Analyzing the effect of customer loyalty on virtual marketing adoption based on theory of technology acceptance model
Bibliographic record
Abstract
One of the most advantages of the internet and its expansion is probably due to its easy and low cost access to unlimited information and easy and fast information exchange.The accession of communication technology for marketing area and emergence of the Internet leads to creation and development of new marketing models such as viral marketing.In fact, unlike other marketing methods, the most powerful tool for selling products and ideas are not done by a marketer to a customer but from a customer to another one.The purpose of this research is to analyze the relationship between customers' loyalty and the acceptance of viral marketing based on the theory of technology acceptance model (TAM) model among the civil engineers and architects who are the members of Engineering Council in Isfahan (ECI).The research method is descriptive-survey and it is applicable in target.The statistical population includes civil engineers and architects who are the members of Engineering Council in Isfahan including 14400 members.The sample size was determined 762 members based on Cochran sampling formula, the sample was selected as accessible.The data was collected by field method.Analyzing the data and recent research hypothesis, the data was extracted from the questionnaires.Then, all the data was analyzed by computer and SPSS and LISREL software.According to the results of the data, the loyalty of the civil engineers and architects members of ECI was associated with the acceptance and practical involvement of viral marketing.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".