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The United Nations Industrial Development Organisation (UNIDO) proposed the development of eco-industrial parks (EIP) related to the 9th, 12th, and 13th of the sustainable development goals (SDGs) for ensuring green industrial practice to deal with climate change. The first approach to EIP is choosing a suitable site, however, it is not a simple task, because it involves spatial factors and is always impaired by uncertainties that require more than one decision module. There is a fewer study in objectively assessing the criteria for the selection of suitable sites for EIP development to contribute to the SDG initiative. This study provides an integrated process for assessing a consistent weight of criteria for EIP site selection. Nine steps were used in the fuzzy-analytical hierarchy process namely criterion identification, hierarchical structure construction, triangular fuzzy number matrix, geometric ratio, fuzzy relative weight, defuzzification, normalisation, sensitivity analysis (SA) and weight ranking. When tested using spatial and non-spatial criteria for EIP site selection, results show transportation infrastructure (13%), raw materials (12%), water bodies (12%), climate (10%), labour (9%), land use (9%), markets (9%), governmental policies (8%), existing industries (7%), urban settlement (6%), and restricted areas (5%). The SA verified that any evaluation error of 2% or 5% on the criteria weight is insignificant, but for 10% error, results can be distorted. The study has developed a consistent, simple approach integrating hierarchical and uncertainty modules for choosing EIP locations, and it is proposed as a guide for selecting suitable greenfield or brownfield EIP sites for sustainable industrial practices.

Outsourcing is a crucial tool for an organization to focus on its core business operations. The purpose of this study is to find critical success factors (CSFs) of outsourcing and to prioritize the performance outcomes of outsourcing because of the adoption of its CSFs.

This research proposes the hybrid framework of fuzzy Delphi, fuzzy analytic hierarchy process (F-AHP) and fuzzy additive ratio assessment (F-ARAS) to fulfill the research objectives. Fuzzy Delphi is used to finalize the CSFs of outsourcing and F-AHP provides the relative weights to it. F-ARAS is used to prioritize the outsourcing performance indicators with respect to CSFs. The empirical case investigation of three Indian large-scale public sector process industries is conducted to demonstrate the applicability of the proposed framework.

The result shows that increased dominance in core activity, ability to increase or decrease capacity, improved financial performance, optimized resource utilization and increased market share are the top five performance outcomes because of the adoption of outsourcing CSFs.

Factors such as cultural, political, environmental and size of the organization may have a significant implication on the research results.

This is a novel approach towards the prioritization of performance outcomes of outsourcing. Moreover, the present research is a helpful tool to manage outsourcing and choosing which performance outcome of outsourcing ought to be used to measure implementation. This will help to recognize potential opportunities and preparing strategic planning for both short-term and long-term goals.

In this paper, the four popular multiple-criteria decision-making (MCDM) methods in fuzzy environment are utilized to reflect the vagueness and uncertainty on the judgments of decision-makers (DMs), because the crisp pairwise comparison in these conventional MCDM methods seems to be insufficient and imprecise to capture the right judgments of DMs. Of these methods, as Fuzzy analytic hierarchy process (F-AHP) is used to calculate criteria weights, the other methods; Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS), Fuzzy Grey relational analysis (F-GRA) and Fuzzy Preference Ranking Organization METhod for Enrichment of Evaluations (F- PROMETHEE II) are used to rank alternatives in the three different ways for a comparative study.

The demand for green products has dramatically increased because the importance and public awareness of the preservation of natural environment was taken into consideration much more in the last two decades. As a result of this, especially manufacturing companies have been forced to design more green products, resulting in a problem of how they incorporate environmental issues into their design and evaluate concept options. The need for the practical decision-making tools to address this problem is rapidly evolving since the problem turns into an MCDM problem in the presence of a set of green concept alternatives and criteria.

The incorporation of fuzzy set theory into these methods is discussed on a real-life case study, and a comparative analysis is done by using its numerical results in which the three fuzzy-based methods reveal the same outcomes (or rankings), while F-GRA requires less computational steps. Moreover, more detailed analyses on the numerical results of the case study are completed on the normalization methods, distance metrics, aggregation functions, defuzzification methods and other issues.

