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Systems
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Data-Driven Formation and Development of Business Ecosystems
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Data-Driven Formation and Development of Business Ecosystems

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

Deadline for manuscript submissions: 27 November 2026 | Viewed by 8456

Special Issue Editors

Dr. Tine Bertoncel

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Guest Editor
Faculty of Management, University of Primorska, Koper, Slovenia
Interests: Industry 4.0; Society 5.0; informatics; data mining; artificial intelligence
Prof. Dr. Maja Meško

E-Mail Website
Guest Editor
Department of Human Resources Systems, University of Maribor, Maribor, Slovenia
Interests: management; Industry 4.0; leadership; decision making; socio-technical systems; psychology in management; occupational health and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in data-intensive fields, including data analytics, automation, and artificial intelligence are catalyzing a profound transformation across all industries. The integration of computational tools and AI-powered platforms is redefining traditional operational pipelines, enabling unprecedented levels of efficiency, complexity, and innovation in the creation of products and services. This technological shift is not just an operational change but is fundamental to the formation and development of new data-driven business ecosystems. These emerging ecosystems foster novel forms of collaboration, value creation, and entrepreneurship by connecting technical implementations with their wider economic and strategic implications. Ultimately, the focus is on understanding how these intelligent, data-centric workflows are actively shaping the future of production, commerce, and business models across the global economic landscape.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Automation and algorithmic thinking in workflows.
  • AI-assisted development and custom tooling.
  • Data visualization and simulation.
  • Generative AI and business ecosystems.
  • Data-driven business models and value chains.
  • AI's impact on education and skill development.

We look forward to receiving your contributions. 

Dr. Tine Bertoncel
Prof. Dr. Maja Meško
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-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 analytics
  • artificial intelligence
  • business ecosystems
  • business models
  • value creation

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Published Papers (4 papers)

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Research

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21 pages, 423 KB  
Article
AI-Enabled Super Apps as Complex Socio-Technical Ecosystems: A Systemic View of User Continuance
by Heetae Yang and Hwansoo Lee
Systems 2026, 14(5), 586; https://doi.org/10.3390/systems14050586 - 20 May 2026
Viewed by 314
Abstract
Super apps have emerged as complex digital service ecosystems that integrate multiple heterogeneous services within a unified platform architecture. As artificial intelligence (AI) capabilities become increasingly embedded into these platforms, understanding how AI-enabled features influence user evaluations has become an important research issue. [...] Read more.
Super apps have emerged as complex digital service ecosystems that integrate multiple heterogeneous services within a unified platform architecture. As artificial intelligence (AI) capabilities become increasingly embedded into these platforms, understanding how AI-enabled features influence user evaluations has become an important research issue. This study develops a new research model by extending the stimulus–organism–response (SOR) framework to examine the determinants of users’ continuance intention toward super apps. Specifically, performance efficacy, service efficiency, and perceived security are conceptualized as stimulus factors. Satisfaction is modeled as the organism variable; and continuance intention represents the behavioral response. In addition, this study conceptualizes AI system capability as a platform-level capability that enables the integration, adaptation, and personalization of heterogeneous services. It examines both its direct effect on user satisfaction and its moderating role in the relationships between functional affordances and satisfaction. Based on survey data collected from 614 super-app users in South Korea, the research model was analyzed using partial least squares structural equation modeling (PLS-SEM). The results reveal that performance efficacy and perceived security significantly influence user satisfaction, whereas service efficiency does not have a significant effect. Furthermore, AI system capability not only directly enhances user satisfaction but also strengthens the relationships between functional affordances and satisfaction. A multi-group analysis comparing financial and non-financial super apps shows that these effects vary depending on the service context. These findings contribute to the literature by conceptualizing AI as a system-level capability that both enables and enhances the realization of functional affordances in complex digital ecosystems. Full article
(This article belongs to the Special Issue Data-Driven Formation and Development of Business Ecosystems)
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41 pages, 3767 KB  
Article
Systemic Innovation Through Non-Dominant Firms: Dual-Path R–S–C Mechanisms in China’s Autonomous Driving Ecosystem
by Shaozhen Hong and Yingqi Liu
Systems 2026, 14(5), 558; https://doi.org/10.3390/systems14050558 - 14 May 2026
Viewed by 349
Abstract
How non-dominant specialized firms sustain systemic innovation influence in modular service ecosystems without occupying architectural control positions remains theoretically underdeveloped. This study develops a dual-path Resource–Strategy–Capability (R–S–C) mechanism framework to explain how structurally distinct network positions generate divergent innovation trajectories among non-dominant firms. [...] Read more.
How non-dominant specialized firms sustain systemic innovation influence in modular service ecosystems without occupying architectural control positions remains theoretically underdeveloped. This study develops a dual-path Resource–Strategy–Capability (R–S–C) mechanism framework to explain how structurally distinct network positions generate divergent innovation trajectories among non-dominant firms. The empirical analysis draws on large-scale patent collaboration network data from China’s autonomous driving industry, covering 26 hidden champion firms and 14 global leading enterprises across 2009–2023. The framework identifies two divergent pathways: firms occupying structural hole positions adopt specialization-deepening strategies that build module-anchoring capabilities, while firms with high betweenness centrality adopt T-shaped strategies that build interface-bridging capabilities—both enabling systemic influence without architectural control. To make the resource construct theoretically precise, the framework distinguishes four categories of network-derived resources operative in the R–S–C mechanism—informational, coordination, reputational, and module-definition resources—and specifies three microfoundational processes through which strategic orientation translates into capability: experiential learning, codification of routines, and legitimation through external recognition. Institutional policy environments moderate these mechanisms by reshaping network structural heterogeneity rather than directly driving firm outcomes. The study challenges the canonical prediction of structural hole theory by demonstrating that brokerage positions generate specialization deepening rather than scope expansion when absorptive capacity constraints are binding, extends service ecosystem theory by introducing non-dominant firm pathways to systemic value co-creation, and reframes institutional policy as a network-structural moderator with transferable implications beyond the Chinese context. Full article
(This article belongs to the Special Issue Data-Driven Formation and Development of Business Ecosystems)
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23 pages, 1822 KB  
Article
A System Model for Valuing Data Assets in Commercial Banks
by Hu Wang, Liangrong Song and Qingying Zong
Systems 2026, 14(1), 115; https://doi.org/10.3390/systems14010115 - 22 Jan 2026
Viewed by 996
Abstract
With the ongoing development of the digital economy, the productive function of data as an economic factor has become increasingly salient. Scientifically and rigorously assessing the value of data assets is essential for improving the national economic accounting system and promoting sustainable economic [...] Read more.
With the ongoing development of the digital economy, the productive function of data as an economic factor has become increasingly salient. Scientifically and rigorously assessing the value of data assets is essential for improving the national economic accounting system and promoting sustainable economic growth. In light of the limitations inherent in existing cost-based and market-based valuation approaches, this paper proposes a comprehensive valuation model that integrates the cost approach with the income approach and applies it to the commercial banking sector. Specifically, text analysis is employed to estimate human capital investment in data assets from the perspective of labor supply and demand, after which total costs are derived based on the proportion of human capital. An ARIMA model is used to forecast future cost inputs and net profits associated with data assets. Furthermore, the income-based approach is adopted to estimate the average present value of data assets, with the results of the two methods serving to validate each other. The comparison of estimation results under the cost approach and the income approach further validates the relationship between input and output in data assets. This also demonstrates that data assets follow the law of diminishing marginal utility, thereby contradicting the notion that data increases in value with greater usage. This study enriches the theoretical framework of data asset valuation, broadens its application scope, and provides meaningful guidance for advancing data asset accounting practices and related research. Full article
(This article belongs to the Special Issue Data-Driven Formation and Development of Business Ecosystems)
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Review

