1, a*
1,b
1,c
1,d
1,e
1 Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia a* libriati@utm.my, b norhamimi2@live.utm.my, c norhazilan@utm.my, d nordiny@utm.my, e ahmadsafuan@utm.my
Keywords: Consequence assessment; pipeline failure; corrosion; reputation loss.
Abstract. In this paper, a qualitative consequence assessment method for damaged urban natural gas pipelines is proposed. It focuses on identifying reputation loss factors according to stakeholders’
(investors, customers, employees, and communities) perceptions. The analytical hierarchy process
(AHP) method is applied to prioritize these factors. Results show that the loss of customer confidence ranks as the highest contributor to an operator’s reputation loss due to a pipeline accident. Thus, better risk assessment of pipeline damage due to corrosion will be achieved with the inclusion of reputation loss in the consequence assessment. Hence, decision making in pipeline repair, inspection, and maintenance can be improved as well as a company’s annual profit margin.
Introduction
In regard to urban natural gas pipeline accidents, corrosion is one of the major causes [1]. This underground pipeline is prone to external corrosion rather than internal. Model of external corrosion specifically designed for buried pipeline improves its reliability [2]. Pipeline failure due to corrosion attack can cause hazards involving multiple fatalities, serious financial loss, bad economic implications, and significant environmental damage [3]. These failures definitely contribute to undesirable costly consequences that will be borne by pipeline operators, resulting in the reduction of annual profit margins. Therefore, the need for awareness among operators to assess the overall consequences in order to forecast the total cost of a pipeline failure event has increased dramatically. Human loss, production loss, asset loss, environmental loss, and reputation loss are the types of losses considered in the consequence assessment, which could exhibit a new methodology of assessing the monetary loss of a pipeline failure event due to corrosion. Hence, the prediction of the total failure cost of an event can be accurately assessed. It could assist pipeline operators in making the right decisions in future pipeline inspections and maintenance program to protect their valuable assets.
The total cost of failure is known as risk. Risk can be defined as the product of an event’s probability of failure (POF) and the consequence of failure (COF). Various industries practice risk assessment to evaluate risk. The consequences of a pipeline accident are commonly assessed based on human loss, environmental loss, asset loss, and production loss. Reputation loss can be included, too, because a pipeline accident event due to corrosion certainly makes a significant impact on the operator’s reputation. Reputation is an intangible asset that can contribute tangible or monetary losses to the pipeline operator. For example, on September 9, 2010, an explosion of a natural gas pipeline in San Bruno, California, owned by Pacific Gas and Electric Company (PG&E), killed eight people, injured 58 others, destroyed 38 houses, and damaged 70 more in a residential neighborhood [4]. The company spent approximately $2.7 billion dollars of shareholders’ money to pay fines and penalties charged by California regulators for the blast as reported in March 2014, almost four years after the event date. Recently, PG&E is reported to have been charged for diverting millions of dollar from pipeline safety allocations to executive bonuses for years prior to the accident. This integrity violation might create a negative perception among stakeholders. It is a sign of reputation decline because reputation is measured by stakeholders’ perceptions [5].

A 20-inch-diameter buried natural gas pipeline owned and operated by Columbia Gas
Transmission Corporation ruptured near a sparsely populated area in Sissonville, West Virginia, on
December 11, 2012. Fortunately, there were no fatalities or serious injuries. However, three houses were destroyed, and the fire ignited approximately 76 million standard cubic feet of high-pressure natural gas, which was released and burned an area 820 feet wide and 1,100 feet along the pipeline right-of-way. The operator spent about $8.4 million for pipeline repair and inspection, and they lost almost $285,000 in released gas. The investigation found that pipeline corrosion was the main source of the accident. Furthermore, there had been no inspection done on the pipeline since 1988
[4]. The integrity of the employee responsible for pipeline inspection and maintenance was questionable and capable of influencing the perception of the stakeholders. Hence, the operator’s good reputation was endangered.
Previous research regarding consequence assessment of pipeline failures has commonly considered the monetary loss of an accident, while reputation loss has been neglected because the data is difficult to quantify [6, 7]. Tangible human, environmental, and asset losses are calculated at the time of the event. On the other hand, reputation loss is time-dependent and it relies on the critical to the event [8, 9]. An effort to measure reputation loss for a pipeline accident is done based on the coverage range the event receives [10] and the level of public concern. However, this method does not portray the actual perceptions of stakeholders, and the definition of reputation is the beliefs of the stakeholders towards a company and its attributes [5]. Reputation is measured using an indexbased method and vital to most organizations [11] because it affects company’s profit margin [12].
This paper will discuss the factors that contribute to the loss of pipeline operator reputation due to accidents caused by corrosion. Six pipeline operator experts were interviewed, and questionnaires were answered. The multiple decision-making criteria of the analytical hierarchy process (AHP) with a weighting method were implemented to process the information received.
Methodology
In order to prioritize the factors of operator reputation loss due to a corroded pipeline, the methodology of the study is as follows:
1.
Gather the factors influencing stakeholders’ perceptions that eventually contribute to the loss of an operator’s reputation in the pipeline accident reports.
2.
Sort the factors corresponding to the stakeholders and structure the decision-making problem into a hierarchical framework.
3.
Design the questionnaire and input the responses in the AHP integrated software called Super
Decision.
4.
Make a pairwise comparison of elements at each hierarchy level with respect to each element on the preceding level.
5.
Prioritize the factors and rank the reputation loss factors.
Data Analysis
Ten cases of natural gas pipeline accidents initiated by corrosion in various countries were studied, and 22 operator reputation loss factors were identified. These factors were then sorted according to their corresponding stakeholders and numbered as indicated in Table 1. As shown in
Figure 1, a hierarchical structure was constructed with attention to creating hierarchical influence between the goal (to prioritize the factors), the criteria (stakeholders), and the subcriteria
(reputation loss factors corresponding to the stakeholders). Based on this structure, a questionnaire was designed and distributed to six experts to achieve the goal. A comprehensive questionnaire and interview sessions were conducted with managers, engineers, and other operator personnel who worked in pipeline integrity management previously or in the present day. The experts were asked to perform a pairwise comparison using an AHP fundamental scale assigning weights as given in
Table 2.
Table 1: Reputation loss factors affecting stakeholders’ perceptions towards pipeline operators

