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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 study aims to calculate the recall ratio of selected MSEs and provide a comprehensive ranking for MSEs using features, precision and recall analysis.

The study was divided into three consecutive sections: Keyword selections and checking demographic searchability; recall calculation among the MSEs and third calculating the Equal Weighted Score by allotting equal weight (0.25) to all MSEs to rank the MSEs based on the re-ranking aggregation approach.

The study clearly shows all the four MSEs considered—Dogpile, Metacrawler, DuckDuckGo and Startpage—Metacrawler (71%) ranked highest for recall, followed by DuckDuckGo (68%), Dogpile (63%) and Startpage (60%). The re-ranking aggregation approach results show DuckDuckGo (2) ranked 1st, followed by Startpage (2.5), Dogpile (2.75) and Metacrawler (2.75); lower scores indicate better performance. The findings indicate that DuckDuckGo is the best MSE regarding user experience (UX) and search quality.

The study used a re-ranking aggregation approach confined to past rankings and limited to four MSEs, limiting its generalizability.

The finding helps users and developers understand the strengths and weaknesses of the different MSEs, enabling more informed decision-making and enhancing UX.

The study selected a novel approach for assessing the MSEs, and no similar study conducted in the past used different performance metrics to rank the MSEs.

With the current developments within the sphere of Internet of Things (IoT) technology, many conventional articles are all being fitted with smart functionalities, ranging from chairs, beds, shoes and caps to underwear. Bags which are utility as well as fashion items have not been left out of this smart craze, albeit to a less popular degree. The purpose of this study is to fill the research gap on the subject of smart bags research and applications and to contribute to the general discourse on IoT.

This study adopts literature search and database review, concept mapping as well as synthesis methodologies. Relevant literature form databases such as Web of Science, Google Scholar and Bing Scholar were interrogated. Manual sifting was done to eliminate papers that do not fit the set inclusion criteria. Literature on smart bags was organized into structured frameworks using concept mapping methodology. Applying a synthesis methodology enabled an exploration of the different technological trends in smart bag research and their areas of application.

The study identified about 15 different smart bag applications and functionalities. Discussed in this study is a classification of bags based on a number of points such as way of carrying, size, utility and fabrication materials. Also discussed are the description of what constitute a smart bag, relevant technologies for smart bag design and engineering and subsequently the current trends in smart bag applications. This study also discovered that the air travel industry tend to have some difficulties with this smart bag technologies, specifically with their built-in batteries.

The results of this study will provide researchers and other stakeholders with key information about existing problems and opportunities in smart bag research and applications. This will go a long way to help in guiding future research as well as policymaking in smart bag design and application.

To the best of the authors’ knowledge, this is the first review on the subject of smart bags even though smart bag research and commercial product design continue to gain momentum in recent years.

Equitable risk allocation is important for the effective management of inevitable risks in International Construction Joint Venture (ICJV) projects. Previous studies have documented risks facing ICJV projects. However, there is a dearth of studies on the risk allocation preferences that take into consideration the opinions of both the local and foreign partners. This study aims to fill this gap by ascertaining the risk allocation preferences of the partners of ICJV projects for effective risk management.

Through a survey, data on risk allocation preferences were collected from both local and foreign partners of ICJV projects using a comprehensive register of 74 risks.

Following analysis, six risks were allocated to the local partner, 11 were allocated to the foreign partner, 51 risks were shared, four were allocated to a third party and two were to be negotiated based on the specific circumstances of the project. Practically, the study’s findings will help ICJV partners in drafting their ICJV contracts to adequately allocate risks and reduce contract negotiation time considerably.

The findings from this study will help partners in drafting their joint venture contract agreement and also reduce the period for contract negotiation. Knowledge of the preferred risk allocation is important in allocating risks in the contract agreement to the relevant partner for effective management.

This study, to the best knowledge of the authors, is one of the early studies to ascertain the risk allocation preferences of ICJV project partners in the Ghanaian construction industry – a departure from previous studies which focused on the identification and evaluation of risks. This study is also different from previous studies by considering the allocation preferences of both partners of the ICJV. The collection of data from both partners of the ICJV helped to consider their perceptions on risk allocation and evaluation, essentially leading to cross-cultural and optimal risk allocation preferences.

The study explores the mechanism between employee demand–ability fit and promotability ratings. Moreover, we examine whether political skill moderates the relationship above.

The present study received 260 effective supervisor-rated questionnaires from various industries in Taiwan.

The results reveal that employee demand–ability fit positively relates to promotability ratings, and task performance mediates the above relationship. In addition, political skill positively moderates the relationship between employee demand–ability fit and task performance.

We used the supervisor rating for our variables to test the hypotheses.

This study investigates the user’s perspective regarding opportunities, challenges and implications related to artificial intelligence technologies such as ChatGPT in several contexts, such as education and healthcare. In detail, the aim was to understand user’ perceptions of the relationship between ChatGPT and sustainability and their views about how companies can contribute to achieving well-being.

To reach the research aim, the study adopts an explorative inductive design, carried out through a qualitative approach and grounded in 34 in-depth consumer interviews.

