E-shopping: an Analysis of the Technology Acceptance Model
Bibliographic record
Abstract
One of the continuing issues in the management of information technologies is the difficulty of identifying significant factors that influences consumers to accept and make use of systems developed and implemented by others. Existing studies have employed the technology acceptance model (TAM) to address this issue and the model has now become one of the most widely used models in information technology. However, an exhaustive review of the literature suggest that findings of TAM relationships are not borne out in all studies - there remains a wide variation of predicted effects in various studies with different types of users and systems. While there are existing studies concentrated on online shopping globally, many conclude with calls for a closer examination of online shopping intentions in specific countries, typically those in developing and less developed countries. Online shopping remains in the early stage of development in Malaysia. Little is known about the acceptance of online shopping and the factors which influence this behaviour. This study attempts to fill in this gap by providing insights on how consumers form their attitudes and online shopping intentions to the existing literature and managerial implications for online shopping retailers and marketers on how best to serve and attract consumers to shop online via the management of online shopping technologies.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".