Beyond The Trade Offs On Facebook: The Underlying Mechanisms of Privacy Choices

Information Systems and e-Business Management (2023) 21:353–387
https://doi.org/10.1007/s10257-023-00622-6
ORIGINAL ARTICLE
Beyond the trade‑offs on Facebook: the underlying

mechanisms of privacy choices
Hung‑Pin Shih1 · Wuqiang Liu1
Received: 7 September 2022 / Revised: 28 November 2022 / Accepted: 5 January 2023 /

Published online: 25 January 2023
© The Author(s) 2023
Abstract
The theory of privacy calculus in terms of the trade-offs between benefits and risks
is believed to explain people’s willingness to disclose private information online.
However, the phenomenon of privacy paradox, referring to the preference-behavior
inconsistency, misfits the risk–benefit analysis. The phenomenon of privacy paradox
matters because it reflects an illusion of personal control over privacy choices. The
anomaly of privacy paradox is perhaps attributed to cognitive heuristics and biases
in making privacy decisions. We consider the stability-instability of privacy choices
is better used to explain the underlying mechanisms of paradoxical relationship. A
rebalanced trade-off, referring to the embeddedness of “bridging” and “bonding”
social support in privacy calculus, is derived to develop the risk–benefit paradigms
to explain the underlying mechanisms. In this study we address the underlying
mechanisms of privacy choices in terms of self-disclosure and user resistance. To
test the hypotheses (or mechanisms) of the research model, we developed the instru-
ment by modifying previous scales. A general sample of 311 experienced Facebook
users was collected via online questionnaire survey. From the empirical results,
perceived benefits based on information support rather than emotion support can
motivate self-disclosure willingness. In contrast, privacy risks rather than privacy
concerns inhibit the willingness to disclose private information. The risk–benefit
paradigms instead of the imbalanced trade-offs help to explain the instability of pri-
vacy choices where privacy calculus sticks with the stability view. Implications for
the theory and practice of privacy choices are discussed accordingly.
Keywords Facebook · Privacy paradox · Privacy calculus · Social support ·

Embeddedness · Self-disclosure
* Hung‑Pin Shih
2387540980@qq.com
Wuqiang Liu
13022289@qq.com
1
Minnan Science and Technology University, Quanzhou 362332, Fujian, China
13
Vol.:(0123456789)
354 H.-P. Shih, W. Liu
1 Introduction
Personal privacy is a general right, just as the right to property, which needs
legal protection for individuals and also received concerns in the United States
and most countries (Warren and Brandeis 1890). Privacy refers to “the claim of
individuals to determine for themselves when, how and to what extent informa-
tion about them is communicated,” (Westin 1967). The concept of privacy for
individuals was considered as a “regulatory process” to control external access
to oneself and information sharing with others (Klopfer and Rubenstein 1977).
Moreover, information (or personal) privacy is a concept of moral and legal right
from the perspectives of regulations, policies, and commercial practices in mod-
ern countries (Clarke 1999). In the digital age, personal privacy becomes a com-
modity on commercial websites that make the collection, storage, transfer, pro-
cessing, and re-use of private information are easy to technology suppliers and
also attractive to personalized practices (Davies 1997). Hence, information pri-
vacy is more related to the ability of personal control over the information (e.g.,
access, storage, use, etc.) about themselves (Bélanger et al. 2002). In the name
of procedural fairness, firms tempt consumers to disclose personal information
on the exchange of free services or online transactions (Culnan and Armstrong
1999). Commercial and defaulted agreements can be regulated to meet privacy
policies and look as sufficient protection for people. But information asymme-
try occurs when consumers surrender personal information to get the required
services from governments or make transactions from firms (Tsai et al. 2011).
Collect customer purchases and profiles for better business opportunities using
the Internet and mobile technologies have become the prevailing competitive
strategies to effectively, i.e., the reduction of time and space barriers to record-
ing, track people’s behavioral patterns (Rust et al. 2002). Obviously, consumers
rather than firms often take compromise on their privacy for the convenience of
mobile applications (Hoffman et al. 1999) or online transactions (Hui et al. 2006).
The phenomenon has occurred, namely privacy paradox, referring to the anom-
aly that consumers behave in a way to contradict their preferences (Sweat 2000),
or the preference-behavior inconsistency in making privacy choices (Dinev and
Hart 2006). The appealing to “fair exchange” of personalized services provides
the reasonable explanation about why consumers express their privacy concerns,
but behave in an opposite way to disclose personal information (Pavlou 2011).
