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Although the literature recognizes that coopetition plays a significant role in the success of international construction joint ventures (ICJVs), the impacts of coopetition on the performance outcomes of ICJVs remain largely unknown. This study extends this line of research by theorizing coopetition from three dimensions, i.e. coopetition intensity, coopetition balance and coopetition structure, and examining the relationships between coopetition and ICJV performance outcomes from both the contingency and configuration perspectives.

The hypotheses were tested using survey data from a sample of 188 ICJVs. Structural equation modelling was employed for the contingency approach to estimate the relationships between the three dimensions of coopetition and performance. For the configuration approach, cluster analysis was utilized to identify coopetition patterns. Subsequently, an analysis of variance was employed to analyse the relationships between these coopetition patterns and performance.

The contingency results indicate that while coopetition intensity is positively related to all types of performance, coopetition balance is only positively related to project performance and partner performance. Moreover, coopetition structure is only related to partner performance and socioenvironmental performance. The configuration approach identifies six patterns of coopetition, manifesting different levels of project, partner and socioenvironmental performance.

These findings, therefore, contribute to the ICJV literature by extending the understanding of how coopetition dimensions individually and jointly influence ICJV performance.

Educational data mining (EDM) discovers significant patterns from educational data and thus can help understand the relations between learners and their educational settings. However, most previous data mining techniques focus on prediction of learning performance of learners without integrating learning patterns identification techniques.

This study proposes a new framework for identifying learning patterns and predicting learning performance. Two modules, the learning patterns identification module and the deep learning prediction models (DNN), are integrated into this framework to identify the difference of learning performance and predicting learning performance from profiles of students.

Experimental results from survey data indicate that the proposed identifying learning patterns module could facilitate identifying valuable difference (change) patterns from student’s profiles. The proposed learning performance prediction module which adapts DNN also performs better than traditional machine techniques in prediction performance metrics.

To our best knowledge, the framework is the only educational system in the literature for identifying learning patterns and predicting learning performance.

This study aims to develop a novel design method to make personalized masks for the effective prevention of pandemic respiratory infectious disease.

The changes in facial shape during speaking were analyzed using a three-dimensional (3D) scanning technique. In total, 13 anthropometric items were measured, and mask patterns were generated using a parametric pattern design method. Three sizing methods were proposed to reflect not only static but also dynamic body dimensions on the mask patterns.

A significant increase or decrease was observed in 10 out of 13 measurement items. Based on this, four items were selected to be used in the mask pattern design. The nose and cheek areas of a mask were fixed to protect the respiratory tract against viruses. The lower jaw area was deformed to improve the fit.

This study is expected to provide fundamental data to understand the changes in facial shape during movement. In addition, it is expected that the development of individualized personal protective equipment with movement adaptability will facilitate an effective response to various pandemic respiratory diseases.

In order to develop a personal protective equipment (PPE) that has a good fit and can protect against pandemic respiratory infectious diseases, morphological analysis was attempted using 3D facial data. It would be possible to design various products and equipment to be worn on the face by using the method proposed in this study.

When emergencies occur, the attention of the public towards emergency information on social media in a specific time period forms the emergency information popularity evolution patterns. The purpose of this study is to discover the popularity evolution patterns of social media emergency information and make early predictions.

We collected the data related to the COVID-19 epidemic on the Sina Weibo platform and applied the K-Shape clustering algorithm to identify five distinct patterns of emergency information popularity evolution patterns. These patterns include strong twin peaks, weak twin peaks, short-lived single peak, slow-to-warm-up single peak and slow-to-decay single peak. Oriented toward early monitoring and warning, we developed a comprehensive characteristic system that incorporates publisher features, information features and early features. In the early features, data measurements are taken within a 1-h time window after the release of emergency information. Considering real-time response and analysis speed, we employed classical machine learning methods to predict the relevant patterns. Multiple classification models were trained and evaluated for this purpose.

The combined prediction results of the best prediction model and random forest (RF) demonstrate impressive performance, with precision, recall and F1-score reaching 88%. Moreover, the F1 value for each pattern prediction surpasses 87%. The results of the feature importance analysis show that the early features contribute the most to the pattern prediction, followed by the information features and publisher features. Among them, the release time in the information features exhibits the most substantial contribution to the prediction outcome.

