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Introducing spatial context in comparative pricing and product search

Published: 28 October 2013 Publication History

Abstract

Price survey services assist consumers interested in purchasing a new product online. Nevertheless, even being efficient in consulting the suppliers and returning up-to-date information about price and availability of products, current services still provide trivial and incomplete information concerning the price dispersion and the price for the final consumer. Usually, only instant prices are provided. These prices change rapidly and do not include shipping costs, which are usually paid by the consumers. In this context, this work proposes an approach based on Big Data to introduce the spatial context supplier-consumer in comparative price surveys and also to store and present the price variation history of the products as a function of their spatial and temporal features. A case study is carried out in order to validate the proposal and to highlight the advantages of this approach for the users of such services.

References

[1]
D. Appelquist, D. Brickley, M. Carvahlo, R. Iannella, A. Passant, and C. Perey. A Standards-based, Open and Privacy-aware Social Web. Technical report, W3C Social Web Incubator Group, 2010. Retrieved April, 18, 2013, data from: http://www.w3.org/2005/Incubator/socialweb/XGRsocialweb-20101206/.
[2]
A. Auinger and M. Fischer. Mining consumers' opinions on the web. FH Science Day, Linz, Osterreich, pages 410--419, 2008.
[3]
M. R. Baye, J. Morgan, and P. Scholten. Information, Search, and Price Dispersion. In Working Papers 2006-11. Indiana University, Kelley School of Business, Department of Business Economics and Public Policy, 2006.
[4]
M. R. Baye, J. Morgan, and P. Scholten. The new economy and beyond, chapter Persistent price dispersion in on-line markets. Edward Elgar Press, Northampton, MA, 2006.
[5]
D. Clark. Shopbots become agents for business change. IEEE Computer, 33(2): 18--21, 2000.
[6]
M. Eirinaki, S. Pisal, and J. Singh. Feature-based opinion mining and ranking. Journal of Computer and System Sciences, 78(4): 1175--1184, 2012.
[7]
L. Garber. Analytics goes on location with new approaches. IEEE Computer, 46(4): 14--17, 2013.
[8]
R. Garfinkel, R. Gopal, B. Pathak, and F. Yin. Shopbot 2.0: Integrating recommendations and promotions with comparison shopping. Decision Support Systems, 46(1): 61--68, 2008.
[9]
R. Garfinkel, R. D. Gopal, A. K. Tripathi, and F. Yin. Design of a bundle shopbot and recommender system for bundle purchases. Decision Support Systems, 42(3): 1974--1986, 2006.
[10]
M. Haynes and S. Thompson. Entry and Exit Behavior in the Absence of Sunk Costs: Evidence from a Price Comparison Site. Review of Industrial Organization, Springer Science Business Media, 42(1): 1--23, 2013.
[11]
O. Hinz and T. Frischmann. Shopbots and Information Quality: Retailers' Strategies for Price Concealment. In 16th European Conference on Information Systems, pages 1847--1858, 2008.
[12]
G. Iyer and A. Pazgal. Internet shopping agents: virtual co-location and competition. Marketing Science, 22(1): 85--106, 2003.
[13]
Y. Jiang. HBase Administration Cookbook. Packt Publishing, 2011.
[14]
M. Kukar-Kinney and A. G. Close. The determinants of consumers' online shopping cart abandonment. Journal of the Academy of Marketing Science, 38(2): 240--250, 2010.
[15]
A. Leff and J. T. Rayfield. Web-Application Development Using the Model/View/Controller Design Pattern. In Proceedings of 5th IEEE International Enterprise Distributed Object Computing Conference, pages 118--127, 2001.
[16]
G. G. Lim, J. Y. Kang, J. K. Lee, and D. C. Lee. Rule-based personalized comparison shopping including delivery cost. Electronic Commerce Research and Applications, 10(6): 637--349, 2011.
[17]
A. L. Montgomery, K. Hosanagar, R. Krishnan, and K. B. Clay. Designing a better shopbot. Management Science, 50(2): 189--206, 2004.
[18]
T. O'Reilly. What Is Web 2.0: Design Patterns and Business Models for the Next Generation of Software. Communications and Strategies, 65(1): 17--37, 2007.
[19]
A. B. Patel, M. Birla, and U. Nair. Addressing big data problem using Hadoop and Map Reduce. In Proceedings of Nirma University International Conference on Engineering, pages 1--5, 2012.
[20]
B. K. Pathak. Comparison Shopping Agents and Online Price Dispersion: A Search Cost based Explanation. Journal of Theoretical and Applied Electronic Commerce Research, 7(1): 64--76, 2012.
[21]
S. Shekhar, V. Gunturi, M. R. Evans, and K. Yang. Spatial Big-Data Challenges Intersecting Mobility and Cloud Computing. In NSF Workshop on Social Networks and Mobility in the Cloud, 2012.
[22]
M. D. Smith and E. Brynjolfsson. Consumer decision making at an internet shopbot: brand still matters. The Journal of Industrial Economics, 49(4): 541--558, 2001.
[23]
J. Star and J. Estes. Geographic Information Systems: An introduction. Prentice Hall series in geographic information science, 1990.
[24]
H. R. Varian. A model of sales. The American Economic Review, 70(4): 651--659, 1980.
[25]
Y. Wan and G. Peng. What's Next for Shopbots? IEEE Computer, 43(5): 20--26, 2010.
[26]
T. White. Hadoop: The Definitive Guide. O'Reilly Media, 3rd edition, 2012.
[27]
Y. Xu and H. W. Kim. Order effect and vendor inspection in online comparison shopping. Journal of Retailing, 84(4): 477--486, 2008.
[28]
J. Zhang and B. Jing. The Impacts of Shopbots on Online Consumer Search. In Proceedings of Hawaii International Conference on Systems Science, pages 1--10. IEEE Computer Society, 2011.
[29]
T. Zhou. An empirical examination of user adoption of location-based services. Electronic Commerce Research, 13(1): 25--39, 2013.

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Published In

cover image ACM Other conferences
MEDES '13: Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
October 2013
358 pages
ISBN:9781450320047
DOI:10.1145/2536146
  • Conference Chairs:
  • Latif Ladid,
  • Antonio Montes,
  • General Chair:
  • Peter A. Bruck,
  • Program Chairs:
  • Fernando Ferri,
  • Richard Chbeir
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • LBBC: Luxembourg Brazil Business Council
  • IPv6 Luxembourg Council: Luxembourg IPv6 Council
  • Luxembourg Green Business Awards 2013: Luxembourg Green Business Awards 2013
  • LUXINNOVATION: Agence Nationale pour la Promotion de l Innovation et de la Recherche
  • Pro Newtech: Pro Newtech
  • CTI: Centro de Tecnologia da Informação Renato Archer

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2013

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Author Tags

  1. big spatial data
  2. e-commerce
  3. product search
  4. shopbots

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MEDES '13
Sponsor:
  • LBBC
  • IPv6 Luxembourg Council
  • Luxembourg Green Business Awards 2013
  • LUXINNOVATION
  • Pro Newtech
  • CTI

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MEDES '13 Paper Acceptance Rate 56 of 122 submissions, 46%;
Overall Acceptance Rate 267 of 682 submissions, 39%

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