Paper Type
Short
Paper Number
PACIS2026-1205
Description
The proliferation of algorithmic recommendation has improved information interaction efficiency while intensifying the “information cocoon” effect, yet existing constructs focus on objective phenomena rather than users' subjective experiences. This paper introduces Perceived Intensity of Information Cocoon (PIIC) — a user-level construct capturing the degree to which individuals feel confined within a narrowed, algorithmically mediated information environment. Study 1 employs grounded theory with multiple sourced data (interviews, surveys, netnography) to develop and validate a 15-item, second-order formative PIIC scale which consists of three dimensions: Perceived Content Homogenization, Perceived Algorithmic Manipulation, and Perceived Opinion Polarization. Study 2 further investigates PIIC's effect on in-feed ad avoidance via two competing mechanisms-processing fluency (negative path) and psychological reactance (positive path),moderated by cognitive resources. This research advances digital media theory and offers practical guidance for platform design, algorithm governance, and advertising strategy.
Recommended Citation
Chang, Yaping; Lin, Weijun; Yan, Jun; and Zhang, Fangfei, "Measuring User Perceived Intensity of Information Cocoon and Its Impact on Ad Avoidance to In-feed Ads" (2026). PACIS 2026 Proceedings. 2.
https://aisel.aisnet.org/pacis2026/isdesign_tam/isdesign_tam/2
Measuring User Perceived Intensity of Information Cocoon and Its Impact on Ad Avoidance to In-feed Ads
The proliferation of algorithmic recommendation has improved information interaction efficiency while intensifying the “information cocoon” effect, yet existing constructs focus on objective phenomena rather than users' subjective experiences. This paper introduces Perceived Intensity of Information Cocoon (PIIC) — a user-level construct capturing the degree to which individuals feel confined within a narrowed, algorithmically mediated information environment. Study 1 employs grounded theory with multiple sourced data (interviews, surveys, netnography) to develop and validate a 15-item, second-order formative PIIC scale which consists of three dimensions: Perceived Content Homogenization, Perceived Algorithmic Manipulation, and Perceived Opinion Polarization. Study 2 further investigates PIIC's effect on in-feed ad avoidance via two competing mechanisms-processing fluency (negative path) and psychological reactance (positive path),moderated by cognitive resources. This research advances digital media theory and offers practical guidance for platform design, algorithm governance, and advertising strategy.
Comments
13-Design