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Building Information Modeling (BIM) competitions are a beneficial approach to enhance BIM education, offering students practical experience in BIM application, including mastering workflows and technical tools. However, research exploring the individual perceptions influencing participation intentions and behaviors in BIM competitions is limited. Therefore, this study aims to investigate the factors affecting university students' behavioral intention and behavior in BIM competitions, providing theoretical support for BIM competitions and educational reform.

This study employs the Structural Equation Modeling (SEM) based on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to analyze the factors influencing BIM competition participation among 970 Architecture, Engineering, and Construction (AEC) university students.

The results of the study show that social influence, attitude, and self-efficacy play critical roles in shaping students' intentions to participate in BIM competitions. Furthermore, self-efficacy, facilitating conditions, and behavioral intention significantly influence students' actual engagement in such competitions. Surprisingly, effort expectancy negatively influences intentions, as less challenging tasks can lead students to perceive their participation as less impactful on their skills and learning, reducing their behavioral intention to participate.

This research provides valuable insights into the effectiveness of BIM competitions in enhancing BIM education for AEC students. Extending the UTAUT model to include self-efficacy and attitude, provides a novel perspective for understanding students' intentions and behaviors regarding BIM competitions. The study’s theoretical support proposes incorporating BIM competitions to augment BIM teaching methods and offers suggestions for advancing the efficacy of students' involvement in BIM competitions within higher education, thus contributing to educational reform in the AEC sector.

The purpose of this paper is to propose a consensus method for multi-attribute group decision-making (MAGDM) problems based on preference-approval structure and regret theory, which can improve the efficiency of decision-making and promote the consensus level among individuals.

First, a new method to obtain the reference points based on regret theory and expert weighting method is proposed. Second, a consensus reaching method based on preference-approval structure is proposed. Then, an adjustment mechanism to further improve the consensus level between individuals is designed. Finally, an example of the assessment of elderly care institutions is used to illustrate the feasibility and effectiveness of the proposed method.

The feasibility and validity of the proposed method are verified by comparing with the advanced two-stage minimum adjustment method. The compared results show that the proposed method is more consistent with the actual situation.

This paper presents a consensus reaching method for MAGDM based on preference-approval structure, which considers the avoidance behaviors of individuals and reference points. Decision makers (DMs) can use this approach to rank and categorize alternatives while further increasing the level of consensus among them. This can further help determine the optimal alternative more efficiently.

A new MAGDM problem based on the combination of regret theory and individual reference points is proposed. Besides, a new method of obtaining experts' weights and a consensus reaching method for MAGDM based on preference-approval structure are designed.

The primary objective of this study is to unveil the relationships that interconnect ESG and three pillars disclosures with technological innovation while also investigating the moderating impact of product market competition. The paper seeks to identify the underlying mechanisms that facilitate technological innovation in sustainable management.

Using data from 8,738 Chinese firms from 2011 to 2019, this study employs quantitative analysis to examine the relationship between ESG disclosure and technological innovation and the moderating effect. Moreover, this study explores the heterogeneous impacts while considering factors such as property rights and firm size.

The findings reveal a positive correlation between ESG disclosure and technological innovation. The study also investigates the moderating role of product market competition and finds that increasing competition mitigates the positive effects of ESG disclosure on technological innovation. Additionally, the conclusions reveal that the relationship between ESG and three pillars disclosures and technological innovation, as well as the moderating role of product market competition, exhibits inconsistency across firms with different property rights and sizes.

This study offers a clear understanding of the relationship between ESG disclosures and technological innovation, and how it varies across businesses of different sizes and ownership structures. It also provides fresh perspectives on the influence of product market competition on this relationship, with implications for strategy development in corporations.

This study examines the relationship between streamers' product descriptions, customer comments and online sales and focuses on the moderating effect of streamer–viewer relationship strength.

