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Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

New business practices and the globalization of markets force firms to take innovation as the fundamental pillar of their competitive strategy. Research and Development (R&D) plays a vital role in innovation. As technology advances and product life cycles become shorter, firms rely on R&D as a strategy to invigorate innovation. R&D project portfolio selection is a complex and challenging task. Despite the management's efforts to implement the best project portfolio selection practices, many projects continue to fail or miss their target. The problem is that selecting R&D projects requires a deep understanding of strategic vision and technical capabilities. However, many decision-makers lack technological insight or strategic vision. This article aims to provide a method to capitalize on the expertise of R&D professionals to assist managers in making informed and effective decisions. It also provides a framework for aligning the portfolio of R&D projects with the organizational vision and mission.

This article proposes a new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps.

The proposed strategy plane helps decision-makers align R&D project portfolios with their strategies to combine a strategic view and numerical analysis in this research. The proposed strategy plane consists of four areas: Exploitation Zone, Challenge Zone, Desperation Zone and Discretion Zone. Mapping the project into this strategic plane would help decision-makers align their project portfolio according to the corporate perspectives.

The new approach combines the efficiency and uncertainty dimensions in portfolio selection into an integrated framework that: (i) provides a complete representation of the stochastic decision-making processes, (ii) models the endogenous uncertainty inherent in the project selection process and (iii) proposes a computationally practical and visually unique solution procedure for classifying desirable and undesirable R&D projects.

The purpose of this study is to evaluate two supply chain resilience key elements of vulnerability and capability in the automotive industry.

We propose a fuzzy approach for statistical hypothesis testing and analyze two research hypotheses by synthesizing the results of a questionnaire given to 44 companies in the Iranian automotive industry.

The results indicate that the automotive industry in Iran should: (1) resist five elements of vulnerability, i.e. “external pressures,” “sensitivity,” “connectivity,” “supplier/customer disruptions,” and “resource limits,” and (2) embrace nine elements of capability, i.e. “flexibility in order fulfillment,” “capacity,” “efficiency,” “visibility,” “adaptability,” “recovery,” “dispersion,” “organization,” “market position” and “security” to achieve greater resiliency elasticity in the supply chain.

This is the first study on the supply chain resilience vulnerabilities and capabilities in the Iranian automotive industry.

This paper aims to study the role of smart technologies (e.g., artificial intelligence, Internet of Things, blockchain and analytics, among others) in the accounting environment (AE). In this context, the nuances of innovation generated by such technologies allow for tracing the merging trends in accounting research.

This paper uses an integrated qualitative methodology composed of structured literature analysis and systematic literature analysis to study scientific papers published and stored in prominent databases from 2000 to 2020. This paper collected a data set sharing topics related to smart technologies and innovation in the AE.

The primary findings reveal four research paths of innovation, impact, implication and intelligence in accounting research as follows: smart technologies as innovations to be managed; smart technologies as impacting tools affecting the AE in certain circumstances; smart technologies as a source generating relevant implications; and smart technologies as factors requiring new and updated knowledge, skills and abilities of actors.

The joint investigation of the AE and smart technologies poses a milestone for future academic and professional accounting research. This paper proposes a new framework (SMATECHacc Framework) consisting of four pathways research that can be used by future researchers to consider and construct their own research designs.

The current paper aims to present a formal model illustrating how payoff imbalances among the members of a team of decision makers (DMs) who must undertake a project condition the final outcome obtained. This result builds on the fact that payoffs imbalances would lead to different performance levels among the employees and managers who compose a team. The analysis is applied to a strategic environment, where a project requiring coordination among the DMs within the team must be developed.

The intuition behind the strategic framework on which the results are based is twofold. The authors build on the literature on social comparisons and assume that employees and managers acquire information on the payoffs received by other members of the team while being affected by the resulting comparisons, and they follow the economic literature on firm boundaries determined via incomplete contracts. In this case, employees and managers may underperform if they feel aggrieved by the outcome of the contract giving place to deadweight losses when developing the project.

The authors illustrate how a team-based performance reward structure may lead to a coordinated equilibrium even when team managers and employees receive different payoffs and exhibit shading incentives based on the payoff differentials between them. The authors will also illustrate how identical shading intensities by both groups of DMs imply that shading by the managers imposes a lower cost on the profit structure of the firm because it leads to a lower decrease in the cooperation incentives of the other members of the team. Finally, the authors show how differences in shading intensity between both types of DMs trigger a strategic defect mechanism within the team that determines the outcome of the project.

The novel environment of team cooperation and defection through shading introduced in this paper is designed to deal with the strategic decisions taken by DMs when undertaking a project within a group. In particular, the intensity of shading applied by the DMs will be endogenously determined by the relative payoffs received, which allows to account for different scenarios, where relative payoff differentials among DMs determine the outcome of the project.

This study aims to propose a method for measuring lean supply chain management (LSCM) maturity based on the main lean practices and existing waste of a supply chain.

