Contributing or receiving-the role of social interaction styles in persuasion over a social networking platform

Abstract

In this paper, the use of the social network platform Twitter is explored in relation to behavior change (BC) for health. We studied the effect of participation style (tweeters vs. non-tweeters) on perceived health behavior change through the constructs "social influence," "efficacy," and "pre-test health behavior." In the experiment (N = 30), participants were given a basic health message, and the tweeters were then asked to actively search, think up, and share tips and guidance with non-tweeters. Results indicate that for both tweeters and non-tweeters, efficacy and social influence were positively related, but that efficacy and perceived health behavior change are only positively related for non-tweeters. From this, we concluded that participation style can impact the effect of efficacy on target behavior. To understand the information processing in the two groups in terms of higher or lower need for cognition, we also studied the participants' thought elaboration. We found that tweeters were more distracted from elaboration on the health message, in particular those tweeters presenting a higher need for cognition. From this, we concluded that taking an active content creating role in peer-to-peer ecoaching systems may lead (a) to higher efficacy appraisal without a positive impact on BC and (b) to reduced attention on the intended behavior change message. In other words, the position of producing content may not translate into better intake of that content. In conclusion, there is a need for investigating strategies for overcoming the distracting nature of an active sharing role in e-coaching systems presented by a social network platform.

Reflection Through Gaming: Reinforcing Health Message Response Through Gamified Rehearsal

“…The ground-up approach of the scale allows users to freely describe their experience rather than having to evaluate their responses against a ready-set frame. The scale has been used earlier in [27].…”

“…Of the total sample of 58 participants 33 were female, 25 male. Average age for the whole sample was 24 years (median 23), average for females being 24 (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) min/max, median 22) and for males 23 (18-33 min/max, median 23). Eight participants reported that they do not usually play any computerized games, 19 used a computer as their main gaming platform, 20 used a gaming console, and 11 reported a mobile device as their primary gaming platform.…”