Between June 2021 and April 2022, the structured data of 965 livestreaming and unstructured text data of 42,956,147 characters from two major live-streaming platforms were collected for the study. Text analysis and regression analysis methods were employed for data analysis.

First, the authors' analysis reveals an inverted U-shaped relationship between comment length and product sales. Notably, comment volume and comment emotion positively influence product sales. Furthermore, the semantic richness, emotion and readability of streamers' product descriptions also positively influence product sales. Secondly, the authors find that the strength of streamer–viewer relationship weakens the positive effects of comment volume and comment emotion without moderating the inverted U-shaped effect of comment length. Lastly, the strength of streamer–viewer relationship also diminishes the positive effects of emotion, semantics and readability of streamers' product descriptions on product sales.

This study is the first to concurrently examine the direct and interactive effects of user-generated content (UGC) and marketer-generated content (MGC) on consumer purchase behaviors in livestreaming e-commerce, offering a novel perspective on individual decision-making and cue utilization in the social retail context.

Drawing on social exchange theory, this study aims to investigate the relation of customer sexual harassment (CSH) and customer-oriented organizational citizenship behaviors (customer-oriented OCB), as well as the mediation of customer–employee exchange (CEX) and the moderation of hostile attribution bias.

The hypotheses were examined through a field study performed in six hotels in three Chinese cities and an experimental study.

The results revealed that CSH undermined the quality of CEX, leading employees to withdraw from customer-oriented OCB. Additionally, the hostile attribution bias of service employees reinforced the direct relationship between CSH and CEX and its indirect relationship with customer-oriented OCB via CEX.

The findings suggest that hospitality organizations should endeavor to reduce the occurrence of CSH, and that by valuing and encouraging the development of high-quality CEX, they can mitigate its detrimental effects. Special attention should also be paid to hospitality employees holding strong hostile attribution bias.

First, to the best of the authors’ knowledge, this is one of the first studies to explore the influence of CSH on customer-oriented OCB among hotel employees. In addition, examining the effect of CSH from the social exchange perspective represents a new theoretical approach. The finding also contributes to the literature on CEX by identifying an important antecedent. Finally, by investigating hostile attribution bias as a moderator, this research provides insights into how individual differences moderate the destructive influence of CSH.

The purpose of this paper is to propose a research model in which coworker service sabotage influences hospitality employees’ service creativity via work engagement. It also aims to test the moderating effect of sensitivity to the interpersonal mistreatment of others (SIMO).

A time-lagged questionnaire study was performed in hotels in China. The hypotheses were tested via hierarchical multiple regression.

Coworker service sabotage is indirectly associated with hospitality employees’ service creativity via work engagement. The trait of SIMO buffers the harmful effect of coworker service sabotage.

Although our research design helps mitigate common method bias, it could still exist. Other coworker behaviors that might influence employees were not included in this research. The findings may also be biased due to the restricted sample from China.

Hospitality organizations should take measures to curb service sabotage. Organizations could also provide supportive resources to suppress the negative impacts of coworker service sabotage. Moreover, organizations should motivate those low in SIMO to care more about customers.

The research takes the lead in investigating the outcomes of service sabotage from a third-party perspective. Work engagement is identified as the mechanism for transmitting the impact of coworker service sabotage to employees. Moreover, a new moderator that attenuates the negative effects of coworker service sabotage is found.

The COVID-19 pandemic has brought tremendous changes in society. Universities were among the few organisations with some previous knowledge of online education, being able to rapidly adapt by transferring already known best practices to the new context. As teaching moved to online, students encountered less sustainable implementation by their universities. This allowed the development of previously planned sustainable strategies so that when face-to-face teaching resumed, universities could be even more sustainable. This paper aims to explore loyalty to the sustainable university during the later COVID-19 pandemic based on the university’s efforts to manage a green campus.

To investigate loyalty towards the sustainable university during the later COVID-19 pandemic, a conceptual model is proposed. This research is grounded in an empirical investigation using a quantitative online survey implemented with online interviews, the relations between all latent constructs being analysed with SmartPLS.

