The OPAD-perception framework: measuring perceptions of online personalized advertising
Abstract
Purpose
The paper aims to develop and validate an instrument to measure users’ perceptions of online personalized advertising.
Design/methodology/approach
First, we identified 12 different aspects of online personalized advertisement and formulated candidate items through a literature review. A card sorting study and expert review were conducted to generate the initial scale items. We then conducted one survey (n = 308) to create a reliable measurement instrument and another (n = 296) to validate the instrument. Finally, we tested how the dimensions of the OPAD-Perception Framework can be used to differentiate between different levels of ad sensitivity, control/no control over the ad personalization process, and different levels of granularity of ad explanation.
Findings
The resulting OPAD-Perception Framework contains 49 Likert-formatted questions measuring ten distinct dimensions of online personalized advertising: reliability, usefulness, transparency, interactivity, targeting accuracy, accountability, creepiness, willingness to rely on, self-actualization, and persuasion.
Originality/value
The OPAD-Perception Framework can serve as a powerful tool to measure users’ attitudes toward online personalized advertising. This will enable advertisers and social media platforms to better support users’ privacy expectations and provide user-friendly interfaces for controlling the ad personalization process.
Keywords
Acknowledgements
This work was partially supported by a 2019 Meta (formerly Facebook) Research Award. RFP: Research to Improve Advertisement Experiences, ID #: 417311752246331. We extend our gratitude to Pardip Kalra (and her team in Meta), Kevin Roundy, and Mahmood, for the expert review on the scale items.
Citation
Guo, L., Wilkinson, D., Namara, M., Patil, K. and Knijnenburg, B.P. (2024), "The OPAD-perception framework: measuring perceptions of online personalized advertising", Internet Research, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/INTR-01-2023-0078
Publisher
:Emerald Publishing Limited
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