Search results

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.

Construction projects involve with various risks during all phases of project lifecycle. Failure mode and effective analysis (FMEA) is a useful tool for identifying and eliminating possible risk of failure modes (FMs) and improving the reliability and safety of systems in a broad range of industries. The traditional FMEA method applies risk priority number method (RPN) to calculate risk of FMs. RPN method cannot consider the direct and indirect interdependencies between the FMs and is not appropriate for complex system with numerous components. The purpose of this study is to propose an approach to consider interdependencies between FMs and also using fuzzy theory to consider uncertainties in experts' judgments.

The proposed approach consist of three stages: the first stage of hybrid model used fuzzy FMEA method to identify the failure mode risks and derive the RPN values. The second stage applied Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) method to determine the interdependencies between the FMs which are defined through fuzzy FMEA. Then, analytic network process (ANP) is applied in the third stage to calculate the weights of FMs based on the interdependencies that are generated through FDEMATEL method. Finally, weight of FMs through fuzzy FMEA and FDEMATEL–ANP are multiplied to generate the final weights for prioritization. Afterward, a case study for a commercial building project is introduced to illustrate proficiency of model.

The results showed that the suggested approach could reveal the important FMs and specify the interdependencies between them successfully. Overall, the suggested model can be considered as an efficient hybrid FMEA approach for risk prioritization.

The originality of approach comes from its ability to consider interdependencies between FMs and uncertainties of experts' judgments.

Due to the high use of mobile devices, the market share of mobile advertisements (Ads) is significantly growing. Although mobile Ads can contact potential customers at any time and in any location depending on their unique demands, one of the biggest problems for advertisers is how to improve customer repurchases with their Ads. The development and empirical support of customer repurchase through mobile Ads context have not been addressed. Therefore, the purpose of this paper is to define and identify the key attributes of customer repurchase in a mobile Ads context.

In this research, the set of attributes was derived from a systematic literature review and finalized by applying the Fuzzy Delphi method. To develop a hierarchical model and classify the cause/effect groups among identified key attributes, the Fuzzy mixed approach uses a combination of Fuzzy interpretive structural modeling-decision-making trial and evaluation laboratory.

The findings suggest that language, type of website and social media are classified to as essential attributes for improving customer repurchase through mobile Ads.

The focus of the current research is limited to identify and develop the hierarchical interrelationships between customer repurchase attributes that are unique to the mobile Ads business context. Additional research may be conducted for various media contexts and other products/services categories.

This study illustrated how multicriteria decision-making techniques could be used effectively using Fuzzy theory to explore the research area of customer repurchase in mobile Ads concept.

By replacing traditional fossil fuels, renewable energy (RE) has the potential to become an outstanding sustainable energy supply. However, owing to technological, economic, social and legal constraints, RE is still in its early stages of development. Hence, this paper aims to analyze the sustainable development hindrances in the RE supply chain (RESC).

Twenty-three hindrances to the sustainable development of the RE industry were investigated in this research, which included a review of the expert opinion and literature. Then, a mutual relationship between the hindrances by integrating interpretive structural modeling and decision-making trial and evaluation laboratory in fuzzy environment was established. Furthermore, using the cross-impact matrix multiplication applied to a classification analysis, these hindrances were grouped.

The findings show that the important hindrances are “lack of standards for the RESC (H19), lack of entrepreneurship support (H21), lack of incentives/subsidies to encourage RE producers to compete (H30) and lack of governmental support for sustainable supply chain solutions (H31).

This research provides unique insights into the area of sustainability in RESC. To the best of the authors’ knowledge, this is the first paper to analyze the sustainability hindrances in the RESC.

One of the most important components of healthcare is the timely delivery of pharmaceutical products, such as life-saving medicines. However, disruptions like COVID-19 bring new challenges and risks to the pharmaceutical supply chain (PSC) and healthcare organizations that impact their operational performance. This study focuses on mitigating risks in India's generic medicine supply chain (GMSC) as a result of various disruptions, which can assist policymakers develop appropriate plans and strategies to build resilience in the Jan Aushadhi Scheme (JAS) of micro, small and medium enterprises (MSMEs) in order to improve their overall performance.

Risk-causing vulnerabilities and resilience capabilities are identified from the literature review and expert's opinions. Following that, the vulnerabilities are classified into cause-and-effect vulnerabilities, and supply chain resilient capabilities (SCRCs) are measured using a hybrid fuzzy DEMATEL and best worst method (FDEMATEL-BMW) framework.

The outcome of the study reveals that transportation breakdown, loss of human resources and loss of suppliers are the potential risk-causing vulnerabilities that lead to vulnerabilities like shortages of medicines, loss of in-hand stock qualities and loss of sales/revenue. In addition, the analysis suggests that the sustainability of an organization with maximum weightage is the critical factor for building resilience in GMSC followed by flexibility, agility and visibility.

The integration of resilience into Jan Aushadhi GMSC can help in managing disruptions efficiently and effectively to mitigate risk and optimize MSMEs overall performance.

To the best of the authors’ knowledge, this work will be the first of its kind to model resilience in GMSC of MSMEs using a hybrid framework.

Although sustainability is a popular topic in the past decade, there is a lack of research to identify the driving factors for developing countries. The purpose of this paper is to investigate the driving factors for achieving eco-innovation.