The phenomenon of privacy paradox matters because it reflects an illusion of
personal control over the misuse of private information where online consum-
ers or platform users just pay attention to short-term risks rather than long-term
ones (Brandimarte et al. 2013). However, Barth et al. (2019) raised one question:
“How much do consumers really value their data and privacy?” (p. 64).
Firms are hard to resist to the temptation of privacy invasion in which the
threats to privacy penetrate through the boundary of personal privacy in collect-
ing and re-using the profiles of consumers and the behaviors of online transac-
tions. From online transactions to social media, people are overwhelmingly sur-
rendering their privacy (e.g., email, mobile phone numbers) in the desire for
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Beyond the trade‑offs on Facebook: the underlying mechanisms… 355
transaction convenience or social connection. The disclosure of personal informa-

tion online is simplistic without rethinking the asymmetric return to an exchange.
Social networking becomes the weapon for the technology giants that do not bear
on the costs or threats to the losses of personal privacy. Social networking sites
(SNSs) are human-created information artefacts that developed for human con-
nections and the spread of influences, which probably promote the invasion of
personal privacy in business contexts (Lowry et al. 2017). The wide use of SNSs,
such as Facebook (or Chinese Weibo), can provide the observation lens to under-
stand how users define their privacy boundaries and make privacy choices.
Interpersonal communication, from offline to online connections, after the Mil-
lennium was not fully predicted by Putnam (2000). Yet, Facebook’s “friending” and
“tagging” are expected to play as the role of social networking to rebuild people’s
social capital or mutual trust. Facebook users believe that they can save the time
of communication and overpass the constraint of space that were considered as
obvious barriers to the connections of civilization before the Internet. In 2021, the
monthly active users on Facebook have reached 2.85 billion (www.facebook.com).
Along with the growing trend of Facebook users associate with booming profits,
the spreading of personal information may squeeze the borders of personal privacy
and cause the obstacles to secured protection. Invisible invasion of personal privacy
on Facebook has affected people’s lives, such as privacy risks (Debatin et al. 2009).
Privacy risk is a risk perception of the misuse of personal information on oppor-
tunistic behaviors (Dinev and Hart 2006). It is easy to claim that people have the
control over privacy risks because the use of Facebook is a personal choice. But,
the presence of privacy paradox reveals that the ability to cope with privacy risks is
irrelevant (Krasnova et al. 2010). Despite the concern of personal privacy, the dis-
closure of personal information is irreversible after surrendering future use of per-
sonal data or having lost control over it.
People are not naive that profit-seeking high-tech giants, such as Facebook and
other SNSs, have the incentives to foster unspoken misuse of user profiles and the
promotion of personalized services. Previous studies often took Facebook as an
example to examine the motives for personal disclosure behaviors (Chang and Heo
2014; Dienlin and Metzger 2016; Tsay-Vogel et al. 2018; Zlatolas et al. 2015). It
holds obviously that Facebook users are willing to disclose personal profiles despite
the threats to personal privacy. The disclosure of private information often lies
in one’s control over current behavior that has possible impacts on future conse-
quences, which is often judged on the calculus of behavior regarding privacy rights
and personal needs of socialization experience (Laufer and Wolfe 1977). Yet, the
unpredictable future consequences of current behavior motivate people to overall
judge privacy choices in terms of anticipated benefits and anticipated risks. Rational
people are supposed to judge the disclosure of private information on a balanced
trade-off between benefit beliefs and privacy risk beliefs, i.e., the theory of privacy
calculus (Culnan and Armstrong 1999). Considering how privacy protection is
hindered by the freedom of privacy choices (Clarke 1999), or the behavior of self-
interested human species (Acquisti et al. 2022), the phenomenon of privacy para-
dox needs more evidences, such as one’s cognitive biases of risk assessment (Barth
and de Jong 2017). We consider the preference-behavior inconsistency of privacy
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choices as the taken-for-granted phenomenon to rethink the theory of privacy cal-

culus. The phenomenon of privacy paradox is better understood that people often
overestimate their rational calculus of privacy benefits and risks, and underestimate
their behavioral change (e.g., user resistance) in the ignorance of cognitive biases
(Adjerid et al. 2018).
We acknowledge that people are willing to disclose personal information if inter-
personal trust can help them reduce opportunistic behaviors (Krasnova et al. 2010),
or because they want to sustain intimate relationships or have more intimate disclo-
sures (Jiang et al. 2013a, b), or seek to bridge online and offline social connections
(Ellison et al. 2007). Yet, social trust or social capital can enable an individual to
get the support of resources from other in-group members of a networked commu-
nity or group (Ellison et al. 2007). We develop an inference about the rebalance of
trade-offs by embedding social support in privacy calculus to reduce the uncertainty
of privacy choices, i.e., more stable social interactions and intimate relationships.
