Employer Policies and Organizational Commitment in Britain 1992–97

An important current of thinking in the last decade has emphasized the need for a shift from control to commitment as the central objective of management employment policies. This paper is concerned to assess whether there was a significant increase in British employees' commitment to their organizations in the 1990s, using comparative data from two large-scale and nationally representative surveys carried out in 1992 and 1997. It finds that there was no evidence of an increase in commitment over the period. As in the early part of the decade, employees had only a weak level of attachment to their organizations. The analysis examines a number of factors that have been seen as important determinants of such commitment: changes in the level of skill, task discretion, controls over work performance, and forms of employee involvement. While there were changes in some of these factors that encouraged higher commitment, these were largely cancelled out by a notable decline in the discretion that employees were allowed to exercise over their work.     

Modeling Clustered Ordered Categorical Data: A Survey

This article surveys various strategies for modeling ordered categorical (ordinal) response variables when the data have some type of clustering, extending a similar survey for binary data by Pendergast, Gange, Newton, Lindstrom, Palta & Fisher (1996). An important special case is when repeated measurement occurs at various occasions for each subject, such as in longitudinal studies. A much greater variety of models and fitting methods are available than when a similar survey for repeated ordinal response data was prepared a decade ago (Agresti, 1989). The primary emphasis of the review is on two classes of models, marginal models for which effects are averaged over all clusters at particular levels of predictors, and cluster-specific models for which effects apply at the cluster level. We present the two types of models in the ordinal context, review the literature for each, and discuss connections between them. Then, we summarize some alternative modeling approaches and ways of estimating parameters, including a Bayesian approach. We also discuss applications and areas likely to be popular for future research, such as ways of handling missing data and ways of modeling agreement and evaluating the accuracy of diagnostic tests. Finally, we review the current availability of software for using the methods discussed in this article.     

DISCOUNT RATES AND ENERGY EFFICIENCY

Empirical studies provide compelling evidence that economic agents do not adopt the complete range of energy-efficient technologies that are cost-effective under prevailing prices and market conditions. Analysts commonly attribute this anomaly to the use of high discount rates in energy-related decisions-an interpretation that is difficult to reconcile with standard models of rational choice. This paper recasts the controversy from the perspective of economic theory and finds that market failures related to asymmetric information, bounded rationality, and transaction costs are major contributors to the so-called "efficiency gap."     

Making water competition a reality

Following a decade since privatisation during which attempts at introducing competition for water services have been slow and somewhat ineffective, the government is now carrying out a review. Large-volume users have a thirst for water competition brought about by competition in other utilities. The economic regulator needs to allow a new contestable market for industrial water services to develop whilst major structural and legislative changes to facilitate competition are contemplated.     

Least squares model averaging by Mallows criterion

This paper is in response to a recent paper by Hansen (2007) who proposed an optimal model average estimator with weights selected by minimizing a Mallows criterion. The main contribution of Hansen’s paper is a demonstration that the Mallows criterion is asymptotically equivalent to the squared error, so the model average estimator that minimizes the Mallows criterion also minimizes the squared error in large samples. We are concerned with two assumptions that accompany Hansen’s approach. The first is the assumption that the approximating models are strictly nested in a way that depends on the ordering of regressors. Often there is no clear basis for the ordering and the approach does not permit non-nested models which are more realistic from a practical viewpoint. Second, for the optimality result to hold the model weights are required to lie within a special discrete set. In fact, Hansen noted both difficulties and called for extensions of the proof techniques. We provide an alternative proof which shows that the result on the optimality of the Mallows criterion in fact holds for continuous model weights and under a non-nested set-up that allows any linear combination of regressors in the approximating models that make up the model average estimator. These results provide a stronger theoretical basis for the use of the Mallows criterion in model averaging by strengthening existing findings.     

Fits,misfits and interactions: learning at work,job satisfaction and job‐related well‐being

The resource‐based view (RBV) of the firm has focused attention on the importance of aligning employees' needs with the requirements of the jobs they do. This article focuses on how these needs and requirements interact in terms of learning. It does so in two ways. First, it develops new survey instruments to capture the learning demands of jobs and the learning dispositions of workers and uses them for the first time in a survey of 2,810 employees. Second, it examines how these person and job characteristics correlate with specific aspects of job satisfaction and job‐related well‐being. The results show that although learning alignment is associated with high levels of satisfaction and well‐being, not all learning misalignments are associated with the reverse.     

Volunteering time decisions: a multi‐factor experimental study

Non‐profit organizations often seek volunteers to help staff a fundraising event. In the present research, an experimental study assessed volunteering time decisions for a fundraising event following manipulations of opportunity cost valence, opportunity cost avoidability and question order involving donating time and donating money. Opportunity costs represent foregone alternatives sacrificed (e.g. working) when engaging in an activity (e.g. volunteering), with valence of the opportunity cost indicating the appeal (positive or negative) of the foregone alternative and avoidability of the opportunity cost suggesting how easy it would be to forgo the alternative. Prospective donors are often asked to consider both volunteering time and contributing money, and these two questions posed to individuals can be varied in terms of order. The results of the experiment revealed that individuals intended to volunteer the most time when an opportunity cost was negative and avoidable. The lowest time donation intentions occurred when an opportunity cost was positive and less avoidable with individuals also being asked about donating money prior to being asked to donate time. The results highlighted the importance to non‐profit firms of considering opportunity costs potential volunteers face and suggested care in structuring the order of questions involving time and money posed to those individuals. Copyright © 2011 John Wiley & Sons, Ltd.     

Computer assisted text analysis in the social sciences

We use the term “Computer Assisted Text Analysis” in a broad sense to refer to a range of current techniques from quantitative social science and content analysis to ‘data mining’ and ‘text classification’, including the analysis of open-ended survey questions, transcribed interviews and speeches, wherever, in fact, the researcher is confronted with data in the form of natural language texts of social scientific interest. These methods are often used in exploratory data analysis, but can also be applied systematically with moderate statistical rigour in the development and testing of hypotheses at various theoretical levels, ranging from the statistics of word usage to changes within or between discourses over time. The general approach is in the tradition of content analysis, by which words which occur together in relatively close proximity in the same context are interpreted as relating to a common theme or concept in the discourse studied. We review a comprehensive set of tools to identify and visualize structures of co-occurrence of words and concepts both within, and in comparing, a number of texts. These produce results not essentially different from those reached by representing word co-occurrences in terms of network analysis or neural network programming using schematic linguistic templates of various kinds. A comparison of the relational data analysis vs. a dictionary-based MDS approach shows that these provide very close if not identical results, despite the fact that the underlying assumptions are frequently represented as different theoretical approaches.