Description
The purpose of this paper is to assess the service requirements of combination air cargo carriers (CACCs).
Firstly, based on the CACC's operational features and relevant literature, the service requirement attributes
(SRAs) of CACCs were investigated. A gap index based on Fuzzy AHP was then proposed to evaluate
the perceived differences toward those SRAs between CACC users and CACC operators. Finally, as an
empirical study, the CACCs in Taiwan and their users were investigated to validate the model. The results
indicate CACC users pay much attention to SRAs: Perfect cargo delivery, Adequate shipping spaces, Accurate
cargo delivery and Staff's professional knowledge. While, the SRAs with higher gaps for CACCs in Taiwan
are: Stable flights, Adequate flight spots and Special cargo delivery. Based on those results, the theoretical
and managerial implications for CACCs in improving service quality are discussed.
Evaluating the service requirements of combination air cargo carriers
Show-Hui S. Huang
a
, Wen-Kai K. Hsu
b, *
a
Department of International Business & Trade, Shu-Te University, 59, Hengshan Rd, Yanchao District, Kaohsiung, Taiwan, ROC
b
Department of Shipping & Transportation Management, National Kaohsiung Marine University, 142, Hai Jhuan Rd, Nanzih District, Kaohsiung, Taiwan, ROC
a r t i c l e i n f o
Article history:
Received 23 March 2015
Accepted 10 May 2015
Available online xxx
Keywords:
Airfreight
Service quality
Gap
AHP
Fuzzy
a b s t r a c t
The purpose of this paper is to assess the service requirements of combination air cargo carriers (CACCs).
Firstly, based on the CACC's operational features and relevant literature, the service requirement attri-
butes (SRAs) of CACCs were investigated. A gap index based on Fuzzy AHP was then proposed to evaluate
the perceived differences toward those SRAs between CACC users and CACC operators. Finally, as an
empirical study, the CACCs in Taiwan and their users were investigated to validate the model. The results
indicate CACC users pay much attention to SRAs: Perfect cargo delivery, Adequate shipping spaces, Accurate
cargo delivery and Staff's professional knowledge. While, the SRAs with higher gaps for CACCs in Taiwan
are: Stable ?ights, Adequate ?ight spots and Special cargo delivery. Based on those results, the theoretical
and managerial implications for CACCs in improving service quality are discussed.
© 2015 College of Management, National Cheng Kung University. Production and hosting by Elsevier
Taiwan LLC. All rights reserved.
1. Introduction
According to the research reports of Boeing company for global
airfreights (Boeing, 2012), shipment quantities will increase
threefold and growat an annual rate of 5.9% over the next 20 years.
Of which, the top ?ve areas with a high growth rate will be Do-
mestic China (9.2%), Intra-Asia (7.9%), AsiaeNorth America (6.7%),
EuropeeAsia (6.6%) and South-Europe (6.5%). The above results
indicate the Asia area can be the focus of airfreight developments in
the future.
For airfreights, the carriers can be classi?ed as three types:
combination air cargo carrier (CACC), conventional all-cargo airline
(CACA) and integrated carrier (IC). In practice, the service model of
airfreights could be explained as Fig. 1. The main shipments of
CACCs come from air freight forwarders (AFFs) consolidating cargo
fromshippers. Thus, AFFs are usually the main customers of CACCs.
As for CACAs and ICs, their shipments may come from both AFFs
and shippers. The latter can even provide a door-to-door service
independently. Currently, the market share of CACCs' shipments is
still higher than both of CACA and IC carriers. However, the IC
carriers have been gradually establishing an integral supply chain
system by which they can provide a complete and prompt service
for shippers. This result has signi?cantly threatened the other two
carriers, especially the CACCs. Thus, it is important for CACCs to
consider how to deal with future competition.
In the relevant literature concerning airfreight services, most of
studies focus on users' service requirements (e.g., Wang, 2007;
Cheng & Yeh, 2007). Few articles examine the perceived gap in
the service requirements between users and service providers. In
practice, the gap may provide improvement information for service
providers and allow them to allocate their resources with ef?-
ciency. Under resource limitations, the information is very useful
for service providers in making policies to improve their service
operations.
The purpose of this paper is to assess the service requirements
of combination air cargo carriers (CACCs). Since AFFs (air freight
forwarders) are the main customers of CACCs, this paper de?ned
AFFs as the users of CACCs. Based on the CACC's operational fea-
tures and relevant literature, the service requirements attributes
(SRAs) of CACC are investigated in this paper. A Fuzzy AHP model is
then proposed to weight those SRAs from both perspectives of
CACC users (i.e., AFFs) and CACC operators respectively. Based on
those weights, a gap assessment model is then proposed to eval-
uate the perceived differences on the SRAs between CACC users and
CACC operators, by which, the CACC operators may make policies in
improving service qualities. Finally, as an empirical study, the
CACCs in Taiwan and their users were investigated to validate the
* Corresponding author.
E-mail address: [email protected] (S.-H.S. Huang).
Peer review under responsibility of College of Management, National Cheng
Kung University.
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Asia Paci?c Management Review xxx (2015) 1e8
Please cite this article in press as: Huang, S.-H. S., & Hsu, W.-K. K., Evaluating the service requirements of combination air cargo carriers, Asia
Paci?c Management Review (2015),http://dx.doi.org/10.1016/j.apmrv.2015.05.001
model. The remainder of the paper is organized as follows: Section
2 presents a literature review. Section 3 explains the research
method used in this paper. A discussion of the results is then pre-
sented in Section 4. Finally, some general conclusions and limita-
tions for further research are given.
2. Literature reviews
2.1. The SERVQUAL scale
For the measurement of service quality for service industries,
the SERVQUAL scale is one of the famous instruments. The
SERVQUAL scale is a multi-item scale developed to assess customer
perceptions of service quality in service and retail businesses
(Parasuraman, Zeithaml, & Berry, 1988). The SERVQUAL scale de-
composes the notion of service quality into ?ve constructs and
develops 22 questions to measure the service quality. The ?ve di-
mensions are de?ned as follows: (1) Tangibles: physical facilities,
equipment, staff appearance, etc. (2) Reliability: ability to perform
service dependably and accurately. (3) Responsiveness: willingness
to help and respond to customer need. (4) Assurance: ability of staff
to inspire con?dence and trust. (5) Empathy: the extent to which
caring individualized service is given. In later studies, the SERVQ-
UAL scale was widely applied for service quality measurements
(e.g., Davis & Mentzer, 2006; Seth, Deshmukh, & Vrat, 2006).
However, the SERVQUAL scale was originally developed for
measuring the perceived service quality of individual customers, so
it may not be adequate for business customers (Durvasula,
Lysonski, & Mehta, 1999). Thus, for measuring the service quality
of business customers, most relevant studies need to revised the
SERVQUAL scale by considering the business' features, such as port
services (Ugboma, Ogwude, Ugboma, & Nnadi, 2007; Pantouvakis,
Chlomoudis, & Dimas, 2008), air cargo services (Wang, 2007),
shipping carrier services (Lai, Chen, Wang, & Lin, 2009), container
terminal services (Hsu, 2013), international port distribution cen-
ters (Hsu & Huang, 2014), etc.
2.2. The service quality in airfreights
In the relevant literature on the service quality of airfreights,
most studies focus on both air cargo carriers and air cargo logistic
providers. For example, for the former, Wang (2007) discussed the
improvement in service quality for the air cargo sector of China
Airlines. The paper identi?ed three service quality dimensions with
20 service requirement attributes (SRAs) to measure the service
quality of air cargo carriers. The three dimensions were Profes-
sionalism, Physical service and Correctness & positivity. The result
indicated the top 3 SRAs in need of improvement for China Airlines
are: Prompt handling of import/export work, Willingness to help solve
customer service and Standard operating procedures. Hsu, Li, Patty,
and Mark (2009) examined the factors affecting ?rms' selection
of air carriers. In the article, six factors were extracted: Product
characteristics, Values, Inventory cost, Shipping charges, Shipping
distance and Time. The results showed shippers with high product
value and short delivery distance focus on the shipping charge and
prefer choosing the air cargo carrier that offers more ?ights.
As for air cargo logistic providers, Cheng and Yeh (2007)
investigated the relationship between core competencies and
sustainable competitive advantage for air-cargo forwarders. The
paper de?ned the core competencies as three variables: Resources,
Capabilities and Logistics services. For the Resources variable, nine
attributes were proposed and three dimensions were extracted:
Corporate scale and information equipment, Relationship with clients,
Upstream and downstream partners, and Corporate reputation and
past delivery quality. For the Capabilities variable, ten attributes
were proposed and three dimensions were extracted: Staff capa-
bility to provide service, Comprehensive management system and
marketing capability and Multiple ?ight selection and price reduction
capability. For the Logistics services, 19 attributes were proposed and
six dimensions were extracted: Logistic information, Customer de-
livery service, Transportation quality and quantity, Upstream and
downstream partner integration, Providing integrated logistic service
and Price ?exibility and prompt response to quoting. The results
indicated Capabilities is the most essential internal variable in?u-
encing the sustainable competitive advantage, in which, the Staff
capability to provide service is the critical factor. Tsai, Wen, and Chen
(2007) examined the demand choices of high-technology industry
for air logistics service providers. The paper proposed 15 SRAs from
shippers' perspectives, which were classi?ed as four constructs:
Service cost, Service performance, Value-added services and Perceived
Capability. The results indicated shippers pay most attention to
Service performance, followed by Service cost and Value-added ser-
vices. Meng, Liang, Lin, and Che (2010) investigated the effects of
logistics services on customer satisfaction with air cargo logistic
providers. In the study, logistic services were assessed by ?ve di-
mensions with 23 SRAs, which were named as Delivery value,
Knowledge innovation value, Service value-added, Information value
and Performance satisfaction value. While, customer satisfactionwas
veri?ed by four constructs with 22 satisfactory indexes, which were
termed as Reliability, Agility, Customization and Flexibility. The re-
sults indicated the Service value-added is the most signi?cant factor
affecting customer satisfaction.
