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Knowledge discovery related to unsafe behaviors promotes the performance of accident prevention in construction. Although numerous studies on accident causation models have discussed the correlations of unsafe behaviors with various factors (e.g., unsafe conditions), limited research explores correlations between unsafe behaviors within accidents. The purpose of this paper is mining strong association rules of unsafe behaviors from historical accidents to clarify this kind of tacit knowledge.

A case study was adopted as the research approach, in which accident records from building and urban railway construction in China were selected as data resources. The groups of unsafe behaviors extracted from accident records were expressed by the definitions of unsafe behaviors from safety regulations and operating procedures. Frequent Pattern (FP)-Growth algorithm was used for association rule mining, and the critical correlations between unsafe behaviors were represented by the effective strong rules.

The findings identify and distinguish correlations between unsafe behaviors within construction accidents. In building construction, workers and managers should pay attention to preventing unsafe behaviors related to personal protective equipment and machines and equipment. In urban railway construction, workers should especially avoid unsafe behaviors of inadequately dealing with environmental factors.

Tacit knowledge is transferred to explicit knowledge as the critical correlations between unsafe behaviors within accidents are determined by the effective strong rules. Additionally, the findings provide practice guidance for safety management, to collaboratively control unsafe behaviors with strong correlations.

This study contributes to the body of safety knowledge in construction and provides a further understanding of how construction accidents are caused by multiple unsafe behaviors.

This study aims to present a conceptual framework aimed at promoting educational innovation in supply chain management and logistics (SCM&L). The framework can help to design active learning experiences regarding student learning outcomes that tackle current challenges in the discipline. Emphasizing the significance of linking students’ learning to real-world scenarios, the framework enables reflective learning through hands-on engagement in a constructive alignment, overcoming existing pedagogical limitations in the field.

This study presents a qualitative research methodology that relies on the case study method. Three instances are presented to illustrate educational efforts of active learning in countries of Latin America, Bolivia, Mexico and Peru, linking real-world relevant situations to disciplinary teaching and learning.

The innovative learning experiences introduced in this study transform real-world SCM&L operations into distinctive educational opportunities. These experiences facilitate learning not only within traditional classrooms but also in urban areas of the Latin American region, enabling students to interact with educational partners in authentic settings to achieve their intended learning outcomes. These experiences are characterized by their focus on establishing meaningful connections between learning and local communities, businesses or specific contexts.

The study recognizes various limitations of conceptual, methodological, execution-related and research process aspects. First, not all academics in the SCM&L discipline may universally acknowledge the importance of educational innovation and active learning experiences because of limited pedagogical awareness. Moreover, execution-related limitations arise from the demanding nature of incorporating active pedagogical approaches into courses, as they can be resource-intensive and time-consuming. Regarding research process limitations, the case study limits generalizability and broader inferences because of its particular views and locations, which require further investigation with other instances across other disciplines and geographical regions for validation.

The practical implementation of this framework within the MIT SCALE network for Latin America and the Caribbean (LAC) demonstrates its potential in meeting diverse academic and institutional expectations and providing educational benefits to students.

The study makes a valuable contribution to prioritizing and coordinating pedagogical research by investigating the success of learning outcomes achieved through active and experiential implementations in various contexts. It provides inspiring examples of innovative learning experiences that can drive new developments not only within the LAC region but also in other areas, prompting a shift away from traditional educational approaches.

This research presents a conceptual framework, which is developed from the insights obtained in the three learning experiences to guide future efforts in SCM&L education. The findings demonstrate how to structure active learning experiences based on authentic assessment and illustrate the potential for increased cooperation among institutions in Latin America. It also promotes the recognition of novel SCM&L active learning experiences and highlights some of the benefits of this approach.

In recent years, the traditional pastries industry has gained popularity among tourists due to the advantage of the pastries location at UNESCO Heritage city, Penang. However, the little research focussed on this particular industry, and there is lack of evidence of the tourists' experience with the traditional pastries and how these attributes affect their revisit decision.

The study utilizes a qualitative research design to gain in-depth understanding on tourists' thought and their repurchase decision. Secondary data were collected via TripAdvisor with 68 tourists who visited the most popular three pastries shops namely, Him Heang, Ghee Hiang and Min Xiang Tai, which are later analysed using qualitative content analysis.

The findings revealed that tourists generally concerned about the service quality, value, brand image and atmospherics that could affect their repurchasing decision. Particularly, the staff service quality has been viewed as the upmost important attribute to influence the tourists' decision. Therefore, the pastries shops shall ensure that the staff who serve the tourists shall be well trained to satisfy the tourists' enquiry.

The limitation concerning the interpretation of the secondary data based on the feedbacks and comments of the tourists may derive the bias possibility. Future research might consider the large-scale primary data to extend the findings.

Limited research exists on the tourists' experience which affects the repurchasing decision in pastries industry. This study provides valuable information for pastries shops and researchers interested in this area.

This paper aims to provide an understanding of how accounting systems have changed across four distinct periods of hegemonic leadership in China.

Using Gramsci's concept of hegemony, periods of leadership and accounting change throughout Chinese history are examined, including the Confucian tradition, the rise of the socialist system followed by the Cultural Revolution in the Maoist era, and the move towards the socialist‐market system in the Dengist era.