An in-depth case study is employed to address the objective outlined above. A Chinese company with more than 1,200 employees was selected to address the research question.

By fuzzy decision-making trial and evaluation laboratory and interpretive structural modelling analysis, the driving factors for eco-innovation are identified, and the priority of different factors has also been extracted.

This is among the first studies to carry similar analysis regarding eco-innovation. More specifically, this is perhaps the first study to take this approach and to analyse this topic in a developing country.

The purpose of this paper is to analyze preferred supplier enablers (PSEs) and measure the interdependencies among themselves for enhancing preferred supplier relationship.

In the current study, an approach has been developed in which the significance of various PSEs was determined by applying Fuzzy Analytic Hierarchy Process (FAHP) and the prominent PSEs were screened out through Pareto analysis. Also, the interdependence among the prominent PSEs was measured by applying the Fuzzy Decision Making Trial and Evaluation Laboratory method in order to select the right PSEs in ascribing the Preferred Supplier Status. Finally, the weakest relationships among the PSEs were confirmed by applying the student’s t-test and then an impact relationship map of PSEs was developed.

The strength of relationships among the PSEs, grouping of PSEs into causes and effects on a causal diagram and a concise impact relationship map of PSEs were determined. Further, the PSEs that a manufacturer must primarily focus and monitor were also obtained.

The study was conducted in an Indian electronic manufacturing environment. Therefore, the results obtained would be more relevant to the high end technology product manufacturers operating in the developing countries.

From the current study, a manufacturer can alleviate, favorably associate and integrate with the good suppliers and then eventually establish a strong supply base.

Manufacturers are looking for the closer and favored relationships by bringing in the concept of preferred supplier while dealing with their key suppliers. Thus, the results obtained from the current study would be of great assistance to a manufacturer in gaining an extra cutting-edge and, in turn, tackling the increased competitive pressures and reduced availability of resources.

Lean Six Sigma (LSS) is receiving a tremendous attention as a modern process of streaming to improve the organizational ability and customer satisfaction. A successful LSS implementation is influenced by various factors and the execution of all the influencing factors simultaneously is a very difficult task for any organization. From the perspective of limitation of resources, this paper aims to present a basic issue in an LSS implementation of clustering complex and impacting factors into groups to achieve them in a stepwise manner. This paper aims to present a fundamental issue of clustering the complex and impacting factors of an LSS implementation into groups to achieve them stepwise.

A total of 40 relevant influencing factors toward an LSS implementation have been identified from the extensive literature review and duly validated with experts’ opinions. Integrated fuzzy set theory and decision-making trial and evaluation laboratory (DEMATEL) approach are demonstrated to explore the causal relationships among influencing factors of the LSS implementation. An empirical case analysis of an Indian manufacturing organization is carried out to illustrate the utilization of the proposed model.

The proposed framework effectively finds out the significance of each influencing factor of an LSS implementation and clustered into cause–effect groups. As per the results of the empirical case analysis, ten critical success factors (CSFs) of the LSS implementation are evaluated for the successful LSS implementation. Top management pays more attention to achieve them and implement them in a phase-wise approach under the limitations of accessible resources.

The presented framework provides an effective, precise and systematic decision support tool for recognizing CSFs of the LSS implementation. The organization, decision-makers, industrial practitioners and academic researchers may be able to comprehend the cause–effect relationship of the influencing factors of the LSS implementation. The exploratory nature and the single case study are two major limitations of this analysis. The developed model is heavily dependent on the experts’ opinions; hence, any bias in judgment will influence the final result.

This analysis is the first of its kind of effort, according to the best of the authors’ knowledge, to classify the influencing factors of LSS implementation into the cause–effect cluster. The outcomes of this analysis make the complexity of a problem easier in handling and assisting the decision-making.

The purpose of this paper is to present a multidimensional evaluation of brand equity performance incorporating dimensions adopted from the balance scorecard (BSC) approach to business performance.

In this study, text mining is used for automatic extraction of valuable information from textual data such as the financial reports of firms. Instead of expert opinions, linguistic scales built upon outcomes of text mining are used as inputs for decision-making. The proposed model combines fuzzy DEMATEL (FDEMATEL), fuzzy ANP (FANP), fuzzy TOPSIS (FTOPSIS) and fuzzy VIKOR (FVIKOR) methods for weighting criteria and ranking alternatives.

Using data from five privatized firms in Turkey, the study’s findings demonstrate that the customer is the most important dimension of brand equity performance evaluation. Cash flow and brand loyalty are identified as the most important criteria in the measurement of brand equity performance.

Findings highlight the importance of firms taking action to increase consumer perceptions, attitudes and behaviors in the privatization processes. For this purpose, privatized firms need to understand the expectations of customers to increase customer satisfaction and loyalty and therefore improve brand equity.

The paper contributes to literature in several important ways. First, by adopting the BSC approach, it proposes a holistic and a multidimensional model for measuring brand equity performance. Second, the study offers a novel methodology using a hybrid multi-criteria decision-making model designed for the fuzzy environment. Third, the study uses the knowledge extraction tool of text mining in the fuzzy decision-making process. Finally, the study evaluates the brand equity performance of privatized firms in an emerging country context.

Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement.

A hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context.

The results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF. A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment. There are 35 countries/territories being categorized into five geographic regions. Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis. Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability.

This study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF.