Synthetic Creativity and the Recomposition of Brand Value: A Dynamic Capability Perspective on Generative AI Intensification
DOI:
https://doi.org/10.66203/manexia.01204Keywords:
synthetic creativity, brand value differentiation, dynamic capabilities, exploration–exploitation, organizational learning, symbolic governanceAbstract
The diffusion of generative AI challenges foundational assumptions in branding and strategic management. Classical brand theory conceptualizes differentiation as the outcome of human-authored creativity, while dynamic capabilities scholarship emphasizes costly exploration and managerial orchestration as the basis of sustained advantage. Generative AI disrupts these premises by enabling large-scale probabilistic recombination of symbolic content at marginal cost. This article advances a conceptual re-theorization of creativity under such conditions by introducing synthetic creativity as an orchestrated dynamic capability through which firms govern probabilistic generative systems to sustain symbolic differentiation. Integrating dynamic capabilities theory, exploration–exploitation logic, organizational learning, and AI research, a mechanism-based model is developed to explain how generative AI intensification produces non-linear effects on brand value. Three interdependent mechanisms are specified: exploration compression, capability substitution versus augmentation, and metric-driven symbolic over-optimization. Together, these mechanisms generate a curvilinear relationship between AI intensification and brand differentiation, moderated by data heterogeneity, governance architecture, and infrastructural control. By shifting analytical focus from AI adoption to orchestration asymmetry, the study reconceptualizes competitive advantage in symbolic markets as dependent on governance of probabilistic infrastructures rather than on ideational originality. The framework establishes theoretical foundations for examining strategic differentiation in the generative economy.
References
Aaker, D. A. (1996). Building strong brands. Free Press.
Amabile, T. M. (1996). Creativity in context. Westview Press.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of Management Review, 28(2), 238–256. https://doi.org/10.5465/amr.2003.9416096
Bohnsack, R., Ciulli, F., & Kolk, A. (2024). Profiting from innovation in digital ecosystems: A configurational approach. Research Policy, 53(3), 104961. https://doi.org/10.1016/j.respol.2024.104961
Chaudhuri, R., et al. (2024). Artificial intelligence, data-driven culture, and innovation capabilities. Technological Forecasting and Social Change, 200, 123165. https://doi.org/10.1016/j.techfore.2023.123165
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553
Fleming, L. (2001). Recombinant uncertainty in technological search. Management Science, 47(1), 117–132. https://doi.org/10.1287/mnsc.47.1.117.10671
Holt, D. B. (2004). How brands become icons: The principles of cultural branding. Harvard Business School Press.
Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1–22. https://doi.org/10.1177/002224299305700101
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how? International Journal of Information Management, 77, 102783. https://doi.org/10.1016/j.ijinfomgt.2024.102783
Levinthal, D. A. (1997). Adaptation on rugged landscapes. Management Science, 43(7), 934–950. https://doi.org/10.1287/mnsc.43.7.934
Levinthal, D. A., & March, J. G. (1993). The myopia of learning. Strategic Management Journal, 14(S2), 95–112. https://doi.org/10.1002/smj.4250141009
March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. https://doi.org/10.1287/orsc.2.1.71
Mariani, M. M., & Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A research agenda. Journal of Business Research, 175, 114542. https://doi.org/10.1016/j.jbusres.2024.114542
Mariani, M. M., et al. (2023). Artificial intelligence in innovation research: A systematic review. Technovation, 122, 102623. https://doi.org/10.1016/j.technovation.2022.102623
Peteraf, M. A. (1993). The cornerstones of competitive advantage: A resource-based view. Strategic Management Journal, 14(3), 179–191. https://doi.org/10.1002/smj.4250140303
Riemer, K., & Peter, S. (2024). Generative AI as style engines: A conceptualization. International Journal of Information Management, 79, 102824. https://doi.org/10.1016/j.ijinfomgt.2024.102824
Ritala, P., Aaltonen, P., Ruokonen, M., & Nemeh, A. (2024). Developing industrial AI capabilities. Technovation, 138, 103120. https://doi.org/10.1016/j.technovation.2024.103120
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Woodman, R. W., Sawyer, J. E., & Griffin, R. W. (1993). Toward a theory of organizational creativity. Academy of Management Review, 18(2), 293–321. https://doi.org/10.5465/amr.1993.3997517
