The Generative Pathway of Data Assets: A Data-Based Bricolage Approach
Bibliographic record
Abstract
Beyond connecting platform participants, industrial digital platforms serve as incubators for diverse business portfolios tailored to address industry-specific challenges. Yet, their development is often hindered by the absence or ineffectiveness of institutions, commonly referred to as institutional voids. While prior research has emphasized data as a strategic asset that empowers platform transformation and drives novel value propositions, the mechanisms through how data assets are formed, and their value creation remain ambiguous. Through a longitudinal case study, this research explores how a digital industry platform rooted in the ceramic industry leverages accumulated data resources to develop data assets that fill in institutional voids. Using the bricolage lens, we identify the platform’s adoption of data-based bricolage process—institutional, platform, and anchor bricolage approaches—to construct digital infrastructure, enrich digital resources, and create data asset products. This process also marks the platform’s evolution from a trading intermediary to an institutional intermediary. This study contributes to the understanding of how digital platforms can leverage benign institutional voids to address industry voids. Additionally, it advances the literature on data asset formation by a bricolage lens, which can drive institutional change rather than only serving as a ‘second-best’ solution. These findings offer valuable practical insights for the evolution of nascent digital platforms.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".