Validation and measurement invariance of the multidimensional qualitative job insecurity scale

This contribution introduces the Multidimensional Qualitative Job Insecurity Scale (MQJIS). Drawing from the qualitative job insecurity literature and addressing some of other scales’ limitations, a multidimensional model is proposed and investigated by means of confirmatory factor analysis and multilevel confirmatory factor analysis. Study 1 aims to explore the psychometric properties and factorial structure of MQJIS in an Italian sample of blue-collar workers (N?=?583), showing that a model with one higher-order factor (i.e., qualitative job insecurity) and four dimensions (i.e., social relationships, employment conditions, working conditions, and work content) shows a good fit to the data and good reliability indices. Study 2 aims to investigate MQJIS measurement invariance across several groups, based on country of origin, age, and gender. Results on a sample of Belgian and Italian workers (N?=?710) show that MQJIS met the criteria for uniqueness invariance across genders and scalar invariance across countries and age groups. Significance, implications, and future directions stemming from the initial validation and the confirmed measurement invariance of this scale are discussed.

     

Designs for smoothing spline ANOVA models

Smoothing spline estimation of a function of several variables based on an analysis of variance decomposition (SS-ANOVA) is one modern nonparametric technique. This paper considers the design problem for specific types of SS-ANOVA models. As criteria for choosing the design points, the integrated mean squared error (IMSE) for the SS-ANOVA estimate and its asymptotic approximation are derived based on the correspondence between the SS-ANOVA model and the random effects model with a partially improper prior. Three examples for additive and interaction spline models are provided for illustration. A comparison of the asymptotic designs, the 2^d factorial designs, and the glp designs is given by numerical computation. Received May 2000     

Relationship between entrepreneurial orientation and social performance in Spanish sports clubs. The effect of the type of funding and the level of competition

The entrepreneurial orientation and social role of sports clubs have recently become topics of great interest to academics and professionals. The aim of this study is to analyse the relationship between entrepreneurial orientation (EO) in sports clubs and their social performance (SP) and the effect that management variables such as the type of funding (public vs private) and the competition level (national vs regional) may have on this relationship. To compare the results, two complementary methodologies have been used to compare the results: linear models with moderation analysis and models based on qualitative comparative analysis (QCA). The study was carried out with a sample of 407 Spanish sports clubs. The instrument used to measure EO was an adaptation of the original Covin and Slevin (1989) approach, while the instrument used to measure SP is a validated scale of our own elaboration presenting good psychometric properties. The results found show a direct effect of the EO dimensions on the SP. In all cases, clubs with major public funding showed higher levels of prediction in SP. On the other hand, as far as QCA is concerned, no necessary condition was found, while the most important condition sufficient to obtain high levels of SP would be high levels of innovation, a high level of risk-taking, and low levels of proactivity in national sports clubs. In general, models based on qualitative comparative analysis (QCA) provide higher predictive values than linear regression models and include variables not considered in linear models.

     

Descriptive statistics of large data sets by scatter plots, an exploratory approach

In the analysis of large tables of M variables on N observations one is interested in the relations between the variables and it is usual to inspect the M(M-1)/2 scatter plots of N points. Clearly, the scatter plot approach relies on visual inspection and is to be preferred in so far as applicable to detect simple relations, namely when M is small. Other approaches are needed for large values of M .
We consider that only the relatively few scatter plots that present a 'structure' are of interest for an exploratory analysis and, by 'structure', we mean a domain of specially high local density in the plot. Based on this concept, we propose a method constructed around two steps: the selection of the possibly interesting pairs of variables and the validation of the corresponding scatter plots. The selection of the pairs results from an algorithm based on a binary partitioning tree. The validation of the corresponding scatter plots enables the production of only those where a structure is found the recognition of a structure is derived from a statistic based on the length of the Minimum Spanning Tree constructed on the N points of the candidate scatter plot.
For illustration, we report on an industrial application where the method is routinely applied for exploratory purposes.     

