Artificial Intelligence, Digital Transformation, and Organizational Performance: Emerging Trends in Management and Evaluation

Dear Colleagues,

The rapid integration of Artificial Intelligence (AI) and the ongoing wave of Digital Transformation (DT) have fundamentally altered the landscape of modern management. As organizations transition from traditional models to data-driven ecosystems, the mechanisms used to evaluate and enhance Organizational Performance must also evolve. This Special Issue aims to bridge the gap between technological advancement and strategic organizational outcomes.

1. Focus, Scope, and Purpose

a. Focus

The primary focus of this Special Issue is the intersection of AI-driven technologies and strategic management. We seek to explore how digital tools do not merely automate tasks but also redefine value creation, leadership roles, and decision-making processes.

b. Scope

The scope of this Issue is multidisciplinary. We welcome contributions that address the following:

• The impact of generative and predictive AI on organizational agility.
• Frameworks for measuring Digital Transformation maturity.
• The role of AI in enhancing human capital and performance management systems.
• Ethical governance and the "black box" challenge in algorithmic management.
• Sector-specific digital shifts (e.g., healthcare, manufacturing, or services).

c. Purpose

The purpose is to provide a comprehensive platform for scholars and practitioners to share high-quality research that evaluates the efficacy of digital shifts. By synthesizing theoretical frameworks with empirical data, we aim to establish new benchmarks for organizational excellence in the digital age.

2. Relationship with Existing Literature

While the existing literature has extensively covered the technical implementation of AI (Brynjolfsson & McAfee, 2014; Wagner et al., 2022) and the broad concepts of digital strategy (Bharadwaj et al., 2013; Hanelt et al., 2021; Ortner et al., 2025; Vial, 2019), there remains a critical “evaluation gap.” Much of the current research is siloed within either computer science or general management, which often treats AI as a “black box” driver of efficiency (Aderemi et al., 2025; Liu et al., 2025). Recent meta-reviews indicate that while organizational, technological, and social dimensions remain pivotal, emerging research from 2023 to 2025 highlights a measurement imbalance (Bean et al., 2026; Melão & Reis, 2023; Palazzo et al., 2025; Schryen et al., 2025). This Special Issue aims to address these gaps by integrating technical and behavioral perspectives, advancing dynamic capability theory (Cavusgil & Deligonul, 2025).

This Special Issue usefully supplements the field by:

• Integrating Technical and Behavioral Perspectives: Moving beyond “what” the technology is to “how” it changes organizational behavior and performance metrics.
• Addressing the Dynamic Capability Gap: Extending the work of Teece (2007) by investigating how AI acts as a micro-foundation for sensing and seizing digital opportunities.
• Empirical Validation: Providing data-driven insights into whether digital transformation actually leads to sustainable competitive advantage, countering the often-anecdotal nature of the early DT literature.

3. Submission Process

We request that prior to submitting a manuscript, interested authors first submit a proposed title and an abstract of 300–500 words summarizing their intended contribution. Please send it to the guest editors (Email: f.qureshi@uwtsd.ac.uk or fq1@bolton.ac.uk) or to Administrative Sciences Editorial Office (admsci@mdpi.com). Abstracts will be reviewed by the Guest Editors for the purposes of ensuring proper fit within the scope of the Special Issue. Full manuscripts will undergo double-blind peer-review.

4. References

• Aderemi, I. A., Kehinde, T. O., Ugochukwu, D. O., Ahmad, K. H., Adjei, K. Y., & Chijioke, C. E. (2025). Beyond the black box: a systematic review of explainable AI for transparent and trustworthy water quality monitoring. IEEE Sensors Reviews.
• Bean, T., Monfared, R., Segura-Velandia, D., & Fuller, L. (2026). To twin or not to twin, that is the question: evaluating the business justification for digital twins in legacy systems. Journal of Intelligent Manufacturing, 1–28.
• Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: toward a next generation of insights. MIS Quarterly, 471–482.
• Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
• Cavusgil, S. T., & Deligonul, S. Z. (2025). Dynamic capabilities framework and its transformative contributions. Journal of International Business Studies, 56(1), 33–42.
• Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of management studies, 58(5), 1159-1197.
• Liu, X., Huang, D., Yao, J., Dong, J., Song, L., Wang, H., ... & Chu, W. (2025). From Black Box to Glass Box: A Practical Review of Explainable Artificial Intelligence (XAI). AI, 6(11), 285.
• Melão, N., & Reis, J. (2026). Generative Artificial Intelligence in HRM Practice: Patterns, Profiles, and Theoretical Insights. Administrative Sciences, 16(3), 113.
• Ortner, T., Hautz, J., Stadler, C., & Matzler, K. (2025). Open strategy and digital transformation: A framework and future research agenda. International Journal of Management Reviews, 27(3), 324–345.
• Palazzo, F., Zambetta, G., & Palazzo, S. (2025). Artificial Intelligence at the Crossroads of Engineering and Innovation. Computing & AI Connect, 2(1), 1–10.
• Schryen, G., Marrone, M., & Yang, J. (2025). Exploring the scope of generative AI in literature review development.Electronic Markets, 35(1), 13.
• Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144.
• Wagner, G., Lukyanenko, R., & Paré, G. (2022). Artificial intelligence and the conduct of literature reviews. Journal of Information Technology, 37(2), 209–226.

Prof. Dr. Fayyaz Qureshi
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 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 double-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Administrative Sciences 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 1600 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.

• intelligence
• (AI) digital transformation
• organizational performance
• strategic management
• innovation & agility
• change management