A Systemic Approach to Next Generation Infrastructure Data Elicitation and Planning Using Serious Gaming Methods

Infrastructure systems are vital to the functioning of our society and economy. However, these systems are increasingly complex and are more interdependent than ever, making them difficult to manage. In order to respond to increasing demand, environmental concerns, and natural and man-made threats, infrastructure systems have to adapt and transform. Traditional engineering design approaches and planning tools have proven to be inadequate when planning and managing these complex socio-technical system transitions. The design and implementation of next generation infrastructure systems require holistic methodologies, encompassing organizational and societal aspects in addition to technical factors. In order to do so, a serious gaming based risk assessment methodology is developed to assist infrastructure data elicitation and planning. The methodology combines the use of various models, commercial-off-the-shelf solutions and a gaming approach to aggregate the inputs of various subject matter experts (SMEs) to predict future system characteristics. The serious gaming based approach enables experts to obtain a thorough understanding of the complexity and interdependency of the system while offering a platform to experiment with various strategies and scenarios. In order to demonstrate its abilities, the methodology was applied to National Airspace System (NAS) overhaul and its transformation to Next Generation Air Transportation System (NextGen). The implemented methodology yielded a comprehensive safety assessment and data generation mechanism, embracing the social and technical aspects of the NAS transformation for the next 15 years

The Use of Expert Judgement Methods for Deriving Accident Probabilities in Aviation

Improving safety has always been the top interest in the aviation industry. The outcomes of safety and risk analyses have become much more thorough and sophisticated. They have become an industry standard of safety investigations in many airlines nowadays. In the past, airlines were much more limited in answering the questions about hazardous situations, accident probabilities, and accident rates. Airlines try hard to cope with stricter safety standards. The objective of this paper is to find out and quantify the extent of the expert judgment in helping airlines in the evaluation of the Flight Data Monitoring (FDM) events. On top of that, the paper reveals the method for a careful choice of experts, so that their estimations will maximize the potential of an accurate and useful outcome. Also, the paper provides details of implementation of the classical model into this research, then continues with the calculations and visualization of the outcomes. The outcomes are probability distributions per each aircraft type, then per IATA accident type and finally per FDM event

Developing a Methodology for Eliciting Subjective Probability Estimates During Expert Evaluations of Safety Interventions: Application for Bayesian Belief Networks

The NASA Aviation Safety Program (AvSP) has defined several products that will potentially modify airline and/or ATC operations, enhance aircraft systems, and improve the identification of potential hazardous situations within the National Airspace System (NAS). Consequently, there is a need to develop methods for evaluating the potential safety benefit of each of these intervention products so that resources can be effectively invested to produce the judgments to develop Bayesian Belief Networks (BBN's) that model the potential impact that specific interventions may have. Specifically, the present report summarizes methodologies for improving the elicitation of probability estimates during expert evaluations of AvSP products for use in BBN's. The work involved joint efforts between Professor James Luxhoj from Rutgers University and researchers at the University of Illinois. The Rutgers' project to develop BBN's received funding by NASA entitled "Probabilistic Decision Support for Evaluating Technology Insertion and Assessing Aviation Safety System Risk." The proposed project was funded separately but supported the existing Rutgers' program

Service Delivery Management: A Process for Proactively Ensuring Customer Satisfaction.

SDM is a process model, based on service marketing components, to position a service while concurrently being a service delivery management tool improving serviceen counter processes. Added to a service script, SDM can increase customer satisfaction, quality perception, voice levels and repurchase intentions in a general service encounter scenario as well as during episodes which include a failure. Addressing mishaps requires particular attention, since Consumer Complaint Behaviour research shows that most customers prefer to switch suppliers, rather than offer constructive feedback. This means that many service failures go unnoticed, with a large proportion of customers defecting. Since most services are performed in real time by service personnel, consistent quality output is a challenge. Therefore, occasional service failures are inevitable. Present research recognises this by offering suggestions, but does not present an integrated framework like SDM, using the presence of a customer during a service encounter as an unique opportunity to resolve issues on the spot. An elicitation process is used as a first step, attempting to improve voice and minimising lost feedback. Step two is a specific service recovery process, adapted to the failure type. SDM processes can also lead to a general increase of satisfaction and quality perception, regardless of whether or not there was a service failure. With satisfaction generally being regarded as an actual repurchase behaviour indicator, this may lead to increased sales turnover, while a higher quality perception may lead to a larger price premium tolerance and therefore higher profits. Higher service quality perceptions can also be used as a marketing positioning tool to differentiate a service from competitors. Data collected supported all hypotheses put forward in this thesis, showing statistically significant improvements on all key variables, including a satisfaction rating increase of 24percent when SDM was applied. In academic terms, the process model tested did not only link separate literature streams, but offered an integrated, proactive tool which is capable of operating in real time. Traditionally, academic models and their processes analyse results after an episode concludes, while SDM allows a provider to positively influence or manage satisfaction levels during the service delivery

