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The short/fast-food and perishable food supply chains (PFSC) have similar characteristics of lower lifespan and variable demand, leading to significant waste. However, the global population surge and increased health awareness make it impossible to continue wasting food because it is responsible for the loss of economy, resources, and biodiversity. A sustainable transition in short and PFSC is necessary; thus, addressing challenges is critical to explore the best strategy for redesigning PFSC.

An extensive literature review helped to identify 40 challenges, while a Delphi study highlighted 21 critical challenges. The fuzzy decision-making trial and evaluation laboratory method establishes a causal relationship between sustainable development (SD) challenges to help redesign PFSC.

From a strategic development perspective, frequent transportation disruption is the main critical challenge. Lack of supplier reliability is the most substantial cause of independence, with a causal value of 2.878. Overhead costs and lack of green maintenance strategies are part of the performance-oriented challenges. As it belongs to the driving zone, the second quadrant requires control while transforming PFSC for better sustainable development.

The study has several implications, such as lack of supplier reliability and frequent transportation disruption, which have the most robust causal value used as short-term strategy development. For short- and fast-food supply chains, it is necessary to study market and consumer behavior patterns to optimize inventory and customer service. Combating transportation disruption and supplier reliability challenges is vital in both PFSC and short and fast-food supply chains to reduce waste and promote sustainability.

The study’s findings are unique and put value toward the sustainable transition of PFSC by revealing critical challenges and their impact.

This paper aims to explore the underlying intention behind using blockchain technology (BLCT) in the agri-food supply chain (AFSC). This is achieved by using a conceptual framework based on technology acceptance models that considers various factors influencing user behavior toward implementing this technology in their practices.

The conceptual framework developed is empirically validated using structural equation modeling (SEM). A total of 258 respondents from agri-food domain in India were involved in this survey, and their responses were analyzed through SEM to validate our conceptual framework.

The findings state that food safety and security, traceability, transparency and cost highly influence the intention to use BLCT. Decision-makers of the AFSCs are more inclined to embrace BLCT if they perceive the usefulness of the technology as valuable and believe it will enhance their productivity.

This study contributes to the existing literature by providing thorough examination of the variables that influence the intention to adopt BLCT within the AFSC. The insights aim to benefit industry decision-makers, supply chain practitioners and policymakers in their decision-making processes regarding BLCT adoption in the AFSC.

This study investigates how decision-makers’ perceptions of BLCT influence their intention to use it in AFSCs, as well as the impact of the different underlying factors deemed valuable in the adoption process of this technology.

This study aims to identify several perspectives that affect the adoption of blockchain technology in India (BCTA) and evaluate their impact. To study the sector’s influence on adoption and the impact of BCTA on the performance of the Indian healthcare supply chain (HSCP) using BCTA as a mediating variable.

In this study, we first developed a conceptual model based on Organizational Information Processing Theory and Technology-Organization-Environment, then formulated hypotheses. Based on this, a questionnaire was developed, and data were gathered from experts in the Indian healthcare industry who were familiar with blockchain technology. AMOS 19 was used to analyze data using structural equation modelling.

All the factors have a significant positive influence on BCTA. Healthcare supply chain factors influenced the adoption most dominantly, followed by technological, environmental, organizational and record-keeping unit factors. Both the public and private sectors of HSCP benefited significantly from BCTA.

This research work is fruitful for healthcare practitioners, top management, academicians and policymakers in assessing BCTA’s impact on the HSCP.

We have attempted to evaluate the possible BCTA impact on HSCP. BCTA as a mediating variable and considering different perspectives for a holistic view of adoption in the Indian context add to this work’s originality.

Although the research promotes deploying renewable energy technologies (RETs) due to their inherently eco-friendly characteristics, consumer acceptance is relatively low, making RETs acceptance a subject of interest for most recent research. This systematic study aims to integrate and synthesize earlier research on the acceptance of RETs, assessing state-of-the-art and painting a complete picture of the consumer's adoption intention.

