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Oil market VAR models have become the standard tool for understanding the evolution of the real price of oil and its impact on the macro economy. As this literature has expanded at a rapid pace, it has become increasingly difficult for mainstream economists to understand the differences between alternative oil market models, let alone the basis for the sometimes divergent conclusions reached in the literature. The purpose of this survey is to provide a guide to this literature. Our focus is on the econometric foundations of the analysis of oil market models with special attention to the identifying assumptions and methods of inference.

This paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned successful innovator and leader regarding the challenges of bringing technological discoveries to fruition. This paper aims to discuss these issues.

The interviewee is Dr Robin R. Murphy, Raytheon Professor of Computer Science and Engineering, Texas A&M University; Co-lead, Emergency Informatics EDGE Innovation Network Center, Texas A&M, Director of the Humanitarian Robotics and AI Laboratory and Vice President of the Center for Robot-Assisted Search and Rescue (CRASAR) http://crasar.org. In this interview, Dr Murphy provides answers to questions regarding her pioneering experiences in rescue robotics.

As a child, Dr Murphy knew she wanted to be a mechanical engineer and obtained her BME degree from Georgia Institute of Technology (Georgia Tech). While working in industry after her BME, she fell in love with computer science and received an MS and PhD in Computer Science at Georgia Tech where she was a Rockwell International Doctoral Fellow. In the mid-1990s, while teaching at the Colorado School of Mines, she pioneered rescue robots after one of her graduate students returned from the Oklahoma City bombing and suggested that small rescue robots should be developed for future disasters. The National Science Foundation awarded Murphy and her students the first grant for search-and-rescue robots. She has since assisted in responses at more than 20 worldwide disasters, including Hurricane Katrina, the Crandall Canyon Mine collapse, the Tohoku Tsunami and the Fukushima Daiichi nuclear accident.

The response to the World Trade Center attacks after September 11, 2001 by Dr Murphy’s team from the University of South Florida (the only academic institution), along with four other teams brought together by CRASAR, marked the first recorded use of a rescue robot at a disaster site. In addition to being a founder in the field of rescue robots, she is also a founder in the field of human–robot interaction and the Roboticists Without Borders. She has written over 100 publications and three books: the best-selling textbook, Introduction to AI Robotics, Disaster Robotics and Robotics-Through-Science-Fiction: Artificial Intelligence Explained Six Classic Robot Short Stories. Dr Murphy has received approximately 20 national awards and honors including: the AUVSI’s Al Aube Outstanding Contributor Award, the Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics, CMU Field Robotics Institute “Pioneer in Field Robotics” and TIME Magazine, Innovators in Artificial Intelligence. She is an IEEE Fellow.

This chapter investigates the history, antecedents and drivers for the latest Fire and Rescue National Framework for England, published in 2018. It reviews the previous five national frameworks published since the first was introduced in 2004 and evaluates them against the model outline in Chapter 2. The authors suggest that that political expediency and speed of delivery have played a greater role in their development than improving services, increasing public safety and providing assurance to the public. It therefore highlights some key areas for improvement in both the national framework and in its implementation.

Ranked preference data arise when a set of judges rank, in order of their preference, a set of objects. Such data arise in preferential voting systems and market research surveys. Covariate data associated with the judges are also often recorded. Such covariate data should be used in conjunction with preference data when drawing inferences about judges.

To cluster a population of judges, the population is modeled as a collection of homogeneous groups. The Plackett-Luce model for ranked data is employed to model a judge's ranked preferences within a group. A mixture of Plackett- Luce models is employed to model the population of judges, where each component in the mixture represents a group of judges.

Mixture of experts models provide a framework in which covariates are included in mixture models. Covariates are included through the mixing proportions and the component density parameters. A mixture of experts model for ranked preference data is developed by combining a mixture of experts model and a mixture of Plackett-Luce models. Particular attention is given to the manner in which covariates enter the model. The mixing proportions and group specific parameters are potentially dependent on covariates. Model selection procedures are employed to choose optimal models.

Model parameters are estimated via the ‘EMM algorithm’, a hybrid of the expectation–maximization and the minorization–maximization algorithms. Examples are provided through a menu survey and through Irish election data. Results indicate mixture modeling using covariates is insightful when examining a population of judges who express preferences.

In this article, we provide evidence that even when Murphy's Law is objectively untrue, because of sampling bias, people perceive the law as true, and this perceptual bias has far-reaching implications in management accounting research. A corollary to Murphy's Law is: “The other lane always moves faster than my lane.” A manager who is aware of this perceptual bias will try to structure her budget cutbacks and all other “negative compensations” in such a way that her employees perceive that the cutback applies to everyone, not just to themselves.

The findings of our study support the wisdom that, whenever managers must implement managerial plans that will be perceived as “negative,” the plans should be implemented all at once. Spreading the implementation over a period of time produces more discontent on the part of the personnel affected. The findings lend credence to a generalization that peoples’ discontent is minimized when the number of observations (and thus the number of chances for forming a negative perception) of undesirable events is minimized.

