Do links matter? An investigation of the impact of consumer feedback, recommendation networks, and price bundling on sales

Link structures among items within an E-commerce Web site can be regarded as a potential recommendation that helps new consumers quickly locate relevant products. In this paper, combining a modified version of Google's PageRank method with economic analysis of word of mouth, we investigate whet...

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Main Authors: HU, Nan, TIAN, Gaoliang, LIU, Ling, LIANG, Bin, GAO, Yunjun
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Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/sis_research/8221
https://doi.org/10.1109/TEM.2010.2064318
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spelling sg-smu-ink.sis_research-92242023-10-25T02:32:05Z Do links matter? An investigation of the impact of consumer feedback, recommendation networks, and price bundling on sales HU, Nan TIAN, Gaoliang LIU, Ling LIANG, Bin GAO, Yunjun Link structures among items within an E-commerce Web site can be regarded as a potential recommendation that helps new consumers quickly locate relevant products. In this paper, combining a modified version of Google's PageRank method with economic analysis of word of mouth, we investigate whether a product's position within a network composed of recommendation links is incrementally informative about its future sales. Based on data from Amazon.com, we document that with consumer word of mouth and other product characteristics controlled, the position of a product within a recommendation network does influence consumers' purchase decisions, and models incorporating link structure have a higher incremental predictive power of future sales than models without. In addition, as time elapses, the relative weights consumers placed on recommendations with price discount and those without are different. Last, we develop a learning mechanism through which we find the optimal damping value of the PageRank IR model in the Amazon context. Our results show that compared to general Internet surfing behavior, consumer consumption on Amazon is less random. We conclude that even though the product position within a recommendation network does influence customers' purchase behaviors, product sales are still mainly driven by their own product characteristics. 2012-05-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/8221 info:doi/10.1109/TEM.2010.2064318 https://doi.org/10.1109/TEM.2010.2064318 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University collaborative-filtering cross sale marketing strategy online word of mouth (WOM) PageRank price strategy Databases and Information Systems E-Commerce
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic collaborative-filtering cross sale
marketing strategy
online word of mouth (WOM)
PageRank
price strategy
Databases and Information Systems
E-Commerce
spellingShingle collaborative-filtering cross sale
marketing strategy
online word of mouth (WOM)
PageRank
price strategy
Databases and Information Systems
E-Commerce
HU, Nan
TIAN, Gaoliang
LIU, Ling
LIANG, Bin
GAO, Yunjun
Do links matter? An investigation of the impact of consumer feedback, recommendation networks, and price bundling on sales
description Link structures among items within an E-commerce Web site can be regarded as a potential recommendation that helps new consumers quickly locate relevant products. In this paper, combining a modified version of Google's PageRank method with economic analysis of word of mouth, we investigate whether a product's position within a network composed of recommendation links is incrementally informative about its future sales. Based on data from Amazon.com, we document that with consumer word of mouth and other product characteristics controlled, the position of a product within a recommendation network does influence consumers' purchase decisions, and models incorporating link structure have a higher incremental predictive power of future sales than models without. In addition, as time elapses, the relative weights consumers placed on recommendations with price discount and those without are different. Last, we develop a learning mechanism through which we find the optimal damping value of the PageRank IR model in the Amazon context. Our results show that compared to general Internet surfing behavior, consumer consumption on Amazon is less random. We conclude that even though the product position within a recommendation network does influence customers' purchase behaviors, product sales are still mainly driven by their own product characteristics.
format text
author HU, Nan
TIAN, Gaoliang
LIU, Ling
LIANG, Bin
GAO, Yunjun
author_facet HU, Nan
TIAN, Gaoliang
LIU, Ling
LIANG, Bin
GAO, Yunjun
author_sort HU, Nan
title Do links matter? An investigation of the impact of consumer feedback, recommendation networks, and price bundling on sales
title_short Do links matter? An investigation of the impact of consumer feedback, recommendation networks, and price bundling on sales
title_full Do links matter? An investigation of the impact of consumer feedback, recommendation networks, and price bundling on sales
title_fullStr Do links matter? An investigation of the impact of consumer feedback, recommendation networks, and price bundling on sales
title_full_unstemmed Do links matter? An investigation of the impact of consumer feedback, recommendation networks, and price bundling on sales
title_sort do links matter? an investigation of the impact of consumer feedback, recommendation networks, and price bundling on sales
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/sis_research/8221
https://doi.org/10.1109/TEM.2010.2064318
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