FEMIB 2026 Abstracts

Area 1 - Accounting and Finance

Full Papers

Paper Nr:	26
Title:	Harnessing Intangible Assets for Firm Value: The Moderating Role of the Corruption Perceptions Index in Southeast Asia
Authors:	Cecilia Christie Haryono and Triasesiarta Nur
Abstract:	This study aims to examine the impact of intangible assets (IA) on firm value by considering the Corruption Perceptions Index (CPI) as a moderating variable. This study employs panel data from public companies across six Southeast Asian countries, covering 40,105 observations over ten years (2015–2024). The findings indicate that IA and identifiable intangible assets (IIA) have a positive and significant effect on firm value. Furthermore, the findings also reveal that CPI positively and significantly moderates the relationship between IIA and firm value. This study contributes to the literature by integrating institutional quality into the IA and firm value nexus. It also provides practical implications for managers and policymakers in optimizing IA investments and strengthening institutional governance to enhance firm value.
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Paper Nr:	43
Title:	From ABC to AIBC: A Systematic Literature Review on the Evolution of Artificial Intelligence-Based Costing
Authors:	Haryanto Haryanto, Amelia Setiawan, Sofk Handoyo and Ankit Nandwal
Abstract:	The present study aims to survey the growing body of research on Artificial Intelligence-Based Costing as a development of traditional Activity-Based Costing in contemporary cost and management accounting. The research aims to examine the body of recent research for signs of promise and also identify the conditions for AIBC adoption. A systematic literature review is conducted based on the PRISMA protocol and Scopus-based search and retrieval methods. The search process resulted in 120 studies, of which 18 studies met the final inclusion criteria. The research found that AIBC is increasingly linked to adaptive cost driver selection, speedup of processing, and improvement of analysis support. However, the research also found that the body of research is limited and uneven and that significant barriers to AIBC adoption include data quality, infrastructure preparedness, human skills, governance, and regulatory compatibility. The research does not find AIBC ready to replace traditional ABC but rather views it as an evolving form of AI-based development of traditional costing systems. The research argues that AIBC holds greater promise than actual research maturity and views itself as a consolidating effort to clarify the conditions for AIBC to become practically relevant and academically defensible.
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Paper Nr:	66
Title:	Digital Risk Management Dimensions in Thailand’s Financial Ecosystem: A Performance Importance Analysis
Authors:	Phoranee Rhuwadhana, Wattana Viriyasitavat, Chavalit Ratanatamskul and Danupol Hoonsopon
Abstract:	As financial services become increasingly digital, organizations face growing pressure to strengthen digital risk management beyond conventional control structures. This study examines digital risk management dimensions in Thailand’s financial ecosystem using Performance Importance Analysis (PIA), supported by Exploratory Factor Analysis (EFA). A cross-sectional survey of 110 professionals examined six dimensions, identified through expert panel consultation and validated through Item Objective Congruence evaluation, with paired performance and importance ratings: cybersecurity risk management, data protection risk management, regulatory compliance risk, regulatory risk foresight, cross-jurisdictional risk management, and third-party risk management. The results reveal consistent gaps across all six dimensions, with perceived importance exceeding perceived performance in every case. The widest gaps appear in regulatory and coordination related areas, particularly regulatory risk foresight and cross-jurisdictional risk management. The EFA results suggest a more integrated pattern in importance ratings, whereas performance ratings are grouped into two clusters: ecosystem governance and execution, and technical protection controls. The findings support a six dimensional view of digital risk management and provide a comparative basis for understanding relative priorities among respondents in Thailand’s financial ecosystem.
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Short Papers

