reputation loss framework for consequence … · abstrak penilaian kesan kegagalan paip saluran...
Post on 05-May-2019
214 Views
Preview:
TRANSCRIPT
REPUTATION LOSS FRAMEWORK FOR CONSEQUENCE ASSESSMENT OF
ONSHORE PIPELINE DAMAGE
LIBRIATI ZARDASTI
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Doctor of Philosophy (Civil Engineering)
Faculty of Civil Engineering
Universiti Teknologi Malaysia
MAY 2016
iii
DEDICATION
In the name of ALLAH, the Most Gracious, Most Beneficent.
I dedicate this thesis especially to:
… My beloved parent for their unconditional love…
Nursinah binti Tanggi and the late Zardasti bin Dawi
… My dear siblings who never giving up on me…
Nurmalawati (Along), Sri Martina (Angah), Elfi Zalena (Kak Epi), Nurmilawati
(Kak Mila), Rudi Hartono (Abang Rudi), Nani Ariani (Kak Pani), Marini (Kak
Erin), the late Muhammad Aswandi whom I never met and Fitrianengsih (Kak
Neneng);
… My adored brother in-laws who endlessly supports me…
Kasniza Effendy Shamsir (Abang Ngah), Muhammad Taufik (Abang Taufik),
Muhammad Khuzaimy Abdullah (Abang Jimmy) and Muhammad Firdaus (Abang
Apis);
… My naughty nieces and nephews who motivates me …
Kasniza Zulkarnain (Naim), Nurul Fatihah (Fatin), Nurul Irfani (Irfan), Kasniza
Khairulnazreen (Krol), Siti Aisyah (Aisyah), Nurasyidah (Syidah), Nurul
Najihah (Jiha), Nur Amrina (Amrina), Muhammad Hafiz (Apis), Putri Natasha
(Tasha), Muhammad Khusyairy (Harry), Muhammad Haikal (Haikal), Putri
Sarah Shakira (Ira), Marissa (Misa), and Muhammad Fayyadh (Fayyadh).
…My super awesome supervisors who inspire me…
Associate Professor Dr. Norhazilan Md. Noor and Professor Dr. Nordin Yahaya.
Thank you for everything. May ALLAH bless and grant us Jannatul Ferdous.
iv
ACKNOWLEDGEMENT
The preparation of this thesis requires me to get in contact with many people,
researchers, academicians and practitioners. They have contributed towards my
understanding and thoughts. In particular, I wish to express my sincere appreciation
to my main thesis supervisor, Associate Professor Dr. Norhazilan Md Noor, for
encouragement, guidance, critics and friendship. I am also very thankful to my co-
supervisors Professor Dr. Nordin Yahaya for their guidance, advices and motivation.
Without their continuous support and interest, this thesis would not have been the
same as presented here.
I am also indebted to Universiti Teknologi Malaysia (UTM) for funding my
Ph.D. study; the staff of PETRONAS Gas Berhad (PGB) Malaysia, especially Encik
Mohd. Nazmi Mohd Ali Napiah and his colleagues; Prof. Dr. Faisal Khan of
Memorial University of Newfoundland (MUN) Canada and his research teammates;
as well as other respondents of this research surveys that also deserve special thanks
for their assistance in supplying the relevant literatures and survey responses.
My fellow postgraduate students should also be recognised for their support.
My sincere appreciation also extends to all my research group members of
Reliability Engineering and Safety Assessment (RESA), colleagues in Faculty of
Civil Engineering, UTM and others who have provided assistance at various
occasions. Their views and tips are useful indeed. Unfortunately, it is not possible to
list all of them in this limited space. I appreciate supports from all in this PhD
journey.
v
ABSTRACT
Consequence assessment for pipeline damage is exercised to determine the
losses of a failure event such as human, asset, production and environmental loss.
However, assessment of reputation loss, which is part of failure impact, is usually
excluded due to its qualitative nature. Therefore, the need for a quantitative model of
reputation loss is of great interest among pipeline risk assessors. The available
current model assesses reputation loss qualitatively; it is a self-centered assessment,
time-independent loss factors and internal stakeholder’s influence are typically
neglected. Therefore, the study aimed to develop a quantitative model to quantify
reputation loss of the pipeline owner in order to improve the calculation of risk of
pipeline damage according to the four different stakeholders’ perceptions. A total of
30 reputation loss factors were identified via 30 case studies related to onshore
pipeline damage accident reports. These factors were included in a structured online
survey which was designed for the stakeholders in Malaysia to rank the factors
according to its influence on owner’s reputation based on a given imaginary worst
case scenario. A total of 200 respondents participated in the survey and the ranking
of the factors based on the four different stakeholders were obtained. All
stakeholders were in agreement that the factor which most influences the loss of
owner reputation is factor D3 “Accident Severity”. It is the parent factor of D31
“Multiple fatality and injuries”, D32 “Fire extinguished in longer duration”, D33
“Destroyed private properties”, and D34 “Damaged of vast environment area”.
Statistical analysis and fuzzy analytic hierarchy process (FAHP) were implemented
to prioritize and weigh the factors according to the four different stakeholders’
preferences. Four reputation loss models were then proposed to predict the reputation
loss due to pipeline explosion. Eight experts from PETRONAS Gas Berhad (PGB)
with 15 years of experience in pipeline integrity management were selected for
model validation. Model development was presented to the experts for validation
survey. The result indicates that this model is considered as comprehensive, fulfilled
the objective, well-defined and practical to be used with a moderate level of overall
reliability which can be improved by utilizing the model in a real case study. Thus,
the proposed model was implemented in a case study of pipeline in Malaysia by
taking the 2014 explosion event occurred in Lawas, Sarawak as a benchmark. The
value of consequence of failure calculated by the proposed model was 7% lower than
the current model from PETRONAS Technical Standard. This reduction has
significantly shifted the risk of pipeline failure from “High” to “Moderate” for the
rural area. To conclude, the inclusion of the proposed reputation loss model may
produce a comprehensive consequence assessment of pipeline damage and provide a
higher level of confidence to the pipeline owner to optimize their risk-based
inspection and maintenance scheme, hence, prolonging the long-term integrity of
their pipeline assets and simultaneously securing the company annual profit margins.
vi
ABSTRAK
Penilaian kesan kegagalan paip saluran minyak dan gas dilakukan untuk
menentukan kerugian terhadap manusia, harta benda, pengeluaran dan alam sekitar.
Walaubagaimanapun, impak lain iaitu kehilangan reputasi kebiasaannya
dikecualikan kerana sifat kualitatifnya. Maka, satu model kuantitatif kehilangan
reputasi amat diperlukan oleh para penilai risiko. Model sedia ada menilai
kehilangan reputasi secara kualitatif. Penilaiannya bersifat kendiri, faktor kehilangan
tidak bersandarkan masa, dan pengaruh pihak berkepentingan dalaman diabaikan.
Oleh itu, kajian dilakukan untuk membina model kuantitatif untuk mengukur
kehilangan reputasi pemilik paip saluran agar penilaian risiko kerosakan paip saluran
diperhebatkan berdasarkan persepsi empat jenis pihak berkepentingan. Sebanyak 30
faktor penyebab kehilangan reputasi dikenalpasti melalui 30 kajian kes laporan
berkaitan kerosakan saluran paip daratan. Faktor-faktor dimuatkan dalam borang
kajiselidik dalam talian yang direkabentuk untuk pihak-pihak berkepentingan di
Malaysia bagi menentukan kedudukan faktor mengikut pengaruhnya terhadap
reputasi pemilik paip saluran berdasarkan senario bayangan kes terburuk yang
disertakan. Seramai 200 responden telah terlibat dan kedudukan faktor-faktor
menurut empat pihak berkepentingan berbeza diperoleh. Kesemua pihak
berkepentingan bersependapat bahawa faktor D3 “Tahap keterukan kemalangan”
adalah faktor yang paling berpengaruh yang merupakan faktor induk kepada faktor
D31 “Kematian dan kecederaan berganda”, D32 “Tempoh panjang untuk memadam
kebakaran”, D33 “Kemusnahan harta benda awam”, dan D34 “Luas kawasan alam
sekitar yang terjejas”. Analisis statistik dan proses hierarki analitik kabur dijalankan
untuk menentukan pemberat faktor menurut tahap keutamaan pihak berkepentingan
berbeza. Empat model kehilangan reputasi kemudian dicadangkan untuk meramal
kehilangan reputasi disebabkan oleh letupan paip saluran. Lapan pakar dari
PETRONAS Gas Berhad (PGB) dengan 15 tahun pengalaman dalam pengurusan
integriti paip saluran dipilih untuk tujuan pengesahan model. Ia didahului dengan
pembentangan pembangunan model untuk tinjauan pengesahan dan keputusan
menunjukkan model dianggap menyeluruh, mencapai matlamat, jelas dan praktikal
untuk digunakan dengan kebolehpercayaan sederhana. Oleh itu, model telah
diimplementasi dalam kajian kes sebenar iaitu kes letupan saluran paip yang berlaku
di Lawas, Sarawak pada tahun 2014 bagi meningkatkan kebolehpercayaan. Nilai
kesan kegagalan berdasarkan model cadangan adalah 7% lebih rendah berbanding
nilai model Piawai Teknikal PETRONAS menyebabkan risiko kegagalan berubah
daripada “Tinggi” kepada “Sederhana” bagi kawasan luar bandar. Kesimpulannya,
model cadangan kehilangan reputasi menghasilkan penilaian kesan kegagalan paip
saluran yang menyeluruh dan meningkatkan tahap keyakinan pemilik untuk
mengoptimumkan skema pemeriksaan dan penyelenggaraannya berasaskan risiko.
Maka, integriti jangka panjang paip saluran dapat dilanjutkan dan sekaligus
melindungi keuntungan tahunan syarikat.
vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xiv
LIST OF FIGURES xxii
LIST OF ABBREVIATIONS xxvi
LIST OF SYMBOLS xxviii
LIST OF APPENDICES xxix
1 INTRODUCTION 1
1.1 Preface
1.2 Background and Motivation
1.3 Research Problem
1.4 Research Aim and Objectives
1.5 Research Scope
1.6 Research Significance
1.7 Research Methodology
1.8 Structure of Thesis
1
2
4
6
7
7
8
9
2 LITERATURE REVIEW 12
2.1 Introduction 12
viii
2.2 Pipeline Accident Documentations
2.2.1 Definition of Pipeline Accident
2.2.2 Pipeline Accident Statistics
2.3 Pipeline Integrity Management Program (PIMP)
2.3.1 Oil and Gas Pipeline Risk Assessment
2.4 Consequence Assessment
2.5 Loss Categories
2.6 Reputation Loss and its Definition
2.7 Reputation and Expectations
2.8 Reputation Loss Model
2.9 Reputation Loss Index
2.10 Reputation Loss Indicators in the Pipeline
Accidents
2.11 Major Onshore Oil and Gas Pipeline Accidents
2.11.1 Pipeline Explosion Cases (1965 – 1974)
2.11.2 Pipeline Explosion Cases (1975 – 1984)
2.11.3 Pipeline Explosion Cases (1985 – 1994)
2.11.4 Pipeline Explosion Cases (1995 – 2004)
2.11.5 Pipeline Explosion Cases (2005 – 2014)
2.13 Prioritization of Reputation Loss Factors
2.13.1 Analytic Hierarchy Process (AHP)
2.13.2 Fuzzy Analytic Hierarchy Process (FAHP)
2.14 Concluding Remarks
13
14
15
18
20
22
32
35
36
38
44
47
50
53
57
59
60
64
70
71
72
73
3 RESEARCH METHODOLOGY 75
3.1 Introduction
3.2 Overview of Research Design
3.3 Data Collection
3.3.1 Literature Review
3.3.2 Case Studies
3.3.3 Interview with Experts
3.3.3.1 Agreements on the Reputation
Loss Factors Identification
75
76
76
78
78
83
85
ix
3.3.3.2 Questionnaire Survey Validations
3.3.3.3 Determination of Reputation Loss
Severity Scale
3.3.4 Questionnaires
3.3.5 Questionnaire Design
3.3.5.1 Preliminary Questionnaire Design
– Type 1
3.3.5.2 Preliminary Questionnaire Design
– Type 2
3.3.6 Main Questionnaire Design
3.3.6.1 Main Questionnaire Design
– Type 1
3.3.6.2 Main Questionnaire Design
– Type 2
3.3.6.3 Main Questionnaire Design
– Type 3
3.3.6.4 Main Questionnaire Design
– Type 4
3.3.6.5 Main Questionnaire Design
– Type 5
3.3.7 Validation of Reputation Loss Model
Questionnaire Design
3.4 Data Analysis
3.4.1 Objective 1: Identification of Reputation
Loss Factors
3.4.1.1 Frequency analysis
3.4.1.2 Sample Size and Return Rate
3.4.1.3 Reliability analysis
3.4.1.4 Mann-Whitney Test
3.4.1.5 Kruskal-Wallis Test
3.4.2 Objective 2: Relationship between
Identified Factor and Reputation
3.4.2.1 Average Index
85
86
87
87
88
90
94
94
96
97
98
99
100
102
104
104
105
106
107
108
109
109
x
3.4.2.2 Relative Importance Index
3.4.2.3 Analytic Hierarchy Process (AHP)
3.4.2.4 Fuzzy Analytic Hierarchy Process
(FAHP)
3.4.2.5 Spearman’s rho Correlation
Analysis
3.4.3 Objective 3: Reputation Loss as a
Consequence Assessment
3.4.3.1 The Scale of Severity of the
Reputation Loss Factor
3.4.3.2 Reputation Loss Model
3.4.3.3 Validation of Reputation Loss
Model by Expert
3.4.3.4 Validation of Reputation Loss
Model by Case Study
3.5 Concluding Remarks
110
111
116
118
119
119
121
121
122
126
4 QUESTIONNAIRE DATA ANALYSIS 128
4.1 Introduction
4.2 Result Reputation Loss Factor Identification
4.2.1 Survey Reliability Analysis
4.2.2 Sample Size and Return Rate
4.2.3 Results of Main Survey – Type 1
4.2.3.1 Section 1: Demographic
4.2.3.2 Section 2: Stakeholder Perceptions
4.2.3.3 Section 3: Operator’s Reputation
4.2.4 Result of Main Survey – Type 2
4.2.4.1 Section 1: Demographic
4.2.4.2 Section 2: Operator’s Reputation
4.2.5 Result of Main Survey – Type 3
4.2.5.1 Section 1: Demographic
4.2.5.2 Section 2: Customer’s Information
128
129
129
130
131
131
132
136
139
139
140
142
142
143
xi
4.2.5.3 Section 3: Level of Company
Reputation Loss
4.2.6 Result of Main Survey – Type 4
4.2.6.1 Section 1: Demographics
4.2.6.2 Section 2: Reputation Loss
Indicators
4.2.7 Result of Main Survey – Type 5
4.2.7.1 Section 1: Demographic
4.2.7.2 Section 2: Public Information
4.2.7.3 Section 3: Level of Company
Reputation Loss
4.3 Comparison of Reputation Loss Factors Rating
between Stakeholders
4.3.1 Factors Rating Comparison between Types
of Customer
4.3.2 Factors Rating Comparison between Types
of Employee
4.3.3 Factors Rating Comparison between Types
of Public
4.3.4 Factors Rating Comparison between Types
of Stakeholders
4.4 Comparison of Reputation Loss Factors Ranking
between Surveys
4.4.1 Comparison of Factor Ranking between
Stakeholder
4.4.2 Comparison of Factor Ranking between
Types of Survey
4.5 Concluding Remarks
145
147
148
148
151
151
152
153
155
155
158
160
164
165
166
169
174
5 PRIORITIZATION OF REPUTATION LOSS
FACTORS
175
5.1 Introduction
5.2 Prioritization of Factors
175
175
xii
5.2.1 Analytic Hierarchy Process (AHP)
Framework
5.2.2 Fuzzy Analytic Hierarchy Process
5.2.3 Priority Vector
5.3 Reputation Loss Factors Weight Validation by
Experts
5.4 Correlation Analysis
5.5 Concluding Remarks
177
181
184
191
193
201
6 DEVELOPMENT OF REPUTATION LOSS MODEL 203
6.1 Introduction
6.2 Reputation Loss Formulation
6.3 Reputation Loss Model
6.4 Model Validation by Expert
6.5 Major Pipeline Accident Reputation Loss
Assessment
6.6 Fluctuation of Share Price prior to Pipeline
Accidents
6.7 Model Validation via Pipeline Case Study in
Malaysia
6.8 Concluding Remarks
203
205
212
213
216
220
225
232
7 DISCUSSION 235
7.1 Overview
7.2 Identification of Reputation Loss Factor
7.2.1 Survey Reliability Test and Return Rate
7.2.2 Selection of Reputation Loss Factor by
Stakeholders
7.3 Reputation Loss Factor Relationship with Pipeline
Owner’s Reputation
7.4 The Assessment of Pipeline Owner Reputation
Loss
235
236
237
238
243
248
xiii
7.5 Validation of Reputation Loss Model by Panels of
Expert
7.6 Limitations of the model
7.7 Model Validation via Pipeline Case Study in
Malaysia
7.8 Concluding Remarks
254
258
258
260
8 CONCLUSION AND RECOMMENDATION 261
8.1 Summary
8.2 Conclusion
8.3 Significant of Research Contributions
8.4 Recommendations
261
262
263
265
REFERENCES 267
Appendix A-G 291 - 311
xiv
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 A Sample of PAER Model 5x5 Risk Assessment Matrix
for Oil and Gas Pipeline Integrity Management Program
(PIMP) (Source: PTS 30.40.60.33, 2012)
21
2.2 Guidelines for assessment of risk category
(Source: PTS 30.40.60.33, 2012)
23
2.3 Impact on People (Source: PTS 30.40.60.33, 2012) 24
2.4 Impact on Asset (Source: PTS 30.40.60.33, 2012) 24
2.5 Impact on Reputation (Source: PTS 30.40.60.33, 2012) 25
2.6 Impact on Environment (Source: PTS 30.40.60.33, 2012) 26
2.7 Identification of potential consequences for pipeline
(Source: DNV-RP-F107, 2010)
27
2.8 Safety consequence ranking (human) (Source: DNV-RP-
F107, 2010)
28
2.9 Spillage ranking (environment) (Source: DNV-RP-F107,
2010)
28
2.10 Economic consequence ranking (economic) (Source:
DNV-RP-F107, 2010)
28
2.11 Third party safety impact (Source: DNV-DSS-316, 2013) 29
2.12 Environmental Impact (Source: DNV-DSS-316, 2013) 30
2.13 Assessment of consequence of pipeline failure
(Source: DNV-RP-F116, 2009)
31
2.14 Six Level Safety, Health and Environmental Consequence
Categories (Source: API-RP-580, 2009)
32
xv
2.15 Loss Costing Guidelines for Oil and Gas Exploration and
Production (Source: E&P 6.54/246, 1996)
34
2.16 A taxonomy of a company’s stakeholders and related
expectations (Source: Scandizzo, 2011)
37
2.17 Reputation Paradigm – The Schools of Thought
(Source: Chun, 2005)
38
2.18 Qualitative reputation models (Source: Trotta and
Cavallaro, 2012)
40
2.19 Summary of reputation loss model characteristics 44
2.20 Components of Reputation Index (Source: Cravens et al.,
2003)
46
2.21 Causes and the percentage of pipeline accident in Europe,
North America and the former Soviet Union in the past
30 years (Source: Janssens et al., 2009)
47
2.22 Summary of the selected major onshore pipeline
accidents case study
53
3.1 Summary of the severity of the selected major onshore
pipeline accident case study
79
3.2 Summary of the extracted reputation-threat factors in the
selected major onshore pipeline accidents case study
81
3.3 Identified reputation loss factor and its indicators found in
the selected major onshore pipeline accidents case study
82
3.4 Fundamental Comparison Scale for AHP method 89
3.5 Scale transformation between Likert and AHP Method 89
3.6 Corrected Scale between Preliminary Survey Design 2
and AHP Method
92
3.7 Qualitative Scale of Preliminary Survey Design 2 and
AHP Method
93
3.8 Valid return rate of questionnaire for analysis and
reporting purposes (Source: Miller, 1991)
105
3.9 Cronbach’s alpha reliability coefficient (Source: Gliem
and Gliem, 2003)
107
3.10 Average Index classification 110
xvi
3.11 5-point rating scale of relative importance index
classification
111
3.12 9-point rating scale of relative importance index
classification
111
3.13 Number of comparisons 113
3.14 Data transformation scheme to pairwise judgment
(Source: Chen, 2010)
114
3.15 Random Index for factors in the process of decision
making
116
3.16 Linguistic scale for importance 116
3.17 Characteristic of selected sites (Source: Nazim, 2015) 123
3.18 Formula of onshore pipeline consequence assessment
with scoring descriptor (Source: PTS 30.40.60.33, 2012)
126
4.1 Result of questionnaires’ Cronbach’s alpha reliability
coefficient
129
4.2 Result of sample size and return rate of main
questionnaire
130
4.3 Respondent’s demographics of main survey type 1 131
4.4 Average index of post-event reputation loss level 133
4.5 Demographics of the employees 134
4.6 The influence of factors on pipeline owner’s reputation 137
4.7 Respondent’s demographic of main survey type 1 140
4.8 Average index of reputation loss factor influence on
pipeline owner
141
4.9 Respondent’s demographic of main survey type 3 142
4.10 Retailer’s information 143
4.11 End-user’s information 144
4.12 End-user’s monthly income and percentage spent on
products
145
4.13 Average index of factors according to customer
perceptions
146
4.14 Respondent’s demographics of main survey type 4 148
xvii
4.15 Average index of reputation loss factor according to
employee perception
149
4.16 Respondent’s demographic of main survey type 5 151
4.17 Public information 152
4.18 Average index of reputation loss factor according to
public perception
154
4.19 Difference of the reputation loss factor ratings on
customer demographic
156
4.20 Difference of the reputation loss factor ratings on
customer information
157
4.21 Difference of the reputation loss factor ratings by the
post-accident product changes preferences
158
4.22 Difference of the reputation loss factor ratings on
employee information
159
4.23 Difference of the reputation loss factor ratings by
company’s sector
160
4.24 Difference of the reputation loss factor ratings on public
information
161
4.25 Difference of the reputation loss factor ratings on
employee information
162
4.26 Difference of the reputation loss factor ratings by the
post-accident product preference changes
163
4.27 Difference of the reputation loss factor ratings by types of
stakeholders
164
4.28 Ranking of reputation loss factor according to different
types of stakeholders
167
4.29 Difference of the reputation loss factor ratings by
different stakeholders
168
4.30 Reputation loss factors ranking according to category of
influence
169
4.31 Reputation loss factors ranking according to all factors 171
4.32 Difference of the reputation loss factor ratings 172
5.1 Reputation loss factors dependency 176
xviii
5.2 Pairwise comparison matrix for Criterion 180
5.3 Pairwise comparison matrix for Sub-criterion 181
5.4 Pairwise comparison matrix for Sub-subcriterion 181
5.5 Calculation of priority vector for Criterion 181
5.6 TFN pairwise comparison matrix for Criterion 182
5.7 Normalized weight vector for factors in Criterion level 184
5.8 Priority vector of factors using AHP, FAHP and Super
Decisions
185
5.9 Difference of priority vector between types of
stakeholders and analysis methods
186
5.10 Ranking of factors by AI, AHP, FAHP and Super
Decisions
187
5.11 Local and global weight for factors according to investor
perceptions
189
5.12 Local and global weight for factors according to customer
perceptions
190
5.13 Local and global weight for factors according to
employee perceptions
190
5.14 Local and global weight for factors according to public
perceptions
191
5.15 Expert profiles 192
5.16 Priority vector of factors between experts and respondents
using FAHP
192
5.17 Ranking of factors between experts and respondents using
FAHP
193
5.18 Correlation analysis between factors in Criterion level
(stakeholder-influenced)
194
5.19 Correlation analysis for between factors Sub-subcriterion
(consequence-influenced)
194
5.20 Correlation analysis between factors in Criterion and
Sub-criterion
195
5.21 Correlation analysis between factors by customers
(employee-influenced)
196
xix
5.22 Correlation analysis between factors by customers
(public-influenced)
196
5.23 Correlation analysis between factors by employees
(investor-influenced)
197
5.24 Correlation analysis between factors by employees
(employee-influenced)
198
5.25 Correlation analysis between factors in Criteria and Sub-
criterion by employees
198
5.26 Correlation analysis between factors in Sub-criterion and
Sub-subcriterion by the public
198
5.27 Correlation analysis between factors in Criterion level by
the public
199
5.28 Correlation analysis between factors by the public
(investor-influenced)
199
5.29 Correlation analysis between factors by the public
(employee-influenced)
200
5.30 Correlation analysis between factors in Criteria and Sub-
criterion by the public (investor-influenced)
200
5.31 Correlation analysis between factors in Criteria and Sub-
criterion by the public (customer-influenced)
201
5.32 Correlation analysis between factors in Criteria and Sub-
criterion by the public (public-influenced)
201
6.1 The equation for reputation loss calculation of the impact
by investor
205
6.2 The equation for reputation loss calculation of the impact
by customers
206
6.3 The equation for reputation loss calculation of the impact
by employee
206
6.4 The equation for reputation loss calculation of the impact
by the public
207
6.5 5-point scale reputation loss severity level (investor-
influenced)
209
xx
6.6 5-point scale reputation loss severity level (customer-
influenced)
209
6.7 5-point scale reputation loss severity level (employee-
influenced)
210
6.8 5-point scale reputation loss severity level (public-
influenced)
211
6.9 Reputation loss model 212
6.10 Relative importance index of factor based on experts’
judgments
213
6.11 Ranking of the factors by experts and various
stakeholders
214
6.12 PETRONAS experts’ profiles 215
6.13 Reputation loss model validation result by experts 215
6.14 Share price details of the pipeline owner of the case study 217
6.15 Reputation loss evaluation for factor A1 “Share price
dropped and market capitalization affected”
218
6.16 Reputation loss evaluation for factor D31“Multiple
fatality and injuries”
218
6.17 Evaluation of Reputation Loss for factor A1 “Share price
