Reconsidering Privacy Choices: The Impact of Defaults, Reversibility, and Repetition

Cameron Kormylo & Idris Adjerid

Pamplin College of Business, Virginia Tech

Blacksburg, VA 24060

ckormylo@vt.edu; iadjerid@vt.edu

Abstract

Despite significant stated privacy concerns among online consumers, research has found almost 

uniform acceptance of online consent decisions. In recent years, policy makers have adopted 

sweeping privacy-related regulations to address these concerns. These regulations change the way 

that online consent is elicited, in hopes of encouraging more deliberative and self-interested 

privacy decision making by consumers. This study aims to explore the effects of these changes on 

consumer consent decisions. We isolate three specific tenets of modern privacy regulation, 

reduction in privacy consent opted-in by default, reversibility, and repeated consent, and explore 

their effects on individual behavior. We conduct an online experiment that presents participants 

with actual disclosure decisions that asks participants to link sensitive disclosures to personal 

information through the decision to “log-in.” Expectedly, we find that active choice consent 

structure and a protective opt-out consent structure decrease log-ins significantly compared to 

default opt-in. However, opposite the expectation that repeated exposure will lead to less 

susceptibility to choice defaults, we find that repeated exposure increases the effect of choice 

defaults, further entrenching their impact. For reversible consent decisions, we also find the 

surprising result that both explicit reversibility and explicit irreversibility increase the impact of 

protective defaults by up to 50%. We conclude that while opt-out defaults can drive more 

protective consumer behavior, in combination with reversibility and repeated exposure, they may 

the unintended effects of an over-reaction by consumers and lead to drastic reductions in consent 

provided. Our results extend the current privacy literature and have significant implications for 

consumers, firms, and policy makers. 

 
1. Introduction

Offering consumers granular privacy choices online promises protections for consumers 

who desire it while allowing innovation for firms reliant on consumer personal information. 

Legal scholars and policy makers however, argue that the potential value of granular privacy 

choices as a mechanism of consumer protection has not been realized (Solove 2013). In online 

settings with well documented consumer privacy concerns, permissions from consumers are 

often astronomically high and this consent elicitation is often never revisited. For example, 

research on the AdChoices program (which gives users the ability to opt-out of behaviorally 

targeted ads), shows that the program was used in only 0.23% of all American ad impressions 

(Johnson et al. 2020). Some scholars interpret high levels of consent as consumer indifference to 

privacy concerns or the high valuation they place on the digital services provided (or both) 

(Luohan Academy 2021). Policy makers disagree that consumers choices online reflect an active 

evaluation of the benefits and costs of data sharing. Rather, they point to implicit and difficult to 

reverse permissions garnered through convoluted privacy policies and privacy choices buried in 

complex menus that require consumers to actively unselect options to opt-out (FTC 2013). 

Research substantiates this premise and finds that individuals overwhelmingly agree to privacy 

policies even when they include naming rights for their first child, access to the airspace above 

their homes for drone traffic, and sharing all of their data with the National Security Agency 

(Obar and Oeldorf-Hirsch 2018).

Recent changes to global privacy regulation reflect policy makers’ concerns with the 

status quo for privacy choice online. The General Data Protection Regulation (GDPR) became 
law in Europe in 20181 and requires, among other things, that permission for collections and use 

of personal information be solicited in an active manner and not through implicit data 

allowances, disallows the use of opt-in defaults for most privacy consents, and requires that 

permissions are easily reversible and are revisited periodically through consent re-elicitation. The 

California Privacy Rights Act (CPRA) amended pre-existing California law soon after2 and 

instituted similar requirements to GDPR. Importantly, the extant privacy literature only partially 

informs the impact of such drastic and widely applicable changes to online consumer privacy 

choices. 

With respect to changes to choice defaults, recent work substantiates the powerful role 

cognitive biases and “choice architecture” (e.g. choice defaults) play in privacy choice settings 

(Acquisti et al. 2017; Adjerid et al. 2019; Egelman et al. 2013; Keller et al. 2011; Thaler and 

Sunstein 2008; Thaler et al. 2013). Thus, policy decisions to prohibit permissive defaults and 

institute active choices or protective defaults are likely to substantially alter consumer privacy 

choices online. What is less clear from the privacy literature is the impact of policy attempts to 

convert online privacy choices from one-off, relatively permanent choices to dynamic choices 

which are re-visited by consumers and easily reversible. The re-visitation and reversibility of 

privacy choices can have intriguing effects on privacy decisions. For example, Peer et al. 2016 

(the only work we are aware of evaluating reversibility in privacy settings) find that providing 

reversible privacy choices has the counter-intuitive effect of decreasing disclosure (despite 

objectively reducing the risk of disclosure). More so, reversibility and revisitation of privacy 

choices are likely to have important intersections with the impact of cognitive biases and choice 

 1

Regulation (EU) 2016/679 https://gdpr-info.eu/ 2 https://www.manatt.com/insights/newsletters/client-alert/the-california-privacy-rights-act-has-passed
architecture online (e.g. choice defaults). For instance, literature in economics is divided on the 

effect of experience on the impact of cognitive biases in decision making. Some present evidence 

that individuals can learn to overcome decision biases with experience (Sellier et al. 2019; Smith 

1976; Smith 1982). Others show that habituation and fatigue can lead to additional biases, further 

hindering decision making (Schaub et al. 2015). 

Given the significance and scope of policy changes around consumer consent and the

limited research informing their impact on consumer behavior, our research aims are to 1) 

evaluate the effect of changing choice architecture on consumer privacy choices and 2) explore 

how reversibility and repeated exposure impact decision making across different manipulations 

of choice architectures. 

We conduct a two factor (3x3) between-subjects online experiment with participants 

recruited through Prolific3

. Participants first create a research profile which asks for demographic 

information (similar to a profile page for a web service) and a social desirability assessment 

(Crowne and Marlowe 1960)

4

. Each participant then takes three separate studies that ask for 

sensitive disclosures related to sexual behavior, romantic involvement, or criminal history. 

Participants are informed that, if they log-in to their research profile, the demographic data in 

their research profile will be associated with their responses. They are also informed that their 

profile would be used to track their responses across multiple studies, and that consenting would 

allow for customized research requests in the future based on their responses.5 Otherwise, their 

 3 Prolific is an online subject recruitment platform designed specifically for researchers. It has been shown to 

provide data quality comparable to other platforms while providing participants that are more naïve to “common 

experimental research tasks” (Peer et al. 2017). 4 We introduced a social desirability assessment because default effects are often a function of one’s desire to adhere 

to a socially optimal choice. 

5 It is against the terms of service for most online research platforms (e.g. Prolific, Amazon Mechanical Turk) to ask 

directly identifying information from participants (e.g. email address, name). 
answers would be disconnected from the research profile and no additional opportunities would 

be available. Participants are randomly assigned to one of nine conditions that manipulate two 

experimental factors. The first factor, choice architecture, alters whether the option log-in choice 

is selected by default (baseline condition), an active choice decision (i.e. no default selected), and 

a protective default where the option to be anonymous is selected by default. The second 

dimension is whether the participant decision is explicitly reversible, the decision is explicitly

irreversible, or the participant is given no information about the reversibility of their choice. 

These conditions are held constant across the three studies. These experimental manipulations 

allow us to evaluate the impact of choice architecture and reversibility while the repeated nature 

of the task allows us to evaluate the impact of multiple exposures to the privacy choice.

In terms of the first research objective, we find large and significant effects of changing 

choice architecture. Those in the default opt-in condition (our control group), the active choice 

condition, and the default opt-out condition logged in approximately 92%, 81%, and 51% of the 

time, respectively. This shows the effectiveness of an active choice while highlighting the 

strength of a more protective opt-out default.

