Conceptual framework to explore artificial intelligence technology (AIT) readiness and adoption intention in records and information management (RIM) practices: a proposal
Abstract
Purpose
This research proposal aims to address the growing significance of artificial intelligence (AI) technology in the field of records and information management (RIM) within the African context. Despite the increasing prevalence of AI, there is a lack of comprehensive understanding regarding the factors influencing AI readiness and adoption in RIM. The primary purpose of this paper is to explore these factors and propose an AI readiness and adoption conceptual framework.
Design/methodology/approach
A comprehensive literature review was conducted to identify the proposed variables and support the hypothesis development. The theoretical foundation of the proposed conceptual framework is based on three theories: the technology acceptance model (TAM), the technology readiness index (TRI) and the cognitive appraisal theory (CAT).
Findings
The literature reveals that there is a lack of empirical investigation of AI readiness and adoption within the RIM context. Through the proposed conceptual model, the researcher anticipates uncovering critical insights into the factors influencing AI readiness and adoption in RIM practices across African nations.
Research limitations/implications
The proposed model is not yet empirically tested and the study's scope is limited to African nations.
Originality/value
The proposed model takes a pioneering approach to empirically investigate AI readiness and adoption within the RIM field, specifically in an African context which is understudied.
Keywords
Acknowledgements
The author acknowledges the use of OpenAI, ChatGPT and QuillBot for paraphrasing, summarizing and grammar checks.
Citation
Shonhe, L. (2024), "Conceptual framework to explore artificial intelligence technology (AIT) readiness and adoption intention in records and information management (RIM) practices: a proposal", Records Management Journal, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/RMJ-09-2023-0046
Publisher
:Emerald Publishing Limited
Copyright © 2024, Emerald Publishing Limited