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This study aims to determine the barriers hindering the incorporation of blockchain technology (BCT) in two key service industries – hotel and health care – as well as to assess their readiness for implementing BCT after overcoming the barriers.

The barriers of this study are determined through two phases: a review of prior literature and obtaining expert opinions, which are then analyzed to identify specific barriers that are impeding the incorporation of BCT. Moreover, to generate a blockchain implementation reluctance index (BIRI), this study presents an interval-valued intuitionistic fuzzy set (IVIFS) that uses graph theory and matrix approach (GTMA). The permanent function in the GTMA approach is computed using the PERMAN algorithm. Finally, to compare the readiness of the hotel and health-care industries to adopt BCT, the BIRI values are plotted and evaluated.

The barriers identified by this study are listed under five major headings, namely, financial, operational, behavioral, technical and legal. This study revealed that the operational and technical barriers of BCT are critically hindering its widespread integration in hotel and health-care industries. Furthermore, on comparing the BIRI values of both industries, the result suggested that the hotel industry needs to work more on these barriers to effectively incorporate BCT. Besides the comparison, the BIRI values clearly indicate that both industries have to put a lot of effort into the mitigation of the barriers found by this study to successfully integrate BCT.

The experts’ opinions are used to evaluate the identified barriers, which raises the chance that the opinions are prejudiced based on the experts’ perspectives and ideologies. The sensitivity of decision-maker loads toward preference outcomes is not analyzed in this manuscript. Therefore, any recent sensitivity analysis may be considered a prospective field for future research. This study applies a multicriteria decision-making (MCDM) approach, IVIFS–GTMA, which limits the evaluation of the influence caused by individual barriers on the integration of BCT in the hotel and health-care industries. Henceforth, in future investigations, alternative MCDM methods may be used to analyze individual barriers.

According to the findings, if the hotel or health-care industry aims to incorporate BCT in its supply chain operations, it is recommended to emphasize more on the operational barriers along with the technical and behavioral barriers. The barriers mentioned in this manuscript can be used as guidance for developers in their development activities, such as scalability concerns, establishment costs, the 51% attack and the inefficient nature of BCT. Furthermore, they may address the potential users’ negative perceptions about security, privacy, trust and risk avoidance through creatively developed blockchain solutions to promote BCT implementation.

To the best of the author’s knowledge, this is the first study that identifies barriers toward BCT incorporation in the major service industries, i.e. hotel and health care. Moreover, this is the first study that compares the preparedness of the hotel and health-care industries to determine the industry that requires more work to implement BCT.

The study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied.

The study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied.

The study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity.

This study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers.

The novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.

In recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.

The authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.

The findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.

The methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.

This research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

The proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.

This research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

The purpose of this paper is to identify factors which influence participation of developing countries in the global value chains (GVC) and to present a mathematical modeling technique to calculate a “National Participation Index of a developing country in the GVC” (hereinafter referred to as G-NPI).

The paper identifies 17 factors, encompassing both country-specific and firm-specific considerations that influence a developing countries’ participation in the GVCs. These factors have been grouped into four sets of enablers, namely, macroeconomic, geo-economic, strategic and geopolitical. The national participation index (G-NPI) has been calculated using the graph theoretic modeling approach.

Besides identifying the factors of a developing country’s participation in the GVC and grouping them as enablers, the paper considers the case of India as an example and assesses the level of interactions within (i.e. among various factors of a given enabler) and among these enablers (leading to a G-NPI value) using graph theory.

The findings can be used to calculate G-NPI for different countries to ascertain their potential for participation and also to find the factors(s) which may be deterring their participation in the GVCs.

This paper can help in policy advocacy to enhance the competitiveness of developing countries and consequently to improve the life of their consumers.

This paper introduces the concept of G-NPI and presents a unique way of assessing a developing country’s participation in the GVCs through a mathematical modeling approach.

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.

Supply chains’ (SCs’) sustainability practices and recovery strategies are attaining popularity in academia and industries to improve the resilience of the SCs and to manage large-scale disruptions. The global pandemic caused by the COVID-19 has raised the question of the sustainability of essential health-care products’ SCs of Bangladesh. It is an essential avenue for making the life of people safe and secure. Despite its importance, most of the health-care SCs in Bangladesh are struggling to meet the demand of its nation due to capacity shortage, technological backwardness of the manufacturers, delivery capacity shortages and less advanced forecasting capabilities. Therefore, this study aims to investigate the key performance indicators (KPIs) of a sustainable recovery strategy in the context of health-care SCs considering the COVID-19 pandemic.

This study used a dynamic method named graph theory and matrix approach to evaluate the most critical KPIs of a sustainable recovery strategy in the context of Bangladeshi health-care SCs.

The result revealed that dynamic forecasting and planning with a smooth delivery and distribution support system, production capacity diversification and having alternative or multiple suppliers during extraordinary disruptions may aid in the sustainability of the health-care SCs in Bangladesh.

This study is unique as no previous study has identified and examined the sustainable recovery strategy perspective KPIs considering the COVID-19 pandemic in the context of Bangladeshi health-care SCs. This study will also add value by guiding decision-makers of the health-care SCs of Bangladesh to adopt strategies toward the sustainability of SCs.

