Journal of Service Science and Management, 2011, 4, 391-399
doi:10.4236/jssm.2011.43045 Published Online September 2011 (
Copyright © 2011 SciRes. JSSM
Sea-Port Operational Efficiency: An Evaluation of
Five Asian Ports Using Stochastic Frontier
Production Function Model
Otieno Robert Kennedy1, Khin Lin1, Hu a lo n g Yang1*, Banomyong Ruth2
1Transportation Management College, Dalian Maritime University, Dalian, China; 2Department of International Business, Logistics
& Transport, Thammasat University, Bangkok, Thailand.
Email: *,, {robertko78, khinlinmmu68},
Received April 25th, 2011; revised June 3rd, 2011; accepted June 23rd, 2011.
Sea-port operational efficiency is critical factor for handling of goods in the international supply chains, and is viewed
to impact transportation and logistics which play an important role in trade exchange with other countries. It is impor-
tant to evaluate operational efficiency of sea-ports to reflect their status and reveal their position in this competitive
environment. Moreover, knowing impacts of efficiency of sea-ports on the supply chain is vital for business survival.
This study uses stochastic frontier and inefficiency models to analyze sea-port operational efficiency and Delphi tech-
nique to seek expert respondents opinion on its characteristics. The research also uses structural equation modeling to
build a model of sea-port operational efficiency as a further step to examine the significance of the characteristics. The
results of this study emphasize the need to improve sea-port operational efficiency, and indicate which characteristics
should be given more attention.
Keywords: Sea-port Operational Efficiency, Supply Chain, Stochastic Frontier Model, Delphi Technique, Asian Ports
1. Introduction
Sea-ports have been considered to be important parts of
international supply chains [1]. They hold a very impor-
tant role and are the most critical nodes in the supply
chain [2]. It is widely believed that sea-ports form a vital
link in the overall trading chain [3]. Sea-ports are a com-
ponent of freight distribution as they offer a maritime to
land interface for cross-border businesses. Therefore, effi-
ciency of sea-port operation is vital for supply chains.
A lot of research has been done in the area of sea-port
operational efficiency. Many of such research dwell on
tactical means of bolstering sea-port operational effi-
ciency [3-6]. Some researchers regard sea-port as Third
Party Logistics (3PL) provider that intervenes in a series
of different companies and supply chains [7]. Three dif-
ferent channels: trade channel, logistics channel and sup-
ply chain channel were identified by [7] as a new frame-
work of measuring performance of sea-ports. However,
there still exists a gap in assessing the sea-port opera-
tional efficiency. The question: “What characteristics are
key to improving sea-port operational and to what extent
they can bolster efficiency?” has not been adequately
addressed in literature. Some research dwell on one or
two aspects at a time leaving out other aspects.
This research seeks to address this concern by exam-
ining sea-port operational efficiency, establishing deter-
minants of such efficiency for its evaluation and building
its model. Since various aspects of efficiency do not lend
themselves to precise analytical techniques but can bene-
fit from subjective judgments on collective basis [8],
Delphi technique was chosen as a feasible method for
identifying key factors that are significant to sea-port
operational efficiency.
The paper will be comprised of five main sections.
Following the introductory section, the paper will present
reviews on related literature concerning sea-port opera-
tional efficiency and logistics flexibility. The section will
be devoted to defining it and outlining its theoretical pre-
cepts. Next section will present the selected research
methodology followed by results of the research as well
as their analyses and related discussions. The paper will
further present implications of research findings and dis-
cussions of limitations of the current study as well as
recommendations for further research. Finally, there will
Sea-Port Operational Efficiency: An Evaluation of Five Asian Ports Using
Stochastic Frontier Production Function Model
be summary and conclusions.
2. Literature Review
Sea-port operation is defined as cargo handling (or mov-
ing) activity, performed by a designed company (gang or
team), consisting of labor and machines. It is also defined
as the operation of a wharf and other port facilities, op-
eration of port passenger transport service, operation of
cargo loading/unloading, haulage and warehousing ser-
vices within a port area and so on [9].
Presently, there is difficulty in defining port efficiency
due to non-universal definition of what indicates an effi-
cient port or what port efficiency entails [10]. An effi-
cient sea-port should be one that is competent in opera-
tions [10]. Based on this definition, efficiency of sea-port
operations is determined by duration (time) of ship’s stay
in a port, quality of cargo handling and quality of service
to inland transport vehicle during passage through the
port [11]. Quality of cargo handling is in the form of
berth throughput [10] and quality of service to inland
vehicle is dependent on port infrastructure. Productivity
has been identified as a measure of sea-port operational
efficiency [3].
