Reference and Literature Review
Azzolina, Stefano, et al. “Price Discrimination in the Online Airline Market: An Empirical Study.” Journal of Theoretical and Applied Electronic Commerce Research, vol. 16, no. 6, Sept. 2021, pp. 2282–303. Crossref, https://doi.org/10.3390/jtaer16060126.
Review: The paper examined price differences between different user types, including frequent travelers, price-sensitive customers and business travelers. The results show that airlines does engage in price discrimination, offering different prices to different types of customers depends on factors such as travel date, time, and booking channel. Frequent flyers and business travelers often charge higher prices, while price-sensitive customers receive more discounts/coupons in their iternery. The study also found that price discrimination had a great impact on consumer behavior, and that different price concessions elicited different responses from different types of customers. For example, business travelers are less sensitive to price changes, while price-sensitive customers are more likely to choose lower-priced options. Overall, the study highlights the prevalence and impact of price discrimination in online airline marketplaces and highlights the need for consumers to be aware of these practices and utilize tools and strategies to ensure they receive the best price available.
Lewis, Matthew S. “Identifying airline price discrimination and the effect of competition.” International Journal of Industrial Organization 78 (2021): 102761. https://www.sciencedirect.com/science/article/pii/S0167718721000540.
Review: The paper focus on different level of airline price discrimination and the impact of competition on these practices. Most findings here agree with the study from Azzolina et al, where airlines do engage in price discrimination behavior and determine prices based on types of customers and other factors. The author argues that competition is critical to reducing price discrimination in this sector. In markets with less competition (some airlines might have exclusive access to certain airports. e.g., most flights out/into DFW airport belong to American Airlines), airlines charge higher prices, suggesting that increased competition leads to lower prices and less price discrimination. This is because airlines need to lower their prices to remain competitive and attract more customers when competition is more intense. In addition, the study highlights the need for policies that promote competition in the airline industry to ensure consumers have access to affordable travel options and reduce price discrimination.
Stavins, Joanna. “Price Discrimination in the Airline Market: The Effect of Market Concentration.” Review of Economics and Statistics, vol. 83, no. 1, 2001,pp.200–202. https://doi.org/10.1162/rest.2001.83.1.200.
Review: This paper examines the effect of market concentration on price discrimination in the airline industry. Despite variations in ticket prices across the spectrum of customers, in highly concentrated markets, airlines charge higher prices, indicating limited competition and more widespread price discrimination. By contrast, airlines charge lower prices in less concentrated markets, suggesting greater competition and less widespread price discrimination.The author underscores the importance of competition in reducing price discrimination and ensuring consumers have access to affordable travel options. And the paper suggests that lower entry barriers and increasing pricing transparency may help reduce price discrimination and improve overall consumer welfare, which seems to become less feasible without government intervention.
Williams, Kevin. Dynamic Airline Pricing and Seat Availability. Cowles Foundation DiscussionPaper No.2103R, May 2020. http://dx.doi.org/10.2139/ssrn.3611696.
Review: The article explains the use of dynamic pricing by airlines to adjust ticket prices in real time based on various factors such as current demand, competition, and market conditions. The airline company closely monitors its revenue through data analysis and continually adjusts prices to optimize revenue. The author also discusses how airlines utilize seat inventory to maximize potential revenue. The author points out that airlines use advanced algorithms and machine learning models to determine the best pricing strategy based on historical/current features such as booking patterns, seasonality, and competitor pricing. Such technology is also standard in seat allocation, where the company strategically allocates seats to different fare classes, such as economy, premium, and business class. The authors argue that advanced analytics and predictive modeling are critical components of revenue management strategies and are essential for airlines to remain competitive and profitable in a highly dynamic and competitive market. In conclusion, machine learning models (or AI models) are prevalent tools in deciding flight ticket prices, implying that unbias machine learning models are detrimental as they will inevitably lead to price discrimination.