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The process of predicting construction costs and forecasting price fluctuations is a significant and challenging undertaking for project managers. This study aims to develop a construction cost index (CCI) for Jordan’s construction industry using fuzzy analytic hierarchy process (FAHP) and predict future CCI values using traditional and machine learning (ML) techniques.

The most influential cost items were selected by conducting a literature review and confirmatory expert interviews. The cost items’ weights were calculated using FAHP to develop the CCI formula.

The results showed that the random forest model had the lowest mean absolute percentage error (MAPE) of 1.09%, followed by Extreme Gradient Boosting and K-nearest neighbours with MAPEs of 1.41% and 1.46%, respectively.

The novelty of this study lies within the use of FAHP to address the ambiguity of the impact of various cost items on CCI. The developed CCI equation and ML models are expected to significantly benefit construction managers, investors and policymakers in making informed decisions by enhancing their understanding of cost trends in the construction industry.

In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI).

The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control.

The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller.

The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions.

The purpose of this research is to present an integrated methodological framework to aid in performance stewardship of management institutions according to their strategies based on a holistic evaluation encompassing social, economic and environmental dimensions.

A Mamdani fuzzy inference system (FIS) approach was adopted to design the quantitative models with respect to balanced scorecard (BSC) perspectives to demonstrate dynamic capability. Individual models were developed for each perspective of BSC using Mamdani FIS. Data was collected from subject matter experts in management education.

The proposed methodology is able to successfully compute the scores for each perspective. Effective placement, teaching learning process, faculty development and systematic feedback from the stakeholders were found to be the key drivers for revenue generation. The model is validated as the results were well accepted by the head of the institution after implementation.

The model resulting from this study will assist the institution to cyclically assess its performance, thus enabling continuous improvement. The strategy map provides the causality of the objectives across the four perspectives to aid the practitioners to better strategize. Also this study contributes to the literature of BSC as well to the applications of multi-criteria decision-making (MCDM) techniques.

Mamdani FIS integrated BSC model is a significant contribution to the academia of management education to quantitatively compute the performance of institutions. This quantified model reduces the ambiguity for practitioners to decide the performance levels for each metric and the priorities of metrics.

The main purpose of this study is to present a new approach to managing process changes in uncertain conditions. The proposed approach is applied in one of the largest production companies in Turkey to manage the changes in their warehouse processes which formed after the merger.

In the model, interval-valued hesitant fuzzy the decision-making trial and evaluation laboratory (IVHF-DEMATEL) methodology is integrated into one of the most used BPR tools, change matrix. The main focus of the proposed model is to increase both flexibility and applicability in uncertain conditions. Thus, while the change matrix enables companies to be agile and responsive to changes, IVHF-DEMATEL provides a better way to continuously evaluate and determine critical processes, and strategies to align with evolving conditions.

Initial analysis revealed two major problems, the slowness of shipments caused by the increase in costs and the confusion in the organizational structure. However, the conventional methods fall short of effectively determination of critical objectives in terms of dealing with uncertainty. Therefore, a comprehensive roadmap for managing the change is developed with the integration of IVHF-DEMATEL and change matrix so that a successful transition is achieved.

It is believed that the study will contribute to the existing literature by providing a novel approach in which the IVHF-DEMATEL methodology is integrated into the change matrix. Also, the study provides a guideline for practical applications by presenting a step-by-step implementation of the model.

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

This study aims to explore the integration of Industry 4.0 technologies with lean six sigma practices in the manufacturing sector for enhanced process improvement.

This study used a fuzzy decision-making trial and evaluation laboratory approach to identify critical Industry 4.0 technologies that can be harmonized with Lean Six Sigma methodologies for achieving improved processes in manufacturing.

The research reveals that key technologies such as modeling and simulation, artificial intelligence (AI) and machine learning, big data analytics, automation and industrial robots and smart sensors are paramount for achieving operational excellence when integrated with Lean Six Sigma.

The study is limited to the identification of pivotal Industry 4.0 technologies for Lean Six Sigma integration in manufacturing. Further studies can explore the implementation challenges and the quantifiable benefits of such integrations.

Integrating Industry 4.0 technologies with Lean Six Sigma enhances manufacturing efficiency. This approach leverages AI for predictive analysis, uses smart sensors for energy efficiency and adaptable robots for flexible production. It is vital for competitive advantage, significantly improving decision-making, reducing costs and streamlining operations in the manufacturing sector.

The integration of Industry 4.0 technologies with Lean Six Sigma in manufacturing has significant social implications. It promotes job creation in high-tech sectors, necessitating advanced skill development and continuous learning among the workforce. This shift fosters an innovative, knowledge-based economy, potentially reducing the skills gap. Additionally, it enhances workplace safety through automation, reduces hazardous tasks for workers and contributes to environmental sustainability by optimizing resource use and reducing waste in manufacturing processes.

