Value Creation in Platform Enterprises: A Fuzzy-Set Qualitative Comparative Analysis
Bibliographic record
Abstract
Platform enterprises have emerged as one of the most popular business models in the era of knowledge economy. The success of platform enterprises relies on continuous value creation by constructing an efficient platform and attracting more users to participate in order to create more value for the users and by the users. This study is to explore the key factors that drive value creation in platform enterprises’ ecosystem to help better understand the management of platform enterprises as the knowledge-based entrepreneurial ventures in emerging markets. This study employs a newly emerged method—a fuzzy-set qualitative comparative analysis to empirically analyze the antecedent configurations of value creation for Chinese platform enterprises. This method extends qualitative comparative analysis by integrating fuzzy-sets and fuzzy-logic principles with qualitative comparative analysis principles, which offers a more realistic approach. This paper identifies different configurations for high and non-high levels of value creation in platform enterprises. The results show that continuous user commitment is crucial to creating positive value for Chinese platform enterprises, and active user participation and knowledge sharing as well as platform construction/improvement are the key elements that determine the platform enterprises’ value creation process. The results also demonstrate a hybrid value creation logic with efficiency and innovation in platform enterprises. This study further identifies an asymmetric causality in the Chinese platform enterprises’ value creation process that is crucial for knowledge sharing and effective management of platform enterprises. The findings can shed light on the strategic management of platform enterprises for emerging markets.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".