3. Research method
For ease of explanations, some notations are used in this paper:
Fig. 1. The service model of airfreights.
SRA Service requirement attribute
CACC Combination air cargo carriers
AFF Airfreight forwarder
RP Responsiveness
IS Integrated service
TG Tangibles
TC Transportation capability
PS Personnel service
GI Gap index
S.-H.S. Huang, W.-K.K. Hsu / Asia Paci?c Management Review xxx (2015) 1e8 2
Please cite this article in press as: Huang, S.-H. S., & Hsu, W.-K. K., Evaluating the service requirements of combination air cargo carriers, Asia
Paci?c Management Review (2015),http://dx.doi.org/10.1016/j.apmrv.2015.05.001
3.1. Research framework
The research framework of this paper is shown in Fig. 2. Based
on the relevant literature and the features of CACCs, the SRAs for
CACC users are ?rst investigated. A fuzzy AHP approach is then
constructed to weight those SRAs from both of CACC operators and
users (AFFs) perspectives. Finally, based on those two weights, a
gap assessment model is proposed, by which CACC operators may
make policies to improve service quality.
3.2. The hierarchical structure of SRAs
Based on the features of CACCs and the relevant literature, the
SRAs of CACCs are de?ned from the following ?ve dimensions
(Cheng & Yeh, 2007; Hsu et al., 2009; Meng et al., 2010;
Parasuraman et al., 1988; Tsai et al., 2007; Wang, 2007):
1 Responsiveness (RP):
RP is de?ned as CACCs' capability to respond to user requests,
such as information systemservice, cargo tracking, answering price
requests, etc.
2 Integrated service (IS)
IS is de?ned as CACCs' capability to provide value-added ser-
vices, such as warehouse services, special cargo services, inland
transportations etc.
3 Tangibles (TG)
TG is de?ned as CACCs' physical services. For CACCs, generally,
the main physical services are adequate shipping spaces, adequate
?ights and routes, service branches etc.
4 Transportation capability (TC)
TC is de?ned as the CACCs' capability to deliver cargo punctually,
perfectly and accurately. Further, it also contains the compensation
for delivery misses.
5 Personnel service (PS)
PS is de?ned as the service of CACC personnel, including a
proactive attitude, professional capability, uni?ed service window,
etc.
Based on the above de?nitions, a two-layer hierarchy structure
of SRAs for CACCs was ?rst constructed. Four practical experts (two
CACC operators and two AFF) were then invited to revise and check
the independences of those SRAs. Further, they also checked if any
important SRAs were missed. After several rounds of discussions
and revisions, the ?nal hierarchy structure of SRAs, shown as
Table 1, contains ?ve dimensions of SRAs for the ?rst layer and 19
SRAs for the second layer.
3.3. Questionnaire design
Since this paper adopts Fuzzy AHP method to weight the SRAs of
CACCs, a pair-wise comparison questionnaire with a nine point
rating scale was used to measure the relative perceived importance
of SRAs for respondents. Based on the hierarchical structure of the
SRAs in Table 1, an AHP questionnaire with ?ve criteria and 19 sub-
criteria was created. To validate the scale, another two CACCs and
two AFFs were invited to pretest the questionnaire and check
whether the statements were clear.
3.4. Research sample
To validate the assessment model, the CACCs in Taiwan were
investigated. In Taiwan, the top ?ve CACCs are China Airline (33%),
Evergreen Airline (21%), Cathay Paci?c (9%), Japan Airline (8%) and
Dragonair (5%). Of which, their total market share of shipments is
over 75%. For the CACC sample, 1e5 subjects for each of the top ?ve
CACCs were surveyed by company size. Meanwhile, each of the
CACCs was asked to provide 2e10 main customers (AFFs) for the
AFF sample. Finally, we had 42 samples in total, including 14 CACCs
and 28 AFFs.
Since this paper used an expert questionnaire of AHP as a
research instrument, all of the above subjects were asked to ful?ll
the following requirements: (1) the respondents must be senior
employees and (2) the respondents must have suf?cient experience
and knowledge of CACC operations. Further, for enhancing the
validity of the survey, an assistant was assigned to help each subject
to complete the questionnaire.
For each of the CACC and AFF samples in the empirical study, the
consistency index (C.I.) was ?rst used to test the consistency of their
Fig. 2. The research framework.
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Please cite this article in press as: Huang, S.-H. S., & Hsu, W.-K. K., Evaluating the service requirements of combination air cargo carriers, Asia
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pair-wise comparison matrix respectively. The results indicated
four samples' C.I. values are higher than 0.1, implying they are
highly inconsistent (Saaty, 1980). Thus, those questionnaires must
be revised, before use. The four respondents were then asked to
reviewand modify their answers again, until the answers ?tted the
consistency (C.I.) tests.
The pro?les of the 42 respondents' characteristics are shown in
Table 2. It can be seen all of the subjects have at least ?ve years of
work experience, with (10/14) CACC and (20/28) AFF subjects
having more than 10 years in their companies. Note, the remarkable
quali?cations of the respondents endorse the reliability of the
survey ?ndings.
3.5. The weights of SRAs
From the sample data (14 CACCs and 28 AFFs), 42 positive
reciprocal matrices were obtained for each pair-wise comparison of
the SRAs in each layer. In the past, most relevant studies used an
arithmetic mean or geometric mean to integrate multiple subjects'
opinions. However, those two means are sensitive to extreme
values. Thus, a fuzzy number is considered to integrate the subjects'
perceptions. First, the geometric mean was employed to represent
the consensus of the respondents (Hsu & Huang, 2014; Hsu, Yu, &
Huang, 2015). A triangular fuzzy number characterized by mini-
mum, geometric mean and maximum of the measuring scores was
then used to integrate the 42 positive reciprocal matrices into two
fuzzy positive reciprocal matrixes, one for CACCs and one for AFFs.
Finally, based on these two matrixes, a fuzzy AHP approach was
employed to determine the weights of the SRAs. The procedure of
the fuzzy AHP is explained in detail in the Appendix.
The results of the SRAs' weights for AFF samples are shown in
Table 3, in which the global weights of the SRAs in the ?rst layer are
shown in the second ?eld, and those of the SRAs in the second layer
are shown in the last ?eld. The results indicate, for the ?rst layer of
SRA constructs, users (AFFs) pay more attention to TC (25.35%), TG
(22.82%) and PS (20.10%). While, for the second layer of SARs, the
top SRAs users perceived importance are: TC2 (8.76%), TG1 (7.56%),
Table 1
The hierarchy structure of the service requirement attributes of CACCs.
Layer 1: Construct Code Layer 2: Service attribute Reference
Responsiveness
(RP)
RP1 Cargo tracking capability [2], [5e6]
RP2 Information system performance [1e2], [5e6]
RP3 Response for price request [2]
RP4 Flexible ordering time Expert
interviews
Integrated service
(IS)
IS1 Inland transportation [2], [5]
IS2 Warehouse services [5]
IS3 Special cargo services [1]
Tangibles (TG) TG1 Adequate shipping spaces [2]
TG2 Adequate shipping routes Expert
interviews
TG3 Adequate shipping ?ights [2], [4]
TG4 Adequate service branches [2], [5]
Transportation
capability (TC)
TC1 Punctual cargo delivery [1e3], [5e6]
TC2 Perfect cargo delivery [2e3], [5e6]
TC3 Accurate cargo delivery [5]
TC4 Compensation for service misses [3], [5e6]
Personal services
(PS)
PS1 Providing bene?cial loading
modes
[2], [6]
PS2 Professional capability [2]
PS3 Response for complaints [1e2], [5e6]
PS4 Uni?ed service window Expert
interviews
Note: [1] Wang (2007); [2] Cheng and Yeh (2007); [3] Tsai et al. (2007); [4] Hsu
et al. (2009); [5] Meng et al. (2010).
Table 2
Pro?le of the respondents.
Features Range CACC AFF
Frequency % Frequency %
Carriers China airline 5 35.71 10 35.71
Evergreen airline 4 28.57 8 28.57
Cathay Paci?c 2 14.29 4 14.29
Japan airline 2 14.29 4 14.29
Dragonair 1 7.14 2 7.14
Work experience
(years)
5e10 2 14.29 8 28.57
11e15 5 35.71 11 35.71
16e20 6 42.86 3 10.71
Above 20 1 7.14 6 21.43
Age (years) 31e40 8 57.14 17 60.71
41e50 5 35.71 8 28.57
Above 50 1 7.14 3 10.71
Job title President level 0 0.00 4 14.29
Manager level 6 42.85 14 50.00
Senior sales 8 57.14 10 35.71
Education level Master 1 7.14 5 17.86
University 8 57.14 15 53.57
College 5 35.71 8 28.57
Table 3
The weights of AFFs' perceived importance toward SRAs.
Layer 1
SRAs
The global weights
of Layer 1
Layer 2
SRAs
The local weights
of Layer 2
The global weights
of Layer 2 (%)
RP 0.1859 RP1 0.2854 5.31
RP2 0.2753 5.12
RP3 0.3002 5.58
RP4 0.1391 2.59
IS 0.1314 IS1 0.3711 4.88
IS2 0.3353 4.41
IS3 0.2936 3.86
TG 0.2282 TG1 0.3318 7.56
TG2 0.1876 4.28
TG3 0.2785 6.36
TG4 0.2022 4.61
TC 0.2535 TC1 0.2457 6.23
TC2 0.3460 8.76
TC3 0.2808 7.12
TC4 0.1275 3.23
TS 0.2010 TS1 0.2987 6.00
TS2 0.3441 6.92
TS3 0.2123 4.27
TS4 0.1449 2.91
Note: The boldfaced numbers present the SRAs with higher weights.
Table 4
The weights of CACCs' perceived importance toward SRAs.
