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The consumption of bubble tea is a huge phenomenon in Southeast Asia, and many bubble tea stores have been decorated uniquely in an attempt to attract more consumers. Hence, the purpose of this study is to investigate the influence of servicescape on experiential value and its influence on consumers' responses (i.e. eWOM, revisit intentions and willingness to pay a price premium) based on the Stimulus-Organism-Response framework.

This research project gathered survey data from 289 consumers of bubble tea. The collected data underwent analysis employing a statistical technique known as partial least squares structural equation modelling to examine and understand the relationships between different variables within the dataset, offering insights into consumer behaviour and preferences regarding bubble tea consumption.

The results show that factors such as facility aesthetics, seating comfort and cleanliness positively affect experiential value. However, layout accessibility and signs and symbols do not affect experiential value. In addition, experiential value positively affects eWOM, revisit intentions and willingness to pay a price premium.

The importance of servicescape in physical stores within the restaurant setting has been extensively documented in existing literature. However, there is a notable lack of research addressing the influence of servicescape on consumer responses, particularly within the bubble tea context. This study stands out as one of the few endeavours to examine the impacts of servicescape on consumer responses within the bubble tea industry.

With an increasing array of new technologies and devices emerging as novel approaches to museum narratives, scholars are intrigued by the narrative effect they produce. Within this context, this study aims to conduct a quantitative case study on the influencing mechanisms among technology embodiment, technology attachment, museum narrative effect and perceived museum image based on embodied cognitive theory (ECT).

By introducing the Opium War Museum, Dongguan City, Guangdong Province, China, as an empirical case study, the authors collected data, through questionnaires, from 425 visitors who had experienced virtual reality (VR) technology in the museum. The data was then analyzed by using the maximum likelihood estimate for structural equation modeling.

Technology embodiment and technology attachment have significant positive correlations with the museum narrative effect. Technology attachment provides a partial mediating effect between technology embodiment and the museum narrative effect. The perceived museum image moderates such mediating effects of technology attachment.

This study brings the emerging idea of developing visitor experiences with VR through expanding the applications of ECT in the museum scenario. First, the authors identified the underlying interaction patterns between various factors that influence the museum narrative effect. Second, the authors verified that technology can optimize the museum narrative effect and help the creative transformation of cultural relics and culture. This study also provides practical recommendations to improve the technological service experience and strengthen the “audience-centric” management concept of museums

随着越来越多的新技术和设备作为博物馆新颖的叙事方法出现, 学者们对它们产生的叙事效果感到兴趣。在这一背景下, 我们基于具身认知理论对技术具身感、技术依恋、博物馆叙事效果和感知博物馆形象之间的影响机制进行了定量案例研究。

通过引入中国广东省东莞市鸦片战争博物馆作为实证案例研究, 我们从体验了博物馆VR技术的425名参观者中收集了问卷数据。然后, 我们使用极大似然法估计结构方程模型对数据进行了分析。

(1) 技术具身感和技术依恋与博物馆叙事效果存在显著正相关关系。 (2) 技术依恋在技术具身感与博物馆叙事效果之间起到了部分中介作用。 (3) 感知博物馆形象调节了技术依恋的上述中介效应。

本研究通过扩展具身认知理论在博物馆场景中的应用, 提出了利用VR优化访客体验的新兴理念。首先, 我们识别了影响博物馆叙事效果的各种因素之间的潜在互动模式。其次, 我们验证了技术可以优化博物馆叙事效果, 并有利于文物和文化的创造性转化。本研究还提出了实际建议, 以改善技术服务体验, 并加强博物馆“以观众为中心”的管理理念。

The use of technological innovations to effectively deliver construction projects is gaining significant coverage. This study aims to assess the inhibiting factors to the utilisation of laser scanners for the delivery of construction projects in developing economies using South Africa as the study area.

Adopting a quantitative technique, this study elicited responses from construction professionals using a questionnaire as the instrument for data collection. A four-pronged data analysis method was used, comprising descriptive statistics, Kruskal−Wallis h-test, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

Findings from the study show that lack of awareness and high cost of acquisition are the barriers rated by the study’s respondents the most. Also, findings from the EFA and CFA conducted showed and affirmed the significance of three constructs inhibiting factors to the utilisation of laser scanners for construction project delivery: technical hindrances, financial impediments and institutional challenges.

This study makes practical contributions to the discourse of using innovative technologies for effective construction project delivery by inhibiting factors to the use of laser scanners.

Evidence from the literature shows that no study has assessed the barriers to the utilisation of laser scanning technology for construction projects in the South African construction industry. This study strives to close this gap in the literature.