Our second research objective considers the intersection of changing choice architecture 

with reversibility in a choice context. Our first major finding shows the surprising result that 

when combined with a protective opt-out default, both reversibility and irreversibility negatively 

affect log-in rates. For those in the protective opt-out condition, log-in rates when the decision 

was made explicitly reversible, explicitly irreversible, or neither reversible nor irreversible were 

47%, 46%, and 56% respectively. Given that these two seemingly opposite constructs affect our 

outcomes in the same direction, we speculate that both reversibility and irreversibility signal to 

the user that the decision introduces privacy concerns, leading to more protective behavior.
Next, we consider the role of repeated exposure on the effects of choice architecture. 

Without a reversibility statement, both defaults get stronger over time in their respective 

directions. The log-in rates increase by ~2% after each exposure to the privacy choice when 

presented as a default opt-in while log-in rates decrease by ~4% after each exposure to the 

privacy choice when presented as a protective opt-out default. This result shows that repeated 

exposure is unlikely to attenuate the effect of decision biases. However, we find that making a 

choice reversible or irreversible counteracts the increase in decision biases for those in the 

default opt-in condition. In other words, without any explicit reversibility, the effect of the opt-in 

default grows over time (as stated above). When, instead, reversibility or irreversibility is 

introduced, the effect of the default remains the same over time, instead of increasing. Our 

interpretation of this result is that the potential of either reversibility or irreversibility to prime 

privacy concerns counter-acts the potential of individuals to be more susceptible to permissive 

defaults over time. Comparatively, when reversibility is introduced to those in the protective optout condition, consent is driven down even further, likely creating ceiling effects. Therefore, 

those prone to be impacted by the default exhibit this behavior at the first exposure to the choice, 

with the effect persisting, but not growing, over time. 

Our findings contribute to several streams of research. First, we contribute to the stream 

of research within the privacy decision making literature highlighting the importance of 

behavioral biases in decision making (Acquisti and Grossklags 2005; Acquisti et al. 2012; Li et 

al. 2008; Tsai et al. 2011). 

Specifically, we reaffirm the influence that default choices have on individuals’ privacy 

related behavior (Gross and Acquisti 2005; Johnson and Goldstein 2003; McKenzie et al. 2006). 

In addition, past work has primarily focused on one-off choices (Acquisti and Grossklags 2005), 
when in reality most modern privacy choices are repetitive. Thus, we extend work on the 

influence of biases in privacy decision making by showing its lasting effects over time. By 

extending our consideration to repeated choices, we are able to better model the progression of 

individuals’ privacy behavior. Finally, with the exception of Peer and Acquisti (2016), to our 

knowledge, no other work has addressed the role of reversibility in privacy decision making. We 

show how explicit reversibility or irreversibility can alter an individual’s perception of privacy 

risk and their willingness to forgo anonymity online. This answers the call for further exploration 

of the effects of two seemingly opposite constructs put forth by Peer and Acquisti (2016). We 

relate the effect of reversibility to that of control and how literature has primarily considered 

control’s effect on gaining initial consent, not preventing a revocation of consent (Whitley 2009). 

We also contribute to the literature in behavioral economics and choice architecture. 

First, we reiterate past findings that show the power of default effects, specifically as they relate 

to an active choice structure (Letzler et al. 2017; Li et al. 2013). Second, we explore the 

persistence of choice defaults across instances of exposure. Despite extensive consideration 

around default effects, little is known about their ability to persist over time (Marteau et al. 

2011). The little evidence that has been presented primarily focuses on the persistence of a single 

exposure to a default effect (Venema et al. 2018). Our consideration allows for a more thorough 

understanding of the effect of defaults over time, specifically in light of multiple presentations of 

the same default. Finally, to the best of our knowledge, no work has evaluated the combined 

effect of explicit reversibility and choice defaults. This consideration grants further insight to the 

changing effects of choice defaults when paired with other interventions. 

Finally, we contribute to the growing literature surrounding the implications of broad 

privacy regulation like GDPR (Adjerid et al. 2015; Batikas et al. 2020; Goldberg et al. 2019; Jia 
et al. 2018). To our knowledge, this is the first work to consider the interactive effects of specific 

tenets of privacy regulation such as repeated exposure and reversibility. We isolate these specific 

enactments to better understanding the far-reaching implications of emerging privacy law. Our 

experimental approach allow us to focus on key aspects of new legislation (as opposed to 

consider these laws as one shock) and extend the current conversation around new privacyrelated legislation and addresses concerns relating to specific aspects of these laws. We further 

contribute to the literature by showing the importance of reversible consent and its impact on 

consumer behavior. 

For firms, our findings are important when navigating the changing regulatory structure 

and when designing user interfaces that abide by said regulation while not being prohibitively 

costly to the firm. Understanding the consumer decision-making process can assist firms in 

designing appropriate interfaces that remain protective while still allowing for considerable rates 

of consent. Additionally, we highlight the balance between protectiveness and economic benefit 

that is impacted by privacy regulation. Policymakers can use these findings to design regulation 

that balances the two considerations and apply them to specific choice contexts. For instance,

one interpretation of our findings is that while each of the changes to how consent is elicited can 

have the desired policy impact (consumers choosing options that better reflect their privacy 

concerns), the scale of our effects suggest that a combination of protective defaults, reversibility, 

and repeated exposure may lead to an overreaction from consumers with respect to consent. 

Finally, consumers benefit from a more thorough understanding of their own decision biases 

when presented with privacy choices. These findings can assist consumers in understanding the 

far reaching implications of consent decisions and navigating changing choice architectures in 

light of new privacy regulation. 
2. Conceptual Background & Hypotheses

This study examines and builds on three existing areas of research. The first is privacy 

decision making, an area of research that has grown in complexity within the last several decades 

(Bélanger and Crossler 2011; Li 2011; Pavlou 2011). While any number of factors may influence 

the outcome of a privacy decision, this work specifically focuses on a second area of research, 

choice architecture – the idea that subtle changes in the design of a choice can drastically impact 

the behavior and decision making of an individual presented with that choice (Egelman et al. 

2013; Johnson et al. 2012; Keller et al. 2011; Thaler et al. 2013). At the intersection of these two 

streams, we consider the effect of choice architecture on privacy decision making and how its 

effect may be influenced the repetitiveness and reversibility of the decision. 

The information systems literature is increasingly adopting insights and rationale from 

behavioral economics (Goes 2013). The traditional view within behavioral economics has been 

that decision makers operate through a rational process, driven by the desire to maximize utility 

(Mullainathan and Thaler 2000). When applied to privacy decision making, this gave rise to the 

development of a privacy calculus, in which consumers systematically weighed the costs and 

benefits of privacy-related disclosures (Dinev and Hart 2006). 

More recent research has softened these assumptions and posits that privacy choices are 

driven by both deliberative assessments of benefits and risks as well as by bounded rationality 

and cognitive biases (Acquisti et al. 2012; Li et al. 2008; Tsai et al. 2011). Manipulations in the 

presentation of a choice, termed choice architecture, take advantage of these biases in order to 

encourage particular decision outcomes. Major findings surrounding the powerful effects of
choice architecture have been highlighted extensively in the literature (Egelman et al. 2013; 

Johnson et al. 2012; Keller et al. 2011; Thaler et al. 2013).

In light of these findings and given the importance of privacy decision making to 

consumer outcomes, we aim to further explore the impacts of choice architecture. We focus 

specifically on one powerful and relevant application of choice architecture, choice defaults and 

how choice defaults intersect with both the repetitiveness and reversibility of many privacy 

decisions. 

