Factors that Influence the Intention to Adopt Halal Logistics Services among Malaysian SMEs: Formation of Hypotheses and Research Model
Bibliographic record
Abstract
This study was aim to understand the influence of adoption factors on the intention of adopting an innovation (Halal Logistics) among Malaysian Halal SMEs. This research employed a quantitative research design using survey research method. Four objectives were established. The first is to formulate a model that identifies the influence of adoption factors on innovation (Halal Logistics) adoption intention among Malaysian Halal SMEs. This was achieved through literature reviews and preliminary study. Five halal compliant logistics service providers (LSPs) were contacted through phone and email correspondences. Seven research hypotheses were derived and seven factors that influenced the innovation (Halal Logistics) adoption intention were identified: the presence of familiarity with innovation, status characteristics, position in social network as internal factors as well as benefit, geographical setting, societal culture and political condition as external factors. The second objective is to develop an instrument that can be used to measure the influence of adoption factors on adoption intention among Halal SMEs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".