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Human-artificial intelligence (AI) collaboration, as a new form of cooperative interaction, has been applied in brainstorming activities. This study aims to explore the impact of performance-reward expectancy (PRE) and creative motivation (CM), along with the search for ideas in associative memory (SIAM) theory, on participants' AI collaboration intent (AICI).

The research employs an online survey targeting users with brainstorming experience. Structural equation modeling (SEM) is applied to analyze the data and validate the proposed hypotheses.

PRE shows a positive correlation with both intrinsic motivation (IM) and extrinsic motivation (EM). Furthermore, EM significantly and positively influences AICI, while IM has a negative significant effect. Additionally, the study confirms the mediating role of social inhibition (SI) between EM and AICI.

This study examines the intent to collaborate with AI in brainstorming, filling a gap in existing research. It integrates SIAM theory to analyze how performance rewards and creative motivation influence this intent. Findings reveal that performance-based rewards effectively motivate creative engagement, but high intrinsic motivation may lead to lower intent to collaborate due to autonomy concerns and trust issues. The study emphasizes the need for an open environment and offers practical insights for fostering AI collaboration while addressing challenges like social inhibition and resistance among participants.

This study provides practical insights for creative teams and individuals, emphasizing the importance of integrating AI in brainstorming to unlock its full potential. While performance rewards are effective, social inhibition may still lead participants to have negative attitudes toward AI collaboration. Creating an open and inclusive environment is essential. Additionally, the “individual + AI” model may provoke resistance among highly intrinsically motivated participants, necessitating training and improved AI transparency to build trust. Although focused on the Chinese market, the findings are applicable globally, highlighting the need to explore effective AI integration methods for innovation.

Our study found that PRE can positively influence intrinsic and extrinsic motivation in creative activities. This finding provides new evidence for our understanding of the role of performance-reward mechanisms in stimulating creativity. At the same time, we also explored how factors such as social inhibition and production blocking can affect individuals’ willingness to work with AI by influencing creativity motivation. This provides new insights to better understand how AI in teams affects individual psychology and team dynamics. These findings not only enrich our understanding of innovation and teamwork but also provide valuable references and directions for future research.

This study systematically examines the influence of PRE on CM within the context of AI-assisted brainstorming for the first time. It further investigates how SIAM theory regulates this process and ultimately shapes participants' willingness to engage in AI collaboration. The findings offer theoretical and practical guidance on designing incentive mechanisms to enhance engagement in AI-supported brainstorming and provide new perspectives on the application of AI in team innovation activities.

The landscape of health information acquisition has shifted from offline to online, and online question-and-answer (Q&A) communities have emerged as prominent sources of health information; however, it is unclear how users identify satisfactory health information. This paper identifies factors that influence users’ adoption of health information in the context of online Q&A communities.

Based on the elaboration likelihood model (ELM) and opinion leader theory, we construct a research model to examine how information quality (complexity, image structure and emotional change) and source credibility (authentication status, follower number) affect health information adoption behavior. We verify the hypotheses by Poisson regression and zero-inflation Poisson regression using the data collected from an online Q&A community.

The empirical results indicate that both information quality and source credibility positively affect users’ adoption of health information.

This research can assist designers and managers of online Q&A communities to better comprehend users’ health information needs and their preferences for adoption. This enhanced understanding can facilitate the provision of superior online health information.

The chapter introduced stealth construction as a transformative concept emerging in the construction industry, emphasising resilience and efficiency through innovative technologies, cutting-edge materials, and advanced methodologies. Drawing parallels to stealth aircraft, stealth construction aims to create buildings and infrastructure that blend seamlessly into their environments, minimising environmental impact and enhancing sustainability. This approach integrates smart materials, strategic planning, and covert practices throughout all construction stages, ensuring robust, adaptive, and discreet structures. Stealth construction represents a significant paradigm shift by prioritising energy efficiency, environmental responsibility, and resilience against natural disasters and cyber threats. It combines modern aerospace engineering principles with diverse construction practices to achieve construction invincibility, setting new standards for the industry regarding sustainability, security, and aesthetics.

This study aims to optimize the preparation conditions and modify the nanofiltration (NF) membranes to prepare high-performance polysulfone/sulfonated polysulfone composite nanofiltration (PSF/SPSF-NF) membranes through interfacial polymerization.

