Adoption enablers of big data analytics in supply chain management practices: the moderating role of innovation culture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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