Layer 1
SRAs
The global weights
of Layer 1
Layer 2
SRAs
The local weights
of Layer 2
The global weights of
Layer 2 (%)
RP 0.1677 RP1 0.2121 3.56
RP2 0.3130 5.25
RP3 0.3138 5.26
RP4 0.1611 2.70
IS 0.1454 IS1 0.2932 4.26
IS2 0.3266 4.75
IS3 0.3802 5.53
TG 0.2955 TG1 0.3592 10.60
TG2 0.1789 5.29
TG3 0.3263 9.64
TG4 0.1356 4.01
TC 0.2065 TC1 0.2919 6.03
TC2 0.2630 5.43
TC3 0.3265 6.74
TC4 0.1187 2.45
TS 0.1849 TS1 0.2375 4.39
TS2 0.3605 6.67
TS3 0.2226 4.12
TS4 0.1795 3.32
Note: The boldfaced numbers present the SRAs with higher weights.
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Please cite this article in press as: Huang, S.-H. S., & Hsu, W.-K. K., Evaluating the service requirements of combination air cargo carriers, Asia
Paci?c Management Review (2015),http://dx.doi.org/10.1016/j.apmrv.2015.05.001
TC3 (7.12%) and PS2 (6.92%). Likewise, the results of the SRAs'
weights for CACC sample are shown in Table 4. The results indicate,
for the ?rst layer of SRA constructs, service providers (CACCs) pay
more attentiontoTG(29.55%) andTC(20.65%). While, for the second
layer of SARs, the top SRAs CACC operators perceived importance
are: TG1 (10.60%), TG3 (9.63%), TC3 (6.74%) and TS2 (6.67%).
3.6. Gap assessment
Obviously, an SRA (service requirement attribute) with higher
AFF' perceived weight and lower CACC's perceived weight should
be a gap with higher improvement priority for CACC operators. In
this paper, when a SRA' AFF weight is higher than its CACC' weight,
we termed this SRA a “negative gap”. In practice, the CACC opera-
tors should consider improving those SRAs' service operations. That
is the policy for those SRAs with negative gaps should be
“concentrate here”. On the contrary, a SRA is de?ned as a “positive
gap” as its AFF weight is lower than its CACC weight. In practice,
those SRAs imply “relative overskill”, and thus the resources
committed to these SRAs could be better employed elsewhere.
Furthermore, a SRA is no positive gap only when its AFF weight
equals its CACC' weight. It is clear that the police for those SRAs is
“keep up the work”
Based on the above concept, a two-dimensional matrix with
both of AFF weights and CACC weights is constructed to assess the
gaps of SARs. The matrix is shown in Fig. 3, in which the CACC
weight is depicted on the x-axis, the AFF weight on the y-axes, and
a 45
+
line divides the matrix into two quadrants. The SRAs in
Quadrant I imply that their AFF weights > their CACC weights. Thus,
those SRAs are evaluated as negative gaps. The management policy
for those SRAs should be “concentrate here”. That is the CACC op-
erators should invest more resources to improve those SRAs.
Furthermore, for assessing the degrees of the SRAs' gaps, a Gap
Index (GI) is thus proposed in this paper, which is evaluated by the
distance from the SRAs' coordinates to the 45
+
line. For example, in
Fig. 3, the SRATG3's coordinate is (9.64, 6.36). Then, its GI is 3.28 by
(9.64À6.36). Thus, the TG3 has a positive gap 3.28. Note, fromFig. 3,
it is clear that the more the SRA's position closes to the 45
+
line, the
smaller the SRA's GI is.
All of the SRAs located in the Quadrant I (with negative gap) are
shown in Fig. 3, and their gaps are listed in the last ?eld of Table 5.
The results indicate there are 11 SRAs with negative gaps (located in
the Quadrant I zone). In practice, the CACC operators should pay
more attention to improving those SRAs. Furthermore, the SRAs in
the Quadrant II are shown in Fig. 3, and their GIs are also shown as
the last ?elds in Table 5. The results indicate there are 7 SRAs
located in the Quadrant II zone (i.e., positive gap). Thus, the re-
sources committed to these SRAs could be better employed else-
where. In practice, for those SRAs with positive gaps, CACC mangers
may consider transferring part of their resources to the SRAs in
Quadrant I.
4. Results and discussions
4.1. The SRA's weights of users perceived importance
The results (Table 3) indicate, for the ?rst layer of SRA con-
structs, AFFs (users) pay more attention to TC (Transportation
capability), TG (Tangibles) and PS (Personal services). While, for the
second layer of SARs, the top SRAs users' perceived importance are:
TC2 (Perfective cargo delivery), TG1 (Adequate shipping spaces), TC3
(Accurate cargo delivery) and PS2 (Professional capability). Based on
those results, we consulted with the four practical experts who had
revised this paper's survey previously, and made some suggestions
for CACC operators as follows.
1 Improving ground operations
For improving the TC2 (Perfective cargo delivery) and TC3
(Accurately cargo delivery), this paper suggests CACC operators
focus on strengthening the ground operations of cargo. Generally,
the ground operations include cargo's discharge, warehousing,
packing and stowage. In practice, most CACC operators may out-
source those operations to ground service ?rms. Thus, for
improving the ground operations of cargo, CACCs have to handle
those ?rms' scheduling operations accurately. This paper suggests
CACCs should choose those outsourcing partners carefully, and
further evaluate their service performance regularly.
2 Adopting policies of strategic alliances
For improving the TG (Adequate shipping spaces), this paper
suggests CACCs adopt policies of strategic alliances with other Fig. 3. The gap assessment of SRAs.
Table 5
The Gap indexes of service requirement attributes.
SRAs AFFs' weights (%) CACCs' weights (%) Quadrant Gap Index (%)
RP1 5.31 3.56 I ¡1.75
RP3 5.58 5.26 À0.32
IS1 4.88 4.26 À0.62
TG4 4.61 4.01 À0.60
TC1 6.23 6.03 À0.20
TC2 8.76 5.43 ¡3.33
TC3 7.12 6.74 À0.38
TC4 3.23 2.45 À0.78
PS1 6.00 4.39 ¡1.61
PS2 6.92 6.67 À0.25
PS3 4.27 4.12 À0.15
RP4 2.59 2.70 II 0.11
IS2 4.41 4.75 0.34
IS3 3.86 5.53 1.67
TG1 7.56 10.60 3.04
TG2 4.28 5.29 1.01
TG3 6.36 9.64 3.28
PS4 2.91 3.32 0.41
Note: The boldfaced numbers present the SRAs with higher negative or positive
gaps.
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CACCs. Generally, the most effective method to improve shipping
spaces is to increase the number of cargo aircrafts. However, pur-
chase of cargo aircrafts is costly, and is a long-term investment
policy. For CACCs, it is usually necessary to be assessed carefully.
Thus, most CACCs would not adopt such a policy, especially for a
variation in short-term needs. Therefore, adopting a strategic alli-
ance policy to increase the supply of shipping spaces is a more
feasible method for CACCs.
3 Enhancing operating staffs' professional capabilities
In general, airfreight cargo has timeliness requests. Thus, the
CACC staff need to complete all of the shipping procedures in a
short time, including freight scheduling, declaration of dangerous
goods, document production etc. In practice, for improving PS
(Personal service), the staff need enough professional and skilled
capabilities to accomplish it. This paper suggests CACCs encourage
their employees to apply for the relevant licenses, such as customs
personnel certi?cate, customs bonded certi?cate, IATA dangerous
goods licenses, etc. In addition, CACCs may ask their employees to
return to school part time to learn new professional knowledge.
This is also an effective way to improve employees' service
capability.
4.2. The SRA's weights of CACCs perceived importance
The results (Table 4) show, for the ?rst layer of SAR constructs,
CACCs pay more attention to Tangibles (TG) and Transportation
capability (TC). While, for the second layer of SARs, the top SRAs
CACCs perceived importance are: Adequate shipping spaces (TG1),
Adequate shipping ?ights (TG3), Cargo delivery accurately (TC3) and
Professional capability (PS2). Furthermore, the results also indicate
the CACCs' perceived importance toward SRAs are consistent with
AFFs', except the TG3. The results imply the CACCs operators in
Taiwan may grasp their customers' (AFFs) requirements well.
Further, the result may also be veri?ed from the SRAs' positions in
Fig. 3. The result of Fig. 3 shows most of the SRAs locate closely to
the 45
+
line (except TG1, TG3, and TC2), which imply those SRAs'
CACC weights approximate its AFF weights, leading to CACCs'
perception of SRAs being roughly consistent with AFFs'.
4.3. The gap index of SRAs
In this paper, a GI (Gap Index) is employed to assess the
perceived gaps of SRAs between CACCs and AFFs. The results
(Table 5) indicate the SRAs with a higher negative gap are Cargo
delivery perfectively (TC2, À3.33%), Cargo tracking capability
(RP1, À1.75%) and Providing bene?cial loading modes (PS1, À1.61%).
While, the SRAs with a higher positive gap are Adequate shipping
?ights (TG3, 3.28%) and Adequate shipping spaces (TG1, 3.04%).
Those results may provide practical information for the reallocation
policy of CACCs' resources. In practice, CACCs managers may
consider transferring some of the resource of the SRAs with higher
positive gaps to the SRAs with higher negative gaps. The SRAs with
greater positive GIs should have higher priority to transfer their
resources to those SRAs with greater negative GIs. Under resource
limitations, this information is very useful for CACC managers.
5. Conclusion
Currently, CACCs are still the main carriers for global airfreights.
However, with providing a door-to-door service for shippers, the
ICs (Integrated carriers) are beginning to threaten the CACCs. Thus,
CACC operators should consider how to deal with this competition
in the future. In the relevant literature concerning CACC services,
few articles examine the perceived gap in the SRAs between users
(AFFs) and CACCs (service providers). In practice, with resource
limitations, the information is very useful for CACC operators to
allocate their resources in improving service quality. In this paper, a
gap assessment model based on Fuzzy AHP is proposed to evaluate
the gaps in SRAs between AFFs and CACCs. The gap assessment
model may provide a theoretical reference for relevant research on
service quality.
