1. 1 The role of interpersonal information in electronic commerce: The case of Internet auctions The role of interpersonal information in electronic commerce: The case of Internet auctions Avi Noy The Graduate School of Business Administration University of Haifa http://research.haifa.ac.il/~avinoy/ avinoy@gsb.haifa.ac.il

2. 2 Contents o Information and interaction in electronic commerce o Internet auctions o A study of interpersonal information in auctions (Supervised by Prof. Sheizaf Rafaeli)

3. 3 Our focus is this study id on the consumer in B2C and C2C oWhat type of information is consumed  Product related, Seller related (and 3 rd party sites), … oSources of information  Public / Interpersonal information (real vs. virtual), Advertisements, Other sites,… oDirection of the information  One way / Two ways oType of communication  Textual, Graphical, audio, video, synchronous/Asynchronous Information and interaction in electronic commerce

4. 4 Current research Virtual Presence Virtual Presence Consumer Behavior Consumer Behavior Internet Auctions Internet Auctions How are these issues related ? How are these issues related ? Interpersonal Influence Interpersonal Influence Computer- Mediated Communication Computer- Mediated Communication Related topics o Human Computer Interaction o Autonomous agents

5. 5 oHow to represent an interaction? Characters (VHost) – OddCast Human click’s interactive salesman BuddySpace Information and interaction in electronic commerce

6. 6 oOddCast  Banners that said, “Chat online with an expert” with a gif of a smiling service vs. [V]Host™ character saying, “Hi, I’m a customer service agent. Click here for live help.” Generated 150% improvement in click through rate to chat  Users create [V]Hosts and email them to friends as part of a contest for the awards night. 62% of unique visitors converted into registrants  Promoting website as alternative to traditional mailing to lower customer service costs and postal fees. With no advertising or change in their search engine status, Merit put a [V]Host™ on their website. Sustained lift of 200% in traffic Information and interaction in electronic commerce

7. 7 oHow to represent an interaction and awareness? Radar- Odigo Bubble - IBM Interaction map

8. 8 FootPrint – MIT Media Lab Crowd – MIT Media Lab Chat Circles – MIT Media Lab Information and interaction in electronic commerce oHow to represent an interaction and awareness?

9. 9 Information and interaction in electronic commerce oInterpersonal information ? – Store rating, opinions

10. 10 Information and interaction in electronic commerce oInterpersonal information in Internet auctions  Forums / Chats  Seller/Buyer reputation systems

11. 11 Determinants of bidding behavior in Internet Auctions The Bidder perceived risk, independent estimates, experience, information, enjoyment The Bidder perceived risk, independent estimates, experience, information, enjoyment Auction mechanism and rules auction type, ending rules, reserved price, proxy bidding Auction mechanism and rules auction type, ending rules, reserved price, proxy bidding The Item Independent private value/Common value Means of item evaluation The Item Independent private value/Common value Means of item evaluation The Seller Reputation (self/site) The Seller Reputation (self/site) Other bidders and other social factors herding, Preceding behavior Other bidders and other social factors herding, Preceding behavior Bidding behavior Bidding behavior Where to buy What item Bidding strategy Bidding proxy How much to bid When to bid How many bids Where to buy What item Bidding strategy Bidding proxy How much to bid When to bid How many bids

12. 12 Pre-Auction Phase Bidding Phase Post-Auction Phase Information Item Auction site Seller Preliminary Decisions On Going Decisions Auction related factors (Auction type and rules) Bidder related factors (Risk, Experience, Enjoyment) Changing factors (Item evaluation, Recent information) Seller related factors (Reputation) Other bidders related factors (Preceding behavior, Evaluation ) Social Influence Virtual, Real Other social factors (Friends, Family) Evaluation Of auction results Post-Auction Decisions Social Influence Virtual, Real Social influence in Internet auctions

13. 13 Theoretical background of the study oNormative vs. Informational influence  According to normative influence, judgment shifts result from exposure to others’ choice preferences and subsequent conformity to the implicit or explicit norms in these preferences.  Informational influence attributes shifts to the incorporation of relevant arguments or information about the issue that are shared between discussants (Kaplan, 1987) oRelated theories  Influence in CMC groups  Social presence theory  Media Richness theory  Auction economics research

14. 14 Research questions oCan the social environment that is part of traditional auctions be replicated in Internet auctions, and how? oHow does other bidder influence bidding behavior ? oWhat are the influencing components of interpersonal interaction in auctions? oHow does bidding behavior affected by different auction models?

15. 15 Research framework oCore simulation  Auction site  Interpersonal information components  Bidding agents  Implemented in Java  Input parameters - control setup and behavior  Output parameters – data collected during the auction oSimulation framework  Client - Web pages, Forms, Java scripts  Server - Perl/CGI scripts oExperimental procedure  Different auction models  Manipulation of the level of interpersonal information

16. 16 Typical eBay auction

17. 17 English Auction

18. 18 Dutch Auction

19. 19 Results – English auction 440 460 480 500 520 540 560 580 Bids HI Participants LI Participants High Bid Win Bid 0 1 2 3 4 5 6 Number of bids HI Participants LI Participants Number of bids Of a winner

20. 20 Results – English auction 20% 30% 40% 50% 60% 70% 80% HI Participants LI Participants % Wins 90% 100% % Continue

21. 21 Results – Dutch auction 480 500 520 540 560 580 600 620 Win Bid Bids HI Participants LI Participants 20% 30% 40% 50% 60% 70% 80% HI Participants LI Participants % Wins 90% 100% % Continue

22. 22 Future research and availability oInterpersonal information in e-commerce  Online Stores  Online games  Online casinos oWeb mining – recommendations based bidding patterns oAutonomous agents oA demo version of the simulations is available at:http://research.haifa.ac.il/~avinoy/auction/ oSimulations can be operated in class or at home oContact: avinoy@gsb.haifa.ac.ilavinoy@gsb.haifa.ac.il Thank You !