Entrepreneurship research in service industries: a literature classification and trend analysis

In this paper, the influence of entrepreneurship on service management research is examined. Entrepreneurship research occupies a central place in organizational theory and strategic thinking, but its theories and models have little influence on business management. Entrepreneurship research can have a better integration with other areas of management, but there is still certain isolation. This article reviews the emerging areas in entrepreneurship research that provides complementary theoretical framework to service business models. In order to determine the areas in service management research in which entrepreneurship theories can be useful, a classification of the papers published in the last 2 years in the JCR service management journals is carried out. The study concludes that innovation is the most outstanding priority in service management research that can be enriched by entrepreneurial theories. The entrepreneurial approach to networks and institutional environment are the theoretical frameworks that better suit to introduce entrepreneurship variables as antecedents in business innovation models.     

Blocked factor aliased effect-number pattern and column rank of blocked regular designs

In factorial experiments, estimation precision of specific factor effects depends not only on design selection but also on factor assignments to columns of selected designs. Usually, different columns in a design play different roles when estimating factor effects. Zhou et al. (Can J Stat 41:540-555, 2013) introduced a factor aliased effect-number pattern (F-AENP) and proposed a column ranking scheme for all the GMC \(2^{n-m}\) designs with \(5N/16+1\le n\le N-1\), where \(N=2^{n-m}\). In this paper, we first introduce a blocked factor aliased effect-number pattern (B-F-AENP) for blocked regular designs as an extension of the F-AENP. Then, by using the B-F-AENP, we propose a column ranking scheme for all the B\(^1\)-GMC \(2^{n-m}:2^s\) designs with \(5N/16+1\le n\le N-1\), as well as an assignment strategy for important factors.     

Foreign remedies for local difficulties: the case of three Scottish manufacturing firms

Literature on Japanese transplant manufacturing firms in the automotive sector often emphasise the importance placed on attitude as opposed to skills in the hiring decisions for line workers. In this paper, a case study of one second-tier components supplier for a major Japanese automotive assembler in the Midwestern United States provides the opinions of senior managers and human resource associates regarding recruitment and selection practices. In-depth interviews, carried out over a two-week period in August 2000, are used to develop an understanding of the recruitment and selection process for line workers as well as to investigate the desired skills and value of previous Japanese experience. Results of the case study analysis are compared with two models from the literature: (1) a model of recruitment and selection at Japanese automotive-related firms in Japan and (2) a model of recruitment and selection at Japanese transplant automotive-related firms abroad. Deviations from the two models point not to a new paradigm of ‘second-generation’ Japanese transplants – those that have moved into regions quite familiar with Japanese firms and related management and production methods – but rather to overall weaknesses in the stereotypical models. Managerial opinions within the case study firm place limited value on familiarity with a Japanese environment, considering such experience secondary to attitudes and work ethics that are in line with the philosophy of the case study firm.     

Fisher information under Gaussian quadrature models

This paper develops formulae to compute the Fisher information matrix for the regression parameters of generalized linear models with Gaussian random effects. The Fisher information matrix relies on the estimation of the response variance under the model assumptions. We propose two approaches to estimate the response variance: the first is based on an analytic formula (or a Taylor expansion for cases where we cannot obtain the closed form), and the second is an empirical approximation using the model estimates via the expectation–maximization process. Further, simulations under several response distributions and a real data application involving a factorial experiment are presented and discussed. In terms of standard errors and coverage probabilities for model parameters, the proposed methods turn out to behave more reliably than does the ‘disparity rule’ or direct extraction of results from the generalized linear model fitted in the last expectation–maximization iteration.     

SWARCH模型下的VaR估计

本文将状态转换下的ARCH模型（SWARCH）引入到估计金融资产VaR中，以上证股票指数为例进行实证分析，并与传统GARCH（1，1）模型中正态分布、t分布、GED分布估计的结果进行了比较，实证显示含有状态转换的VaR具有较好的估计效果。     

Estimation in a growth study with irregular measurement times

Between 1982 and 1988 a growth study was carried out at the Division of Pediatric Oncology of the University Hospital of Groningen. A special feature of the project was that sample sizes are small and that ages at entry may be very different. In addition the intended design was not fully complied with. This paper highlights some aspects of the statistical analysis which is based on (1) reference scores, (2) statistical procedures allowing for an irregular pattern of measurement times caused by missing data and shifted measurement times.     

