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Research Article

Predictors of organ donation-related cognitions and intentions in China: communication variables and cultural values

 

ABSTRACT

China is in the process of establishing a more regulated organ procurement and distribution system. Previous media reports stated that misperceptions toward organ donation and value-related beliefs (e.g., helping others) were two important reasons that prevented or facilitated donor registration in China. Based on a sample of 472 Chinese non-registered donors, the present analysis found that compared with subjective norms, self-efficacy, and anticipated guilt, misperceptions toward organ donation were a weak predictor of donor registration intentions, whereas value-related beliefs did not predict donor registration intentions. In addition, collectivistic values were negatively related to misperceptions and positively related to value-related beliefs and subjective norms. Lastly, organ donation-related media use and interpersonal discussion predicted subjective norms, self-efficacy, and anticipated guilt. Overall, results indicate that for internalized beliefs (e.g., misperceptions and value-related beliefs), media and interpersonal discussion of organ donation may exert less influence.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. Future orientation is defined as one ability and tendency to consider possible future consequences when making decisions (Strathman et al., Citation1994). Because it has been found to predict many health behaviors (or intentions), it is included as a control variable.

Additional information

Funding

This work was supported by the College of Liberal Arts, Rochester Institute of Technology [internal faculty development fund].

Notes on contributors

Xiao Wang

Xiao Wang (Ph.D., Florida State University) is an associate professor of communication at Rochester Institute of Technology. His research interests include health and environmental communication, intercultural communication, persuasion, and big data.

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