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25 pages, 3609 KB  
Review
Generative Artificial Intelligence and the Creative Industries: A Bibliometric Review and Research Agenda
by Mitja Bervar, Tine Bertoncel and Mirjana Pejić Bach
Systems 2026, 14(2), 138; https://doi.org/10.3390/systems14020138 - 29 Jan 2026
Cited by 1 | Viewed by 6074
Abstract
Generative artificial intelligence (GenAI) is increasingly transforming creative industries through its ability to generate high-quality content, raising critical questions about authorship, ownership, and the future of creative labor. This paper addresses these challenges by conducting a systematic bibliometric review of 119 peer-reviewed articles [...] Read more.
Generative artificial intelligence (GenAI) is increasingly transforming creative industries through its ability to generate high-quality content, raising critical questions about authorship, ownership, and the future of creative labor. This paper addresses these challenges by conducting a systematic bibliometric review of 119 peer-reviewed articles on GenAI in the creative sectors, published between 2023 and 2025. The study applies PRISMA 2020 guidelines and keyword co-occurrence analysis using VOSviewer to identify thematic clusters and map research trends. The central research question is how the academic literature conceptualizes the role and impact of GenAI within creative industries and how this has evolved over time. Findings reveal nine major thematic areas, ranging from technical implementations to ethical, economic, and institutional perspectives. The analysis shows that recent research emphasizes not only the technological capacities of GenAI, but also its implications for value creation, creative agency, and industry structures. The main contribution of the paper lies in offering a structured overview of current research trajectories, clarifying conceptual ambiguities, and highlighting understudied areas—particularly regarding the intersection of GenAI, platform economies, and labor dynamics. The review also identifies a methodological gap in comparative empirical studies and proposes directions for future research. By mapping the evolving discourse on GenAI in creative industries, this study contributes to both scholarly understanding and policy development. It provides a foundation for interdisciplinary inquiry and a forward-looking agenda for critically assessing GenAI’s role in reshaping creative work. Full article
(This article belongs to the Special Issue Data-Driven Formation and Development of Business Ecosystems)
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