Paper Nr:	30
Title:	Behavioural Trends Affecting the Intention to Adopt Islamic Financial Products and Services in UAE Banks: A Social Cognitive Theory Perspective
Authors:	Ayman Abdalla Mohammed Abubakr and Adel Salem Allouzi
Abstract:	This study examines the behavioral determinants influencing customers’ intention to adopt Islamic financial products and services in UAE banks within the framework of Social Cognitive Theory (SCT). The United Arab Emirates operates under a dual banking system in which Islamic and conventional financial institutions coexist, creating a unique financial decision-making environment shaped by cognitive, behavioral, and environmental factors. The proposed model integrates three key constructs within a unified SCT-based structural framework: financial self-efficacy, Islamic financial literacy, and financial behavior. A quantitative approach was employed using survey data collected from 303 customers across eight Islamic banks in the UAE. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypothesized relationships. The findings indicate that all hypothesized relationships are statistically significant and positive. Financial behavior (β = 0.573) emerged as the strongest predictor of adoption intention, followed by Islamic financial literacy (β = 0.206) and financial self-efficacy (β = 0.109) at a significance level of p < 0.01. These results suggest that customers learned financial practices, their familiarity with Shariah-compliant financial principles, and their confidence in managing financial matters directly influence their intention to engage with Islamic financial products. The results demonstrate the strong explanatory power of the proposed model and support the applicability of Social Cognitive Theory in explaining Islamic finance adoption behavior within the UAE’s dual banking system. The study makes an original contribution to the literature by providing empirical evidence on the behavioral drivers of Islamic finance adoption and by offering regulatory and managerial recommendations aimed at enhancing financial literacy, strengthening digital Islamic banking platforms, and promoting greater customer adoption of Islamic financial services.
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Paper Nr:	39
Title:	The AI Revolution in Finance: A Double-Edged Sword of Efficiency and Systemic Risk
Authors:	Baojie Wang
Abstract:	Artificial Intelligence (AI) is revolutionizing finance, driving advances in predictive analytics and algorithmic trading. However, this review argues that the pursuit of performance is a double-edged sword. The "black-box" nature of complex models, their reliance on historical data, and regulatory lag are creating systemic risks and threatening financial exclusion. An examination of AI applications reveals fundamental trade-offs between accuracy, interpretability, and robustness. Technical challenges like concept drift further amplify regulatory and ethical dilemmas. Therefore, the future of financial AI requires a pivot towards building a "Trustworthy AI" framework. We propose a research agenda focusing on explainable AI in legal contexts, causal inference for robustness, and economic models for federated learning. This shift is crucial to ensure AI fosters not only efficiency but also stability, fairness, and accountability.

