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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.

The carbon complementary supply chain (CCSC) is a collaborative framework that facilitates internal carbon credit trading agreements among supply chain agents in compliance with prevailing carbon regulations. Such agreements are highly beneficial, prompting agents to consider joint investment in emission reduction initiatives. However, capital investments come with inevitable opportunity costs, compelling agents to weigh the potential revenue from collaborative investments against these costs. Thus, this paper mainly explores carbon abatement strategies and operational decisions of the CCSC members and the influence of opportunity costs on the strategic choice of cooperative and noncooperative investment.

The authors propose a novel biform game-based theoretical framework that captures the interplay of pricing competition and investment cooperation among CCSC agents and assesses the impact of opportunity costs on CCSC profits and social welfare. Besides, the authors also compare the biform game-based collaborative scenario (Model B) to the noncooperative investment scenario (Model N) to investigate the conditions under which collaborative investment is most effective.

The biform game-based collaborative investment strategy enhances the economic performance of the traditional energy manufacturer, who bears the risk of opportunity costs, as well as the retailer. Additionally, it incentivizes the renewable energy manufacturer to improve environmental performance through renewable projects.

This research contributes significantly by establishing a theoretical framework that integrates the concepts of opportunity costs and biform game theory, offering new insights into the strategic management of carbon emissions within supply chains.

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。

This study aims to assess the mediating and driving roles of knowledge cooperation in the effectiveness of G60 Sci-tech Innovation Corridor (G60 STIC) for regional collaborative innovation within the knowledge economy context. Furthermore, it focuses on whether knowledge cooperation is more effective than resource cooperation in terms of spatial spillover and its mediating effects on collaborative innovation.

This study employs multiple statistical and econometric approaches, including social cooperation network, Super-DEA, spatial difference-in-difference model (SDID) and mediating effect model, to measure the effectiveness of knowledge cooperation and resource cooperation paths within the framework of the G60 STIC on regional collaborative innovation in the Yangtze River Delta region (YRD) from 2002 to 2022.

First, the knowledge cooperation networks validate the strengthening of collaborative innovation is primarily centred on provincial cities and leading manufacturing locales, with smaller cities radiating outwards from these centres. The knowledge cooperation network was generally stronger than the resource cooperation network. Second, the G60 STIC significantly enhances collaborative innovation efficiency by intensifying knowledge, resource and interactive cooperation networks. Third, within the context of the knowledge economy, knowledge cooperation presents a stronger spillover and mediating effect in stimulating collaborative innovation than resource cooperation.

This study clarifies the existence of a knowledge cooperation network and its mediating role in stimulating the effectiveness of strategic, innovative platforms on collaborative innovation. This further verifies the stronger role of the knowledge cooperation than the resource cooperation, which serves as a vital element in promoting strategic innovative platforms to optimise collaborative innovation.

This study delves into the multifaceted landscape of online advertising, focusing specifically on the customer perceptions of advertisements from prominent e-commerce platforms, AJIO and Myntra. With a sample size of 137 participants, the research employs robust statistical techniques, including simple percentage analysis and chi-square, to unravel insights embedded within the collected data.

Exploring how customers perceive online advertisements, particularly those originating from AJIO and Myntra, and how these perceptions shape their purchasing decisions, the study underscores the importance of transparent and accurate representation of products and services in online ads to foster trust and confidence among consumers. The study provides insights specifically relevant to AJIO and Myntra customers.

This research adds to the existing understanding of online advertising, presenting insights that hold significance for both advertisers and consumers navigating this complex landscape. With the digital domain continuing its rapid evolution, this study serves as a valuable resource, aiding in comprehending.

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 introduces the heritage city risk dimension of the urban rail transit (URT) projects. It aims to identify the risk factors affecting URT projects within the unique context of heritage-rich cities, exploring their interrelation and evaluating critical factors.

The research adopts a multi-case exploratory study to identify the unique challenges faced by URT projects in heritage-rich environments, followed by a comprehensive risk assessment framework integrating Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP) and Risk Interaction Network (RIN) analysis to assess identified risks in the context of Kathmandu Valley. Additionally, a risk response action is simulated using RIN analysis.

About 16 risk factors were identified from the case studies and evaluated using the proposed risk assessment methodology. The study reveals a highly interconnected risk environment, with heritage impact-related factors exerting the strongest causative influence on cost and social engagement factors. Community opposition (R8) shows the highest betweenness centrality, indicating its central position in risk propagation across the network. Cost-related risk, social demand contingency (R2) ranked as the most critical. Simulations of a targeted risk avoidance strategy showed that addressing only three key high-betweenness centrality factors (R5, R8 and R15) reduced overall risk interactions by 46%, simplifying the risk network, reducing project complexity and improving manageability.

The findings emphasize that project managers, urban planners and policymakers should integrate heritage preservation concerns when planning and executing URT projects in heritage-rich cities. Moreover, the research highlights that effective community engagement serves as a key strategy for reducing risk propagation and plays a crucial role in overall project risk management.

The study contributes to the underexplored context of URT projects in heritage-rich cities, providing a comprehensive risk management framework for identifying and assessing project risks intersecting with urban development imperatives and heritage conservation objectives.

The wheel wear of high-speed trains is the main concern in the operation process of the high-speed trains. Wheel reprofile usually adopts the thin flange wheel profiles for the economic wheel reprofile strategy; however, the wheel wear evolution law of the thin flange wheel profile is not clear. The purpose of this paper mainly researches the wheel wear evolution law of thin flange wheel profile and the influence factors of thin flange wheel.

To solve this problem, this paper analyzed the wheel-rail contact relationship and established a vehicle dynamic model of high-speed train. The Jendel wheel wear model was used to analyze the evolution of thin flange wheel wear and rail deviation affecting wheel wear under thin flange wheel profiles.

Due to the consistency of the tread area, the distribution of wheel-rail contact points for thin flange wheels in the tread area is close. As the wheel flange increases, wheel wear also increases. Thin flange wheel profiles reduce wheel wear to a certain extent. The maximum wheel wear depths with rail deviations of −0.4 mm, 0 mm and 0.4 mm are 0.9844 mm, 1.077 mm and 1.142 mm, respectively. The negative rail deviation suppresses wheel wear, but the positive rail deviation increases wheel wear.

The evolution of wheel wear for thin flange wheels after reprofiling is not clear. This paper analyzed the wear characteristics of thin flange wheel wear and rail deviation affecting wheel wear under thin flange wheels.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0401/