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Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

To cater to the senior tourist market, it is essential to comprehend the factors motivating and deterring them from international travel post-COVID-19. This study aims to focus on senior citizens’ destination choice intentions and aims to develop a model that prioritizes positive and negative factors leading to international travel destination choices. It uses push–pull factors, perceived travel risks (PTRs) and perceived travel constraints (PTCs).

Decision-making trial and evaluation laboratory (DEMATEL) and fuzzy technique for order of preference by similarity to ideal solution (Fuzzy TOPSIS) are two multi-criteria decision-making (MCDM) techniques used to identify connections between variables and determine their relative importance in the decision-making model.

DEMATEL found push and pull factors are “effects” while PTCs and PTRs are “causes” affecting senior citizens’ destination choices. Push factors and PTCs have a greater impact than pull factors and PTRs. Fuzzy TOPSIS highlighted “improving health and wellness” and “self-fulfillment and spirituality” as key push factors, “health safety and security quotient” as the most important pull factor, and “interpersonal constraints” as the most critical PTC. Finally, “health risks” is the top PTR.

This paper adds to the tourism literature by looking at the relationship between senior tourists’ motivation, PTRs and PTCs and showing how the subfactors affect their choice of destination rank. The data analysis techniques used in this study are also novel, having never been used before in senior tourism research. Finally, even though there is a lot of research on senior tourism, not much is known about how Indian senior tourists act. In light of this study’s findings, practical recommendations were offered to tourism stakeholders worldwide, interested in tapping into the market of Indian outbound senior tourists or repositioning product or destination offerings to take this promising market or similar markets into account.

为了成功迎合蓬勃发展的老年旅游市场, 了解激励和阻止老年人国际旅行的因素尤为重要, 尤其是在 COVID-19 之后。本研究侧重于老年人的目的地选择意向, 并基于推拉因素、感知旅行风险 (PTR) 和感知旅行限制 (PTC), 旨在开发影响老年人国际旅游目的地选择的积极和消极因素的模型。

决策试验和评估实验室 (DEMATEL), 和与理想解决方案相似度的模糊偏好顺序 (Fuzzy TOPSIS) 是两种多标准决策 (MCDM) 技术, 用于识别变量之间的联系并找出它们在决策模型中的相对重要性。

DEMATEL的结果表明, 推力和拉力因素是“影响”, 而感知旅行约束（PTC）和感知旅行风险（PTR）是影响老年人目的地选择意愿的因素中的“原因”。推动因素和 PTC 比拉动因素和 PTR 发挥更重要的作用。 Fuzzy TOPSIS分析结果表明, “改善健康”和“自我实现和精神”是推动因素下最重要的因素。此外, 目的地的“健康安全商数”是拉动因素中最重要的, “人际约束”是PTC中最重要的。最后, 研究结果表明, “健康风险”是 PTR 中最重要的。

本文通过评估旅游动机、PTR 和老年游客 PTC 之间的相互关系, 为现有的旅游文献做出了贡献。此外, 该研究展示了影响老年游客目的地选择意愿的因素中各个子因素的比较优先级。本研究中使用的数据分析技术也很新颖, 以前从未在老年人旅游研究中使用过。最后, 虽然对老年旅游有丰富的研究, 但印度老年旅游者的行为相对不为人知。研究结果向有兴趣进入印度出境老年游客市场或重新定位产品或目的地的全球旅游利益相关者提供了切实可行的建议, 以考虑这个有前景的市场或类似市场。

Para atender a un mercado turístico de la tercera edad, es esencial comprender los factores que les motivan y les disuaden de realizar viajes internacionales tras el COVID-19. Este estudio se centra en las intenciones de elección de destino de las personas mayores y pretende desarrollar un modelo que priorice los factores positivos y negativos que conducen a la elección de un destino de viaje internacional. Utiliza los factores push-pull, los riesgos de viaje percibidos (PTR) y las limitaciones de viaje percibidas (PTC).

