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Service Quality Evaluation of Container Terminals through AHM and Membership Degree Transformation
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ScienceDirect Materials Today: Proceedings 18 (2019) 155–164
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ICAMME-2018
Service Quality Evaluation of Container Terminals through AHM and Membership Degree Transformation S Hemalathaa, Lingaraju Dumpalab, B Balakrishnac a
Research Scholar in Department of Mechanical Engg, JNTUCEK, JNTUK, Kakinada-533 001,India b Department of Mechanical Engg, JNTUCEK, JNTUK, Kakinada-533 001,India c Department of Mechanical Engg, JNTUCEK, JNTUK, Kakinada-533 001,India
Abstract In today’s competitive world, it is important to determine the relative importance of attributes that have an effect on the service quality to improve the business performance. As Container terminals plays major role in national economy, the study focuses on the evaluation of container terminal service quality. The five main attributes considered in measuring service quality of container terminals are Container terminal Assurance, Empathy, Responsiveness, Reliability and Tangibles. In this paper, Relative weights of each criteria and sub-criteria are determined by using AHM and overall performance of container terminals is also evaluatedby M (1, 2, 3)Degree transformation. © 2019 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Engineering, ICAMME-2018. Keywords: Container terminals; Service quality, Attribute Hierarchy Model (AHM), Membership Degree transformation;
1. Introduction In view of the container terminals customers the service quality of container terminals is a key element affecting their choice of terminal in today’s competitive environment (Ha, 2003). Service quality is impossible to define and measure (Brown and Swartz 1989, Carman 1990). The survey outcome achieved by Bolton and Drew (1991), customer expectations depend on the perceptions of current and previous service quality. This concludes that there is different quality measurements derived from customers with their experience, expectations and perception. Clarke (2001) stated that physical service measures are not sufficient to evaluate the container terminal performance and quality of its services unless it is compelled with some service quality indicators. These indicators could e measured by determining the difference between client’s anticipations and delivered service. ∗ Hemalatha Somireddi. Tel.: +0-728-590-7861; E-mail address: [email protected]
2214-7853 © 2019 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Engineering, ICAMME-2018.
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Through the literature, it is observed that the service quality of any container terminal depends upon specific requirements, namely; Port Assurance, Empathy, Responsiveness, Reliability and Tangibles. Consequently, prioritization of the customer requirements is essential to progress container terminal service quality by quickly responding to the variations in the customer demand with effective cost reduction. In this study, five attributes Port Assurance, Empathy, Responsiveness, Reliability and Tangibles are considered as customer requirements. 2. Literature Well-known SERVQUAL (Parasuraman et al., 1988) model is useful to evaluate the service quality of service organizations. Container terminal service quality analysis allows forms an appropriate profile to face effectively the volatility of market segments and fight to get competitive advantages in maritime industry. Chinonye Ugboma, Innocent C. Ogwude et al. (2007) identified and assessed the key determinants of port service quality and determined the quality of service offered by two ports in Nigeria using SERVQUAL model and also Customer Satisfaction Index is determined to measure port users’ level of satisfaction at these ports. I. Kolanovićet al. (2008) mainly focused to reduce a great number of the port service quality attributes to a smaller number of attributes, grouped in common factors. The hypothesis stated that the two dimensions of the port service quality: reliability and competence are best among the attributes presented. An optimal selection of attributes has been made by the principal component analysis, while the attribute distribution has been obtained by the exploratory factor analysis. Alireza Miremadi et al. (2011) found five generic dimensions (tangible, responsive, assurance, empathy, and reliability) as the key determinants of service quality and determined the quality of service offered considering Shahid Rajaie Port (SRP) in Bandar Abbas with the SERVQUAL model. Ines Kolanovićet al. (2011) applied method of principal components analysis and factor analysis, to the model indicated by five factors influencing the port service quality dimensions measurable by a set of 25 attributes. The five factors extracted with regard to the port service quality pertaining attributes are called: accessibility factor, reliability factor, functionality factor, information availability factor, and flexibility factor. Lei Wang (2011) evaluated container terminal service attributes by Internal-Consistency Reliability, Factor Analysis, Cluster Analysis, Importance-Satisfaction Analysis and analysis of variance methods. Also the work suggested that reliability is the most important service attribute and quality of port facility is the most satisfactory service attribute. Gi Tae Yeo et al. (2015) investigated the port service quality influence on customer satisfaction. Partial least squares structural equation modeling (PLS-SEM) was conducted to confirm the PSQ dimensions and to examine their relationship with customer satisfaction. PSQ is found to be a five-factor construct, and its management, and image and social responsibility factors have significant positive effects on customer satisfaction. Nguyen Hoang Viet (2015) synthesized the elements of quality of container terminal services Resources; Capacities; Process of services; Management capacities; Image and reputation from which analyzed the relationship between the elements of the service quality to the level of customer satisfaction. The research model is recognized by a group of customer at 6 container terminals of Saigon Newport Corporation. Jing Lu, Xiaoxing Gong and Vinh V Thai (2016) examined the concept of Port Service Quality (PSQ) and analyzed its influence on customer satisfaction in the port sector. A confirmatory factor analysis followed by multiple regression is conducted to confirm the PSQ construct and examine the relationship between PSQ and customer satisfaction. It is found that PSQ is a four-dimensional construct namely outcomes, management, process and image and social responsibility and that the relationship between PSQ and customer satisfaction is positively significant. Jafar Sayarehet al. (2016) used five main dimensions tangibles, Reliability, Assurance, Responsiveness and Empathy evaluated the service quality offered at Shafid Rajaee Container terminal by using SERVQUAL model. And also identified the gaps between service expectations and perceptions by performing t-test. Chinedum Onyemechiet al. (2017) focused on service quality assessment of the Nigerian ports and evaluated them based on the service quality model developed by Parasuraman within the core dimensions of Empathy, Responsiveness, Tangibles, Reliability and Assurance. The expectations and perceptions raised within these dimensions were addressed with two hypotheses and were addressed with the results of Factor Analysis which identified the significance port users attach to service quality dimensions and their respective attributes.
