Modelling the Antecedents of Perceived Trust in AI-Based Records and Information Management Systems

Liah Shonhe

doi:10.25159/2663-659X/21085

Authors

Liah Shonhe Dalian University of Technology https://orcid.org/0000-0003-2233-2164

DOI:

https://doi.org/10.25159/2663-659X/21085

Keywords:

Artificial intelligence (AI), behavioural use intention (BUI), perceived trust, Trust-based extended valence framework (TEVF), records and information management (RIM)

Abstract

This study explores the antecedents of trust and its impact on the intention to use artificial intelligence (AI)-based records and information management systems (RIMS), applying the trust-based extended valence framework (TEVF) to an African context, where research on AI trust in records and information management remains limited. Using a mixed methods approach, snowball and purposive sampling techniques were used to gather data online from 890 participants. Correlation analysis and ANOVA were conducted to examine the TEVF dimensions, while thematic analysis was applied to qualitative data. The findings revealed that perceived trust was positively correlated with behavioural use intention and perceived benefits. However, perceived risks was not significantly correlated with either behavioural use intention or perceived benefits. The relationship between perceived trust and perceived risks was found to be weakly negative. Perceived trust and behavioural use intention exhibited significant group differences based on geographic location and work experience. Transparency, understanding basic functionality, human oversight and control, ease of use, explainability, and regulatory framework were identified as key trust enablers, while cybersecurity concerns, ethical risks, resources constraints, and skills constraints emerged as major barriers to behavioural use intention. The study posits that sustainable AI integration into records and information management hinges on transparent governance, targeted training, and strategic advocacy, ensuring efficiency while mitigating adoption barriers.

Author Biography

Liah Shonhe, Dalian University of Technology

Dr. Liah is a dynamic Records and Information Management Specialist and Lecturer with the following research interest: technology readiness and adoption, AI-induced professional identity threat, government AI readiness, digital transformation, open data, continuous professional development, archives and records management, library science, and organizational change. My research explores how emerging technologies enhance information governance, particularly in developing regions, leveraging advanced analytical tools like SPSS-AMOS, SmartPLS and R. Passionate about education, I integrate cutting-edge technology into teaching to equip students with lifelong, adaptable learning skills. With leadership roles in professional associations, and a strong publication record, I bridge theory and practice to drive innovation in knowledge management.

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