Crashed! Why Drone Delivery Is Another Tech Idea not Ready to Take Off
Bibliographic record
Abstract
The objective of this study was to examine why drone delivery is another tech idea not ready to take off. Qualitative methods, which involved inductive, exploratory and interpretivism approaches, were used in this conceptual study. The inductive approach was used to generate propositions based on secondary data obtained from journal articles, authorized website contents, and books. On the other hand, exploratory and interpretivism approaches were used to undertake in-depth analysis and to have a complete description and understanding of the factors that shape consumers’ behavioural intentions to use drone food delivery services, respectively. Based on research findings and news related to consumers’ behavioural intentions to use drone delivery services, conceptual frameworks have been proposed to show the four main independent variables, which are functions, hedonic motivation, cognition, and social factors, that affect the dependent variable, which is behavioural intentions of consumers to use drone for food delivery services. Overall, factors that were hindering consumers’ behavioural intentions to use drone food delivery services were consumers’ unfamiliarity and negative perceptions toward drone delivery in that it is unregulated, dangerous, risky, lack of quantified risk assessments, intimidating, related to military and defence, and lengthy in the process to obtain authority’s permission to operate food delivery services by drones. Nevertheless, they may be influenced to use drone delivery if their friends and family were using it.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".