How Posting Online Reviews Can Influence the Poster's Evaluations.

The current research demonstrates that posting online reviews can influence the evaluations of the individual posting the review. Across four studies, we examine the impact of individuals' naive theories about the meaning of their own posting on subsequent attitudes. In these experiments, individuals were assigned to write either positive or negative reviews about various products and services and then post them. The meaning associated with posting a review was varied to indicate either high validity (e.g., saving, extending, sharing) or low validity (e.g., deleting, hiding, archiving) with respect to their previously written reviews. When posting was associated with a high validity meaning, it increased reliance on those thoughts polarizing attitudes and behavioral intentions compared with when the posting was associated with a low validity meaning. These findings were mediated by the impact of meaning on thought confidence.

Artificial Intelligence and Persuasion: A Construal-Level Account.

Although more individuals are relying on information provided by nonhuman agents, such as artificial intelligence and robots, little research has examined how persuasion attempts made by nonhuman agents might differ from persuasion attempts made by human agents. Drawing on construal-level theory, we posited that individuals would perceive artificial agents at a low level of construal because of the agents' lack of autonomous goals and intentions, which directs individuals' focus toward how these agents implement actions to serve humans rather than why they do so. Across multiple studies (total N = 1,668), we showed that these construal-based differences affect compliance with persuasive messages made by artificial agents. These messages are more appropriate and effective when the message represents low-level as opposed to high-level construal features. These effects were moderated by the extent to which an artificial agent could independently learn from its environment, given that learning defies people's lay theories about artificial agents.

Alpha's standard error (ASE): an accurate and precise confidence interval estimate.

This research presents the inferential statistics for Cronbach's coefficient alpha on the basis of the standard statistical assumption of multivariate normality. The estimation of alpha's standard error (ASE) and confidence intervals are described, and the authors analytically and empirically investigate the effects of the components of these equations. The authors then demonstrate the superiority of this estimate compared with previous derivations of ASE in a separate Monte Carlo simulation. The authors also present a sampling error and test statistic for a test of independent sample alphas. They conclude with a recommendation that all alpha coefficients be reported in conjunction with standard error or confidence interval estimates and offer SAS and SPSS programming codes for easy implementation.