Special Issue 2026

Abstract Reliable estimation of loan default risk plays a vital role in ensuring financial system resilience and enabling sound credit decision-making in today’s lending landscape. Although conventional statistical techniques offer transparency, they frequently struggle to model the intricate nonlinear patterns embedded in high-dimensional financial datasets. To address this limitation, the present study introduces a robust framework for default risk evaluation that employs a CatBoost model optimized through a Tree-Structured Parzen Estimator (TPE). This methodology combines Bayesian-driven hyperparameter tuning with gradient boosting techniques to improve both predictive accuracy and the calibration of probability estimates. The performance of the proposed model is validated using three widely recognized benchmark datasets: German Credit Risk, Polish Companies Bankruptcy, and Taiwan Credit Card Default. The proposed approach is evaluated in comparison with several conventional machine learning models, namely Logistic Regression, Random Forest, and Support Vector Machine. The assessment is carried out using a diverse set of performance indicators, including ROC–AUC, recall, F1-score, Brier score, and the Kolmogorov–Smirnov (KS) measure. Empirical analysis across multiple datasets reveals that the CatBoost model enhanced through TPE-based optimization consistently delivers superior results relative to the benchmark methods, particularly in terms of classification strength, probability estimation accuracy, and effective differentiation of risk levels. Notably, the improvement is more pronounced in datasets characterized by class imbalance, emphasizing the advantage of the adopted optimization technique. The results indicate that combining advanced boosting methods with systematic hyperparameter tuning yields a dependable and scalable framework for credit risk evaluation, offering meaningful benefits to financial institutions through more precise and data-informed lending decisions.

Keywords: CatBoost, Credit Risk Assessment, Gradient Boosting, Hyperparameter Optimization, Loan Default Prediction, Tree-Structured Parzen Estimator (TPE)

Abstract This study examines how cognitive job demands relate to employee work engagement with emotional exhaustion as a mediating mechanism among managerial-level employees in export-oriented apparel firms in Sri Lanka. Drawing on Job Demands–Resources (JD-R) theory, cognitive job demands were operationalized as a reflective–formative (Type II) higher-order construct comprising four first-order dimensions namely information processing, decision making, time pressure and sustained attention. Data were collected from 758 managerial employees across 45 firms in the Western Province using a structured questionnaire. Partial least squares structural equation modeling indicated that cognitive job demands were positively associated with emotional exhaustion and negatively associated with employee work engagement. Emotional exhaustion was also negatively associated with engagement and partially mediated the relationship between cognitive job demands and engagement. The indirect effect accounted for approximately 48.7% of the total effect, indicating that emotional exhaustion plays a substantial but not exclusive role in linking cognitive demands to engagement outcomes. Cognitive job demands explained about 34.7% of the variance in emotional exhaustion and together with emotional exhaustion, explained about 80.6% of the variance in employee work engagement. Given the cross-sectional, self-reported design and voluntary participation, these findings should be interpreted as associative rather than causal. The study contributes to JD-R research by testing the health-impairment pathway in a developing economy, export-manufacturing context and by modeling cognitive demands as a multidimensional formative construct. The results suggest that managing excessive cognitive demands and supporting employee recovery may be relevant to sustaining engagement among managerial employees in Sri Lankan apparel firms.

Keywords: Apparel industry, Cognitive job demands, Emotional exhaustion, Employee Work Engagement, Job Demands–Resources theory, Sri Lanka

