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The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud.

This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer.

The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia.

One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked.

Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

Contemporary frameworks on institutional theory and corporate environmentalism observe that institutional fields positively influence a firm’s environmental response in the form of implementation of environmental practices. These frameworks, however, provide little evidence on why firms facing similar institutional field differ in their environmental response. This paper aims to incorporate the intra-organizational dynamics within the traditional institutional theory framework to address this heterogeneity, examining specifically the role of absorptive capacity for environmental knowledge in the organizational implementation of corporate environmental practices.

Integrating the institutional theory and resource-based view, this paper examines the mediating role of absorptive capacity in the relationship between institutional pressure for corporate environmentalism vis-a-vis the implementation of corporate environmental practices. Partial least square structural equation modeling was used for hypotheses testing based on data obtained from the Indian apparel and textile industry.

The results support the mediating role of absorptive capacity in the relationship between institutional pressure and implementation of corporate environmental practices. Further, this study highlights the importance of acquisition and utilization of environmental knowledge in driving environmentalism through developing absorptive capacity; the findings also suggest that the role of institutional pressure in the implementation of environmental practices should not be analyzed in isolation but rather in conjunction with the development of absorptive capacity that forms the internal basis of implementation.

Managers need to focus on the development of organizational capabilities for acquiring and exploiting environmental knowledge to complement their preparedness in responding to any institutional pressures for environmental sustainability. Firms also need to link their environmental orientation with various sources of environmental knowledge and capabilities residing outside the organizational boundaries. It is important to note here that the development of absorptive capacities for the exploration and exploitation of external knowledge is indeed both required and necessary to build sustainable organizational capabilities.

This paper is among the very few studies that address the issue of knowledge and development of related organizational capabilities for corporate environmentalism. Recognizing that environmental knowledge resides outside organizational boundaries with regulatory agencies and special interest groups, this paper highlights the importance of developing organizational capabilities for the acquisition and exploitation of environmental knowledge.

The concept of sustainable livelihoods – commonly understood as managing of means of living without undermining natural resources – has gained momentum in international development discourse. This review focuses on broadening of conceptualisation of policy intervention process for sustainable livelihoods approach and synthesis of evidence to track development in their knowledge structure.

Evidence available in the literature systematically reviewed with the established methods and themes of interest are curated, to assess the characteristics, mechanisms and caveats arising in policy and practice.

The authors found seventy-five independent studies that fit into the pre-specified research protocol and objectives. The authors classify these studies into six major themes: Economic wellbeing; Opportunities and capabilities; Climate and natural resources; Policy design; Formal-informal institutional environment; and Labour, entrepreneurship and exports. Various research gaps and directions for future research are also discussed.

The study provides a typology of reviewed literature examining the profile dynamics. Thematic analysis resulted into identifying complementarities and trade-offs and results demonstrate multiple heterogeneity in structures, processes and outcomes.

The definitive areas of interventions are discussed to broaden the utility of the concept in a structured way. This review paper brings a comprehensive view of livelihood intervention system and contribute in enriching the social policy perspective.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2022-0402

The ongoing transition in the attitude of consumers toward health and environment has a direct implication on the organic food industries, making it necessary to examine the drivers of the purchase intention of organic food items, specifically in developing economies like India. Therefore, this study tries to frame and validate the instrumental scale by collecting data from 574 organic food item consumers to examine the determinants of purchase intention among consumers in India.

This study has employed advanced statistical tools i.e. Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Harmon’s single factor test and other statistical measures using SPSS and AMOS 23, for framing and validating the instrumental scale for this study.

The results of EFA explain 67.714% variance of total research variable variance with six major constructs. Moreover, the result of the CFA confirms the six factors and the proposed instrumental scale. The finding explicates that health consciousness, ecological trustworthiness and functional value are the major drivers of the purchase intention of organic food items.

This study has major policy implications for organic producers, processor and marketers for understanding the complex phenomenon of organic consumer behavior. The result explains that marketers and producers should adopt ad hoc marketing strategies that aim to promote the organic food items as healthy and safe.

There is hardly any study that has proposed and validated an instrumental scale with these factors collectively in India for studying the purchase intention of organic food consumers in India.

Additive manufacturing has rapidly developed in terms of technology and its application in various types of industries. With this rapid development, there has been significant research in the area of materials. This has led to the invention of Smart Materials (SMs). The 4D printing is basically 3D printing of these SMs. This paper aims to focus on novel materials and their useful application in various industries using the technology of 4D printing.

Research studies in 4D printing have increased since the time when this idea was first introduced in the year 2013. The present research study will deeply focus on the introduction to 4D printing, types of SMs and its application based on the various types of stimulus. The application of each type of SM has been explained along with its functioning with respect to the stimulus.

SMs have multiple functional applications pertaining to appropriate industries. The 4D printed parts have a distinctive capability to change its shape and self-assembly to carry out a specific function according to the requirement. Afterward, the fabricated part can recover to its 3D printed “memorized” shape once it is triggered by the stimulus.

The present study highlights the various capabilities of SMs, which is used as a raw material in 4D printing.

The readers will gain practical insights on the key attributes of a women entrepreneur, the role of innovative product design and usage, the core challenges and opportunities and the strategies to overcome entrepreneurial hurdles in the Moonj handicraft business.

