E-shopping: An Analysis of the Uses and Gratifications Theory
Bibliographic record
Abstract
The Internet has experienced an exponential growth in the number of users and has created enormous increases in its marketing and communication applications during a considerably short period of time. Although both scholars and practitioners have jointly acknowledged the capabilities of the Internet as a marketing tool that offers great potentials and advantages, there remains a scarcity of knowledge pertaining to the motivations for using the Internet and associated online consumer behaviours in more web-specific scenarios. The uses and gratifications theory (U&G) provides a theoretical grounding and an avenue to further understand consumers’ attitude and intention of using the Internet as a shopping channel from a media perspective. While most of the studies done on the U&G in the Internet are situated in American and European contexts, this paper considers the U&G structure of online shoppers in the Asian context (more specifically, in Malaysia). More specifically, this study attempts to shed some light on how consumers form their attitude and online shopping intention based on the uses and gratifications structure to the existing literature and managerial implications for entrepreneurs and marketers of electronic businesses on how best to serve and attract consumers to shop online via the management of online shopping technologies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".