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The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their digital works. Despite this, no studies have examined the drivers of continuous content contribution behavior (CCCB) toward NFTs. Hence, this study draws on the theory of relational bonds to examine how various relational bonds affect feelings of psychological ownership, which, in turn, affects CCCB on metaverse platforms.

Using structural equation modeling and importance-performance matrix analysis, an online survey of 434 content creators from prominent NFT platforms empirically validated the research hypotheses.

Financial, structural, and social bonds positively affect psychological ownership, which in turn encourages CCCBs. The results of the importance-performance matrix analysis reveal that male content creators prioritized virtual reputation and social enhancement, whereas female content creators prioritized personalization and monetary gains.

We examine Web 3.0 and the NFT creators’ network that characterizes the governance practices of the metaverse. Consequently, the findings facilitate a better understanding of creator economy and meta-verse commerce.

The proliferation of digital communication on social media provides new opportunities for businesses to take advantage of Internet memes to boost customer engagement. Academic literature on digital communications mostly focuses on popular forms such as selfies, branded posts, and branded emoticons. Less attention has been paid to brand memes and their implications for brand management. Based on the cue utilization theory, this research aims to investigate the informational cues of brand memes foster brand partnerships.

The structural equation modeling and importance-performance matrix analysis were used to empirically validate the research hypotheses with 595 respondents to an online survey.

Three informational cues of brand memes (i.e. comprehensibility, novelty, and meme-brand congruity) stimulated consumers' attitudes, which in turn impacted consumer-brand relationships. Another brand meme informational cue, sarcasm, negatively moderated the relationships between the three informational cues and consumer-brand relationships.

Our findings indicate that a brand can engage consumers in conversations on social media and foster long-term consumer-brand relationships through brand memes.

The purpose of this study is to improve the thermal management of lithium-ion batteries. The phase change material (PCM) cooling does not require additional equipment to consume energy. To improve the heat dissipation capacity of batteries, fins are added in the PCM to enhance the heat transfer process.

Computational fluid dynamics method is used to study the influence of number of vertical fins and ring fins (i.e. 2, 4, 6 and 8 vertical fins, and 2, 3, 4 and 5 ring fins) and the combination of them on the cooling performance.

The battery maximum temperature can be decreased by the PCM with vertical or ring fins, and it can be further decreased by the combination of them. The PCM with eight vertical fins and five ring fins reduces the battery maximum temperature by 5.21 K. In addition, the temperature and liquid-phase distributions of the battery and PCM are affected by the design of the cooling system.

This work can provide guidelines for the development of new and efficient PCM cooling systems for lithium-ion batteries.

The combination of PCM and fins can be used to reduce the battery maximum temperature and temperature difference.

This study aims to examine how governing bodies demonstrated stakeholder engagement during the time of the COVID-19 crisis in South Africa.

This study uses a qualitative approach based on semi-structured interviews with 18 participants, comprising of preparers of financial statements, board members and management consultants/advisors. The study also relied on the analysis of articles on corporate webpages and publications produced by professional bodies on the economic, social and environmental impact of COVID-19.

The results of this study indicated that governing bodies demonstrated stakeholder engagement during times of crisis through transparent reporting, corporate social responsibility initiatives and active stakeholder inclusivity.

This study contributes to the body of research on stakeholder engagement during a crisis and provides evidence of the role stakeholder inclusivity can play in responding to a crisis. The findings will be useful in understanding the importance of stakeholder engagement during times of crisis. The study is one of the first, to the best of the authors’ knowledge, to evaluate how stakeholder engagement principles can be followed by governing bodies during a crisis.

The purpose of this paper is to analyze the impact of different information source on consumer attitudes toward genetically modified food.

The data used in this study are obtained from a large-scale nationwide consumers' survey in urban China conducted by the China Center for Agricultural Policy, Peking University, in 2020. A descriptive analysis between information sources and consumer attitudes toward GM food was conducted. Based on the collected data, an econometric model on the determinants of consumer attitudes was constructed and used for analysis.

This study shows that the impact of new media is currently no different than that of traditional media, indicating that the media campaign successfully reduced the spread of rumors and misinformation regarding GM food. Moreover, this study also shows that consumers whose main information source regarding GM foods is school hold more positive feelings toward such food.

This study contributes to the existing literature by examining the impact of information source on consumer attitudes toward GM food. To reverse consumers' negative attitudes, China has launched a widespread media campaign since the first decade of the 2000s. Results of this study show that authorities' efforts to manage and surveil new media have yielded the desired outcome.

In tourism live streaming (TLS), streamers strive to capture viewers’ attention by responding quickly to viewers’ requests and providing tourism-related knowledge. However, the effectiveness of such practices in the TLS context remains unclear. Accordingly, based on flow theory, this study aims to uncover the effects of responsiveness and knowledge spillover on viewers’ travelling intentions.

The authors collected 319 valid questionnaires to examine the proposed model. Followingly, the authors used a partial least squares structural equation modelling approach using SmartPLS 4 to analyse the survey data.

