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Record W4407848090 · doi:10.1016/j.jbusres.2025.115251

Social, economic, and environmental implications of drones in marketing: A framework of safeguards for sustainable technology implementation

2025· article· en· W4407848090 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Business Research · 2025
Typearticle
Languageen
FieldComputer Science
TopicAI in Service Interactions
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsDroneSocial marketingBusinessMarketingEnvironmental economicsEconomics

Abstract

fetched live from OpenAlex

Emerging technologies in marketing can bring sustainability benefits but can also cause harms to the environment, the economy, and society. To better realize global sustainability goals, this empirically grounded study contributes a framework of safeguards for technologies in marketing. Focusing on the increasingly prevalent technology of commercial aerial drones, the authors employed inductive research involving a fully qualitative survey of 240 commercial drone experts, marketing professionals, and the general public to explore the sustainability implications of commercial drones. The findings reveal that commercial drones are linked to a range of benefits and harms, and that three groups of safeguards – technical safeguards, policy safeguards, and inclusive public safeguards – could create value by reducing the possible harms and bolstering the benefits of commercial drones in marketing.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.415
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it