2.1.1 Choice Defaults

As defined by the choice architecture literature, a default is the first considered option 

when making a decision and is the “status quo” for the decision maker before they consider other 

options (Huh et al. 2014). Generally, defaults are presented with one option as the opt-out choice 

(meaning that the decision maker must exert effort to change their decision from that choice; e.g. 

the default) and all others as opt-in (Johnson and Goldstein 2003). Decision makers presented 

with these defaults are given a default configuration that they can then add or subtract features 

from (Park et al. 2000). Defaults could also present as a choice between two options but where 

only one is given and the other must be requested (McKenzie et al. 2006). 

Whichever way a default is presented, extensive evidence exists showing that this 

specific choice architecture will have significant effects on consumer outcomes (Johnson et al. 

2002; Johnson and Goldstein 2003; Thaler and Sunstein 2008). A classic example in the 

literature shows how presenting an organ donation decision with an opt-in design can drastically 

alter the decision maker’s willingness to donate (Johnson and Goldstein 2003). Similar effects 

have been shown for consumer product choices among other decisions (Brown and Krishna 

2004; Dinner et al. 2011).
Discussions on the causes of defaults have provided a number of valuable findings. First, 

a default choice is often “easy.” Identifying the best option among choices and analyzing 

underlying tradeoffs takes time and increases cognitive effort (Tversky and Kahneman 1974), 

whereas no effort is required of the decision maker when a default choice is presented. A 

decision maker exhibiting cognitive laziness may be more susceptible to a less effortful choice 

(Fiske and Taylor 1991; Samuelson and Zeckhauser 1988; Thaler and Sunstein 2008). 

Consumers have shown this affinity for less effortful choices when alternatives require higher 

levels of cognitive effort (Brown and Krishna 2004; Camerer et al. 2003; Johnson et al. 2002). 

Additionally, when decision makers are tired (Levav et al. 2010) or when their self-control has 

been taxed (Evans et al. 2011) they have shown to be more susceptible to defaults. These effort 

based accounts show that default effects are most impactful when people fail to align the effort 

required to make the choice with the importance of its outcome (McKenzie et al. 2006). 

Additionally, research on social influence has been used to describe the effects of defaults 

(Huh et al. 2014). It has been shown that decision makers often view default choices as an 

implicit recommendation on the part of the person or organization that is giving them that choice 

(Brown and Krishna 2004; McKenzie et al. 2006). Defaults have an inherently social nature, that 

can be exploited by (1) the belief that the behavior of others provides “diagnostic information” 

[informational influence] and (2) the desire of an individual to adhere to the expectations of 

society [normative influence] (Deutsch and Gerard 1955). Informational influence is based on an 

individual’s desire to be accurate (e.g. they believe that others give information that is evidence 

about reality) and normative influence is based on the desire to behave “appropriately in a social 

setting” (Campbell and Fairey 1989; Cialdini and Goldstein 2004). Huh et al. (2014) show that 

“social defaults” arise outside of conscious awareness. However, these automatic processes can 
be disrupted when decision makers have the cognitive resources necessary to think through their 

decisions (Bargh 1994; Fiske 1998). 

Given the extensive findings on default effects and identifying our choice context as the 

decision to log-in to a webservice, we hypothesize that:

H1a: Presenting a log-in decision as an active choice will result in lower log-in rates when 

compared with a default opt-in.

H1b: Presenting a log-in decision as a default opt-out will result in lower log-in rates when 

compared with either an active choice or a default opt-in.

2.1.2 Reversibility 

This study considers the role of reversibility in privacy policy requirements. We explore 

how reversibility changes a person’s willingness to consent along with its interaction with choice 

architectures. Allowing for reversible consent is a central tenet of most modern privacy 

regulation, most notably GDPR (Article 29 2017). These changes are intended to grant further 

control to consumers over their privacy. Giving consumers control over their personal data is an 

essential aspect for maintaining privacy related trust (Whitley 2009). When trust in an online 

entity is high, individuals tend to make riskier privacy decisions (Lauer and Deng 2007). This 

has the potential to show that allowing for reversible consent makes consumers more willing to 

disclose of personal information initially online, even if the disclosure is risky. 

However, most reversibility-related empirical conclusions drawn from literature are 

outside of the scope of online privacy. For example, it has been shown that consumers have 

greater trust in retailers when they have more lenient return policies (Bonifield et al. 2010). Few 

findings exist that substantiate reversibility as a trust-invoking element of control within online 
privacy. By contrast, the evidence that does exist, primarily shows the reverse. Peer and 

Acquisti (2016) show that reversibility instead cues the individual as to the seriousness of the 

decision at hand. This leads to less risky decision making in the form of less disclosure. 

Additionally, they show that both reversibility and irreversibility have the same directional 

effect, so long as they are made salient. While individuals may still prefer a reversible decision, 

this does not entice them to disclose more as it entices them to, say, purchase more from a 

vendor (Wood 2001).

Applying these results to choice architecture gives rise to a number of interesting 

conclusions. First, when making a decision explicitly reversible or irreversible, consumers’ 

perceived stakes of the decision rise. This leads to lower rates of fatigue and lower cognitive 

laziness, two important drivers of default effects. However, given the implicit preference for 

reversible choices shown extensively in the extant literature, individuals may feel more 

comfortable with a reversible choice, leading to higher rates of cognitive laziness. Therefore, we 

expect that salient reversibility may increase the effect of defaults while irreversibility may 

lessen those effects.

H2a: Informing a decision maker of the reversibility of a decision will increase the effects of 

choice defaults.

H2b: Informing a decision maker of the irreversibility of a decision will decrease the effects of 

choice defaults. 

2.1.3 Experiential Effects

While the use of choice architecture and the effects of defaults have been introduced to 

the privacy decision making literature (Adjerid et al. 2019; Almuhimedi et al. 2015; Ariely and 
Holzwarth 2017), few works exist that evaluate its impact on decision making and, to our 

knowledge, the works that do exist focus solely on one-off choices. In reality, privacy policy 

requirements, such as consent re-elicitation, require uses to make a privacy choice numerous 

times, likely influencing consumers’ decision-making processes. We address this gap in the 

literature by considering the effect of repeated privacy choices on the impact of choice defaults.

When presented with the same privacy choice numerous times, decision makers have the 

potential to rethink the decision as they gain experience in the choice context. This is critical to 

the impact of choice defaults (and other manipulations of choice architecture) since the repetition 

of these choices has the potential to improve consumer outcomes and their choice-making 

“muscles” can be exercised to think through and make better decisions (York et al. 2018). In 

particular, realizing the consequences of past decisions and gaining new information can result in 

a revised belief system that may change the outcome of the decision the next time it is presented 

(Carlsson et al. 2012; Hagmayer and Meder 2013). 

Critically, some of the prior literature has shown that an individual can learn to overcome 

behavioral biases typically leveraged by choice architecture (e.g. the status quo bias) (Sellier et 

al. 2019; Smith 1976; Smith 1982). Learning to make better privacy decisions often comes from 

receiving feedback from the choice context to encourage a revision of one’s belief system 

(Lagnado et al. 2007). Specific to privacy, tools meant to increase privacy awareness can be suboptimally configured if they lack proper feedback for their users (Leon et al. 2012). Further 

complicating the matter, feedback that pushes users’ existing causal models might irritate the 

user and have the opposite of the intended effect (i.e. strict password suggestion meters) (Ur et 

al. 2012). 
However, the current literature is divided on the impact of repetition on decision making. 

Despite the above findings, others have shown that behavioral biases persist, or even strengthen, 

when individuals make repeated choices. More so, repetition of choices can introduce new 

decision biases; for example, (Schaub et al. 2015) find that habituation in repeated choice 

contexts prevents the retrieval of new information. Past literature has shown that individuals 

exhibit what has been termed “privacy fatigue,” where they disclose more information over time 

when faced with increasing complexity and less usability in privacy controls (Keith et al. 2014). 

