Consumers as time travellers: the moderating effects of risk perception and construal level on consumers’ responses to temporal framing

2019 ◽  
Vol 38 (8) ◽  
pp. 1070-1097 ◽  
Author(s):  
Hyuksoo Kim ◽  
Seounmi Youn
2019 ◽  
Vol 38 (5) ◽  
pp. 528-536
Author(s):  
Ruishi Si ◽  
Meizhi Wang ◽  
Qian Lu ◽  
Shuxia Zhang

Carcass waste recycling exerts an important influence on preventing epidemic diseases, improving the ecological environment, and promoting sustainable development of the livestock industry although it has rarely aroused widespread attention throughout the world. Based on the data of 470 households engaged in breeding pigs in Hebei, Henan, and Hubei, China, and considering dead pigs as an example, this study employed the Double Hurdle model to assess impact of risk perception on household dead pig recycling behaviour and further tested the moderating effects of environmental regulation on the impact of risk perception on household dead pig recycling behaviour. The results show that: (1) Risk perception has a positive and significant influence on household dead pig recycling behaviour; however, this influence is mainly caused by households’ production and public health safety risk perceptions. Food and ecological safety risk perceptions have no significant influence on household dead pig recycling behaviour. (2) Environmental regulation has enhanced moderating effects on the impact of risk perception on household dead pig recycling behaviour, but the moderating effects mainly arose from imperative, guiding, and voluntary regulations. A moderating effect of incentive regulation is not obvious. (3) The moderating effects of environmental regulations present strong heterogeneity when different breeding scales and recycling technical attributes are considered. Finally, some policy implications, such as improving households’ risk perception level, enhancing environmental regulation intensity, and classifying to formulate measures, are proposed.


2018 ◽  
Vol 9 (4) ◽  
pp. 587-607 ◽  
Author(s):  
Weiwei Li ◽  
Yang Yuan

Purpose The paper aims to explore the factors that influence the risk perception. The moderating roles of purchase experience and purchase involvement are also investigated. Design/methodology/approach The conceptual model is framed into the SOR (stimulus-organism-response) model, which was tested using data collected from the users of a leading group buying platform in China, with the help of LISREL. Findings The result indicates that both vendor- and intermediary-related factors exert negative yet significant influences on perceived risk. The relationships between vendor-related attributes and perceived risk are contingent on purchase involvement. The relationships between intermediary-related attributes and perceived risk are subject to purchase experience. Originality/value This study explores the moderating effects of purchase experience and purchase involvement based on the characteristics of group buying, demonstrating the differences from traditional online shopping.


2017 ◽  
Vol 45 (11) ◽  
pp. 1865-1877 ◽  
Author(s):  
Lin Wu ◽  
Jing Lin

We developed a platform to consider both optimism and pessimism biases in the same context using construal level theory as a basis, and gained the following key results: An increase in event abstractness increased differences in self–other risk perception (Experiment 1). Perceived level of social distance determined whether optimism or pessimism bias occurred (Experiment 2). Compared with their closest other, people displayed more pessimism bias when estimating the risk of a concrete, compared to an abstract, event. In contrast, when comparing themselves with their most distant other, people displayed less optimism bias when estimating the risk of a concrete event relative to an abstract event, illustrating that the occurrence and extent of optimism or pessimism depends on an interactive effect between social distance and event abstractness (Experiment 3). Our results suggest that people should consider both risk target and risk characteristics when estimating risk.


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