dropped and market capitalization affected” and D31
“Multiple fatality and injuries”
219
6.18 Increment of reputation loss value by different numbers
of factor
219
6.19 100 data of share price of recent onshore pipeline
accidents
223
6.20 Pipeline owners’ share price details 225
6.21 Reputation loss result of each stakeholder for the case
study
230
6.22 Risk score for pipeline at rural area 231
6.23 Risk score for pipeline at urban area 232
7.1 Percentage of difference of the reputation loss factors’
priority vector between expert and respondents
238
7.2 Ranking of reputation loss factors by the stakeholder 240
xxi
7.3 Correlation of factor in two consecutive AHP levels 244
7.4 Correlation between factors in similar AHP levels
according to Customer
246
7.5 Correlation between factors in similar AHP levels
according to Employee
247
7.6 Correlation between factors in similar AHP levels
according to the Public
248
7.7 Stakeholders with similar opinions on reputation loss
factors correlations
248
7.8 Expert opinions for validation criteria of level of
comprehensiveness
255
7.9 Expert opinions for validation criteria of level of
objectivity
255
7.10 Expert opinions for validation criteria of level of clarity 256
7.11 Expert opinions for validation criteria of level of
reliability
256
7.12 Expert opinions for validation criteria of level of
practicality
257
xxii
LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Annual number of incident (1970 – 2013) (Source:
EGIG, 2014)
16
2.2 Cumulative number of incidents (1970 – 2013) (Source:
EGIG, 2014)
16
2.3 Annual number of accidents and incidents in TSB (2004
– 2013) (Source: TSB, 2014)
17
2.4 Annual serious and significant incidents in PHMSA
(2004 – 2013) (Source: PHMSA, 2015)
17
2.5 PETRONAS PIMP elements (Source: PTS 30.40.60.33,
2012)
19
2.6 Illustration of threats, values and ‘loss categories’ in risk
– and vulnerability management (Source: Hokstad and
Steiro, 2006)
33
2.7 Consequence categories with their loss indicators
(Source: Arunraj and Maiti, 2009)
33
2.8 The reputation loss model of Dutch private banks
caused by IT-security incidents (Source: Bie, 2007)
41
2.9 The retail industry reputation model (Source: APCO
Insight, 2010)
42
2.10 The retail industry reputation index performance score
(Source: APCO Insight, 2010)
45
2.11 Accident frequency data in various energy sector
(Source: Sovacool, 2008)
51
xxiii
2.12 Craters beneath a demolished three-storey building at
the LaSalle Height (Source: Gravenor, 2008)
54
2.13 Aerial view of burning buildings at Richmond, Indiana
(Source: Burke, 2015)
55
2.14 Osaka gas pipeline explosion (Source: Akatsuka and
Kobayashi, 2010)
56
2.15 Modern shops damaged in Clarkston Toll, Renfrewshire
(Source: Brown, 2011)
57
2.16 Smoke all over a burning hotel in Fremont (Source:
Real-McKeighan, 2002)
58
2.17 Trans-Siberian Railway tracks scattered upon explosion
(Source: Onliner, 2015)
59
2.18 Guadajalara streets destroyed after pipeline explosion
(Source: Expansion, 2012)
60
2.19 Search and rescue workers begin operation in shoe store
at Rio Pedras (Source: Wikipedia, 2015)
61
2.20 Aerial view of Pecos River campsite post-accident
(Source: NTSB/PAR-03/01, 2003)
63
2.21 Ariel view of Ghislenghein pipeline explosion (Source:
ARIA, 2009)
64
2.22 Burning oil pipeline in Abule Egba, Lagos (Source:
China.org.cn, 2006)
65
2.23 Fire burning Xingang Port after pipeline explosion in
Dalian (Source: Boston.com, 2010)
66
2.24 Cars destroyed after Pemex pipeline explosion in
Puebla, Mexico (Source: People's Daily Online, 2010)
67
2.25 Ripping roads and damaged vehicles after explosion in
Shandong (Source: Barber, 2013)
68
2.26 Fire after explosion of GAIL gas pipeline in India
(Source: Janyala, 2014)
69
2.27 Vehicles overturned and trapped in the middle of split
road at Kaohsiung, Taiwan (Source: The Malaysian
Insider, 2014)
70
xxiv
2.28 A general analytic hierarchy process framework 71
3.1 Overview of research design 77
3.2 The proposed reputation loss model framework 83
3.3 Interview sessions objectives and results 84
3.4 Preliminary Questionnaire Design – Type 1 90
3.5 Preliminary Questionnaire Design – Type 2 93
3.6 Main Questionnaire Design – Type 1 95
3.7 Main Questionnaire Design – Type 2 96
3.8 Main Questionnaire Design – Type 3 97
3.9 Main Questionnaire Design – Type 4 98
3.10 Main Questionnaire Design – Type 5 99
3.11 Validation of Reputation Loss Model Questionnaire
Design
101
3.12 Framework of data analysis 102
3.13 Table for determining minimum returned sample size
for a given population size for continuous and
categorical data (Source: Bartlett et al., 2001)
106
3.14 Hierarchical breakdown of reputation loss model
framework
112
3.15 Peninsular Gas Utilisation networks in Peninsular
Malaysia (Source: Oil Peak, 2012)
123
3.16 Pictures of topographical area at the five selected sites
(Source: Nazim, 2015)
124
3.17 Arial view the two selected urban sites (Source: Google
Map)
125
4.1 Method used by the customer to express dissatisfactory
towards the pipeline owner
134
4.2 Customer purchase preference after event 134
4.3 The highest contributor of pipeline owner reputation
loss
136
4.4 Monthly income spent for oil and gas products by the
end-users
145
5.1 AHP process flowchart 177
xxv
5.2 AHP framework in Super Decisions main window 178
5.3 A snapshot of pairwise comparison window of Super
Decisions
179
5.4 A snapshot of priorities window of Super Decisions 180
5.5 FAHP process flowchart 182
6.1 Process flowchart of the results of previous chapters 206
6.2 The fluctuation of pipeline owner’s share price (400
data)
221
6.3 The fluctuation of pipeline owner’s share price (100
data)
221
6.4 Conceptual framework of the proposed research 234
7.1 Local priority vector for public influence factors of each
stakeholder
240
7.2 Local priority vector of different types of stakeholder
perceptions
242
7.3 The aerial view of deep trenches of splitting roads by
Kaohsiung pipeline blast (Source: Lux, 2014)
252
xxvi
LIST OF ABBREVIATIONS
AHP - Analytic hierarchy process
AI - Average Index
ALARP - As Low As Reasonably Practicable
BP - British Petroleum
CEO - Chief Executive Officer
CGTD - China General Terminal and Distribution Corporation
CI - Consistency Index
CNPC - China National Petroleum Corporation
CR - Consistency Ratio
CPC - CPC Corporation
DNV - Det Norske Veritas
EGIG - European Gas pipeline Incident data Group
EPNG - El Paso Natural Gas Company
E&P - Exploration and Production
FAHP - Fuzzy analytic hierarchy process
GAIL - Gas Authority of India Limited
HCA - High Consequence Area
IT - Information Technology
KPC - Kenya Pipeline Company
LCY - LCY Chemical Corporation
LNG - liquefied natural gas
MCDM - Multi criteria decision making
MYR - Malaysia Ringgit
NGO - Non-Government Organisation
NIL - Not in list
NNPC - Nigerian National Petroleum Corporation
xxvii
NTSB - National Transportation Safety Board
OGP - International Association of Oil and Gas Producers.
PAER - People, Asset, Environment and Reputation
Pemex - Petroleos Mexicanos
PETRONAS - Petroliam Nasional Berhad
PCG - PG&E Corp.’s
PHMSA - Pipeline Hazardous Materials Safety Administration
PGB - PETRONAS Gas Berhad
PGU - Peninsular Gas Utilisation
PG&E - Pacific Gas and Electric Co.
PTS - PETRONAS Technical Standards
QNG - Quebec Natural Gas Corporation
RI - Random Index
RII - Relative Importance Index
RL - Reputation loss
RQ - Reputation Quotient
SD - Super Decisions
SS - Sample Size
SPSS - Statistical Packages for the Social Sciences
SSGP - Sabah-Sarawak Gas Pipeline
S&P - Standard & Poor's
TFN - Triangular Fuzzy Number
TGT - Tennessee Gas Transmission Co.
TSB - Transportation Safety Boards
UK - United Kingdom
US - United States
USA - United States of America
USD - United States Dollar
WMAC - World’s Most Admired Companies
xxviii
LIST OF SYMBOLS
A - the comparison matrix
α - Cronbach’s alpha reliability coefficient
ai - is the constant expressing the weight given to i,
d - degree of accuracy expressed as a proportion (0.05)
d - ordinate of the highest intersection point D
f - frequency of an observation and
Indexmax - maximum index
Indexmin - minimum index
λmax largest eigenvalue
K - sample with more than two groups
k - convex fuzzy numbers
N -
number of respondents; population size; number of rating
scale index
ni - number of respondents who rate the importance or
influence of the factor as i = 1 as “very low”; 2 as
“low”;3 as “moderate”; 4 as “high”; and5 as “very high”.
n - the dimension of the matrix
P - population proportion
s - the required sample size
W - a non-fuzzy number
w - eigenvector
wi - weight of factor i
X2 - the table value of chi-square
x - variable expressing the frequency response for i = 1, 2, 3,
4, and 5.
LIST OF APPENDICES
APPENDIX TITLE PAGE
A PRELIMINARY SURVEY – 1st trial 291
B PRELIMINARY SURVEY – 2nd trial 293
C MAIN QUESTIONNAIRE – SURVEY TYPE 1 296
D MAIN QUESTIONNAIRE – SURVEY TYPE 2 301
E MAIN QUESTIONNAIRE – SURVEY TYPE 3 304
F MAIN QUESTIONNAIRE – SURVEY TYPE 4 308
G MAIN QUESTIONNAIRE – SURVEY TYPE 5 311
CHAPTER 1
INTRODUCTION
1.1 Preface
Onshore oil and gas pipeline damage due to explosion, leakage, and etc. is an
undesired event. Accidents cause significant negative impact such as loss of life,
destruction of private and public property and serious environmental damage. In fact,
this event is capable of tarnishing the pipeline owner’s reputation as well as
jeopardizes the confidence level of their internal and external stakeholders such as
investors, employees, customers, public etc. In a famous quote, Warren Buffet, the
chief executive officer (CEO) of Berkshire Hathaway warned: “It takes 20 years to
build a reputation and five minutes to ruin it. If you think about that you will do
things differently”, (Rochette, 2007; Vallens, 2008; Gaultier-Gaillard, Louisot and
Rayner, 2009; Bibi, 2011). This quote emphasized the importance of protecting and
enhancing the stability of company reputation. Hence, negative perception among
stakeholders decreases and eventually improves company’s profit margin.
In the risk assessment of pipeline damage, the impact of an accident in terms
of monetary value is most preferable by the owner. Thus human, environment, and
economic losses are converted into dollars to assist them in forecasting their losses in
each occurrence to obtain risk value in monetary terms. Nevertheless, reputation loss
is a function of the impact or consequence of failure as well. It can be included in the
2
consequence assessment of pipeline damage as the impact of the accident on owner’s
reputation is certainly significant. Therefore, the presence of reputation loss
assessment provides comprehensive risk estimation and subsequently allows the
owner to prepare an optimum inspection and maintenance schedule, hence boosting
annual corporate profit.
1.2 Background and Motivation
Pipelines are susceptible to failure even though it is the most economical,
fastest, and safest means of transporting natural gas and hazardous liquids in large
amount (Dziubínski et al., 2006; Carvalho et al., 2008; Brito and Almeida, 2009;
Brito et al., 2010; Furchtgott-Roth, 2013). A comprehensive Pipeline Integrity
Management Program (PIMP) is vital for the maintenance of a safe and reliable oil
and gas pipeline. It consists of a foundation of pipeline inspection, assessment,
mitigation and communication aimed at minimizing the risk of the pipeline failure to
As Low As Reasonably Practicable (ALARP). This program has experienced
significant changes since the early 2000’s. The number of gas transmission pipeline
incidents had increased over the past 15 years according to the United States
Department of Transportation (USDOT) of Pipeline and Hazardous Material Safety
Administration (PHMSA) online data source (PHMSA, 2015). These failure events
can harm the public, the environment, assets and production. The reputation of
pipeline owners is endangered regardless of how the pipeline had been operating
prior to the failure event. Pipeline failure has great financial costs for pipeline
owners. For gas transmission pipelines alone, failure events have cost pipeline
owners approximately one billion US Dollars over the last 15 years (2000 – 2015)
(PHMSA, 2015). A well-planned pipeline inspection and maintenance program is
necessary to avoid pipeline damage and reduce the impact of failure events. PIMP
secures the annual profit margins of pipeline owners and protects its reputations.
3
The previous time-based inspection of pipeline integrity management was
improved by the implementation of Risk-Based Inspection (RBI). RBI allows
pipeline owners to choose the most cost effective pipeline inspection scheme. RBI
optimizes maintenance scheduling and reduces unnecessary inspections. As a part of
a RBI module, pipeline damage risk is assessed as a product of the likelihood or
frequency of pipeline damage probability and the impact or consequence of such an
event. Existing consequence assessments are quite effective in evaluating the
monetary loss of pipeline failure, such as the number of fatalities and injuries, cost of
asset damages, cost of production loss, and the cost of environmental pollution fines.
This assessment does not calculate the actual cost of pipeline damage due to the
qualitative nature of a company’s reputation influencing factors. This may be due to
time dependency, difficulties in quantifying factors into monetary value, or lack of
identification of reputation loss impact on local conditions. Onshore pipelines buried
underground are laid across various types of geographical surfaces with different
demographic populations. These various conditions contribute to different impacts
on company’s reputation due to a failure event. The impact of pipeline failure
causing an explosion in Europe is different from an explosion in Nigeria due to
different education levels. Public awareness of safe and reliable pipeline operation
varies between countries.
The reputation of a company depends on stakeholder beliefs. Each company
has at least four major stakeholders, including investors, customers, employees and
the public. Pipeline accidents impact all stakeholders directly or indirectly.
Stakeholder post-accident negative beliefs and responses to loss of human life,
economic damage and environmental damage due to a pipeline damage event can be
considered indicators of company reputation loss. Stakeholder perceptions and
expectations differ and are highly incident-dependent. Pipeline damage may affect
stakeholders physically or mentally and has a negative impact on the pipeline
owner’s reputation – an intangible asset that could be capable of generating tangible
loss. Current risk assessment for pipeline damage includes an assessment of failure
event effects on owner reputation. If a pipeline owner can identify the reputation loss
factors influenced by the views of external and internal stakeholder prior to a failure
4
event, a comprehensive consequence assessment for pipeline damage can be
established.