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3. Methodology Step 1: Identify Service Quality Evaluation Attributes of Container Terminals To assess the service quality of container terminals, at first the container terminal needs to identify the attributes on which the service quality depends. Step 2: Determine relative importance weights of the attributes To calculate the relative weights of the criteria and sub criteria Attribute Hierarchical Model (AHM) is used, discussed by Xiao el at (2013). In general, Attribute hierarchical model (AHM) is an unstructured decision making method obtained from analytic hierarchy process (AHP). Contrast with the AHP, which depend on the weight of the model, the majorbenefit from the game-based type of AHM will be the matrix aboutrelationship between every a pair of indexes does not exist (consistency check difficulty),Hence there is no need for a extensive amount calculation. Step 2.1: Prepare pair wise comparison matrices for criteria/sub criteria Pair wise comparison matrix can be prepared basing on the relative significance about the criteria over a size involving 1-5. (VeryDisatisfied-1; Disatisfied-2; General-3; Satisfied-4; VerySatisfied-5). The values taken in pair wise comparison matrices would be the aggregated values of the all of the viewpoints. Step 2.2: Prepare Attribute matrix Attribute matrix A = (µ ij) nxn equation.
ij
k k 1 1 k 1 0 .5 0
is formulated from pair wise comparison matrix using the following conversion
a ij k 1 k 1, i j
a ij a ij
a i j 1, i j
Step 2.3: Determine relative weights of the Attributes Relative attribute weight is determined from the following relation
Wc j
J 2 * ij J * ( J 1) i 1
j 1, 2....J
Step 3: Membership Transformation through “Effective, Comparison and Composition” Membership transformation method – M (1,2,3) proposed by Hemalatha (2015) is adopted to find out the evaluation matrix of the alternative. Step 3.1: Determine Evaluation Membership (
jk (Q )
)
Percentage of satisfaction among the domain experts under each class is considered as evaluation matrix of each criterion.
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jk (Q)
=membership of jth sub-criteria of the criteria group ‘Q’ belonging to the kth fuzzy membership class.
Step 3.2: Determine Distinguishable Weights (
j (Q )
)
Distinguishable weight represents the normalized and quantized value obtained from the following relation.
j (Q) vj (Q)/ j1vj (Q) m
( j 1..m)
vj (Q) 1 (1/log( p))* H j (Q)
Where
p
Hj (Q) jk (Q)*log jk (Q) k1
v j (Q)
= weight of the jth sub criteria of the evaluation criteria object ‘Q’ obtained from uncertainty in the payoff information of the sub criteria
H j (Q)
= Measure of uncertainty in the payoff information of the jth sub criteria of the evaluation criteria object
‘Q’ Step 3.3: Determine Comparable sum Vector ( M k (Q) ) Comparable sum vector of the sub criteria under the given criteria is evaluated from the following relation.
Mk (Q) = j1j (Q)*j (Q)*jk (Q) m
j (Q)
= Importance Weight Vector of sub-criteria
Step 3.4: Determine Membership Vector
k (Q)
Membership vector of the object ‘Q’ belonging to class ‘k’ is calculated from the following relation. p
k (Q) =
Mk (Q)/ Mk (Q) k1
Step 3.5: Establish Evaluation Matrix of the alternative U(S) Membership matrix of all the attributes of the object ‘Q’ is determined and evaluation matrix is formed as shown below.
U(S) =
.. ..
( C 1) (C 2 ) (C 3) (C 4 )
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Step 4: Find out Final membership Vector
159
(S )
Once the weights of the each attribute and evaluation matrix are evaluated the procedure is repeated from the steps 3.1 to 3.5 to obtain the final membership vector. Step 5: Evaluate the grade of overall Performance (KO) Confidence recognition rule (Confidence degree: alternative by applying k
KO = min {k| k 1 4.
k
>0.7) is used to evaluate Overall performance of the
(S )
}
Case study 4.1 Service Quality Evaluation Index System of Container Terminals
The container terminals play an important role in the economic development of the country. Service quality of container terminals has become very significant. In this paper, Service quality evaluation of container terminals using proposed methodology is illustrated. The attributes Port Assurance, Empathy, Responsiveness, Reliability and Tangibles are considered at criterion level. Sub-criteria under each criterion are given below (Table.1). Table.1 Evaluation Index System for Service quality of container terminals Criteria Sub-Criteria Knowledge on needs and requirements of the customers Meet the service requirements anytime and anywhere Assurance Knowledge and competent human resource Social responsibility to their employees and other stakeholder Feedback mechanism about the services Emphasizes on environmentally responsible operations Empathy Relationship with customers, other ports and land transport service providers Customer-oriented operations and management processes Quick service High level of efficiency in operations and management Responsiveness: Comprehensive use of ICT applications Respond quickly to the enquiries and requests Provide service in a reliable manner Ensure safety and security to ships/shipments Reliability Error free invoice and related documents Shipment track and trace capability Availability of Physical infrastructure such as berths, yards, warehouses, distribution centers, and hinterland connection networks Tangibles Availability of Material handling equipments and facilities Professional attitude and behavior of human resource Strong and stable financial stability
Code ASS1 ASS2 ASS3 ASS4 EMP1 EMP2 EMP3 EMP4 RES1 RES2 RES3 RES4 REL1 REL2 REL3 REL4 TAN1 TAN2 TAN3 TAN4
4.2 Relative weights of the criteria/sub-criteria Necessary data on the relative importance of criteria/sub-criteria gathered from discussions with the 180 employees of twelve major Indian container terminals. These container terminals need to improve their service quality by concentrating issues to face with the uncertainty within the business environment.Relative weights of each criteria and sub-criteria are determined by using Attribute Hierarchy Model (AHM) discussed by Xiao et al(2013). Relative weights of criteria and sub-criteria are shown in below tables.
