Search results

The viewpoint paper aims to highlight the assistive role that Generative artificial intelligence (Gen AI) can play in the design of learning and development programs for employees with special needs. The article discusses the challenges, benefits and reasons why Gen AI should be used to manage diversity, equity and inclusion by creating personalized and customized training and development programs.

The viewpoint paper is based on reviewing articles and videos on the application of Gen AI in learning and development.

Gen AI offers immense opportunities to design personalized learning solutions for employees with special needs due to disability that can be physical or cognitive. The AI-based solutions support special learners by customizing assistive technology-based solutions and content based on the level of disability and need of the learner. This paper also highlights the importance of synergy between the training department, government and technology solution providers.

The viewpoint paper fills in an important gap by discussing the role that Gen AI can play by facilitating the learning and development of employees with unique skills.

The purpose of this study is to provide an overview of artificial intelligence (AI) in the talent management sphere. The study seeks to contribute to the body of knowledge with respect to human resource management and AI by conducting a literature review on the integration of AI in talent management, synthesising existing approaches and frameworks, as well as emphasising potential benefits.

The study adopts desk research, computational literature review (CLR) and uses topic modelling [with bidirectional encoder representations from transformers (BERTopic)] to throw light on the diffusion of AI in talent management.

The study’s main finding is that the area of AI in talent management is on the verge of gradual development and is in tandem with the growth of AI. We deduced that there is a link between talent management practices (planning, recruitment, compensation and rewards, performance management, employee empowerment, employee engagement and organisational culture) and AI. Though there are some known fears with regards to using the innovation, the benefits outweigh the demerits.

The current study has some limitations. The scope and size of the sample are the primary limitations of this study. No form of qualitative analytics was used in this study; as a result, the information obtained was limited. The study provides a snapshot of AI in talent management and contributes to the lack of literature in the joint fields. Also, the study provides practitioners and experts an overview of where to target investments and resources if need be.

The originality of this study comes from the combination of CLR methods and the use topic modelling with BERTopic which has not been used by previous reviews. In addition, the salient machine learning algorithms are identified in the study, which other studies have not identified.

Deep-learning techniques are recently gaining a lot of importance in the field of turbulence. This study focuses on addressing the problem of data imbalance to improve the performance of an existing deep learning neural network to infer the Reynolds-averaged Navier–Stokes solution, proposed by Thuerey et al. (2020), in the cases of airfoils with high wake formation behind them. The model is based on a U-Net architecture, which calculates pressure and velocity solutions for fluid flow around an airfoil.

In this work, we propose various methods for training the model on selectively generated data with different distributions, which would be representative of the under-performing test samples. The property we chose for selectively generating data was the fraction of negative x-velocity in the domain. We have used Grad-CAM to compare the layer activations of different models trained using the proposed methods.

We observed that using our methods, the average performance on the samples with high wake formation (i.e. flow over airfoils at high angle of attack) has improved. Using one of the proposed methods, an average performance improvement of 15.65% was observed for samples of unknown airfoils compared to a similar model trained using the original method.

This work demonstrates the use of imbalanced learning in the field of fluid mechanics. The performance of the model is improved by giving significance to the distribution of the training data without changes to the model architecture.

Previous research has indicated that entrepreneurial marketing (EM) positively influences small and medium-sized enterprises’ (SMEs) performance. While most studies have examined the relationship in a stable environment, EMs’ effectiveness during environmental instability remains uncertain. Therefore, the study aims to investigate the influence of EM on Indian manufacturing-based SMEs’ performance during the COVID-19-induced environmental instability. Additionally, it explores the mediating role of innovative performance in the relationship between EM and SME performance.

The data were collected by distributing a structured survey questionnaire to 302 owners/managers of SMEs. Hypotheses were tested using structural equation modeling (SEM).

The result indicates that EM significantly impacts both innovation and SME performance. Furthermore, the innovative performance partially mediates the link between EM and SME performance. The findings suggest that even within severely affected sectors (manufacturing) during the pandemic, SMEs can achieve growth and innovation through effective EM practices.

This study validates the theoretical notion that EM remains effective even in unpredictable environments such as the COVID-19 pandemic. The findings offer valuable insights for SMEs seeking innovative strategies to enhance their performance, particularly those in emerging economies.

