Smart Human Augmentation Through Artificial Intelligence Tools Using Technology Acceptance Model an Impact Analysis on Performance of IT Employees Bengaluru

Abstract

This thesis explores the acceptance and adoption of AI tools within the framework of the Technology Acceptance Model (TAM). The TAM serves as a theoretical foundation for understanding users' attitudes and behaviors towards the adoption of new technologies, ultimately leading to the practical utilization of AI tools and enhancing employee performance. Employing a descriptive study design, cross-sectional data were collected from IT professionals in Bengaluru City, with a sample size of 409 identified through convenience sampling. Normality checks were conducted using both the Kolmogorov- Smirnov and Shapiro-Wilk tests. Internal consistency was assessed using Cronbach’s Alpha via IBM SPSS (26). Convergent validity was examined using the Gaskin tool, yielding favorable results indicative of the measurement instrument's reliability. Confirmatory Factor Analysis (CFA) was performed to evaluate the reliability and validity of the data. Structural Equation Modeling (SEM) was employed to assess model fit using IBM AMOS (26). Mediation analysis was conducted to analyze significant indirect effects using IBM AMOS (26) employing the User Estimation method. Comparative analysis was undertaken to test differences among categorical variables using the Mann-Whitney U test and Kruskal-Wallis Test. Through empirical analysis, this study provides deeper insights into how individuals perceive and adopt AI tools in their professional environments. By identifying drivers and barriers to AI tool adoption, this research offers valuable insights to inform strategies aimed at facilitating the successful integration of AI technologies into the workplace. Ultimately, this study contributes to advancing knowledge in the field of AI adoption and its implications for enhancing job performance and productivity, often referred to as smart human augmentation. The study's scope is confined to IT professionals in Bengaluru, which may impact generalizability.