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The coronavirus disease 2019 (COVID-19) pandemic has been reported to have a major impact on the mental health of an individual. Healing the mental stress, anxiety, depression and insomnia of an individual's immediate surroundings play a major role. Therefore, this study reviews how the built environment impacts the healing of an individual's state of mind.

Various works of literature on healing environments were analysed to create frameworks that can facilitate psychological healing through architectural elements. Articles were selected from various journals like SAGE, PubMed, Journal of Applied and Computational Mechanics (JACM), Routledge Taylor and Francis, Journal of Contemporary Urban Affairs (JCUA), ScienceDirect, and Emerald databases, news articles, official web pages, and magazines that have been referred.

Indicators (spatial, sensory comfort, safety, security, privacy and social comfort) are linked to sub-indicators (access, distractions and views) and design characteristics (indoor climate, interior view, outside view, privacy, communication, noise, daylighting, temperature) which help in better connection of the built environment with individual's mental health. From the above indicators, sub-indicators and design characteristics, the authors have come to a conclusion that a view to the outside with better social interaction has an in-depth effect on an individual's mental health.

This study predominantly talks about healing in hospitals but quarantining of COVID-19 patients happens in residences too. So, it is important to find the healing characteristics in residences and in which typology the recovery process is high.

This paper has been written completely by the author and the co-authors and has not been copied from any other sources.

This case is about the way Pragati Software Private Limited, a small but profitable software training company set up in Mumbai by an alumnus of Indian Institute of Management Ahmedabad sent away half its employees in three rounds of layoff in its tenth year.

Food systems research is typically focused on productivity and efficiency. But in the face of impending challenges of climate, investment, markets, and incomes small holders may do well to shift to diversity and sufficiency. The transition requires institutions such as Farmer Producer Organisations (FPOs) to play the role of intermediaries. This paper aims to understand this challenging phenomenon using a case from India.

In this article, drawing from the emerging literature of PO as a sustainability transition intermediary, this paper uses the case study of a women-owned FPO and explores its role in contributing to sustainable food systems through practices of non-pesticide management of agriculture. This paper explores, through non-participant observer methods, focus group discussions and interviews with multiple stakeholders how an FPO embeds sustainability in its purpose and the challenges faced in transforming producer and consumers towards sustainable food systems.

The study argues for early articulation of the “sustainability transition intermediary” role in the FPO’s vision and mission. Second, FPOs’ role of being a transition intermediary is impacted by the key stakeholders and the durability of relationship with them.

By studying FPOs in India, from the framework of sustainability transitions, this article adds to the limited literature that looks as POs as sustainability transition intermediaries.

In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).

This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.

This study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.

This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.

A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.

Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.

This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.

For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

The purpose of this paper is to analyse proposed cloud computing integration (CCI) and external integration (EI) effects on the relationship between the integration of supply chain and business performance of the organisation in the Indian context.

A two-stage, structural equation modelling (SEM) and artificial neural network (ANN) methodology are employed for the analysis, and for verifying the robustness of the developed model sensitivity analysis is performed.

The results of SEM revealed that out of 14 hypotheses, 12 hypotheses were supported. Furthermore output of SEM was used as input for the ANN model and the results highlighted that production flexibility is an essential factor for operational business performance (OBP) followed by customer integration, supplier integration, product quality, internal integration and on-time delivery (OD).

This study focussed on the emerging economies context and cannot be applied to all the countries, and there could be other derived variables from the real factors. This investigation is intended to guide various policy and decision makers of the case domain.

This study has introduced new factors such as CCI, EI and organisational business performance.

This study aims to investigate the mediating role of “Big Data Analytics” played between “Project Performance” and nine factors including top management, project knowledge management focus on sustainability, green purchasing, environmental technologies, social responsibility, project operational capabilities, project complexity, collaboration and explorative learning, and project success.

A sample of 321 responses from 106 Indian manufacturing small and medium-scaled enterprises (SMEs) was collected. Data were analyzed using empirical analysis through structural equation modeling.

The result shows that project knowledge management, green purchasing and project operational capabilities require the mediating support of big data analytics. The adoption of big data analytics has a positive influence on project performance in the manufacturing sector.

This study is useful to SMEs managers, practitioners and government policymakers to develop an understanding of big data analytics, eliminate challenges in the adoption of big data, and formulate strategies to handle projects efficiently in SMEs in the context of Indian manufacturing.

For the first time, big data for manufacturing firms handing innovative projects was discussed in the Indian SME context.

The purpose of this paper is to identify and model critical barriers to cloud computing adoption (CCA) in Indian MSMEs by the interpretive structural modeling (ISM) approach.

In this paper, through a literature survey and expert opinions, 14 critical barriers were identified, and the ISM tool was used to establish interrelationship among the identified barriers and to determine the key barriers having high driving power.

After analyzing the barriers, it was found that three barriers, namely, lack of confidentiality (B8), lack of top management support (B3) and lack of sharing and collaboration (B2) were most significant.

The developed model is based on the expert opinions, which may be biased, influencing the final output of the structural model. The research implications of the developed model are to help managers of the organization in the understanding significance of the barriers and to prioritize or eliminate the same for the effective CCA.

This study is for the first time an attempt that has been made to apply the ISM methodology to explore the interdependencies among the critical barriers for Indian MSMEs. This paper will guide the managers at various levels of an organization for effective implementation of the cloud computing practices.

The study examined a wide range of proactive supply chain practices to demonstrate a cross-linkage among them and to understand their effects on both practitioners of previous decision-making models, frameworks, strategies and policies. Here, six supply chain practices are empirically evaluated based on 28 constructs to investigate a comprehensive model and confirm the connections for achieving performance and competence. The study presents a conceptual model and examines the influence of many crucial factors, i.e. supply chain collaboration, knowledge, information sharing, green human resources (GHR) management and lean-green (LG) practices on supply chain performance.

Structural equation modeling (SEM) examines the conceptual model and allied relationship. A sample of 175 respondents' data was collected to test the hypothesized relations. A resource based view (RBV) was adopted, and the questionnaires-based survey was conducted on the Indian supply chain professionals to explore the effect of LG and green human resource management (GHRM) practices on supply chain performance.

The study presented five constructs for supply chain capabilities (SCCA), five constructs for supply chain collaboration and integration (SCIN), four constructs for supply chain knowledge and information sharing (SCKI), five constructs for GHR, five constructs for LG practices (LGPR) and four constructs for lean-green SCM (LG-SCM) firm performance to be utilized for validation by the specific industry, company size and operational boundaries for attaining sustainability. The outcome emphasizes that SCCA positively influence GHRM, LG practices and LG supply chain firm performance. However, LG practices do not influence LG-SCM firm performance, particularly in India.

The study exploited multiple practices in a conceptual model to provide a widespread understanding of decision-making to assist in developing a holistic approach based on different practices for attaining organizational sustainability. The study stimulates the cross-pollination of ideas between many supply chain practices to better understand SCCA, SCIN, SCKI, GHRM and LG-SCM under a single roof for retaining organization performance.