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The objective of this study is to accurately predict the cost of green buildings to provide quantifiable criteria for investment decisions from investors.

This study proposes a hybrid prediction model ML-based for cost prediction of GBPs and obtains prediction parameters (PPs) associated with project characteristics through data mining (DM) techniques. The model integrates a principal component analysis (PCA) method to perform parameter dimensionality reduction (PDR) on a large number of raw variables to provide independent characteristic terms. Moreover, the support vector machine (SVM) algorithm is improved to optimize the prediction results and integrated with parameter dimensionality reduction and cost prediction.

The prediction results show that the mean absolute and relative errors of the hybrid prediction model proposed in this study are equal to 39.78 and 0.02, respectively, which are much lower than those of the traditional SVM model and MRA prediction model. Moreover, the hybrid prediction model with parameter dimensionality reduction also achieved better prediction accuracy (R² = 0.319) and superior prediction accuracy for different cost terms.

Theoretically, the hybrid prediction model developed in this study can reliably predict the cost while accurately capturing the characteristics of GBPs, which is a bold attempt at a comprehensive approach. Practically, this study provides developers with a new ML-based prediction model that is capable of capturing the costs of projects with ambiguous definitions and complex characteristics.

It is found that previous studies only focus on how digital transformation contributes to individual firms’ green innovation performance while ignoring the important role that it plays in the spillover and diffusion of green innovations among peer firms. Therefore, this study aims to investigate the influence of focal firms’ digital transformation on the spillover of green innovation among peer firms in heavily polluting industries mediated by environmental, social and governance (ESG) performance and agency conflict. Further, this study is also expected to explore the effects of digital transformation’s green innovation spillover.

This study chooses 6,438 A-share heavily polluting listed firms in the stock exchanges based in Shanghai and Shenzhen in China during 2010–2020 as samples and tests the hypothesis with ordinary least squares (OLS) regression. Results prove to be robust to a battery of robustness analyses the authors performed to take care of endogeneity.

The results show that the focal firm’s digital transformation may trigger their peer firms’ green innovation spillover and prompt them to engage in green innovation activities actively. The mechanism test shows that peer firms’ ESG performance and agency conflict mediate the influence path between digital transformation and peer firms’ green innovation spillover. Finally, among heavily polluting firms with high industry competition and large scale, digital transformation’s green innovation spillover effects are more significant in conventional energy-based source control, end-of-pipe treatment and substantive green innovation.

This study is possible to provide a potential driving mechanism of green innovation spillovers. The findings lay a sound foundation for future research, providing important theoretical support and practical insights for digital transformation to empower heavily polluting industries to achieve green transformation and low-carbon development.

This chapter examines the relative influence of parents (vertical channel) and friends (horizontal channel) when deciding between employment in the public or private sector. Using a novel database and applying network analysis, the influence of peers is first measured. Next, the peer effect is compared with the impact of parental background on individual preferences. For the private sector, findings indicate that the influence (marginal effect) of friends is greater than that of parents. The opposite is observed for the public sector. However, in the case of public sector employment, the overall effect of the horizontal channel may surpass the vertical channel, as individuals typically have two parents but may have many friends. Additionally, it is found that both parents and friends exert a greater influence on women than on men.

In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

External relational capital is the value created by an organization’s relationships with outside stakeholders, such as customers. This study introduces and examines the concept of sustainable enclothed cognition to support it, aligning rational reasons, personal values and emotions with sustainable clothing choices not only fosters envisioning sustainable learning from a user perspective but also holds the potential to help companies quickly adapt and find alternative solutions, thereby minimizing production impacts on the environment and promising the future for sustainable fashion in the industry.

This study aims to explore how sustainable enclothed cognition, combined with envisioning sustainable learning, can enhance external relational capital in the fashion industry by fostering deeper connections between fashion brands and environmentally conscious consumers. Data collection took place between May and September 2021. A survey of 211 young workers was conducted, and the data were analyzed using partial least squares-structural equation modeling (PLS-SEM).

