342 |
6182.495 |
-.805 |
-4.498 |
.001 |
|
|
a. Dependent Variable: TOTAL CARGO MOVEMENT |
||||||
Using
the result shown in Table 11, we can predict total cargo movement from total
number of employees as follows:
This
suggests that for every variable of cargo movement predicted there will be a
negative increase in the number of employee by a figure of about 27810. The
p-value (< 0.05) obtained for the total number of employees implies that
this variable is significant in explaining total cargo movement.
5. CONCLUSION
In conclusion, the study observed
that the air transportation industry has been facing turbulence over the years,
suggested by the result from air transport operation analysis which shows
growth within year 2007 to 2014 notably. The result shows a growth in passenger
throughput from 8,409,944 to 15,722,423, aircraft movement increased from
177,627 to 258,624, and cargo throughput increased from 94,523,909kg to
201,208,117kg.
This steady increase was as a result
of concession and privatization of some airports. Furthermore, from year 2015
it was observed that there was a steady and serious decline in the level of air
transport operational performance. Passenger throughput declined to 14, 562,191
in 2016, while the cargo throughput decreased to 165,568,809kg, and aircraft
movement experienced a significant decrease to 225,922.
Overall, it means that Nigeria’s air
transport performance is sub-optimal because of the serious decline in the
various output variables. Officially, Nigeria faced recession from the year
2015 resulting to an increase in the price of dollar. This increase in dollar
in relation to the value of Naira has put many airlines out of business at
worse or rationalisation of operations at best. The outcome of the hypothesis
tested shows that number of employee has a significant relationship with
aircraft throughput, passenger throughput, and cargo carried respectively.
The
work has been constrained by the dearth of data on total operational cost,
total assets, total check-in counters, number of gates, and terminal capacity.
This greatly affected the level of statistical analysis carried out in this
study. For instance, while it would have been desirable to use multiple
regression (instead of simple regression analysis) to determine which variable
impacts more on the response variable, this was not possible because there was
only one predictor variable (TNE).
The
above limitations notwithstanding, the outcome of the study has some policy
implications. For instance, in view of the significant impact of number of
employees on total aircraft movement, total passenger movement as well as total
cargo movement, the Federal Airports Authority of Nigeria (FAAN), Nigerian
Airspace Management Agency (NAMA), Nigerian Civil Aviation Authority (NCAA) and
the operating airlines should be staffed with adequately motivated staff in
order to ensure and maintain optimal performance. Furthermore, based on the
commendable performance attributable to the concession arrangement in some of
the airports, there is need for divestment of government interest in these
airports in order to enhance performance.
REFERENCES
ACZEL,
A. D.; SOUNDERPANDIAN, J. (2009) Complete
Business Statistics, USA, McGraw−Hill Primis.
AHMAD, S.; SCHROEDER, R. G. (2003) The
impact of human resource management practices on operational performance:
recognizing country and industry differences. Journal of operations Management, n. 21, p. 19-43.
ANDERSON, D. R.; SWEENEY, D. J.;
WILLIAMS, T. A.; CAMM, J. D.; COCHRAN, J. J. (2015) Essentials of Statistics for Business and Economics, USA, Cengage
Learning.
ANFOFUM, A. A.; SAHEED, S. Z.; ILUNO, C.
(2015) Air Transportation Development and Economic Growth in Nigeria. Journal of Economics and Sustainable
Development, n. 6, p. 1-11.
BALTACI, N.; SEKMEN, O.; AKBULUT, G. (2015)
The Relationship between Air Transport and Economic Growth in Turkey:
Cross-Regional Panel Data Analysis Approach. Journal of Economics and Behavioral Studies, n. 7, p. 89.
BERENSON, M. L.; LEVINE, D. M.;
KREHBIEL, T. C. (2012) Basic Business
Statistics: Concepts and Applications, New Jersey, Prentice Hall.
BON, A. T.; MUSTAFA, E. M. A. (2013)
Impact of Total Quality Management on Innovation in Service Organizations:
Literature Review and New Conceptual Framework. Procedia Engineering, n. 53, p. 516-529.
