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Using STOPS to predict prosocial behavioral intentions: Disentangling the effects of passive and active communicative action
Public Relations Review ( IF 4.1 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.pubrev.2020.101956
Geah Pressgrove , Cristobal Barra , Melissa Janoske

Abstract This study seeks to understand the confluence of factors that might lead individuals to engage in prosocial action around a societal issue that has received little attention– the mass incarceration of women in the United States. Through qualitative and quantitative inquiry, the authors employ the situational theory of problem solving (STOPS) to disentangle active and passive communicative action in a model that predicts three common supportive prosocial behaviors--financial support, volunteerism, and political support. Findings demonstrate an asymmetry between passive and active communicative actions, both with respect to antecedents and the strength with which they predict prosocial behavioral support. While both situational motivation and referent criterion predict active communicative action; only situational motivation predicts passive communicative action. Further, in this context, passive communicative action is the best predictor of common support behaviors including donating money, volunteering time and participating in policy advocacy. Theoretical and practical implications are discussed.

中文翻译:

使用 STOPS 预测亲社会行为意图:解开被动和主动交流行为的影响

摘要 本研究旨在了解可能导致个人围绕一个鲜为人知的社会问题(美国大规模监禁妇女)参与亲社会行动的因素的汇合。通过定性和定量调查,作者运用问题解决情境理论 (STOPS) 在预测三种常见支持性亲社会行为的模型中解开主动和被动交流行为——财务支持、志愿服务和政治支持。研究结果表明,被动和主动交流行为之间存在不对称性,无论是在前因还是它们预测亲社会行为支持的强度方面。而情境动机和指称标准都预测了积极的交际行为;只有情境动机才能预测被动的交流行为。此外,在这种情况下,被动交流行动是共同支持行为的最佳预测指标,包括捐款、志愿时间和参与政策倡导。讨论了理论和实践意义。
更新日期:2020-11-01
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