Abstract

PurposeThe purpose of this paper is to investigate how individuals respond to messages asking for donations in broadcast advertising. It does so by considering both preexisting attitudes and beliefs related to donating, as well as message processing. The goal is to uncover messages that may help nonprofit organisations increase donations.Design/methodology/approachThe research combines the theory of planned behaviour (TPB) to measure preexisting beliefs and the elaboration likelihood model (ELM) to measure involvement in an investigation of donation responses to broadcast-quality advertisements developed by a professional ad agency featuring the following two messages: one that leverages social norms and another that legitimises minimal giving. Two studies collected data from a total of 544 respondents in two between-subjects 2 × 2 × 2 experiments.FindingsInjunctive norm messages affect the intended donation behaviour of individuals who are pre-disposed to donating, but only if they are highly involved with the ad. Social legitimisation messages affect donations from individuals who look to referents to direct behaviour, but unlike what was expected, only by those not highly involved with the ad. Similarly, individuals who do not think they can donate increased donations when they saw the legitimisation message and had low advertisement involvement.Research limitations/implicationsResults extend the ELM-TPB integrated framework by discovering when and how involvement drives intended donation behaviour. The research also sheds light on message processing by focussing on the preexisting characteristics of recipients.Practical implicationsThe results provide nonprofit managers with strategies to increase donations with targeted messages. Those who pay attention to the ad and have a positive attitude toward giving are going to donate if they are told others support the cause. Therefore, the focus should be on those who are not involved with the ad but still believe giving is appropriate.Originality/valueThis research is the first to use the ELM-TPB framework to discover that ELM has varying utilities and values from TPB in different ad contexts.

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