商业银行行长胜任力模型研究

商业银行行长对于商业银行的发展和成长至关重要,采用什么标准来选拔和培育银行行长成为银行利益相关者关注的焦点.胜任力模型的研发为此提供了一个全新的视角.本文应用行为事件访谈法(BEI),并通过对不同绩效行长胜任力特征的差异比较,构建了商业银行行长胜任力模型.行长胜任力模型包括9项超越性胜任力特征和13项基准性胜任力特征.最后,本文就如何将行长胜任力模型运用于商业银行行长选拔、培训、绩效考核,后备行长选拔以及行长职业生涯规划等人力资源模块进行了深入探讨.     

商业银行行长胜任力模型研究

商业银行行长对于商业银行的发展和成长至关重要，采用什么标准来选拔和培育银行行长成为银行利益相关者关注的焦点。胜任力模型的研发为此提供了一个全新的视角。本文应用行为事件访谈法（BEI），并通过对不同绩效行长胜任力特征的差异比较，构建了商业银行行长胜任力模型。行长胜任力模型包括9项超越性胜任力特征和13项基准性胜任力特征。最后，本文就如何将行长胜任力模型运用于商业银行行长选拔、培训、绩效考核，后备行长选拔以及行长职业生涯规划等人力资源模块进行了深入探讨。     

城市商业银行经营管理层胜任力与经营绩效的关系

本文构建了中国城市商业银行经营战略、企业文化与经营管理层胜任力与经营绩效的结构模型,运用探索性因素分析法和验证性因素分析法进行实证研究.结果表明,城市商业银行经营管理层胜任力模型包括内在素质、管理技能、关系管理三维结构;在中国城市商业银行,经营管理层管理技能胜任力对经营战略与财务绩效间的关系起到完全中介作用,经营管理层...     

商业银行行长胜任力模型应用探索——基于中国工商银行广东省分行的实践与启示

胜任力模型是行之有效的管理工具。本文立足于商业银行运营实践，选取分支行行长这一重要岗位构建起了“商业银行行长胜任力模型”，并以此为基础研发了“行长胜任力测评系统”。基于在中国工商银行广东省分行的成功运用，本文就如何构筑基于胜任力的人力资源管理体系进行了探讨。     

新形势下我国企业人力资源绩效管理体系构建——基于胜任力模型视角

胜任力与绩效管理的关系模型近年来成我国企业人力资源绩效管理研究的热点,胜任力模型为企业绩效管理体系的研究打开了一个新的突破口。本文基于胜任力的内涵、特征和核心胜任力的形成入手,分析了胜任力与人力资源绩效的关系。同时,本文提出了模型绩效结构性要求和方法。研究认为,胜任力对企业人力资源绩效的提升的,要加强模型构建和体系构建,以提高人力资源绩效管理水平。     

基于岗位胜任力的中层经理绩效考评体系探索——以G公司为例

在梳理相关文献的基础上,对G公司100多名的中层经理为对象进行实证研究,结果表明:综合胜任力指标、财务及非财务绩效的绩效考核,不断有效发现中层经理的短板,还能有针对性提升中层经理的胜任力。因此,G公司可根据研究成果形成以胜任力为基础的绩效考评体系,对激发中层经理的关键胜任力起到一定的作用。     

浅谈员工胜任素质模型的构建及应用

胜任素质又称能力素质,在组织管理中是指驱动员工做出卓越绩效的一系列综合素质,是员工通过不同方式表现出来的知识、技能或能力、职业素养、自我认知、特质等素质的集合.胜任素质模型被应用到人力资源管理的各个方面,对提高企业的人力资源质量,提升组织竞争力,从而推进企业发展战略目标的实现.     

国内商业银行中层管理人员胜任力模型研究

本文基于企业发展战略提出了建立胜任力模型的理论框架，探索构建了国内商业银行中层管理人员胜任力模型，设计了生理要素、心理要素和能力要素等模型要素。其中，生理要素包括身体健康等3个二级指标，心理要素包括诚信等10个二级指标，能力要素包括风险管理能力等17个二级指标。通过运用结构方程进行分析和检验，结果表明：结合企业发展战略...     

