Predicting industrial ad readership

This study examined the effect of visual and psycholinguistic elements (such as picture size and the number of adjectives in the headline) on industrial advertisement readership. Multiple regression techniques were used to analyze the data, which were obtained from AD-CHART. AD-CHART is a research firm which specializes in the measurement of industrial ad readership. The analyses showed that visual and psycholinguistic elements are better predictors of industrial advertising readership than more complicated models.     

Predicting Commercial Mortgage Foreclosure Experience

This study has two objectives: (1) it directly evaluates the relationship between commercial mortgage default incidence and characteristics of the mortgage, borrower, property, market, and general economic conditions, and (2) it uses this relationship to predict the exposure of life insurers to future mortgage defaults and to examine the relative importance of various causes of current and past credit quality problems. A theoretical model of the default decision predicts that the decision would be expected to be driven primarily by the borrower's current equity stake in the property, or the ratio of the market value of the loan to property value (M_t/V_t), but that the presence and magnitude of transaction costs associated with default would be expected to result in underexercise of the default option. Empirical estimation making use of American Council of Life Insurance (ACLI) and National Council of Real Estate Investment Fiduciaries (NCREIF) data confirms both expectations. A high proportion of the longitudinal variation in foreclosure incidence is explained by variations in M_t/V_t, but even at high ratios M_t/V_t in excess of 1.1. only 5% to 8% of mortgagors default, although this magnitude of underexercise is probably overstated because of problems in measuring M^t and for other reasons. Simulations using the model provide a pessimistic outlook for future defaults. Default rates are predicted to double in the five-year period 1988–93. Other simulations examine the relative importance of interest rate fluctuations, property value declines, and geographic or temporal correlations in lending during the 1976–88 period on current default experience.     

软岩非线性蠕变行为的西原模型改进

为了有效控制地下工程中围岩失稳现象的发生,在描述软岩非线性蠕变行为的基础上,借鉴元件模型的建模方式,将分数阶软体元件替代传统西原模型Kelvin体中的黏滞体,得到基于分数阶微积分的定常蠕变本构模型。引入非线性黏滞系数表达式来改进模型黏弹性部分,通过损伤力学理论描述岩石黏塑性应变,得到一个新的非线性四元件蠕变本构模型。通过所建模型辨识冻结软岩、片岩和泥岩的蠕变试验数据,采用Levenberg-Marqud算法求解模型参数,对比分析试验数据和辨识曲线。结果表明,所建模型对于研究软岩非线性蠕变行为是可行的,具有较好的适用性。在分数阶微积分的基础上通过损伤力学理论描述岩石黏塑性应变,得到的非线性四元件蠕变本构模型可为进一步研究软岩非线性蠕变行为提供参考。     

Predicting organizational identification at the CEO level

To develop further insight into antecedents of the CEO's psychological orientation toward the firm, we investigate what might lead CEOs to identify with their firms. Although research suggests that CEO organizational identification can be quite consequential for the firm, little research attention has been paid to its determinants. To predict how the special context of the CEO position might lead to identification, we consider a set of motives that members have for identifying with their organizations and consider how unique features of the CEO position might be relevant to those motives. Our theory and supportive findings help explain how the context of the CEO position, including variables often conceptualized as control mechanisms in agency theory research, can have important effects on subsequent CEO organizational identification. Copyright © 2014 John Wiley & Sons, Ltd.     

Predicting Failure of Savings & Loan Associations

Early-warning systems are intended to provide regulators with identification of problem institutions sooner than is possible with the present system of call reports and periodic on-site examinations. Given an earlier indication of potential problems, resources can be focused on those institutions most in need of monitoring, thus potentially reducing both the number of failures and FSLIC financial assistance in the remaining failures. While some early-warning systems were developed in the mid-1970s for use by commercial bank regulators, very little attention has been focused on developing similar systems for the thrift industry. To address this issue, multiple discriminant analysis was used to develop an early-warning system for savings and loan associations in the Boston district of the Federal Home Loan Bank system. The results suggest that use of this model would have given signals of impending trouble well before the actual failure occurred. Consequently, an early-warning system could provide information to regulators sooner, permitting scarce resources to be allocated more effectively. In addition, earlier intervention could reduce the amount of FSLIC financial assistance required. By intervening earlier, FSLIC may be able to arrest the failure-promoting activities in which the association is engaged.     