Embeddedness refers to the functioning of a real world that could be understood
associated with various modes of social actions on different conditions or the eco-
nomic life of social beings governed by social customs and norms, which act as
parts of historically derived, institutional, or social structures (Polanyi 1957). In the
context of networked firms, embeddedness refers to the cultivation of long-term and
cooperative relationships (Uzzi 1997). In the context of human-technology interac-
tions, embeddedness refers to usages of information systems in work routines to sus-
tain stable socialization process (Polites and Karahanna 2013). To extend the knowl-
edge about personal choices of privacy uncertainty (Acquisti et al. 2015), we address
the risk–benefit paradigms of self-disclosure and user resistance. The risk–benefit
paradigms are developed to examine the underlying mechanisms of privacy choices,
but not to eliminate the phenomenon of privacy paradox.
We take Facebook users as the target and thereby examine the fostering and
inhibiting forces of privacy choices. The approach to uncover the black-box of
privacy paradox is beyond the research scope. The uncertainty of privacy choices
would always exist, and people try to make against it using their own ways stem-
ming from specific beliefs that need better explanations than the existing ones from
privacy calculus. In this study we address the following research questions concern-
ing privacy choices involved in embedding digital technologies into people’s life:
Question-1: What’s the alternative explanation of privacy paradox and why it

works?
Question-2: How to conduct a rebalanced trade-off of privacy choices on Face-
book?
2 Competing views of information privacy
2.1 Social interactions on Facebook
The evolution of online connections, stemming from SNSs to multi-sided platforms

for social app development, fosters Facebook to become a technology giant that
13
increases its influence on social media, political dispute, and advertising by collect-
ing personal data and breaking privacy boundary (Helmond et al. 2019). Among
digitalized platforms in the world, Facebook, as founded in 2004, is one of the most
successful company in terms of user populations, geographical coverages, social
influences, media advertisements, and business profits (Hutchinson 2020). The suc-
cess of Microsoft company would remind general impressions of the importance of
network effects and the war on standards for operating systems (Bresnahan 2001).
New rules of digitalized platforms and strong congestion between sellers have
tempted the desire for privacy protection and the business opportunities underly-
ing social-culture dissociation (Tucker 2018). Facebook has adapted the competitive
strategy via mergers and acquisitions for the past decade, from Instagram to What-
sApp, and from social connections to online advertisements and transactions (Srad-
ers 2020). Facebook persuades people to believe that the anticipated benefits would
compensate their potential losses of personal disclosure in the digital platform. The
lock-in strategy of Microsoft has adapted switching costs and network effects to
win the war for software competition. However, the competition in the digital econ-
omy has changed from the war on standards for compatibility (e.g., Microsoft) to
the embeddedness of social capital or social support for shared interests and social
connections (e.g., Facebook). Social capital or social support helps individuals con-
nect with others and behave sticky to a community or a group (Putnam 2000). Many
Facebook users knew each other offline (Ellison et al. 2007), the networking trend
from offline to online would reinforce their social connections. Facebook users that
engaged in social information-seeking behaviors via online and offline connection
strategies are more likely to develop social support (Ellison et al. 2010).
Online social interaction is the key driving force for Facebook users from the
past to the present (Acquisti et al. 2015). The use of Facebook “friending” and “tag-
ging” to intensify the current relationship can provide explanations about why users
are willing to disclose their profiles and ignore privacy concerns in the disclosure.
The major sources of privacy-related threats to Facebook users mostly come from
institutions and peers in which the latter is related to social privacy that probably
received more concerns (Raynes-Goldie 2010). The research indicates that Face-
book users are more concerned about the disclosure of private information on the
peer context (Rogers 1975). But the growing trend of Facebook users (e.g., over 1
billion after 2012) seems to manifest their need for social interactions, which contra-
dicts general impressions of privacy concerns. People’s desire for social interactions
and privacy protection are unlikely to be simultaneously achieved in the digital age.
Owing to the spreading from offline to online social connections, we consider that
Facebook users’ concerns about the misuse of private information would be totally a
different story that desires for a new approach to examine privacy choices (Acquisti
et al. 2015, 2022).
