How social comparison influences reference price formation in a service context

What is the influence on reference price when the source of price information is anonymous versus social? This article investigates the formation of reference prices given an observed sequence of past prices in a service context. An experimental study suggests that, considering the same price information, if the source is social (i.e., the prices paid by colleagues), then consumers want to pay less. More specifically, social comparison changes the way individuals weigh information, attributing more importance to the lowest historical prices and to the range in price variations

Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research

This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyzes prior studies in the context of Internet applications to Tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing

Consumer Search in the U.S. Auto Industry

Consumers may become aware of products and their characteristics either passively or actively. While passive awareness may arise through conversations with friends or consumption of advertising, active awareness occurs through a process of canvassing sellers. Consumers often refer to this activity as browsing or shopping; economists refer to this activity as search. This dissertation begins with a summary of the current economic models of consumer search and the econometric methods used to estimate those models. An emphasis will be placed on models that assume consumers search sequentially for product fit, as opposed to non-sequential search or search for observable product characteristics such as price. The bulk of this dissertation then provides an empirical analysis of consumer search through which I demonstrate identification of -- and provide a first estimation of -- a key parameter in sequential search models, the standard deviation of product fit. I devote separate chapters to detailed explanations of the data acquired for the analysis; the model, its estimation, and identification of this key parameter; and important post-estimation analysis including an assessment of model fit, calculations of consumer surplus and price elasticities, and implementation of counterfactual analyses. The setting of my empirical analysis is the U.S. automotive market. I assemble a unique data set containing individual-level smartphone geolocation data that inform me about dealership visits which I combine with proprietary DMV registration data that inform me of new vehicle purchases. I model consumers' dealership visits and purchase decisions using a discrete choice model of demand with optimal sequential search for product fit. In these models, the benefit of searching is measured by the standard deviation of the product fit. This benefit is parametrically identified by the functional form of the match-value, but in practice it is difficult to jointly estimate along with consumer search costs. I use the distance a consumer must travel to visit a dealership as an exogenous search cost shifter, which enables me to identify and estimate the standard deviation of the product fit. My results show that the benefit provided by dealerships to consumers is substantial and that failure to estimate the standard deviation of the product fit leads to biased estimates of search costs and consumer surplus, as well as to inaccurate predictions regarding the number of searches that consumers conduct. These effects can impact managerial decisions, as demonstrated through my counterfactual analyses

Consumer online search with partially revealed information

Modern-day search platforms generally have two layers of information presentation. The outer layer displays the collection of search results with attributes selected by platforms, and consumers click on a product to reveal all its attributes in the inner layer. The information revealed in the outer layer affects the search costs and the probability of finding a match. To address the managerial question of optimal information layout, we create an information complexity measure of the outer layer, namely orderedness entropy, and study the consumer search process for information at the expense of time and cognitive costs. We first conduct online random experiments to show that consumers respond to and actively reduce cognitive cost for which our information complexity measure provides a representation. Then, using a unique and rich panel tracking consumer search behaviors at a large online travel agency (OTA), we specify a novel sequential search model that jointly describes the refinement search and product clicking decisions. We find that cognitive cost is a major component of search cost, while loading time cost has a much smaller share. By varying the information revealed in the outer layer, we propose information layouts that Pareto-improve both revenue and consumer welfare for our OTA.This paper was accepted by Juanjuan Zhang, marketing

Essays on consumer search

This thesis consists of three essays on different topics within the field of consumer search. The first essay proposes and solves a model for how consumers decide between discovering more products and searching among alternatives they are already aware of. The second essay builds on this model and quantifies the effects of different product rankings on consumer surplus and an online search intermediary’s revenues. The third and final essay proposes and solves a model for how consumers allocate time to searching across different product categories

GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM

A rating system or reviews are generally used to assist in making decisions. Rating system widely used as a technique in the recommendation of one of them used by the customer, as in determining the resort to be used. However, the credibility of the rating looks vague because the rating could only represent some points of service. So that customer preference with each other is very different. Personalized recommendation systems offer more personalized advice, precisely knowing the preferences or tastes of the customers. Especially for customers who have a transaction history or reservation as at their resorts provide good information used by managers to design a recommendation model for their customers. In this study aims to create a model of resort recommendations based on a rating of frequency. This frequency is the number of resort use by the customer within the specified time frame. With the frequency can represent the preferences of customers. The RFM method is used to measure the reservation frequency value of the customer. The K-Means method is used to categorize customer data with its frequency and classify the type of resort. Recommendation resort to the customer based on the dominant use in one of the resort types. The recommended type of resort based on the similarity between the types of resorts used with other types of resorts

Disintermediation in the United States air travel industry who hold the power of booking strength

The purpose of this study is to investigate the correlation of U.S. domestic air travelers and patterns of air travel purchase within the U.S. air travel industry. Influences on the patterns of air travel purchase involved 1) demographic characteristics of travelers 2) benefits to the customer 3) customer attitude toward service providers\u27 (defined as airlines, web-based travel agents, and traditional travel agents and 4) information sources used to select and book a flight for business and leisure trips

Strategic alliance and loyalty marketing: Do partnerships affect loyalty customers?

It becomes essential for hospitality professionals to explore the concepts that underlie in strategic alliances of loyalty programs to assess business strategies as a source of competitive advantage to improve and sustain customer loyalty which will eventually bring growth to a company. Having such a numerous number of selections of hotel brands at the present time, are customers loyal to a hotel because of their preference in a specific brand or are they loyal because of the additional benefits they can receive from partnering companies? In other words, does strategic alliance create value and increase customer satisfaction which leads to customer loyalty? The purpose of this paper is to determine the strategic alliance impact on loyalty customers; This study designed a questionnaire and conducted a survey and performed regression analysis to test the hypotheses. This study tried to examine how customers perceive the value creation factors initiated by hotels in relation with partnerships to measure the strategic alliance impact. (Abstract shortened by UMI.)

Estimating Search with Learning

In this paper we estimate a structural model of search for differentiated products, using a unique dataset of consumer online search for hotels. We propose and implement an identification strategy that allows us to separately estimate consumer's beliefs, search costs and preferences. Learning plays an essential role in this strategy. It creates variation of posterior beliefs across consumers that is orthogonal to the variation in search costs. We show that ignoring endogeneity of choice sets due to search may lead to significant biases in estimates of consumer demand: from 50 to more than 200 percent depending on informational assumptions. Second, th median search cost is about 25 dollars per 15 hotels; there is also a significant heterogeneity of search costs among the population. We perform a statistical test between models of search from known (Stigler 1967) and from unknown (Rothschild 1974) distribution and find that our data favors the latter: we find a statistically significant amount of Bayesian learning