A systematic literature review and bibliometric analysis on the topic of RETs acceptance have been conducted to understand the spectrum of theoretical and methodological methods used in the past, as well as how the users perceive and react to the adoption of it.

The study of selected 70 research articles showed the role of technology, consumer acceptance and decision-making process as factors influencing the acceptance of RET. The findings suggest that it is crucial to comprehend these factors since users' opinions significantly affect the acceptance and use of renewable technologies.

This paper reviews the research on adopting RETs and identifies factors influencing it. The study further endorses that technology like digitalization can promote the acceptability of renewable energy sources and, inadvertently, assist managers and policymakers in managing business models and making better decisions for sustainable investments.

Research on accepting RETs by integrating the social and technological components is still in its infancy. This study is a novel attempt to identify broad patterns of RETs that can improve communication between decision-makers, technologists and users and aid in the switch to a sustainable energy transition.

Educational policies, integrated practices, obliged strategies and notable benchmarks are always required by the higher educational institutions (HEIs) for operating business ventures into competent boundaries and to preside toward the overall new business density. The same are needed to be evaluated based on student's concerns for road-mapping sustainability. Accordingly, authors conducted present study to identify crucial quality characteristics (measures) under the origins of HEIs based on student's concerns using qualitative medium under Indian economy. The study is presenting critical dimensions and quality characteristics, which are seeking by the students for selecting HEIs for their studies.

Kano integrated-Grey-VIKOR approach is utilized in present study for road-mapping sustainability based on the determination of priority index and ranking. The study utilized three segments of methodology, where in the first segment, Kano technique is implicated to define priority index of quality characteristics. In the second segment, grey sets theory is implicated to capture the perceptions of the respondents. In the third segment, VIKOR technique is implicate to rank the HEIs.

The findings of the study will assist administrators in planning the prominent strategies that can embrace performance traits under HEI, which in turn will participate in growth and development of an economy. The findings have revealed “PPCS, ICMC, TSTR, PICM, AFEP, IMIS as Attractive performance characteristics,” “IEAF, OIAR, INET as One dimensional performance characteristics,” “QTCS, PORE, SIRD as Must-be performance characteristics” and “PQPE, PCTM as Indifferent performance characteristics.” Additionally, “Professional and placement characteristics of institute” is found as the most significant measure inspiring students for admiring engineering institutes. It is found that “Observance of institutional affiliation and recognition” and “Infrastructure, classroom management and control methods” are found as the second significant measures. “Patterns of question papers and evaluation medium” and “Personal characteristics of teacher and management” are found as the least competent characteristics admiring stakeholders for selecting HEI.

The present study can assist administrators in drafting refined policies and strategies for practising quality outputs by HEI. The study suggested critical quality characteristics, which in respond will aid in attracting more number of students toward educational institutes. A study under Indian context is demonstrated for presenting critical facts and attaining higher student's enrolment rates.

The advent of Internet of Things, cloud computing and advanced computing has endowed smart manufacturing environments with resilience, reconfigurability and intelligence, resulting in the emergence of novel capabilities. These capabilities have significantly reshaped the manufacturing ecosystem, enabling it to effectively navigate uncertainties. The purpose of this study is to assess the operational transformations resulting from the implementation of smart manufacturing, which distinguish it from conventional systems.

A list of qualitative and quantitative smart manufacturing performance metrics (SMPMs) are initially suggested and categorized into strategic, tactical and operational levels. The SMPMs resemble the capabilities of smart manufacturing systems to manage disruptions due to uncertainties. Then, industry and academia experts validate the SMPMs through the utilization of the Delphi method, enabling the ranking of the SMPMs.

The proposition of the SMPMs serves as a metric to assess the digital transformation capabilities of smart manufacturing systems. In addition, the ranking of the proposed SMPMs shows a degree of relevance of the measures in smart manufacturing deployment and managing the disruptions caused due to the COVID-19 pandemic

The findings benefit managers, consultants, policymakers and researchers in making appropriate decisions for deploying and operationalizing smart manufacturing systems by focusing on critical SMPMs.

