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Gamification has been widely applied in mobile fitness apps to motivate users to exercise continuously. Based on the affordances–psychological outcomes–behavioral outcomes framework, this study explores the roles of three specific gamification affordances (competition, visibility of achievement and interactivity) in self-health management (continuous use behavior and health behavior) from the perspectives of achievement satisfaction and gamification exhaustion.

We test the research model using a structural equation model (SEM) with 505 self-reported data points. Furthermore, we apply fuzzy-set qualitative comparative analysis (fsQCA) to explore configurations of gamification affordances associated with self-health management behavior, reinforcing the SEM results.

Results indicate that competition, visibility of achievement and interactivity can enhance achievement satisfaction, which further boosts self-health management behavior. However, competition and interactivity can also cause gamification exhaustion, which undermines self-health management behavior to some extent. Overall, the positive impacts of the three affordances outweigh the negative impacts.

This study provides new insights for relevant practitioners on designing gamification affordances, aiding the sustainable development of mobile fitness apps and their long-term effects on self-health management. Visibility of achievement should be emphasized, and competition and interactivity should be thoughtfully designed to minimize their negative effects.

This study extends the affordances–psychological outcomes–behavioral outcomes framework and the literature on gamification and health management by applying both SEM and fsQCA methodologies to examine the relationship between specific gamification affordances and self-health management behavior.

As project managers are in the central position of sustainable project management (SPM), their competencies become an important factor that affects the outcome of SPM. However, literature lacks a clear description of the project manager competence system required by SPM. The purpose of this study is to explore what competencies are required by sustainable project management and analyze the hierarchy and attributes of these competencies.

Aiming to address the problem, several methods were applied in this study. First, with a literature review, semi-structured interviews and Delphi technology, 23 project manager competencies required by SPM are identified. Second, the fuzzy interpretive structural modeling–matrix impact cross-reference multiplication applied to classification (FISM–MICMAC) method is used to analyze the data from 21 experienced project managers in the construction industry, revealing the hierarchy and attributes of the project manager competency system required by SPM.

The results indicate that the project manager competency system required by SPM includes nine micro levels. According to the nature of the competencies, these nine levels can be summarized into five macro levels. Furthermore, all competencies can be divided into three categories: independent, autonomous and dependent.

This study not only provides project managers and scholars with a further understanding of project manager competencies but also helps contractors make informed and objective judgments in the selection and/or appointments of project managers who have the appropriate competencies for SPM.

The study aims to review how the use of technology enhances the authentic tourist experience. Technology and digitalization have enhanced tourist experiences. However, critiques comment on its ability to over-commercialize activity and lack of authenticity. Thus, there is a need to synthesize knowledge of technology usage to increase authentic tourist experience.

The study carries out a bibliometric review of the studies focusing on the use of technology in enhancing tourist experiences. Two hundred journal articles, published between 1997 and 2023 were retrieved from the Web of Science (WoS) database to carry out descriptive and network analysis using the Gephi, VOSviewer and Science of Science (Sci2) software. The components of authentic tourism experience are identified from the literature through a content analysis.

The findings of the study are broadly classified into two: first, the most frequently used keywords in the study include tourist experience and satisfaction, co-creation, virtual reality, smart tourism, technology, authenticity and heritage tourism. Second, the five major themes studied in the topic include virtual reality and tourist experience; media, tourist experience and encounters; technology, smart tourism and tourist experience; digital transformation, social media and tourist experience; and virtual reality and tourist experience which are still relevant in the literature because of the presence of study gaps.

The findings are used to develop a conceptual framework for the role of technology in enhancing authenticity in tourism typologies where authenticity is critical.

This study aims to seek and identify key enablers influencing the integration of deconstruction and carbon finance to improve sustainable and resilient construction in the Industry 5.0 era and then examines how these enablers relate to one another and rank, classify and prioritize them appropriately.

This study adopts the Integrated Decision-Making Trial and Evaluation Laboratory-Interpretive Structural Modeling-Matrice d’impacts Croises Multiplication Appliquee a un Classment (DEMATEL-ISM-MICMAC) methodology.

