Abstract
Identification, the experience of a psychological merging between the self and a character, is a key mechanism underlying the power of narratives to influence attitudes, beliefs, intentions, and behaviors in story-consistent ways. Similarity between audience members and characters has been intuitively thought to be an antecedent of identification, but experimental studies have yielded inconsistent findings regarding the effectiveness of manipulating similarity on eliciting identification. The current meta-analysis synthesized and quantified the evidence from 39 studies (k = 50, N = 11,077) and investigated several potential causes of heterogeneity at both the narrative and study levels. The data revealed a small but significant and robust overall effect of similarity on identification (g = 0.19, 95% CI [0.10, 0.28], p < .001), with little evidence of publication bias. A notable narrative-level moderator was type of similarity, with manipulations of psychological similarity yielding larger effects than manipulations of objective similarity. In addition, study design emerged as a significant study-level moderator, with the similarity–identification effect being stronger in studies that manipulated similarity using a within-subjects design than those that used a between-subjects design. Insights gained from this meta-analysis can help to address some ill-defined aspects of the similarity–identification hypothesis, contributing to a better understanding of involvement with narrative characters. Practically, the results can inform the design of more effective targeted and tailored narrative messages that are intended to engage and persuade audiences using the tactic of incorporating similar characters.
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Data availability
Data and scripts used in the analyses are available on OSF: https://osf.io/gmc9t/?view_only=1a3b9e4815d34614a735f755abcb9bbe
Notes
The analysis for the similarity–identification effect was based on only six effects from four studies that all manipulated similarity in demographic characteristics. Moreover, a closer look at the included studies reveals that half of them either conflated identification with other forms of character involvement (Williams, 2011) or did not provide a concrete definition of identification (either conceptually or to participants; Appiah, 2001).
The comparison of interest here is between the first- and third-person POV, since few studies in this area have used narratives that are predominantly told from the second-person POV (e.g., “You shook your head and sighed deeply”). Moreover, the second-person POV may also be less relevant to identification as it directly references audience members (Chen & Bell, 2021).
Two additional moderators were considered but ultimately not included. The first was perceived similarity (i.e., whether the similarity manipulation indeed affected participants’ level of perceived similarity to the character), which was excluded due to the fact that few studies measured this variable. Moreover, operationalizations might not be comparable across studies, in that some studies asked how similar participants felt toward the character on a global level (e.g., Cohen et al., 2018), while others assessed whether participants felt that the narrative content had been tailored to them (e.g., Lu, 2013). The second moderator that was excluded was narrative topic (e.g., health, social, political), as the narratives used in studies covered a wide range of topics and not all of them could be easily classified (e.g., some narratives were simply about the daily life of the protagonist).
In the sensitivity analysis excluding the three influential cases (k = 47), Egger’s regression test yielded a significant result, b = 0.34, 95% CI [0.14, 0.53], t(45) = -2.09, p = .043, indicating funnel plot asymmetry. However, the trim-and-fill procedure similarly added 11 studies on the right side, resulting in a larger corrected effect size estimate, g = 0.23, 95% CI [0.15, 0.30], p < .001.
When the three influential cases were excluded (k = 45), the difference in effect size between psychological manipulations (k = 9, g = 0.28) and objective manipulations (k = 36, g = 0.11) did not reach conventional levels of statistical significance, Q(1) = 3.69, p = .055.
It is possible that these results were influenced by the fact that in some within-subjects studies (i.e., Hoeken et al., 2016, Studies 1 and 2; Tukachinsky et al., 2019; Van Krieken & Sanders, 2017, Medical students, Art students), narrative voice remained the same across story versions (e.g., the story was narrated in the first person by a character referring to themselves as “I”) but the perspectivizing character was manipulated such that the story could be told from the viewpoint of either the similar or dissimilar character. To test this idea, three types of sensitivity analyses were performed for this moderator: (a) one in which the three influential cases were removed (k = 47), (b) one in which the studies manipulating the perspectivizing character were removed (k = 45), and (c) one in which both of the aforementioned cases were removed (k = 43). However, in all three analyses, the difference in the magnitude of the similarity–identification effect between narrative POVs did not reach statistical significance, ps > .056.
When excluding the three influential cases (k = 47), the magnitude of the effect no longer varied significantly between published studies (k = 38, g = 0.17) and unpublished studies (k = 9, g = 0.08), Q(1) = 2.32, p = .128.
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Huang, K.Y., Fung, H.H. & Sun, P. The effect of audience–character similarity on identification with narrative characters: A meta-analysis. Curr Psychol 43, 7026–7043 (2024). https://doi.org/10.1007/s12144-023-04842-4
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DOI: https://doi.org/10.1007/s12144-023-04842-4