Categorization of disaster decision support needs for the development of an integrated model for DMDSS

The wide variety of disasters and the large number of activities involved have resulted in the demand for separate Decision Support System (DSS) models to manage different requirements. The modular approach to model management is to provide a framework in which to focus multidisciplinary research and model integration. A broader view of our approach is to provide the flexibility to organize and adapt a tailored DSS model (or existing modular subroutines) according to the dynamic needs of a disaster. For this purpose, the existing modular subroutines of DSS models are selected and integrated to produce a dynamic integrated model focussed on a given disaster scenario. In order to facilitate the effective integration of these subroutines, it is necessary to select the appropriate modular subroutine beforehand. Therefore, subroutine selection is an important preliminary step towards model integration in developing Disaster Management Decision Support Systems (DMDSS). The ability to identify a modular subroutine for a problem is an important feature before performing model integration. Generally, decision support needs are combined, and encapsulate different requirements of decision-making in the disaster management area. Categorization of decision support needs can provide the basis for such model selection to facilitate effective and efficient decision-making in disaster management. Therefore, our focus in this paper is on developing a methodology to help identify subroutines from existing DSS models developed for disaster management on the basis of needs categorization. The problem of the formulation and execution of such modular subroutines are not addressed here. Since the focus is on the selection of the modular subroutines from the existing DMDSS models on basis of a proposed needs classification scheme.

Discovering implicit criteria preferences reflected in tourism journal ranking lists using the Choquet Integral

Variations between journal rankings may cause confusion. As such, prior attempts were made to compare and evaluate journal ranking criteria for obtaining insightful knowledge on how different research communities have ranked journals. However, existing approaches are unable to model the journal ranking process closely enough as they are incapable of considering the relationship between multiple criteria simultaneously. In this paper, we address the challenges by introducing the Choquet Integral (CI) for evaluating journal ranking criteria. The new approach is able to account for interactions between criteria in relation to overall ranking score, using a fuzzy measure in its computation. Its properties, the Shapley value and the Interaction index, allow for good representations of importance and interactions between criteria. We demonstrate the efficiency of the CI through a case study of journal ranking lists in tourism and service journals.

Children's implicit recall of junk food, alcohol and gambling sponsorship in Australian sport

BACKGROUND: In Australia, sport is saturated by the promotion of junk food, alcohol and gambling products. This is particularly evident on player jerseys. The effect of this advertising on children, who are exposed to these messages while watching sport, has not been thoroughly investigated. The aim of this research study was to investigate: (1) the extent to which children implicitly recalled shirt sponsors with the correct sporting team; (2) whether children associated some types of sponsors with certain sporting codes more than others; and (3) whether age of the children influenced the correct recall of sponsoring brands and teams. METHOD: This experimental study conducted in New South Wales, Australia used projective techniques to measure the implicit recall of team sponsorship relationships of 85 children aged 5-12 years. Participants were asked to arrange two sets of magnets - one which contained sporting teams and one which contained brand logos - in the manner deemed most appropriate by them. Children were not given any prompts relating to sporting sponsorship relationships. RESULTS: Three quarters (77 %) of the children were able to identify at least one correct shirt sponsor. Children associated alcohol and gambling brands more highly with the more popular sporting code, the National Rugby League compared to the Australian Football League sporting code. Results showed that age had an effect on number of shirt sponsors correctly recalled with 9-12 year olds being significantly more likely than 5-8 year olds to correctly identify team sponsors. CONCLUSIONS: Given children's ability to implicitly recall shirt sponsors in a sporting context, Australian sporting codes should examine their current sponsorship relationships to reduce the number of unhealthy commodity shirt sponsors. While there is some regulation that protects children from the marketing of unhealthy commodity products, these findings suggest that children are still exposed to and recall these sponsorship relationships. Results suggest that the promotion of unhealthy commodity products during sporting matches is contributing to increased awareness amongst children of unhealthy commodity brands. Further investigation is required to examine the extent and impact of marketing initiatives during televised sporting matches on children.

Towards a conceptual framework on the categorization of stereotypical perceptions in accounting

The stereotypical image of the profession is poor with accountants appearing in the popular media as either the object of satire or the criminally inclined expert who deceives the public for self-gain. Extant research on the portrayal of the stereotypic accountant is limited in two ways: (1) existing research assumes a unitary concept by inferring a dominant image when the accountant stereotype is multifaceted; and (2) it is unclear from existing research whether the dominant image results from perceived character traits or the duties undertaken by accountants. This paper relies on qualitative methods of data analysis to unpack the elements that underpin stereotypical images in accounting to develop a framework of external perceptions that distinguishes one image from another. The framework is constructed on two broad criteria that comprise accountants (personality traits and physical characteristics) and accounting (task functionality). The interplay of these two criteria creates four subtypes representing positive (Scorekeeper and Guardian) and negative (Beancounter and Entrepreneur) interpretations of the two basic categorizations: bookkeeper and business professional. Further analysis revealed four primary dimensions (Ethics and Sociable, Skill and Service) that underlie the construction of the subtypes. In general, the 'Scorekeeper' rates more highly than the 'Beancounter' on 'Ethics and Sociable' and the 'Guardian' rates more highly than the 'Entrepreneur' on 'Ethics'. Accounting researchers and the profession could benefit from understanding how stereotypical perceptions are constructed and managed.

A graph-based model for context-aware recommendation using implicit feedback data

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.

Implicit and explicit motor learning: Application to children with Autism Spectrum Disorder (ASD)

Aims and objectives: This study aims to determine whether children with Autism Spectrum Disorder (ASD) are capable of learning a motor skill both implicitly and explicitly. Methods: In the present study, 30 boys with ASD, aged 7-11 with IQ average of 81.2, were compared with 32 typical IQ- and age-matched boys on their performance on a serial reaction time task (SRTT). Children were grouped by ASD and typical children and by implicit and explicit learning groups for the SRTT. Results: Implicit motor learning occurred in both children with ASD (p = .02) and typical children (p = .01). There were no significant differences between groups (p = .39). However, explicit motor learning was only observed in typical children (p = .01) not children with ASD (p = .40). There was a significant difference between groups for explicit learning (p = .01). Discussion: The results of our study showed that implicit motor learning is not affected in children with ASD. Implications for implicit and explicit learning are applied to the CO-OP approach of motor learning with children with ASD.