Topic Editors

School Management Science and Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
School of Management, Shanghai University, Shanghai 200444, China
Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan

Data-Driven Group Decision-Making

Abstract submission deadline
31 July 2024
Manuscript submission deadline
31 December 2024
Viewed by
20083

Topic Information

Dear Colleagues,

With the existence of both global integration and reverse integration, as well as the rapid development of Internet technology and communication technology, the environment of governments, enterprises and other organizations is becoming more complex. Currently, there are major problems with group decision-making that require solving with the help of group wisdom. As the basic form of decision-making in social activities, group decision-making takes into account a variety of interests and overcomes the shortcomings of individual knowledge, information and ability. It has been widely used in many fields, such as emergency decision-making in major emergencies, major strategic decision-making in the government, logistics and supply chain management decision-making, etc. However, with the rapid development and deep integration of information technology, a new chapter of digital life has begun and we have been placed into the era of big data. Because of the characteristics of large volume, diversity, dynamic and low-value density of big data, dynamic and social group decision-making in a big data environment creates new challenges in the decision-making field, which are worthy of further exploration. Consistent or compromised schemes are more effective than the traditional decision-making method. Therefore, applying data-driven technology to carry out more research and innovation in group decision-making has extensive theoretical and practical significance. This Special Issue's aim is to solicit the latest research and review articles on group decision-making driven by data. We hope to combine the two studies, including new theoretical methods based on existing theories. We welcome new ideas to explore the future development direction of intelligent group decision-making. You are invited to provide original contributions of novel theories, methods and applications to the problems of data-driven group decision-making research. Potential topics:

  • Application of robust optimization method in group decision-making; 
  • Application of data driven method in group decision-making; 
  • Application of machine learning method in group decision-making; 
  • Analysis and application of data-driven group decision-making behavior; 
  • Clustering method of preference data in group decision-making; 
  • Multistage dynamic group decision-making method; 
  • Data-driven preference learning method; 
  • Data-driven preference clustering method; 
  • Large-group emergency decision based on decision-maker behavior data mining; 
  • Data collection and extraction in online reviews; 
  • Data mining for feature learning, classification, regression and clustering.

Prof. Dr. Shaojian Qu
Prof. Dr. Ying Ji
Dr. M. Faisal Nadeem
Topic Editors

Keywords

  • decision making
  • data-driven
  • optimization
  • algorithm
  • applications

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Big Data and Cognitive Computing
BDCC
3.7 4.9 2017 18.2 Days CHF 1800 Submit
Digital
digital
- - 2021 22.7 Days CHF 1000 Submit
Information
information
3.1 5.8 2010 18 Days CHF 1600 Submit
Mathematics
mathematics
2.4 3.5 2013 16.9 Days CHF 2600 Submit
Systems
systems
1.9 3.3 2013 16.8 Days CHF 2400 Submit

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Published Papers (13 papers)

