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This paper aims to investigate whether the establishment of commodity futures can effectively hedge systemic risk in the spot network, given the context of financialization in the commodity futures market.

Utilizing industry association data from the Chinese commodity market, the authors identify systemically important commodities based on their importance in the production process using multiple graph analysis methods. Then the authors analyze the effect of listing futures on the systemic risk in the spot market with the staggered difference-in-differences (DID) method.

The findings suggest that futures contracts help reduce systemic risks in the underlying spot network. Systemic risk for a commodity will decrease by approximately 5.7% with the introduction of each corresponding futures contract, since the hedging function of futures reduces the timing behavior of firms in the spot market. Establishing futures contracts for upstream commodities lowers systemic risks for downstream commodities. Energy commodities, such as crude oil and coal, have higher systemic importance, with the energy sector dominating systemic importance, while some chemical commodities also have considerable systemic importance. Meanwhile, the shortest transmission path for risk propagation is composed of the energy industry, chemical industry, agriculture/metal industry and final products.

The paper provides the following policy insights: (1) The role of futures contracts is still positive, and future contracts should be established upstream and at more systemically important nodes in the spot production chain. (2) More attention should be paid to the chemical industry chain, as some chemical commodities are systemically important but do not have corresponding futures contracts. (3) The risk source of the commodity spot market network is the energy industry, and therefore, energy-related commodities should continue to be closely monitored.

Soft actuators using pneumatic-chamber (PneuNet)-based designs have been of interest in the area of soft robotics with scope of application in the area of biomedical assistance and smart agriculture. Researchers have attempted to investigate multiple chambers in parallel to examine their deformation characteristics. However, there is a lacuna for investigation of the deformation characteristics of four parallel chambered soft actuators. The purpose of this study is to comprehensively investigate the different possible actuation scenarios and the resulting bending/deformation behaviours.

Therefore, in this study, a four-chambered PneuNet actuator is numerically investigated to evaluate the effects of pressurization scenarios and pressure levels on its performance, operating reaching and working volume.

The results of this study revealed that two-adjacent chamber equal pressurization and three-chamber pressurizations result in increased bending. However, two-opposite chamber pressurization reduces the bending angle with pressure levels in the lower pressure chamber. The maximum bending angle of 97° was achieved for single-chamber pressurization of 300 kPa. The two-adjacent chamber unequal pressurization can achieve a sweeping motion in the actuator along with bending. The working volume and reaching capability analysis revealed that the actuator can reach around 71% of the dimensional operating space.

The results provide fundamental guidance on the output nature of motion which can be obtained under different pressurization scenarios using the four-chambered design soft actuator, thereby making it a practical guide for implementation for useful applications.

The comprehensive pressurization scenarios and pressure level variations reported in this study will serve as fundamental operating guidelines for any practical implementation of the four-chambered PneuNet actuator.

The purpose of this study is to explore the suggestions that construction processes could be considerably improved by integrating building information modelling (BIM) with 3D laser scanning technologies. This case study integrated 3D laser point cloud scans with BIM to explore the effects of BIM adoption on ongoing construction project, whilst evaluating the utility of 3D laser scanning technology for producing structural 3D models by converting point cloud data (PCD) into BIM.

The primary data acquisition adopted the use of Trimble X7 laser scanning process, which is a set of data points in the scanned space that represent the scanned structure. The implementation of BIM with the 3D PCD to explore the precision and effectiveness of the construction processes as well as the as-built condition of a structure was precisely captured using the 3D laser scanning technology to recreate accurate and exact 3D models capable of being used to find and fix problems during construction.

The findings indicate that the integration of BIM and 3D laser scanning technology has the tendency to mitigate issues such as building rework, improved project completion times, reduced project cost, enhanced interdisciplinary communication, cooperation and collaboration amongst the project duty holders, which ultimately enhances the overall efficiency of the construction project.

The acquisition of data using 3D laser scanner is usually conducted from the ground. Therefore, certain aspects of the building could potentially disturb data acquisition; for example, the gable and sections of eaves (fascia and soffit) could be left in a blind spot. Data acquisition using 3D laser scanner technology takes time, and the processing of the vast amount of data acquired is laborious, and if not carefully analysed, could result in errors in generated models. Furthermore, because this was an ongoing construction project, material stockpiling and planned construction works obstructed and delayed the seamless capture of scanned data points.

