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This study aims to explore the influence of Fourier-feature enhanced physics-informed neural networks (PINNs) on effectively solving two-dimensional local time-fractional anomalous diffusion equations with nonlinear thermal diffusivity. By tackling the shortcomings of conventional numerical methods in managing fractional derivatives and nonlinearities, this research addresses a significant gap in the literature regarding efficient solution strategies for complex diffusion processes.

This study uses a quantitative methodology featuring a feed-forward neural network architecture combined with a Fourier feature layer. Automatic differentiation is implemented to ensure precise gradient calculations for fractional derivatives. The effectiveness of the proposed approach is showcased through numerical simulations across various sub-diffusion and super-diffusion scenarios, with fractal space parameters adjusted to examine behavior. In addition, the training process is assessed using the Fisher information matrix to analyze the loss landscape.

The results demonstrate that the Fourier-feature enhanced PINNs effectively capture the dynamics of the anomalous diffusion equation, achieving greater solution accuracy than traditional methods. The analysis using the Fisher information matrix underscores the importance of hyperparameter tuning in optimizing network performance. These findings support the hypothesis that Fourier features improve the model’s capacity to represent complex solution behaviors, providing the relationship between model architecture and diffusion dynamics.

This research presents a novel approach to solving fractional anomalous diffusion equations through Fourier-feature enhanced PINNs. The results contribute to the advancement of computational methods in areas such as thermal engineering, materials science and biological diffusion modeling, while also providing a foundation for future investigations into training dynamics within neural networks.

Human resources analytics (HRA) applications are of theoretical and practical interest to both researchers and practitioners. While organizations have begun to implement HRA applications, there is currently no established approach for measuring their performance. This study aims to address this gap in the literature by proposing a new approach for measuring the performance of HRA applications.

This study proposes a hybrid approach that combines the cumulative belief degree (CBD) and partial least squares structural equation modeling (PLS-SEM) to measure organizational HRA performance.

The performance measurement approach proposed in this study has the capacity to reveal the total HRA performance level of an organization, while also providing the opportunity to measure the performance of the subdimensions that make up HRA. These subdimensions include data sufficiency, technological capability, workforce capability, application level of HRA and organizational climate. This approach has the potential to assist organizations that do not currently utilize HRA in their operations to make an informed decision regarding the implementation of HRA and enables organizations to assess their potential performance if they were to implement HRA.

The proposed approach allows organizations to assess the performance of analytical applications in the human resources (HR) field. This assessment can be carried out at both the pre-implementation and post-implementation stages of HRA applications. Therefore, the approach provides a valuable contribution to organizations, enabling them to enhance their capabilities in this domain. Consequently, the study addresses a significant gap in practical research. Furthermore, in terms of the applicability of the developed HRA performance measurement model to diverse analytical domains, it paves the way for the advancement of other performance measurement studies.

The HRA performance assessment process encompasses multiple interrelated HRA subdimensions and performance indicators that can be measured using different scales. It is therefore essential to implement a flexible methodology that can convert diverse forms of evaluation into a unified scale and integrate them in order to effectively manage the inherent complexities and uncertainties associated with the assessment process. In this regard, the CBD approach proves particularly effective. In the CBD approach, a fuzzy set of linguistic terms is used to convert the performance indicator scores into a common scale and therefore takes into account the uncertainty inherent in the assessment process. In addition, it is also proposed to use the PLS-SEM method to combine CBDs.

During the pandemic, restaurants closed due to restrictions and economic instabilities. This research paper aims to investigate the motivational effect of nostalgia induced by fear of missing out (FOMO) upon the e-WOM about the dining place. Did FOMO evoke nostalgic memories and emotional symptoms with the dining place? It further inspected if the diners trust the restaurant for these nostalgic memories and their emotional connect.

A survey-based questionnaire was applied to collect data. A two-step method for analyzing the measurement and structural model was applied by using the principles of SEM to analyze the associations among key constructs in the proposed model.

Psychological constructs like nostalgia and personality drove the diners’ behavior of trust and e-WOM. The findings also confirmed the mediation effect of brand nostalgia, brand personality and brand trust between FOMO and e-WOM. Further, the model has elucidated the elaboration likelihood model (ELM) and usage gratification theories.

