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This study aims to chart the impact of technological advancements on tourism from the post–Second World War era to the present and forecast their influence until 2050. It assesses how technologies have reshaped travel experiences and operations, with a focus on upcoming innovations such as the metaverse, Web 3.0 and AI, and their implications for sustainable and ethical tourism.

This study uses a hybrid approach, combining historical analysis and future projections. It analyzes archival data, industry reports and academic literature.

This study identifies crucial technological milestones that have significantly impacted tourism, including the rise of commercial aviation, the internet and AI. Future trends suggest emerging technologies will further transform the sector. Challenges in sustainability, ethics and inclusivity are highlighted as critical considerations for future development.

This paper offers a unique longitudinal perspective on technology’s influence on tourism, bridging past trends with future projections.

本研究采取混合方法, 融合历史分析与未来趋势预测。研究分析了丰富的档案数据、行业报告以及学术文献。

旨在勾勒从二战后至今技术进步对旅游业的影响, 并展望至2050年的潜在影响。本研究着重评估技术如何重塑旅游体验和运作, 特别是对元宇宙、网络3.0和人工智能等即将到来的创新技术及其对可持续和伦理旅游的意义。

识别了旅游业中关键的技术里程碑, 包括商业航空、互联网和人工智能的崛起。研究指出, 未来趋势显示新兴技术将继续深刻改变旅游业。同时强调, 可持续性、伦理和包容性是未来发展中不可忽视的关键要素。

本文从独特的纵向视角出发, 深入探讨了技术对旅游业的历史与未来影响, 将过去发展趋势与未来展望紧密结合。

Este estudio emplea un enfoque híbrido que combina el análisis histórico y las proyecciones de futuro. Analiza datos de archivo, informes del sector y bibliografía académica.

La investigación pretende trazar el impacto de los avances tecnológicos en el turismo desde la era posterior a la Segunda Guerra Mundial hasta la actualidad y prever su influencia hasta 2050. Evalúa cómo las tecnologías han reconfigurado las experiencias y las operaciones de viaje, centrándose en las próximas innovaciones como el Metaverso, la Web 3.0 y la IA, y sus implicaciones para un turismo sostenible y ético.

El estudio identifica hitos tecnológicos cruciales que han tenido un impacto significativo en el turismo, como el auge de la aviación comercial, Internet y la IA. Las tendencias futuras sugieren que las tecnologías emergentes transformarán aún más el sector. Los retos en sostenibilidad, ética e inclusividad se destacan como consideraciones críticas para el desarrollo futuro.

Este artículo ofrece una perspectiva longitudinal única sobre la influencia de la tecnología en el turismo, tendiendo un puente entre las tendencias pasadas y las proyecciones futuras.

As technology increasingly integrates into the restaurant industry, avatar servers present a promising avenue for promoting healthier dining habits. Grounded in the halo effect theory and social comparison theory, this study aims to delve into the influence of avatars' appearance, humor and persuasion on healthier choices and customer satisfaction.

This paper comprises three experimental studies. Study 1 manipulates avatar appearance (supermodel-looking vs normal-looking) to examine its effects on perceived attractiveness, warmth and relatability. These factors influence customer satisfaction and healthy food choices through the psychological mechanisms of social comparison and aspirational appeal. Studies 2 and 3 further refine this theoretical model by assessing the interplay of appearance with humor (presence vs absence) and persuasion (health-oriented vs beauty-oriented), respectively.

Results suggest that avatars resembling supermodels evoke stronger aspirational appeal and positive social comparison due to their attractiveness, thus bolstering healthier choices and customer satisfaction. Moreover, humor moderates the relationship between appearance and attractiveness, while persuasion moderates the effects of appearance on social comparison and aspirational appeal.

This research bridges the halo effect theory and social comparison theory, offering insights enriching the academic discourse on technology’s role in hospitality.

The findings provide actionable insights for managers, tech developers and health advocates.

Despite its significance, avatar design research in the hospitality sector has been overlooked. This study addresses this gap, offering a guideline for crafting attractive and persuasive avatars.

This study aims to investigate the integration of blockchain technology into the food supply chain within the restaurant industry. It focuses on how blockchain can be applied to enhance transparency and trust in tracking food sources, ultimately impacting customer satisfaction.

A service design workshop (Study 1) and three between-subjects experiments (Studies 2–4) were conducted.