The designing and manufacturing environmental-friendly products in a product design process has been a vital issue for many companies which take care of reflecting environmental issues into their product design and meeting standards of recent green guidelines. These companies have utilized these guidelines by following special procedures at the design phase. Along the design process consisting of various steps, the environmental issues have been considered an important factor in the end-of-life of products since it can reduce the impact on the nature. In the stage of developing a new product with the aim of environmental-friendly design, the green thinking should be incorporated as early as possible in the process.

The case study was inspired from the previous work of the author, which was realized in a hot runner systems manufacturer, used in injection molding systems in a Canada. In a new product development process, the back- and front-ends of development efforts mainly determine the following criteria: cost, risk, quality and green used in this paper. The case study showed that the three fuzzy MCDM methods come to the same ranking outcomes. F-GRA has a better time complexity compared to the other two methods and uses a smaller number of computational steps. Moreover, a comparative analysis of the three F-MCDM methods; F-PROMETHEE II, F-TOPSIS and F-GRA used in ranking for green concept alternatives using the numerical results of the case study. For the case study; as seen in table 20, the three F-MCDM methods produced the numerical results on the rankings of the green concept alternatives as follows; {Concept A-Concept C–Concept B–Concept D}.

Inclusion of environmental-related criteria into concept selection problem has been gaining increasing importance in the last decade. Therefore, to facilitate necessary calculations in applying each method especially with its fuzzy extension, it can be developed a knowledge-based (KB) or an expert system (ES) to help the DMs make the required calculations of each method, and interpret its results with detailed analysis.

The objective of the research was to propose a F-AHP based F-MCDM approach to green concept selection problem through F-PROMETHEE II, F-TOPSIS and F-GRA methods. As the F-AHP is used to weight evaluation criteria, the other methods are respectively used for ranking the concept alternatives and determine the best concept alternative.

From the last few decades, environmental issues have become a global concern. Consumption activities are given much attention in the marketing literature, whenever the question about protecting the environment arises. The responsibility of the consumer toward the environment is a major concern and the purpose of this study is to prioritize factors responsible for determining environmental responsibility among young consumers. This research will provide valuable insights to the marketers in targeting those particular areas which according to young consumers are highly ranked/prioritized for being environmentally responsible.

In this study, data were collected from 400 young consumers from Punjab and Chandigarh. Fuzzy analytical hierarchy process (F-AHP) was applied to prioritize or rank the factors on the basis of significance for being environmentally responsible. The primary factors considered for further analysis were knowledge and awareness, attitude, green consumer value, emotional affinity toward nature, willingness to act and environment-related past behavior.

Results of the study depicted knowledge and awareness as the highest-ranked and prioritized factor for young consumers to become environmentally responsible, while environment-related past behavior emerged as the least important factor for consumers' environmental responsibility.

Data were collected from the young consumers of Punjab and Chandigarh only and only a few factors of consumers' environmental responsibility were considered for further analysis which depicts the limitation of the study.

The research study is highly useful for the government and the business firms to target the highly prioritized factors of environmental responsibility which will promote green consumption practices and behavior among young consumers.

Previous researches have explored the factors of environmental responsibility and modeled their relationships. However, the present study has employed the multi-criteria decision-making technique to provide valuable insights for marketers, academicians and practitioners about the drivers of consumers' environmental responsibility which adds value to the existing knowledge base.

The purpose of this paper is to identify, model, analyze and prioritize the barriers in implementing sustainable practices in rubber products manufacturing industry in Kerala, a state in South India. This research provides a more reliable quantitative measure of association of the barriers in the implementation of sustainable practices in the rubber products manufacturing sector.

Interpretive structural modeling (ISM) is used to develop the hierarchical relationships among the barriers in the implementation of sustainable supply chain practices in the rubber products manufacturing sector. The hierarchical structure among the barriers is framed using the experts’ opinion. Cross-impact matrix multiplication applied to the classification (MICMAC) analysis is integrated with the output of ISM to classify the barriers into different categories based on the driving power and the dependence power. The driving power and the dependence power of the barriers obtained from ISM constitute two criteria for prioritizing the barriers. The change management required is considered as the third criterion. The fuzzy-analytic hierarchy process (F-AHP) methodology is adopted to prioritize the barriers based on these three criteria.