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Table.2 Pair wise comparisons and relative weights of the major criterion. ASS EMP RES REL TAN ASS EMP
RES
REL
TAN
WEIGHTS
ASS
1.000
0.600
0.525
0.450
0.775
ASS
0.000
0.231
0.208
0.184
0.279
0.090164
EMP
1.667
1.000
0.900
0.600
0.700
EMP
0.769
0.000
0.310
0.231
0.259
0.15696
RES
1.905
1.111
1.000
0.525
0.725
RES
0.792
0.690
0.000
0.208
0.266
0.195571
REL
2.222
1.667
1.905
1.000
0.600
REL
0.816
0.769
0.792
0.000
0.231
0.260841
TAN
1.290
1.429
1.379
1.667
1.000
TAN
0.721
0.741
0.734
0.769
0.000
0.296464
From Table.2 it is observed that highest relative weight is obtained with Tangibles followed by Reliability, Responsiveness, Empathy and Assurance. Table.3 Pair wise comparisons and relative weights of the sub-criteria under Assurance criteria. ASS1
ASS2
ASS3
ASS4
ASS1
ASS2
ASS3
ASS4
WEIGHTS
ASS1
1.000
0.125
0.150
0.150
ASS1
0.000
0.059
0.070
0.070
0.03306
ASS2
8.000
1.000
0.200
0.200
ASS2
0.941
0.000
0.091
0.091
0.187166
ASS3
6.667
5.000
1.000
0.075
ASS3
0.930
0.909
0.000
0.036
0.312578
ASS4
6.667
5.000
13.333
1.000
ASS4
0.930
0.909
0.964
0.000
0.467196
From Table.3 it is observed that highest relative weight is obtained with ASS4 (Social responsibility to their employees and other stakeholder). Table.4 Pair wise comparisons and relative weights of the sub-criteria under Empathy criteria. EMP1
EMP2
EMP3
EMP4
EMP1
EMP2
EMP3
EMP4
WEIGHTS
EMP1
1.000
0.175
0.150
0.325
EMP1
0.000
0.080
0.070
0.140
0.048335
EMP2
5.714
1.000
0.200
0.200
EMP2
0.920
0.000
0.091
0.091
0.18356
EMP3
6.667
5.000
1.000
0.275
EMP3
0.930
0.909
0.000
0.121
0.3267
EMP4
3.077
5.000
3.636
1.000
EMP4
0.860
0.909
0.879
0.000
0.441404
From Table.4 it is observed that highest relative weight is obtained with EMP4 (Social responsibility to their employees and other stakeholder). Table.5 Pair wise comparisons and relative weights of the sub-criteria under Responsiveness criteria. RES1
RES2
RES3
RES4
RES1
1.000
0.400
0.350
0.275
RES2
2.500
1.000
0.425
RES3
2.857
2.353
RES4
3.636
13.333
RES1
RES2
RES3
RES4
WEIGHTS
RES1
0.000
0.167
0.149
0.121
0.072747
0.075
RES2
0.833
0.000
0.175
0.036
0.174123
1.000
0.075
RES3
0.851
0.825
0.000
0.036
0.285325
13.333
1.000
RES4
0.879
0.964
0.964
0.000
0.467805
From Table.5 it is observed that highest relative weight is obtained with RES4 (Respond quickly to the enquiries and requests). Table.6 Pair wise comparisons and relative weights of the sub-criteria under Reliability criteria. REL1
REL2
REL3
REL4
REL1
REL2
REL3
REL4
WEIGHTS
REL1
1.000
0.850
0.750
0.825
REL2
1.176
1.000
0.825
0.700
REL1
0.000
0.298
0.273
0.292
0.143835
REL2
0.702
0.000
0.292
0.259
0.208842
REL3
1.333
1.212
1.000
0.725
REL3
0.727
0.708
0.000
0.266
0.283549
REL4
1.212
1.000
2.000
1.000
REL4
0.708
0.500
0.800
0.000
0.334661
From Table.6 it is observed that highest relative weight is obtained with REL4 (Shipment track and trace capability).
S. Hemalatha et al. / Materials Today: Proceedings 18 (2019) 155–164 Table.7 Pair wise comparisons and relative weights of the sub-criteria under Tangibles criteria. TAN1 TAN2 TAN3 TAN4 TAN1 TAN2 TAN3 TAN4
161
WEIGHTS
TAN1
1.000
0.550
0.550
0.625
TAN1
0.000
0.216
0.216
0.238
0.111578
TAN2
1.818
1.000
0.650
0.075
TAN2
0.784
0.000
0.245
0.036
0.177624
TAN3
1.818
1.538
1.000
0.075
TAN3
0.784
0.755
0.000
0.036
0.262529
TAN4
1.538
1.000
2.000
1.000
TAN4
0.755
0.500
0.800
0.000
0.342453
From Table.7 it is observed that highest relative weight is obtained with TAN4 (Strong and stable financial stability). The relative weights of all criteria and sub criteria are as shown in Table.8. Table.8 Relative weights of the criteria and sub-criteria Major Major criterion Sub-Criteria criterion weight
ASS
EMP
RES
REL
TAN
0.090164
0.156960
0.195571
0.260841
0.296464
Sub-Criteria Weights
Synthetic weights
ASS1
0.033060
0.003
ASS2
0.187166
0.017
ASS3
0.312578
0.028
ASS4
0.467196
0.042
EMP1
0.048335
0.008
EMP2
0.183560
0.029
EMP3
0.326700
0.051
EMP4
0.441404
0.069
RES1
0.072747
0.014
RES2
0.174123
0.034
RES3
0.285325
0.056
RES4
0.467805
0.091
REL1
0.143835
0.038
REL2
0.208842
0.054
REL3
0.283549
0.074
REL4
0.334661
0.087
TAN1
0.111578
0.033
TAN2
0.177624
0.053
TAN3
0.262529
0.078
TAN4
0.342453
0.102
4.3 Evaluation Membership Data for container terminals performance is obtained from 180 employees of container terminals. No of employees responded regarding the satisfaction levels in five classes (Very Satisfied-VS, Satisfied-SA, General-GE, Dissatisfied-DS, and Very Dissatisfied-VD) and the membership values are shown in Table.9. The final membership vectors for each criteria are determined by using Membership degree Transformation M (1,2,3).
