Factors influencing attitudes and intentions towards smart retail technology
Bibliographic record
Abstract
Today, the ever-changing technology development is bringing an impact to the Malaysian retail industry. Smart Retail Technology is one of the outcomes of Industry Revolution 4.0 to deepen integration between digital and physical characteristics that can serve extraordinary shopping experiences in brick-and-mortar stores. Smart retail technology is attaining greater attention in the information system literature. In this study, the researchers aimed to investigate the relationships between perceived usefulness (PU), perceived ease of use (PEOU), perceived enjoyment (PE), perceived risk (PR), attitudes (ATD) and behavioral intentions towards smart retail technology (BI). The 170 respondents were collected through an online survey and processed in the PLS-SEM data analysis. The results indicated that perceived ease of use is the main variable influencing people's acceptance of SRT, followed by perceived enjoyment. However, perceived usefulness and perceived risk performed insignificant roles to link with attitudes and behavioral intentions towards SRT. This study provided some insightful implications for both academicians and practitioners to understand the current situation of smart retail technology in developing countries, especially Malaysia.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".