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The study aims to develop an in-depth understanding of challenges faced by Indian women professionals during the pandemic and the human resource (HR) initiatives like effective communication, taken by the organizations to mitigate the plight of these professionals.

A mix of two qualitative research methods namely focus groups in-depth and one-to-one in-depth interviews was used. A total of 32 females working with different organizations participated.

The thematic analysis revealed themes related to challenges faced by working women-gendered burnout, mental health issues, increased household responsibilities, job insecurity, work-life conflict, gender inequalities, reduced internal communication and financial independence, domestic violence and exploitation. The major themes that emerged for the organizational initiatives were flexible working hours, equal women representation in response to planning and decision making, driving transformative change for gender equality, paid leaves for family care, caregiving bonus, leadership development seeds, increased female recruitments, transparent communication and counseling sessions.

The study establishes a holistic understanding of the plight of Indian women professionals and the consequent organizational interventions accompanied by transparent communication. It adds rigor to the evolving literature on COVID-19 and enriches the theoretical narrative of policy adaptations by industry practitioners for aligning them with employee needs. This helps in routing the policy design and implementation in light of the challenges faced.

The study presents an in-depth understanding of challenges faced by women employees; and provides a foundation for identifying human resource management (HRM) interventions customized for working females. It also proposes a framework implementable in the recovery phase, deploying critical strategic shifts like reflection, recommitment and re-engagement of the women workforce in order to maximize their efficacy for rapidly evolving organizational priorities.

The purpose of this paper is to facilitate perishable product supply chain (PPSC) managers and practitioners to assess PPSC failure events. The paper proposed fault tree methodology for assessing failures associated with PPSC for evaluating the performance in terms of effective PPSC management adoption.

Initially, different failure events were identified from literature and semi-structured interviews from experts. Fault tree model was developed from the identified failure events. Probability of failure events was calculated using Poisson distribution based on the annual reports and interviews conducted from experts. Further, qualitative analysis – minimum cut sets (MCSs), structural importance coefficient (SIC) – and quantitative analysis – Birnbaum importance measure (BIM), criticality importance factor (CIF) and diagnosis importance factor (DIF) – were performed for ranking of failure events. In this study, fault tree development and analysis were conducted on apple supply chain to present the authenticity of this method for failure analysis.

The findings indicate that the failure events, given as failure at production and procurement (A2), that is, involvement of middleman (BE3), handling and packaging failure (BE4) and transportation failure (A3), hold the highest-ranking scores in analysis of PPSC using fault tree approach.

This research uses the modularization approach for evaluation of failure events of PPSC. This paper explores failures related to PPSC for efficient management initiatives in apple supply chain context. The paper also provides suggestion from managerial perspective with respect to each failure event.

The purpose of this paper is to estimate the impact of the COVID-19 pandemic on the patterns of convergence/divergence among the districts in India. Specifically, this paper investigates if the impact is heterogeneous among different cohorts of districts (based on income distribution). The differential impact may lead to heterogeneous long-run growth paths, resulting in unbalanced development across regions within the country. A study of convergence can ascertain the possible trajectory of such development across regions. Investigation of this phenomenon is the primary aim of this study.

This paper uses the panel regression method for estimation. This paper uses high-frequency nighttime light intensity data as a proxy for aggregate output.

The authors observe a significant reduction in the convergence rate as a result of the pandemic. Across the cluster of districts, the drop in ß-convergence rate, compared to the pre-pandemic period, varied from approximately 33% for the poorer districts to close to zero for the richest group of districts. These findings suggest that the pandemic may lead to a wider disparity among different regions within the country.

This paper contributes to the literature in the following ways. First, to the best of the authors’ knowledge, this is the first paper to investigate the impact of COVID-19 on the convergence rate. A detailed look into the possible disparity in convergence among various regions is critical because a larger drop in convergence, especially among the poorer regions, may call for policy attention to attain long-term equitable development. The authors perform this exercise by dividing the districts into four quantile groups based on the distribution of night-light intensity. Second, while previous studies on convergence using nighttime light data have used a cross-sectional approach, this study is possibly the first attempt to use the panel regression method on this data. The application of this method can be useful in tackling district-level omitted variables bias. Finally, the heterogeneity analysis using different quantiles of the distribution of night-light intensity may help in designing targeted policies to mitigate the disparity across districts due to the shock.

This paper proposes a family of estimators of population mean using information on several auxiliary variables and analyzes its properties in the presence of measurement errors.

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.

This paper aims to identify the role of internet of things (IoT) in water supply chain management and helps to understand its future path from the junction of computer science and resource management.

The current research was studied through bibliometric review and content analysis, and various contributors and linkages were found. Also, the possible directions and implications of the field were analyzed.

The paper’s key findings include the role of modern computer science in water resource management through sensor technology, big data analytics, IoT, machine learning and cloud computing. This, in turn, helps in understanding future implications of IoT resource management.

A more extensive database can add up to more combinations of linkages and ideas about the future direction. The implications and understanding gained by the research can be used by governments and firms dealing with water management of smart cities. It can also help find ways for optimizing water resources using IoT and modern-day computer science.

This study is one of the very few investigations that highlighted IoT’s role in water supply management. Thus, this study helps to assess the scope and the trend of the case area.