Pro-environmental or pro-social: Which motivation is stronger in the case of charity retailing engagement?

B. Borusiak, B. Kucharska
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Abstract

The aim of the study is to examine the intention to engage in charity retailing using the norm activation model which has been widely used to predict people’s altruistic and pro-social behavior (De Groot and Steg, 2009, 425–449). Pro-social behavior refers to a person’s action that is intended to help other people and consists of a broad range of helping, sharing, and cooperating behaviors. Three forms of engagement in charity retailing were taken into account: buying at a charity shop, volunteering in a charity store, and donating. The current study was conducted in 2019 using the CAWI method on 766 Polish students coming from 16 universities; for data analysis, SEM was applied. We tested two variants of the model. In the first one we connected personal norm, awareness of consequences, and ascription of responsibility with the context of the natural environment protection, whereas in the second the same variables were connected with helping people in general. The findings show that the norm activation model may be applied as a mediator model for explaining intention to engage in shop charity. Both tested models were positively verified — all relations but one were proven to be statistically significant. The only relation which turned out to be statistically insignificant was the relation between awareness of consequences and personal norm. The results show that environmental context increased the strength of the relations between the analyzed variables.
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亲环境还是亲社会:在慈善零售参与的情况下,哪个动机更强?
本研究的目的是使用规范激活模型来检验从事慈善零售的意愿,该模型已被广泛用于预测人们的利他主义和亲社会行为(De Groot and Steg, 2009, 425-449)。亲社会行为是指一个人旨在帮助他人的行为,包括广泛的帮助、分享和合作行为。慈善零售的三种参与形式被考虑在内:在慈善商店购物、在慈善商店做志愿者和捐赠。目前的研究是在2019年使用CAWI方法对来自16所大学的766名波兰学生进行的;数据分析采用扫描电镜。我们测试了该模型的两种变体。在第一个实验中,我们将个人规范、后果意识和责任归属与自然环境保护联系起来,而在第二个实验中,同样的变量与帮助人们联系在一起。研究结果表明,规范激活模型可以作为解释商店慈善意向的中介模型。两种被测试的模型都得到了正验证——除了一种关系外,所有关系都被证明具有统计显著性。唯一在统计上不显著的关系是后果意识和个人规范之间的关系。结果表明,环境背景增加了被分析变量之间关系的强度。
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