Choi et al. (2018) show how privacy fatigue leads to a perceived loss of control and a sense of 

futility with protecting one’s privacy that results in less informed privacy decision making. This 

theory has also been applied to privacy and security notices (Schaub et al. 2015). When the 

complexity of a notice is high and there is a lack of choices, it becomes unimportant to the 

consumer (Cate 2010). Individuals who see these seemingly unimportant notices repeatedly tend 

to habituate to the notices and dismiss them without any consideration (Anderson et al. 2015). 

Given that two key mechanisms underlying default effects are (1) cognitive laziness and 

(2) social norm adherence, we can consider how experience in a choice context would impact 

these effects. Experience may lead to the collection of new information, a refinement of 

preferences, and a resulting revision of belief systems. This could make an individual less 

susceptible to default effects. However, as stated above, repetitive complex choices may lead to 

privacy fatigue. In addition, complex decisions require more cognitive effort which is 

undesirable to decision makers. As a primary cause of default effects, it would follow that 

privacy fatigue from multiple choice presentations and the cognitive effort required of each 

decision would have at least an additive effect on the impact of defaults. Additionally, some 

evidence exists to show that the subtle pressure to adhere to social norms increases over time 
(Sen and Airiau 2007). If the pressure to adhere to social norms increase with experience, then 

the effect of defaults should strengthen with experience as well. 

The lack of work evaluating the experience with a privacy context in addition to the 

intersection of experience and choice architecture in the privacy decision making literature is a 

significant research gap with important implications for research and practice. Upon review of 

the existing literature, we hypothesize that: 

H3: Experience in a choice setting will result in an increased effect of choice defaults.

3. Experimental Design 

3.1 Two Factor Between-Subjects Design

We conducted a two factor between-subjects online experiment to evaluate our 

hypotheses6

. We chose to conduct an online experiment, instead of analyzing secondary data, so 

that causal relationships could be developed without interference from endogeneity and so that 

we could achieve a level of precision not possible otherwise (Gupta et al. 2018). Our two factors 

differentiated 1) the structure of consent retrieval and 2) the structure of consent reversal. We 

also considered a third factor experimental factor to exogenously shift an individual’s level of 

social desirability bias (SDB). The intervention did not have an effect on behavior and is not 

included in our analysis for clarity. Because these are exogenous manipulation, their 

inclusion/exclusion does not impact the estimates for other treatment conditions (Appendix A 

shows consistent results while controlling for attempted manipulations of SDB). 

3.1.1 Consent Retrieval

 6 An earlier iteration of this paper attempted to exogenously shift social desirability bias in addition to manipulating 

consent retrieval and consent reversal, making our experiment a three-factor design. This attempt proved 

unsuccessful and our SDB conditions had no effect on the outcomes of our other factors. 
Participants were first separated into three dimensions for consent retrieval: default 

logged-in (our control dimension, indicative of the traditional universal opt-in structure), active 

choice (the structure required by privacy policy such as GDPR), and default not logged-in (a 

highly protective opt-out privacy structure). Participants in the opt-in and opt-out groups were 

presented with one choice for the log-in decision: “Sign into my research profile” or “Continue 

as guest” respectively. In either case, the option was pre-selected. If the participant wanted to 

change the selection, they had to manually uncheck the box. In the active choice dimension, they 

were presented with both of the above options: “Sign into my research profile” and “Continue as 

guest.” Neither option was pre-selected, requiring these participants to manually choose one of 

the two options. Participants were presented with this choice three times, holding the consent 

structure constant, to measure the change in structure effects that comes with experience in a 

choice context. The three default presentations can be seen in Figures 1a-1c.

Figure 1a: Opt-in default presentation 
Figure 1b: Active choice default presentation

Figure 1c: Opt-out default presentation
3.1.2 Consent Reversal 

We also manipulated the reversibility of the log-in decisions. Each participant saw their 

log-in decision presented with one of three options: a statement making the reversibility of the 

log-in decision explicit, a statement making the irreversibility of the log-in decision explicit, or 

no statement regarding reversibility (our control dimension). The reversible statement read, 

“This decision can be changed by contacting survey administrators.” Conversely, the irreversible 

statement informed the participant that, “This decision cannot be changed and is final.” 

Examples of these statements can be seen in Figures 2a and 2b. These figures display the 

reversibility/irreversibility statements for those in the active choice default dimension. It is 

important to note that those in the opt-in default dimension and the opt-out default dimension 

were divided in the same way. 

Figure 2a: Reversible presentation
Figure 2b: Irreversible presentation

3.2 Participants and Procedure

We recruited 1600 participants for this experiment. After filtering out participants that 

did not complete the entirety of the study, participants that gave non-engaged responses, and 

participants that asked for their data to be removed after debriefing, 1526 participants were left. 

Participants were recruited through Prolific, an online subject recruitment platform that 

specifically caters to researchers, unlike the more commonly used Amazon Mechanical Turk 

(AMT) (Palan and Schitter 2018). Prolific has been shown to provide data quality comparable to 

AMT while providing participants that are more naïve to “common experimental research tasks” 

(Peer et al. 2017). Participants were compensated $3.40 upon completing all three studies. 

Participants began by creating a research profile. The profile consisted of two parts. The 

first part included demographic information such as gender, ethnicity, geographical location, and 

education/work status. The second part asked participants to complete an evaluation that 
measured social desirability bias.7 This research profile simulated a personal profile that one 

would create on a popular web service. 

Participants were then directed to take three studies, in a random order. Participants were 

informed at the beginning of each study that we were exploring potentially unethical behavior 

and that some of the questions that would be asked dealt with adult or sensitive material. The 

participants were also informed that they may skip any questions that they wish not to answer. 

The three studies contained questions related to criminal activity, sexual activity, and romantic 

involvement, respectively. These studies were randomly ordered and counterbalanced. The 

questions asked in each study can be found in Appendix B. 

The experiment was designed to mimic a user’s repeated interactions with an online 

webservice. Measures were taken to ensure that the participants felt that each study was separate 

and unique. First, each of these studies began by providing the consent form which listed details 

related to the study, requirements, risks, benefits, and compensation. The consent form was 

provided three times to give the impression to the participants that each study was different from 

the previous one. Second, each study had a different theme (e.g. colors, fonts, layouts). Finally, 

at the end of each study, participants were asked to watch a video relating to the context of the 

specific study that acted as a time lag between studies. 

At the onset of each study, the participants were given the choice to log-in to their 

research profile before answering the questions. Depending on their randomly assigned 

condition, this choice was presented as either defaulted to log-in (the control group), an active 

choice to either log-in or not, or defaulted to remain as a “guest” (see Figures 1a-1c). They were 

 7 Prior to completing this evaluation, participants watched one of three videos that were intended to shift SDB. 

These videos had no effect on log-in rates across conditions (see Appendix A)
informed that logging-in would allow us to track their responses and that by doing so, their 

responses would be linked back to them. To make the decision salient, participants who chose to 

sign into their research profile saw their Prolific ID listed at the top of each subsequent page of 

the study. Participants who chose to not login saw “Guest” listed instead. Additionally, the 

participants either saw a statement making the choice explicitly reversible, explicitly irreversible, 

or no statement (the control group)—see Figures 2a-2b. The conditions related to defaults and 

reversibility remained the same for the respective participant across each of the studies. 

The participant then was directed to a series of 5 questions related to the study topic. 

After the 5 questions were answered, they were shown a short video that they had to watch and 

subsequently summarize and reflect on. They were then randomly directed to one of the 

remaining studies. At the conclusion of the third and final study, they were asked a set of exit 

questions (Appendix C) and were then debriefed to let them know that they were participating in 

one study, not three separate studies as we had led them to believe. 