2.2 Instability of privacy choices
Privacy choices are more related to the control over personal information, but not the
restriction on social interactions (Klopfer and Rubenstein 1977). The phenomenon
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of privacy paradox reveals the presence of cognitive dissonance in privacy choices,

or the conflict between privacy preferences and actual disclosure. Privacy prefer-
ences concerning framing, biases, and heuristics that are not stable in different
contexts in which people’s lives indicate changing boundaries between private and
public spheres regarding the concealment or disclosure of personal information over
time or across cultures (Acquisti 2009, 2022; Brandimarte and Acquisti 2012; John
et al. 2011). Personal control over private information often depends on platform
self-regulation and government legislation on privacy protection (Brandimarte and
Acquisti 2012), those changes in privacy boundaries would make privacy choices
unpredictable or unreliable, i.e., the instability of privacy choices or the instability
of behavioral outcomes. The divulgence of personal data has shaped privacy as peo-
ple’s concerns about online social interactions that associate with economic, legal,
technical, social, and ethical issues surrounded by market opportunities and privacy
intrusion (Sraders 2020).
From the selected previous work about the explanations of privacy paradox, the
risk–benefit analysis of privacy calculus is rarely stable, depending on people’s
rational boundaries, psychological processes, or cognitive heuristics and biases
(Table 1). For example, incorporating behavioral influences into rational privacy
calculus of risks and benefits, the instability of privacy choices is shaped by both
objective and relative perceptions, leading to the phenomenon of privacy paradox
(Adjerid et al. 2018). Alternatively, the phenomenon of privacy paradox prob-
ably comes from people’s self-interested choices of private information and diverse
boundaries of privacy (Acquisti et al. 2022). From the previous work, the dispute of
privacy choices is inconclusive and even more complicate for people to get involved
in SNSs for social interactions.
Our framework of privacy choices is consistent with Westin’s view that people
want to restore the balance between privacy and necessary surveillance (Laughlin
1968). However, two types of uncertainty would hinder privacy choices, leading
to the instability of privacy choices. First, the uncertainty of information misuses
to shape privacy risks. Second, the uncertainty of information disclosure to shape
anticipated benefits, such as discounting late rewards would cause overweight on
the existing benefits. The instability of privacy choices, referring to the instability of
underlying mechanisms, may come from possible changes of risk beliefs and/or ben-
efit beliefs along with changing borders of privacy or different frames of reference
from the existing states to the future states. Also, the trade-offs between long-term
privacy risks and short-term benefits may cause the instability of privacy choices
(Acquisti and Grossklags 2005).
According to privacy calculus, risk beliefs and benefit beliefs are supposed to
independently affect self-disclosure intention. Building on privacy calculus, we
develop the risk–benefit paradigms by taking a relative weighing on the comparison
between perceived benefits and perceived risks of private information disclosure. A
relative weighing approach to privacy choices was less examined in the literature
(Adjerid et al. 2018). Using the relative weighing approach instead of the risk–ben-
efit analysis in the literature (Keith et al. 2013; Xu et al. 2009), we assume that
experienced people (e.g., existing Facebook users) perceived the risks of informa-
tion disclosure prior to judging the anticipated benefits of that disclosure. Moreover,
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Table 1 Selected literature about the explanations of privacy paradox
Theory Assumption Trade-off/context Privacy choice References
Procedural justice fairness 1. Fairness is a basic need for Procedural fairness, as regulated Customer trust in procedural Clarke (1999), Culnan and
humans in justice in policies, increasing the fairness would reduce privacy Armstrong (1999)
2. Privacy regulations or protec- perceptions of fairness that concern associated with
tions for fair information weaken the tensions between disclosure
practices that rely on freedom the collection (giving benefits)
of information and use (creating risks) of
personal information
Context: Internet commerce
Hyperbolic discounting/Psycho- Hyperbolic discount functions An inconsistency between People tend to under-discount Acquisti and Grossklags (2004)
logical distortions are developed in terms of a privacy attitudes and privacy long-term risks and losses
high discount rate over short behavior while acting in privacy-sensi-
horizons and a low discount Context: Interview surveys and tive situations
rate over long horizons. (A experiments
conflict between present and
future preferences)
Social contract 1. The perceptions of fairness The trust-risk framework as Less sensitive information Malhotra et al. (2004)
regarding the collection and shaped by privacy concerns is would increase trusting beliefs
Beyond the trade‑offs on Facebook: the underlying mechanisms…
use of personal information better to deal with the disclo- and reduce risk beliefs of
are not stable sure intention in an uncertain privacy disclosure
2. The shifting dimensions of environment
privacy concerns from offline Context: Field surveys of Inter-
to online are assumed to lie in net users from household
fairness perceptions
3. Privacy concerns exhibited
three psychometric properties,
namely collection, control, and
awareness, in online contexts
359
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Table 1 (continued)
360
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Incomplete information and 1. People have limited ability An imbalanced trade-off People show the dichotomy Acquisti and Grossklags (2005)
