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Systems
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Advancing Open Innovation in the Age of AI and Digital...
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Advancing Open Innovation in the Age of AI and Digital Transformation

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2049

Special Issue Editors

Prof. Dr. Jun Li

E-Mail Website
Guest Editor
SP Jain London School of Management, 2 Harbour Exchange Square, London E14 9GE, UK
Interests: entrepreneurship; innovation management; SMEs; emerging economies; creative problem solving; research methods
Dr. Sadia Riaz

E-Mail Website
Guest Editor
SP Jain London School of Management, 2 Harbour Exchange Square, London E14 9GE, UK
Interests: technology management; disruptive innovation; AI frameworks and architecture; innovation management
Dr. Sawsan Malik

E-Mail Website
Guest Editor
Faculty of Business Studies, Arab Open University, Ardiya Industrial Area, Al-Safat 13033, Kuwait
Interests: entrepreneurship; smart city management; informal economy; theory of planned behaviour; SMEs; Business creation and startups; entrepreneurship education; innovation; research methods
Dr. Shijie Zhang

E-Mail Website
Guest Editor
Business School, Xiangtan University, Xiangtan 411105, China
Interests: big data marketing; consumer behavior analysis; AI empowerment for the growth of small and medium-sized enterprises; “AI + scenarios” and innovation and entrepreneurship ecosystems

Special Issue Information

Dear Colleagues,

The digital era has fundamentally transformed how organizations innovate, collaborate, and create value. Artificial intelligence (AI), data analytics, and platform technologies are redefining the boundaries of openness, shifting open innovation from a firm-centric process to a networked, data-driven, and algorithmically mediated phenomenon. In this evolving context, traditional models of open innovation must be re-examined through the lenses of digital transformation, marketing innovation, and information systems.

This Special Issue aims to advance theoretical and empirical understanding of how AI and digital transformation are reshaping open innovation processes, strategies, and outcomes across industries, organizational forms, and ecosystems. It welcomes interdisciplinary submissions that bridge insights from marketing, information systems, innovation management, and public policy. Conceptual, empirical, methodological, and case-based studies are equally encouraged.

This Special Issue seeks to attract contributions under (but not limited to) the following thematic clusters:

  1. Customer-Centric Open Innovation

Indicative topics include:

  • Digital co-creation, crowdsourcing, and participatory innovation;
  • AI-driven customer insights and predictive innovation;
  • Brand communities and innovation contests;
  • Human–AI collaboration in marketing creativity;
  • Open innovation for sustainable and responsible marketing.
  1. AI-Enabled Innovation Systems

Indicative topics include:

  • AI and machine learning for knowledge discovery and innovation intelligence;
  • Generative AI as a creative partner in product and service innovation;
  • Algorithmic openness, transparency, and accountability;
  • Data-driven innovation and dynamic capabilities;
  • AI-augmented decision-making in innovation management.
  1. Digital Platforms and Ecosystem Orchestration

Indicative topics include:

  • Platform business models and innovation ecosystems;
  • Governance and boundary resources (APIs, SDKs);
  • Data sharing and interoperability across platforms;
  • Digital intermediaries and cross-industry innovation;
  • Orchestration of open innovation networks.
  1. Data Governance, Trust, and Digital Ethics

Indicative topics include:

  • Data governance for open innovation ecosystems;
  • Privacy, security, and consent in AI-driven innovation;
  • Blockchain and distributed trust mechanisms;
  • Intellectual property, openness, and knowledge protection;
  • Ethical AI and responsible openness.
  1. Organizational Capabilities and Digital Transformation

Indicative topics include:

  • IT and IS capabilities for open innovation;
  • Digital transformation and organizational ambidexterity;
  • Leadership and absorptive capacity in open innovation contexts;
  • SMEs and startups in digital innovation ecosystems;
  • Dynamic capabilities for managing openness.
  1. Policy, Society, and the Future of Openness

Indicative topics include:

  • National and regional policies for AI and open innovation;
  • Open science, open data, and public–private knowledge sharing;
  • Socio-technical systems and digital inclusion;
  • Sustainability and societal impacts of openness;
  • The future of human–AI collaboration in innovation.

We invite scholars to submit their research to this Special Issue, which illuminates the emerging logics, capabilities, and governance mechanisms that underpin open innovation in the digital era.