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Table.9 Evaluation Responses and Memberships Criteria
ASS
EMP
RES
REL
TAN
Evaluation Responses
Evaluation Memberships
SubCriteria
VS
SA
GE
DS
VD
VS
SA
GE
DS
ASS1
49
41
40
34
16
0.2722
0.2278
0.2222
0.1889
0.0889
ASS2
38
40
44
32
26
0.2111
0.2222
0.2444
0.1778
0.1444
ASS3
34
41
31
41
33
0.1889
0.2278
0.1722
0.2278
0.1833
ASS4
30
36
46
22
46
0.1667
0.2000
0.2556
0.1222
0.2556
EMP1
44
37
30
25
44
0.2444
0.2056
0.1667
0.1389
0.2444
EMP2
61
35
35
18
31
0.3389
0.1944
0.1944
0.1000
0.1722
EMP3
51
43
39
29
18
0.2833
0.2389
0.2167
0.1611
0.1000
EMP4
51
42
34
23
30
0.2833
0.2333
0.1889
0.1278
0.1667
RES1
52
42
34
19
33
0.2889
0.2333
0.1889
0.1056
0.1833
RES2
45
35
31
41
28
0.2500
0.1944
0.1722
0.2278
0.1556
RES3
36
48
51
32
13
0.2000
0.2667
0.2833
0.1778
0.0722
RES4
37
37
50
20
36
0.2056
0.2056
0.2778
0.1111
0.2000
REL1
72
33
37
11
27
0.4000
0.1833
0.2056
0.0611
0.1500
REL2
27
35
40
26
52
0.1500
0.1944
0.2222
0.1444
0.2889
REL3
50
32
34
41
23
0.2778
0.1778
0.1889
0.2278
0.1278
REL4
23
35
31
30
61
0.1278
0.1944
0.1722
0.1667
0.3389
TAN1
60
37
30
20
33
0.3333
0.2056
0.1667
0.1111
0.1833
TAN2
22
34
43
38
43
0.1222
0.1889
0.2389
0.2111
0.2389
TAN3
47
46
45
27
15
0.2611
0.2556
0.2500
0.1500
0.0833
TAN4
44
27
48
43
18
0.2444
0.1500
0.2667
0.2389
0.1000
VD
4.4 Evaluation matrix Table.10 Evaluation matrix VS
S
G
DS
VD
ASS
20.97%
21.94%
22.36%
17.92%
16.81%
EMP
28.75%
21.81%
19.17%
13.19%
17.08%
RES
23.61%
22.50%
23.06%
15.56%
15.28%
REL
35.15%
18.42%
20.58%
9.25%
16.60%
TAN
24.03%
20.00%
23.06%
17.78%
15.14%
From Table.10 it is understood that criteria Reliability attribute has highest percentage 35.15% in very satisfied level, under very dissatisfied level Empathy attribute has highest percentage of 18.80%. 4.5 Final membership Vector Table.11 Final membership values Satisfaction VS S level mew(c1) 0.2915 0.1987
G
DS
VD
0.2097
0.1345
0.1656
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4.5 Grade of Overall Performance of container terminals From the final membership vector (Table.11), it is identified that the service quality of the container terminals belongs to the ‘General’ level with the confidence level of 69.99% (29.15%+19.87%+20.97%). 5. Results and discussions From the comparison judgment matrix it is understood that Tangibles (TAN) showing high weight 0.296, followed by Reliability (0.260), Responsiveness (0.195), Empathy (0.15), and Assurance (0.090). Which means Tangibles has maximum difference and Empathy has minimum difference between client’s anticipations and delivered service. Evaluation membership of container terminals is shown in Fig.1. From the figure, it is understood that Reliability (REL) of the container terminals is showing relatively high confidence level of performances of 35.15% in ‘Very Satisfied’ level.
Fig.1.Evaluation memberships of container terminal service quality attributes
From the results of the final membership values, it can be judged that the overall service quality of the container terminals is considered as ‘General’ level as the confidence level of 69.99% (29.15%+19.87%+20.97%) is nearly equal to the minimum confidence level of 70%. Overall confidence level with ‘Very Satisfied’ is only 29.15% indicates that the container terminals should enhance the servicequality from every criteria. Since the attribute weights are the very significant factor for the analysis of service quality. So, in order to compare the suggested results with the previous results Onyemechi [3],and found that the suggestedresults coincidetogether with his results, that is tangible has more effect on container terminal service quality. The results of this study offersignificant implications for container terminalmanagers. Based on this understanding, port managers can develop a standard scale to measure service quality. Over time, the use of such a standard measurement tool would facilitate the assessment and benchmarking between container terminals in terms of their service quality performance, and through that implement necessary solutions to enhance container terinal service quality. 6. Conclusions The main purpose of the current study is to evaluate the factors that effect on container terminal service quality and to evaluate the overall performance of the container terminals. The article used fuzzy AHP to calculate the
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weights of all criteria and sub-criteria which affect the service quality of container terminals, from the results it is understood that tangibles criteria mostly affect the port service quality. To find the service quality of container terminals membership degree transformation is used and it is observed that overall service quality is in general level. References [1] Ael Luttenberger (2010), Achieving the quality of services in sea ports through regulations, Tourism and hospitality management 2010 conference proceedings, pp. 1034-1040. [2] Alireza Miremadi , Shermineh Ghalamkari , Farhad Sadeh (2011), Customer Satisfaction In Port Industry (A Case Study Of Iranian Shipping), International Conference on Sociality and Economics Development 2011, Vol.10, pp.58-62. [3] Chinedum Onyemechi, Azubuike Chibuzo Amanze, Chinemerem Igboanusi and Abiodun Sule (2017), Port Service Quality Study of Nigerian Sea Ports, Journal of Shipping and Ocean Engineering, Vol. 7, pp. 59-64. [4] Chinonye Ugboma, Innocent C. Ogwude, Ogochukwu Ugboma and Kenneth Nnadi (2007), Service quality and satisfaction measurements in Nigerian ports: an exploration, Maritime Policy & Management Vol. 34, No. 4, pp. 331-346. [5] Gi Tae Yeo , Vinh V. Thai and Sae Yeon Roh (2015), An Analysis of Port Service Quality and Customer Satisfaction: The Case of Korean Container Ports, The Asian Journal of Shipping and Logistics, Vol.31, No.4, pp. 437-447. [6] Ines Kolanović, Čedomir Dundović and Alen Jugović (2011), Customer-based port service quality model, Promet – Traffic &Transportation, Vol. 23, No. 6, pp. 495-502. [7] Jafar Sayareh, Sobhan Iransahi and Neda Golfakhrabadi (2016), Service quality evaluation and ranking of Container terminal operators, The Asian Journal of Shipping and Logistics, Vol.32, No.4, pp. 203-212. [8] Jing Yang, Hua Jiang (2012),”Fuzzy evaluation on supply chains overall performance based on AHM and M(1,2,3)” Journal of Software, Vol .7,No 12. [9] Jing Lu, Xiaoxing Gong and Lei Wang (2011), An Empirical Study of Container Terminal’s Service Attributes, Journal of Service Science and Management, Vol.4, pp. 97-109. [10] Kolanović, j. Skenderović, and z. Zenzerović (2008), Defining the port service quality model by using the