Many researchers have used various approaches to
evaluate sea-port efficiency. Annual firm level surveys
have been employed as indicators of sea-port operational
efficiency, but “there was almost no information on how
port efficiencies evolve over time from these studies” [11,
p. 3]. A number of studies have used data on inputs, out-
puts and production function theory, by means of data
envelopment analysis (DEA), to estimate the most effi-
cient production frontier across a set of sea-ports [6,12,
13]. The approaches using this method have the advan-
tage of economies of scale derived from econometric
evidence but the drawback is that they typically assume
constant return to scale [11]. To address the issue of error
estimation and statistical confidence, another approach,
econometric estimation of cost functions, was developed
by [11]. The method, however, has “difficulties with data
requirements, particularly measurement of labor, capital
and other requirements” [11, p. 5] which limit its appli-
cation to many sea-ports at a time.
Some research has been done on the contribution of
port ownership to efficiency. Transformation from public
to private ownership is believed to improve sea-port op-
erational efficiency even without change in level of com-
petition [14]. Some researchers contended this position
[14] and have opinion that principal-agent problems may
also arise in the private sector as a result of capital mar-
ket imperfections [3]. Reference [15] applied stochastic
production function to evaluate technical efficiency but
did not show that port ownership has significant effects
on sea-port operational efficiency. Moreover, [16] de-
veloped stochastic frontier model and carried out com-
parison of efficiency level of 40 container terminals, but
also failed to establish the relationship between terminal
ownership, operations and efficiency level. On the con-
trary, a number of studies have shown relationship be-
tween port ownership and sea-port operational efficiency
[3,11,17]. Relative efficiency of a number of Asian ports
was assessed by [17] using a combination of cross-sec-
tional and panel data versions of stochastic frontier
model and the finding was that there seems to be some
support that privatization should have some relationship
with improvement in efficiency [3]. These efforts by the
researchers show that port ownership is a likely determi-
nant of operational efficiency.
It has been found that size of sea-port has positive ef-
fects on its efficiency [18]. Also, it has been shown that
ports with larger throughput seem to have certain per-
formance advantage over those with smaller throughput
[17]. In research on 15 sea-ports [19] showed that port
efficiency has no clear relationship with its size and func-
tion (hub or feeder) [3].
3. Methodology and Data
3.1. Methodology
Researchers identified research tools and strategies that
will be employed and related their application to speci-
fied research objectives. Questions to be addressed by
this research set up the direction that the research will
take and are tied to research objectives. The research
questions were: 1) What are the operational efficiencies
of a set of sea-ports? 2) What are the key characteristics
of sea-port operational efficiency? 3) What is the model
of sea-port operational efficiency?
Based on the research questions, the objectives of this
research were to: evaluate operational efficiencies of a
number of selected sea-ports; examine the characteristics
of sea-ports’ operational efficiency and build its struc-
tural model.
Stochastic frontier production function model in [19]
was used to evaluate the efficiency of selected sea-ports.
This method was selected because of its ability to esti-
mate technical inefficiency [19] and simultaneously es-
timates parameters of an inefficiency model with those of
stochastic frontier production model [3]. Delphi tech-
nique was employed to seek expert respondents’ opinion
on the characteristics of sea-port operational efficiency.
3.1.1. S tochasti c Frontier and Inefficiency Models
Stochastic frontier, also known as composed error, model
for production function i
 (i = 1, 2,
3, , N), [20] where is the output for observation i,
Copyright © 2011 SciRes. JSSM
Sea-Port Operational Efficiency: An Evaluation of Five Asian Ports Using 393
Stochastic Frontier Production Function Model
is vector of inputs for observation i,
is the vector
of parameters, i
is error term for observation i, postu-
lates that the error term i
is made up of two inde-
pendent components, iii
where, i
v is a two-
sided error term representing statistical noise in any rela-
tionship; i is one-sided error term representing
technical inefficiency.
