Digital Business Systems for Entrepreneurial Innovation and Strategic Development

A Special Issue of Systems (ISSN 2079-8954) belonging to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 716

Editors


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Guest Editor
ISCA-UA, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Interests: strategic marketing; digital transformation; dynamic capabilities; innovation; digital twins; creativity; business models

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Guest Editor
ESTGA-UA, Universidade de Aveiro, Rua Comandante Pinho e Freitas 28, 3750-127 Águeda, Portugal
Interests: artificial intelligence; machine learning; data science; business intelligence; blockchain; human–computer interaction; education and technology

Special Issue Information

Dear Colleagues,

We kindly invite you to submit articles to our Special Issue, including conceptual and empirical studies. Please find below the background and the aim of this Special Issue, as well as the themes we are interested in.

Digital technologies are shaping how organizations organize, coordinate and innovate. The integration of artificial intelligence, data infrastructures, platforms and interconnected digital architectures has led to the emergence of digital business systems that operate across organizational and institutional boundaries (Bharadwaj et al., 2013; Yoo et al., 2012; Verhoef et al., 2021). Digital business systems refer to integrated configurations of digital infrastructures, organizational capabilities and multi-actor coordination mechanisms through which entrepreneurial innovation and value creation are achieved, reflecting the increasing relevance of ecosystem-based and platform-enabled forms of organizing (Adner, 2017; Jacobides et al., 2018; Compagnucci et al., 2025; Francksen et al., 2025).

Research on digital transformation examined changes in business models, organizational processes and strategic alignment. Digital transformation is understood as a set of changes affecting technologies, structures and organizational practices, with implications for competitiveness and value creation (Vial, 2019; Verhoef et al., 2021). More recent studies extend this perspective by looking at digital transformation in relation to data-driven capabilities, platform structures and organizational redesign (e.g., Hess, 2024; Olan et al., 2024; Hajli et al., 2025).

Within the strategy and innovation literature, the dynamic capabilities perspective helps analyze how organizations adapt to changing environments. These capabilities refer to the ability of organizations to sense opportunities, seize them and reconfigure resources accordingly (Teece et al., 1997; Teece, 2007; Helfat et al., 2007). Subsequent research has examined micro-foundations and capability development across levels (Eisenhardt and Martin, 2000; Barreto, 2010), including recent work linking these capabilities to digital transformation contexts (Leso et al., 2024; Rummel et al., 2022) and foundational capabilities and change readiness as conditions for transformation (Dirnböck et al., 2025).

At the same time, research on emerging digital technologies such as digital twins developed along separate trajectories. Digital twins are defined as virtual representations of physical entities that enable monitoring, simulation and performance evaluation (Tao et al., 2019). Recent contributions start addressing this gap by examining digital twins in relation to business models, platform integration as well as enablers of innovation and cross-organizational coordination (e.g., Fan et al., 2026; Jacobson et al., 2026; Wang et al., 2026; Hamadi et al., 2026).

However, the literature remains fragmented across disciplines, with a limited integration of technological, organizational and ecosystem perspectives. This Special Issue addresses this gap by focusing on digital business systems as units of analysis and examining how they enable entrepreneurial innovation and strategic development across organizational and institutional levels.

This Special Issue aims to advance theoretical and empirical knowledge on how digital business systems influence entrepreneurial innovation and strategic development.

Building on prior research on digital transformation, dynamic capabilities and digital innovation, we are looking for a system-level perspective in which innovation outcomes emerge from the interaction between technological infrastructures, organizational capabilities and multi-actor coordination mechanisms.

We seek contributions that look at digital transformation as a system-based process, analyze how digital infrastructures support innovation, explore how organizations develop capabilities and address cross-organizational coordination and ecosystem dynamics.