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18 pages, 754 KiB  
Article
Beyond Boundaries: The AHP-DEA Model for Holistic Cross-Banking Operational Risk Assessment
by Yuan Hong and Shaojian Qu
Mathematics 2024, 12(7), 968; https://doi.org/10.3390/math12070968 - 25 Mar 2024
Viewed by 437
Abstract
Operational risk assessment has received considerable attention in bank risk management. However, current assessment methods are primarily designed to assess the risk profile of individual banks. To enable cross-bank operational risk assessment, we propose an integrated AHP-DEA (analytic hierarchy process–data envelopment analysis) method. [...] Read more.
Operational risk assessment has received considerable attention in bank risk management. However, current assessment methods are primarily designed to assess the risk profile of individual banks. To enable cross-bank operational risk assessment, we propose an integrated AHP-DEA (analytic hierarchy process–data envelopment analysis) method. This method determines the importance of assessment criteria by calculating the weighted sum of rank votes after obtaining the importance values for specific rankings with DEA. This procedure replaces the pairwise comparisons in AHP and addresses the challenge of traditional AHPs in determining appropriate importance values when dealing with a large number of indicators. We applied this method to assess the operational risks of three Chinese commercial banks, and the empirical results indicate that this integrated AHP-DEA method is simple and user-friendly, making it suitable for cross-bank operational risk assessment. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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21 pages, 474 KiB  
Article
Assessing Competitiveness in New Energy Vehicle Enterprises: A Group Decision Model with Interval Multiplicative Preference Relations
by Huimin Zhang, Meng Li and Wen Chen
Mathematics 2024, 12(1), 23; https://doi.org/10.3390/math12010023 - 21 Dec 2023
Viewed by 739
Abstract
New energy vehicles (NEVs) are the main direction for the development of the global automobile industry. Evaluating and analyzing the competitiveness of new energy vehicle enterprises (NEVEs) is of great significance for promoting their development. In order to explore the current situation of [...] Read more.
New energy vehicles (NEVs) are the main direction for the development of the global automobile industry. Evaluating and analyzing the competitiveness of new energy vehicle enterprises (NEVEs) is of great significance for promoting their development. In order to explore the current situation of NEVEs in Henan Province, this paper firstly constructs a competitiveness evaluation index system for NEVEs, comprising both quantitative and qualitative indexes. Then, a new definition of consistency, the consistency measure level, and corresponding improvement methods for interval multiplicative preference relations (IMPRs) are proposed. On this basis, fuzzy group decision-making models with IMPRs are constructed to deal with the ambiguity and uncertainty of the decision information, where consistency and consensus are both considered. In our case study, decision results are derived using Lingo 11.0 software. The results of this paper show that the degree of specialization has the greatest impact on the competitiveness of NEVEs, and some NEVEs are deficient in this regard. Related suggestions based on expert evaluation results are also provided. In addition, a comparison with other consistency improvement methods of IMPRs reveals that the methods proposed utilize the original information provided to decision-makers to the utmost degree. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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20 pages, 1115 KiB  
Article
Enhancing the Performance of High-Growth Small- and Medium-Sized Enterprises through Effective Project-Management Processes and Stakeholder Engagement: A Systems Perspective
by Igor Vrečko, Polona Tominc and Karin Širec
Systems 2023, 11(10), 511; https://doi.org/10.3390/systems11100511 - 12 Oct 2023
Viewed by 2067
Abstract
This study examines the impact of project-management practices on high-growth small and medium-sized enterprises (HG SMEs) from a systems perspective, utilizing structural equation modelling (SEM) and data from a diverse SME sample. It investigates the intricate relationships among several factors: project management system [...] Read more.
This study examines the impact of project-management practices on high-growth small and medium-sized enterprises (HG SMEs) from a systems perspective, utilizing structural equation modelling (SEM) and data from a diverse SME sample. It investigates the intricate relationships among several factors: project management system support, project-management processes, stakeholder involvement, project management success, project success, and HG SME growth. Our findings highlight the substantial positive influence of project-management processes and stakeholder engagement on project management success. These factors subsequently contribute significantly to both project success and the overall growth of HG SMEs. Notably, project management system support does not exhibit a substantial influence on these success factors. Furthermore, our research uncovers important indirect effects. Project-management processes indirectly impact both project success and HG SME growth, underscoring their central role. Similarly, stakeholder involvement indirectly influences HG SME growth through its impact on project success, emphasizing its significance. This study contributes to the existing body of knowledge by emphasizing the critical roles of project-management processes, stakeholder engagement, and project success as drivers of SME growth. These insights have valuable implications for SME managers, project leaders, and policymakers, highlighting the essential nature of effective project management in shaping the growth trajectory of SMEs. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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16 pages, 1537 KiB  