bounded rationality to calculate privacy risks and between risks and benefits of between claimed privacy
disclosure benefits privacy concerns and their intention to
2. People do not have all the disclose
information required for judg-
ing privacy risks
Privacy calculus 1. Consumers are rational to A cost–benefit analysis of Internet trust and personal Dinev and Hart (2006)
balance anticipated risks and privacy calculus in terms of interest raise the willingness to
anticipated benefits in surren- risk beliefs and confidence disclose personal information
dering personal information and enticement beliefs are Privacy concerns reduce disclo-
2. Risk perceptions are related to conducted to predict the dis- sure intention
the uncertainty of oppor- closure behavior of personal
tunistic behavior, i.e., risk information
perceptions are not stable in Context: e-commerce transac-
the decision prior to privacy tions
disclosure
3. From Internet experiences to
online transactions, trust and
personal interest may outweigh
privacy risk perceptions
H.-P. Shih, W. Liu
Table 1 (continued)
Utility maximization 1. A trade-off based on consum- Consumers that perceive the The potential benefit of the Awad and Krishnan (2006)
ers’ utility function of benefits value (or outcome utility) of service outweighs the potential
and costs online service exceeds online risk of privacy invasion in
2. Benefit is derived through the advertising are more willing to online personalized service
degree of personalization disclose private information The risk of an intrusion
3. Cost is a function of con- Consumes perceive different outweighs the benefit of adver-
sumer privacy concerns, values of online service and tising in online personalized
previous privacy invasion, advertising associated with advertising
consumer-rated importance of varying perceived benefits
information transparency and Context: Online personalization
privacy policies by a large Internet service
provider
Heuristic processing and routini- 1. The temporal stability of A trade-off between risks and Risk perceptions hinder indi- Norberg et al. (2007)
zation of behavior intention for planned and trust instead of the cost–ben- viduals’ stated intention to
voluntary behavior efit analysis disclose personal information,
2. Actual disclosure behavior Context: Marketing exchange while trust promotes actual
was less planned environments disclosure
Quantum theory 1. Indeterminacy: decision Stated preferences and actual People’s willingness to disclose Flender and Müller (2012)
outcome is determined at the disclosure behavior are neces- personal data are observed
decision time rather than the sary but mutually exclusive to against their own privacy
decision-making process observations concerns claimed
2. Noncommutativity: two People modify their preferences
decisions (stated preferences in response to discomfort
and actual disclosure) are not stemming from conflicting
interchangeable or compatible preferences
in privacy decision making Context: Meta-analysis
361
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Table 1 (continued)
362
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Privacy calculus Social 1. Mediated communication for A shift from privacy calculus to Privacy concerns reduce self- Jiang et al. (2013a, b)
exchange intimate and social desirable social exchange disclosure
Media richness relationship Context: College students in Social rewards increase self-
2. The disclosure of private online chat rooms disclosure
information in synchronous
online social interactions
Information boundary The control over private and A risk-control trade-off instead A tension between person- Sutanto et al. (2013)
valued information in the col- of a risk–benefit trade-off to alization and privacy can
lection and storage processes judge psychological comfort be reduced by technological
with personalized application solutions
and the intention toward the
collection of personal data
Context: A field experiment of
smartphone users
Privacy calculus 1. The trade-offs between A trade-off between privacy High affective commitment Kordzadeh and Warren (2017)
Social exchange privacy and benefits of concerns and positive out- reduces the impact of privacy
information disclosure along comes of disclosure concerns on the willingness to
with the outcomes of trade- Context: The sample of students, disclose
offs constitute the calculus of faculty, staffs from southern High affective commitment
behavior region, as well as patients of increases the impact of posi-
2. The cost-effectiveness clinics from the local hospital tive outcomes on the willing-
analysis of privacy makes university in USA ness to disclose
individuals seeking for health
communication in online
platforms despite the presence
of privacy concerns
H.-P. Shih, W. Liu
Table 1 (continued)
Behavioral economics 1. Co-existence between norma- Consumers may overestimate Objective benefits and costs, i.e., Adjerid et al. (2018)
tive and behavioral influences their response to normative normative influences, deter-
2. Privacy preferences are influences, while underesti- mine disclosure intention
unstable in privacy choices mate their response to behav- Relative perceptions of risks
because of cognitive heuristics ioral influences and benefits, i.e., behavioral
and biases Context: There were two sam- influences, determine actual
3. Privacy behavior is context- ples. Participants of the first disclosure
dependent sample were recruited from
Amazon Mechanical Turk for
Experiments 1 and 2. Par-
ticipants of the second sample
were recruited from Prolific
Academic for Experiment 3
363
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a rebalanced trade-off is also assumed to lie in the risk–benefit paradigms. In this

study we focus on general privacy risks, but not context-specific privacy risks (Kar-
watzki et al. 2022). Alternatives that take self-disclosure to predict the anticipated
benefits are considered, but such a causal link may violate the reasoning of user
resistance, and lead to an inverse of trade-offs.
