Framing the usefulness of eHRM in talent management: A case study of talent identification in a professional services firm

As talent management evolves from intuitive to evidence‐based decision‐making, the role of electronic Human Resource Management (eHRM) to gather, distribute, and analyze data becomes more critical. However, surprisingly few academic studies investigate the role of technology in talent management. Drawing on a qualitative case study of talent management in a large professional services firm, this paper critically examines how eHRM information technologies are framed as useful within talent identification discourses. The findings reveal two distinct but interrelated sets of processes employed to identify talent and suggest that the perceived usefulness and centrality of eHRM are influenced by how stakeholders shape their understanding of effective talent management. Copyright © 2016 ASAC. Published by John Wiley & Sons, Ltd.     

The effects of perceived service quality on repurchase intentions and subjective well-being of Chinese tourists: The mediating role of relationship quality

The current study provides and tests an integrated model that examines two relationship quality constructs (overall customer satisfaction, customer-company identification) as mediating variables between Chinese tourists' lodging service quality perceptions and two outcomes (repurchase intentions, subjective well-being). The results of a study with domestic Chinese hotel guests (n = 451) provide support for the proposed model. Specifically, the results indicate that overall customer satisfaction fully mediates the relationship between perceived service quality and repurchase intentions and subjective well-being, respectively. Customer-company identification partially mediates the relationship between perceived service quality and repurchase intentions and subjective wellbeing, respectively. We provide empirical validation that customers do, indeed, identify with hospitality providers, and this, in-turn, provides positive consequences for both the service provider (i.e., repurchase intentions) and the customer (i.e., subjective well-being). Managerial implications are provided, limitations noted, and future research directions suggested.     

Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism

We evaluate the short term forecasting performance of methods that systematically incorporate high frequency information via covariates. Our study provides a thorough introduction of these methods to the tourism literature. We highlight the distinguishing features and limitations of each tool and evaluate their forecasting performance in two tourism-specific applications. The first uses monthly indicators to predict quarterly tourist arrivals to Hawaii; the second predicts quarterly labor income in the accommodations and food services sector. Our results indicate that compared to the exclusive use of low frequency aggregates, including timely intra-period data in the forecasting process results in significant gains in predictive accuracy. Anticipating growing popularity of these techniques among empirical analysts, we present practical implementation guidelines to facilitate their adoption.     

汽车保险精算定价模型研究综述

汽车保险定价模型在非寿险精算领域内占有重要地位,本文对车险定价模型一百多年来的研究进展作了综述性的回顾。首先,本文介绍了车险定价模型的先验估费方法;其次着重介绍了时齐的后验估费方法,以及时变的先验后验相结合的精算模型;最后提出了车险定价模型的未来发展方向。     

大型交叉安全风险识别与评估

大型交叉施工具有建筑结构复杂、设备集中、人员密度大、环境密闭等特点,一旦结构发生安全事故,后果严重,而风险识别和评估可以结合施工的实际情况,对其中的各种危险源进行分类,并进行定量与定性评价。文章从风险特点及类型、风险分析依据及步骤、风险评估流程,最后得出风险识别与评估结论:工程项目风险识别与评估应是一个经常性反复循环的活动,是一个动态的过程。     

高压变频器在电厂凝结水泵上的应用

文章论述了高压变频器在燃煤发电厂大容量电动机上应用的必要性,介绍了凝结水泵的变频改造方案,分析了高压变频器改造的经济性,为同类型发电厂的节能降耗提供了借鉴经验和参考。     

广播供电系统内高压柜双电源自备投装置的故障处理和分析

随着广播电视工程的扩建升级,高科技广播电视导航设备得到了普及应用。供电系统在供应设备电能使用方面的作用越来越显著。现代广播电视供电系统要求开关柜保持良好的功能状态,设置自检测或自报警功能模块,提前发出系统感应到的异常信号。     

提升算法对传统车险索赔频率建模模型的改进——基于我国五省交强险保单数据

广义线性模型作为非寿险定价的经典模型,在非寿险定价中得到了广泛的应用。近年来,以提升算法为代表的机器学习算法在保险领域取得了很好的效果,为保险产品定价提供了一种新的选择。本文将提升算法思想分别融入到回归树模型和广义线性模型(GLM)中去,用得到的新模型对我国车险索赔频率进行预测建模分析,并与传统的回归树模型和GLM进行比较。结果表明,加入提升算法后传统车险索赔频率建模模型的效果得到了很大的改善,并且在不存在过拟合的前提下,随着模型深度和迭代次数的增加,模型的效果也在不断优化。