The meaning of empowerment: the interdisciplinary etymology of a new management concept

Empowerment has become a widely used management term in the last decade or so, though, in practical terms, it shares the ambiguity of its predecessors in the HRM tradition. This paper sets out to unravel the web of meaning surrounding empowerment to show what a contested concept it is, and hence why its application in organizational settings is fraught with misunderstanding and tension. It does so by taking an approach that contributes to the examination of HRM discourse and management rhetoric. To demonstrate the ambiguity of empowerment as a concept, the paper reviews the various ways in which the term has been used across non-management disciplines (women, minority groups, education, community care, politics), culminating with a review of the use of empowerment in contemporary management theory. The paper concludes that organizations and managers have chosen to coin a phrase which is open to different, sometimes contradictory, meanings and which, when applied, evokes both subjective attitudes and objective behaviour, means different things in varying contexts, and is affected fundamentally by individual differences in perception and experience. Unless organizations offer clear operational definitions when using empowerment, instead of purely acquiescing to a vague and seductive version of the concept, they are abdicating responsibility for the unpredictable consequences that result.     

Ethical Considerations in Management Research: A ‘Truth’ Seeker's Guide

Within a growing Knowledge-Management context, it would appear that Management Research calls for researchers to explicitly understand their own values, have increasing capacity for reflexivity and anticipate various ethical dilemmas that emerge from the research endeavour. This paper provides an overview of the philosophy of ethics and focuses on a researcher's ethical reasoning. It provides a framework for understanding causes of ethical dilemmas in the context of management research and provides strategies for a way forward through such dilemmas. The paper, finally, outlines exercises pertinent to research ethics and ‘hands on’ research praxis.     

The Job Demands-Job Control Model and absence behaviour: results of a 3-year longitudinal study

Empirical results of earlier studies only marginally supported the relevance of Karasek's Job Demands-Job Control Model for absence behaviour. Since longitudinal studies with respect to these relations were largely lacking, a four-wave panel study was carried out using data from 1755 male employees of a technical maintenance firm in the public sector. Job demands, job control, physical working conditions, and the employee's age, education, and health were measured in one year and absenteeism in the same year and in the next 3 years. Data were analysed with linear regression and Poisson regression techniques. The Poisson regression technique was superior to the linear regression technique in explaining absence. Age, health and prior absence were the best predictors of later absence behaviour. With respect to the Job Demands-Job Control Model, the main findings of the study were (1) that job control was significantly associated with a low number of simultaneous and later absence days, and (2) that, contrary to expectations, job demands were also related to a low number of simultaneous and later absence days. These results hold when age, health, education, prior absence, and working conditions are controlled for. Job control and job demands did not predict later absence frequency. In the discussion it is suggested that a high level of job demands may not only be harmful for the well-being of employees but also work as 'a pressure to attend'.     

Triangle plots of three-level explanatory variables

Thalassaemias are genetic blood disorders which cause varying degrees of anaemia. Their geographical distribution suggests a compensating protection against malaria, which kills between 0.5 and 2.5 million people per year in developing countries. Neal Alexander describes a study in Papua New Guinea which estimated this association more directly, and a triangle plot which clarified, for himself and his non-statistician colleagues, the relative risks of malaria for those with none, one or two copies of the relevant haemoglobin mutation.     

Interval estimation for log-linear models with one variable subject to non-ignorable non-response

Summary.  Log-linear models for multiway contingency tables where one variable is subject to non-ignorable non-response will often yield boundary solutions, with the probability of non-respondents being classified in some cells of the table estimated as 0. The paper considers the effect of this non-standard behaviour on two methods of interval estimation based on the distribution of the maximum likelihood estimator. The first method relies on the estimator being approximately normally distributed with variance equal to the inverse of the information matrix. It is shown that the information matrix is singular for boundary solutions, but intervals can be calculated after a simple transformation. For the second method, based on the bootstrap, asymptotic results suggest that the coverage properties may be poor for boundary solutions. Both methods are compared with profile likelihood intervals in a simulation study based on data from the British General Election Panel Study. The results of this study indicate that all three methods perform poorly for a parameter of the non-response model, whereas they all perform well for a parameter of the margin model, irrespective of whether or not there is a boundary solution.     

A Hybrid Geometric Phase II/III Clinical Trial Design based on Treatment Failure Time and Toxicity

The problem of comparing several experimental treatments to a standard arises frequently in medical research. Various multi-stage randomized phase II/III designs have been proposed that select one or more promising experimental treatments and compare them to the standard while controlling overall Type I and Type II error rates. This paper addresses phase II/III settings where the joint goals are to increase the average time to treatment failure and control the probability of toxicity while accounting for patient heterogeneity. We are motivated by the desire to construct a feasible design for a trial of four chemotherapy combinations for treating a family of rare pediatric brain tumors. We present a hybrid two-stage design based on two-dimensional treatment effect parameters. A targeted parameter set is constructed from elicited parameter pairs considered to be equally desirable. Bayesian regression models for failure time and the probability of toxicity as functions of treatment and prognostic covariates are used to define two-dimensional covariate-adjusted treatment effect parameter sets. Decisions at each stage of the trial are based on the ratio of posterior probabilities of the alternative and null covariate-adjusted parameter sets. Design parameters are chosen to minimize expected sample size subject to frequentist error constraints. The design is illustrated by application to the brain tumor trial.     

Efficient likelihood-free Bayesian Computation for?household epidemics

Considerable progress has been made in applying Markov chain Monte Carlo (MCMC) methods to the analysis of epidemic data. However, this likelihood based method can be inefficient due to the limited data available concerning an epidemic outbreak. This paper considers an alternative approach to studying epidemic data using Approximate Bayesian Computation (ABC) methodology. ABC is a simulation-based technique for obtaining an approximate sample from the posterior distribution of the parameters of the model and in an epidemic context is very easy to implement. A new approach to ABC is introduced which generates a set of values from the (approximate) posterior distribution of the parameters during each simulation rather than a single value. This is based upon coupling simulations with different sets of parameters and we call the resulting algorithm coupled ABC. The new methodology is used to analyse final size data for epidemics amongst communities partitioned into households. It is shown that for the epidemic data sets coupled ABC is more efficient than ABC and MCMC-ABC.     

Modelling spatially correlated data via mixtures: a Bayesian approach

Summary. The paper develops mixture models for spatially indexed data. We confine attention to the case of finite, typically irregular, patterns of points or regions with prescribed spatial relationships, and to problems where it is only the weights in the mixture that vary from one location to another. Our specific focus is on Poisson-distributed data, and applications in disease mapping. We work in a Bayesian framework, with the Poisson parameters drawn from gamma priors, and an unknown number of components. We propose two alternative models for spatially dependent weights, based on transformations of autoregressive Gaussian processes: in one (the logistic normal model), the mixture component labels are exchangeable; in the other (the grouped continuous model), they are ordered. Reversible jump Markov chain Monte Carlo algorithms for posterior inference are developed. Finally, the performances of both of these formulations are examined on synthetic data and real data on mortality from a rare disease.