Determining the Effects of Perceived Utilitarian and Hedonic Value on Online Shopping Intentions
Bibliographic record
Abstract
In today’s digital world, the Internet is having vigorous and transformational effects on consumer’s behavior. Over the past ten years, consumers all over the world have increasingly used the Internet as an efficient medium in their shopping experience. Online retailers are trying to influence consumers shopping attitude and behavior by creating renewed shopping experiences in order to sustain their business under the catastrophic destructive competition among online and offline retailers. In the catastrophic destructive rivalry environment, it is vital for retailers to understand online consumers’ beliefs, attitudes, shopping intentions and behavior toward online shopping. Therefore, this study was designated to clarify consumers’ online shopping intentions within the online shopping environment. This study extends the technology acceptance model (TAM) and consumer perceived value theory. In the data gathering process, we used convenience sampling and face-to-face interviews techniques. The 400 valid questionnaires were gathered from the Internet shoppers who voluntarily participated with in our research in Osmaniye, Turkey. In order to test the research model, we used Partial Least Squares (PLS-PM) analysis method. The analysis results provide strong support for the research model. Particularly, perceived usefulness, hedonic value, and online shopping satisfaction dimensions have statistically positive effect on online shopping intentions. The findings suggest that perceived usefulness and positive online shopping attitude plays a significant role in increasing both perceived utilitarian and hedonic online shopping value. In addition, online shopping satisfaction and hedonic value have a significant effect on consumer online shopping intentions. Finally, analysis results give some useful insights into the consumers’ online shopping intentions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".