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This study aims to explore how sustainable business models (SBM) can be developed within agri-innovation systems (AIS) and emphasize an integration of the two with a systemic understanding for reducing food loss and value loss in postharvest agri-food supply chain.

This study conducted longitudinal qualitative research in a developing country with food loss challenges in the postharvest supply chain. This study collected data through multiple rounds of fieldwork, interviews and focus groups over four years. Thematic analysis and “sensemaking” were used for inductive data analysis to generate rich contextual knowledge by drawing upon the lived realities of the agri-food supply chain actors.

First, this study finds that the value losses are varied in the supply chain, encompassing production value, intrinsic value, extrinsic value, market value, institutional value and future food value. This happens through two cumulative effects including multiplier losses, where losses in one model cascade into others, amplifying their impact and stacking losses, where the absence of data stacks or infrastructure pools hampers the realisation of food value. Thereafter, this study proposes four strategies for moving from the loss-incurring current business model to a networked SBM for mitigating losses. This emphasises the need to redefine ownership as stewardship, enable formal and informal beneficiary identification, strengthen value addition and build capacities for empowering communities to benefit from networked SBM with AIS initiatives. Finally, this study puts forth ten propositions for future research in aligning AIS with networked SBM.

This study contributes to understanding the interplay between AIS and SBM; emphasising the integration of the two to effectively address food loss challenges in the early stages of agri-food supply chains. The identified strategies and research propositions provide implications for researchers and practitioners seeking to accelerate sustainable practices for reducing food loss and waste in agri-food supply chains.

The case aims to deal with multigenerational entrepreneurship. The family business of sports goods was initiated by Yashpal Aggarwal and his friends in the 1950s. Yashpal acquired the art of doing business and started initially with trading of sporting goods in Mumbai. Yashpal had three sons, Ashok, Ashwini and Rajesh, who ventured into sports trading business as well. After the demise of Yashpal, Ashok shifted to Jalandhar and started a manufacturing unit, producing roller skates. Ashwini, being entrepreneurial and innovative, had always desired to professionalise the business and hence started with Okini Sports. Okini Sports emerged as the first organised professional sports mall in India.

Based on case research, individual interviews at different levels, testing the case several times and filling the case gaps during the process to authenticate information, multiple sources of information have been used.

Businesses largely compete on the basis of available talent, competency and capability. Family businesses must be open to induct competent people within the family with the required skills to lead the company. If a family nurtures a member with requisite skills, values, to keep shareholders, key customers, and suppliers loyal to the business, then family leadership is the best option. As the business grows in dimension, differential capabilities are required to run the business competitively, hence, inducting talented individuals as professionals is a better option. A family must be realistic about the talents available internally.

This case is restricted to sports family business entrepreneurship in the context of India, but has a great learning towards multigenerational entrepreneurship.

The case is original with the family in its fourth generation, the youngest looking to diversify and professionalize the business, set his family dreams of setting up the biggest sports mall in India.

This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context.

20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of “Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used.

The study's outcome indicates that “lack of central and state regulations and rules” and “lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties.

This study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care.

This study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP.

This paper aims to discover and analyze the challenges hampering blockchain technology’s (BT’s) implementation in the Indian health-care sector. A total of 18 challenges have been prioritized and modeled based on an extensive literature search and professional views.

An integrated multi-criteria decision-making approach has been used in two phases. Best worst method (BWM) is used in the first phase to prioritize the challenges with sensitivity analysis to validate the findings and eliminate a few challenges. In the second phase, interpretive structural modeling is applied to the remaining 15 challenges to obtain relative relationships among them with cross-impact matrix multiplication applied to classification analysis for their categorization.

The study’s results reveal that limited knowledge and expertise, cost and risk involved, technical issues, lack of clear regulations, resistance to change and lack of top management support are the top-ranked or high-intensity challenges according to the BWM. Interpretive structural modelling findings suggest that the lack of government initiatives has been driving other challenges with the highest driving power.

This work has been conducted in the Indian context, so careful generalization of the results is needed.

This work will give health-care stakeholders a better perspective regarding blockchain’s adoption. It will help health-care stakeholders, service providers, researchers and policymakers get a glimpse of the strategies for eradicating mentioned challenges. The analysis will help reduce the challenges’ impact on blockchain’s adoption in the Indian health-care sector.

The adoption of BT is a novel concept, especially in developing countries such as India. This is one of the few works addressing the challenges to BT adoption in the Indian health-care sector.

India started economic reforms at a rapid pace to catch up the world economy by following the services-led-growth model during the post-liberalisation period. Over the years, the growing unemployment rate posits a re-look into the dynamics of growth model for wider work force participation. In this backdrop, the paper aims to examine the dynamics of structural changes in employment pattern in view of economic growth led by services-led growth model in India.