For validating the model, the CACCs in Taiwan and their users
were empirically investigated. The results indicate CACC users pay
much attention to SRAs: Perfect cargo delivery, Adequate shipping
spaces, Accurate cargo delivery and Staff's professional knowledge. In
practice, when resources is suf?cient, CACC operators may input
resource to those SRAs directly to improve their corresponding
service operations. However, CACC operators may need to adjust
their resource inputs when the resource is inadequate. The GIs (gap
index) proposed in this paper may provide practical information for
CACC operators to reallocate resources inputs ef?ciently.
Regarding the service quality gap, the ?ve-gap PZB model
(Parasuraman et al., 1988) is one of the most famous models. In the
PZB model, the service quality gap is broken down into four sub-
gaps, in which the Gap 1 (knowledge cap) is de?ned as the differ-
ences between consumers' expectations and managers' percep-
tions toward the consumer expectations. In fact, the gap discussed
in the paper, the difference between CACCs and AFFs' perceptions
on SRAs, is very similar to the Gap1. In PZB model, Gap 1 is the
initial gap and is the most important gap (Zeithaml, Parasuraman,
& Berry, 1990).
In practice, to increase completeness, CACC operators may
outsource some service operations to their partner ?rms. In this
paper, the proposed model did not examine the service quality of
those partner ?rms. In practice, the performance of partner ?rms
may also be a determinant of service quality for IDC operators.
Thus, further research may also involve the partner factor in the
model.
To validate the proposed model, 14 AFFs and 28 CACCs in Taiwan
were surveyed in this paper. For enhancing the validity of the
questionnaire investigation, besides the respondents being asked
to have enough operation experience, this paper adopted an
interviewsurvey with an assistant. Thus, the validity and reliability
of the ?ndings in this paper could be endorsed. However, for better
con?rming the empirical results, more representative samples may
be necessary in future research.
Acknowledgements
The authors greatly appreciate the anonymous reviewers for
their very valuable comments on this paper. Besides, this paper was
funded by National Science Council of Taiwan (NSC 101-2410-H-
022-005).
Appendix
The procedure of fuzzy AHP can be shown as the following
steps:
1 The de?nition of fuzzy positive reciprocal matrix
Suppose a fuzzy positive reciprocal matrix
~
A ¼ ½~ a
ij
?
nÂn
with n
criteria, where the criterion ~ a
ij
(i.e., SRA) is a triangular fuzzy
number with parameters:
_
l
ij
; m
ij
; u
ij
_
¼
_
½1; 1; 1?; if i ¼ j;
_
1
_
u
ji
; 1
_
m
ji
; 1
_
l
ji
¸
if isj:
S.-H.S. Huang, W.-K.K. Hsu / Asia Paci?c Management Review xxx (2015) 1e8 6
Please cite this article in press as: Huang, S.-H. S., & Hsu, W.-K. K., Evaluating the service requirements of combination air cargo carriers, Asia
Paci?c Management Review (2015),http://dx.doi.org/10.1016/j.apmrv.2015.05.001
The fuzzy positive reciprocal matrix
~
A ¼ ½~ a
ij
?
nÂn
can then be
expressed as follows:
~
a
ij
¼
_
½1; 1; 1?; if i ¼ j;
_
~
a
ji
_
À1
; if isj:
(A1)
2 The consistency tests
Before calculating the weights of the SRAs (i.e., ~ a
ij
) in
~
A, an
immediate problem is how to test the consistency of such a fuzzy
positive reciprocal matrix. Since the criterion ~ a
ij
, within the
~
A are
fuzzy numbers, the consistency of the
~
A can not be tested directly
as done in traditional AHP. Buckley (1985) conducted the consis-
tency test for a fuzzy positive reciprocal matrix whose criteria are
trapezoid fuzzy numbers. He used geometric mean to defuzzify
the criteria and thus convert the fuzzy positive reciprocal matrix
into a crisp matrix. Then the consistency test can be undertaken
for the crisp matrix by the same way in traditional AHP. In this
paper, the Buckley' method is adopted to test the consistence of
~
A.
Speci?cally, the fuzzy criterion ~ a
ij
¼ ½l
ij
; m
ij
; u
ij
? in the
~
A can be
?rst defuzzi?ed as:
a
ij
¼
_
l
ij
$m
ij
$m
ij
$u
ij
_
1=4
; i ¼ 1; 2; …; n; j ¼ 1; 2; …; n (A2)
Then, the
~
A ¼ ½~ a
ij
?
nÂn
can be converted into a crisp matrix
A ¼ ½a
ij
?
nÂn
.
Generally, the following C.I. (Consistency Index) and C.R. (Con-
sistency Ratio) are two indexes used to test the consistency of a
crisp positive reciprocal matrix:
C:I: ¼
l
max
Àn
n À1
(A3)
and
C:R: ¼
C:I:
R:I:
(A4)
where l
max
is the maximum eigenvalue of the matrix and n is the
number of criteria of the matrix. Saaty (1980) suggested that the
C.I. 0.1 is an acceptable range. While, the R.I. represents a ran-
domized index whose values are shown in Table A1 (Hsu & Huang,
2014).
The results of consistency tests for the fuzzy positive reciprocal
matrixes in the empirical study are shown in Table A2. Since all of
the C.I. and C.R. indexes are less than 0.1, all the positive reciprocal
matrixes in the sample data are consistent.
3 The local weights of the SRAs
Basically, the local weights of SRAs can be determined from
the eigenvectors of
~
A. However, due to the special structure of
~
A, Saaty (1980) suggested four solution methods to ?nd the
eigenvectors: Average of Normalized Columns (ANC), Normal-
ization of the Row Average (NRA), Normalization of the
Reciprocal of Columns Sum (NRCS), and Normalization of the
Geometric Mean of the Rows (NGMR). In this paper, the NGMR
method was used to determine the local weights of the SRAs
in matrix
~
A.
For the matrix
~
A, the geometric means of triangular fuzzy
numbers for the ith SRA (i ¼1,2,…,n) can be found as:
~ w
i
¼
_
_
n
j¼1
~
a
ij
_
_
1=n
¼
_
¸
_
_
_
n
j¼1
l
ij
_
_
1=n
;
_
_
n
j¼1
m
ij
_
_
1=n
;
_
_
n
j¼1
u
ij
_
_
1=n
_
¸
_; i
¼ 1; 2; …; n: (A5)
0
n
i¼1
~ w
i
¼
_
¸
_
n
i¼1
_
_
n
j¼1
l
ij
_
_
1=n
;
n
i¼1
_
_
n
j¼1
m
ij
_
_
1=n
;
n
i¼1
_
_
n
j¼1
u
ij
_
_
1=n
_
¸
_:
(A6)
From the Equations (A5) and (A6), the local weight for the ith
SRAs (i ¼1,2,...,n) can then be obtained as:
~
W
i
¼ ~ w
i
_
n
i¼1
~ w
i
¼
_
¸
¸
¸
¸
¸
_
_
n
j¼1
l
ij
_
_
1=n
n
i¼1
_
n
j¼1
u
ij
_
_
1=n
;
_
n
j¼1
m
ij
_
_
1=n
n
i¼1
_
n
j¼1
m
ij
_
_
1=n
;
_
n
j¼1
u
ij
_
_
1=n
n
i¼1
_
n
j¼1
l
ij
_
_
1=n
_
¸
¸
¸
¸
¸
_
; i
¼1;2;…;n
(A7)
4 The defuzziness process
Since the local weight
~
W
i
of the ith SRA (i ¼1,2,…,n) is fuzzy, this
paper adopted Yager's index (1981) to defuzzify the
~
W
i
into a crisp
number W
i
, i ¼1,2,...,n. For convenience of explanations, let
~
W
i
¼ ½l
W
i
; m
W
i
; u
W
i
?, where
Table A1
The randomized index of R. I.
n 3 4 5 6 7 8 9 10 11 12
R.I. 0.525 0.882 1.115 1.252 1.341 1.404 1.452 1.484 1.513 1.535
Table A2
The results of the consistency tests.
Subjects Layer C.I. R.I. C.R. (C.I./R.I.)
CACCs Layer 1 0.0075 1.115 0.0067
Layer2: TG 0.0092 0.882 0.0104
Layer2: RB 0.0123 0.882 0.0139
Layer2: AA 0.0101 0.525 0.0192
Layer2: EP 0.0325 0.525 0.0619
Layer2: DV 0.0258 0.882 0.0293
AFFs Layer 1 0.0102 1.115 0.0091
Layer2: TG 0.0154 0.882 0.0175
Layer2: RB 0.0221 0.882 0.0251
Layer2: AA 0.0433 0.525 0.0825
Layer2: EP 0.0178 0.525 0.0339
Layer2: DV 0.0210 0.882 0.0238
S.-H.S. Huang, W.-K.K. Hsu / Asia Paci?c Management Review xxx (2015) 1e8 7
Please cite this article in press as: Huang, S.-H. S., & Hsu, W.-K. K., Evaluating the service requirements of combination air cargo carriers, Asia
Paci?c Management Review (2015),http://dx.doi.org/10.1016/j.apmrv.2015.05.001
Then the Yager's index (1981) of the
~
W
i
(i ¼1,2,...,n) is de?ned
as:
W
i
¼
_
l
W
i
þ2 m
W
i
þ u
W
i
__
4; i ¼ 1; 2; …; n: (A8)
Finally, normalizing the W
i
(i ¼1,2..,n) then the local weight of
the ith SRAs can be obtained as:
u
i
¼ W
i
_
n
i¼1
W
i
; i ¼ 1; 2; …; n (A9)
5 The global weights of the SRAs
The global weights of SRAs can be found by multiple the low
level of local weights by their corresponding high level of global
weights.
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_
l
W
i
;m
W
i
;u
W
i
_
¼
_
¸
¸
¸
¸
¸
_
_
n
j¼1
l
ij
_
_
1=n
n
i¼1
_
n
j¼1
u
ij
_
_
1=n
;
_
n
j¼1
m
ij
_
_
1=n
n
i¼1
_
n
j¼1
m
ij
_
_
1=n
;
_
n
j¼1
u
ij
_
_
1=n
n
i¼1
_
n
j¼1
l
ij
_
_
1=n
_
¸
¸
¸
¸
¸
_
; i¼1;2;…;n:
S.-H.S. Huang, W.-K.K. Hsu / Asia Paci?c Management Review xxx (2015) 1e8 8
Please cite this article in press as: Huang, S.-H. S., & Hsu, W.-K. K., Evaluating the service requirements of combination air cargo carriers, Asia
Paci?c Management Review (2015),http://dx.doi.org/10.1016/j.apmrv.2015.05.001
doc_664933384.pdf
The purpose of this paper is to assess the service requirements of combination air cargo carriers (CACCs).
