Adoption enablers of big data analytics in supply chain management practices: the moderating role of innovation culture
Bibliographic record
Abstract
The enablers of Big Data Analytics (BDA) on the BDA adoption intention of consumer goods’ retailing firms were measured in this study along with innovation culture as a moderator. Based on a literature review, six BDA adoption intention enablers: financial readiness, perceived advantages, top management support, IT infrastructure, technology sophistication, and data quality were identified. The study collected data from different levels of managers in the consumer goods’ retailing sector in Jordan to test the proposed study framework. To obtain primary data, a quantitative method was used, and a survey (structured questionnaire) was conducted. SmartPLS version 3.3 was used to analyze and test the proposed study model, which included 211 respondents. Three BDA enablers, including perceived advantages, top management support, and IT infrastructure, were found to have a statistically significant effect on BDA adoption intention in their supply chain operations. Furthermore, the relationship between financial readiness and BDA adoption intention was significantly moderated by innovation culture. This research model can be used to determine the challenges and enablers to BDA adoption in supply chain operations for both developed and developing countries. Future research may replicate the model in various sectors or the same sector in different countries.
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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 | 0.010 | 0.034 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".