Katterfeld, Eva-Maria
(2016)
Advertising Avoidance on the Internet: An Empirical Study about the Antecedents of Ad Blocking.
[Dissertation (University of Nottingham only)]
Abstract
The introduction of ad blocking software provides consumers with a mechanical means to avoid advertisements on the Internet. Growing usage rates of ad blockers around the world put online marketers under increasing pressure. This master dissertation aims to identify key antecedents of online advertising avoidance and, in particular, of ad blocking. Based on findings of previous research (e.g. Baek and Morimoto, 2012; Cho and Cheon, 2004; Edwards, Li and Lee, 2002; Kelly, 2014; Li, Edwards and Lee, 2002; Seyedghorban, Tahernejad and Matanda, 2016; Walsh, 2010), a conceptualised model of online advertising avoidance is developed. The model is studied with survey data from 140 respondents. Multicollinearity in the multiple regression models and constraints in using a more advanced data analysis method hindered an assessment of the overall model. Instead, simple regression analyses were used to investigate the independent impact of each variable separately. Results suggest that Internet consumers will use an ad blocker if they feel disrupted or distracted by online ads, were not able to perceive any benefits or incentive when they previously clicked on online ads, are sceptical towards online ad’s claims, perceive online ads as intrusive, have privacy concerns, or a general negative attitude towards online ads. Gender was identified as the most important predictor of ad blocking. In addition, it was found that targeted online ads are less avoided than non-targeted ones and that user mode has an impact on how Internet consumers respond to online ads. Online marketers should refrain from using intrusive ad formats and instead aim to provide Internet consumers with useful information through better targeted ads. In addition, they should seek a dialogue with ad blocker users to make them aware of the negative impact of ad blocking. Further research should replicate the present study and use a more advanced data analysis method such as “Structural Equation Modelling” to test the proposed model as a whole.
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