This study investigates the impact of digital leadership on employees' innovative work behavior and knowledge sharing behavior, with a particular focus on the mediating role of psychological ownership. The independent variable, digital leadership, is examined through its supportive and innovative sub-dimensions, while psychological ownership is explored through emotional and work-based ownership. The dependent variables are innovative work behavior and knowledge sharing behavior. To test the research model, eight sub-models were developed and analyzed using PLS-SEM. Mediation was assessed through the Variance Accounted For (VAF) metric. Data were collected from 287 managers and employees working in IT departments within technology parks located in Ankara, Türkiye. The results reveal that both supportive and innovative forms of digital leadership have a positive and statistically significant influence on employees' innovative behavior and knowledge sharing. However, a portion of this effect operates indirectly through psychological ownership.

Bu araştırma, turizm sektöründe yapay zekâ temelli kişiselleştirme uygulamalarını veri mahremiyeti ve dijital haklar bağlamında incelemeyi amaçlamaktadır. Çalışmada, yapay zekâ temelli kişiselleştirme modelleri aracılığıyla turist deneyimlerini dönüştürme potansiyeli ele alınmış; aynı zamanda gizlilik, etik sorumluluk ve dijital hakların korunması konularında ortaya çıkan riskler tartışılmıştır. Araştırma kapsamında literatür incelemesi yöntemi kullanılmıştır. Bulgular, yapay zekânın turizmde müşteri memnuniyetini, destinasyon yönetimini ve rekabet avantajını güçlendirdiğini; ancak kişisel verilerin korunması, şeffaflık ve kullanıcı rızasının sağlanması gibi konularda önemli sorunların devam ettiğini göstermektedir. Çalışma, etik yaklaşımlar (faydacılık, deontoloji ve etik tasarım) ışığında turizmde yapay zekâ uygulamalarının dengeli bir şekilde benimsenmesi gerektiğini vurgulamaktadır. Ayrıca çalışmada, kişiselleştirme ile mahremiyet arasındaki hassas denge vurgulanmakta; etik tasarım prensipleri, şeffaflık ve veri güvenliği gibi yaklaşımların önemine dikkat çekilmektedir Sonuç olarak, turizmde yapay zekâ destekli kişiselleştirme stratejilerinin, kullanıcı haklarını gözeten politikalarla desteklenmesi halinde sektöre yenilikçi ve sürdürülebilir katkılar sağlayabileceği ortaya konmuştur.

The rapid adoption of large language models (LLMs) transformed the way users obtained travel recommendations and generated personalized travel plans. However, standardized methods for comparing AI-generated travel itineraries across heterogeneous providers remained limited. This study presented the design, implementation, and experimental evaluation of a web-based benchmarking framework for the standardized and automated comparison of AI-generated travel plans. The framework integrated standardized prompt generation, structured JSON response processing, field-level comparison, deterministic rule-based scoring, model ranking, and exportable benchmarking reports. It was evaluated across three travel-planning scenarios involving Prague, Rome, and Tokyo, with three independent benchmark executions attempted for each scenario, resulting in 27 provider-level requests. Eight executions produced complete three-provider comparisons; one Rome execution was incomplete because Microsoft Phi returned malformed JSON. Across the eight complete executions, Gemini achieved a mean structural-completeness score of 57.00/57 (SD = 0.00), ChatGPT achieved 47.38/57 (SD = 3.02), and Microsoft Phi achieved 34.63/57 (SD = 5.26), with the same rank ordering observed in all complete comparisons. Supplementary constraint-compliance analysis demonstrated that structural completeness did not necessarily imply full satisfaction of travel requirements. Sustainability-related outputs were additionally examined as a separate dimension. The results demonstrated consistent framework operation under the tested scenarios and repetitions while emphasizing the need to distinguish structural completeness from constraint compliance, factual accuracy, practical feasibility, and sustainability-related performance.