Managers' personal values as predictors of importance attached to training and development: a cross-country exploratory study

Few empirical studies have examined the effect of personal values on the importance attached by individuals to training and development in organizations. We argue that personal values play an important role in decision-making processes (i.e. commitment to training and development) and that such values are the product of socialization processes operating at an organizational and societal level. Questionnaire data were collected from 340 Irish and Canadian line managers to test the hypothesis that personal values affect the importance attached by respondents to training and development. Capability values were found to be a significant positive predictor of the perceived importance of training and development. The findings emphasize the need for simultaneously examining both personal values and organizational factors as predictors of training and development activity.     

Technical rationality and professional artistry in HRD practice

Abstract

This paper outlines the limitations of a technical rationalist approach to HRD practice without seeking to negate it. It then offers a complementary view based on Schon's notion of the reflective practitioner which exhorts HRD practitioners to embrace complexity and reflection. We outline, first, a number of dimensions of diversity which confer complexity upon HRD practice, and, second, a series of suggested questions that may aid the framing of practical problems in a more effective way than might be the case with a purely technical rationalist approach. We urge novice (and expert) HRD practitioners to adopt a mind set that is contemplative of the diversities that they may encounter in practice and which is conjectural with regard to how these diversities may impact upon problems and their solutions.     

Corporate Political Activity: A Literature Review and Research Agenda

This paper reviews the diverse literature on corporate political activity (CPA) and develops a framework that details and integrates existing research in this field. A systematic analysis of extant CPA literatures is conducted to order them into domains that have implications for organizational performance. The paper is structured into three such domain emphases, which require further research investigation: resources and capabilities focus; institutional focus; and political environment focus. The contribution of each to an understanding of CPA in pursuit or defence of corporate competitive advantage is discussed. The authors also suggest that the internationalization of business, including the more recent emergence of developing country economies and companies, presents scholars with the challenge of understanding CPA in more varied institutional settings. CPA practices continue to expand as commerce goes increasingly global and, consequently, involves a wider array of political actors and institutions. The paper contributes by increasing the clarity of CPA classification, reflecting on the implications of a multi‐polar world for CPA research and advancing future agendas for scholars in this research community.     

Coaching‐Kompetenzen: Behauptungen und Kritik

The term “competence” is widely used, but hardly ever defined. The author combines the four levels of evaluation sensu Kirkpatrick, Evaluation of training, Training and Development Handbook (1976) with the three dimensions of quality management to review continuous assumptions of competence in the light of a theoretical model. He concludes that competence remains to be seen as performance by improvement in practice.     

A novel approach to subgraph selection with multiple weights on arcs

In this paper, an extension of the minimum cost flow problem is considered in which multiple incommensurate weights are associated with each arc. In the minimum cost flow problem, flow is sent over the arcs of a graph from source nodes to sink nodes. The goal is to select a subgraph with minimum associated costs for routing the flow. The problem is tractable when a single weight is given on each arc. However, in many real-world applications, several weights are needed to describe the features of arcs, including transit cost, arrival time, delay, profit, security, reliability, deterioration, and safety. In this case, finding an optimal solution becomes difficult. We propose a heuristic algorithm for this purpose. First, we compute the relative efficiency of the arcs by using data envelopment analysis techniques. We then determine a subgraph with efficient arcs using a linear programming model, where the objective function is based on the relative efficiency of the arcs. The flow obtained satisfies the arc capacity constraints and the integrality property. Our proposed algorithm has polynomial runtime and is evaluated in rigorous experiments.

     

Logistic retainment interval dose exploration design for Phase I clinical trials of cytotoxic agents

Phase I studies of a cytotoxic agent often aim to identify the dose that provides an investigator specified target dose-limiting toxicity (DLT) probability. In practice, an initial cohort receives a dose with a putative low DLT probability, and subsequent dosing follows by consecutively deciding whether to retain the current dose, escalate to the adjacent higher dose, or de-escalate to the adjacent lower dose. This article proposes a Phase I design derived using a Bayesian decision-theoretic approach to this sequential decision-making process. The design consecutively chooses the action that minimizes posterior expected loss where the loss reflects the distance on the log-odds scale between the target and the DLT probability of the dose that would be given to the next cohort under the corresponding action. A logistic model is assumed for the log odds of a DLT at the current dose with a weakly informative t-distribution prior centered at the target. The key design parameters are the pre-specified odds ratios for the DLT probabilities at the adjacent higher and lower doses. Dosing rules may be pre-tabulated, as these only depend on the outcomes at the current dose, which greatly facilitates implementation. The recommended default version of the proposed design improves dose selection relative to many established designs across a variety of scenarios.     

Conditional payments and self-protection

Self-protection can be financed either by upfront payments or by conditional payments (or contingent fees) in which individuals pay a provider only when a favorable outcome is realized. We find that, from the vantage point of a risk-averse individual, conditional payments welfare dominate upfront payments in the state-independent framework, but upfront payments welfare dominate conditional payments in the state-dependent framework. Moreover, more risk aversion always implies more self-protection under conditional payments, for both state-independent and state-dependent frameworks. In contrast, it has been previously argued that more risk aversion does not necessarily imply more self-protection under upfront payments.

Collective Action as the Material Expression of Opinion-Based Group Membership

In this article, we argue that progress in the study of collective action rests on an increasingly sophisticated application of the social identity approach. We develop the view, however, that the application of this theoretical perspective has been limited by theoretical and empirical difficulties in distinguishing between social categories and psychological groups. These problems have undermined the ability of researchers to correctly specify the collective identities that actually underpin many instances of collective action. As a partial solution to this problem we focus on collective identities based on shared opinion (opinion-based groups). We develop the proposition that much collective action reflects the crystallization or instantiation of opinion-based groups. We also outline an intervention aimed at stimulating commitment to collective action through group-based interaction involving opinion-based group members. We conclude by emphasizing that opinion-based groups tend to be most successful when they present themselves as being representative or aligned with dominant, positively valued social categories such as nations.     

Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects

Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer. The two fundamental challenges in the analysis of bottom-up MS-based proteomics are: (1) Identifying the proteins that are present in a sample, and (2) Quantifying the abundance levels of the identified proteins. Both of these challenges require knowledge of the biological and technological context that gives rise to observed data, as well as the application of sound statistical principles for estimation and inference. We present an overview of bottom-up proteomics and outline the key statistical issues that arise in protein identification and quantification.     

On the estimation and application of max-stable processes

The theory of max-stable processes generalizes traditional univariate and multivariate extreme value theory by allowing for processes indexed by a time or space variable. We consider a particular class of max-stable processes, known as M4 processes, that are particularly well adapted to modeling the extreme behavior of multiple time series. We develop procedures for determining the order of an M4 process and for estimating the parameters. To illustrate the methods, some examples are given for modeling jumps in returns in multivariate financial time series. We introduce a new measure to quantify and predict the extreme co-movements in price returns.