The phenomenon of privacy paradox is better understood by the stability-change
dichotomy in which mechanisms and outcomes of human-technology interactions
(e.g., social interactions on Facebook) are often assumed complementary but not
mutually exclusive. We consider stability and change (or instability) as two essential
elements to explain the underlying mechanisms of paradoxical relationship, such as
the paradoxical relationship between privacy concerns and information disclosure
(Acquisti and Grossklags 2004, 2005). Stability and change are fundamentally inter-
dependent or contradictory but mutually enabling and constituting each other, i.e.,
the duality view (Farjoun 2010). According to the duality view, the change of mech-
anisms enables stability outcomes, while the stability of mechanisms enables change
outcomes. The approach to consider privacy calculus as the two-sided forces (e.g.,
risks and benefits) for either constraining or enabling self-disclosure is inconsistent
with the phenomenon of privacy paradox. From the duality view, we consider the
proposed risk–benefit paradigms as two-sided factors that are conditionally balanced
to enable the resistance and willingness toward privacy disclosure. The examination
of user resistance helps to address the potential instability of privacy choices.
According to psychological reactance (Brehm and Brehm 1981), people that
enjoy their behavioural freedom would achieve a negative emotion response to the
threats to behavioral freedom. The restoring of behavioral freedom is called reac-
tance. Facebook users may not consider the disclosure of private information as the
threats to behavioral freedom given that the disclosure freedom is not completely
eliminated or restricted, such as partial disclosure of sensitive information (Core
2001). We adopt “resistance toward information disclosure” to define people’s psy-
chological reaction to the concern on negative behavioral outcomes (the weaker
reaction) or the violation of personal autonomy (the stronger reaction) but not the
notion of negative emotional response. For consumers, resistance to use an innova-
tion mostly stems from functional barriers (e.g., incompatibility) and/or psychologi-
cal barriers (e.g., conflicting with belief structure) (Ram and Sheth 1989). For Face-
book users, we consider user resistance mostly stems from psychological barriers
to privacy disclosure. People often resist to an innovation in the way of rejection,
postponement, or opposition in order to avoid, reduce, or control the uncertainty
of behavioral outcomes, respectively (Szmigin and Foxall 1998). In the context of
privacy choices, the fear of losses and the willingness to against privacy risks may
cause people’s resistance toward privacy disclosure (Hirschheim and Newman 1988;
Klaus and Blanton 2010).
2.3 A trade‑off between risks and benefits
The boundary of privacy between private (e.g., incomes) and public (e.g., honours)
information would define personal privacy (Petronio 2002), and also define the red
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lines of privacy invasion for an individual. Privacy is a concern for individuals and
linked to the ability to the control of private information on making privacy protec-
tions or valuable exchanges in specific contexts (Smith et al. 2011). Privacy calculus
refers to the calculus of behavior conducted on personal information processing in
the context of changing environments, unpredictable behavioral outcomes, and new
technologies that emerge at various stages of the life (Laufer and Wolfe 1977). Pri-
vacy calculus also refers to the disclosure of private information in exchange for an
individual’s economic and social benefits (Culnan and Armstrong 1999). According
to Dinev et al. (2008), the information asymmetry between people and government
under institutional surveillance rendering privacy risks about the abuse of personal
information by the government. Likewise, the phenomenon of information asym-
metry exists in which Facebook users lose the control about the processing of their
disclosed profiles behind the platform. According to privacy calculus theory, people
would develop the beliefs of risk–benefit analysis of information disclosure under
the influence of privacy concerns and privacy risks (Dinev and Hart 2006). Privacy
concern is a specific belief driven by experience and context (Culnan and Armstrong
1999). Privacy risk is a general belief of one’s expected losses from the disclosure of
personal sensitive information (Xu et al. 2011a, b). Privacy concern is more related
to one’s perceived ability to control the disclosure of personal information, privacy
risk is more related to the possibility of loss in information disclosure (Dinev and
Hart 2006). Privacy concerns reflect perceived control over privacy choices accord-
ing to one’s confidence in specific contexts, while privacy risks are considered as
perceived losses from privacy disclosure according to the estimation of probabili-
ties. From the information privacy framework (Ozdemir et al. 2017), privacy con-
cerns and privacy risks differ in term of antecedents and outcomes.
