Digital transformation in SMEs: Assessing the impact of big data capabilities on project success, business continuity, and sustainability
Bibliographic record
Abstract
During the innovation era and in the highly competitive environment, big data capabilities (BDCs) play a pivotal role in shaping competitive dynamics; the influence of these technologies on small and medium-sized enterprises (SMEs) operating in the retail sector is critically significant. This study is specifically focused on the retail industry, with a particular emphasis on how BDCs impact the project success, business continuity, and sustainability of SMEs within this industry. Our theoretical model was tested using a survey of 300 operations managers working in SMEs in the retail sector in the Middle East. PLS-SEM was conducted to analyze our collected data. Our results reveal that BDCs enhance project success and promote sustainability practices. The findings also reveal that BDCs have no impact on business continuity. By shedding light on the nuanced impact of BDCs on SMEs in the retail sector, this study contributes valuable insights to the existing literature, offering a deeper understanding of how these technological capabilities can drive success and sustainability in a highly competitive market environment.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.015 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".