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This study aims to examine the relationships between quality management, human capital and innovation (both incremental and radical), and social sustainability practices in hospitality. Also considered are the mediating roles of human capital and innovation.

The study considers 365 hotels located in Spain, using a structural equation model based on Partial Least Squares (PLS) analysis.

The findings show that quality management practices, human capital and incremental innovation all have a direct relationship with social sustainability practices. Human capital and incremental innovation partially mediate the relationship between quality management and social sustainability practices. Radical innovation has no impact on social sustainability practices and does not play a mediating role.

This study enriches the literature on social sustainability in hospitality by showing that quality management, human capital and innovation can enhance social sustainability practices. It offers practical insights by understanding key drivers for promoting social sustainability in the hospitality sector.

Prior research in hospitality has not used a mediation model to empirically examine the aforementioned relationships.

This study aims to investigate the mediating role of green employee empowerment (GEE) in the relationship between green discipline management and green involvement with green employee creativity and innovation (GECI) within public universities in Yemen. The research explores how the components of green human resource management (GHRM) influence green creativity and innovation through employee empowerment.

The study utilizes both primary and secondary data. The primary data were gathered through structured questionnaires distributed to 363 employees in public universities in Yemen. The data were analyzed using structural equation modeling (SEM) with AMOS to assess the relationships among the variables.

The findings reveal that green discipline management and green involvement have a significantly positive impact on GEE and GECI. Furthermore, GEE partially mediates this relationship, which demonstrates that empowered employees are more likely to contribute to environmentally sustainable creativity and innovation.

This research helps better understand how GHRM practices, such as discipline management and involvement, can promote green creativity and innovation through employee empowerment. By examining the mediating role of GEE, the study provides valuable insights for academic institutions and organizational leaders seeking to integrate sustainability into human resource (HR) practices and policies.

This study aims to apply machine learning techniques to efficiently predict leisure firms’ financial performance. Accurate financial forecasting is crucial in leisure and tourism, greatly affecting firms’ strategic decisions and competitive positioning. This study emphasizes the roles of intellectual capital to offer a nuanced understanding of how these types of capital influence firm success.

Using comprehensive firm-level data, this study examines several machine learning algorithms’ predictive capacity across a spectrum of industry sectors (general, manufacturing, service) to identify the most effective model and training dataset. These tools are used to evaluate financial metrics such as return on sales, return on assets and sales growth. A range of variables are incorporated into this process to enhance model accuracy and relevance.

Results demonstrate the support vector machine algorithm’s exceptional performance based on a training data set from the service sector in predicting leisure firms’ return on sales and sales growth. This algorithm is thus an efficacious strategic forecasting instrument. The variables significantly affecting firm performance include demand variation; organizational, product and technological innovation; synergistic innovation between multiple domains; salary levels; market strategy; and the number of employees.

By integrating advanced machine learning techniques with the strategic management of intellectual capital, this study presents a sophisticated approach to predicting leisure firms’ financial performance. Findings enrich the discourse on firm performance forecasting and offer actionable insights into strategic planning and resource allocation for practitioners in the leisure and tourism sectors.

本研究应用机器学习技术来高效预测休闲企业的财务表现。准确的财务预测对于休闲和旅游业至关重要, 极大影响企业的战略决策和竞争定位。本研究强调智力资本的作用, 以深入理解这些资本类型如何影响企业的成功。

本研究使用全面的企业层面数据, 考察多种行业领域（综合、制造、服务）中多个机器学习算法的预测能力, 识别出最有效的模型和训练数据集。这些工具用于评估财务指标, 如销售回报率、资产回报率和销售增长率。过程纳入多种变量, 以提高模型的准确性和相关性。

结果表明, 基于服务行业训练数据集的支持向量机算法在预测休闲企业的销售回报率和销售增长率方面表现出色。因此, 该算法是一种有效的战略预测工具。影响企业绩效的显著变量包括需求变化、组织、产品和技术创新、多领域之间的协同创新、工资水平、市场战略和员工数量。

通过将先进的机器学习技术与智力资本的战略管理相结合, 本研究提出了一种复杂的方法来预测休闲企业的财务表现。研究结果丰富了关于企业绩效预测的讨论, 并为休闲和旅游业的从业者提供了有关战略规划和资源分配的实用见解。