Research and Publications
Focus Areas
My research program investigates the intersection of accounting with technology, governance, education, and analytics. Key themes include the application and impact of Accounting AI and Machine Learning in Accounting, the evolution of Accounting Technology, data-driven Accounting Analytics, innovations in Accounting Pedagogy, and aspects of Corporate Governance and Corporate Disclosures, particularly in international contexts. I aim to produce research that is both rigorous and relevant to academics, practitioners, and policymakers navigating the changing landscape of accounting and finance.
Forthcoming Peer-Reviewed Publications
Exploring Gamification Approaches in Accounting Courses: A Systematic Review
Anwar, Y., & Mulyadi, M. (2025, forthcoming). Exploring Gamification Approaches in Accounting Courses: A Systematic Review. Journal of Accounting and Finance, 25(2), 24-33. Accepted for publication.
Abstract: This systematic review synthesizes recent research about gamification in accounting education, where student disengagement and motivational obstacles frequently hinder learning. This analysis synthesizes empirical research demonstrating how gamified elements can improve student motivation, engagement, and, in numerous instances, learning results. Self-Determination Theory and Problem-Based Learning frameworks explain gamification's ability to meet students' demands for autonomy, competence, and relatedness within accounting contexts. Although most research indicates favorable effects on exam scores, problem-solving abilities, and enjoyment, conflicting results highlight the necessity for meticulous design and alignment with learning objectives. Additionally, resource limitations, technical obstacles, and student diversity present tangible impediments to the efficient implementation of gamification. This paper outlines the primary advantages of gamified accounting education, specifies circumstances in which gamification may not enhance performance, and offers suggestions to implement or improve game-based methodologies. This study identifies critical deficiencies, providing a framework to direct future research and improve the educational efficacy of gamification in accounting instruction.
Dynamic Pricing in U.S. Soccer Tournaments: A Business Analytics Perspective on Revenue Management
Mulyadi, M., & Anwar, Y. (2025, forthcoming). Dynamic Pricing in U.S. Soccer Tournaments: A Business Analytics Perspective on Revenue Management. Management and Economics Research Journal. Accepted for publication.
Abstract: This conceptual paper analyzes the use of dynamic pricing in soccer competitions in the United States, emphasizing the role of business analytics in enabling data-driven ticket pricing methods. Based on economic, marketing, and sports management perspectives, we identify a gap in the literature about dynamic pricing in soccer events, which have traditionally depended on static pricing models. Our research examines how dynamic ticket pricing might enhance income and attendance for soccer competitions while ensuring fairness for fans. Our research reveals that dynamic pricing enables organizers to maximize revenue from high-demand matches by increasing ticket prices to align with real-time market value and to enhance attendance for low-demand matches by strategically reducing prices and providing discounts. These modifications are facilitated by business analytics, encompassing demand forecasts and price optimization algorithms, which guarantee that pricing decisions are informed by current data on consumer willingness to pay. Our analysis emphasizes that dynamic pricing in sports functions not merely as a mechanism for price augmentation but may also augment consumer value and accessibility, as demonstrated by its capacity to lower ticket prices when warranted. Drawing on existing literature, the paper provides a theoretical framework and practical recommendations, highlighting areas for future empirical research. We offer practical insights into the implementation of dynamic pricing in soccer, emphasizing the significance of analytical models, effective stakeholder engagement, and policy considerations to balance revenue objectives with fan experience. These findings enhance sports management literature by applying dynamic pricing theory to U.S. soccer and providing a framework for utilizing business analytics in the optimization of sports revenue.
The Boardroom's Digital Footprint: Exploring the Impact of Diversity on Web-Based Disclosures
Mulyadi, M., & Anwar, Y. (forthcoming). The Boardroom's Digital Footprint: Exploring the Impact of Diversity on Web-Based Disclosures. International Journal of Web Based Communities. Accepted for publication.
Abstract: This study investigates the influence of board diversity on the extent of web-based corporate disclosures within the top publicly listed corporations in Indonesia and Malaysia. Grounded in media agenda-setting theory and using multiple regression analysis, it reveals a significant but nuanced impact of board diversity on disclosure practices. The study finds that while board nationality diversity negatively impacts web-based disclosures, robust corporate governance can mitigate this effect. Conversely, the diversity of foreign educational backgrounds and board gender diversity does not significantly influence web-based disclosures. These findings underscore the importance of a nuanced understanding of board diversity and its interaction with corporate governance in shaping disclosure practices. The research contributes to corporate web disclosure theory and offers practical insights for corporations seeking to optimise their web-based disclosures.
Selected Peer-Reviewed Publications
Below is a representative selection of recent peer-reviewed publications. Please see my Google Scholar profile for a comprehensive list and citation details.
