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Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

The paper aims to identify the critical constraints that impede heritage building (HB) facility managers from discharging their duties effectively and develop an index model to guide HB maintenance management (HBMM) practitioners to the critical constraints.

A literature review was conducted to identify HBMM constraints. Facilty management practitioners assessed the constraints' significance through an online survey. The factor analysis was used to shortlist and group the constraints, and the constraint clusters were analyzed by the fuzzy synthetic evaluation technique. A significant index cluster to determine HBMM constraints criticality was generated using the linear additive model.

Embracing a total of 16 HBMM constraints, the three clusters identified are: (1) managerial and inadequacy constraints, (2) pressure and bureaucracy constraints and (3) HB peculiarities constraints. Based on the generated significant index, the HB peculiarities cluster was identified as the most significant.

The study was conducted in a particular jurisdiction, limiting the generalizability of the result. Future research should address this limitation by covering more jurisdictions.

The significant index model (SIM) developed enables HBMM practitioners to objectively assess the criticality of HB constraints and facilitates them to effectively strategize and allocate resources for HBMM.

The SIM, which transforms subjective judgment into the objective assessment of the HBMM constraints' criticality, can assist practitioners, policymakers and other HBMM stakeholders in implementing strategies for the sustainability of HBs.

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.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

The primary objective of this study is to ascertain how Metaverse technologies, primarily virtual reality (VR) and augmented reality (AR), redefine tourism experiences. It aims to delve into the capabilities of these technologies in eliminating conventional physical and economic barriers and enabling virtual exploration of destinations. Additionally, this study seeks to understand how tourism enterprises can leverage Metaverse technologies for operational efficiency and enhance customer satisfaction.

This research uses a two-pronged methodological approach, incorporating bibliometric analysis techniques and a systematic literature review. Bibliometric analysis provides a quantitative assessment of existing literature, identifying key trends, authors and publications in the domain of Metaverse tourism. Concurrently, this systematic literature review qualitatively evaluates the content and context of these studies, focussing on the applications of VR and AR in tourism as well as the managerial and consumer implications therein.

The findings reveal a significant uptick in literature production in the Metaverse tourism domain post-2018, indicating a burgeoning interest in this field. Metaverse facilitates a unique blend of engaging and personalised tourism experiences from the comfort of one’s home. Moreover, tourism enterprises stand to gain from streamlined booking processes and to improve operational efficiency, which in turn augments customer satisfaction.

This research is confined to the analysis of the existing literature and does not involve primary data collection, which could limit the comprehensiveness of the insights. Moreover, the nascent stage of Metaverse technology in tourism presents a challenge in deriving conclusive implications. This study underscores the need for further empirical research to validate the theoretical frameworks discussed and explore the long-term implications of Metaverse technologies in the tourism sector.

Metaverse opens avenues for tourism enterprises to enhance their customer engagement and operational efficiency. It offers a novel platform for immersive and personalised travel experiences, thereby potentially increasing the market reach and customer satisfaction. The streamlined processes facilitated by Metaverse could also lead to cost reduction and increased profitability for tourism enterprises.

The integration of Metaverse technologies could democratise travel experiences, allowing individuals who might be economically or physically constrained to virtually explore global destinations. However, it also raises concerns regarding the digital divide and potential loss of authentic human interactions and cultural exchanges that traditionally characterise tourism.

This study is among the pioneering efforts to quantitatively and qualitatively analyse the burgeoning field of metaverse tourism. By amalgamating bibliometric analysis with a systematic literature review, it unveils the potential of Metaverse technologies in transcending conventional tourism paradigms, presenting a comprehensive understanding of the practical, managerial and consumer-centric implications therein.

本研究采用了双管齐下的方法, 既有文献计量分析技术, 又有系统的文献综述。文献计量分析对现有文献进行定量评估, 确定元旅游领域的主要趋势、作者和出版物。同时, 系统性文献综述对这些作品的内容和背景进行了定性评估, 重点关注虚拟现实（VR）和增强现实（AR）在旅游业中的应用以及对管理和消费者的影响。

本研究的主要目的是确定元宇宙技术（主要是 VR 和 AR）如何重新定义旅游体验。研究旨在深入探讨这些技术在消除传统物理和经济障碍以及实现目的地虚拟探索方面的能力。此外, 本研究还试图了解旅游企业如何利用元数据技术提高运营效率和客户满意度。

研究结果表明, 2018 年后, Metaverse 旅游领域的文献产量大幅上升, 表明人们对这一领域的兴趣日益浓厚。Metaverse 可以让人们在家中就能享受到独特的个性化旅游体验。此外, 旅游企业可从简化预订流程和提高运营效率中获益, 进而提高客户满意度。

本研究仅限于对现有文献的分析, 不涉及原始数据收集, 这可能会限制研究见解的全面性。此外, Metaverse 技术在旅游业中的应用尚处于起步阶段, 这对得出结论性影响提出了挑战。本研究强调有必要开展进一步的实证研究, 以验证所讨论的理论框架, 并探索元数据技术在旅游业中的长期影响。