1.3 Research Problem
Onshore pipeline accidents have become common in recent years. In 2014,
there were a number of pipeline explosion events such as Kaohsiung in Taiwan,
Andhra Pradesh in India, and Sarawak in Malaysia. Current consequence of failure
assessment calculates the monetary losses of these pipeline damage events i.e.
human loss, production loss, asset loss and environmental loss because they are
quantitatively countable and visible, in addition to reputation loss. This loss
assessment is assessor-centered and ranges from very low to very high. The effects
of post-accident reputation loss on stakeholder perceptions is neglected due to
difficulties in quantifying factors (Khan and Haddara, 2004; Arunraj and Maiti,
2009). The effects of post-accident reputation loss are vital to most organizations
(Cravens et al., 2003); as it endangers profit margins (Money and Hillenbrand,
2006).
Most industry players choose to exclude post-accident reputation loss due to
its qualitative nature and the subjectivity of its factors. The factors for reputation loss
are as follows: time-dependent (Dunbar and Schwalbach, 2000; Bie, 2006);
multidimensional (Fombrun, 1996); behavior-dependent (Bie, 2006); and influenced
by stakeholder experience (Spence, 2011). Current practices for pipeline risk
assessment assume that the cost of reputation loss is equivalent to business
interruption costs (Muhlbauer, 2004). The loss of company reputation is judged and
calculated based on fluctuations in share price over a period of time in order to
simplify assessment procedures (Vergin and Qoronfleh, 1998; Money and
Hillenbrand, 2006; Tonello, 2007; Scandizzo, 2011). This type of reputation loss
quantification is time-dependent, but affects only a single stakeholder (investors).
The expectations of other stakeholders have similar impacts i.e. jeopardizing the
5
reputation of the company and significantly influencing company operations
(Macnamara, 2006). Public perception prior to a pipeline damage event is crucial as
it forces pipeline operators to apply mitigation measures. Public pressure for pipeline
safety differs by geographical location and the status of the pipeline owner.
Efforts have been made to quantify reputation loss for pipeline owners (E&P
6.54/246, 1996; Muhlbauer, 2004; PTS 30.40.60.33, 2012). Despite these efforts, a
model to calculate intangible reputation loss based on overall stakeholders’
perspectives, whether internal or external is currently unavailable. None of the
current available models prioritize reputation loss factors to assists operators in
responding to the most severe factors affecting the perspective of company
stakeholders. Reputation loss models for onshore pipeline damage do not yet exist,
but models covering reputation loss in other industries such as the banking and retail
do (Muller and Vercouter, 2008; APCO Insight, 2010; Li et al., 2010; Cherchiello,
2011; He and Wu, 2013).
As pipelines age and the risk of a failure event increases, there is a need to
account for additional factors in pipeline risk assessments such as reputation loss.
The inclusion of reputation loss in pipeline risk assessments makes those
assessments more conservative. If reputation loss, which is currently neglected in
calculations of monetary impact, has a significant contribution to total cost of a
failure event, neglecting it may result in the inaccurate assessment of failure
consequences. Planning errors for pipeline inspections and maintenance program
impose additional costs due to unnecessary inspections programs, affecting a
company’s annual profit margins. If a reputation loss model is successfully
developed, pipeline damage can be prevented with reasonable increments in
inspection frequency as pipeline operators pay more attention to higher risk
pipelines. Great effort is needed to develop a comprehensive consequence
assessment model incorporating the intangible elements of reputation loss for
comprehensive risk assessment. To reach this milestone, a detail investigation on
reputation loss factors is crucial.
6
1.4 Research Aim and Objectives
This study aims to develop a quantitative consequence assessment model for
pipeline failure associated with the reputation loss of the owner, based on a
Malaysian stakeholder perspective. The proposed model is unique in that reputation-
threat factors are both time-dependent and time-independent. This model is tailored
for onshore oil and gas pipeline damage resulting from an explosion. The objectives
of this study are:
1. To identify owner reputation-threat factors which lead to negative
perceptions among stakeholders in Malaysia prior to pipeline damage as
reported in selected onshore pipeline explosion case studies.
2. To determine the priority vector of the identified stakeholder-influenced
reputation-threat factors according to the degree of negative perceptions
among the major constituents of a company i.e. investor, customer, employee
and the public using Multi Criteria Decision Making (MCDM) method.
3. To evaluate the impact of reputation loss on pipeline owners by applying
stakeholder-oriented priority vectors for reputation-threat factors as a
consequence assessment of pipeline damage, including the prediction and
validation of the model via expert interviews and case studies in Malaysia.
These outcomes may contribute to the consequence assessment for pipeline
damage by exploring a selection of reputation loss factors for future pipeline owner
reputation loss modelling.
7
1.5 Research Scope
There are numbers of factors that may influence an oil and gas company’s
reputation. This study focuses on the relationship between reputation loss and
stakeholder perceptions resulting from a pipeline explosion by observing company
stakeholders’ responses (investor, customer, employee and public) prior to the event.
This study focuses on the geographical area of Malaysia, limited to the stakeholders
of the country’s oil and gas companies. Unstructured interviews and questionnaire
surveys are carried out for this study. Qualitative experts’ judgments were
transformed into quantitative information using fuzzy and Analytic Hierarchy
Processes (AHP) in order to reduce errors and increase accuracy. Statistical analysis
was used to identify the relationships between reputation loss and stakeholder
perceptions. The index method was used to rate the severity level of a company’s
reputation loss for modelling purposes. The model was then validated via expert
interviews and case studies in Malaysia.
1.6 Research Significance
The main challenge of reputation loss model development is to understand
factor selection in order to obtain an accurate model. This model is to be used by
pipeline risk assessors for engineering analysis. Previous studies did not include
reputation-threat factors towards pipeline owner in their assessment of pipeline
damage. The outcome of this study shows the influence negative stakeholder
perceptions have on a company’s reputation loss prior to an accident. A
comprehensive consequence assessment for onshore oil and gas pipeline damage can
be obtained by incorporating post-accident owner reputation-threat factors.
8
1.7 Research Methodology
Qualitative and quantitative approaches were conducted in this study. It
consists of four stages: preliminary study, data collection, data interpretation and
analysis and conclusions and recommendations. The first stage requires a
comprehensive review of literatures on pipeline risk assessment and reported
onshore pipeline post-accident damages in the past 50 years on a worldwide basis. It
was achieved through literature search via reports, journals, articles, books, internet
sources, online newspapers archives, informal discussion with experts and
researchers. This stage attains background knowledge of the topic, knowledge gaps
relating to the research problems, which eventually produces research aim and
objectives within a reachable research scope and the significance.
Second stage requires a wide-ranging literature search as well. All reviews
are supported with trusted sources to comply with statement validity. It includes
collecting data from case study, interviews with experts in pipeline risk management
and questionnaire survey distribution to the respective stakeholders namely investor,
customer, employee and public. This secondary data is able to identify the following
requirements: post-accident reputation-threat factor; the importance of the reputation
loss factor; the influence of reputation loss factor towards pipeline owner; the impact
of reputation loss factors on pipeline company’s stakeholders; and validation of
reputation loss severity scale for modelling.
The next stage interpreted and analyzed successfully answered questionnaire
surveys. The significance of reputation loss factors are tested before modelling is
developed. Statistical analysis, AHP method and fuzzy AHP method were
implemented in the priority vector calculation processes with the aid of Microsoft
Excel and Super Decisions. The experts are involved in the validation process to
justify the obtained values of factors priority. The evaluation of reputation loss is
formulated later using the priority vector for each factor obtained from different
stakeholders. The model is developed to classify the level of degradation of pipeline
owner’s reputation. It is then imposed on the selected onshore pipeline explosion
9
case studies to assess the respective level of reputation loss of pipeline owners. The
final stage concluded the findings that attain the research objectives and derive
recommendations within the research scope along with advice for future study.
1.8 Structure of Thesis
This thesis consists of eight chapters structured in the following manner:
1. Chapter 1 shares the introduction of the study. It covers the
motivation and background of the research, problems, aim and
objectives, scope and significance of the research. A brief research
methodology with the approach and method is stated. The outlines of
the research are mentioned at the end of this chapter.
2. Chapter 2 provides a comprehensive literature review which covers
the pipeline accident documentations, the overviews of oil and gas
pipeline risk assessment and the consequence assessment including
the loss categories in terms of risk, the extensive explanation on
reputation loss and its definition, the relationship between reputation
and expectations, the current reputation model and reputation index in
various research field, the reputation loss indicators in various
pipeline explosion case, and the prioritization method of reputation
loss is reviewed in this chapter.
3. Chapter 3 demonstrates the overall methodology of the study.
Overview of research design, data collection methods, techniques to
identify reputation loss factors and the prioritization is explained
comprehensively. The reputation loss severity level and the model are
presented and the model validation procedures are described.
10
4. Chapter 4 produces the analysis and results for questionnaire survey
distributed to the respective pipeline owner’s stakeholder namely
investor, customer, employee and public. The reliability of the survey,
sample size and return rate, the demographic of the respondents and
the given 5-point rating of reputation loss level of each factor from all
types of survey is presented. The average index and ranking for each
factor in various surveys is calculated and the significant difference in
rating given by the respondents of all surveys between stakeholder
and the significant difference between surveys is tested.
5. Chapter 5 presented the priority vector of the reputation-threat or
reputation loss factor using analytic hierarchy process method and
fuzzy analytic hierarchy process with the aid of Microsoft Excel
software and Super Decisions software. The significant difference of
rating given by respondents between methods of obtaining priority
vector is tested. These priority vectors are validated done by the
experts. The correlated factors according to respective stakeholders
are extracted as well.
6. Chapter 6 transforms the previous priority vectors into a formulation
to assess pipeline owner’s reputation loss prior to pipeline accident.
The level of severity for all reputation-threat factors discussed with
the experts is listed in a scale of 1 to 5. This severity level produces
range of reputation loss values, which the class of reputation loss
index is explained. Simultaneously, a reputation loss model with
ranges of reputation loss index is presented, and the model validation
scores given by the experts are provided. The implementation of the
model in the selected case studies is applied.
7. Chapter 7 discusses the results obtained in previous three chapters.
This chapter deliberates on the relevance of analysis in order to
11
accomplish all research objectives. The achievement of the aim of this
study is declared at the end of this chapter.
8. Chapter 8 concludes the accomplishment of the research objectives
and the aim of the study. It also stated the contribution of this study
towards the industry of oil and gas. Research limitation and
recommendation is specified for future study.
267
REFERENCES
Adams, D. (1965). The 1965 Montreal Canada Apartment House Explosion: Some
Nites and Comparisons with the Indianapolis, Indiana Coliseum Explosion.
Research Note #12. Ohio State University, Columbus, Ohio.
Agren, D. (2010, December 21) Oil: The Mexican cartels' other deadly business. The
Globe and Mail. Retrieved on July 30, 2014, from
http://www.theglobeandmail.com/news/world/americas/oil-the-mexican-cartels-
other-deadly-business/article1845378/
Akatsuka, H. and Kobayashi, H. (2010). Gas Explosion at a Subway Construction
Site. Hatamura Institue for Advancement of Technology - Failure Knowledge
Database. Retrieved on October 10, 2014, from
http://www.sozogaku.com/fkd/en/cfen/CB1012037.html
Akinola, W., Oyewole, S., Usman, E., Adegboye, K., Adeoye, Y., Akoni O., and
Austi. (2008, May 16). Nigeria: 39 Die in Lagos Pipeline Fire. AllAfrica.
Retrieved on July 30, 2014, from
http://web.archive.org/web/20121003155127/http://allafrica.com/stories/2008051
60001.html
Amini, S. and Jochem, R. (2011). Fuzzy Performance Measurement and Evaluation
of Service Processes. The European Association for Research on Services
(RESER) Conference 2011. September 5-7. Hamburg, Germany, 1–20.
Andersen, M. and Subbaraman, R. (1996). Share Prices and Investment. Research
Discussion Paper 9610. Reserve Bank of Australia, Australia.
Anderson, J., and Smith, G. (2006). A Great Company Can Be a Great Investment.
Financial Analysts Journal, 62(4), 86-93.
APCO Insight. (2010). ROR Return on Reputation Indicator-State of the Retail
Industry: Executive Summary. General Findings Report. APCO Worldwide Inc.,
Washington D.C.
268
ARIA (2009). Rupture and ignition of a gas pipeline, July 30, 2004, Ghislenghien,
Belgium. Pipeline Accident Report No. 27681. French Ministry for Sustainable
Development, France.
Arunraj, N. S. and Maiti, J. (2009). A Methodology for Overall Consequence
Modeling In Chemical Industry. Journal of Hazardous Materials, 169, 556–574.
Arunraj, N. S. and Maiti, J. (2010). Risk-Based Maintenance Policy Selection Using
AHP And Goal Programming. Safety Science, 48(2), 238–247.
Aydin, O. and Pakdil, F. (2008). Fuzzy SERVQUAL Analysis in Airline Services.
Journal of Management, Information Systems and Human Resources, 41(3),
108–115.
Barber, G. (2013, November 11). Photos: Oil pipeline explosion kills 35 in eastern
China. The Denver Post. Retrieved on October 10, 2014, from
http://photos.denverpost.com/2013/11/22/photos-oil-pipeline-explosion-kills-35-
in-eastern-china/#3
Bartlett, J. E., Kotrlik, J. W. K. J. W., and Higgins, C. (2001). Organizational
Research: Determining Appropriate Sample Size In Survey Research Appropriate
Sample Size In Survey Research. Information Technology, Learning, and
Performance Journal, 19(1), 43.