Paper Nr:	45
Title:	An Integrated Conceptual Framework for Cloud Accounting Adoption and SMEs’ Financial Performance: Synthesizing TAM, ISSM, and ECT Perspectives
Authors:	Sri Wahyuni and Rindang Widuri
Abstract:	Cloud accounting has emerged as a strategic tool for small and medium-sized enterprises (SMEs) to enhance financial management and financial performance in the context of digital transformation. Nevertheless, both adoption and post-adoption outcomes remain uneven due to differences in perceived ease of use, perceived usefulness, system quality, and trust in cloud-based technologies, particularly among SMEs in emerging economies. Drawing on the Technology Acceptance Model (TAM) (Davis, 1989), the Information Systems Success Model (ISSM) (DeLone & McLean, 2003), and Expectation Confirmation Theory (ECT) (Bhattacherjee, 2001), and supported by prior empirical evidence in the cloud accounting and SME context (e.g., Musyafi et al.; Xu et al., 2024), this study develops an integrated conceptual framework to explain initial acceptance, post-use evaluation, and continued use of cloud-based accounting systems among SMEs in Indonesia. The proposed framework explains how perceived ease of use shapes perceived usefulness, while system-related attributes, such as accuracy and report quality, operational efficiency, and security and system credibility, drive user satisfaction and intention to continue system use through confirmation of expectations, consistent with established technology acceptance, information systems success, and post-adoption continuance research (Davis, 1989; DeLone & McLean, 2003; Bhattacherjee, 2001). Sustained system use enables SMEs to realize financial advantages in the form of cost efficiency, improved cash flow management, and reduced user errors, which ultimately translate into improved financial performance, as evidenced in prior cloud-based accounting studies in the SME context (Musyafi et al.; Xu et al., 2024). By explicitly linking pre-adoption perceptions with post-adoption experiences and performance outcomes, this study offers a comprehensive and theory-driven explanation of how cloud accounting generates sustainable financial value for SMEs.
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Paper Nr:	54
Title:	Designing Pairs Trading Strategies Based on Subsample Selection and Sharpe Ratio Optimization: Evidence from China's A-Share Market
Authors:	Fengqing Chen
Abstract:	This paper evaluates pairs trading strategies in China’s A-share market using high-frequency minute-level data over the period from June 2019 to June 2020. We construct over 2,000 stock pairs based on distance metrics and industry-level filters, with trading signals generated via z-score thresholds grounded in the mean-reversion principle. Performance is evaluated using both in-sample (IS) and out-of-sample (OOS) designs. To enhance robustness and mitigate overfitting under realistic market frictions, we implement a rolling subsample window and screen pairs based on their in-sample Sharpe ratios. The empirical results indicate that this diagnostic subsample-based screening leads to materially improved OOS performance, characterized by higher cumulative returns and lower volatility relative to full-sample selection approaches. Importantly, the strategy remains effective after accounting for practical trading constraints, including T+1 settlement and execution costs, highlighting its applicability in friction-bound environments. These results are interpreted within a limits-to-arbitrage framework, in which institutional constraints specific to China’s A-share market—including state-ownership heterogeneity and regulatory price limits—help explain why robust screening is especially important for deployable out-of-sample performance. Rather than proposing a new trading model, this study contributes by validating a transparent, scalable, and deployable high-frequency screening framework that is robust to the structural frictions specific to emerging markets such as China.
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Paper Nr:	55
Title:	Public Debt Management: A New Perspective
Authors:	Martin Soucy, Hakim Lounis and Serge Robert
Abstract:	In the last decades, a popular approach among public debt managers from advanced countries was to rely on numerical approach models to define a set of optimal alternative financing strategies to guide their strategic financing plans. However, these models face important challenges when used to guide public debt managers in choosing a financing strategy. Consequently, this position paper proposes a new perspective on the public debt managers’ planning problem that better aligns with the need to support their decision-making process. Indeed, it is proposed to focus research on developing a response capability, defined here as any set of mechanisms that adjust decision variables according to states of the public debt manager’s environment. By understanding how these mechanisms adapt decision variables such as issuance weights to different states of the environment, public debt managers will be better informed to plan and make strategic financing choices in real situations. Four criteria are proposed to measure the usefulness of a response capability in supporting public debt managers’ decision-making process. Finally, it is argued that a cognitive model derived from CLARION cognitive architecture presents an interesting approach to meet these four criteria.