Predicting and measuring alliance performance: a multidimensional analysis

The strategic alliance literature demonstrates that alliances create value for the partners, but also that many alliances fall short of expectations. This study addresses the complex issue of alliance performance. We follow 100 contractual alliances over a 5-year period, and study their performance in terms of abrupt termination, short-term performance, and long-term performance. The results indicate that alliances that are considered strategically important are less likely to be abruptly terminated. We also find that newly established alliances have a higher termination rate than older alliances. Short-term performance is primarily affected by access to complementary and strategically important resources, whereas long-term performance is related to specific investments in human capital combined with the partners' ability to develop and expand alliance activities over time. Copyright © 2007 John Wiley & Sons, Ltd.     

国际原油价格预测因素探讨

随着中国进口原油数量的不断增加，国际原油价格成了影响我国经济运行的重要因素。首先从经济学角度分析了合理油价的空间为15－21美元／桶，而后进一步分析了影响油价的非供求因素如期货市场、石油库存、气候以及欧佩克的影响力，力图提供一个预测国际原油价格走势的基本框架。     

Predicting Joining and Participating in Minority Employee Network Groups

Do minority employees join network groups due to social identity, dissatisfaction with conditions at work, or career costs and benefits? Results show that joining is driven by social identity as well as expected costs (backlash) and benefits (career enhancement) but not by dissatisfaction, making it unlikely that they will become oppositional. Participation is also driven by cost-benefit calculations and social identity (via the effect social identity has on the perceived benefits of network groups).     

Predicting new product success with prediction markets in online communities

The prediction of new product success is still a challenging task. Traditional market research tools are expensive, time consuming, and error prone. Prediction markets have been introduced as a viable alternative. Utilizing inputs from various participants in game‐like environments, they have been shown to produce accurate results by combining dispersed knowledge via market‐based aggregation mechanisms. While most previous studies use employees or experts as a sample, we test whether online consumer communities can be used to predict the sale of new skis via prediction markets. Sixty‐two users took part in the study. The prediction market was open for 12 days before the main skiing season 2010/2011 began. The outcomes of the prediction markets were compared with the actual sales numbers provided by the ski producers. The mean average errors were between 2.74% and 9.09% in the four markets. Overall, it can be concluded that the prediction markets based on consumer communities produce accurate results.     

Predicting Household Switching Behavior and Switching Costs at Depository Institutions

This paper uses new survey data to investigate the covariates of self-reported switching costs and switching behavior by deposit account holders. Factors affecting geographic mobility appear to be most important in explaining the duration of deposit relationships. Both younger and older respondents are more likely than others to be at their first bank ever, suggesting a cohort effect in deposit relationships. Households reporting switching costs, net of the benefits from switching, are less likely than others to have stayed with a bank for prices or customer service, suggesting that switching costs may decrease price sensitivity. Switching costs appear more severe for households with high income or education and for households with very low income or minority ethnicity. These findings imply that banking markets characterized by such households may present greater entry costs for new firms.     

Predicting Counterproductive Performance Among Temporary Workers: A Note*

Using prehire biographic and work history data, temporary help agency workers (N= 201) were classified as marginal or satisfactory. Marginal temps had characteristics suggesting poor work histories and willingness to accept any kind of employment. In prior jobs, they were paid less and more likely to have been laid off. They had also been temps for longer periods of time, and were more willing to work weekends and nights. The classification of temps as marginal versus satisfactory was validated using posthire data, showing that marginal temps had lower performance evaluations, and exhibited more counterproductive behaviors (e.g., late, absent, unsafe, or careless).     

