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Motivational orientation for word-of-mouth and its relationship with WOM messages

김나민|Francis Ulgado
한국마케팅과학회 (Korean Scholars of Marketing Science)|2014.03|Journal of Global Scholars of Marketing Science(마케팅과학연구) (全球营销科学学报)|제24권 제2호|pp. 223-240|DOI http://dx.doi.org/10.1080/21639159.2014.881115

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참고문헌

키워드

motivation for WOMWOM messagespositive vs. negative WOMhedonic vs. utilitarian attributes口碑宣传动因口碑宣传信息积极与消极口碑宣传享乐型与功力型特性
주제분류경영학

초록

In this study, we investigate how consumers’ motivation to spread messages via word-ofmouth(WOM) influences the content of the WOM messages. We classify consumers’motivation into two categories: hedonic and utilitarian motivation. Then we analyzehow each motivation affects the valence of the message (e.g., positive vs. negative)and the attributes that the consumers mention (e.g., hedonic vs. utilitarian). Using ascenario-based experiment, we found that the content of the messages was stronglyrelated to the motivation of the consumers. Consumers who are hedonically motivatedtend to mention hedonic attributes more, and consumers who are utilitarian-motivated arelikely to mention utilitarian attributes more. The valence of the message, however, is notaffected by consumers’ motivation.

Abstract

In this study, we investigate how consumers’ motivation to spread messages via word-ofmouth(WOM) influences the content of the WOM messages. We classify consumers’motivation into two categories: hedonic and utilitarian motivation. Then we analyzehow each motivation affects the valence of the message (e.g., positive vs. negative)and the attributes that the consumers mention (e.g., hedonic vs. utilitarian). Using ascenario-based experiment, we found that the content of the messages was stronglyrelated to the motivation of the consumers. Consumers who are hedonically motivatedtend to mention hedonic attributes more, and consumers who are utilitarian-motivated arelikely to mention utilitarian attributes more. The valence of the message, however, is notaffected by consumers’ motivation.

참고문헌 (52)

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관련 논문AI추천

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