Search results

With the widespread popularity of Esports, Esports game addiction has attracted wide attention. This research explores the causes of Esports game addiction among college students in Mainland China and the influence of specific participation motivation and self-control on college students’ Esports game addiction.

This research applied the organismic integration theory (OIT) instead of the basic psychological needs theory using Honor of Kings, the most popular Esports game in the form of multiplayer online battle arena (MOBA) among Chinese college students. A total of 339 completed questionnaires were finally analyzed. SmartPLS 2.0 M3 was used to perform the PLS-SEM analysis on the relationship between self-control, participation motivation and Esports game addiction.

Only introjected regulation and a motivation positively impacted Esports game addiction, while intrinsic motivation, integrated regulation, identified regulation and external regulation had surprisingly no effect on Esports game addiction.

Scant studies have explored Esports game addiction in MOBA games, especially in Asia. Results showed that parents, schools and society should consciously strengthen the education and exercise of students’ self-control ability to prevent Esports game addiction. Game developers should appropriately adjust game structures and functions to prevent college students from using them as social tools or games to escape reality.

The primary goal of this research is to introduce a data-driven Problem-Solving Approach for Performance Improvement in Healthcare Systems (DPAPIH). This approach combines process mining and data mining techniques to enhance operational efficiency by identifying bottlenecks in Coronary Artery Bypass Grafting (CABG) procedures, particularly focusing on variability in Length of Stay (LOS) in the Intensive Care Unit (ICU). The study, implemented at Tehran Heart Center, aims to optimize patient flow, reduce ICU congestion and improve hospital efficiency by predicting and managing the occurrence of postoperative Atrial Fibrillation (AF), a significant cause of prolonged ICU stays.

The study introduces a data-driven problem-solving approach that integrates process mining and data mining techniques to improve performance in healthcare systems. Focusing on coronary artery bypass grafting (CABG) at Tehran Heart Center, the approach identifies bottlenecks, particularly variability in ICU length of stay (LOS) and predicts postoperative atrial fibrillation (AF). A mixed-methods approach is employed, combining quantitative process mining analyses with qualitative insights from expert consultations. The CHAID decision tree algorithm, alongside other models, is used to predict AF, enabling preemptive interventions, improving patient flow and optimizing resource allocation to reduce hospital congestion and costs.

The study reveals that postoperative Atrial Fibrillation (AF) significantly increases the length of stay (LOS) in the Intensive Care Unit (ICU), creating bottlenecks that delay subsequent surgeries and elevate hospital costs. A predictive model developed using CHAID decision tree algorithms achieved a prediction accuracy of 71.4%, allowing healthcare providers to anticipate AF occurrences. This capability enables proactive measures to reduce ICU congestion, improve patient flow and optimize resource allocation. The findings emphasize the importance of AF management in enhancing operational efficiency and improving patient outcomes in Coronary Artery Bypass Grafting (CABG) procedures.

This study presents an innovative integration of fuzzy process mining and data mining algorithms to address performance bottlenecks in healthcare systems, specifically within the coronary artery bypass surgery process. By identifying atrial fibrillation as a key factor in length of stay fluctuations and developing a robust predictive model, the research offers a novel, data-driven approach to performance improvement. The implementation at Tehran Heart Center validates the model’s practical applicability, demonstrating significant potential for enhancing patient outcomes, optimizing resource allocation and informing decision-making in healthcare management.

Structural mentoring barriers are policies, practices and cultural norms that collectively disadvantage marginalized groups and perpetuate disparities in mentoring. This study aims to better understand structural mentoring barriers at the postdoctoral training stage, which has a direct impact on faculty diversity and national efforts to retain underrepresented groups in research careers.

A diverse sample of postdoctoral scholars (“postdocs”) from across the USA were asked to participate in focus groups to discuss their training experiences. The authors conducted five 90-min focus groups with 32 biomedical postdocs, including 20 (63%) women and 15 (47%) individuals from underrepresented racial/ethnic groups (URG).

A social-ecological framework was used to categorize both the upstream and downstream manifestations of structural mentoring barriers, as well as mentoring barriers, overall. Notable structural barriers included: academic politics and scientific hierarchy; inequalities resulting from mentor prestige; the (over) reliance on one mentor; the lack of formal training for academic and non-academic careers; and the lack of institutional diversity and institutional mentor training. To overcome these barriers, postdocs strongly encouraged developing a network or team of mentors and recommended institutional interventions that create more comprehensive professional development, mentorship and belonging.

