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Value co-creation is a new initiative for enterprises to form a competitive advantage, and user engagement is the basis for achieving value co-creation; nevertheless, few studies have discussed the influence mechanisms of user engagement on value co-creation behavior. In this study, the authors aim to reveal the influence mechanisms of online fitness user engagement on value co-creation behavior by considering emotional resonance and immersive experience as mediating variables.

The authors proposed and empirically tested a research model based on a survey involving 461 Chinese respondents through partial least squares structural equation modeling (PLS-SEM).

The results of this study confirm that consumer engagement, contributing engagement and social engagement are important drives of value co-creation behavior among online fitness users. Furthermore, emotional resonance and immersive experience have been revealed as important mediating mechanisms to explain why user engagement drives value co-creation behavior.

The results of this study suggest that practitioners need to focus on the social engagement and consumer engagement of users in online fitness communities and to provide the appropriate environment and conditions for online fitness user to achieve mutual value co-creation.

This study makes two main contributions. It examines user engagement in an online fitness community context and helps to understand its applicability in other contexts. It explains the influence mechanisms of online fitness user engagement on value co-creation behavior and enriches the studies related to the drivers of value co-creation behavior.

This study aims to modify the human factors analysis and classification system (HFACS) to make it suitable for collapse accident analysis in construction. Based upon the modified HFACS, distribution patterns of causal factors across multiple levels were discerned among causal factors of various stakeholders at construction sites. It explored the correlations between two causal factors from different levels and further determined causation paths from two perspectives of level and stakeholder.

The main research framework consisted of data collection, coding and analysis. Collapse accident reports were collected with adequate causation information. The modified HFACS was utilized for coding causal factors across all five levels in each case. A hybrid approach with two perspectives of level and stakeholder was proposed for frequency analysis, correlation analysis and path identification between causal factors.

Eight causal factors from external organizations at the fifth level were added to the original HFACS. Level-based correlation analyses and path identification provided safety managers with a holistic view of inter-connected causal factors across five levels. Stakeholder-based correlation analyses between causal factors from the fifth level and its non-adjacent levels were implemented based on client, government and third parties. These identified paths were useful for different stakeholders to develop specific safety plans for avoiding construction collapse accidents.

This paper fulfils an identified need to modify and utilize the HFACS model for correlation analysis and path identification of causal factors resulting in collapse accidents, which can provide opportunities for tailoring preventive and protective measures at construction sites.

This study aims to enhance the assessment of green governance in energy projects along the Belt and Road, reduce the influence of fuzzy judgment, and construct a grey network analysis model from the perspective of Environmental, Social, and Governance (ESG).

The ESG concept is used to establish an evaluation indicator system. The Analytic Network Process (ANP) and the Grey System Theory are applied sequentially to determine the green governance grade of energy projects, exemplified by an evaluation of five projects.

The Karot hydropower project has the best green governance status among the five projects and is of excellent grade. This is followed by the Hongfeng photovoltaic project, the De Aar wind power project, and the Yamal liquefied natural gas project, which are of good grade. The Lamu coal power station project has the worst green governance and is at a medium level.

This study can assist Belt and Road energy projects in identifying their deficiencies and promoting sustainable development by providing a robust framework for green governance evaluation.

The indicator system developed in this study includes social and project governance aspects in addition to environmental performance, reflecting the comprehensive green governance status of projects. The combined use of ANP and grey system theory fully considers the mutual influence relationship between indicators and improves the objectivity of green governance grade judgment.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

Three-way decision (3WD) and probabilistic rough sets (PRSs) are theoretical tools capable of simulating humans' multi-level and multi-perspective thinking modes in the field of decision-making. They are proposed to assist decision-makers in better managing incomplete or imprecise information under conditions of uncertainty or fuzziness. However, it is easy to cause decision losses and the personal thresholds of decision-makers cannot be taken into account. To solve this problem, this paper combines picture fuzzy (PF) multi-granularity (MG) with 3WD and establishes the notion of PF MG 3WD.

An effective incomplete model based on PF MG 3WD is designed in this paper. First, the form of PF MG incomplete information systems (IISs) is established to reasonably record the uncertain information. On this basis, the PF conditional probability is established by using PF similarity relations, and the concept of adjustable PF MG PRSs is proposed by using the PF conditional probability to fuse data. Then, a comprehensive PF multi-attribute group decision-making (MAGDM) scheme is formed by the adjustable PF MG PRSs and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, an actual breast cancer data set is used to reveal the validity of the constructed method.

The experimental results confirm the effectiveness of PF MG 3WD in predicting breast cancer. Compared with existing models, PF MG 3WD has better robustness and generalization performance. This is mainly due to the incomplete PF MG 3WD proposed in this paper, which effectively reduces the influence of unreasonable outliers and threshold settings.

