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This paper aims at the analytical formulation of the electromagnetic features of flux switching permanent magnet (PM) machines with emphasis on the PM air gap flux density and armature magnetic reaction.

The PM air gap flux density is formulated considering three different analytical models. These differ by the incorporation of the air gap magnetic saliency level from the stator side. In addition, the armature magnetic reaction is investigated based on a simplified magnetic reluctance circuit that considers the flux switching permanent magnet machines magnetic circuit geometry specification. Then, the no- and on-load torque is predicted based on the two air gap flux densities.

It has been found that the PM air gap flux density considering the stator saliencies with trapezoidal magnetomotive force waveform presents the highest accuracy. Despite the simplicity of the magnetic equivalent circuit-based approach, the predicted air gap armature magnetic reaction is in good agreement with the finite element analysis (FEA) one. These lead to the analytical predictions of the no- and on-load torque which is characterized by an acceptable accuracy.

This work should be extended to experimental validation of the FEA results regarding the torque production generation.

The paper proposes an improved design-oriented analytical approach with emphasis on the PM air gap flux density and the armature magnetic reaction of flux switching PM machines.

SETARA is a well-known university rating tool in Malaysia. The study aims to enhance the transparency, accuracy, and reliability of SETARA assessment instrument by improving its weighting scheme for the domains, sub-domains, criteria, and indicators.

The study utilized a quantitative research design and collected responses from 29 academic administrators in both public and private higher education institutions in Malaysia. The four steps of the Analytic Hierarchy Process (AHP) were employed. An AHP questionnaire was developed, adopting the domains, sub-domains, criteria, and indicators of the SETARA assessment instrument. The “Superdecision” software was used to synthesize the pairwise comparison judgments and compute the consistency ratio for all group pairwise comparison matrices.

The AHP-assigned weights differed significantly from those assigned by the SETARA rating system. For instance, the weight for “Input” increased from 20 to 41.05 out of 100, and the weight for “Output” decreased from 40 to 25.52. The new SETARA rating instrument is expected to be used by the Ministry of Higher Education (MOHE) due to its scientifically justified foundation to ensure transparency, accuracy, and reliability.

The study is limited by small sample size for data collection. Future research could benefit from a larger and more diverse sample, including participants from a number of national-level universities and relevant agencies like Malaysia Qualification Agency (MQA) and MOHE. Additionally, the research focused solely on recalculating weights and did not consider modifying the criteria set. Further investigation is needed to determine if some criteria can be added or removed.

This research offers practical implications for refining the SETARA assessment mechanism and improving the quality of higher education in Malaysia. The modified weights provide guidance to Higher Education Institutions (HEIs) on where to focus their efforts to ensure quality. The transparent methodology introduced by this study can serve as a model for enhancing educational quality evaluations in Malaysia and potentially in other higher education systems worldwide.

The originality of this study lies in its innovative application of the AHP to the SETARA assessment tool, addressing a crucial gap in the assessment process. By providing a comprehensive and transparent methodology for assigning percentages and weights, this research offers a practical blueprint for enhancing the precision and impact of educational quality evaluations in Malaysia and beyond.

The pandemic has necessitated employees to work virtually due to mandatory work-from-home setup. Since every employee is not comfortable working online owing to their individual differences which impact performance, thus, it is essential to identify individual characteristics governing performance. As per conventional theories, cognition and metacognition have a significant impact on employee performance, and the key to performance in a collaborative online environment also is metacognition. However, this has been scarcely explored in the context of virtual workspace. This study, therefore, empirically investigates the influence of metacognition and its sub-domains on employees' virtual performance given the challenges they face in a virtual work environment.

The cross-sectional study used a purposive sampling technique for data collection. Data collected from 534 professionals with high and low levels of metacognitive ability is analysed using univariate analysis to ascertain whether metacognitive ability helps employees deal with challenges associated with virtual work environments and perform better.

Results confirm a significant relationship between the level of metacognitive ability and virtual performance. Further, the findings also confirm the interaction effect of the level of metacognitive ability and challenge of maintaining work and non-work boundaries and the need for the physical presence of team member/s in predicting virtual performance.

This study is the first empirical attempt to examine the linkage between metacognitive ability and performance among professionals in the context of post-pandemic virtual work environment and challenges.

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 principal objective of the study is to analyze the influence of ethnicity, culture and collective intelligence in entrepreneurial creativity, innovation and marketing of artisanal beer in Mexico.

The qualitative data have been gathered by conducting four workshops with twelve respondents in each workshop across four states of Mexico comprising Mexico City, Puebla, Queretaro and Guadalajara. These workshops were held for four hours during the pre-lunch period over the weekends, which was participated by a mix of entrepreneurs and consumers.

Artisanal entrepreneurship is driven by the culture, ethnicity, collective intelligence and frugal innovations. Ethnic products generate patriotic feeling and consumption for a social cause to encourage artisans at the grassroots with the local tags. Results also indicate that social media and crowd cognition play an important role in developing creative artisanal beer.

This study is founded on the theoretical maxims of social learning theory (SCT), social cognitive theory and theory of creativity. The contextual interpretation of SCT explains the socialization of concepts by modelling emotions and behavior to derive structural experiences as observed in artisanal entrepreneurship.

Entrepreneurs can develop brand emotions, boost anthropomorphic feelings and inculcate the sense of nationalism among consumers to market ethnic brands and develop social consciousness towards consumption of “Made in Mexico” products.

Artisanal beer face major challenge of customer outreach by enhancing the brand proximity and ethnic values. Ethnic products hold a strong image in niche market and need to be stimulated by the experience sharing through social media and community interactions.

This research study significantly contributes to the existing literature on ethnic entrepreneurship and creativity using innovative research approach.

