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Electronics
Special Issues
Information Systems, Management, and Digital Innovation: Complexity, Integration, and Transformation
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Information Systems, Management, and Digital Innovation: Complexity, Integration, and Transformation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 1603

Editors

Dr. Peng Qin

E-Mail Website
Guest Editor
Faculty of Data Science, City University of Macau, Macau 999078, China
Interests: information system; e-commerce technology; AI for business
Dr. Jiepeng Wang

E-Mail Website
Guest Editor
Department of Systems Science, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: complex adaptive systems; complex networks; agent-based modeling
Dr. Yufei Yuan

E-Mail Website
Guest Editor
Department of Transport & Planning, Delft University of Technology, 2628 CN Delft, The Netherlands
Interests: traffic flow theory; AI for transportation; active model modeling and simulation; data mining

Special Issue Information

Dear Colleagues,

In the context of the rapid digital transformation, the deep integration of the digital economy with artificial intelligence (AI) is fundamentally reshaping how information systems, organizations, and innovation ecosystems evolve. Advances in big data analytics, complex networks, platform technologies, and generative AI have intensified the complexity, interdependence, and dynamism of contemporary digital innovation. As a result, traditional boundaries between technology development, organizational management, and value creation are being increasingly blurred.

Despite significant technological progress, a persistent gap remains between technological innovation and managerial practice. Existing theories related to Information Systems (IS) and management often struggle to fully explain or guide organizational transformation under conditions of high complexity, uncertainty, and rapid technological change. Addressing these challenges requires integrative perspectives that combine IS research, management theory, and digital innovation studies while embracing complexity, emergence, and system-level interactions.

This special issue, ‘Information Systems, Management, and Digital Innovation: Complexity, Integration, and Transformation’, aims to provide a broad interdisciplinary forum for research at the intersection of IS, management, and digital innovation in complex socio-technical systems. We invite cutting-edge contributions that explore how organizations design, govern, and leverage digital technologies to achieve sustainable transformation, resilience, and value creation in an increasingly complex digital environment.

Topics of interest include, but are not limited to, the following:

  • Design, implementation, and governance of AI-enabled and data-driven information systems;
  • Digital innovation, digital transformation, and platform-based ecosystems;
  • Management and strategic decision-making in complex digital environments;
  • AI, big data, and algorithmic governance in organizations and societies;
  • Complex networks, multi-agent systems, and digitally enabled organizational coordination;
  • Data governance, data trust, and ethical challenges in digital systems;
  • Digital innovation in business, e-commerce, public services, and cross-sector contexts;
  • Organizational resilience, sustainability, and green digital transformation.

We welcome original empirical studies, theoretical and conceptual contributions, meta-analyses, and in-depth case studies from scholars working in the fields of Information Systems, management, economics, public administration, complexity science, or related disciplines. By integrating theoretical advances with insights from real-world digital innovation practices, this special issue seeks to advance the next generation of IS and management research.

Overall, this Special Issue aims to deepen the academic understanding of the complex and multi-dimensional interactions between AI-driven technological change, organizational and ecosystem governance, and digital value-creation in the evolving digital economy.

Dr. Peng Qin
Dr. Jiepeng Wang
Dr. Yufei Yuan
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly 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

  • information systems (IS)
  • digital innovation
  • complex systems and networks
  • data governance
  • strategic management
  • algorithmic governance
  • digital strategy
  • organizational resilience
  • green digital transformation
  • data trust
  • digital platforms

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Published Papers (2 papers)

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Research

25 pages, 2669 KB  
Article
Deterministic Data Governance in Hybrid Financial Architectures
by Sergiu-Alexandru Ionescu, Vlad Diaconita, Andreea-Oana Radu, Laurentiu Gabriel Dinca and Ioana Nagit
Electronics 2026, 15(8), 1716; https://doi.org/10.3390/electronics15081716 - 18 Apr 2026
Viewed by 463
Abstract
Today, financial institutions’ architecture does not rely on one single technology. Instead, it uses a multi-technology approach in order to cover modern requirements and, at the same time, remain relevant. It integrates technologies such as relational databases, Big Data for analysis, and Cloud [...] Read more.
Today, financial institutions’ architecture does not rely on one single technology. Instead, it uses a multi-technology approach in order to cover modern requirements and, at the same time, remain relevant. It integrates technologies such as relational databases, Big Data for analysis, and Cloud environments for distributed capacities within a complex data architecture. At the same time, due to European data governance regulations, governance mechanisms such as encryption, pseudonymization, and incremental versioning must be applied on each architectural layer in order to comply with strict European governance rules. In this study, the impact of data governance is assessed by applying these mechanisms from the data-ingestion level, using diverse data types such as structured, semi-structured, and unstructured data, across relational databases, Big Data analysis, and Cloud distributed systems. In doing so, metrics such as execution time, CPU, and memory usage are assessed in order to properly evaluate the impact of governance mechanisms on financial systems. The results show that governance can be successfully integrated, provided these mechanisms are embedded at the architectural level, ensuring that performance, scalability, and compliance are maintained across the entire processing pipeline. Full article
(This article belongs to the Special Issue Information Systems, Management, and Digital Innovation: Complexity, Integration, and Transformation)
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20 pages, 1396 KB  
Article
A Cascaded Framework for Vehicle Detection in Low-Resolution Traffic Surveillance Videos
by Tao Yu and Laura Sevilla-Lara
Electronics 2026, 15(5), 1119; https://doi.org/10.3390/electronics15051119 - 8 Mar 2026
Viewed by 670
Abstract
Traffic surveillance cameras, as core sensing devices in smart cities, are crucial for traffic management, violation detection, and autonomous driving. However, due to deployment constraints and hardware limitations, the videos they capture often suffer from low resolution and noise, leading to missed and [...] Read more.
Traffic surveillance cameras, as core sensing devices in smart cities, are crucial for traffic management, violation detection, and autonomous driving. However, due to deployment constraints and hardware limitations, the videos they capture often suffer from low resolution and noise, leading to missed and false detections in traditional object detection algorithms trained on high-resolution data. To address this issue, this study proposes a cascaded collaborative framework that integrates video super-resolution (VSR) and object detection for robust perception in low-quality traffic surveillance scenarios. First, a transformer-based VSR model with masked intra- and inter-frame attention (MIA-VSR) is employed to reconstruct temporally coherent high-resolution video sequences from degraded inputs. A domain-specific super-resolved dataset is subsequently constructed to train a lightweight one-stage detector (You Only Look One-level Feature, YOLOF) for efficient vehicle localisation. Extensive experiments on public datasets (REDS, Vimeo90k, UA-DETRAC) demonstrate that the proposed framework achieved a 56.89 mAP@0.5 on low-resolution UA-DETRAC, outperforming both direct low-resolution inference (39.17 mAP@0.5) and conventional fine-tuning strategies (45.70 mAP@0.5) by 17.72 and 11.19 points, respectively. These findings indicate that super-resolution-driven data reconstruction provides an effective pathway for mitigating feature degradation in low-quality surveillance environments, offering both theoretical insight and practical value for intelligent transportation perception systems. Full article
(This article belongs to the Special Issue Information Systems, Management, and Digital Innovation: Complexity, Integration, and Transformation)
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