Online Demand Under Limited Consumer Search

By Jun B. Kim, Paulo Albuquerque, Bart J. Bronnenberg. Marketing science (Providence, R.I.), 2010

304 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e4dbdd32243d15f3f2ac580f61d740d907e7a11c

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Citations in the corpus

A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries (2018, background): “There has been a stream of recent research in marketing that applies natural language processing (NLP) to analyze online user-generated content (Archak et al. 2011, Lee and Bradlow 2011, Ghose et al. 2012, Netzer et al. 2012).”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, background): “These patterns can be exploited to quantify their search behavior using ML techniques [198].”
How is the Mobile Internet Different (2012, background): “…that the reduction in search costs associated with the internet affected prices, price dispersion, product quality, online demand, market structure, unemployment, and many other areas of economic life (see Lynch and Ariely 2000, Autor 2001, Ellison and Ellison 2009, Kim et al. 2010, etc.).”
Advertising, Consumer Awareness, and Choice: Evidence from the U.S. Banking Industry (2017, background): “…2006), in the most recent search literature, search is observed in the data either directly through data on the consumers’ consideration sets (e.g., De los Santos, Hortaçsu, and Wildenbeest, 2012; Honka, 2014) or indirectly through other variables (e.g., Kim, Albuquerque, and Bronnenberg, 2010).”
The impact of search costs on consumer behavior: A dynamic approach (2013, background): “Papers like Kim, Albuquerque, and Bronnenberg (2011), Goeree (2008), Moraga-Gonzalez, Sandor, and Wildenbeest (2009) and Koulayev (2009) show that including imperfect information has an impact on the estimates of consumer preferences which is also the case here.”
What Makes Them Click: Empirical Analysis of Consumer Demand for Search Advertising (2012, background): “Moreover, we observe more or less the same fit across search strings, which reassures us that we do not overfit or underfit any of the search strings.”