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Humanitarian supply chains (HSCs) by their very nature require urgent reaction to unforeseeable needs, making it difficult to properly plan for the support of actual demands. As such, integrating sustainability into traditional HSC practices continues to present a challenge to governments, nongovernmental organizations (NGOs) and other humanitarian-related agencies. This study focuses on identifying and categorizing the leading enablers to green humanitarian supply chains (GHSCs) and proposes a model for improving the responsiveness based upon a fuzzy total interpretive structural modelling approach.

Total interpretive structural modelling (TISM) uses group decision-making to identify contextual relationships among each pair of enablers and elucidates the nature of each underlying relationship. The fuzzy TISM shows the level of strength (very high influence, high influence, low influence and very low influence) of each enabler in relation to other enablers, which can help to inform management decision-making.

GHSC management requires strategic planning of inventory and logistics management. The importance of collaborative relationship building with HSC partners for developing capability and the effective use of available resources are keys to success. These improved relationships also help to promote postponement and similar speculation-based logistics strategies, as well as advanced purchasing and pre-positioning strategies. Finally, the speed and quality of response is found to be the top enabler in GHSC management.

One noted shortcoming of the chosen research method is its reliance on subjective expert judgement. However, collecting judgements is at the basis of many research methods, and the research team took utmost care throughout the research process to allay biases. Future empirical research can further examine the relationships suggested herein. Managers can use the model developed in this research to consider impactful ways to design and execute sustainable HSCs.

To the best of the authors' knowledge, this is a novel attempt to identify enablers to GHSC management. Secondly, the research team has used an advanced methodology (fuzzy TISM) to develop the contextual inter-relationships among the enablers which has not been used earlier in this direction before and thus advances the GHSC literature.

The inclusion of sustainable practices in the service only supply chain (SOSC) is less evident in the literature. The aim of this research is to analyse the enablers of sustainability to be implemented in (SOSC).

A rigorous literature review and experts’ outlook are used to identify fifteen key enablers of sustainability to be implemented in the SOSC. These key enablers are analysed by the interpretive structural modelling (ISM) and fuzzy decision making trial and evaluation laboratory (DEMATEL) technique. A case study of an Indian hospital is taken for testing the relevancy of the research.

ISM identified the six hierarchical levels of the enablers and MICMAC classified the enablers into four categories based on the driving and dependence power of the enablers. The fuzzy DEMATEL technique categorized the enablers into cause and effect groups. Four enablers were found in the effect group and eleven enablers were identified in the cause group.

This research will help the managers in identifying the enablers of sustainability to implement in the SOSC and to handle the critical enablers with care.

This is the first of its kind of study which not only identifies the key enablers to achieve sustainability in the SOSC but also derives the hierarchy levels of enablers and categorizes these enablers into cause and effect groups.

Recent years have witnessed a significant rise in Indian healthcare establishments (HCEs) which indicate that there is a constant need to improve the healthcare quality services through the adoption and implementation of TQM enablers. The purpose of this paper is to identify such enablers and then propose a ranking model for TQM implementation in Indian HCEs for improved performance.

The study identifies 20 TQM enablers through comprehensive literature survey and expert’s opinion, and classifies them into five main categories. The prominence of these enablers is established using a recently developed novel multi-criteria decision making (MCDM) method, i.e. best-worst method (BWM). The importance of the various main category and sub-category enablers is decided on the basis of their weights which are determined by the BWM. In comparison to other MCDM methods, such as analytical hierarchy process, BWM requires relatively lesser comparison data and also provides consistent comparisons which results in both optimal and reliable weights of the enablers considered in this paper. Further, a sensitivity analysis is also carried out to ensure that the ranking (based on the optimal weights) of the various enablers is reliable and robust.

The results of this study reveal that out of five main category enablers, the “leadership-based enablers (E1)” and the “continuous improvement based enablers (E5)” are the most and the least important enablers, respectively. Similarly, among the 20 sub-category enablers, “quality leadership and role of physicians (E14)” and “performing regular survey of customer satisfaction and quality audit (E52)” are the most and the least dominating sub-category enablers, respectively.

This study does not explore the interrelationship between the various TQM enablers and also does not evaluate performance of the various HCEs based on the weights of the enablers.

The priority of the TQM enablers determined in this paper enables decision makers to understand their influence on successful implementation of the TQM principles and policies in HCEs leading to an overall improvement in the system’s performance.

This study identifies the various TQM enablers in HCEs and categorizes them into five main categories and ranks them using the BWM. The findings of this research are quite useful for management of the HCEs to properly understand the relative importance of these enablers so that managers can formulate an effective and efficient strategy for their easy and smooth implementation which is necessary for continuous improvement.

