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Venkatesh Shankar - One of the best experts on this subject based on the ideXlab platform.
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Price Levels and Price Dispersion Within and Across Multiple Retailer Types: Further Evidence and Extension
Journal of the Academy of Marketing Science, 2004Co-Authors: F. Ancarani, Venkatesh ShankarAbstract:In this article, the authors develop hypotheses on how Prices and Price Dispersion compare among pure-play Internet, bricks-and-mortar (traditional), and bricks-and-clicks (multichannel) retailers and test them through an empirical analysis of data on the book and compact disc categories in Italy during 2002. Their results, based on an analysis of 13,720 Price quotes, show that when posted Prices are considered, traditional retailers have the highest Prices, followed by multichannel retailers, and pure-play e-tailers, in that order. However, when shipping costs are included, multichannel retailers have the highest Prices, followed by pure-play e-tailers and traditional retailers, in that order. With regard to Price Dispersion, pure-play e-tailers have the highest range of Prices, but the lowest standard deviation. Multichannel retailers have the highest standard deviation in Prices with or without shipping costs. These findings suggest that online markets offer opportunities for retailers to differentiate within and across the retailer types.
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Price Dispersion on the internet a review and directions for future research
Journal of Interactive Marketing, 2004Co-Authors: Xing Pan, Brian T Ratchford, Venkatesh ShankarAbstract:The explosive growth in Internet retailing has sparked a stream of research on online Price Dispersion, defined as the distribution of Prices (such as range and standard deviation) of an item with the same measured characteristics across sellers of the item at a given point in time. In this paper, we review the empirical and analytical literature on online Price Dispersion and outline the future directions in this research stream. We address the issue of whether Price Dispersion is greater or smaller online than off-line, examine whether Price Dispersion on the Internet has changed over time, discuss multichannel retailing and measurement of Price Dispersion, explore why Internet Price Dispersion exists, and investigate the drivers of online Price Dispersion.
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can Price Dispersion in online markets be explained bydifferences in e tailer service quality
Social Science Research Network, 2002Co-Authors: Xing Pan, Brian T Ratchford, Venkatesh ShankarAbstract:It has been hypothesized that the online medium and the Internet lower search costs and that electronic markets are more competitive than conventional markets. This suggests that Price Dispersion - the distribution of Prices of an item indicated by measures such as range and standard deviation - of an item with the same measured characteristics across sellers of the item at a given point in time for identical products sold by e-tailers online (on the Internet) should be smaller than it is offline, but some recent empirical evidence reveals the opposite. A study by Smith et al. (2000) speculates that this is due to heterogeneity among e-tailers in such factors as shopping convenience and consumer awareness. Based on an empirical analysis of 105 e-tailers comprising 6739 Price observations for 581 items in eight product categories, we show that online Price Dispersion is persistent, even after controlling for e-tailer heterogeneity. Our general conclusion is that the proportion of the Price Dispersion explained by e-tailer characteristics is small. This evidence is contrary to the hypothesis that search costs in online markets are low, or that online markets are highly competitive. The results also show that after controlling for differences in e-tailer service quality, Prices at pure play e-tailers are equal to or lower than those at bricks-and-clicks e-tailers for all categories except books and computer software.
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Can Price Dispersion in online markets be explained by differences in e-tailer service quality?
Journal of the Academy of Marketing Science, 2002Co-Authors: Xing Pan, Brian T Ratchford, Venkatesh ShankarAbstract:It has been hypothesized that the online medium and the Internet lower search costs and that electronic markets are more competitive than conventional markets. This suggests that Price Dispersion of an item with the same measured characteristics across sellers at a given point in time for identical products sold by e-tailers online should be smaller than it is offline, but some recent empirical evidence reveals the opposite. Based on an empirical analysis of 105 e-tailers comprising 6,739 Price observations for 581 items in eight product categories, the authors show that online Price Dispersion is persistent, even after controlling for e-tailer heterogeneity. The general conclusion is that the proportion of the Price Dispersion explained by e-tailer characteristics is small. Also, after controlling for differences in e-tailer service quality, Prices at pure-play e-tailers are equal to or lower than those at bricks-andclicks e-tailers for all categories except books and computer software.