The participant’s decision to log-in and their level of disclosure (e.g. the number of 

unethical behaviors they admitted to having done) was measured across the three studies. This 

was intended to measure the impact that experience has on the effect of differing structures of 

consent retrieval. In addition, the effect of differing structures of consent reversal was measured 

both initially and over time. 

4. Analysis

4.1 Balance Check

We evaluate observable variables for each participant (age, race, gender, education, and 

employment) and find balance across most variables. The primary exception is gender across 

default and reversibility conditions. We show that the inclusion of these controls in the analysis 
has no bearing on our results. Descriptive statistics and a table of pairwise comparisons are in 

Appendix D. 

4.2 Initial Effect of Changing Choice Architecture

4.2.1 Estimation Approach

The dependent variable in our regression is a binary indicator variable that represents 

whether or not the participant i chose to log-in to their research profile. For our initial 

consideration of default effects, we consider the participant’s first exposure to the choice.

LLLLLLLLnnii = ββ1AAAAAAAAAAAAAAh ii + ββ2OOOO OOOO ii + ααiiDDDDDD + γγii + ii

ActiveChoicei is a binary indicator for whether or not a participant was in an active 

choice condition. Opt-Outi is a binary indicator for whether or not a participant was in an opt-out 

condition. Additionally, we control for participant demographics (age, gender, race, education, 

and employment) and include study context fixed effects (e.g. Sex, Romance, Crime) γγii

Estimates on randomly assigned treatments are unbiased due to assumed exogeneity of 

experimental manipulations. 

4.2.2 Results

One thousand five hundred and twenty-six participants from Prolific took part in our 

experiment. Our control group (opt-in) had expectedly the highest baselines for log-in 

percentage. This shows the consequences of a universal opt-in structure for consent retrieval that 

resulted in roughly 92% of participants in these groups choosing to log-in during their first 

exposure to the choice. Both the active choice conditions and the protective opt-out conditions 

had strong initial effects. An active choice structure decreased initial log-in rates in Study 1 by 

11.5%. The protective opt-out conditions decreased initial log-in rates by 41%. This confirms 
hypotheses H1a and H1b. Figure 3 is a graphical representation of the results and Table 1 shows 

estimates of our above model with and without demographic controls. 

 

Figure 3: Log-In Rates Across Consent Retrieval Structure

(1) (2)

VARIABLES Log-In Log-In

Active Choice -0.115*** -0.115***

(0.0211) (0.0212)

Opt-Out -0.409*** -0.409***

(0.0254) (0.0256)

Constant 0.916*** 0.923***

(0.0271) (0.0468)

Controls No Yes

Context FE Yes Yes

Observations 1,526 1,526

R-squared 0.158 0.161

Robust standard errors in parentheses 

*** p<0.01, ** p<0.05, * p<0.1 

Table 1: Initial Regression Results (Consent Retrieval)

4.3 Effect of Consent Reversal Structure on Choice Architecture 

4.3.1 Estimation Approach 
The dependent variable in our second regression is the same binary indicator used above. 

For our initial consideration of reversibility and default effects, we consider the participant’s first 

exposure to the choice.

LLLLLLLLnnii = ββ1AAAAAAAAAAAAAAh ii + ββ2OOOO OO ii + ββ3RR RRRR RRRR ii + ββ4 ii

+ββ5AAAAAAAAAAAAAAh ii × RR RRRR RRRR ii + ββ6AAAAAAAAAAAAAAh ii × ii

+ ββ7OOOO OOOO ii × RR RRRR RRRR ii + ββ8OOOO OOOO ii × ii + ααiiDDDDDD + γγii + ii

Reversiblei is a binary indicator for whether or not a participant was in a reversible 

condition. Irreversiblei is a binary indicator for whether or not a participant was in an irreversible 

condition. We also include interaction terms between the consent retrieval and consent reversal 

structures. 

4.3.2 Results

Our analysis first confirms significant initial effects of consent retrieval structure. When 

there is no explicit reversibility or irreversibility, an active choice structure decreases log-in rates 

by 12% and an opt-out default structure decreases log-in rates by 34%. Most notably, our results 

show us that when paired with an opt-out default structure, both reversibility and irreversibility 

decrease log-in rates by an additional 11%. 

Additionally, neither reversibility nor irreversibility has an effect on an opt-in default 

structure or an active choice structure. This implies that reversibility and irreversibility do act as 

a cue as to the seriousness of the decision being made. However, this seriousness is only made 

salient if the participant is also given a default opt-out structure (which selects the protective 

option). Our results provide partial support for H2, showing that neither reversibility nor 
irreversibility has an effect on an opt-in default but both strengthen the effect of an opt-out 

default. Table 2 shows the preliminary analysis for the effect of consent reversal structure in 

Study 1 with and without controls and Figure 4 is a graphical representation of the results. 

Figure 4: Log-In Rates Across Consent Retrieval & Consent Reversal Structure
(1) (2)

VARIABLES Log-In Log-In

Active Choice -0.120*** -0.118***

(0.0376) (0.0379)

Opt-Out -0.339*** -0.336***

(0.0434) (0.0435)

Reversibility 0.000952 0.00205

(0.0286) (0.0291)

Irreversibility 0.000332 0.000859

(0.0288) (0.0288)

Active*Reversibility 0.0170 0.0147

(0.0519) (0.0527)

Active*Irreversibility -0.00270 -0.00485

(0.0523) (0.0524)

Opt-Out*Reversibility -0.106* -0.112*

(0.0620) (0.0624)

Opt-Out*Irreversibility -0.105* -0.109*

(0.0618) (0.0619)

Constant 0.916*** 0.923***

(0.0329) (0.0501)

Controls No Yes

Context FE Yes Yes

Observations 1,526 1,526

R-squared 0.163 0.165

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 2: Initial Regression Results (Consent Reversal)
4.4 Effect of Experience on Choice Architecture

4.4.1 Estimation Approach

The dependent variable in our third regression is the same binary indicator used above. 

This log-in decision is a repeated measure and therefore we use a panel random effects 

regression for our estimation.

LLLLLLLLnniiii = ββ1AAAAAAAAAAAAAAh ii + ββ2OOOO OOOO ii + ββ3SS SSSSSS SS iiii

+ ββ4AAAAAAAAAAAAAAh ii × SS SSSSSS SS iiii + ββ5OOOO OOOO ii × SS SSSSSS SS iiii + γγiiii + ααii + ii

StudyNumberij is a continuous variable representing whether the participant is partaking 

in their first, second, or third study. This variable evaluates the impact over time for the baseline 

condition (opt-in). We also include interaction terms between study number and choice 

architecture. To first isolate the effects of experience, we estimate three separate models, 

controlling for reversibility condition. 

4.4.2 Results

The results of our analysis can be found in Table 3 and Table 4 below. We first break 

down the regression by each of the three studies, controlling for the reversibility condition. Table 

3 estimates the model only for those that were shown no statement on reversibility or 

irreversibility. The first three columns show the effects of choice architecture across the three 

studies. The log-in rate for the control condition (shown by the constant) is 92.3%, 95.2%, and 

95.8% respectively. This shows an increasing effect of an opt-in default. Likewise, the effect of a 

protective opt-out default is -33.9%, -39.9%, and -40.5% respectively. This shows an increasing 

effect of an opt-out default (decreasing log-ins over time). Columns 4-6 estimate the same model 

with demographic controls included to show the consistency of our results. Finally, Column 7 is 
the estimated panel data model. This estimation assigns significance to our results. The 

coefficient on StudyNumberi represents the effect of experience on the opt-in default, showing 

that for each study, participants log-in 1.78% more (p<0.05). The interaction term between the 

study number and the protective opt-out default is also significant (p<0.05) and shows an 

additional -3.27% decrease in log-in rates for each study. 

