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This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

This study aims to investigate the different effects of physical and social servicescapes on brand love for internet-famous restaurants, focusing on their pathways and strengths of influence.

Structural equation modeling was applied to analyze data from 387 online questionnaires in China.

Results indicate that social servicescape directly influences brand love for internet-famous restaurants, while physical servicescape does not. The effect of physical servicescape on brand love for internet-famous restaurants is mediated by perceived coolness and perceived enjoyment, whereas social servicescape’s influence is mediated solely by perceived enjoyment. Overall, physical servicescape has a stronger impact on brand love for internet-famous restaurants compared to social servicescape.

The findings help internet-famous restaurants create effective physical and social servicescapes to enhance brand love, underscoring that physical servicescape is more crucial than social servicescape for cultivating this love.

This study contributes to the literature by analyzing the heterogeneous pathways and strengths of physical and social servicescapes influencing brand love for internet-famous restaurants, while highlighting the mediating role of perceived coolness and expanding the application scope of cognitive appraisal theory.

Although artificial intelligence (AI) is an essential component of hospitality in the technological empowerment era, AI’s effectiveness as an attraction in this context remains unclear. Grounded in Herzberg’s motivation theory and complexity theory, this study aims to explore configurational paths whereby combinations of qualities lead to success for different types of AI-themed hotels.

This study innovatively blends topic modeling and fuzzy-set qualitative comparative analysis (fsQCA) to investigate configurational paths whereby combined qualities produce positive guest evaluations of 12 AI-themed hotels as evidenced by 7,431 customer reviews.

The results indicate that AI could serve as a “theme” to attract customers under certain circumstances. First, “attractive” and “must-be” qualities are first identified for different types of AI-themed hotels. Furthermore, 6, 15 and 15 configurational paths inspiring favorable guest evaluations of luxury-independent, budget-independent and chain AI-themed hotels, respectively. Technology-related qualities are found to be especially attractive for luxury-independent AI-themed hotels, whereas the role of technology is minimal for budget AI-themed hotels. The impact of technology is salient for chain AI-themed hotels when combined with other factors. In addition, the effect of price differs among the configurational paths for the three hotel types.

This study expands the understanding of AI applications within the hospitality context by exploring the role of AI in AI-themed hotels and comparing its effectiveness in attracting customers across various hotel types. It also provides operational strategies for adopting AI for different types of hotels and for other hospitality and tourism sectors.

This study represents an early attempt to integrate topic modeling and fsQCA to clarify customers’ perceptions of AI-themed hotels and the combined impacts of various qualities. The findings expand on Kano’s model by classifying technology-related qualities into attractive qualities within AI-themed hotels.

Binder jetting of 17-4PH martensitic stainless steel is of great interest to several industries that could exploit the higher degree of geometrical complexity granted by 3D printing, thus this paper aims at providing a comprehensive description of the material development throughout the multiple manufacturing stages and treatments.

In this work, insight into the as-sintered 17-4PH microstructure and the manufacturing process effects on the copper precipitation is provided. Furthermore, conventional ageing treatments were applied to the specimens starting from either the as-sintered or the solution-annealed state and studied with particular attention to the microstructure and the resulting mechanical properties.

The analysis of the as-sintered microstructure revealed a continuous δ ferrite network along martensitic grains, with Cu-rich phases within the ferritic phase. Solution annealing was able to redistribute the alloying elements within the matrix and limit the continuity of the ferritic network leading to an enhancement in ductility. On the contrary, the direct ageing treatments performed on the as-sintered microstructure lead to overageing of the Cu-precipitates and impairing of the tensile properties compared to those starting from solution annealed condition. Nonetheless, hardness remains comparable independently from the ageing temperature and the ferritic network retains its morphology and distribution.

A stepwise description of the microstructural development throughout thermal treatments is provided granting the chance to design the most convenient post-processing route to achieve the required mechanical properties with a minimisation of energy and cost consumption.

