Suchergebnisse

We investigated the effects of subliminal affective priming on implicit and explicit occupational gender stereotypes and their correspondence. First, we manipulated 3 types of affective priming (positive, neutral, and negative) and utilized the Implicit Association Test to find that
positively affective priming decreased, and negatively affective priming increased, implicit occupational gender stereotyping at the subliminal level. We then measured participants' explicit occupational gender stereotypes and found that, at the subliminal level, positive affective priming
deterred, and negative affective priming enhanced, explicit occupational gender stereotypes. Also, in the condition of subliminal affective priming, implicit and explicit occupational gender stereotypes were not significantly related and affect did not moderate the correspondence between them.
In sum, our results showed that affect moderated both implicit and explicit occupational gender stereotypes in the condition of subliminal affective priming, but did not moderate the correspondence between them.

A public bicycle system (PBS) is a promising countermeasure for the traffic issues induced by rapid urbanization, and it is widely acknowledged that the built environment has a significant impact on the use of a PBS. However, as the urban built-up area expands, different regions within a city can exhibit diverse characteristics. The spatial effects and differences among regions have been neglected by existing studies. To better understand how the urban built environment affects PBS ridership, this study conducts a quantitative analysis of the spatial relationship. It introduces a multi-scale geographically weighted regression (MGWR) to accomplish this task and conducts and evaluates a case study of the PBS in Nanjing, China. Six types of "D" variables (density, diversity, design, destination accessibility, distance to transit, and demand management) are involved in the analysis. The proposed method outperforms linear regression and standard geographically weighted regression (GWR) in terms of explanatory power. The modeling results demonstrate different influencing patterns between traditional downtown areas and newly built-up areas, especially for the density of population, road network, parking space, and various points of interest.