Digital marketing and its role in achieving customer’s happiness: Evidence Jordanian five-star hotels
Bibliographic record
Abstract
The study aimed at bridging or reducing the knowledge gap between digital marketing di-mensions and customer’s happiness, by diagnosing both the level of digital marketing adoption and the level of customer’s happiness achievement, determining the nature of the relationship between digital marketing and customer’s happiness, and recognizing the level of effect and contribution of digital marketing dimensions in achieving customer’s happiness. This is based on the main idea that digital marketing activities are the main basis for achieving customer’s happiness, when designing digital services. Opinions of (360) customers in Amman five-star hotels were surveyed and viewed to achieve this direction, by designing and distributing a questionnaire. The most important results of the study showed that all digital marketing dimensions positively affect customer’s happiness, as well, the level of using dimensions of both digital marketing, and achieving customer happiness is lower than the required level, by five-star hotels in Amman. The most important recommendations were using of distinguished digital channels tools to respond to attract customers, such as the use of advertisements that accustom customers to use of products or services based on virtual reality or through the applications that use live streaming marketing system (photos or video) in order to bring customers close to what services the hotel offers. Using creative, and effective methods in communicating with customers through human emotions to create delicious marketing for customers, which makes them engaged with the offers they receive, whether through e-mail or SMS. Interest in providing real and effective content to digitally marketed service, in order not to create a gap between hotel and its customers as a result of the mismatch between what is advertised and what is actually provided. Determining success indicators of a hotel digital marketing campaigns by knowing customer’s feelings and their happiness (i.e. through customers’ positive feedback).
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| 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 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".