Processing TV textual and sub-textual messages: the impact of audience goal processing and thematic coordination between messages on recalling

Montazeri, Behnaz; Hakim Ara, Mohammad Ali

Processing TV textual and sub-textual messages: the impact of audience goal processing and thematic coordination between messages on recalling

Document Type : Research Paper

Authors

Behnaz Montazeri

Mohammad Ali Hakim Ara

Abstract

Today development of technology has facilitated the simultaneous broadcasting of multiple messages in the form of textual and sub-textual messages, and these appear regularly on television screens, in addition to computer monitors. While textual messages convey the main message of the media and occupy the major portion of the screen, sub-textual messages appear in the form of rolling strips. Given the Dual Process Theory, it is vital to ask whether or not such simultaneous textual and sub-textual messages can be processed by the audience. In fact, psychological theories run counter to the keenness of the media to transmit multiple messages. The present research empirically studies the impact of audience goal processing (as human factor) in relation to thematic coordination/non-coordination of textual/sub-textual messages (as media factor), on the degree of message recalling. 120 participants in four groups of 30, with assorted goals and motivations, were exposed to different forms of multiple textual and sub-textual messages. Notwithstanding the thematic coordination or non-coordination between textual and sub-textual messages, the results of different tests confirm the impact of goal processing on optimal message recalling. Also, when messages were presented as coordinated or non-coordinated, the media factor showed that each case had impact in its own way on the degree to which the information was recalled by audiences who goal-processed or, conversely, did not goal-process.

Keywords

Recalling

Message processing

Textual message

Subtextual message

subtitle

Dual-Process Theory

Elaborating Likelihood model (ELM)