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This study aims to understand the importance and challenges of adopting artificial intelligence (AI) in the banking industry in Malaysia and examine the factors that are important in investigating consumers' intention to adopt AI in banking services.

The qualitative research was carried out using in-depth interviews from officials in the baking industry to understand the importance and challenges of adopting AI in the banking industry. In the quantitative study, a total of 302 completed questionnaires were received from Malaysian banking customers. The data were analysed using the SmartPLS 3.0 software to identify the important predictors of their intention to adopt AI.

The qualitative results reveal that AI is an essential tool for fraud detection and risk prevention. The absence of regulatory requirements, data privacy and security, and lack of relevant skills and IT infrastructure are significant challenges of AI adoption. The quantitative results indicate that attitude towards AI, perceived usefulness, perceived risk, perceived trust, and subjective norms significantly influence intention to adopt AI in banking services while perceived ease of use and awareness do not. The results also show that attitude towards AI significantly mediates the relationship between perceived usefulness and intention to adopt AI in banking services.

Financial technology (FinTech) is regarded as a critical determinant of strategic planning in the banking industry. While AI provides various disruptive opportunities in the FinTech space in terms of data collection, analysis, safeguarding and streamlining processes, it also poses a sea of threats to incumbent banks. This study provides vital insights for the policymakers of the banking industry to address the challenges of adopting AI in banking. It also provides the important predictors of the bank customers' intention to adopt AI in banking services. Policymakers can devise their strategies to enhance AI adoption considering the facts.

This study is amongst the pioneer in exploring the importance and potential challenges in implementing AI technology in banking services and identifying the essential factors influencing the intention to adopt AI in Malaysia's banking services.

This study aims to explore, in the context of Machinery and Equipment sector of Malaysia, the association between average wages and share of employment in automatable jobs, specifically whether the association between average wages and share of employment automatable jobs is asymmetric in nature.

The responses obtained from the structured interview of 265 firms are used to build up the empirical models (conditional mean regression and quantile regression).

The conditional mean regression findings show that employment levels in some low-waged, middle-skilled jobs are negatively associated with average wages. Furthermore, the quantile regression results add that firms that possess higher levels of share of employment in automation jobs are found to have a stronger association to average wages than those possessing a lower share of employment in automation jobs.

From the theoretical perspective, the findings of this study add to the body of knowledge of the theory of minimum wages and the concept of job polarization. From a policy perspective, the findings of this study can serve as a critical input to standard setters and regulators in devising industrial and as education policies.

Based on the assumption of a constant average policy effect on automatable jobs, conditional mean regression models have been commonly used in prior studies. This study makes the first attempt to employ the quantile regression method to provide a deeper understanding of the relationship between wages and employment in automatable jobs.

The purpose of this study is to examine the key challenges currently prevalent in the Machinery and Equipment (M&E) sector of Malaysia and to offer an integrative Industry 4.0 strategic roadmap. The Environmental Scan 2016 and 2018 provides a basis for the identification of the challenges in the M&E sector of Malaysia. The study further investigates the challenges by analyzing the responses of four major stakeholders in a Focus Group Discussion. The findings reveal that the M&E sector suffers from very low automation adoption. This study is among the first few to analyze the challenges in the M&E sector and lay out a strategic roadmap encompassing the role of each stakeholder at every phase of the transition toward Industry 4.0. The proposed method of transitioning through targeted incentive schemes will help academics and practitioners in developing concrete and workable action plans to conduct the transition process.