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In the present era, the achievement of employee Islamic performance has become a significant challenge for organizations. The purpose of the study is to examine the effect of Islamic leadership on employee Islamic performance directly and indirectly by bridging the connections between employees’ Islamic organizational values, Islamic organizational culture, and Islamic work motivation among the employees of Egyptian banks.

The authors used quantitative methods in this study and based its findings on the data received from 312 respondents in response to a questionnaire.

By using SmartPLS 4, this study’s findings demonstrate that Islamic leadership has a positive and significant effect on Islamic organizational values, culture, employee Islamic performance and work motivation. While Islamic organizational values and Islamic organizational culture do not significantly impact employee Islamic performance, Islamic work motivation is a significant predictor of employee Islamic performance. On the one hand, Islamic organizational values and Islamic organizational culture do not mediate the relationship between Islamic leadership and employee Islamic performance. On the other hand, Islamic work motivation is a mediating variable that significantly develops the relationship between Islamic leadership and employee Islamic performance.

The study’s findings support policymakers and human resource management practitioners to develop plans and strategies which enhance the Islamic performance of organizations’ employees. In addition, this study’s findings provide insights for researchers and academicians in developing Islamic leadership within their organizations so that they operate by Islamic values and codes.

Finally, by offering an integrated model of Islamic leadership, Islamic organizational values, Islamic organizational culture and employee Islamic performance, this study’s findings fill the gaps in the context of bank employees in a developing country, namely, Egypt.

A transmission tower usually experiences bolt loosening under long-term alternating cyclic load, which may lead to collapse of the tower in extreme operating conditions. The paper aims to propose a data-driven identification method for bolt looseness of complicated tower structures based on reduced-order models and numerical simulations to perceive and evaluate the health state of a tower in operation.

The equivalent stiffnesses of three types of bolt joints under various loosening scenarios are numerically determined by three-dimensional finite element (FE) simulations. The order of the FE model of a tower structure with bolt loosening is reduced by means of the component modal synthesis method, and the dynamic responses of the reducer-order model under calibration loads are simulated and used to create the dataset. An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed.

An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed and the applicability of the model is investigated. It is shown that the proposed method has a high identification accuracy and strong robustness to data noise and data missing. Meanwhile, the method is less dependent on the number and location of sensors and is easier to apply in real transmission lines.

This paper proposes a data-driven identification method for bolt looseness of a complicated tower structure based on reduced-order models and numerical simulations. Non-linear relationships between equivalent stiffness of bolted joints and bolt preload depicting looseness are obtained and reduced-order model of tower structure with bolt looseness is established. Finally, this paper investigates applicability of identification model for bolt looseness.

The study seeks to explore the intricate dynamics among customer relationship management (CRM) practices, guest satisfaction and loyalty in the hospitality context. Additionally, it aims to examine the moderating influence of guest engagement on the relationships between CRM practices and guest satisfaction and loyalty.

An integrated theoretical framework is developed by incorporating CRM practices and guest engagement into the satisfaction-loyalty framework. Two research instruments were adapted from the literature to assess the perspectives of customers and employees in the hotel industry in Kashmir. The customer survey measured guest satisfaction, loyalty, and engagement, while the employee survey focused on CRM practices, including key customer focus and CRM organization. Data was collected using a pen-and-paper survey with convenience sampling across 10 qualifying hotels, each classified as 3-star or above. A total of 270 matched responses from guests and employees were obtained and analyzed using descriptive analysis, structural equation modeling (SEM), and moderation analysis with SPSS and AMOS software. The study utilized a rigorous data matching process to ensure reliability, with guest-employee pairs verified and cross-checked with hotel records.

