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Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.

The study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.

This study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.

This study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.

By considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.

This study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.

This study investigates the enablers and challenges of digital supply chains (DSCs) adoption and develops a digital supply chain maturity (DSCM) model as a basis for developing guidelines for DSC adoption in the digital transformation journey.

The research involves a systematic literature review (SLR) of Industry 4.0 (I4) adoption in supply chain (SC) practices to identify key enablers and associated maturity levels. The literature search of published articles during the 1997–2020 period and subsequent screening resulted in 64 articles. A DSCM model was developed using the categorization of important enablers and associated levels transitioning from the traditional SC to the DSC ecosystem.

Four broader categories of DSC enablers and challenges were identified from the content analysis of SLR. Digital strategy alongside I4 technologies and human capital were prominent in DSC adoption as I4 technologies and human capital depend on other enablers such as dynamic capabilities (DCs). Lack of infrastructure and financial constraints to implementing I4 were significant challenges in the DSC adoption.

The proposed DSCM model provides a holistic view of enablers and maturity levels from traditional SC to DSC adoption. However, the DSCM model needs to be empirically validated and streamlined further using inputs from practitioners.

The proposed DSCM model can be used as a framework to guide practitioners in assessing maturity and developing implementation plans for successful DSC adoption.

This research introduces a novel DSC maturity model through a holistic view of enablers and maturity levels from traditional SC to DSC adoption.

This study aims to identify the key enablers for the adoption of Industry 4.0 (I4.0) in the automobile industry of India, which has been severely impacted by COVID-19. Adopting I4.0 will provide organizations greater flexibility and resilience during the COVID-19 pandemic.

Based on the literature review and experts’ opinions, 21 enablers were identified. Further, contextual relationships among the identified factors and a hierarchical digraph was developed by using the total interpretive structural modelling (TISM) technique. Finally, fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was conducted to classify the enablers into different categories based on their dependence and driving power.

The results indicate that top management support, clarity on government policy, strategic vision on I4.0 and development of new industrial policy are the most influential factors, with the highest driving power placed at the bottom of the TISM hierarchical model. Furthermore, agile workforce, smart HR practices and IT standardization and security are identified as linkage enablers with the most driving and dependency power.

The hierarchical TISM model and fuzzy MICMAC approach provide a comprehensive understanding of the I4.0 implementation process through a visual, logical structure to the managers. It will help the researchers and practitioners understand the contextual relationship among various enablers in fostering the I4.0 adoption process and digital reorganization in the automobile industry during the COVID-19 pandemic.

This study provides a holistic TISM hierarchical framework on I4.0 adoption that will elevate the next maturity level of innovation adoption and may act as a blueprint for automobile industries during the COVID-19 pandemic.

A crucial contemporary policy question for financial service organizations of being resilient across the globe calls for rethinking and renovating by adopting and adapting to the technologies of artificial intelligence (AI). The purpose of this study is to propose a policy framework for adoption of AI in the finance sector by exploring the driving factors through systems approach.

Based on literature review and discussions with experts from both industry and academia, nine enablers were shortlisted, which were used in the questionnaire survey to determine ranks of enablers. Further, the study developed the interpretive structural model (ISM) with the help of experts.

The ISM digraph developed with the help of the experts, resulted in the enablers like anticipated profitability, contactless solutions, credit risk management and software vendor support as dependent factors and stood at the top of the ISM. On the other hand, factors like availability of the data, technical infrastructure and funds are the most driving factors, which lie on the bottom of the ISM.

The study provides implications and policy recommendations for the practicing managers and government agencies approaching the digital transformation towards the adoption of AI in the finance ecosystem.

The paper uses the systems approach for the development of the ISM of the enabling factors for the adoption of AI technology. On the basis of the results, the study proposes a policy framework to accelerate the functioning of the finance ecosystem with AI technology.

This research aims to identify and prioritize the circular economy (CE) benefits (CEBs) due to the adoption of CE enablers (CEEs) in the Indian manufacturing organization context.

