How well can experienced Internet shoppers detect new forms of seller deception on the Internet? This study examines consumer evaluations of a real commercial web site and a fraudulent site that imitates it. The forged site contains malicious manipulations designed to increase trust in the site, decrease perceived risk, and ultimately increase the likelihood that visitors would buy from it. Besides measuring the consumer's willingness to buy from the site, this study recorded the actual ordering of a laptop. Results show that most subjects failed to detect the fraud manipulations, albeit a few succeeded. The fraud has the effect of increasing the consumers' reliance in assurance mechanisms and trust mechanisms, which in turn decrease perceived risk and increase trust in the store. The study confirms hypothesized relationships between purchase behavior, willingness to buy, attitudes toward the store, risk, and trust that are consistent with other trust models found in the literature. Past research is augmented by showing that perceived risk and trust interact in their effects on consumer attitudes, by distinguishing between the notions of assurance and trust, and by identifying the effects of perceived deception on risk and trust. Overall, the study sheds light on consumers' vulnerability to attack by hackers posing as a legitimate site.
The work presented here investigates the process by which one group of individuals solves the problem of detecting deceptions created by other agents. A field experiment was conducted in which twenty-four auditors (partners in international public accounting firms) were asked to review four cases describing real companies that, unknown to the auditors, had perpetrated financial frauds. While many of the auditors failed to detect the manipulations in the cases, a small number of auditors were consistently successful. Since the detection of frauds occurs infrequently in the work of a given auditor, we explain success by the application of powerful heuristics gained from experience with deceptions in everyday life. These heuristics implement a variation of Dennett's intentional stance strategy, which is based on interpreting detected inconsistencies in the light of the Deceiver's (i.e., management's) goals and possible actions. We explain failure to detect deception by means of perturbations (bugs) in the domain knowledge of accounting needed to apply these heuristics to the specific context of financial statement fraud. We test our theory by showing that a computational model of fraud detection that employs the proposed heuristics successfully detects frauds in the cases given to the auditors. We then modify the model by introducing perturbations based on the errors made by each of the auditors in the four cases. The resulting models account for 84 of the 96 observations (i.e., 24 auditors x four cases) in our data.
The work presented here investigates the process by which one group of individuals solves the problem of detecting deceptions created by other agents. A field experiment was conducted in which twenty-four auditors (partners in international public accounting firms) were asked to review four cases describing real companies that, unknown to the auditors, had perpetrated financial frauds. While many of the auditors failed to detect the manipulations in the cases, a small number of auditors were consistently successful. Since the detection of frauds occurs infrequently in the work of a given auditor, we explain success by the application of powerful heuristics gained from experience with deceptions in everyday life. These heuristics implement a variation of Dennett's intentional stance strategy, which is based on interpreting detected inconsistencies in the light of the Deceiver's (i.e., management's) goals and possible actions. We explain failure to detect deception by means of perturbations (bugs) in the domain knowledge of accounting needed to apply these heuristics to the specific context of financial statement fraud. We test our theory by showing that a computational model of fraud detection that employs the proposed heuristics successfully detects frauds in the cases given to the auditors. We then modify the model by introducing perturbations based on the errors made by each of the auditors in the four cases. The resulting models account for 84 of the 96 observations (i.e., 24 auditors x four cases) in our data.
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