This document discusses the role of trust in e-commerce systems and winning business online. It covers key topics related to trust including familiarity, reputation, and imprudence. The goal of computer scientists is to develop systems that increase trust and familiarity through good user interface design, reputation tracking, and preventing seller imprudence as their reputation grows. Factors like brand recognition, UI consistency, and reputation systems can help build the trust necessary for online purchases and use of software/websites.

1. THE ROLE OF TRUST
SYSTEMS IN
E-COMMERCE:
Winning Business with
Psychology, The Internet
and Good UI Development

2. INTRODUCTION
Who I am:
Jessica Van Meter, transfer CS undergrad
jessvee on Github
jessv on ACM Slack
My undergraduate research lecture
Talk to a researcher
Read articles
Get started now…
While you still have multiple semesters to commit

3. OVERVIEW
Trust
Familiarity
Reputation
Imprudence

4. TRUST Our goal as Computer
Scientists

5. TRUST
Trust- The expectation that an uncontrollable second party with
unknown motives will produce results that are the same or similar
to those anticipated
“You can't trust Melanie but you can trust Melanie to be Melanie” – Ordell Robbie in Jackie Brown
Sometimes, these expectations are not voiced but are implied
Places in Computer Science where trust needs to occur:
Chat Rooms
Safe Downloading of Files
Online Journalism
Research
Electronic Voting Systems
Online Purchases*
And more…
ULTIMATE GOAL:
 Trust leads to increases in purchases, voting, subscriptions, etc.

6. WHY TRUST MATTERS ONLINE
Trust is more difficult to gain online
Because there are no physical interactions
Often, we do not even know the party we are
subscribing to
A huge, developing marketplace occurs online,
everyday
In 2000, Amazon had 6.2 million customers
The Better Business Bureau has identified trust as a
major issue in online commerce

7. TYPES OF TRUST
Goal-based
Two people have a common goal
Calculative
Uses evidence to make an assumption about a party
Knowledge-based
Similar to Calculative
But instead of evidence, experience is used
Respect-based
Akin to the trust gained by friends
Parties have similar world views and keep open dialogue to maintain the relationship
The most long-lasting and evolving form of trust
As such, the most sought after by companies

8. DISPOSITION TO TRUST
Strongest factor in whether trust will occur
Factors Affecting Disposition to Trust:
Personal History/Experience
Personal Tastes
Personality Style
Least understood factor in trust
Most difficult factor of trust to harness

9. FAMILIARITY A Way to Increase Trust in
Our Websites and Software

10. FAMILIARITY
A learning process based on prior experiences
Trust vs. Familiarity:
Familiarity concerns the present relationship, and trust concerns
the future
Familiarity is users knowing a website, and trust is users putting
their credit card information into it
Even if trust does not apply to our software, familiarity always does
Examples:
Brand Familiarity
Familiarity with User Interface

11. BRAND AND PRODUCT
FAMILIARITY
How many people like the
new MS logo best?

12. USER INTERFACE FAMILIARITY
In many cases, the user interface to a
program is the most important part
for a commercial company: whether
the program works correctly or not
seems to be secondary.
-- Linus Torvalds

13. USER INTERFACE FAMILIARITY
Ways to increase familiarity:
Present seller/company identity clearly
Uphold transparency in actions
Integrate user reviews and feedback
Provide good customer service avenues
Seek buyer’s informed consent
End User Agreements aim to do this

14. USER INTERFACE FAMILIARITY
More ways to increase familiarity through user
interface design:
Initiate repeat interactions
Anticipate problems and repair damages
A common thread with Ethics covered this
semester
Provide recourse for users who lose their trust in our
companies
Keep user expectations in check
Clear communication

15. REPUTATION The Current Appraisal
System

16. REPUTATION
Is the seller worthy of trust?
Measured with Reputation Systems
Bayesian Models :
Take discrete ratings and generate a global reputation
score
Binomial
(Good/Bad)
or Multinomial
 (1-5 stars, for ex.)
Prevents polarized results
Does not account for Imprudence

17. IMPRUDENCE
Imprudence- the observed tendency for a
seller to act more carelessly while fulfilling a
particular transaction after a high reputation
rating has been achieved
Because of imprudence, Bayesian Models of
Reputation Appraisal only work for sellers or
companies with small data sets

18. SELLER REPUTATION APPRAISAL
ON EBAY

19. MULTINOMIAL REPUTATION RATING
FOR MOVIES
Local Rating (for the user)
Global Rating (seen by
everyone)

20. TRUE GLOBAL RATINGS ON
AMAZON

21. SUMMARY
Trust- a big factor deciding if a user will use our website
Disposition- an unchangeable facet of a user’s personality
Familiarity- how well the user knows our product
Reputation- is the product or company trustworthy?
Imprudence- the inherent laziness of a seller with almost immutable
reputation
The Role of Computer Scientists and Programmers:
Good UI Development
Intuitive Software
Trust and Reputation Generation Systems Development and Research

22. REFERENCES
Fink, R. A., Sherman, A. T., and Carback, R. Tpm meets dre: reducing the trust base for electronic
voting using trusted ​platform modules. Information Forensics and Security, IEE Transactions on
4, 4 (2009), 628 – 637.
Gefen, D., Karahanna, E., and Straub, D. W. Inexperience and experience with online stores: the
importance of tam and trust. Engineering Management, IEEE Transactions on 50, 3 (2003), 307-
321.
Gefen, D. E-commerce: the role of familiarity and trust. Omega 28, 6 (2000), 725-737.
Josang, A., Bhuiyan, T., Xu, Y., and Cox, C. Combining trust and reputation management for web-
based services. In Trust, Privacy and Security in Digital Business. Springer, 2008, pp. 90-99.
Koehn, D. The nature of and conditions for online trust. Journal of Business Ethics 43, 1-2 (2003), 3-
19.
Liu, X. Datta, A., Fang, H., and Zhang, J. Detecting imprudence of reliable sellers in online auction
sites. In Trust, Security and Privacy in Computing and Communications (TrustCom), 2012 IEEE
11th International Conference on(2012), IEEE, pp. 246-253.
Udo, G. J. Privacy and security concerns as major barriers for e- commerce: a survey
study. Information Management & Computer Security 9, 4 (2001), 165-174
http://tech-insider.org/linux/research/1997/0920.html

23. QUESTIONS, COMMENTS?

24. THANK YOU.