The results indicate CRM practices play a pivotal role in shaping guest satisfaction and loyalty. Notably, personalization and a targeted customer approach emerged as the most influential factors in enhancing tourist satisfaction. Similarly, prospecting, personalization, and effective knowledge management significantly contributed to visitor loyalty. The establishment of robust relationships is underscored through collaborative active guest engagement. Furthermore, the study highlights the nuanced relationship between satisfaction and loyalty moderated by guest engagement. High levels of guest engagement amplify the positive impact of satisfaction on loyalty, while lower engagement levels attenuate this effect. Moreover, the moderating influence of guest engagement on the relationships between CRM practices and guest satisfaction and CRM practices and guest loyalty was notably strong at elevated guest engagement levels and relatively weaker at lower engagement levels.

While the study findings encourage organizations to prioritize customer relationship development, hospitality entities must emphasize the adoption of CRM philosophy and robust guest engagement measures. Actively involving guests in co-creating services can yield incremental benefits in terms of attracting, retaining, and effectively serving guests.

This study introduces novel dimensions to the existing CRM framework within the hospitality context, specifically exploring the impact of hotel-specific elements (personalization and prospecting) on customer satisfaction and loyalty. Furthermore, it innovatively investigates the moderating role of guest engagement in the satisfaction-loyalty relationship, expanding its scope to include the relationships between CRM practices and guest satisfaction and guest loyalty.

This chapter delves into the captivating world of Spice Tourism, exploring its transformative potential within the enchanting landscapes of Nepal. Amidst the rich tapestry of culture, history and natural beauty, this chapter unravels the intricate interplay between spices, meaningful tourism and sustainability. It sheds light on how Spice Tourism has the power to redefine travel experiences and infuse meaning into tourism in the region. Unveiling the path to meaningful tourism, it offers invaluable insights into the untapped possibilities of Spice Tourism in Nepal. This chapter serves as a beacon for practitioners, scholars and policymakers, illuminating strategic approaches to harness the allure of spices and promote Nepal as a prime Spice Tourism destination in South Asia.

This study aims to analyze the stock market reaction of the small and medium sectors in the Southeast Asian countries (SEACs), including China, to the Russia–Ukraine war.

This study uses the event study methodology to quantify the magnitude and direction of the impact of the Russia–Ukraine war on the performance of small and medium indices of SEACs. Assuming market efficiency, this study uses the market model to capture abnormal returns on the event date and across the event window.

The Russia–Ukraine war adversely affected most of the sample countries, with a prolonged impact over the 50-day post-event window. In most cases, the study has failed to establish statistical significance for the results. One major reason for the low impact is the proactive role of the governments of the SEACs in mitigating the war’s impact on their respective economies by providing subsidies, exercising price controls and stockpiling.

To the best of the authors’ knowledge, this study is the first to analyze the impact of the Russia–Ukraine war on the small and medium sectors of SEACs, including China. It highlights the importance of geographic proximity and the proportion of bilateral trade in understanding how war and geopolitical tensions affect the stock market performance in other countries in the current global scenario. In addition, this study uses a range of parametric and nonparametric tests that adjust for the cross-country dependence and event-induced volatility to substantiate the statistical significance of the results and this supports the robustness of the results.

The application of steel fiber reinforced concrete (SFRC) beams is limited in practice, partially due to the lack of accurate shear strength prediction models. This study aims to develop a reliable shear strength prediction model for SFRC beams.

In this study, an artificial neural network was employed to predict the shear strength of SFRC beams, utilizing a comprehensive database of 562 experimental studies. Multiple neural networks were established with varying hyperparameters, and their performance was evaluated using statistical parameters.

The neural network with 11 neurons showed superior results than other networks. The performance evaluation, efficiency and accuracy of the selected neural network were examined using margin of deviation, k-fold cross-validation, Shapley analysis, sensitivity analysis and parametric analysis. The proposed artificial neural network model accurately predicts the shear strength and outperforms other existing equations.

This research contributes to overcoming the limitations of existing prediction models for shear strength of SFRC beams without stirrups by developing a highly accurate model based on ANN. Utilizing a comprehensive database and rigorous evaluation techniques enhances the reliability and applicability of the proposed model in practical engineering applications.