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Paper Nr:	34
Title:	A Hybrid AI-Fundamental Analysis Framework for Enhancing Short-Term Investment Decisions in Volatile Markets
Authors:	Abeba N. Turi, Julio Meza, Paulos Hedru and Oludamola Durodola
Abstract:	This paper introduces a hybrid investment decision framework that integrates classical Chartered Financial Analyst (CFA)- based fundamental analysis with AI–driven sentiment extraction to enhance short-term trading performance in volatile financial markets. Traditional fundamental analysis provides a structured and interpretable methodology for estimating intrinsic value based on financial statements, macroeconomic conditions, and industry dynamics, but it is inherently limited in responsiveness to rapid shifts in market perception and information flow. In contrast, advances in natural language processing (NLP) like FinBERT, enable the real-time extraction of market sentiment from unstructured textual data, including financial news, earnings call transcripts, and digital media, capturing behavioural and informational signals that precede short-term price movements. The proposed hybrid framework combines these complementary strengths through a three-stage process: (i) an initial CFA-based fundamental assessment to establish intrinsic valuation and financial robustness, (ii) sentiment extraction and aggregation using domain-specific NLP models to quantify short-term market emotions, and (iii) an integration layer that jointly evaluates fundamental signals and sentiment indicators to inform tactical investment decisions. This structure allows for cross-validation between economic fundamentals and collective market psychology, improving early risk detection and responsiveness under conditions of heightened uncertainty. The paper contributes to the literature by bridging the gap between structured financial analysis and AI-based sentiment modelling, offering a practical and theoretically grounded framework for short-term investment decision-making in volatile environments.
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Paper Nr:	50
Title:	Revitalising Loan Securitization in Germany: How Much Regulatory Capital Relief for (Green) Lending?
Authors:	Dominic Strube, Christian Daase and Matija Denise Mayer-Fiedrich
Abstract:	Europe’s green transformation requires substantial private financing, while banks’ lending is constrained by regulatory capital requirements. This paper quantifies, under deliberately optimistic assumptions, to what extent placed true-sale securitizations can reduce risk-weighted assets (RWA) and thereby create additional lending capacity for German banks. Using aggregated data from the Deutsche Bundesbank, securitisable loan pools are derived for large banks, other credit banks, and regional banks, and the implied RWA relief for securitization shares of 1% to 10% is translated into additional lending capacity. A Monte Carlo sensitivity analysis varies key parameters such as the pool share, average risk weights, and the capital treatment of securitization positions. The results indicate a positive capacity effect that is driven primarily by regional banks, reflecting their larger securitisable portfolios and higher average risk weights. Even this upper-bound effect is unlikely to close the green financing gap on its own, implying that securitization should be viewed as a complementary lever.
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Paper Nr:	59
Title:	Optimizing Capital Structure Decisions: A Mathematical Model for Forecasting and Managing Corporate Asset Formation
Authors:	Sunnatov Yusuf Usmonovich
Abstract:	This study develops a comprehensive mathematical framework for optimizing capital structure decisions and forecasting corporate asset formation. The model analyzes the interrelationships between equity capital (EC) and debt capital (DC) in forming long-term (LTA) and current assets (CA), establishing six fundamental relative coefficients (k₁–k₆) along with their algebraic and trigonometric interdependencies. The methodology employs parametric equations to determine optimal boundaries for asset allocation strategies, offering practical tools for managing emission policies, dividend strategies, investment decisions, and debt financing. Empirical validation confirms the model’s applicability across diverse corporate contexts. Ultimately, the research contributes a novel quantitative methodology that integrates capital structure theory with practical asset management tools, providing significant value for financial decision-makers seeking to enhance organizational financial stability and strategic capital allocation efficiency.