Predicting the Market Share of a New Airport in Multi-Airport Cities: the Case of Lagos

The primary objective of the study was to develop an empirical model that combines the contingent valuation method (CVM) with the isochrone analysis to predict the market shares of new airports in multi-airport cities and to apply the model to the case of Lekki International Airport (LIA), the proposed second airport in Lagos, Nigeria. In addition to predicting the market share that LIA could attain, the study also identified and analyzed the catchment areas as well as the willingness to pay (WTP) of would-be LIA passengers. Furthermore, the research identified the determinants of airport choice in the Nigerian market. The CVM was used for the collection of the data; 1,176 valid in-person interviews were conducted at Murtala Mohammed International Airport (MMIA), Lagos. Descriptive statistics and logistic regression analysis were used to predict LIA’s market share and identify the factors that influenced passengers’ choice between the existing and the proposed second airport. Further, isochrones and passenger stated preference data were analyzed for the determination of the LIA’s catchment areas for the business and non-business segments of the Nigerian market as well as the areas of spatial competition between MMIA and LIA. With regard to the passengers’ willingness to pay, the median of the WTP values was determined through descriptive statistics. The determinants of the WTP were also identified using a multiple regression analysis. Using the combination of CVM and isochrone analysis, the present research predicted that LIA will attain 28.9% of the market share based on the contingent scenario presented to the passengers. Further, the study found that the exclusive catchment areas of LIA for business and non-business passengers were limited to two Local Government Areas (LGAs) of Lagos State. Passengers who chose LIA as their first choice were willing to pay NGN3000 (about $15 or 15% of an average domestic one-way ticket price) as additional fare to fly from the airport. However, the realization of the predicted market share will be contingent on LIA’s ability to attract airlines, remedy the isolation of the proposed airport site, and apply the appropriate pricing policy

Service Delivery Management: A Process for Proactively Ensuring Customer Satisfaction.

SDM is a process model, based on service marketing components, to position a service while concurrently being a service delivery management tool improving serviceen counter processes. Added to a service script, SDM can increase customer satisfaction, quality perception, voice levels and repurchase intentions in a general service encounter scenario as well as during episodes which include a failure. Addressing mishaps requires particular attention, since Consumer Complaint Behaviour research shows that most customers prefer to switch suppliers, rather than offer constructive feedback. This means that many service failures go unnoticed, with a large proportion of customers defecting. Since most services are performed in real time by service personnel, consistent quality output is a challenge. Therefore, occasional service failures are inevitable. Present research recognises this by offering suggestions, but does not present an integrated framework like SDM, using the presence of a customer during a service encounter as an unique opportunity to resolve issues on the spot. An elicitation process is used as a first step, attempting to improve voice and minimising lost feedback. Step two is a specific service recovery process, adapted to the failure type. SDM processes can also lead to a general increase of satisfaction and quality perception, regardless of whether or not there was a service failure. With satisfaction generally being regarded as an actual repurchase behaviour indicator, this may lead to increased sales turnover, while a higher quality perception may lead to a larger price premium tolerance and therefore higher profits. Higher service quality perceptions can also be used as a marketing positioning tool to differentiate a service from competitors. Data collected supported all hypotheses put forward in this thesis, showing statistically significant improvements on all key variables, including a satisfaction rating increase of 24percent when SDM was applied. In academic terms, the process model tested did not only link separate literature streams, but offered an integrated, proactive tool which is capable of operating in real time. Traditionally, academic models and their processes analyse results after an episode concludes, while SDM allows a provider to positively influence or manage satisfaction levels during the service delivery

How do full-service carriers and low-cost carriers passengers perceived service dimensions, passengers’ satisfaction, and loyalty differently? An empirical study

