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This study aims to explore the connection between travel content consumption in social networks and social comparison, envy as well as travel intention. It analyzes whether tourists’ travel intention is affected by travel content consumption in social networks, and more importantly, whether social comparison and envy play a mediating role in this process.

Data was collected through intercept in four popular tourist spots in Guangzhou and Zhuhai in South China. A self-administered questionnaire was used. A total of 400 participants were recruited, and 291 valid questionnaires were obtained. Bias-corrected nonparametric percentile bootstrap mediation variable test method was used to test hypotheses.

The study yielded three results. First, travel content consumption in the social networks positively influences travel intention. Second, travel content consumption in social networks indirectly affects travel intention through social comparison and envy. Third, the control variables, such as gender, age, education and income, mainly affect envy.

This study constructs a theoretical framework of stimulus–cognitive appraisal–emotion–behavioral responses. To the best of the authors’ knowledge, it is the first study to reveal that the internal psychological mechanism of travel content consumption affects travel intention. It also discloses that envy of seemingly negative emotions can encourage positive behaviors in certain situations.

Cesspits are the means for each house to dispose of wastewater in the Bani Kinanah District (BKD) of Jordan, which creates severe environmental complications. This research aimed to find a suitable site for a wastewater treatment plant (WWTP) in BKD.

Geographic Information System (GIS)-based multicriteria decision analysis (MCDA) was used for an optimal site selection for a sewage treatment plant. Several datasets were obtained to prepare the maps of the criteria influencing the choice of the most suitable site for the WWTP. The analytic hierarchy process was used to apply the weights for each factor.

Five classes of suitability were generated: 0.23% very high suitability, 8.49% high suitability, 47.12% moderate suitability, 37.67% low suitability and 6.49% very low suitability. According to Analytical Hierarchy Process (AHP) results, the elevations, slope and groundwater depth have high importance; where their weights 21%, 19% and 17%, respectively. The most suitable site for establishing a WWTP was found in the northern part of the study area, where it is characterized by relatively low elevations (−90 to −93 m), low slope (0–2.5 %), distance from groundwater level (47–82 m) and the space is sufficient for building the plant (25328 m², 8861 m² and 8586 m²).

This research is limited by the availability of data.

The research is invaluable for decision makers involved in urban planning.

Wastewater treatment plants are essential for communities with limited resources such as Jordan. It has also profound impacts on the surrounding environment.

From the present study, it can be concluded that GIS is essential in urban utility establishment, like urban domestic wastewater treatment site selection. Although the study area has adequate potential areas for establishing WWTP, further assessment of flood vulnerability, wastewater amount quantification, population growth and urban expansion must be seriously considered before implementation.

The objective of this study is to investigate the concept of “earthquake resistance” in high-rise buildings and assess the current level of structural strength in the areas where these structures are situated. The study aims to identify and implement necessary measures to enhance resilience in these areas. A comprehensive literature review was conducted to develop a conceptual framework focusing on earthquake resistance's meanings, stages and physical elements to achieve these goals.

This study focuses on Istanbul, a city known for its high earthquake risk, specifically targeting the Atasehir district. The research utilizes the DEMATEL (Decision-Making Trial and Evaluation Laboratory) method to evaluate urban resilience parameters. Additionally, the Fuzzy TOPSIS (Preference Ranking Technique by Similarity) method is employed to analyze the location of five buildings in Atasehir, using criterion weights derived from this methodology.

The findings indicate that resilience varies depending on the distance of the buffers. Moreover, the amount and quality of urban equipment in the study areas have a significant impact on the earthquake resistance level of the surrounding areas where high-rise buildings are situated. Building upon this analysis, the study suggests the implementation of measures aimed at augmenting the quantity and quality of urban facilities in the study areas, consequently enhancing urban resilience.

The originality and value of this study lie in its examination of seismic resilience within the context of high-rise buildings and the identification of necessary measures to increase resilience in areas where these structures are prevalent. By focusing on Istanbul, a city with a high earthquake risk, and specifically selecting the Atasehir district as the study area, this research provides a comprehensive conceptual framework for understanding urban resilience and its physical components. Moreover, the study offers a fresh perspective on urban resilience by highlighting the influence of tall buildings on the surrounding areas. Ultimately, it provides practical recommendations for architects, urban planners and other stakeholders to improve regional earthquake resilience.

