Understanding Consumers’ Adoption Behavior of Driverless Delivery Vehicles: Insights from the Combined Use of NCA and PLS-SEM
Bibliographic record
Abstract
The rapid development of autonomous driving technology has been a key driver for the emergence of driverless delivery vehicles. To promote wider adoption, it is essential to address consumers’ concerns about safety and reliability, leverage psychological factors, and implement supportive policies that encourage technology adoption while ensuring public safety and privacy. Therefore, it is necessary to explain and predict consumers’ behavior and intention to adopt driverless delivery vehicles. To this end, this study extends the Technology Acceptance Model (TAM) to include technological complexity and perceived trust. This study evaluates the model by applying necessary condition analysis (NCA) and partial least squares structural equation modeling (PLS-SEM) to analyze data from 579 respondents from Jiangsu Province, China. This study explores the sustainability implications of autonomous delivery vehicles, highlighting their potential to reduce environmental impact and promote a more sustainable transportation system. The outcomes indicate that perceived ease of use (PEU), attitude, perceived trust, technological complexity (TECOM), and perceived usefulness (PU) are significant determinants and necessary conditions of consumers’ intention to adopt driverless delivery vehicles. Perceived trust and TECOM had a significant and indirect influence on consumers’ intention to adopt driverless delivery vehicles via PU and PEU. Perceived trust and technological complexity had a substantial impact on consumers’ adoption intention of driverless delivery vehicles. The study recommends that managers work closely with regulators to ensure their technologies meet all local standards and regulations. It also recommends its potential to reduce carbon emissions, improve energy efficiency, and contribute to a more sustainable transportation system.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".