Area 2 - Emerging Areas in FEMIB

Short Papers

Paper Nr:	61
Title:	LLM-Based CV Matching for Best Candidate Job Fit Estimation
Authors:	Thomas Weber, Bakir Hadžić, Deepu Krishnareddy and Matthias Rätsch
Abstract:	This study investigates the potential of large language models (LLMs) to support automated candidate assessment in recruitment. Using a dataset of three real-world job openings and 50 anonymised CVs, we evaluated five LLM variants (GPT-4.1, GPT-4o, GPT-5-nano, GPT-5-mini, GPT-5) across three approaches: a modular skill-oriented pipeline, holistic CV job scoring, and comparative candidate ranking. Model performance was measured using F1 scores, area under the ROC curve (AUC), and, for rankings, the normalised difference between the predicted and actual top-ranked candidate. Overall, GPT-5-nano and GPT-5-mini performed best, particularly in ranking candidates and identifying the top applicant. Results indicate that LLMs can effectively shortlist suitable candidates, with holistic CV evaluation generally outperforming skill-oriented methods. Future studies with larger datasets should further assess the generalisability, robustness, and practical utility of these approaches.

Area 3 - IT Business

Full Papers

Paper Nr:	35
Title:	Real-Time Predictive Maintenance with XGBoost RUL Estimates and a Streaming Simulation Built on N-CMAPSS
Authors:	Khaled Mohammad Alomari, Safwan Maghaydah, Said A. Salloum, Hassan Ali Al-Ababneh and Tareq Hamadneh
Abstract:	Industry 4.0 has made predictive maintenance a central topic; however, much of the research still focuses on offline benchmarking rather than deployable systems. This paper presents a real-time predictive maintenance framework built around XGBoost models, the N-CMAPSS turbofan engine dataset, and a streaming simulation environment. The approach relies on feature engineering to support both Remaining Useful Life (RUL) prediction and short-term failure classification. A lightweight FastAPI backend connects the simulation engine to an interactive Streamlit dashboard, allowing users to observe model behavior as it evolves. XGBoost is used to capture nonlinear degradation patterns through engineered features such as sensor ratios, rolling statistics, and a health index. Experimental results show strong performance, with an R² of 0.9004 for RUL prediction and an AUC close to 0.99 for failure classification, while maintaining low inference latency suitable for real-time operation. The system supports parallel monitoring of multiple engines and allows rapid exploration of degradation trajectories when abnormal behavior appears. Beyond predictive accuracy, the framework demonstrates how predictive maintenance models can be operationalized in real-world settings, offering a deployment-oriented solution suitable for real-time monitoring environments, while benchmarking against deep sequence models is left for future work.
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Area 4 - Management

Full Papers

Paper Nr:	60
Title:	Determining Factors of Technological Innovation Capability in the Technology Industry
Authors:	Jing Li, Chien-Ke Huang and Chi-Hui Wu
Abstract:	Advanced technological innovation capability is the lifeblood of the technology industry and a crucial factor for enterprises to acquire core competencies and competitive advantages, as well as a strategic approach to improve corporate performance. However, technological innovation capability is a complex, multifaceted, and uncertain concept with no consistent conclusions. Therefore, this study identifies the determinants of technological innovation capability in the technology industry and clarifies the causal relationships between these facets and criteria, aiming to help technology firms grasp a few key determinants and maximize the effectiveness of limited resources. Through a literature review and expert survey, a preliminary list of suitable evaluation criteria was identified. Key criteria were constructed using the fuzzy Delphi method, and fuzzy DEMATEL analysis was employed to establish causal relationships between dimensions/criteria and determine critical decision-making dimensions/criteria. The findings reveal that, among the dimensions, innovation management capability is the decisive dimension for technological innovation capability in the technology industry, influencing the other five dimensions. Among the criteria, learning capability emerges as the decisive factor. Therefore, in a competitive environment characterized by continuously shortening product cycles and rapidly changing customer demands, firms must prioritize enhancing innovation management capability to improve technological innovation capability. Specifically, they must strengthen learning capability - the capability to identify, absorb, and utilize knowledge within the organization - to enhance knowledge management and innovation decision-making capabilities, thereby bolstering overall innovation capability. This study contributes to identifying and prioritizing key determinants/criteria that influence technological innovation capabilities in high-tech industries. It serves as a strategic planning reference for enterprises within the industry to enhance their technological innovation capabilities and maintain competitive advantages.
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Paper Nr:	67
Title:	Artificial Intelligence in Teams: A Systematic Literature Review
Authors:	Ilda Maria Coniglio and Luca Famà
Abstract:	Artificial intelligence (AI) is increasingly embedded in teamwork and collaborative environments. Prior research has primarily examined AI at the individual level. Organizations, however, rely heavily on teams, making it important to understand how AI influences collective dynamics. Existing studies on AI in teams remain fragmented across disciplines and focus on isolated aspects of teamwork. This paper presents a systematic literature review of research on artificial intelligence in teams using the PRISMA methodology. A total of 102 peer-reviewed articles indexed in Scopus were analyzed through bibliometric and qualitative approaches. The findings reveal a rapidly growing research field structured around several thematic areas, including human-AI collaboration, team dynamics, decision-making processes, and AI-enabled decision-support systems. The review maps the main topics and research clusters in the literature and identifies key gaps related to team-level processes and AI integration. By synthesizing existing research, this study provides a structured overview of the field and outlines directions for future research on hybrid human-AI teams.

