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This paper takes the Shanghai-Shenzhen-Hong Kong Stock Connect as a quasi-natural experiment and investigates the impact of capital market liberalization on the corporate debt maturity structure. It also aims to provide some policy implications for corporate debt financing and further liberalization of the capital market in China.

Employing the exogenous event of Shanghai-Shenzhen-Hong Kong Stock Connect and using the data of Chinese A-share firms from 2010 to 2020, this study constructs a difference-in-differences model to examine the relationship between capital market liberalization and corporate debt maturity structure. To validate the results, this study performed several robustness tests, including the parallel test, the placebo test, the Heckman two-stage regression and the propensity score matching.

This paper finds that capital market liberalization has significantly increased the proportion of long-term debt of target firms. Further analyses suggest that the impact of capital market liberalization on the debt maturity structure is more pronounced for firms with lower management ownership and non-Big 4 audit. Channel tests show that capital market liberalization improves firms’ information environment and curbs self-interested management behavior.

This research provides empirical evidence for the consequences of capital market liberalization and enriches the literature on the determinants of corporate debt maturity structure. Further this study makes a reference for regulators and financial institutions to improve corporate financing through the governance role of capital market liberalization.

ChatGPT has been receiving mounting research attention recently. However, its application and challenges to adopt for tourism and hospitality businesses remain relatively unexplored. To address this research gap, this study aims to systematically assess the application of ChatGPT and its challenges within the domain of tourism and hospitality.

This study conducts bibliometric and content analyses of papers retrieved from Web of Science and Scopus. Particularly, it systematically reviewed the tourism and hospitality research to identify critical applications of ChatGPT in the context of tourism and hospitality. In addition, this study identified challenges associated with the application of ChatGPT in this context.

It has been revealed that the use of generative artificial intelligence (AI), such as ChatGPT, in tourism and hospitality research is ascending, with an opportunity to advance the existing knowledge in customer service research. In addition, the results suggest an ongoing interest in assessing the role of AI and language modeling for tourism education and human resource management.

The results are constrained by the used search keywords and electronic databases. Additionally, this study covered only papers published in English. However, the findings shed light on existing knowledge concerning ChatGPT’s transformative potential, identify areas for further exploration and offer guidelines for practice in the tourism and hospitality industry. The findings also revealed various challenges that various stakeholders should keep a closer eye on to ensure the effective and safe use of ChatGPT accordingly.

This study initiates a discussion on ChatGPT’s role in tourism and hospitality and underscores the importance of comprehensive AI integration within the sector.

近年来, ChatGPT受到了越来越多的研究关注。然而, 它在旅游和酒店业中的应用及其面临的挑战仍然相对未被探索。为填补这一研究空白, 本研究系统评估了ChatGPT在旅游和酒店业中的应用及其挑战。

本研究通过对从Web of Science（WoS）和Scopus检索的文献进行文献计量分析和内容分析。特别是, 系统回顾了旅游和酒店业的研究, 以识别ChatGPT在这一背景下的关键应用, 并识别了与其应用相关的挑战。

研究揭示了生成式人工智能（如ChatGPT）在旅游和酒店业研究中的应用日益增多, 为推动客户服务研究的现有知识提供了机会。此外, 研究结果表明, 对人工智能和语言建模在旅游教育和人力资源管理中的作用存在持续的兴趣。

本研究开启了对ChatGPT在旅游和酒店业中作用的讨论, 并强调了在该行业中全面整合人工智能的重要性。

本研究受限于所用的搜索关键词和电子数据库。此外, 本研究仅涵盖了英文论文。然而, 研究结果揭示了关于ChatGPT变革潜力的现有知识, 确定了进一步探索的领域, 并为旅游和酒店业实践提供了指导。研究还揭示了各利益相关者应密切关注的各种挑战, 以确保ChatGPT的有效和安全使用。

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This paper aims to study the impact of the interaction effects between live-streaming marketing and clothing type on consumers' intention to purchase clothing, and the mediating effect of internalization and identification on the relationship between them.

This paper conducts a scenario experiment to 486 consumers who had experience in purchasing clothing on the live-streaming platform and employs the analysis of variance, structural equation model and multivariate regression model.

Our findings reveal that professional live-streaming marketing (PLSM) can better stimulate consumers' intention to purchase formal clothing than entertainment live-streaming marketing (ELSM) does. Compared with PLSM, ELSM can better stimulate consumers' intention to purchase casual clothing. When PLSM promotes formal clothing, it triggers the internalization mechanism of consumers, so as to improve their purchase intention. When ELSM promotes casual clothing, it triggers consumers' identification mechanism, so as to improve their purchase intention.

This paper helps to identify the differences in the impact of different types of live-streaming marketing on consumers' intention to purchase different types of clothing, as well as the mediating role of internalization and identification mechanisms. This paper provides a theoretical reference for clothing firms to strategically select the appropriate type of live-streaming marketing.

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 examine the impact of peer-to-peer (P2P) accommodation platforms’ green marketing on consumers’ pro-environmental behavioural intention through the mediation of consumer trust and engagement, following the social influence theory and the stimulus–organism–response model.

A questionnaire survey was designed to collect data from American P2P accommodation consumers. Data collection was conducted through an outsourced survey company. Partial least squares structural equation modelling was used to analyse the data.

The study reveals that P2P accommodation platforms’ green marketing orientation was positively associated with consumer trust in the platform and consumer engagement with the platform. Both consumer trust and consumer engagement positively enhanced consumers’ pro-environmental behavioural intention in the P2P accommodation consumption, serving as effective mediators between consumers’ perceptions of green marketing orientation and pro-environmental behavioural intention.