The findings demonstrate that prioritizing sustainable enclothed cognition can satisfy consumer demands, strengthen customer relationships and enhance competitive positioning in the fashion industry. Furthermore, the study provides actionable strategies for implementing envisioning sustainable learning, highlighting its transformative role in turning consumer alignment into external relational capital. This insight inspires a new perspective on the potential of sustainable learning in the fashion industry.

This research offers a deeper understanding of how companies can strategically manage their external customer relationships by using sustainable enclothed cognition to drive eco-innovation and enhance relational capital in the sustainable fashion industry. Findings support that textile companies provide fresh insights into their innovative capacity by aligning consumer rational reasons, values and emotions with learning practices. The study also underscores the pivotal role of envisioning sustainable learning in embedding sustainability into the core of fashion industry practices, delivering both theoretical and practical guidance on achieving long-term business success through sustainability.

This study is of great significance in revealing the interactive and coordinated relationship between tourism development and the ecological environment, improving the level of environmental governance in the process of tourism economic development and realising the dual-carbon goal and sustainable development of the tourism industry.

This paper performs slack-based measure (SBM)-data envelopment analysis (DEA) analysis, benchmark regression and threshold effect measurement on a sample of 277 cities in China from 2011 to 2019, to estimate the impact of the digital economy on tourism eco-efficiency and to identify the moderating role of environmental regulation.

China’s tourism eco-efficiency shows a spatio-temporal characteristic of steady growth amid fluctuations. The digital economy has a positive facilitating effect on tourism eco-efficiency, which is non-linear with a single threshold effect (0.631), and when the level of the digital economy exceeds the threshold value, its facilitating effect increases from 0.696 to 0.927. Environmental regulation does not play the role of “the icing on the cake” during the digital economy’s impact on tourism eco-efficiency.

This study for the first time includes the digital economy, eco-efficiency and environmental regulation in the research perspective, and analysed the mechanism of action between the three, expanding the depth of research on the digital economy and environmental regulation in the field of tourism. Meanwhile, based on the development needs of policy specialisation and industrial refinement, this study has strong practical significance when conducted at the city level.

Given the importance of chatbots in customer service in tourism, this paper aims to understand the drivers that predispose regular consumers of restaurant recommendation chatbots to continue using them.

A total of 386 regular consumers of a chatbot via WhatsApp restaurant recommender responded to an online questionnaire (inspired by scales found in the literature on technology adoption). Structural equation modeling was used to test the hypotheses.

Significant predictors of intention to continue using these chatbots included “effort expectancy (EE),” “hedonic motivation (HM),” “price value (PV)” and “habit (HT).” Specifically, HT still has a long way to go in terms of its performance, and it will be possible to work on it. Furthermore, two variables, EE and HM, act as a bottleneck when it comes to explaining this recurrent usage intention. Factors such as “performance expectancy (PE),” “facilitating conditions (FC)” and “social influence (SI)” did not influence “behavioral intention (BI).” Likewise, the moderating variables, age and gender, are not significant. Finally, the predictive capability of the model is demonstrated. The study findings will enable the development of effective strategies to foster consumer loyalty to this new technology in the restaurant industry.

This study contributes, building on the suitability of the unified theory of acceptance and use of technology 2 model, to explain users’ intention to continue using chatbot tourism services in the context of an information search for an unplanned and varied purchase decision, namely, restaurant recommendation services. To the best of the authors’ knowledge, this is the first analysis of tourist’s intention to reuse a real and fully functional chatbot via mobile instant messaging.