基于岗位胜任能力的企业KPI绩效管理体系研究

经济全球化已经是一种发展趋势,企业要发展,就必须采取精细化分工协作的管理模式,而基于岗位胜任能力的企业K PI绩效管理正是这一模式的核心,是将企业个人绩效与公司的任务与目标相结合的有效工具。本文从构建岗位胜任能力模型和K PI体系两方面入手,对企业绩效管理进行科学分析。     

服务地方经济 创建精品银行—中国工商银行山东省聊城分行改革发展创佳绩

2008年度中国工商银行二级分行行长经营绩效考评第6名;山东省分行二级分行行长经营绩效考核第3名;连续5年进入全国工行二级分行经营30强,其中2008年位列第8位……在山东省,聊城地区的经济并不算发达。中国工商银行聊城分行主动融入地方经济发展的大环境,通过创新发展思路、增强营销力度、提高服务能力、强化风险管理、培育企业文化等一系列举措,在服务地方经济建设的同时,实现了自身的健康、快速发展。     

LOCAL AUTHORITY E-GOVERNMENT PARTNERSHIPS IN ENGLAND: A CASE STUDY

This paper uses a case study of an e-government partnership of ten local authorities to consider how governance structures, financial arrangements and performance management systems may promote or inhibit successful collaborative working. The case is considered within the contexts of continuing new public management reforms including the balance between formal performance measurement indicators imposed by higher authorities and the scope for dialogue in the development of performance management systems. The case supports a view that collaborative working can benefit from a governance structure supporting a consistency of objectives of the partners operating within similar political and regulatory environments. Externally imposed performance indicators are significant to the partner authorities but their influence does not exclude the possibility of more dialogue-driven performance management systems being developed within the partnership. Sustainability of the partnership is never assured and tensions develop as a result of developing priorities of individual authorities, the possibility of local government reorganisation and changing national policy initiatives.     

The impact of abnormal real earnings management to meet earnings benchmarks on future operating performance

Motivated by agency conflicts of real earnings management (e.g., opportunistic and signalling perspectives), this study investigates the association between firms that manipulate their business operations to meet earnings benchmarks (i.e., zero earnings, last year's earnings) and subsequent operating performance. We examine the effects of the magnitude of real earnings management on firms' future performance for the period 2009 to 2015 for UK firms. Our analysis shows that the manipulation of operating activities such as sales, discretionary expenditures, and production costs to meet earnings benchmarks has a significantly positive consequence for firms' subsequent operating performance and signals firms' good future performance. We also find that firms that manipulate their operating activities in the absence of meeting earnings benchmarks experience a decline in their subsequent operating performance. The findings of this research lend support to our understanding of the process that management follows to evaluate costs and benefits of real earnings management.     

Open‐Market Stock Repurchases by Insurance Companies and Signaling

The signaling hypothesis of share repurchases implies that management uses repurchases to signal either that their firm's future operating performance will improve or that shares of their stock are simply underpriced by the market. This study examines which of the two interpretations can better explain open‐market share repurchase programs announced by insurance companies. We find no evidence that future‐operating performance of insurers improves following the repurchase announcement. In addition, changes in future operating performance cannot explain the announcement‐period abnormal return. Instead, the stock undervaluation prior to the repurchase announcement can significantly explain the announcement‐period abnormal return, particularly for life insurers. Overall, our results suggest that the positive market reaction to insurers’ open‐market share repurchase announcements is due to the stock undervaluation by the market, but not due to positive information content about future operating performance conveyed in the repurchase announcement.     

The information content of stock splits

This study examines whether stock splits contain information content about future operating performance or whether splits are undertaken by firms to realign their share prices and to improve trading liquidity. In the four years following split announcements, splitting firms do not experience improved operating performance relative to non-splitting firms. Furthermore, stock split signals are not related to future profitability. The positive announcement effect can be explained by lower share prices and improved market liquidity following stock splits but not by split signals and post-split operating performance. Our results show very little evidence that stock splits signal improvement in long-run operating performance and are more consistent with the trading range/liquidity hypothesis.     