Short Papers

Paper Nr:	29
Title:	Analysing the Impact of Strategic Human Resource Management Practices on Institutional Performance: A Case Study of Abu Dhabi National Energy Company (TAQA)
Authors:	Ahmed Mahade and Ahmed Ali Alnaqbi
Abstract:	The study aimed to analyse the impact of strategic human resource management SHRM practices on enhancing organizational performance at Abu Dhabi National Energy Company (TAQA), by SHRM practices in their four dimensions: competency-based recruitment, continuous training and development, talent management, and performance and compensation management. The dependent variable is institutional performance. The study adopted a descriptive, analytical, quantitative approach using a questionnaire with simple random sampling. The data were analysed, and the study hypotheses were tested. The study concluded that the level of SHRM practices was high and that the level of organizational performance was also high. The results also showed a statistically significant effect of strategic human resource management practices on organizational performance. The study recommended that Abu Dhabi National Energy Company should continue to develop SHRM practices through continuous alignment between HR plans and organizational objectives, and linking individual performance to corporate work results to achieve excellence and operational sustainability.
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Paper Nr:	42
Title:	Enhancing Job Performance through Skill Development: Evidence from Blue-Collar Myanmar Migrant Workers in Thai Food SMEs
Authors:	Saung Htet Nao and Pittawat Ueasangkomsate
Abstract:	Low job performance among Myanmar migrant workers in Thai food SMEs remains a critical management challenge affecting productivity and organizational efficiency. As the industry increasingly depends on migrant labor, understanding the mechanisms that enhance performance has become essential. This study examines how skill development enhances job performance through the mediating roles of dynamic capabilities, job satisfaction, and work motivation. A quantitative cross-sectional survey of 410 blue-collar Myanmar migrant workers was conducted in Thai food SMEs using a non-probability convenience sampling method. Data were collected through validated questionnaire items measured using multi-item scales with a 5-point Likert response format and analyzed using multiple regression analysis and Hayes’ PROCESS macro to examine the hypothesized relationships and indirect effects among the variables. Results showed that skill development has a significant positive effect on job performance and positively affects dynamic capabilities, which in turn enhance job satisfaction and work motivation. Job satisfaction and work motivation both significantly contribute to job performance, and all six hypothesized relationships were supported. Mediation analysis confirms partial indirect effects, demonstrating that skill development influences performance through both capability-based and psychological pathways. The findings highlight a practical pathway for improving workforce productivity and stability in Thai food SMEs.
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Paper Nr:	36
Title:	Business Resilience and Tionghoa Culture: A Phenomenological Study of Entrepreneurs in Medan, Pekanbaru, and Jakarta
Authors:	Stephanie Amelia and Marko S Hermawan
Abstract:	This research paper examines the significance of the local socio-cultural environment of the Chinese-Indonesian ethnicity (Tionghoa) in the endeavors to establish and sustain business resilience in the urban centers of Pekanbaru, Medan, and Jakarta. By employing an interpretive phenomenological methodology, this study investigates the manner in which Chinese-Indonesians perceive and enact values that fundamentally influence their quotidian business activities. Furthermore, the results indicate that the participants do not overtly recognize their business practices as fundamentally grounded in "Tionghoa culture" in an essentialist or doctrinal manner. Instead, values such as trust, perseverance, discipline, frugality, relational commitment, and long-term orientation are perceived as pragmatic norms that have been deeply woven into the local Indonesian social fabric. These values are expressed implicitly through daily practices and social interactions, rather than being explicitly articulated as reflections of ethnic or Confucian identity.The study further demonstrates that the notion of a singular “Tionghoa culture” is problematic, as business practices and meanings of resilience are strongly shaped by the specific local contexts of each city. In Medan, historical experiences of ethnic tension foster inward-oriented solidarity and cautious relational strategies; in Pekanbaru, family and religious networks shape conservative and risk-averse business behavior; while in Jakarta, practices reflect greater openness, professionalization, and integration with modern market systems. Despite variations in local socio-economic conditions across the three cities, the finding business resilience among Chinese-Indonesian entrepreneurs is better understood as a locally embedded and acculturated process rather than as a direct continuation of an inherited Tionghoa cultural root. Resilience emerges not from a homogeneous ethnic culture, but from the interaction between lived historical experiences, local social norms, and adaptive market rationality.
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