The purpose of this study is to identify factors that are important for logistics organizations from the perspective of manpower readiness for digitization of logistics operations. The study also prioritizes the identified factors and also evaluates the readiness index of manpower for the digitalization of logistics processes.

The factors for manpower readiness are identified through literature review and analysis of a case study. Three major categories of factors are identified. These are organizational, behavioural and technological factors. Under these three major categories of factors, 18 sub-factors are identified. Thereafter, with experts' inputs, the factors are prioritized using Fuzzy analytic hierarchy process (AHP). Further, a case illustration of an Indian logistics company has been taken to understand the current processes, technical capabilities, manpower skills and organization culture. After the case analysis and expert inputs, the manpower readiness index has been evaluated by using graph theory matrix approach (GTMA).

The prioritization of manpower readiness factors has been done using Fuzzy AHP. Organizational factors are found to be the most important factors which require quick attention. Sub-factors that are most important for building competencies in the logistics sector are providing the right training on functional skill development (0.129), top management support and commitment for digitalization (0.117), and organizational culture for process digitalization (0.114), etc. Finally, framework for evaluation of manpower readiness index for logistics operations in the digital age has been illustrated for a case company.

Indian logistics companies can benchmark their readiness index with respect to the best in the industry. Based on the readiness index, logistics companies can analyse their position, gaps from best and worst and can also identify potential areas for improvement.

The novelty of the study lies in the development of a framework for manpower readiness for digitalization in the logistics sector. In literature, this field is very less researched and provides the scope for developing strategies for improving manpower competencies for Industry 4.0. Logistics companies can improve their performance by making their manpower ready based on results obtained for readiness index.

The purpose of this study is to identify sustainability practices and to propose a framework for evaluating the sustainability index of logistics service providers (LSPs).

Sustainable practices followed by LSPs are identified through literature review and analysis of a case study. Thirteen such sustainable practices are identified. Thereafter, with expert inputs, nine sustainable practices are shortlisted and considered for the evaluation of the sustainability index in the proposed framework. Graph Theory Matrix Approach has been applied to evaluate the sustainability index of an LSP.

Major practices identified for evaluating sustainability index include the use of recyclable packaging, use of renewable energy sources, green procurement, reduction in carbon emissions, use of CNG/electric vehicles, rainwater harvesting and so on. The sustainability index of an LSP is evaluated by using the proposed framework.

LSPs can benchmark their sustainability index with respect to the best in the industry. Based on it, LSPs can also identify potential areas for improvement.

Novelty of the study lies in the proposed framework for evaluation of sustainability index which can be used to develop strategies for green logistics. LSPs can also improve their performance in terms of sustainability measures by adopting green logistics.

The purpose of the study is two-fold. First, to find out the different barriers that prevent the implementation of sustainable manufacturing practices in the case of Micro, Small and Medium Enterprises (MSMEs). Second, quantifying the relative intensity of these barriers to understand the importance and dependence power of the observed variables.

Primary data were collected via structured questionnaires from 150 Indian MSMEs working in the automobile industry. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) was performed. Finally, barriers are ranked using graph theory and matrix approach (GTMA), considering the inheritance and interaction among the observed variables.

There are a total of 22 barriers to “sustainable manufacturing”. EFA and CFA models confirmed four different categories of barriers. In order of importance, they are ranked as – production and operations related; organization related; collaboration related; and government rules and regulations.

The findings show that there are multiple barriers to sustainable manufacturing. The barriers are categorized into four major categories, and their ranking provides an opportunity for prioritization from both policy and managerial perspectives. The barriers can be overcome through both policy interventions as well as through best practice sharing, benchmarking and organizational-wide initiatives.

The study is among the first studies that provide valuables insights on what are the 22 different barriers to sustainable manufacturing and its relevance to the MSMEs working in the automobile industry. The study would provide further opportunities for similar studies in different sectors.

Sustainability has been on the executive agenda for years and it is now one of the fastest growing supply chain management trends. The purpose of this paper is to analyze the barriers for the adoption and implementation of the sustainable supply chain management (SSCM) concept.

This study has been divided into two phases such as identification of barriers and qualitative analysis. First, to identify the most influential barriers, the authors offer a systematic literature review, taking 188 papers published from 2010 to November 2016 into account. The investigation phase led to the selection of 15 barriers based on the literature in consultation with industrial experts and academicians. Second, the interpretive structural modeling qualitative analysis was used to find out the mutual influences between the 15 barriers by a survey.

Further, the authors propose and illustrate the cross-impact matrix multiplication applied to classification analysis to test a framework that extrapolates SSCM barriers and their relationships. “Inadequate information technology implementation” has been identified as the most important barrier that may force organizations to implement SSCM practices to ensure their business sustainability.

The authors presented some limitations in their research in some fields which could allow new researchers and practitioners to conduct the future research to grow in different dimensions.

Practitioners or policymakers usually are not familiar with these types of research works; that is why most of these surveys remain theoretical and conceptual. Future investigation needs to be done in practical application domain instead of merely giving opinions.

Based on the authors’ research, the researchers have more attention to work in conceptual analysis due to other fields, but the authors believe that even with the implementation of SSCM, many remarkable areas still exist for future research which could help in development. The authors also provide more details in this paper.