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This study aims to explore the interplay between community dynamics and knowledge production using the quantum computing research field as a case study. Quantum computing holds the promise of dramatically increasing computation speed and solving problems that are currently unsolvable in a short space of time. In this highly dynamic area of innovation, computer companies, research laboratories and governments are racing to develop the field.

After constructing temporal co-authorship networks, the authors identify seven different events affecting communities of researchers, which they label: forming, growing, splitting, shrinking, continuing, merging, dissolving. The authors then extract keywords from the titles and abstracts of their contributions to characterize the dynamics of knowledge production and examine the relationship between community events and knowledge production over time.

The findings show that forming and splitting are associated with retaining in memory what is currently known, merging and growing with the creation of new knowledge and splitting, shrinking and dissolving with the curation of knowledge.

Although the link between communities and knowledge has long been established, much less is known about the relationship between the dynamics of communities and their link with collective cognitive processes. To the best of the authors’ knowledge, the present contribution is one of the first to shed light on this dynamic aspect of community knowledge production.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

This study investigates the optimum size for manufacturing firms and the impact of subcontracting on firms' likelihood of achieving their optimal scale in Vietnam.

Using data from the enterprise census in 2017 and 2021, the paper first estimates the production function to identify the optimum firm size for manufacturing firms and then, applies the logit model to investigate factors associated with the optimal firm size.

The study reveals that medium-sized firms exhibit the highest level of productivity. Nevertheless, a consistent trend emerges, indicating that nearly 90% of manufacturing firms in Vietnam operated below their optimal scale in both 2017 and 2021. An analysis of the impact of subcontracting on firms' likelihood to achieve their optimal scale emphasizes its crucial role, especially for foreign firms, exerting an influence nearly five times greater than that of the judiciary system.

The paper's findings offer crucial policy implications, suggesting that initiatives aimed at enhancing the overall productivity of the manufacturing sector should prioritise facilitating contract arrangements to encourage firms to reach their optimal size. These insights are also valuable for other countries with comparable firm size distributions.

This paper provides the first empirical evidence on the relationship between firm size and productivity as well as the role of subcontracting in firms' ability to reach their optimal scale in a country with a right-skewed distribution of firm sizes.

The purpose of this paper is twofold: first, to present a rigorous bibliometric analysis and a systematic literature review of the critical success factors (CSFs) for Building information modelling (BIM)-based digital transformation; second, to identify the relationship between the dimensions in favour of BIM implementation.

This study adopts a two-step approach to combine bibliometric and systematic literature review to explore the research topic of BIM and CSFs. Bibliometric tools such as Biblioshiny in R language and Ucinet software were applied to this study.

Besides identifying the two most influential authors (e.g. Bryde and Antwi-Afari), the key journal for disseminating articles, and the most influential countries in this discourse (e.g. Hong Kong and Australia), the study also identifies four pivotal research themes derived from the co-occurrence analysis of keywords: the fusion of sustainability and technology with BIM; practical application and its integration within construction management; innovation and engineering paradigms; and the advent of emerging technologies (e.g. Blockchain) within developing nations. Additionally, the paper introduces a comprehensive framework for selecting CSFs pertinent to BIM-centred digital transformation as viewed through the lens of dynamic capabilities.

This paper establishes a link between dynamic capabilities theory, CSFs, and BIM dimensions, presenting a multifaceted framework guiding future paths and offering practical insights for managerial and political decision-makers engaged in digital transformation endeavours. The study positions dynamic capabilities as pivotal, aligning digital technologies with continuous business performance, and advocates for a strategic focus on digital transformation.

This paper uses the complex proportionality assessment (COPRAS) method to examine the driving factors of Industry 4.0 (I4) technologies for lean implementation in small and medium-sized enterprises (SMEs).

Adopting I4 technology is imperative for SMEs seeking to maintain competitiveness within the manufacturing sector. A thorough understanding of the driving factors involved is required to support the implementation of I4. For this objective, the multi-criteria decision-making (MCDM) tool COPRAS was used to efficiently analyze and rank these driving elements based on their importance. These factors can help small and medium-sized firms (SMEs) prioritize their efforts and investments in I4 technologies for lean implementation.

This study evaluates and prioritizes the nine I4 factors according to the perceptions of SMEs. The ranking offers significant insights into the factors SMEs consider more accessible and effective when adopting I4 technologies.

The author's original contribution is to examine I4 driving factors for lean implementation in SMEs using COPRAS.

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

The existing literature offers various perspectives on integrating cryptocurrencies into investment portfolios; yet, there is a gap in understanding the behaviours, attitudes and cross-investment links of individual investors. This study, grounded in the modern portfolio theory and the random walk theory, aims to add empirical insights that are specific to the UK context. It explores four hypotheses related to the influence of socio-demographics, digital adoption, cross-investment behaviours and financial attitudes on cryptocurrency owners.

This study uses a logistic regression model with secondary data from the Financial Lives Survey 2020 to assess the factors impacting cryptocurrency ownership. A total of 29 variables are used, categorized into four groups aligned with the hypotheses. Additionally, hierarchical clustering analysis was conducted to further explore the cross-investment links.

The study reveals a significant lack of diversification among UK cryptocurrency investors, a pronounced inclination towards high-risk investments such as peer-to-peer lending and crowdfunding, and parallels with gambling behaviours, including financial dissatisfaction and a propensity for risk-taking. It highlights the influence of demographic traits, risk tolerance, technological literacy and emotional attitudes on cryptocurrency investment decisions.

This study provides valuable insights into cryptocurrency regulation and retail investor protection, underscoring the necessity for tailored financial education and a holistic regulatory approach for investment products with comparable risk levels, with the aim of minimizing regulatory arbitrage. It significantly enhances our understanding of the unique dynamics of cryptocurrency investments within the evolving financial landscape.