TY - GEN
T1 - Ad blocking and counter-ad blocking
T2 - America�s Conference on Information Systems: A Tradition of Innovation, AMCIS 2017
AU - Zhao, Shuai
AU - Wang, Chong
AU - Kalra, Achir
AU - Vaks, Leon
AU - Borcea, Cristian
AU - Chen, Yi
N1 - Funding Information:
This work is partially supported by National Science Foundation (NSF) CAREER Award IIS-0845647, NSF Grant No.CNS 1409523, Google Cloud Service Award and the Leir Charitable Foundations. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.
Publisher Copyright:
© 2017 AIS/ICIS Administrative Office. All Rights Reserved.
PY - 2017
Y1 - 2017
N2 - Compared with online advertising industry, there is an even faster increase of ad blocker usage, which influence badly on publishers’ and advertisers’ business. Thus more and more companies initialize their counter-ad blocking strategies, in which customers choose to either disable their ad blockers or leave without seeing the content. There are also companies which abandon their counter-ad blocking strategies after conducting them for a while due to insufficient understanding of users’ ad blocking behavior. In this study, we employed a quasi-experiment framework and collected a large-size data with the cooperation with Forbes Media. We aim to identify factors influencing ad blocker usage. Furthermore, we will model the interaction effects among user profile, online behavior patterns, device features on ad blocker usage propensity. Our study contributes the literature of understanding ad blocker usage by evaluating those principles using big amount of real-world data.
AB - Compared with online advertising industry, there is an even faster increase of ad blocker usage, which influence badly on publishers’ and advertisers’ business. Thus more and more companies initialize their counter-ad blocking strategies, in which customers choose to either disable their ad blockers or leave without seeing the content. There are also companies which abandon their counter-ad blocking strategies after conducting them for a while due to insufficient understanding of users’ ad blocking behavior. In this study, we employed a quasi-experiment framework and collected a large-size data with the cooperation with Forbes Media. We aim to identify factors influencing ad blocker usage. Furthermore, we will model the interaction effects among user profile, online behavior patterns, device features on ad blocker usage propensity. Our study contributes the literature of understanding ad blocker usage by evaluating those principles using big amount of real-world data.
KW - Ad avoidance
KW - Ad blocker
KW - Big data analytics
KW - Online advertising
UR - http://www.scopus.com/inward/record.url?scp=85048356008&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048356008&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85048356008
T3 - AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation
BT - AMCIS 2017 - America's Conference on Information Systems
PB - Americas Conference on Information Systems
Y2 - 10 August 2017 through 12 August 2017
ER -