Unleashing big data analytics to enhancing customer happiness in digital marketing 4.0 era, evidence from health care sector
Bibliographic record
Abstract
This study aims to explore the impact of Big Data Analytics (BDA) on Customer Happiness (CH) in Marketing 4.0 (M4.0) Era in the Saudi healthcare sector. The purpose of the study is to examine how the integration of data-driven decision making and modern marketing strategies can enhance patient happiness. The sample consisted of 450 employees from various levels within healthcare organizations across Saudi Arabia. A quantitative research approach was used, using a structured survey to collect data on perceptions of BDA and M4.0 and their impact on CH. Statistical analyses were conducted to test the proposed hypotheses. The results indicate that both BDA and M4.0 have a statistically significant positive impact on customer happiness, with BDA enhancing personalized healthcare services and M4.0 improving patient happiness. Based on these findings, healthcare organizations are encouraged to invest in Big Data analytics tools and adopt Marketing 4.0 strategies, such as personalized marketing and digital patient engagement, to enhance patient experiences and happiness. It is also recommended that future studies explore patient happiness through big data analytics, and to expand understanding of these technologies in diverse healthcare settings.
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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 | 0.025 |
| 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 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".