三维多箱模型预测大气环境质量

建立了16个风向下的三维多箱模式,该模式充分考虑了污染源的空间变化、气象条件和周围环境对污染扩散的影响,以及干沉积和化学转化等物理化学机理对SO2的去除作用,根据质量守恒定律,得到了箱体方程,最后将该模式用于石家庄市区SO2质量浓度的预测,并对其进行了验证,结果表明多箱模型是预测市区和经济开发区的大气质量最佳方法之一。     

Predicting and controlling flammability of multiple fuel and multiple inert mixtures

Le Chatelier's Rule is in wide use for predicting the flammability of mixtures with multiple fuels present. The rule does not conveniently handle multiple inerts or elevated temperatures and pressures. This paper describes an alternate method, developed at Air Products, called FLAMCHEK^TM, which conveniently handles these variables. This method for predicting flammability is based upon the commonality of the adiabatic flame temperature of a wide variety of fuels at their upper and lower flammable limits. The method, if PC based, can be extended to automatically control the addition of inerts, fuels, or oxidizers in order to avoid flammable conditions. The concept may be extended to more involved applications, such as within an oil well with fuel gas mixtures containing oxygen. In this case, the location from which a gas sample is obtained for analysis (wellhead) may have a different fuel analysis and flammability condition than the location where an explosion is likely to initiate (bottom of well). Hence a correction of the fuels analysis is required.     

Predicting Spatial Patterns of House Prices Using LPR and Bayesian Smoothing

This article is motivated by the limited ability of standard hedonic price equations to deal with spatial variation in house prices. Spatial patterns of house prices can be viewed as the sum of many causal factors: Access to the central business district is associated with a house price gradient; access to decentralized employment subcenters causes more localized changes in house prices; in addition, neighborhood amenities (and disamenities) can cause house prices to change rapidly over relatively short distances. Spatial prediction (e.g., for an automated valuation system) requires models that can deal with all of these sources of spatial variation. We propose to accommodate these factors using a standard hedonic framework but incoporating a semiparametric model with structure in the residuals modeled with a partially Bayesian approach. The Bayesian framework enables us to provide complete inference in the form of a posterior distribution for each model parameter. Our model allows prediction at sampled or unsampled locations as well as prediction interval estimates. The nonparametric part of our model allows sufficient flexibility to find substantial spatial variation in house values. The parameters of the kriging model provide further insights into spatial patterns. Out–of–sample mean squared error and related statistics validate the proposed methods and justify their use for spatial prediction of house values.     

Predicting the level of B2B e-commerce in industrial organizations

Through the use of business-to-business electronic commerce (B2B e-commerce), leading companies are transforming interorganizational transaction processing, trading, and collaboration into a competitive advantage. The research empirically examines several models with B2B e-commerce overall use as the dependent variable and innovation characteristics, context, channel factors, and organizational structure as the predictor variables. The results demonstrate that compatibility with existing systems, cooperative norms with customers, lateral integration within a firm, technocratic specialization, and decentralization of information technology decisions facilitate B2B e-commerce overall use. In addition, large firms are more likely than small firms to have greater levels of B2B e-commerce and firms are more likely to use B2B e-commerce with customers that use recommendations rather than threats to encourage e-commerce use.     

Predicting Organizational and Union Commitment: The Effect of Industrial Relations Climate

Research evidence from North America shows that employees can be committed simultaneously to both their union and their company. Moreover, a co-operative industrial relations climate has been seen to be conducive to the existence of higher levels of commitment to both organizations. This study utilized a sample of white-collar unionists in Australia to identify whether union and company commitment could be predicted by the same factors and whether positive perceptions of the industrial relations climate were related to dual commitment. The research found no evidence of dual commitment. Furthermore, company and union commitment were predicted by different factors, and employee perceptions of a co-operative industrial relations climate were associated with higher employee commitment to the company but lower commitment to the union.     

油气管道腐蚀剩余寿命的预测方法

针对管道腐蚀诸多因素的复杂性和不确定性以及管道腐蚀剩余寿命预测的困难性．系统地阐述了在役管道的腐蚀剩余寿命预测方法。评述了灰色预测方法，概率统计方法，人工神经网络方法和可靠度函数分析四种国内外正在研究和使用的方法。这些方法对于管道操作者做出正确的管道检测、维修以及更换决策起到了重要的指导作用。