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Balancing Technical Convergence and Platform Agility: An M&A Integration Framework for Digital Mergers

Sidharth Ramsinghaney

AIMS International Journal of Management2025https://doi.org/10.26573/2025.19.2.3article
ABDC C
Weight
0.50

What the paper says

This research examines the architectural challenges and strategic approaches in integrating disparate software platforms during mergers and acquisitions in the technology sector.Through an in-depth case study of a USD3B+ acquisition, we analyze how technical architecture decisions influence integration outcomes and innovation capabilities.The study introduces a novel theoretical framework that addresses the fundamental tension between achieving technical synergies and maintaining platform agility.Our findings reveal that selective integration approaches, when strategically implemented, enable value creation while preserving essential innovation capabilities.The research contributes to both integration theory and practice by providing technology leaders with empirically grounded guidance for architecting platform integrations while maintaining development velocity and customer commitments.This work extends current platform integration literature by incorporating the dynamic nature of modern SaaS architectures and their impact on merger success.

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https://doi.org/https://doi.org/10.26573/2025.19.2.3

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@article{sidharth2025,
  title        = {{Balancing Technical Convergence and Platform Agility: An M&A Integration Framework for Digital Mergers}},
  author       = {Sidharth Ramsinghaney},
  journal      = {AIMS International Journal of Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.26573/2025.19.2.3},
}

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Balancing Technical Convergence and Platform Agility: An M&A Integration Framework for Digital Mergers

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.