Replication For: How proximity and trust of policy narrators motivate their audience

The narrator, or entity telling a story, is assumed to be a critical element for understanding how narratives affect public policy processes; however, for the Narrative Policy Framework, the role of the narrator is largely understudied. We offer a systematic way to approach the study of policy narrators that includes (i) a definition, (ii) features (e.g., proximity to audience), and (iii) functions (e.g., audience trust) of narrators. We then present an exploratory study (n = 2268) that affixes narrators to visual messages about getting the COVID-19 vaccine. Informed by Construal Level Theory, narrators were assigned proximal to distal characteristics (“your friend,” “your doctor,” “the CDC,” and a control “someone”) to investigate the extent to which proximity, trust, and congruence between narrator and narrative form predicts an individual’s motivation to get the COVID-19 vaccine. We found that the narrator alone had no significant effect, but the proximal narrator in the context of proximal characters in the policy message did have significant effects on motivation to vaccinate. Additionally, individual trust of distal narrators elicits affective responses, whereas individual trust of the proximal narrator is associated with motivation. Taken together, these results suggest effects of narrator feature, characteristic, and function are dynamic and contextual.
* Encoding: UTF-8. /* The macro definition is written by Andrew F Hayes for PROCESS version 4.3.1 and must be accessed for SPSS, R and SAS here: www.processmacro.org*/. /* www.afhayes.com */. /* www.processmacro.org */. /* Copyright 2017-2023 by Andrew F Hayes */. /* Documented in http://www.guilford.com/p/hayes3 and supplements */. /* The macro call below runs the macro and identifies the variable inputs and indicator coding for X variable for the 4 models */. PROCESS y=C_Motiv / x=C_narrs / m=C_affect / COV=RP_sev RP_lik TR_F TR_dr TR_cdc gender RaceR age educ income kids flushot polide relig / model=4 /total = 1 / mcx=1 / center=0 / intprobe=.10 / conf=95 / boot=5000 / seed=12345. PROCESS y=Y_mot / x=Y_narr / m=Y_affect / COV=RP_sev RP_lik TR_F TR_dr TR_cdc gender RaceR age educ income kids flushot polide relig / model=4 /total = 1 / mcx=1 / center=0 / intprobe=.10 / conf=95 / boot=5000 / seed=12345. PROCESS y=Cir_mot / x=Cir_narr / m=Cir_affe / COV=RP_sev RP_lik TR_F TR_dr TR_cdc gender RaceR age educ income kids flushot polide relig / model=4 /total = 1 / mcx=1 / center=0 / intprobe=.10 / conf=95 / boot=5000 / seed=12345. PROCESS y=Com_mot / x=Com_narr / m=Com_affe / COV=RP_sev RP_lik TR_F TR_dr TR_cdc gender RaceR age educ income kids flushot polide relig / model=4 /total = 1 / mcx=1 / center=0 / intprobe=.10 / conf=95 / boot=5000 / seed=12345.