The effects of consumer knowledge on responses to event sponsorships

Research into consumer responses to event sponsorships has grown in recent years. However, the effects of consumer knowledge on sponsorship response have received little consideration. Consumers' event knowledge is examined to determine whether experts and novices differ in information processing of sponsorships and whether a sponsor's brand equity influences perceptions of sponsor-event fit. Six sponsors (three high equity/three low equity) were paired with six events. Results of hypothesis testing indicate that experts generate more total thoughts about a sponsor-event combination. Experts and novices do not differ in sponsor-event congruence for high-brand-equity sponsors, but event experts perceive less of a match between sponsor and event for low-brand-equity sponsors. (C) 2004 Wiley Periodicals, Inc.

Attitudes to living and working in pandemic conditions among emergency prehospital medical care personnel

Introduction: Little is known about the risk perceptions and attitudes of healthcare personnel, especially of emergency prehospital medical care personnel, regarding the possibility of an outbreak or epidemic event. Problem: This study was designed to investigate pre-event knowledge and attitudes of a national sample of the emergency prehospital medical care providers in relation to a potential human influenza pandemic, and to determine predictors of these attitudes. Methods: Surveys were distributed to a random, cross-sectional sample of 20% of the Australian emergency prehospital medical care workforce (n = 2,929), stratified by the nine services operating in Australia, as well as by gender and location. The surveys included: (1) demographic information; (2) knowledge of influenza; and (3) attitudes and perceptions related to working during influenza pandemic conditions. Multiple logistic regression models were constructed to identify predictors of pandemic-related risk perceptions. Results: Among the 725 Australian emergency prehospital medical care personnel who responded, 89% were very anxious about working during pandemic conditions, and 85% perceived a high personal risk associated with working in such conditions. In general, respondents demonstrated poor knowledge in relation to avian influenza, influenza generally, and infection transmission methods. Less than 5% of respondents perceived that they had adequate education/training about avian influenza. Logistic regression analyses indicate that, in managing the attitudes and risk perceptions of emergency prehospital medical care staff, particular attention should be directed toward the paid, male workforce (as opposed to volunteers), and on personnel whose relationship partners do not work in the health industry. Conclusions: These results highlight the potentially crucial role of education and training in pandemic preparedness. Organizations that provide emergency prehospital medical care must address this apparent lack of knowledge regarding infection transmission, and procedures for protection and decontamination. Careful management of the perceptions of emergency prehospital medical care personnel during a pandemic is likely to be critical in achieving an effective response to a widespread outbreak of infectious disease.

Report from the Knowledge Exchange Event: Pathways to Open Scholarship

The report on the ‘Pathways to Open Scholarship’ conference, organised by Knowledge Exchange to celebrate its 10th anniversary, contains an overview of the views that invited speakers and experts in the audience expressed on Open Scholarship developments. Open Scholarship is not yet a reality of everyday research and education. The event reported on, addressed the concept of "open scholarship" by debating issues and key challenges with the aim to identify pathways forward. Four central themes which represent crucial dimensions of open scholarship have been explored: Benefits, risks and limitations of Open Scholarship; Success as a researcher; Technology; and Publishing and publication services. The report contains views, expectations, wishes and frustrations that experts have on open scholarship, as well as new and promising initiatives and approaches. The report is a starting point for future planning of KE activity and input for a range of consultation talks with respected experts and leaders in the research community in KE countries and beyond.

Utilising Intranet technologies in construction sector SMEs:building up knowledge bases for extreme weather event risk management

Intranet technologies accessible through a web based platform are used to share and build knowledge bases in many industries. Previous research suggests that intranets are capable of providing a useful means to share, collaborate and transact information within an organization. To compete and survive successfully, business organisations are required to effectively manage various risks affecting their businesses. In the construction industry too this is increasingly becoming an important element in business planning. The ability of businesses, especially of SMEs which represent a significant portion in most economies, to manage various risks is often hindered by fragmented knowledge across a large number of businesses. As a solution, this paper argues that Intranet technologies can be used as an effective means of building and sharing knowledge and building up effective knowledge bases for risk management in SMEs, by specifically considering the risks of extreme weather events. The paper discusses and evaluates relevant literature in this regard and identifies the potential for further research to explore this concept.

Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. © 2014 The Author 2014. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.