Industry publications abound with tips on how to create and nurture customer evangelism. Scholarly publications note the effects of evangelism to firms. Consultants promote evangelism creation as part of their skill set. Yet the existence customer evangelism and its effects remain unsupported by empirical evidence. The purpose of this paper is to quantitatively explore customer evangelism.

This paper takes one of the first steps towards empirical analysis of customer evangelism by using a formative composite latent variable model to identify customer evangelists from a survey population. The authors then compare customer evangelists against non-customer evangelists on key characteristics, as per the claims in the qualitative literature, to verify the accuracy of the selection model.

The analysis demonstrates that key claims in the qualitative literature in regard to customer evangelists are supported by quantitative data in this study, namely that customer evangelists are focused on authenticity, cultishness and sharing knowledge, and have a deep emotional and spiritual connection to the brand. They also have higher intentions to purchase the product in future than do non-customer evangelists. However, other claims in the qualitative literature – such as that customer evangelists are more socially oriented, knowledge-seeking, experientially oriented or idealistic than are non-customer evangelists – are not supported by the data in this study, or are inconclusive.

This study is one of the first to attempt to empirically identify customer evangelists, and is part of a movement to study consumer religiosity in an empirical context. This study paves the way for further empirical research into customer evangelism, consumer religiosity and consumer collectivism.

Patients with autism spectrum disorder (ASD) present with specific assessment, specific difficulties, needs and therapeutic issues and therefore are a challenging group for forensic services. Given the challenge that individuals with ASD present to forensic services, the suggested increase in the number of this group within this setting and the relatively little amount of research which suggests they face a number of difficulties within the prison environment, the purpose of this paper is to identify and review all the studies which have been carried out investigating any aspect of ASD in relation to secure hospital settings.

Seven internet-based bibliographic databases were used for the present review. The review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines.

A total of 12 studies were included in this review; 3 looked at the prevalence of ASD in secure psychiatric hospitals. One study evaluated the clinical utility of the AQ screening tool to assess self-reported autistic traits in secure psychiatric settings. Three explored any type of characteristics of patients with ASD detained in secure psychiatric hospitals. One study investigated the experiences or quality of life of patients with an ASD detained in secure psychiatric care. Two studies investigated awareness, knowledge and/or views regarding patients with ASD held by staff working within secure psychiatric hospitals. Lastly, three studies (one of which was also included in the prevalence category above) looked at the effectiveness of interventions or treatment of patients with ASD in secure psychiatric hospitals. Clinical recommendations and future research directions are discussed.

To the author’s knowledge, this is the first review to explore what research has been carried out looking specifically at patients with ASD in relation to secure forensic settings.

The study reported on in this article examines how instructional leadership is exercised by superintendents in effective school districts. We employ concepts drawn from school effectiveness studies and from organizational literature on coordination and control in an attempt to understand how superintendents organize and manage instruction and curriculum in these effective districts. Specific instructional management practices are examined within a framework of six major functions, setting goals and establishing expectations and standards, selecting staff, supervising and evaluating staff, establishing an instructional and curricular focus, ensuring consistency in technical core operations, and monitoring curriculum and instruction. Based on interviews with superintendents from 12 of the most instructionally effective school districts in California and analysis of selected district documents, we present descriptions of district‐level policies and practices that these superintendents use to coordinate and control the instructional management activities of their principals. Similarities and differences in the patterns of control and coordination found in these districts are highlighted. The implications of the findings are then examined in light of recent findings regarding coupling and linkages in schools. The results of this study suggest that superintendents in instructionally effective school districts are more active “instructional managers” than previous descriptions of superintendents would have led us to expect. In particular, coordination and control of the technical core appears more systematic in these districts. The results do not, however, provide a uniform picture of how instruction is coordinated and controlled. A wide range of both culture building activities and bureaucratic policies and practices were emphasized by the superintendents in this study as they exercised their instructional leadership roles.

In this study the authors set out to investigate the nature of administrative control in school districts in general and the control processes and activities employed in instructionally effective school districts in particular. Nine control functions are identified which are assumed to affect student outcomes by influencing the culture and technology (curriculum and instruction) of schools. Data were collected from interviews of superintendents in 12 effective school districts in California. The findings revealed inter alia more district‐level control of principal behavior and site activity than anticipated; control functions that were pervasive and connected; a wide range of control mechanisms; and the key role of the superintendent in connecting schools and district offices.

This study examines the effect of delegation of decision rights, moral justification (MJ), and ethical climate (EC) on managers’ misreporting in the financial services sector. We employed an online research panel called Qualtrics, to collect data based on a sample of 127 middle-level managers from various US financial services firms. We find that MJ mediates the relation between delegation and misreporting, suggesting delegation of decision rights increases employees’ misreporting indirectly by increasing MJ. We also find that EC significantly moderates the relationship between MJ and misreporting. Furthermore, our test of the moderated-mediation effect reveals that the indirect effect of the delegation of decision rights on misreporting through MJ is stronger when there is a higher level of instrumental climate (IC) and a lower level of principle climate (PC).