Privacy calculus theory that develops based on a risk–benefit analysis has been
widely used to predict consumers’ willingness toward private information disclosure
in online transactions (Dinev and Hart 2006), online chat rooms (Jiang et al. 2013a,
b), mobile applications (Kehr et al. 2015; Wang et al. 2016), location-based services
(Xu et al. 2011a, b; Xu et al. 2009), virtual health communities (Kordzadeh and
Warren 2017), SNSs (Dienlin and Metzger 2016), and ride-sharing services (Cheng
et al. 2021). In sum, privacy calculus was used to address how to make the trade-offs
between perceived benefits and perceived risks. The early privacy calculus model
examined the balance between privacy risk beliefs (e.g., privacy risks and concerns)
and confidence-enticement beliefs (e.g., trust and personal interest) regarding online
disclosure of personal information (Dinev and Hart 2006). The reality of Internet
privacy for the majority of people is far from the balance between privacy concerns
and personal interests because individual needs of privacy protection and social
environments of personal life are constantly interacting and changing (Acquisti
et al. 2015). People may perceive the rewards outweigh the risks of information dis-
closure, i.e., an imbalanced trade-off, when they focus on the value of relationship
intimacy by minimizing the risk beliefs of self-disclosure (Jiang et al. 2013a, b).
Privacy calculus theory takes an exchange view to address whether consumers are
willing to disclose private information (e.g., anticipated risks) to get the return of
value (e.g., anticipated benefits) in the personalization (Xu et al. 2011a, b). How-
ever, the calculation of privacy in the disclosure of private information is beyond
13
the trade-offs framework because anticipated benefits and anticipated risks are not
evaluated on the same time point. For example, mobile phone users tend to exhibit
dichotomous attitudes toward the disclosure of privacy in the imbalanced risk–bene-
fit trade-offs (Goes 2013). The theory of hyperbolic discounting explains that people
prefer an early reward to a late reward (Ainslie 2002). Beyond a rational trade-off,
prior habits provide the reason to explain the irrational disclosure of private infor-
mation (Fernandes and Pereira 2021). The disclosure of personal information looks
like a context-dependent decision-making process (Acquisti et al. 2015; Punj 2019;
Yu et al. 2020). The underlying assumptions of privacy calculus such as rational
(e.g., cost–benefit analysis) and irrational (e.g., the embeddedness of habits), as well
as context-dependence of privacy concerns (Acquisti et al. 2015), may overlook the
stability of privacy choices in theory development. Moreover, the assumption of sta-
ble preferences for privacy calculus does not fit the reality of privacy choices (Adj-
erid et al. 2018). We thus address the anomaly of privacy paradox that lies in the
stability-instability dichotomy of privacy decisions.
2.4 Embeddedness of social support
Social capital refers to public resources in a community, such as information, social

relationship and capacity, that can be shared with people for better collective actions
or social outcomes (Coleman 1988; Paxton 1999; Woolcock and Narayan 2000).
The concepts of social capital help individuals to achieve a stable balance between
private and collective benefits. There are two forms of social capital, “bridging”
social capital that enables individuals to access the information from external ties
(Nahapiet and Ghoshal 1998), and “bonding” social capital that helps individuals
pursue cohesive relations through internal ties (Adler and Kwon 2002). The influ-
ence of external and internal ties on social interactions and human behaviors pro-
vides possible incentives to foster the use of social networks for personal benefits
(Newell et al. 2004). Bridging social capital refers to loose connections between
networked members that can provide and receive useful information (Ellison et al.
2007), which rely on the approach to promote casual acquaintances and connec-
tions of view via information support (Claridge 2018). Bonding social capital refers
to strong ties between networked members or those members holding emotionally
close relationships (Ellison et al. 2007), which rely on the approach to reinforce inti-
mate interpersonal relationship via emotion support (Claridge 2018).
Compared with offline communication, online communication is more crucial
to the competition among profit-seeking SNSs. Facebook has adapted social infor-
mation-seeking and relational communication strategies to embed social capital in
the structure of networks and the habit of individuals (Ellison et al. 2007, 2010;
Valenzuela et al. 2009). Social capital is localized at individuals to reinforce their
belongingness to a group or a community (Adler and Kwon 2002). The development
of social capital helps people produce positive social outcomes, such as civic trust,
mutual cooperation, and behave reciprocity in social life (DeFilippis 2001; Ellison
et al. 2007). Facebook users that have developed their social ties would also extend
their online connections. For Facebook users, such online connections help to
13
develop their social support that can be directly personalized to meet personal inter-
ests. Compared with the three-dimension framework of social capital that focused
on both personal and collective goals and actions (Treacy et al. 2017; Wasko and
Faraj 2005), we adopt “social support” instead of “social capital” to address how
Facebook users develop their online connections to pursue the individual preference
of benefits and whether the support from information and emotion can work (Liang
et al. 2011; Taylor et al. 2004).