Investigating the impacts of anhydrous piperazine (PIP) concentration, trimesoyl chloride (TMC) concentration and basement membrane type on NF membrane performance, the optimal membrane was prepared. In addition, nano-SiO₂ was added to the active separation layer to modify the NF membranes.

The comprehensive performance of PSF/SPSF-NF membranes was optimized when the concentration of PIP was 0.75 Wt.% and the concentration of TMC was 0.15 Wt.%, at which time the water flux was 66.1 L·m⁻²·h⁻¹ and the retention rate of Na₂SO₄ was 98.1%. The comprehensive performance of polysulfone/sulfonated polysulfone-SiO₂ nanofiltration (PSF/SPSF-SiO₂-NF) membranes was optimized when the blending ratio of nano-SiO₂ to PIP was 2:3, with a pure water flux of 81.9 L·m⁻²·h⁻¹ and a Na₂SO₄ retention rate of 95.9%. Compared to polysulfone nanofiltration (PSF-NF) membranes and PSF/SPSF-NF membranes, NF membranes with nano-SiO₂ increased the flux recovery rate by 22.9% and 8.7%.

PSF/SPSF-SiO2-NF membrane exhibits excellent antifouling properties.

There is currently no literature available on the preparation of NF membranes using polysulfone/sulfonated polysulfone (PSF/SPFS) as a substrate. This provides a method for modifying NF membranes, starting with the modification of the basement membrane and then modifying the active separation layer.

A live-stream failure occurs when the product which is highly recommended by the influencers and exhibited quality problems. This study investigated how brand trust and influencers trust affect live-streaming purchase intentions (PIs) of consumers when live-streaming e-commerce failures occur and the role of the Stealing Thunder (ST).

Totally 584 adults with live-streaming shopping experience were invited to investigate the impact and mechanisms of live-streaming failure incidents, as well as the moderating mechanisms of ST.

Firstly, live-streaming failure accidents decreased the PI of consumers by reducing both brand trust and influencer trust, with trust in the influencers having a stronger impact on PI than brand trust. Secondly, if the influencers used the ST after a live-stream failure, the PI was the same as that in a non-failure scenario. Thirdly, the ST acts as a moderating variable neutralized the negative impact of live-streaming failure on trust in the influencers. Lastly, the negative impact of brand trust on PI was less significant using the ST.

This research deepens the understanding of service failures in live-streaming e-commerce. It provides insights into the consumer behavior and practical guidance on how influencers can actively respond to live-streaming failure to sustain reciprocal relationships in live-streaming e-commerce. The study addresses the effects of the coping strategies of influencers on the interests of product brands and discusses the potential solutions.

This study aims to explore the determinants of green electronics purchase intention in Malaysia by extending existing knowledge on green consumer behavior and contributing to the field of sustainable consumption.

A quantitative research approach was adopted, with data collected from 250 Malaysian consumers. The proposed model was tested using partial least squares structural equation modeling to assess the relationships between various determinants and green purchase intention.

The results demonstrate that perceived consumer effectiveness, green advertising and monetary cost positively affect environmental attitudes, which subsequently influence green purchase intention. The study also identifies that brand image and information quality significantly enhance green brand trust (GBT), leading to stronger intentions to engage in green purchasing. Additionally, it finds that environmental knowledge and environmental concern shape perceived behavioral control, which further impacts green purchasing intention.

The study focuses on Malaysian consumers, which may limit the generalizability of the findings to other cultural contexts. Future research could expand the scope to include cross-cultural comparisons to validate the model in different settings.

By providing insights into the key factors driving consumers’ intention to purchase green electronics, the study offers valuable guidance for marketers and manufacturers to develop targeted strategies that promote sustainable consumption and capitalize on the growing demand for green products in Malaysia.

This study is unique in measuring the influence of green attitude, GBT and perceived behavioral control on green purchase intention specifically within the electronics sector, offering a novel contribution to the literature on sustainable consumer behavior.

Health technologies are an issue that directly affects the sustainability and quality of health services. Due to budget constraints, it is not financially possible for businesses to apply comprehensive improvement strategies to all these criteria. In this case, it is possible for businesses to implement more priority strategies. Accordingly, the main purpose of this study is to evaluate the important performance indicators of health technology investments.

