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This paper aims to develop a new 3D analytical model in cylindrical coordinates to study radial flux eddy current couplers (RFECC) while considering the magnetic edge and 3D curvature effects, and the field reaction due to the induced currents.

The analytical model is developed by combining two formulations. A magnetic scalar potential formulation in the air and the magnets regions and a current density formulation in the conductive region. The magnetic field and eddy currents expressions are obtained by solving the 3D Maxwell equations in 3D cylindrical coordinates with the variable separation method. The torque expression is derived from the field solution using the Maxwell stress tensor. In addition to 3D magnetic edge effects, the proposed model takes into account the reaction field effect due to the induced currents in the conducting part. To show the accuracy of the developed 3D analytical model, its results are compared to those from the 3D finite element simulation.

The obtained results prove the accuracy of the new developed 3D analytical model. The comparison of the 3D analytical model with the 2D simulation proves the strong magnetic edge effects impact (in the axial direction) in these devices which must be considered in the modelling. The new analytical model allows the magnetic edge effects consideration without any correction factor and also presents a good compromise between precision and computation time.

The proposed 3D analytical model presents a considerably reduced computation time compared to 3D finite element simulation which makes it efficient as an accurate design and optimization tool for radial flux eddy current devices.

A new analytical model in 3D cylindrical coordinates has been developed to find the electromagnetic torque in radial flux eddy current couplers. This model considers the magnetic edge effects, the 3D curvature effects and the field reaction (without correction factors) while improving the computation time.

The purpose of the study is to investigate the relationship between workplace incivility (WIN) and ineffectual employee silence (IES) through rationalized knowledge-hiding (RKH) and regulation of emotion, drawing on the conservation of resources (COR) and social exchange theory (SET).

The study employed a cross-sectional design and used the partial least squares (PLS)-structural equational modeling (SEM) algorithm to test the reliability, validity of the measurement and hypotheses using a sample of 252 information technology (IT) professionals.

The results demonstrate that experienced WIN and RKH behavior significantly exacerbate IES. On the other side, the regulation of emotion decreases the negative influence of WIN and aids in the reduction of IES.

The study suggests that organizations should take appropriate measures to alleviate WIN, which might prevent concealing information/knowledge, IES and encourage employees to practice regulation of emotion.

The study significantly contributes to the relationship between uncivil behavior and ES and expands the knowledge on the mediating roles of RKH and regulation of emotion.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

In today’s business world, adopting social commerce for day-to-day operations has increasingly become an important phenomenon. Several factors have been identified by previous researchers regarding the adoption of social commerce, but academic research is scarce on the relationship between the factors influencing social commerce adoption and small and medium-sized enterprises (SMEs) in the post-COVID-19 situation. This study aims to identify the impact of technological, environmental and entrepreneurial factors on the adoption of social commerce by SMEs in Sri Lanka.

A quantitative study utilised the deductive approach and collected data through a field survey by distributing a five-point Likert scale questionnaire to conveniently selected respondents from Sri Lankan SMEs. Structural equation modelling (SEM) was used for the analysis of 384 responses.

The results revealed that technological factors [technology availability (TA) and cost-effectiveness (CE)], environmental factors [bandwagon effect (BE)] and entrepreneurial factors [attitude (AT), innovativeness (IN) and IT knowledge (IK)] have a significant impact on the social commerce adoption of SMEs in Sri Lanka. This study, as the first of its type, offers insightful information on the influence of variables on the adoption of social commerce after the COVID-19 pandemic.

Similar to any research, this study also has inherent limitations. Due to time and financial restrictions, the study’s convenience sampling method was adopted. The study’s possible limitation is its narrow focus, which could mean that it only examines a select few social media (SM) networks. The study’s conclusions might be less generalised since it focused on the western province of Sri Lanka. Future studies should take a cross-cultural strategy to explore the influence of social commerce adoption to improve the generalisability of research findings.