Analysis of marginally specified semi-nonparametric models for clustered binary data

Generalized linear mixed models are widely used for analyzing clustered data. If the primary interest is in regression parameters, one can proceed alternatively, through the marginal mean model approach. In the present study, a joint model consisting of a marginal mean model and a cluster-specific conditional mean model is considered. This model is useful when both time-independent and time-dependent covariates are available. Furthermore our model is semi-parametric, as we assume a flexible, smooth semi-nonparametric density of the cluster-specific effects. This semi-nonparametric density-based approach outperforms the approach based on normality assumption with respect to some important features of 'between-cluster variation'. We employ a full likelihood-based approach and apply the Monte Carlo EM algorithm to analyze the model. A simulation study is carried out to demonstrate the consistency of the approach. Finally, we apply this to a study of long-term illness data.     

The twenty‐first‐century HR organization

Like any value‐creating staff function, HR departments should operate as a business within a business. Others have focused on the strategy and direction of HR departments. This article examines the next evolution for how HR department organization structure can deliver value based on two premises: (1) HR organization should be structurally aligned with the organization structure of the business and (2) because diversified/allied business models prevail, it is important to lay out the five roles and responsibilities of HR that respond to this organization model: service centers, corporate, centers of expertise, embedded HR, and operational HR. The article lays out the duties of each role, the relationship among these roles, and suggestions for implementing this new HR structure. © 2008 Wiley Periodicals, Inc.     

城市女性休闲活动的影响因素及差异分析——基于休息日与工作日的对比

对女性休闲活动的研究是西方休闲研究中的重要主题,而国内针对女性休闲活动的直接研究较少.本研究利用2007年北京市民活动日志的第一手数据,建立女性参与休闲的多项logit模型,分析女性休闲活动的影响因素并探讨休息日与工作日影响因素的差异.     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

精准营销在B2C电子商务中的应用分析

B2C电子商务是消费者与企业利用互联网直接开展交易的一种商业模式,电子商务的蓬勃发展对传统的经营模式形成了巨大挑战,本文主要分析了精准营销在B2C电子商务中的应用现状,探讨了如何利用电子商务提升企业精准营销的效率。     

On the Comparison of Run Orders of Unreplicated 2<Superscript><Emphasis Type="Italic">k</Emphasis>–<Emphasis Type="Italic">p</Emphasis></Superscript> Designs in the Presence of a Time Trend

The response from a factorial experiment carried out in a time sequence may be affected by uncontrollable variables that are highly correlated with the time in which they occur. In such a situation, one possibility is to randomize the run order of the experiment. Another possibility is to use a systematic run order that is robust against time trends. Since randomized run orders make the time trend part of the error, it can be hoped that systematic run orders will be more effective to identify truly active factors. In this paper, a simulation study is used to compare the performances of the randomized and the systematic run orders. The response from an experiment where we have observed a strong time trend is used to demonstrate the influence of a realistic time trend on the run orders under consideration. The performance of the run orders is then measured by taking the probabilities of false rejection and the probabilities of detection of active contrasts. Our results show that the randomized run order managed to keep the nominal level, while the systematic did not. Additionally, when there were active factors, then the systematic run orders did not achieve more power than did the randomized run order.     

An approximation to the limit distribution of the epps-pulley test statistic for normality

Summary The approach of Epps and Pulley (1983) based on the empirical characteristic function is one of the most powerful tools for detecting any departures from normality. We obtain the first four moments of the limiting null distribution of the Epps-Pulley Statistic. Johnson- and Pearson curve fitting yields excellent approximations to simulated quantiles, and by modifying the test statistic the procedure may be carried out easily without the use of extensive tables for all sample sizes. Research done while the author was on leave at the University of Gie?en.     

Testing the adequacy of varying coefficient models with missing responses at random

In this paper, we investigate checking the adequacy of varying coefficient models with response missing at random. In doing so, we first construct two completed data sets based on imputation and marginal inverse probability weighted methods, respectively. The empirical process-based tests by using these two completed data sets are suggested and the asymptotic properties of the test statistics under the null and local alternative hypotheses are studied. Because the limiting null distribution is intractable, a Monte Carlo approach is applied to approximate the distribution to determine critical values. Simulation studies are carried out to examine the performance of our method, and a real data set from an environmental study is analyzed for illustration.