Decision support systems in crowdfunding: A fuzzy cognitive maps (FCM) approach

Borrero-Domínguez, Cinta; Escobar‐Rodríguez, Tomás

doi:10.1016/j.dss.2023.114000

Cited by 9 publications

(5 citation statements)

References 45 publications

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“…The conceptual model of integrated risk management of a scientific project in the sustainable development of industry and the transition to a circular economy is based on the model of "Change Management Iceberg" [29,30]. This is based on the fact that a feature of the iceberg itself is that there is a larger volume of ice under the water than on the surface of the water.…”

Section: Resultsmentioning

confidence: 99%

Accelerating Sustainable and Economic Development via Scientific Project Risk Management Model of Industrial Facilities

Ragas,

Chupin,

Bolsunovskaya

et al. 2023

Sustainability

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This study presents a systemic and causal model of integrated stakeholder risk management of industrial facilities under sustainable development conditions. This model allows us to analyze the main factors of stakeholder influence, namely personnel risks, conflicts, and behavioral economic factors on a scientific project. This method is based on the identification of stakeholders and determining the possibility of the presence in their activities or inaction of personnel risks, conflicts, and behavioral economic factors that can affect the success of the production of industrial facilities, as well as on the calculation of toxicity indicators for each stakeholder. This study presents information technologies for the integrated management of industrial facilities in the context of sustainable development and transition to a circular economy, which, under conditions of uncertainty, allow the manager of an industrial enterprise and his team to implement the methodology of integrated management of industrial facilities in the context of sustainable development and transition to a circular economy to ensure the successful and timely implementation of these projects to meet the needs of stakeholders.

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Section: Resultsmentioning

confidence: 99%

Accelerating Sustainable and Economic Development via Scientific Project Risk Management Model of Industrial Facilities

Ragas,

Chupin,

Bolsunovskaya

et al. 2023

Sustainability

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“…The subject, reflecting theoretical and practical aspects in the field of DSS, is of interest to different groups of specialists. The main part of the publications is devoted to general issues of a canonical nature in this subject area, for example [20], the use of spe- cific methods and models, a description of the application and related features of DSS in various functional areas (medicine [21][22][23], specialized universities [24], defense technology [25], coal preparation enterprises [26], forestry [27][28], transport [29], financial issues [30][31], etc. ), as well as a description of the DSS used in the form of specific software.…”

Section: Discussionmentioning

confidence: 99%

Influence of algorithmization and interface for the preparation of management decisions

Gutgarts

2023

Bus. Inform.

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In modern conditions, managerial decision-making is carried out using automated systems under the general name “Decision Support Systems” (DSS). When creating them, it is important to consider two key points. The first is the algorithmic component, which reflects the logic of the system as a whole and its individual parts. The second is the application interface through which the user interacts with it. The interface is a graphical interpretation of the algorithms that are implemented within the system. Therefore, it is very important to design and create such a relationship between the algorithm and the interface so that the user is as comfortable as possible using the DSS to solve current tasks (information input, its processing, presentation and analysis for decision making). Thus, there is a directly proportional relationship between the interface and the algorithm. Moreover, despite the fact that there are many studies on these aspects, both theoretical and practical, there are still questions to which one should pay attention to in terms of application. The purpose of this study is to formulate practical recommendations to prevent the entry of incorrect information into the DSS database and to present the results in a form convenient for its analysis. The main tasks of the work are to show by means of examples which errors can contribute to the entry of unreliable information into the database, as well as how best to present information on the monitor screen in accordance with the psychophysiological characteristics of a person in order to reduce the time for its analysis and decision-making.

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“…FCM is a versatile tool for modeling complex, dynamic systems and understanding the interdependencies among various factors. It allows for the representation of uncertainty and imprecision, making it suitable for situations where precise data are unavailable or where human judgment plays a significant role in understanding and managing complex systems [72]. In our study, FCM is used as a mind map that depicts the total of the deployed relationships among the study's variables [82].…”

Section: Fcm Model For Conceptual Frameworkmentioning

confidence: 99%

“…Their application aids the decision-making processes of businesses since these methods provide valuable information regarding the various factors of firms' internal and external environments. For the development of the hybrid modeling process, the DSS software AnyLogic 8.8.5 PLE [72] was utilized. The inputs for the hybrid model were the coefficients of the correlation and regression analyses, as well as the descriptive statistics of the included variables.…”

Section: Hybrid Model and Ai Proceduresmentioning

confidence: 99%

“…Then, the correlation and linear regression coefficients (OLS), as well as the variables' descriptive statistics were inserted into the FCM model. In this way, the FCM output provided this study with a concept for representing the interrelationships of the total of the analyzed factors [72]. The latter model served as a conceptual framework for understanding the overall environment and the included variables in the analysis.…”

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confidence: 99%

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Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling

Giannakopoulos,

Terzi,

Sakas

et al. 2024

Information

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Agriculture firms face an array of struggles, most of which are financial; thus, the role of decision making is discerned as highly important. The agroeconomic indexes (AEIs) of Agriculture Employment Rate (AER), Chemical Product Price Index (CPPI), Farm Product Price Index (FPPI), and Machinery Equipment Price Index (MEPI) were selected as the basis of this study. This research aims to examine the connection between digital marketing analytics and the selected agroeconomic indexes while providing valuable insights into their decision-making process, with the utilization of AI (artificial intelligence) models. Thus, a dataset of website analytics was collected from five well-established agriculture firms, apart from the values of the referred indexes. By performing regression and correlation analyses, the index relationships with the agriculture firms’ digital marketing analytics were extracted and used for the deployment of the fuzzy cognitive mapping (FCM) and hybrid modeling (HM) processes, assisted by using artificial neural network (ANN) models. Through the above process, there is a strong connection between the agroeconomic indexes of AER, CPPI, FPPR, and MEPI and the metrics of branded traffic, social and search traffic sources, and paid and organic costs of agriculture firms. It is highlighted that agriculture firms, to better understand their sector’s employment rate and the volatility of farming, chemicals, and machine equipment prices for future investment strategies and better decision-making processes, should try to increase their investment in the preferred digital marketing analytics and AI applications.

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Decision support systems in crowdfunding: A fuzzy cognitive maps (FCM) approach

Cited by 9 publications

References 45 publications

Accelerating Sustainable and Economic Development via Scientific Project Risk Management Model of Industrial Facilities

Accelerating Sustainable and Economic Development via Scientific Project Risk Management Model of Industrial Facilities

Influence of algorithmization and interface for the preparation of management decisions

Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling

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