This study provides an in-depth assessment of critical factors, facilitating policymakers, owners, leaders and managers (decision-makers) to gain insight into the real influencing factors on social commerce adoption and the significance of SM. The study helps them comprehend how outstanding governance and knowledge of influencing factors can boost SME success in various ways. For example, research reveals that various factors have a major influence on social commerce adoption.

There has been limited research conducted on social commerce adoption after the COVID-19 pandemic period; thus, this study looked at the variables influencing it amongst SMEs in a South Asian developing country like Sri Lanka after the pandemic lasted for two years. By placing a strong emphasis on the role of entrepreneurial characteristics and the available technology within one single framework in the context of SMEs and their involvement with social commerce adoption, this study contributes to the past literature by emphasising the role of several significant factors in SMEs' adoption of social commerce. Whilst previous studies looked at multiple factors influencing the adoption of social commerce globally, this study focussed on how these factors have a significant impact on SMEs in Sri Lanka.

This study developed a multi-perspective framework combining technological, environmental and entrepreneurial factors influencing SMEs to adopt social commerce. The study provides a contribution to the literature on social commerce adoption from the perspective of SMEs in a developing country like Sri Lanka after COVID-19. Exclusively, it examines the impact of entrepreneur-related factors on social commerce adoption.

In parallel with the rising importance of innovation and digital technologies, research on platform ecosystems is rapidly accumulating. This study aims to summarize the current research published in leading technical and innovation management (TIM) journals and provide recommendation for future research.

In this paper, the authors combine multiple quantitative literature review methods (social network analysis, citation analysis and co-citation analysis) with a systematic literature review of articles published in the 13 most influential TIM journals over a 15-year period.

The citation network of 168 selected papers is sparse, with low network density indicating the emerging nature of the field. Using network centrality values, the authors identified the 33 most influential articles. The review of methodologies in these articles found that conceptual and case study research dominate, suggesting the need for additional confirmatory and quantitative analysis. Co-citation analysis of the references identified six research clusters: foundation, network-centric innovation, complementor management, platform competition, ecosystemsand product platforms.

This analysis is among the first to examine the knowledge structure of platform ecosystem research in the TIM domain by using multiple evidence-based analysis methods. The authors also apply the theory, context, characteristics and methodology framework to suggest areas for future research.

The objective of the study is to systematically review the existing research in the topical domain of farmers’ direct link with supermarkets. The authors present a state-of-the-art structure of the field and provide directions for future research in the domain. The major aim of this study is to synthesize the research field and answer some specific questions, like what do we know about this field and where should we be heading.

A pool of 275 articles published from 2002 to 2022 were retrieved from the Scopus database and analyzed using the R-based Biblioshiny and visualized using VOSviewer. The research design is a mix of quantitative bibliometric technique and qualitative content analysis. Bibliometric method ensures the objectivity while content analysis ensures the scholarly evaluation.

Major findings include production trend, dominant keywords, leading publication outlets and country-wise analysis of the selected articles. The authors found that sub-domains like economic aspects, participation hurdles and the rise of supermarkets are the most researched topics while operational issues, their pragmatic solutions, sustainability and innovation are the emerging sub-fields that need more academic attention.

The major limitation is the use of single data source, i.e. Scopus, and it is quite possible that useful studies that are not covered by Scopus remain excluded.

This study is one of the first attempts to systematically review the previous research on the selected topic. It will help researchers to understand the present status, identify future research directions, and pursue more reasonable and relevant topics of research.

This paper aimed to explore the presence of altmetric data across the sub-categories of the medical science discipline and also explore whether the openness of articles results in (dis)advantage for altmetrics mentions.

The research implies data carpentry methods for gathering bibliographic data related to narrow fields of medical science discipline from the Scopus database with at least one Indian author affiliation during 2012–2021. The corresponding data were also collected from three different sources: Altmetric.com, Mendeley.com and Unpaywall.org, using OpenRefine and REST/API calls. Further, the authors observed open access altmetric advantages (OAAA) and categorical OAAA (COAAA) across seven altmetric platforms for all articles as well as discipline-wise.