These findings highlight the significance of integrating BIM and 3D laser scanning technology in the construction process and emphasise the value of advanced data collection methods for effectively managing construction projects and streamlined workflows.

The primary objective was to develop a user-centered mobile health application (app) tailored to the specific health information needs of among immigrant women from diverse backgrounds in Korea.

In-depth interviews were conducted with 24 immigrant women to gain insights into their health information-seeking behavior. Based on the findings, a mobile app was designed and developed. A beta version of the app underwent validation by an MD and seven expert reviewers who assessed the app for content accuracy and conformance to mobile heuristics. Last, immigrant women (n = 12) evaluated the usability of the app.

The study revealed that the interviewed immigrant women had strong health information needs related to pregnancy and parenting. Most of them used multiple sources to find and verify health information. Language barriers were identified as a major obstacle to accessing and evaluating health information. The results of the user test indicated that the app effectively facilitated study participants' search for reliable health information, meeting their specific needs.

This research extended the literature by addressing the limited availability of mobile apps tailored to the health information needs of immigrant women in Korea.

By incorporating multilingual support and focusing on pregnancy and parenting information, the health app serves as a valuable tool to bridge the gap in health information access and to facilitate the well-being of immigrant women in the country.

This study investigates the key factors that influence customer satisfaction when interacting with augmented reality shopping assistance applications (ARSAPs). ARSAPs grant consumers the capability to experience products in a virtually simulated user environment before product acquisition. With the development of mobile e-commerce due to breakthroughs in smartphone and augmented reality (AR) technologies, there is an increasing potential for these emergent AR mobile services, yet there is a need for further improvement.

This study initially explored the key satisfaction factors for ARSAPs by utilizing topic modeling of a collection of actual user reviews. These factors are subsequently revisited and complemented by existing literature, and finally verified through logistic regression analysis supported by sentiment analysis.

This study identified the key factors that influence customer satisfaction with ARSAPs, including visuality, sense of reality, credibility, format, completeness, understandability, relevance, flexibility, response time, reliability, availability, ease of use and privacy. In particular, two additional factors (i.e. visuality and sense of reality) were newly identified as important in the context of AR, despite their previous omissions in existing literature.

This study is the first to investigate the key factors that influence customer satisfaction with ARSAPs from users' perspectives, utilizing topic modeling of a large amount of real-world data on actual user feedback. By identifying new factors (i.e. visuality and sense of reality) that were not identified in previous literature, this study provides important academic implications for a broader understanding of AR and related technologies that are essential elements of the metaverse. This study also provides valuable insights for developers and companies in the e-commerce industry on how to optimize AR applications and develop more targeted and effective marketing strategies in this field.

This paper aims to explore the matching effect of hospitality brand image (cool vs non-cool) and service agents (Artificial intelligence [AI] vs human staff) on brand attitude, with a focus on assessing the role of feeling right as a mediator and service failure as a moderator.

This paper tested the hypotheses through three experiments and a Supplementary Material experiment, which collectively involved 835 participants.

The results indicated that the adoption of AI by cool brands can foster the right feeling and enhance consumers’ positive brand attitudes. In contrast, employing human staff did not lead to improved brand attitudes toward non-cool brands. Furthermore, the study found that service failure moderated the matching effect between service agents and cool brand images on brand attitude. The matching effect was observed under successful service conditions, but it disappeared when service failure occurred.

The findings offer practical guidance for hospitality companies in choosing service agents based on brand image. Cool brands can swiftly transition to AI, reinforcing their modern, cutting-edge image. Traditional brands may delay AI adoption or integrate it strategically with human staff.

To the best of the authors’ knowledge, this paper represents one of the first studies to address the issue of selecting the optimal service agent based on hospitality brand image. More importantly, it introduces the concept of a cool hospitality brand image as a boundary condition in the framework of AI research, providing novel insights into consumers’ ambivalent responses to AI observed in previous studies.

This paper aims to explore the impact of live streamer authenticity (LSA) on purchase intention in tourism e-commerce live streaming, with a focus on boundary conditions and underlying mechanisms.

The data collected from 451 participants were analyzed using structural equation modeling.

This paper found that four dimensions of LSA – sincerity, truthfulness endorsement, expertise and uniqueness – positively influenced purchase intention, while visibility did not. In addition, sincerity, truthfulness endorsement and uniqueness had an indirect influence on purchase intention through flow experience, while sincerity, truthfulness endorsement, expertise and uniqueness had an indirect effect through perceived trust. Furthermore, self-construal moderated the effect of sincerity and truthfulness endorsement on purchase intention, with the positive effect being stronger for the dependent self-construal.