This paper has contributed to the branding literature by examining how brand constructs have mediated the path between FOMO and e-WOM. Multiple serial mediation effect among the key variables has been a novel contribution of this study. The ELM and the usage gratification theory have been integrated to extend the theoretical underpinnings in the hospitality sector.

This study aims to identify and model deterrents to adopt and institutionalize analytics and artificial intelligence in modern human resource (HR) using interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) approach.

A comprehensive investigation of the literature and feedback from experts led to the identification of 16 deterrents in this study. After that, the ISM tool is used to find connections between the identified deterrents in the HR ecosystem and MICMAC which helps in categorising deterrents on the basis of driving and dependence power and provides deeper insights into their roles and significance.

Employee resistance and HR transformation are highly influenced by other factors but exert minimal driving power. Data availability, leadership support, communication and collaboration, legal, ethical and regulatory compliance, and infrastructure and resources exhibit strong influence and dependence, making them highly sensitive and crucial. Training and development, learning culture and change management, and data privacy and security have strong driving power with minimal dependence, indicating their foundational role in shaping HR transformation.

This study will assist policymakers and owners/managers in the HR ecosystem in recognising and comprehending the importance and applicability of analytics and AI obstacles while developing HR strategies.

This study explicitly focuses on data analytics and AI technology in the current scenario. It also explores the relationship between deterrents and their driving and dependence powers.

This research examines the influence of the digital voice of consumers (DVoC) on the resilience of supply chains (SCR) within the renewable energy sector. The main goal is to identify and highlight the crucial aspects that enable the examination of this impact.

A comprehensive literature analysis was performed utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to ascertain pertinent factors. This research utilizes a comprehensive methodology that merges interpretive structural modeling (ISM) with Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis to assess the discovered variables.

The study identified 16 factors that contribute to both DVoC and SCR. Key influential factors include top management support, collaboration, feedback influence and flexibility. These factors play a significant role in enhancing SCR by facilitating better adaptation and response to disruptions.

The study is confined to the renewable energy sector and may lack generalizability to other businesses without additional validation. Subsequent studies ought to investigate the implementation of these findings across various industrial contexts to enhance comprehension of DVoC’s influence on SCR.

This study presents a new integrated framework for comprehending the function of DVoC in improving SCR. It presents significant practical consequences for stakeholders in the renewable energy sector, such as researchers, regulators, manufacturers and consumers, highlighting the necessity for a resilient and flexible supply chain.

This paper aims to investigate the extent of transgender (TG) inclusivity in workplaces, particularly comparing developed and developing economies. It seeks to address the social stigma faced by TG individuals in Asian economies, notably focusing on India. Through qualitative studies, the research identifies barriers hindering TG employment and proposes a theoretical framework to guide organizations in creating TG-inclusive work environments. The research intends to provide insights that enhance the experiences of TG employees while promoting diversity and inclusion in organizations.

This research comprises two qualitative studies aimed at first identifying barriers to TG employment through the lens of human resources (HR) professionals and then suggesting a theoretical framework to organizations for overcoming these barriers to achieve a TG-inclusive workplace. Thematic analysis, using Atlas.Ti software, and the interpretive structural modelling technique were used to synthesize a conceptual framework.

This study's outcomes highlight a substantial gap in achieving truly TG-inclusive workplaces, particularly in economies with prevalent social stigma like India. The findings show that traditional means may not be enough for building a TG-inclusive workplace, and organizations need to go the extra mile by promoting positive deviance and TG-centric corporate social responsibility initiatives to achieve results.

The results of this study provide insights for organizations seeking to improve the experiences of their TG employees and promote diversity and inclusion in the workplace. The incorporation of HR professionals' insights offers a practical perspective, and the conceptual framework proposed for organizations to overcome TG employment barriers is a novel contribution.

The recent pandemic (COVID-19) and the continuous ICT advancements have resulted in increased levels of technostress. On this basis, the present work tried to explore how technostress influences employees’ turnover intention with the mediation of work-exhaustion. Deploying the theoretical lens of job demands-resources theory, the authors also aim to investigate the part that positive psychological capital (PsyCap) has to play as a moderator in between technostress and work-exhaustion.

The study utilizes a time-lagged methodological design; data was gathered from 544 Indian IT employees. Additionally, PLS-SEM was used to carry out the aforementioned moderation-mediation analysis.

