مطالب

In today’s data-driven hospitality sector, customer interactions increasingly occur through digital platforms, generating extensive behavioral and transactional information. This study analyse the prediction of Customer Lifetime Value (CLV) using machine learning models—Linear Regression, Random Forest, and LightGBM—trained on features derived from hotel website interactions and booking records. After comprehensive data preprocessing, the models were evaluated using MAE, RMSE, and R² metrics. LightGBM achieved the highest predictive performance (R² = 0.504), followed by Random Forest (R² = 0.497), while Linear Regression underperformed (R² = 0.386), highlighting the advantages of non-linear models in modeling intricate customer patterns. Residual analyses confirmed LightGBM's stability and low bias across diverse customer profiles. Apart from prediction, the study applies Recency-Frequency-Monetary (RFM) analysis to segment customers into distinct value-based groups. These segments form the basis for tailored marketing strategies, allowing hotels to allocate resources more efficiently, enhance customer retention, and develop targeted campaigns aligned with customer potential. By integrating web-derived behavioral data with advanced modeling and segmentation, this research offers hotel managers practical tools for strategic planning in customer relationship management.

Objective: This study aimed to explore how narrative reframing functions as a coping and meaning-making mechanism among Taiwanese families experiencing trauma. Methods and Materials: A qualitative research design was adopted using purposive sampling to recruit 26 participants from Taiwan who had directly experienced family trauma, including bereavement, illness, or sudden life disruptions. Semi-structured interviews were conducted until theoretical saturation was reached. Each interview lasted 60–90 minutes, was audio-recorded, and transcribed verbatim. Data were analyzed using thematic analysis facilitated by NVivo 14 software. An inductive coding approach was applied to identify open codes, subcategories, and overarching themes, with constant comparison used to refine findings. Research rigor was ensured through reflexive memo-writing, triangulation of coding among team members, and maintaining an audit trail. Findings: Analysis yielded three overarching themes: (1) Emotional reconstruction through narrative reframing, where participants processed trauma-related emotions, reduced guilt and shame, regulated distress, and cultivated hope; (2) Relational transformation within the family system, including improved communication, redefined roles, conflict resolution, trust rebuilding, and strengthened intergenerational understanding; and (3) Identity and future orientation, where families reframed themselves as survivors, emphasized personal growth, redefined goals and values, and expressed aspirations to transmit resilience across generations. Participants highlighted that narrative reframing enabled them to transform suffering into continuity, strengthen bonds, and situate trauma within cultural and spiritual frameworks that emphasized endurance and meaning. Conclusion: The findings demonstrate that narrative reframing is a powerful coping process in families experiencing trauma, allowing for emotional healing, relational adaptation, and the construction of resilient family identities. This study underscores the importance of integrating narrative approaches into therapeutic interventions to support families in navigating trauma, rebuilding trust, and envisioning hopeful futures.