Securing Strategic Advantage: The Role of Cybersecurity Actions and Government Support in Enhancing Digital Platform Capabilities
Bibliographic record
Abstract
ABSTRACT In today's digital age, digital platforms are foundational to the business strategies of major firms but are increasingly vulnerable to cyber‐attacks that can severely impact user trust and operational functionality. This study examines the impact of cybersecurity actions on the strategic capabilities of digital platforms, moderated by government support for cybersecurity policy. Drawing on the resource‐based view (RBV) with the core competence perspective, this research posits that proactive cybersecurity measures are not merely protective mechanisms but strategic assets that enhance digital platforms' reliability, innovation potential, and competitive positioning in the digital economy. Based on 1017 survey responses from 817 firms in Taiwan, this study validates a model that confirms the positive impact of cybersecurity actions on the strategic capabilities of digital platforms and elucidates the moderating role of government support. The findings reveal that government policies not only boost the effectiveness of cybersecurity measures but also align corporate strategies with broader policy frameworks, enhancing the resilience and strategic agility of digital platforms. This study extends the RBV by incorporating cybersecurity as a pivotal intangible resource and highlights the necessity for integrated approaches where government support amplifies the strategic benefits of cybersecurity actions. The insights provided offer substantial implications for policymakers and business leaders in navigating the complexities of cybersecurity in the digital age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".