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11 – 20 of 330Juita-Elena (Wie) Yusuf, Lenahan O’Connell, David Chapman, Meagan M. Jordan and Khairul Azfi Anuar
The purpose of this paper is to examine drivers’ willingness-to-pay (WTP) tolls using data from a survey of drivers in the Hampton Roads region of Southeastern Virginia. The…
Abstract
Purpose
The purpose of this paper is to examine drivers’ willingness-to-pay (WTP) tolls using data from a survey of drivers in the Hampton Roads region of Southeastern Virginia. The theory of planned behavior is applied to understand the different factors contributing to WTP tolls. The study measures different dimensions of WTP, offers a two-stage approach that aligns correlates of WTP tolls in logical sequence, and assesses the role of price information (toll rates) as an anchor heuristic in WTP.
Design/methodology/approach
Three WTP measures are elicited via contingent valuation method using three survey questions that incorporate different price information. The study tests the role of price information as an anchor heuristic. WTP is analyzed using a two-stage decision process. Drivers first decide whether, in-principle, to support tolls, followed by the amount they are willing to pay (maximum and peak amounts). Three regression models are run to test the impact of ability to pay on amount WTP, impact of in-principle WTP on maximum WTP, and impact of maximum WTP on peak WTP given an anchor toll rate.
Findings
Attitudes supportive of tolls and the ability to pay are predictors of in-principle WTP, while in-principle WTP predicts amount (maximum and peak) WTP. Price information, as an anchor heuristic, reduces variability in amount WTP and conditions the amounts WTP.
Originality/value
The value and originality of this study lie in the application of the theory of planned behavior to study WTP tolls, the use of contingent valuation, and the effect of anchor heuristics.
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Gautam Gulati, Brendan D. Kelly, Conor O’Neill, Paul O’Connell, Sally Linehan, Eimear Spain, David Meagher and Colum P. Dunne
The assessment and management of prisoners on hunger strikes in a custodial setting is complex. There is limited clinical guidance available for psychiatrists to draw upon in such…
Abstract
Purpose
The assessment and management of prisoners on hunger strikes in a custodial setting is complex. There is limited clinical guidance available for psychiatrists to draw upon in such cases. The purpose of this paper is to develop a management algorithm through expert elicitation to inform the psychiatric care of prisoners on a hunger strike.
Design/methodology/approach
A Delphi method was used to elicit views from Irish forensic psychiatrists, a legal expert and an expert in ethics using a structured questionnaire. Themes were extracted from the results of the questionnaire to propose a management algorithm. A consensus was reached on management considerations.
Findings
Five consultant forensic psychiatrists, a legal expert and an expert on psychiatric ethics (n=7) consented to participation, with a subsequent response rate of 71.4 per cent. Consensus was achieved on a proposed management algorithm. Assessment for mental disorder, capacity to refuse food and motivation for food refusal are seen as key psychiatric tasks. The need to work closely with the prison general practitioner and the value of multidisciplinary working and legal advice are described. Relevant aspects of law included mental health, criminal law (insanity) and capacity legislation.
Originality/value
This study outlines a management algorithm for the psychiatric assessment and management of prisoners on a hunger strike, a subject about which there is limited guidance to date. Although written from an Irish perspective, this study outlines key considerations for psychiatrists in keeping with international guidance and therefore may be generalisable to other jurisdictions.
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How do transnational social movements organize? Specifically, this paper asks how an organized community can lead a nationalist movement from outside the nation. Applying the…
Abstract
How do transnational social movements organize? Specifically, this paper asks how an organized community can lead a nationalist movement from outside the nation. Applying the analytic perspective of Strategic Action Fields, this study identifies multiple attributes of transnational organizing through which expatriate communities may go beyond extra-national supporting roles to actually create and direct a national campaign. Reexamining the rise and fall of the Fenian Brotherhood in the mid-nineteenth century, which attempted to organize a transnational revolutionary movement for Ireland’s independence from Great Britain, reveals the strengths and limitations of nationalist organizing through the construction of a Transnational Strategic Action Field (TSAF). Deterritorialized organizing allows challenger organizations to propagate an activist agenda and to dominate the nationalist discourse among co-nationals while raising new challenges concerning coordination, control, and relative position among multiple centers of action across national borders. Within the challenger field, “incumbent challengers” vie for dominance in agenda setting with other “challenger” challengers.
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Yakub Kayode Saheed, Usman Ahmad Baba and Mustafa Ayobami Raji
Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).Need for the study: With the advance of technology, the world is…
Abstract
Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).
Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.
Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.
Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.
Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.
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Monica Buchtmann, Russell Wise, Deborah O'Connell, Mark Crosweller and Jillian Edwards
There are many pragmatic challenges and complex interactions in the reduction of systemic disaster risk. No single agency has the mandate, authority, legitimacy or resources to…
Abstract
Purpose
There are many pragmatic challenges and complex interactions in the reduction of systemic disaster risk. No single agency has the mandate, authority, legitimacy or resources to fully address the deeper socio-economic, cultural, regulatory or political forces that often drive the creation and transfer of risk. National leadership and co-ordination are key enablers. This paper shares Australia's progress in building an enabling environment for systemic disaster risk reduction, and specifically how a change in thinking and resolve to work differently is beginning to shape nation-wide reforms and national programs of work.
Design/methodology/approach
The project and program of work adopted an inclusive, collaborative, co-design and co-production approach, working with diverse groups to create new knowledge, build trust, ongoing learning and collective ownership and action. Values- and systems-based approaches, and ethical leadership were core aspects of the approach.
Findings
Co-creating a more comprehensive and shared understanding of systemic disaster risk, particularly the values at risk and tensions and trade-offs associated with the choices about how people prevent or respond, has contributed to a growing shift in the way disasters are conceptualised. New narratives about disasters as “unnatural” and the need for shared responsibilities are shaping dialogue spaces and policy frameworks. The authors’ experience and ongoing learning acknowledge pragmatic challenges while also providing evidence-based ideas and guidance for more systems and transformative styles and competencies of leadership that are needed for convening in contested and complex environments.
Practical implications
This work built networks, competencies and generated ongoing momentum and learning. The lessons, evidence and reports from the work continue to be accessed and influential in research, emergency management and disaster mitigation practices (e.g. engagement, communications, training) and policy. Most significantly, the National Disaster Risk Reduction Framework provides the basis, justification and guidance for the nation's policy reform agenda around disaster risk reduction and is catalysing national efforts in developing a national action plan and systemic measurement, evaluation and learning to ensure the realisation of disaster risk reduction priorities.
Originality/value
A practical example is offered of a nation actively learning to navigate the governance challenges and implement strategies to address the reduction of complex, systemic risks.
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David O’Donnell, Thomas N. Garavan and Alma McCarthy
Neoclassical approaches continue to dominate evaluations of national skill‐formation systems. Argues for the benefits of including alternative interdisciplinary and theoretically…
Abstract
Neoclassical approaches continue to dominate evaluations of national skill‐formation systems. Argues for the benefits of including alternative interdisciplinary and theoretically grounded approaches in any evaluation of the Irish system as it relates to its economic system. This broader focus, it is argued, could lead to more informed policy formulation and implementation. Following the “societal effect” approach, argues here that vocational education and training systems can only be adequately understood with reference to the set of inter‐relationships between the education system, industrial training system, the organizational structure of industry, the industrial relations system and the class and status relations of the wider society as reflected in its political system.
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