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In this paper, a new grey Cosine New Structured Grey Model (CNSGM(1,N)) prediction power model is constructed for the small-sample modeling and prediction problem with complex nonlinearity and insignificant volatility.

Firstly, the weight of some relevant factors is determined by the grey comprehensive correlation degree, and the data are preprocessed. Secondly, according to the principle of “new information priority” and the volatility characteristics of the sequence growth rate, the ideas of damping accumulation power index and trigonometric function are integrated into the New Structured Grey Model (NSGM(1,N)) model. Finally, the non-structural parameters are optimized by the genetic algorithm, and the structural parameters are calculated by the least squares method, so a new CNSGM(1,N) predictive power model is constructed.

Under the principle of “new information priority,” through the combination with the genetic algorithm, the traditional first-order accumulation generation is transformed into damping accumulation generation, and the trigonometric function with the idea of integer is introduced to further simulate the phenomenon that the volatility is not obvious in the real system. It is applied to the simulation and prediction of China’s carbon dioxide emissions, and compared with other comparison models; it is found that the model has a better simulation effect and excellent performance.

The main contribution of this paper is to propose a new grey CNSGM(1,N) prediction power model, which can not only be applied to complex nonlinear cases but also reflect the differences between the old and new data and can reflect the volatility characteristics of the characteristic behavior sequence of the system.

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.

This study aims to investigate how small and medium-sized enterprises (SMEs’) supply chain specific investment (SCSIs) affects supply chain financing performance (SCFP) in the innovative industrial finance model, and further analyze the internal mechanisms and important contextual factors.

Based on signaling theory, this study constructs a mediating and moderating model to examine the influencing mechanisms of SMEs’ SCSIs on SCFP, including the mediating effect of opportunism and the moderating effect of digital technology deployment (DTD). A multiple regression analysis is conducted to verify the theoretical hypotheses, using questionnaire data collected from 288 SMEs in China.

The empirical findings indicate that both SMEs’ supply chain asset-specific and relationship-specific investments can significantly promote SCFP. Also, SMEs’ SCSIs can improve SCFP by reducing the occurrence of opportunism perceived by supply chain partners. The breadth of DTD positively moderates the relationship between the two types of SCSIs and SCFP, while the depth of DTD has no significant moderating effect on the relationship between SCSIs and SCFP.

This study has discussed the important and novel issue of how financially distressed SMEs can send effective signals to financial institutions by increasing their SCSIs in supply chain finance mode. By revealing the influencing mechanisms of SMEs’ SCSIs on SCFP, this study contributes to expanding the research on the antecedents of SCFP from the dimension of interorganizational transactions. This study also enriches the perspectives of signaling theory by exploring the interaction between signal sender and signal intermediary.

A global industrial revolution driven by carbon neutrality and digital technologies (DTs) is fast gaining momentum. However, the present understanding of how firms should leverage digitalization for sustainability is underdeveloped. This paper aims to explore how digital orientation can improve environmental performance from the natural resource-based view.

Using a sample set of 132 Chinese firms, the authors adopt the hierarchical regression analysis and bootstrap approach to examine the hypotheses.

The empirical results show that green DT usage, green DT disposal and green practices-DT fit are three factors that mediate the positive relationship between digital orientation and environmental performance. In addition, of the five possible mediational paths, only the serial mediation of green DT usage and green practices-DT fit, as well as the serial mediation of green DT disposal and green practices-DT fit, show significant effects.

The authors contribute to the current digitalization and sustainability literature by demonstrating the processes through which digital orientation influences environmental performance. The study also provides managerial implications for firms to adjust their operations.

Electromagnetic shielding (EMS) fabrics composed of cotton, polyester and other high-polymer short-staple fibers are widely utilized in various fields. However, the inevitable pores in these fabrics lead to the leakage of electromagnetic waves, which severely diminishes the fabric’s shielding effectiveness (SE). To address this issue, this paper proposes the implantation of a metamaterial structure known as the “split ring resonator (SRR)” into the fabric.

Firstly, the types and principles of SRRs are analyzed. Through electromagnetic simulation and emulation, the effectiveness of SRRs in dissipating electromagnetic waves is confirmed. By selecting different embroidery methods, various shapes of SRRs are implanted into the fabric. Subsequently, through testing and analysis of sample fabrics embroidered with SRRs, it is concluded that implanting appropriate SRRs into pure cotton fabrics and cotton/polyester/stainless steel-blended EMS fabrics can effectively impart or enhance the SE of these fabrics.