Results indicate that blockchain adoption significantly improves traceability and trust in the food supply chain. This improvement in turn enhances customer satisfaction through perceived improvements in food safety, quality and naturalness. This study also notes that the effects of blockchain technology vary depending on the type of restaurant (casual or fine dining) and its location (tourist destinations or residential areas).

The findings offer practical insights for restaurant owners, technology developers and policymakers. Emphasizing the benefits of blockchain adoption, this study guides decision-making regarding technology investments for enhancing customer service and satisfaction in the hospitality sector.

This research contributes novel insights to the field of technology innovation in the hospitality industry. It extends the understanding of signaling theory by exploring how blockchain technology can serve as a tool for signal transmission in restaurant food supply chains.

Using intraday data, the authors explore the forecast ability of one high frequency order flow imbalance measure (OI) based on the volume-synchronized probability of informed trading metric (VPIN) for predicting the realized volatility of the index futures on the China Securities Index 300 (CSI 300).

The authors employ the heterogeneous autoregressive model for realized volatility (HAR-RV) and compare the forecast ability of models with and without the predictive variable, OI.

The empirical results demonstrate that the augmented HAR model incorporating OI (HARX-RV) can generate more precise forecasts, which implies that the order imbalance measure contains substantial information for describing the volatility dynamics.

The study sheds light on the relation between high frequency trading behavior and volatility forecasting in China's index futures market and reveals the underlying market mechanisms of liquidity-induced volatility.

It is of a great significance for the health monitoring of a liquid rocket engine to build an accurate and reliable fault prediction model. The thrust of a liquid rocket engine is an important indicator for its health monitoring. By predicting the changing value of the thrust, it can be judged whether the engine will fail at a certain time. However, the thrust is affected by various factors, and it is difficult to establish an accurate mathematical model. Thus, this study uses a mixture non-parametric regression prediction model to establish the model of the thrust for the health monitoring of a liquid rocket engine.

This study analyzes the characteristics of the least squares support vector regression (LS-SVR) machine . LS-SVR is suitable to model on the small samples and high dimensional data, but the performance of LS-SVR is greatly affected by its key parameters. Thus, this study implements the advanced intelligent algorithm, the real double-chain coding target gradient quantum genetic algorithm (DCQGA), to optimize these parameters, and the regression prediction model LSSVRDCQGA is proposed. Then the proposed model is used to model the thrust of a liquid rocket engine.

The simulation results show that: the average relative error (ARE) on the test samples is 0.37% when using LS-SVR, but it is 0.3186% when using LSSVRDCQGA on the same samples.

The proposed model of LSSVRDCQGA in this study is effective to the fault prediction on the small sample and multidimensional data, and has a certain promotion.

The original contribution of this study is to establish a mixture non-parametric regression prediction model of LSSVRDCQGA and properly resolve the problem of the health monitoring of a liquid rocket engine along with modeling the thrust of the engine by using LSSVRDCQGA.

The purpose of this paper is to prepare a multifunctional nanocomposite that is slow-release and resistant to seawater corrosion and biofouling corrosion and to explore the synergistic effect between the two corrosion inhibitors.

The morphology, structure and release properties of CAP@HNTs, BTA@HNTs and CAP/BTA@HNTs were investigated by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, specific surface area analysis and UV spectrophotometry. The corrosion resistance and antimicrobial properties were investigated by electrochemical measurements and bioinhibition rate tests, and the synergistic effect between the two corrosion inhibitors was explored by X-ray photoelectron spectroscopy.

The CAP/BTA@HNTs are responsive to acidic environments and have significantly improved antibacterial and corrosion resistance compared with CAP@HNTs and BTA@HNTs. CAP and BTA have a positive synergistic effect on anticorrosion and antifouling.

Two types of inhibitors, anticorrosion and antifouling, were loaded into the same nanocontainer to prepare a slow-releasable and multifunctional nanocomposite with higher resistance to seawater corrosion and biocorrosion and to explore the synergistic effect of CAP and BTA on corrosion resistance.

This study aims to using Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and scanning electron microscope/energy dispersive Xray spectrometer to identify different automotive coatings for forensic purpose.

Two four-layered samples in a hit-and-run case were compared layer by layer with three different methods. FTIR spectroscopy was used to primarily identify the organic and inorganic compositions. Raman spectrum and scanning electron microscope/energy dispersive Xray spectrometer (SEM-EDS) were further used to complement the FTIR results.