Building partnerships as a key strategy in developing an event management model for an international dance event : a case study of the 2008 WDA (World Dance Alliance) Global Summit, Brisbane, Australia

With the increasing growth of cultural events both in Australia and internationally, there has also been an increase in event management studies; in theory and in practice. Although a series of related knowledge and skills required specifically by event managers has already been identified by many researchers (Perry et al., 1996; Getz, 2002 & Silvers et al., 2006) and generic event management models proposed, including ‘project management’ strategies in an event context (Getz, 2007), knowledge gaps still exist in relation to identifying specific types of events, especially for not-for-profit arts events. For events of a largely voluntary nature, insufficient resources are recognised as the most challenging; including finance, human resources and infrastructure. Therefore, the concepts and principles which are adopted by large scale commercial events may not be suitable for not-for-profit arts events aiming at providing professional network opportunities for artists. Building partnerships are identified as a key strategy in developing an effective event management model for this type of event. Using the 2008 World Dance Alliance Global Summit (WDAGS) in Brisbane 13-18 July, as a case study, the level, nature and relationship of key partners are investigated. Data is triangulated from interviews with organisers of the 2008 WDAGS, on-line and email surveys of delegates, participant observation and analysis of formal and informal documents, to produce a management model suited to this kind of event.

Improving service innovation through structured process-oriented knowledge infrastructure design

Formalised service innovation is a central tenet of enterprise systems lifecycle phases. Event driven process models extended with knowledge objects are found to be not useful in early lifecycle phases. When an upgrade is required, a map of the knowledge infrastructure is needed to better design further service innovation because functional maps no longer adequately describe the context adequately. By looking at formal changes to business processes as service innovations, and recognising the knowledge infrastructure inherent in services generally, changes driven through technology such as ES can be better understood with the application of frameworks such as B-KIDE.

Knowledge-Intensive Business Processes: Proceedings of the 1st International Workshop on Knowledge-intensive Business Processes [CEUR Workshop Proceedings, Volume 861]

Nowadays, Workflow Management Systems (WfMSs) and, more generally, Process Management Systems (PMPs) are process-aware Information Systems (PAISs), are widely used to support many human organizational activities, ranging from well-understood, relatively stable and structures processes (supply chain management, postal delivery tracking, etc.) to processes that are more complicated, less structured and may exhibit a high degree of variation (health-care, emergency management, etc.). Every aspect of a business process involves a certain amount of knowledge which may be complex depending on the domain of interest. The adequate representation of this knowledge is determined by the modeling language used. Some processes behave in a way that is well understood, predictable and repeatable: the tasks are clearly delineated and the control flow is straightforward. Recent discussions, however, illustrate the increasing demand for solutions for knowledge-intensive processes, where these characteristics are less applicable. The actors involved in the conduct of a knowledge-intensive process have to deal with a high degree of uncertainty. Tasks may be hard to perform and the order in which they need to be performed may be highly variable. Modeling knowledge-intensive processes can be complex as it may be hard to capture at design-time what knowledge is available at run-time. In realistic environments, for example, actors lack important knowledge at execution time or this knowledge can become obsolete as the process progresses. Even if each actor (at some point) has perfect knowledge of the world, it may not be certain of its beliefs at later points in time, since tasks by other actors may change the world without those changes being perceived. Typically, a knowledge-intensive process cannot be adequately modeled by classical, state of the art process/workflow modeling approaches. In some respect there is a lack of maturity when it comes to capturing the semantic aspects involved, both in terms of reasoning about them. The main focus of the 1st International Workshop on Knowledge-intensive Business processes (KiBP 2012) was investigating how techniques from different fields, such as Artificial Intelligence (AI), Knowledge Representation (KR), Business Process Management (BPM), Service Oriented Computing (SOC), etc., can be combined with the aim of improving the modeling and the enactment phases of a knowledge-intensive process. The 1st International Workshop on Knowledge-intensive Business process (KiBP 2012) was held as part of the program of the 2012 Knowledge Representation & Reasoning International Conference (KR 2012) in Rome, Italy, in June 2012. The workshop was hosted by the Dipartimento di Ingegneria Informatica, Automatica e Gestionale Antonio Ruberti of Sapienza Universita di Roma, with financial support of the University, through grant 2010-C26A107CN9 TESTMED, and the EU Commission through the projects FP7-25888 Greener Buildings and FP7-257899 Smart Vortex. This volume contains the 5 papers accepted and presented at the workshop. Each paper was reviewed by three members of the internationally renowned Program Committee. In addition, a further paper was invted for inclusion in the workshop proceedings and for presentation at the workshop. There were two keynote talks, one by Marlon Dumas (Institute of Computer Science, University of Tartu, Estonia) on "Integrated Data and Process Management: Finally?" and the other by Yves Lesperance (Department of Computer Science and Engineering, York University, Canada) on "A Logic-Based Approach to Business Processes Customization" completed the scientific program. We would like to thank all the Program Committee members for the valuable work in selecting the papers, Andrea Marrella for his valuable work as publication and publicity chair of the workshop, and Carola Aiello and the consulting agency Consulta Umbria for the organization of this successful event.

