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Wearable health devices (WHDs) have demonstrated significant potential in assisting elderly adults with proactive health management by utilizing sensors to record and monitor various aspects of their health, including physical activity, heart rate, etc. However, limited research has systematically explored older adults’ continued usage intention toward WHD. By utilizing the extended unified theory of acceptance and use of technology (UTAUT2), this paper aims to probe the precursors of elderly adults’ continuance intention to use WHD from an enabler–inhibitor perspective.

The research model was developed based on UTAUT2 and examined utilizing the partial least squares technique (PLS). The research data were collected through in-person meetings with older people (n = 272) in four cities in China.

Results reveal that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic values and perceived complexity are the positive predictors of elderly adults’ continuance intention to use WHDs. Technology-related anxiety and usage cost negatively influence the formation of older people’s continuance intention.

This work is an original empirical investigation that draws on several theories as guiding frameworks. It adds to the existing literature on the usage of wearable technologies and offers insights into how the elderly’s intentions to continue using WHDs can be developed. This study broadens the scope of the UTAUT2 application and presents an alternative theoretical framework that can be utilized in future research on the usage behavior of wearable devices by individuals.

This study aims to examine the role of big data analytics (BDA) powered by artificial intelligence (AI) in improving sustainable performance (SP) through green supply chain collaboration (GSCC), sustainable manufacturing (SM) and environmental process integration (EPI).

Data was collected from 249 supply chain professionals working at various manufacturing firms, and hypotheses were tested through a quantitative method using PLS-SEM with the help of SmartPLS version 4 to validate the measurement model.

This study identified that BDA-AI significantly and positively affects GSCC, SM and EPI. Similarly, the results showed that GSCC significantly and positively affects SP. At the same time, SM and EPI have an insignificant effect on SP. The GSCC found a significant relationship between BDA-AI and SP for mediation. However, SM and environmental performance integration did not mediate the relationship between BDA and AI and SP.

This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan. Therefore, this research could be beneficial for researchers, manufacturers and policymakers to attain sustainable goals by implementing the BDA-AI in the supply chain.

The social media industry has entered a new stage with intensifying competition and heightened uncertainty about future directions. The purpose of this paper is to provide analyses of the current challenges and to identify industry-wide trends that may offer a roadmap for the future.

Drawing on publicly available key performance metrics, company reports and press reports, this paper offers critical analyses of the challenges facing the major social media platforms and new trends in the social media industry.

This study identified five major trends in the current social media industry: 1) content is king, and that content is moving to visual; 2) artificial intelligence is key to competitive advantage; 3) network effects still matter, but business model innovation can overcome that barrier; 4) the need to broaden revenue sources; and 5) the strive for the everything app. In this changing environment, social media companies need to adapt and innovate their business models proactively to stay ahead.

This paper not only sheds light on the current challenges of individual social media platforms but also identifies industry-wide trends that may apply across all platforms. Taken together, these insights paint a comprehensive picture of the current industry landscape, as well as offer clues about its future directions.

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.

In recent years, there has been growing interest in the use of stainless steel (SS) in reinforced concrete (RC) structures due to its distinctive corrosion resistance and excellent mechanical properties. To ensure effective synergy between SS and concrete, it is necessary to develop a time-saving approach to accurately determine the ultimate bond strength τ_u between the two materials in RC structures.

Three robust machine learning (ML) models, including support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost), are employed to predict τ_u between ribbed SS and concrete. Model hyperparameters are fine-tuned using Bayesian optimization (BO) with 10-fold cross-validation. The interpretable techniques including partial dependence plots (PDPs) and Shapley additive explanation (SHAP) are also utilized to figure out the relationship between input features and output for the best model.

Among the three ML models, BO-XGBoost exhibits the strongest generalization and highest accuracy in estimating τ_u. According to SHAP value-based feature importance, compressive strength of concrete f_c emerges as the most prominent feature, followed by concrete cover thickness c, while the embedment length to diameter ratio l/d, and the diameter d for SS are deemed less important features. Properly increasing c and f_c can enhance τ_u between ribbed SS and concrete.

An online graphical user interface (GUI) has been developed based on BO-XGBoost to estimate τ_u. This tool can be utilized in structural design of RC structures with ribbed SS as reinforcement.

The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.

The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.

The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.

The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.

Developments regarding the use of artificial intelligence (AI) in transportation systems, one of the important stakeholders of tourism, are remarkable. However, no review thus far has provided a comprehensive overview of research on AI in transportation systems.

To fill this gap, this study uses the VOSviewer software to present a bibliometric review of the current scientific literature in the field of AI-related tourism research. The theme of AI in transportation systems was explored in the Web of Science database.

The original search yielded 642 documents, which were then filtered by parameters. For publications related to AI in transportation systems, the most cited documents, leading authors, productive countries, co-occurrence analysis of keywords and bibliographic matching of documents were examined. This report shows that there has been a recent increase in research on AI in transport systems. However, there is only one study on tourism. The country that contributed the most is China with 298 studies. The most used keyword in the documents was intelligent transportation system.

The bibliometric analysis of the existing work provided a valuable and seminal reference for researchers and practitioners in AI-related in transportation system.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Grounded in dynamic capabilities theory, this study aims to examine how dynamic capabilities and a transactive memory system (TMS) can build the resilience of service organizations and improve their financial performance. Limited studies examine the link between a TMS and organizational resilience.

The authors test a theoretical model on a sample of 350 UK service firms that were impacted by the COVID-19 pandemic and analyze the data using partial least square structural equation modeling.

Results highlight the positive effects of a TMS and dynamic capabilities on organizational resilience. Only a TMS and organizational resilience have direct positive effects on financial performance.

To the best of the authors’ knowledge, this is the first study to ascertain the influence of a TMS on organizational resilience in service firms following adversity.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.