In the evolving digital work landscape, where cyberloafing has become a notable challenge, this study aims to investigate the mechanisms through which organizations can effectively reduce such behaviors. Specifically, the research explores the role of employee adaptability in mitigating cyberloafing, taking into account the influences of temporal leadership, teamwork attitudes, and competitive work environments.

Utilizing the broaden-and-build theory and the job demands-resources (JD-R) model, we analyzed data from 245 employees through structural equation modeling (SEM) to investigate how various factors influence cyberloafing.

The results indicate that employee adaptability significantly mitigates cyberloafing and serves as a mediating factor between temporal leadership, teamwork attitudes, and the impact of competitive work environments on cyberloafing. Temporal leadership and teamwork attitudes positively correlate with increased adaptability, thereby reducing cyberloafing. Conversely, competitive work environments, while slightly enhancing adaptability, substantially increase cyberloafing.

The study contributes new insights into the dynamics of cyberloafing, emphasizing the critical roles of adaptability, teamwork attitudes, and temporal leadership in reducing such behaviors. It underscores the need for organizations to foster a supportive culture that minimizes competitive pressures and promotes teamwork and leadership strategies conducive to high productivity and minimal cyberloafing. This research offers practical implications for designing workplace strategies aimed at boosting productivity and curbing undesirable online behaviors during work hours.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

The rapid expansion of online education in the 21st century, driven by technological advancements and the COVID-19 pandemic, has highlighted the critical role of massive open online courses (MOOCs) in higher education. This study aims to investigate student satisfaction with the instructional design of MOOCs at a private university in Vietnam.

This mixed-methods research integrates quantitative data from a survey of 225 students with qualitative insights from interviews with 10 students. The study examines key determinants of student satisfaction, including course content, instructional methodologies, assessment systems, engagement in discussion forums and the overall online learning environment.

The findings reveal high levels of student satisfaction with the quality of course materials, the flexibility of the platform and the usability of the interface. However, areas of dissatisfaction include limited interactive engagement, inadequate motivational elements, suboptimal assessment strategies and insufficient staff support. The study underscores the need for comprehensive instructor evaluations, increased student-instructor interactions, improved plagiarism detection mechanisms and timely academic support to enhance the instructional design and educational outcomes of MOOCs.

This study provides a nuanced understanding of student satisfaction with MOOCs, specifically within the context of a private university in Vietnam. By integrating both quantitative and qualitative data, the research offers valuable insights into specific elements that contribute to or detract from learner satisfaction. These findings can inform practical enhancements in MOOC design and delivery, ultimately aiming to improve educational outcomes in online learning environments.

This paper aims to explore the underlying mechanisms and boundary conditions through which equipment manufacturing enterprises can capture market value from digital transformation, with a specific focus on the roles of knowledge search and knowledge recombination.

This study uses a double fixed-effects model to test the hypotheses, using a unique data set of “firm-year” observations from 739 publicly listed equipment manufacturing companies in China, spanning the period from 2018 to 2022.

Digital transformation drives market value creation in equipment manufacturing enterprises through both breakthrough knowledge recombination (BKR) and progressive knowledge recombination (PKR). In addition, the analysis of marginal conditions reveals that diversified knowledge search serves as a substitute for digital transformation in promoting BKR, while also positively moderating the relationship between digital transformation and PKR.

Grounded in the knowledge-based view theoretical framework, this study introduces the novel concepts of BKR and PKR and systematically examines how digital transformation impacts market value in equipment manufacturing enterprises.

Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology, resulting in inadequate target feature information. In real-world mission scenarios, such as military information acquisition or medical image enhancement, the prominence of target feature information is of paramount importance. To address these challenges, this paper introduces a novel infrared-visible light fusion model.

Leveraging the foundational architecture of the traditional DenseFuse model, this paper optimizes the backbone network structure and incorporates a Unique Feature Encoder (UFE) to meticulously extract the distinctive features inherent in the two images. Furthermore, it integrates the Convolutional Block Attention Module (CBAM) and the Squeeze and Excitation Network (SE) to enhance and replace the original spatial and channel attention mechanisms.

Compared to other methods such as IFCNN, NestFuse, DenseFuse, etc., the values of entropy, standard deviation, and mutual information index of the method presented in this paper can reach 6.9985, 82.6652, and 13.6022, respectively, which are significantly improved compared with other methods.

This paper presents a UFEFusion framework that synergizes with the CBAM attention mechanism to markedly augment the extraction of detailed features relative to other methods. Moreover, the framework adeptly extracts and amplifies unique features from disparate images, thereby elevating the overall feature representation capability.