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Price Levels and Price Dispersion on the Internet: A Comparison of Pure Play Internet, Bricks-and-Mortar, and Bricks-and-Clicks Retailers
2002Co-Authors: F. Ancarani, Università Commerciale Luigi Bocconi Milan, Venkatesh ShankarAbstract:Price levels and Price Dispersion on the Internet have attracted a lot of research and managerial attention. Contrary to initial predictions that the Internet would lead to the emergence of a frictionless economy, empirical research shows that online Price Dispersion is persistent and is no lower than offline Price Dispersion. There have been a few studies comparing Price levels at online and offline retailers, but not much is known about how Prices compare among three types of retailers, namely, pure play, bricks-and-mortar (traditional), and bricks-and-clicks (multichannel) retailers. We address this important issue in this paper through an empirical analysis of Price levels and Price Dispersion in the book and compact disc (CD) categories among the three types of retailers in Italy during early 2002. Our results, based on an analysis of 13720 Price quotes, show that when list Prices are considered, traditional retailers have the highest Prices, followed by multichannel and pure play e-tailers, in that order. However, when shipping costs are included, multichannel retailers have the highest Prices, followed by pure play e-tailers and traditional retailers, in that order. With regard to Price Dispersion, pure play e-tailers have the highest range of Prices, but the lowest variability (standard deviation). Multichannel retailers have the highest standard deviation in Prices with o
Xing Pan - One of the best experts on this subject based on the ideXlab platform.
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Price Dispersion on the internet a review and directions for future research
Journal of Interactive Marketing, 2004Co-Authors: Xing Pan, Brian T Ratchford, Venkatesh ShankarAbstract:The explosive growth in Internet retailing has sparked a stream of research on online Price Dispersion, defined as the distribution of Prices (such as range and standard deviation) of an item with the same measured characteristics across sellers of the item at a given point in time. In this paper, we review the empirical and analytical literature on online Price Dispersion and outline the future directions in this research stream. We address the issue of whether Price Dispersion is greater or smaller online than off-line, examine whether Price Dispersion on the Internet has changed over time, discuss multichannel retailing and measurement of Price Dispersion, explore why Internet Price Dispersion exists, and investigate the drivers of online Price Dispersion.
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can Price Dispersion in online markets be explained bydifferences in e tailer service quality
Social Science Research Network, 2002Co-Authors: Xing Pan, Brian T Ratchford, Venkatesh ShankarAbstract:It has been hypothesized that the online medium and the Internet lower search costs and that electronic markets are more competitive than conventional markets. This suggests that Price Dispersion - the distribution of Prices of an item indicated by measures such as range and standard deviation - of an item with the same measured characteristics across sellers of the item at a given point in time for identical products sold by e-tailers online (on the Internet) should be smaller than it is offline, but some recent empirical evidence reveals the opposite. A study by Smith et al. (2000) speculates that this is due to heterogeneity among e-tailers in such factors as shopping convenience and consumer awareness. Based on an empirical analysis of 105 e-tailers comprising 6739 Price observations for 581 items in eight product categories, we show that online Price Dispersion is persistent, even after controlling for e-tailer heterogeneity. Our general conclusion is that the proportion of the Price Dispersion explained by e-tailer characteristics is small. This evidence is contrary to the hypothesis that search costs in online markets are low, or that online markets are highly competitive. The results also show that after controlling for differences in e-tailer service quality, Prices at pure play e-tailers are equal to or lower than those at bricks-and-clicks e-tailers for all categories except books and computer software.
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Can Price Dispersion in online markets be explained by differences in e-tailer service quality?
Journal of the Academy of Marketing Science, 2002Co-Authors: Xing Pan, Brian T Ratchford, Venkatesh ShankarAbstract:It has been hypothesized that the online medium and the Internet lower search costs and that electronic markets are more competitive than conventional markets. This suggests that Price Dispersion of an item with the same measured characteristics across sellers at a given point in time for identical products sold by e-tailers online should be smaller than it is offline, but some recent empirical evidence reveals the opposite. Based on an empirical analysis of 105 e-tailers comprising 6,739 Price observations for 581 items in eight product categories, the authors show that online Price Dispersion is persistent, even after controlling for e-tailer heterogeneity. The general conclusion is that the proportion of the Price Dispersion explained by e-tailer characteristics is small. Also, after controlling for differences in e-tailer service quality, Prices at pure-play e-tailers are equal to or lower than those at bricks-andclicks e-tailers for all categories except books and computer software.
