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The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

This paper aims to construct a novel measure of firms’ perceived degree of economic policy uncertainty (EPU) and to examine its impact on imitative innovation.

This paper uses text mining methods to extract valid information for management discussion and analysis from the annual reports of Chinese A-share-listed companies from 2009 to 2021.

The empirical findings demonstrate that firms that perceive higher uncertainty in economic policies promote increased imitative innovation behaviour. In addition, when uncertainty is high, overly optimistic managers can reduce the imitative innovation of the firm to other firms.

The paper uses some of the more recent textual analysis techniques in the field of innovation to measure listed firms' perceptions of external EPU and to examine the impact of uncertainty on imitative innovation. Such research enriches the literature on innovation and fills some gaps.

Advertisements facilitate certain emotions, subsequently influencing purchase intentions. Humor, as an influential way of information expression, is frequently used in ads to elicit emotions. Drawing upon literature on advertisement humor and new product purchase intention and the theory of planned behavior, the study proposes that humor stimulation in advertisements can affect consumers' new product purchase intentions, in which two process mechanisms, namely, emotional arousal and cognitive flexibility, play a mediating effect.

To test the assertions, the authors conduct three experimental studies. The authors' first study assesses the main effect between advertisement humor and purchase intentions. In the second study, the authors show the mediating effects of emotional pleasure, emotional arousal, and cognitive flexibility on the relationship between advertisement humor and purchase intentions. In the first two experiments, the authors study incremental new products. In the third study, the authors study the same mediating relationships for radically new products.

This study's results show consumers that watch humorous ads are more likely to choose new products than those who watch non-humorous ads (Study 1); compared with non-humorous ads, humorous ads can enhance emotional arousal, thus promoting cognitive flexibility and making consumers more inclined to choose new products (Study 2 and Study 3). That said, the authors find that these mediation effects are only partial.

This study's results have important implications for firms vying to enhance consumers' new product purchase intentions by deploying humorous ads.

Football stadiums, traditionally linked to local landmarks, now see a shift as corporate brands engage in naming rights through sponsorship. However, limited scholarly attention has focused on the perception of stadium toponyms. This research initiative aims to gauge attention garnered by football stadiums in Europe and China based on various naming rights options.

Commencing with a well-known European stadium: Allianz Arena (Munich) and new constructed stadium in China, the original naming rights have been proffered as stimuli, in comparison with two major technology brands as well as Coca-Cola, the historical sponsor of sport mega-events. A sample of 100 participants was analyzed using the eye-tracking technique to compare the perceptions of European and Chinese youth.

The conclusion drawn from the study is that for stadiums with a history of sponsorship, unedited versions of the pictures attract the most attention. Compared with technology brand, Coca-cola is the brand with the greatest ability to attract the attention of young people in both historical stadiums or new constructed stadium, acting as an impulse to the activation of the naming rights strategy due to its historical character as a sponsor of mega sporting events.

The research makes dual contributions, both theoretically and practically. It enhances comprehension of audience visual attention patterns in the context of football stadium naming rights sponsorship, employing cognitive attention theory to substantiate empirical evidence. Furthermore, it advances the existing literature on football stadium naming rights research. Additionally, it proposes an optimization tool to assess the effectiveness of naming rights sponsorships, offering valuable insights for companies and brands seeking to enhance their marketing strategies.

This study aims to discuss whether the lasting Confucian philosophy could be used in responding to the consequences of technological impacts on jobs in the Age of Robots from a human resource management and development (HRMD) perspective.

Related literature concerning traditional Confucian philosophy and the power of Confucianism was examined. Key perspectives on this topic relating to smart technology were analyzed. Whether Confucian humanity could be used to promote ethical behavior and continuous improvement in the workplace in the Age of Robots was then discussed.

Three propositions were made: humans can better coexist with artificial intelligence (AI) and robots if humanity is valued, cultivated and practiced; some concepts of traditional Confucian philosophy can be applied to support management, employees and organizations to go through the technology-driven social change; and managements and human resource professionals can be the change agent and adopt Confucian paradigm for employees’ and organizational effectiveness in the Age of Robots.

Future research on human–machine interactions and strategic plans to apply Confucian humanity on job restructuring in robotic workplace is recommended.

For organizational development implication, human resource professionals may identify business opportunities, develop human–machine interactions strategic plans, build out creative process and promote moral behaviors and ethical conduct with a growth mindset.

For corporate social responsibility, management and human resource professionals can upskill and reskill employees to develop talents, avoid technology unemployment and advance their human skills to be competitive in the robotic workplace.

This study highlighted how human workers should work like a human, not as a robot, by building a lifelong character through a moral refinement process for self-fulfillment, social responsibility and social stability.