BOSEDE, A.; ABALABA, B.; AFOLABI, D. (2013)
Transport Infrastructure Improvement and Economic Growth in Nigeria. International Journal of Humanities and
Social Science Invention, n. 2, p. 23-31.
BROWN, S. (2011) Measures of Shape: Skewness and Kurtosis. MATH200 - Statistics. New
York, USA: Tompkins Cortland Community College/The State University of New
York.
BROWN, S. (2016) Measures of Shape: Skewness and Kurtosis [Online]. Available: https://brownmath.com/stat/shape.htm
[Accessed 5 May 2018].
BULMER, M. G. (1979) Principles of Statistics, Dover.
CROCKATT, M. A. (2000) Airport Infrastructure and Regional
Development: A Case for Resurrecting the Growth Pole Concept. Master of
Arts Thesis, University of Manitoba.
DHARMAWAN, I. G. N. I. (2012) The effect of air transport to economic
development in Indonesia, Erasmus University.
DOGANIS, R. (2005) The Airport Business, London, Routledge.
EASTERLY, W.; REBELO, S. (1993) Fiscal
policy and economic growth. Journal of
monetary economics, n. 32, p. 417-458.
FEDERAL AVIATION ADMINISTRATION. Airport Categories [Online].
Washington, DC: United States Department of Transportation. Available: https://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/categories/
[Accessed 17 March 2018].
GILLEN, D.; LALL, A. (2001)
Non-parametric measures of efficiency of US airports. International Journal of Transport Economics/Rivista internazionale di
economia dei trasporti, p. 283-306.
IHS GLOBAL INC. (2015) EViews 9 User’s Guide I, Irvine CA,
USA, IHS Global Inc.
ISHUTKINA, M.; HANSMAN, R. J. (2008) Analysis
of Interaction between Air Transportation and Economic Activity. The
26th Congress of ICAS and 8th AIAA ATIO. 8888.
JARŽEMSKIENĖ, I. (2012) Applying the
method of measuring airport productivity in the Baltic region. Transport, n. 27, p. 178-186.
JUAN, E. J. (1995) Airport infrastructure: the emerging role of the private sector,
recent experiences based on 10 case studies. CFS Discussion Paper Series (No.
CFS 115). Washington, DC: World Bank.
JUAN, E. J. (2001) Financing Air Navigation Infrastructure in Emerging Markets. Air
Finance, Fall.
KAYDOS, W. (1998) Operational Performance Measurement: Increasing Total Productivity,
Boca Raton, Florida, CRC Press.
KELLER, G. (2014) Statistics for Management and Economics, Boston, USA, Cengage
Learning.
MINITAB BLOG EDITOR. (2013) Multiple Regression Analysis: Use
Adjusted R-Squared and Predicted R-Squared to Include the Correct Number of
Variables [Online]. Pennsylvania: Minitab Inc. Available: http://blog.minitab.com/blog/adventures-in-statistics-2/multiple-regession-analysis-use-adjusted-r-squared-and-predicted-r-squared-to-include-the-correct-number-of-variables
[Accessed 5 May 2018].
NEWBOLD, P.; CARLSON, W. L.; THORNE, B.
M. (2013) Statistics for Business and
Economics (Global Edition), Harlow, England, Pearson Education Limited.
NIGERIAN NATIONAL PETROLEUM CORPORATION.
(2016) Nigeria Profile [Online].
Abuja, Nigeria: Nigerian National Petroleum Corporation. Available: http://www.nnpcgroup.com/NNPCBusiness/BusinessInformation/OilGasinNigeria/NigeriaProfile.aspx
[Accessed 17 April 2018].
NWAOGBE, O. R.; OGWUDE, I. C.; IBE, C.
C. (2017ª) Efficiency Analysis of the Nigerian Airports: An Application of
DEA-BCC Model. International Scientific
Journal of Air Transport Industry (AERO-Journal), n. 2, p. 28-39.
NWAOGBE, O. R.; PIUS, A.; IDOKO, F. W. (2017b)
Estimating Nigerian Airports Production Functions Using Cobb Douglas Analytical
Model. International Scientific Journal
of Air Transport Industry (AERO-Journal), n. 2, p. 40-45.
