The impact of digital marketing strategies on innovation: The mediating role of AI: A critical study of SMEs in the KSA market
Bibliographic record
Abstract
The purpose of this study is to investigate the moderating role of digital marketing strategies on innovation through Artificial Intelligence (AI) mediating impact in Small and Medium Enterprises within Saudi Arabia. Advanced analytics tools analyzed data from KSA SMEs to establish the role of AI, customer behavior, and experiences in product and process innovation. Artificial intelligence boosts product and process innovation with unconventional customer knowledge. Integrating AI combined with digital marketing to improve decisions and efficiency, to increase understanding of customer dynamics for sustaining growth and promoting collaborations. Using AI-empowered digital marketing strengthens Saudi SMEs advancement by promptly reacting to market motions. Sustainability practices attract the environmentally conscious consumer. This research provides actionable insights for SMEs who want to employ digital marketing and AI strategies and contribute to building an economic environment in Saudi Arabia.
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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 | 0.004 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| 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; a candidate call from one teacher head, 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".