Considering the importance of green knowledge in firms' sustainability, this study investigates the mediating mechanism of green knowledge acquisition (GKA) and the moderating role of resource orchestration capability (ROC) in the relationship between green entrepreneurial orientation (GEO) and corporate sustainable performance (CSP).

Using a sample of 388 executives from 195 small and medium-sized enterprises (SMEs) in the UAE, this study used partial least squares structural equation modelling to examine the proposed relationships among the constructs.

The research shows that GEO affects CSP's environmental, economic, and social aspects of CSP. This study also highlights the mediating role of GKA in the relationship between GEO and CSP. The moderated mediation analysis results indicate that when ROC is elevated, GEO's indirect influence on environmental and economic performance through GKA is more pronounced.

This study provides useful insights and a novel approach for manufacturing industries and authoritative bodies to alleviate environmental deterioration and improve CSP by encouraging GKA through green entrepreneurship.

This study enriches the existing literature on GEO, GKA, and CSP by focusing on environmental challenges and applying the resource-based view (RBV) framework. The study's findings broaden the theoretical basis for green entrepreneurship, provide guidance on enhancing CSP in manufacturing firms, and advance green entrepreneurship research.

Mobile edge computing (MEC) services have long been used by private enterprises in Saudi Arabia with considerable success; however, there has been a stark lack of insight into how these services can be used to improve mobile government (M-Government) services for KSA citizens. This study aims to bridge this gap by integrating MEC with an enhanced version of the technology acceptance model (TAM) and examining its effects on user behavior and acceptance.

A closed-ended survey was administered to 1,500 people, and the responses were analyzed using sophisticated advanced statistical techniques to test an expanded TAM, using a quantitative method that uses structural equation modeling to validate the proposed model and hypotheses.

This study reveals that MEC significantly influences users’ intentions about using M-Government services and their tolerance for new technology adoption. Specifically, service cost and social influence are positively linked with end users’ intention to adopt M-Government services.

The novelty and contribution of this paper to existing literature are in highlighting the pivotal role of MEC in transforming public sector service delivery through technology. This study not only supports the adoption of M-Government services to enhance social welfare but also demonstrates and concludes some practical and theoretical ramifications of MEC service adoption.

This study aims to investigate the work experience of women in hospitality with the job demands-resources (JD-R) theory. By focusing on job resources, job demands (emotional and physical), work engagement, emotional exhaustion and turnover intention, this study examines (1) female workers’ perceptions of these variables; (2) how race, age and job positions affect perceptions and (3) structural relationships among these variables.

The study collected 412 responses from American female hospitality workers via an online survey. Descriptive statistics and independent T-tests were conducted using SPSS to analyze professional experiences and differences based on individual characteristics. Path analysis was conducted using Amos 28 to assess the structural relationships among variables.

Female hospitality workers generally feel engaged, experience low emotional exhaustion and have low turnover intention. Younger or front-line women reported higher emotional exhaustion, lower engagement and greater likelihood to quit. Unexpectedly, emotional resources do not significantly affect work engagement, but physical resources increase it.

The study provides directions to establish specific well-being and organizational support initiatives to retain female hospitality workers.

This study offers fresh insights into the JD-R theory by examining the experiences of American female hospitality employees using a non-comparative lens. Although existing literature highlights women’s unfavorable positions relative to male counterparts, this study reveals rather positive perspectives. Additionally, it presents a dual psychological process of how job resources and demands affect women’s work experience and the varying impacts of job demands on work engagement.

This study aims to enhance real estate price prediction accuracy using advanced machine learning models, minimizing biases and inconsistencies inherent in traditional appraisal methods. By leveraging support vector regression (SVR) and gradient boosting machine (GBM), this study provides a data-driven approach to property valuation, improving decision-making for buyers, sellers and policymakers. This study also seeks to bridge the gap in machine learning applications for emerging markets like Jordan. This study’s research’s broader goal is to offer a transparent, efficient and reliable tool for property valuation that improves market efficiency and reduces transaction uncertainty.