Decision Making Trial and Evaluation Laboratory (DEMATEL) y Fuzzy Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) son dos técnicas de toma de decisiones multicriterio (MCDM) utilizadas para identificar las conexiones entre variables y determinar su importancia relativa en el modelo de toma de decisiones.

DEMATEL descubrió que los factores de empuje y atracción son “efectos,” mientras que las PTC y las PTR son “causas” que afectan a las elecciones de destino de las personas mayores. Los factores de empuje y los PTC tienen un mayor impacto que los factores de atracción y los PTR. El Fuzzy TOPSIS destacó la “mejora de la salud y el bienestar” y la “autorrealización y espiritualidad” como factores de empuje clave, el “cociente de seguridad y protección de la salud” como el factor de atracción más importante y las “limitaciones interpersonales” como el PTC más crítico. Por último, los “riesgos para la salud” son el principal PTR.

Este artículo se suma a la literatura turística al estudiar la relación entre la motivación de los turistas sénior, los PTR y los PTC y mostrar cómo afectan los subfactores a su elección del destino. Las técnicas de análisis de datos empleadas en este estudio también son novedosas, ya que nunca se habían utilizado en la investigación sobre el turismo senior. Por último, aunque existen muchas investigaciones sobre el turismo sénior, el comportamiento de los turistas de la tercera edad en la India es relativamente desconocido. A la luz de los resultados del estudio, se ofrecen recomendaciones prácticas a las partes interesadas en el turismo de todo el mundo, interesadas en aprovechar el mercado de los turistas senior indios emisores o en reposicionar la oferta de productos o destinos para tener en cuenta este prometedor mercado o mercados similares.

Delay and disruption claims involve a complex process that often result in disputes, unnecessary expenses and time loss on construction projects. This study aims to review and synthesize the contributions of previous research undertaken in this area and propose future directions for improving the process of delay and disruption claims.

This study adopted a holistic systematic review of literature following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A total of 230 articles were shortlisted related to delay and disruption claims in construction using Scopus and Web of Science databases.

Six research themes were identified and critically reviewed including delay analysis, disruption analysis, claim management, contract administration, dispute resolution and delay and disruption information and records. The systematic review showed that there is a dearth of research on managing the wide-ranging information required for delay and disruption claims, ensuring the transparency and uniformity in delay and disruption claims’ information and adopting an end-user’s centred research approach for resolving the problems in the process of delay and disruption claims.

Complexities in delay and disruption claims are real-world problems faced by industry practitioners. The findings will help the research community and industry practitioners to prioritize their energies toward information management of delay and disruption claims.

This study contributes to the body of knowledge in delay and disruption claims by identifying the need for conducting more research on its information requirements and management. Subsequently, it provides an insight on the use of modern technologies such as drones, building information modeling, radio frequency identifiers, blockchain, Bigdata and machine learning, as tools for more structured and efficient attainment of required information in a transparent and consistent manner. It also recommends greater use of design science research approach for delay and disruption claims. This will help to ensure delay and disruption claims are the least complex and less dispute-prone process.

Nuclear energy investments contribute significantly to the energy independence of countries. This situation helps countries to develop both economically and socially. Therefore, countries need to develop strategies to increase their nuclear energy investments. However, radioactive waste generated in nuclear reactors is one of the most important disadvantages of this type of energy. Due to this situation, the wastes generated must be disposed of effectively for nuclear energy investments to be effective. In this study, it is aimed to create optimal strategies for the effective waste managements generated in nuclear power plants. Within this framework, four different balanced scorecard perspectives are considered as the criteria. In the analysis process, Analytical Hierarchy Process (AHP) methodology is taken into consideration. It is identified that learning and development is the most critical factor for the effective radioactive waste management created in nuclear power plants. On the other side, internal factors are the second most important criterion in this framework. Hence, it is understood that nuclear energy investors should give priority to the technological development for effective waste management in nuclear power plants. With the help of these technological improvements, the wastes can be disposed more successfully. As a result, environmental sustainability can be provided much easily.