It is unclear whether information obtained from a one parent can be used to infer the other parent's history of psychopathology. Two hundred and one parental dyads were asked to complete psychiatric interviews. Based on maternal report, non-participating husbands/ fathers had higher rates than participating fathers of psychiatric illness. For fathers who did participate, maternal report did not match direct interview of paternal psychopathology with sensitivities less than 0.40 and positive predictive values of 0.33 to 0.74. Psychopa -thology may be over-represented among fathers who do not participate in research. Mother report of paternal symptoms is not an effective proxy. Alternative methods need to be developed to: i) improve father participation or ii) identify psychiatric status in fathers who do not participate in research projects.

The purpose of this paper is two-fold: to expand the understanding of brand equity for places (e.g. countries, regions or cities) and propose two frameworks to increase its value.

By interviewing international participants, this study was exploratory and interpretative; thematic analysis was performed for a broader understanding about place brand equity.

The Roadmap of Brand Equity is provided by proposing three “pathways” founded on core value drivers as “road signs” (potential influences or main variables of the value of a place brand), which are required to be strategically monitored and aligned to place branding activities to enhance the value of a city, region or country. Value drivers of place brand equity and the roadmap of place brand equity are proposed through the figures.

A natural progression of this study is the investigation of place brand equity by applying statistical procedures for measuring places. Due to specific locations’ (often) unfavorable and threatening reputations worldwide, the key value-drivers (government initiatives, stakeholders' perceptions, residents’ engagement, news media, social media and real data indexes) are noted as influential partakers – either separated or combined – when analyzing their brand equity.

Both proposed archetypes suggest applications for several co-creators involved in public or private places, which can be beneficial for both emerging and non-emerging countries, regions or cities. Furthermore, both may be applied to the analysis of other places (e.g. universities, schools, museums, public squares, airports, hospitals, etc.).

This study may inspire planning and actions for public policies, including private partnerships, government initiatives and practical endeavors.

This is one of the first studies to perform an analysis of brand equity of places under a qualitative approach and to propose strategical frameworks for both research and practice.

The purpose of this paper is to provide an insight about factors affecting Big Data Analytics (BDA) utilization and adoption in Indian firms. Research studies have so far focused on BDA adoption in developed economies. This study examines the factors that influence BDA usage and adoption in the context of emerging economies.

This study proposed a theoretical model of factors influencing BDA utilization and adoption. Two independent research streams – first, the top managers’ perceived strategic value (PSV) in BDA and second, the factors that influence the adoption of BDA theoretically – have been integrated with the technology-organization-environment (TOE) framework. In the BDA context, there was a theoretical necessity to identify the driver and barriers of BDA from the TOE framework on PSV and adoption of BDA. A qualitative exploratory study using face-to-face semi-structured interviews was carried out to collect data from 22 different enterprises and service providers in India. India was selected as the context as it is one of the fastest growing large economies of the world with huge potential of BDA to improve the business landscape.

The results showed that the major reason behind BDA non-adoption is that the organizations did not realize the strategic value (SV) of BDA, and they were not ready to make the changes because of technological, organizational and environmental difficulties. The findings corroborate previous results about significant factors affecting IT adoption and implementation and provide new and interesting insights. The main factors identified as playing a significant role in organizations’ adoption of BDA were SV of BDA, complexity, compatibility, IT assets, top management support, organization data environment, perceived costs, external pressure and industry type.

The main limitation related to this study is the difficulty in generalizing the findings to a larger population of enterprises. To overcome this, a statistical survey has been planned to be conducted in the future.

The BDA adoption model in this study will have both managerial implications for practitioners in India, as well as those in other developing countries, and academic implications for researchers who are interested in BDA adoption in developing counties, in terms of formulating better strategies for BDA adoption. For managers, using the research model of this study could assist in increasing their understanding of why some organizations choose to adopt BDA, while similar ones facing similar conditions do not. Also, the understanding of the strategic utilization of BDA in different business processes may improve the adoption of BDA in organizations.

This paper contributes in exploring and enhancing the understanding of the factors affecting the utilization and adoption of BDA in organizations from an Indian perspective. This study is an attempt to develop and explore a BDA adoption model by the fusion of PSV and TOE framework. The effect of the three contexts of this framework (technological, organizational and environmental) on the strategic utilization of BDA has been studied for the first time.

This paper intends to explore and appraise the humanitarian supply chain management enablers (HSCMEs) for efficient and effective humanitarian operations. This research aims to analyze the interaction of enablers for humanitarian supply chain management (HSCM) using a proposed hybrid framework consists of fuzzy Delphi (FD), interpretive structural modeling (ISM)–matriced impacts croises multiplication appliquee a un classement (MICMAC) and revised Simos approach.

This research is deliberate to identify 28 HSCMEs through a literature review and experts' opinions; out of which 20 HSCMEs are selected using FD. ISM is applied to know contextual relationship among the selected HSCMEs for developing a hierarchical model. The MICMAC analysis classifies the HSCMEs based on driving power and dependence power to validate the developed hierarchical ISM structure. The revised Simos technique is used to prioritize the HSCMEs to access its relative significance in humanitarian operations.

The finding of the analysis suggests that government policy and leadership support obtained the highest priority, having high driving power and low dependence power is significantly strategic and emerged as the leading driver for the HSCM implementation.