The hierarchical relationship obtained through ISM methodology shows that lack of government initiatives and lack of benchmark on sustainability measurement in Indian conditions are the major barriers in implementing sustainable practices in the rubber products manufacturing sector. These barriers lead to the lack of management commitment for the implementation of sustainable practices in the organizations under study. The prioritization obtained through the F-AHP method suggests that lack of top-level management commitment, lack of motivation, lack of government initiatives and high initial cost of implementation are some of the major barriers in implementing sustainable practices in the organizations.

In the application of the ISM methodology, the contextual relationship between the barriers specified by the experts and their preferences may involve bias. Another limitation of this research is that the modeling and prioritization of the barriers are executed based on the opinion of experts from rubber product manufacturing companies in one state only.

To the best knowledge of the authors, this research is the first study on the identification and prioritization of the barriers in sustainable supply chain implementation in the rubber products manufacturing sector. Modeling the inter-relationship among the barriers using ISM technique and prioritizing the barriers using F-AHP are the novel features of the contributions of this work.

Business organisations are under serious threat to sustain their business due to globalisation, challenging market and recent economic competitiveness. The aim of this study is to address various pressures to circular supply chain management (CSCM) implementation for sustainability.

The present study is based on two research levels. Initially, extensive literature review has been made to identify 31 pressures to CSCM, and eight categories of pressures have been identified. At the second level, fuzzy analytical hierarchy process (F-AHP) has been applied to rank the identified pressures to CSCM implementation for sustainability.

“Financial Pressures (FP)” has been identified as most significant pressure to CSCM implementation for sustainability. Further, “Lack of support of top management (MP1)”, “Lack of implementation of laws and policies (GP2)” and “Lack of vision for CSCM (GP1)” have been found most critical sub-pressures CSCM implementation for sustainability.

The final results give the prioritised list of all identified 31 sub-pressures and eight main pressure heads, which will be helpful in their removal for achieving the goal of CSCM implementation. It will be helpful for managers to take decisions promoting circular practices in supply chains to achieve truly sustainable supply chains. It will also be help for SC managers to understand the flow of activities and materials in CSC to get good results and remove pressures.

The present study plays an important role in circular activities implementation in supply chain for profit gain, and their pressure ranking may help the mangers to implement the CSCM effectively.

Reverse logistics has attracted many industries due to product recalls, enormous waste generation, competitive reasons, vast opportunity in the waste management market, and to get the maximum value out of waste recovery. Selection of the right implementation strategy is vital for reverse logistics to function efficiently. Therefore, this research aims to evaluate the criteria for selecting reverse logistics strategy and help to choose the preferred strategy for its implementation.

Three reverse logistics implementation strategies, namely, in-house, joint venture and outsourcing, are proposed. A novel hybrid fuzzy analytical hierarchy process (F-AHP) and fuzzy measurement of alternatives and ranking according to COmpromise Solution (F-MARCOS) based framework is developed to fulfil the research objective. A survey is performed on Indian manufacturing industry to demonstrate the applicability of the proposed framework.

The result shows that government policy and regulations, reverse logistics risks and reduced emission have prime importance for a manufacturing industry which needs to implement reverse logistics into its supply chain. Outsourcing is the preferred reverse logistics strategy followed by joint venture and in-house that a manufacturing firm in India can implement.

The research results are based on the responses of the survey received. This research considers various industry sectors to test the applicability of the framework. However, for actual implementation, this survey must first be limited to a particular industry as the results will apply to that industrial sector only.

This developed framework simplifies the procedure of selecting the strategy when the industry needs to implement reverse logistics. For industries working with a smaller set of criteria, this framework is a powerful and dynamic approach for reducing and choosing the most pertinent one that helps accomplish their objectives of reverse logistics implementation strategy selection.

Based on the literature and current applicability of reverse logistics, this research proposes three models to implement reverse logistics in Indian industries. A novel hybrid F-AHP and F-MARCOS based framework is developed to handle the selection of suitable reverse logistics strategy.

Over the years, despite the best efforts to evaluate service quality through multiple techniques, the connotation between service deliverance and consumer expectation remains sporadic. In the quest to quantify service quality from an innovative perspective, the current study has leveraged consumer-perceived experiences to unveil the novel intricacies of healthcare quality.

