Effect of interactive empowerment between digital technology and social network on manufacturing firm growth: evidence from China
Bibliographic record
Abstract
Digital technology and social network have been widely recognised as critical drivers of firm growth. However, existing studies often treat them as independent processes, overlooking the potential synergistic effects arising from their joint development. Drawing on Resource Orchestration Theory, this study examines how interactive empowerment between digital technology and social networks (IEDS) promotes firm growth through strategic entrepreneurship. We conceptualise IEDS as a mutually reinforcing and spiralling process through which digital technology and social network co-evolve to expand and enrich the resources available to firms. We further posit that strategic entrepreneurship serves as a critical mechanism linking IEDS to firm growth by leveraging the enriched resource base to align opportunity-seeking and advantage-seeking behaviours. Using data of 794 publicly listed Chinese manufacturing firms for the period 2015–2021, we conduct regression analysis to test the proposed relationships, and the empirical results provide strong support to our theoretical hypotheses. This study contributes to the literature on empowerment theory and strategic entrepreneurship by introducing the concept of interactive empowerment, demonstrating that strategic entrepreneurship mediates the relationship between digital technology and social network in driving firm growth, and enriching empirical research in the context of the digital economy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".