Firstly, based on the CACC's operational features and relevant literature, the service requirement attributes
(SRAs) of CACCs were investigated. A gap index based on Fuzzy AHP was then proposed to evaluate
the perceived differences toward those SRAs between CACC users and CACC operators. Finally, as an
empirical study, the CACCs in Taiwan and their users were investigated to validate the model. The results
indicate CACC users pay much attention to SRAs: Perfect cargo delivery, Adequate shipping spaces, Accurate
cargo delivery and Staff's professional knowledge. While, the SRAs with higher gaps for CACCs in Taiwan
are: Stable flights, Adequate flight spots and Special cargo delivery. Based on those results, the theoretical
and managerial implications for CACCs in improving service quality are discussed.
Evaluating the service requirements of combination air cargo carriers
Show-Hui S. Huang
a
, Wen-Kai K. Hsu
b, *
a
Department of International Business & Trade, Shu-Te University, 59, Hengshan Rd, Yanchao District, Kaohsiung, Taiwan, ROC
b
Department of Shipping & Transportation Management, National Kaohsiung Marine University, 142, Hai Jhuan Rd, Nanzih District, Kaohsiung, Taiwan, ROC
a r t i c l e i n f o
Article history:
Received 23 March 2015
Accepted 10 May 2015
Available online xxx
Keywords:
Airfreight
Service quality
Gap
AHP
Fuzzy
a b s t r a c t
The purpose of this paper is to assess the service requirements of combination air cargo carriers (CACCs).
Firstly, based on the CACC's operational features and relevant literature, the service requirement attri-
butes (SRAs) of CACCs were investigated. A gap index based on Fuzzy AHP was then proposed to evaluate
the perceived differences toward those SRAs between CACC users and CACC operators. Finally, as an
empirical study, the CACCs in Taiwan and their users were investigated to validate the model. The results
indicate CACC users pay much attention to SRAs: Perfect cargo delivery, Adequate shipping spaces, Accurate
cargo delivery and Staff's professional knowledge. While, the SRAs with higher gaps for CACCs in Taiwan
are: Stable ?ights, Adequate ?ight spots and Special cargo delivery. Based on those results, the theoretical
and managerial implications for CACCs in improving service quality are discussed.
© 2015 College of Management, National Cheng Kung University. Production and hosting by Elsevier
Taiwan LLC. All rights reserved.
1. Introduction
According to the research reports of Boeing company for global
airfreights (Boeing, 2012), shipment quantities will increase
threefold and growat an annual rate of 5.9% over the next 20 years.
Of which, the top ?ve areas with a high growth rate will be Do-
mestic China (9.2%), Intra-Asia (7.9%), AsiaeNorth America (6.7%),
EuropeeAsia (6.6%) and South-Europe (6.5%). The above results
indicate the Asia area can be the focus of airfreight developments in
the future.
For airfreights, the carriers can be classi?ed as three types:
combination air cargo carrier (CACC), conventional all-cargo airline
(CACA) and integrated carrier (IC). In practice, the service model of
airfreights could be explained as Fig. 1. The main shipments of
CACCs come from air freight forwarders (AFFs) consolidating cargo
fromshippers. Thus, AFFs are usually the main customers of CACCs.
As for CACAs and ICs, their shipments may come from both AFFs
and shippers. The latter can even provide a door-to-door service
independently. Currently, the market share of CACCs' shipments is
still higher than both of CACA and IC carriers. However, the IC
carriers have been gradually establishing an integral supply chain
system by which they can provide a complete and prompt service
for shippers. This result has signi?cantly threatened the other two
carriers, especially the CACCs. Thus, it is important for CACCs to
consider how to deal with future competition.
In the relevant literature concerning airfreight services, most of
studies focus on users' service requirements (e.g., Wang, 2007;
Cheng & Yeh, 2007). Few articles examine the perceived gap in
the service requirements between users and service providers. In
practice, the gap may provide improvement information for service
providers and allow them to allocate their resources with ef?-
ciency. Under resource limitations, the information is very useful
for service providers in making policies to improve their service
operations.
The purpose of this paper is to assess the service requirements
of combination air cargo carriers (CACCs). Since AFFs (air freight
forwarders) are the main customers of CACCs, this paper de?ned
AFFs as the users of CACCs. Based on the CACC's operational fea-
tures and relevant literature, the service requirements attributes
(SRAs) of CACC are investigated in this paper. A Fuzzy AHP model is
then proposed to weight those SRAs from both perspectives of
CACC users (i.e., AFFs) and CACC operators respectively. Based on
those weights, a gap assessment model is then proposed to eval-
uate the perceived differences on the SRAs between CACC users and
CACC operators, by which, the CACC operators may make policies in
improving service qualities. Finally, as an empirical study, the
CACCs in Taiwan and their users were investigated to validate the
* Corresponding author.
E-mail address: [email protected] (S.-H.S. Huang).
Peer review under responsibility of College of Management, National Cheng
Kung University.
HOSTED BY
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j ournal homepage: www. el sevi er. com/ l ocat e/ apmrvhttp://dx.doi.org/10.1016/j.apmrv.2015.05.001
1029-3132/© 2015 College of Management, National Cheng Kung University. Production and hosting by Elsevier Taiwan LLC. All rights reserved.
Asia Paci?c Management Review xxx (2015) 1e8
Please cite this article in press as: Huang, S.-H. S., & Hsu, W.-K. K., Evaluating the service requirements of combination air cargo carriers, Asia
Paci?c Management Review (2015),http://dx.doi.org/10.1016/j.apmrv.2015.05.001
model. The remainder of the paper is organized as follows: Section
2 presents a literature review. Section 3 explains the research
method used in this paper. A discussion of the results is then pre-
sented in Section 4. Finally, some general conclusions and limita-
tions for further research are given.
2. Literature reviews
2.1. The SERVQUAL scale
For the measurement of service quality for service industries,
the SERVQUAL scale is one of the famous instruments. The
SERVQUAL scale is a multi-item scale developed to assess customer
perceptions of service quality in service and retail businesses
(Parasuraman, Zeithaml, & Berry, 1988). The SERVQUAL scale de-
composes the notion of service quality into ?ve constructs and
develops 22 questions to measure the service quality. The ?ve di-
mensions are de?ned as follows: (1) Tangibles: physical facilities,
equipment, staff appearance, etc. (2) Reliability: ability to perform
service dependably and accurately. (3) Responsiveness: willingness
to help and respond to customer need. (4) Assurance: ability of staff
to inspire con?dence and trust. (5) Empathy: the extent to which
caring individualized service is given. In later studies, the SERVQ-
UAL scale was widely applied for service quality measurements
(e.g., Davis & Mentzer, 2006; Seth, Deshmukh, & Vrat, 2006).
However, the SERVQUAL scale was originally developed for
measuring the perceived service quality of individual customers, so
it may not be adequate for business customers (Durvasula,
Lysonski, & Mehta, 1999). Thus, for measuring the service quality
of business customers, most relevant studies need to revised the
SERVQUAL scale by considering the business' features, such as port
services (Ugboma, Ogwude, Ugboma, & Nnadi, 2007; Pantouvakis,
Chlomoudis, & Dimas, 2008), air cargo services (Wang, 2007),
shipping carrier services (Lai, Chen, Wang, & Lin, 2009), container
terminal services (Hsu, 2013), international port distribution cen-
ters (Hsu & Huang, 2014), etc.
2.2. The service quality in airfreights
In the relevant literature on the service quality of airfreights,
most studies focus on both air cargo carriers and air cargo logistic
providers. For example, for the former, Wang (2007) discussed the
improvement in service quality for the air cargo sector of China
Airlines. The paper identi?ed three service quality dimensions with
20 service requirement attributes (SRAs) to measure the service
quality of air cargo carriers. The three dimensions were Profes-
sionalism, Physical service and Correctness & positivity. The result
indicated the top 3 SRAs in need of improvement for China Airlines
are: Prompt handling of import/export work, Willingness to help solve
customer service and Standard operating procedures. Hsu, Li, Patty,
and Mark (2009) examined the factors affecting ?rms' selection
of air carriers. In the article, six factors were extracted: Product
characteristics, Values, Inventory cost, Shipping charges, Shipping
distance and Time. The results showed shippers with high product
value and short delivery distance focus on the shipping charge and
prefer choosing the air cargo carrier that offers more ?ights.
As for air cargo logistic providers, Cheng and Yeh (2007)
investigated the relationship between core competencies and
sustainable competitive advantage for air-cargo forwarders. The
paper de?ned the core competencies as three variables: Resources,
Capabilities and Logistics services. For the Resources variable, nine
attributes were proposed and three dimensions were extracted:
Corporate scale and information equipment, Relationship with clients,
Upstream and downstream partners, and Corporate reputation and
past delivery quality. For the Capabilities variable, ten attributes
were proposed and three dimensions were extracted: Staff capa-
bility to provide service, Comprehensive management system and
marketing capability and Multiple ?ight selection and price reduction
capability. For the Logistics services, 19 attributes were proposed and
six dimensions were extracted: Logistic information, Customer de-
livery service, Transportation quality and quantity, Upstream and
downstream partner integration, Providing integrated logistic service
and Price ?exibility and prompt response to quoting. The results
indicated Capabilities is the most essential internal variable in?u-
encing the sustainable competitive advantage, in which, the Staff
capability to provide service is the critical factor. Tsai, Wen, and Chen
(2007) examined the demand choices of high-technology industry
for air logistics service providers. The paper proposed 15 SRAs from
shippers' perspectives, which were classi?ed as four constructs:
Service cost, Service performance, Value-added services and Perceived
Capability. The results indicated shippers pay most attention to
Service performance, followed by Service cost and Value-added ser-
vices. Meng, Liang, Lin, and Che (2010) investigated the effects of
logistics services on customer satisfaction with air cargo logistic
providers. In the study, logistic services were assessed by ?ve di-
mensions with 23 SRAs, which were named as Delivery value,
Knowledge innovation value, Service value-added, Information value
and Performance satisfaction value. While, customer satisfactionwas
veri?ed by four constructs with 22 satisfactory indexes, which were
termed as Reliability, Agility, Customization and Flexibility. The re-
sults indicated the Service value-added is the most signi?cant factor
affecting customer satisfaction.