Article
Decision-Making on Selection of Talent Management Methods in the Era of Digitalization
by Lihong Cai, Ying Ji, Chethana Wijekoon and Yangyun Yuan
Systems 2023, 11(9), 450; https://doi.org/10.3390/systems11090450 - 31 Aug 2023
Viewed by 1479
Abstract
The application of digital technologies has a significant impact on organizational performance by way of different talent management methods, thereby enabling the maintenance of the organization’s continuous competitive advantage. Therefore, this paper studied the four key factors that influence organizational performance: digital technology [...] Read more.
The application of digital technologies has a significant impact on organizational performance by way of different talent management methods, thereby enabling the maintenance of the organization’s continuous competitive advantage. Therefore, this paper studied the four key factors that influence organizational performance: digital technology application (DTA), inclusive talent management (ITM), exclusive talent management (ETM), and non-equilibrium investment (NET), aiming to investigate how digital technology application and talent management methods positively affect organizational performance, explore how this relationship is regulated by NET, and provides suggestions for selecting appropriate talent management methods. To conduct quantitative analysis, questionnaires were used with a sample size of 534 middle and senior managers as well as human resources practitioners from different enterprises. The structural equation model (SEM) was employed along with 5000 iterations to test the research hypotheses. The results indicate a positive correlation between digital technology application and organizational performance. Furthermore, ITM and ETM act as intermediaries between digital technology application and organizational performance, whereas NET plays a regulatory role in relation to ITM and organizational performance. This paper offers comprehensive insights into the factors influencing organizational performance and sheds light on how organizations make decisions regarding data-driven talent management methods at different stages of development. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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17 pages, 493 KiB  
Article
Research on the Non-Linear Relationship between Information Disclosure and Subsequent Purchases: The Moderating Effect of Membership Level
by Chunming Qin and Le Zuo
Systems 2023, 11(8), 398; https://doi.org/10.3390/systems11080398 - 02 Aug 2023
Viewed by 794
Abstract
In order to provide better marketing services to customers, companies often want to obtain as much customer information as possible. However, for customers, as well as leading to better service, information disclosure may also put them at risk of information leakage, so customers [...] Read more.
In order to provide better marketing services to customers, companies often want to obtain as much customer information as possible. However, for customers, as well as leading to better service, information disclosure may also put them at risk of information leakage, so customers may respond in two different ways to firms’ invitations to disclose information. This paper applies theories of social capital and communication boundary management to developing a framework for understanding the psychological mechanisms behind individuals’ responses to disclosure invitations and their subsequent purchase behavior. The results of this study show that under the combined effect of social capital and communication boundaries, subsequent purchases show an inverted-U-shaped relationship, initially increasing and then decreasing as the level of disclosure increases. Furthermore, because the social capital of high-level members and firms is higher than that of low-level members, it moderates the inverted-U-shaped relationship; that is, the higher the level of membership, the more moderate the inverted-U-shaped relationship. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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25 pages, 956 KiB  
Article
Evaluation of Enterprise Decarbonization Scheme Based on Grey-MEREC-MAIRCA Hybrid MCDM Method
by Moses Olabhele Esangbedo and Mingcheng Tang
Systems 2023, 11(8), 397; https://doi.org/10.3390/systems11080397 - 02 Aug 2023
Cited by 3 | Viewed by 1241
Abstract
Engineering and technological breakthroughs in sustainability play a crucial role in reducing carbon emissions. An important aspect of this is the active participation of enterprises in addressing carbon reduction as a systemic approach. In response to government incentives in the People’s Republic of [...] Read more.