Sources of Corporate Financing and Operating Performance: The effects of strategic ownership and financial restatements

Using a sample from European markets this study documents that changes in external financing, both in the form of equity and debt, can predict future operating performance (profitability and cash flows). In terms of future profitability, increases in equity (debt) financing particularly benefit large-size growth firms (large-size value firms). It is notable that a firm environment of low information quality, indicated by the presence of accounting restatements, intensifies the association between external financing and operating performance, due to the heightened scrutiny investors/lenders apply to firms that have recently restated their financials. In addition, strategic ownership in the firm has no significant effect on the financing – operating profitability association but may amplify the positive effects of equity financing on future operating cash flows. Moreover, financial analysts' forecasts of operating profitability and operating cash flows reflect the impact of external financing changes on future operating performance but exhibit a financing-related systematic inefficiency particularly for firms that have recently announced a material restatement of their prior financial results. Finally, controlling for information contained in analyst forecast surprises, the market is efficient overall and incorporates the effects of equity and debt financing changes into stock prices.     

IPO or SBO?: The Increasing Importance of Operational Performance for Private Equity Exits Following the Global Financial Crisis of 2007–08

The authors report the findings of their recent study of the role of portfolio company operating performance in determining the choice of exit options by private equity firms between initial public offerings (IPOs) and secondary buyouts (SBOs), and how that role may have changed since the Global Financial Crisis of 2007–2008. Virtually all studies of PE exits in all countries have found that portfolio companies that exit through IPOs tend to be larger and have higher operating returns than companies that exit through SBOs or sales to other companies. After examining the exits of PE portfolio companies based in Denmark and Sweden during the period 2003–2013, the authors report that, although general market conditions continue to be a major factor, operating performance and size have become even more important requirements for IPO exits since the crisis. And thus PE firms that fail to make operating improvements in their portfolio companies are likely to find their exit options limited.     

A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection

Several state‐of‐the‐art binary classification techniques are experimentally evaluated in the context of expert automobile insurance claim fraud detection. The predictive power of logistic regression, C4.5 decision tree, k‐nearest neighbor, Bayesian learning multilayer perceptron neural network, least‐squares support vector machine, naive Bayes, and tree‐augmented naive Bayes classification is contrasted. For most of these algorithm types, we report on several operationalizations using alternative hyperparameter or design choices. We compare these in terms of mean percentage correctly classified (PCC) and mean area under the receiver operating characteristic (AUROC) curve using a stratified, blocked, ten‐fold cross‐validation experiment. We also contrast algorithm type performance visually by means of the convex hull of the receiver operating characteristic (ROC) curves associated with the alternative operationalizations per algorithm type. The study is based on a data set of 1,399 personal injury protection claims from 1993 accidents collected by the Automobile Insurers Bureau of Massachusetts. To stay as close to real‐life operating conditions as possible, we consider only predictors that are known relatively early in the life of a claim. Furthermore, based on the qualification of each available claim by both a verbal expert assessment of suspicion of fraud and a ten‐point‐scale expert suspicion score, we can compare classification for different target/class encoding schemes. Finally, we also investigate the added value of systematically collecting nonflag predictors for suspicion of fraud modeling purposes. From the observed results, we may state that: (1) independent of the target encoding scheme and the algorithm type, the inclusion of nonflag predictors allows us to significantly boost predictive performance; (2) for all the evaluated scenarios, the performance difference in terms of mean PCC and mean AUROC between many algorithm type operationalizations turns out to be rather small; visual comparison of the algorithm type ROC curve convex hulls also shows limited difference in performance over the range of operating conditions; (3) relatively simple and efficient techniques such as linear logistic regression and linear kernel least‐squares support vector machine classification show excellent overall predictive capabilities, and (smoothed) naive Bayes also performs well; and (4) the C4.5 decision tree operationalization results are rather disappointing; none of the tree operationalizations are capable of attaining mean AUROC performance in line with the best. Visual inspection of the evaluated scenarios reveals that the C4.5 algorithm type ROC curve convex hull is often dominated in large part by most of the other algorithm type hulls.