Consumer Decision‐making at an Internet Shopbot: Brand Still Matters

Smith, Michael D.; Brynjolfsson, Erik

doi:10.1111/1467-6451.00162

Cited by 501 publications

(279 citation statements)

References 23 publications

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“…Furthermore, we also show that price dispersion in the electronic market is significantly lower than in the traditional market. This result is consistent with some work in the earlier literature, all of which used posted prices, supporting the theoretical argument that search cost in electronic markets is lower than that in traditional markets (Bakos 1997, Smith and Brynjolfsson 2001, Smith 2001. Given that our findings are different from those in many earlier studies, we point out that our findings should be interpreted in light of the differences in the research settings.…”

Section: Introductionsupporting

confidence: 90%

“…The summary shows that published price dispersion varies greatly, from as low as 4% to as high as 57%. 2 Earlier theoretical and empirical work suggests that price dispersion may result from bundling products with services Smith 2000, Baye et al 2004); differences in brand, reputation, and trust across sellers , Baye et al 2006, Chen and Hitt 2003; retailer heterogeneity (Smith and Brynjolfsson 2001, Baylis and Perloff 2002, Bailey et al 1999; product heterogeneity (Baye et al 2006); price discrimination (Clemons et al 2002); randomized pricing strategies by firms (Varian 1980, Chen and Hitt 2003, Ghose et al 2007; interaction between retailer and market characteristics (Venkatesan et al 2007), multiple channel operations (Ancarani andShankar 2004, Pan et al 2003b); and differences in vendor price format such as everyday low prices (EDLP) (Sin et al 2007, Chellappa et al 2007.…”

Section: Theory and Hypothesismentioning

confidence: 99%

“…First, electronic markets reduce search costs (Bakos 1997). Smith and Brynjolfsson (2001) and Smith (2001) estimate that search costs in electronic markets may be reduced by "at least 30-fold," compared to those in telephonebased shopping, and even more compared to the price of physically visiting the retailers. Second, electronic markets increase information transparency in both B2C and B2B scenarios (Granados et al 2009) and reduce information asymmetry (Clemons et al 1993).…”

Section: Conceptual Modelmentioning

confidence: 99%

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Using Transaction Prices to Re-Examine Price Dispersion in Electronic Markets

Ghose

Yao

2011

Information Systems Research

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P rice dispersion is an important indicator of market efficiency. Internet-based electronic markets have the potential to reduce transaction and search costs, thereby creating more efficient, "frictionless" markets, as predicted by theories in information economics. However, earlier work has reported significant levels of price dispersion on the Internet, which is in contrast to theoretical predictions. A key feature of the existing stream of work has been its use of posted prices to estimate price dispersion. In theory, this can lead to an overestimation of price dispersion because a sale may not have occurred at the posted price. In this research, we use a unique data set of actual transaction prices collected from both the electronic and offline markets of buyers in a businessto-business market to evaluate the extent of price dispersion. We find that price dispersion in the electronic market is as low as 0.22%, which is substantially less than that reported in the existing literature. This near-zero price dispersion suggests that in some electronic markets the "law of one price" can prevail when we consider transaction prices, instead of posted prices. We further develop a theoretical framework that identifies several new drivers of price dispersion using transaction data. In particular, we focus on four product-level and market-level attributes-product cost, order cycle time, own price elasticity, and transaction quantity, and we estimate their impact on price dispersion. We also examine the electronic market's moderating role in the relationship between these drivers and price dispersion. Finally, we estimate the efficiency gains that accrue from transactions in the relatively friction-free market and find that the electronic market can enhance consumer surplus by as much as $97.92 million per year.

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Section: Introductionsupporting

confidence: 90%

Section: Theory and Hypothesismentioning

confidence: 99%

Section: Conceptual Modelmentioning

confidence: 99%

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Using Transaction Prices to Re-Examine Price Dispersion in Electronic Markets

Ghose

Yao

2011

Information Systems Research

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“…Athey and Meidan (2011) and Athey and Nekipelov (2011) are important papers on the analysis of user behavior in the paid results. Previous research has indicated that CTRs can increase markedly for results placed at the top of a results page compared to other ranks and pages (Smith and Brynjolfsson 2001;Xu and Kim 2008;Ghose and Yang 2009. Of these studies, Xu and Kim (2008) is based on a small-sample laboratory experiment, and Xu and Kim (2008) uses data on paid search from an advertiser.…”

Section: Introductionmentioning

confidence: 99%

How Does Ranking Affect User Choice in Online Search?

