Identifying Research Fields within Business and Management: A Journal Cross-Citation Analysis

TL;DR

This paper uses cross-citation analysis of 450 business and management journals to rigorously identify sub-fields within the discipline, aiding normalization and classification efforts.

Contribution

It introduces a data-driven method to delineate B&M sub-fields based on actual citation patterns, improving upon ad hoc classifications.

Findings

Identified coherent sub-fields within B&M based on citation data.

Reduced overlap of journals across multiple categories.

Provided a framework for more rigorous discipline classification.

Abstract

A discipline such as business and management (B&M) is very broad and has many fields within it, ranging from fairly scientific ones such as management science or economics to softer ones such as information systems. There are at least two reasons why it is important to identify these sub-fields accurately. Firstly, for the purpose of normalizing citation data as it is well known that citation rates vary significantly between different disciplines. Secondly, because journal rankings and lists tend to split their classifications into different subjects, for example the the Association of Business Schools (ABS) list, which is a standard in the UK, has 22 different fields. Unfortunately, at the moment these are created in an ad hoc manner with no underlying rigour. The purpose of this paper is to identify possible sub-fields in B&M rigorously based on actual citation patterns. We have examined 450 journals in B&M which are included in the ISI Web of Science (WoS) and analysed the cross-citation rates between them enabling us to generate sets of coherent and consistent sub-fields that minimise the extent to which journals appear in several categories. Implications and limitations of the analysis are discussed