The Impact of Sales Promotions on Sales Turnover in Airlines Industry in Nigeria
Bibliographic record
Abstract
This study seeks to determine the impact of sales promotions on sales turnover in the airlines industry in Nigeria. Ex post facto and survey research design were adopted. Secondary data on average monthly passenger turnover covering a period of 25 years (1991-2015) were collected from the records of airport authority. A questionnaire was also administered to 450 air travellers to ascertain the extent to which sales promotions incentives stimulate them to travel by air within Nigeria. Frequency analysis, regression and t-Test methods of analysis were applied. The results show that sales promotions incentives significantly impacted sales turnover in the airlines industry; and air travellers prefer non-monetary sales promotional offers and off-line incentives to monetary and online offers. The study, therefore, recommends that the management of the airlines need to be more innovative in making promotion offers to air travellers so as to optimize the opportunity. Non-monetary and offline incentives offerings should be emphasized to meet the preference of air travellers. For future research, the study suggests that the focus can be on identifying other salient factors that motivate passengers to travel by air so as to boost industry sales.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".