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In recent decades, many managers have conceived the radical role of knowledge in acquiring a competitive advantage and pursuing organizational strategic goals. Therefore, knowledge management is now a competitive requirement in organizations. However, if proper infrastructures and necessary prerequisites are not provided for executing such a process, the result will be nothing except failure and resource waste. Therefore, it is necessary that organizations identify effective factors for their readiness in executing knowledge management. The purpose of this study is identifying and prioritizing of effective constructs in readiness of knowledge management implementation by using fuzzy analytic hierarchy process (AHP).

In the present study, the relevant literature is initially reviewed and then seven factors and 19 affecting indicators on knowledge management readiness are identified by using a questionnaire for knowledge management elites which also constitute the research conceptual model. Finally, the weight of each factor is determined by fuzzy analytical hierarchy process and the readiness priority and preference of each factor are discussed.

The results of fuzzy analytic hierarchy process show that the management factor is recognized as the most effective factor in knowledge management execution readiness.

The model proposed in this study is applicable to the academic culture. Modification of the proposed model may be needed in applying this model to other environments.

As limited studies have been carried out on identifying and prioritizing of effective constructs in readiness of knowledge management implementation by using fuzzy analytic hierarchy process, the findings of this study will be useful in identifying and assessing the main components in readiness of knowledge management implementation in the research and training centers.

The need for the thermal insulation of masonry buildings in Algeria is no longer debated. This paper aims to propose an integrated fuzzy multi-criteria decision aid method for the thermal insulation of masonry buildings to rank the thermal insulation solutions.

The proposed method combines the fuzzy analytical hierarchy process with the fuzzy preference ranking organization method for enrichment evaluation.

A case study using the proposed method is detailed in this paper. The building users’ preferences obtained by the fuzzy analytical hierarchy process had a higher level of consistency and accuracy. The case study demonstrates how in a highly uncertain field such as thermal insulation of masonry buildings, the fuzzy preference ranking organization method for enrichment evaluation can prevent the loss of valuable evaluation data and overcome difficulty in integrating linguistic assessments of the thermal insulation alternatives.

The proposed method extends current knowledge by using the fuzzy analytical hierarchy process to consider uncertainties regarding the building users’ preferences, and the fuzzy preference ranking organization method for enrichment evaluation to get a complete ranking of the thermal insulation solutions taking into account the uncertainties related to the alternatives’ evaluations.

The purpose of this research is to determinate the criteria weight in a fashion design scheme evaluation system.

The first stage is to use the fuzzy Delphi method (FDM) by fashion design experts of academia and industries for fashion design evaluation criteria. The second stage is based on the use of a fuzzy analytic hierarchy process (FAHP) to find the criteria weight. Finally, an empirical example is used to illustrate the procedure of obtaining the criteria weights for the evaluation of a fashion design scheme.

The result shows that there are eight evaluation criteria to be obtained for fashion design scheme selection. The evaluation characteristic weights of theme and innovation score almost 90 percent (88.93 percent), the criteria weights of the first five, fashion forecast theme story, best‐seller modification, new idea and product position, score almost 80 percent (79.96 percent) and the criteria weights of the first two, fashion forecast and theme story, score almost 40 percent (39.93 percent) when selecting a design scheme in the fashion design process.

This paper proposes the vital characteristic and criteria for the selection of the fashion design scheme. In selecting fashion design scheme, this study uncovers that the marketing is less important than theme and innovation characteristics. Additionally, the results of this study, indicate the important five criteria, offered designer a set of useful indicators in preparing fashion design scheme and improving the quality of fashion design decision.

Nowadays, there are various internet of things (IoT) applications covering many aspects of daily life. Many people own numerous smart objects that use these IoT applications. The purpose of this study is determining suitable IoT applications for each user which is a relevant challenge because it is amulti-criteria decision-making.

To solve this challenge, the authors propose fuzzy analytical hierarchy process model. Based on the opinions of IoT experts, the model and the hierarchy were designed to assess and compare three crucial IoT criteria, namely, object, application and providers.

The results indicated that the application criterion is far more relevant for users other than the two criteria. The findings of this study offer insights into more effective decision-making for IoT application developers and providers.

This study contributes to the IoT through proposing a fuzzy model to classify IoT applications. The findings provide meaningful implications for IoT application providers.

This paper aims to describe an application of hybrid Multi Criteria Decision Making (MCDM) technique for the selection of wastewater treatment (WWT) technology for treating wastewater.

The proposed approach is based on Analytical Hierarchy Process (AHP) under fuzzy environment, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) and hierarchy Grey Relation Analysis (GRA) techniques. Two models are proposed to evaluate the best WWT. The first model, Fuzzy Analytical Hierarchy Process (FAHP) is integrated with Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) technique. The second model, FAHP is integrated with hierarchy Grey Relation Analysis (GRA) technique. The Fuzzy Analytical Hierarchy Process (FAHP) is used to determine the weights of criteria and then ranking of the WWT technology is determined by PROMETHEE and GRA.