This study reveals the phenomena and special patterns of growth and decline, appearance and disappearance of social media emergency information popularity from the time dimension and identifies the patterns of social media emergency information popularity evolution. Meanwhile, early prediction of related patterns is made to explore the role factors behind them. These findings contribute to the formulation of social media emergency information release strategies, online public opinion guidance and risk monitoring.

This study explored how the use of different social media is related to subjective well-being among university students during the COVID-19 pandemic in Japan.

We surveyed 1,681 university students in the Kanto region of Japan in May 2021 to investigate how social media use relates to subjective well-being. We also examined the effects of self-consciousness and friendship, self-presentation desire, generalized trust, online communication skills, depression tendency and social support from others.

The responses revealed 15 possible patterns of social media usage on four widely used social media in Japan (LINE, Twitter, Instagram and Facebook). We selected users with the top five patterns for further statistical analyses: LINE/Twitter/Instagram/Facebook, LINE/Twitter/Instagram, LINE/Twitter, LINE/Instagram and LINE only. Overall, self-establishment as a factor of self-consciousness and friendship, and social support from others had positive effects on the improvement of subjective well-being, whereas depression tendency had negative effects on their subjective well-being regardless of their usage patterns, of which the results of social support from others and depression tendency were consistent with the results of previous studies. Regarding other factors, they had different effects on subjective well-being due to different patterns. Effects on subjective well-being from self-indeterminate and self-independency as factors of self-consciousness and friendship, praise acquisition, self-appeal and topic avoidance as factors of self-presentation desire were observed.

This is among the earliest studies on the relationship between young generations’ social media use and subjective well-being through social media usage patterns during the COVID-19 pandemic in Japan.

E-commerce companies often manipulate customer decisions through dark patterns to meet their interests. Therefore, this study aims to identify, model and rank the enablers behind dark patterns usage in e-commerce companies.

Dark pattern enablers were identified from existing literature and validated by industry experts. Total interpretive structural modeling (TISM) was used to model the enablers. In addition, “matriced impacts croisés multiplication appliquée á un classement” (MICMAC) analysis categorized and ranked the enablers into four groups.

Partial human command over cognitive biases, fighting market competition and partial human command over emotional triggers were ranked as the most influential enablers of dark patterns in e-commerce companies. At the same time, meeting long-term economic goals was identified as the most challenging enabler of dark patterns, which has the lowest dependency and impact over the other enablers.

TISM results are reliant on the opinion of industry experts. Therefore, alternative statistical approaches could be used for validation.

The insights of this study could be used by business managers to eliminate dark patterns from their platforms and meet the motivations of the enablers of dark patterns with alternate strategies. Furthermore, this research would aid legal agencies and online communities in developing methods to combat dark patterns.

Although a few studies have developed taxonomies and classified dark patterns, to the best of the authors’ knowledge, no study has identified the enablers behind the use of dark patterns by e-commerce organizations. The study further models the enablers and explains the mutual relationships.

This study aims to explore how users’ movement is influenced by different hospital ward layouts, by using space syntax theory. This study also compared four circulation patterns to find the best one for the study goal.

The authors used both qualitative and quantitative methods to study how users’ wayfinding in hospitals depends on various indicators and factors. The study used Depth Map software to do case studies and then analyzed the indicators from theoretical foundations and used the Pearson Test to check the correlation between indicators. This study also looked at Iran’s Ministry of Health standards for ward layouts. Finally, the results obtained from the research data were compared to achieve a suitable model based on the research objectives.

The linear-patterned plan was the best for easy wayfinding and accessibility among four patterns. The optimal hospital circulation patterns can improve wayfinding and reduce wayfinding problems and user movement.

By pioneering space syntax in hospital research, this study unveils the novel interaction between path architecture and user movement. It gives new insights into current trends, helping architects, administrators and policymakers improve health-care design, efficiency and patient experience.

This study explores the career trajectories of Chief Executive Officers (CEOs) to uncover unique characteristics that contribute to their success. By utilizing web scraping and machine learning techniques, over two thousand CEO profiles from LinkedIn are analyzed to understand patterns in their career paths. This study offers an alternative approach compared to the predominantly qualitative research methods employed in previous research.

