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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.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

Driven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs), which reduces energy consumption and lineside inventory of workstations (LSI). Nevertheless, the previous part feeding scheduling method was designed for conventional material handling tools without considering the flexible spatial layout of the robotic mobile fulfillment system (RMFS). To fill this gap, this paper focuses on a greening mobile robot part feeding scheduling problem with Just-In-Time (JIT) considerations, where the layout and number of pods can be adjusted.

A novel hybrid-load pod (HL-pod) and mobile robot are proposed to carry out part feeding tasks between material supermarkets and assembly lines. A bi-objective mixed-integer programming model is formulated to minimize both total energy consumption and LSI, aligning with environmental and sustainable JIT goals. Due to the NP-hard nature of the proposed problem, a chaotic differential evolution algorithm for multi-objective optimization based on iterated local search (CDEMIL) algorithm is presented. The effectiveness of the proposed algorithm is verified by dealing with the HL-pod-based greening part feeding scheduling problem in different problem scales and compared to two benchmark algorithms. Managerial insights analyses are conducted to implement the HL-pod strategy.

The CDEMIL algorithm's ability to produce Pareto fronts for different problem scales confirms its effectiveness and feasibility. Computational results show that the proposed algorithm outperforms the other two compared algorithms regarding solution quality and convergence speed. Additionally, the results indicate that the HL-pod performs better than adopting a single type of pod.

This study proposes an innovative solution to the scheduling problem for efficient JIT part feeding using RMFS and HL-pods in automobile MMALs. It considers both the layout and number of pods, ensuring a sustainable and environmental-friendly approach to production.

This study aims to explain the influence of four socio-psychological variables: social comparison orientation, face saving (FS), status consumption (STC) and frugality (FGL) on consumers’ value perception toward ride-sharing services – one of the most widely used collaborative consumption models. Furthermore, it assesses how perceived value affects consumers’ intention to use (IU) the ride-sharing services and intentions to substitute ride-sharing services for using a personally owned car. It also assesses the moderating effect of psychographics on the relationship between consumers’ perception and behavioral intention.

A structured questionnaire was developed using existing scales adapted from the literature to test the hypothesized relationships. The data for the study were collected from 489 users of ride-sharing services in India. Structural equation modelling was performed to test the proposed model using AMOS 18 and moderation analysis was performed using PROCESS MACRO.

The findings of the study suggest that social comparison, FS, STC and FGL have a significant influence on consumers’ value (utilitarian and hedonic) perception. Furthermore, the results supported the effect of consumers’ value perception on their IU the ride-sharing services as well as their intention to substitute ride-sharing services for using a personally owned car. Lastly, the results also evidenced the moderating role of psychographic variables.

Very few studies have examined the role of psychographics in the adoption of collaborative consumption services. The paper attempts to fill this gap. It assesses the effect of four relevant consumer traits on perceived value in the ride-sharing services context. Furthermore, it expands the understanding of the role of psychographics by measuring their moderating effects apart from direct effects. The results of the study bear important implications for academicians, policymakers and marketers.

Manufacturing companies are increasingly using green smart production (GSM) as a tactic to boost productivity since it has a number of advantages over conventional manufacturing methods. It costs a lot of money and takes a lot of work to create an SMS since it combines a lot of different technologies, including automation, data exchanges, cyber-physical systems (CPS), artificial intelligence, the Internet of things (IoT) and semi-autonomous industrial systems. Green smart manufacturing (GSM) activities provide the foundation for creating ecologically friendly and green products. However, there are a number of other significant barriers obstacles to GSM deployment. As a result, removing this identification of these hurdles in a systematic manner should be a top focus of this study.

This article seeks to identify and prioritize the nine barriers based on research and expert viewpoints on GSM challenges. The analytical hierarchy process (AHP) is used to prioritize the barriers.

The result depicts that, financial constraints is the most important barrier that followed by scarcity of dedicated suppliers, concern to data security lack of understanding of the surroundings, inadequate top management commitment, proper handling of data interfaces lack of support by government, employees' lack of training, concern to data security lack of environment knowledge, fear of change/resistance and constraints of technology.

The current research will help the manufacturing industry in Industry 4.0 to identify potential barriers to GSM implementation.

Green manufacturing (GM) entails the implementation of renewable production methods and eco-friendly procedures in manufacturing businesses. This study helps manufacturers come up with recycling and creative products, and manufacturers can give back to the environment by protecting natural areas by getting rid of the obstacles that get in the way.

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.

There is a growing trend in online shopping in developing and underdeveloped economies. Measuring web retail service quality is vital to enhance and retain online customers for higher profits. As a result, it is essential to understand e-service quality and its relationship with other e-commerce-related variables identified as customer satisfaction, word-of-mouth (WOM), loyalty and repurchase intention. The main objective of this research is to validate the psychometric properties of E-TailQ and ES-Qual scales to measure e-service quality in the context of an emerging economy.

The study was administered by surveying online customers through a convenience sampling approach. The sample data consisted of 329 valid responses. The data were analyzed using confirmatory factor analysis (CFA) and structural equation modeling (SEM).

The results suggest that both the scales are valid, reliable and adequate and confirm the psychometric properties of E-TailQ and ES-Qual scales in an emerging economy context. There is a positive and significant relationship between latent variables (E-TailQ and ES-Qual) and customer outcome variables like satisfaction, loyalty, WOM and repurchase intention. Additionally, the coefficients of the E-TailQ scale have higher values than ES-Qual scales in all dimensions. The perceived quality proved to be a precedent of satisfaction to a lesser extent than other scales' constructs, and it proved to be a superior predictor of WOM for the E-TailQ scale and loyalty for the ES-Qual scale. Overall, when measured by the ES-Qual scale, perceived quality could explain much of the sample's satisfaction, loyalty and repurchase intention compared to E-TailQ.

One of the significant limitations of this study is related to the data collection, as individual responses were collected at one particular point in time. Thus, it is impossible to verify the development of perceived service quality outcomes over a specific time. Another possible limitation is the selection of only two widely used scales, although other options are available in the literature. Finally, developing a new scale to address future challenges might be another alternative.

The results obtained in the study will help managers assess customer satisfaction, the reputation of the business through WOM, and customer intentions for the repurchase that will lead to loyalty by emphasizing e-service quality. It will enable them to design strategies for improving business performance. The developed instrument will assist in identifying the gaps and describing how service quality can achieve higher levels of customer satisfaction.

Several studies in the literature have used E-TailQ and ES-Qual scales to measure e-service quality in developed countries. Only a few studies addressed the validity of these widely used competing scales in an emerging economy like Pakistan. This study addresses the literature gap by addressing the reliability and validity of these scales and assesses the impact of e-commerce-related variables. Besides, the study reveals insights into the literature by addressing multiple dimensions of e-service quality and summarizes a deep understanding of each scale item that affects the perceived quality in a developing economy like Pakistan.