factor analysis, Vol. 22, No. 2, pp. 283-297. [11] Kum Fai Yuen and Vinh Van Thai (2015), Service quality and customer satisfaction in liner shipping, International Journal of Quality and Service Sciences, Vol. 7, No. 2/3, pp.170-183. [12] Leili Afkhama, Farshid Abdia and Alireza Rashidi Komijan (2012) “Evaluation of service quality by using fuzzy MCDM: A case study in Iranian health-care centers”, Management Science Letters, Vol.2, pp.291–300. [13]Milad Shafii, Sima Rafiei, Fatemeh Abooee, Mohammad Amin Bahrami, Mojtaba Nouhi, Farhad Lotfi, Khatere Khanjankhani (2016) “Assessment of Service Quality in Teaching Hospitals of Yazd University of Medical Sciences: Using Multi-criteria Decision Making Techniques”, Osong Public Health Res Perspect ,Vol.7, No.4, pp.239-247. [14]Nguyen Hoang Viet (2015), Service Quality at the Container terminal System of Saigon Newport Corporation, International Journal of Marketing Studies, Vol. 7, No. 6, pp.145-154, [15] Paul T-W Lee and Kai-Chieh Hu (2012), Evaluation of the service quality of container ports by importance-performance analysis, international Journal of Shipping and Transport Logistics Volume 4, No. 3, pp.197-211. [16] S Hemalatha, K Narayana Rao, G Rambabu and K Venkata subbaiah (2017), Supply chain performance evaluation through AHM and Membership degree transformation, Materials today Proceedings, Vol.4, pp.7848-7858. [17] Vinh V Thai (2016), The impact of port service quality on customer satisfaction: The case of Singapore, Maritime Economics & Logistics, Vol. 18, No. 4, pp. 458–475. [18] Xia, L.X.X., Bin Ma, Lim, R. (2007),”AHP based supply chain performance measurement system” Emerging Technologies and Factory Automation, 2007. ETFA. IEEE Conference on Page(s):1308 - 1315. [19]Zoraghi, N., Amiri, M., Talebi, G. and Mahdi Zowghi (2013) “A fuzzy MCDM model with objective and subjective weights for evaluating service quality in hotel industries”, J Ind Eng Int, vol. 9, No.38, pp.1-13.
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ICAMME-2018
Service Quality Evaluation of Container Terminals through AHM and Membership Degree Transformation S Hemalathaa, Lingaraju Dumpalab, B Balakrishnac a
Research Scholar in Department of Mechanical Engg, JNTUCEK, JNTUK, Kakinada-533 001,India b Department of Mechanical Engg, JNTUCEK, JNTUK, Kakinada-533 001,India c Department of Mechanical Engg, JNTUCEK, JNTUK, Kakinada-533 001,India
Abstract In today’s competitive world, it is important to determine the relative importance of attributes that have an effect on the service quality to improve the business performance. As Container terminals plays major role in national economy, the study focuses on the evaluation of container terminal service quality. The five main attributes considered in measuring service quality of container terminals are Container terminal Assurance, Empathy, Responsiveness, Reliability and Tangibles. In this paper, Relative weights of each criteria and sub-criteria are determined by using AHM and overall performance of container terminals is also evaluatedby M (1, 2, 3)Degree transformation. © 2019 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Engineering, ICAMME-2018. Keywords: Container terminals; Service quality, Attribute Hierarchy Model (AHM), Membership Degree transformation;
1. Introduction In view of the container terminals customers the service quality of container terminals is a key element affecting their choice of terminal in today’s competitive environment (Ha, 2003). Service quality is impossible to define and measure (Brown and Swartz 1989, Carman 1990). The survey outcome achieved by Bolton and Drew (1991), customer expectations depend on the perceptions of current and previous service quality. This concludes that there is different quality measurements derived from customers with their experience, expectations and perception. Clarke (2001) stated that physical service measures are not sufficient to evaluate the container terminal performance and quality of its services unless it is compelled with some service quality indicators. These indicators could e measured by determining the difference between client’s anticipations and delivered service. ∗ Hemalatha Somireddi. Tel.: +0-728-590-7861; E-mail address: [email protected]
2214-7853 © 2019 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Engineering, ICAMME-2018.
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Through the literature, it is observed that the service quality of any container terminal depends upon specific requirements, namely; Port Assurance, Empathy, Responsiveness, Reliability and Tangibles. Consequently, prioritization of the customer requirements is essential to progress container terminal service quality by quickly responding to the variations in the customer demand with effective cost reduction. In this study, five attributes Port Assurance, Empathy, Responsiveness, Reliability and Tangibles are considered as customer requirements. 2. Literature Well-known SERVQUAL (Parasuraman et al., 1988) model is useful to evaluate the service quality of service organizations. Container terminal service quality analysis allows forms an appropriate profile to face effectively the volatility of market segments and fight to get competitive advantages in maritime industry. Chinonye Ugboma, Innocent C. Ogwude et al. (2007) identified and assessed the key determinants of port service quality and determined the quality of service offered by two ports in Nigeria using SERVQUAL model and also Customer Satisfaction Index is determined to measure port users’ level of satisfaction at these ports. I. Kolanovićet al. (2008) mainly focused to reduce a great number of the port service quality attributes to a smaller number of attributes, grouped in common factors. The hypothesis stated that the two dimensions of the port service quality: reliability and competence are best among the attributes presented. An optimal selection of attributes has been made by the principal component analysis, while the attribute distribution has been obtained by the exploratory factor analysis. Alireza Miremadi et al. (2011) found five generic dimensions (tangible, responsive, assurance, empathy, and reliability) as the key determinants of service quality and determined the quality of service offered considering Shahid Rajaie Port (SRP) in Bandar Abbas with the SERVQUAL model. Ines Kolanovićet al. (2011) applied method of principal components analysis and factor analysis, to the model indicated by five factors influencing the port service quality dimensions measurable by a set of 25 attributes. The five factors extracted