Table 4 shows the same analysis for those in either the reversible or irreversible 

conditions. Interestingly, the increase in default effects was not replicated for these participants. 

We combine reversible and irreversible conditions due to their similar directional effects. 

Appendix E shows this analysis broken down further by reversible and irreversible.

Our results show that in absence of explicit reversibility or irreversibility, individuals 

may be subject to some level of fatigue that allows the effects of defaults to grow. However, 

when the same decision is made reversible or irreversible, while there is an initial decrease in 

log-in rates, the effects of the defaults do not grow over time. Therefore, we can partially 

confirm H3 that experience does increase the effect of defaults, however with the caveat that this 

result only holds when the decision is not explicitly reversible or irreversible. Similar to Peer and 

Acquisti (2016), our interpretation of these results is that reversibility introduces privacy 

concerns for consumers. However, these concerns affect the two default options differently. For 

those in the opt-in group, the elevated concerns diminish the propensity of permissive defaults to 

increase opt-in over time. For the protective opt-out group, consent is driven down significantly 

by reversibility, perhaps introducing ceiling effects. In other words, most who would be 

impacted by the default observe that effect immediately and this large effect persists, but does 

not grow, over time. 
Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 3: Effect of Experience on Choice Architecture (No Reversibility or Irreversibility)

No 

Statement

No 

Statement

No 

Statement

No 

Statement

No 

Statement

No 

Statement

No 

Statement

(Study 1) (Study 2) (Study 3) (Study 1) (Study 2) (Study 3) (Panel)

VARIABLES Log-In Log-In Log-In Log-In Log-In Log-In Log-In

Active Choice -0.120*** -0.144*** -0.137*** -0.115*** -0.135*** -0.126*** -0.108**

(0.0376) (0.0351) (0.0339) (0.0386) (0.0361) (0.0348) (0.0442)

Opt-Out -0.339*** -0.399*** -0.405*** -0.333*** -0.392*** -0.399*** -0.309***

(0.0434) (0.0419) (0.0415) (0.0441) (0.0424) (0.0416) (0.0501)

Study Number 0.0178**

(0.00835)

Active*Number -0.00848

(0.0116)

Opt-Out*Number -0.0327**

(0.0136)

Constant 0.923*** 0.952*** 0.958*** 0.922*** 1.061*** 1.089*** 0.865***

(0.0207) (0.0165) (0.0155) (0.0940) (0.0897) (0.0888) (0.0803)

Controls No No No Yes Yes Yes Yes

Context FE No No No Yes Yes Yes Yes

Observations 498 498 498 498 498 498 1,494

R-squared 0.112 0.156 0.165 0.120 0.170 0.180

Number of ID 498
Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 4: Effect of Experience on Choice Architecture (Explicit Reversibility or Irreversibility)

Reversible or 

Irreversible

Reversible or 

Irreversible

Reversible or 

Irreversible

Reversible or 

Irreversible

Reversible or 

Irreversible

Reversible or 

Irreversible

Reversible or 

Irreversible

(Study 1) (Study 2) (Study 3) (Study 1) (Study 2) (Study 3) (Panel)

VARIABLES Log-In Log-In Log-In Log-In Log-In Log-In Log-In

Active Choice -0.113*** -0.119*** -0.116*** -0.114*** -0.120*** -0.118*** -0.115***

(0.0254) (0.0254) (0.0260) (0.0259) (0.0259) (0.0264) (0.0272)

Opt-Out -0.445*** -0.463*** -0.461*** -0.446*** -0.463*** -0.460*** -0.441***

(0.0312) (0.0310) (0.0313) (0.0315) (0.0312) (0.0315) (0.0339)

Study Number -0.00289

(0.00390)

Active*Number -0.00157

(0.00586)

Opt-Out*Number -0.00795

(0.00837)

Constant 0.923*** 0.926*** 0.918*** 0.903*** 0.899*** 0.819*** 0.879***

(0.0140) (0.0138) (0.0144) (0.0644) (0.0596) (0.0642) (0.0540)

Controls No No No Yes Yes Yes Yes

Context FE No No No Yes Yes Yes Yes

Observations 1,028 1,028 1,028 1,028 1,028 1,028 3,084

R-squared 0.184 0.197 0.193 0.188 0.201 0.199

Number of ID 1,028
4.5 Effects on Disclosure

The above regressions were estimated replacing the dependent variable, LogIni, with 

Disclosurei, an ordinal variable (1-5) representing the level of disclosure for each participant i. In 

each study, 5 questions were asked pertaining to potentially incriminating behavior. The value in 

Disclosurei is a summation of each behavior the participant admitted to partaking in. We start by 

only considering a participant’s first exposure to the choice.

DD ii = ββ1AAAAAAAAAAAAAAh ii + ββ2OOOO OOOO ii + ααiiDDDDDD + γγii + ii

4.5.1 Initial Effect of Changing Choice Architecture on Disclosure

Considering the effect of choice architecture on subsequent disclosures, initial 

exploration appears to show increasing disclosure as choice architecture gets more protective. 

Those in the universal opt-in, active choice, and protective opt-out conditions disclosed 2.04, 

2.10, and 2.21 behaviors on average, respectively. This is shown in Figure 5 below. 

Figure 5: Disclosure Across Choice Architectures
Despite the visual trend in disclosure rates across choice architectures, initial regression 

results do not show significant effects. These regression results can be seen in Table 5 below 

with and without demographic controls. This is consistent with prior work suggesting that subtle 

changes to choice architecture can have powerful impacts on initial privacy choices that do not 

translate to downstream changes in disclosure (Adjerid et al. 2019). 

(1) (2)

VARIABLES Disclosure Disclosure

Active Choice 0.0590 0.0838

(0.0888) (0.0882)

Opt-Out 0.137 0.145

(0.0909) (0.0902)

Constant 0.839*** 0.798***

(0.108) (0.185)

Controls No Yes

Context FE Yes Yes

Observations 1,526 1,526

R-squared 0.108 0.126

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 5: Effect of Choice Architecture on Disclosure

4.5.2 Effect of Consent Reversal Structure on Disclosure

We estimate the above model replacing ActiveChoicei and OptOuti with Reversiblei and 

Irreversiblei, respectively. Table 6 shows the estimated model with and without controls. 

Interestingly, both reversibility and irreversibility have significant positive effects on disclosure 

(p≈0.10 and p<0.05). These effects could be the result of lower log-in rates for those in 

reversible or irreversible conditions, therefore leading to higher rates of disclosure.8 These 

findings highlight that while reversibility can decrease up front consent, unlike subtle changes to 

 8 Given that the log-in choice is an intermediate outcome, we do not include it as a regressor in our model.
choice architecture, it can have important compensatory reactions by consumers that make them 

more open to disclosing information in downstream choices. This result points to an important 

distinction between a salient stimulus that impacts upstream privacy choices vs. more subtle 

stimuli (e.g. changes in choice defaults) which can influence users to be more privacy conscious 

but will not result in more openness in other downstream choices made by consumers. 

(1) (2)

VARIABLES Disclosure Disclosure

Reversibility 0.152* 0.130

(0.0910) (0.0906)

Irreversibility 0.230** 0.211**

(0.0900) (0.0896)

Constant 0.763*** 0.746***

(0.107) (0.185)

Controls No Yes

Context FE Yes Yes

Observations 1,526 1,526

R-squared 0.110 0.128

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 6: Effect of Reversibility and Irreversibility on Disclosure

5. Discussion & Conclusion

5.1 Theoretical and Research Implications

These findings contribute to the privacy decision making literature. Specifically, past 

work has primarily focused on one-off choices (Acquisti and Grossklags 2005), thus, we extend 

work on the influence of biases in privacy decision making by showing its lasting effects over 

time. Additionally, with the exception of Peer and Acquisti (2016), to our knowledge, no other 

work has addressed the role of reversibility in privacy decision making. We also contribute to the 
literature in behavioral economics and choice architecture by exploring the persistence of choice 

defaults across instances of exposure. In addition, to the best of our knowledge, no work has 

evaluated the combined effect of explicit reversibility and choice defaults. Finally, we contribute 

to the work considering the far-reaching implications of GDPR and similar changes in privacy 

regulation (Adjerid et al. 2015; Goldberg et al. 2019; Goldfarb and Tucker 2011b; Johnson and 

Shriver 2020; Miller and Tucker 2009; Miller and Tucker 2017). To our knowledge, this is the 

first work that considers the interactive effects of changes enacted by privacy policies with 

experiential effects and the effects of stated reversibility. 