Two weak and tiny peaks in one layer found between two samples by FTIR, Raman microscope and SEM-EDS verified the result of differences. The study used the three instruments in combination and found it’s effective in sensing coatings, especially in the inorganic additives.

Using these three instruments in combination is more accurate than individually in multilayered coating analysis for forensic purpose.

The three different instruments all present unique information on the composition, and provided similar and mutually verifiable results on the two samples.

With this method, scientists could identify and discriminate important coating evidences with tiny but characteristic differences.

University–industry (UI) collaboration is essential for knowledge and technology exchange between higher education institutions and industries, enabling enterprises to accelerate innovation. However, few studies have investigated the collaborative innovation mechanism through which UI collaboration can enhance the accumulation of firms' intellectual capital (IC) and how this, in turn, affects their innovation-driven development.

Drawing from the knowledge management and collaborative innovation theory, this research proposes a theoretical framework of the inter-organization relationship between enterprises and universities to investigate the influence mechanism of UI collaboration, including academic engagement and commercialization, on corporate performance as well as the mediating role of IC by employing survey that covers 177 UI collaborations.

Empirical results show that human capital and relational capital fully mediate the relationship between academic engagement UI collaboration and corporate economic performance, while human capital partially mediates the relationship between commercialization UI collaboration and corporate economic performance. Additionally, structural capital and relational capital partially mediate the relationship between academic engagement and corporate innovation performance, while structural capital fully mediates the relationship between commercialization and corporate innovation performance.

This study empirically investigates how academic engagement and commercialization impact corporate performance (i.e. innovation dimension or economic dimension). It uncovers this relationship's underlying mechanism by documenting the IC's mediating impact.

The purpose of this study is to investigate which of the two carbon allowance allocation methods (CAAMs), i.e. grandfathered system carbon allowance allocation (GCAA) and baseline system carbon allowance allocation (BCAA), is more beneficial to capital-constrained supply chains under the carbon emission allowance repurchase strategy (CEARS).

Adopting CEARS to ease the capital-constrained supply chains, this study develops two-period game models with manufacturers as leaders and retailers as followers from the perspective of profit and social welfare maximization under two CAAMs (GCAA and BCAA), where the first period produces normal products, and the second period produces low-carbon products.

First, higher carbon-saving can better use CEARS and achieve a higher supply chain profit under the two CAAMs. However, the higher the end-of-period carbon price is, the lower the social welfare is. Second, when carbon-saving is small, GCAA achieves both economic and environmental benefits; BCAA reduces carbon emissions at the expense of economic benefit. Third, the supply chain members gain higher profits and social welfare under GCAA, so the government and supply chain members are more inclined to choose GCAA.

By analyzing the profits and total carbon emissions of capital-constrained supply chains under GCAA and BCAA, this study provides theoretical references for retailers and capital-constrained manufacturers. In addition, by comparing the difference in social welfare under GCAA and BCAA, it provides a basis for the government to choose a reasonable CAAM.

Based on sensory marketing theory and cognitive appraisal theory, this study investigates whether and how the background visual complexity of live-streaming affects consumers' purchase intention and reveals the underlying mechanisms through which background visual complexity influences consumers' purchase decisions.

The experiment was conducted with 180 college students, using eye-tracking technology to explore the impact mechanism of live background visual complexity on consumers' purchase intention, considering three types of background visual complexity (high vs medium vs low) and two levels of need for cognitive closure (high vs low).

Firstly, the background visual complexity of live-streaming positively influences consumers' purchase intention by eliciting positive emotions (pleasure and arousal), and the relationship between consumer emotions and purchase intention is nonlinear. Secondly, need for cognitive closure to significantly moderate the influence of background visual complexity on purchase intention.

The limited sample size makes it difficult to generalize to other consumer groups. Also, the study only focuses on one visual factor, lacking comprehensive analysis from multiple perspectives.

It is recommended that live e-commerce companies optimize the visual design of live-streaming backgrounds and identify consumer traits to match the visual complexity with consumers' level of need for cognitive closure, thereby stimulating positive emotions and facilitating more satisfactory shopping decisions.

This paper addresses an interesting and practical issue related to the effects of live background visual complexity on consumers' purchase intention.