Emoticons are graphic representations of facial expressions that many e-mail users embed in their messages. These symbols are widely known and commonly recognized among computer-mediated communication (CMC) users, and they are described by most observers as substituting for the nonverbal cues that are missing from CMC in comparison to face-to-face communication. Their empirical impacts, however, are undocumented. An experiment sought to determine the effects of three common emoticons on message interpretations. Hypotheses drawn from literature on nonverbal communication reflect several plausible relationships between emoticons and verbal messages. The results indicate that emoticons ’ contributions were outweighed by verbal content, but a negativity effect appeared such that any negative message aspect—verbal or graphic—shifts message interpretation in the direction of the negative element.

Abstract. This paper explores authentication techniques based on pictures as a possible solution to the most important problems concerning traditional passwords. The aim of this work is to bring together the technical (cryptological) and non-technical (psychological) awareness into the research on passwords. Security issues of any authentication mechanism (relying on knowledge) should not be considered without analysis of the human factor − since the users ’ human nature was identified as a source of major weaknesses of conventional authentication. Several issues of security and possibility of practical application were discussed in the paper. In the first place the statistically significant superiority of picture passwords over alphanumerical ones was presented. Then, it was shown that the crucial weaknesses of the picture passwords follow less from pictures locations and more from their meaning and ability to be recognized. Moreover, the techniques leading to resistance to ‘key logging ’ and ‘mouse tracking ’ were discussed. Finally, methods guaranteeing that users choose dissimilar, personalized and cryptographically strong graphical passwords were proposed. Key words: graphical authentication, picture passwords (p.p.), p.p. superiority, p.p. cryptanalysis, p.p. dictionary attacks, strong passwords, human factor 1.

insightful comments and suggestions. 2 Two experiments examined the effect of interacting with a virtual object (i.e., object-interactivity) on true and false memories. Although object-interactivity will likely improve memory of associations compared to static pictures and text, it may lead to the creation of vivid internally-generated recollections that pose as real memories. Consequently, compared to information conveyed via static pictures and text, object-interactivity may cause people to falsely recognize more non-presented features. The results support these hypotheses and provide converging evidence that this false recognition effect is due to using imagery during retrieval and is robust, emerging regardless of individuals ’ goals (to search or browse) or learning intent. 3 “The difference between false memories and true ones is the same as for jewels: it is always the false ones that look the most real, the most brilliant. ” Salvador Dali As reflected in the above quote, separating true from false memories is often difficult. Though important, understanding how marketing communication affects both true and false

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Abstract. Recruiters are increasingly turning to the Web in order to compete in the “war for talent”. This paper develops a conceptual framework comprising antecedents of websites ’ attractiveness and its influence on application behavior. We show the contribution aesthetics, content, navigation, interactivity and data security make to the attractiveness of a website. Furthermore, we state that employer knowledge serves as a partial mediator between a website’s attractiveness and the application tendency. Employer knowledge, on the other hand, is directly influenced by the aesthetics and the content of the webpage. Keywords: Web-based recruiting; Website characteristics; Employer knowledge; Elaboration Likelihood Model