There are three main limitations to our study. First, our work is likely subject to ceiling 

effects with respect to measuring the effect of default opt-in choices. Further work is needed to 

better understand the persistence of these default effects over time. Second, we did not measure 

the effect of changing choice architecture with respect to specific decision biases. Evaluating this 

effect with regards to cognitive laziness, privacy fatigue, and other biases will lend a clearer 

understanding of this phenomenon. Finally, our work was done in an isolated environment where 

the risks of disclosure, while real, were not overly intrusive. Expanding these ideas to a field 

environment where real life privacy decisions are being made would grant further credence to the 

results. 

Future research can contribute to these results and expand their scope. First, the privacy 

decision making literature can benefit from analyzing interactive effects on behavior. This study 

considers the interaction between defaults and experience and defaults and reversibility. 

Examples of further routes of exploration could be framing effects and reversibility, the effect of 

reference dependence over time, or the interaction between multiple cognitive biases (e.g. 

optimism bias and overconfidence) with reversibility in a privacy setting. The behavioral 
economics literature can also benefit from similar explorations contextualized outside of privacy 

decision making. 

The growing literature surrounding the effects of privacy regulation can also benefit from 

the findings presented here. Regulation like GDPR is often broad, covering a sweeping array of 

privacy-related issues. Isolating specific tenets of said regulation will further lend to the insights 

surrounding its enactment. However, these tenets do not exist in a vacuum and likely interact, 

adding further complexity to their effects. Addressing the effects of privacy-regulation from both 

the perspective of individual changes and the interaction of such changes will allow for further 

development of the both the academic literature and policy discussions. Possible examples could 

include the interaction between data security requirements and privacy by design or the effects of 

net neutrality (which classifies internet service providers as telecommunications services instead 

of information services, thus dividing privacy regulation enforcement between the FCC and the 

FTC9

) on the right to be forgotten. 

5.2 Practical and Policy Implications

Our findings also have significant implications for firms, policymakers, and consumers. 

With respect to firms, it is widely known the role they play in utilizing consumer’s behavioral 

biases (Conti and Sobiesk 2010). Receiving consent for data tracking practices can have 

significant economic benefit for firms. Navigating the changing privacy landscape in the face of 

new regulation can prove challenging to those firms that rely heavily on data. Understanding the 

intricacies of the decision-making process, and the biases that influence it, can assist firms in 

 9 https://www.govtech.com/policy/gao-report-the-net-neutrality-debate-complicates-dataprivacy.html#:~:text=It%20defines%20%E2%80%9CInternet%20data%20privacy,locations%2C%20and%20travel

%20routes.%E2%80%9D&text=The%20Trump%20administration's%20FCC%20then,with%20the%20FCC's%20p

rivacy%20rules.
designing interfaces that are appropriately protective while still allowing for considerable rates of 

consent. Firms also will likely benefit from understanding the role that reversibility plays in 

consent elicitation. A common practice for firms is to avoid “ringing the alarm bells” when it 

comes to privacy (Goldfarb and Tucker 2011a). Given that reversibility and irreversibility do cue 

individual’s as to the seriousness of the decision, this will likely “ring the bell” in a way that may 

be undesirable to firms.

Policymakers will also benefit from these findings. Primarily, we highlight the delicate 

balance between protectiveness and economic benefit that is influenced by changing regulation. 

Adapting an active choice structure in favor of a universal opt-in has a modest impact for 

consumer protectiveness while still allowing for the considerable level of consent that firms 

desire. Further, a more protective opt-out default may be desirable in situations in which giving 

consent allows for highly intrusive practices or grants access to sensitive data. However, when 

the decision is repetitive, the protectiveness of the opt-out default increases, striking less of an 

economic balance. Finally, making reversibility salient in combination with default opt-out 

choices provides extreme protectiveness with questionable utility. Understanding the various 

levels of protectiveness can assist policymakers in designing regulation that strikes a proper 

balance with respect to specific choice contexts. 

Finally, our findings have important implications for consumers. Privacy decisions are 

constantly being presented to individuals and have significant consequences. Data tracking 

practices can become highly intrusive and impact consumers in ways that are often unbeknownst 

to them. Understanding one’s own behavioral biases can assist consumers in overcoming them 

through more careful consideration of consent decisions. Learning to navigate changing choice 

architectures in light of privacy regulation can be confusing to consumers. A careful 
consideration of the benefits and drawbacks to various regulatory instantiations can prove 

essential to an individual’s privacy decision making. 
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7. Appendices

7.1 Appendix A – Exogeneity of Social Desirability Manipulation

7.1.1 A-1 – Effect of Choice Architecture with SDB Treatment

(1) (2)

VARIABLES Log-In Log-In

Active Choice -0.115*** -0.115***

(0.0211) (0.0213)

Opt-Out -0.409*** -0.408***

(0.0254) (0.0256)

SDB Treatment -0.00286 -0.00368

(0.0123) (0.0123)

Constant 0.921*** 0.924***

(0.0359) (0.0544)

Context FE Yes Yes

Controls No Yes

Observations 1,526 1,526

R-squared 0.158 0.161

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1
7.1.2 A-2 – Effect of Reversibility with SDB Treatment

(1) (2)

VARIABLES Log-In Log-In

Active Choice -0.120*** -0.117***

(0.0377) (0.0380)

Opt-Out -0.338*** -0.334***

(0.0435) (0.0436)

Reversible 0.00118 0.00286

(0.0286) (0.0292)

Irreversible 0.00133 0.00213

(0.0290) (0.0290)

Active*Reversible 0.0173 0.0143

(0.0518) (0.0527)

Active*Irreversible -0.00318 -0.00517

(0.0524) (0.0526)

Opt-Out*Reversible -0.107* -0.113*

(0.0621) (0.0625)

Opt-Out*Irreversible -0.106* -0.111*

(0.0619) (0.0619)

SDB Treatment -0.00449 -0.00538

(0.0123) (0.0123)

Constant 0.924*** 0.893***

(0.0394) (0.0608)

Context FE Yes Yes

Controls No Yes

Observations 1,526 1,526

R-squared 0.163 0.165

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1
7.1.3 A-3 – Effect of Experience with SDB Treatment

No 

Statement

No 

Statement

No 

Statement

No 

Statement

No 

Statement

No 

Statement

No 

Statement

(Study 1) (Study 2) (Study 3) (Study 1) (Study 2) (Study 3) (Panel)

VARIABLES Log-In Log-In Log-In Log-In Log-In Log-In Log-In

Active Choice -0.120*** -0.143*** -0.120*** -0.115*** -0.135*** -0.115*** -0.108**

(0.0379) (0.0354) (0.0379) (0.0389) (0.0363) (0.0389) (0.0445)

Opt-Out -0.337*** -0.396*** -0.337*** -0.332*** -0.391*** -0.332*** -0.307***

(0.0437) (0.0421) (0.0437) (0.0444) (0.0427) (0.0444) (0.0501)

Study Number 0.0178**

(0.00835)

Active*Number -0.00848

(0.0116)

