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To present a three‐layer hierarchical structure of the factors involved in adopting an electronic marketplace (EM) model and to examine the relative weightings given to various strategic factors by the securities industry (SI) and the heavy electric machinery industry in Taiwan.

A literature review and a review of nine Taiwanese industries allow the formulation of a three‐layer hierarchical structure of adoption factors. A fuzzy analytic hierarchical process (AHP) is then undertaken to ascertain the relative weightings of factors that affect entry to an EM in two of these industries which are studied in more detail.

The weights of “proactive” factors are found to be greater than those of “defensive” factors. For example, contrary to previous findings in this area, the “risk of adopting new technology” is not found to be the major factor influencing decision making. Various factors are found to have different routes of influence in determining decision making in different industries.

Enterprises that appreciate the weightings of factors to be considered will be able to facilitate the adoption of an EM model with lower costs and greater efficiency.

The study provides novel and reliable information about strategic factors that are involved in corporate decisions about entering an EM.

Implementing food safety measures (FSMs) have become a prerequisite for food firms looking to export internationally. Many exporters find it difficult to comply with multiple regulations, and their consignments are often rejected at borders due to food safety concerns. Hence, harmonization in food safety standards is arguably the most contentious topic regarding the export market since it affects international trade. Accordingly, the paper uses the case of Indian seafood exporters to identify key FSMs, investigate stringency associated with them and rank international markets based on degree of stringency for selected FSMs.

First, the authors identify the key FSMs by using the Delphi method. Then, the authors apply the Fuzzy analytical hierarchical process (FAHP) method to calculate weights of the FSMs as criteria. Lastly, the authors apply the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to rank markets. To compute fuzzy TOPSIS, weights are derived from fuzzy AHP.

This study’s findings suggest that product and process standards, traceability requirements and tolerance limits for residues are the most stringent FSMs, among others. Besides, the overall ranking of markets reveal that the European Union (EU), the USA and Japan ranked lowest and perceived to have the most stringent food safety requirements.

The paper offers guidance to firms and policymakers to manage their efforts and resources during food safety implementation by focussing on critical FSMs. Researchers will get insights about FSMs for further empirical investigation. To the authors’ knowledge, no study examined the stringency associated with various FSMs in the seafood industry.

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 behavioural decision-making process of individuals highlights the importance of investors’ sentiment and their correlation with the real economy. This paper aims to contribute to the literature of behavioural finance by examining the influence of contextual factors on investment decision-making.

Using a questionnaire, a total of 445 valid responses were collected from March to May 2021 through online sources. The current study uses a technique of Fuzzy-analytical hierarchical process (AHP) to assign relative weights to various contextual factors influencing investment decision-making. Harman’s single factor test was used to check common method bias.

Results of the study reveal that accounting information, self-image/firm-image coincidence, and neutral information as the top-ranked factors in influencing investment decisions, whereas advocate recommendation and personal financial needs emerged as less important factors in influencing investment decisions.

The current study collects data from Indian stock market investors, which may limit the generalization of the study to India only. Moreover, this study is cross-sectional in nature, and there are numerous factors that are not part of the study but might significantly influence the investors’ decision-making process.

The research has implications for both academicians working in the area of behavioural finance and practitioners’ who are active in stock markets, more specifically dealing with retail investors and in the domain of personal finance. Also, the current study will accommodate different groups, i.e. policy makers, financial advisors, investors, investment professionals, etc. in carrying out their professional work.

The current study will provide a comprehensive overview of individual investor behaviour. To the best of the authors’ knowledge, the present study is one of its kind to use the Fuzzy-AHP technique for evaluating the relative ranks of contextual factors influencing investment decision-making.

Recent literature has proposed many theoretical methods to help decision makers choose an appropriate project delivery system (PDS) in a rational manner. None of these articles however systematically compare and systematize the available PDS selection methods and guide decision makers in choosing a method that best meets their PDS decision‐making circumstances. This paper aims to bridge this gap.

Four groups of PDS selection methods, namely, guidance (e.g. decision charts and guidelines), multi‐attribute analysis (e.g. multi‐attribute utility theory and analytical hierarchical process), knowledge‐ and experience‐based (e.g. case‐based reasoning), and mix‐method approaches are reviewed, compared and systematized.

The discussed methods vary in their underlying concepts, complexities of implementation and levels of required information. They also differ in the ways how decision makers' preferences are elucidated, expressed and measured. A conceptual framework is proposed to help decision makers match a PDS selection method with their decision‐making circumstances.

The paper highlights limitations of the discussed methods, and presents areas for future research.

This paper helps decision makers develop a fundamental understanding of the available PDS selection methods, and match a PDS selection method with their unique decision‐making circumstances. Using a suitable method will improve the decision‐making efficiency.

The existing literature on venture capitalists’ (VCs’) exits provides insufficient evidence regarding factors affecting the exit decision. This study aims to identify these factors and examine how VC firms do ranking or prioritize these factors.

The study is based on primary data. The qualitative analysis was done to develop the survey instrument. Fuzzy analytical hierarchical process, which is a popular method of multi-criteria decision modeling, is used to identify or rank the determinants of exit strategy by venture capital firms in India.

Broadly, eight determinants of exit strategy are ranked by VCs. A total of 33 statements describe these eight determinants. The results are analyzed on the basis of four measures of VCs’ profile, i.e. age of VC firm, number of start-ups in portfolios, type of investment and amount of investment.

The survey instrument needs to be validated with a larger sample size and other financial backers than VCs.

The study has direct managerial implication for VC firms as it provides useful information regarding the determinants of exit strategy by VC firms in India. These findings can provide necessary information to other financial backers too, viz., angel investors, banks, non-banking financial institutions and other individual and syndicated set-ups providing funding to start-ups.