Purpose: In this study, group differences between full-service carriers (FSC) and low-cost carriers (LCC) in loyalty constructs are investigated, revealing the relationship between service quality and loyalty. This work focuses on five dimensions, including tangibility, empathy, assurance, responsiveness, and reliability, constitute service quality. Design/methodology/approach: 248 questionnaires were collected in the first half of 2019. The antecedents of customer loyalty are explored, and the group differences between FSC and LCC are analyzed. For assessing the path model with the consideration of group variance, the Partial Least Squares Multiple Group Analysis (PLS-MGA) was adopted to analyze the differences of the estimated inter-group coefficient. Findings: Our findings suggest that service assurance, service empathy, and service reliability positively impact the value perceived. The impact of service empathy on customer satisfaction in FSC is significantly diverse from LCC. Several suggestions are provided to FSC and LCC on improving their services in view of passengers’ wants and interests. Originality/value: With the data collected at the HKIA, this study examined the relationships among service quality, perceived value, customer satisfaction, and customer loyalty and divided service quality into five dimensions. The findings show that assurance, empathy, and reliability of service quality positively affect the value perceived, and the effects of responsiveness and tangibility of service quality on perceived value are insignificant. Among the five aspects of service quality, assurance, reliability, responsiveness, and tangibility of the service quality are the pre-conditions of customer satisfaction. However, only the reliability of service is the antecedent of customer loyalty. Besides, the value perceived positively affects customers to be satisfactory and loyal. Furthermore, satisfaction degree also significantly influences the degree of customers’ loyalty. As to the role of airline types, the sole effect is on customers' satisfaction is service empathy, with a significant difference between FSC and LCCPeer Reviewe

Identification of Causal Paths and Prediction of Runway Incursion Risk using Bayesian Belief Networks

In the U.S. and worldwide, runway incursions are widely acknowledged as a critical concern for aviation safety. However, despite widespread attempts to reduce the frequency of runway incursions, the rate at which these events occur in the U.S. has steadily risen over the past several years. Attempts to analyze runway incursion causation have been made, but these methods are often limited to investigations of discrete events and do not address the dynamic interactions that lead to breaches of runway safety. While the generally static nature of runway incursion research is understandable given that data are often sparsely available, the unmitigated rate at which runway incursions take place indicates a need for more comprehensive risk models that extend currently available research. This dissertation summarizes the existing literature, emphasizing the need for cross-domain methods of causation analysis applied to runway incursions in the U.S. and reviewing probabilistic methodologies for reasoning under uncertainty. A holistic modeling technique using Bayesian Belief Networks as a means of interpreting causation even in the presence of sparse data is outlined in three phases: causal factor identification, model development, and expert elicitation, with intended application at the systems or regulatory agency level. Further, the importance of investigating runway incursions probabilistically and incorporating information from human factors, technological, and organizational perspectives is supported. A method for structuring a Bayesian network using quantitative and qualitative event analysis in conjunction with structured expert probability estimation is outlined and results are presented for propagation of evidence through the model as well as for causal analysis. In this research, advances in the aggregation of runway incursion data are outlined, and a means of combining quantitative and qualitative information is developed. Building upon these data, a method for developing and validating a Bayesian network while maintaining operational transferability is also presented. Further, the body of knowledge is extended with respect to structured expert judgment, as operationalization is combined with elicitation of expert data to create a technique for gathering expert assessments of probability in a computationally compact manner while preserving mathematical accuracy in rank correlation and dependence structure. The model developed in this study is shown to produce accurate results within the U.S. aviation system, and to provide a dynamic, inferential platform for future evaluation of runway incursion causation. These results in part confirm what is known about runway incursion causation, but more importantly they shed more light on multifaceted causal interactions and do so in a modeling space that allows for causal inference and evaluation of changes to the system in a dynamic setting. Suggestions for future research are also discussed, most prominent of which is that this model allows for robust and flexible assessment of mitigation strategies within a holistic model of runway safety

Data-Informed Calibration and Aggregation of Expert Judgment in a Bayesian Framework

Historically, decision-makers have used expert opinion to supplement lack of data. Expert opinion, however, is applied with much caution. This is because judgment is subjective and contains estimation error with some degree of uncertainty. The purpose of this study is to quantify the uncertainty surrounding the unknown of interest, given an expert opinion, in order to reduce the error of the estimate. This task is carried out by data-informed calibration and aggregation of expert opinion in a Bayesian framework. Additionally, this study evaluates the impact of the number of experts on the accuracy of aggregated estimate. The objective is to determine the correlation between the number of experts and the accuracy of the combined estimate in order to recommend an expert panel size