3. Research method
For ease of explanations, some notations are used in this paper:
Fig. 1. The service model of airfreights.
SRA Service requirement attribute
CACC Combination air cargo carriers
AFF Airfreight forwarder
RP Responsiveness
IS Integrated service
TG Tangibles
TC Transportation capability
PS Personnel service
GI Gap index
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3.1. Research framework
The research framework of this paper is shown in Fig. 2. Based
on the relevant literature and the features of CACCs, the SRAs for
CACC users are ?rst investigated. A fuzzy AHP approach is then
constructed to weight those SRAs from both of CACC operators and
users (AFFs) perspectives. Finally, based on those two weights, a
gap assessment model is proposed, by which CACC operators may
make policies to improve service quality.
3.2. The hierarchical structure of SRAs
Based on the features of CACCs and the relevant literature, the
SRAs of CACCs are de?ned from the following ?ve dimensions
(Cheng & Yeh, 2007; Hsu et al., 2009; Meng et al., 2010;
Parasuraman et al., 1988; Tsai et al., 2007; Wang, 2007):
1 Responsiveness (RP):
RP is de?ned as CACCs' capability to respond to user requests,
such as information systemservice, cargo tracking, answering price
requests, etc.
2 Integrated service (IS)
IS is de?ned as CACCs' capability to provide value-added ser-
vices, such as warehouse services, special cargo services, inland
transportations etc.
3 Tangibles (TG)
TG is de?ned as CACCs' physical services. For CACCs, generally,
the main physical services are adequate shipping spaces, adequate
?ights and routes, service branches etc.
4 Transportation capability (TC)
TC is de?ned as the CACCs' capability to deliver cargo punctually,
perfectly and accurately. Further, it also contains the compensation
for delivery misses.
5 Personnel service (PS)
PS is de?ned as the service of CACC personnel, including a
proactive attitude, professional capability, uni?ed service window,
etc.
Based on the above de?nitions, a two-layer hierarchy structure
of SRAs for CACCs was ?rst constructed. Four practical experts (two
CACC operators and two AFF) were then invited to revise and check
the independences of those SRAs. Further, they also checked if any
important SRAs were missed. After several rounds of discussions
and revisions, the ?nal hierarchy structure of SRAs, shown as
Table 1, contains ?ve dimensions of SRAs for the ?rst layer and 19
SRAs for the second layer.
3.3. Questionnaire design
Since this paper adopts Fuzzy AHP method to weight the SRAs of
CACCs, a pair-wise comparison questionnaire with a nine point
rating scale was used to measure the relative perceived importance
of SRAs for respondents. Based on the hierarchical structure of the
SRAs in Table 1, an AHP questionnaire with ?ve criteria and 19 sub-
criteria was created. To validate the scale, another two CACCs and
two AFFs were invited to pretest the questionnaire and check
whether the statements were clear.
3.4. Research sample
To validate the assessment model, the CACCs in Taiwan were
investigated. In Taiwan, the top ?ve CACCs are China Airline (33%),
Evergreen Airline (21%), Cathay Paci?c (9%), Japan Airline (8%) and
Dragonair (5%). Of which, their total market share of shipments is
over 75%. For the CACC sample, 1e5 subjects for each of the top ?ve
CACCs were surveyed by company size. Meanwhile, each of the
CACCs was asked to provide 2e10 main customers (AFFs) for the
AFF sample. Finally, we had 42 samples in total, including 14 CACCs
and 28 AFFs.
Since this paper used an expert questionnaire of AHP as a
research instrument, all of the above subjects were asked to ful?ll
the following requirements: (1) the respondents must be senior
employees and (2) the respondents must have suf?cient experience
and knowledge of CACC operations. Further, for enhancing the
validity of the survey, an assistant was assigned to help each subject
to complete the questionnaire.
For each of the CACC and AFF samples in the empirical study, the
consistency index (C.I.) was ?rst used to test the consistency of their
Fig. 2. The research framework.
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pair-wise comparison matrix respectively. The results indicated
four samples' C.I. values are higher than 0.1, implying they are
highly inconsistent (Saaty, 1980). Thus, those questionnaires must
be revised, before use. The four respondents were then asked to
reviewand modify their answers again, until the answers ?tted the
consistency (C.I.) tests.
The pro?les of the 42 respondents' characteristics are shown in
Table 2. It can be seen all of the subjects have at least ?ve years of
work experience, with (10/14) CACC and (20/28) AFF subjects
having more than 10 years in their companies. Note, the remarkable
quali?cations of the respondents endorse the reliability of the
survey ?ndings.
3.5. The weights of SRAs
From the sample data (14 CACCs and 28 AFFs), 42 positive
reciprocal matrices were obtained for each pair-wise comparison of
the SRAs in each layer. In the past, most relevant studies used an
arithmetic mean or geometric mean to integrate multiple subjects'
opinions. However, those two means are sensitive to extreme
values. Thus, a fuzzy number is considered to integrate the subjects'
perceptions. First, the geometric mean was employed to represent
the consensus of the respondents (Hsu & Huang, 2014; Hsu, Yu, &
Huang, 2015). A triangular fuzzy number characterized by mini-
mum, geometric mean and maximum of the measuring scores was
then used to integrate the 42 positive reciprocal matrices into two
fuzzy positive reciprocal matrixes, one for CACCs and one for AFFs.
Finally, based on these two matrixes, a fuzzy AHP approach was
employed to determine the weights of the SRAs. The procedure of
the fuzzy AHP is explained in detail in the Appendix.
The results of the SRAs' weights for AFF samples are shown in
Table 3, in which the global weights of the SRAs in the ?rst layer are
shown in the second ?eld, and those of the SRAs in the second layer
are shown in the last ?eld. The results indicate, for the ?rst layer of
SRA constructs, users (AFFs) pay more attention to TC (25.35%), TG
(22.82%) and PS (20.10%). While, for the second layer of SARs, the
top SRAs users perceived importance are: TC2 (8.76%), TG1 (7.56%),
Table 1
The hierarchy structure of the service requirement attributes of CACCs.
Layer 1: Construct Code Layer 2: Service attribute Reference
Responsiveness
(RP)
RP1 Cargo tracking capability [2], [5e6]
RP2 Information system performance [1e2], [5e6]
RP3 Response for price request [2]
RP4 Flexible ordering time Expert
interviews
Integrated service
(IS)
IS1 Inland transportation [2], [5]
IS2 Warehouse services [5]
IS3 Special cargo services [1]
Tangibles (TG) TG1 Adequate shipping spaces [2]
TG2 Adequate shipping routes Expert
interviews
TG3 Adequate shipping ?ights [2], [4]
TG4 Adequate service branches [2], [5]
Transportation
capability (TC)
TC1 Punctual cargo delivery [1e3], [5e6]
TC2 Perfect cargo delivery [2e3], [5e6]
TC3 Accurate cargo delivery [5]
TC4 Compensation for service misses [3], [5e6]
Personal services
(PS)
PS1 Providing bene?cial loading
modes
[2], [6]
PS2 Professional capability [2]
PS3 Response for complaints [1e2], [5e6]
PS4 Uni?ed service window Expert
interviews
Note: [1] Wang (2007); [2] Cheng and Yeh (2007); [3] Tsai et al. (2007); [4] Hsu
et al. (2009); [5] Meng et al. (2010).
Table 2
Pro?le of the respondents.
Features Range CACC AFF
Frequency % Frequency %
Carriers China airline 5 35.71 10 35.71
Evergreen airline 4 28.57 8 28.57
Cathay Paci?c 2 14.29 4 14.29
Japan airline 2 14.29 4 14.29
Dragonair 1 7.14 2 7.14
Work experience
(years)
5e10 2 14.29 8 28.57
11e15 5 35.71 11 35.71
16e20 6 42.86 3 10.71
Above 20 1 7.14 6 21.43
Age (years) 31e40 8 57.14 17 60.71
41e50 5 35.71 8 28.57
Above 50 1 7.14 3 10.71
Job title President level 0 0.00 4 14.29
Manager level 6 42.85 14 50.00
Senior sales 8 57.14 10 35.71
Education level Master 1 7.14 5 17.86
University 8 57.14 15 53.57
College 5 35.71 8 28.57
Table 3
The weights of AFFs' perceived importance toward SRAs.
Layer 1
SRAs
The global weights
of Layer 1
Layer 2
SRAs
The local weights
of Layer 2
The global weights
of Layer 2 (%)
RP 0.1859 RP1 0.2854 5.31
RP2 0.2753 5.12
RP3 0.3002 5.58
RP4 0.1391 2.59
IS 0.1314 IS1 0.3711 4.88
IS2 0.3353 4.41
IS3 0.2936 3.86
TG 0.2282 TG1 0.3318 7.56
TG2 0.1876 4.28
TG3 0.2785 6.36
TG4 0.2022 4.61
TC 0.2535 TC1 0.2457 6.23
TC2 0.3460 8.76
TC3 0.2808 7.12
TC4 0.1275 3.23
TS 0.2010 TS1 0.2987 6.00
TS2 0.3441 6.92
TS3 0.2123 4.27
TS4 0.1449 2.91
Note: The boldfaced numbers present the SRAs with higher weights.
Table 4
The weights of CACCs' perceived importance toward SRAs.
