Author ORCID Identifier
Behrooz Davazdahemami: https://orcid.org/0000-0003-2885-6014
Hamed Zolbanin: https://orcid.org/0000-0001-5783-1495
Amir Zadeh: https://orcid.org/0000-0002-3171-5629
Abstract
This study addresses the crucial task of maintaining the academic journal "Aims and Scope" (A&S), particularly in the Information Systems (IS) field. The A&S serves as a defining guide for editorial decisions and audience engagement. Outdated A&S can hinder innovative research and new ideas. To tackle this, the paper introduces an innovative automated framework that employs Large Language Models (LLMs), like Generative Pre-trained Transformer (GPT) models, to update A&S statements. First, using a series of quantitative analyses based on state-of-the-art natural language processing methods we show the ability of LLMs to rate the fitness of research articles to the journals’ A&Ss. Following that, evaluating more than 4000 abstracts from 13 top-tier IS journals, the study reveals potentially outdated A&S statements. Using the GPT-3.5 API, the framework assesses recent papers for alignment with the journal’s scope, identifying emerging areas and interdisciplinary aspects, and suggests a new improved version of A&S encompassing those areas. This enables periodic A&S updates to mirror the dynamic IS trends. Also, by investigating the proposed A&S updates, we discuss the evolution of the IS discipline over the past several years.
Recommended Citation
Davazdahemami, B., Zolbanin, H., & Zadeh, A. (In press). Revitalizing Scholarly Compass: Harnessing GPT-Powered Automation for Dynamic Aims & Scope Evolution in Academic Journals. Communications of the Association for Information Systems, 55, pp-pp. Retrieved from https://aisel.aisnet.org/cais/vol55/iss1/37
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