with regard to the port service quality pertaining attributes are called: accessibility factor, reliability factor, functionality factor, information availability factor, and flexibility factor. Lei Wang (2011) evaluated container terminal service attributes by Internal-Consistency Reliability, Factor Analysis, Cluster Analysis, Importance-Satisfaction Analysis and analysis of variance methods. Also the work suggested that reliability is the most important service attribute and quality of port facility is the most satisfactory service attribute. Gi Tae Yeo et al. (2015) investigated the port service quality influence on customer satisfaction. Partial least squares structural equation modeling (PLS-SEM) was conducted to confirm the PSQ dimensions and to examine their relationship with customer satisfaction. PSQ is found to be a five-factor construct, and its management, and image and social responsibility factors have significant positive effects on customer satisfaction. Nguyen Hoang Viet (2015) synthesized the elements of quality of container terminal services Resources; Capacities; Process of services; Management capacities; Image and reputation from which analyzed the relationship between the elements of the service quality to the level of customer satisfaction. The research model is recognized by a group of customer at 6 container terminals of Saigon Newport Corporation. Jing Lu, Xiaoxing Gong and Vinh V Thai (2016) examined the concept of Port Service Quality (PSQ) and analyzed its influence on customer satisfaction in the port sector. A confirmatory factor analysis followed by multiple regression is conducted to confirm the PSQ construct and examine the relationship between PSQ and customer satisfaction. It is found that PSQ is a four-dimensional construct namely outcomes, management, process and image and social responsibility and that the relationship between PSQ and customer satisfaction is positively significant. Jafar Sayarehet al. (2016) used five main dimensions tangibles, Reliability, Assurance, Responsiveness and Empathy evaluated the service quality offered at Shafid Rajaee Container terminal by using SERVQUAL model. And also identified the gaps between service expectations and perceptions by performing t-test. Chinedum Onyemechiet al. (2017) focused on service quality assessment of the Nigerian ports and evaluated them based on the service quality model developed by Parasuraman within the core dimensions of Empathy, Responsiveness, Tangibles, Reliability and Assurance. The expectations and perceptions raised within these dimensions were addressed with two hypotheses and were addressed with the results of Factor Analysis which identified the significance port users attach to service quality dimensions and their respective attributes.
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3. Methodology Step 1: Identify Service Quality Evaluation Attributes of Container Terminals To assess the service quality of container terminals, at first the container terminal needs to identify the attributes on which the service quality depends. Step 2: Determine relative importance weights of the attributes To calculate the relative weights of the criteria and sub criteria Attribute Hierarchical Model (AHM) is used, discussed by Xiao el at (2013). In general, Attribute hierarchical model (AHM) is an unstructured decision making method obtained from analytic hierarchy process (AHP). Contrast with the AHP, which depend on the weight of the model, the majorbenefit from the game-based type of AHM will be the matrix aboutrelationship between every a pair of indexes does not exist (consistency check difficulty),Hence there is no need for a extensive amount calculation. Step 2.1: Prepare pair wise comparison matrices for criteria/sub criteria Pair wise comparison matrix can be prepared basing on the relative significance about the criteria over a size involving 1-5. (VeryDisatisfied-1; Disatisfied-2; General-3; Satisfied-4; VerySatisfied-5). The values taken in pair wise comparison matrices would be the aggregated values of the all of the viewpoints. Step 2.2: Prepare Attribute matrix Attribute matrix A = (µ ij) nxn equation.
ij
k k 1 1 k 1 0 .5 0
is formulated from pair wise comparison matrix using the following conversion
a ij k 1 k 1, i j
a ij a ij
a i j 1, i j
Step 2.3: Determine relative weights of the Attributes Relative attribute weight is determined from the following relation
Wc j
J 2 * ij J * ( J 1) i 1
j 1, 2....J
Step 3: Membership Transformation through “Effective, Comparison and Composition” Membership transformation method – M (1,2,3) proposed by Hemalatha (2015) is adopted to find out the evaluation matrix of the alternative. Step 3.1: Determine Evaluation Membership (
jk (Q )
)
Percentage of satisfaction among the domain experts under each class is considered as evaluation matrix of each criterion.
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jk (Q)
=membership of jth sub-criteria of the criteria group ‘Q’ belonging to the kth fuzzy membership class.
Step 3.2: Determine Distinguishable Weights (
j (Q )
)
Distinguishable weight represents the normalized and quantized value obtained from the following relation.
j (Q) vj (Q)/ j1vj (Q) m
( j 1..m)
vj (Q) 1 (1/log( p))* H j (Q)
Where
p
Hj (Q) jk (Q)*log jk (Q) k1
v j (Q)
= weight of the jth sub criteria of the evaluation criteria object ‘Q’ obtained from uncertainty in the payoff information of the sub criteria
H j (Q)
= Measure of uncertainty in the payoff information of the jth sub criteria of the evaluation criteria object
‘Q’ Step 3.3: Determine Comparable sum Vector ( M k (Q) ) Comparable sum vector of the sub criteria under the given criteria is evaluated from the following relation.
Mk (Q) = j1j (Q)*j (Q)*jk (Q) m
j (Q)
= Importance Weight Vector of sub-criteria
Step 3.4: Determine Membership Vector
k (Q)
Membership vector of the object ‘Q’ belonging to class ‘k’ is calculated from the following relation. p
k (Q) =
Mk (Q)/ Mk (Q) k1
Step 3.5: Establish Evaluation Matrix of the alternative U(S) Membership matrix of all the attributes of the object ‘Q’ is determined and evaluation matrix is formed as shown below.
U(S) =
.. ..
( C 1) (C 2 ) (C 3) (C 4 )
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Step 4: Find out Final membership Vector
159
(S )
Once the weights of the each attribute and evaluation matrix are evaluated the procedure is repeated from the steps 3.1 to 3.5 to obtain the final membership vector. Step 5: Evaluate the grade of overall Performance (KO) Confidence recognition rule (Confidence degree: alternative by applying k
KO = min {k| k 1 4.