This research proposes a hybrid framework of Pythagorean fuzzy analytic hierarchy process (PF-AHP) and Pythagorean fuzzy TODIM (an acronym in Portuguese for Interactive Multicriteria Decision-Making) techniques. It identifies the CEEs and CEBs based on literature review and validated through industrial experts. Further, this research conducts an empirical case study to demonstrate the applicability of the proposed framework.

The result shows that CE enabler SE1 (clear vision, support and commitment from top management for CE adoption) is the most critical enabler for CE implementation. The CE benefit CEB1 (improves the value chain of products and mitigating environmental damage during product life cycle phase) is the most significant benefit derived from the adoption of CEEs. The proposed framework will provide a more accurate, structural and systematic approach to the business organizations for achieving the CEBs in a stepwise manner through the effective adoption of CEEs.

The findings of this research are nation-specific and based on a case study of single manufacturing industry. Thus, the result obtained can vary from case to case and nation to nation.

A deep understanding of each CEEs and CEBs would help build confidence among decision-makers and industrial practitioners to eliminate the risks associated with CE implementation.

The objective of the paper is to identify and model the relevant enablers related to the issue of adoption and implementation of project-based learning (PjBL) in higher educational institutions.

The present study has developed an integrated model using interpretive structural modeling (ISM) and the Fuzzy Matrice d' Impacts Croises Multiplication Appliqué an Classement (FMICMAC) approach, which helps to identify and classify the important enablers and reveal the direct and indirect effects of each enabler on the PjBL implementation in higher educational institutions.

The paper has identified the key enablers and presented an integrated model using ISM and FMICMAC. The result shows that there exists a group of enablers having a high driving power and low dependence requiring maximum attention and strategic importance, while another group consists of those enablers that have high dependence and are the resultant actions.

The study proposes a scientific way to model the relevant enablers to implementation of PjBL. This would help higher educational institutions to prioritize the enablers as these are hierarchically structured. The model is based on the experts' opinions, which may be biased, influencing the final output of the structural model.

Enablers are building blocks for the adoption of PjBL. The study presents an integrated model using ISM and FMICMAC to identify and categorize various key enablers of PjBL adoption in higher education institutions. The results will help higher educational institutions to focus on the right enablers for the successful implementation of PjBL in their programs.

The purpose of this study is two-fold. First, to identify and encapsulate the enablers that can facilitate technology integration in higher education and second, to understand and analyze the interplay between technology agility enablers.

The study used the Total Interpretive Structural Modeling (TISM) approach to construct a theoretical model of the technology agility enablers in higher education and MICMAC analysis for ranking and segregating the enablers based on their dependence power into four categories: Autonomous, Dependent, Linkage and Independent.

The study helped identify eight technology agility enablers, with the Covid-19 pandemic as the most significant enabler. The Covid-19 pandemic has catalyzed the diffusion of technology across the education sector in India, including tertiary higher education. The study revealed government initiatives and institutional commitment as other enablers that can promote technology agility in higher education.

The results of this study would assist the policymakers and management of universities and colleges in understanding the important enablers that can facilitate technology integration in higher education.

Research in the past on technology adoption in higher education has looked into each enabler in isolation. This research provides a comprehensive view of the enablers and has attempted to establish a multidirectional interplay between the enablers.

This study focuses on the adoption of wearable technologies in a context where care-providing organizations can offer, in collaboration with caregivers, better care. Drawing on dual-factor theory and from the caregiver perspective, this study identifies and examines factors of technology adoption in four developing countries.

This study was undertaken using a quantitative approach. A survey was distributed among 1,013 caregivers in four developing countries in Asia including Iran, Azerbaijan, Turkmenistan and Iraq and collected quantitative data for model validation and hypotheses analysis. Building on the technology adoption literature, we identified six constructs that impact the behavioral intention of caregivers to use wearable technologies in aged care-providing organizations.