The model employs the VIKOR method for optimal granularity selections, which takes into account both group utility maximization and individual regret minimization, while incorporating decision-makers' subjective preferences as well. This ensures that the experiment maintains higher exclusion stability and reliability, enhancing the robustness of the decision results.

This study aims to investigate a chromium-free sealing treatment process to replace the chromate sealing process in response to the environmental hazards caused by chromate in the Phosphate chemical conversion (PCC) coating post-treatment sealing process.

In this paper, chromium-free sealing technology was used to post-treat PCC coatings. Scanning electron microscopy was used to investigate the structure of the surface of the PCC coatings after the sealing treatment, and the corrosion resistance, hydrophobicity and bonding were tested using an electrochemical workstation, a copper sulfate spot-drop test, a lacquer bonding test, a contact angle meter and a neutral salt spray test.

Chromium-free closure makes the grain distribution on the surface of the PCC coating more uniform and dense, and forms an organic film on the surface of the coating, which significantly improves the corrosion resistance and hydrophobicity of the PCC coating, does not affect the coating film bonding force and has similar performance with potassium dichromate solution.

The results show that the corrosion resistance of PCC coatings after chromium-free sealing treatment is improved, and chromium-free sealing has the potential to replace chromium sealing.

The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing.

This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics.

The experiment results show that the proposed method outperforms the baseline methods.

This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

This study aims to investigate the relationship between geopolitical risk (GPR) and gold price bubbles to determine whether rising GPR can drive deviations in the fundamental value of gold, thus leading to speculative bubbles.

Using a data set that spans from January 2002 to December 2023 and covers both GPR data and gold price data, this study applies the log-periodic power law singularity (LPPLS) model to identify gold price bubbles. The analysis explores the effects of GPR and its sub-indices – geopolitical risk–acts (GPRA) and geopolitical risk–threats (GPRT) – on gold price bubbles. The causal relationships are examined through logistic regression, Tobit modelling and machine learning, with a focus on different countries, including major gold producers and consumers.

The results indicate a significant relationship between GPR and gold price bubbles, particularly with GPRA, which exerts a stronger influence than GPRT does. Peaks in GPR often align with the formation of gold price bubbles, both positive and negative. Additionally, geopolitical instability in Russia has a significant effect on US gold price bubbles.

The findings provide valuable insights for investors and policymakers by emphasizing the importance of GPR in shaping gold price dynamics. Investors are advised to consider the nuanced roles of GPRA and GPRT when using gold as a hedge during periods of heightened geopolitical tension.

Understanding the role of GPR in gold price bubbles can help mitigate the financial risk associated with speculative bubbles, thereby offering a better framework for managing assets during geopolitical crises.

This study extends existing research by directly linking GPR with gold price bubbles via the LPPLS model, with a novel emphasis on the differentiation between GPRA and GPRT, providing new perspectives on the safe-haven role of gold during geopolitical uncertainty.

Morality in the workplace has become a significant determinant of organizational effectiveness and employee well-being. This research aims to conduct an in-depth review of the past literature on multidimensional morality and provide insight into its impact on the modern workplace and employee well-being.

The literature search resulted in 3,589 papers published between 2012 and 2024. This paper analyzed 30 research studies on workplace morality based on predetermined inclusion and exclusion criteria to ensure methodological rigor.

Research findings show the need to promote workplace ethics to avoid counterproductive workplace behaviors. Also, effective leadership adheres to ethical principles, together with the ethical framework of an organization, which includes codes of ethics and a commitment to corporate social responsibility, contributing to the overall well-being of individuals by providing a sense of support and resolving conflicts. Ethical conflicts are associated with decreased well-being and increased turnover, underscoring the need for organizational solutions.

Understanding intricate morality is essential for organizations to foster moral cultures and improve performance. HR specialists can use this knowledge to create policies that uphold moral principles and improve employee job satisfaction.

This study extends the current body of knowledge by synthesizing research on multifaceted moral issues in modern workplaces. It provides useful insights for researchers, practitioners, and policymakers who are interested in promoting ethical organizational cultures by addressing ethical shortcomings.

The purpose of the present research is to address the issue by conceptualizing artificial intelligence (AI) experience quality and its dimensions, and furthermore, to empirically test the relationships among AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention.

The data were collected from an AI community canteen in Shanghai. They were also analyzed using exploratory and confirmatory factor analyses (EFA and CFA) and structural equation modeling (SEM).

Four primary dimensions and 15 sub-dimensions of AI experience quality for community canteens were identified. The hypothesized paths between the higher-order constructs – AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention – were confirmed as well.

This is the first study to synthesize AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention in an AI restaurant setting.