Anchoring on the dimensions of patient experience (PX), field data were collected from 244 patients at different private hospitals operating across India. Further, this study incorporated a fuzzy analytic hierarchy process (F-AHP) to evaluate consumer preferences and prioritised the dimensions of PX in three categories of Indian hospitals, namely nursing homes (NHs), mid-tier corporate hospitals (MCHs) and top-tier corporate hospitals (TCHs).

The results establish a performance ranking by demonstrating that MCHs outperform the rest alternatives on the grounds of perceived experiences. Tukey's honestly significance difference (HSD) test was executed to confirm the heterogeneity among the participants' preferences across three different hospital categories. The results reveal that for most of the alternatives, the mean scores of the criterion were statistically significantly different.

In healthcare studies, PX dawned as an entity with an ability to propel healthcare quality in a better way than the classical techniques did. The study's findings present a comprehensive picture of a care delivery system by identifying relatively significant dimensions of PX, hence improving the quality quotients by adjusting healthcare offerings in alignment with consumer expectations and organisational strategies.

Research in the area of behavioral finance has demonstrated that investors exhibit irrational behavior while making investment decisions. Investor behavior usually deviates from logic and reason, and consequently, investors exhibit various behavioral biases which impact their investment decisions. The purpose of this paper is to rank the behavioral biases influencing the investment decision making of individual equity investors from the state of Punjab, India. This research would provide valuable insight into the different behavioral biases to investors and other participants of the capital market and help them in improving investment decisions.

The research is conducted on the individual equity investors of Punjab, India. Fuzzy analytic hierarchy process was applied to rank the factors influencing the decision making of individual equity investors of Punjab. The primary factors considered for the study are overconfidence bias, representative bias, anchoring bias, availability bias, regret aversion bias, loss aversion bias, mental accounting bias and herding bias.

The three most influential criteria were herding bias, loss aversion bias and overconfidence bias. The five most influential sub-criteria were “I readily sell shares that have increased in value (C61),” “News about the company (Newspapers, TV and magazines) affects my investment decision (C84),” “I invest each element of my investment portfolio separately (C71)” and “I usually hold loosing stock for long time, expecting trend reversal (C52).”

Although sample survey conducted in the present study was based on a limited sample selected from a particular area that truly represented the total population, it is considered as the limitation of this study.

The outcome of this research provides investors with a better understanding of behavioral biases that influence their decision making. This study provides them a guideline on different behavioral biases that they should consider while making investment decisions.

The research model is based on the available literature on behavioral finance and the research results and findings would add value to the existing knowledge base.

The paper covers mega infrastructure construction supply chain (MICSC) in Engineering-Procurement-Construction (EPC) projects, where the frequent occurrence of risk incidents has greatly affected human life. The research aims to establish a risk evaluation index system for MICSC in EPC projects, exploring what risk factors lead to risk incidents and measure the importance and causality of all these risk factors.

The research applies a combination of quantitative and qualitative analysis methodology to process data sequentially. In the first place, risk factors for MICSC in EPC projects are extracted and identified from literature survey and expert interviews. In the second place, an integration model fuzzy Analytic Hierarchy Process (f-AHP) and fuzzy Decision-making Trial and Evaluation Laboratory (f-DEMATEL) is constructed to comprehensively analyze all these risk factors.

12 primary risk factors and 36 secondary risk factors comprise the risk evaluation index system for MICSC in EPC projects from 178 literature and 5 professionals. The results indicate that Political Situation (F1), Social Security (F2) and Management Mode (F8) are critical risk factors, where F1 and F2 are cause factors and F8 is an effect factor.

There are three main contributions of this paper. First and foremost, from the perspective of the research content, no other study has been able to assess risk factors for MICSC in EPC projects, while embedding nine phases of the whole project life cycle and six subjects of stakeholders into a risk evaluation index system. Additionally, from the perspective of research method, a combined model incorporating f-AHP and f-DEMATEL is constructed to avoid the one-sidedness of a single model. Last but not least, from the perspective of practical significance, focusing on the critical risk factors, a series of effective measures are formulated to make appropriate management decisions for nodal enterprises of MICSC, which can improve their risk management capabilities.