The Effectiveness of Emotional and Rational Regulatory (In)congruent Messages for a Fair Trade Campaign

Abstract

AbstractAccording to research on message framing, messages that are factually equivalent, but framed differently, often lead to differences in persuasion (Yi and Baumgartner, 2009). Most previous research on message framing has compared message frames that differ in overall valence, i.e., positive frames versus negative frames (Rothman and Salovey, 1997; Levin et al., 1998). Message framing in terms of a prevention versus promotion outcome focus lately received an increased academic interest. This way of framing is based on the self-regulatory focus theory of Higgins (1997), stating that people can be classified as either relatively more promotion-focused or relatively more prevention-focused. Furthermore, this theory proposes that message (frame) effectiveness depends on consumers’ underlying motivational orientations (e.g., Aaker and Lee, 2001). That is, when the message frame is congruent to the viewer’s self-regulatory focus, the message is assumed to be more effective (Higgins, 2002). Although ample evidence supporting this regulatory congruency principle has been found (e.g., Latimer, et al., 2007; Yi and Baumgartner, 2008), the boundaries of this principle are still insufficiently studied. So far, studies have mainly focused on differently framed rational messages. To our knowledge, no studies were conducted yet to scrutinize the effectiveness of differently framed emotional messages despite the importance of emotions in consumer decision-making and their use in realistic ads (Pham, 1998). Therefore, the current study wants to complement prior academic research by examining the moderating role of message type (i.e., rational vs. emotional) on the effectiveness of regulatory congruent versus incongruent messages.KeywordsFair TradeCongruency EffectPurchase IntentionMessage TypeMessage FrameThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.