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Decreasing sources of fossil fuels has caused an increase in importance of the renewable energy resources and systems that directly utilize renewable energy are even more important. The purpose of the paper is to compare the most common solar cooling technologies against the most important requirements.

A multi-criteria decision methodology, analytical hierarchical process, has been used to prioritize these technologies with respect to each other.

The findings of this study are the priorities of selected solar cooling concepts against performance affecting criteria. The solar vapour adsorption cooling system has been found to be the optimum solar cooling concept with practically the highest performance number compared with the other cooling systems.

This study can be used in the future development of solar cooling technologies to benefit from the best collective features of the specific technologies.

The purpose of this study is to identify and rank the effective factors on customer values from the perspective of banking customers.

This study is a practical and descriptive survey. In order to rank the factors affecting customer values, a fuzzy analytical hierarchical process has been used. The data were gathered through Delphi method and questionnaires.

The results of this study indicate that the factors affecting customer values include costs, relational benefits, brand perceptions, and services quality. These factors influence customer values, respectively. The results also indicate that financial costs among costs, operational benefit among relational benefits, trust in employee behavior in customer brand perceptions, and finally confidence in service quality dimensions have the highest priority.

The proposed model and research findings will greatly help researchers and practitioners understand the factors influencing customer value using a fuzzy analytic hierarchy process (FAHP) in the banking industry.

In this study, for the first time, factors influencing customer value are studied by using a FAHP in the banking industry. The use of this method in this study has a certain authenticity.

The purpose of this paper is to prioritize 21 healthcare wastes in public and private hospitals of United Arab Emirates (UAE).

Seven healthcare wastes linked with lean management are further decomposed in to sub-criteria and to deal with this complexity of multi criteria decision-making process, analytical hierarchical process (AHP) method is used in this research.

AHP framework for this study resulted in a ranking of 21 healthcare wastes in public and private hospitals of UAE. It has been found that management in private healthcare systems of UAE is putting more emphasis on the inventory waste. On the other hand, over processing waste has got highest weight in public hospitals of UAE.

The future directions of this research would be to apply a lean set of tools for the value stream optimization of the prioritized key improvement areas.

This is a contribution to the continuing research into lean management, giving practitioners and designers a practical way for measuring and implementing lean practices across health organizations.

The contribution of this research, through successive stages of data collection, measurement analysis and refinement, is a set of reliable and valid framework that can be subsequently used in conceptualization, prioritization of the waste reduction strategies in healthcare management.

This purpose of this paper is to identify and prioritize the critical factors impacting knowledge sharing (KS) in technology-intensive manufacturing organizations in the United Arab Emirates (UAE) and to propose a decision-making framework for KS.

Analytical Hierarchical Process method is used to identify these critical factors impacting KS in technology-intensive manufacturing organizations in the UAE.

Results show that organizational leadership and culture are the most critical factors impacting KS in the technology-intensive manufacturing organizations.

The data are collected from technology-intensive manufacturing organizations in the UAE; therefore, these cannot be generalized to other locations. Future research in different countries is required.

To implement successful KS practices in technology-intensive manufacturing organizations, it is essential that all impacting factors and sub-factors are well understood within the organizations.

This study is among the first studies in the region that presents a comprehensive framework for KS in manufacturing sector.

The purpose of this paper is to identify attributes of knowledge management (KM) for large and small manufacturing companies in United Arab Emirates (UAE) and to propose a process to evaluate the competitive priorities of these companies.

This research is designed by composing multi criteria decision making of KM attributes in order to identify competitive priorities of large and small manufacturing companies to achieve better customer focus. To deal with this complexity of multi criteria decision-making process, Analytical Hierarchical Process (AHP) method has been used in this research.

Results show that large manufacturing companies are putting more emphasis on “Know-Why” knowledge attribute in all four competitive priorities for achieving customer focus. While, small size manufacturing companies put higher weight on “Know-What” knowledge attribute by focussing on two competitive priorities (flexibility and quality).