The current research is unique as no prior study has explored the determinants of VCs exit strategy and prioritizing these determinants.

Blockchain has been considered a disrupting technology that can add value in various supply chains differently. The provenance framework matches the four blockchain capabilities of traceability, certifiability, trackability and verifiability to the five generic risks, namely, the financial risk, psychological risk, social risk, physical risk and performance risk. This will help in uncording which specific risk gets mitigated by the use of blockchain in a specific supply chain.

This study illustrates four supply chains, namely, pharmaceutical industry, fast moving consumer goods industry, precious metal and automotive industry, and maps the risks associated with them to the provenance framework wherein the applicability of blockchain is mapped. Fuzzy analytical hierarchical processing (F-AHP) is used to rank the risks in the supply chain.

Blockchain capabilities can elevate the provenance knowledge leading to assurance in terms of origin, authenticity, custody and integrity to mitigate the supply chain risks. Present work highlights the thrust areas across various supply chains and identifies the risk priority tasks aligning the contextual supply chain risks. This study has covered five major risk perceptions. This study contributes to the literature on blockchain, customer perceived risk, provenance and supply chain.

This methodology can be adopted to understand and market the application of blockchain in a supply chain. It brings the marketers and marketing perspective to the supply chain. Exhaustive risk perception can be included to get more comprehensive data on mapping the risks along different supply chains. Vertical extensions of this work can be consideration of other supply chains including dairy, fruits and vegetables, electronics and component assemblies to derive the comprehensive framework for mapping risk perceptions and thereby supply chain risk mitigation through blockchain technology.

This linkage between blockchain, perceived risk, applications in the supply chain and a tool to convince the customers about the blockchain applicability has not been discussed in the literature. Adopting the multi-criteria decision-making F-AHP approach, this study attempt to rank the risks and stimulate conversations around a common framework for multiple sectors.

The product‐mix decision has received considerable attention in management accounting and economics literatures. However, many studies in these literatures are contradicting, inconclusive and lack rigorous analysis of this complex decision. They seek to develop weights for the products in the product mix based on one objective, to maximize the firm’s profit ability. But before developing these weights, the studies must first rank these products, Ranking is a complex endeavor since it is often driven by a multitude of hierarchical financial and non‐financial goals and objectives. Ranking is also difficult due to the use of complex concepts such as time, uncertainty, cost and interdependencies between accounting systems and manufacturing systems and among the products of the product mix. These concepts are inherently fuzzy and coextensively applied often with a confluence of variables operating simultaneously. This paper applies an advanced mathematical model to account for the product mix decision. The model combines the powers of fuzzy‐set theory (Zadeh, 1965) and the analytic hierarchy process (Saaty, 1978). The fuzzy‐analytic‐hierarchical process (FAHP), developed by de Korvin and Kleyle (1999), is sufficiently powerful to account for the ambiguous variables and the web of prioritized strategies and goals of cost leadership, product differentiation, financial objectives of earnings, cash flows and market share and non financial goals such as tradition and owners’ convictions and philosophies underlying the ranking of the products in the product mix. By way of example, the paper applies the FAHP model to rank order four products subject to these strategies and goals.

This paper aims to provide a tool for decision makers to help them with selection of the appropriate supplier.

Companies often depend on their suppliers to meet customers' demands. Thus, the key to the success of these companies is selection of the appropriate supplier. A methodology is proposed to address this issue by first identifying the appropriate selection criteria and then developing a mechanism for their inclusion and measurement in the evaluation process. Such an evaluation process requires decision maker's preferences on the importance of these criteria as inputs.

Human assessments contain some degree of subjectivity that often cannot be expressed in pure numeric scales and requires linguistic expressions. To capture this subjectivity the authors have applied fuzzy logic that allows the decision makers to express their preferences/opinions in linguistic terms. Decision maker's preferences on appropriate criteria as well as his/her perception of the supplier performance with respect to these criteria are elicited. Fuzzy membership functions are used to convert these preferences expressed in linguistic terms into fuzzy numbers. Fuzzy mathematical operators are then applied to determine a fuzzy score for each supplier. These fuzzy scores are in turn translated into crisp scores to allow the ranking of the suppliers. The proposed methodology is multidisciplinary across several diverse disciplines like mathematics, psychology, and operations management.

The procedure proposed here can help companies to identify the best supplier.

The paper describes a decision model that incorporates decision maker's subjective assessments and applies fuzzy arithmetic operators to manipulate and quantify these assessments.

The efficiency and effectiveness of reverse logistics (RL) is dependent on collection methods as the collection activities are critical in determining the economic viability of the entire recovery chain. The purpose of this paper is to evaluate the various collection methods used in RL under uncertain environment.

An integrated fuzzy multi-criteria decision model has been developed for the evaluation of various collection methods. The evaluation has been done based on the criteria of initial investment, value added recovery, return volume, operating cost, degree of supply chain control, and level of customer satisfaction. The three alternatives used in the study are collection by the manufacturer directly from the customer, collection by the retailer, and collection by the third party. The fuzzy analytical hierarchy process has been used to compute the criteria weights and fuzzy technique for order preference by similarity to ideal solution has been used to rank the alternative collection methods. Fuzzy mathematics has been used to take care of uncertainties in the RL.

Selection and evaluation of alternative collection methods is affected by multiple criteria like initial investment, value added recovery, return volume, operating cost, degree of supply chain control, and level of customer satisfaction. The utility of the proposed evaluation methodology has been validated by solving a case example from automotive company.

The proposed methodology will provide a useful tool to the decision maker for the evaluation and selection of the alternative collection methods in RL. This will help companies in strategic decision making to prioritize and develop collection facilities accordingly.