Factors Influencing Consumer Intention to Purchase via Omni-Channel Fashion Retail in Malaysia
Bibliographic record
Abstract
The enormous development of technology and e-commerce in Malaysia creates a vast potential for traditional retailers to emerge with new technology and apply the omnichannel strategy concept.Customers nowadays use personal devices to enhance their shopping experience.The primary approach to promoting customization in a customer's journey to purchase is to integrate online and offline practices.This cross-sectional study focuses on factors influencing consumer intention to purchase via omni-channel fashion retail in Malaysia.The study sample consisted of 415 consumers, purposefully selected among Malaysia's fashion retailing population.The respondents were given questions regarding the perceived value of webrooming, the perceived value of showrooming, perceived compatibility, perceived risk, and the purchasing intention of omni-channel which were used to help gather responses on the intention of consumer purchase.The results revealed that perceived compatibility is the essential factor in consumer intention to purchase via omni-channel fashion retail, followed by the perceived value of webrooming, the perceived value of showrooming, and the perceived value of webrooming.This study is limited to the knowledge of consumers since the omni-channel strategy concept is still new in Malaysia and cannot yet be generalized.Due to that, the data obtained still does not reflect an accurate result regarding factors influencing consumers' intentions to purchase via omni-channel fashion retail in Malaysia.Therefore, the results suggest that the current cross-sectional study be further analyzed using qualitative research.It would be beneficial to replicate the study by region in Malaysia and add on cultural variables such as differences in gender and generation to get more generalizability.While most literature on omni-channel focuses on consumer attitudes and behavior perspectives, the current study aims to gain insight from consumer perspectives on factors that influence their intention.The study contributes to gaining knowledge on the primary factor of purchasing intention through omni-channel retail services in Malaysia, which deepens the understanding of consumer behavior to purchase from fashion retailers using the omni-channel strategy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".