k
>0.7) is used to evaluate Overall performance of the
(S )
}
Case study 4.1 Service Quality Evaluation Index System of Container Terminals
The container terminals play an important role in the economic development of the country. Service quality of container terminals has become very significant. In this paper, Service quality evaluation of container terminals using proposed methodology is illustrated. The attributes Port Assurance, Empathy, Responsiveness, Reliability and Tangibles are considered at criterion level. Sub-criteria under each criterion are given below (Table.1). Table.1 Evaluation Index System for Service quality of container terminals Criteria Sub-Criteria Knowledge on needs and requirements of the customers Meet the service requirements anytime and anywhere Assurance Knowledge and competent human resource Social responsibility to their employees and other stakeholder Feedback mechanism about the services Emphasizes on environmentally responsible operations Empathy Relationship with customers, other ports and land transport service providers Customer-oriented operations and management processes Quick service High level of efficiency in operations and management Responsiveness: Comprehensive use of ICT applications Respond quickly to the enquiries and requests Provide service in a reliable manner Ensure safety and security to ships/shipments Reliability Error free invoice and related documents Shipment track and trace capability Availability of Physical infrastructure such as berths, yards, warehouses, distribution centers, and hinterland connection networks Tangibles Availability of Material handling equipments and facilities Professional attitude and behavior of human resource Strong and stable financial stability
Code ASS1 ASS2 ASS3 ASS4 EMP1 EMP2 EMP3 EMP4 RES1 RES2 RES3 RES4 REL1 REL2 REL3 REL4 TAN1 TAN2 TAN3 TAN4
4.2 Relative weights of the criteria/sub-criteria Necessary data on the relative importance of criteria/sub-criteria gathered from discussions with the 180 employees of twelve major Indian container terminals. These container terminals need to improve their service quality by concentrating issues to face with the uncertainty within the business environment.Relative weights of each criteria and sub-criteria are determined by using Attribute Hierarchy Model (AHM) discussed by Xiao et al(2013). Relative weights of criteria and sub-criteria are shown in below tables.
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Table.2 Pair wise comparisons and relative weights of the major criterion. ASS EMP RES REL TAN ASS EMP
RES
REL
TAN
WEIGHTS
ASS
1.000
0.600
0.525
0.450
0.775
ASS
0.000
0.231
0.208
0.184
0.279
0.090164
EMP
1.667
1.000
0.900
0.600
0.700
EMP
0.769
0.000
0.310
0.231
0.259
0.15696
RES
1.905
1.111
1.000
0.525
0.725
RES
0.792
0.690
0.000
0.208
0.266
0.195571
REL
2.222
1.667
1.905
1.000
0.600
REL
0.816
0.769
0.792
0.000
0.231
0.260841
TAN
1.290
1.429
1.379
1.667
1.000
TAN
0.721
0.741
0.734
0.769
0.000
0.296464
From Table.2 it is observed that highest relative weight is obtained with Tangibles followed by Reliability, Responsiveness, Empathy and Assurance. Table.3 Pair wise comparisons and relative weights of the sub-criteria under Assurance criteria. ASS1
ASS2
ASS3
ASS4
ASS1
ASS2
ASS3
ASS4
WEIGHTS
ASS1
1.000
0.125
0.150
0.150
ASS1
0.000
0.059
0.070
0.070
0.03306
ASS2
8.000
1.000
0.200
0.200
ASS2
0.941
0.000
0.091
0.091
0.187166
ASS3
6.667
5.000
1.000
0.075
ASS3
0.930
0.909
0.000
0.036
0.312578
ASS4
6.667
5.000
13.333
1.000
ASS4
0.930
0.909
0.964
0.000
0.467196
From Table.3 it is observed that highest relative weight is obtained with ASS4 (Social responsibility to their employees and other stakeholder). Table.4 Pair wise comparisons and relative weights of the sub-criteria under Empathy criteria. EMP1
EMP2
EMP3
EMP4
EMP1
EMP2
EMP3
EMP4
WEIGHTS
EMP1
1.000
0.175
0.150
0.325
EMP1
0.000
0.080
0.070
0.140
0.048335
EMP2
5.714
1.000
0.200
0.200
EMP2
0.920
0.000
0.091
0.091
0.18356
EMP3
6.667
5.000
1.000
0.275
EMP3
0.930
0.909
0.000
0.121
0.3267
EMP4
3.077
5.000
3.636
1.000
EMP4
0.860
0.909
0.879
0.000
0.441404
From Table.4 it is observed that highest relative weight is obtained with EMP4 (Social responsibility to their employees and other stakeholder). Table.5 Pair wise comparisons and relative weights of the sub-criteria under Responsiveness criteria. RES1
RES2
RES3
RES4
RES1
1.000
0.400
0.350
0.275
RES2
2.500
1.000
0.425
RES3
2.857
2.353
RES4
3.636
13.333
RES1
RES2
RES3
RES4
WEIGHTS
RES1
0.000
0.167
0.149
0.121
0.072747
0.075
RES2
0.833
0.000
0.175
0.036
0.174123
1.000
0.075
RES3
0.851
0.825
0.000
0.036
0.285325
13.333
1.000
RES4
0.879
0.964
0.964
0.000
0.467805
From Table.5 it is observed that highest relative weight is obtained with RES4 (Respond quickly to the enquiries and requests). Table.6 Pair wise comparisons and relative weights of the sub-criteria under Reliability criteria. REL1
REL2
REL3
REL4
REL1
REL2
REL3
REL4
WEIGHTS
REL1
1.000
0.850
0.750
0.825
REL2
1.176
1.000
0.825
0.700
REL1
0.000
0.298
0.273
0.292
0.143835
REL2
0.702
0.000
0.292
0.259
0.208842
REL3
1.333
1.212
1.000
0.725
REL3
0.727
0.708
0.000
0.266
0.283549
REL4
1.212
1.000
2.000
1.000
REL4
0.708
0.500
0.800
0.000
0.334661
From Table.6 it is observed that highest relative weight is obtained with REL4 (Shipment track and trace capability).
