Gadjah Mada International Journal of Business

Behavioral Intention to Use Artificial Intelligence (AI) Among Accounting Students: Evaluating the Effect of Job Relevance

2025-09-02T08:16:15+07:00

This research meticulously evaluates the influence of job relevance on accounting undergraduates' behavioral intentions toward utilizing Artificial Intelligence (AI), scrutinizing the mediating role of perceived usefulness. Anchored in the extended Technology Acceptance Model, this study employs a cross-sectional, survey-based methodology to gather data from 136 undergraduate students across various public and private Malaysian universities. The empirical evidence elucidates that job relevance positively influences the students’ behavioral intentions regarding AI integration. In tandem, perceived usefulness emerges as a significant mediator, revealing its critical role in this relationship, thus manifesting a partial mediation effect. The findings highlight the necessity of strategically reconfiguring accounting education curricula to incorporate pedagogical approaches aligned with the influential factors of job relevance and perceived usefulness, thereby intensifying students’ intentions to engage with AI in academic and professional settings. Such an educational evolution is paramount, equipping accounting students with the requisite competencies and insights to navigate the accounting profession’s rapidly transforming, technologically driven landscape.

Machine Learning vs. Human Investors: Analyzing Adaptive Herding Behaviour in U.S. Stocks vs. Shariah-Compliant Stocks in Malaysia and Indonesia

2025-09-02T08:16:15+07:00

This study examined the effectiveness of machine learning models in capturing adaptive herding behaviour in the US, Malaysia, and Indonesia. Utilising data from January 2010 to December 2023, the study incorporates market sentiment (Thomson Reuters MarketPsych Indices), news sentiment (Bloomberg Sentiment Analysis), and investor happiness measures (Hedonometer). The methodology employs both static and adaptive herding analyses using the CSAD approach, enhanced by real-time sentiment analysis and various machine learning models, including single- and multi-layer neural networks. The results indicate significant differences in herding behaviour across the three markets, with machine learning models demonstrating superior performance in capturing herding behaviour and faster normalisation after major macroeconomic events than traditional methods. These findings highlight the potential of machine learning models to challenge the static assumptions of the Efficient Market Hypothesis and provide insights for designing better trading algorithms by considering the impact of market sentiment, news sentiment, and investor happiness.

MSME Loan Composition, Financial Stability, and Government Ownership: Evidence from Indonesia’s Banking Sector

2025-09-02T08:16:15+07:00

The Indonesian government has promoted growth in MSMEs (micro, small, and medium enterprises) by targeting banks to allocate at least 30% by Q2 2022 of their loan portfolios to MSMEs. However, by Q4 2022, this target had not been met, partly due to the high credit risk and information asymmetry in the MSME sector. While past studies often suggest that MSME lending improves bank stability, this study finds otherwise. Using panel data from 96 banks between Q1 2019 and Q4 2022 and applying the GMM method, the result shows that a higher MSME loan share tends to reduce bank stability. Interestingly, when government ownership is considered, the effect turns positive, suggesting that government-owned banks may manage MSME risks better. This may be due to stronger oversight, policy support, their experience with development-focused lending, a broader business focus beyond profits, and their role as agents of change in supporting financial inclusion and economic stability. These findings suggest the need for better MSME policy alignment, risk mitigation tools, and a centralized MSME database to balance financial inclusion with banking sector stability.

The Impact of Green Bonds on Firm Value and the Role of ESG: Revisiting the Stakeholder Value Maximization Theory

2025-09-02T08:16:14+07:00

This study evaluates managers’ motivations in decision-making and whether they aim to maximize value or meet environmental and social pressures at the expense of value. The authors utilize green bonds as a treatment and measure changes in firm value data using Tobin’s Q, controlling for times and individual fixed effects and several company characteristics. This research documents the positive impact of issuing green bonds on a firm's value using the difference-in-differences (DD) method. Our findings are confirmed over the aggregate sample and most of the industry subsample, whereas we observed negative associations between green bonds and firm value only in the industrial subsample (comprising industrial and commercial services, industrial goods, and transportation). These findings align with the hypothesis of the stakeholder value maximizing theory. So, we suggest that, based on the data we analyzed about green bond issuances, companies choose to issue green bonds to increase their overall value.

ESG Controversy as a Moderator of the Impact of Environmental, Social, and Governance Indicators on High-Profile Companies' Performance in Asia

2025-09-02T08:16:14+07:00

The purpose of this research paper is to analyze the relationship of each environmental, social, and governance (ESG) aspect to the performance of high-profile companies, and the purpose of this study further analyzes the role of moderating ESG controversies on the relationship of every aspect of ESG disclosure to corporate performance. Application of research observation data, using observations from 2010 to 2019, is a company in an Asian country listed on the Sustainable Stock Exchange, which requires companies to publish and report ESG aspects. This study excludes the period 2020-2024 from the analysis because the global economic crisis due to the COVID-19 pandemic has caused the company to change its priorities. The purposive sampling method was used in this study, obtaining 847 samples of observational data from seven countries in the Asian region. Regression moderation analysis was used to examine the relationship between each aspect of ESG and the performance of high-profile companies with the controversy of ESG as a moderation variable. The study results obtained findings that social and governance aspects have a positive and significant effect on the performance of high-profile companies. However, environmental aspects were found to be insignificant. The findings of the ESG controversy weaken the relationship between social and governance aspects of high-profile company performance, and the ESG controversy does not moderate the relationship of environmental disclosure to company performance.