广告诉求-品牌来源国刻板印象匹配度对品牌态度的影响机制

基于刻板印象内容模型,作者将广告诉求分为诚意诉求和能力诉求,并研究了广告诉求和品牌来源国刻板印象对品牌态度的交互影响。通过两个实验,作者发现广告诉求和来源国刻板印象之间的匹配程度正向地影响消费者处理广告信息的流畅性,进而导致积极的情绪体验。消费者将这种积极情绪错误地归因到品牌上,从而产生积极的品牌态度。这一发现证实品牌来源国信息可以在消费者无意识的状态下通过情绪体验影响不同广告诉求的说服效果。     

Correlation of Self-Perception and Image of Christ Using the Five-Factor Model of Personality

In this study, the relationship between one’s self-perception of personality and his or her image of Jesus Christ was studied within a sample of 153 undergraduate students, of whom 130 were Christian and 23 were non-Christian; 23 Protestant pastors; and 55 Protestant laypersons. Using two forms of the NEO Personality Inventory (Self and Observer), ratings of the Big Five personality factors were obtained for both self and Jesus Christ. Results indicated significant positive correlations between ratings of self and Christ in each of the subgroups of Christian individuals. No correlations were found between ratings of self and Christ for the non-Christians. A repeated-measures ANOVA revealed significant differing correlations between groups. Christian persons may project perceptions onto Christ or attempt to mold themselves in a way more consistent with their own images of Christ. Further implications of the results as well as direction of the relationship between self-perception and image of Christ are discussed.     

平面性诉求广告记忆效果的加工分离研究

运用PDP（过程分离程序）,以122名大学生为被试,以自编性诉求广告及自编包含与排除问卷为实验材料,采用3×2×2多因素设计,探讨不同裸露类型（含蓄、一般、直露）和代言人类型（名人、一般人）的性诉求广告在不同阶段（控制化加工、自动化加工）认知信息加工的模式差异。结果发现： 1）外显的控制化加工,含蓄型性诉求广告记忆效果最好;2）直露型性诉求广告内隐的自动化加工最高;3）未发现性诉求广告中存在名人效应。     

Learning From Peers’ Eye Movements in the Absence of Expert Guidance: A Proof of Concept Using Laboratory Stock Trading,Eye Tracking,and Machine Learning

Existing research shows that people can improve their decision skills by learning what experts paid attention to when faced with the same problem. However, in domains like financial education, effective instruction requires frequent, personalized feedback given at the point of decision, which makes it time‐consuming for experts to provide and thus, prohibitively costly. We address this by demonstrating an automated feedback mechanism that allows amateur decision‐makers to learn what information to attend to from one another, rather than from an expert. In the first experiment, eye movements of N = 100 subjects were recorded while they repeatedly performed a standard behavioral finance investment task. Consistent with previous studies, we found that a significant proportion of subjects were affected by decision bias. In the second experiment, a different group of N = 100 subjects faced the same task but, after each choice, they received individual, machine learning‐generated feedback on whether their pre‐decision eye movements resembled those made by Experiment 1 subjects prior to good decisions. As a result, Experiment 2 subjects learned to analyze information similarly to their successful peers, which in turn reduced their decision bias. Furthermore, subjects with low Cognitive Reflection Test scores gained more from the proposed form of process feedback than from standard behavioral feedback based on decision outcomes.