READINESS OF THE OUT-OF-HOME ADVERTISING INDUSTRY FOR THE 4TH INDUSTRIAL REVOLUTION
Bibliographic record
Abstract
The shift to the 4th Industrial Revolution (4IR) is said to have potential to increase productivity and improve the quality of life. In the context of the Out-of-Home (OOH) advertising industry, the 4IR era is characterized by using emerging technologies to provide targeted advertising. Without the shift, the industry runs the risk of losing out on revenue growth prospects that are brought about by the adoption of emerging technologies such as Internet of Things (IoT), big data, machine learning and advanced real imaging technology. The aim of the study was to investigate and highlight the main actors, state of play and constraints of the South African OOH industry in relation to the shift towards the 4IR era. Coupled with the use of a technology adoption model, a customized framework was developed on how a South African OOH advertising organisation could go about transitioning towards the 4IR. The successful outcome of the research project would increase the body of knowledge of 4IR transformation strategies in an industry in which limited research has been done. The theory covered applies not only to the OOH advertising industry but could be beneficial to other industries through its contribution to the technology adoption theory.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".