BUSINESS INFORM JOURNAL.

УКР

ENG

Search:

REGISTRATION CERTIFICATE

KV #19905-9705 PR dated 02.04.2013.

FOUNDERS

RESEARCH CENTRE FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS (KHARKIV, UKRAINE)

According to the decision No. 802 of the National Council of Television and Radio Broadcasting of Ukraine dated 14.03.2024, is registered as a subject in the field of print media.
ID R30-03156

PUBLISHER

Liburkina L. M.

SITE SECTIONS

The AI-Enhanced Intelligent Business Diagnostics for Predictive Assessment of Organizational Resilience in Digital Transformation
Zvarych O. I., Kafka S. M.

Zvarych, Olena I., and Kafka, Sofiia M. (2025) “The AI-Enhanced Intelligent Business Diagnostics for Predictive Assessment of Organizational Resilience in Digital Transformation.” Business Inform 10:57–57.
https://doi.org/10.32983/2222-4459-2025-10-57-57

Section: Management and Marketing

Article is written in English
Downloads/views: 0

UDC 658.012.2:658.012.32:004.8

Abstract:
The aim of the article is to develop a theoretical conception for transforming organizational resilience through AI-mediated business diagnostics. Traditional theories of dynamic capabilities, adaptive capacity, and organizational learning do not explain organizations where algorithms make critical decisions, machines learn from experience, and artificial agents interact with humans. The article presents a systematic review of over 120 articles from leading journals (2015–2025), a conceptual analysis for the development of theoretical constructs, and a synthesis of dynamic capabilities theory, organizational learning, and computer science to create an integrative conception. The conclusion introduces «algorithmic reflexivity» – the organization’s ability to understand itself through computational processes that simultaneously shape organizational reality. Three paradoxes of AI-enhanced resilience have been identified: transparency through opacity (clarity through algorithmic inscrutability); autonomy through dependence (independence through technological reliance); stability through fluidity (changes generate meta-stability). A hybrid human-machine intelligence model with emergent properties has been developed. In addition, 13 empirically verified propositions related to organizational adaptation, transformation of managerial agency, and algorithmic competition have been formulated. Boundary conditions include digital infrastructure, cultural acceptability of algorithms, and scale thresholds. Empirical operationalization and new methodologies (computational ethnography, algorithmic audit) are needed. The practical significance of this article lies in the recommendation that organizations should develop algorithmic governance instead of direct control, invest in skills to create meaning for interpreting AI analytics, and design systems that enable human-machine interaction. Leaders evolve from decision-makers to creators and facilitators of collaborative work. The originality of this work is that organizational resilience is conceptualized, for the first time, as an emergent property of human-machine interaction rather than as a human capability. A new ontology of organizational knowledge is proposed, transcending the human-machine divide and theorizing hybrid intelligence as a result of integration. The paradoxical logic of AI-enhanced resilience challenges linear adaptation models and calls for a rethinking of management theory for the era of hybrid organizations.

Keywords: artificial intelligence, organizational resilience, business diagnostics, algorithmic reflexivity, strategic management, organizational development, digital transformation, managerial innovations.

Bibl.: 55.

Zvarych Olena I. – Doctor of Sciences (Economics), Associate Professor, Professor, Department of Management and Marketing, Vasyl Stefanyk Carpathian National University (57 Shevchenka Str., Ivano-Frankіvsk, 76018, Ukraine)
Email: [email protected]
Kafka Sofiia M. – Doctor of Sciences (Economics), Professor, Professor, Department of Management and Business Administration, Vasyl Stefanyk Carpathian National University (57 Shevchenka Str., Ivano-Frankіvsk, 76018, Ukraine)
Email: [email protected]

