Model uncertainty in the evaluation of the impact of interdisciplinary research in Business Studies: A Multiverse Analysis
Year: 2025
Type: article
Abstract: There are many operational definitions and many indicators of interdisciplinary research. As a consequence, claims about its scientific impact may suffer from high model uncertainty and low credibility, since they can be based on specific results highly dependent on implicit choices of the modelling process. This study addresses the issue with Multiverse Analysis, a protocol for multi-model inference that specifies the modelling factors that can generate variability in the results. Combinations of these factors are fit and analysed simultaneously, so that inference cannot be selective in reporting results. 1,344 regression models are fit to a sample of 5,828 articles from journals of Business Studies. The Multiverse Analysis does not find support for the claim that interdisciplinary articles of Business are more cited. Even though a minority of the models reach statistical significance, the median estimates for the effect size are close to zero. Through an analysis of variance it is established that the choice of the indicator is the most influential, reproducing 45% of the total variance. This result confirms that claims on the scientific impact of IDR are highly dependent on the operational definition. The choice of the sources of metadata on articles, a factor previously overlooked in literature, reproduce 10% of the total variance. Findings suggest caution in accepting generic claims about the scientific impact of interdisciplinary research in social sciences. • The scientific impact of interdisciplinarity depends on modelling choices. • Multiverse Analysis allows simultaneous analysis of many models. • Analysis rejects that interdisciplinarity is successful in Business Studies. • Further analysis of variance assesses the most influential choices. • The variance induced by choosing the source of data is concerning.
Source: Journal of Informetrics
Authors: Giulio Giacomo Cantone, Paul Nightingale
Institutions: Magna Graecia University, University of Sussex
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,220
Funder: Schmidt Family Foundation
OpenAlex ID: W4415608095