BBC News (2000, July 14). Nigerian fire: A Familiar Tragedy. BBC News.
Retrieved on July 30, 2014, from http://news.bbc.co.uk/2/hi/africa/833439.stm
BBC News (2003, June 22). Nigeria Oil Blast Kills Scores. BBC News. Retrieved on
April 20, 2014, from http://news.bbc.co.uk/2/hi/africa/3009756.stm
BBC News (2004, March 18). Search Abandoned in Russia Rubble. BBC News.
Retrieved on July 30, 2014, from http://news.bbc.co.uk/2/hi/europe/3522832.stm
BBC News (2006a, May 12). Scores Die in Nigeria Fuel Blast. BBC News.
Retrieved on July 30, 2014, from http://news.bbc.co.uk/2/hi/africa/4765695.stm
BBC News (2006b, December 26). Lagos pipeline blast kills scores. BBC News.
Retrieved on July 30, 2014, from http://news.bbc.co.uk/2/hi/africa/6209845.stm
BBC News (2011a, October 21). Clarkston Remembers 22 Dead From 1971 Gas
Explosion. BBC News. Retrieved on October 20, 2013, from
http://www.bbc.com/news/uk-scotland-glasgow-west-15393422
BBC News (2011b, September 12). Kenya fire: Nairobi pipeline blaze 'kills at least
75'. BBC News. Retrieved on July 30, 2014, from
http://www.bbc.com/news/world-africa-14879401
269
Beitler, S. (1965, March 4). Gas Blast Kills 17 In Louisiana. GenDisasters.com.
Retrieved on January 10, 2014, from
http://www3.gendisasters.com/louisiana/53/natchitoches-la-gas-pipeline-
explosion-mar-1965
Bertolini, M., Bevilacqua, M., Ciarapica, F. E., and Giacchetta, G. (2009).
Development of Risk-Based Inspection and Maintenance Procedures for An Oil
Refinery. Journal of Loss Prevention in the Process Industries, 22, 244-253.
Bibi, W. (2011). Reputational Risk or Risks to Reputation? Bibi Consulting Inc.
Retrieved on May 12, 2014, from
http://www.bibiconsulting.net/Reputational_risk_article.pdf
Bie, C. de. (2006). Exploring Ways To Model Reputation Loss: A Case Study On
Information Security At Dutch Private Banks. Master Thesis. Delft University of
Technology, Netherlands.
Billingsley, E. (2002). El Paso Corp. Settles With Victims' Families. Albuquerque
Business First. Retrieved on April 20, 2014, from
http://www.bizjournals.com/albuquerque/stories/2002/09/16/story3.html?page=al
l
Boston.com (2010, July 21). The Big Picture: Oill Spill in Dalian, China.
Boston.com. Retrieved on October 10, 2013, from
http://archive.boston.com/bigpicture/2010/07/oil_spill_in_dalian_china.html
Bozbura, F. T. and Beskese, A. (2007). Prioritization of Organizational Capital
Measurement Indicators Using Fuzzy AHP. International Journal of
Approximate Reasoning, 44, 124–147.
Brandsæter, A. (2002). Risk Assessment In The Offshore Industry. Safety Science,
40, 231–269.
Bridgers, J. (2004). Natural Gas Pipeline Safety is a Myth. Earth Alert! Retrieved on
April 20, 2014, from http://earthalert.org/articles/gas_pipeline.html
Brito, A. J. and Almeida, A. T. De. (2009). Multi-Attribute Risk Assessment For
Risk Ranking of Natural Gas Pipelines. Reliability Engineering and System
Safety, 9, 187–198.
Brito, A. J., Almeida, A. T. De, and Mota, C. M. M. (2010). A Multicriteria Model
for Risk Sorting of Natural Gas Pipelines Based on ELECTRE TRI Integrating
Utility Theory. European Journal of Operational Research, 200(3), 812–821.
270
Brown, J. D. (2011). Likert Items and Scales of Measurement? SHIKEN: JALT
Testing and Evaluation SIG Newsletter, 15(1), 20–24.
Burke, R. J. (2011). Chapter 1: Corporate Reputations: Development, Maintenance,
Change and Repair. In Burke, R. J., Martin, G., and Cooper, C. L. (Ed.)
Corporate Reputation: Managing Opportunities and Threats (pp. 4–43). United
Kingdom: Gower Publishing @ Ashgate Publishing.
Byrnes, M. (2013, November 2013).Scenes of Destruction From China's Deadly
Pipeline Explosion. The Atlantic Citylab. Retrieved on July 30, 2014, from
http://www.citylab.com/politics/2013/11/scenes-destruction-chinas-deadly-
pipeline-explosion/7694/
Carvalho, A. A., Rebello, J. M. A., Souza, M. P. V, Sagrilo, L. V. S., and Soares, S.
D. (2008). Reliability of Non-Destructive Test Techniques In The Inspection of
Pipelines Used In The Oil Industry. International Journal of Pressure Vessels
and Piping, 85(11), 745–751.
Celik, M., Er, I. D., and Ozok, A. F. (2009). Application of Fuzzy Extended AHP
Methodology on Shipping Registry Selection: The Case of Turkish Maritime
Industry. Expert Systems with Applications, 36(1), 190–198.
Chen, Z. (2010). A Cybernetic Model For Analytic Network Process. Ninth
International Conference on Machine Learning and Cybernetics. 11-14 July.
Qingdao, 1914–1919.
Cherchiello, P. (2011). Statistical Models to Measure Corporate Reputation. Journal
of Applied Quantitative Methods, 6(4), 58–71.
Chicago Tribune (1973, February 3). Blast Wrecks Cafe in Iowa: Hunt Bodies.
Chicago Tribune. Retrieved on October 10, 2013, from
http://archives.chicagotribune.com/1973/02/03/page/1/article/blast-wrecks-cafe-
in-iowa-hunt-bodies
Chiroweb (2015). Deadly Explosion and Its Aftermath: Tragedy in San Juan Brings
Volunteers and a Chiropractor. Chiroweb. Retrieved on August 17, 2015 from
http://www.chiroweb.com/mpacms/dc_ca/article.php?id=38045
Chuang, M. L., Chen, W. M., and Liou, J. J. H. (2009). A Fuzzy MCDM Approach
For Evaluating Corporate Image and Reputation in The Airline Market. The
International Symposium on the Analytic Hierarchy Process (ISAHP). July 29-
August 1. Pittsburg, USA, 1–15.
271
Chun, R. (2005). Corporate Reputation : Meaning and Measurement. International
Journal of Management Reviews, 7(2), 91–109.
Clarin.com (2013, September 10). Tragedia De Rosario: Son 22 Los Muertos Por La
Explosión" [Tragedy In Rosario: The Deaths Of The Explosion Are 22].
Clarin.com. Retrieved on July 30, 2014, from
http://www.clarin.com/sociedad/Tragedia-Rosario-muertos-
explosion_0_1007899257.html
CNN.com (2004, September 17). Nigeria pipeline blast kills 50. CNN.com. Retrieved
on July 30, 2014, from
http://edition.cnn.com/2004/WORLD/africa/09/17/nigeria.pipeline/
CNN.com (2006, December 16). 200 dead in Nigeria pipeline blast, Red Cross
confirms. CNN.com. Retrieved on July 30, 2014, from
http://web.archive.org/web/20070102080252/http:/www.cnn.com/2006/WORLD
/africa/12/26/nigeria.blast.ap/index.html
Cordeiro, J. J. and Schwalbach, J. (2000). Preliminary Evidence on the Structure and
Determinants of Global Corporate Reputations. ECONIS-Online Catalogue of
the ZBW.
Coulter, E. D., Coakley, J., and Sessions, J. (2006). The Analytic Hierarchy Process :
A Tutorial for Use in Prioritizing Forest Road Investments to Minimize
Environmental Effects. International Journal of Forest Engineering, 17(2), 51–
69.
Cravens, K.S., Oliver, E. G., and Ramamoorti, S. (2003). The Reputation Index :
Measuring and Managing Corporate Reputation. European Management Journal,
21(2), 201–212.
Cravens, K. S., and Oliver, E. G. (2006). Employees: The Key Link to Corporate
Reputation Management. Business Horizons, 49, 293–302.
Crisis and Emergency Management Centre (CEMAC) (2014, 30 July). The
Ghislenghien Gas Pipeline Explosion… 10 Years. Crisis and Emergency
Management Centre. Retrieved on July 30, 2014, from
http://www.cemac.org/cbe/?p=668
Daily Sabah (2014, August 1). Blast Kills At Least 25, injures 270 in Taiwan. Daily
Sabah. Retrieved on May 1, 2015, from
http://www.dailysabah.com/asia/2014/08/01/blast-kills-at-least-25-injures-270-
in-taiwan
272
Dalalah, D., Al-Oqla, F., and Hayajneh, M. (2010). Application of the Analytic
Hierarchy Process (AHP) in Multi- Criteria Analysis of the Selection of Cranes.
Jordan Journal of Mechanical and Industrial Engineering, 4(5), 567–578.
Das, P. (2010). Selection of Business Strategies for Qualiy Improvement using Fuzzy
Analytic Hierarchy Process. International Journal for Quality Research, 4(4),
283–289.
Denis, D. St., Farooqui, K., and Scheller, E. (2013). Pipeline Integrity Management :
An Operating Model For The Midstream Industry. Oilpro. Retrieved September
9, 2014, from http://oilpro.com/post/1331/pipeline-Integrity-Management-an-
Operating-Model-for-the-Midstream-Industry
Det Norske Veritas (2010). DNV-RP-F107. Retrieved on April 10, 2013, from
http://rules.dnvgl.com/docs/pdf/DNV/codes/docs/2010-10/RP-F107.pdf
Det Norske Veritas (2013). DNV-DSS-316. Retrieved on April 20, 2016, from
https://rules.dnvgl.com/docs/pdf/DNV/codes/docs/2014-01/Dss-316.pdf
Dixon, N. (2000, July 19). NIGERIA: Hundreds Die in Pipeline Disaster. Green Left
Weekly. Retrieved on April 10, 2014, from
https://www.greenleft.org.au/node/21954
DNA India (2014a, June 27). Lighting of stove might have sparked GAIL pipeline
fire:Police. DNA India. Retrieved on July 30, 2014, from
http://www.dnaindia.com/india/report-lighting-of-stove-might-have-sparked-gail-
pipeline-firepolice-1998146
DNA India (2014b, June 29). GAIL gas pipeline explosion: 2 Senior GAIL Officials
Suspended Over Andhra Pradesh Pipeline Mishap. DNA India. Retrieved on July
30, 2014, from http://www.dnaindia.com/india/report-gail-gas-pipeline-
explosion-2-senior-gail-officials-suspended-over-andhra-pradesh-pipeline-
mishap-1998115
Dunbar, R. L. M., and Schwalbach, J. (2000). Corporate Reputation and Performance
in Germany. Corporate Reputation Review, 3(2), 115–124.
Dziubínski, M., Fratczak, M., and Markowski, A. S. (2006). Aspects of Risk
Analysis Associated with Major Failures of Fuel Pipelines. Journal of Loss
Prevention in the Process Industries, 19, 399–408.
E&P Forum (1996). No. 6.54/246. Retrieved on June 13, 2012, from
http://www.ogp.org.uk/pubs/246.pdf
273
Eisner, P. (1992, April 24). Mexico Reels from Explosion. The Tech Online Edition.
Retrieved on April 20, 2014, from
http://tech.mit.edu/V112/N22/mexico.22w.htm
European Gas Pipeline Incident Data Group (EGIG) (2014). EGIG 14.R.0403.
Retrieved on June 13, 2015, from
https://www.egig.eu/uploads/bestanden/ba6dfd62-4044-4a4d-933c-
07bf56b82383
Elnuevodia (2010, November 21). Así Huele La Muerte: Cada Cual Conserva Su
Propio Recuerdo De La Tragedia De Humberto Vidal. El Nuevo Dia. Retrieved
on April 20, 2014, from
http://www.elnuevodia.com/Xstatic/endi/template/imprimir.aspx?id=822160&t=
3
Expansion (2012, Apil 22). Guadalajara, 20 Años Después De Las Explosiones.
Expansion (Guadalajara, 20 Years After Explosions). Retrieved on April 20,
2014, from http://expansion.mx/fotogalerias/2012/04/22/guadalajara-20-anos-
despues-de-las-explosiones
Feng, W., Crawley, E. F., Weck, O. De, Keller, R., and Robinson, B. (2010).
Dependency Structure Matrix Modelling for Stakeholder Value Networks. 12th
International Dependency and Structure Modelling Conference. July 22-23.
Cambridge, UK, 3–16.
Firestein, P. J. (2006). Building and Protecting Corporate Reputation, Strategy and
Leadership, 34(4), 25-31.
FitzGerald, D. (2011). S&P Cuts PG&E By A Notch As Utility Rebuilds Reputation.
Wall Street Journal, Retrieved October 17, 2013, from
online.wsj.com/article/BT-CO-20111208-713981.html.
Fombrun, C. J. (1996). Reputation: Realizing Value from the Corporate Image.
United States of America: Harvard Business School Press.
Fombrun, C. J. (2007). List of Lists : A Compilation of International Corporate
Reputation Ratings. Corporate Reputation Review, 10(2), 144–153.
Fombrun, C., and Riel, C. B. M. Van. (1996). The Reputational Landscape.
Corporate Reputation Review, 1(1), 5–13.
