Exploring factors influencing actual usage of freight forwarding services in Indonesia: A study on desire, outcome expectations, perceived self-efficacy and moderating roles of delivery risk and perceived trust
Bibliographic record
Abstract
This study investigates the factors influencing the actual usage of freight forwarding services in Indonesia, focusing on the roles of desire, outcome expectancy, and perceived self-efficacy, with delivery risk and perceived trust acting as moderating factors. Grounded in Social Cognitive Theory (SCT), this study examines how personal cognitive factors and external risks influence users' attitudes and behaviors toward freight forwarding services. Data were collected from 616 respondents across Jakarta, Surabaya, and Makassar utilizing a structured questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test the hypothesized relationships. The findings reveal that desire significantly influences both attitude and delivery risk, while attitude has a strong direct effect on actual usage. Outcome expectancy and perceived self-efficacy demonstrated weaker effects, particularly on attitudes, suggesting that other factors, such as trust and risk perceptions, play a more significant role in this context. Additionally, delivery risk was found to moderate the relationship between desire and attitude, while perceived trust did not moderate the link between attitude and actual usage. The research underscores the importance of trust-building and risk mitigation strategies for freight forwarding service providers in Indonesia. The study also provides theoretical contributions by applying SCT to the logistics sector and offers practical implications for enhancing service adoption in emerging markets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".