Metaverse 为旅游企业提高客户参与度和运营效率开辟了道路。它为身临其境的个性化旅游体验提供了一个新颖的平台, 从而有可能提高市场覆盖率和客户满意度。Metaverse 推动的简化流程还可降低旅游企业的成本, 提高其盈利能力。

Metaverse 技术的整合可以使旅游体验平民化, 让经济或身体条件有限的人也能以虚拟方式探索全球旅游目的地。然而, 它也引发了人们对数字鸿沟的担忧, 以及对传统旅游业所特有的真实人际互动和文化交流的潜在损失的担忧。

本研究是定量和定性分析新兴的 Metaverse 旅游领域的开创性研究之一。通过将文献计量学分析与系统的文献综述相结合, 本研究揭示了元数据技术在超越传统旅游范式方面的潜力, 并对其中的实用、管理和以消费者为中心的含义提出了全面的理解。

Esta investigación utiliza un enfoque metodológico doble, que incorpora técnicas de análisis bibliométrico y una revisión sistemática de la literatura. El análisis bibliométrico proporciona una evaluación cuantitativa de la literatura existente, identificando tendencias, autores y publicaciones clave en el ámbito del turismo en el Metaverso. Al mismo tiempo, la revisión sistemática de la literatura evalúa cualitativamente el contenido y el contexto de estos trabajos, centrándose en las aplicaciones de la Realidad Virtual (RV) y la Realidad Aumentada (RA) en el turismo, así como en sus implicaciones para la gestión y el consumidor.

El objetivo principal de este estudio es determinar cómo las tecnologías asociadas al Metaverso, principalmente la RV y la RA, redefinen las experiencias turísticas. Pretende profundizar en las capacidades de estas tecnologías para eliminar las barreras físicas y económicas convencionales y permitir la exploración virtual de los destinos. Además, este estudio pretende entender cómo las empresas turísticas pueden aprovechar las tecnologías relacionadas con el Metaverso para mejorar la eficiencia operativa y la satisfacción del cliente.

Los resultados revelan un aumento significativo en la producción de literatura en el ámbito del turismo en el Metaverso desde el año 2018, lo que indica un creciente interés en este campo. El Metaverso facilita una combinación única de experiencias turísticas atractivas y personalizadas desde la comodidad del hogar. Además, las empresas turísticas se benefician de la agilización de los procesos de reserva y la mejora de la eficiencia operativa, lo que a su vez aumenta la satisfacción del cliente.

La investigación se limita al análisis de la bibliografía existente y no incluye la recopilación de datos primarios, lo que podría limitar la exhaustividad de las conclusiones. Además, el estado incipiente de la tecnología relacionada con el Metaverso en el turismo supone un reto a la hora de extraer implicaciones concluyentes. Este estudio subraya la necesidad de realizar más estudios empíricos para validar los marcos teóricos discutidos y explorar las implicaciones a largo plazo de estas tecnologías en el sector turístico.

El Metaverso abre vías para que las empresas turísticas mejoren su compromiso con el cliente y su eficiencia operativa. Ofrece una plataforma novedosa para experiencias de viaje inmersivas y personalizadas, con lo que aumenta potencialmente el alcance del mercado y la satisfacción del cliente. La racionalización de los procesos facilitados por el Metaverso también podría conducir a la reducción de costes y al aumento de la rentabilidad de las empresas turísticas.

La integración de las tecnologías relacionadas con el Metaverso podría democratizar las experiencias de viaje, permitiendo a personas con limitaciones económicas o físicas explorar virtualmente destinos globales. Sin embargo, también suscita preocupación en relación con la brecha digital y la posible pérdida de interacciones humanas auténticas e intercambios culturales que tradicionalmente caracterizan al turismo.

Este estudio es uno de los pioneros en analizar cuantitativa y cualitativamente el creciente campo del turismo en el Metaverso. Amalgamando el análisis bibliométrico con una revisión sistemática de la literatura, desvela el potencial de las tecnologías relacionadas con el Metaverso para sacar a la luz los paradigmas turísticos convencionales, presentando una comprensión exhaustiva de sus implicaciones prácticas, de gestión y centradas en el consumidor.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

This study aims to investigate how university students experience a skill transformation process aligned with the sustainable development goals (SDGs). This transformation occurs through their participation in a service-learning programme alongside an international volunteering project. The theoretical framework for understanding this skill transformation process is based on the “rite of passage”.

Qualitative methodology is adopted by conducting 23 online surveys with volunteers (virtual and onsite) and five with coordinators across the rite of passage phases. Volunteering was carried out in five Mayan indigenous communities in Mexico as part of an international cooperation project with the goals of supporting community-based tourism development and strengthening volunteers’ skills in accordance with the SDGs.

Results show that international volunteering programmes for university students significantly enhance their interpersonal and professional skills, demonstrating strong potential for implementing the SDGs. These programmes provide learning and education opportunities for both volunteers and local communities. Volunteers gain a broader perspective on gender equality and cultural barriers. Additionally, volunteering supports sustainable tourism, economic worth and collaboration among institutions. Both volunteers’ personal characteristics (educational level and sociocultural context), as well as their sociocultural context, influenced the perception of the skill transformation process and learning about the SDGs. Finally, a new educational university programme in volunteering aligned with SDGs is proposed.