A three-stage approach was developed. First, a thorough literature review was performed to raise concepts and previous findings on maturity models (MMs) and LSCM. This review’s outcomes were then validated by experts in the field using the fuzzy Delphi method (FDM). Subsequently, the proposed model was illustrated and assessed based on a multi-case study.

All companies attained high outcomes in the elimination of the waste pillar. The pillars of logistics management, continuous improvement and information technology management also stood out in the three organizations’ results. The company with the lowest maturity level operates in a make-to-order production policy, which may harm the lean supply in its supply chain.

The proposed model can reveal external opportunities and threats and internal strengths and weaknesses in supply chains (SCs). It is also capable of providing a clear roadmap for SC improvement in companies.

To the best of the authors’ knowledge, no study to date has proposed a MM in the LSCM context using FDM and considering the crucial relationship between lean practices and wastes.

Customer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.

The authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.

As a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.

The authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.

Delivering premium services and quality products are critical strategies for success in manufacturing. Continuous improvement (CI), as an underlying foundation for quality management, is an ongoing effort allowing manufacturing companies to see beyond the present to create a bright future. We propose a novel integrated fuzzy framework for analyzing the barriers to the implementation of CI in manufacturing companies.

We use the fuzzy failure mode and effect analysis (FMEA) and a fuzzy Shannon's entropy to identify and weigh the most significant barriers. We then use fuzzy multi-objective optimization based on ratio analysis (MOORA), the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) and fuzzy simple additive weighting (SAW) methods for prioritizing and ranking the barriers with each method. Finally, we aggregate these results with Copeland's method and extract the main CI implementation barriers in manufacturing.

We show “low cooperation and integration of the team in CI activities” is the most important barrier in CI implementation. Other important barriers are “limited management support in CI activities,” “low employee involvement in CI activities,” “weak communication system in the organization,” and “lack of knowledge in the organization to implement CI projects.”

We initially identify the barriers to the implementation of CI through rigorous literature review and then apply a unique integrated fuzzy approach to identify the most important barriers based on the opinions of industry experts and academics.

The purpose of this paper is to provide a theoretical framework for predicting the next period financial behavior of bank mergers within a statistical-oriented setting.

Bank mergers are modeled combining a discrete variant of the Smoluchowski coagulation equation with a reverse engineering method. This new approach allows to compute the correct merging probability values via the construction and solution of a multi-variable matrix equation. The model is tested on real financial data relative to US banks collected from the National Information Centre.

Bank size distributions predicted by the proposed method are much more adherent to real data than those derived from the estimation method. The proposed method provides a valid alternative to estimation approaches while overcoming some of their typical drawbacks.

Bank mergers are interpreted as stochastic processes focusing on two main parameters, that is, number of banks and asset size. Future research could expand the model analyzing the micro-dynamic taking place behind bank mergers. Furthermore, bank demerging and partial bank merging could be considered in order to complete and strengthen the proposed approach.

The implementation of the proposed method assists managers in making informed decisions regarding future merging actions and marketing strategies so as to maximize the benefits of merging actions while reducing the associated potential risks from both a financial and marketing viewpoint.

To the best of the authors’ knowledge, this is the first study where bank merging is analyzed using a dynamic stochastic model and the merging probabilities are determined by a multi-variable matrix equation in place of an estimation procedure.

Promoting security, stability and cooperation is the raison d'être of the North Atlantic Treaty Organization (NATO) and these are the aims of its strategy of membership enlargement. The incentive of NATO membership has led some former Warsaw Pact applicant countries to reform their political systems, transform their economies, deal with corruption and improve social justice and human rights. However, controversy has surrounded NATO's enlargement because of the current ambiguous and subjective decision‐making process and the effect that it could have on the organization. This paper aims to present the results of a study to develop a benchmarking model as a means to assist NATO evaluate and screen potential applicant countries.

A novel and structured multiple‐criteria decision analysis model that considers specific NATO applicant evaluation criteria and environmental forces is offered and a template for a membership evaluation process is proposed. A total of 120 researchers in France, Germany, Switzerland and the USA provided the necessary data on the 23 countries that are analyzed in order to develop the benchmarking model. Four distinct categories were established to categorize these countries. The ranking of the countries based on Euclidean distance from the ideal state is illustrated with a classification schema outlining four typologies as beneficial believers, detrimental disadvantaged, perilous partners and apathetic acquaintances.

Among the potential applicant countries considered as “beneficial believers” are Sweden, Austria, Switzerland, Finland and Ireland while other countries, such as, Kazakhstan, Azerbaijan, Uzbekistan, Turkmenistan, Georgia, Montenegro, Kyrgyzstan and Tajikistan are considered as “detrimental disadvantaged”. Furthermore, Russia and Ukraine were identified as “perilous partners” and Malta, FYR Macedonia, Cyprus, Serbia, Belarus, Bosnia and Herzegovina, Armenia and Moldova were identified as “apathetic acquaintances”.

This model could be applied to other supranational organizations and multinational firms when assessing international strategic alliances.

The paper presents the results of a study to develop a benchmarking model as an aid in evaluating and screening potential NATO applicant countries.