To the best of the authors’ knowledge, it is the first study to examine LSA dimensions and their consequences. This paper not only provides a better and more detailed understanding of the complexity of LSA but also contributes to the development of authenticity theory by responding to individual authenticity studies.

本文旨在研究旅游直播电商中主播真实性对旅游购买意向的影响, 及其内在机制和边界条件。

本文采用问卷调查的方法收集451名参与者的数据。然后用结构方程模型（SEM）对收集的数据进行分析。

本文发现, 主播真实性的四个维度–真诚性、真实背书、专业性和独特性–对购买意愿有积极影响, 而主播的透明性没有影响。此外, 真诚性、真实背书和独特性通过心流体验对购买意愿产生间接影响, 此外真诚性、真实背书、专业性和独特性还通过感知信任产生间接影响。此外, 自我建构调节了真诚性和真实背书对购买意向的影响。对于依赖型自我建构, 真诚性和真实背书对购买意愿的影响更强。

本文是最早探讨主播真实性维度及其后果影响的研究之一。本文不仅对主播真实性的复杂性有了更好更详细的了解, 而且响应了对个体真实性研究的呼吁, 为真实性理论的发展做出了贡献。

Este artículo explora el impacto de la autenticidad del streamer en la intención de compra en la retransmisión en directo de comercio electrónico turístico, centrándose en las limitaciones y los procesos subyacentes.

Se analizaron los datos recopilados de 451 participantes utilizando un modelo de ecuaciones estructurales (SEM).

Este trabajo halló que cuatro dimensiones de la autenticidad de los streamers en directo -la sinceridad, el respaldo a la veracidad, la pericia y la singularidad- influían positivamente en la intención de compra, mientras que la visibilidad no lo hacía. Además, la sinceridad, el respaldo de veracidad y la singularidad influyeron indirectamente en la intención de compra a través de la experiencia de flujo, mientras que la sinceridad, el respaldo de veracidad, la pericia y la singularidad tuvieron un efecto indirecto a través de la confianza percibida. Además, la autoconstrucción moderó el efecto de la sinceridad y el respaldo de veracidad sobre la intención de compra, siendo el efecto positivo más fuerte para la autoconstrucción dependiente.

Es el primer estudio que examina las dimensiones de la autenticidad de los retransmisores en directo y sus consecuencias. Este trabajo no sólo proporciona una comprensión mejor y más detallada de la complejidad de la autenticidad de los streamers en directo, sino que también contribuye al desarrollo de la teoría de la autenticidad al responder a los estudios sobre autenticidad individual.

Drawing on power approach-inhibition theory, this study develops a conditional indirect effect model to explore how team vertical leader position and expert power indirectly impact members’ shared leadership through vertical leader’s empowering behaviors.

Multi-source data was collected using a field survey research design. The final sample includes 944 employees in 164 teams from 14 companies in China.

This study found that the interaction of team vertical leader position power and expert power was positively related to their empowering behaviors, which in turn were positively associated with shared leadership. Moreover, our post hoc-analysis revealed the moderating effect of team power distance orientation on the relationship between vertical leader empowering behaviors and shared leadership.

This study sheds light on shared leadership literature by examining vertical leader position and expert power as antecedents. We also offer new directions for exploring how power functions by discussing leadership through the lens of power approach-inhibition theory.

This study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years 2005–2021.

Using monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 indices.

The PC algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of cities.

This suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities.

Online reviews derived from peer communications have been increasingly viewed as an important approach for consumers to gather pre-purchase information. This study aims to examine factors affecting online reviews adoption in social network communities and then indicates the underlying mechanism of this process based on an extended information adoption model (IAM).

Using the data collected from 242 users of a social network community via an online survey, the proposed model is empirically assessed by partial least squares-based structural equation model (PLS-SEM).

The results show that both perceived diagnosticity and perceived serendipity are drivers of online reviews adoption in social network communities. Meanwhile, community identification is not only an antecedent of diagnosticity and serendipity perceived by community members, but also motivates source credibility which, in turn, positively influences argument quality. Finally, the importance of argument quality and source credibility in reviews adoption process is also presented.

This study extends the IAM and enriches the literature regarding online reviews adoption. It deepens the understanding of serendipitous experiences and community identification in social networking context by addressing their important roles in the authors' extended IAM.