The result shows that the overall coverage of altmetric events is still low, but it shows an increasing trend from the past. Mendeley has the largest coverage; almost 97.12% of publications are covered. The health policy discipline has extensive coverage across altmetric platforms (nearly 57.40% of publications in altmetrics and 99.23% in Mendeley), whereas the drug guides has the lowest (almost 0.92% in Altmetrics and 77.05% in Mendeley). Moreover, the OA articles have been highly covered in altmetrics than those of non-OA articles, and bronze OA articles covered mostly compared to others. News registered with the significant OA altmetric advantages across disciplines. Categorically, bronze and hybrid OA have the largest altmetric advantages.

This research is a unique attempt to apply OAAA and COAAA to explore OA altmetric advantages of narrow subject categories of medical science disciplines.

This study presents a systematic review of the literature on monetary policy and corporate investment together with bibliometric analysis.

This study selected 455 articles from databases, Scopus and Web of science and the papers are reviewed systematically to identify their theoretical and empirical contributions.

The results reveal that monetary policy can influence corporate investment. Post to the economic crisis (2008), there is an exponential growth in the number of publications. Berger, A., and Hubbard are the two prominent authors based on the highest citation score, whereas Marquez, R., and Vermuelen, P. are the two prolific authors, subject to their highest h-index. Journal of Banking & Finance was the top journal (total citations = 1482) and 5 publications.

This study positively contributes to the comprehensive understanding of corporate investment, monetary policy transmission and firm capital structure choices.

To the best of the author’s knowledge, this is the first study that conducts a systematic review of the influence of monetary policy on corporate investment.

This study aims to examine the current state of academic research pertaining to the role played by artificial intelligence (AI) in the achievement of a critical sustainable development goal (SDG) – poverty alleviation and describe the field’s development by identifying themes, trends, roadblocks and promising areas for the future.

The authors analysed a corpus of 253 studies collected from the Scopus database to examine the current state of the academic literature using bibliometric methods.

This paper identifies and analyses key trends in the evolution of this domain. Further, the paper distils the extant literature to unpack the intermediary mechanisms through which AI and related technologies help tackle the critical global issue of poverty.

The corpus of literature used for the analysis is limited to English language studies from the Scopus database. The paper contributes to the extant research on AI for social good, and more broadly to the research on the value of emerging technologies such as AI.

Policymakers and government agencies will get an understanding of how technological interventions such as AI can help achieve critical SDGs such as poverty alleviation (SDG-1).

The primary focus of this paper is on the role of AI-related technological interventions to achieve a significant social objective – poverty alleviation.

To the best of the authors’ knowledge, this is the first study to conduct a comprehensive bibliometric analysis of a critical research domain such as AI and poverty alleviation.

The purpose of this paper is to conduct a critical analysis of the commonly projected visions on the future of built environment, focusing on transformative research. The primary question is will the construction sector be able to make the projected transformative leap even if the history of technology adoption in construction suggests otherwise? And, what role can academic research play?

This paper is based on a reflective research and qualitative review of academic articles, white papers and reported projections for the future of construction. The reflections are based on discussions with colleagues and students, including thought experiments.

There is a general agreement across various sources about the key technical and social drivers for the future of construction. However, these projections seem to be emanating from industry insiders, and more diversity and creativity is needed in exploring alternative possibilities.

The paper should be useful for researchers in assessing their research strategy, especially those aiming to focus on the future of construction and transformative research. The findings of this paper suggest the need for collaboration and explorations with diverse disciplines, including those that may not appear immediately connected to digital construction.

The paper should be useful for individuals and organizations, especially start-ups that are seeking novel opportunities to disrupt the future of construction.

The originality and value of this research lies in a timely critique of the commonly projected trends in the future of digital construction. The use of reflective research and thought experiments emphasizes the need for divergent thinking and creative research methods in construction research.