Innovation within the Australian outdoor hospitality parks industry
Bibliographic record
Abstract
Purpose Traditional innovation typologies within the extant literature are not compatible with the innovation levels found within the Australian outdoor hospitality parks (OHP) sector, given its tourism and small business characteristics. This paper seeks to introduce an innovation typology specific to the Australian OHP sector. Design/methodology/approach A two‐phase qualitative research method was employed, whereby 30 semi‐structured interviews were conducted with OHP operators/administrators who were identified as being “innovative” by four industry executives. Based on the 30 interviews carried out in Phase 1, six industry individuals who demonstrated a wider and deeper approach to innovation than the others were further interviewed in Phase 2. Findings A small percentage of Australian OHP industry operators and executive officers showcase a level of innovation that is beyond incremental in character, but is not radical, revolutionary or disruptive. This group of “strategic innovators” are the first to adopt ideas from other sources and adapt them to fit within the Australian context. These new ideas are introduced in three‐ to four‐year increments, providing the individuals with sufficient time to assess the market's reaction to the changes, and to measure increased value to their situation. The three‐ to four‐year time span dovetails with the length of time taken by the majority of competitors to imitate the new concepts. Originality/value The paper introduces an innovation typology applicable to the Australian outdoor hospitality parks sector.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".