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Aarhus Kommunes Biblioteker (Teknisk Bibliotek), Ingerslevs Plads 7, Aarhus, Denmark. Representative: V. NEDERGAARD PEDERSEN (Librarian).

Communications regarding this column should be addressed to Mrs. Cheney, Peabody Library School, Nashville, Term. 37203. Mrs. Cheney does not sell the books listed here. They are available through normal trade sources. Mrs. Cheney, being a member of the editorial board of Pierian Press, will not review Pierian Press reference books in this column. Descriptions of Pierian Press reference books will be included elsewhere in this publication.

It has often been said that a great part of the strength of Aslib lies in the fact that it brings together those whose experience has been gained in many widely differing fields but who have a common interest in the means by which information may be collected and disseminated to the greatest advantage. Lists of its members have, therefore, a more than ordinary value since they present, in miniature, a cross‐section of institutions and individuals who share this special interest.

Effective management and utilisation of plant history data can considerably improve plant and equipment performance. This rationale underpins statistical and mathematical models for exploiting plant management data more efficiently, but industry has been slow to adopt these models. Reasons proffered for this include: a perception of models being too complex and time consuming; and an inability of their being able to account for dynamism inherent within data sets. To help address this situation, this research developed and tested a web‐based data capture and information management system. Specifically, the system represents integration of a web‐enabled relational database management system (RDBMS) with a model base management system (MBMS). The RDBMS captures historical data from geographically dispersed plant sites, while the MBMS hosts a set of (Autoregressive Integrated Moving Average – ARIMA) time series models to predict plant breakdown. Using a sample of plant history file data, the system and ARIMA predictive capacity were tested. As a measure of model error, the Mean Absolute Deviation (MAD) ranged between 5.34 and 11.07 per cent for the plant items used in the test. The Root Mean Square Error (RMSE) values also showed similar trends, with the prediction model yielding the highest value of 29.79 per cent. The paper concludes with direction for future work, which includes refining the Graphical User Interface (GUI) and developing a Knowledge Based Management System (KBMS) to interface with the RDBMS.

High-involvement work processes (HIWPs) are associated with high levels of employee influence over the work process, such as high levels of control over how to handle individual job tasks or a high level of involvement at team or workplace level in designing work procedures. When implementations of HIWPs are accompanied by companion investments in human capital – for example, in better information and training, higher pay and stronger employee voice – it is appropriate to talk not only of HIWPs but of “high-involvement work systems” (HIWSs). This chapter reviews the theory and practice of HIWPs and HIWSs. Across a range of academic perspectives and societies, it has regularly been argued that steps to enhance employee involvement in decision-making create better opportunities to perform, better utilization of skill and human potential, and better employee motivation, leading, in turn, to various improvements in organizational and employee outcomes.

However, there are also costs to increased employee involvement and the authors review the important economic and sociopolitical contingencies that help to explain the incidence or distribution of HIWPs and HIWSs. The authors also review the research on the outcomes of higher employee involvement for firms and workers, discuss the quality of the research methods used, and consider the tensions with which the model is associated. This chapter concludes with an outline of the research agenda, envisaging an ongoing role for both quantitative and qualitative studies. Without ignoring the difficulties involved, the authors argue, from the societal perspective, that the high-involvement pathway should be considered one of the most important vectors available to improve the quality of work and employee well-being.

Investigates the differences in protocols between arbitral tribunals and courts, with particular emphasis on US, Greek and English law. Gives examples of each country and its way of using the law in specific circumstances, and shows the variations therein. Sums up that arbitration is much the better way to gok as it avoids delays and expenses, plus the vexation/frustration of normal litigation. Concludes that the US and Greek constitutions and common law tradition in England appear to allow involved parties to choose their own judge, who can thus be an arbitrator. Discusses e‐commerce and speculates on this for the future.

Qualitative information‐gathering techniques are focused on to determine whether they can be adapted or adopted to support strategic goal‐setting. Much of the literature suggests that if planning is based on information gathered and presented in a manner which managers can understand they are more likely to act on it, and, for this reason, qualitative rather than quantitative techniques are stressed here. Factors which are not amenable to numerate analysis but which are useful to the strategic planner, such as experience, judgement and intuition, are also isolated and analysed. An attempt is made to facilitate the use of qualitative data‐gathering methods and suggestions are made as to where particular techniques may prove beneficial, together with their limitations. Research, from a small (n = 20), in‐depth survey of small business owners/ managers in Canada, is included which shows that they do not use quantitative planning processes but that judgemental techniques were most widely used; in general, the less sophisticated the planning process the higher it would be ranked among the survey participants. The research from other surveys also shows that scientific mathematically based models often do not fit with small business organisational reality and that methodologies should be developed that integrate research into the decision‐making process.