The interviewees perceive ChatGPT as a helpful tool that simplifies activities and reduces time and human errors. However, risks associated with using this tool have been identified, such as the reliability of the information provided, the need for more privacy for the requested personal data and the lack of empathy and human interaction. AI tools can support businesses and governments in creating and promoting sustainable strategies and reducing waste. Furthermore, the study shows how these tools can improve the community's well-being by disseminating knowledge and information, supporting workers and generating less stress.

The paper describes the users' perspective on the opportunities of ChatGPT in promoting sustainable development and well-being, providing suggestions for future research.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.

Building a smart city is a necessary path to achieve sustainable urban development. Smart city public–private partnership (PPP) project is a necessary measure to build a smart city. Since there are many participants in smart city PPP projects, there are problems such as uneven distribution of risks; therefore, in order to ensure the normal construction and operation of the project, the reasonable sharing of risks among the participants becomes an urgent problem to be solved. In order to make each participant clearly understand the risk sharing of smart city PPP projects, this paper aims to establish a scientific and practical risk sharing model.

This paper uses the literature review method and the Delphi method to construct a risk index system for smart city PPP projects and then calculates the objective and subjective weights of each risk index through the Entropy Weight (EW) and G1 methods, respectively, and uses the combined assignment method to find the comprehensive weights. Considering the nature of the risk sharing problem, this paper constructs a risk sharing model for smart city PPP projects by initially sharing the risks of smart city PPP projects through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the independently borne risks and the jointly borne risks and then determines the sharing ratio of the jointly borne risks based on utility theory.

Finally, this paper verifies the applicability and feasibility of the risk-sharing model through empirical analysis, using the smart city of Suzhou Industrial Park as a research case. It is hoped that this study can provide a useful reference for the risk sharing of PPP projects in smart cities.

In this paper, the authors calculate the portfolio assignment by EW-G1 and construct a risk-sharing model by TOPSIS-Utility Theory (UT), which is applied for the first time in the study of risk sharing in smart cities.

The purpose of this study is to examine the moderating effect of personality traits on social responsibility-oriented human resources management (SR-HRM) practices and job performance.

The sample of the study consists of 442 employees working in firms developing knowledge and innovation-based systems in Ankara (Turkey). The strengths of this study stem from its methodology, which contributes to the literature in terms of analysis comparing linear and fuzzy set qualitative comparative analyzes (fsQCA).

Both linear analyses and fsQCA results show the moderating effect of conscientiousness on the relationship between job performance and SR-HRM. Both models demonstrate the impact of agreeableness on business performance. Study results indicate that linear and fsQCA examine the moderating effect of conscientiousness in the same way as the literature. The linear analysis results differ from fsQCA, which examined the moderating effect of compatibility in this study. The fsQCA results of this study show that SR-HRM affects job performance when extraversion and conscientiousness are taken into account.

Further research may compare linear and fuzzy logic models about moderating and mediating effects. Future researchers may highlight the increasing importance of linear and fsQCA in any organizational behavior or organizational theory subject.

The main implication is that managers should consider the significant impacts of effective SR-HRM development on employees’ job performance. Personality traits should be taken into account in HRM processes such as recruitment and retention.

The main contribution of the current study is the insight it provides into the moderating effects of the big five personality traits on the relationship between job performance and SR-HRM and how they differ in the linear model and fsQCA.

The current study contributes to the relevant literature by presenting the research design and questions. Using a multi-method approach, this research provides a better understanding of patterns between variables and contributes to the existing literature.

Many investors have less interest in tendering for public-private partnership (PPP) construction projects as a result of the uncertain risks associated with the project delivery. Moreover, PPP project stakeholders have inadequate information about the probable impacts of time-based delay imposed on PPP projects under the PPP arrangement. This study aims to identify and categorize construction stakeholders’ perceptions of the impact of time-based delays on PPP construction projects.

A purposive sampling technique was adopted where questionnaires were used as the primary instrument for gathering data from PPP experts. Cronbach’s alpha coefficient and Kendall’s concordance were used to measure the reliability of the scale and the respondent’s level of agreement, respectively. One sample t-test, mean score ranking and principal component analysis were used to analyse the identified time-based delay impacts.

The study revealed seven significant impacts of time-based delay on PPP construction project delivery as: “project schedule overrun”, “idling of project resources”, “project cost overrun”, “poor quality of completed works”, “delayed realization of project benefits”, “frequent arbitration/litigation in PPP projects” and “total abandonment of PPP projects”. The study further identified the top five significant impacts of time-based delay on PPP project stakeholders as: “reduction in motivation to attract investment”, “high interest on finance (loans)”, “contractor in financial crisis”, “loss of public confidence in government” and “reduction in parties’ reputation”.

The identified significant impacts of time-based delays will increase stakeholders’ awareness of the repercussions and effects that time-based delays may impose on PPP construction projects if not appropriately managed throughout the project implementation. This awareness will further guide stakeholders to implement targeted risk management strategies to minimize the negative consequences of delays on PPP project performance.

As a pioneering study that provides a better understanding of the impacts of time-based delays on PPP construction projects, this study enhances knowledge of PPP construction project implementation.