article · Discover Artificial Intelligence
An investigation of 503 academic researchers across 20 public and private universities in Ghana evaluates how artificial intelligence tools influence scholarly productivity. Assessing ChatGPT-4 and SciSpace Ai through a socio-technical framework reveals that both technologies are positively associated with academic research output. The cognitive orientation of researchers also connects positively to research productivity and acts as an explanatory mechanism linking AI tool adoption to higher output. However, research orientation alters the direct influence of each tool differently. A stronger research orientation diminishes the link between ChatGPT-4 and research output, whilst enhancing the positive association between SciSpace Ai and output. These observations indicate that the practical utility of generative and discovery-focused AI tools depends heavily on human cognitive orientation, highlighting the need for targeted training, institutional backing, and formal governance frameworks for ethical integration in higher education.
Artificial intelligence tools are rapidly entering academia, but technological access alone does not ensure productive outcomes. Understanding how personal research orientation shapes the effectiveness of tools such as ChatGPT-4 and SciSpace Ai helps universities design better training programmes. It also guides policymakers in establishing responsible governance frameworks that balance technological capabilities with individual cognitive skills across higher education institutions.
The findings offer early-stage empirical insights that can guide university leadership, education technology vendors, and research administrators in deploying workflow-specific AI software. While the study itself is foundational academic research rather than a commercial product, its results can inform the development of targeted AI training programmes and institutional adoption frameworks designed for higher education researchers.
AI-generated from the published abstract. Always read the original work before citing.
Abstract This study addresses a critical gap in the literature by examining the limited empirical evidence on how artificial intelligence (AI) tools interact with researchers’ cognitive orientations to influence research productivity. Specifically, it develops and tests the novel AI–Research Output (AI-RO) Model, which integrates the Technology Acceptance Model (TAM) and Socio-Technical Systems Theory to explain both the direct and conditional relationships between AI use and research output. Using an explanatory cross-sectional design, data were collected from 503 academic researchers across 20 public and private universities in Ghana. Structural Equation Modeling (SEM) was employed to examine direct, mediating, and moderating relationships among ChatGPT-4, SciSpace Ai, research orientation (RO), and research output (Rout). The findings show that ChatGPT − 4 (β = 0.387, p < 0.001) and SciSpace Ai (β = 0.182, p < 0.001) are positively associated with research output, while both tools are also significantly associated with research orientation (ChatGPT-4: β = 0.395; SciSpace Ai: β = 0.435; p < 0.001). Research orientation is positively associated with research output (β = 0.289, p < 0.001). Mediation analysis indicates that RO is significantly associated with the relationships between ChatGT- 4 (β = 0.114, p = 0.002) and SciSpace Ai (β = 0.126, p = 0.001) and research output. Moderation results reveal a differential interaction pattern, in which RO weakens the association between ChatGPT − 4 and research output (β = −0.204, p = 0.002) but strengthens the association with SciSpace AI (β = 0.130, p = 0.036). The study contributes to theory by advancing a dual mediation–moderation framework that explains how human cognitive orientation conditions AI–research relationships, extending TAM and Socio-Technical Systems perspectives. From a practical standpoint, the findings highlight the importance of aligning AI tools with researchers’ methodological capabilities through targeted training and institutional support. From a policy perspective, the results underscore the need for structured AI governance frameworks that promote responsible, ethical, and context-sensitive integration of AI in academic research. The study demonstrates that the effectiveness of AI in research is contingent not only on technological capability but also on the cognitive and institutional conditions under which it is used.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1007/s44163-026-01997-4
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