Contemporary coliving is a rapidly developing housing typology, characterised by high-density private living spaces integrated with various shared, mixed-use amenities. The purpose of this research is to quantitatively examine the spatial configurations of coliving building systems, and the integration of programmatic space labels, to provide insights for architects and researchers into the homogeneity and genotypical patterns embedded within these contexts.

Coliving buildings of various scales from the United Kingdom and the USA were examined using small graph matching and inequality genotypes. The former was adopted to identify a genotype signature and assess homogeneity levels, whilst the latter provided a comparative analysis of the ranked integration values for space labels within these building systems.

Although local samples exhibited superior levels of homogeneity compared to the sample population (n = 18), the latter still evinced a marked homogeneity and no statistical difference in building system integration (mean real relative asymmetry (RRA)). Local large-scale samples showed the greatest homogeneity and building system integration of all sample groups, whilst a statistically significant distinction in building system integration was evident between large- and small-scale samples. However, a comparison of space label integration (RRA) across different building scales demonstrated that a potential genotypical pattern exists between small- and large-scale samples.

Through the identification of homogeneity and integration values related to scale and location, this research establishes an empirical, methodological framework for the generalisable spatial analysis of contemporary coliving buildings. Furthermore, genotypical patterns provide insights into space labels that are most likely to encourage copresence and social encounters between residents.

This study aims to examine the enablers of productivity of enterprise-level Agile development process using modified total interpretative structural modeling (TISM). The two main objectives of the current study are to determine the variables influencing enterprise-level agile development productivity and to develop modified TISM for the corresponding components.

To identify enablers of the productivity of enterprise-level agile software development process a literature review and opinions of domain experts were collected. A hierarchical relationship among variables that show direct and indirect influence is created using the modified TISM (M-TISM) technique with Cross Impact Matrix-Multiplication Applied to Classification analysis. This study examined and analyzed the relationships between the determinants within the enterprise using a M-TISM technique.

With the literature review, the study could identify ten enabling factors of the productivity of Agile development process at the enterprise level. Results depict that program increment (PI) planning and scalable backlog management, continuous integration and continuous delivery (CI/CD), agile release trains (ART), agile work culture, delivery excellence, lean and DevOps practices, value stream mapping (VMS), team skills and expertise, collaborative culture, agile coaching, customer engagement have an impact on the productivity of enterprise-level Agile development process. The results show that team collaboration, agile ways of working and customer engagement have a greater impact on productivity improvement for enterprise-level Agile development process.

The developed model is useful for organizations employing scaled Agile development processes in software development. This study provides a recommended listing of key enablers, that may enable productivity improvements in the Agile development process at the enterprise level. Strategists should focus on team collaboration and Agile project management. This study offers a modified TISM model to academicians to help them understand the effects of numerous variables on maintaining the productivity of an enterprise-level Agile. The identified characteristics and their hierarchical structure can help project managers during the execution of Agile projects at the enterprise level, more effectively, increasing their success and productivity.

The study addresses the gap in the literature by interpretative relationships between the identified enabling factors. The model validation is carried out by a panel of nine experts from several information technology organizations deploying Agile software development at the enterprise level. This unique method broadens the knowledge base in Agile software development at scale and provides project managers and practitioners with a practical foundation.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

This study examined the psychometric properties of intention to drop out and study satisfaction measures for first-year South African students. The factorial validity, item bias, measurement invariance and reliability were tested.

A cross-sectional design was used. For the study on intention to drop out, 1,820 first-year students participated, whilst 780 first-year students participated in the study on satisfaction with studies. Confirmatory factor analysis (CFA), differential item functioning (DIF), measurement invariance and internal consistency were used to test the scales.