The study offers practical insights for P2P accommodation platforms and operators in engaging in green marketing and fostering consumers’ pro-environmental consumption behaviours in P2P accommodations.

The study addresses the grand question of whether business operators’ responsible production behaviour can possibly lead to consumers’ responsible consumption behaviour in the P2P accommodation sector. It contributes to the literature on P2P accommodation by providing evidence to show green marketing practices of P2P accommodation platforms can lead to consumers’ pro-environmental behavioural intention. It provides both theoretical value for knowledge advancement and practical value to guide more sustainable industry practices.

Intellectual experiences focus on users’ information processing and critical thinking toward stimuli. The deployment of humanoid service robots as novel stimuli in tourism and hospitality has influenced users’ perceptions and may affect their intellectual engagement. This paper aims to connect four contemporary theoretical concepts: the service robot acceptance model, technological fear, the uncanny valley theory and the stereotype content model, to investigate users’ perceptions and intellectual experiences toward humanoid service robots.

Scale development procedures were conducted: literature review, checking face and content validity, factorizing items and dimensions, achieving construct and criterion validity and testing predictive validity.

Through literature review and free-response tasks, 43 measurement items were generated. Next, 1,006 samples from two cross-cultural groups refined the scale. Finally, a reliable and valid scale with four dimensions measuring users’ perceptions of humanoid service robots was determined.

Humanoid service robots should be designed to enhance functionality and innovativeness while minimizing stiffness, inflexibility, unsafety and danger to improve users’ intellectual engagement.

This study provides a novel examination of users’ intellectual experiences toward humanoid service robots by connecting four contemporary theories of users’ perceptions. This study enriches human–robot experience through an integrated perspective and presents a rigorous examination of the scale’s psychometric properties. A reliable and valid scale for measuring users’ perceptions toward humanoid service robots fills the gaps and serves as an effective predictor of intellectual experience in human–robot literature.

This paper aims to regulate the oil retention rate and tribological properties of pored polytetrafluoroethylene (PPTFE) using polyvinyl alcohol (PVA)-based oil gel.

PPTFE was first prepared by using citric acid (CA) as an efficient pore-making agent. Subsequently, PVA and chitosan solution was introduced into the pores and experienced a freezing-thawing process, forming PVA-based gels inside the pores. Then, the PPTFE/PVA composite was impregnated with polyethylene glycol 200 (PEG200), yielding an oil-impregnated PPTFE/PVA/PEG200 composite.

It was found that the oil-impregnated PPTFE/PVA/PEG200 composite exhibited advanced tribological properties than neat PTFE with reductions of 53% and 70% in coefficient of friction and wear rate, respectively.

This study shows an efficient strategy to regulate the tribological property of PTFE using a PVA-based oil-containing gel.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0432/

The development of financial technology (fintech), especially digital payments (e-payments), aims to increase the efficiency and effectiveness of economic transactions. This study aims to see the extent to which microentrepreneurs in Indonesia and Malaysia take advantage of the existence of e-payments in developing their business and the factors that influence the adoption of e-payments.

This study uses qualitative and quantitative approach. For quantitative approach, partial least squares structural equation modeling (PLS-SEM 4.0) was used to analyze the data. Using the nonprobability convenient sampling technique, this study collected 400 respondents from microenterprises in Indonesia and Malaysia in various regions.

Most of the microentrepreneurs in Indonesia and Malaysia have used fintech platforms, especially e-wallet and ATM debit. However, for quick response code-based fintech for business transactions, most microentrepreneurs have not taken advantage of the platform. Then the results of the digital payment adoption factor also differ for each country.

This study is valuable for decision-makers and regulators. These results can be used to find a roadmap for regulators to build a digital economy, especially digital payments for microenterprises in both countries. In addition, these results can be used as a basis for making policies regarding digital payments.

To the best of the authors’ knowledge, this is the first study to compare e-payment adoption by microentrepreneurs in Indonesia and Malaysia. Indonesia and Malaysia are two countries in the Southeast Asia region that have great attention in fintech development. This study provides new insights about fintech, especially digital payments as a strategic approach in the digitalization era.

In the evolution process of building construction accidents, there are key nodes of risk change. This paper aims to quickly identify the key nodes and quantitatively assess the node risk. Furthermore, it is essential to propose risk accumulation assessment method of building construction.

Authors analyzed 419 accidents investigation reports on building construction. In total, 39 risk factors were identified by accidents analysis. These risk factors were combined with 245 risk evolution chains. Based on those, Gephi software was used to draw the risk evolution network model for building construction. Topological parameters were applied to interpret the risk evolution network characteristic.

Combining complex network with risk matrix, the standard of quantitative classification of node risk level is formulated. After quantitative analysis of node risk, 7 items of medium-risk node, 3 items of high-risk node and 2 items of higher-risk nodes are determined. The application results show that the system risk of the project is 44.67%, which is the high risk level. It can reflect the actual safety conditions of the project in a more comprehensive way.

This paper determined the level of node risk only using the node degree and risk matrix. In future research, more node topological parameters that could be applied to node risk, such as clustering coefficients, mesoscopic numbers, centrality, PageRank, etc.

This article can quantitatively assess the risk accumulation of building construction. It would help safety managers could clarify the system risk status. Moreover, it also contributes to reveal the correspondence between risk accumulation and accident evolution.

This study comprehensively considers the likelihood, consequences and correlation to assess node risk. Based on this, single-node risk and system risk assessment methods of building construction systems were proposed. It provided a promising method and idea for the risk accumulation assessment method of building construction. Moreover, evolution process of node risk is explained from the perspective of risk accumulation.