The Evolution of Low Cost Carriers in Australia

Srisaeng, Panarat; Baxter, Glenn; Wild, Graham

doi:10.3846/16487788.2014.987485

Cited by 14 publications

(12 citation statements)

References 38 publications

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“…The only exception was Tiger, which began operating in late 2007. Tiger's operations actually resulted in more competition being created with the Qantas group changing its policy of not operating Qantas on routes served by Jetstar, the Qantas' LCC subsidiary (Srisaeng et al, 2014). Overall, Tiger has never been a strong competitor, struggling on a number of issues, such as delays and later being grounded, in 2011, for safety issues.…”

Section: Resultsmentioning

confidence: 97%

Air route suspension: The role of stakeholder engagement and aviation and non-aviation factors

Lohmann

Vianna²

2016

Journal of Air Transport Management

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a b s t r a c tThis study provides insight into how aviation and non-aviation factors affect the decision to suspend air routes. Using examples from Australian domestic routes, the paper analyses the business relationships and negotiation processes followed by airports, airlines, and destination management organizations (DMOs) to avoid air route suspensions. Data were collected through semi-structured interviews with key aviation and tourism stakeholders directly impacted by suspended routes. The outcomes of this paper demonstrate that while most of the major reasons for air route suspension in Australia are mentioned in existing literature and are linked to demand, other factors have not previously been deeply investigated, including how stakeholders can be involved to avoid air route suspension. The paper also explores and identifies strengths and weaknesses in the relationship among airlines, DMOs and airports.

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Section: Resultsmentioning

confidence: 97%

Air route suspension: The role of stakeholder engagement and aviation and non-aviation factors

Lohmann

Vianna²

2016

Journal of Air Transport Management

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“…LCCs are different from conventional airlines and their business model is very simple (Srisaeng et al, 2014). They implement strategies to reduce costs such as using secondary airports because these tend to have lower airport charges (Pantazis & Liefner, 2006;Albers et al, 2010) and operate point-to-point systems.…”

Section: Measuring the Variablesmentioning

confidence: 99%

Influence of Airlines’ Size and Labour Costs on Profitability

2020

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It is important to examine profitability in the aviation sector and the factors that may affect it, due to their relevance for the airlines’ survival. The main purpose of this paper is to analyse the effect of three internal business characteristics,namely labour costs, labour productivity and company size, on airlines’ profitability, considering the airline type as a moderating effect. We have collected data of 190 European airlines during a 10-year period (2004–2013) which allowed us to create an unbalanced panel of 1,364 observations. Four types of airlines are considered. Results show that cost per employee has a positive influence on economic performance, especially for major carriers and regional carriers. It is also confirmed that the carrier type influences the relationship between labour costs, labour productivity and company size on the one hand, and economic returns on the other. These findings are relevant as they improve our understanding of economic profitability in different airlines’ types.

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“…Following the deregulation of Australia's domestic airline market in 1990, which permitted other airlines to compete with the established carriers (Forsyth 2003;Nolan 1996), a number of low cost carriers (LCCs) have entered the market. The LCCs now have around a 35 per cent market share, with the two major incumbent LCCs being Jetstar and Tiger Airways (Srisaeng et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Demand for Low Cost Carriers in Australia Using an Artificial Neural Network Approach

2015

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This study focuses on predicting Australia's low cost carrier passenger demand and revenue passenger kilometres performed (RPKs) using traditional econometric and artificial neural network (ANN) modelling methods. For model development, Australia's real GDP, real GDP per capita, air fares, Australia's population and unemployment, tourism (bed spaces) and 4 dummy variables, utilizing quarterly data obtained between 2002 and 2012, were selected as model parameters. The neural network used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The ANN was applied during training, testing and validation and had 11 inputs, 9 neurons in the hidden layers and 1 neuron in the output layer. When comparing the predictive accuracy of the two techniques, the ANNs provided the best prediction and showed that the performance of the ANN model was better than that of the traditional multiple linear regression (MLR) approach. The highest R-value for the enplaned passengers ANN was around 0.996 and for the RPKs ANN was round 0.998, respectively.

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The Evolution of Low Cost Carriers in Australia

Cited by 14 publications

References 38 publications

Air route suspension: The role of stakeholder engagement and aviation and non-aviation factors

Air route suspension: The role of stakeholder engagement and aviation and non-aviation factors

Influence of Airlines’ Size and Labour Costs on Profitability

Forecasting Demand for Low Cost Carriers in Australia Using an Artificial Neural Network Approach

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