Modelling business and management systems using Fuzzy cognitive maps: A critical overview

A critical overview of modelling Business and Management (B&M) Systems using Fuzzy Cognitive Maps is presented. A limited but illustrative number of specific applications of Fuzzy Cognitive Maps in diverse B&M systems, such as e business, performance assessment, decision making, human resources management, planning and investment decision making processes is provided and briefly analyzed. The limited survey is given in a table with statics of using FCMs in B&M systems during the last 15 years. The limited survey shows that the applications of Fuzzy Cognitive Maps to today’s Business and Management studies has been steadily increased especially during the last 5-6 years. Interesting conclusions and future research directions are highlighted

Modelling business and management systems using Fuzzy cognitive maps: A critical overview

A critical overview of modelling Business and Management (B&M) Systems using Fuzzy Cognitive Maps is presented. A limited but illustrative number of specific applications of Fuzzy Cognitive Maps in diverse B&M systems, such as e business, performance assessment, decision making, human resources management, planning and investment decision making processes is provided and briefly analyzed. The limited survey is given in a table with statics of using FCMs in B&M systems during the last 15 years. The limited survey shows that the applications of Fuzzy Cognitive Maps to today’s Business and Management studies has been steadily increased especially during the last 5-6 years. Interesting conclusions and future research directions are highlighted

A Learning Fuzzy Cognitive Map (LFCM) approach to predict student performance

Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student performance. Although the available sets of common prediction approaches, such as Artificial Neural Networks (ANN) and regression, work well with large datasets, they face challenges dealing with small sample sizes, limiting their practical applications in real practices. Methodology: Six distinct categories of performance antecedents are adopted here as course characteristics, LMS characteristics, student characteristics, student engagement, student support, and institutional factors, along with measurement items within each category. Furthermore, we assessed the student’s overall performance using three items of student satisfaction score, knowledge construction level, and student GPA. We have collected longitudinal data from 30 postgraduates in four subsequent semesters and analyzed data using the Learning Fuzzy Cognitive Map (LFCM) technique. Contribution: This research proposes a brand new approach, Learning Fuzzy Cognitive Map (LFCM), to predict student performance. Using this approach, we identified the most influential determinants of student performance, such as student engagement. Besides, this research depicts a model of interrelations among the student performance determinants. Findings: The results suggest that the model reasonably predicts the incoming sequence when there is a limited sample size. The results also reveal that students’ total online time and the regularity of learning interval in LMS have the largest effect on overall performance. The student engagement category also has the highest direct effect on student’s overall performance. Recommendations for Practitioners: Academic institutions can use the results and approach developed in this paper to identify students’ performance antecedents, predict the performance, and establish action plans to resolve the shortcomings in the long term. Instructors can adjust their learning methods based on the feedback from students in the short run on the operational level. Recommendation for Researchers: Researchers can use the proposed approach in this research to deal with the problems in other domains, such as using LMS for organizational/institutional education. Besides, they can focus on specific dimensions of the proposed model, such as exploring ways to boost student engagement in the learning process. Impact on Society: Our results revealed that students are at the center of the learning process. The degree to which they are dedicated to learning is the most crucial determinant of the learning outcome. Therefore, learners should consider this finding in order the gain value from the learning process. Future Research: As a potential for future works, the proposed approach could be used in other contexts to test its applicability. Future studies could also improve the performance level of the proposed LFMC model by tuning the model’s elements

Evaluating the quality of planning in new product development projects

The research and development industry shifts significant resources, from physical products to software. This is triggered by the need to stay competitive in a tough market. However, the poor performance of new product development in the field of software development may restrict this trend. Following a research stream that focuses on NPD planning, we introduce the quality of planning evaluation model (QPEM) and a knowledge base for improving the quality of planning evaluation. QPEM suggests planning quality should be evaluated using two distinct and complementary approaches of top-down and bottom-up for enhancing the accuracy of planning: a) an established measure that assesses 16 planning products and b) a novel measure that assesses 55 factors that affect project performance. This second measure uses cognitive maps, which is a methodology based on expert knowledge that graphically describes the behaviour of a system, and represents the project manager’s know-how and R&D Management Conference 2017, 1 - 5 July 2017, Leuven, Belgium characteristics, technological expertise, top management support, enterprise environmental factors, and the quality of methods and tools in a form that corresponds closely with humans’ perceptions. The alignment between these two approaches is demonstrated through multiple case studies

Evaluating the quality of planning in new product development projects

The research and development industry shifts significant resources, from physical products to software. This is triggered by the need to stay competitive in a tough market. However, the poor performance of new product development in the field of software development may restrict this trend. Following a research stream that focuses on NPD planning, we introduce the quality of planning evaluation model (QPEM) and a knowledge base for improving the quality of planning evaluation. QPEM suggests planning quality should be evaluated using two distinct and complementary approaches of top-down and bottom-up for enhancing the accuracy of planning: a) an established measure that assesses 16 planning products and b) a novel measure that assesses 55 factors that affect project performance. This second measure uses cognitive maps, which is a methodology based on expert knowledge that graphically describes the behaviour of a system, and represents the project manager’s know-how and R&D Management Conference 2017, 1 - 5 July 2017, Leuven, Belgium characteristics, technological expertise, top management support, enterprise environmental factors, and the quality of methods and tools in a form that corresponds closely with humans’ perceptions. The alignment between these two approaches is demonstrated through multiple case studies

Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes

In this paper, we propose a Fuzzy Cognitive Map (FCM) learning approach with a multi-local search in balanced memetic algorithms for forecasting industrial drying processes. The first contribution of this paper is to propose a FCM model by an Evolutionary Algorithm (EA), but the resulted FCM model is improved by a multi-local and balanced local search algorithm. Memetic algorithms can be tuned with different local search strategies (CMA-ES, SW, SSW and Simplex) and the balance of the effort between global and local search. To do this, we applied the proposed approach to the forecasting of moisture loss in industrial drying process. The thermal drying process is a relevant one used in many industrial processes such as food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries, and others. This research also shows that exploration of the search space is more relevant than finding local optima in the FCM models tested

A Novel Approach for Performance Assessment of Human-Robotic Interaction

Robots have always been touted as powerful tools that could be used effectively in a number of applications ranging from automation to human-robot interaction. In order for such systems to operate adequately and safely in the real world, they must be able to perceive, and must have abilities of reasoning up to a certain level. Toward this end, performance evaluation metrics are used as important measures. This research work is intended to be a further step toward identifying common metrics for task-oriented human-robot interaction. We believe that within the context of human-robot interaction systems, both humans' and robots' actions and interactions (jointly and independently) can significantly affect the quality of the accomplished task. As such, our goal becomes that of providing a foundation upon which we can assess how well the human and the robot perform as a team. Thus, we propose a generic performance metric to assess the performance of the human-robot team, where one or more robots are involved. Sequential and parallel robot cooperation schemes with varying levels of task dependency are considered, and the proposed performance metric is augmented and extended to accommodate such scenarios. This is supported by some intuitively derived mathematical models and some advanced numerical simulations. To efficiently model such a metric, we propose a two-level fuzzy temporal model to evaluate and estimate the human trust in automation, while collaborating and interacting with robots and machines to complete some tasks. Trust modelling is critical, as it directly influences the interaction time that should be directly and indirectly dedicated toward interacting with the robot. Another fuzzy temporal model is also presented to evaluate the human reliability during interaction time. A significant amount of research work stipulates that system failures are due almost equally to humans as to machines, and therefore, assessing this factor in human-robot interaction systems is crucial. The proposed framework is based on the most recent research work in the areas of human-machine interaction and performance evaluation metrics. The fuzzy knowledge bases are further updated by implementing an application robotic platform where robots and users interact via semi-natural language to achieve tasks with varying levels of complexity and completion rates. User feedback is recorded and used to tune the knowledge base where needed. This work intends to serve as a foundation for further quantitative research to evaluate the performance of the human-robot teams in achievement of collective tasks

Startup’s critical failure factors dynamic modeling using FCM

The emergence of startups and their influence on a country's economic growth has become a significant concern for governments. The failure of these ventures leads to substantial depletion of financial resources and workforce, resulting in detrimental effects on a country's economic climate. At various stages of a startup's lifecycle, numerous factors can affect the growth of a startup and lead to failure. Numerous scholars and authors have primarily directed their attention toward studying the successes of these ventures. Previous research review of critical failure factors (CFFs) reveals a dearth of research that comprehensively investigates the introduction of all failure factors and their interdependent influences. This study investigates and categorizes the failure factors across various stages of a startup's life cycle to provide a deeper insight into how they might interact and reinforce one another. Employing expert perspectives, the authors construct fuzzy cognitive maps (FCMs) to visualize the CFFs within entrepreneurial ventures and examine these factors' influence across the four growth stages of a venture. Our primary aim is to construct a model that captures the complexities and uncertainties surrounding startup failure, unveiling the concealed interconnections among CFFs. The FCMs model empowers entrepreneurs to anticipate potential failures under diverse scenarios based on the dynamic behavior of these factors. The proposed model equips entrepreneurs and decision-makers with a comprehensive understanding of the collective influence exerted by various factors on the failure of entrepreneurial ventures

ANÁLISIS DE MODELOS MENTALES Y SU PAPEL EN LA COMPRENSIÓN DE SISTEMAS COMPLEJOS PARA ESTUDIANTES DE INGENIERÍA EN SISTEMAS COMPUTACIONALES

Los modelos causales son instrumentos empleados para comprender y modelar los sistemas complejos. Con el fin de representar computacionalmente el conocimiento causal se debe recurrir a estructuras grafos dirigidas. El objetivo del presente artículo consiste en aplicar el modelo de relación entre los factores críticos, ilustrando las ventajas de los mapas cognitivos difusos en la representación de la causalidad, para la contribución a la comprensión de los sistemas. Se muestra un procedimiento para la obtención de modelos causales. Se presenta adicionalmente un estudio de caso donde se muestra la aplicabilidad de la propuesta y el uso de la computación con palabras, en la representación del conocimiento causal en una situación determinada. Ello facilita la comprensión de sistemas complejos, en especial, la presencia de vaguedad y de retroalimentación

Perceived key determinants of payment instrument usage: a fuzzy cognitive mapping-based approach

The recent economic climate has had direct repercussions on people’s daily lives. This has occurred not only in how they use payment instruments, but is also evinced in new concerns adjacent to technological advances, people’s safety and the credibility of financial institutions. In this regard, the banking sector has had a crucial role in countries’ economic development, making it increasingly important to understand how the banking system operates and what payment instruments are available to users. Relying on specialized literature and the application of fuzzy cognitive mapping, this study aims to understand the cause-and-effect relationships between customers’ preference factors in using payment instruments. The results show that usability aspects and safety concerns constitute the factors which users pay more attention to. Strengths and limitations of our proposal are also discussed.info:eu-repo/semantics/publishedVersio