How much airline customers are willing to pay: An analysis of price sensitivity in online distribution channels

Garrow, Laurie A.; Jones, Stephen; Parker, Roger

doi:10.1057/palgrave.rpm.5160052

Cited by 54 publications

(38 citation statements)

References 6 publications

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“…Walker (2006) finds that the business and leisure segments put different dollar figures on factors such as total time for the trip, number of stops, aircraft changes, and so on. This is confirmed by the work of Garrow, Jones, and Parker (2007), who also showed that departure versus arrival sensitivity can affect preferred travel time, with departure-sensitive travellers showing strong morning and evening peaks, with arrival-sensitive passengers having a midday peak.…”

Section: Introductionsupporting

confidence: 55%

“…Garrow et al (2007) finds that this is true for the majority of passengers, and also finds that the minority arrival-time-sensitive passengers are generally midday travellers, speculating that hotel check-in times are the cause. In the context of short-haul operations and midday travel, when time zone impacts are relatively minor, we believe preferred arrival time can reasonably be "mapped back" to preferred departure time, and so restricting our attention to departure-time sensitive groups is a reasonable approximation (see also Evans (2009)).…”

Section: Assumptions Notation and Utility Curve Templatesmentioning

confidence: 99%

“…As time constraints are tighter for the first two types, such passengers are willing to pay a higher fare to travel at their preferred time compared to the third type (noted also by Garrow et al (2007)). …”

Section: Assumptions Notation and Utility Curve Templatesmentioning

confidence: 99%

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Airline planning benchmark problems—Part II: Passenger groups, utility and demand allocation

Akartunalı

Boland

Evans³

et al. 2013

Computers & Operations Research

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This paper is the second of two papers entitled "Airline Planning Benchmark Problems", aimed at developing benchmark data that can be used to stimulate innovation in airline planning, in particular, in flight schedule design and fleet assignment. The former has, to date, been under-represented in the optimization literature, due in part to the difficulty of obtaining data that adequately reflects passenger choice, and hence schedule revenue. Revenue models in airline planning optimization only roughly approximate the passenger decision process. However there is a growing body of literature giving empirical insights into airline passenger choice. Here we propose a new paradigm for passenger modelling, that enriches our representation of passenger revenue, in a form designed to be useful for optimization. We divide the market demand into market segments, or passenger groups, according to characteristics that differentiate behaviour in terms of airline product selection. Each passenger group has an origin, destination, size (number of passengers), departure time window, and departure time utility curve, indicating willingness to pay for departure in time sub-windows. Taking as input market demand for each origin-destination pair, we describe a process by which we construct realistic passenger group data, based on analysis of empirical airline data collected by our industry partner. We give the results of that analysis, and describe 33 benchmark instances produced.

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Section: Introductionsupporting

confidence: 55%

Section: Assumptions Notation and Utility Curve Templatesmentioning

confidence: 99%

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Airline planning benchmark problems—Part II: Passenger groups, utility and demand allocation

Akartunalı

Boland

Evans³

et al. 2013

Computers & Operations Research

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“…Focusing exclusively on the analysis of the online distribution channels, a greater number of quantitative studies, particularly those applied to the tourist industry, have been directed at: (i) quantifying the price elasticity of consumers making online purchases in different agents (Chevalier & Goolsbee, 2003); (ii) determining the predisposition to pay for a product in an online environment (Garrow, Jones & Parker, 2007), as well as whether or not the website's informative content affects the predisposition to pay (Diehl, Kornish & Lynch, 2003;Miao & Mattila, 2007); (iii) segmenting the market to understand the weight of the clients who are more sensitive to price, who have a lower per capita cost but more intensely evaluate their purchase experiences and the use of tourist services (Petrick, 2005); (iv) analyzing the influence of online evaluations-e.g. scores provided by tourists in the websites of the hotels and intermediaries, stars given by travellers, etc.-on the perceived value of each hotel offer and on future buying intention (Vermeulen & Seegers, 2009;Ogut & Taj, 2012;Sparks, Perkins & Buckley, 2013).…”

Section: Distribution Channelmentioning

confidence: 99%

Do Low Price Signals Influence Online Purchases of Tourist Accommodation Services? The Moderating Role of Gender

Vega¹,

Gil²,

Vecchio³

2014

BMR

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Based on an experimental design applied to online purchases of hotel bookings, this study analyzes the influence of three low price signals and the distribution channel on perceived value and behavioral intentions. Positive influences on perceived value and buying intention were found for price beating guarantee and always low price signals but the distribution channel was only found to have an effect on behavioral intentions. Also, it has been seen that gender plays a moderating role on both perceived value and buying intention. Finally, the most effective low price differ in function of the type of enterprise that is transmitting the information to the consumers: price beating guarantee is the best option for hotel website but always low price guarantee is better for electronic intermediaries.

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“…However, there has been a growing body of both empirical and theoretical research seeking to provide insight into airline passenger decision processes and to develop models of passenger utility. See, for example, Coldren, Koppelman, Kasturirangan, and Mukherjee (2003), Garrow, Jones, and Parker (2007), Koppelman, Coldren, and Parker (2008), Walker (2006), and Wojahn (2002). The insights provided in these papers, combined with an empirical analysis of rich data sets from a wide range of airlines worldwide, including all airlines in the Star and oneworld alliances, has led to the development of a new approach to representing airline demand data, and a methodology for generating realistic demand data sets.…”

Section: Introductionmentioning

confidence: 99%

Airline planning benchmark problems—Part I:

Akartunalı

Boland

Evans³

et al. 2013

Computers & Operations Research

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This paper is the first of two papers entitled "Airline Planning Benchmark Problems", aimed at developing benchmark data that can be used to stimulate innovation in airline planning, in particular, in flight schedule design and fleet assignment. While optimisation has made an enormous contribution to airline planning in general, the area suffers from a lack of standardised data and benchmark problems. Current research typically tackles problems unique to a given carrier, with associated specification and data unavailable to the broader research community. This limits direct comparison of alternative approaches, and creates barriers of entry for the research community. Furthermore, flight schedule design has, to date, been under-represented in the optimisation literature, due in part to the difficulty of obtaining data that adequately reflects passenger choice, and hence schedule revenue. This is Part I of two papers taking first steps to address these issues. It does so by providing a framework and methodology for generating realistic airline demand data, controlled by scalable parameters. First, a characterisation of flight network topologies and network capacity distributions is deduced, based on analysis of airline data. Then a bi-objective optimisation model is proposed to solve the inverse problem of inferring OD-pair demands from passenger loads on arcs. These two elements are combined to yield a methodology for generating realistic flight network topologies and OD-pair demand data, according to specified parameters. This methodology is used to produce 33 benchmark instances exhibiting a range of characteristics. Part II will extend this work by partitioning the demand in each market (OD pair) into market segments, each with its own utility function and set of preferences for alternative airline products. The resulting demand data will better reflect recent empirical research on passenger preference, and is expected to facilitate passenger choice modelling in flight schedule optimisation.

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How much airline customers are willing to pay: An analysis of price sensitivity in online distribution channels

Cited by 54 publications

References 6 publications

Airline planning benchmark problems—Part II: Passenger groups, utility and demand allocation

Airline planning benchmark problems—Part II: Passenger groups, utility and demand allocation

Do Low Price Signals Influence Online Purchases of Tourist Accommodation Services? The Moderating Role of Gender

Airline planning benchmark problems—Part I:

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