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The main aim of this study is to identify and prioritize the factors that influence the adoption of big data analytics (BDA) within the supply chain (SC) of the food industry in India.

The study is carried out in two distinct phases. In the first phase, barriers hindering BDA adoption in the Indian food industry are identified. Subsequently, the second phase rates/prioritizes these barriers using multicriteria methodologies such as the “analytical hierarchical process” (AHP) and the “fuzzy analytical hierarchical process” (FAHP). Fifteen barriers have been identified, collectively influencing the BDA adoption in the SC of the Indian food industry.

The findings suggest that the lack of data security, availability of skilled IT professionals, and uncertainty about return on investments (ROI) are the top three apprehensions of the consultants and managers regarding the BDA adoption in the Indian food industry SC.

This research has identified several reasons for the adoption of bigdata analytics in the supply chain management of foods in India. This study has also highlighted that big data analytics applications need specific skillsets, and there is a shortage of critical skills in this industry. Therefore, the technical skills of the employees need to be enhanced by their organizations. Also, utilizing similar services offered by other external agencies could help organizations potentially save time and resources for their in-house teams with a faster turnaround.

The present study will provide vital information to companies regarding roadblocks in BDA adoption in the Indian food industry SC and motivate academicians to explore this area further.

The study aims to facilitate the medical tourists visiting emerging countries for various kinds of ailments by ranking the possible destinations to avail medical treatments.

A Fuzzy Analytical Hierarchical Process (FAHP) with a mixed-method approach is applied to analyze data collected from patients and substantiate it with medical tour operators in India to gain managerial insights on the choice-making patterns of the patients.

India is a preferred emerging market location due to the low cost and high medical staff quality. India offers value for money, whereas Singapore and Thailand are preferred destinations for quality and technology.

The study will facilitate the emerging markets' governments, hospitals and medical tourists to understand the importance of various determinants responsible for availing medical treatment outside their country.

The study recommends that cost and quality care are the patients' prime focus; government policies must provide clear guidelines on what the hospitals and country environment can offer and accordingly align the marketing strategies.

This study is the first attempt to rank various factors affecting medical tourism using the FAHP approach.

This research has been carried out to study the impact of critical success factors (CSFs) on successful project management of projects in the manufacturing sector. These success factors will pave the way for the successful completion of projects for the manufacturing sector. CSFs play a vital role in the timely conclusion of projects in any organization. For the projects to be successful certain essential factors must be taken into account. These essential factors are identified through this research.

During this study an adaptive survey of the literature was conducted, after a detailed literature review certain success factors were identified for project management in the manufacturing sector. The number of success factors was reduced to 40 factors based on the level of incidence in the literature. The length of the questionnaire was also given due importance to make the survey more interesting and effective. After that Fuzzy Delphi Method (FDM) was employed to screen the most essential factors. In the end, the Fuzzy Analytical Hierarchical Process (FAHP) was used to rank these factors in order of importance.

Project Manager Leadership Skills, Economic Environment, Top Management Support, Project Planning, Clear and realistic Goals, and Financial Support have come out to be the most important CSFs for successful project management in the manufacturing sector.

This is novel research to identify CSFs for project management in the manufacturing sector. Previously, most of the studies remained focused on construction and software projects.

The purpose of this research is to present an integrated methodological framework to aid in performance stewardship of management institutions according to their strategies based on a holistic evaluation encompassing social, economic and environmental dimensions.

A Mamdani fuzzy inference system (FIS) approach was adopted to design the quantitative models with respect to balanced scorecard (BSC) perspectives to demonstrate dynamic capability. Individual models were developed for each perspective of BSC using Mamdani FIS. Data was collected from subject matter experts in management education.

The proposed methodology is able to successfully compute the scores for each perspective. Effective placement, teaching learning process, faculty development and systematic feedback from the stakeholders were found to be the key drivers for revenue generation. The model is validated as the results were well accepted by the head of the institution after implementation.

