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This paper explores the management of emotions and emotional challenges that human trainees face when interacting with a robot or a humanoid trainer.

This study draws on existing academic and grey literature on robot and humanoids-based training with algorithms, bots, and artificial intelligence (AI).

The study highlights the need for personalized feedback, clear communication, and the establishment of trust between the trainee and robotic trainer. The study discusses the strategies to manage emotions like anger, disgust, fear, happiness, sadness, and surprise that are experienced by human trainees.

The research provides an accessible summary of setting realistic expectations for the emotional experience of working with a robotic trainer to help manage expectations and reduce disappointment.

The managers in charge of implementing robotic training programs can provide education and resources to help individuals effectively manage emotions when working with a robotic trainer.

This study systematically reviews the existing literature on the application of gamification in logistics and supply chain management (LSCM). This study aims to categorise the literature in various logistics and supply chain domains, to conceptualise the gamification framework pertinent in the context of LSCM and to provide a research agenda for scholars in the area.

The study’s methodology adopts the Preferred Reporting Items for Systematic Review and Meta-Analysis framework to conduct a systematic literature review. Forty relevant papers published from 2012 to 2023 are included in the analysis.

Augmented reality, blockchain, education, sustainability and warehousing have been identified as the key focus areas in which gamification is applied. Furthermore, the paper highlights different research approaches used to study these domains, maps the literature with gamification constructs (affordances, psychological outcomes and behavioural outcomes) and provides potential research avenues for future scholars.

This review offers evidence of the impact of gamification on workforce dynamics, employee motivation, job satisfaction, trust, employee engagement and productivity. The study significantly contributes to the academic community by offering a conceptual framework and meaningful avenues for future researchers.

This research work contributes to the gamification, logistics and supply chain literature by providing a more comprehensive and methodical knowledge of the field. This study adds to the body of knowledge by offering a reference framework for future scholars based on a synthesis of the studies published so far in the area.

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.

Scholarly works on sex work and sex workers are mostly confined to discourses on human trafficking and the incidence of HIV/STIs among sex workers. Although crucial, this restricted focus has neglected the reality that sex workers are a diverse community, and while their challenges may appear to be linked at first glance, they differ greatly. While extensive research has been conducted on sex workers working in more open settings like brothels, hotels, and streets, there is a scarcity of research on sex workers working in more private spaces, such as, for instance, their own homes. Within the hierarchy of sex workers, home-based sex workers (HBSWs) among the indoor sex workers dominate commercial sex transactions. However, they are often overlooked due to their covert nature and invisible landscape. This chapter addresses the knowledge gap by examining the work lives and conditions of home-based female sex workers (FHBSWs) in Punjab. The study analyzes the complex lives of sex workers who use their home as both a family unit and a workplace. A detailed analysis of the risks and vulnerabilities they face in their daily lives and their coping strategies is also examined in this chapter. The study points out that although working from home may have positive outcomes for sex workers, the integration of sex work into the home environment exposes them to several challenges. Hence, the study emphasizes the need for tailoring interventions for sex workers who operate in different physical environments so that their unique needs and challenges are well addressed.

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.

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.

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.

Authors analyze a business model (BM) based on organizational performance. BMs are strategic tools used to achieve high performance. This study is based on two characteristics of causal complexity: conjunction and equifinality. Authors also examine the applicability of causal asymmetry in the relationship between BMs and organizational performance.

Generally, the relationship between BM elements and organizational performance is analyzed using a correlational approach. This relationship is marked by causal complexity, which cannot be analyzed via such approach. Authors applied a fuzzy-set qualitative comparative analysis with data from three time-periods and two performance variables for pharmaceutical firms.

Qualitative comparative analysis revealed that high performance resulted from configurations (combinations) of BM elements and not from the effects of individual elements. In addition, multiple configurations are available for achieving high performance. Causal asymmetry was observed in the configuration of the BM elements for high and low performances.

Using qualitative comparative analysis of data sets from three time-periods in the context of the pharmaceutical industry BM, authors integrated the theoretical constructs of causal complexity, namely conjunction, equifinality and causal asymmetry.

Findings related to conjunctions will help managers shift their focus from individual BM elements to combinations of BM elements. Additionally, the findings related to equifinality and causal asymmetry will allow flexibility in designing their company’s BM according to the resource constraints their company faces.

This was one of the first few studies on BMs using the twin indicators of the organizational performance relationship and causal complexity.

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.