Engineering and technological breakthroughs in sustainability play a crucial role in reducing carbon emissions. An important aspect of this is the active participation of enterprises in addressing carbon reduction as a systemic approach. In response to government incentives in the People’s Republic of China, Chinese enterprises have developed carbon reduction systems to align their organizational goals with national long-term plans. This paper evaluates the carbon reduction schemes employed by six companies as a multi-criteria decision-making (MCDM) problem. To this end, we propose a new hybrid MCDM method called the grey-MEREC-MAIRCA method. This method combines the recently developed method based on the removal effects of criteria (MEREC) for weighting and multi-attribute ideal-real comparative analysis (MAIRCA) based on the grey system theory. The proposed hybrid method provides the additional benefit of accounting for uncertainty in decision making. Notable findings of this research, based on the decision-maker scores, are that the control of direct carbon emissions and energy-saving efficiency are top priorities. In contrast, committing to corporate social responsibility through carbon public welfare and information disclosure are considered lesser priorities. Furthermore, the ranking results obtained using this method are compared with those from the classical weighted sum model and the technique for order preference by similarity to ideal solution (TOPSIS), confirming the selection of the best company. Despite the limitation of the proposed method and the additional steps needed in the evaluation, it opens up opportunities for future research to develop simpler MCDM methods under uncertainty. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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13 pages, 2342 KiB  
Article
Data-Driven Decision-Making (DDDM) for Higher Education Assessments: A Case Study
by Samuel Kaspi and Sitalakshmi Venkatraman
Systems 2023, 11(6), 306; https://doi.org/10.3390/systems11060306 - 13 Jun 2023
Cited by 1 | Viewed by 3556
Abstract
The higher education (HE) system is witnessing immense transformations to keep pace with the rapid advancements in digital technologies and due to the recent COVID-19 pandemic compelling educational institutions to completely switch to online teaching and assessments. Assessments are considered to play an [...] Read more.
The higher education (HE) system is witnessing immense transformations to keep pace with the rapid advancements in digital technologies and due to the recent COVID-19 pandemic compelling educational institutions to completely switch to online teaching and assessments. Assessments are considered to play an important and powerful role in students’ educational experience and evaluation of their academic abilities. However, there are many stigmas associated with both “traditional” and alternative assessment methods. Rethinking assessments is increasingly happening worldwide to keep up with the shift in current teaching and learning paradigms due to new possibilities of using digital technologies and a continuous improvement of student engagement. Many educational decisions such as a change in assessment from traditional summative exams to alternate methods require appropriate rationale and justification. In this paper, we adopt data-driven decision-making (DDDM) as a process for rethinking assessment methods and implementing assessment transformations innovatively in an HE environment. We make use of student performance data to make an informed decision for moving from exam-based assessments to nonexam assessment methods. We demonstrate the application of the DDDM approach for an educational institute by analyzing the impact of transforming the assessments of 13 out of 27 subjects offered in a Bachelor of Information Technology (BIT) program as a case study. A comparison of data analysis performed before, during, and after the COVID-19 pandemic using different student learning measures such as failure rates and mean marks provides meaningful insights into the impact of assessment transformations. Our implementation of the DDDM model along with examining the influencing factors of student learning through assessment transformations in an HE environment is the first of its kind. With many HE providers facing several challenges due to the adoption of blended learning, this pilot study based on a DDDM approach encourages innovation in classroom teaching and assessment redesign. In addition, it opens further research in implementing such evidence-based practices for future classroom innovations and assessment transformations towards achieving higher levels of educational quality. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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14 pages, 1452 KiB  
Article
A Measuring Method Based on Graph Structure for Decision-Making Complexity in Major Science and Technology Projects
by Zhifeng Wu and Yisheng Liu
Systems 2023, 11(5), 234; https://doi.org/10.3390/systems11050234 - 07 May 2023
Viewed by 1252
Abstract