ISM model presents an insight into interrelationship among HSCMEs, but this cannot quantify the impact of each HSCMEs.

Disaster relief aid agencies and stakeholders may focus on the enablers having high driving power and higher weight in designing and executing an effective and efficient humanitarian supply chain and to improve their activities and strategies of HSCM.

This research helps humanitarian logisticians and humanitarian organizations to make better decisions to improve their operational performance in pre and postdisaster phases.

This paper explores the application of proposed hybrid framework to analyze the HSCMEs that can be considered as the original contribution.

Due to the rapid surge in the number of COVID-19 cases in India, the health-care supply chain (HCSC) disruptions and uncertainties have increased manifold posing severe challenges to health-care facilities and significantly hampering the functioning of the health industry. This study aims to propose a hierarchical structural model of enablers of HCSC in the COVID-19 outbreak and identifies inter-relationships among them in the health-care market.

Enablers of emergency HCSC have been identified through extensive literature review and experts’ opinions. Subsequently, total interpretive structural modeling (TISM) and cross-impact matrix-multiplication (MICMAC) analysis have been implemented to determine the hierarchical inter-relationships among enablers and classify them according to their contribution to the overall system.

The research has identified and validated 15 enablers of the emergency supply chain in health-care businesses. The study resulted in a seven-level hierarchical structural model based on enabler’s driving and dependence powers. Further, the application of MICMAC analysis resulted in the classification of enablers into four groups, namely, autonomous, dependent, linkage and independent group.

This study would help health professionals, policymakers and academia to implement the theoretical model constructed to alleviate the effect of COVID-19 by improving the HCSC performances in pandemic situations. This study has social and economic implications in terms of cost-effective and efficient delivery of care services in health emergencies.

The proposed theoretical model constructed is a new effort addressing the issues of HCSC in the COVID-19 crisis. Procedural implementation of TISM and MICMAC analysis in this study would help researchers to grasp concepts in a very lucid manner. The present study is one of the very few studies analyzing enablers in pandemic situations by implementing the TISM approach.

The paper focuses on reviewing and theorizing the factors that affect the adoption of cloud computing in the education sector narrowing the focus to developing countries such as India.

Through an extensive literature survey, critical factors of cloud computing for education were identified. Further, the fuzzy DEMATEL approach was used to define their interrelationship and its cause and effect.

A total of 17 factors were identified for the study based on the literature survey and experts' input. These factors were classified as causes and effects and ranked and interrelated. “Required Learning Skills and Attitude,” “Lack of Infrastructure,” “Learners' Ability” and “Increased Investment” are found to be the most influential factors.

The resultant ranking factors can be used as a basis for managing the process of cloud adoption in several institutions. The study could guide academicians, policymakers and government authorities for the effective adoption of cloud computing in education.

The study investigates interdependency amongst the factors of cloud computing for education in context with developing economy. This is one of first study in higher education institutes of India.

In a globalized environment, small and medium enterprises (SMEs) are facing formidable challenges. Not only do they have to keep up their profitability, but there is also a pressure from various stakeholders to add to their environmental and quality performance .The solution obviously lies in continuously adopting and improving upon lean-green practices in their operations. This work aims at identifying, classifying and building up a duly tested robust ranked-order model of such “enablers”, related to lean-green practices, that puts them (the enablers) in an order of being the most significant to being the least significant further to be accorded the same or similar weight in strategy formulation and implementation stage by Indian SMEs for enhancing their overall organizational performance.

The study identifies 20 enablers (12 lean and 08 green manufacturing enablers) through extensive literature review and experts' opinion survey and classifies them into three main categories. The ranking and significance of each of the main and subcategory enablers is evaluated according to its weight which is determined by the best-worst method (BWM) approach, one of the novel multi-criteria decision-making (MCDM) methods. Further, the results have been drawn after running accuracy check of the rankings (based upon optimal weights) and testing the robustness of the ranked-order model through sensitivity analysis.

The results of this study reveal that out of the three main category enablers, “operational performance enablers (E1)” and “quality performance enablers (E3)” are the most and the least significant enablers, while in the group of 20 subcategory enablers, “Kaizen (E17)” and “environment emission control (E28)” are the most and the least significant subcategory enablers, respectively.

The prioritization model or ranked-order model of the lean-green manufacturing enablers proposed through this study may serve as a standard model to managers to help them decide and allocate their efforts and resources accordingly in managing their operations. This will also help them adopt high-ranking lean-green manufacturing enablers in their firms and benchmark and standardize their existing practices accordingly, leading to greater competitive advantage.

The study identifies various green-lean manufacturing enablers in SMEs, classifies them into three main categories and ranks them using BWM approach. The findings of this study should be extremely relevant to managers, manufacturing engineers and practitioners in Indian SMEs from the perspective of developing deeper appreciation of these enablers as per their relative ranked importance to further formulating an effective and efficient strategy for their implementation resulting in optimal results.