鉴于聊天机器人在旅游客户服务中的重要性, 本研究旨在了解驱动消费者持续使用WhatsApp餐厅推荐聊天机器人的因素。

共收集386名WhatsApp餐厅推荐聊天机器人的常规用户在线问卷数据（问卷设计参考技术采纳相关文献中的量表）。研究采用结构方程模型（SEM）验证假设。

影响用户持续使用意图的显著预测因素包括“努力期望（EE）”、“享乐动机（HM）”、“价格价值（PV）”和“习惯（HT）”。其中, “习惯（HT）”表现仍有提升空间, 而“努力期望（EE）”和“享乐动机（HM）”是解释持续使用意图的瓶颈因素。此外, “绩效期望（PE）”、“促进条件（FC）”和“社会影响（SI）”对“行为意图（BI）”无显著影响。性别和年龄等调节变量同样不显著。研究结果验证了模型的预测能力, 能够为餐厅行业制定有效策略以增强消费者对这一新技术的忠诚度提供指导。

本研究基于UTAUT2模型, 首次分析了消费者在餐厅推荐服务中持续使用移动即时通讯（MIM）聊天机器人的意图, 为探索非计划性和多样化购买决策背景下的信息搜索服务提供了新见解。

This paper aims to focus on the research progress of intelligent self-healing anti-corrosion coatings in the aviation field in the past few years. The paper provides certain literature review supports and development direction suggestions for future research on intelligent self-healing coatings in aviation.

This mini-review uses a systematic literature review process to provide a comprehensive and up-to-date review of intelligent self-healing anti-corrosion coatings that have been researched and applied in the field of aviation in recent years. In total, 64 articles published in journals in this field in the last few years were analysed in this paper.

The authors conclude that the incorporation of multiple external stimulus-response mechanisms makes the coatings smarter in addition to their original self-healing corrosion protection function. In the future, further research is still needed in the research and development of new coating materials, the synergistic release of multiple self-healing mechanisms, coating preparation technology and corrosion monitoring technology.

To the best of the authors’ knowledge, this is one of the few systematic literature reviews on intelligent self-healing anti-corrosion coatings in aviation. The authors provide a comprehensive overview of the topical issues of such coatings and present their views and opinions by discussing the opportunities and challenges that self-healing coatings will face in future development.

This study aims to explore the factors affecting sustainable workplace remodeling in Saudi Arabia.

A literature review was conducted, resulting in 30 factors grouped into five categories. A questionnaire was designed and tested, and stakeholders including architects/engineers (A/Es), contractors and facilities managers were surveyed to evaluate the importance of these factors. The responses were analyzed to determine the relative importance index (RII), importance rating (IR) and ranking of each factor.

All 30 factors were evaluated as “Extremely Important” and “Very Important” in the collective assessment conducted by the three stakeholder categories. The findings revealed that the key factors, unanimously highlighted by all groups, include “The highest-ranking factors Constrained budget for the project”, “Availability of an experienced contractor in the remodeling field”, “Compliance with safety codes and standards”, “Underestimation of project complexity” and “Availability of specifications”.

The study presents an approach for assessing stakeholder consensus, using the RII and Spearman’s rank correlation coefficient to analyze the results. This approach aims to improve understanding of how various stakeholders influence and perceive sustainability in construction projects.

The study offers guidance for various stakeholders, to integrate sustainable practices into their operations. It emphasizes communication, implementing cost-efficient designs and ensuring compliance with sustainability standards to enhance the longevity and efficiency of remodeled workplaces.

The study adds to the current knowledge base by providing insights into sustainable practices of workplace remodeling specific to the context of Saudi Arabia.

This study aims to explore the relationship between the innovation capacity and performance of manufacturing firms in the Ecuadorian pharmaceutical and chemical sectors using strategic foresight analysis.

From an extensive literature review, the fundamental variables related to manufacturing firms’ innovation for better performance were identified. Six hypotheses and actions were proposed related to financing, economic resources, capacities and research and development. Several scenarios were tested through foresight methodology to determine the more appropriate to be implemented by manufacturing firms over the next five years.

Scenario 01, where all the hypotheses resulted positive, has a 15.3% probability of occurrence. The results offer a relevant understanding of the behavior of the variables proposed as strategic actions for the sector.

To the best of the authors’ knowledge, foresight methodology is applied for the first time to analyze the manufacturing sector in Ecuador. The authors propose a plan of action from the strategic scenario identified in this study, supporting the development of the industrial sectors under study.