Unlike general large-scale projects, major science and technology projects (MSTPs) are strategically positioned to meet national needs, reflecting the forward-looking direction of science and technology development. The correctness of decision making for MSTPs is crucial for the long-term development and strategic interests of [...] Read more.
Unlike general large-scale projects, major science and technology projects (MSTPs) are strategically positioned to meet national needs, reflecting the forward-looking direction of science and technology development. The correctness of decision making for MSTPs is crucial for the long-term development and strategic interests of the country. To measure decision complexity accurately, we propose a graph-based approach that utilizes information entropy theory. This approach provides decision makers with a theoretical foundation for effectively managing decision complexity. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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26 pages, 29573 KiB  
Article
Evolutionary Analysis of the Regulation of Data Abuse in Digital Platforms
by Zhen Wang, Chunhui Yuan and Xiaolong Li
Systems 2023, 11(4), 188; https://doi.org/10.3390/systems11040188 - 07 Apr 2023
Cited by 5 | Viewed by 1667
Abstract
This study proposes a tripartite evolutionary game model to investigate the interactions among digital platforms, governments, and users to address the negative consequences of data abuse. The paper identifies that the high tax incentives and low penalties set by the government will increase [...] Read more.
This study proposes a tripartite evolutionary game model to investigate the interactions among digital platforms, governments, and users to address the negative consequences of data abuse. The paper identifies that the high tax incentives and low penalties set by the government will increase the incentive for data abuse by platforms of different sizes, and the government can try to set up a tax ladder policy for platforms of different sizes and a dynamic penalty amount based on platform revenue. The study also reveals that user participation in supervision can reduce information asymmetry, and decrease the cost of government regulation. However, the single constraint of users is less effective than government regulation or dual user-government regulation. Additionally, the presence of privacy leakage risks prompts digital platforms to adopt compound engines to implement data abuse. Hence, the relevant government regulatory policies should consider the efficiency and cost of data security technology for timely adjustments. This research contributes to understanding the complex relationships among digital platforms, governments, and users and highlights the need for appropriate measures to mitigate the negative effects of data abuse. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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17 pages, 1016 KiB  
Article
Based on Improved NSGA-II Algorithm for Solving Time-Dependent Green Vehicle Routing Problem of Urban Waste Removal with the Consideration of Traffic Congestion: A Case Study in China
by Zhenhua Gao, Xinyu Xu, Yuhuan Hu, Hongjun Wang, Chunliu Zhou and Hongliang Zhang
Systems 2023, 11(4), 173; https://doi.org/10.3390/systems11040173 - 27 Mar 2023
Cited by 4 | Viewed by 1682
Abstract
The dense population and the large amount of domestic waste generated make it difficult to determine the best route and departure time for waste removal trucks in a city. Aiming at the problems of municipal solid waste (MSW) removal and transportation not in [...] Read more.
The dense population and the large amount of domestic waste generated make it difficult to determine the best route and departure time for waste removal trucks in a city. Aiming at the problems of municipal solid waste (MSW) removal and transportation not in time, high collection and transportation costs and high carbon emissions, this paper studies the vehicle routing problem of municipal solid waste removal under the influence of time-dependent travel time, traffic congestion and carbon emissions. In this paper, a dual objective model with the lowest total economic cost and the highest garbage removal efficiency is established, and a DCD-DE-NSGAII algorithm based on Dynamic Crowding Distance and Differential Evolution is designed to improve the search ability, improve the convergence speed and increase the diversity of the optimal solution set. The results show that: according to the actual situation of garbage collection and transportation, the method can scientifically plan the garbage collection and transportation route, give a reasonable garbage collection scheme and departure time, and effectively avoid traffic congestion time; Through algorithm comparison, the algorithm and model proposed in this paper can reduce collection and transportation costs, improve transportation efficiency and reduce environmental pollution. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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22 pages, 2159 KiB  
Article
The Strategic Weight Manipulation Model in Uncertain Environment: A Robust Risk Optimization Approach
by Shaojian Qu, Lun Wang, Ying Ji, Lulu Zuo and Zheng Wang
Systems 2023, 11(3), 151; https://doi.org/10.3390/systems11030151 - 15 Mar 2023
Viewed by 1010
Abstract