All the hypotheses proposed were confirmed. It was found that technostress significantly impacts employees’ turnover intention. Additionally, work-exhaustion does mediate the relationship between technostress and employees’ turnover intention. Furthermore, PsyCap did play the role of a moderator between Technostress and work-exhaustion.

This paper provides an augmented understanding of technostress in IT organizations and highlights the role of personal resources in aiding employees’ to deal with technostress.

This study is one of the early studies to highlight the role of positive psychological capital in mitigating the impact of technology-induced exhaustion and employees’ turnover intention.

The purpose of this study is to explore the mediation of organizational identification (OI) in the relationship between employees’ psychological capital (PsyCap) and their engagement in organizational-oriented citizenship behaviors (OCBO). We examine the proposed theoretical model through the lens of the job demand and resources (JD-R) model and social identity theory (SIT), focusing specifically on the healthcare setting.

Employing a structured questionnaire, responses were collected from 240 healthcare professionals working in multinational pharmacovigilance firms in select Tier-I Indian cities using purposive sampling. Model testing constituted performing statistical analyses using partial least squares-structural equation modelling (PLS-SEM) in the R environment, an open-source software.

Results revealed a significant positive association between PsyCap and OCBO. In addition, we found that OI mediated the relationship between PsyCap and OCBO. This suggests that practitioners can stimulate extra-role performance in employees through enhancing PsyCap and fostering psychological oneness with the organization.

The research emphasizes the significant role of employees’ personal resources, manifested in the form of PsyCap, in having a bearing on their attitudes and feelings towards the organization. We explore the under-researched latent mechanism of the PsyCap and OCBO relationship through employees’ social identification process.

In this research, we uncover the underlying mechanism of PsyCap and OCBO association by highlighting the latent role of OI on a unique set of healthcare professionals. 

Grounded in self-congruency theory, this study aims to investigate the impact of different types of congruence in social media influencer marketing on consumers’ online impulse buying intentions under the moderating influence of wishful identification.

This study collects survey responses from an online sample of 232 social media users and analyses them using partial least squares structural equation modelling.

This study delineates two distinct pathways influencing online impulse buying intentions within influencer marketing: direct consumer–product congruence and the conditional role of consumer–influencer congruence. Particularly, the alignment between a consumer’s self-image and the product’s attributes independently drives online impulse buying intentions. Conversely, consumer–influencer congruence, despite high alignment, fails to spur online impulse buying intentions unless amplified by wishful identification – the consumer’s aspirational desire to emulate the influencer. This finding underscores the complexity of impulsive consumer behaviours in the digital marketplace, highlighting the pivotal role of product appeal and the conditional influence of influencer relationships on spontaneous purchasing decisions.

This study pioneers by elucidating the congruence interplay between consumers, influencers and products in online impulse buying, emphasising wishful identification as a critical moderating factor. Theoretically, it expands self-congruency theory by detailing the distinct roles of congruence types on impulsive behaviours, notably underlining the essential role of wishful identification for the effect of consumer–influencer congruence. Practically, the insights equip brands with a deeper understanding of the key drivers behind impulsive purchases in an influencer-centric digital marketplace, offering strategic guidance for optimising influencer collaborations and product presentations to enhance consumer engagement and sales.

This study investigated the predictors of overall value for money in the hospitality industry concerning routine practices reinforcing SDG goals. The sequential mediation underlines the mechanism of theoretical viability and sustainability relevance of restaurant environment factors and customer-generated communication on social media to influence the value impressions of hospitality customers.

Cross-sectional design was utilized to obtain empirical data from customers of luxury restaurants in mega cities of Pakistan. Non-probability purposive sampling was deployed to collect data from 370 customers. The chained relationship of predictors was analyzed using Smart PLS.

The SDGs' integration with predictors provided theoretical insights into the role of physical spaces in achieving SDG 14 of sustainable consumption and production. The role of social media communication explains the mechanism of online discourse, which shapes perceptions of sustainable dining experiences.

This study explained sustainable consumption's relation to consumers' psychological undertakings and reinforced the dinners' decision-making processes to evaluate expectations influencing dining choices. This study helps to understand how sustainability issues transformed consumers' value perception and helps them take measures to minimize the environmental impact of their dining choices.

The sequential mediation model investigated with the lens of expectation disconfirmation theory provided relational clarity, which is difficult to achieve when comparing the perception of customers with performance variables. Distinguishing overall value for money from perceived value is essential for making data-driven decisions to utilize monetary resources efficiently.