SELECTED PUBLICATIONS 

S. Grazioli & S. Jarvenpaa (2003).
Deceived! Under target on line.
Communications of the ACM, 46(12), pp. 196-205

This paper offers empirical data on average losses from Internet deception and fraud based on an analysis of over 200 cases.  A classification scheme of the deceptive tactics used on the Internet is also proposed and illustrated with examples.

S. Grazioli & S. Jarvenpaa (2003).
Consumer and Business Deception on the Internet: Content Analysis of Documentary Evidence.
International Journal of Electronic Commerce, 7(4) 93-118 (email me for a copy)

This paper investigates the deceptive tactics available to businesses and consumers to deceive others on the Internet.  Hypotheses about some of the factors that make these hypotheses more or less likely to be adopted are proposed and tested using data from 201 cases of internet deception occurred from 1995 to 2000.

S. Grazioli & R. Carrell (2002).
Exploding Phones and Dangerous Bananas: Perceived Precision and Believability of Deceptive Messages Found on The Internet.
Proceedings of the Americas Conference on Information System. Dallas, TX., 1977-1984

Urban legends are untrue messages that circulate widely in a social environment (e.g., the Internet) and that have a wide audience that believes them to be true. We show that readers judge low-precision Urban Legends to be untruthful, and that some (but not all) readers incorrectly use high precision as a proxy for truth.

S. Grazioli & A. Wang (2001).
Looking without seeing: Understanding Naïve Consumers’ Success and Failure to Detect Internet Deception.
Proceedings of the International Conference on Information Systems, New Orleans,  LA. 193-204 Nominated for Best Completed Research Paper Award.

Why do naive consumers fail to become suspicious when browsing a fraudulent web site?  If they become suspicious, how does the perception of deception affects their intention to purchase from a web store? This paper investigates three determinants of  the intention to buy from an Internet store: Deception, Trust, and Risk (DTR model) and adds a cognitive processes perspective to previous work.  It is found that naive consumers identify relevant clues, but do not seem to be able to process  them effectively. 

P. E. Johnson, S. Grazioli, K. Jamal & G. R. Berryman (2001).
Detecting deception: Adversarial problem solving in a low base rate world.
Cognitive Science, 25(3), May/June 2001, 355-392.

This is currently our best attempt to create a theory of deception applicable to a wide range of organizational settings.  The work proposes an information processing model of deception detection and tests it with an experiment where experienced auditors (senior partners in accounting firms) detect frauds presents in the financial statement of real companies.  Why people succeed and fail at detecting deception is explained. 

S. Grazioli & S. Jarvenpaa (2000).
Perils of internet fraud: an empirical investigation of deception and trust with experienced internet consumers.
IEEE transactions on Systems, Man, and Cybernetics, 30(4), 395-410.

This paper proposes a model that explains purchases from a web store based on state-of-the-art theory on trust, risk, and deception.  The model is tested using data from an experiment where consumers visited a real commercial site, as well as a fraudulent 'copy cat' site (a 'page jacking' deception). We found that even experienced consumers are easily victimized by internet deception.

My CV contains a statement of research that summarizes the work I have done in these areas, as well as other research projects I have been involved in: information retrieval from databases, errors in information processing, and knowledge management in information-rich environments and highly volatile environments.