Corporate Control in Emerging Markets: The Non-Linear Dynamics of Foreign Board Involvement
Anwar, Y., & Mulyadi, M. (2024). Corporate control in emerging markets: The non-linear dynamics of foreign board involvement. Corporate Ownership & Control, 21(2), 45–51. https://doi.org/10.22495/cocv21i2art4
Abstract: This study examines the complex dynamics of foreign board membership in corporate governance within 266 family-owned corporations in Indonesia and Malaysia. By employing multiple regression analysis, we have determined a turning point in the level of foreign board representation. Once this threshold is surpassed, the advantages of governance start to diminish, indicating the necessity for a more balanced and diverse board composition. Surprisingly, the presence of women on corporate boards did not have a significant effect on governance practices. The findings indicate that although foreign expertise might be advantageous, relying too much on it can have negative consequences. These observations encourage a reassessment of the makeup of boards in developing economies, suggesting areas for further investigation into governance methods in different types of organizations and cultural contexts.
Beyond Traditional Analysis: Using Machine Learning to Investigate Intellectual Capital Disclosures
Anwar, Y., & Mulyadi, M. (2023). Beyond traditional analysis: Using machine learning to investigate intellectual capital disclosures. Corporate Ownership & Control, 20(3), 437–446. https://doi.org/10.22495/cocv20i3siart16
Abstract: This research aims to conduct a thorough examination of the practices related to the disclosure of intellectual capital (IC). In the context of the dynamic knowledge-based economy, it is crucial for organizations to recognize the significance of IC. Intellectual capital comprises three key components: internal capital, external capital, and human capital. These elements play a pivotal role in generating value for organizations and positioning them competitively in the market. The current body of literature is constrained in its empirical investigation of IC disclosures and their congruence with organizational strategies. This research utilizes a combination of textual analysis and machine learning techniques, specifically K-means clustering, to examine the practices of IC disclosure. The integration of machine learning techniques facilitates the identification of patterns and interdependencies among diverse IC attributes. Notably, while the current literature has predominantly focused on IC disclosures within established frameworks, it often falls short of empirically exploring patterns and interconnections between different IC attributes. The results of the study indicate a notable emphasis on the disclosure of human capital and provide valuable insights into the different strategic priorities based on the clustering of IC attributes. The insights provided offer significant value to organizations as they facilitate the improvement of transparency and the effective communication of the strategic importance of IC. Furthermore, this study makes a valuable contribution to the existing theoretical framework on IC by identifying the interconnections that exist between various attributes of IC. The utilization of these findings by policymakers and standard- setting bodies can be instrumental in the development of more extensive guidelines for IC disclosures.
Machine Learning in Accounting: Insight from the March 2023 Bank Failures
Mulyadi, M., & Anwar, Y. (2023). Machine learning in accounting: Insight from the March 2023 bank failures. Risk Governance and Control: Financial Markets & Institutions, 13(2), 28–36. https://doi.org/10.22495/rgcv13i2p3
Abstract: This research investigates the bank failures in the United States in March 2023, concentrating on the impact of held-to-maturity debt instruments in the event and the implications for accounting methods. Our research deciphers the alleged “accounting loophole” (Farrell, 2023) associated with these securities and provides an in-depth analysis of the associated accounting treatment. We analyze the accounting treatment using the Accounting Standards Codification (ASC) and International Financial Reporting Standards (IFRS). Furthermore, our study employs automated machine learning techniques and the local interpretable model-agnostic explanations (LIME) method to identify key accounting features that could explain bank failures. The research identifies five essential accounting aspects, two of which are related to held-to- maturity assets. The findings underscore the importance of these accounting features in evaluating financial institutions, thereby providing valuable insights for stakeholders, decision-makers, and future research. Our research also advocates for increased transparency and accuracy in accounting practices, via ASC 825 (Financial Accounting Standards Board [FASB], n.d.-a), particularly related to the fair value of held-to-maturity securities.
Working Papers & Ongoing Research
Jackson, G., Doedderlin, J., & Mulyadi, M. (Working Paper). Valuing Ecosystem Services in the Ecuadorian Amazon: Strategic Applications of ESVD-Based Benefit Transfer for Conservation Finance.
Abstract: This paper demonstrates how ecosystem service valuation can inform conservation finance strategies using standardized benefit transfer methodologies and internationally recognized valuation databases. We apply the Ecosystem Services Valuation Database (ESVD) to estimate the Total Economic Value (TEV) of ecosystem services provided by a 60-hectare property in the Ecuadorian Amazon. Using the benefit transfer method (BTM), we compare Net Present Value outcomes across three scenarios: Carbon Sequestration Only, Extractive Land Use, and Full Ecosystem Services Valuation. The results reveal that TEV far exceeds the value of both Carbon-Only and Extractive-Use scenarios, underscoring the importance of recognizing ecological assets beyond tradable commodities. We then explore practical financing pathways including payments for ecosystem services (PES), biodiversity credits, conservation easements, and REDD+, and propose multiple investment structuring options based on ESVD-derived valuation. Drawing from recent developments, we argue for a diversified, multi-service approach to ecosystem finance. The paper concludes with strategic recommendations for aligning conservation investments with ecosystem-based valuation models and outlines future research directions in ecosystem service accounting, blended finance, and community co-design.