This study proposes a framework for analyzing CEO career patterns. Job titles and company information are encoded using the Standard Occupational Classification (SOC) scheme. The study employs the Needleman-Wunsch optimal matching algorithm and an agglomerative approach to construct distance matrices and cluster CEO career paths.

This study gathered data on the career transition processes of graduates from several renowned public and private universities in the United States via LinkedIn. Employing machine learning techniques, the analysis revealed diverse career trajectories. The findings offer career guidance for individuals from various academic backgrounds aspiring to become CEOs.

The building of a career sequence that takes into account the number of years requires integers. Numbers that are not integers have been rounded up to facilitate the optimal matching process but this approach prevents a perfectly accurate representation of time worked.

This study makes an original contribution to the field of career pattern analysis by disclosing the distinct career path groups of CEOs using the rich LinkedIn online dataset. Note that our CEO profiles are not restricted in any industry or specific career paths followed to becoming CEOs. In light of the fact that individuals who hold CEO positions are usually perceived by society as successful, we are interested in finding the characteristics behind their success and whether either the title held or the company they remain at show patterns in making them who they are today.

As a matter of fact, nearly all CEOs had previous experience working for a non-Fortune organization before joining a Fortune company. Of those who have worked for Fortune firms, the number of CEOs with experience in Fortune 500 forms exceeded those with experience in Fortune 1,000 firms.

Effective communication is crucial in the medical field where different stakeholders use various terminologies to describe and classify healthcare concepts such as ICD, SNOMED CT, UMLS and MeSH, but the problem of polysemy can make natural language processing difficult. This study explores the contextual meanings of the term “pattern” in the biomedical literature, compares them to existing definitions, annotates a corpus for use in machine learning and proposes new definitions of terms such as “Syndrome, feature” and “pattern recognition.”

Entrez API was used to retrieve articles form PubMed for the study which assembled a corpus of 398 articles using a search query for the ambiguous term “pattern” in the titles or abstracts. The python NLTK library was used to extract the terms and their contexts, and an expert check was carried out. To understand the various meanings of the term, the contextual environment was analyzed by extracting the surrounding words of the term. The expert determined the appropriate size of the context for analysis to gain a more nuanced understanding of the different meanings of the term pattern.

The study found that the categories of meanings of the term “pattern” are broader in biomedical publications than in common definitions, and new categories have been emerging from the term's use in the biomedical field. The study highlights the importance of annotated corpora in advancing natural language processing techniques and provides valuable insights into the nuances of biomedical language.

The study's findings demonstrate the importance of exploring contextual meanings and proposing new definitions of terms in the biomedical field to improve natural language processing techniques.

The existing traditional recommendations based on content-based filtering (CBF), collaborative filtering (CF) and hybrid approaches are inadequate for recommending practice challenges in programming online judge (POJ). These systems only consider the preferences of the target users or similar users to recommend items. In the learning environment, recommender systems should consider the learning path, knowledge level and ability of the learner. Another major problem in POJ is the learners don't give ratings to practice challenges like e-commerce and video streaming portals. This purpose of the proposed approach is to overcome the abovementioned shortcomings.

To achieve the context-aware practice challenge recommendation, the data preparation techniques including implicit rating extraction, data preprocessing to remove outliers, sequence-based learner clustering and utility sequence pattern mining approaches are used in the proposed approach. The approach ensures that the recommender system considers the knowledge level, learning path and learning goals of the learner to recommend practice challenges.

Experiments on practice challenge recommendations conducted using real-world POJ dataset show that the proposed system outperforms other traditional approaches. The experiment also demonstrates that the proposed system is recommending challenges based on the learner's current context. The implicit rating extracted using the proposed approach works accurately in the recommender system.

The proposed system contains the following novel approaches to address the lack of rating and context-aware recommendations. The mathematical model was used to extract ratings from learner submissions. The statistical approach was used in data preprocessing. The sequence similarity-based learner clustering was used in transition matrix. Utilizing the rating as a utility in the USPAN algorithm provides useful insights into learner–challenge relationships.