OAKLAND, J. S. (1995) Total Quality Management: The Route To
Improving Performance, Oxford; Boston, Butterworth-Heinemann.
OAKLAND, J. S. (2003) Total Quality Management: Text with
Cases, Butterworth-Heinemann.
OGWUDE, I. C.; NWAOGBE, O. R.; PIUS, A.;
EJEM, E. A.; IDOKO, F. O. (2018) Performance Appraisal of Nigerian Airports:
Stochastic Frontier Analysis. Transport & Logistics: The International Journal, n. 18, p. 1-14.
OWEN, W. (1987) Transportation and world development, Baltimore, Maryland, U.S.A,
Johns Hopkins University Press.
OXFORD ECONOMIC FORECASTING (2003) The contribution of air transport to
sustainable development in Africa. Unpublished Final Report for the Air
Transport Action Group. Geneva, Switzerland: Air Transport Action Group.
PABEDINSKAITĖ, A.; AKSTINAITĖ, V. (2014)
Evaluation of the airport service quality. Procedia-Social
and Behavioral Sciences, n. 110,
p. 398-409.
PIUS, A.; NWAOGBE, O.; AKERELE, O.;
MASUKU, S. (2017ª) An Appraisal of Airport Terminal Performance: Evidence from
Murtala Muhammed International Airport (MMIA). International Journal of Professional Aviation Training & Testing
Research, n. 9.
PIUS, A.; NWAOGBE, O.; IDOKO, F. W. (2017)
Estimation of Nigerian Airports’ Production Functions: An Application of Cobb
Douglas Model. In: KAZDA, A.; SMOJVER, I., eds. 6th International Conference on Air Transport (INAIR 2017): Air
Transport Growth- What are the Limits?, 14-16 November 2017 2017b Prague, Czech
Republic. Elsevier Procedia, 225.
RENCHER, A. C. (2003) Methods of Multivariate Analysis, New
York, Wiley.
SALVATORE, D.; REAGLE, D. (2002) Statistics and Econometrics, New York,
McGraw-Hill.
SARKIS, J. (2000) An analysis of the
operational efficiency of major airports in the United States. Journal of Operations management, n. 18, p. 335-351.
STEPHENS, M. S.; UKPERE, W. I. (2011)
Airport capacity utilization in Nigeria: A performance and efficiency analysis.
African Journal of Business Management,
n. 5, p. 11104.
SUTIA, S.; SUDARMA, M.; ROFIATY, D. (2013)
The Influence of Human Capital Investment, Leadership and Strategic orientation
on Airport Performance. International
Journal if Business and Management Invention, n. 2, p. 26-32.
TABACHNICK, B. G.; FIDELL, L. S. (2013) Using Multivariate Statistics, New
Jersey, Pearson Education, Inc.
TERZIOVSKI, M. (2006) Quality management
practices and their relationship with customer satisfaction and productivity
improvement. Management Research News,
n. 29, p. 414-424.
TSENG, K.; HO, J.-F.; LIU, Y.-J. (2008)
A study on the performance evaluation of major international airports in the
world. Journal of Modelling in
Management, n. 3, p. 71-81.
UGBOAJA, P. C. (2013) A Sustainability
Assesment of Nigerian Transport Policy. International
Journal of Asian social science, n. 3,
p. 1208-1226.
WANKE, P.; BARROS, C.; NWAOGBE, O. R. (2016)
Assessing productive efficiency in Nigerian airports using Fuzzy-DEA. Transport Policy, n. 49, p. 9-19.
WEISS, N. A. (2012) Introductory Statistics, Boston, Addison-Wesley.
WORLD BANK (1996) Sustainable transport: priorities for policy reform. Washington,
D.C.: The International Bank for Reconstruction and Deelopment/The World Bank.
ZHANG, B.; WANG, J.; LIU, C.; ZHAO, Y. (2012)
Evaluating the technical efficiency of Chinese airport airside activities. Journal of Air Transport Management, n.
20, p. 23-27.