Bibliographic record
Abstract
The purpose of this research is to investigate the relationship between the knowledge of IT managers and the use of its types with the attraction and loyalty of the customers of Tehran's gym clubs.The research has descriptive-correlation method and field method.The statistical population of this research is Tehran's gym clubs' managers whose number is about 2000 people.The sample size was 400 people according to Morgan table.384 questionnaires were used for data analysis.The method used to select samples was non-random and available method.The tools usedwereMohammadis21 questions questionnaire (2013), standardized questionnaire 6 absorption and standardized questionaires,6questions of effective recruiting, and8 questions of customer loyalty questionnaire.The content validity of the questionnaires was also approved by a group of university sport management professors.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 for cause and effect relationships, structural equations variables were used.The most important findings of the research showed that there is a significant relationship between the IT knowledge with the absorpation and variables and the effectiveness of customer attraction and loyalty(p<0.05),also according to (R2=0.09,GOF=0.941) and combined AVE and extruded structures were fitted to the approved model.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.892 | 0.881 |
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".