Prof. Dr. Jun Li
Dr. Sadia Riaz
Dr. Sawsan Malik
Dr. Shijie Zhang
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

  • open innovation
  • Artificial Intelligence (AI)
  • digital transformation
  • innovation ecosystems
  • co-creation
  • platform economy
  • data governance
  • human–AI collaboration
  • organizational capabilities
  • sustainability

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

36 pages, 8897 KB  
Article
Evolutionary Game Analysis of AI-Generated Disinformation Governance on UGC Platforms Based on Prospect Theory
by Licai Lei, Yanyan Wu and Shang Gao
Systems 2026, 14(4), 416; https://doi.org/10.3390/systems14040416 (registering DOI) - 9 Apr 2026
Abstract
While Generative Artificial Intelligence technology empowers content production on user-generated content platforms, it also gives rise to novel risks of disinformation dissemination. The effective governance of these risks is critical to ensuring the cybersecurity of the online ecosystem and maintaining long-term social stability. [...] Read more.
While Generative Artificial Intelligence technology empowers content production on user-generated content platforms, it also gives rise to novel risks of disinformation dissemination. The effective governance of these risks is critical to ensuring the cybersecurity of the online ecosystem and maintaining long-term social stability. To address the collaborative governance dilemma, this study constructs a tripartite “platform-user-government” evolutionary game model based on prospect theory. It explores the evolutionarily stable strategies and stability conditions of each actor, supplemented by numerical simulations and practical case validation. The results indicate that: (1) under specific conditions, the system can converge to an ideal equilibrium {active platform governance, engaged user participation, stringent government supervision}; (2) the government’s reward–penalty mechanisms can drive the system towards this ideal equilibrium; (3) users’ digital literacy is a key variable influencing the system’s evolutionary path; (4) both the risk preference coefficient (β) and loss aversion coefficient (λ) from prospect theory have a significant moderating effect on the system’s evolution. Finally, targeted recommendations are proposed for the three aforementioned stakeholders to accelerate the improvement of China’s collaborative governance of the content ecosystem. Full article
(This article belongs to the Special Issue Advancing Open Innovation in the Age of AI and Digital Transformation)
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28 pages, 1460 KB  
Article
Firms’ Structural Positions in Patent Citation Networks and Innovation Performance: Evidence from a Large-Scale Chinese Dataset
by Yan Qiao and Siyu Wang
Systems 2026, 14(4), 351; https://doi.org/10.3390/systems14040351 - 25 Mar 2026
Viewed by 349
Abstract
Using a panel of Chinese A-share listed companies from 2007 to 2022, this study examines how firms’ structural positions in patent citation networks affect innovation efficiency. We construct a firm-level patent citation network and use betweenness centrality to capture firms’ brokerage-oriented positions in [...] Read more.
Using a panel of Chinese A-share listed companies from 2007 to 2022, this study examines how firms’ structural positions in patent citation networks affect innovation efficiency. We construct a firm-level patent citation network and use betweenness centrality to capture firms’ brokerage-oriented positions in knowledge flows. Based on firm- and year-fixed-effects models, instrumental-variable estimation, and robustness checks, we find that stronger brokerage positions significantly improve innovation efficiency. Mechanism analyses show that this effect operates through two channels: cross-domain knowledge recombination and organizational boundary spanning. Firms in stronger brokerage positions are more likely to access technologically heterogeneous external knowledge and interact with a wider range of external knowledge-bearing entities, thereby improving the efficiency with which innovation inputs are transformed into patent-based outputs. We further find that digital transformation negatively moderates the relationship between brokerage centrality and innovation efficiency. This suggests that digital transformation reduces firms’ marginal dependence on external brokerage positions by strengthening internal data-processing, coordination, and knowledge-integration capabilities. Additional analyses show that the positive effect of brokerage centrality is broadly shared across ownership groups. Regional heterogeneity is more evident in the stronger brokerage premium observed in the western region than in the eastern region. Full article
(This article belongs to the Special Issue Advancing Open Innovation in the Age of AI and Digital Transformation)
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26 pages, 2187 KB  
Article
How Does Digital Transformation Affect Cross-Regional Collaborative Innovation: Evidence from A-Share Listed Firms
by Binyu Wei, Xiaoyu Hu, Yushan Wang and Guanghui Wang
Systems 2026, 14(4), 337; https://doi.org/10.3390/systems14040337 - 24 Mar 2026
Viewed by 215
Abstract
This study utilizes digital transformation and patent data from A-share listed companies on the Shanghai and Shenzhen stock exchanges in China between 2011 and 2021 to examine the influence of digital transformation on the quality of cross-regional collaborative innovation. The findings reveal that [...] Read more.
