Electronic networks of practice (ENPs) have become an important mechanism for knowledge exchange among loosely connected individuals who share common knowledge interests. While prior research has explored factors that influence knowledge contribution in such networks, less is understood about the process by which individuals evaluate and ultimately adopt knowledge from ENPs. This study examines the process of knowledge filtering in online ENP forums. Drawing from dual process and information-evaluation theories, we hypothesize that performance on a knowledge-filtering task will be influenced by the constancy and directionality of search patterns employed by knowledge seekers. Hypotheses are tested in an experiment that utilized an eye tracker to record gaze data from professional software developers using an experimental ENP forum. By combining information-evaluation and dual process theory perspectives, our results deepen the insights offered in extant information-processing literature by showing that higher filtering accuracy is associated with (a) constant evaluation of some types of information attributes (solution content) but not others (peripheral cues), and (b) increasing attribute-based processing over time. > >
Electronic networks of practice have become a prevalent means for acquiring new knowledge. Knowledge seekers commonly turn to online repositories constructed by these networks to find solutions to domain-specific problems and questions. Yet little is understood about the process by which such knowledge is evaluated and adopted by knowledge seekers. This study examines how individuals filter knowledge encountered in online forums, a common platform for knowledge exchange in an electronic network of practice. Drawing on dual process theory, we develop research hypotheses regarding both central and peripheral evaluation of knowledge. These hypotheses are examined in a field experiment in which participants evaluate online solutions for computer programming problems. Results show that peripheral cues (source expertise and validation) have a greater influence on knowledge filtering decisions than does the content quality of the solution. Moreover, elaboration increases the effect of content quality but does not seem to attenuate the effect of peripheral cues. Implications for research and practice are discussed.
Phishing is a major threat to individuals and organizations. Along with billions of dollars lost annually, phishing attacks have led to significant data breaches, loss of corporate secrets, and espionage. Despite the significant threat, potential phishing targets have little theoretical or practical guidance on which phishing tactics are most dangerous and require heightened caution. The current study extends persuasion and motivation theory to postulate why certain influence techniques are especially dangerous when used in phishing attacks. We evaluated our hypotheses using a large field experiment that involved sending phishing messages to more than 2,600 participants. Results indicated a disparity in levels of danger presented by different influence techniques used in phishing attacks. Specifically, participants were less vulnerable to phishing influence techniques that relied on fictitious prior shared experience and were more vulnerable to techniques offering a high level of self-determination. By extending persuasion and motivation theory to explain the relative efficacy of phishers' influence techniques, this work clarifies significant vulnerabilities and lays the foundation for individuals and organizations to combat phishing through awareness and training efforts.
Historically, inaccurate credibility assessments have resulted in tremendous costs to businesses and to society. Recent research offers unobtrusive credibility assessment aids as a solution; however, the accuracy of these decision aids is inadequate, and users often resist accepting the aids' recommendations. We follow the principles of signal detection theory to improve the accuracy of recommendations in computer-aided credibility assessment by combining automated and participatory decision support. We also leverage participation in decision-making theory to explain and predict an increased acceptance of assessment aid recommendations when perceptual cues are elicited from users. Based on these two theories, we design and test a hybrid decision aid to perform automated linguistic analysis and to elicit and analyze perceptual cues from an observer. Results from a laboratory experiment indicate that decision aids that use linguistic and perceptual cues offer more accurate recommendations than aids that use only one type of cue. Automatic analysis of linguistic cues improved both the decision aid's recommendations and the users' credibility assessment accuracy. Challenging the generalizability of past findings, the elicitation of perceptual cues did not improve the decision aid's recommendations or the users' assessment accuracy. Elicitation of perceptual cues, however, did improve user acceptance of the decision aid's recommendations. These findings provide guidance for future development of credibility assessment decision aids.
The article presents the results of an experiment which involved novices and experts using a decision aid to detect deception in a law enforcement scenario. Both groups improved their assessment accuracy using the decision support system, but tended to ignore it when it contradicted them. The professionals often did not even look at the decision aid's reasons for contradicting them. The theory of technology dominance (TTD) is discussed in this context, and it is noted that the experiment's results contradicted TTD in two respects.