The key enablers are the circular economy approach with closed-loop material flow, prioritizing material recovery and reuse, and structuring carbon credits for affordability.

The construction industry can advance toward environmentally friendly, sustainable building techniques and help achieve the larger objectives of carbon neutrality and environmental preservation by adopting the study’s guiding principles.

This study implies switching to circular rather than linear economic methods. It is still unrealistic to achieve zero carbon emissions, which is why carbon finance is important because it allows companies to trade carbon credits and encourages negative carbon activities and to structure and trade carbon credits affordably so that businesses of all sizes can participate. To extend the lives of current materials and reduce waste, it is imperative to maximize their reuse and minimize their disposal.

The novelty of this research lies in introducing carbon trade finance concepts to the construction sector, particularly focusing on the deconstruction phase, to enhance sustainability and resilience in construction practices in the Industry 5.0 era.

This study aims to provide new insights into aerodynamic drag reduction for increasingly faster blunt-nosed trains, such as urban and freight trains. Specifically, this work investigates two distinctly different wake structures and associated aerodynamic drag of blunt-nosed trains.

Three typical cases of blunt-nosed trains with 1-, 2- and 3-m nose lengths are selected. The time-averaged and unsteady flow structures around the trains are analyzed using the improved delayed detached eddy simulation model and proper orthogonal decomposition method.

The simulation results indicate that for 2- and 3-m nose lengths, the flow separates at first and then reattaches to the slanted surface of the tail, with a pair of longitudinal vortices dominating the wake. In contrast, for the 1-m nose length case, the wake structure is characterized by complete separation, attributed to the larger curvature of the slanted tail surface. Consequently, the total time-averaged drag coefficient is reduced by 27.2% and 19.2% for the 1-m nose length case compared to the 2- and 3-m cases, respectively. Moreover, the predominant unsteady structures with Strouhal numbers St = 0.30 and St = 0.28 are detected in the near-wake of the 2- and 3-m nose length cases, respectively. These structures result from periodic vortex shedding at the lower slanted tail surface. In contrast, for the 1-m nose length case, the predominant unsteady structure with St = 0.19 is induced by the nearly periodic expansion and contraction of the upper bubbles.

Two distinctly different wake structures in blunt-nosed trains are identified. Unlike high-speed trains with longer, streamlined noses, for blunt-nosed trains, shorter nose lengths result in lower aerodynamic drag. Insights for reducing energy consumption in blunt-nosed trains are provided.

The purpose of this study is to explore the motivational role of feedback information (positive and negative) provided by the firm in the face of participant heterogeneity, in terms of past success experience, under the research setting of crowdsourcing contests.

Taking insights from feedback studies and the dynamics of self-regulation theory, four theoretical hypotheses are proposed. An integrated dataset of 4,880 contest-participant pairs, which is obtained from an online contest platform and a survey, is empirically analyzed.

Empirical analysis shows that both positive feedback and negative feedback are able to stimulate the inner needs of participants. Notably, negative (positive) feedback becomes more (less) effective in intrinsically motivating crowds as they gain more successful experience during contest participation.

This study brings some new knowledge for the intrinsic motivation of crowds by exploring its antecedents, which have been undervalued in extant literature. The motivational role of feedback information is particularly explored.

The potential of generative AI (GenAI) to stimulate employee creativity has received extensive attention from industry and academia. However, there is still limited research on strategically using GenAI to leverage its positive effects on employee creativity. This study aims to clarify the effects of different GenAI use purposes on employee creativity.

Based on self-determination theory, this study explores the effects of work-related and nonwork-related GenAI use on incremental and radical creativity through the mediator role of exploratory and exploitative learning and the boundary role of perceived ease of use. This study constructs a theoretical model and uses structural equation modeling to test the model by analyzing survey data from 330 employees.