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why aren t the Prices of the same item the same at me com and you com drivers of Price Dispersion among e tailers
Social Science Research Network, 2001Co-Authors: Xing Pan, Brian T Ratchford, Venkatesh ShankarAbstract:Frictionless e-commerce implies that Price Dispersion for identical products sold by different e-tailers should be smaller than it is offline, but some recent empirical evidence reveals the opposite. A study by Smith et al. (2000) suggests that such a phenomenon may be due to heterogeneity among e-tailers in such factors as shopping convenience, consumer awareness, and trust. These hypotheses, however, remain untested. In this paper, we extend previous research by developing a comprehensive framework of the drivers of online Price Dispersion that includes market characteristics such as number of competitors, consumer involvement, and product popularity, in addition to e-tailer characteristics and product category differences. We also empirically test our propositions in a more comprehensive manner than prior research by using a range of Price Dispersion measures covering 6,739 Price quotes for 581 products from 105 e-tailers in a variety of product categories including books, CDs, DVDs, desktop computers, laptop computers, PDAs, computer software, and consumer electronics. Specifically, we (1) identify some key dimensions of e-tailer heterogeneity using factor analysis; (2) identify clusters of e-tailers on these dimensions using cluster analysis; (3) analyze how market factors affect Price Dispersion using regression analyses; and (4) examine how heterogeneity among e-tailers is related to their Prices using hedonic regressions by category and by cluster. Our results show that e-tailer services can be characterized by five underlying factors, namely, shopping convenience, reliability in fulfillment, product information, shipping and handling, and pricing policy. There are three clusters of e-tailers who target different consumer groups and position themselves differently along these five factors. Even after controlling for e-tailer characteristics, online Price Dispersion is large. Market characteristics drive a large portion of this e-tailer Price Dispersion. Specifically, Price Dispersion increases with involvement or average Price level of items, albeit at a decreasing rate, and decreases with the number of competitors, but at a diminishing rate. The models explain over 92% of the variance in Price Dispersion. E-Tailers charge Prices in line with their characteristics, but do not necessarily command higher Prices for superior services. The drivers of e-tailer Prices also vary significantly by the cluster to which the e-tailers belong.
Yuanfang Lin - One of the best experts on this subject based on the ideXlab platform.
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why is Price Dispersion higher online than offline the impact of retailer type and shopping risk on Price Dispersion
Journal of Retailing, 2018Co-Authors: Hejun Zhuang, Peter Popkowski T L Leszczyc, Yuanfang LinAbstract:When physically similar products, of similar quality, are offered by retailers both online and offline, we often observe that the Dispersion in Prices of these products online is greater than the Price Dispersion offline. This observation runs counter to early theories that suggested Price Dispersion online would be smaller than that offline due to the ease of search and information availability online. This paper investigates and provides an explanation for this puzzling phenomenon by examining the impact of two important drivers of Price Dispersion: retailer type and consumers’ shopping risk. Retailer type refers to whether a retailer is a pure offline, pure online, or dual channel retailer. Shopping risk is defined as the product of consumers’ perceived risk of shopping and the transaction uncertainty related to shopping at different types of retailers.
