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Systems
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Data-Driven Methods in Business Process Management
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Data-Driven Methods in Business Process Management

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 2668

Special Issue Editor

Dr. Snezhana Dineva Sulova

E-Mail Website
Guest Editor
Department of Informatics, University of Economics – Varna, 9002 Varna, Bulgaria
Interests: data mining; text mining; e-commerce; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data analysis from various sources plays a key role in modern business process management because it aids in proof-based decision making. Data processing assists businesses in identifying trends, forecasting, and optimizing processes. The main methods used for data processing include the use of big data analyses, machine learning, and artificial intelligence. They allow companies to customize their products and offered services, improve supply chain management, and optimize resources. In addition, data-driven methods increase the transparency and traceability of business processes, which eases monitoring and controlling of their performance. The methods used for working with data and metadata are constantly evolving and transforming. The main challenges to using them are related to the need for quality data, dealing with complex infrastructures for processing and analysis, using reliable ways to protect data, and complying with data privacy legislations.

Dr. Snezhana Dineva Sulova
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data-driven decision making
  • business process management (BPM)
  • big data analytics
  • machine learning in business
  • artificial intelligence in BPM
  • predictive analytics
  • process optimization

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Published Papers (5 papers)

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Research

18 pages, 1541 KiB  
Article
The Development of a Methodology for Assessing Data Value Through the Identification of Key Determinants
by Daye Lee and Byungun Yoon
Systems 2025, 13(4), 305; https://doi.org/10.3390/systems13040305 - 21 Apr 2025
Abstract
This study introduces a methodology for assessing data value by identifying the key determinants that influence it. As data represents critical assets in modern business, companies must evaluate and use them strategically to maintain competitiveness. However, the intangible and complex nature of data [...] Read more.
This study introduces a methodology for assessing data value by identifying the key determinants that influence it. As data represents critical assets in modern business, companies must evaluate and use them strategically to maintain competitiveness. However, the intangible and complex nature of data makes objective valuation difficult. The proposed methodology categorizes data value determinants into two groups: essential value factors (completeness, accuracy, uniqueness, and consistency) and value-of-use factors (risk, timeliness, restrictive use, accessibility, and utility). This study analyzes the impact of each factor on the data value using quantitative methods. A regression analysis reveals the influence, interactions, and relative importance of these determinants. A real-world case study on the “Papers with Code” platform—widely used in machine learning research—demonstrates the methodology in practice. The results indicate that essential value factors, such as Percentage Correct and Task, have the strongest positive effect on data value, which underscores the importance of accuracy and relevance to specific applications. In contrast, factors such as Similar Datasets and Benchmarks reduce the data value, which highlights the need for uniqueness and differentiation in determining the value of a company’s data assets. This study provides practical guidelines for companies on the key factors to focus on when evaluating and managing data value. This study offers practical guidance on prioritizing value-related factors and enables more effective investment and utilization strategies. By addressing current limitations in data valuation and presenting a new approach, this study enhances data-driven decision-making and strengthens its associated competitive advantage. Full article
(This article belongs to the Special Issue Data-Driven Methods in Business Process Management)
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38 pages, 4338 KiB  
Article
Exploring MSME Owners’ Expectations of Data-Driven Approaches to Business Process Management
by Gelmar García-Vidal, Alexander Sánchez-Rodríguez, Laritza Guzmán-Vilar, Rodobaldo Martínez-Vivar and Reyner Pérez-Campdesuñer
Systems 2025, 13(4), 265; https://doi.org/10.3390/systems13040265 - 8 Apr 2025
Viewed by 307
Abstract
This study explores the adoption of data-driven approaches to business process management (BPM) by micro, small, and medium enterprises (MSMEs), which are crucial for economic growth and job creation but often face challenges in adopting advanced technologies. The research aims to understand the [...] Read more.
This study explores the adoption of data-driven approaches to business process management (BPM) by micro, small, and medium enterprises (MSMEs), which are crucial for economic growth and job creation but often face challenges in adopting advanced technologies. The research aims to understand the perceived benefits and persistent barriers that MSMEs encounter when implementing data-driven BPM. A critical review of academic literature was conducted, focusing on factors influencing adoption, perceived benefits, and existing challenges. Literature consistently highlights benefits such as process optimization, informed decision making, customer personalization, operational efficiency, adaptability, risk mitigation, automation, organizational culture transformation, innovation, and transparency. However, the review also identifies significant challenges, including technological and infrastructural limitations, resource constraints, training and skill gaps, organizational and cultural resistance, data management and quality issues, difficulties in implementation and change management, and analytical and technical complexities. These findings emphasize that, while data-driven BPM offers substantial opportunities for MSMEs to improve competitiveness and efficiency, successful adoption requires careful consideration of these multifaceted challenges and the development of tailored strategies to overcome them. Full article
(This article belongs to the Special Issue Data-Driven Methods in Business Process Management)
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28 pages, 2298 KiB  
Article
Data-Driven Business Process Evaluation in Commercial Banks: Multi-Dimensional Framework with Hybrid Analytical Approaches
by Zaiwen Ni, Binqing Xiao and Yanying Li
Systems 2025, 13(4), 256; https://doi.org/10.3390/systems13040256 - 6 Apr 2025
Viewed by 155
Abstract