274
Fong, A . (2015, July 18). Kaohsiung agrees to gas blast settlement. 2014. The China
Post. Retrieved on October 1, 2015, from
http://www.chinapost.com.tw/taiwan/national/national-
news/2015/07/18/441020/Kaohsiung-agrees.htm
Fortune (2015). 316. CPC. FORTUNE. Retrieved on June 20, 2015, from
http://fortune.com/global500/cpc-316/
Fowler, T. (2007, July 27). El Paso agrees to fine, settling explosion case. Chron.
Retrieved on April 20, 2014, from
http://www.chron.com/business/energy/article/El-Paso-agrees-to-fine-settling-
explosion-case-1820725.php
Furchtgott-Roth, D. (2013). Issue Brief: Pipelines are Safest for Transportation of
Oil and Gas. http://www.manhattan-institute.org/pdf/ib_23.pdf: Manhattan
Institute.
Gaultier-Gaillard, S., Louisot, J., and Rayner, J. (2009). Managing Risks to
Reputation – From Theory to Practice. Reputation Capital: Building and
Maintaining Trust in the 21st Century (pp. 161-178). London: Springer-Verlag.
Gaz Métro (2011). Gaz Métro and Valener complete reorganization. Gaz Métro: Life
in Blue. Retrieved on Augut 25, 2014, from
http://www.corporatif.gazmetro.com/investisseurs/default.aspx?culture=en-ca
Gettleman, J. (2011, September 12). Leaked Fuel Lures Needy Kenyans, Then
Ignites. The New York Times. Retrieved on July 30, 2014 from
http://www.nytimes.com/2011/09/13/world/africa/13kenya.html?_r=0
Gharakhani, D., Taromian, S., Dadras, K., and Fakhar, A. (2014). Fuzzy AHP and
Fuzzy DEMATEL Methods for Supplier Selection Criteria. International
Conference on Computer Science and Information Systems (ICSIS’2014). Oct 17-
18. Dubai, UAE, 112–115.
Gillis, C. (2011, July 13). Nigeria's Oil: A Brief History. Counterspill. Retrieved on
April 20, 2014, http://www.counterspill.org/article/nigerias-oil-brief-history
Gliem, J. A, and Gliem, R. R. (2003). Calculating, Interpreting, and Reporting
Cronbach’s Alpha Reliability Coefficient for Likert-Type Scales. Midwest
Research-to-Practice Conference in Adult, Continuing, and Community
Education. Columbus, Ohio, 82–88.
275
Goldstein, S. (1989, June 5). Soviet Rail Fire Kills 650 2 Trains Caught In Gas
Explosion. Philly.com. Retrieved on October 20, 2013, from
http://articles.philly.com/1989-06-05/news/26106226_1_gas-explosion-gas-
pipeline-passenger-trains
Google Finance (2014). El Paso LLC (NYSE:EP). Google Finance. Retrieved on
April 20, 2014, from https://www.google.com/finance?cid=666007 .
Gould. J. E. and Rodriguez, C. M. (2010, December 21). Pemex Pipeline Blast
Blamed on Criminals Kills 28. Bloomberg. Retrieved on July 30, 2014 from
http://www.bloomberg.com/news/articles/2010-12-20/pemex-pipeline-blast-
blamed-on-gang-kills-27-in-rivers-of-fire-
Graham, J. (2009). Reputational Risk: Managing Reputational Risk and Preventing
Value Destruction. Seminar of Reputational Risk – Protecting the Reputation of
the UK’s Leading Companies, Association of Insurance and Risk Managers
(AIRMIC).
Gravenor (2008, June 10). Montreal, QB Apartment Building Explosion.
GenDisaster.com. Retrieved on March 12, 2015, from
http://www3.gendisasters.com/quebec/6807/montrealqbapartmentbuildingexplosi
onmar1965
Greenpeace (2010, August 2). Dalian Oil Spill: Preliminary Findings and
Recommendations. Greenpeace. Retrieved on July 30, 2014, from
http://www.greenpeace.org/eastasia/press/releases/climate-energy/2010/dalian-
oil-recommendations-steiner/
Han, Z. Y., and Weng, W. G. (2011). Comparison Study on Qualitative and
Quantitative Risk Assessment Methods for Urban Natural Gas Pipeline Network.
Journal of Hazardous Materials, 189(1-2), 509–518.
Hanafiah, N. M., Zardasti, L., Yahaya, N., Noor, N., and Safuan, A. A. (2015).
Comparison of Human Health and Safety Loss Due to Corroded Gas Pipeline
Failure in Rural and Urban Areas : A Case Study in Malaysia. Solid State
Phenomena, 227, 221–224.
He, C. and Wu M. (2013). Discrete Markov Chain Model for Reputation Estimation
of Unstructure P2P Network, Journal of Theoretical and Applied Information
Technology, 48(3), 1410–1416.
Helm, S. (2007). The Role of Corporate Reputation in Determining Investor
Satisfaction and Loyalty. Corporate Reputation Review, 10(1), 22–37.
276
History (2009a, Jun 3). Natural gas explosion kills 500 in Russia. History. Retrieved
on October 9, 2013, from http://www.history.com/this-day-in-history/natural-gas-
explosion-kills-500-in-russia
History (2009b, October 18). Pipeline explosions kills 700 in Nigeria. 2009. History.
Retrieved on April 20, 2014, from http://www.history.com/this-day-in-
history/pipeline-explosions-kills-700-in-nigeria
Hogg, C. (2010, July 30). China Struggles To Recover From 'Worst Ever' Oil Spill.
2010. BBC News. Retrieved on July 30, 2014, from
http://www.bbc.com/news/world-asia-pacific-10819987
Hokstad, P., and Steiro, T. (2006). Overall strategy for Risk Evaluation and Priority
Setting of Risk Regulations. Reliability Engineering and System Safety, 91, 100-
111.
Hossain, F., Adnan, Z. H., and Hasin, M. A. A. (2014). Improvement in Weighting
Assignment Process in Analytic Hierarchy Process by Introducing Suggestion
Matrix and Likert Scale. International Journal of Supply Chain Management,
3(4), 91–95.
Hsiao, A. (2014). CPC Pipelines Illegal : Legislator. Taipei Times. Retrieved on
January 15, 2015, from
http://www.taipeitimes.com/News/front/archives/2014/08/07/2003596833.
International Business Times (2006, December 29). Nigerian Pipeline Blast Spurs
UN Call for Fuel Management Review. International Business Times. Retrieved
on July 30, 2014, from http://www.ibtimes.com/nigerian-pipeline-blast-spurs-un-
call-fuel-management-review-196974
International Association of Oil and Gas Producer (OGP) (2010). Risk Assessment
Data Directory: Major accidents. Retrieved on May 10, 2013, from
http://www.ogp.org.uk/pubs/434-17.pdf
IOL (2003, June 22). Red Cross: Nigerian Pipeline Fire Killed 105. IOL. Retrieved
on October 20, 2014, from http://www.iol.co.za/news/africa/red-cross-nigerian-
pipeline-fire-killed-105-1.108583?noredirect=20#.VnDlX0p97IV
IRIN (2003, June 22). Over 100 killed in oil pipeline explosion and fire. IRIN.
Retrieved on April 20, 2014, from http://www.irinnews.org/report/44471/nigeria-
over-100-killed-in-oil-pipeline-explosion-and-fire%22
277
IRIN (2004, September 17). NIGERIA: More Than 30 Killed in Lagos Fuel Pipeline
Explosion. IRIN. Retrieved on April 20, 2014, from
http://www.irinnews.org/news/2004/09/17/more-30-killed-lagos-fuel-pipeline-
explosion
Janssens, L. (2009). Manual: Dealing with incidents involving pipelines - General
Action Cards and Natural Gas Action Cards. (Ver. 2.2) Leuvenseweg, Brussel:
the Directorate-General for Civil Security.
Janyala, S. (2014, June 28). GAIL pipeline explosion: 'Ignored' gas leak kills 15 in
Andhra Pradesh. The Financial Express. Retrieved on October 10, 2014, from
http://archive.financialexpress.com/news/gail-pipeline-explosion-ignored-gas-
leak-kills-15-in-andhra-pradesh/1264816
Javanbarg, M. B., Scawthorn, C., Kiyono, J., and Shahbodaghkhan, B. (2012).
Expert Systems with Applications Fuzzy AHP-based Multicriteria Decision
Making Systems Using Particle Swarm Optimization. Expert Systems With
Applications, 39(1), 960–966.
Jo, Y. and Jong, B. (2005). A Method of Quantitative Risk Assessment For
Transmission Pipeline Carrying Natural Gas. Journal of Hazardous Materials,
123(1-3), 1–12.
Johnson, T. (2000, July 14). More than 250 feared dead in Nigerian pipeline
explosion. World Socialist Web Site. Retrieved on April 20, 2014, from
https://www.wsws.org/en/articles/2000/07/nig-j14.html
Johnston, G. (1965, March 4). Blast Probe Under Way; Death Toll Reaches 27. The
Montreal Gazette. Retrieved on October 10, 2013, from
https://news.google.com/newspapers?nid=Fr8DH2VBP9sC&dat=19650304&pri
ntsec=frontpage&hl=en
Jones-Lee, M. and Aven, T. (2011). ALARP — What Does It Really Mean?
Reliability Engineering and System Safety, 96(8), 877–882.
Jonkman, S. N., Gelder, P. H. A. J. M. Van, and Vrijling, J. K. (2003). An Overview
of Quantitative Risk Measures for Loss of Life and Economic Damage. Journal
of Hazardous Materials, 99, 1–30.
Junior, F. R. L., Osiro, L., and Carpinetti, L. C. R. (2014). A Comparison Between
Fuzzy AHP and Fuzzy TOPSIS Methods to Supplier Selection. Applied Soft
Computing, 21, 194–209.
278
Kabir, G. and Hasin, M. A. A. (2011). Comparative Analysis of AHP and Fuzzy
AHP Models for Multicriteria Inventory Classification. International Journal of
Fuzzy Logic Systems, 1(1), 1–16.
Kallas, Z. (2011). Butchers ’ Preferences For Rabbit Meat : AHP Pairwise
Comparisons Versus A Likert Scale Valuation. Proceedings of the International
Symposium on the Analytic Hierarchy Process for Multicriteria Decision
Making. June 15-18. Sorrento, Italy, 1–6.
Keller, B. (1989, June 5). 500 on 2 Trains Reported Killed By Soviet Gas Pipeline
Explosion. The New York Times. Retrieved on October 10, 2013, from
http://www.nytimes.com/1989/06/05/world/500-on-2-trains-reported-killed-by-
soviet-gas-pipeline-explosion.html?_r=1
Khan, F. I. and Abbasi, S. A. (1999). Major Accidents in Process Industries and An
Analysis of Causes and Consequences. Journal of Loss Prevention in the Process
Industries, 12(5), 361–378.
Khan, F. I. and Haddara, M. M. (2003). Risk-Based Maintenance (RBM): A
Quantitative Approach for Maintenance / Inspection Scheduling and Planning.
Journal of Loss Prevention in the Process Industries, 16(6), 561–573.
Khan, F. I. and Haddara, M. R. (2004). Risk-Based Maintenance Of Ethylene Oxide
Production Facilities. Journal of Hazardous Materials, 108(3), 147–159.
Kim, B.K., Krams, J. Krug, E., Leaseburge, M., Lemley, J., Alkhawaldeh, A.,
Mentzer, R.A., and Mannan, M. S. (2012). Case Study Analysis of The Financial
Impact of Catastrophic Safety Events. Journal of Loss Prevention in the Process
Industries, 25(5), 780-787.
Kiris, S. (2013). Multi-Criteria Inventory Classification by Using a Fuzzy Analytic
Network Process (ANP) Approach. INFORMATICA, 24(2), 199–217.
Kong, F., and Liu, H. (2005). Applying Fuzzy Analytic Hierarchy Process to
Evaluate Success Factors of E-Commerce. International Journal of Information
and Systems Sciences, 1(3-4), 406–412.
Kordi, M. (2008). Comparison of Fuzzy and Crisp Analytic Hierarchy Process
(AHP) Methods for Spatial Multicriteria Decision Analysis in GIS. Master
Thesis. University of Gavle, Sweden.
Krejcie, R. V, and Morgan, D. W. (1970). Determining Sample Size for Research
Activities. Educational And Psychological Measurement, 30, 607–610.
279
Kumar, M., Talib, S.A. and Ramayah, T. (2013). Business Research Methods.
Malaysia: Oxford University Press.
La Capital (2013, November 29). Ahora Litoral Gas les propone un arreglo
económico a las víctimas de Salta 2141" [Now Litoral Gas proposes an economic
agreement to the victims of Salta 2141]. La Capital. Retrieved on July 30, 2014,
from http://www.lacapital.com.ar/la-ciudad/Ahora-Litoral-Gas-les-propone-un-
arreglo-economico-a-las-victimas-de-Salta-2141-20131129-0041.html
La Nacion (2013a, August 6). Un escape de gas, la causa del estallido ocurrido en
Rosario [A gas leak, the cause of the outbreak occurred in Rosario]. La Nacion.
Retrieved on July 30, 2014, from http://www.lanacion.com.ar/1608024-un-
escape-de-gas-la-causa-del-estallido-ocurrido-en-rosario
La Nacion (2013b, August 6). Litoral Gas dice que no tuvieron reclamos en el call
center [Litoral Gas said they had no complaints at the call center]. La Nacion.
