Determining the Effects of Perceived Utilitarian and Hedonic Value on Online Shopping Intentions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>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.</p><p>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.</p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it