The Dark Side of Powerful Platform Owners: Aspiration Adaptations of Digital Firms
Bibliographic record
Abstract
This study provides a timely and critical perspective on the negative influences of powerful platform owners (PPOs) on the strategy aspiration adaptations of digital firms operating on these platforms. Contrary to the tenets of the behavioral theory of the firm, the aspiration adaptation of digital firms is largely influenced by PPOs rather than autonomously based on market- and competition-based referents. We call this Faustian bargain—the trade-off between the utility and advantages offered by PPOs’ technology affordance and the loss of aspiration adaptation control of independent digital firms—the “dark side” of powerful platforms. Drawing on resource-dependency logic and illustrative cases, we uncover how PPO characteristics, structural mechanisms, and the use of undesirable tactics manifest this phenomenon. In addition to uncovering the dynamics of an increasingly critical managerial and scholarly phenomenon, we provide much-needed implications for PPO governance practices and policies. Further, we advocate the formation of new institutions that can match the dynamic development of the digital platform economy with adaptive regulations and enforcement, superseding existing but ineffective antitrust laws mainly based on the pre-digital world.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".