Retrieved on July 30, 2014, from h http://www.lanacion.com.ar/1608004-litoral-
gas-dice-que-no-tuvieron-reclamos-en-el-call-center
La Nacion (2013c, August 7). Uno de los detenidos habría huido cuando vio la
magnitud del escape de gas" [One of the detained employees may have escaped
when he saw the severity of the gas leak]. La Nacion. Retrieved on July 30, 2014,
from http://www.lanacion.com.ar/1608407-uno-de-los-detenidos-huyo-cuando-
vio-la-magnitud-del-escape-de-gas
Lee, A. and Meng, A. (2014, August 4). Owners never inspected Taiwan gas pipeline
that exploded because they 'didn't have the keys'. South China Morning Post.
Retrieved on May 1, 2015, from
http://www.scmp.com/news/china/article/1566101/owner-pipeline-caused-
deadly-blasts-kaohsiung-never-inspected-it
Levinson, L. (1965, November 24). LaSalle Victim Sues Gas Firm. The Montreal
Gazette. Retrieved on October 10, 2013, from
https://news.google.com/newspapers?id=JcQtAAAAIBAJ&sjid=W58FAAAAIB
AJ&pg=4506,5663456&dq=lasalle-heights&hl=en
Li, J., Liu, L., and Xu, J. (2010). A P2P E-Commerce Reputation Model Based on
Fuzzy Logic. 10th IEEE International Conference on Computer and Information
Technology. June 29 - July 1. Bradford, United Kingdom, 1275–1279.
280
Li, L., and Poh, K. L. (2010). Does “ Fuzzifying ” AHP Improve the Quality of
Multi-Attribute Decision Making? Proceeding of the 15th National
Undergraduate Research Opportunities (NUROP) Congress 2010. March 13.
Singapore, 1–14.
Liu, J. (2014, August 3). Investigators found pipeline with evidence of gas leakage:
report. The China Post. Retrieved on May 1, 2015, from
http://www.chinapost.com.tw/taiwan/national/national-
news/2014/08/03/413933/Investigators-found.htm
Liu, L. (2014, August 3). LCY apologizes over Kaohsiung blasts. The China Post.
Retrieved on May 1, 2015, from
http://www.chinapost.com.tw/taiwan/national/national-
news/2014/08/04/414000/LCY-apologizes.htm
Lux (2014, August 7). Photo of the Day. Whale Oil. Retrieved on December 24,
2014, from http://www.whaleoil.co.nz/2014/08/photo-day-342/
Macnamara, J. (2006). Reputation: Measurement and Management. Australia:
CARMA Asia Pacific-Media Monitor.
Mejri, M., and Wolf, D. De. (2013). Crisis Management: Lessons Learnt from the BP
Deepwater Horizon Spill Oil. Business Management and Strategy, 4(2), 67.
Memon, Z. A., Abd Majid, M. Z., and Mustaffar, M. (2006). Digitalizing
Construction Monitoring (DCM): An Overview of Malaysian Construction.
Proceedings of the 6th Asia-Pacific Structural Engineering and Construction
Conference (APSEC 2006). September 5 – 6. Kuala Lumpur, Malaysia, D15–
D27.
Mikhailov, L., and Tsvetinov, P. (2004). Evaluation of Services Using A Fuzzy
Analytic Hierarchy Process. Applied Soft Computing, 5, 23–33.
Miller, D. (1991). Handbook of Research Design and Social Measurement.
California: SAGE Publications Ltd.
Miller, J. (2011, May 8). Dalian oil spill photographer receives award at World Press
Photo. Greenpeace. Retrieved on July 30, 2014, from
http://www.greenpeace.org/international/en/news/Blogs/makingwaves/dalian-oil-
spill-photographer-receives-award-/blog/34625/
281
Money, K., and Hillenbrand, C. (2006). Beyond Reputation Measurement : Placing
Reputation Within A Model of Value Creation by Integrating Existing Measures
into A Theoretical Framework. 10th International Conference on Corporate
Reputation, Image, Identity and Competitiveness. 25-28 May. New York, 1–17.
Mott, G. D. (1978, November 3). Pipeline Explosion in Mexico Kills 52. The
Telegraph. Retrieved on October 10, 2013, from
https://news.google.com/newspapers?nid=2209&dat=19781103&id=uhxSAAAA
IBAJ&sjid=JjUNAAAAIBAJ&pg=4978,558758&hl=en
Muhlbauer, W. K. (2004). Pipeline Risk Management Manual, Third Edition.
Houston, Texas: Gulf Publishing Company.
Muhlbauer, W. K. (2006). Enhanced Pipeline Risk Assessment: Part 2 - Assessments
of Pipeline Failure Consequence. Retrieved on May 10, 2013, from
http://www.pipelinerisk.com/pdf/Enhanced_PL_Risk_Assess_Part_2%20rev3.3.
Muhlbauer, W. K. (2012). Pipeline Risk Assessment-The Essential Elements: An
Initiative Through Collaboration of DNV and W. Kent Muhlbauer. Retrieved on
February 26, 2014, from http://www.pipelinerisk.net/articles/Pipeline-Risk-
Assessment-Essential-Elements-Sample-Case_PGJ0113.pdf
Muller, G., and Vercouter, L. (2008). Computational Trust and Reputation Models.
Proceedings of the10th European Agent Systems Summer School (EASSS '08). 5-
9 May. Lisbon, Portugal, 1-13.
Nasr, G. G., and Connor, N. E. (2014). Chapter 2: Transmission and Distribution
System and Design. In Nasr, G. G., and Connor, N. E. (Ed.). Natural Gas
Engineering and Safety Challenges: Downstream Pocess, Analysis, Utilization
and Safety. (pp. 17–43). Switzerland: Springer International Publishing
National Fire Protection Association (2008). Summary of Fire Incidents 1934-2006
in Hotel Fires in the United States as Reported to the NFPA , with Ten or more
Fatalities. Quincy, Massachusetts: National Fire Protection Association
National Transportation Safety Board (1976). NTSB/PAR-76/06. Retrieved on
September 10, 2013, from
http://www.ntsb.gov/investigations/AccidentReports/Pages/PAR7606.aspx
National Transportation Safety Board (1997). NTSB/PAR-97/01. Retrieved on
September 10, 2013, from
http://www.ntsb.gov/investigations/AccidentReports/Pages/PAR9701.aspx
282
National Transportation Safety Board (2003). NTSB/PAR-03/01. Retrieved on
September 10, 2013, from
http://www.ntsb.gov/investigations/AccidentReports/Pages/PAR0301.aspx
National Transportation Safety Board (2015). NTSB SS-15/01. Retrieved on January
20, 2016, from http://www.ntsb.gov/safety/safety-studies/Pages/SS1501.aspx
Navarro, M. (2002, January 2002). Enron’s Collapse: Five Uncertain Years; A
Bankruptcy Freezes The Settlement of Claims In a Puerto Rico Explosion. The
Nww York Times. Retrieved on October 10, 2013 from
www.nytimes.com/2002/01/21/us/enron-s-collapse-five-uncertain-years-
bankruptcy-freezes-settlement-claims.html?pagewanted=allandsrc=pm
Nazim, F. A. M. (2015). Soil Corrosivity Condition Index for Buried Steel Pipeline.
Master Thesis. University Teknologi Malaysia, Malaysia.
Ndonga, S. (2011, September 12). 116 Pipeline Fire Survivors in Hospital. Capital
News. Retrieved on July 30, 2014 from
http://www.capitalfm.co.ke/news/2011/09/116-pipeline-fire-survivors-in-
hospital/
New Straits Times Online. (2014, June 13). Lawas explosion: PETRONAS to
completely remove residual gas. New Straits Times. Retrieved on June 15, 2014,
from http://www.nst.com.my/node/2447.
Nilashi, M., and Janahmadi, N. (2012). Assessing and Prioritizing Affecting Factors
in E-Learning Websites Using AHP Method and Fuzzy Approach. Information
and Knowledge Management, 2(1), 46–62.
Noor, N. M. M., Sabri, I. A. A., Hitam, M. S., Ali, N. H., and Ismail, F. (2012).
Fuzzy Analytic Hierarchy Process (FAHP) Approach for Evaluating Tourism
Islands in Terengganu, Malaysia. International Conference on Communications
and Information Technology (ICCIT-2012). 26-28 June. Hammamet, Tunisia,
62–66.
Oğuztimur, S. (2011). Why Fuzzy Analytic Hierarchy Process Approach For
Transport Problems? Proceedings of 2011 International Conference on
Engineering of Reconfigurable Systems & Algorithms. July 18-21. Nevada, USA,
1–19.
Oil Peak (2012, April 19). Malaysia Energy Report. endofcrudeoil.com. Retrieved on
April 30, 2016, from http://www.endofcrudeoil.com/2012/04/malaysia-energy-
report.html
283
Onliner (2015, July 22). Ад на Транссибе: крупнейшая железнодорожная
катастрофа в истории СССР (Hell on Transsib: the largest railway accident in
the history of the Soviet Union). Onliner. Retrieved on July 30, 2015, from
https://realt.onliner.by/2015/07/22/ufa
Özdağoğlu, A., and Özdağoğlu, G. (2007). Comparison of AHP and Fuzzy AHP for
the Multi- Criteria Decision Making Processes With Linguistic Evaluations.
Istanbul Commerce University Journal of Social Sciences, 11, 65–85.
Paez, J. and Roy, A. (2010). Developing A Pipeline Risk Assessment Tool for the
Upstream Oil and Gas Industry. Houston, Texas: NACE International.
Palmer, A. (1965, March 2). Other Nearby Buildings Ordered Evacuated As 24 Die,
5 Missing In LaSalle Explosion: New Blast Feared; Families Moved Into School.
The Gazette. Retrieved on October 9, 2013, from
https://news.google.com/newspapers?nid=Fr8DH2VBP9sC&dat=19650302&pri
ntsec=frontpage&hl=en
Pandey, M., Khare, N. and Shrivastava, S. (2013). Transform for Simplified Weight
Computations. Intelligent Informatics, 182, 109–117.
Park, K. S., Lee, J. H. and Jo, Y. D. (2004). An Approach to Risk Management of
City Gas Pipeline. Process Safety and Environmental Protection, 82(B6), 446-
452.
Pearson, D. (1965, June 18). Classified Report on Pipeline Blast Says Pressure
Excessive. The Press-Courier. Retrieved on January 10, 2014, from
https://news.google.com/newspapers?id=jiJLAAAAIBAJ&sjid=aCMNAAAAIB
AJ&pg=6968,2455002&dq=gas+line+blast&hl=en
Petroliam Nasional Berhad (PETRONAS) (2012). PTS 30.40.60.33. Malaysia:
PETRONAS.
Pipeline and Hazardous Materials Safety Administration (PHMSA) (2015). About
Data & Statistics. Retrieved on 2013, Jun 24 from
http://www.phmsa.dot.gov/pipeline/library/data-stats
Press Trust of India (2014, June 27). Prime Minister Narendra Modi condoles loss of
lives in GAIL pipeline fire. The Indian Express. Retrieved on July 30, 2014, from
http://indianexpress.com/article/india/india-others/pm-condoles-loss-of-lives-in-
gail-pipeline-fire/
284
Presse, L. (2010, February 26). A Natural Gas Explosion Kills 28 People. Le
Messager LaSalle. Retrieved on October 9, 2013, from
www.messagerlasalle.com/Societe/L%26rsquohistoire-de-LaSalle/2010-02-
26/article-1018620/Une-explosion-au-gaz-naturel-tue-28-personnes/1
Pula, R., Khan, F. I., Veitch, B., and Amyotte, P. R. (2005). Revised Fire
Consequence Models For Offshore Quantitative Risk Assessment. Journal of
Loss Prevention in the Process Industries, 18, 443–454.
Rayner, J. (2003). Managing Reputational Risk: Leveraging Opportunities, Curbing
Threats. England: John Wiley & Sons Ltd.
Reuters (2013, November 25). Police detain nine after Qingdao oil blasts: media.
Reuters. Retrieved on July 30, 2014, from http://www.reuters.com/article/us-
china-sinopec-blasts-idUSBRE9AP02N20131126
Real-McKeighan, T. (2002, January 10). Hotel Pathfinder: Man believes survivors of
1976 explosion can inspire people today. Fremont Tribune. Retrieved on October
10, 2013, from http://fremonttribune.com/hotel-pathfinder-man-believes-
survivors-of-explosion-can-inspire-people/article_991b058b-bd23-5d4d-936d-
2116655517b0.html
Rochette, M. (2007). Reputation Risk: Also known as the Cinderella Asset!
Reputation Risk, Tillinghast-Tower Perrins.
Rose, C., and Thomsen, S. (2004). The Impact of Corporate Reputation on
Performance : Some Danish Evidence. European Management Journal, 22(2),
201–210.
Roth, A. T. (2003). Managing Risk Issues vs . Managing Risk: Energy Pipeline
Communications And Public Awareness Programs. Proceeding of 2003
International Oil Spill Conference, April 6-11. Vancouver, British Columbia,
Canada, 357-365.
Rourke, J. S. O. (2011). Putting Reputation At Risk The Seven Factors of
Reputational Management. The United Nations Millennium Development Goals:
The Global Compact and the Common Good. March 2011. Notre Dame, Indiana,
1–19.
Saaty, T.L. (2008). Decision Making With the Analytic Hierarchy Process.
International Journal of Services Sciences, 1(1), 83-98.
285
Sato, Y. (2003). Questionnaire Design For Survey Research : Employing Weighting
Method. International Symposium on the Analytic Hierarchy Process (ISAHP)
2005. July 8-10. Honolulu, Hawaii, 1–8.
Saudi Press Agency (2013, November 23). Death Toll Rises to 47 from Chinese Oil
Pipeline Explosions. High Beam Research. Retrieved on July 30, 2014, from
https://www.highbeam.com/doc/1G1-350201800.html
Scandizzo, S. (2011). A Framework For The Analysis Of Reputational Risk. The
Journal of Operational Risk, 6(3), 41–63.
