The Role of Big Data Analytics in Manufacturing Agility and Performance: Moderation–Mediation Analysis of Organizational Creativity and of the Involvement of Customers as Data Analysts
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The involvement of customers as data analysts enables firms to gain valuable insights and create value from big data. We provide a theoretical explanation, drawn from the resource‐based view, for the influence of the involvement of customers as data analysts and of the development of big data analytics (BDA) capabilities in business‐to‐business contexts as routes to manufacturing agility and performance. Our study empirically tested a framework in which organizational creativity and the involvement of customers as data analysts may differentially influence the relationship between BDA capabilities and manufacturing agility. We further tested whether the relative impact of manufacturing agility depends on organizational creativity and the involvement of customers as data analysts. To test our proposed framework, we took a partial least‐squares structural modelling approach using data collected through a survey involving 179 engineering manufacturers operating across different industrial sectors in Pakistan. We provide evidence for organizational creativity and customer involvement, presenting a promising opportunity for manufacturers to gain better insights from resources, and for the deployment of BDA capabilities leading to better manufacturing agility and performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it