This research study aims to conduct a cross-cultural analysis by examining case studies that explore the topic of “Empowering Communities and Preserving Heritage: Case studies on Socially Responsible Travel through Fair Trade Tourism.” This study investigates the effects and long-term viability of endeavors that combine responsible tourism strategies with fair trade ideals within various cultural settings. This study centers on three specific case studies, namely community-based ecotourism in Costa Rica, fair trade tourism in South Africa's Wild Coast, and responsible cultural tourism in India's Rajasthan. The research findings demonstrate that the practice of socially responsible travel and fair trade tourism make substantial contributions toward the promotion of sustainable tourism and responsible travel. Furthermore, the implementation of responsible tourism practices has made a significant contribution to the preservation of environment, resulting in the understanding of ecological impact on both natural resources and species. The case studies reveal recurring themes and patterns that underscore the significance of community engagement, environmental sustainability, and cultural interchange in the responsible advancement of tourism. Cultural elements have a significant impact on the implementation of sustainable practices since a strong sense of identity and adherence to cultural values play a pivotal role in shaping the acceptance of responsible tourist efforts. The study adds the expansion of corpus of literature about sustainable tourism practices and ethical travel. This statement underscores the capacity of responsible tourism to have a positive impact on economic growth, cultural safeguarding, and environmental protection. It also emphasizes the need of tailoring methods to individual cultural contexts.

This study aims to introduce a conceptual framework that links gender microaggressive experiences at the workplace to women’s propensity to quiet quit.

Using conservation of resources theory and social exchange theory this study develops a conceptual framework to explain women employees’ quiet quitting (QQ) behaviour when they experience gender microaggressions (GM) in workplaces.

The conceptual framework provides a comprehensive understating of antecedents to QQ behaviour of women employees. Affective commitment is identified to mediate and supervisory support to moderate the relationship between QQ and GM.

To the best of the authors’ knowledge, this is the first study to examine the QQ process through a gendered lens.

With the development of Web information systems, steel e-commerce platforms have accumulated a large number of quality objection texts. These texts reflect consumer dissatisfaction with the dimensions, appearance and performance of steel products, providing valuable insights for product improvement and consumer decision-making. Currently, mainstream solutions rely on pre-trained models, but their performance on domain-specific data sets and few-shot data sets is not satisfactory. This paper aims to address these challenges by proposing more effective methods for improving model performance on these specialized data sets.

This paper presents a method on the basis of in-domain pre-training, bidirectional encoder representation from Transformers (BERT) and prompt learning. Specifically, a domain-specific unsupervised data set is introduced into the BERT model for in-domain pre-training, enabling the model to better understand specific language patterns in the steel e-commerce industry, enhancing the model’s generalization capability; the incorporation of prompt learning into the BERT model enhances attention to sentence context, improving classification performance on few-shot data sets.

Through experimental evaluation, this method demonstrates superior performance on the quality objection data set, achieving a Macro-F1 score of 93.32%. Additionally, ablation experiments further validate the significant advantages of in-domain pre-training and prompt learning in enhancing model performance.

This study clearly demonstrates the value of the new method in improving the classification of quality objection texts for steel products. The findings of this study offer practical insights for product improvement in the steel industry and provide new directions for future research on few-shot learning and domain-specific models, with potential applications in other fields.

Despite the increase in public spending by Sub-Saharan African (SSA) Governments in a bid to foster the growth of human capital and sustainable development, they continue to experience a very slow rate of progress. This study aims to investigate the impacts of public spending on sustainable economic development in SSA.

The study adopts the system generalized method of moments to account for cross-sectional dependence and endogeneity for 38 SSA countries from 1996 to 2019.

The findings indicate that public spending inhibits sustainable economic development while human capital enhances sustainable economic development in SSA. Furthermore, the study equally reveals that the development-inhibiting role of public spending is modulated through human capital and governance quality. Public spending interacts with human capital and governance quality to produce negative net effects and positive synergy effects, respectively.

Based on these findings, the study suggests that governments in SSA countries should discourage heavy dependence on public spending. Policies that provide a framework for financial incentives in the domain of health and education should be encouraged to increase investment in human capital.