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Nowadays, the tourism industry is considered the largest and most diverse industry in the world, which can play a significant role in the economic growth and development of a country as the main source of income and employment. In this regard, the purpose of this study is to focus on evaluating the competitiveness indicators of Shiraz city tourism destination based on a combined model.

For this purpose, a questionnaire with 78 questions was used to evaluate the indicators, in which a total of 1,432 tourists participated, including 927 domestic tourists and 505 international tourists. The collected data were analyzed to determine the hierarchical relationship between the indicators using equation structural modeling.

The research findings indicate that the most effective and influential indicators in the field of tourism competitiveness are natural and handmade resources, and the most influential factors are demand conditions and cultural and natural resources. In this hierarchy, each factor affects its previous level and influences the next level.

This study offers significant potential for uncovering credible and robust approaches to further investigate the contextualization of tourism competitiveness on both national and international scales, thereby generating valuable new insights. By conceptualizing the diverse dimensions of tourism competitiveness and delving into the variations in its impacts across multiple levels, this research not only challenges existing notions but also aids destinations in maintaining and enhancing their market position and share over time. This study offers valuable insights and practical implications for both researchers and practitioners in the field of urban tourism. It enhances the understanding of destination competitiveness, informs policy decisions, facilitates benchmarking and best practices, guides strategic decision-making and promotes sustainable tourism development.

Although prior research has employed various variables to predict player churn, the dynamic evolution of the behavioral patterns of players has received limited attention. In this study, churn prediction models are developed by incorporating the progress level, in-game purchase, social interaction, behavioral pattern and behavioral variability (BV) of players in social casino games (SCGs). The study distinguishes churn prediction between two player groups: monetizers and non-monetizers.

This study employs three machine learning techniques—logistic regression, decision trees and random forests—using real-world player data from an SCG company to construct churn prediction models. Two experiments were conducted. In Experiment 1, BV was combined with four other variable categories to effectively predict churn behaviors across all players (n = 52,246). In Experiment 2, churn prediction models were developed separately for monetizers (n = 16,628) and non-monetizers (n = 35,618).

The findings from Experiment 1 indicate that incorporating BV significantly improves the overall performance of churn prediction models. Experiment 2 demonstrates that churn prediction models achieve better performance and predictive accuracy for monetizers and non-monetizers when BV is calculated over the 3-day to 7-day and 7-day to 14-day windows, respectively.

This study introduces BV as a novel variable category for churn prediction, emphasizing within-person variability and demonstrating its effectiveness in enhancing model performance. Churn prediction models were independently constructed for monetizers and non-monetizers, utilizing different time windows for variable extraction. This approach improves predictive performance and highlights key differences in critical variables influencing churn across the two player groups. The findings provide valuable insights into churn management strategies tailored for monetizers and non-monetizers.

This study explores the formation process of cognitive destination image among first-time Chinese millennial tourists visiting Iran.

Employing a comprehensive mixed-method exploratory approach, blending qualitative and quantitative methodologies, we aim to construct a conceptual model explaining the complex process of tourists’ cognitive destination image development. In the qualitative phase, interviews with 15 Chinese millennials unveiled key factors such as perceived behavioral control, perceived product quality, on-site involvement, engagement, and prior destination image as fundamental influencers of tourists’ cognitive destination image. Subsequently, in the quantitative phase, involving a questionnaire survey with 229 first-time Chinese travelers, we proposed a conceptual model to validate our primary findings.

The results highlight the emergence of on-travel cognitive image as a developmental concept originating from tourists' on-site participation and pre-existing perceptions of the destination. Moreover, we identified perceived behavioral control as the primary antecedent of millennials’ image formation, directly affecting the destination’s image development. Furthermore, increased involvement and engagement were found to diminish perceived behavioral control. Additionally, perceived product quality prompted millennials to engage more with cultural attractions and activities, enriching their in-situ image.

The findings offer valuable insights for destination managers, enabling them to construct tailored strategies to augment the cognitive destination image.

This study provides a novel understanding of the cognitive destination image formation process among Chinese millennial tourists, highlighting the crucial role of perceived behavioral control, involvement, engagement, and perceived product quality. These insights can guide destination managers in enhancing the overall tourist experience and destination appeal.

This study evaluates the reliability of a multi-state system (MSS) with n components, each having two s-dependent components via copulas.

The study employs copula functions to model dependencies between components in an MSS. Specifically, it analyzes a (1,1)-out-of-n three-state system using Frank and Clayton copulas for reliability evaluation. A simulation-based case study of a micro-inverter solar panel system is also conducted using the Farlie–Gumbel–Morgenstern (FGM) copula.

The study finds that incorporating component dependencies significantly impacts the reliability of multi-state systems. Using Frank and Clayton copulas, the analysis shows how dependency structures alter system performance compared to independent models. The case study on a micro-inverter solar panel system, using the FGM copula, demonstrates that real-world systems with dependent components exhibit different performances. Also some effects of dependence parameters on the performance characteristics of the system such as mean residual lifetime and mean past lifetime are also examined.

This study is original in its use of copula functions to evaluate the performance of multi-state systems, particularly focusing on a (1,1)-out-of-n three-state system with dependent components. By applying Frank and Clayton copulas, the research advances reliability analysis by considering component dependencies, often overlooked in traditional models. Additionally, a case study on a micro-inverter solar panel system using the FGM copula highlights the practical application of these methods.

Maintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0.

The maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization.

The main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance.

This research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry.

