Over time, scholars with different perspectives have examined information privacy behavior, making distinct contributions to advertising (Li, 2012). Consequently, online advertising has progressively transitioned, leaving user information as a fundamental asset for advertisers to achieve the most effective and efficient personalized targeting (Estrada-Jiménez et al., 2017). Thus, advertisers aim to assimilate the consumer-provided information to establish a continuing relationship between the brand and the consumer (Rapp et al., 2009). This form of personalized targeting is, in the literature, identified and known as the so-called online behavioural advertising (OBA) (Aiolfi et al., 2021). Hence, advertisers perceive OBA as an important tool because it allows precise consumer targeting and boosting ad effects (Boerman et al., 2017). Yet, the use of this technology poses privacy concerns among consumers. Therefore, advertisers must examine and incorporate privacy and protection strategies (Estrada-Jiménez et al., 2017) that align with privacy regulations such as the General Data Protection Regulation (GDPR) (Jin & Skiera, 2022).

Research indicates that a substantial majority, two out of three internet users, express concerns about the unauthorised use of their private data (Estrada-Jiménez et al., 2017). Brockner (2002) asserts the importance for advertisers to understand how consumers react to procedural fairness and unfairness in online profiling. Accordingly, as consumers are exposed to advertisements, they engage in a sensemaking process, enabling them to decide whether to enter into a social contract or not (Martin & Murphy, 2017). Similarly, consumers’ responses to data surveillance involve a trade-off between feeling ownership over their personal data (Chen et al., 2019) and deriving benefits from the access to companies (Plangger & Montecchi, 2020). Therefore, consumer intention is analyzed regarding consumers’ privacy perceptions (Wang et al., 2016). Further, Laufer and Wolfe (1977) relate it to the idea of control or choice.

The Privacy Calculus Theory outlined by Culnan and Armstrong (1999) builds upon a rational choice principle (Satz & Ferejohn, 1994). The theory posits that individuals disclose information based on a calculus of behavior (Pentina et al., 2016). In simpler terms, it represents a trade-off between expected risks and expected benefits by the consumer (Li, 2012). Essentially, Wang et al. (2016) observed that when the expected benefits exceed the risks, an individual would be more likely to disclose their personal information.

According to Culnan and Armstrong (1999), professionals in the advertising field have seen a paradigm shift from mass production of information to mass customization and personal services. Thus, organizations, platforms, and advertisers must balance the power of information as the constructive effect of transaction data and incorporating fair procedures is essential for the generation of value (Culnan & Armstrong, 1999). In the quest to understand what drives users’ willingness to disclose private information, the personalization-privacy paradox has to be assessed (Yeh et al., 2018). The personalization-privacy paradox explains that the intention does not match the behavior (Norberg et al., 2007). In the literature the phenomenon is well-known (Gerber et al., 2018; Hayes et al. 2021; Kokolakis, 2017) as disparities are highlighted between consumers’ stated privacy concern and the actual behavior, giving rise to a contradiction.

The Privacy Calculus Theory serves as a fundamental theory in different studies, including those by Gutierrez et al. (2019) and Hayes et al. (2021), which is applied in the realm of Mobile Location-Based Advertising (MLBA) and social media. The study by Gutierrez et al. (2019) uses the Privacy Calculus Theory to examine four specific risks and benefits that influence the acceptance of MLBA; internet privacy concerns, intrusiveness, personalization, and money rewards. The results showed that users generally seem to accept a certain degree of personal privacy loss in the trade-off of guarantee of specific benefits. Another approach is seen by Hayes et al. (2021) who investigate Privacy Calculus in relation to consumer-brand relationships. In their study, they focus on the emotional attachment consumers have with a brand. Accordingly, it was found that when people feel attached to a brand it is more likely that they will disclose information as it seems less risky. Therefore in sum, the two studies use the Privacy Calculus Theory as a lens to examine specific expected benefits over specific expected risks.

In order to grasp the arising consumers’ inconsistencies in privacy concerns and behavior, Wisniewski and Page (2022) introduce various privacy frameworks. These frameworks impose limitations on the Privacy Calculus Theory since it is a complex network (Li, 2012). Fox et al. (2021) highlight the importance of understanding the difference between privacy concerns at a general level and situational level. In addition to the Privacy Calculus (Culnan & Armstrong, 1999), the risk-benefit analysis must consider factors such as interpersonal boundary regulation, contextual norms, and individual differences to be appraised (Wisniewski & Page, 2022).

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