The results show that university sustainability reflects student loyalty, outlining the image developed under the influence of green campus management. The results show that universities must intensify their efforts to support the sustainable agenda and create a sustainable academic brand, inducing student loyalty. The findings may attract the attention of other universities wishing to gain knowledge about the factors that students consider important in generating their loyalty.

As this research was carried out in the later COVID-19 pandemic context from 2021, the students already had experience of online teaching, so their assessment of the sustainability strategies implemented could be quite different from their perceptions in the first months of the pandemic. This research provides a comprehensive insight into the overall strategy that a sustainable university might apply in a crisis context.

Research has shown that green campus management has a positive impact on the sustainability of a university’s image, on how students perceive the university at which they study and on the university’s efforts to ensure efficient campus management. These factors contribute to the development of a strong and sustainable image of the university within the community.

The originality of this paper lies in the research questions designed to conceptualise and operationalise the generation of students' loyalty towards their university by encouraging and implementing sustainable strategies on campus. This paper highlights a structural model that combines strategic practices to determine students' loyalty towards a sustainable university during the COVID-19 pandemic.

Gallic acid is a substance that is widely found in nature. Initially, it was only used as a corrosion inhibitor to retard the rate of corrosion of metals. In recent years, with intensive research by scholars, the modification of coatings containing gallic acid has become a hot topic in the field of metal protection. This study aims to summarize the various preparation methods of gallic acid and its research progress in corrosion inhibitors and coatings, as well as related studies using quantum chemical methods to assess the predicted corrosion inhibition effects and to systematically describe the prospects and current status of gallic acid applications in the field of metal corrosion inhibition and protection.

First, the various methods of preparation of gallic acid in industry are understood. Second, the corrosion inhibition principles and research progress of gallic acid as a metal corrosion inhibitor are presented. Then, the corrosion inhibition principles and research progress of gallic acid involved in the synthesis and modification of various rust conversion coatings, nano-coatings and organic resin coatings are described. After that, studies related to the evaluation and prediction of gallic acid corrosion inhibition on metals by quantum chemical methods are presented. Finally, new research ideas on gallic acid in the field of corrosion inhibition and protection of metals are summarized.

Gallic acid can be used as a corrosion inhibitor or coating in metal protection.

There is a lack of research on the synergistic improvement of gallic acid and other substances.

The specific application of gallic acid in the field of metal protection was summarized, and the future research focus was put forward.

To the best of the authors’ knowledge, this paper systematically expounds on the research progress of gallic acid in the field of metal protection for the first time and provides new ideas and directions for future research.

The incidents of hotel theft constitute a significant cause of concern for the hotel industry. Such incidents result in substantial financial implications, inventory management issues and complications in finding exact replacements. This study, therefore, investigates customer stealing practices in Indian hotel industry.

A structured email interview was conducted with twenty-five senior hotel managers. The data were analyzed by adopting a coding technique using inductive content analysis.

The results were discussed under seven headings-extent of such stealing incidents, financial consequences of such practices, strategies to prevent such instances and, finally, how to deal with customers when caught stealing.

This study highlighted themes related to the profile of customers involved in stealing practices, motives, consequences and strategies required to manage such customers. Future studies can focus on restaurants, airlines, airports and tourist attractions to identify strategies and best practices for preventing such practices.

The study's findings have tremendous potential for the hospitality industry. Dealing with customers’ stealing practices is a very sensitive issue. Furthermore, hotels bear lots of expenses if the policy for tackling the problem still needs to be implemented. Furthermore, if not handled professionally, a lack of effective guidelines can cause embarrassment for managers.

The study has the potential to raise awareness among the members of society to be more responsible toward hotel property.

This study extends to the limited literature on hotel guests' theft. The comprehensive analysis of the themes generated from an interview with the senior managers of the hotel industry provides a unique insight into the nature of the problem. The solutions offered in this study are an original contribution to the literature.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.