Family firms and high technology Mergers & Acquisitions

We examine whether family firms undertake value creating high technology M&A. We also examine whether level of ownership, diversification, agency issues and CEO type matter. Our sample consists of high-technology M&A undertaken by Canadian firms over the period 1997–2006. Canada offers a setting with many family firms and the use of control enhancing mechanisms such as dual class shares and pyramid structures. We find a positive relationship between family ownership and announcement period abnormal returns. This relationship, however, starts to decrease at higher levels of ownership but remains overall positive. We also show that the agency conflict between shareholders and professional managers has a detrimental impact on announcement period abnormal returns whereas the conflict between controlling and minority shareholders via control enhancing mechanisms does not. Finally, we document that founder CEO undertake better high tech M&A than descendant or hired CEO.     

Strategic planning for telecommunications—a systems approach

This paper outlines the practical application of a systems approach to designing and implementing the Strategic Planning Process in a mid-sized, capital/technology intensive Canadian telecommunications company. The basic General Systems concepts used are briefly mentioned, the design of the Organization Structure and the Information Structure for the Strategic Planning Process are reviewed, the Operational Dynamics of the Strategic Planning Process outlined, outputs and application case described and conclusions reached.     

Demographic scenarios by age,sex and education corresponding to the SSP narratives

In this paper, we translate the five narratives as defined by the Shared Socioeconomic Pathways (SSPs) research community into five alternative demographic scenarios using projections by age, sex and level of education for 171 countries up to 2100. The scenarios represent a significant step beyond past population scenarios used in the Intergovernmental Panel for Climate Change context, which considered only population size. The definitions of the medium assumptions about future fertility, mortality, migration and education trends are taken from a major new projections effort by the Wittgenstein Centre for Demography and Global Human Capital, while the assumptions for all the other scenarios were defined in interactions with other groups in the SSP community. Since a full data base with all country-specific results is available online, this paper can only highlight selected results.     

You Thought That This Would Be Easy? Seeking an Understanding of Episodic Volunteering

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This article is intended as the leading article in a Special Issue of Voluntas devoted to episodic volunteering from a...     

Uniformly implementable small sample integrated likelihood ratio test for one-way and two-way ANOVA under heteroscedasticity and normality

AStA Advances in Statistical Analysis - ANOVA under normally distributed response and heteroscedastic variances is commonly encountered in biological, behavioral, educational and agricultural...     

Network design for reverse logistics

Collection and recycling of product returns is gaining interest in business and research worldwide. Growing green concerns and advancement of green supply chain management (GrSCM) concepts and practices make it all the more relevant. Inputs from literature and informal interviews with 84 stakeholders are used to develop a conceptual model for simultaneous location–allocation of facilities for a cost effective and efficient reverse logistics (RL) network. We cover costs and operations across a wide domain and our proposed RL network consists of collection centers and two types of rework facilities set up by original equipment manufacturers (OEMs) or their consortia for a few categories of product returns under various strategic, operational and customer service constraints in the Indian context.     

Bayesian Identification of Multivariate Autoregressive Processes

Identification is one of the most important stages of a time series analysis. This paper develops a direct Bayesian technique to identify the order of multivariate autoregressive processes. By employing the conditional likelihood function and a matrix normal-Wishart prior density, or Jeffrey' vague prior, the proposed identification technique is based on deriving the exact posterior probability mass function of the model order in a convenient form. Then one may easily evaluate the posterior probabilities of the model order and choose the order that maximizes the posterior mass function to be the suitable order of the time series data being analyzed. Assuming the bivariate autoregressive processes, a numerical study, with different prior mass functions, is carried out to assess the efficiency of the proposed technique. The analysis of the numerical results supports the adequacy of the proposed technique in identifying the orders of multivariate autoregressive processes.     

Statistical Security for Social Security

The financial viability of Social Security, the single largest U.S. government program, depends on accurate forecasts of the solvency of its intergenerational trust fund. We begin by detailing information necessary for replicating the Social Security Administration's (SSA's) forecasting procedures, which until now has been unavailable in the public domain. We then offer a way to improve the quality of these procedures via age- and sex-specific mortality forecasts. The most recent SSA mortality forecasts were based on the best available technology at the time, which was a combination of linear extrapolation and qualitative judgments. Unfortunately, linear extrapolation excludes known risk factors and is inconsistent with long-standing demographic patterns, such as the smoothness of age profiles. Modern statistical methods typically outperform even the best qualitative judgments in these contexts. We show how to use such methods, enabling researchers to forecast using far more information, such as the known risk factors of smoking and obesity and known demographic patterns. Including this extra information makes a substantial difference. For example, by improving only mortality forecasting methods, we predict three fewer years of net surplus, $730 billion less in Social Security Trust Funds, and program costs that are 0.66% greater for projected taxable payroll by 2031 compared with SSA projections. More important than specific numerical estimates are the advantages of transparency, replicability, reduction of uncertainty, and what may be the resulting lower vulnerability to the politicization of program forecasts. In addition, by offering with this article software and detailed replication information, we hope to marshal the efforts of the research community to include ever more informative inputs and to continue to reduce uncertainties in Social Security forecasts.     

Shape-constrained Gaussian process regression for surface reconstruction and multimodal,non-rigid image registration

We present a new statistical framework for landmark ?>curve-based image registration and surface reconstruction. The proposed method first elastically aligns geometric features (continuous, parameterized curves) to compute local deformations, and then uses a Gaussian random field model to estimate the full deformation vector field as a spatial stochastic process on the entire surface or image domain. The statistical estimation is performed using two different methods: maximum likelihood and Bayesian inference via Markov Chain Monte Carlo sampling. The resulting deformations accurately match corresponding curve regions while also being sufficiently smooth over the entire domain. We present several qualitative and quantitative evaluations of the proposed method on both synthetic and real data. We apply our approach to two different tasks on real data: (1) multimodal medical image registration, and (2) anatomical and pottery surface reconstruction.     

An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming

In this paper, we present an aggregate production planning (APP) model applied to a Portuguese firm that produces construction materials. A multiple criteria mixed integer linear programming (MCMILP) model is developed with the following performance criteria: (1) maximize profit, (2) minimize late orders, and (3) minimize work force level changes. It includes certain operational features such as partial inflexibility of the work force, legal restrictions on workload, work force size (workers to be hired and downsized), workers in training, and production and inventory capacity. The purpose is to determine the number of workers for each worker type, the number of overtime hours, the inventory level for each product category, and the level of subcontracting in order to meet the forecasted demand for a planning period of 12 months. Additionally, a decision support system (DSS) based on the MCMILP model is proposed. It will help practitioners find the “best” solution for an APP problem without having to familiarize themselves with the mathematical complexities associated with the model. An example to illustrate the use of the DSS is also included.