FROM PERSONAL AND ENVIRONMENTAL FACTORS TO AI IMPACT: RETHINKING THE ACCOUNTING CAREER PATHWAY

Abstract

This study investigates how personal traits (self-efficacy and motivation) and environmental influences (parental and peer influence) shape accounting career choices while emphasizing the growing impact of artificial intelligence (AI) on the profession. AI is transforming accounting by automating repetitive tasks, redefining required competencies, and creating demand for digital literacy, data analytics, and technological adaptability. This study, which involved 103 accounting students, used a quantitative methodology with purposive sampling and was based on Bandura's Social Cognitive Career Theory (SCCT). Descriptive statistics and Spearman correlation were used to analyse the data, which were gathered using a structured questionnaire with career-related scales and demographic questions. Findings revealed that all four factors recorded high mean scores, with motivation ranking the highest. Correlation analysis, however, revealed that only motivation had a marginally positive association with students' profession choices; peer, parental, and self-efficacy factors exhibited negative associations. These results suggest that traditional personal and environmental factors may no longer decisively influence accounting career pathways. Instead, AI-driven changes, such as evolving skill requirements, professional certifications are linked to technology readiness, and new career opportunities shaped by automation—are becoming more influential. By integrating traditional factors with AI’s disruptive role, this study explores how future accountants must adapt to shifting expectations, where success depends not only on personal drive but also on digital competence in an AI-augmented profession. The study provides theoretical insights by extending SCCT into the Malaysian context and practically by offering guidance for educators, policymakers, and parents to align career support with the realities of an AI-driven labour market. Future research should examine additional predictors such as digital readiness, career exposure, and global employability trends.