List of references in article

Agrawal A., Gans J. & Goldfarb A. Prediction Machines: The Simple Economics of Artificial Intelligence. Boston: Harvard Business Review Press.
Aguinis H., Gottfredson R. K. & Culpepper S. A. (2013). Best-Practice Recommendations for Estimating Cross-Level Interaction Effects Using Multilevel Modeling. Journal of Management, 6(39), 1490–1528. https://doi.org/10.1177/0149206313478188
Aguinis H. & Bakker R. M. (2021). Time is of the essence: Improving the conceptualization and measurement of time. Human Resource Management Review, 2(31). https://doi.org/10.1016/j.hrmr.2020.100763
Berends H. & Antonacopoulou E. (2014). Time and Organizational Learning: A Review and Agenda for Future Research. International Journal of Management Review, 4(16), 437–453. https://doi.org/10.1111/ijmr.12029
Brynjolfsson E. & McElheran K. (2016). The Rapid Adoption of Data-Driven Decision-Making. American Economic Review, 5(106), 133–139. https://doi.org/10.1257/aer.p20161016
Burrell J. (2016). How the machine ’thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 1(3). https://doi.org/10.1177/2053951715622512
Creswell J. W. & Plano Clark V. L. Designing and conducting mixed methods research. (3rd ed.). Thousand Oaks, CA: Sage.
Cyert R. M. & March J. G. A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice-Hall.
Daugherty P. R. & Wilson H. J. Human + machine: Reimagining work in the age of AI. Boston: Harvard Business Review Press.
Duan Y., Cao G. & Edwards J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research, 3(281), 673–686. https://doi.org/10.1016/j.ejor.2018.06.021
Duchek S. (2020). Organizational resilience: A capability-based conceptualization. Business Research, 13, 215–246. https://doi.org/10.1007/s40685-019-0085-7
Eisenhardt K. M. (1989). Building Theories from Case Study Research. Academy of Management Review, 4(14), 532–550. https://doi.org/10.5465/amr.1989.4308385
Faraj S., Pachidi S. & Sayegh K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 1(28), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005
Grieves M. & Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In: Transdisciplinary Perspectives on Complex Systems. Cham: Springer. https://doi.org/10.1007/978-3-319-38756-7_4
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, 5(58), 1159–1197. https://doi.org/10.1111/joms.12639
Hine C. Ethnography for the Internet: Embedded, Embodied and Everyday. London: Bloomsbury Academic.
Hinton G. (2018). Deep Learning – A Technology with the Potential to Transform Health Care. JAMA, 11(320), 1101–1102. https://doi.org/10.1001/jama.2018.11100
Hitt M. A., Beamish P. W., Jackson S. E. & Mathieu J. E. (2007). Building Theoretical and Empirical Bridges Across Levels: Multilevel Research in Management. Academy of Management Journal, 6(50), 1385–1399. https://doi.org/10.5465/amj.2007.28166219
Hofstede G. Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations. (2nd ed.). Thousand Oaks, CA: Sage.
House R. J., Hanges P. J., Javidan M., Dorfman P. W. & Gupta V. (Eds.) Culture, Leadership, and Organizations: The GLOBE Study of 62 Societies. Thousand Oaks, CA: Sage.
Iansiti M. & Lakhani K. R. Competing in the Age of AI: Strategy and Leadership when Algorithms and Networks Run the World. Boston: Harvard Business Review Press.
Introna L. D. (2016). Algorithms, Governance, and Governmentality: On Governing Academic Writing. Science, Technology, & Human Values, 1(41), 17–49. https://doi.org/10.1177/0162243915587360
Jarzabkowski P., Le J. K. & Van de Ven A. H. (2013). Responding to competing strategic demands: How organizing, belonging, and performing paradoxes coevolve. Strategic Organization, 3(11), 245–280. https://doi.org/10.1177/1476127013481016
Kaminski M. E. (2019). The Right to Explanation, Explained. Berkeley Technology Law Journal, 1(34), 189–218. https://doi.org/10.15779/Z38TD9N83H
Kellogg K. C., Valentine M. A. & Christin A. (2020). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 1(14), 366–410. https://doi.org/10.5465/annals.2018.0174
An L., Grimm V., Sullivan A. & Turner B. L. et al. (2021). Challenges, tasks, and opportunities in modeling agent-based complex systems. Ecological Modelling, 457. https://doi.org/10.1016/j.ecolmodel.2021.109685
Langley A. & Tsoukas H. Introduction: Process theorizing and perspectives on process and organization. In: The SAGE. Handbook of Process Organization Studies. London: SAGE Publications. https://doi.org/10.4135/9781473957954.n1
Levinthal D. A. & Marino A. (2015). Three Facets of Organizational Adaptation: Selection, Variety, and Plasticity. Organization Science, 3(26), 743–755. https://doi.org/10.1287/orsc.2014.0956