The study employs a non-linear autoregressive model (NARDL) to examine the effect of the growth rates in three broad economic sectors namely agriculture and allied, services and industry on work force participation representing the employment opportunities in India.

The results highlight that the rapid expansion of the service sector has not occurred with enough employment opportunities by the same rate. By contrast, the growth in the industrial sector significantly creates employment opportunities in the short and long run. These results support the industry led growth model over the services for sustainable and inclusive economic growth in the country.

The study relies on combined labour force participation rates rather than gender-specific rates. Further, the regulatory, working conditions and economic incentives may affect the gender-specific engagement of the labour force in three broad sectors.

The results offer important insight into changing patterns in employment with policy lessons. A wider workforce force participation calls for expansion of manufacturing activities through pro-industry programmes.

The study makes pioneer efforts to examine the dynamics of labour force participation with respect to the growth of three broad economic sectors of the Indian economy. The results provide new insights with policy implications for the changing employment pattern and policy response.

Textile and Apparel (T&A) sector significantly influences socio-economic and environmental dimensions of the sustainability. The purpose of this paper is proposed to establish the interrelationship among the critical barriers to the sustainable development of T&A supply chains by using a multi-criteria decision-making approach and to obtain a ranking of the barriers.

In the present investigation through literature review and from expert opinions, 14 significant challenges to the sustainable growth of T&A sector have identified. For establishing the interrelationship and for developing a structural model of the identified challenges, interpretive structural modelling (ISM) methodology is employed.

The results of the investigation revealed that lack of effective governmental policies (B8), poor infrastructure (B4), lack of effective level of integration (B6), low foreign investment (B13) and demonetization (B12) are the top most significant challenges.

The model development based on the expert inputs from the industry and academia, these inputs could be biased influencing the accuracy of the model. Also, inclusion more factors for the analysis will improve the reliability of the model.

This research is intended to guide the policy and decision makers for improving overall the growth of the T&A supply chain.

In the modern digital age technologies like Industry 4.0 has revolutionized the manufacturing sector. There are many economic and technological advantages of digitalization. Along with economic benefits, manufacturing enterprises especially small and medium enterprises (SMEs) need to take advantage of digitalization in strengthening their social performance. Observing the importance of social performance of Indian SMEs and availability of limited research in this direction, the current study has done a holistic analysis of social performance enablers.

A systematic literature review with a series of personal interviews of experts has been conducted to identify the enablers of social performance and supportive digital technologies. A fuzzy decision-making trial and evaluation laboratory (DEMATEL) approach has been applied to find the degree of influence and interrelation between enablers. Sensitivity analysis is also performed to validate the results obtained.

Authors observed that policies of corporate and social responsibility, support by top management, awareness of social performance issues, and ethical practices and collaboration amongst supply chain members are leading causing enablers of social performance. Preparedness for the uncertainty of pandemics, improved work conditions and ergonomics, green practices, improvement in global business and improved living standards of employees and their families are leading the effect group of enablers. Further authors also observed that social media, information and communication technologies (ICT), websites, smart surveillance, e-mails and cloud computing are few supportive digital technologies of social performance issues.

SMEs all over the globe are passing through a transition due to digitalization and influence of pandemics. The finding of current study highlights the importance of strategic management of social performance enablers. Since the research is very limited in the social performance area, especially in Indian SMEs, this study makes a notable contribution to the literature too.

Novelty of this study is that social performance enablers of Indian SMEs in the digital era have been analysed holistically. The content of this research is the original work of the authors and has not been submitted for any other publication.

The purpose of this study is to investigate the role of ethical obligation and environmental concern in young consumers’ green purchase behaviour using Ajzen’s (1991) theory of planned behaviour as underpinning theoretical model.

Data collected from 253 college students of a major higher education institution in Northern India were analysed by using structural equation modelling and moderated mediation analysis in AMOS 22.0 and SPSS 20.0.

Investigating why environmental concern does not significantly translate into green purchase intention, the study demonstrates that the effect of environmental concern was mediated through attitude and ethical obligation. In addition, the study also found that the effect of environmental concern on attitude was positively moderated by ethical obligation. Moreover, subjective norms were found to exhibit no direct effect on green purchase intention; rather, this effect was indirect and mediated through attitude and perceived behavioural control. The results of moderated mediation analysis further demonstrated that the indirect effect of subjective norms on green purchase intention through attitude was found conditional on the values of perceived behavioural control.

The study offers a valuable contribution by signifying the moderating role of ethical obligation in green purchase behaviour. It also contributes to the existing knowledge by assessing the conditional indirect effect of subjective norms on green purchase intention which is rarely examined in the literature.

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.

This study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability.

Selection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria.

The results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed.

Challenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency.

The novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.