Stakeholders Factors contributing to pipeline operator reputation loss
A: Investors
B: Customers
A1: Sudden drop of share price and market capitalization
A2: Services or sales progress disturbed
A3: Ranking downgraded
B1: Loss of customer confidence
C: Employees
B2: Bad word-of-mouth among customers
C1: CEO responds with unreasonable actions towards victims
C2: CEO neglects victims’ welfare
C3: CEO hides facts about the accident
C4: CEO refuses to take responsibility
C5: CEO mismanages allocations to lobby politicians
D: Communities D1: Recurrence of similar accident
D2: Loss of public trust
D3: Severity of accident
D4: Mishandling public reports
D5: Negative media reports
A4: Reduction of credit rating
A5: Loss of new pipeline contracts
A6: Loss of sponsorship opportunity
C6: Employees demotivated
C7: Job applications for position reduced
C8: Skilled worker resignations
C9: Employee(s) caused accident
Table 2: AHP fundamental scale
Definition Equal importance
Moderate importance
Strong importance
Very strong importance
Extreme importance
Intermediate values
Intensity of importance
1 3 5 7 9 2, 4, 6, 8
The experts’ responses were stored in the AHP software. The respective importance of the reputation loss factors were done by pairwise comparison between reputation loss factor with respect to stakeholders, and stakeholders with respect to goal. The comparison of the stakeholders
(A, B, C, D) node was carried out according to their influence on the reputation loss factor. For example, investors as a primary standard were denoted as A, and the corresponding reputation loss factors were written as Ai (i = 1, 2, 3, 4, 5, 6) as secondary standards as shown in Figure 1.
Similarly, the goal G was compared to the influence on the stakeholders.
An example of a pairwise comparison matrix is given in Table 3. The local priority vector was calculated using the principal eigenvector of a comparison matrix. After all priority vectors were computed, multiplying the criteria-specific priority vector of the alternatives with the corresponding criterion weight and summing up the results obtained the overall priorities for the reputation loss factor with respect to the goal . The consistency according to the pairwise comparison was checked using the consistency ratio (CR). If the consistency value of CR is less than 0.1, it proves that such pairwise comparison matrix consistency is satisfactory.
The priority vectors for all six experts were transferred to Microsoft Excel for further analysis.
The vectors of the stakeholders and reputation loss factor priority values were averaged respectively. To rank the reputation loss factor, the reputation loss factor priority vector must be multiplied by its corresponding stakeholders . Rearranging the values in descending order has shown the ranking of the reputation loss factor. B1 ranked as the highest contribution to the loss of operator reputation with regard to an urban gas pipeline accident initiated by structural failure due to corrosion. C9 is the lowest.
Goal
G1
Stakeholders
A, B, C, D
Investors
A1, A2, A3, A4, A5, A6
Customers
B1, B2
Employees
C1, C2, C3, C4, C5, C6, C7, C8, C9
Communities
D1, D2, D3, D4, D5
Figure 1: AHP structure
Table 3: Pairwise Comparison Matrix Between Reputation Loss Factor and Stakeholders
Investors
A1
A1
1
A2
2
A3
1/2
A4
1
A5
1
A6
1/2
Priority vector
0.135

A2
A3
A4
A5
A6
1/2
2
1
1
2
1
3
2
2
3
1/3
1
1/2
1/2
1
1/2
2
1
1
2
1/2
2
1
1
2
1/3
1
1/2
1/2
1
0.075
0.259
0.136
0.136
0.259
Conclusion
Reputation loss is difficult to quantify and commonly neglected in the consequence assessment.
It is dependent on time and perceptions. This paper endeavored to identify the factors of stakeholders’ perceptions that result in pipeline operator reputation loss. The AHP approach was adopted to prioritize the reputation loss factor. The results show that the factor contributing to the highest priority value is B1 (loss of customer confidence). The AHP method is capable of identifying contributors to reputation loss. Thus, better risk assessment of pipeline damage due to corrosion will be achieved with the inclusion of reputation loss in the consequence assessment.
Hence, decision making in pipeline repair, inspection, and maintenance will be improved as well as the company’s annual profit margin.
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