Bibliographic record
Abstract
In order to conduct the research, a descriptive-correlation method, as well as, a field study approach was employed.The statistical population of the research comprised approximately 2000 people and the size of the statistical sample was determined as 400 individuals based on the Morgan table.384 questionnaires were distributed among the participants for further data analysis.The method used to select the samples was nonrandom and available sampling method.In terms of the instruments, a 21-item questionnaire designed by Mohammadi (2013) on information technology, a standard 6-item questionnaire on the utilization of attraction and a 6-item questionnaire on the attraction efficiency by Hajebrahimi (2016) and finally an 8-item questionnaire on customer loyalty by Liu (2008) were employed.The content validity of the questionnaires was also approved by a group of university sport management professors, as well as, the managers of sports clubs.Reliability of the questionnaire was 0.88, 0.85, 0.80, and 0.79, respectively, through Cronbach's alpha.In order to analyze the data, Kolmogorov-Smirnov test, binomial test and Spearman correlation coefficient were used and as for the cause and effect relationships of the variables, structural equations were employed.According to the major findings of the research, there is a significant relationship between the information technology knowledge and the attraction efficiency and customer loyalty (p<0/05).Moreover, according to (R2=0.09,GOF=0.941),combined reliability and the AVE extracted from the constructs, the fitness of the model was confirmed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.922 | 0.926 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".