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Library and information science (LIS) is an academic, intellectual and industrial field with a large international reach. LIS educates library and information professionals, and is an active field in research and practice with a tradition of research development, standards, networks and distribution worldwide. The field has in recent years experienced a significant growth and development in all parts of the world, however, the field's long-term future is at the same time being challenged by new technologies, education changes and the development of new industries. A refocusing from a library to an information focus is in development within the LIS field. However, the field of information is also being grasped by the technology fields on the one hand and the psychological/behavioural fields on the other. Unfortunately for the field of LIS, information is now everyone's problem and of greater interest to more scientific fields and in addition, industry and government are looking for information management solutions that require technological development based on the psychological quality research. How the LIS field survives over the next 20 years will be played out in educational and industry environments globally.

Purpose — This chapter provides an historical overview of assessments of research quality conducted by the UK funding councils in the period 1986–2008, with special reference to the assessments that have been carried out of departments in the library and information management (LIM) sector.

Methodology/approach — A literature review covering both LIM-specific material and more general sources discussing the assessment of research quality in UK universities.

Findings — There is clear evidence of an increase in the general quality of the research carried out by the LIM sector over the review period. This has been accompanied by a decrease in the number of traditional LIM departments submitting themselves for assessment, with these being replaced in the assessment process largely by information systems departments. The rankings over the review period have been dominated by a small number of departments with long-established research traditions.

Originality/value of the paper — While there is an extensive literature describing research assessment in general, and a few articles describing individual assessments in the LIM sector, there is no overview of the involvement of the LIM departments over the whole series of assessment exercises that has been carried out.

David Ellis is Professor in the Department of Information Studies at Aberystwyth University. He was previously lecturer and senior lecturer in the Department of Information Studies, University of Sheffield. He has a PhD and an MA in Information Studies from the University of Sheffield, and a BA in Philosophy and Politics from the University of Durham. His PhD study of the information behaviour of academic social scientists represented one of the first attempts to apply a rigorous qualitative methodology to modelling the information seeking patterns of social science researchers and was subsequently extended to studies of scientists in both academic and industrial research environments. These interests were further developed in the course of the uncertainty in information seeking project carried out in collaboration with researchers at the University of Sheffield and the University of North Texas. Professor Ellis has published extensively in the information studies field, his work has been recognised as representing a distinct, substantive and methodological contribution to the fields of information behaviour and information retrieval research, and is widely cited in both. His current research interests are in the areas of information behaviour, information and knowledge management and information systems. Professor Ellis's professional activities have included service on the UK Research Assessment Exercise, Peer Review Panel for Library and Information Management, and Research Convenor of the Arts and Humanities Research Council (AHRC) Peer Review Panel for Librarianship, Information and Museum Studies. He is a member of the AHRC and the Economics and Social Sciences Research Council (ESRC) Peer Review Colleges and Research Notes Editor of the International Journal of Information Management.

In March 2002, the Bush administration unveiled what it deems to be a “new global development compact”: the Millennium Challenge Account (MCA). This new compact builds upon the Millennium Development Goals, e.g. halving world poverty by 2015, put forward by 189 countries at the Millennium General Assembly at the United Nations in September 2000. However, and in stark contrast with the latter strategy, which is aimed at addressing human security issues, the MCA is tied to the objectives of the 2002 National Security Strategy of the United States. As such, the MCA is primarily aimed at bringing excluded states (or, “failed states”) into the bounds of disciplinary role of capital. For instance, one of the most novel, and coercive, features of this development compact is the “pre-emptive” method in which it will administer aid. Under the MCA, only countries that govern justly, invest in their people, and open their economies to foreign enterprise and entrepreneurship will qualify for funding. In what follows, I argue that while the form of the MCA represents an unabashed articulation of U.S.-led imperialism vis-à-vis the poorest regions in the South, witnessed by the growing privatization of development aid and military intervention, its content reflects the same goals and interests that underlie the proceeding development agenda (i.e. the Washington consensus), namely promoting the idea that the “only” path to increased growth and prosperity is to be found in countries’ willingness and ability to adopt policies that promote economic freedom and the rule of law.

The procedures and rules the insurance industry writes in the polices it sells amount to a form of private legislation enforced by the state. Such authority creates a powerful lever for social change, from easing the diffusion to new technologies to slowing climate change. What maintains a sense of fairness in the way insurance firms shape society is an informal but stable network that interconnects them around the globe. A handful of specialist firms occupy key notes in maintaining this network. While these specialists aggressively compete over market share and profits, they also prioritize long-term relationships with their clients and competitors.

This chapter describes how to use popular software programs (Hierarchical Linear Modeling, LISREL) to analyze multiwave panel data. We review prevailing methods for panel data analyzes in strategic management research and identify their limitations. Then, we explain how multilevel and latent growth modeling provide more rigorous methodologies for studying dynamic phenomena. We present an example illustrating how firm performance can initiate temporal change in the human and social capital of members of Board of Directors, using hierarchical linear modeling. With the same data set, we replicate this test with first-order factor latent growth modeling (LGM). Next, we explain how to use second-order factor LGM with panel data on employee cognitions. Finally, we review the relative advantages and disadvantages of these new data-analytical approaches.

In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.