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Purpose One of the most effective tools used by interactive marketers is personalized advertising, which allows consumers to directly respond to customized offers to purchase a brand’s products and services. Yet, recent studies show many consumers are installing ad blockers to avoid personalized ads. This study aims to examine how ad skepticism, ad relevance and ad irritation predict ad avoidance directly, as well as indirectly through consumers’ attitudes toward personalized advertising. Also, considered were how these antecedents’ study in tandem to trigger consumers’ desire to avoid ads by installing ad-blocking software. Design/methodology/approach An online survey was administered to a pool of 1,313 paid panelists who were familiar with ad blocking and reported that they either currently used an ad blocker, previously used an ad blocker, were considering using an ad blocker or did neither use nor were they considering using an ad blocker. All hypotheses were addressed via path modeling using PROC CALIS in SAS 9.4. Findings Results indicate that attitudes toward personalized advertising are more complex than attitudes toward advertising in general and mediate the effect of ad relevance on ad avoidance. Likewise, trust in interactive marketers moderates attitude toward personalized advertising and the negative outcomes of ad avoidance and ad blocker usage among skeptical consumers. Also, the reported differences in ad avoidance based on participants’ current vs previous ad blocker usage suggest that former users are using a more sophisticated evaluation of the costs and benefits of using ad blockers. Practical implications Consumers’ trust in an interactive marketer to properly collect and use their information plays an important role in moderating negative outcomes associated with personalized advertising. Also, the key is the use of high-quality data (best obtained through a permission-based relationship with the consumer) to deliver relevant ads without stimulating reactance or (privacy-related) boundary turbulence. Findings suggest that bolstering trust by engaging in a transparent, permission-based relationship with consumers may mitigate the tendency to adopt ad blockers and enhance the effectiveness of interactive marketing efforts. Originality/value Ad blocking presents a significant threat to the effectiveness of interactive marketing efforts like personalized advertising. Previous research on the antecedents of ad blocking is limited, considers a broad range of factors and offers mixed findings. The present study examines an informed set of cognitive and affective factors suggested by previous ad blocking studies to predict consumers’ desire to avoid personalized ads by installing ad-blocking software. Given the continued threat to the interactive marketing industry posed by ad blocking, a greater understanding of consumers’ motivations to adopt and use ad blockers is critical.

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Summary Processes of viral diffusion are important in biological, technological, and social systems alike. Several mathematical models of infection have been developed to predict diffusion through networks, such as nonlinear dynamical systems (NLDSs). Such models generally offer accurate representations of real‐world diffusion, particularly for networks with static topologies. However, simulations of viral diffusion are computationally expensive, rendering them infeasible for large‐scale networks. Here, a new approach is shown that leverages quantum computing to make viral diffusion simulations feasible for large networks, independent of network topology. Simulations of an error‐free quantum circuit accurately modeled viral diffusion, with multivariate Euclidean distances from predicted infection probabilities capped near 8% for a network with N = 5 nodes and t = 20 time‐steps. This is sufficient accuracy to distinguish the relative susceptibility of nodes and to identify significant changes, such as periods of especially high susceptibility. The results illustrate the potential for quantum computational network simulation to provide accurate models of diffusion through large networks, an important real‐world application of quantum computing. The ability to simulate viral diffusion is invaluable for researchers across disciplines who aim to understand, anticipate, prepare for, and intervene in ongoing diffusion processes.

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