Opt-Out*Number -0.0327**

(0.0136)

SDB Treatment -0.00814 -0.0102 -0.00814 -0.00716 -0.00673 -0.00716 -0.00931

(0.0215) (0.0213) (0.0215) (0.0219) (0.0216) (0.0219) (0.0204)

Constant 0.938*** 0.971*** 0.938*** 0.934*** 1.072*** 0.934*** 0.882***

(0.0435) (0.0418) (0.0435) (0.0995) (0.0936) (0.0995) (0.0885)

Context FE No No No Yes Yes Yes Yes

Controls No No No Yes Yes Yes Yes

Observations 498 498 498 498 498 498 1,494

R-squared 0.113 0.156 0.113 0.120 0.170 0.120

Number of IDs 498

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Reversible or Reversible or Reversible or Reversible or Reversible or Reversible or Reversible or 
Irreversible Irreversible Irreversible Irreversible Irreversible Irreversible Irreversible

(Study 1) (Study 2) (Study 3) (Study 1) (Study 2) (Study 3) (Panel)

VARIABLES Log-In Log-In Log-In Log-In Log-In Log-In Log-In

Active Choice -0.113*** -0.119*** -0.113*** -0.114*** -0.120*** -0.114*** -0.115***

(0.0254) (0.0254) (0.0254) (0.0259) (0.0258) (0.0259) (0.0272)

Opt-Out -0.445*** -0.463*** -0.445*** -0.446*** -0.463*** -0.446*** -0.441***

(0.0312) (0.0311) (0.0312) (0.0315) (0.0313) (0.0315) (0.0339)

Study Number -0.00289

(0.00390)

Active*Number -0.00157

(0.00586)

Opt-Out*Number -0.00795

(0.00837)

SDB Treatment -0.00261 -0.00419 -0.00261 -0.00359 -0.00508 -0.00359 -0.00247

(0.0149) (0.0150) (0.0149) (0.0149) (0.0150) (0.0149) (0.0145)

Constant 0.928*** 0.934*** 0.928*** 0.910*** 0.909*** 0.910*** 0.884***

(0.0327) (0.0331) (0.0327) (0.0731) (0.0687) (0.0731) (0.0635)

Context FE No No No Yes Yes Yes Yes

Controls No No No Yes Yes Yes Yes

Observations 1,028 1,028 1,028 1,028 1,028 1,028 3,084

R-squared 0.184 0.197 0.184 0.188 0.201 0.188

Number of IDs 1,028

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1
7.2 Appendix B – Study Questions

Each question had the following possible answers: Yes, No, Prefer Not to Answer

Question Study

While in a relationship, have you ever flirted with 

somebody other than your partner? 

Romance

Have you ever used a dating app for the sole purpose of 

engaging in sexual activity?

Romance

Have you ever encouraged someone to drink when you 

were trying to seduce them?

Romance

Have you ever cheated while in a relationship? Romance

Have you ever refrained from dating someone because of 

their skin color?

Romance

Have you ever looked at pornographic material? Sex

Have you ever had a one night stand? Sex

Have you ever taken nude pictures of yourself or a partner? Sex

Have you ever had sexual thoughts about a member of 

your same sex? 

Sex

Have you ever showered with a partner? Sex

Have you ever used drugs of any kind (e.g. weed, heroin, 

crack)?

Crime

Have you ever let a friend drive after you thought he or she 

had had too much to drink?

Crime

Have you ever made up a serious excuse, such as grave 

illness or death in the family, to get out of doing 

something?

Crime

Have you ever stolen anything worth more than $50? Crime

Have you ever downloaded pirated content from the 

internet?

Crime
7.3 Appendix C – Exit Questions

Each question was answered using a likert scale with the following responses: Strongly 

Agree, Agree, Neither Agree nor Disagree, Disagree. 

Question

I was concerned about my personal privacy when completing this study.

My responess could be used in a way that may harm me.

My responses are valuable to the researchers.

Maintaining the privacy of one’s personal information is very important.

I trust the researchers with my responses.

I was comfortable signing into my research profile.

I was less honest when signed into my profile.

My decision to log in during one survey impacted my decision to log in for 

subsequent surveys.

I thought more about my decision each time I was asked to log in.

I am logged into my account most times that I use an online web service 

(Google, Amazon, YouTube, etc.).
7.4 Appendix D – Balance Check

7.4.1 D-1 – Group Means 

Variable Opt-In Active Choice Opt-Out No Statement Reversible Irreversible

Age 34.4 years 33.8 years 34.1 years 33.7 years 34.1 years 34.5 years

White 69.5% 68.1% 67.6% 69.3% 66.8% 69.2%

Black 10.7% 11.1% 11.6% 11.2% 11.4% 10.8%

Other Race 19.7% 20.8% 20.8% 19.5% 21.8% 20.0%

Male 53.0% 48.6% 45.3% 48.6% 50.7% 48.0%

< Bachelor 38.7% 37.7% 37.8% 37.1% 38.1% 41.2%

Bachelor 41.5% 42.3% 40.2% 41.0% 41.8% 38.9%

Advanced Degree 19.7% 20.0% 22.0% 21.9% 20.0% 19.8%

Full-Time 

Employed

44.9% 40.3% 44.1% 40.2% 44.6% 44.5%

7.4.2 D-2 – Balance Across Default and Reversibility Conditions

Variable Opt-In vs. Active

Choice

Opt-In vs. Opt-Out No Statement vs. 

Reversible

No Statement vs. 

Irreversible

Age 0.193 0.454 0.390 0.101

White 0.369 0.234 0.144 0.950

Black 0.723 0.420 0.897 0.688

Other Race 0.447 0.458 0.114 0.698

Male 0.014 0.000 0.250 0.733

< Bachelor 0.557 0.582 0.584 0.880

Bachelor 0.684 0.452 0.622 0.314

Advanced Degree 0.833 0.117 0.212 0.165

Full-Time Employed 0.009 0.639 0.014 0.015
7.4 Appendix E – Consideration of Experience with Reversibility and Irreversibility Separated

Reversible Reversible Reversible Reversible

(Study 1) (Study 2) (Study 3) (Panel)

VARIABLES Log-In Log-In Log-In Log-In

Active Choice -0.0992*** -0.132*** -0.127*** -0.0935**

(0.0375) (0.0381) (0.0387) (0.0394)

Opt-Out -0.441*** -0.460*** -0.448*** -0.438***

(0.0453) (0.0452) (0.0455) (0.0491)

Study Number 2.79e-05

(0.00550)

Active*Number -0.0123

(0.00919)

Opt-Out*Number -0.00617

(0.0122)

Constant 0.866*** 0.908*** 0.822*** 0.834***

(0.0801) (0.0770) (0.0824) (0.0784)

Controls Yes Yes Yes Yes

Context FE Yes Yes Yes Yes

Observations 509 509 509 1,527

R-squared 0.194 0.206 0.198

Number of ID 509

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Irreversible Irreversible Irreversible Irreversible

(Study 1) (Study 2) (Study 3) (Panel)

VARIABLES login login login login

Active Choice -0.122*** -0.103*** -0.0999*** -0.129***

(0.0360) (0.0355) (0.0364) (0.0377)

Opt-Out -0.446*** -0.461*** -0.467*** -0.440***

(0.0446) (0.0445) (0.0449) (0.0473)

Study Number -0.00564

(0.00553)

Active*Number 0.00851

(0.00736)

Opt-Out*Number -0.00970

(0.0115)

Constant 0.958*** 0.955*** 0.827*** 0.913***

(0.0793) (0.0746) (0.0898) (0.0754)

Controls Yes Yes Yes Yes

Context FE Yes Yes Yes Yes

Observations 519 519 519 1,557

R-squared 0.195 0.205 0.210

Number of ID 519

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1