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Information behaviour meta‐models

Andrew K. Shenton (Monkseaton High School, Monkseaton, UK)
Naomi V. Hay‐Gibson (Business School, Newcastle University, Newcastle upon Tyne, UK)

Library Review

ISSN: 0024-2535

Article publication date: 24 February 2012

1792

Abstract

Purpose

The purpose of this paper is to explore meta‐models that pertain to information behaviour. It seeks to highlight the possibilities they offer to researchers wishing to develop their own and to readers more generally interested in information behaviour literature.

Design/methodology/approach

Various frameworks that may be regarded as information behaviour meta‐models were examined and three separate types were identified. These are discussed in turn, with particular characteristics of individual meta‐models used to illustrate the types.

Findings

A meta‐model is considered here to be a model that has been derived from one or more existing models. Information behaviour meta‐models fall into three categories: those that unify, within one framework, disparate models/theories from a number of areas; those that integrate the fundamentals of several models which share common strands; and those that recast an established model for a particular purpose.

Research limitations/implications

The extent of the typology presented in the paper is bound by the limits of the authors' endeavours in uncovering relevant meta‐models. Should further meta‐models be traced, it is possible that other types would also come to light.

Originality/value

The work will aid the reader's understanding of how theoretical frameworks in information behaviour are developed. It will help those who study the field's literature to grasp variations in the origin of the individual models they see, by demonstrating that models may be derived from others in different ways. It will also enable readers intent on constructing their own models to understand some of the courses of action open to them.

Keywords

Citation

Shenton, A.K. and Hay‐Gibson, N.V. (2012), "Information behaviour meta‐models", Library Review, Vol. 61 No. 2, pp. 92-109. https://doi.org/10.1108/00242531211220735

Publisher

:

Emerald Group Publishing Limited

Copyright © 2012, Emerald Group Publishing Limited

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