S. Hemalatha et al. / Materials Today: Proceedings 18 (2019) 155–164 Table.7 Pair wise comparisons and relative weights of the sub-criteria under Tangibles criteria. TAN1 TAN2 TAN3 TAN4 TAN1 TAN2 TAN3 TAN4
161
WEIGHTS
TAN1
1.000
0.550
0.550
0.625
TAN1
0.000
0.216
0.216
0.238
0.111578
TAN2
1.818
1.000
0.650
0.075
TAN2
0.784
0.000
0.245
0.036
0.177624
TAN3
1.818
1.538
1.000
0.075
TAN3
0.784
0.755
0.000
0.036
0.262529
TAN4
1.538
1.000
2.000
1.000
TAN4
0.755
0.500
0.800
0.000
0.342453
From Table.7 it is observed that highest relative weight is obtained with TAN4 (Strong and stable financial stability). The relative weights of all criteria and sub criteria are as shown in Table.8. Table.8 Relative weights of the criteria and sub-criteria Major Major criterion Sub-Criteria criterion weight
ASS
EMP
RES
REL
TAN
0.090164
0.156960
0.195571
0.260841
0.296464
Sub-Criteria Weights
Synthetic weights
ASS1
0.033060
0.003
ASS2
0.187166
0.017
ASS3
0.312578
0.028
ASS4
0.467196
0.042
EMP1
0.048335
0.008
EMP2
0.183560
0.029
EMP3
0.326700
0.051
EMP4
0.441404
0.069
RES1
0.072747
0.014
RES2
0.174123
0.034
RES3
0.285325
0.056
RES4
0.467805
0.091
REL1
0.143835
0.038
REL2
0.208842
0.054
REL3
0.283549
0.074
REL4
0.334661
0.087
TAN1
0.111578
0.033
TAN2
0.177624
0.053
TAN3
0.262529
0.078
TAN4
0.342453
0.102
4.3 Evaluation Membership Data for container terminals performance is obtained from 180 employees of container terminals. No of employees responded regarding the satisfaction levels in five classes (Very Satisfied-VS, Satisfied-SA, General-GE, Dissatisfied-DS, and Very Dissatisfied-VD) and the membership values are shown in Table.9. The final membership vectors for each criteria are determined by using Membership degree Transformation M (1,2,3).
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Table.9 Evaluation Responses and Memberships Criteria
ASS
EMP
RES
REL
TAN
Evaluation Responses
Evaluation Memberships
SubCriteria
VS
SA
GE
DS
VD
VS
SA
GE
DS
ASS1
49
41
40
34
16
0.2722
0.2278
0.2222
0.1889
0.0889
ASS2
38
40
44
32
26
0.2111
0.2222
0.2444
0.1778
0.1444
ASS3
34
41
31
41
33
0.1889
0.2278
0.1722
0.2278
0.1833
ASS4
30
36
46
22
46
0.1667
0.2000
0.2556
0.1222
0.2556
EMP1
44
37
30
25
44
0.2444
0.2056
0.1667
0.1389
0.2444
EMP2
61
35
35
18
31
0.3389
0.1944
0.1944
0.1000
0.1722
EMP3
51
43
39
29
18
0.2833
0.2389
0.2167
0.1611
0.1000
EMP4
51
42
34
23
30
0.2833
0.2333
0.1889
0.1278
0.1667
RES1
52
42
34
19
33
0.2889
0.2333
0.1889
0.1056
0.1833
RES2
45
35
31
41
28
0.2500
0.1944
0.1722
0.2278
0.1556
RES3
36
48
51
32
13
0.2000
0.2667
0.2833
0.1778
0.0722
RES4
37
37
50
20
36
0.2056
0.2056
0.2778
0.1111
0.2000
REL1
72
33
37
11
27
0.4000
0.1833
0.2056
0.0611
0.1500
REL2
27
35
40
26
52
0.1500
0.1944
0.2222
0.1444
0.2889
REL3
50
32
34
41
23
0.2778
0.1778
0.1889
0.2278
0.1278
REL4
23
35
31
30
61
0.1278
0.1944
0.1722
0.1667
0.3389
TAN1
60
37
30
20
33
0.3333
0.2056
0.1667
0.1111
0.1833
TAN2
22
34
43
38
43
0.1222
0.1889
0.2389
0.2111
0.2389
TAN3
47
46
45
27
15
0.2611
0.2556
0.2500
0.1500
0.0833
TAN4
44
27
48
43
18
0.2444
0.1500
0.2667
0.2389
0.1000
VD
4.4 Evaluation matrix Table.10 Evaluation matrix VS
S
G
DS
VD
ASS
20.97%
21.94%
22.36%
17.92%
16.81%
EMP
28.75%
21.81%
19.17%
13.19%
17.08%
RES
23.61%
22.50%
23.06%
15.56%
15.28%
REL
35.15%
18.42%
20.58%
9.25%
16.60%
TAN
24.03%
20.00%
23.06%
17.78%
15.14%
From Table.10 it is understood that criteria Reliability attribute has highest percentage 35.15% in very satisfied level, under very dissatisfied level Empathy attribute has highest percentage of 18.80%. 4.5 Final membership Vector Table.11 Final membership values Satisfaction VS S level mew(c1) 0.2915 0.1987
G
DS
VD
0.2097
0.1345
0.1656
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4.5 Grade of Overall Performance of container terminals From the final membership vector (Table.11), it is identified that the service quality of the container terminals belongs to the ‘General’ level with the confidence level of 69.99% (29.15%+19.87%+20.97%). 5. Results and discussions From the comparison judgment matrix it is understood that Tangibles (TAN) showing high weight 0.296, followed by Reliability (0.260), Responsiveness (0.195), Empathy (0.15), and Assurance (0.090). Which means Tangibles has maximum difference and Empathy has minimum difference between client’s anticipations and delivered service. Evaluation membership of container terminals is shown in Fig.1. From the figure, it is understood that Reliability (REL) of the container terminals is showing relatively high confidence level of performances of 35.15% in ‘Very Satisfied’ level.
Fig.1.Evaluation memberships of container terminal service quality attributes
From the results of the final membership values, it can be judged that the overall service quality of the container terminals is considered as ‘General’ level as the confidence level of 69.99% (29.15%+19.87%+20.97%) is nearly equal to the minimum confidence level of 70%. Overall confidence level with ‘Very Satisfied’ is only 29.15% indicates that the container terminals should enhance the servicequality from every criteria. Since the attribute weights are the very significant factor for the analysis of service quality. So, in order to compare the suggested results with the previous results Onyemechi [3],and found that the suggestedresults coincidetogether with his results, that is tangible has more effect on container terminal service quality. The results of this study offersignificant implications for container terminalmanagers. Based on this understanding, port managers can develop a standard scale to measure service quality. Over time, the use of such a standard measurement tool would facilitate the assessment and benchmarking between container terminals in terms of their service quality performance, and through that implement necessary solutions to enhance container terinal service quality. 6. Conclusions The main purpose of the current study is to evaluate the factors that effect on container terminal service quality and to evaluate the overall performance of the container terminals. The article used fuzzy AHP to calculate the
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