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Video game streaming (VGS) is emerging as an extremely popular, highly interactive, inordinately subscribed and very dynamic form of digital media. Incorporated environmental elements, gratifications and user pre-existing attitudes in VGS, this paper presents the development of an extended model of uses and gratification theory (EUGT) for predicting users' behavior in novel technological context.

The proposed model was empirically tested in VGS context due to its popularity, interactivity and relevance. Data collected from 308 VGS users and structural equation modeling (SEM) was employed to assess the hypotheses. Multi-model comparison technique was used to assess the explanatory power of EUGT.

The findings confirmed three significant types elements in determining VGS viewers' engagement, including gratifications (e.g. involvement), environmental cues (e.g. medium appeal) and user predispositions (e.g. pre-existing attitudes). The results revealed that emerging technologies provide potential opportunities for new motives and gratifications, and highlighted the significant of pre-existing attitudes as a mediator in the gratification-uses link.

This study is one of its kind in tackling the criticism on UGT of considering media users too rational or active. The study achieved this objective by considering environmental impacts on user behavior which is largely ignored in recent UGT studies. Also, by incorporating users pre-existing attitudes into UGT framework, this study conceptualized and empirically verified the higher explanatory power of EUGT through a novel multi-modal approach in VGS. Compared to other rival models, EUGS provides a more robust explanation of users' behavior. The findings contribute to the literature of UGT, VGS and users' engagement.

This study elicits the critical attributes, consequences and values associated with the purchasing process in the context of cross-border e-commerce (CBEC). The purpose is to provide a better understanding of the fundamental factors that determine consumer values in CBEC.

The study applies the means-end-chain theory and soft-laddering techniques to interview 60 CBEC consumers to construct an implication matrix and a hierarchical value map (HVM) of the consumer purchasing process, consisting of attribute-consequence-value (A-C-V) paths.

By analyzing the significant linkages, elements, ladders and chains in the HVM, four dominant A-C-V paths were identified: economic-driven, efficiency-driven, progress-driven and quality-driven paths.

This study included only Chinese CBEC buyers. This limitation might affect the generalizability of the conclusions as culture, purchase habits and economic development differ between China and other countries.

The results of this study provide CBEC practitioners an understanding of the consumer purchasing process and how consumer values are associated with platform characteristics. Thus, the results aid practitioners in allocating resources and developing CBEC platforms in an appropriate manner and direction.

This study sheds lights on the emerging phenomenon of CBEC. By applying the means-end-chain approach, the study provides a comprehensive HVM for interpreting the consumer online purchasing process in this novel context. By illustrating the dominant paths, this research provides deeper theoretical insights into the specific focuses of CBEC consumer purchasing.

The aim of this research is to quantitatively synthesize empirical findings of the effect of discount level on consumer response to the coupon.

The authors used the meta-analysis method to synthesize coupons' discount level effects on consumer response. Meta-regression was used to examine the moderating factors that affect the relationship between discount level and consumer response.

The average effect size of the discount level is 0.331, indicating that higher discount levels lead to higher consumer responses. The effect of discount level on consumer response to the coupon is stronger when the discount is displayed in proportion format (vs amount format), when consumers are distant (vs near) to the coupon-issuing stores, and when consumers have not opted-in to receive promotional information. The discount level effect is weaker for coupons that can be redeemed online (vs offline only), for hedonic products (vs utilitarian products) and for products of real brands.

From information processing and cost–benefit trade-off perspectives, this research proposes a comprehensive research framework that synthesizes a variety of contextual factors. It identifies several contextual factors that may reconcile several inconsistent findings in the existing literature. It also addresses how the new-technology related factors affect coupon redemption under different discount levels.

The major problem that limits the widespread use of WAAM technology is the forming quality. However, most of the current research focuses on post-process detections that are time-consuming, expensive and destructive. This paper aims to achieve the on-line detection and classification of the common defects, including hump, deposition collapse, deviation, internal pore and surface slag inclusion.

This paper proposes an in-process multi-feature data fusion nondestructive testing method based on the temperature field of the WAAM process. A thermal imager is used to collect the temperature data of the deposition layer in real-time. Efficient processing methods are proposed in this paper, such as the temperature stack algorithm, width extraction algorithm and a classification model based on a residual neural network. Some features closely related to the forming quality were extracted, containing the profile image and width curve of the deposition layer and abnormal temperature features in longitudinal and cross-sections. These features are used to achieve the detection and classification of defects.

Thermal non-destructive testing is a potentially superior technology for in-process detection in the industrial field. Based on the temperature field, extracting the most relevant features of the defect information is crucial. This paper pushes current infrared (IR) monitoring methods toward real-time detection and proposes an in-process multi-feature data fusion non-destructive testing method based on the temperature field of the WAAM process.

In this paper, the single-layer and multi-layer WAAM samples are preset with various defects, such as hump, deposition collapse, deviation, pore and slag inclusion. A multi-feature nondestructive testing methodology is proposed to realize the in-process detection and classification of the defects. A temperature stack algorithm is proposed, which improves the detection accuracy of profile change and solves the problem of uneven temperature from arc striking to arc extinguishing. The combination of residual neural network greatly improves the accuracy and efficiency of detection.

Western research on teams has paid an increasing attention to team's emotional process and corresponding outcomes. However, there were seldom studies examining the influence mechanism of team leader emotional intelligence on subordinate outcomes in teams, and the previous studies often neglected the team context. The aim of the study is to explore the cross‐level effect of team leader emotional intelligence and team emotional climate on team member job satisfaction.

Questionnaire survey on 148 team leaders and 840 team members was conducted. The paper applied hierarchical linear modeling analyses with the software HLM 6.06 to test the hypotheses.

