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
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.
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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".