The exponential form of the proposed model giving
production function in Equation (1) as,
exp it
u (1)
where, it is the production at the tth observation (t = 1,
2, , T) for the ith firm (i = 1, 2,, N); it
is logarithm
of input variables it is random error assumed to be
truncated normal distribution with respect to mean and
0, v
, and independently distributed of
non-negative random variable, it . The truncated normal
distribution using Wald or generalized likelihood-ratio
test [20] is specified in this research to justify the selec-
tion of distribution form for technical inefficiency effects
it it
Regression of effects of inefficiency on the variables
that explain inefficiency is given by Equation (2) as,
 (2)
where zit is a vector of explanatory variables;
is a
vector of unknown scalar parameters; Wit is truncation of
normal distribution,
0, v
, such that the point of
truncation is such that point of truncation is it
To avoid serial correlation among random errors, this
part of the research will employ cross-sectional data and
use cross-sectional analysis to address concerns about
correlations of inefficiencies and input choices [15,17].
We propose maximum likelihood method for simultane-
ous estimation of parameters of stochastic frontier model
and those of technical inefficiencies model [3]. The like-
lihood function is expressed in terms of variance pa-
rameters 2
can therefore be defined in terms of the ratio between
observed output and potential output given input xit as
TE Wex
it exp
it yit
3.1.2. Delphi Technique
Delphi, a systematic interactive forecasting technique
which depends on a panel of independent, carefully se-
lected expert respondents [21], was used to identify char-
acteristics of efficient sea-port operations. Delphi was
used because researchers felt that expert opinion was the
best available evidence. The method has the ability to
provide anonymity to respondents and controlled feed-
back process as well as allows application of variety of
statistical analysis techniques to interpret data [8].
A group of selected 32 expert respondents in port
management, shipping and logistics field drawn mainly
from China, Hong Kong, Singapore and Korea partici-
pated in the study by answering questionnaires sent
through email. Sample size was kept reasonably small, so
as to do justice to the rich evidence given by qualitative
studies [22]. Table 1 shows sample of expert respondents
who participated in this part of the research.
The process was carried out in three rounds as recom-
mended by literature [21]. Caution was exercised to deal
with concerns of Delphi such as time consumption [21],
molding of opinion, subjectivity versus objectivity and
the assumption that the participants have equivalent
knowledge and experience [21].
Round 1 questionnaire was unstructured with ques-
tions and statements phrased to increase chances of ac-
curacy of responses [21]. In Round 1 the respondents
were asked to identify key characteristics of sea-port
operational efficiency and provide their comments as to
why they thought the identified items were important.
After Round 1 deadline, two weeks as recommended by
literature between rounds [21], results were analyzed and a
summary of the same was included in the design of Round
2 questionnaire. A review of questionnaire statements was
done to remove any possible influences by monitor team’s
views [9]. The questionnaire was sent to the experts to
refine ideas, explore agreements and disagreements and to
probe strengths and weaknesses of opinions.
After deadline of Round 2, responses were analyzed and
Round 3 questionnaire was prepared. In this round re-
spondents were asked to revise judgments or specify why
they remain out of consensus [21]. Round 2 and Round 3
question was,
“To what extent do you agree that the following fac-
tors contribute to sea-port operational efficiency? Please,
provide your comments or additional factors that in your
view are significant to sea-port efficiency.”
The experts were required to rate the characteristics
using Likert-scale (1 = strongly disagree, 2 = disagree, 3 =
moderately agree, 4 = strongly agree, 5 = very strongly
3.2. Data
Since the main activity of container ports is handling
containers only one output will be identified in this study.
The total throughput is a good measurement for the out-
put of a container terminal [3]. Table 2 shows container
throughput of five container ports being investigated.
Literature argues that only the input factors: quay
length; terminal area; and the number of quay cranes are
relevant variables affecting container terminal opera-
ional efficiency [17]. Table 3 shows these input factors t
Copyright © 2011 SciRes. JSSM
Sea-Port Operational Efficiency: An Evaluation of Five Asian Ports Using
Stochastic Frontier Production Function Model
Copyright © 2011 SciRes. JSSM
Table 1. Sample of Delphi respondents.
No. of port/firm
No. of employees
% employees the ports/firms
in all
Respondents expected
Respondents who participated
100 - 249 1,505 43 22 14
250 - 499 770 22 11 7
500 - 999 525 15 8 5
1,000+ 700 20 10 6
test (= =7.089, df = 3, p = 0.0691)
Job title
CEO/president 805 23 11 7
Vice president 525 15 8 5
Manager 1,890 54 27 17
Director 280 8 4 3
test (= =6.533, df = 3, p = 0.0884)
Total 3,500 100 50 32
is obtained using the formula:
Table 2. Port container throughput in TEUs.