An efficient pair‐wise comparison process and ranking of alternatives can be achieved for WWT technology selection through the integration of FAHP and PROMETHEE, FAHP and GRA.

The paper highlights a new insight into MCDM techniques to select an optimum WWT technology selection for the paper manufacturing industry.

This paper aims to identify and reduce possible process failures occurred in warehouse.

This research used risk analysis method FMEA combined with fuzzy-analytical hierarchy process (AHP). Design FMEA will direct the failure mode or failure of components into levels and will use it to analyze the product before it used in manufacturing processes. Design FMEA has a major point on the failure mode that caused inefficiency in the design while fuzzy-AHP used to reduce subjectivity in the weighting process.

The results show that high inventory is the dominant factor that must be controlled by the company to prevent the risk of failure processes in the warehouse, followed by the number of stocks that do not match with existing records, and misplacement of machines and/or materials.

This research used risk analysis method FMEA combined with fuzzy-AHP to identify and reduce the possible process failures in warehousing.

Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.

Based on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.

The outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.

This study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.

One of the most important issues in supply chain (SC) management is the identification and management of the risk involved in it. The purpose of this paper is to propose a comprehensive model of supply chain risk management (SCRM) in the product life cycle (PLC) and the operational process cycle (OPC). To decrease the risks in a fuzzy environment, the model considers the organizational performance factors (OPF) and the risk operational practices (ROP).

Fuzzy analytic hierarchy process is used to determine the weights of the relationships between the PLC, OPC and OPF in the hierarchical structure of the decision problem. In addition, the fuzzy technique for order preference by similarity to ideal solution is employed to recognize the priority of ROPs in dealing with the performance factors. The integrated framework is evaluated using the case study of an automotive company in Iran.

The results demonstrated that the proposed model can be used to formulate an appropriate method for prioritizing defined alternatives to decrease risk and improve the organizational performance in SCRM under fuzzy conditions.

A major limitation of the study is that a few of the selected criteria for risk assessment are focused only on economic factors. Another limitation of the current study is related to the PLC, OPC and OPF being based on the work of Xia and Chen (2011).

The current study identified the more important stage in the PLC. More significant process in each stage of the PLC and weightier risk factors in each process of the OPC were determined. Some strategies for reducing risk in each stage of the PLC were presented. The best alternatives for reducing risks in SC were indicated.

It is worth mentioning that previous studies have not applied multiple criteria and alternatives to decrease the risks involved in the PLC and OPC parts of the SC under fuzzy conditions. However, it should be stated that some academics have used these techniques separately, in other specialized areas of the SC.

Small and medium enterprises (SMEs) have been reported as a credit-constrained sector in the earlier literature. Amidst the available external financing options, SMEs are dependent upon banks for their financial needs, hence they offer an important profitable segment for banks. Commercial banks need to develop effective targeting strategies for this segment and ranking the priorities of SMEs in selecting commercial banks will be of great help to them. The purpose of this paper is to implement a fuzzy analytic hierarchy process (FAHP) multi-criteria decision model for commercial bank’s selection by SMEs.

The research process was carried out in two phases. In Phase I, a self-structured scale was developed to measure bank selection criteria of SMEs after an extensive review of the literature of relevant studies on the topic. A sample of 600 SMEs was selected through non-proportionate quota sampling and only 313 valid responses were received. Phase II was conducted to prioritize the extracted factors through FAHP, a multi-criteria decision-making technique. For this purpose, another questionnaire was designed in the form of pair-wise evaluation and the response was taken on the same from those 313 SMEs again.

The results showed that SMEs bank selection criteria can be categorized under six heads, namely, bank attributes, accommodation of credit needs (AC), bank personnel, financial factors (FF), service quality (SQ) and business knowledge. The research study produced a reliable and valid instrument for studying the bank selection criteria of SMEs. The results further revealed that AC is the most important factor considered by SMEs followed by FF and SQ. Going further, global weights were also calculated through the FAHP which revealed that the most important consideration (variable) viewed upon by SMEs is willingness to accommodate credit needs followed by flexible collateral requirements and absence of hidden charges.

The results of the present study offer significant insights as to the factors SMEs consider while making a bank selection decision. It is of utmost importance for banks to identify true determinant factors used by SMEs while making bank choice decisions as they offer ample profit and revenue opportunities to banks. The results of the study provide a practical approach to banks that would help them in framing strategies for SMEs customers.

This is the first study of its kind which has not only focused on the hierarchy of factors measuring bank selection criteria of SMEs rather on the hierarchy of single variables also through the calculation of global weights. As banks cannot focus on all the dimensions of the criteria, they can focus on the spirit of that particular criteria.

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.