This paper shows how political leaders in these different time periods effectively achieved leadership by destroying an existing hegemony, creating a new ideology, and implanting this into people's daily lives in order to successfully mobilise their ideological systems. Consistent with changes in leadership, Chinese accounting systems are shown to have responded to hegemonic shifts across these periods.

This paper contributes to understandings of Gramsci's concept of hegemony, explanations of, and motivations for, accounting change, and provides an insight into the evolution of accounting systems throughout time in the context of China.

This study aims to investigate the relevance, accuracy, specificity and justification of investment recommendations of generative artificial intelligence (GenAI) chatbots for different investment capitals and countries (UK and Bulgaria).

A two-stage mixed methods approach was used. Prompts were queried into OpenAI’s ChatGPT, Microsoft Bing and Google Bard (now Gemini). Finance and investment practitioners and finance and investment lecturers assessed the chatbots’ recommendations through an online questionnaire using a five-point Likert scale. The Chi-squared test, Wilcoxon-signed ranks test, Mann–Whitney U test and Friedman test were used for data analysis to compare GenAIs’ recommendations for the UK and Bulgaria across different amounts of investment capital and to assess the consistency of the chatbots.

GenAI chatbots’ responses were found to perform medium-to-high in terms of relevance, accuracy, specificity and justification. For the UK sample, the amount of investment had a marginal effect but prompt timing had an interesting impact. Unlike the British sample, the GenAI application, prompt timing and investment amount did not significantly influence the Bulgarian respondents’ evaluations. While the mean responses of the British sample were slightly higher, these differences were not statistically significant, indicating that ChatGPT, Bing and Bard performed similarly in both the UK and Bulgaria.

The study assesses the relevance, accuracy, specificity and justification of GenAI chatbots’ investment recommendations for two different periods, investment amounts and countries.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

The construction collapse is one of the most serious accidents since it has several attributes (e.g. accident type and consequence) and its occurrence involves various kinds of causal factors (e.g. human factors). The impact of causal factors on construction collapse accidents and the interrelationships among causal factors remain poorly explored. Thus, the purpose of this paper is to use association rule mining (ARM) for cause analysis of construction collapse accidents.

An accident analytic framework is developed to determine the accident attributes and causal factors, and then ARM is introduced as the method for data mining. The data are from 620 historical accident records on government websites of China from 2010 to 2020. Through the generated association rules, the impact of causal factors and the interrelationships among causal factors are explored.

Collapse accident is easily caused by human factors, material and machine condition and management factors. Furthermore, the results show a close interrelationship between many causal factors and construction scheme and organization. The earthwork collapse is greatly related to environmental condition and the scaffolding collapse is greatly related to material and machine condition.

This study found relevant knowledge about the key causes for different types of construction collapses. Besides, several suggestions are further provided for construction units to prevent construction collapse accidents.

This study uses data mining methods to extract knowledge about the causes of collapse accidents. The impact of causal factors on various types of construction collapse accidents and the interrelationships among causal factors are explained from historical accident data.

A humanoid intelligent robot (HIR) possessing a human-like appearance can undertake human jobs, interact, communicate and even transmit emotions to human beings. Such robots have gradually been integrated into people's daily life and production scenarios. However, it is unclear whether and by what mechanism HIRs can stimulate people’s risk perception and its impact on consumption attitudes. Based on the risk decision theory, this study aims to take the social value substitution attribute of a HIR as the incentive and analyzes the influence of social value substitution and risk perception on the customers’ consumption attitudes.

Three experiments were conducted to investigate the related questions about the social value substitution attribute of a HIR, its impact on risk perception and the customers’ consumption attitudes.

The results reveal that physical labor, intellectual labor, friendship, kinship and the ego constitute the hierarchical elements of social value substitution. Among them, physical labor and intellectual labor pertain to the dimension of social function value substitution, while friendship, kinship and ego pertain to the dimension of social presence value substitution; social function value substitution and social presence value substitution affect the subjects’ risk perception positively, but the latter arouses a stronger risk perception; the 2 (risk perception of social function value: security/danger) × 2 (risk perception of social presence value: security/danger) condition corresponds to diverse consumption attitudes.

The results enrich the theories of the “cha-xu pattern” and “uncanny valley” and provide reference for the healthy development of the HIR industry.

This study aims to report the development and experimental evaluation of two kinds of PANI@semiconductor based photocathodic anti-corrosion coating, for application on stainless steel substrates.

PANI was in situ chemical polymerized on TiO₂ and BiVO₄ particles, and FT-IR and SEM/EDS were used to understand the characteristics and elemental distribution of the composite particles. Composite coatings, which consisted of epoxy, PANI@TiO₂ or PANI@BiVO₄ and graphene, were prepared on the 304L stainless steel. Photoelectrochemical response measurement, electrochemical tests and immersion tests were used to assess the anti-corrosion performance of the prepared coatings in 45°C 3.5 wt.% NaCl solution. And the corrosion protection mechanism was further explained by combining with surface observation.

The photoelectrochemical response tests revealed the good photocathodic effect of the coatings, and the reversible oxidation-reduction properties of PANI (pseudocapacitive effect) leading to the repeated usage of the coatings. Consequently, the anti-corrosion mechanism of the composite coating is attributed to the physical barrier effect of the coating, the anodic protection effect of PANI and the photocathodic and energy store effect.

These kind coatings could prevent corrosion from day to night for stainless steel, which has great engineering application prospects on stainless steel corrosion protection.