E-marketing, EWOM, and social media influencers' effects on Intention to purchase and custom-er’s happiness at Amman Stock Exchange
Bibliographic record
Abstract
The goal of this study was to measure the main effects of e-marketing, e-WOM, and social media influencers on increasing the intention to purchase and enhancing customers’ happiness in the Amman stock exchange. 285 samples represented the research study samples which have been collected, analyzed, and used to discuss the research hypotheses. The research study gave results which showed that e-marketing, e-WOM, and social media influencers’ effects positively on increasing customers’ intention to buy and enhancing customers’ satisfaction and happiness. This research represented each main research variable through its main keys, this research represented e-marketing through internet usage benefits received, simple use with low cost, and behavior and action. The second main variable is the e-WOM, and it is represented in this research through: satisfaction, dissatisfaction and perceived novelty. The third main variable is the social media influencers and it’s represented by: expertise, trustworthiness, and attractiveness. The main output of this study is represented that using digital marketing channels, with knowing peoples’ opinion, and following social media influencers can give customers ability to decide which product have to buy in the way which they can get the maximum benefits. The novelty of this study lies in giving more details about the effects of e-marketing, e-WOM, and social media influencers which are still new fields, and it needs more research for discovering all dimensions. Also, this research is useful and innovative based on choosing the field of this study and it is the Amman stock exchange which can help people to know useful information about the nature of stock investment.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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; 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".