For postdoctoral scientists, structural mentoring barriers can permeate down to institutional, interpersonal and individual levels, impeding a successful transition to an independent research career. This work provides strong evidence for promoting mentorship networks and cultivating a “mentoring milieu” that fosters a supportive community and a strong culture of mentorship at all levels.

This study aims to examine the intricate relationship between Chile’s coastal communities and the increasing effects of climate change, with a focus on Caleta Lenga as a case study. Chile’s extensive coastline is closely linked to the country’s economic well-being and the livelihoods of millions of people. The mounting threats posed by climate change require immediate action. To strengthen resilience and reduce risk, adaptive measures are imperative. However, effective adaptation is hindered by numerous barriers, including adaptive capacity and governance challenges.

This study uses a case study approach, which centres on Caleta Lenga’s unique socio-environmental and demographic context. This study used semi-structured interviews and historical reconstruction to reveal a socio-economic and environmental narrative that was influenced by significant events and transitions.

The residents’ perception of climate change impacts was viewed through the lens of industrial activities and changing weather patterns. This study emphasizes the community’s resilience in the face of changing socio-environmental dynamics. It highlights the importance of informed decision-making, community cooperation and the preservation of ancestral knowledge in promoting adaptive strategies based on community solidarity and collective decision-making. The findings underscore the need for effective adaptation measures that address both adaptive capacity and governance challenges to bolster climate resilience in vulnerable coastal communities.

The findings underscore the need for effective adaptation measures that address both adaptive capacity and governance challenges to bolster climate resilience in vulnerable coastal communities.

The persistence and virality of conspiracy theories online have raised significant concerns. This study revisits Rogers’ Diffusion of Innovations theory to examine the spread of conspiracy theories on social media, specifically focusing on how factors influencing their diffusion evolve over time.

The study analyzes over 1.18 million COVID-19-related tweets using a combination of natural language processing, social network analysis and machine learning techniques. It explores the dynamic roles of novelty, content negativity, influencers, echo chamber members and social bots in the diffusion of conspiracy theories.

The results indicate that novelty, influencers, echo chamber members and social bots are positively associated with the spread of conspiracy theories. The initial dissemination of conspiracy theories is primarily driven by content novelty and influencer involvement. Over time, the perpetuation of these theories becomes increasingly influenced by content negativity and the involvement of echo chamber members and social bots. Social bots serve as important connectors within echo chambers and their removal significantly reduces network cohesion.

The findings provide practical guidance for social media platforms and policymakers in monitoring diffusion patterns and applying targeted interventions.

This study introduces a time-sensitive approach to understanding the spread of conspiracy theories on social media. By identifying the key drivers at different stages of the diffusion process, this study offers valuable insights for developing effective strategies to counteract the proliferation of conspiracy theories at various points in their lifecycle.

This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.

A new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.

According to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.

An integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

Across the globe, cities have contemplated practical measures to handle sustainable development issues which vary from environmental, social and to economic problems. Despite this, Africa has continued to fall behind in the implementation of smart city development. Yet, one of Africa's most cherished goals is achieving sustainable human settlements to keep up with the present and future urbanisation rates. The backbone to achieving sustainable human settlements is having efficient, well-capacitated, municipal systems and committed public servants. African Governments, however, continue to have failing municipalities with corruption being rife in both small towns and cities, yet there are chances to evolve and become smarter. Blockchain technology is a novel and disruptive innovation that has the potential to empower smart cities by providing a platform for interoperability, coordination and governance among multiple smart city initiatives and actors. However, the adoption of blockchain technology also faces several challenges and barriers to its adoption and implementation in smart cities, especially in Africa, where there is lack of awareness, regulation, infrastructure and access to ICT. This chapter examines the current state, opportunities and challenges in the adoption of blockchain technology in smart city development in Africa, a continent that faces multiple urbanisation issues, such as poverty, inequality, environmental degradation and lack of infrastructure. As such, the study adopts two theories, the diffusion of innovation (DOI) and the technology–organisation–environment (TOE) framework to view the use and opportunities placed by adopting blockchain technology through municipal stakeholders and citizens to enhance smart city development.