The research provides a metric to assess the operational transformations during the deployment of smart manufacturing systems. Also, it states the role of the metric in managing the potential disruptions that can alter the performance of the business due to the COVID-19 pandemic.

In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem.

In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores.

Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A₂ and A₁₁ occupy the top two positions having almost the same performance with respect to the considered financial metrics.

Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.

The study aims to explore the available academic literature on the decision-making frameworks used in additive manufacturing management (AMM).

This research formulates a systematic literature review to determine the research trend of the decision-making framework in AMM. Further, the theory, context, characteristics, and methodology (TCCM) framework is used to identify the research gaps and suggest future research directions.

The systematic literature review (SLR) delves into overarching research themes within decision-making frameworks in AMM. Additionally, it uncovers trends in article publication, geographical distribution, methodologies utilized, and industry applications. This review not only reveals research gaps but also proposes directions for future exploration.

The key novelty of this research lies in revealing the five most contributing themes of decision-making frameworks in AMM, with the highest contributing theme being AM process selection, followed by part selection for AM. This finding enables decision-makers to make informed decisions to address similar problems while exploring AM technology. Moreover, this research introduces an AM part fabrication roadmap inspired by the literature review. Lastly, the paper highlights key research gaps for future research.

The paper proposes a framework for the successful deployment of Industry 4.0 (I4.0) principles in the aerospace industry, based on identified success factors. The paper challenges the perception of I4.0 being aligned with de-skilling and personnel reduction and instead promotes a route to successful deployment centred on upskilling and retaining personnel for future role requirements.

The research methodology involved a literature review and industrial data collection via questionnaires to develop and validate the framework. The questionnaire was sent to a purposive sample of 50 respondents working in operations, and a response rate of 90% was achieved. Content analysis was used to identify patterns, themes, or biases, and the data were tabulated based on specific common attributes. The proposed framework consists of a series of gates and criteria that must be met before progressing to the next gate.

The proposed framework provides a feedback mechanism to review minimum standards for successful deployment, aligned with new developments in capability and technology, and ensures quality assessment at each gate. The paper highlights the potential benefits of I4.0 implementation in the aerospace industry, including reducing operational costs and improving competitiveness by eliminating variation in manufacturing processes. The identified success factors were used to define the framework, and the identified failure points were used to form mitigation actions or controls for inclusion in the framework.

The paper provides a framework for the successful deployment of I4.0 principles in the aerospace industry, based on identified success factors. The framework challenges the perception of I4.0 as being aligned with de-skilling and personnel reduction and instead promotes a route to successful deployment centred on upskilling and retaining personnel for future role requirements. The framework can be used as a guideline for organizations to deploy I4.0 principles successfully and improve competitiveness.

This paper aims to determine the key factors and provide an effective model to enhance the performance of sustainable supply chain management (SSCM).

Data were collected using a semi-structured interview technique, a snowball sampling method and qualitative study method. For this purpose, ten supply chain and food production managers and experts were interviewed semi-structured. The data were analyzed using open, central and selective coding methods with grounded theory approach. In the proposed model, 13 principal codes have been specified, including organizational productivity, sustainable supply chain (SSC), industry supply chain, macro policies, organizational performance, social factors, economic factors, organizational factors, political factors, technology, manufactured products, customer and supply chain failures.

The model and concepts obtained from the participants clearly show that several reasons and motivations are involved in increasing the performance of SSCM. Moreover, the designed model indicates that the motives and reasons for turning to this system are satisfactory when implemented.

The distinctive and knowledge-enhancing feature of this paper compared to previous studies is the focus on the selected background, intervening and causal factors with the influence of strategies designed to achieve a new and local model for the SSC model and assess its impact on organizational performance and productivity. The proposed components of this paper have not been investigated so far.