Falls after discharge from hospital : is there a gap between older peoples' knowledge about falls prevention strategies and the research evidence?

Purpose: The aim of this study was to examine whether older people are prepared to engage in appropriate falls prevention strategies after discharge from hospital. Design and Methods We used a semi-structured interview to survey older patients about to be discharged from hospital and examined their knowledge regarding falls prevention strategies to utilize in the post-discharge period. The study was part of a prospective cohort study, nested within a larger, randomized controlled trial. Participants (n = 333) were asked to suggest strategies to reduce their falls risk at home after discharge, and their responses were compared with current reported research evidence for falls prevention interventions.  Results Participants’ strategies (n = 629) were classified into 7 categories: behavioral, support while mobilizing, approach to movement, physical environment, visual, medical, and activities or exercise. Although exercise has been identified as an effective falls risk reduction strategy, only 2.9% of participants suggested engaging in exercises. Falls prevention was most often conceptualized by participants as requiring 1 (35.4%) or 2 (40.8%) strategies for avoiding an accidental event, rather than engaging in sustained multiple risk reduction behaviors.  Implications Results demonstrate that older patients have low levels of knowledge about appropriate falls prevention strategies that could be used after discharge in spite of their increased falls risk during this period. Findings suggest that health care workers should design and deliver falls prevention education programs specifically targeted to older people who are to be discharged from hospital.

A Comprehensive Benchmarking Framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM

Process mining encompasses the research area which is concerned with knowledge discovery from information system event logs. Within the process mining research area, two prominent tasks can be discerned. First of all, process discovery deals with the automatic construction of a process model out of an event log. Secondly, conformance checking focuses on the assessment of the quality of a discovered or designed process model in respect to the actual behavior as captured in event logs. Hereto, multiple techniques and metrics have been developed and described in the literature. However, the process mining domain still lacks a comprehensive framework for assessing the goodness of a process model from a quantitative perspective. In this study, we describe the architecture of an extensible framework within ProM, allowing for the consistent, comparative and repeatable calculation of conformance metrics. For the development and assessment of both process discovery as well as conformance techniques, such a framework is considered greatly valuable.

How activation, entanglement, and searching a semantic network contribute to event memory

Free association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist cuing, primed free association, intralist cuing, and single-item recognition tasks. The findings also show that when a related word is presented to cue the recall of a studied word, the cue activates it in an array of related words that distract and reduce the probability of its selection. The activation of the semantic network produces priming benefits during encoding and search costs during retrieval. In extralist cuing recall is a negative function of cue-to-distracter strength and a positive function of neighborhood density, cue-to-target strength, and target-to cue strength. We show how four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks indicating that the contribution of the semantic network varies with the context provided by the task. We evaluate spreading activation and quantum-like entanglement explanations for the priming effect produced by neighborhood density.

Profiling event logs to configure risk indicators for process delays

Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators(PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company.

An extensible framework for analysing resource behaviour using event logs

Business processes depend on human resources and managers must regularly evaluate the performance of their employees based on a number of measures, some of which are subjective in nature. As modern organisations use information systems to automate their business processes and record information about processes’ executions in event logs, it now becomes possible to get objective information about resource behaviours by analysing data recorded in event logs. We present an extensible framework for extracting knowledge from event logs about the behaviour of a human resource and for analysing the dynamics of this behaviour over time. The framework is fully automated and implements a predefined set of behavioural indicators for human resources. It also provides a means for organisations to define their own behavioural indicators, using the conventional Structured Query Language, and a means to analyse the dynamics of these indicators. The framework's applicability is demonstrated using an event log from a German bank.