Glick

Richards²,

Sapozhnikov³

et al. 2014

Rev Ind Organ

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This paper investigates whether a search engine's ordering of algorithmic results has an important effect on website traffic. A website's ranking on a search engine results page is positively correlated with the clicks that it receives. This could result from the search engine's accurately predicting the websites relevance to users. Or it could result from users merely clicking on the highest ranked links, regardless of the website's relevance. Using a unique dataset, we find that a website's rank, not just The analysis for this paper was performed in conjunction with ongoing work for Microsoft Corporation; Keystone and Seabright acknowledge financial support from Microsoft. The primary data that were used in the analysis come from log files from Microsoft's Bing search engine and include the website names, search result rankings, and click-through rates for results that were presented in response to user search queries on Bing.com. Supplementary data for additional website click-through rates were derived from additional Microsoft opt-in consumer panel data that contained online behavior. The authors are grateful to Elan Fuld, who provided excellent research assistance; to Scott Gingold, who was involved with the project from the beginning and gave us invaluable feedback, ideas, encouragement and support; and to Thierry Magnac, who gave us invaluable econometric expertise and spent much time helping us to understand the data.M. Glick Department of Economics, University of Utah, Salt Lake City, UT 84112, USA e-mail: glick@economics.utah.edu its relevance, strongly and significantly affects the likelihood of a click. We also find evidence that rank influences CTRs partly by controlling access to the scarce attention of users, but primarily by substituting the reputational capital of the search engine for the reputation of individual websites.

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“…This is unobservable for Geizhals.at and thus for us. Therefore, following Smith and Brynjolfsson (2001) and Bai (2004), we use the concept of last-click-through (LCT) as a proxy for the purchasing decision. If a customer is searching for a product, she might meander around different web sites, comparing characteristics of the shops, but she will finally settle for the preferred shop and buy there online.…”

Section: Data and Prevalence Of Price Pointsmentioning

confidence: 99%

99 Cent: Price Points in E-Commerce

2010

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW.Download this ZEW Discussion Paper from our ftp server:ftp://ftp.zew.de/pub/zew-docs/dp/dp10022.pdf 99c: Price-Points in E-Commerce: Non -Technical SummaryThis paper attempts to shed light on the pricing behaviour of firms or sellers in market places with price competition. In particular, it aims at testing the mechanism that lies at the heart of one of the most famous models in economics, namely the Bertrand model of competition. This model suggests that firms' profits will be zero as soon as there are at least two sellers in the market. The model is built on the idea that consumers will go to the cheapest shop, even if the differences in prices are "infinitesimal". As a result, firms are tempted to undercut their opponents' price by a small amount and they do so over and over until they end up charging the cost at which they produce their products.However, the underlying assumption is at odds with the observation that consumers typically do not care very much about a single cent and the predicted outcome clearly goes against the fact that many prices end in 99c. If sellers really charged the price at which they buy or produce a good or a service, we should observe all possible price endings. Basu (2006) argues that the prevalence of 99 cent prices in shops can be explained by rational consumers who disregard the rightmost digits of the price. This "bounded rational" behaviour leads to an Bertrand equilibrium with positive mark-ups allowing firms to make at least small profits.We use data from an Austrian price comparison site, which bears the advantage that in such an environment other factors than price both can be observed online and play a minor role. Price comparison sites are therefore highly suited for our test.We find results highly compatible with Basu's theory. We can show that price points -in particular prices ending in 9 -are more frequent than other endings and have significant impact on consumer demand. This shows that the mechanism that was postulated by Bertrand is not in place. Consumers do not necessarily choose the cheapest price and they even less do so when the Euro digits of the price are the same (i.e. when the difference is small). Moreover, the nine-ending prices are sticky: neither the price-setter itself wants to change them nor the rivals do underbid these prices, if they represent the cheapest price on the market. This might either show that shops seem to believe that it is no use to only slightly undercut a rival's price if this do...

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Consumer Decision‐making at an Internet Shopbot: Brand Still Matters

Cited by 501 publications

References 23 publications

Using Transaction Prices to Re-Examine Price Dispersion in Electronic Markets

Using Transaction Prices to Re-Examine Price Dispersion in Electronic Markets

How Does Ranking Affect User Choice in Online Search?

99 Cent: Price Points in E-Commerce

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