Artificial Intelligence Acceptance, Uncertainty Avoidance, and Job Performance among Academicians at UiTM Cawangan Pahang: : An Exploratory Factor Analysis

Sharifah Norhuda  Syed Wahid; Mastura Mohamad; Hazlin Hasan; Juhaida Ismail; Siti Fazilah  Hamid; Nor Tasnim Syahera  Rasak

Authors

Sharifah Norhuda Syed Wahid Universiti Teknologi MARA Cawangan Pahang
Mastura Mohamad Universiti Teknologi MARA Cawangan Pahang
Hazlin Hasan Universiti Teknologi MARA Cawangan Pahang
Juhaida Ismail Universiti Teknologi MARA Cawangan Pahang
Siti Fazilah Hamid Universiti Teknologi MARA Cawangan Selangor
Nor Tasnim Syahera Rasak Universiti Teknologi MARA Cawangan Perak

Keywords:

artificial intelligence, uncertainty avoidance, job performance, exploratory factor analysis

Abstract

This exploratory study evaluated the validity and reliability of a modified Technology Acceptance Model (TAM) incorporating Uncertainty Avoidance (UA) and Job Performance (JP) among academicians at Universiti Teknologi MARA (UiTM) Cawangan Pahang. Data were gathered through online questionnaires distributed to 32 academicians across 11 faculties in a pilot study using stratified random sampling. The measurement instrument assessed three main constructs, Acceptance of Artificial Intelligence (AI), comprising Perceived Usefulness (PU) and Perceived Ease of Use (PEoU), UA, and JP. Exploratory Factor Analysis (EFA) using Principal Component Analysis with Varimax Rotation confirmed strong construct validity. The Kaiser-Meyer-Olkin (KMO) measures were satisfactory across all constructs (AI Acceptance = 0.867, UA = 0.899, JP = 0.903), with significant Bartlett’s tests of sphericity (p < 0.001). All items demonstrated factor loadings above the 0.60 threshold. The total variance explained exceeded the 60% benchmark for AI Acceptance (96.39%), UA (92.81%), and JP (88.82%). Reliability testing yielded exceptionally high Cronbach’s alpha coefficients for PU (0.997), PEoU (0.986), UA (0.980), and JP (0.981), demonstrating robust internal consistency. The findings confirm that the adapted instrument is valid and reliable for large-scale data collection to investigate the impact of AI acceptance on job performance, with uncertainty avoidance as a moderating factor.

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