درخت حوزه‌های تخصصی

The sharing business model (SBM) has introduced a new type of business model that combines collaborative technologies with idle resources to improve product and service utilization efficiency. However, in recent years, the sharing economy has had a tremendous impact on the tourism business. While some initially suggest that the sharing economy (SE) is beneficial to sustainability, we believe that extensive adoption may have unwanted consequences due to negative cultural effects in tourism. This study uses a thematic analysis to classify SBM's cultural dark side. Using a systematic literature review approach and the PRISMA protocol on 117 eligible papers, we identify ten negative cultural effects: nuisance for local residents, unethical practices, perceived risk, social inequality, customer misbehavior, safety concerns, employee rights violations, lobbying, consumerism, and overcrowding. These negative effects are impacting tourism's cultural sustainability. We also help to develop a framework for SBM's cultural sustainability in tourism. The framework suggests some cultural sustainability strategies, such as affordable and accessible products, the development of a niche market, local empowerment, simple production, and ease of use. We address the theoretical and practical implications, as well as the limitations. Finally, we discussed future research in three major directions.

This research aims to examine the impact of Big Data Quality (BDQ) components, including completeness, accuracy, format, and currency, on Big Data-driven Human Resources (BDHRP) management practices and Decision-Making Quality (DMQ) from the viewpoint of HR managers. It also seeks to identify the most impactful components among completeness, accuracy, format, and currency in the context of BDHRP and DMQ. A survey of HR professionals in 108 French organizations deploying Big Data Analytics systems revealed positive relationships between BDQ, BDHRP, and DMQ. Statistical analyses conducted with the Partial Least Squares Structural Equation Modeling (PLSEM) method showed a positive relationship between BDQ components and BDHRP, with currency and accuracy emerging as the most influential factors. Additionally, a strong positive relationship was found between BDQ components and DMQ, with currency and accuracy leading the way. The research also found a significant connection between BDHRP and DMQ, further underscoring the importance of effective HRM practices in enhancing decision-making quality. These findings contribute significantly to understanding the crucial role played by big data quality in BDHRP and decision-making, highlighting the potential for organizations to improve outcomes by focusing on currency and accuracy-related concerns. In practical terms, this research offers insights that can guide data quality practices, resource allocation, and strategic decision-making within organizations.