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Journal of the Academy of Marketing Science

18-11-2023 | Original Empirical Research

Toward a theory of consumer digital trust: Meta-analytic evidence of its role in the effectiveness of user-generated content

Authors: Rachel E. Hochstein, Colleen M. Harmeling, Taylor Perko

Published in: Journal of the Academy of Marketing Science

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Abstract

Consumers seek out online user-generated content to inform their purchase decisions because they perceive content created by other consumers as more believable than marketing communications. This research provides a theory of consumer digital trust in which consumer trust in user-generated content requires a digital environment that minimizes consumer suspicion of misrepresented or missing content. The theory is supported with empirical evidence from a hierarchical meta-analysis of 128 effects from 19 online platforms over 19 years (2004–2022). Account verification features, which alleviate suspicions of misrepresented content creator identities, increase the effect of user-generated content on firm performance, but content-enhancing features, such as photo filters, that can prompt suspicion of misrepresented brand experiences, weaken this link. Content-removal features that can spark speculation of missing information in content creators’ historical content and platform moderation media, which creates questions about missing content in brand conversations, weaken the influence of some user-generated content.

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Appendix
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Footnotes
1
Percent change calculated based on a mean-split of effect sizes at the average volume of media coverage considering the average effect size according to high (i.e., above the mean) or low (i.e., below the mean) amounts of media coverage and type of user-generated content (restricted or unrestricted).
 
2
Sales rank has an inverse relationship with sales, such that we reverse the sign of the effect size unless the original study already did so (Babić-Rosario et al., 2016).
 
3
Following Babic-Rosario et al. (2016) and Rosenthal (1988), we use the following formula to calculate the partial correlation coefficient effect sizes: rxy = t/sqrt(t2 + df), where t is the t-value associated with the regression parameter, and df are the degrees of freedom of the reported regression model. We ran an additional model, controlling for whether an effect size reflects a full or partial correlation. Including this control variable does not influence the results.
 
4
We use the midpoint of a study’s data collection period rather than the year the study was published to account for the delay between when data are collected from platforms and when a study is published. In instances where the authors of the original study were not able to provide the exact dates of data collection, we substituted the publication year.
 
5
Variance inflation factors are < 5, and correlations are < .70, limiting multicollinearity concerns (Chang and Taylor 2016).
 
6
To calculate a pseudo R2 for the model, we compare a baseline version without moderators with a model that includes moderators to determine the amount of heterogeneity explained by the addition of the moderating variables.
 
7
With small samples (19 platforms) and limited variance in measures (binary measures for content-enhancing and content-removal features), the power to detect an effect is significantly impaired (38 possible unique values), so for directional hypotheses, less conservative significance values (p < .10; one-tailed p < .05) can provide valid tests of significant relationships (Sawyer and Ball 1981).
 
8
To calculate standardized effect sizes, we used the formula \(b(\frac{sd\left(x\right)}{sd\left(y\right)})\), where \(b\) is the estimated meta-regression coefficient, sd(x) is the standard deviation of the focal independent variable, and sd(y) is the standard deviation of the effect size.
 
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Metadata
Title
Toward a theory of consumer digital trust: Meta-analytic evidence of its role in the effectiveness of user-generated content
Authors
Rachel E. Hochstein
Colleen M. Harmeling
Taylor Perko
Publication date
18-11-2023
Publisher
Springer US
Published in
Journal of the Academy of Marketing Science
Print ISSN: 0092-0703
Electronic ISSN: 1552-7824
DOI
https://doi.org/10.1007/s11747-023-00982-y