No abstract

Past studies suggest that computer security countermeasures such as security policies, systems, and awareness programs would be effective in preventing computer abuse in organizations. They are based on the general deterrence theory, which posits that when an organization implements countermeasures that threaten abusers, its computer abuse problems would be deterred. However, computer abuse problems persist in many organizations despite these measures. This article proposes a new model of computer abuse that extends the traditional model with the social criminology theories. Focusing on computer abuse within organizations, the model explains the phenomenon through social lenses such as social bonds and social learning. The new model contributes to our theoretical body of knowledge on computer abuse by providing a new angle for approaching the problem. It suggests to practitioners that both technical and social solutions should be implemented to reduce the pervasive computer abuse problems.

No abstract

Social influence on technology acceptance behavior has been acknowledged but needs to be further articulated. While Subjective Norm (SN) has been dominantly used to capture the essence of social influence, the findings to date has led some researchers to question whether it captures the full extent of social influence. Recently, social psychologists have examined Self-Identity as a construct reflecting social influence on behavior. In particular, Self-Identity has been shown to have significant influence on voluntary behavior and have enduring effects, situations where the Subjective Norm had little effect. This study examines the effect of Self-Identity on technology acceptance decision in the context of a web-based class support system under the Technology Acceptance Model (TAM). The result demonstrates a significant direct and indirect effect of Self-Identity on technology acceptance. The result also confirms that Self-Identity has significant direct effect on the acceptance in voluntary and experienced situations, while Subjective Norm has no significant effect in both situations. Key implications for theory and practice are discussed.

The rapid and wide dissemination of up‐to‐date, localized information is a central issue during disasters. Being attributed to the original 140‐character length, Twitter provides its users with quick‐posting and easy‐forwarding features that facilitate the timely dissemination of warnings and alerts. However, a concern arises with respect to the terseness of tweets that restricts the amount of information conveyed in a tweet and thus increases a tweetʼs uncertainty. We tackle such concerns by proposing entropy as a measure for a tweetʼs uncertainty. Based on the perspectives of Uncertainty Reduction Theory (URT), we theorize that the more uncertain information of a disaster tweet, the higher the entropy, which will lead to a lower retweet count. By leveraging the statistical and predictive analyses, we provide evidence supporting that entropy validly and reliably assesses the uncertainty of a tweet. This study contributes to improving our understanding of information propagation on Twitter during disasters. Academically, we offer a new variable of entropy to measure a tweetʼs uncertainty, an important factor influencing disaster tweetsʼ retweeting. Entropy plays a critical role to better comprehend URLs and emoticons as a means to convey information. Practically, this research suggests a set of guidelines for effectively crafting disaster messages on Twitter.