Contributions are expected to address interactions, interdependencies and multi-level dynamics within digital business systems. These include, but are not limited to, the following themes:

Digital Transformation and System-Level Analysis

  • Digital transformation as a multi-level and system-based process
  • Interaction between digital infrastructures and organizational structures
  • Strategic alignment in digitally mediated environments
  • Organizational architectures and system integration

Dynamic Capabilities and Foundational Capabilities

  • Dynamic capabilities in digital contexts
  • Foundational capabilities and change readiness
  • Micro-foundations of capability development
  • Capability deployment across organizational levels

Entrepreneurial Innovation and Digital Systems

  • Digital entrepreneurship and opportunity recognition
  • Innovation processes in digitally mediated environments
  • Start-ups and scale-ups within digital ecosystems
  • Corporate entrepreneurship and digital venturing

Digital Business Models, Innovation and Value Creation

  • Business model innovation enabled by digital systems
  • Data-driven and platform-based business models
  • Value creation and capture mechanisms
  • Product–service systems and hybrid offerings

AI, Data, and Smart Decision Systems

  • AI-supported decision-making processes
  • Data infrastructures and analytics capabilities
  • Human–AI interaction in organizational contexts
  • Governance and transparency of algorithmic systems

Digital Ecosystems and Platform Governance

  • Platform governance and orchestration
  • Multi-actor coordination and ecosystem dynamics
  • Inter-organizational systems and networked innovation
  • Institutional and regulatory dimensions of digital systems

Digital Twins and Innovation

  • Digital twins as components of digital business systems
  • Digital-twin-enabled innovation within and across organizations
  • Integration of digital twins with AI and data infrastructures
  • Business models and value creation mechanisms based on digital twins

Human, Organizational and Societal Dimensions

  • Skills, competencies and digital work systems
  • Organizational culture and transformation processes
  • Leadership and governance in digital environments
  • Ethical and societal implications of digital systems

References:

Adner, R. (2017). Ecosystem as structure: An actionable construct for strategy. Journal of Management, 43(1), 39–58.

Barreto, I. (2010). Dynamic capabilities: A review of past research and an agenda for the future. Journal of Management, 36(1), 256–280.

Bharadwaj, A.; El Sawy, O.A.; Pavlou, P.A.; Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482.

Cho, H., & Cho, K. (2025). Impact of Security Management Activities on Corporate Performance. Systems, 13(8), 633.

Compagnucci, L., Spigarelli, F., Sernani, P., Frontoni, E., & Seri, P. (2025). A systematic literature review of business-to-business platforms for the digital transformation of the manufacturing industry: taking stock and advancing through research. Technovation, 148, 103330.

Dirnböck, M.; Madaleno, M.; Saur-Amaral, I.; Au-Yong-Oliveira, M. (2025). Dynamic Capabilities and Change Readiness: A Systematic Literature Review. ISPIM Norway.

Eisenhardt, K.M.; Martin, J.A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10–11), 1105–1121.

Fan, Y., Assimakopoulos, D., Carayannis, E., & Wang, J. (2026). Digital innovation capabilities: a systematic review, synthesis and research agenda. Technovation, 152, 103496.

Francksen, S., Ghaziani, S., & Bahrs, E. (2025). Digital Maturity of Administration Entities in a State-Led Food Certification System Using the Example of Baden-Württemberg. Foods, 14(11), 1870.

Gong, C.; Ribiere, V. (2021). Developing a unified definition of digital transformation. Technovation, 102, 102217.

Gupta, R., Mejia, C., & Kajikawa, Y. (2019). Business, innovation and digital ecosystems landscape survey and knowledge cross sharing. Technological Forecasting and Social Change, 147, 100–109.

Hajli, N., Baydarova, I., & Nisar, T. (2025). Digital entrepreneurial ecosystem: the role of the sharing economy in driving innovation. Entrepreneurship and Regional Development, 37(5-6), 785–815.

Hamadi, I. (2026). Toward a meta-framework for digital ecosystem concepts: A comparative review on the state of research, concept relationships, and future directions. Technological Forecasting and Social Change, 223, 124413.

Helfat, C.E.; Finkelstein, S.; Mitchell, W.; Peteraf, M.; Singh, H.; Teece, D.J.; Winter, S.G. (2007). Dynamic Capabilities: Understanding Strategic Change in Organizations. Blackwell.

Hess, T., Riedl, R., & Becker, L. (2024). Digital Business as a Field for Research and Education. Electronic Markets, 34(1), 46.

Hu, B., Yang, W., Zhang, S., Yan, S., & Xiang, Y. (2025). Linking the top management team transactive memory system, strategic flexibility and digital business model innovation: a dynamic capabilities perspective. Technology Analysis & Strategic Management, 37(11), 1621–1633.

Jacobides, M.G.; Cennamo, C.; Gawer, A. (2018). Towards a theory of ecosystems. Strategic Management Journal, 39(8), 2255–2276.