The model resulting from this study will assist the institution to cyclically assess its performance, thus enabling continuous improvement. The strategy map provides the causality of the objectives across the four perspectives to aid the practitioners to better strategize. Also this study contributes to the literature of BSC as well to the applications of multi-criteria decision-making (MCDM) techniques.

Mamdani FIS integrated BSC model is a significant contribution to the academia of management education to quantitatively compute the performance of institutions. This quantified model reduces the ambiguity for practitioners to decide the performance levels for each metric and the priorities of metrics.

Effective leaders have emerged as the cornerstone of project success. The major purpose of this paper is to propose a framework to categorize and prioritize leadership competencies for project managers in megaprojects.

In the first stage, this study utilizes PMBOK 6th Edition, IPMA ICB 4.0 frameworks to develop a hierarchy-based four clusters of leadership competencies. In the second stage, a Fuzzy-AHP (Analytic Hierarchy Process) approach was employed to prioritize the leadership competencies for an organization dealing in megaprojects. Finally, using ordinal priority approach (OPA), the results of Fuzzy-AHP method are validated.

Based on PMBOK, IPMA and literature, the proposed framework deduced twenty-four leadership competencies and grouped them in four clusters. The Fuzzy-AHP results indicate that among clusters, people competencies cluster is ranked most important, followed by perspective, practice, and innovation competencies. Considering the sub-categories and global weights, culture/values, governance, interpersonal skills, and development and growth emerged as the most important leadership competencies. The results from OPA corroborate the findings of Fuzzy-AHP method.

Megaprojects are characterized by massive investments and extensive economic and social impact. The proposed framework would be an important aid for policymakers to develop suitable strategies and programs to inculcate leadership competencies that would lead to successful project managers and improved project performance.

The need for this research stems from the need to integrate popular project management frameworks in leadership competencies development in project based organizations. The proposed integrated framework, based on PMBOK 6th Edition and IPMA ICB 4.0 competency frameworks, is an original contribution to understand and prioritize leadership competencies for megaproject success.

Identifying the barriers that hinder the circularity in the wine industry in Bulgaria and proposing a preliminary circular economy (CE) index for ranking in order of importance the barriers for providing a suitable and replicable model through the contextualization of the study applied at the territorial level.

The article adopts a Fuzzy AHP methodology based on 6 main barriers and 19 sub-barriers to find the weights of the barriers and rank them. The study was conducted in Bulgaria, and it consists of a questionnaire with pairwise comparisons to ask for expert opinions of members of the Executive Board of the National Association of Bulgarian Vinegrowers and additional members of the regional vine and winegrowing chambers.

The study identifies training and education and environmental barriers as the most important among the main barriers whereas, economic barriers are assessed as low impact. Considering the sub-barriers, lack of consumer awareness and interest, unclear taxation for CE, lack of knowledge about environmental emissions (carbon, water etc.) within the supply chains, and lack of digital trends are determined as the most significant sub-barriers.

The preliminary CE index can be practically used and tested, when necessary, by each winemaker according to their understanding and opinion of CE barriers and sub-barriers. The ranking within the CE index can lead winemakers also to decisions related to the company's social policy.

The study uses the Fuzzy AHP methodology with expert opinions to analyze and weigh the main barriers to achieving CE at the micro-level.

One of the critical reasons for the nonacceptance of additive manufacturing (AM) processes is the lack of understanding and structured knowledge of design for additive manufacturing (DfAM). This paper aims to assist designers to select the appropriate AM technology for product development or redesign. Using the suggestion provided by the design assist tool, the user’s design alterations depend on their ability to interpret the suggestion into the design without affecting the design’s primary objective.

This research reports the development of a tool that evaluates the efficacy values for all seven major standard AM processes by considering design parameters, benchmark standards within the processes and their material efficacies. In this research, the tool provides analytical and visual approaches to suggestion and assistance. Seventeen design parameters and seven benchmarking standards are used to evaluate the proposed product and design quality value. The full factorial design approach has been used to evaluate the DfAM aspects, design quality and design complexity.

