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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.

Virtual reality (VR) offers unprecedented immersion and interactivity in education, and working and learning from home have become the norm during the COVID-19 pandemic. This study empirically investigated the factors affecting the use of a VR online learning system (VROLS).

To explore factors affecting users’ continuance behavioral intentions toward using VROLSs, a research framework was formed comprising factors that constitute benefits (i.e. pull factors) and costs (i.e. push factors); these factors included perceived value, flow and social influence. The data for this study were collected via online survey questionnaires. A total of 307 valid responses were used to examine the hypotheses in the research model, employing structural equation modeling (SEM) techniques.

Perceived value, flow experience and the number of peers using VR primarily affect the decision to adopt a VROLS. The pull factors of spatial presence, entertainment and service compatibility, along with the push factors of complexity and visual fatigue, affect perceived value. Therefore, we conclude that perceived value is a primary factor positively influencing both flow experience and the decision to adopt the service.

This study contributes to a theoretical understanding of factors that explain users’ intention to use VROLSs.

The objective is to develop an empirical model estimating the relationship and interaction amongst the factors affecting and enhancing green finance (GF) in developing economies like India.

Around nine growth-accelerating enablers of green financing were found through literature and unstructured interviews and analysed using the total interpretive structural modelling (TISM) method. The hierarchical link between each factor is established using TISM, and further to evaluate the driver-dependent relationship the Matriced’ Impacts Croises Appliquee Aaun Classement (MICMAC) approach is utilised.

The findings demonstrate an interrelationship between growth-accelerating factors, where the political environment and information and communication technology (ICT), have minimal dependency but a strong driving force. Political environment and ICT are found as strategic-level factors lying at the bottom of the model driving towards the dependent variables. The government should focus on enacting effective policies such as the green credit guarantee scheme and carbon credit and establishing a regulatory framework to enhance green financing.

This study examines the literature to generalise the findings and focus on the primary motivators for developing green financing. To increase green financial activity, practitioners must concentrate on aspects with significant driving forces. Furthermore, it makes organisations more profitable, efficient and competitive and promotes long-term growth.

The study is the first in the literature which identifies the growth-accelerating factors of green financing using the TISM and MICMAC-based hierarchical models.

The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

This study investigated multiple factors associated with librarians’ intentions to engage in virtual storytimes in public libraries. Based on the theory of planned behavior, the study examined the aspects of attitudes, social norms, behavioral controls and organizational support about librarians’ intentions to engage in virtual storytime practices.

A survey was conducted to investigate the perceptions of public librarians who work in children’s services across the USA. The study collected 365 valid responses to assess the impact of the selected factors on librarians’ intentions. Structural equation modeling was employed for statistical analysis.

The results revealed that attitudes, subjective norms and behavioral controls were significant factors associated with librarians’ intention to engage in virtual storytimes. Among these, attitudinal factors were the most influential; however, organizational support was not a significant factor.

Minimal research has been conducted to explore factors associated with online storytime practices, which served as a primary delivery mode during the COVID-19 pandemic. This study is among the first attempts to investigate various factors related to virtual storytimes in public libraries from the perspectives of librarians.

Many countries have a significant vaccination hesitancy rate regardless of vaccine prosperity. This study aims to identify factors restricting hesitancy and fostering vaccination intention and uptake against coronavirus in Vietnam.

The study has proposed an extended COM-B model based on the Theoretical Domains Framework to explore critical factors influencing vaccination intention and uptake in Vietnam. A database was collected from 1,015 suitable respondents who had received at least one dose of the COVID-19 vaccine, and ten hypotheses were tested by the partial least squares structural equation model.

The findings showed that six factors, including knowledge, experience, resource, social influence, belief and reinforcement, have either direct or indirect positive effects on COVID-19 vaccine uptake behavior. The output also indicated that personal experience positively affects vaccination intention and uptake.