Asian Port 2005 2006
Singapore 23,192,000 24,792,400 27,932,000 29,918,200 25,866,400 28,400,000
Hong Kong 22,602,000 23,538,580 23,998,000 24,248,400 21,040,000 23,699,000
Shanghai 18,084,000 21,710,000 26,168,000 28,006,000 25,002,000 29,100,000
Shenzhen 16,197,173 18,468,900 21099,000 21,416,000 18,250,100 22,510,000
Busan 11,840,000 12,030,000 13,261,000 13,425,000 11,954,000 14,180,000
Sources: China Port Industry Report, 2010; Container Throughput Hong Kong [available online] United Nations (2010)
Review of Maritime Transport, Chapter 5.
Table 3. Input factors of five major Asian ports. vate sector participation is given as 0/3 for purely public
ownership; 1/3 for public regulator and landownership
while private sector acts as operators; 2/3 is given for
public being regulator while the private sector perform
the role of landowner and operator and 3/3 is given for
purely private ownership.
Quay Length
Terminal Area
No. of Quay
Singapore 16.945 600 190
Hongkong 19 285 93
Shanghai 20 401 240
Shenzhen 5.543 344 71
Busan 9.95 292.5 70
Cobb-Douglas and Translog functional forms for sto-
chastic production function forms are tested based on
maximum likelihood method by applying FRONTIER
package version 4.1. The following, Equation (4), is the
stochastic production function to be tested,
  
 
011 22 33
41 5263
71 28193
92 3
lnln lnln
ii i
 
 
Source: Global Container Terminal Operators Annual Review, 2010.
for the five ports studied.
We will also use the port ownership [3] to analyze port
ownership structure. According to [3] the degree of pri- Technical inefficiencies are defined by,
Sea-Port Operational Efficiency: An Evaluation of Five Asian Ports Using 395
Stochastic Frontier Production Function Model
011 22 32
 
  (5)
ln: natural logarithm;
i: total throughput in TEU on container port (termi-
nal) i in a given year;
: terminal quay length in metres of port i;
: terminal area in hectares of port i;
: number of quay cranes used in port i;
1i: size of port i, dummy binary variable that distin-
guishes whether annual port throughput exceeds 15 mil-
lion TEUs or not (i.e. 1 if throughput is 15 million
TEUs, and 0 otherwise);
Generalized likelihood method was used to test func-
tional forms. The method is as follows, Likelihood Ratio
LR = –2{ln[L(H0)] – ln[L(H1)]}, where L(H0) and L(H1)
are values of likelihood function under null hypothesis
(H0: 456789
z: the extent of private sector participation.
) and the alterna-
tive H1, respectively. The presence of inefficiency effects
of ui was examined using one-sided generalized likeli-
hood-ratio statistics [23,24].
4. Results, Discussions and Structural Model
4.1. Results of Stochastic Production and
Inefficiency Models
Empirical results based on data from the five sea-ports
are shown in Table 4. All beta were statistically signifi-
cant at p < 5%, showing that the three inputs: total quay
length, terminal area and quay cranes, have significant
effects on production, consistent with result of [3] and
[18]. The estimate of č is 0.8283 implying that 82.83%
of the total variability is associated with technical effi-
ciency of production and it is very significant, p < 1%.
The coefficient, 2
, is negative implying that there is
positive relationship between technical efficiency and
privatization in sea-ports. These results concur with those
found by [3].The coefficient 3
is positive implying an
inverted U-shaped relationship of with sea-port pri-
vatization. The best level of privatization for the seaports
studied is given by: 22
obtained from
Table 4. Final estimates of time-invariant technical efficiencies for period 2005 to 2010.