This study utilizes digital transformation and patent data from A-share listed companies on the Shanghai and Shenzhen stock exchanges in China between 2011 and 2021 to examine the influence of digital transformation on the quality of cross-regional collaborative innovation. The findings reveal that the cooperative innovation network exhibits pronounced small-world characteristics. In terms of spatio-temporal evolution, China’s urban collaborative innovation network demonstrates a notable quadrilateral spatial structure and has evolved toward a multicenter pattern. Moreover, the advancement of digital transformation positively contributes to both the quality and quantity of cross-regional cooperative innovation. By enhancing the relational embeddedness among cities, digital transformation facilitates improved outcomes in collaborative innovation. Furthermore, when the volume of digital patent applications surpasses a certain threshold, its positive effect on the quality of cross-regional collaborative innovation accelerates. These results provide empirical evidence from a major emerging economy, offering insights that can inform policies and strategies in other regions undergoing digital transition. The mechanisms identified, such as network structure evolution and relational embeddedness, contribute to a broader understanding of how digital transformation shapes innovation dynamics across geographical boundaries in a globalized knowledge economy. Full article
(This article belongs to the Special Issue Advancing Open Innovation in the Age of AI and Digital Transformation)
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35 pages, 1076 KB  
Article
Digital Transformation in SMEs: Governance Performance Mediated by AI-Enabled Analytics and Process Integration
by Sultan Bader Aljehani, Khalid Waleed Ahmed Abdo, Imdadullah Hidayat-ur-Rehman, Doaa Mohamed Ibrahim Badran and Mahmoud Abdelgawwad Abdelhady
Systems 2026, 14(3), 324; https://doi.org/10.3390/systems14030324 - 18 Mar 2026
Viewed by 487
Abstract
Digital transformation has become important for SMEs that want better control, transparency, and coordinated operations. Yet, many studies treat digital tools in isolation and do not explain how AI and big data capabilities, together with process integration, drive governance outcomes. This gap limits [...] Read more.
Digital transformation has become important for SMEs that want better control, transparency, and coordinated operations. Yet, many studies treat digital tools in isolation and do not explain how AI and big data capabilities, together with process integration, drive governance outcomes. This gap limits a clear understanding of how digital transformation supports governance performance in SMEs. This study examines how digital transformation (DT) influences digital governance performance (DGP) in SMEs, with AI and big data analytical capability (AIBDAC) and process integration capability (PIC) as mediators. The research is grounded in the Resource-Based View, Dynamic Capabilities Theory, and the Technology Organization Environment framework. Data were collected from SMEs across five regions of Saudi Arabia using cluster and purposive sampling to target employees and managers involved in digital, analytical, and process integration work. A total of 396 valid responses were included in the analysis. Partial Least Squares Structural Equation Modelling (PLS SEM) was used to assess the measurement model, test the hypothesized paths, and evaluate mediation and moderation effects. The findings show that DT, AIBDAC, PIC, and top management support (TMS) have significant direct effects on DGP. AIBDAC and PIC act as key mediators, fully transmitting the effects of digital innovation capability and strategic readiness and partially mediating the effects of DT and TMS. Multi-group analysis shows that small and medium-large firms rely on different capability combinations. The study contributes by explaining how SMEs strengthen governance through capability development and offers practical guidance for improving governance through digital transformation. Full article
(This article belongs to the Special Issue Advancing Open Innovation in the Age of AI and Digital Transformation)
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26 pages, 12179 KB  
Article
Analysis of Influencing Factors and Prediction of Provincial Energy Poverty in China Based on Explainable Deep Learning
by Zihao Fan, Pengying Fan and Yile Wang
Systems 2026, 14(3), 319; https://doi.org/10.3390/systems14030319 - 17 Mar 2026
Viewed by 334
Abstract
Energy poverty remains an important challenge for sustainable development in China, with pronounced regional disparities and evolving temporal dynamics that require accurate and interpretable prediction tools. This study develops a provincial panel-based framework that combines Energy Poverty Index (EPI) construction, SSA-LSTM prediction, SHAP-based [...] Read more.
Energy poverty remains an important challenge for sustainable development in China, with pronounced regional disparities and evolving temporal dynamics that require accurate and interpretable prediction tools. This study develops a provincial panel-based framework that combines Energy Poverty Index (EPI) construction, SSA-LSTM prediction, SHAP-based model interpretation, and two-way fixed effects (TWFE) regression analysis. Using provincial data for China (2003–2022), we first construct a composite EPI with the entropy weight method, then apply a Sparrow Search Algorithm (SSA) to optimize LSTM hyperparameters for EPI forecasting. SHAP is used to interpret feature contributions to model-predicted EPI, and TWFE regression is used to provide complementary panel-data evidence on factor–EPI associations. The results show that the SSA-LSTM model outperforms benchmark machine learning and deep learning models in out-of-sample prediction performance. SHAP-based interpretation indicates that variables such as GDP, energy intensity, and power generation per capita contribute strongly to prediction variation, with notable regional heterogeneity. TWFE results are broadly consistent with several key patterns identified in the SHAP analysis. Overall, the proposed framework provides an accurate and interpretable provincial energy poverty prediction approach and offers a useful empirical reference for energy poverty monitoring and policy discussion. Full article
(This article belongs to the Special Issue Advancing Open Innovation in the Age of AI and Digital Transformation)
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