Firstly, with the help of the artificial intelligence system, a decision matrix is established. Secondly, spherical fuzzy total order of preference decision-making trial and evaluation laboratory methodology is taken into consideration for weighting the criteria. Thirdly, emerging seven countries are ranked by using spherical fuzzy MultiAtributive Ideal-Real Comparative Analysis (MAIRCA).

The findings demonstrate that the criteria of health policies and research and development are defined as the most significant factor in this regard. China and Turkey are also found to be the most successful emerging countries with respect to the performance of health technology investments.

The main contribution of this study is that a novel decision-making model is generated by integrating artificial methodology into the spherical fuzzy sets.

High-speed turnouts are more complex in structure and thus may cause abnormal vibration of high-speed train car body, affecting driving safety and passenger riding experience. Therefore, it is necessary to analyze the data characteristics of continuous hunting of high-speed trains passing through turnouts and propose a diagnostic method for engineering applications.

First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. Then, the Short-Time Fourier Transform (STFT) is performed to calculate the marginal spectra. Finally, the presence of a continuous hunting problem is determined based on the results of the comparison calculations and diagnostic thresholds. To improve computational efficiency, permutation entropy (PE) is used as a fast indicator to identify turnouts with potential problems.

Under continuous hunting conditions, the PE is less than 0.90; the ratio of the maximum peak value of the signal component to the original signal peak value exceeded 0.7, and there is an energy band in the STFT time-frequency map, which corresponds to a frequency distribution range of 1–2 Hz.

The research results have revealed the lateral vibration characteristics of the high-speed train’s car body during continuous hunting when passing through turnouts. On this basis, an effective diagnostic method has been proposed. With a focus on practical engineering applications, a rapid screening index for identifying potential issues has been proposed, significantly enhancing the efficiency of diagnostic processes.

This paper examines the effect of managerial interlocking networks (MINs) on firm risk spillover by using a sample of Chinese A-share listed firms.

Applying the complex network approach, we build managerial interlocking networks (MINs) and leverage degree centrality to quantify a manager’s network position. To gauge firm risk spillover, we utilize the conditional autoregressive value at risk (CAViaR) model to compute the value-at-risk. Subsequently, we employ ordinary least squares to investigate the influence of MINs on firm risk spillover.

Our research uncovers a direct correlation between a firm risk spillover and the status of network positions within managerial interlocking networks; namely, the more central the position, the greater the risk spillover. This increase is believed to be due to central firms in MINs having greater connectedness and influence. This fosters a similarity in decision-making across different firms through interfirm managerial communication, thus amplifying the risk spillover. Economic policy uncertainty (EPU) and Guanxi culture furtherly intensify the effects of MINs. Additional analysis reveals that the impact of MINs on the firm risk spillover is significantly noticeable in non-state-owned enterprises, while good corporate governance diminishes the risk spillover prompted by MINs.

Our findings offer fresh insights into the interfirm risk outcome associated with MINs and extend practical guidelines for attenuating firm risk spillover with a view toward mitigating systemic risk.

This study aims to explore the impact mechanism of social support on individual health knowledge creation among users in online patient communities, guide and promote the creation of health knowledge and provide insights into managing online patient communities.

A theoretical model was constructed by integrating social impact and social support theories. Data were collected through questionnaires, and 750 valid responses were analysed using a structural equation model.

This study found the following. (1) Social support influences individual health knowledge creation through the mediating effects of creative self-efficacy and positive emotions. (2) The general rule of the strength of the influencing factors on individual health knowledge creation is that creative self-efficacy > positive emotions. (3) The general pattern of the mediating effect of attitude factors between social support and health knowledge creation is that creative self-efficacy > positive emotions. (4) The key path for social support to influence individual health knowledge creation is “social support → creative self-efficacy → health knowledge creation”. (5) The dimensions of social support in online patient communities can be divided into information, emotional, respect and network support. Individual health knowledge creation can be divided into health knowledge externalisation, combination, socialisation and internalisation.

This study expands the application scope of social influence theory and opens up the “black box” of the impact of social support on individual health knowledge creation behaviour. Simultaneously, the dimensions of social support, individual health knowledge creation and the mediating role between social support and health knowledge creation are discussed.