This study contributes to understanding COVID-19 vaccine uptake behavior by identifying several direct and indirect factors of the extended COM-B model that include “knowledge” and “reinforcement” in shaping behavior change. The study adds to the literature on COVID-19 vaccine uptake behavior and could help achieve higher vaccination rates, ultimately leading to better control of the pandemic.

Iron deficiency anemia (IDA) is the most common nutritional anemia in the world and a pervasive health problem, especially in developing countries. Children under two years of age are more prone to be affected by IDA. The best strategy to prevent and treat IDA is to use iron supplements. This study aims to examine the factors associated with noncompliance (such as non-utilization or inconsistent usage) of iron drop supplementation among infants between 6 and 24 months old.

Online databases (PubMed, Scopus and SID) were searched to retrieve relevant articles published from inception up to July 2023. Among the 2,177 articles detected, after removing duplicate and irrelevant titles, 21 cross-sectional studies that met the authors’ inclusion criteria were included. Screening for articles and data extraction were conducted separately by two researchers.

The findings suggest that some factors related to mothers, such as education, knowledge, attitude and performance; some factors associated with child such as child’s gastrointestinal and dental complications; taste and smell of iron drops; and birth order and gender are the main determinants of adherence to iron supplementation.

It can be proposed that the most significant factors affecting the feeding of iron drops to children under the age of two include: the level of mother’s awareness, socio-economic status of the household and the occurrence of digestive complications following the supplementation. Given these observations, adopting proper policies toward improving the nutritional awareness of mothers and producing iron supplements with minimal side effects seems crucial.

By using a wide range of macro and micro factors, this paper aims to provide a new assessment of the recent literature on inbound tourism demand models.

This study examines the determinants and spatial effects of inbound tourism using Hausman–Taylor and spatial econometric models.

Several important factors were identified, including local economic growth, openness to the outside world, regional size, geographic distance, foreign direct investment, level of innovation and average annual temperature. In addition, the study found strong cross-city competition effects on tourism resource endowment and hotel infrastructure.

Inbound tourism is a crucial link in achieving high-quality economic development. However, previous studies have mainly focused on the analysis of single influencing factors, ignoring the spatial spillover effects of factors.

Robots have been adopted in numerous tourism and hospitality sectors, including restaurants. This study aims to investigate fast-food employees' use of service robots (SRs) in Pakistan.

This study used a conceptual model based on innovation resistance theory (IRT). By employing structural equation modeling (SEM) in Smart-PLS 3.2.8, we evaluated data from 247 valid respondents.

The findings demonstrated that drivers of robot adaptation significantly influenced image barriers, risk barriers, traditional barriers, usage barriers and value barriers. The results also revealed that usage, image and traditional barriers significantly affect usage intention.

This study enhances the research on robotics acceptance in tourism and hospitality and subsequently aids in the planning for post-COVID-19 resumption. This study offers several practical and theoretical insights for further investigation.

The purpose of this study is to develop a new hybrid method that combines interpretative structural modeling (ISM) and matrix cross-impact multiplication applied to classification (MICMAC) to investigate the influencing factors of sustainable infrastructure vulnerability (SIV).

(1) Literature review and case study were used to identify the possible influencing factors; (2) a semi-structured interview was conducted to identify representative factors and the interrelationships among influencing factors; (3) ISM was adopted to identify the hierarchical structure of factors; (4) MICMAC was used to analyze the driving power (DRP) and dependence power (DEP) of each factor and (5) Semi-structured interview was used to propose strategies for overcoming SIV.

Results indicate that (1) 18 representative factors related to SIV were identified; (2) the relationship between these factors was divided into a five-layer hierarchical structure. The 18 representative factors were divided into driving factors, dependent factors, linkage factors and independent factors and (3) 12 strategies were presented to address the negative effects of these factors.

The findings illustrate the factors influencing SIV and their hierarchical structures, which can benefit the stakeholders and practitioners of an infrastructure project by encouraging them to take effective countermeasures to deal with related SIVs.