This study uses machine learning techniques – SVR and GBM – to predict real estate prices in Amman, Jordan. Data was collected from the Department of Lands and Survey, covering residential property sales from March 2023 to December 2023. The data set underwent preprocessing, including one-hot encoding for categorical variables and logarithmic normalization for skewed data. Hyperparameter tuning was performed using grid search, and an ensemble approach compared multiple algorithms. Performance was evaluated using root mean squared error (RMSE), mean absolute percentage error (MAPE) and MAE. The findings were implemented into a user-friendly “PRICE IT” application for real-world application.

The results demonstrate that SVR outperforms GBM in predicting real estate prices, achieving the lowest RMSE (0.31) and MAPE (25%). The most influential factors in price determination are property area, location and apartment type. The study highlights that machine learning models provide superior accuracy compared to traditional appraisal methods. The findings support the integration of data-driven valuation techniques in real estate markets, reducing reliance on subjective human judgment. A user-friendly application was developed to enable nontechnical users to estimate property prices, making the research practical and impactful.

This study contributes to the growing field of machine learning applications in real estate by demonstrating the effectiveness of SVR and GBM in an emerging market context. Unlike previous research, it focuses on Amman, Jordan, where limited studies have explored advanced machine-learning models for price prediction. The study offers a practical, user-friendly valuation tool that real estate stakeholders can widely adopt. This research enhances decision-making and market efficiency by providing a transparent and objective alternative to traditional appraisal methods.

A fully coupled multi-physics model is established to simulate thermal, hydraulic, chemical and mechanical (THCM) behaviors of the sediments with the hydrate dissociation.

Linear hydrate-soil constitutive model; Mohr–Coulomb yield criterion; Thermal, hydraulic and chemical (THC) model.

(1) The linear internal friction angle model proposed in this study can more reasonably simulate the effect of hydrate saturation on the mechanical properties of sediment than the fixed internal friction angle model. (2) The established THCM model can not only accurately simulate temperature variations and gas production efficiency but also reasonably simulate effective stress reduction and volumetric contraction of sediment during the process of hydrate dissociation. (3) The dissociation of hydrate not only decreases the instability coefficient of sediment but also increases the moving distance and equivalent plastic strain of the sediment.

The hydrate sediment with large deformations should be considered in the future.

The submarine landslide will be investigated in the future.

(1) A linear hydrate-soil constitutive model based on the Mohr–Coulomb yield criterion is proposed, which defines the internal friction angle, the elastic modulus and the cohesion as a linear function of hydrate saturation. (2) A fully coupled multi-physics THCM model is established to simulate thermal, hydraulic, chemical and mechanical behaviors of hydrate-bearing sediment.

Choosing the proper selling format for online retail has long been a critical issue for many platforms to consider, whereas the emergence and popularity of live-streaming have had a massive impact on the platform's business. As a result, selecting the suitable operating strategy for the live channel has become another critical issue for platforms. In such a context, what will be the impact of live-streaming on selling formats?

In order to explore these issues, we identified two selling formats (wholesale reselling or agency selling) as well as two operating strategies (introduce or discard). Thereby, four channel-structures are constructed, namely the reselling-discard model (WN), the reselling-introduce model (WL), the agency-discard model (AN), and the agency-introduce model (AL). We comprehensively compare how different structures affect stakeholders' interests, consumer surplus, and social welfare through equilibrium analyses.

These results help clarify the impact of critical factors (e.g. self-effort attribute, cross-effort attribute, and commission ratio) on the choice of models. We find that regardless of the selling agreement between the manufacturer and the platform, the introduction of a live store is necessary; specifically, when the commission ratio is high, the platform's optimal decision is first to sign an agency agreement and then apply live selling (AL); conversely, when the commission ratio is low, the platform's optimal strategy is first to enable the live channel and then to select the reselling format (WL), together, this also reveals, from a theoretical perspective.

Our study includes the dual analysis of selling formats and channel operations, considering the inherent dual attributes of service efforts and the external competitive environment.