CEOs’ temporal focus,firm strategic change,and performance: Insights from a paradox perspective

Recent research indicates that CEOs’ temporal focus (the degree to which individuals attend to the past, present, and future) is a critical predictor for strategic outcomes. Building on paradox theory and the attention-based view, we examine the implications of CEOs’ past and future focus for strategic change. Results from polynomial regression analysis reveal that CEOs who cognitively embrace both the past and the future at the same time engage more in strategic change. In addition, our results reveal that the positive strategic change−firm performance relationship is enhanced when CEOs’ past focus is high, whereas CEOs’ future focus mitigates the translation of strategic change into firm performance (when their past focus is low at the same time). In addition, supplemental analyses indicate that the impact of CEOs’ temporal focus turns out differently in stable and dynamic environments. Our study thus extends the literature on both individual’s temporal focus and strategic change.     

On regression modelling with dummy variables versus separate regressions per group: Comment on Holgersson et al.

In a recent issue of this journal, Holgersson et al. [Dummy variables vs. category-wise models, J. Appl. Stat. 41(2) (2014), pp. 233–241, doi:10.1080/02664763.2013.838665] compared the use of dummy coding in regression analysis to the use of category-wise models (i.e. estimating separate regression models for each group) with respect to estimating and testing group differences in intercept and in slope. They presented three objections against the use of dummy variables in a single regression equation, which could be overcome by the category-wise approach. In this note, I first comment on each of these three objections and next draw attention to some other issues in comparing these two approaches. This commentary further clarifies the differences and similarities between dummy variable and category-wise approaches.     

Mixtures of regression models with incomplete and noisy data

The estimation of the mixtures of regression models is usually based on the normal assumption of components and maximum likelihood estimation of the normal components is sensitive to noise, outliers, or high-leverage points. Missing values are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this article, we propose the mixtures of regression models for contaminated incomplete heterogeneous data. The proposed models provide robust estimates of regression coefficients varying across latent subgroups even under the presence of missing values. The methodology is illustrated through simulation studies and a real data analysis.     

Similar coefficient for cluster of probability density functions

In this article, we propose a new criterion to evaluate the similarity of probability density functions (pdfs). We call this the criterion on similar coefficient of cluster (SCC) and use it as a tool to deal with overlap coefficients of pdfs in normal standard on [0;1]. With the support of the self-update algorithm for determining the suitable number of clusters, SCC then becomes a criterion to establish the corresponding cluster for pdfs. Moreover, some results on determination of SCC in case of two and more than two pdfs as well as relations of different SCCs and other measures are presented. The numerical examples in both synthetic data and real data are given not only to illustrate the suitability of proposed theories and algorithms but also to demonstrate the applicability and innovation of the proposed algorithm.     

Promoting sustainable development through civil society: A case study of the EU’s NSA/LA thematic programme in Kyrgyzstan

The article focuses on the contribution of the European Union (EU) in promoting sustainable development through the involvement of civil society in partner countries. More specifically, it analyses the main features and outcomes of the projects implemented by civil society organizations (CSOs) in Kyrgyzstan under the EU thematic programme Non‐State Actors and Local Authorities in Development (NSA/LA). Despite its importance—this is the only EU programme providing direct support to non‐state actors and local authorities engaged in poverty reduction—to date, there has been very little research on the functioning of this instrument on the ground. This article seeks to fill this gap in the literature by examining the EU’s contribution to sustainable development through a case study on Kyrgyzstan. The study is based on primary data: 10 semi‐structured interviews conducted with the EU‐funded organizations implementing the NSA/LA programme. The NSA/LA projects were analysed by considering two major fields of engagement of non‐state actors in the development process: as service providers and as advocates (Banks & Hulme, 2012). Overall, the organizations awarded EU support were not only focused on fulfilling short‐term needs but also sought to introduce new ways of dealing with poverty and inequality, positioning themselves between the “Big‐D” and the “little‐d” approaches to development (Bebbington, Hickey, & Mitlin, 2008). Nonetheless, the EU‐funded projects were too limited and fragmented to be able to sustain long‐term structural change. Therefore, the EU should place new emphasis on creating synergies between new and old structures at the grassroots level and establishing mechanisms and bodies that could merge and co‐ordinate their efforts. In addition, the calls for proposals could highlight the need to share the lessons learnt by “obliging” the beneficiaries to act as multipliers and to pass on their positive experience to neighbouring communities. Finally, the EU could stimulate the funded organizations to experiment with innovative mechanisms of involvement in the policy‐making process, by making this aspect a mandatory requirement of the projects implemented with its support.     

Conditional Akaike Information Criteria for a Class of Poisson Mixture Models with Random Effects

Focusing on the model selection problems in the family of Poisson mixture models (including the Poisson mixture regression model with random effects and zero‐inflated Poisson regression model with random effects), the current paper derives two conditional Akaike information criteria. The criteria are the unbiased estimators of the conditional Akaike information based on the conditional log‐likelihood and the conditional Akaike information based on the joint log‐likelihood, respectively. The derivation is free from the specific parametric assumptions about the conditional mean of the true data‐generating model and applies to different types of estimation methods. Additionally, the derivation is not based on the asymptotic argument. Simulations show that the proposed criteria have promising estimation accuracy. In addition, it is found that the criterion based on the conditional log‐likelihood demonstrates good model selection performance under different scenarios. Two sets of real data are used to illustrate the proposed method.     

A robust test for autocorrelation in the presence of a structural break in variance

It has been known that when there is a break in the variance (unconditional heteroskedasticity) of the error term in linear regression models, a routine application of the Lagrange multiplier (LM) test for autocorrelation can cause potentially significant size distortions. We propose a new test for autocorrelation that is robust in the presence of a break in variance. The proposed test is a modified LM test based on a generalized least squares regression. Monte Carlo simulations show that the new test performs well in finite samples and it is especially comparable to other existing heteroskedasticity-robust tests in terms of size, and much better in terms of power.     

Optimal quantization applied to sliced inverse regression

In this paper we consider a semiparametric regression model involving a d-dimensional quantitative explanatory variable X and including a dimension reduction of X via an index β′X. In this model, the main goal is to estimate the Euclidean parameter β and to predict the real response variable Y conditionally to X. Our approach is based on sliced inverse regression (SIR) method and optimal quantization in L^p-norm. We obtain the convergence of the proposed estimators of β and of the conditional distribution. Simulation studies show the good numerical behavior of the proposed estimators for finite sample size.     

An m-estimation-based model selection criterion with a data-oriented penalty

In Wu and Zen (1999), a linear model selection procedure based on M-estimation is proposed, which includes many classical model selection criteria as its special cases, and it is shown that the selection procedure is strongly consistent for a variety of penalty functions. In this paper, we will investigate its small sample performances for some choices of fixed penalty functions. It can be seen that the performance varies with the choice of the penalty. Hence, a randomized penalty based on observed data is proposed, which preserves the consistency property and provides improved performance over a fixed choice of penalty functions.     

BUSDM – an algorithm for the bottom-up search of departures from a model

Searching for regions of the input space where a statistical model is inappropriate is useful in many applications. The study proposes an algorithm for finding local departures from a regression-type prediction model. The algorithm returns low-dimensional hypercubes where the average prediction error clearly departs from zero. The study describes the developed algorithm, and shows successful applications on the simulated and real data from the steel plate production. The algorithms that have been originally developed for searching regions of the high-response value from the input space are reviewed and considered as alternative methods for locating model departures. The proposed algorithm succeeds in locating the model departure regions better than the compared alternatives. The algorithm can be utilized in sequential follow-up of a model as time goes along and new data are observed.