The role of destination personality fit in destination branding: Antecedents and outcomes

Drawing from fit research in strategic management, this study develops and investigates a model predicting destination attitude and (re)visit intention. The study introduces the concept of destination personality fit on the basis of how well consumer perceptions of a tourist destination's brand personality fits that of what the destination brand manager wishes to convey. A model incorporating destination advertising awareness as an antecedent of destination personality and consumer‐manager destination personality fit is tested on international consumers with the destination personality of Switzerland as the study setting. Structural equation modeling results reveal that destination advertising awareness does indeed relate positively to both stronger perceived destination personality and destination personality fit in consumers’ minds. Interestingly, the subsequent destination personality–destination attitude relationship is moderated by consumer–manager destination personality fit in such a way that the link grows stronger in cases where fit is high. The results have important implications for destination brand managers in that they reinforce the importance of strong and distinct destination personalities. The findings also show the importance of actively communicating the destination brand to consumers since the positive outcomes of a strong destination personality increase in magnitude when successfully communicated, and the vision of the destination brand manager has been adopted by the consumer.     

基于VVA的多分辨率交通仿真建模过程评价框架研究

文章从多分辨率交通仿真的建模过程入手,研究了系统仿真的校核、验证与确认(Verification Validation Accreditation,VVA)过程评价的原理。在此基础上,提出了基于VVA的多分辨率交通仿真建模的过程评价框架。通过研究仿真系统生命周期中的一系列活动来评价研究结果的正确性和有效性,提高了系统的可信性,使仿真系统能够满足实际应用的需要。     

设施直线型自动布置的算法之二

基于从至表矩阵变换,给出了设施直线型自动布置一种算法,数值实验表明这种方法是十分有效的。     

Fraudster’s and victims’ profiles and loss predictors’ hierarchy in the mental healthcare industry in the US

Abstract

Background

Fraud- or theft-related crimes account for the highest number of crimes in the mental health industry in the US.     

Assessing the reversal of investor sentiment

Assessing the reversal of sentiment in stock markets is needed because, according to the social mood cycle, the change of social mood over time is an antecedent of price movements. The purpose of this study is to empirically assess reversal of investor sentiment, to show the phases of social mood cycle from increasing mood to decreasing mood, and to explain the dynamic change in market inefficiency from increasing to decreasing. Growth modeling, developed particularly for dealing with the change over time, is used in this study for assessing the reversal of investor sentiment. The autocovariance structure of errors and the variances/covariances of the random coefficients are all taken into account in the model. The results have indicated that the change in investor sentiment over time is inverted U-shaped for the entire market. Moreover, arbitrage constraint and stock characteristics exert a joint moderating effect on sentiment reversal. Less arbitrage constraint can strengthen sentiment reversal only when the market for individual stocks is dominated by noise traders. Based on the results obtained, we discuss asset pricing, liquidity management, and market intervention.     

Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting

The Global Energy Forecasting Competition 2017 (GEFCom2017) attracted more than 300 students and professionals from over 30 countries for solving hierarchical probabilistic load forecasting problems. Of the series of global energy forecasting competitions that have been held, GEFCom2017 is the most challenging one to date: the first one to have a qualifying match, the first one to use hierarchical data with more than two levels, the first one to allow the usage of external data sources, the first one to ask for real-time ex-ante forecasts, and the longest one. This paper introduces the qualifying and final matches of GEFCom2017, summarizes the top-ranked methods, publishes the data used in the competition, and presents several reflections on the competition series and a vision for future energy forecasting competitions.     

Small system dynamics model for alleviating the general practitioners rural care gap in Ontario,Canada

The disproportionate concentration of healthcare professionals in urban areas is a concern in many countries, including Canada. A need to address this rural care gap has driven a large number of government led initiatives worldwide over the years. This paper presents a model that can be used as a tool to examine the efficacy of such policies on the workforce distribution in the long term. A small system dynamics model is employed to simulate the current and future distribution of general physicians at a jurisdictional level. The model represents the transition of general practitioners to provide insight into the dynamics of care provision over time. The movement, and competition, between rural and urban areas is modeled to enable detailed exploration of the ability for proposed measures to alleviate the care gap in the future. Among the tested policies are such commonly used initiatives as financial incentives to rural professionals, promotion of medical education in rural areas, expansion of rural education programs and the engagement of international medical graduates etc. We demonstrate how the model can be used as a tool to determine an efficient and well-chosen combination of policies which can help alleviate the rural care gap in the future, given that some policies are more effective than others alone but also combined with other initiatives. The presented small system dynamics model is tested on Canada's reality, but its simple nature lends itself to easy application to other countries that experience a similar problem.     

The effect of controversial global sourcing practices on the ethical judgments and intentions of U.S. consumers

Global sourcing has led to lower cost and more effective supply chains for many companies. However, when the cost-driven practices of many suppliers in these chains come to light there is often considerable debate over the ethics of these practices. This research uses the well-known Hunt–Vitell framework as the theoretical foundation for a structural equation model of the deontological and teleological evaluations used by consumers when making ethical judgments of a firm's controversial cost-driven global sourcing practices. Data from a large-scale U.S. consumer survey show the importance of deontological and teleological evaluations in forming consumers’ ethical judgments of global sourcing practices, and establish a strong relationship between ethical judgment and the intention of consumers to alter consumption of a firm's products. Extensions to the framework and demographic analyses for age, gender, and income provide insights as to how perceptions of these practices affect consumer evaluations of a company involved in global sourcing and how consumers actualize their resultant intentions.     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.