An investigation on the use of digital marketing towards the customer satisfaction and brand loyalty of hotels/ restaurants sector in Saudi Arabia
Bibliographic record
Abstract
The goal of this study is to evaluate the way digital marketing (DM) works in increasing customer satisfaction (CS) and brand loyalty (BL) at the Saudi Arabian Restaurants. The study uses 7 variables for analysis such as Service quality satisfaction (SQS) Digital engagement satisfaction (DES) Recommendation Likelihood (RL) Digital Promotions (DP) Online Presence Perception (OPP) Promotions Effectiveness (PE) Social Media Engagement (SME). Data from customers using digital media has been gathered through questionnaires. 410 respondents provided the data, which was then examined using SPSS and AMOS. The study will give management the knowledge they need to modify procedures and train employees in order to satisfy customers and promote BL. Future research can be done across several corporate sectors and cultural contexts. The premise for this study is provided by this paper, which also offers managers useful guidance regarding how to train employees to increase consumer satisfaction and BL.
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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 | 0.000 |
| 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 | 0.000 | 0.004 |
| Open science | 0.000 | 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".