Anwar, Y., & Mulyadi, M. (Working Paper). Enhancing Accounting Instruction with Excel: A Theoretical Perspective on Learning and Cognitive Load.
Abstract: This paper explores how Excel-based instruction can enhance learning in undergraduate accounting through Active Learning Theory and Cognitive Load Theory. Studies on technology-enhanced accounting instruction underscore spreadsheets’ utility, yet few examine their theoretical underpinnings. We adopt a conceptual approach with classroom examples drawn from Principles of Managerial Accounting and Intermediate Accounting courses. Our observation shows that students were more involved, solved problems in real-time, and had less superfluous work. Well-scaffolded Excel projects enhanced professional skills and conceptual clarity. Including Excel promotes work preparedness, better retention, and practical learning. These results provide both a strong theoretical underpinning and practical guidance for including spreadsheets in accounting courses.
Mulyadi, M., & Anwar, Y. (Working Paper). Integrating Artificial Intelligence in Accounting: An Empirical and Theoretical Synthesis.
Abstract: Artificial intelligence (AI) and machine learning (ML) are increasingly prevalent in the accounting field, offering the potential to automate routine tasks, enhance data analysis, and provide deeper insights across auditing, financial accounting, and management accounting. Despite a growing body of research, the literature remains fragmented, with many studies focusing on isolated applications rather than a unified exploration of AI’s impact on the profession. This paper presents a comprehensive literature review that synthesizes research in these areas. Through a systematic analysis, we classify studies by key accounting domains and AI/ML techniques, highlighting both the benefits—such as efficiency gains, improved accuracy, and real-time reporting—and the persistent challenges, including data quality issues, model explainability, ethical considerations, and skill gaps among practitioners. Drawing on theoretical frameworks like agency theory and the technology acceptance model, we discuss how AI adoption is reshaping accountants’ roles, emphasizing the need for human oversight and strategic judgment. We also map critical research gaps, including the importance of explainable AI, the under-explored domain of management accounting, and emerging opportunities for interdisciplinary collaboration. The paper concludes with recommendations for practitioners and researchers aimed at guiding responsible and effective AI deployment, ensuring that AI-driven innovations align with the core objectives of accounting: transparency, integrity, and stakeholder trust.
Mulyadi, M., & Anwar, Y. (Working Paper). The Changing Landscape of Accounting in the Digital Age.
Abstract: The accounting profession is rapidly evolving in response to the rise of artificial intelligence (AI) and data analytics. This paper examines the emerging convergence of accounting and data science, proposing the concept of the “Accounting Data Scientist” as a hybrid role. Through a qualitative meta-analysis of recent literature, we explore how accountants are expanding their expertise beyond conventional compliance tasks to embrace technologies like machine learning and big data analytics. The findings reveal that AI is not displacing accountants but rather augmenting their capabilities, thereby shifting the profession’s identity. We argue that future accountants will need dual competencies in both financial principles and data-driven methodologies. This evolution has broad implications for education, professional bodies, and firms: curricula must integrate programming and analytical skills, while policies and certification frameworks should reflect the profession’s technological transformation. Overall, this study underscores that combining accounting and data science capabilities can enhance the profession’s relevance, strategic influence, and long-term value.
Mulyadi, M., & Anwar, Y. (Working Paper). Business-Focused Explainable Artificial Intelligence (XAI): Methods, Bias Audits, and Trust Building.
Abstract: This paper provides an integrative review of explainable and ethical Artificial Intelligence (AI) within business analytics. After outlining challenges posed by complex black-box models, we discuss how explainability techniques—ranging from feature attribution to rule-based surrogates—can enhance managerial trust and foster transparent decision-making. We then examine fairness audits, focusing on bias detection and mitigation through established frameworks and toolkits. Recognizing that user adoption is driven by trust, the paper explores how well-crafted AI explanations can build confidence, especially when combined with consistent governance practices. The contributions include a structured synthesis of XAI tools for practical business deployment, a roadmap for bias auditing, and a framework linking explainability to trust-driven adoption of AI. Implications for both researchers and practitioners are outlined.
Research Impact
You can find further details and citation metrics for my research on my Google Scholar Profile. As of April 13, 2025, my research has received 716 citations, with an h-index of 13 and an i10-index of 15.