A one-factor structure was confirmed for both scales. For the intention to drop out scale, Items 3 and 4 were identified with statistically significant item bias; however, these differences had no practical impact. Except for scalar invariance for language, sufficient measurement invariance was established. No problematic items were identified for the study satisfaction scale.

In essence, this study provides evidence of two short measures that are culturally sensitive that could be used as short and valid measures across contextual boundaries as practically valuable tools to measure intention to drop out and study satisfaction in diverse and multicultural contexts.

This study contributes to limited research on bias and invariance analyses for scales that can be used in interventions to identify students at risk of leaving the university and utilising psychometric analyses to ensure the applicability of these two scales in diverse and multicultural settings.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

Organizations that prioritize humanistic responsibility create an environment of value for their employees as the most important stakeholders. However, despite the numerous corporate social responsibility (CSR) models and research highlighting stakeholder considerations, the long-standing “social” aspect of CSR has inhibited its humanism responsibility. In response, this study proposes to move beyond the antecedents and outcomes of CSR to explore how perceived CSR can promote its humanistic responsibility both inside and outside of organizations.

The authors followed Sendjaya et al. (2008) ’s methodology for developing and validating the perceived corporate humanistic responsibility (CHR) scale. Study 1 validated the CHR's content. Study 2 established the measure’ reliability, internal consistency, unidimensionality and discriminant validity. The authors describe each of the studies in the forthcoming sections.

This research has produced a comprehensive set of perceived CHR items for business leaders based on earlier CHR/humanism concepts. Through the deconstruction of CHR theory, the granular conceptualization provides employee-centric workplaces, healthy internal communication, holistic compensation, CSR-committed behaviors and holistic training and development, equipped to assess how their CHR fosters humanistic workplaces that encourage socially responsible behaviors. This, in turn, would have an immense impact on employee well-being that, in turn, flourishes societal well-being.

Although the perceived CHR scale's psychometric properties were confirmed using multiple tests ranging from qualitative to quantitative studies, this newly developed scale requires further investigation to explore whether internal or external relevance factors affect organizations' humanistic responsibility.

CSR is about caring for humans and the planet. The authors have unpacked what and how the human side of CSR operates for business leaders to advance their CHR practices and responsible management learning. The perceived CHR dimensions can guide business leaders to promote multidimensional humanistic behaviors inside and outside workplaces that transcend how to strengthen the humanistic responsibility behaviors of corporations to promote CHR by articulating how the “Social” aspect of CSR ought to function for employee well-being first.

This study responds to Sustainable Development Goals (SDGs) most aligned with the SDG 3 (good health and well-being) and SDG 8 (decent work and economic growth) by promoting humanistic workplaces with implications for United Nation's Principles for Responsible Management that encourages universities to educate students on humanism concepts in business management.

The originality lies in the empirical study of CHR. By incorporating the original concepts of humanism/humanistic management and CHR, the authors empirically articulate how CHR may be practically implemented as an elaborated humanistic synthesis for corporations.

This paper reviews 18 workplace spirituality scales developed over a period of 23 years from year 2000 to early 2023. The objective is to provide inputs which can guide future researchers in choosing an existing appropriate workplace spirituality scale and in adopting sound scale development procedures for the development of new workplace spirituality scales.

This paper outlines the need for and importance of carrying out a review of workplace spirituality scales. Thereafter, it carries out a review of each of the 18 workplace spirituality scales. It describes 10 overall limitations of the collective set of workplace spirituality scales. Finally, five future research directions are outlined.

The review indicates that most of the workplace spirituality scales have limitations associated with them. The collective set of workplace spirituality scales also has several overall limitations.

The review suggests that workplace spirituality researchers need to exercise caution in choosing an appropriate existing workplace spirituality scale for their research. The review also provides directions for the future research for developing more adequate workplace spirituality scales.

The inputs from this review will contribute to improve the practice of workplace spirituality research.

As workplace spirituality can make organizations more beneficial for employees and the society, this review's contribution to improving workplace spirituality research has an indirect social significance.

This is, to the author's knowledge, the first comprehensive review of 18 workplace spirituality scales developed over a period of 23 years from year 2000 to 2023.