The efficiency and reliability of business processes in commercial banks are critical to financial stability and compliance. However, traditional evaluation methods that rely on retrospective qualitative assessments and static frameworks struggle to address the dynamic complexities inherent in modern banking operations. These approaches [...] Read more.
The efficiency and reliability of business processes in commercial banks are critical to financial stability and compliance. However, traditional evaluation methods that rely on retrospective qualitative assessments and static frameworks struggle to address the dynamic complexities inherent in modern banking operations. These approaches lack real-time monitoring, fail to leverage granular event log data, and overlook organizational interdependencies, hindering proactive risk management and optimization. To bridge these gaps, this study proposes a data-driven evaluation framework that integrates three core dimensions: efficiency, quality, and flexibility. We developed a hybrid analytical model by integrating process mining with DEMATEL-AHP to analyze a Chinese bank’s performance guarantee process, comparing pre- and post-centralization workflows. The analysis revealed that post-centralization processes exhibited improved flexibility but reductions in efficiency and quality. Moreover, the social network analysis highlighted structural shifts, including expanded audit participation and reduced departmental cohesion, contributing to inefficiencies. This study advances business process management by demonstrating that a data-driven process evaluation framework offers greater persuasiveness and methodological rigor than traditional qualitative approaches. Full article
(This article belongs to the Special Issue Data-Driven Methods in Business Process Management)
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35 pages, 9453 KiB  
Article
A Two-Layer Causal Knowledge Network Construction Method Based on Quality Problem-Solving Data
by Yubin Wang, Shirong Qiang, Xin Yue, Tao Li and Keyong Zhang
Systems 2025, 13(3), 142; https://doi.org/10.3390/systems13030142 - 20 Feb 2025
Viewed by 475
Abstract
“Cause analysis” constitutes an indispensable component in quality management systems, serving to systematically identify the causes of quality defects, thereby enabling the development of targeted improvement strategies that concurrently address surface-level manifestations and fundamental drivers. However, relying solely on personal experience makes it [...] Read more.
“Cause analysis” constitutes an indispensable component in quality management systems, serving to systematically identify the causes of quality defects, thereby enabling the development of targeted improvement strategies that concurrently address surface-level manifestations and fundamental drivers. However, relying solely on personal experience makes it challenging to conduct a comprehensive and in-depth analysis of quality problems. The reason is that, when analyzing the causes of quality problems, it is essential not only to consider the specific context in which the problems occur. This enables “specific problems” to be “specifically analyzed” for the formulation of temporary containment measures. Additionally, the context of the problem needs to be stripped. This allows for a general and in-depth analysis of the “class problem” or the causal linkages underlying the problem, thereby determining the root cause of the problem and formulating a corresponding long-term program. The analysis of the causes of quality problems exhibits “duality” characteristics. Based on this, this study proposes and constructs a two-layer causal knowledge network by leveraging the causal knowledge generated and applied in the process of quality problem solving to address the “duality” characteristic of the cause analysis of quality problems. The proposed network can assist front-line employees in analyzing the quality problems of products from diverse perspectives and overcome the challenge of relying solely on personal experience to comprehensively and profoundly analyze the causal relationships of quality problems. Our method not only contributes to enhancing the efficiency of quality problem solving but also makes a valuable contribution to the advancement of theories and methods related to quality management and knowledge management. Full article
(This article belongs to the Special Issue Data-Driven Methods in Business Process Management)
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29 pages, 1602 KiB  
Article
Financing Mechanisms and Preferences of Technology-Driven Small- and Medium-Sized Enterprises in the Digitalization Context
by Jing Hu, Lianming Huang, Weifu Li and Hongyi Xu
Systems 2025, 13(2), 68; https://doi.org/10.3390/systems13020068 - 21 Jan 2025
Cited by 1 | Viewed by 1032
Abstract
In the context of digitalization, this study investigated the financing mechanisms and preferences of technology-driven small and medium-sized enterprises (TDSMEs) listed on the National Equities Exchange and Quotations (NEEQ) in China. Its primary objective was to identify the factors influencing financing decisions and [...] Read more.
In the context of digitalization, this study investigated the financing mechanisms and preferences of technology-driven small and medium-sized enterprises (TDSMEs) listed on the National Equities Exchange and Quotations (NEEQ) in China. Its primary objective was to identify the factors influencing financing decisions and to elucidate how TDSMEs choose their financing options in a rapidly evolving digital environment. To achieve this goal, we constructed a panel regression model using financial data from 41 TDSMEs (2017–2023), identifying the key determinants of financing decisions while examining the impact of regional heterogeneity and validating the model’s robustness. The empirical findings indicated that various independent variables, including a firm’s capital structure, significantly influenced both internal and external financing. Additionally, six machine learning (ML) algorithms were employed to predict financing preferences. Among them, the random forest (RF) model achieved the best financing preferences performance, with an average F1 score of 0.814, indicating its robust predictive capability for TDSMEs’ financing preferences. To further validate the proposed models, we conducted a case study on a TDSME newly recognized in 2024 (named TS Pharmaceutical). Both the Lasso and RF models demonstrated outstanding predictive accuracy, confirming the practicality of the ML models. These results provide valuable insights into navigating the ever-changing digital financing landscape, offering recommendations for policymakers and financial institutions to better support TDSMEs. The key innovation of this study lies in its novel integration of conventional panel regression analysis and ML techniques, thereby bridging the gap between digital transformation and financing strategies while contributing both theoretically and practically to the field. Full article
(This article belongs to the Special Issue Data-Driven Methods in Business Process Management)
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