Drawing on the pleasure-arousal-dominance (PAD) emotion model, the emotional states of consumers embedded in online reviews can be described through three dimensions, that is, pleasure, arousal and dominance, rather than only the one-dimensional positive and negative polarity, as in previous studies. Therefore, this study aims to explore the effect of online review emotion on perceived review helpfulness based on these three basic emotional dimensions.

A lexicon-based method is developed to analyze PAD emotions of online reviews from JD.com. The zero-inflated negative binomial regression is utilized to empirically validate the study hypothesis. The authors examine the influence of pleasure, arousal, dominance, emotion diversity and emotion deviation on review helpfulness, as well as the moderating effect of product type on the relationship between all independent variables and online review helpfulness.

The study results show that the pleasure emotion impairs the helpfulness of online reviews, while the arousal and dominance emotions have a positive impact. Moreover, the authors find that compared with search products, the effects of pleasure, arousal and dominance on perceived helpfulness are strengthened for experience products. However, the emotional diversity and emotional deviation have opposite effects on the helpfulness of search products and experience products. Additionally, the results show that dominance emotion plays a more important role in the interaction effect.

The empirical findings confirm the applicability of PAD in the online review context and extend the existing knowledge of the influence of review emotion on helpfulness. A feasible scheme for extracting PAD variables from Chinese text is developed. The study findings also have significant implications for reviewers, merchants and platform managers of e-commerce websites.

This site-based study employed a sociolinguistic approach to investigate the linguistic landscape and multilingualism displayed on signs in Marib, a historical and tourist city in Yemen. The study focused on the types and functions of these signs, as well as people's perceptions of the status quo of the linguistic landscape.

A mixed-methods approach was used to collect data. Signs in tourist areas were photographed, and subsequent semi-structured interviews were conducted with relevant individuals to achieve the study's objectives.

The results revealed that both multilingual and monolingual signs coexist, with multilingual signs being more prevalent. Multilingual signs, which were official (top-down), served informational and symbolic functions, while monolingual signs, which were informal (bottom-up), served purely informational purposes. Bilingual signs were not present at the research sites. Interviewees generally expressed their satisfaction with multilingualism, as both national (Arabic and Musnad) and international (English) languages coexisted. However, they highlighted a deficiency in the number of signs at the research sites.

This study suggested intensifying efforts to include more signs, augmented with QR codes, to convey an appealing image of the linguistic landscape and provide visitors with information about these sites. Exposing students to the linguistic landscape can significantly enhance their English learning.

This study contributes to existing literature by providing further insights into the linguistic landscape in tourist destinations in Marib city. It offers the local and global community a better understanding of why and how the linguistic landscape should be improved and presented more appropriately.

As the current Coronavirus 2019 pandemic eases, international tourism, which was greatly affected by the outbreak, is gradually recovering. The attraction of countries to overseas tourists is related to their overall performance in the pandemic. This research integrates the data of vaccination of different countries, border control policy and holidays to explore their differential impacts on the overseas tourists’ intention during the pandemic. This is crucial for destinations to built their tourism resilience. It will also help countries and industry organizations to promote their own destinations to foreign tourism enterprises.

This study proposes an analysis based on panel data for ten countries over 1,388 days. The coefficient of variation is used to measure monthly differences of Chinese tourists’ intention to visit overseas country destinations.

Results show that, for tourist intention of going abroad: border control of the destination country has a significant negative impact; daily new cases in the destination country have a significant negative impact; domestic daily new cases have a significant positive impact; holidays have significant negative impact; daily vaccination of the destination countries has significant positive impact; and domestic daily vaccination have negative significant impact.

First, there is a large uncertainty in studying consumers’ willingness to travel abroad in this particular period because of unnecessary travel abroad caused by the control of the epidemic. Second, there are limitations in studying only Chinese tourists, and future research should be geared toward a broader range of research pairs.

First, from the government perspective, a humane response can earn the respect and trust of tourists. Second, for tourism industry, to encourage the public take vaccine would be beneficial for both the tourism destination and foreign tourism companies. The same effect can be achieved by helping tourists who are troubled by border control.

First, this research provides suggestions for the government and the tourism industry to deal with such a crisis in the future. Second, this study found that vaccination has a direct impact on tourism. This provides a basis for improving people’s willingness to vaccinate. Thirdly, this study proves suggestion for the destinations to build tourism resilience.

This study analyzes the unique control measures and vaccination in different countries during the pandemic, then provides suggestions for the tourism industry to prepare for the upcoming postpandemic tourism recovery. This study is valuable for improving the economic resilience of tourism destinations. Additionally, it helps to analyze the advantages and disadvantages of different restrain policies around the world.

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.