(1) Work-related and nonwork-related GenAI use positively impacts incremental and radical creativity through exploratory and exploitative learning; (2) work-related GenAI use contributes more to exploitative learning than to exploratory learning, while nonwork-related GenAI use contributes more to exploratory learning than to exploitative learning; (3) exploitative learning has a stronger positive impact on incremental creativity, and exploratory learning has a stronger positive impact on radical creativity; (4) perceived ease of use weakens the positive effects of nonwork-related GenAI use on exploratory and exploitative learning.

First, this study enriches employee creativity research by revealing the relationship between different GenAI use purposes and incremental and radical creativity. Second, this study enriches employee creativity research by revealing the mediating role of exploratory and exploitative learning between GenAI use and incremental and radical creativity. Finally, this study enriches GenAI use research by revealing the moderating role of perceived ease of use between GenAI use and employee learning.

This paper aims to explore how psychological perceptions and family situations drive women into necessity- or opportunity-based female entrepreneurship (NBFE or OBFE) and the moderating role of gender equality.

This study adopts multilevel logistic regression analysis to examine relationships based on a sample of 6,843 women across eight developing countries drawn from the Global Entrepreneurship Monitor (GEM).

The findings suggest that capability and opportunity perceptions positively affect NBFE and OBFE. Family responsibility burden positively affects NBFE and has a U-shaped relationship with OBFE. Household income negatively affects NBFE but positively affects OBFE. Gender equality weakens the U-shaped relationship between family responsibility burden and OBFE but strengthens the positive relationship between capability perception and NBFE and between opportunity perception and NBFE.

The study highlights the need for targeted policies and support that consider the distinct antecedents and mechanisms of NBFE and OBFE, as well as the importance of promoting gender equality and entrepreneurial education to empower women in their entrepreneurial endeavors. A limitation of this study is the reliance on older data from the GEM, which may not fully capture the current dynamics of developing societies. While the study provides valuable insights, future research should incorporate more recent data to enhance the applicability of the results.

This study deepens the understanding of antecedents of NBFE and OBFE, breaking through the existing literature that neglects the heterogeneity of female entrepreneurship (FE).

This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

Accurate predictions of the steady-state corrosion phase and service life to achieve specific safety limits are crucial for assessing the service of reinforced concrete (RC) structures. Forecasting the service life (SL) of structures is imperative for devising maintenance and repair strategy plans. The optimization of maintenance strategies serves to prolong asset life, mitigate asset failures, minimize repair costs and enhance health and safety standards for society.

The well-known empirical conventional (traditional) approaches and machine learning (ML)-based SL prediction models were presented and compared. A comprehensive parametric study was conducted on existing models, considering real-world conditions as reported in the literature. The analysis of traditional and ML models underscored their respective limitations.

Empirical models have been developed by considering simplified assumptions and relying on factors such as corrosion rate, steel reinforcement diameter and concrete cover depth, utilizing fundamental mathematical formulas. The growth of ML in the structural domain has been identified and highlighted. The ML can capture complex relationships between input and output variables. The performance of ML in corrosion and service life evaluation has been satisfactory. The limitations of ML techniques are discussed, and its open challenges are identified, along with insights into the future direction to develop more accurate and reliable models.

To enhance the traditional modeling of service life, key areas for future research have been highlighted. These include addressing the heterogeneous properties of concrete, the permeability of concrete and incorporating the interaction between temperature and bond-slip effect, which has been overlooked in existing models. Though the performance of the ML model in service life assessment is satisfactory, models overlooked some parameters, such as the material characterization and chemical composition of individual parameters, which play a significant role. As a recommendation, further research should take these factors into account as input parameters and strive to develop models with superior predictive capabilities.

Recent deployment has revealed that ML algorithms can grasp complex relationships among key factors impacting deterioration and offer precise evaluations of remaining SL without relying on traditional models. Incorporation of more comprehensive and diverse data sources toward potential future directions in the RC structural domain can provide valuable insights to decision-makers, guiding their efforts toward the creation of even more resilient, reliable, cost-efficient and eco-friendly RC structures.