Jacobson, N.G.; Saur-Amaral, I.; Martins, C.; Torres, D.F.M. (2026). Digital Twin-Enabled Business Innovation Within and Beyond the Firm: A Systematic Literature Review and Innovation Typology. Systems (in review–reference to be completed).

Leso, B., Cortimiglia, M., Ghezzi, A., & Minatogawa, V. (2024). Exploring digital transformation capability via a blended perspective of dynamic capabilities and digital maturity: a pattern matching approach. Review of Managerial Science, 18(4), 1149–1187.

Olan, F., Troise, C., Damij, N., & Newbery, R. (2024). Refocusing digital entrepreneurship: an updated overview of the field, emerging opportunities and challenges. International Journal of Entrepreneurial Behavior & Research, 30(2/3), 238–257.

Rummel, F., Hüsig, S., & Steinhauser, S. (2022). Two archetypes of business model innovation processes for manufacturing firms in the context of digital transformation. R & D Management, 52(4), 685–703.

Tao, F.; Zhang, H.; Liu, A.; Nee, A.Y.C. (2019). Digital twin in industry: State-of-the-art. Journal of Manufacturing Systems, 51, 1–15.

Teece, D.J. (2007). Explicating dynamic capabilities: The nature and microfoundations of enterprise performance. Strategic Management Journal, 28(13), 1319–1350.

Teece, D.J.; Pisano, G.; Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

Ursic, D., & Cater, T. (2025). Digital innovation in management and business: A comprehensive review, multi-level framework, and future research agenda. Journal of Business Research, 197, 115475.

Verhoef, P.C.; Broekhuizen, T.; Bart, Y.; Bhattacharya, A.; Dong, J.Q.; Fabian, N.; Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901.

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118–144.

Wang, C., & Thai, M. (2026). Beyond digital anxiety: Leveraging digital platform capability and entrepreneurial orientation for SME digital business model innovation. Journal of Innovation & Knowledge, 15, 975–975, 100975.

Yoo, Y.; Boland, R.J.; Lyytinen, K.; Majchrzak, A. (2012). Organizing for innovation in the digitized world. Organization Science, 23(5), 1398–1408.

Prof. Dr. Irina Saur-Amaral
Prof. Dr. Ciro Martins
Guest Editors

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Keywords

  • digital business systems
  • digital transformation
  • entrepreneurial innovation
  • dynamic capabilities
  • business model innovation
  • digital ecosystems
  • digital twins
  • strategic development

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Published Papers (1 paper)

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25 pages, 9899 KB  
Article
Unveiling the Black Box: Nonlinear Effects of Digital Transformation on Financial Distress with XGBoost-SHAP Model
by Guhao Zhang, Hao Yang, Chenkai Wang, Zhipeng Zhou and Shilei Hu
Systems 2026, 14(8), 905; https://doi.org/10.3390/systems14080905 - 1 Aug 2026
Viewed by 280
Abstract
Existing research lacks consensus on how digital transformation affects financial distress, and traditional linear models struggle to capture complex variable dynamics. This study examines the link from prediction and association perspectives by constructing a high-precision financial risk prediction model and using explainable methods [...] Read more.
Existing research lacks consensus on how digital transformation affects financial distress, and traditional linear models struggle to capture complex variable dynamics. This study examines the link from prediction and association perspectives by constructing a high-precision financial risk prediction model and using explainable methods to explore feature contributions. Using 2010–2024 data from Chinese A-share listed companies, we apply Logit regression to assess the association between digital transformation and financial distress, and introduce an XGBoost model with SHAP for prediction and interpretability. Results show that XGBoost outperforms Logit (AUC, PR_AUC). Logit regression reveals a negative correlation between digital transformation and distress probability, which SHAP further supports. Higher digitalization corresponds to lower predicted risk, with non-linear characteristics: at low levels, marginal contributions fluctuate; beyond a threshold, the mitigation effect stabilizes. Feature importance identifies the asset-liability ratio as the top risk factor, while digital transformation and operating profit margin serve as key protective features. This paper contributes by constructing a digital transformation index and integrating it into the financial distress prediction model, comparing traditional econometric models with machine learning models, and using SHAP to provide an interpretable framework for understanding the digital transformation–financial distress association. Full article
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