Prior studies have relied on a single layer of abstraction to analyze the impact of EM. The present study is the first to extend standard construct (EM) conceptualization. Furthermore, it evaluated the efficiency of EM in situations characterized by instability, which is rare in the EM and SME literature.

This study aims to examine empirically the nature of supply chain disruptions caused by the COVID-19 pandemic, particularly on the Indian automobile sector.

The authors evaluate the stock market performance of individual company and its quantitative relationship to certain variables related to company’s supply chain.

The authors analysed the company’s operations considering several ratios like asset intensity, company size, labour intensity and inventory to revenue.

The results of analysis can help the companies to understand how disruptions in the supply chain can affect the company’s operations and how it is perceived by the investors in the stock market.

Also, investors are benefitted, as they can understand how different companies with different operational characteristics react to global disruptions in supply chains, which in turn would help them to find better investment opportunities.

Although there is some literature available on the qualitative as well as quantitative analysis, the authors go further to analyse the impact of supply chain disruption on the stocks of the automobile sector.

COVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.

The eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.

The empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.

The present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.

The purpose of this paper is to study the influence of inflated energy prices on the capital structure of Indian manufacturing corporations and to investigate whether the capital structure of Indian firms is driven by demand shocks or supply shocks during the study period.

After conducting a thorough review of the capital structure and inflation-based research studies, panel data-based regression model and correlation matrix have been used as statistical tools for Indian manufacturing sector available with the Centre for Monitoring Indian Economy Prowess database.

The results suggest that variables like the presence of inflated energy prices had adversely influenced the capital structure of Indian corporations. Not only this, the study also highlights that factors pertaining to the demand shock had induced Indian corporations to have higher debt levels in the capital structure.

This study has laid some ground work to explore the influence of inflation on capital structure of Indian firms upon which a more detailed evaluation could be based.

To the best of the authors’ knowledge, this study is the first that explores the influence of inflated energy prices on the capital structure of manufacturing firms in India by using the most recent data.

Gourd fibres (GF) are a natural biodegradable fibre material with excellent mechanical properties and high tensile strength. The use of natural fibres in composite materials has gained popularity in recent years due to their various advantages, including renewability, low cost, low density and biodegradability. Gourd fibre is one such natural fibre that has been identified as a potential reinforcement material for composites. However, it has low surface energy and hydrophobic nature, which makes it difficult to bond with matrix materials such as polyester. To overcome this problem, chemically adapted gourd fibre has been proposed as a solution. Chemical treatment is one of the most widely used methods to improve the properties of natural fibres. This research evaluates the feasibility and effectiveness of incorporating chemically adapted gourd fibre into polyester composites for industrial fabrication. The purpose of this study is to examine the application of chemically modified GF in the production of polyester composite engineering materials.

This work aims to evaluate the effectiveness of chemically adapted gourd fibre in improving the adhesion of gourd fibre with polyester resin in composite fabrication by varying the GF from 5 to 20 wt.%. The study involves the preparation of chemically treated gourd fibre through surface modification using sodium hydroxide (NaOH), permanganate (KMnO₄) and acetic acid (CH₃COOH) coupling agents. The mechanical properties of the modified fibre and composites were investigated. It was then characterized using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) to determine the changes in surface morphology and functional groups.

FTIR characterization showed that NaOH treatment caused cellulose depolymerization and caused a significant increase in the hydroxyl and carboxyl groups, showing improved surface functional groups; KMnO₄ treatment oxidized the fibre surface and caused the formation of surface oxide groups; and acetic acid treatment induced changes that primarily affected the ester and hydroxyl groups. SEM study showed that NaOH treatment changed the surface morphology of the gourd fibre, introduced voids and reduced hydrophilic tendencies. The tensile strength of the modified gourd fibres increased progressively as the concentration of the modification chemicals increased compared to the untreated fibres.

This work presents the designed composite with density, mechanical properties and microstructure, showing remarkable improvements in the engineering properties. An 181.5% improvement in tensile strength and a 56.63% increase in flexural strength were got over that of the unreinforced polyester. The findings from this work will contribute to the understanding of the potential of chemically adapted gourd fibre as a reinforcement material for composites and provide insights into the development of sustainable composite materials.