Abstract Supply chain resilience has emerged as a critical capability for firms operating in increasingly fragile, uncertain, and disruption-prone global supply networks. For export-oriented industries such as apparel, the ability to respond rapidly and recover effectively from disruption is central to sustaining competitiveness. In this context, digital supply chain traceability (DSCT) has become an important digital capability because it enhances the capture, tracking, and retrieval of critical supply chain information across sourcing, production, and distribution activities. Yet, despite growing interest in digital transformation, existing research has paid limited attention to DSCT as a distinct organizational capability and has provided insufficient explanation of how it is associated with resilience outcomes. This study addresses this gap by examining the association between DSCT and supply chain resilience (SCR), with operational decision-making speed (ODMS) examined as a mediator, in apparel exporting firms in the Western Province of Sri Lanka. Drawing on Organizational Information Processing Theory and the Dynamic Capabilities View, the study examines the associations among DSCT, ODMS, and SCR. Survey data were collected from managerial and executive-level employees (N = 210) and analysed using partial least squares structural equation modelling (PLS-SEM). The findings indicate significant positive associations between DSCT, ODMS, and SCR, with ODMS partially mediating the association between DSCT and SCR. These findings suggest that the strategic value of traceability extends beyond visibility and transparency, as DSCT is also associated with faster operational decision-making and stronger resilience. The study advances resilience literature by identifying a process-based explanation linking digital capability and resilience, while providing actionable implications for apparel exporters seeking to develop more responsive and robust supply chains.

Keywords: Digital supply chain traceability, Operational decision-making speed, Supply chain resilience, Apparel exporting firms

Abstract The growing importance of experiential tourism has increased the need for effective digital marketing strategies to promote niche tourism destinations. Despite Sri Lanka's global recognition for Ceylon tea, limited empirical evidence exists regarding how digital marketing tools contribute to strengthening tea tourism destination image. Therefore, this study examines the influence of digital marketing tools on tea tourism destination image while investigating the mediating role of visitor experience. A mixed-method research approach was employed by combining quantitative and qualitative techniques. Quantitative data were collected from 382 foreign tea tourists using structured questionnaires, while qualitative data were gathered through semi-structured interviews with 12 tea tourism stakeholders. Quantitative data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4, whereas qualitative data were analyzed using thematic content analysis. The findings reveal that digital marketing tools significantly enhance visitor experience but do not exert a significant direct influence on tea tourism destination image. However, visitor experience significantly mediates the relationship between digital marketing tools and destination image, demonstrating that memorable tourism experiences transform digital promotional efforts into favorable destination perceptions. The qualitative findings further indicate limited adoption of immersive digital technologies, fragmented promotional strategies, and inadequate stakeholder collaboration within Sri Lanka's tea tourism sector. The study extends the Stimulus-Organism-Response (S-O-R) framework within the tea tourism context and provides practical recommendations for tourism authorities and industry stakeholders to strengthen digital engagement, improve visitor experiences, and enhance the international competitiveness of Sri Lanka's tea tourism destinations.

Keywords: Destination Image; Digital Marketing Tools; Experiential Tourism; Mediation Analysis; PLS-SEM; Tea Tourism; Visitor Experience.

Abstract While AI is often viewed as a strategic resource for improving efficiency and competitiveness, its outcomes remain uneven across organizations. Limited conceptual clarity remains regarding how artificial intelligence adoption contributes to organizational resilience, particularly in turbulent South Asian contexts. This paper argues that the missing link lies in AI readiness, conceptualized as a multidimensional capability that reflects the extent to which organizations and their employees are prepared to adopt, use, and adapt to AI technologies. Based on an integrative review, the study draws on the Resource-Based View, dynamic capabilities theory, and leadership perspectives to develop a conceptual framework that positions AI readiness as a mediating mechanism between leadership and organizational resilience. Leadership is conceptualized as an enabling force that shapes organizational conditions and supports capability development, while AI readiness facilitates the translation of technological potential into adaptive capacity. Organizational resilience is viewed as a dynamic capability involving anticipation, coping, and adaptation under disruption. The study further situates this framework within the context of South Asian economies characterized by institutional volatility, resource constraints, and uneven digital maturity. It argues that under such conditions, leadership-enabled AI readiness becomes particularly critical for sustaining organizational performance. The paper contributes to theory by integrating leadership, AI readiness, and resilience within a capability-based framework and offers practical insights for organizations seeking to navigate technological change in turbulent environments.

Keywords: Artificial Intelligence, Leadership, Organizational Resilience, South Asia, Technology Readiness