Layer 1
SRAs
The global weights
of Layer 1
Layer 2
SRAs
The local weights
of Layer 2
The global weights of
Layer 2 (%)
RP 0.1677 RP1 0.2121 3.56
RP2 0.3130 5.25
RP3 0.3138 5.26
RP4 0.1611 2.70
IS 0.1454 IS1 0.2932 4.26
IS2 0.3266 4.75
IS3 0.3802 5.53
TG 0.2955 TG1 0.3592 10.60
TG2 0.1789 5.29
TG3 0.3263 9.64
TG4 0.1356 4.01
TC 0.2065 TC1 0.2919 6.03
TC2 0.2630 5.43
TC3 0.3265 6.74
TC4 0.1187 2.45
TS 0.1849 TS1 0.2375 4.39
TS2 0.3605 6.67
TS3 0.2226 4.12
TS4 0.1795 3.32
Note: The boldfaced numbers present the SRAs with higher weights.
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TC3 (7.12%) and PS2 (6.92%). Likewise, the results of the SRAs'
weights for CACC sample are shown in Table 4. The results indicate,
for the ?rst layer of SRA constructs, service providers (CACCs) pay
more attentiontoTG(29.55%) andTC(20.65%). While, for the second
layer of SARs, the top SRAs CACC operators perceived importance
are: TG1 (10.60%), TG3 (9.63%), TC3 (6.74%) and TS2 (6.67%).
3.6. Gap assessment
Obviously, an SRA (service requirement attribute) with higher
AFF' perceived weight and lower CACC's perceived weight should
be a gap with higher improvement priority for CACC operators. In
this paper, when a SRA' AFF weight is higher than its CACC' weight,
we termed this SRA a “negative gap”. In practice, the CACC opera-
tors should consider improving those SRAs' service operations. That
is the policy for those SRAs with negative gaps should be
“concentrate here”. On the contrary, a SRA is de?ned as a “positive
gap” as its AFF weight is lower than its CACC weight. In practice,
those SRAs imply “relative overskill”, and thus the resources
committed to these SRAs could be better employed elsewhere.
Furthermore, a SRA is no positive gap only when its AFF weight
equals its CACC' weight. It is clear that the police for those SRAs is
“keep up the work”
Based on the above concept, a two-dimensional matrix with
both of AFF weights and CACC weights is constructed to assess the
gaps of SARs. The matrix is shown in Fig. 3, in which the CACC
weight is depicted on the x-axis, the AFF weight on the y-axes, and
a 45
+
line divides the matrix into two quadrants. The SRAs in
Quadrant I imply that their AFF weights > their CACC weights. Thus,
those SRAs are evaluated as negative gaps. The management policy
for those SRAs should be “concentrate here”. That is the CACC op-
erators should invest more resources to improve those SRAs.
Furthermore, for assessing the degrees of the SRAs' gaps, a Gap
Index (GI) is thus proposed in this paper, which is evaluated by the
distance from the SRAs' coordinates to the 45
+
line. For example, in
Fig. 3, the SRATG3's coordinate is (9.64, 6.36). Then, its GI is 3.28 by
(9.64À6.36). Thus, the TG3 has a positive gap 3.28. Note, fromFig. 3,
it is clear that the more the SRA's position closes to the 45
+
line, the
smaller the SRA's GI is.
All of the SRAs located in the Quadrant I (with negative gap) are
shown in Fig. 3, and their gaps are listed in the last ?eld of Table 5.
The results indicate there are 11 SRAs with negative gaps (located in
the Quadrant I zone). In practice, the CACC operators should pay
more attention to improving those SRAs. Furthermore, the SRAs in
the Quadrant II are shown in Fig. 3, and their GIs are also shown as
the last ?elds in Table 5. The results indicate there are 7 SRAs
located in the Quadrant II zone (i.e., positive gap). Thus, the re-
sources committed to these SRAs could be better employed else-
where. In practice, for those SRAs with positive gaps, CACC mangers
may consider transferring part of their resources to the SRAs in
Quadrant I.
4. Results and discussions
4.1. The SRA's weights of users perceived importance
The results (Table 3) indicate, for the ?rst layer of SRA con-
structs, AFFs (users) pay more attention to TC (Transportation
capability), TG (Tangibles) and PS (Personal services). While, for the
second layer of SARs, the top SRAs users' perceived importance are:
TC2 (Perfective cargo delivery), TG1 (Adequate shipping spaces), TC3
(Accurate cargo delivery) and PS2 (Professional capability). Based on
those results, we consulted with the four practical experts who had
revised this paper's survey previously, and made some suggestions
for CACC operators as follows.
1 Improving ground operations
For improving the TC2 (Perfective cargo delivery) and TC3
(Accurately cargo delivery), this paper suggests CACC operators
focus on strengthening the ground operations of cargo. Generally,
the ground operations include cargo's discharge, warehousing,
packing and stowage. In practice, most CACC operators may out-
source those operations to ground service ?rms. Thus, for
improving the ground operations of cargo, CACCs have to handle
those ?rms' scheduling operations accurately. This paper suggests
CACCs should choose those outsourcing partners carefully, and
further evaluate their service performance regularly.
2 Adopting policies of strategic alliances
For improving the TG (Adequate shipping spaces), this paper
suggests CACCs adopt policies of strategic alliances with other Fig. 3. The gap assessment of SRAs.
Table 5
The Gap indexes of service requirement attributes.
SRAs AFFs' weights (%) CACCs' weights (%) Quadrant Gap Index (%)
RP1 5.31 3.56 I ¡1.75
RP3 5.58 5.26 À0.32
IS1 4.88 4.26 À0.62
TG4 4.61 4.01 À0.60
TC1 6.23 6.03 À0.20
TC2 8.76 5.43 ¡3.33
TC3 7.12 6.74 À0.38
TC4 3.23 2.45 À0.78
PS1 6.00 4.39 ¡1.61
PS2 6.92 6.67 À0.25
PS3 4.27 4.12 À0.15
RP4 2.59 2.70 II 0.11
IS2 4.41 4.75 0.34
IS3 3.86 5.53 1.67
TG1 7.56 10.60 3.04
TG2 4.28 5.29 1.01
TG3 6.36 9.64 3.28
PS4 2.91 3.32 0.41
Note: The boldfaced numbers present the SRAs with higher negative or positive
gaps.
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CACCs. Generally, the most effective method to improve shipping
spaces is to increase the number of cargo aircrafts. However, pur-
chase of cargo aircrafts is costly, and is a long-term investment
policy. For CACCs, it is usually necessary to be assessed carefully.
Thus, most CACCs would not adopt such a policy, especially for a
variation in short-term needs. Therefore, adopting a strategic alli-
ance policy to increase the supply of shipping spaces is a more
feasible method for CACCs.
3 Enhancing operating staffs' professional capabilities
In general, airfreight cargo has timeliness requests. Thus, the
CACC staff need to complete all of the shipping procedures in a
short time, including freight scheduling, declaration of dangerous
goods, document production etc. In practice, for improving PS
(Personal service), the staff need enough professional and skilled
capabilities to accomplish it. This paper suggests CACCs encourage
their employees to apply for the relevant licenses, such as customs
personnel certi?cate, customs bonded certi?cate, IATA dangerous
goods licenses, etc. In addition, CACCs may ask their employees to
return to school part time to learn new professional knowledge.
This is also an effective way to improve employees' service
capability.
4.2. The SRA's weights of CACCs perceived importance
The results (Table 4) show, for the ?rst layer of SAR constructs,
CACCs pay more attention to Tangibles (TG) and Transportation
capability (TC). While, for the second layer of SARs, the top SRAs
CACCs perceived importance are: Adequate shipping spaces (TG1),
Adequate shipping ?ights (TG3), Cargo delivery accurately (TC3) and
Professional capability (PS2). Furthermore, the results also indicate
the CACCs' perceived importance toward SRAs are consistent with
AFFs', except the TG3. The results imply the CACCs operators in
Taiwan may grasp their customers' (AFFs) requirements well.
Further, the result may also be veri?ed from the SRAs' positions in
Fig. 3. The result of Fig. 3 shows most of the SRAs locate closely to
the 45
+
line (except TG1, TG3, and TC2), which imply those SRAs'
CACC weights approximate its AFF weights, leading to CACCs'
perception of SRAs being roughly consistent with AFFs'.
4.3. The gap index of SRAs
In this paper, a GI (Gap Index) is employed to assess the
perceived gaps of SRAs between CACCs and AFFs. The results
(Table 5) indicate the SRAs with a higher negative gap are Cargo
delivery perfectively (TC2, À3.33%), Cargo tracking capability
(RP1, À1.75%) and Providing bene?cial loading modes (PS1, À1.61%).
While, the SRAs with a higher positive gap are Adequate shipping
?ights (TG3, 3.28%) and Adequate shipping spaces (TG1, 3.04%).
Those results may provide practical information for the reallocation
policy of CACCs' resources. In practice, CACCs managers may
consider transferring some of the resource of the SRAs with higher
positive gaps to the SRAs with higher negative gaps. The SRAs with
greater positive GIs should have higher priority to transfer their
resources to those SRAs with greater negative GIs. Under resource
limitations, this information is very useful for CACC managers.
5. Conclusion
Currently, CACCs are still the main carriers for global airfreights.
However, with providing a door-to-door service for shippers, the
ICs (Integrated carriers) are beginning to threaten the CACCs. Thus,
CACC operators should consider how to deal with this competition
in the future. In the relevant literature concerning CACC services,
few articles examine the perceived gap in the SRAs between users
(AFFs) and CACCs (service providers). In practice, with resource
limitations, the information is very useful for CACC operators to
allocate their resources in improving service quality. In this paper, a
gap assessment model based on Fuzzy AHP is proposed to evaluate
the gaps in SRAs between AFFs and CACCs. The gap assessment
model may provide a theoretical reference for relevant research on
service quality.
For validating the model, the CACCs in Taiwan and their users
were empirically investigated. The results indicate CACC users pay
much attention to SRAs: Perfect cargo delivery, Adequate shipping
spaces, Accurate cargo delivery and Staff's professional knowledge. In
practice, when resources is suf?cient, CACC operators may input
resource to those SRAs directly to improve their corresponding
service operations. However, CACC operators may need to adjust
their resource inputs when the resource is inadequate. The GIs (gap
index) proposed in this paper may provide practical information for
CACC operators to reallocate resources inputs ef?ciently.