Sevkli, M., Koh, S. C. L., Zaim, S., Demirbag, M., and Tatoglu, E. (2007). Hybrid
Analytical Hierarchy Process Model For Supplier Selection. Industrial
Management and Data, 108(1), 122–142.
Sheely, R.E. (2015, April 5). Scars From 1968 Explosions That Killed 41 In
Richmond Gas Explosion Remain. Indiana Economic Digest. Retrieved on July
18, 2014, from
http://indianaeconomicdigest.com/Main.asp?SectionID=31&SubSectionID=135
&ArticleID=79123
Smith, H. (2011). BP one year on : How events unfolded. Professional Adviser.
Retrieved on May 18, 2013, from
http://www.professionaladviser.com/ifaonline/news/2044806/bpeventsunfolded
Soekiman, A., Pribadi, K. S., Soemardi, B. W., and Wirahadikusumah, R. D. (2008).
Study On Factors Affecting Project Level Annals of Faculty Engineering Hune.
Doara-International Journal of Engineering, 10(3), 35–40.
Sovacool, B. K. (2008). The Costs Of Failure: A Preliminary Assessment of Major
Energy Accidents , 1907 – 2007. Energy Policy, 36, 1802–1820.
Spence, D. B. (2011). Corporate Social Responsibility in the Oil and Gas Industry:
The Importance of Reputational Risk. Chicago-Kent Law Review, 86(1), 59–85.
Sputnik International (2009, June 4). Russia remembers 1989 Ufa train disaster.
Sputniknews. Retrieved on October 9, 2013, from
http://sputniknews.com/russia/20090604/155167464.html
Suburban Emergency Management Project (SEMP) (2006, May 3). The Guadalajara
1992 Sewer Gas Explosion Disaster: Biot Report #356. Suburban Emergency
Management Project. Retrieved on April 20, 2014, from
http://web.archive.org/web/20090210235229/http://www.semp.us/publications/bi
ot_reader.php?BiotID=356
286
Sulaiman, N. S., and Tan, H. (2014). Third Party Damages of Offshore Pipeline
海底管道的第三方损伤. Journal of Energy Challenges and Mechanics, 1(1), 1–
6.
Sun, N. Y. (2014, August 8). Taiwan explosions under investigation, chemical
supply chain under pressure. Plastic News. Retrieved on May 1, 2015, from
http://www.plasticsnews.com/article/20140807/NEWS/140809943/taiwan-
explosions-under-investigation-chemical-supply-chain-under-pressure
Taiwan's News.Net (2014, August 2). Many dead in Taiwan city gas blasts. Taiwan's
News.Net. Retrieved on May 1, 2015, from
http://www.taiwansnews.net/index.php/sid/224358099
The Associated Press (2006, May 12). Nigerian pipeline blast kills up to 200. USA
Today. Retrieved on April 20, 2014, from
http://usatoday30.usatoday.com/news/world/2006-05-12-nigeria_x.htm
The Associated Press (2010, December 19). Thieves blamed in Mexico pipeline blast
that kills 28. The Seattle Times. Retrieved on April 20, 2014, from
http://www.seattletimes.com/nation-world/thieves-blamed-in-mexico-pipeline-
blast-that-kills-28/
The Blade (1978, November 2). 52 Killed In Pipeline Blast In Small Mexican
Village. The Blade. Retrieved on October 10, 2013, from
https://news.google.com/newspapers?nid=1350&dat=19781102&id=1RBPAAA
AIBAJ&sjid=fAIEAAAAIBAJ&pg=6863,705956&hl=en
The Borneo Post Online. (2014). Anyi: Keep Public Informed on Latest Concerning
Gas Pipeline Explosion. The Borneo Post Online. Retrieved on August 30, 2014,
from http://www.theborneopost.com/2014/06/12/anyi-keep-public-informed-on-
latest-concerning-gas-pipeline-explosion/ 5/5.
The Free Library (1998, March 25). Shizuoka Gas, plaintiffs reach settlement over
explosion. The Free Library. Retrieved on October 10, 2013, from
http://www.thefreelibrary.com/Shizuoka+Gas%2c+plaintiffs+reach+settlement+
over+explosion-a050306543
The Guardian (2006, May 12). Hundreds killed in Nigerian pipeline explosion. The
Guardian. Retrieved on July 30, 2014, from
http://www.theguardian.com/world/2006/may/12/oil.business
287
The Guardian (2014, August 1). Taiwan Gas Explosion Kills Dozens: Hundreds
More Injured As Streets Of Kaohsiung Are Ripped Open By Petrochemical
Pipeline Blast. The Guardian. Retrieved on May 1, 2015, from
http://www.theguardian.com/world/2014/jul/31/taiwan-city-kaohsiung-gas-
explosion
The Glasgow Herald (1972, February 11). Disaster Jury Say No One Was to Blame.
The Glasgow Herald. Retrieved on October 10, 2013, from
https://news.google.com/newspapers?id=HZZAAAAAIBAJ&sjid=I6UMAAAAI
BAJ&pg=5007,2094378&dq=clarkston+explosion&hl=en
The Herald (2002, October 19). The Day 22 Died As Explosion Blasted Shopping
Centre EVENT THE CLARKSTON DISASTER DATE October 21, 1971. The
Herald Scotland. Retrieved on October 10, 2010, from
http://www.heraldscotland.com/news/11913558.The_day_22_died_as_explosion
_blasted_shopping_centre_EVENT_THE_CLARKSTON_DISASTER__DATE_
October_21__1971/
The Huffington Post (2010, July 30). China Oil Spill Far Bigger Than Stated, U.S.
Expert Says. The Huffington Post. Retrieved on July 30, 2014, from
http://www.huffingtonpost.com/2010/07/30/china-oil-spill-far-
bigge_n_665038.html
The Huffington Post (2010, July 21). China Oil Spill PHOTOS: Environmental and
Economic Damage Becomes Clear. The Huffington Post. Retrieved on July 30,
2014, from http://www.huffingtonpost.com/2010/07/21/china-oil-spill-grows-
off_n_653852.html
The Malaysian Insider (2014, August 10). Taiwan Opens Investigation Into Gas
Explosion Disaster. The Malaysian Insider. Retrieved on January 20, 2015, from
http://www.themalaysianinsider.com/world/article/taiwan-opens-investigation-
into-gas-explosion-disaster
The New York Times (2000). Pipeline Explosion Kills 30 in Nigeria. The New York
Times. Retrieved on April 20, 2014 from
http://www.nytimes.com/2000/07/17/world/pipeline-explosion-kills-30-in-
nigeria.html
288
The Scotsman (2011, October 18). Forty years on: Remembering the Clarkston Toll
disaster. The Scotsman. Retrieved on October 10, 2013 from
http://www.scotsman.com/news/forty-years-on-remembering-the-clarkston-toll-
disaster-1-1915939
The Telegraph (1980, August 16). Japan Explosion Leaves 12 Dead. The Telegraph.
Retrieved on October 10, 2013, from
https://news.google.com/newspapers?nid=2209&dat=19800816&id=oaArAAAA
IBAJ&sjid=YvwFAAAAIBAJ&pg=4354,3160162&hl=en
The Washington Observer (1965, March 5). 17 Killed As Gas Line Explodes: Nine
Children Among Victims In Louisiana. The Washington Observer. Retrieved on
July 26, 2015, from
https://news.google.com/newspapers?id=e95dAAAAIBAJ&sjid=x14NAAAAIB
AJ&pg=6377,556265&dq=pipeline+fire&hl=en
The Windstor Star (1967, February 23). Gas Company Sued After Fatal Explosion.
The Windstor Star. Retrieved on July 26, 2015, from
https://news.google.com/newspapers?id=gkU_AAAAIBAJ&sjid=FlIMAAAAIB
AJ&pg=6187,4967515&dq=lasalle-heights&hl=en
The World Post (2010, July 30). China Oil Spill Far Bigger Than Stated, U.S. Expert
Says. The World Post. Retrieved on July 30, 2014 from
http://www.huffingtonpost.com/2010/07/30/china-oil-spill-far-
bigge_n_665038.html
TIME (1992, May 11). Pemex Is Blamed for The Sewer Explosio: Damages in
Guadalajara will cost $300 million to repair. TIME. Retrieved on April 20, 2014,
from http://content.time.com/time/magazine/article/0,9171,975488,00.html
Tonello, M. (2007). Reputation Risk: A Corporate Governance Perspective. United
States of America: The Conference Board, Inc.
Toosi, N. M., and Kohanali, R. A. (2011). The Study Of Airline Service Quality In
The Qeshm Free Zone By Fuzzy Logic. The Journal of Mathematics and
Computer Science, 2(1), 171–183.
Torfi, F., Farahani, R. Z., and Rezapour, S. (2010). Fuzzy AHP to Determine the
Relative Weights of Evaluation Criteria and Fuzzy TOPSIS to Rank the
Alternatives. Applied Soft Computing Journal, 10(2), 520–528.
289
Transportation Safety Board of Canada (TSB) (2014). Statistical Summary - Pipeline
Occurrences 2013. Retrieved on July 17, 2015 from
http://www.tsb.gc.ca/eng/stats/pipeline/2013/sspo-2013.asp
TransWorldNews (2008, May 16). Pipeline Blast in Nigeria Kills at least 100.
TransWorldNews. Retrieved on July 30, 2014, from
http://www.transworldnews.com/NewsStory.aspx?id=46897&cat=0
Trotta, A., and Cavallaro, G. (2012). Measuring Corporate Reputation : A
Framework For. International Journal of Economics and Finance Studies, 4(2),
21–30.
United States Coast Guard National Response Team (2011). On Scene Coordinator
Report: Deepwater Horizon Oil Spill. Retrieved on June 12, 2012, from
http://www.uscg.mil/foia/docs/dwh/fosc_dwh_report.pdfUSA Today (2006, May
12). Nigerian pipeline blast kills up to 200. USA Today. Retrieved on April 20,
2014, from http://usatoday30.usatoday.com/news/world/2006-05-12-
nigeria_x.htm
United States Department of Justice (USDOJ) (2007, July 26). El Paso Natural Gas
to Pay $15.5 Million Penalty and Perform Comprehensive Reforms to Pipeline
System. United States Department of Justice. Retrieved on April 20, 2014, from
http://www.justice.gov/archive/opa/pr/2007/July/07_enrd_548.html
Xinhua News Agency (2006, December 27). 269 People Killed in Nigerian Pipeline
Explosion. China.org.cn. Retrieved on January 20, 2015, from
http://www.china.org.cn/english/international/194010.htm
Vallens, A. (2008). The Importance of Reputation. Questia, 55(4), 1–4.
Van Nesss Feldman (2015). Natural Gas Pipeline Safety and Reliability: An
Assessment of Progress. Retrieved on April 20, 2016 from
http://www.napsr.org/SiteAssets/mediainfo/Pipeline%20Safety-
AGF%20Report%202015.pdf
Vergin, R. C. and Qoronfleh, M.W. (1998). Corporate Reputation and the Stock
Market. Business Horizons. 41(1), 19–26.
Voice of America (VOA) (2010, July 19). China Port Reopens After Oil Spill,
Cleanup Continues. Voice of America. Retrieved on July 30, 2014, from
http://www.voanews.com/content/china-port-reopens-after-oil-spill-cleanup-
continues-98853794/166050.html
290
Wang, A. (2014, August 1). Powerful Taiwan gas explosions kill 20, injure 270.
Inqurer.net. Retrieved on May 1, 2015, from
http://newsinfo.inquirer.net/625423/powerful-taiwan-gas-explosions-kill-20-
injure-270
Weiss, L. (2013). How Share-Price Fixation Killed Enron. Harvard Business Review.
Retrieved on May 19, 2015, from https://hbr.org/2013/07/how-share-price-
fixation-kille
Wikipedia (2015). Humberto Vidal Explosion. Wikipedia. Retrieved on October 20,
2014, from https://en.wikipedia.org/wiki/Humberto_Vidal_explosion
WOW News (2002, December 20). Settlement Reached In Humberto Vidal
Explosion Case. Puerto Rico Herald. Retrieved on April 20, 2014, from
http://www.puertorico-herald.org/issues/2002/vol6n52/Media3-en.html
Yeung, J. F., Chan, A. P. and Chan, D. W. (2009). A Computerized Model for
Measuring and Benchmarking the Partnering Performance of Construction
Projects. Automation in Construction, 18(8), 1099-1113.
Yuan, W. J. (2014). Safety, Heath, and Environment (SHE) Case for Oil andamp;
Gas Operating Company. Advanced Materials Research, 937, 520–525.
Zadeh, L. A. (2008). Is There a Need for Fuzzy Logic? Information Sciences,
178(13), 2751-2779.
Zakzak (2012, August 16). Casualties are more than 200 people gas explosion in the
underground mall of Railways Shizuoka Station. Zakzak. Retrieved on October
10, 2013 from
http://www.zakzak.co.jp/society/domestic/news/20100816/dms1008161621007-
n1.htm
Zeynali, M., Aghdaie, M. H., Rezaeiniya, N., and Zolfani, S. H. (2012). Full Length
Research Paper A Hybrid Fuzzy Multiple Criteria Decision Making ( MCDM )
Approach to Combination of Materials Selection. African Journal of Business
Management, 6(45), 11171–11178.
Zhang, Y. (2009). A Study of Corporate Reputation’s Influence on Customer Loyalty
Based on PLS-SEM Model. International Business Research, 2(3), 28–35.
top related