Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Price reaction in New Zealand’s duopolistic airline market
    (Taylor and Francis Group, 2023-02-11) Ngo T; Tsui KWH
    This study investigates the price reaction in a duopolistic market where two airlines (a full-service and a low-cost carrier) dominate the domestic aviation market. Utilising a rich dataset of 53,463 matched airfares for Air New Zealand and Jetstar in New Zealand from September to December 2019, we found a U-shaped relationship (from Jetstar to Air New Zealand) and an inverted U-shaped relationship (from Air New Zealand to Jetstar) between the two airfares. While distinguishing their different business models, such behaviour has not been empirically discussed in the literature. This study can be extended to larger and more complex markets.
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    Airfares data in New Zealand domestic aviation market
    (MDPI (Basel, Switzerland), 2021-08-09) Ho TH; Nguyen DT; Ngo T; Le TDQ; Dray L
    Price competition has been a growing concern of worldwide researchers and managers. In the aviation market, especially with the help from e-commerce platforms such as Expedia, TripAdvisor, and SkyScanner, airfares are now available to customers in the easiest and quickest way. It thus allows airlines to match their fares immediately and simultaneously upon any changes of their rivals, given that customer’ choices are made with regard to their incomes. This study provides a dataset on domestic airfares in New Zealand that could be useful for future studies in the fields of marketing, business and economics, transportation and aviation, or management. The dataset covers 12 trunk routes and 40 secondary routes in New Zealand from 19 September 2019 to 18 December 2019, a total of 90 days. It provides a rich dataset of more than 162,000 observations regarding the airfare, departure time, arrival time, flight duration, airline, departure airport, arrival airport, transiting airport, and so on. There are possibilities to extend the dataset (e.g., in terms of flying distance, airport characteristics, and airline characteristics) to make it be valuable for future study.