Innovative technologies have gained popularity in recent years as a means of improving workers' general well-being at work. Among these exciting new technologies is virtual reality (VR). With a focus on enhancing individual performance, this chapter explores the application of VR as a human resource (HR) intervention to improve spirituality in the workplace. To offer a theoretical foundation for comprehending the possible effects of VR interventions on workplace spirituality, this chapter thoroughly evaluates the literature on mindfulness, quantum consciousness and workplace spirituality. By integrating VR technology with the ideas of these theoretical frameworks, HR professionals may create interventions that foster employee effectiveness, resilience and personal growth. It has also been suggested that HR managers employ VR mindfulness and meditation sessions, virtual retreats, values alignment workshops, empathy-building simulations and spiritual reflection spaces as practical VR interventions to enhance workplace spirituality. To illustrate the usefulness of VR in enhancing workplace spirituality, a few companies that have successfully implemented VR therapies are also cited. Lastly, the challenges and moral dilemmas associated with utilising VR to promote workplace spirituality have been examined. These include privacy difficulties, possible biases in VR content and the requirement for ongoing evaluation and feedback techniques. This chapter highlights how VR has the potential to be a game-changing tool for improving workplace spirituality and boosting individual effectiveness.

The uncertain supply chain network design (SCND) problem, considering suppliers’ environmental, social and governance (ESG) ratings, has been infrequently addressed in the literature. Looking at the importance of ESG ratings in achieving supply chain sustainability, this study aims to fill the gap by incorporating supplier ESG factors into SCND within an uncertain environment.

This paper presents a multi-period, multi product SCND model that integrates ESG factors and accounts for uncertainties in supply and production capacities. The model seeks to minimize total operational costs by determining the optimal selection of plant and warehouse locations across multiple time periods. Uncertainties in supply and production capacities are managed through a chance-constrained programming approach with right-hand side stochasticity. A Lagrangian relaxation-based heuristic method is applied to address the NP-hard nature of the problem.

The efficacy of the proposed model is illustrated through a numerical example, demonstrating its capability to optimize material flows across the supply chain under uncertain conditions. The model simultaneously considers economic and ESG factors in procurement decisions. A sensitivity analysis is conducted to examine different operational scenarios and their implications on the model’s outcomes.

To the best of the authors’ knowledge, this study is one of the first to integrate ESG factors into SCND under uncertainty. The proposed model provides a robust framework for decision-makers to optimize supply chain operations while considering both economic and ESG objectives in an uncertain environment.

Human resources analytics (HRA) applications are of theoretical and practical interest to both researchers and practitioners. While organizations have begun to implement HRA applications, there is currently no established approach for measuring their performance. This study aims to address this gap in the literature by proposing a new approach for measuring the performance of HRA applications.

This study proposes a hybrid approach that combines the cumulative belief degree (CBD) and partial least squares structural equation modeling (PLS-SEM) to measure organizational HRA performance.

The performance measurement approach proposed in this study has the capacity to reveal the total HRA performance level of an organization, while also providing the opportunity to measure the performance of the subdimensions that make up HRA. These subdimensions include data sufficiency, technological capability, workforce capability, application level of HRA and organizational climate. This approach has the potential to assist organizations that do not currently utilize HRA in their operations to make an informed decision regarding the implementation of HRA and enables organizations to assess their potential performance if they were to implement HRA.

The proposed approach allows organizations to assess the performance of analytical applications in the human resources (HR) field. This assessment can be carried out at both the pre-implementation and post-implementation stages of HRA applications. Therefore, the approach provides a valuable contribution to organizations, enabling them to enhance their capabilities in this domain. Consequently, the study addresses a significant gap in practical research. Furthermore, in terms of the applicability of the developed HRA performance measurement model to diverse analytical domains, it paves the way for the advancement of other performance measurement studies.

The HRA performance assessment process encompasses multiple interrelated HRA subdimensions and performance indicators that can be measured using different scales. It is therefore essential to implement a flexible methodology that can convert diverse forms of evaluation into a unified scale and integrate them in order to effectively manage the inherent complexities and uncertainties associated with the assessment process. In this regard, the CBD approach proves particularly effective. In the CBD approach, a fuzzy set of linguistic terms is used to convert the performance indicator scores into a common scale and therefore takes into account the uncertainty inherent in the assessment process. In addition, it is also proposed to use the PLS-SEM method to combine CBDs.