Passenger choice attributes in choosing a secondary airport: A study of passenger attributes in using Lanseria International Airport
Bibliographic record
Abstract
Background: The economic deregulation of the airline industry in South Africa in 1991 was a landmark event and brought about various changes in the air transport market, both locally and internationally. One important after-effect of deregulation was the entry of low-cost carriers (LCCs) in 2001, which increased competition in the market and offered passengers the freedom to choose between full-cost carriers and LCCs. It is generally accepted that LCCs have been very successful across the globe, and the main reason for this lies in their simplified lower cost business models. One way of achieving lower costs is for LCCs to operate from secondary or alternative airports. This trend is observed in most regions of the world. In South Africa, and more specifically the Gauteng province, Lanseria International Airport is considered as an alternative airport to OR Tambo International Airport (the main international airport of South Africa and located about 30 km east of the Johannesburg Central Business District [CBD]). Currently, two LCCs operate from this airport with a third LCC airline indicating that it will shortly begin operations from this airport.Objectives: The research presented here reflects on the aspects passengers consider when selecting a secondary airport for their travel needs. It also compares the research findings of passenger attributes when choosing Lanseria Airport as a secondary airport in 2010 to a similar study in 2013 after another LCC commenced operations from the airport.Method: In this exploratory research a face-to-face survey was used as the quantitative data collection method in order to identify the factors that influenced passengers’ airport choice decisions at Lanseria International Airport.Results: From this research it emerged that when airports in a metropolitan area are close to one another, one of the main considerations for passengers is access time when selecting an airport. Even after a second LCC started operating from Lanseria International Airport, the attributes passengers regard as important in their decision to fly from the airport remained unchanged.Conclusion: The aim of the research is to gain a deeper understanding of the factors involved in secondary airport selection and, building on this knowledge, to assist airport owners and managers in positioning their airports in a multi-airport competitive environment. Similarly, the findings of the research could assist airlines in their decision-making process to operate from secondary airports
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".