Stochastic Frontier Model Inefficiency Model
Coefficient Standard errort-ratio Coefficient Standard error t-ratio
13.758 2.214 6.213
0.457 0.205 2.229
0.172 0.078 2.202
0.609 0.195 3.123
0.278 0.124 2.242
0.667 0.800 0.833
0.010 0.003 3.333
0.444 0.871 0.510
parameter 2
0.0732 0.0429 1.7068
0.8283 0.1080 7.6696
Note: approximate critical value for t-ratio at p = 5% is 2.131 and at p = 1% is 2.947 log likelihood function = 17.332313; LR test of the one-sided error =
3.041894; [note that this statistic has a mixed chi-square distribution]; number of iterations = 13; (maximum number of iterations set at: 100). 2
Copyright © 2011 SciRes. JSSM
Sea-Port Operational Efficiency: An Evaluation of Five Asian Ports Using
Stochastic Frontier Production Function Model
Substituting the values of the coefficients gives the
best level of privatization as 0.751 which falls between
Private/Public (0.667) and Private (1.000). The estimate
of coefficient of port size, 1
is negative meaning that
large sea-ports are more efficient than smaller ones.
Technical efficiencies using production function of the
ports were found and the yearly trends are as in Figures
1 and 2. In Figure 1 inefficiency effects were considered,
for 0
) and for ownership effects, for
) the results are as in Figure 2. Since
null hypotheses: no inefficiency, 0123
Likelihood Ratio (LR) = 30.5544 greater than critical
value 2
(5%) = 10.371; no inefficiency effects, 1
, LR = 21.1298 greater than critical value
(5%) = 7.81 and ownership effects (no privatization),
, LR = 23.0419 greater than critical value
(5%) = 5.99, the null hypotheses were rejected [24].
When inefficiency effects are considered, port of Sin-
gapore is found to be more efficient than that of Shang-
hai that catches up and overtakes it in the second half of
Figure 1.Technical efficiencies (inefficiency effects).
Figure 2.Technical efficiencies (ownership effects).
the year 2009.
However, when ownership effects are considered, the
port of Shanghai overtakes that of Singapore just before
mid 2006, to be the third most efficient after Shenzhen
and Hong Kong. Between 2008 and 2009 all the trends
were on downward trend. This is possibly due to global
recession. After 2009, the trends were upwards with
Shenzhen having highest rise in efficiency followed by
Shanghai port. Time invariant efficiencies were found as
follows: Singapore, 0.9116; Hong Kong, 0.9443; Shang-
hai, 0.9029; Shenzhen, 0.9412 and Busan, 0.5963. Mean
technical efficiency for all the ports was 0.8593.
4.2. Results of Delphi Survey
4.2.1. R ound 1
Round 1 responses were analyzed after all the 32 experts
had submitted their responses (after a period of two
weeks). Items generated by respondents were checked
for their similarity and were refined with great care to
avoid losing their initial meanings. Finally, a summary of
eight items identified by respondents was drawn. Items
generated in Round 1 were: “Quality of port infrastruc-
ture i.e. informat ion systems, storage area” (PE1); “Size
of sea-port, terminal area, quay length, quay cranes,
berths” (PE2); “Quality of cargo/container handling”
(PE3); “Port throughput” (PE4); “Measures to reduce
ship turn-round time” (PE5); “Level of private sector
participation” (PE6); “Nature of value added services”
(PE7) and “Port charges and other costs incurred by port
users” (PE8).
4.2.2. R ound 2
The results of Round 2 are presented in Table 5. Litera-
ture recommends using median with inter-quartile range
when applying Likert scale [25]. The results show that
both PE1 and PE8 were rated highest followed by PE3.
The range of respondent rating was same for all the
characteristics except PE2 whose ratings had inter-quar-
tile range of 1.75 showing that there was comparatively
less consensus on its level of contribution.
4.2.3. R ound 3
The Round 3 results in Table 5 show that PE1, PE3 and
PE8 were rated highest, the rating of PE3 improved from
median of 4.5 in Round 2 to 5 in Round 3. Consensus
also improved for both PE2 and PE4, evidenced by lower
inter-quartile range in Round 3 than in Round 2. These
results show that respondents either strongly or very
strongly agree that the factors contribute to efficiency of
sea-port operations.
4.3. Structural Model
odel of sea-port operational efficiency was built using
Copyright © 2011 SciRes. JSSM
Sea-Port Operational Efficiency: An Evaluation of Five Asian Ports Using 397
Stochastic Frontier Production Function Model
Table 5. Median and inter-quartile r a nge of Rosunds 2 and 3 responses.
