JAR Inside the Research Podcast
Sep 3, 2026 · 17 min · 10 segments
Send us Fan Mail In this episode, we welcome back Kirsten Cowan (The University of Edinburgh) to discuss her article, "(How) Does the Number of…
Kirsten CowanGuestColinHostI'm impressed as well too because this article has a lot of data in it as well too.
So I wonder if you want to talk briefly, you've got secondary data, you've got three experiments as well too.
Is there, could you give us like a brief overview of just what all of those different sort of pieces kind of contribute in terms of evidence?

So I guess this kind of falls into why there is a bit of challenges of of doing this type of research, because if you if you look at influencer research, it's a bit tricky because if you're putting an influencer in the lab, it's not the same as the real world.

They have feelings about them, and these existing attachments can create bias and challenges in doing experimental type of research.

So the alternative is using fake influencers, but they don't have the same type of parasocial relationships.

They don't have the same type of interaction and connection that they do with a real audience.

So there's always a trade-off between realism and control, and that's one of the reasons why we've decided to use multiple studies and multiple methods, mixed methods, to start with Instagram data, allowing us to look at raw data from Instagram and whether they tag brands, encoding all these features, and then creating an efficiency ratio to understand across the different types of influencers how and when are they most effective based on whether they tag the brand in the post or not.

Another big challenge for this was looking at the different types of influencers and understanding what their likes, what their engagement levels were because it's not really easy to compare against the smaller influencers with the larger ones.

So we had to move beyond some of that raw data to understand engagement more proportionally.

So we created this efficiency ratio, as I mentioned, that kind of took the likes from each post and divided it by the number of followers they had in order to better capture how they performed relative to other influencers.

So we collected the data from Instagram and analyzed it And then we also triangulated that with two experimental studies where we manipulated the follower size.

We also manipulated whether the brand was tagged or not, and also the type of post channel, whether it was the brand's channel or their own channel, and measured their persuasiveness.
I'm impressed as well too because this article has a lot of data in it as well too.
So I wonder if you want to talk briefly, you've got secondary data, you've got three experiments as well too.
Is there, could you give us like a brief overview of just what all of those different sort of pieces kind of contribute in terms of evidence?

So I guess this kind of falls into why there is a bit of challenges of of doing this type of research, because if you if you look at influencer research, it's a bit tricky because if you're putting an influencer in the lab, it's not the same as the real world.

They have feelings about them, and these existing attachments can create bias and challenges in doing experimental type of research.

So the alternative is using fake influencers, but they don't have the same type of parasocial relationships.

They don't have the same type of interaction and connection that they do with a real audience.

So there's always a trade-off between realism and control, and that's one of the reasons why we've decided to use multiple studies and multiple methods, mixed methods, to start with Instagram data, allowing us to look at raw data from Instagram and whether they tag brands, encoding all these features, and then creating an efficiency ratio to understand across the different types of influencers how and when are they most effective based on whether they tag the brand in the post or not.

Another big challenge for this was looking at the different types of influencers and understanding what their likes, what their engagement levels were because it's not really easy to compare against the smaller influencers with the larger ones.

So we had to move beyond some of that raw data to understand engagement more proportionally.

So we created this efficiency ratio, as I mentioned, that kind of took the likes from each post and divided it by the number of followers they had in order to better capture how they performed relative to other influencers.

So we collected the data from Instagram and analyzed it And then we also triangulated that with two experimental studies where we manipulated the follower size.

We also manipulated whether the brand was tagged or not, and also the type of post channel, whether it was the brand's channel or their own channel, and measured their persuasiveness.
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