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why is Price Dispersion higher online than offline the impact of retailer type and shopping risk on Price Dispersion
Social Science Research Network, 2018Co-Authors: Hejun Zhuang, Peter Popkowski T L Leszczyc, Yuanfang LinAbstract:When physically similar products, of similar quality, are offered by retailers both online and offline, we often observe that the Dispersion in Prices of these products online is greater than the Price Dispersion offline. This observation runs counter to early theories that suggested Price Dispersion online would be smaller than that offline due to the ease of search and information availability online. This paper investigates and provides an explanation for this puzzling phenomenon by examining the impact of two important drivers of Price Dispersion: retailer type and consumers’ shopping risk. Retailer type refers to whether a retailer is a pure offline, pure online, or dual channel retailer. Shopping risk is defined as the product of consumers’ perceived risk of shopping and the transaction uncertainty related to shopping at different types of retailers. A game-theoretic approach is adopted to model consumers’ Price search and product purchase, as well as Price competition within and across retailer types in online and offline markets. Equilibrium pricing strategies are derived for different retailer types competing for different consumer segments with different levels of perceived shopping risk. The impact of retailer type and shopping risk on online versus offline Price Dispersion are quantified, and conditions when Price Dispersion is greater online than offline are identified. Results indicate that Price Dispersion is greater online when the number of pure online retailers is sufficiently large and is increasing in the number of pure online retailers. In addition, a reduction in online shopping risk may actually increase online Price Dispersion. Results further suggest that even without any online sales, dual channel retailers should maintain their online presence for the purpose of information dissemination, which justifies the importance for pure offline retailer to incorporate webrooming strategies, where consumers can search for Prices online but purchase offline.
Brian T Ratchford - One of the best experts on this subject based on the ideXlab platform.
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Price Dispersion on the internet a review and directions for future research
Journal of Interactive Marketing, 2004Co-Authors: Xing Pan, Brian T Ratchford, Venkatesh ShankarAbstract:The explosive growth in Internet retailing has sparked a stream of research on online Price Dispersion, defined as the distribution of Prices (such as range and standard deviation) of an item with the same measured characteristics across sellers of the item at a given point in time. In this paper, we review the empirical and analytical literature on online Price Dispersion and outline the future directions in this research stream. We address the issue of whether Price Dispersion is greater or smaller online than off-line, examine whether Price Dispersion on the Internet has changed over time, discuss multichannel retailing and measurement of Price Dispersion, explore why Internet Price Dispersion exists, and investigate the drivers of online Price Dispersion.
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can Price Dispersion in online markets be explained bydifferences in e tailer service quality
Social Science Research Network, 2002Co-Authors: Xing Pan, Brian T Ratchford, Venkatesh ShankarAbstract:It has been hypothesized that the online medium and the Internet lower search costs and that electronic markets are more competitive than conventional markets. This suggests that Price Dispersion - the distribution of Prices of an item indicated by measures such as range and standard deviation - of an item with the same measured characteristics across sellers of the item at a given point in time for identical products sold by e-tailers online (on the Internet) should be smaller than it is offline, but some recent empirical evidence reveals the opposite. A study by Smith et al. (2000) speculates that this is due to heterogeneity among e-tailers in such factors as shopping convenience and consumer awareness. Based on an empirical analysis of 105 e-tailers comprising 6739 Price observations for 581 items in eight product categories, we show that online Price Dispersion is persistent, even after controlling for e-tailer heterogeneity. Our general conclusion is that the proportion of the Price Dispersion explained by e-tailer characteristics is small. This evidence is contrary to the hypothesis that search costs in online markets are low, or that online markets are highly competitive. The results also show that after controlling for differences in e-tailer service quality, Prices at pure play e-tailers are equal to or lower than those at bricks-and-clicks e-tailers for all categories except books and computer software.
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Can Price Dispersion in online markets be explained by differences in e-tailer service quality?
Journal of the Academy of Marketing Science, 2002Co-Authors: Xing Pan, Brian T Ratchford, Venkatesh ShankarAbstract:It has been hypothesized that the online medium and the Internet lower search costs and that electronic markets are more competitive than conventional markets. This suggests that Price Dispersion of an item with the same measured characteristics across sellers at a given point in time for identical products sold by e-tailers online should be smaller than it is offline, but some recent empirical evidence reveals the opposite. Based on an empirical analysis of 105 e-tailers comprising 6,739 Price observations for 581 items in eight product categories, the authors show that online Price Dispersion is persistent, even after controlling for e-tailer heterogeneity. The general conclusion is that the proportion of the Price Dispersion explained by e-tailer characteristics is small. Also, after controlling for differences in e-tailer service quality, Prices at pure-play e-tailers are equal to or lower than those at bricks-andclicks e-tailers for all categories except books and computer software.
