Investigation of the Influence of Perceived Quality, Price and Risk on Perceived Product Value for Mobile Consumers
Bibliographic record
Abstract
This article aims to investigating the effects of perceived quality, risk and relative price on the perceived value and purchase intentions of mobile phones. The population comprises 293 persons of Business Administration MA students from the Islamic Azad University of Zanjan. The questionnaires were validated using face validity. In addition, the reliability was calculated using Cronbach Alpha, Split-half and Test- re-test. To analyze the data, with SPSS and LISREL software, Exploratory and Confirmatory Factor Analyses (to identify the effective factors) and Structural Equation Modeling (SEM) (to test the hypotheses) were calculated The findings indicate that perceived value does not influence purchase intentions. Perceived quality, perceived relative price and perceived risk do not influence purchase intentions through perceived value. Perceived risk, perceived quality and relative price all influence perceived value. Relative price influences perceived product quality and the latter, in turn, affects perceived risk. The results of the study show that it is essential to develop an understanding of value in the purchasing process. Moreover, the risk should be reduced to a minimum.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".