Fused deposition modeling (FDM) is an extensively used additive manufacturing method with the capacity to build complex functional components. Due to the machinery and environmental factors during manufacturing, the FDM parts inevitably demonstrated uncertainty in properties and performance. This study aims to identify the stochastic constitutive behaviors of FDM-fabricated polylactic acid (PLA) tensile specimens induced by the manufacturing process.

By conducting the tensile test, the effects of the printing machine selection and three major manufacturing parameters (i.e., printing speed S, nozzle temperature T and layer thickness t) on the stochastic constitutive behaviors were investigated. The influence of the loading rate was also explained. In addition, the data-driven models were established to quantify and optimize the uncertain mechanical behaviors of FDM-based tensile specimens under various printing parameters.

As indicated by the results, the uncertain behaviors of the stiffness and strength of the PLA tensile specimens were dominated by the printing speed and nozzle temperature, respectively. The manufacturing-induced stochastic constitutive behaviors could be accurately captured by the developed data-driven model with the R² over 0.98 on the testing dataset. The optimal parameters obtained from the data-driven framework were T = 231.3595 °C, S = 40.3179 mm/min and t = 0.2343 mm, which were in good agreement with the experiments.

The developed data-driven models can also be integrated into the design and characterization of parts fabricated by extrusion and other additive manufacturing technologies.

Stochastic behaviors of additively manufactured products were revealed by considering extensive manufacturing factors. The data-driven models were proposed to facilitate the description and optimization of the FDM products and control their quality.

This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

Social network group decision-making (SNGDM) has rapidly developed because of the impact of social relationships on decision-making behavior. However, not only do social relationships affect decision-making behavior, but decision-making behavior also affects social relationships. Such complicated interactions are rarely considered in current research. To bridge this gap, this study proposes an SNGDM model that considers the interaction between social trust relationships and opinion evolution.

First, the trust propagation and aggregation operators are improved to obtain a complete social trust relationship among decision-makers (DMs). Second, the evolution of preference information under the influence of trust relationships is measured, and the development of trust relationships during consensus interactions is predicted. Finally, the iteration of consensus interactions is simulated using an opinion dynamics model. A case study is used to verify the feasibility of the proposed model.

The proposed model can predict consensus achievement based on a group’s initial trust relationship and preference information and effectively captures the dynamic characteristics of opinion evolution in social networks.

This study proposes an SNGDM model that considers the interaction of trust and opinion. The proposed model improves trust propagation and aggregation operators, determines improved preference information based on the existing trust relationships and predicts the evolution of trust relationships in the consensus process. The dynamic interaction between the two accelerates DMs to reach a consensus.

This article aims to investigate the status of and the trends in the intertwining of crowdfunding and innovation literature by identifying, evaluating and synthesizing the findings from previous research. This paper provides a bibliometric meta-analysis of the already substantial and growing literature on innovation and crowdfunding research.

Using a bibliometric approach, this research scrutinizes all articles that include terms related to “crowdfunding” and “innovation” (in their title, abstract or keywords) in Elsevier’s Scopus database. VosViewer and Bibliometrix package in R have been used to analyse 150 articles.

The results suggest that there are three main research clusters in the innovation and crowdfunding literature. The first cluster highlights the role of crowdfunding in fostering radical and incremental innovation. The second cluster focuses on the concept of openness and its effect on innovation in crowdfunding campaigns, while the third cluster explains the role of platforms’ innovation in crowdfunding success.

Taking a holistic perspective, this contribution advances new knowledge on the intertwining of crowdfunding and innovation research fields. It implies that crowdfunding is facilitating the flow of knowledge between different stakeholders, including project initiators and crowd investors, among others, as they all benefit from open innovation platforms.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.