Round 2 Round 3
Quartiles Quartiles
Item Median
range Median
First Third
the characteristics. Testing of model fit was done using
Normed Fit Index (NFI), Incremental Fit Index (IFI),
Tucker-Lewis Index (TLI), Root-Mean-Square Error
Approximation (RMSEA) and Standardized Root Mean
Residual (SRMR). NFI, IFI and TLI 0.9 imply accept-
able model.
RMSEA and SRMR 0.08 show an acceptable model.
For RMSEA and SRMR = 0.00 the model is perfect [26].
Factor of loading for characteristics in model should be
0.7 for acceptable models [27].
Reliability of characteristics and that of model esti-
mates was tested by determining Cronbach’s alpha [28].
Reliability occurs for Cronbach’s alpha 0.7 [26]. Cron-
bach’s alpha for characteristics in the models was 0.735.
Convergent validity of model estimates was measured
by Average Variance Extracted (AVE) with acceptable
values of 0.5 [27]. The factors of loading, λ, necessary
for calculation of AVE were obtained using principal
component analysis (PCA) extraction capability of
SPSS® software version 19.
4.3.1. Structu r al Model o f Po rt Operational Efficiency
Figure 3 shows model of sea-port operational efficiency.
The characteristic “Quality of cargo-handling” (PE3) and
“Port throughput” (PE5) have the highest regression
weights of 3.00 and 2.38, respectively. The mean rating
by respondents appear as 4.41 and 4.34 respectively for
the two characteristics with their residual error terms e3
and e5 being the lowest at 0.10 and 0.19 respectively.
Model fit indices were as follows: NFI = 0.901; IFI =
1.101; TLI = 1.187; RMSEA = 0.000; SRMR = 0.0307;
{N = 32, df = 8, p = 0.0342} = 16.637. The fit indices
show that the model is acceptable.
Figure 3. Structural model of port efficiency.
5. Implications of Research Findings
The empirical results provide some valuable implications
for port authorities, operators, shipping companies and
logistics providers. It is found that, generally, large sea-
ports are more efficient than smaller ones possibly be-
cause of the quality of port infrastructure, storage and
cargo-handling. Apart from port size, the level of priva-
tization is also an important factor for efficiency. It is,
however, noted that full privatization is not effective in
increasing efficiency of port operations; meaning that the
Copyright © 2011 SciRes. JSSM
Sea-Port Operational Efficiency: An Evaluation of Five Asian Ports Using
Stochastic Frontier Production Function Model
relationship between private sector participation and sea-
port operational efficiency is not linear. It is shown that
the best extent of privatization is between public/private
(0.67) and Private (1.00) mode, according to literature.
This means that private sector participation should be
limited to “landowner and operator” functions while port
authorities should t ake the role of regulator.
Survey results for Delphi technique show that the re-
spondents had consensus that all the eight factors identi-
fied (see Table 5 ) were important determinants of opera-
tional efficiency of sea-ports. They had opinion that port
infrastructure, quality of cargo-handling and port charges
including other costs are the top ranking determinants of
port efficiency. These results imply that port authorities
should charge reasonable amounts since shippers use
costs in selecting port to use, therefore it is a measure of
competitiveness as supported by [3].
Model results in show that sea-port management need
to focus mainly on boosting quality of container-handling,
putting measures aimed at reducing ship turn-round time,
improving quality of port infrastructure and equipment.
This is evidenced by the regression weights of the char-
acteristics in the model.
6. Limitations and Further Research
It is worth noting that this research did not examine the
effects of cost due to unavailability of data. Another limi-
tation is that analyses were limited to containers and left
out cargo which could have provided interesting form of
results. The third limitation was that this research relied on
Delphi interviews to examine the determinants of effi-
ciency perhaps a different approach could yield another
scenario. Further research is therefore recommended to
address these issues and shed more light in this area, and
possibly with a wide range of ports.
7. Summary and Conclusions
The objective of this paper is to evaluate operational ef-
ficiencies of a number of selected sea-ports; examine the
characteristics significant to sea-ports’ operational effi-
ciency and build a structural model of sea-port opera-
tional efficiency. The obtained results provide valuable
implications to port authorities, operators and business
practitioners depending on port. The results show that
port size and infrastructure, private sector participation
and quality of both cargo-handling and logistics services
are important determinants of efficiency.
8. Acknowledgements
We thank the editor in chief and the reviewers very much
for their constructive pieces of advice. This research is
supported by the National Natural Science Foundation of
China (Grant Nos. 70972008 and 70971014).
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