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why aren t the Prices of the same item the same at me com and you com drivers of Price Dispersion among e tailers
Social Science Research Network, 2001Co-Authors: Xing Pan, Brian T Ratchford, Venkatesh ShankarAbstract:Frictionless e-commerce implies that Price Dispersion for identical products sold by different e-tailers should be smaller than it is offline, but some recent empirical evidence reveals the opposite. A study by Smith et al. (2000) suggests that such a phenomenon may be due to heterogeneity among e-tailers in such factors as shopping convenience, consumer awareness, and trust. These hypotheses, however, remain untested. In this paper, we extend previous research by developing a comprehensive framework of the drivers of online Price Dispersion that includes market characteristics such as number of competitors, consumer involvement, and product popularity, in addition to e-tailer characteristics and product category differences. We also empirically test our propositions in a more comprehensive manner than prior research by using a range of Price Dispersion measures covering 6,739 Price quotes for 581 products from 105 e-tailers in a variety of product categories including books, CDs, DVDs, desktop computers, laptop computers, PDAs, computer software, and consumer electronics. Specifically, we (1) identify some key dimensions of e-tailer heterogeneity using factor analysis; (2) identify clusters of e-tailers on these dimensions using cluster analysis; (3) analyze how market factors affect Price Dispersion using regression analyses; and (4) examine how heterogeneity among e-tailers is related to their Prices using hedonic regressions by category and by cluster. Our results show that e-tailer services can be characterized by five underlying factors, namely, shopping convenience, reliability in fulfillment, product information, shipping and handling, and pricing policy. There are three clusters of e-tailers who target different consumer groups and position themselves differently along these five factors. Even after controlling for e-tailer characteristics, online Price Dispersion is large. Market characteristics drive a large portion of this e-tailer Price Dispersion. Specifically, Price Dispersion increases with involvement or average Price level of items, albeit at a decreasing rate, and decreases with the number of competitors, but at a diminishing rate. The models explain over 92% of the variance in Price Dispersion. E-Tailers charge Prices in line with their characteristics, but do not necessarily command higher Prices for superior services. The drivers of e-tailer Prices also vary significantly by the cluster to which the e-tailers belong.
Henry S Schneider - One of the best experts on this subject based on the ideXlab platform.
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search costs and equilibrium Price Dispersion in auction markets
European Economic Review, 2014Co-Authors: Matthew Backus, Joseph Podwol, Henry S SchneiderAbstract:Abstract A leading explanation for Price Dispersion in posted-Price markets is search costs. We incorporate this insight into a model of competing second-Price auctions similar to eBay. By doing so, we extend the narrow literature on competing auctions to capture Price Dispersion, and grow the already large literature on Price Dispersion to include auctions. We provide evidence on search costs and Price Dispersion using data collected from eBay, identifying search costs by exploiting a discontinuity in the visibility of auctions due to eBay׳s search tool.
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search costs and equilibrium Price Dispersion in auction markets
Social Science Research Network, 2013Co-Authors: Matthew Backus, Joseph Podwol, Henry S SchneiderAbstract:A leading explanation for Price Dispersion in posted-Price markets is search costs. We incorporate this insight into a model of competing second-Price auctions similar to eBay. By doing so, we extend the narrow literature on competing auctions to capture Price Dispersion, and grow the already vast literature on Price Dispersion to include auctions. We provide evidence for the model using data collected from eBay, identifying search costs by exploiting a discontinuity in the visibility of auctions due to eBay’s search tool.Published as "Search Costs and Equilibrium Price Dispersion in Auction Markets," European Economic Review 74, 2014, 173-192.
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search costs and equilibrium Price Dispersion in auction markets
Research Papers in Economics, 2013Co-Authors: Matthew Backus, Joseph Podwol, Henry S SchneiderAbstract:A leading explanation for Price Dispersion in posted-Price markets is search costs. We incorporate this insight into a model of competing second-Price auctions similar to eBay. By doing so, we extend the narrow literature on competing auctions to capture Price Dispersion, and grow the already vast literature on Price Dispersion to include auctions. We provide evidence for the model using data collected from eBay, identifying search costs by exploiting a discontinuity in the visibility of auctions due to eBay's search tool.