Regarding the service quality gap, the ?ve-gap PZB model
(Parasuraman et al., 1988) is one of the most famous models. In the
PZB model, the service quality gap is broken down into four sub-
gaps, in which the Gap 1 (knowledge cap) is de?ned as the differ-
ences between consumers' expectations and managers' percep-
tions toward the consumer expectations. In fact, the gap discussed
in the paper, the difference between CACCs and AFFs' perceptions
on SRAs, is very similar to the Gap1. In PZB model, Gap 1 is the
initial gap and is the most important gap (Zeithaml, Parasuraman,
& Berry, 1990).
In practice, to increase completeness, CACC operators may
outsource some service operations to their partner ?rms. In this
paper, the proposed model did not examine the service quality of
those partner ?rms. In practice, the performance of partner ?rms
may also be a determinant of service quality for IDC operators.
Thus, further research may also involve the partner factor in the
model.
To validate the proposed model, 14 AFFs and 28 CACCs in Taiwan
were surveyed in this paper. For enhancing the validity of the
questionnaire investigation, besides the respondents being asked
to have enough operation experience, this paper adopted an
interviewsurvey with an assistant. Thus, the validity and reliability
of the ?ndings in this paper could be endorsed. However, for better
con?rming the empirical results, more representative samples may
be necessary in future research.
Acknowledgements
The authors greatly appreciate the anonymous reviewers for
their very valuable comments on this paper. Besides, this paper was
funded by National Science Council of Taiwan (NSC 101-2410-H-
022-005).
Appendix
The procedure of fuzzy AHP can be shown as the following
steps:
1 The de?nition of fuzzy positive reciprocal matrix
Suppose a fuzzy positive reciprocal matrix
~
A ¼ ½~ a
ij
?
nÂn
with n
criteria, where the criterion ~ a
ij
(i.e., SRA) is a triangular fuzzy
number with parameters:
_
l
ij
; m
ij
; u
ij
_
¼
_
½1; 1; 1?; if i ¼ j;
_
1
_
u
ji
; 1
_
m
ji
; 1
_
l
ji
¸
if isj:
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Please cite this article in press as: Huang, S.-H. S., & Hsu, W.-K. K., Evaluating the service requirements of combination air cargo carriers, Asia
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The fuzzy positive reciprocal matrix
~
A ¼ ½~ a
ij
?
nÂn
can then be
expressed as follows:
~
a
ij
¼
_
½1; 1; 1?; if i ¼ j;
_
~
a
ji
_
À1
; if isj:
(A1)
2 The consistency tests
Before calculating the weights of the SRAs (i.e., ~ a
ij
) in
~
A, an
immediate problem is how to test the consistency of such a fuzzy
positive reciprocal matrix. Since the criterion ~ a
ij
, within the
~
A are
fuzzy numbers, the consistency of the
~
A can not be tested directly
as done in traditional AHP. Buckley (1985) conducted the consis-
tency test for a fuzzy positive reciprocal matrix whose criteria are
trapezoid fuzzy numbers. He used geometric mean to defuzzify
the criteria and thus convert the fuzzy positive reciprocal matrix
into a crisp matrix. Then the consistency test can be undertaken
for the crisp matrix by the same way in traditional AHP. In this
paper, the Buckley' method is adopted to test the consistence of
~
A.
Speci?cally, the fuzzy criterion ~ a
ij
¼ ½l
ij
; m
ij
; u
ij
? in the
~
A can be
?rst defuzzi?ed as:
a
ij
¼
_
l
ij
$m
ij
$m
ij
$u
ij
_
1=4
; i ¼ 1; 2; …; n; j ¼ 1; 2; …; n (A2)
Then, the
~
A ¼ ½~ a
ij
?
nÂn
can be converted into a crisp matrix
A ¼ ½a
ij
?
nÂn
.
Generally, the following C.I. (Consistency Index) and C.R. (Con-
sistency Ratio) are two indexes used to test the consistency of a
crisp positive reciprocal matrix:
C:I: ¼
l
max
Àn
n À1
(A3)
and
C:R: ¼
C:I:
R:I:
(A4)
where l
max
is the maximum eigenvalue of the matrix and n is the
number of criteria of the matrix. Saaty (1980) suggested that the
C.I. 0.1 is an acceptable range. While, the R.I. represents a ran-
domized index whose values are shown in Table A1 (Hsu & Huang,
2014).
The results of consistency tests for the fuzzy positive reciprocal
matrixes in the empirical study are shown in Table A2. Since all of
the C.I. and C.R. indexes are less than 0.1, all the positive reciprocal
matrixes in the sample data are consistent.
3 The local weights of the SRAs
Basically, the local weights of SRAs can be determined from
the eigenvectors of
~
A. However, due to the special structure of
~
A, Saaty (1980) suggested four solution methods to ?nd the
eigenvectors: Average of Normalized Columns (ANC), Normal-
ization of the Row Average (NRA), Normalization of the
Reciprocal of Columns Sum (NRCS), and Normalization of the
Geometric Mean of the Rows (NGMR). In this paper, the NGMR
method was used to determine the local weights of the SRAs
in matrix
~
A.
For the matrix
~
A, the geometric means of triangular fuzzy
numbers for the ith SRA (i ¼1,2,…,n) can be found as:
~ w
i
¼
_
_
n
j¼1
~
a
ij
_
_
1=n
¼
_
¸
_
_
_
n
j¼1
l
ij
_
_
1=n
;
_
_
n
j¼1
m
ij
_
_
1=n
;
_
_
n
j¼1
u
ij
_
_
1=n
_
¸
_; i
¼ 1; 2; …; n: (A5)
0
n
i¼1
~ w
i
¼
_
¸
_
n
i¼1
_
_
n
j¼1
l
ij
_
_
1=n
;
n
i¼1
_
_
n
j¼1
m
ij
_
_
1=n
;
n
i¼1
_
_
n
j¼1
u
ij
_
_
1=n
_
¸
_:
(A6)
From the Equations (A5) and (A6), the local weight for the ith
SRAs (i ¼1,2,...,n) can then be obtained as:
~
W
i
¼ ~ w
i
_
n
i¼1
~ w
i
¼
_
¸
¸
¸
¸
¸
_
_
n
j¼1
l
ij
_
_
1=n
n
i¼1
_
n
j¼1
u
ij
_
_
1=n
;
_
n
j¼1
m
ij
_
_
1=n
n
i¼1
_
n
j¼1
m
ij
_
_
1=n
;
_
n
j¼1
u
ij
_
_
1=n
n
i¼1
_
n
j¼1
l
ij
_
_
1=n
_
¸
¸
¸
¸
¸
_
; i
¼1;2;…;n
(A7)
4 The defuzziness process
Since the local weight
~
W
i
of the ith SRA (i ¼1,2,…,n) is fuzzy, this
paper adopted Yager's index (1981) to defuzzify the
~
W
i
into a crisp
number W
i
, i ¼1,2,...,n. For convenience of explanations, let
~
W
i
¼ ½l
W
i
; m
W
i
; u
W
i
?, where
Table A1
The randomized index of R. I.
n 3 4 5 6 7 8 9 10 11 12
R.I. 0.525 0.882 1.115 1.252 1.341 1.404 1.452 1.484 1.513 1.535
Table A2
The results of the consistency tests.
Subjects Layer C.I. R.I. C.R. (C.I./R.I.)
CACCs Layer 1 0.0075 1.115 0.0067
Layer2: TG 0.0092 0.882 0.0104
Layer2: RB 0.0123 0.882 0.0139
Layer2: AA 0.0101 0.525 0.0192
Layer2: EP 0.0325 0.525 0.0619
Layer2: DV 0.0258 0.882 0.0293
AFFs Layer 1 0.0102 1.115 0.0091
Layer2: TG 0.0154 0.882 0.0175
Layer2: RB 0.0221 0.882 0.0251
Layer2: AA 0.0433 0.525 0.0825
Layer2: EP 0.0178 0.525 0.0339
Layer2: DV 0.0210 0.882 0.0238
S.-H.S. Huang, W.-K.K. Hsu / Asia Paci?c Management Review xxx (2015) 1e8 7
Please cite this article in press as: Huang, S.-H. S., & Hsu, W.-K. K., Evaluating the service requirements of combination air cargo carriers, Asia
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Then the Yager's index (1981) of the
~
W
i
(i ¼1,2,...,n) is de?ned
as:
W
i
¼
_
l
W
i
þ2 m
W
i
þ u
W
i
__
4; i ¼ 1; 2; …; n: (A8)
Finally, normalizing the W
i
(i ¼1,2..,n) then the local weight of
the ith SRAs can be obtained as:
u
i
¼ W
i
_
n
i¼1
W
i
; i ¼ 1; 2; …; n (A9)
5 The global weights of the SRAs
The global weights of SRAs can be found by multiple the low
level of local weights by their corresponding high level of global
weights.
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_
l
W
i
;m
W
i
;u
W
i
_
¼
_
¸
¸
¸
¸
¸
_
_
n
j¼1
l
ij
_
_
1=n
n
i¼1
_
n
j¼1
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ij
_
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1=n
;
_
n
j¼1
m
ij
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_
1=n
n
i¼1
_
n
j¼1
m
ij
_
_
1=n
;
_
n
j¼1
u
ij
_
_
1=n
n
i¼1
_
n
j¼1
l
ij
_
_
1=n
_
¸
¸
¸
¸
¸
_
; i¼1;2;…;n:
S.-H.S. Huang, W.-K.K. Hsu / Asia Paci?c Management Review xxx (2015) 1e8 8
Please cite this article in press as: Huang, S.-H. S., & Hsu, W.-K. K., Evaluating the service requirements of combination air cargo carriers, Asia
Paci?c Management Review (2015),http://dx.doi.org/10.1016/j.apmrv.2015.05.001
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