Our dual-factor model was successfully validated, and all hypotheses were supported. However, different results were found in the selected countries within the cross-country analysis.

This study has significant implications for the study of emerging technologies in aged care service operations. It provides a theoretical framework that may be adapted for future research, enabling practitioners in aged care to better understand the crucial role of technology adoption in service operations. Less attention was paid to the adoption of wearable technologies in aged care, particularly in developing countries, where healthcare services in aged care impose heavy costs on care providers.

The purposes of this paper are (1) to identify and rank the various enablers for an integrated sustainable-green-lean-six sigma-agile manufacturing system (ISGLSAMS), and (2) to study their correlations and their impact on organizational performance.

Three tiers methodology is used to analyze the enablers for the successful adoption of ISGLSAMS. First, a total of 32 ISGLSAMS enablers are identified through a comprehensive literature review. Then, data are collected with a structured questionnaire from 108 Indian manufacturing industries. Then, an analytic approach is used to analyze (1) the relevance and significance of enablers and (2) their correlations (1) with each other, and (2) with the organizational performance outcomes, to strengthen the understanding of ISGLSAMS.

The findings suggest that top management commitment, sustainable reconfigurable manufacturing system, organization resources for 6 Rs, customers' and stakeholders' involvement, corporate social responsibility (CSR), customers and stakeholders-focused strategic alliances, dynamic manufacturing strategies, use of information and communication technology, concurrent engineering, standardized tasks for continuous improvement, virtual network of supply chain partners, real-time monitoring and control, training and education, employees' involvement and empowerment enablers are the higher level enablers for the adoption of ISGLSAMS. Findings also suggest that there is a scope for research in the incorporation of lot size reduction, Keiretsu-Kraljic supply chain relationship strategy, external collaborations with the stakeholders other than supply chain members, matrix flatter organization structure, employees' career development, justified employees' wages, government support for research fund and subsidies and vendor-managed inventory practices for ISGLSAMS. Top management commitment, sustainable reconfigurable manufacturing system, organization resources for 6 Rs, corporate social responsibility (CSR), dynamic manufacturing strategies, use of information and communication technology, concurrent engineering, virtual network of supply chain partners, real-time monitoring and control, training and education, employees' involvement and empowerment have a significant effect on (1) sustainable product design, (2) sustainable production system, (3) improvement in the sale, (4) improvement in market responsiveness, (5) improvement in the competitive position and (6) improvement in the global market image.

Through this study of ISGLSAMS enablers and their interdependence, and their impact on ISGLSAMS performance outcomes government, organizations, stakeholders, policymakers and supply chain partners may plan the policy, roadmap and strategies for the successful adoption of (1) ISGLSAMS in the organizational value chain, and (2) Industrial ecology and industrial symbiosis in India. The study also contributes to the industrial managers, and value chain partners a better understanding of ISGLSAMS.

This study is the first attempt to understand (1) the ISGLSAMS enablers and their correlations, and (2) the effect of ISGLSAMS enablers on ISGLSAMS performance outcomes to get the competitive and sustainability advantage. The study contributes to the practitioners, policymakers, organizations, government, researchers and academicians a better understanding of ISGLSAMS enablers and its performance outcomes.

This paper aims to intend to help focal firms which are keen to develop a sustainable supply chain by identifying enablers, in knowing the interrelationships involved and in ranking the enablers.

Interpretive structural modeling and fuzzy MICMAC were used for the modeling and clustering of the enablers and fuzzy analytical hierarchy process has been used for the ranking purpose.

Awareness about sustainability incentives, pressure from stakeholders, support from supply chain partners and demand from customer for sustainable products were found very important for developing a sustainable supply chain.

This research will help practitioners to appreciate the importance of the enablers to focus on the making sustainability adoption feasible across the supply chain. This would also facilitate focal firm management to develop a sustainability culture across the supply chain.

Similar work has not been carried before in which interaction among enablers and their priorities were analyzed using hybrid methodologies in developing country context.