For pure cotton fabric without inherent SE, the peak SE value can reach over 30 dB within the 6.57 GHz–7 GHz frequency band, and the minimum SE is greater than 10 dB in the 7 GHz–9.99 GHz frequency band. For the cotton/polyester/stainless steel-blended EMS fabric, the improvement in SE across all frequency bands exceeds 10 dB, averaging around 15.6 dB. The circular type SRR demonstrates the most significant improvement in fabric SE. When the substrate is composed of pure cotton or a cotton/polyester/stainless steel blend, the circular SRRs provide an average enhancement of more than 4 dB and 6 dB, respectively, than other shapes. The fewer the holes created by the implantation method, the higher the SE of the fabric after SRR implantation, with the invisible embroidery technique being the most effective. It improves the fabric’s SE by an average of about 2 dB more than flat embroidery and can be up to an average of around 6 dB higher than the backstitch embroidery technique. For every 0.2 cm increase in the size of the SRRs, the average SE increases by about 4 dB, and for every 0.5 cm increase in the spacing between them, the fabric’s SE decreases by an average of more than 2.7 dB.

This paper offers a novel approach to counteract the issue of pores reducing the SE of EMS fabrics and provides a new method for developing lightweight, thin, low-cost and high-performance EMS fabric composite materials.

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。

Machine learning (ML) technologies are increasingly being applied in building cost estimation as an advanced method to overcome the challenge of insufficient data and subjective effects of experts. To address the gap of lacking a review of ML applications in building cost estimation, this research aimed to conduct a systematic literature review to provide a robust reference and suggest development pathways for creating novel ML-based building cost prediction models, ultimately enhancing construction project management capabilities.

A systematic literature review according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) was adopted using quantitative bibliographic analysis and qualitative narrative synthesis based on the 70 screened publications from Web of Science (WOS) and Scopus databases. The VOSviewer software was used to prepare the thematic focus from the bibliographic data garnered.

Based on the results of a bibliographic analysis, current research hotspots and future trends in the application of ML to building cost estimation have been identified. Additionally, the mechanisms behind existing ML models and other key points were analyzed using narrative synthesis. Importantly, the weaknesses of current applications were highlighted and recommendations for future development were made. These recommendations included defining the availability of building attributes, increasing the application of emerging ML algorithms and models to various aspects of building cost estimation and addressing the lack of public databases.

The findings are instrumental in aiding project management professionals in grasping current trends in ML for cost estimation and in promoting its adoption in real-world industries. The insights and recommendations can be utilized by researchers to refine ML-based cost estimation models, thereby enhancing construction project management. Additionally, policymakers can leverage the findings to advocate for industry standards, which will elevate technical proficiency and ensure consistency.

Compared to previous research, the findings revealed research hotspots and future trends in the application of ML cost estimation models in only building projects. Additionally, the analysis of the establishment mechanisms of existing ML models and other key points, along with the developed recommendations, were more beneficial for developing improved ML-based cost estimation models, thereby enhancing project management capabilities.

The article investigates different types of strategies for managing user-generated content (UGC) and provides some insights into their implications.

A unique sample of Italian hotels with current and prospective customers in the digital environment is investigated. A taxonomy of user-provider interactions mediated by UGC is developed. A mixed approach was designed to meet the study aims. Firstly, an exploratory factor analysis was performed in order to illuminate different strategies of UGC and electronic word-of-mouth (E-WOM) management. Secondly, a cluster analysis was implemented in order to explain hoteliers' behavior toward users' contents.

The study results suggested the existence of three clusters, which reflected three different types of interactions between hotels and customers in the digital domain. Interestingly, most of Italian hotels were found to adopt a reductionist approach to UGC and E-WOM management, turning out to be ineffective to exploit them for the purpose of quality improvement and hospitality service excellence.

Hotels were found to be largely unaware of the importance of UGC and web-based communication with customers to improve their digital business strategy. Tailored management approaches are needed to realize the full potential of hotels' online content responsiveness for the purpose of value co-creation and service co-production.

This is one of the first studies investigating the strategic and management perspectives embraced by hotels to handle their interactions with customers in the digital arena.

This paper examines the evolving landscape of accounting and finance research on the Chinese capital market, building on a previous study published at Abacus in 2018.

By incorporating data from 1999 to 2023, our analysis offers a detailed examination of shifts in academic focus, methodological advancements and thematic expansions over the last quarter-century.

The study reveals a substantial increase in accounting and finance publications related to the Chinese capital market in both Tier 1 and Asia-Pacific journals. The dynamic growth of the Chinese capital market during this period reflects profound economic transformations, characterized by technological innovations, sustainability commitments and regulatory reforms.

We conclude that the globally important Chinese capital market has attracted increasing academic attention, significantly advancing the understanding of accounting and finance research in China’s capital market.