The authors found that responsiveness could enhance viewers’ flow experience and destination attachment, fostering travelling intentions. The authors further found that knowledge spillover strengthened the relationship between responsiveness and travelling intentions and responsiveness and flow experience.

This study broadens the scope of extant tourism research by juxtaposing the effects of responsiveness and knowledge spillover on viewers’ travelling intentions in the TLS context. Practically, the findings provide valuable insights for streamers to conduct appropriate viewer–streamer interaction strategies by providing instant responses and tourism-related knowledge to viewers.

在旅游直播中, 主播常常通过快速响应观众并提供目的地知识, 以吸引观众注意。然而这种策略是否有效地提升了观众的旅游意愿仍需进一步验证。因此, 本研究基于心流理论验证了响应性及知识溢出效应对观众旅游意愿的影响。

我们通过评估319份有效问卷来检验所提出的模型, 采用了SmartPLS软件构建偏最小二乘结构方程模型（PLS-SEM）分析问卷数据。

我们发现, 响应性将增强观众的心流体验和目的地依恋感, 从而促进旅游意愿。此外, 知识溢出效应强化了响应性和旅游意愿及响应性和心流体验之间的关系。

本研究同时关注响应性及知识溢出在旅游直播情境下对观众旅游意愿的影响机制。从实践层面, 本研究为旅游主播提供了高效互动及目的地推广的策略。

En las retransmisiones turísticas en directo (TLS), los organizadores se esfuerzan por captar la atención de los espectadores respondiendo de forma rápida a sus peticiones y aportando conocimientos relacionados con el turismo. Sin embargo, la eficacia de estas prácticas en el contexto de la retransmisión turística en directo sigue sin estar clara. Por consiguiente, este estudio, basado en la teoría del flujo, trata de descubrir los efectos de la capacidad de respuesta y la difusión de conocimientos en la intención de viajar de los espectadores.

Se recogieron 319 cuestionarios válidos para examinar el modelo propuesto. Seguidamente, se aplicó la técnica de ecuaciones estructurales con mínimos cuadrados parciales (PLS-SEM) mediante el software SmartPLS para analizar los datos de la encuesta.

Se concluye que la capacidad de respuesta mejoraría la experiencia de flujo de los espectadores y el apego al destino, fomentando su intención de viajar. Además, se comprueba que la difusión de conocimientos fortalece la relación entre (1) la capacidad de respuesta y la intención de viajar y (2) la capacidad de respuesta y la experiencia de flujo.

La presente investigación amplía el enfoque de los estudios existentes en la investigación turística al aproximar los efectos de la capacidad de respuesta y la difusión de conocimientos sobre la intención de viajar de los espectadores en el contexto de retransmisiones turísticas en directo. Desde el punto de vista práctico, los resultados aportan ideas para que los streamers empleen estrategias de interacción apropiadas con los espectadores, proporcionándoles respuestas instantáneas y transmitiéndoles conocimientos relacionados con el turismo.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

To study these issues, the authors chose a GFSC with one producer and one material supplier as research object, the supplier will offer green material to the producer and the producer will make green food using green production technology. Then, the authors proposed that consumers' perceived value was determined by the trustworthiness levels of the related green and quality-safety information provided by the supplier and the producer. Then, considering the trustworthiness levels of the green and quality information provided by the supplier and the producer, the authors improved the demand function. Afterwards, we constructed four investment models and their income models are built and then a cost-sharing and revenue-sharing contract (hereafter, CSRS) was adopted to coordinate the GFSC.

With the growth of consumers environmental awareness and life level, consumers' requirements for green and high quality food are growing. In recently years, to increase consumers' perceived trustworthiness on the product greenness and quality levels, stakeholders in green food supply chain (hereafter, GFSC) start to adopt the blockchain-based traceability system (hereafter, BLTS). For investors, they need to know the investment conditions and how to coordinate the GFSC.

(1) When the revenue-sharing coefficient is less than three-fourths and higher then a certain vaule, the cost-sharing and revenue-sharing contract can make the GFSC coordinate. (2) The investment cost threshold of the BLTS has a positive relationship with the trustworthiness improvement levels of the green and quality information, the green degree of food products and the quality of food products. (3) In the proposed four investment situations, as the growth of consumers perceived credibility coefficient about the greenness information and the quality information, chain members' revenues will increase. In addition, comparing with co-investing the BLTS, benefits of chain members are lower than them in the sole investment model.

(1) The demand function we proposed can help chain members forecast market demand to support production or ordering decisions. (2) The investment decision policies can offer a theoretical reference for chain members to use the BLTS. (3) The CSRS will offer the theoretical reference for coordinating the supply chain after using the BLTS. Furthermore, our study method can be referenced by other scholars. (4) The study method can offer a method reference for researchers who do a similar discussion in a manufacturing supply chain. Although, our research cannot guide the industrial practices, it can serve as a reference of the similar research in industry.