This study offers a novel perspective on synergizing advanced Industry 4.0 technologies with established Lean Six Sigma practices for enhanced process improvement in manufacturing. The findings can guide industries in prioritizing their technological adoptions for continuous improvement.

This study aims to develop cause-and-effect relationships among the critical success factors (CSFs) of fintech adoption and rank these CSFs based on their importance in the model.

To achieve the objectives of the study, the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) approach was used. The data was collected from 16 experts using a questionnaire.

The findings demonstrated the interrelationships among the CSFs. In total, 16 critical factors were recognized as causal factors, and the remaining eight were considered effect factors. The CSFs were ranked based on their importance in fintech adoption.

This study is novel as it investigates CSFs of fintech adoption using FDEMATEL, and it contributes to understanding the nature of these factors and how they affect fintech adoption. The findings propose a significant basis to deepen fintech adoption and deliver a clue to design a practical framework for fintech adoption.

Sustainable construction re-engineers the conventional project lifecycle to integrate sustainability solutions. The additional sustainability requirements introduce new layers of complexity, challenges and risks that if unaddressed, can derail the gains in sustainable construction projects. This study developed a multidimensional risk assessment model for sustainable construction projects in the United Arab Emirates (UAE).

The research activities a comprised comprehensive literature review to shortlist relevant risks, an analysis of the probability – impact rating of the shortlisted risks – and the development of a risk assessment model for SC projects in the UAE. The model is developed based on the multicriteria framework and mathematical formulation of the fuzzy synthetic evaluation approach.

The developed model quantified the overall risk level in sustainable construction projects to be 3.71 on a 5-point Likert scale, indicating that investment in SC projects in the UAE is risky and should be carefully managed. The developed model further revealed that each of the risk groups, comprising management (3.82), technical (3.78), stakeholder (3.68), regulatory (3.66), material (3.53) and economic risks (3.502), presents a significant threat to realizing outcomes typical of SC projects.

This study developed a multidimensional risk assessment model capable of objectively quantifying the overall risk level and provides decision support to project teams to improve risk management in sustainable construction projects.

Workforce insufficiencies can impair firms' competitiveness in the hospitality industry. The problem of finding suitably trained employees has been aggravated by changes in consumer preferences, and the development of advanced technologies has led to the ‘smartization’ of upscale hotels. The consequent updating of business models means that decisive indicators of worker competence and employability are different from those applied previously. Thus, the aim of this study is to develop an indicator framework for assessing workforce employability with consideration of competence with artificial intelligence (AI) applications.

The initial indicators for the framework are obtained based on an intensive review of the relevant literature and roundtable meetings with academics and practitioners. The Delphi method is used to collect the data, and a hybrid fuzzy approach, which combines the modified Z-number and modified trapezoidal fuzzy number set techniques, is applied to quantify the information originating from the experts’ judgments. The interquartile range approach is applied to optimize the validity of the indicators.

The assessment framework is applied to evaluate workforce employability at an upscale hotel from the perspective of hotel executives. The capability of the workforce for the adoption of advanced technologies, including familiarity with AI, are considered.

The contributions of this research involve the identification of an updated list of determinants for the evaluation of workforce employability for hotels in today’s world, highlighting the value of AI applications to help ameliorate labor shortage problems. The results should benefit practitioners, allowing them to improve the efficiency of their operations, services and management practices, leading to sustainability and competitiveness in the upscale hotel industry.

The European Innovation Scoreboard is an important indicator of innovation performance across European Member States. Despite its wide application, the indicator fails to highlight the interlinkages that exist among innovation measures and focuses primarily on the linear relationship between the individual measures and the predicted outcome. This study aims to address this gap by applying a novel technique, the fuzzy-set qualitative comparative analysis (fsQCA), to shed light on these interlinkages and highlight the complexity of the determinants underlying innovation performance.

The authors adopted a configurational approach based on fsQCA that is implemented on innovation performance data from European Member States for the period 2011–2018. The approach is based on non-linearity and allows for the analysis of interlinkages based on equifinality, that is, the model recognises that there are different potential paths of high and low innovation performance. In addition, the approach allows for asymmetric relations, where a low innovation outcome is not the exact inverse of that which leads to high innovation outcome.

The results clearly indicate that innovation outcomes are not based on simple linear relations. Thus, to reap the desired effects from investments in innovation inputs, the complex set of indicators on which innovation performance is based should be taken into consideration. The results clearly indicate the elements of equifinality and asymmetric relations. Different paths lead to high innovation performance and low innovation performance.

The method applied to investigate the determinants of innovation performance is the prime original factor of this study. Thus, the study contributes to literature by highlighting the complexity involved in understanding innovation. By recognising and attempting to detangle this complexity, this study will assist not just academics but also policymakers in designing the necessary measures required to reach this important outcome for a country’s competitive edge.