Technology serves as a key catalyst in shaping society and the economy, significantly altering customer dynamics. Through a deep understanding of these evolving behaviors, a service can be tailored to address each customer's unique needs and personality. We introduce a strategy to integrate customer complaints with their personality traits, enabling responses that resonate with the customer’s unique personality.

We propose a strategy to incorporate customer complaints with their personality traits, enabling responses that reflect the customer’s unique personality. Our approach is twofold: firstly, we employ the customer complaints ontology (CCOntology) framework enforced with multi-class classification based on a machine learning algorithm, to classify complaints. Secondly, we leverage the personality measurement platform (PMP), powered by the big five personality model to predict customer’s personalities. We develop the framework for the Indonesian language by extracting tweets containing customer complaints directed towards Indonesia's three biggest e-commerce services.

By mapping customer complaints and their personality type, we can identify specific personality traits associated with customer dissatisfaction. Thus, personalizing how we offer the solution based on specific characteristics.

The research enriches the state-of-the-art personalizing service research based on captured customer behavior. Thus, our research fills the research gap in considering customer personalities. We provide comprehensive insights by aligning customer feedback with corresponding personality traits extracted from social media data. The result is a highly customized response mechanism attuned to individual customer preferences and requirements.

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.

Extant research indicates that fear of missing out (FoMO) caused by the negative influence of media and word-of-mouth (WOM) leads to panic buying and generates a negative impact on consumer well-being. However, the mechanism that can minimise or abort this impact remains understudied. Therefore, in this study, we examine how consumer self-regulation functions as a brake mechanism to intervene with the negative influences of media and WOM on FoMO.

Data were collected from a representative sample in Australia. Hypotheses were tested by applying generalised structural equation modelling (GSEM), and analysis was conducted using the statistical software Stata 17.

Self-regulation is negatively influenced by media channels and WOM but is positively influenced by media content. Consumer self-regulation acts as a brake mechanism for FoMO. Panic buying, which is triggered by FoMO, has a significant impact on negative emotional well-being.

The limitations of the study are associated with the survey data collection.

We extend the knowledge of how self-regulation works as a brake mechanism for the complex FoMO construct consisting of a perception of missing out accompanied by irrational behaviours. Self-regulation emerges as a brake mechanism for FoMO. Hence, if self-regulation is practiced at the inception of the media and WOM exposure, it can counteract FoMO and potentially abort its’ impact on panic buying.

From a practical perspective, policymakers could help emotionally vulnerable individuals better engage in self-control practices through support programmes and workshops aimed at assisting the public in coping with overwhelming and intense adverse emotions experienced during and following various crises. Vulnerable cohorts, particularly the younger generation who are arguably more susceptible to FoMO, need to be studied more thoroughly in the marketing domain.

The role of self-regulation has been studied thinly in marketing literature, particularly in relation to offsetting irrational consumer behaviours. The originality of our study is that it extends and broadens the understanding of the role of self-regulation in the context of pandemics and addresses the inconclusive evidence of the impact of self-regulation on FoMO.

Competency-based human resource management (CBHRM) is a key component of all organisations but needs to be regularly reviewed and evaluated to ensure the quality of healthcare professionals. One common taxonomy of competency domains for health professions is from Englander et al., where this paper aims to conduct a large-scale analysis based on topic modelling to investigate the extent to which the competency framework for the healthcare sector is applied in the German job market of health professions.

The quantitative NLP analysis of a dataset consisting of 3,362 online job advertisements of nurses and doctors was scraped from a German job portal. The data was pre-processed according to Miner et al. For the analysis, the authors applied unsupervised (e.g. HDP, LDA) and supervised (BERTopic) methods and content analysis. Based on the extracted topics a word list was created and these words were coded to existing dimensions of the competency framework of Englander et al. or new dimensions were created.

Comparing methodologies, HDP (unsupervised) and BERTopic (supervised) were the best performing while the BERTopic algorithm outperforms HDP. For the doctor dataset 46% of one main dimension was identified but with an overall coverage of 69%, for the care dataset is weaker with 30.8% but an overall coverage of 100%. Additionally, the taxonomy was enhanced with supplementary competencies of “personality/characteristics” and “leadership” as well as two facets of job description which are “place of work” and “job conditions”.

On the one hand selected dimensions of the taxonomy could be clearly identified but on the other hand, there is a documented gap between the taxonomy and the competencies advertised. One cause may lie in the NLP algorithms but applicants may also have the same difficulties when reading the OJAs. Thus, practitioners should carefully review OJAs regarding better separating explicit competencies they are searching for. For the scientific development of new competency frameworks, our data-driven approach exemplified an extension of a given taxonomy.

This paper aims to provide a method for obtaining physically sound temperature fields to be used in geophysical inversions in the presence of immersed essential conditions.

The method produces a thermal field in agreement with a given location of the interface between the Lithosphere and Asthenosphere. It leverages the known location of the interface to enforce the location of a given isotherm while relaxing other constraints known with less precision. The method splits the domain: in the Lithosphere the solution is immediately obtained by standard procedures, while in the Asthenosphere a minimization problem is solved to fulfill continuity of temperatures (strongly imposed) and fluxes at the interface (weakly imposed).

The numerical methodology, based on the relaxation of the bottom fluxes, correctly recovers the thermal field in the complete domain. To obtain bottom fluxes following geophysical expected values, a constrained minimization strategy is required. The sensitivity of the method could be improved by relaxing other quantities such as lateral fluxes or mantle velocities.

A statement of the energy balance problem in terms of a known immersed condition is presented. A novel numerical procedure based on a domain-splitting strategy allows the solution of the problem. The procedure is tailored to be used within geophysical inversions and provides physically sound solutions.