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There is a void in systematically assessing obstructive factors as antecedents in the technology acceptance model (TAM). Three obstructive factors are investigated, namely, discomfort, insecurity and risk. Besides, this study aims to propose the moderator – personal capability (PC) and tested its effects on the relationship between perceived usefulness (PU) and perceived ease of use (PEU) and behavioral intention (BI).

Quantitative data analysis was used to verify the adapted model by using Smart PLS4. The data from 327 valid respondents are analyzed.

Discomfort is a significant antecedent impacting both PU and PEU. Insecurity and risk are inhibitors of PEU and PU, respectively. This study contributes to the scarce literature on the moderating effects of PC that moderates the influence between PU and BI positively.

This study contributes a new TAM extension by incorporating obstructive factors and exploring the moderating role of personal capability about AR Glasses. This research also enables innovation companies to enhance the design of their products and services via users’ feedback.

系统评估阻碍因素作为技术接受模型（TAM）的先驱方面存在一定的空白。本研究调查了三个阻碍因素, 即不适感, 不安全感和风险。此外, 本研究提出了调节变量 - 个人能力（PC）, 并测试其对感知有用性(PU); 感知易用性（PEU）和行为意图（BI）之间关系的影响。

使用量化数据分析验证了通过Smart PLS4使用的调整模型。对327名有效受访者的数据进行了分析。

不适感是影响PU和PEU的显著先驱因素。不安全感和风险分别是PEU和PU的抑制因素。本研究在稀缺文献中贡献了个人能力的调节效应, 积极调节PU和BI之间影响的研究。

本研究通过纳入阻碍因素并探索个人能力在AR眼镜方面的调节作用, 为TAM提供了一种新的拓展。此外, 该研究还使创新公司能够通过用户的反馈来增强其产品和服务的设计。

As a distinctive form of communist heritage tourism, the ideology and government-led form of red tourism warrants an in-depth examination of how tourists consume and perceive it. This study aims to reveal tourists’ perception of red tourism through the lens of destination image.

This study collected 9,819 user-generated photographs within four types of red tourism destinations (RTDs) and used a computer visual and semiotic analysis approach to conduct photograph-based cognitive and affective attributes extraction. Network analysis further visualized the co-relations between cognitive images and affective images. ANOVA analysis compared the differences of the four types of destination images.

Ten dimensions of cognitive image and eight categories of affective image of red tourism were identified. It found that monuments, statues, memorial symbols were the distinctive cognitive features, and admiration was the most dominant emotion. Heterogeneity of destination images was identified among the four types of RTDs.

To the best of the authors’ knowledge, the study is one of the first to explore tourists’ consumption of red tourism through the lens of destination image, which reveals the inconsistencies between the officially projected images and tourists’ perceived images of red tourism. Using Plutchik’s model, it validates a series of positive and negative emotions contributing to the affective images of red tourism, which expands the findings of emotions within the extant red tourism research. Through combined applications of computer visual and semiotic analysis, ANOVA, network analysis and model visualization, the study provides an important methodological triangulation for photograph-based destination image studies.

红色旅游作为共产主义旅游的独特形式, 游客如何感知这种国家意识形态植入与政府主导型旅游值得深入研究。本研究旨在从目的地意象视角揭示游客红色旅游感知。

本研究收集四种类型的红色旅游地9819张用户生成照片, 利用计算机视觉-情感析法对照片进行认知和情感元素提取。复杂网络分析揭示了认知意象与情感意象之间的关联。方差分析比较了四种红色旅游地意象的差异。

本研究确定了红色旅游认知意象的十个维度和情感意象的八个类别。研究发现, 纪念碑、雕像、纪念符号是其独特的认知意象元素, 钦佩是其最主要的情感，四种类型红色旅游地意象存在差异性。

本文是同类研究中首次从目的地意象视角探索游客对红色旅游地感知, 揭示了红色旅游官方投射意象与游客感知意象之间的差异。利用Plutchik情感之轮模型, 验证了一系列积极和消极情绪构成红色旅游地情感意象, 拓展了红色旅游的情感发现。综合运用计算机视觉-情感分析、方差分析、网络分析和模型可视化等方法, 为基于照片的旅游目的地意象研究提供了一个重要方法。

Como forma distintiva del turismo del patrimonio comunista, la ideología y la forma gubernamental del turismo rojo justifican un examen en profundidad de cómo lo consumen y perciben los turistas. Este estudio pretende revelar la percepción que tienen los turistas del turismo rojo desde la perspectiva de la imagen del destino.

Este estudio recopiló 9.819 fotografías generadas por los usuarios dentro de cuatro tipos de destinos de turismo rojo, y utilizó un enfoque de análisis visual y semiótico por ordenador para llevar a cabo la extracción de atributos cognitivos y afectivos basados en fotografías. El análisis de redes visualizó además las correlaciones entre las imágenes cognitivas y las imágenes afectivas. El análisis ANOVA comparó las diferencias de los cuatro tipos de imágenes de destino.

Se identificaron diez dimensiones de imagen cognitiva y ocho categorías de imagen afectiva del turismo rojo. Se descubrió que los monumentos, las estatuas y los símbolos conmemorativos eran los rasgos cognitivos distintivos, y la admiración la emoción más dominante. Se identificó una heterogeneidad de imágenes de destino entre los cuatro tipos de destinos de turismo rojo.

El estudio es uno de los primeros en explorar el consumo de turismo rojo por parte de los turistas a través de la lente de la imagen del destino, lo que revela las incoherencias entre las imágenes proyectadas oficialmente y las imágenes percibidas por los turistas del turismo rojo. Utilizando el modelo de Plutchik, valida una serie de emociones positivas y negativas que contribuyen a las imágenes afectivas del turismo rojo, lo que amplía los hallazgos sobre las emociones dentro de la investigación existente sobre el turismo rojo. Mediante aplicaciones combinadas de análisis visual y semiótico por ordenador, ANOVA, análisis de redes y visualización de modelos, el estudio proporciona una importante triangulación metodológica para los estudios de la imagen del destino basados en fotografías.

The hospitality industry has been debating the merits of sustainable tourism for some time, but more and more people are becoming interested in regenerative tourism. The concept of regenerative tourism goes beyond that of sustainable tourism in that it seeks to restore and regenerate both the natural environment and the communities in which it operates. The Chinese hospitality industry has been expanding at a rapid rate and has the potential to adopt practices that are associated with regenerative tourism. This chapter investigated the hospitality industry of China and the shift from sustainable tourism to regenerative tourism. Consumer expectations, government programmes, and AI are driving this shift from sustainable to regenerative tourism. Despite the challenges of transitioning tourism from sustainable to regenerative, many Chinese initiatives and programmes are helping the environment and communities.