The outcome is evaluated by the product and design quality value, material suit and material-product-design (MPD) value proposed in this work for a comparative assessment of the AM processes for a design. The higher the MPD value, the better the process. The visual aspect of the evaluation uses spider diagrams, which are evaluated analytically to confirm the results’ appropriateness with the proposed methodology.

The data used in the database is assumed to make the study comprehensive. The output aims to help opt for the best process out of the seven AM techniques for better and optimized manufacturing. This, as per the authors’ knowledge, is not available yet.

The fourth industrial revolution and digital transformation have caused paradigm changes in the procedures of goods production and services through disruptive technologies, and they have formed new methods for business models. Health and medicine fields have been under the effect of these technology advancements. The concept of smart hospital is formed according to these technological transformations. The aim of this research, other than explanation of smart hospital components, is to present a model for evaluating a hospital readiness for becoming a smart hospital.

This research is an applied one, and has been carried out in three phases and according to design science research. Based on the previous studies, in the first phase, the components and technologies effecting a smart hospital are recognized. In the second phase, the extracted components are prioritized using type-2 fuzzy analytic hierarchical process based on the opinion of experts; later, the readiness model is designed. In the third phase, the presented model would be tested in a hospital.

The research results showed that the technologies of internet of things, robotics, artificial intelligence, radio-frequency identification as well as augmented and virtual reality had the most prominence in a smart hospital.

The innovation and originality of the forthcoming research is to explain the concept of smart hospital, to rank its components and to provide a model for evaluating the readiness of smart hospital. Contribution of this research in terms of theory explains the concept of smart hospital and in terms of application presents a model for assessing the readiness of smart hospitals.

This paper aims to examine and compare extant literature on the application of multi-criteria decision-making (MCDM) techniques in humanitarian operations (HOs) and humanitarian supply chains (HSCs). It identifies the status of existing research in the field and suggests a roadmap for academicians to undertake further research in HOs and HSCs using MCDM techniques.

The paper systematically reviews the research on MCDM applications in HO and HSC domains from 2011 to 2022, as the field gained traction post-2004 Indian Ocean Tsunami phenomena. In the first step, an exhaustive search for journal articles is conducted using 48 keyword searches. To ensure quality, only those articles published in journals featuring in the first quartile of the Scimago Journal Ranking were selected. A total of 103 peer-reviewed articles were selected for the review and then segregated into different categories for analysis.

The paper highlights insufficient high-quality research in HOs that utilizes MCDM methods. It proposes a roadmap for scholars to enhance the research outcomes by advocating adopting mixed methods. The analysis of various studies revealed a notable absence of contextual reference. A contextual mind map specific to HOs has been developed to assist future research endeavors. This resource can guide researchers in determining the appropriate contextual framework for their studies.

This paper will help practitioners understand the research carried out in the field. The aspiring researchers will identify the gap in the extant research and work on future research directions.

To the best of the authors’ knowledge, this is the first literature review on applying MCDM in HOs and HSCs. It summarises the current status and proposes future research directions.

This research aims to measure the public sector comparator (PSC) to reach public–private partnership (PPP) projects' negotiable price range for water and sewage companies in Iran. PSC measurement drives the public sector to make valid decisions about costs.

Around 170 risks were primarily determined through studying numerous articles. Then, risk effects were specified by distributing questionnaires in two steps. The questionnaires are distributed among experts on PPP-related projects and the Monte Carlo simulation method is used for confidence factors of 70, 80 and 90%. PSC is measured based on these results to study cases of Sirjan’s sewerage and sewage purification systems.

11 risks were identified as the main risks that are effective on PSC, and project implementation costs were specified based on the modeling. The corruption of the private and public sectors was identified as the most effective risk in this research. It can affect a project’s cost up to 158% in the construction period and up to 134% in the operation period. Based on the obtained results, 63% of this risk’s cost goes to the public sector.

The originality of this research is the PSC measurement method and appointing the risk share of each private and public sector. The results of this research can be applied to all the infrastructure and PPP projects in Iran and other developing countries as a way for employers to estimate accurate negotiable price ranges.