Linnenluecke M. K. (2017). Resilience in Business and Management Research: A Review of Influential Publications and a Research Agenda. International Journal of Management Reviews, 1(19), 4–30. https://doi.org/10.1111/ijmr.12076
Marcus G. & Davis E. Rebooting AI: Building artificial intelligence we can trust. New York: Pantheon.
Mikalef P., Boura M., Lekakos G. & Krogstie J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261–276. https://doi.org/10.1016/j.jbusres.2019.01.044
Mitleton-Kelly E., Paraskevas A. & Day C. (Eds.) Handbook of research methods in complexity science: Theory and applications. Cheltenham: Edward Elgar Publishing. https://doi.org/10.4337/9781785364426
Muller O., Fay M. & vom Brocke J. (2018). The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics. Journal of Management Information Systems, 2(35), 488–509. https://doi.org/10.1080/07421222.2018.1451955
Moeuf A., Pellerin R. & Lamouri S. et al. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research, 3(56), 1118–1136. https://doi.org/10.1080/00207543.2017.1372647
Nambisan S., Wright M. & Feldman M. (2019). The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Research Policy, 8(48). https://doi.org/10.1016/j.respol.2019.03.018
Orlikowski W. J. & Scott S. V. (2015). Exploring Material-Discursive Practices. Journal of Management Studies, 5(52), 697–705. https://doi.org/10.1111/joms.12114
Pasquale F. The Black Box Society: The secret Algorithms that Control Money and Information. Cambridge, MA: Harvard University Press. https://doi.org/10.4159/harvard.9780674736061
Pearl J. & Mackenzie D. The Book of Why: The New Science of Cause and Effect. New York: Basic Books.
Railsback S. F. & Grimm V. Agent-Based and Individual-Based Modeling: A Practical Introduction. (2nd ed.). Princeton: Princeton University Press.
Raisch S. & Krakowski S. (2021). Artificial Intelligence and Management: The Automation-Augmentation Paradox. Academy of Management Review, 1(46), 192–210. https://doi.org/10.5465/amr.2018.0072
Raji I. D., Smart A. & White R. N. et al. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 33–44. https://doi.org/10.1145/3351095.3372873
Ross J. W., Beath C. M. & Mocker M. Designed for Digital: How to Architect Your Business for Sustained Success. Cambridge, MA: The MIT Press.
Schad J., Lewis M.W., Raisch S. & Smith W.K. (2016). Paradox research in management science: Looking back to move forward. Academy of Management Annals, 1(10), 5-64. https://doi.org/10.5465/19416520.2016.1162422
Schmidhuber J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
Selbst A. D. & Powles J. (2017). Meaningful information and the right to explanation. International Data Privacy Law, 4(7), 233–242. https://doi.org/10.1093/idpl/ipx022
Shrestha Y. R., Ben-Menahem S. M. & von Krogh G. (2019). Organizational Decision-Making Structures in the Age of Artificial Intelligence. California Management Review, 4(61), 66–83. https://doi.org/10.1177/0008125619862257
Sturm T., Gerlach J. P. & Pumplun L. et al. (2021). Coordinating human and machine learning for effective organizational learning. MIS Quarterly, 3(45), 1581–1602. https://doi.org/10.25300/MISQ/2021/16543
Teece D., Pisano G. & Shuen A. (1997). Dynamic Capabilities and Strategic Management. Strategic Management Journal, 7(18), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Teece D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 13(28), 1319–1350. https://doi.org/10.1002/smj.640
Teece D. J., Peteraf M. & Leih S. (2016). Dynamic Capabilities and Organizational Agility: Risk, Uncertainty, and Strategy in the Innovation Economy. California Management Review, 4(58), 13–35. https://doi.org/10.1525/cmr.2016.58.4.13
Vial G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 2(28), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
von Krogh G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409. https://doi.org/10.5465/amd.2018.0084
Wamba S. F., Gunasekaran A., Akter S. & Ren S. J. F. et al. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
Williams T. A., Gruber D. A. & Sutcliffe K. M. et al. (2017). Organizational Response to Adversity: Fusing Crisis Management and Resilience Research Streams. Academy of Management Annals, 2(11), 733–769. https://doi.org/10.5465/annals.2015.0134
Zuboff S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: PublicAffairs.

FOR AUTHORS

License Contract

Conditions of Publication

Article Requirements

Regulations on Peer-Reviewing

Publication Contract

Current Issue

Frequently asked questions

INFORMATION

The Plan of Scientific Conferences

OUR PARTNERS

Journal «The Problems of Economy»

The site and its metadata are licensed under CC BY-SA.

Write to webmaster