The analyses on the matched sample showed that team negative emotional climate has negative effect on team member job satisfaction, and team emotional climate moderates the relationship between leader emotional intelligence and team member job satisfaction such that leader emotional intelligence has stronger effect on member job satisfaction as team emotional climate becomes more negative.

First, it is a single time period study and cross‐sectional research design, which limits our ability to make causal inferences about the relationships proposed. Second, team leader emotional intelligence was reported by team leader themselves, and it will be better to combine self‐report and other‐report measures of emotional intelligence in the future study.

This study extends the existing theory through a cross‐level framework that examines how team emotional climate interacts with team leader emotional intelligence to affect member job satisfaction, thus enriches the studies on emotional intelligence and leadership, team emotional climate and team effectiveness. The study also provides evidence of group‐level emotion in China, a non‐Western cultural country, which enriches the existing body of studies sampling exclusively from Western cultural groups and the research of emotions in organizations at group level.

This paper aims to figure out the relationship between energy consumption flow from a new perspective of embodied energy inventory index (EEII) and regional economic growth.

The input-output approach has been applied to calculate embodied energy inventory (EEI) and EEII using the data of 25 economies. Meanwhile, cluster analysis and panel data modeling were applied to carry out detailed research.

The results of cluster analysis show that there is a roughly negative relationship between EEII and gross domestic product (GDP) per capita, although there are some exceptions, such as Russia and Taiwan (Province of China). Panel data model results provide further evidence that there is a negative relationship between EEII and GDP per capita. Population is an important productive factor in the regional economic development. The study showed a positive relationship between EEII and population. Therefore, energy consumption flow is closely related to regional economic development.

The value of this paper is to use EEI and EEII to comprehensively clarify the energy consumption flow. The advantage of EEII is that it can reflect the energy embodied in fixed assets and infrastructure.

This research addresses the pressing need for comprehensive studies in the rapidly evolving field of city tourism. This study aims to understand the overall performance of the International Journal of Tourism Cities (IJTC), the structure of knowledge in city tourism research and the prevalent themes and trends arising from IJTC.

A bibliometric analysis was conducted to scrutinize the publication patterns in IJTC. This involved examining parameters such as the annual count of published articles, the keywords used in them and their respective authors.

The findings reveal that IJTC has a growing and diverse publication output, establishing itself as a reputable and influential publication within urban tourism research. The results reflect various aspects and themes in city tourism research.

The study has certain limitations. The data used for analysis was obtained exclusively from the Scopus database. The analysis was conducted using only one software package, Bibliometrix. Other software packages may offer different features for bibliometric analysis. The study relied exclusively on quantitative methods for data analysis. Qualitative methods could have provided more nuanced interpretations of the data.

Comparative analyses could be conducted between IJTC and other journals within urban tourism or related disciplines. Such research would yield valuable insights into the current state of the field and aid in identifying areas warranting further investigation.

The findings from this study can inform the decisions and actions of various stakeholders involved in urban tourism. Practitioners and policymakers can draw from this research to make informed decisions. Existing and emerging authors can identify relevant topics for their research. Readers can access pertinent information for their needs.

This study offers a unique contribution by thoroughly comprehending the performance of IJTC between 2015 and 2023. It progresses the existing body of knowledge on city tourism research by examining its current status and future trends.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

The purpose of this study is to identify the situation of spatial inequality in the residential system of Tehran city in terms of housing prices in the year 2021 and to examine its changes over time (1991–2021).

In terms of purpose, this study is applied research and has used a descriptive-analytical method. The statistical population of this research is the residential units in Tehran city 2021. The average per square meter of a residential unit in the level of city neighborhoods was entered in the geographical information system (GIS) in 2021. Moran’s spatial autocorrelation method, map cluster analysis (hot and cold spots) and Kriging interpolation have been used for spatial analysis of points. Then, the change in spatial inequality in the residential system of Tehran city has been studied and measured based on the price per square meter of a residential unit for 30 years in the 22 districts of Tehran by using statistical clustering based on distance with standard deviation.

The result of spatial autocorrelation analysis with a score of 0.873872 and a p-value equal to 0.000000 indicates a cluster distribution of housing prices throughout the city. The results of hot spots show that the highest concentration of hot spots (the highest price) is in the northern part of the city, and the highest concentration of cold spots (the lowest price) is in the southern part of Tehran city. Calculating the area and estimating the quantitative values of data-free points by the use of the Kriging interpolation method indicates that 9.95% of Tehran’s area has a price of less than US$800, 17.68% of it has a price of US$800 to US$1,200, 25.40% has the price of US$1,200 to US$1,600, 17.61% has the price of US$1,600 to US$2,000, 9.54% has the price of US$2,000 to US$2,200, 6.69% has the price of US$2,200 to US$2,600, 5.38% has the price of US$2,600 to US$2,800, 4.59% has the price of US$2,800 to US$3,200 and finally, the 3.16% has a price more than US$3,200. The highest price concentration (above US$3,200) is in five neighborhoods (Zafaranieh, Mahmoudieh, Tajrish, Bagh-Ferdows and Hesar Bou-Ali). The findings from the study of changes in housing prices in the period (1991–2021) indicate that the southern part of Tehran has grown slightly compared to the average range, and the western part of Tehran, which includes the 21st and 22nd regions with much more growth than the average price.

There is massive inequality in housing prices in different areas and neighborhoods of Tehran city in 2021. In the period under study, spatial inequality in the residential system of Tehran intensified. The considerable increase in housing prices in the housing market of Tehran has made this sector a commodity, intensifying the inequality between owners and non-owners. This increase in housing price inequality has caused an increase in the informal living for the population of the southern part. This population is experiencing a living situation that contrasts with the urban plans and policies.