Primed Authenticity: How Priming Impacts Authenticity Perception of Social Media Influencers

Figures & data

Figure 1. Conceptual framework.

Figure 2. Groups and manipulation conditions.

Table 1. Examples of coding of the open-ended responses.

Verbatim	Code
“She was genuine and charismatic.”	endorser authentic
“Her upbeat optimism seems fake and unreal.”	endorser inauthentic
“She doesn’t come across as entirely authentic.”
“The endorser is a little excited for my taste but not so much that I would avoid her vlogs.”	endorser neutral

Table 2. Summary of the results of the influencer content perceptions.

		Positive text introductory text		Negative introductory text
		Participation (encouraged)	Participation (discouraged)	Participation (encouraged)	Participation (discouraged)
		Group 1	Group 2	Group 3	Group 4	Total
Source authenticity*
Authentic	Count	14	12	13	4	43
	% within group	27%	22%	28%	7%	21%
Inauthentic	Count	9	11	18	25	63
	% within group	17%	20%	39%	45%	30%
No mention/Neutral	Count	29	31	15	26	101
	% within group	56%	57%	33%	47%	49%
Total	Count	52	54	46	55	207
Attitudes towards the video
Video positive**	Count	45	42	28	24	139
	% within group	87%	81%	65%	48%	71%
Video negative***	Count	6	9	15	20	50
	% within group	12%	17%	35%	40%	25%
Video neutral	Count	2	3	2	6	13
	% within group	4%	6%	5%	12%	7%
Total	Count	52	52	43	50	197
Attitudes towards the products
Products positive****	Count	40	42	27	26	135
	% within group	89%	81%	69%	57%	74%
Products negative*****	Count	4	8	5	12	29
	% within group	9%	15%	13%	26%	16%
Products neutral	Count	1	3	6	7	17
	% within group	2%	6%	15%	15%	9%
Total	Count	45	52	39	46	182

* X² = 20.865, p-value 0.002

** X² = 21.863, p-value 0.000

*** X² = 14.743, p-value 0.002

**** X² = 14.354. p-value 0.002

***** X² = 5.412, p-value 0.144 (No statistical significance)