Due to the complexity and uncertainty of decision-making circumstances, it is difficult to provide an accurate compensation cost in strategic weight manipulation, making the compensation cost uncertain. Simultaneously, the change in the attribute weight is also accompanied by risk, which brings a greater [...] Read more.
Due to the complexity and uncertainty of decision-making circumstances, it is difficult to provide an accurate compensation cost in strategic weight manipulation, making the compensation cost uncertain. Simultaneously, the change in the attribute weight is also accompanied by risk, which brings a greater challenge to manipulators’ decision making. However, few studies have investigated the risk aversion behavior of manipulators in uncertain circumstances. To address this research gap, a robust risk strategic weight manipulation approach is proposed in this paper. Firstly, mean-variance theory (MVT) was used to characterize manipulators’ risk preference behavior, and a risk strategic weight manipulation model was constructed. Secondly, the novel robust risk strategic weight manipulation model was developed based on the uncertainty caused by the estimation error of the mean and covariance matrix of the unit compensation cost. Finally, a case of emergency facility location was studied to verify the feasibility and effectiveness of the proposed method. The results of the sensitivity analysis and comparative analysis show that the proposed method can more accurately reflect manipulators’ risk preference behavior than the deterministic model. Meanwhile, some interesting conclusions are revealed. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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19 pages, 2487 KiB  
Article
Online-Review-Driven Products Ranking: A Hybrid Approach
by Shaojian Qu, Yang Zhang, Ying Ji, Zheng Wang and Ruijuan Geng
Systems 2023, 11(3), 148; https://doi.org/10.3390/systems11030148 - 12 Mar 2023
Cited by 2 | Viewed by 1469
Abstract
Online customer reviews (OCRs) are the real feelings of customers in the process of using products, which have great reference value for potential customers’ purchase decisions. However, it is difficult for consumers to extract helpful information from very large numbers of OCRs. To [...] Read more.
Online customer reviews (OCRs) are the real feelings of customers in the process of using products, which have great reference value for potential customers’ purchase decisions. However, it is difficult for consumers to extract helpful information from very large numbers of OCRs. To support consumers’ purchase decisions, this paper proposes a hybrid method to rank alternative products through OCRs. In this method, we use the fine-grained Bidirectional Encoder Representation from Transformers (BERT) model for aspect-level sentiment analysis (SA) and convert SA results of sub-criteria into a corresponding interval intuitionistic fuzzy number, accurately extracting customer satisfaction in OCRs and reducing the errors caused by different amounts of OCRs. Furthermore, in order to obtain the ranking results of products, the subjective and objective weights are combined to determine weight of feature. Subsequently, an improved interval intuitionistic fuzzy VIKOR method is proposed to rank mobile games. Finally, we conduct a case study and make some comparisons, which show that our method can reduce the complexity of accurately obtaining consumers’ personal preferences and help consumers make more accurate decisions. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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14 pages, 284 KiB  
Article
Multiperson Decision-Making Using Consistent Interval-Valued Fuzzy Information with Application in Supplier Selection
by Xiaodong Yu, Atiq ur Rehman, Samina Ashraf, Muhammad Hussain and Shahzad Faizi
Mathematics 2023, 11(4), 879; https://doi.org/10.3390/math11040879 - 09 Feb 2023
Viewed by 873
Abstract
This study describes a consistency-based approach for multiperson decision-making (MPDM) in which decision-makers’ suggestions are expressed as incomplete interval-valued fuzzy preference relations. The presented approach utilizes Lukasiewicz’s t-norm in conjunction with additive reciprocity to obtain comprehensive interval valued fuzzy preference relations from each [...] Read more.
This study describes a consistency-based approach for multiperson decision-making (MPDM) in which decision-makers’ suggestions are expressed as incomplete interval-valued fuzzy preference relations. The presented approach utilizes Lukasiewicz’s t-norm in conjunction with additive reciprocity to obtain comprehensive interval valued fuzzy preference relations from each expert, and the transitive closure formula also produces L-consistency. We would evaluate the consistency weights of the experts using consistency analysis. Experts are allocated final priority weights by combining the consistency weights and preset weights. A collective consistency matrix is then constructed from the weighted sum of preference matrices. After computing the possibility degrees, the normalization procedure is utilized to generate complimentary matrices, and the final ranking values of alternatives are derived as well. Finally, a numerical example demonstrates the efficacy of the suggested approach following a comparison analysis. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
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