The case is about “Rekhaakriti”, a handicraft artisan-oriented firm, which deals in the business of selling handicrafts items like traditional wooden toys, Moonj baskets, rice bowls and vermilion boxes, in both business-to-business (B2B) and business-to-consumer (B2C) markets. Incorporated as a non-governmental organization (NGO) in the year 2014, “Rekhaakriti” was founded with the purpose of preserving, promoting and advancing the Moonj handicraft. Throughout its formative years, the organization experienced several organizational and operational challenges and got almost on the edge of collapse. The dilemma that surrounded Rekha Sinha, a key founding member of “Rekhaakriti”, was whether to carry or dissolve the organization. But, after much contemplation, she decided to convert the organization to a sole proprietorship firm in the year 2017. However, the restructuring decision was also proving to become less effective in attaining the objectives for which the firm got established. This led the owner, Rekha Sinha, to further dug deep to identify obstacles (both internal and external) that impede her firm’s expansion and growth. This case narrates Rekha Sinha's intricate entrepreneurial path in building an innovative handicraft organization and explains how she overcame the overall organizational and operational obstacles. The case provides the context for students to assume the role of protagonist and explore creative strategies for overcoming market obstacles through upskilling, design intervention and product innovation.

The case study is intended for the students pursuing their graduation and post-graduation courses in business, management studies, marketing and entrepreneurship. The case also provides suitable insights to management trainees and executives.

Teaching notes are available for educators only.

CSS 8: Marketing.

In the recent few decades, there has been a rampant increase in the demand of sustainable food products around the world because of high cognizance of consumers toward environment and health. Asian countries, especially India and China, are also expecting a tremendous boost in the domestic demand for organic food products in the upcoming few years. Therefore, it becomes pertinent to explore the factors affecting the purchase intention behavior of organic food items, especially in emerging economies, i.e. India. Hence, the paper aims to explore the factors driving the purchase decision of organic consumers by collecting data set from 603 organic food item consumers in India.

The study has applied advanced statistical tools, i.e. structural equation modeling, Harman’s single factor test and other statistical measures, to analyze the collected research data.

The results posit that consumers’ purchase intention has a favorable impact on health aspects; trustworthiness; social innovativeness; functional value; subjective norms and organic product knowledge. Moreover, the result explicates that health consciousness and trustworthiness are vital predictors of organic food purchase intention.

The findings may assist the producers, processors, marketers, policymakers and regulators in devising appropriate policies and strategies for comprehending the complex phenomenon of organic consumers’ purchase behavior.

To the best of the authors’ knowledge, this is the first study to explore the drivers of purchase intention of organic food items by collecting data from well-defined consumers of organic food items in India.

Globally, the oil and gas (OG) industries are under pressure from numerous stakeholders for their sustainable operations against the backdrop of climate change, ecological damage and social challenges. Drawing on the twin theoretical frameworks of the institutional theory and dynamic capability perspective, this study aims to examine the impact of the institutional pressures and dynamic capabilities on the overall sustainability performance of OG industry.

This study uses survey method to analyze the responses from 275 middle management professionals of OG industry in India using partial least squares structural equation modeling. Further, focused group discussions with the select industry leaders validate the empirical findings of this study.

The research reveals that both institutional pressures and firm’s dynamic capabilities have significant positive impact on its economic and environmental performances in OG sector in India. However, they do not have any impact on social performance, unlike earlier findings.

The main limitation of the study is generalizability of the findings, given the cross-sectional design of the study.

Insights of this study will help regulators and policymakers in formulating effective regulatory and policy frameworks, besides creating awareness amongst the organizations to simultaneously focus on all the three aspects of sustainability performance.

The research has bearing on policy formulation and creating a regulatory ecosystem to ensure overall sustainability performance of OG industry in India.

Differences in institutional environment and governance structures pave the way for heterogeneous nature of different businesses; this, in turn, shapes the way various sections of society act toward each other enacting their responsibilities. Taking into account the unique institutional environment and governance structures of firms in developing economies, this paper aims to build on the “stakeholder theory” to address the issue of the implementation of corporate social responsibilities (CSR) practices in these economies, particularly India. This paper also aims to uncover the saliency (legitimacy and power) of different stakeholder groups on different aspects of a firm’s CSR activities. Further, as most of the firms in developing economies are family-run firms, the paper examines role of organizational leadership in shaping firms’ CSR strategies.

Integrating literature on “stakeholder theory” and CSR, this paper examines the implementation of different CSR practices by family-run firms in India. This paper uses survey research to collect data from 80 privately held family firms operating in apparel and textiles industry in India. The data have been collected from respondents holding top leadership positions in the sample firms.

The findings indicate that pressure from primary stakeholders (i.e. customers, employees and shareholders) and CSR-oriented leadership belief significantly influence organizational implementation of CSR practices, whereas pressure from secondary stakeholder (i.e. community groups and non-governmental organizations) was found to be insignificant. Further, CSR-oriented leadership belief moderated the relationship between primary stakeholder pressure and organizational implementation of CSR practices. The findings equally highlighted lower saliency of secondary stakeholder’s legitimacy and power because of weak institutional mechanisms, while on the other hand, the primary stakeholders exert considerable power because of the direct nature of transactional legitimacy, further accentuated by the governance structure in family firms.

This paper is among the very few studies that address the issue of CSR among family-run businesses in developing economies. Existing frameworks on analyzing firm’s implementation of CSR practices does not recognize the inherent heterogeneity among different stakeholder groups. Recognizing that different stakeholders have different levels of influence over firms, this paper categorized the stakeholders’ groups into primary and secondary to analyze their differential impact over firms. Additionally, given the critical role of leadership belief in the implementation of CSR practices, this paper analyzed the moderated effect of CSR-oriented leadership belief toward developing a more robust model of CSR implementation.