The Future of Dataveillance in Advertising Theory and Practice

Figures & data

Table 1. Glossary of main concepts.

Concepts	Definitions	Source
Computational advertising	“Broad, data-driven advertising approach relying on or facilitated by enhanced computing capabilities, mathematical models/algorithms, and the technology infrastructure to create and deliver messages and monitor/surveil an individual’s behaviors.”	Huh and Malthouse 2020, p. 1
Data collection	Monitoring of personal information (behavior, interests, traits, beliefs, and intentions)	Acquisti et al. 2015
Dataveillance	Automated, continuous, and (unspecific) collection, retention, and analysis of digital traces by people or groups, by means of personal data systems by state and corporate actors, to regulate or govern their behavior	Based on Büchi, Festic, and Latzer 2022, Degli-Esposti 2014
Digital data-driven advertising	Message of persuasion (regarding products, services, or ideas) that interacts with consumers through digital media and requires consumer data to be collected, processed, or stored	Based on Lee and Cho 2020
Perceived surveillance	The feeling of being watched, listened to, or having personal data recorded	Segijn et al. 2022
Personalization/(micro)targeting/tailoring	“The strategic creation, modification, and adaptation of content and distribution to optimize the fit with personal characteristics, interests, preferences, communication styles, and behavior.”	Bol et al. 2018, p. 373
Surveillance beliefs	“The ideas an individual holds regarding the extent and aim of surveillance”	Based on the concept of beliefs in Yzer 2012
Surveillance culture	“This term describes how people are not only under surveillance by government or corporate interests—even in our most mundane day-to-day tasks—but also engage and participate in surveillance of other people, like observing others on social media.”	Penney 2021, p. 1511
Surveillance episode	Directly observable instance of data collection (for example, through a digital ad that is perceived as data driven)	Based on the concept of persuasion episode by Friestad and Wright 1994
Surveillance imaginaries	Collective understanding of the extent and aim of surveillance and expectations about an individual’s own behavior	Duffy and Chan 2019
Surveillance response	Responsive practices that relate to being surveilled	Lyon 2017

Table 2. Overview of past research on perceived surveillance in digital data-driven advertising.

Authors (Year)	Independent Variables	Dependent Variables	Mediators	Main Findings/Arguments Related to Surveillance
Campbell and Carlson (2002)	N/A	N/A	N/A	In a discourse analysis of marketing models of the Web, the authors explore social implications of corporate surveillance. They conclude that participation of individuals in the online gathering of data about themselves is the result of the commodification of privacy.
Farman, Comello, and Edwards (2020)	Behavioral targeting	Purchase intentions	Perceived surveillance Threat Affective and cognitive reactance Attitude toward ad	Behavioral targeting (versus general product ad) leads to more perceived surveillance, which increases perceived threat.
Frick et al. (2021)	Security perception Privacy perception Trust in vendors	Perceived surveillance of conversations	Trust in smart devices	The study identified three predictors that affect the perceived surveillance of conversations: trust in smart devices, computer anxiety, and prior negative experience.
Segijn and Van Ooijen (2020)	Personalization techniques	Perceived surveillance		Perceived surveillance differs per personalization technique. In addition, older generations experience more perceived surveillance than younger generations. The more surveillance is perceived, the less likely people would accept a personalization technique.
Segijn, Kim, et al. (2021)	Synced advertising knowledge	Resistance	Perceived surveillance Synced advertising literacy/ knowledge Critical attitudes	Informing consumers with limited advertising knowledge increases perceived surveillance. Increased perceived surveillance leads to more resistance (counterarguing).
Sifaoui (2021)	A personalized ad in which an Instagram ad displaces an ad in which the brand or product has a (mis)match with a previous search	Advertising avoidance Ad, product, brand attitude	Perceived surveillance	A brand match leads to more perceived surveillance than a brand mismatch. Product (mis)match does not have an influence on surveillance and there is no interaction effect.
Sifaoui, Lee, and Segijn (2022)	Brand match versus brand mismatch in personalized advertising	Advertising avoidance	Perceived surveillance	Increased perceived surveillance increases ad avoidance.

Figure 1. Dataveillance effects in advertising landscape (DEAL) framework. The dotted line between surveillance episode and surveillance beliefs indicates that such an episode may activate one’s beliefs but also can update them.

Table 3. Future research directions overview.

Research Direction	Research Topic	Related (Advertising) Theories	Potential Contributions
1. Surveillance beliefs	How beliefs are developed	Persuasion knowledge model (Friestad and Wright 1994) Covert advertising recognition and effects model (Wojdynski and Evans 2020)	Consumer empowerment through transparency Effectiveness of data collection disclosures
	Effects of beliefs	Transparency-awareness-control framework (Segijn, Strycharz, et al. 2021)
	Combating misinformation/ conspiracy theories in surveillance	Folk theories (DeVito, Gergle, and Birnholtz 2017)
2. Perceived surveillance	Dimensions of perceived surveillance in advertising		Role of perceived surveillance in advertising effectiveness Tipping point in digital data-driven advertising
	Relation between perceived surveillance and persuasion coping mechanisms	Reactance theory (Brehm and Brehm 1981) Resistance typology (Fransen et al. 2015)
3. Surveillance responses	Studying chilling effects in response to corporate surveillance	Theory of the chilling effects of dataveillance (Büchi, Festic, and Latzer 2022)	Unintended effects of digital data-driven advertising
	Role of individual characteristics (e.g., privacy concerns, desire for privacy, online experience, knowledge, awareness)		Identification of groups more likely to show surveillance responses

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