Results are derived from a limited number of empirical data only in one country, therefore these cannot be generalized. Future research with larger samples of small and large manufacturing firms from other countries is needed.

To be competitive, manufacturing companies must reshuffle their production strategies to allow them to play a role in global knowledge-intensive market. Therefore, they must incorporate knowledge attributes as a way to achieve higher levels of performance.

This study is among the first and most exhaustive ones carried out in the small and large size firms operating in the manufacturing sector of UAE. It provides a systematic approach to identify the operational competencies and knowledge attributes being followed by large and small manufacturing companies.

The purpose of this paper is to introduce lean concept to the field of healthcare management, expands the conceptualization of lean management beyond the manufacturing companies to consider key waste reduction opportunities which are posited to be requisites to lean practices and implements the proposed framework in the three public hospitals in Abu Dhabi.

This research is designed by decomposing complex and unstructured issue into a set of components organized in a multi-level hierarchical form. To deal with this complexity of multi criteria decision-making process, analytical hierarchical process (AHP) method is used in this research.

AHP framework for this study resulted in a ranking of 21 healthcare wastes based on the evaluations of local situations by experienced healthcare professionals. It has been found that management in healthcare systems of Abu Dhabi is putting more emphasis on the inventory waste.

The future directions of the research would be to apply a lean set of tools for the value stream optimization of the prioritized key improvement areas.

This is a contribution to the continuing research into lean management, giving practitioners and designers a practical way for measuring and implementing lean practices across health organizations.

The contribution of this research, through successive stages of data collection, measurement analysis and refinement, is a set of reliable and valid framework that can be subsequently used in conceptualization, prioritization of the waste reduction strategies in healthcare management.

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 analytical hierarchical process (AHP)’s main purpose is to assess higher hierarchy levels based on the cooperation of its various levels. It results in a well-designed model-based method in which the weights for the selected attribute are calculated using dimensions, criteria, and indicators. This paper aims to highlight the benefits of blockchain in supply chain management with the help of a literature review along with opinions of experts from various sectors.

With the goal of enhancing the use of blockchain technology in supply chain management, particularly when comparing within the same industry, the AHP methodology has been used. In order to develop the AHP model, a total of eight elements are examined in this study, which are decentralization, resiliency, security, smart contracts, sustainability, traceability, transparency and trust. A calculation of a Desirability Index for conventional supply chain and blockchain-enabled supply chains has been also developed.

Findings where that in a blockchain-enabled supply chain, the global weights of individual benefit variables are considerably larger than in conventional supply chains. When the score of the Desirability Index for conventional supply chain and blockchain-enabled supply chain is compared, the blockchain-enabled supply chain significantly surpasses the conventional supply chain in terms of increasing sustainable development in today’s supply networks.

This study takes into account the AHP methodology applying it on blockchain. This has not been done before in the academic world, at least as far as the authors may be aware of. The originality of combining such process with a recent technology such as blockchain highlights the value of this research.

Spiritual tourism is becoming a significant growth area of the Indian travel market, with more Indians opting to go on pilgrimage to popular religious cities. There are many spiritual destinations where some of this life's essences can be sought to enjoy harmony and peace. The study aims to prioritize motivators driving the intentions of the tourists to visit the spiritual destination.

The current study applied the analytical hierarchical process, a multi-criteria decision-making technique, on the sample of visitors from all the six spiritual destinations to rank the motivational factors that drive the intentions of the tourist to visit a spiritual destination.

The study's results postulated that spiritual fulfillment motives and destination atmosphere are the top prioritized motivations, while destination attributes and secular motives emerged as the least prioritized.

The research study provides valuable insights to the spiritual tourism industry stakeholders to target the tourists' highly prioritized motivations to augment the visits to a particular spiritual destination.

Previous research has explored the motivations and modeled their relationships with tourists' satisfaction and intentions. But, the present study has applied a multi-criteria decision-making technique to add value to the existing knowledge base.

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.