Examining digital platform resilience: a social–ecological systems approach
Bibliographic record
Abstract
Purpose We examined digital platform resilience through a social–ecological systems lens, treating platforms as socio–technical entities embedded in and interacting across, broader environmental scales. Prior work has offered limited conceptual clarity and empirical tests of which attributes drive resilience. We addressed this gap by investigating the antecedents and consequences of platform resilience. Design/methodology/approach We developed a research model in which socio–technical and cross-scale interaction factors act as antecedents of digital platform resilience, which in turn influences platform performance. We analyzed survey data from 252 business-to-business (B2B) e-marketplaces using the partial least squares structural equation modeling (PLS-SEM) method. Findings Socio–technical factors, namely innovation capacity, diversity, IT infrastructure (ITI) flexibility and ITI efficiency, along with a cross-scale interaction factor, sustainability positioning, serve as critical drivers of digital platform resilience. These antecedents explained 71.6% of the variance in platform resilience. Innovation capacity mediates the effects of diversity and ITI flexibility on platform resilience. Originality/value This research provides a systematic, empirically grounded account of digital platform resilience from a social–ecological systems perspective. It clarifies the antecedents and mechanisms that strengthen platforms against disruption and offers insights for managerial action.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.008 |
| 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".