This paper aims to discuss the place-making processes of street art within the context of Toronto, Canada, and potential for street art as alternative tourism to contribute to new urban tourism and encourage urban regeneration in the city.

The study applies reflexive thematic analysis to analyse secondary data sources such as reports, maps, videos, websites, news articles and official documents alongside photographic documentation and field research.

Street art in Toronto has been found to coincide closely with processes of creative place-making. While there is some indication that municipal street art organizations and destination marketing organizations are aware of the possibilities for street art to contribute to tourism in the city, it remains an untapped resource for new urban tourism. As a component of creative place-making, it has great potential as a form of alternative tourism to regenerate a still struggling tourism economy.

This paper explores the nascent research area and practical application of street art as an alternative form of urban tourism in Toronto, Canada. It also fills a gap by connecting the concept of creative place-making with street art, urban regeneration and tourism specifically; a focus that needs wider attention.

Bangladesh is one of the leading countries that has been facing serious air pollution issues, with an exponentially higher death rate attributed to it than other environmental pollution. This study aims to identify the sources and dynamics of particulate matter (PM) pollution across different micro-environments in Rajshahi City.

PMs’ concentration data were collected from 60 sampling stations, located across the six micro-environments of the study area, throughout the year using “HT 9600 Particle Counter.” To assess the level of pollution, the air quality index (AQI) was calculated, and different methods, including observation, group discussion, interview and questionnaire survey, were used to identify the pollution sources.

Both PM_2.5 and PM₁₀ exhibit varied concentrations in different micro-environments, and the area covered by different AQI classes differs considerably throughout the year. The monthly average concentration of PM_2.5 and PM₁₀ was highest in January, 200 and 400 µg/m³ and was lowest in September, 46 and 99 µg/m³, respectively. Among the total 1,440 observations, 853 observations (59.24%) exceeded the national standard. Based on the pollution level, different months and micro-environments in the city have been ranked in descending order as January > December > February > March > April > November > October > May > June > July > August > September and traffic > commercial > industrial > residential > green cover > riverine environment.

Although numerous research has been conducted on air pollution in Bangladesh, the authors are certain that no attempt has been made to address the issue from a multi- micro-environmental perspective. This makes the methodology and findings truly unique and significant in the context of air pollution research in Bangladesh.

This study aims to examine the nexus of corporate governance with firms' financial risk-taking behavior under the corporate social responsibility (CSR) disclosures in the context of non-financial listed firms of an emerging economy.

This study investigates the relationship between corporate governance as evaluated by an index and several financial risks, including idiosyncratic, default and systematic risks. The connection of corporate governance with financial risks is also studied while considering the moderation of CSR disclosures. The data are collected from 2014 to 2018 of 73 top 100-index listed non-financial firms of Pakistan Stock Exchange (PSX). Panel regression fixed effect and 2-step generalized method of moments techniques are applied to confirm the hypothesis along with the diagnostic tests to confirm that all outcomes of models must be authentic and reliable.

The study’s findings confirm that enhancing the overall corporate governance measures resulted in an augment in the firm’s risk due to weak control and regulations prevailing in emerging economies. Moreover, CSR disclosures enhance stakeholder information, lessen information asymmetry about management policies and mitigate the risk associated with operational uncertainties.

This study has a practical implementation to policymakers that effective monitoring and controlling measures facilitate the corporate management for minimizing the financial risks. Further, the study’s findings shed light that implementing corporate governance measures is not enough to mitigate financial risks until supervisory measures in the form of CSR disclosures are not taken to analyse corporate governance effectiveness.

This paper enhances the key findings in the literature by examining the role of corporate governance measures with respect to firms’ financial risks considering the moderating role of CSR disclosures. Furthermore, this research adds to the body of knowledge regarding the implementation of monitoring measures that assist in the mitigation of firms’ financial risks hence firm value.

The purpose of this study is to investigate the effect of catalyst distribution in the combustion catalytic layer on heat and mass transport characteristics of the auto-thermal methanol steam reforming microchannel reactor.

Computational fluid dynamics (CFD) method is used to study four different gradient designs. The corresponding distributions of temperature, species and chemical reaction rate are provided and compared.

The distributions of species, temperature and chemical reaction rate are significantly affected by the catalyst distribution in the combustion catalytic layer. A more uniform temperature distribution can be observed when the gradient design is used. Meanwhile, the methanol conversion rate is also improved.

This work reveals the effect of catalyst distribution in the combustion catalytic layer on heat and mass transport characteristics of the auto-thermal methanol steam reforming microchannel reactor and provides guidance for the design of reactors.

The temperature uniformity and hydrogen production performance can be improved by the gradient design in the combustion catalytic layer.

Most Asians are directly or indirectly dependent on agriculture, and most live in rural areas. Since Asia is primarily an agricultural economy, rural tourism and agrotourism are critical in enabling growth. Agrotourism is a term that refers to the fusion of tourism and agriculture. The freshwater turtles (Batagur sp.) are a group of six turtles that once roamed southern Asia's great rivers and estuaries from the Indian Subcontinent to the Indonesian islands. Therefore, this review is to investigate the current state of agrotourism in Batagur sp. conservation and assess the associated challenges and opportunities. This review paper recognized the impacts and benefits for the rural site as an agrotourism industry. Moreover, three initiatives in Batagur sp. conservation programs were also discussed. Agrotourism, with a focus on the conservation of Batagur sp., is a vital driver of economic growth in rural Asian regions. It diversifies rural communities' income sources, enhancing economic stability and resilience.