This research examines the practical ramifications of incorporating volunteer programmes into university courses. Universities must include these initiatives in their educational systems as a means of enhancing student learning.

A new educational university programme in volunteering aligned with SDGs is proposed. This study suggests a shift in university mindset, as well as increased funding for training and adherence to the SDGs.

This study pioneers the rite of passage framework in an international volunteer tourism project facilitated by universities, emphasizing volunteering as a valuable tool for SDG implementation, considering the interrelationships between objectives.

Artificial intelligence (AI) agents have been increasingly applied in the tourism and hospitality industry. However, AI service failure is inevitable. Thus, AI service recovery merits empirical investigation. This study aims to explore how AI empathic accuracy affects customers’ satisfaction in the context of AI service recovery.

A moderated mediation model was presented to describe the effect of empathic accuracy on customer satisfaction via four scenario-based experiments.

The results reveal the positive impact of AI empathic accuracy on customer satisfaction and the mediating effects of perceived agency and perceived experience. Moreover, anthropomorphism moderates the empathic accuracy effect.

This paper expanded AI service studies by exploring the significance of empathic accuracy in customer recovery satisfaction. The results provide a novel theoretical viewpoint on retaining customers following AI service failure.

人工智能（AI）设备已越来越多地应用于旅游业和酒店业。然而, AI服务失败是不可避免的。因此, AI服务补救值得进一步实证研究。本研究探讨了AI共情准确性如何影响顾客对AI服务补救的满意度。

通过四个基于场景的实验, 提出了一个有调节的中介模型来描述共情准确性对顾客满意度的影响。

结果揭示了AI共情准确性对顾客满意度有积极影响, 感知能动性和感知感受性具有中介效应。此外, 拟人化调节了共情准确性的效应。

本文通过探讨共情准确性在顾客服务补救满意度中的作用, 拓展了AI服务研究。研究结果为AI服务失败后如何留住顾客提供了新的理论视角。

Las agentes de inteligencia artificial (IA) se aplican cada vez más en el sector del turismo y la hostelería. Sin embargo, los fallos de los servicios de IA son inevitables. Por lo tanto, la recuperación de servicios de IA merece una investigación empírica. Esta investigación explora cómo la precisión empática de la IA afecta a la satisfacción de los clientes con la recuperación del servicio de IA.

Se presentó un modelo de mediación moderado para describir el efecto de la precisión empática en la satisfacción del cliente mediante cuatro experimentos basados en escenarios.

Los resultados revelan el impacto positivo de la precisión empática de la IA en la satisfacción del cliente y los efectos mediadores de la agencia percibida y la experiencia percibida. Además, el antropomorfismo modera el efecto de la precisión empática.

Este artículo amplía los estudios sobre los servicios de IA al investigar el papel de la precisión empática en la satisfacción del cliente. Los resultados aportan un punto de vista teórico novedoso sobre la retención de clientes tras el fallo de un servicio de IA.

The ascendancy of modular offsite construction (MOSC) over traditional construction methods is well known. Despite the known potential of this construction approach, its adoption is minimal in New Zealand construction industry. This article investigates the potential benefits of using MOSC for delivery of high-rise buildings in New Zealand, underlying factors responsible for its low uptake and the measures that can facilitate its improved uptake.

This study utilised a mixed research approach. An empirical questionnaire survey was carried out with New Zealand construction industry professionals with expertise in MOSC. Factor analysis of survey data was carried out using SPSS software. Semi-structured interviews were carried out with subject matter experts to get further insights and expand the survey findings. Interview data were analysed using thematic analysis.

Study identified benefits of MOSC, thus establishing potential of its uptake for high-rise building construction. Constraining factors were investigated, most pronounced being low level of skills in construction industry to design, manufacture and integrate supply chain of MOSC, high initial investment, high cost of importing modules and negative perception about offsite manufactured buildings. This study also highlighted the enablers to improve uptake of MOSC. These enablers included; loan and mortgage policies to suit MOSC paradigm, building regulations to support OSC industry, increased support from the government and awareness and acceptance of standardised building designs among the clients.

Originality of this paper harps from little to no research carried out to investigate use of MOSC for high-rise buildings in New Zealand context.

This paper aims to establish the causal relationship between destination image and tourist motivation and engagement.

A causal model with seven hypotheses was tested into a sample of 438 domestic tourists in Acapulco (Mexico).

Cognitive destination image is the main antecedent of tourist engagement, exerting an important direct and indirect effect through push and pull motivations. Affective image also exerts a direct effect on tourist engagement and an indirect effect through push and pull motivations. Only pull motivations exert an influence on tourist engagement.

The study is based on domestic tourists at a sun and beach destination.

Destination management organisations should invest in the care, improvement and promotion of tourism resources. Online and offline communication campaigns should be based on tourism resources and experiences.

This paper fulfils three research gaps: destination image is associated with tourist engagement; tourist motivations affect tourist engagement; and destination image is associated with tourist motivations.