Predicting Adoption of Artificial Intelligence Tools among the Researcher Scholars

Abstract

Purpose: The research evaluates the predictors of Artificial Intelligence (AI) tool adoption among research scholars.

Design/Methodology/Approach: 360 research scholars were chosen for the current study, and the Model was built using Multiple linear regression techniques.

Findings: Effort expectancy, Performance expectancy, and Personal Innovativeness significantly influence the research scholars' Intention to adopt AI tools.

Practical Implication: The researcher assists in evaluating predictors of adoption to use AI tools; thus, AI companies can use the antecedents to retain existing users and convert non-users into users.

Originality/Values: There are many studies conducted on the “Unified Theory of Acceptance and Use of Technology” (UTAUT) model & “Technical Opinion Leadership” (TOL). However, this study extends the literature by integrating two theories and building “Multiple Linear Regression” (MLR) in AI.

Funding Statement: No organisation or person has provided financial assistance or money for this study. Without outside funding, the study's research and analysis were carried out independently. The study was conducted entirely on an unsupported basis, including data collection, processing, and interpretation. The integrity and calibre of this study are entirely the result of the authors' time, labour, and resources. The lack of outside financing does not lessen the relevance of the findings reported here, and the study's results are exclusively the result of the author’s painstaking work.

Ethical Compliance: Strict ethical standards were maintained throughout this investigation, and participants' anonymity and informed permission were guaranteed. It complies with pertinent laws and is sanctioned by the proper ethics board. The study upholds the rights of participants, conducts itself honestly, and is neutral while correctly presenting its findings.