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Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] International Journal of Consumer Studies [ISSN:] 1470-6431 [Volume:] 46 [Issue:] 3 [Publisher:] Wiley Periodicals, Inc. [Place:] Hoboken, USA [Year:] 2021 [Pages:] 850-869
Verlag: 
Wiley Periodicals, Inc., Hoboken, USA
Zusammenfassung: 
Consumers are increasingly sharing their opinions on societal issues and products online. We studied the implications of such online word‐of‐mouth for consumer judgement and decision‐making. The case used is organic food, which is the most successful among the currently emerging, sustainability‐differentiated food product categories. We first analysed the online discussion on organic food by conducting a text‐mining study of reader comments (N = 63,379) from the comments section of a major German online news outlet. Topics therein are discussed with differing frequency, thereby indicating the salience of the various issues to online readers and consumers. One organic food topic of high salience (animal welfare) and one of low salience (biodiversity) were selected to investigate the behavioural relevance of salient online topics in a subsequent priming experiment (online survey of German consumers; N = 1,118). In particular, we tested whether the relative online salience of the two topics used as primes influenced the likelihood of choosing organic instead of conventional eggs and milk in a choice experiment and the acceptance of policies supporting organic farming. Although ineffective for the choice of milk, the priming worked as hypothesized regarding the choice of eggs and policy acceptance. Priming the topic with high online salience is more effective at promoting pro‐organic behaviour than priming the topic with low online salience. Priming effects also depended on prime strength, attitude strength and experience with buying organic food. We discuss how insights from text mining of online word‐of‐mouth can be employed to promote sustainable consumption behaviour.
Schlagwörter: 
associative network
consumer choices
organic food
priming
salience
text mining
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