Non-predictive decision-making, market-oriented behaviours, and smaller-sized firms’ performance
Bibliographic record
Abstract
Owner-managers of smaller-sized businesses often possess limited resources/capabilities affecting their ability to pursue opportunities. Earlier research supports the need to be market-oriented, but such practices can sometimes be time-consuming and expensive. Although owner-managers in the tourism/hospitality industry can estimate broad demand associated with seasonality, they nonetheless face a degree of uncertainty. In fact, they can typically never be sure how many customers will enter their respective businesses on any given day no matter how much they try to anticipate market trends; therefore, predictive decision-making is often ineffective. This study employs a quantitative research design, drawing on data from a survey of 184 smaller-sized firms within the tourism/hospitality industry in New Zealand. The findings contribute to the cross-disciplinary literature positioned at the strategic marketing/entrepreneurship interface. Specifically, unique insights utilising an effectuation lens, illustrate that if managed effectively, decision-makers’ non-predictive effectual logic facilitates market-oriented behaviour, which in turn, enhances firm performance.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".