|dc.description.abstract||According to eMarketer (2015), approximately 60% of travel product purchases are made online, a figure that will continue to increase due to the widespread use of smartphones and the booming of mobile device booking. To motivate customers to book in advance using their own domains, all major hotel chains, airline companies, car rental firms, cruise lines, and third-party online travel agencies (OTAs) have launched various lowest price guarantee policy (LPGP) programs (Garrido, 2012). These policies promise to match the lower rate (some offer additional incentives) within a certain period of time since purchase. To have a successful LPGP, travel companies not only need to find a desirable combination of policy features (e.g., policy duration, refund depth) palatable to the shoppers in the targeted market but also to handle them carefully to fit their overall financial capabilities and risk appetite.
This dissertation consists of three studies on LPGPs from different perspectives, the results of which complement each other, thus providing a strong managerial and theoretical guidance to the travel industry. The basic methodologies proposed are general and can be readily applied to different sectors of the travel industry. The Monte Carlo real option pricing method can be used to estimate companies’ financial costs of offering an LPGP, while choice-based conjoint analysis (CBCA) can help in assessing the policy value perceived by customers. As a result, management can leverage both tools to design a competitive LPGP without losing cost-effectiveness.
The first study documents the existing LPGPs in the current travel market and summarizes them in five key features using data (policies) published by travel websites of a majority of service sectors in the US. In addition, it infers the motives, policy effects, and financial risks from these features for companies that adopt them. It also examines the restrictions and hassle costs for customers who use LPGPs to obtain refunds. It also provides insight on policies’ similarities and differences between two major distribution channels (brand official websites and online travel agencies) as well as within and across diverse service sectors (hotels, airlines, car rental firms, and cruise lines). The results suggest that a majority of LPGPs are inconsistent with their use as a facilitating device because travel companies add numerous restrictions to mitigate the financial risks involved in LPGPs and customers’ refunds are associated with relatively high hassle costs. It also shows that price-beating LPGPs (PB LPGPs) and price-matching LPGPs (PM LPGPs) differ significantly in their features, with PB LPGPs being linked with higher hassle costs and being more likely to have restricted features than customer favored features. Furthermore, it is observed that LPGPs vary across distribution channels and service sectors while having more homogeneity in terms of features within the service sector. The evidence reveals that a great number of brand official websites offer PB LPGPs while a majority of online travel agencies employ PM LPGPs.
The second study focuses on LPGPs from a risk management perspective. It examines the cost of promoting LPGPs from the standpoint of real option pricing, simulating the price paths of underlying assets (travel products or services) using the Monte Carlo method, and the necessity of provisions as tools for managing policy risk exposure. The study presents numerical examples using data from Orbitz.com and applies the parameters derived from real-world data to simulate the price paths of airfares. The simulation results show that the probability of a lower price occurring throughout the booking period up until departure is 92% and that the average affordability of offering Orbitz Price Assurance is 19%, which means for every US$100 worth of air ticket sales, a maximum US$19 provision should be made to satisfy potential customer refund claims.
The third study is an extension of the other two studies and analyzes LPGPs from the perspective of customers. It provides significant insight into customers’ perceptions and preferences regarding the LPGP features and calibrates the importance of each feature and the customer utility of different feature levels by using a fractional orthogonal design and CBCA in questionnaire development and preference modeling. The findings show that customers perceive duration as the most important feature, followed by refund, scope, and required customer action as the least important; the threshold feature is not significant in the model. The results show that “any time before departure” carries the highest utility score among the 17 tested feature categories, indicating customers assign very high importance to it in their decision-making process. Furthermore, the survey finds only 6.2% of customers who are experienced online travel product shoppers have used an LPGP to claim a refund. To leverage the results of the second study, the author can estimate that the overall cost of LPGPs (featuring duration throughout the booking period until departure) is approximately 1.26% of total sales. Last, this study and its conclusion provide a strong managerial and theoretical implication for the travel industry and offer a fundamental framework to design an LPGP in a presumably wide range of target markets.||en_US