People can easily find the current benefits of personal decisions, but feel the diffi-
culty or perceive the uncertainty to judge future benefits of personal choices. Hence,
people tend to discount future benefits of personal choices. The disclosure of private
information on Facebook is a good example to explain why people need “social sup-
port” or “social trust” to against privacy risks or the uncertainty of personal con-
trol over information abuse. It is not surprising that people “need” (e.g., rely on,
use, develop) social communication or interpersonal relationship even in the risks
of privacy invasion or information abuse. According to privacy calculus, people are
supposed to take privacy risks as an exchange of the existing benefits. Obviously,
the risk estimate of information disclosure is not completely reduced by the borders
of privacy. It is overconfident on the estimation of privacy risks according to the
frequency of past privacy invasion events (Knight 1921). The misuse and misinfor-
mation on profit-seeking SNSs to invade the boundaries of privacy may cause unex-
pected losses of privacy (Smyth 2019). The unstable states between “existing ben-
efits” and “future benefits”, the estimate of uncertainty in terms of “current risks”
and “future risks” in information disclosure, and thereby the imbalance between risk
and benefit beliefs would cause the instability of privacy choices. Using a risk–ben-
efit analysis to make the decision to disclose or withhold privacy, the benefits of
emotion support and instrumental support are better to maximize social support and
thereby encourage the disclosure of private information (Kingsley Westerman et al.
2022). Without the instability of privacy choices, instrumental support and informa-
tion support are good measures of social support. Building on the risk–benefit para-
digms, we consider the embeddedness of “bridging” and “bonding” social support
in privacy calculus is better to reduce the instability of privacy choices given that
human behaviors are enabled and constrained by the current social systems (Gid-
dens 1979).
3 Research model and hypothesis development
We address self-disclosure willingness rather than actual disclosure because the

latter is often hidden by platform regulations. Moreover, actual disclosure is more
related to the behavior of context-dependent privacy choices. Privacy calculus is
the underlying theory adopted to develop the theoretical lens (Fig. 1). We develop
the risk–benefit paradigms to extend the anticipated risks to encompass privacy
concerns and privacy risks. The perceived benefits stem from information sup-
port and emotion support are conducted on the view of social support. Given the
influence route from offline to online connections between Facebook users (Ellison
et al. 2007), we assume that information support and emotion support have been
13
Fig. 1 Research model of self-disclosure
embedded in online social interactions, i.e., the structuration process of technology

(Orlikowski 1992). Previous studies considered Internet trust or trusting beliefs as
an enabler of self-disclosure (Dinev and Hart 2006; Malhotra et al. 2004). But, other
research argued that the disclosure of private information would make people to
trust in interpersonal relationship (Schoenbachler and Gordon 2002).
Users develop their beliefs of system usages, including internally (e.g.,
human–computer interactions) and externally (e.g., system functions) oriented
beliefs of the system. Cenfetelli (2004) defines enablers and inhibitors as those
external beliefs of system usages that would encourage and discourage adoption
behavior, respectively. Hence, enablers and inhibitors are not opposites of one
another; meanwhile, both beliefs differ in terms of causes and effects. Resistance to
a change in either organizational routines or personal habits seems to act as an inhib-
itor of innovation (Bovey and Hede 2001; Mani and Chouk 2017). From the study
of physician’s resistance behaviors (Lapointe and Rivard 2005), the interaction with
new technology would lead to the perception of threats or stresses, and individual
user might respond with resistance behaviors, from passive to active, as well as from
individual-level to group-level. A threat is an external stimulus (Witte 1992). A fear
emerges from a threat in which individuals respond with fear-inducing perceptions
of the threat (Rogers 1975). Taken together, fears and perceived threats from privacy
invasion may cause behavioral change. Bhattacherjee and Hikmet (2007) took resist-
ance to change as an external inhibitor under the threat condition. In contrast, we
consider user resistance as an internal inhibitor of self-disclosure under the uncer-
tainty of losses in information misuse in which people would make against the mis-
use. User resistance is a psychological reaction to the situation that individuals skin
to frustration of personal control over the misconduct of private information and the
threats to privacy in the data-driven algorithms (Hirschheim and Newman 1988).
We take privacy risks and privacy concerns rather than perceived threats as the
13
antecedents of user resistance toward information disclosure. User resistance was

less examined in prior work, it is more related to one’s implicit unwillingness based
on conscious thoughts that should be detailed in the study of privacy choices.
3.1 Information and emotion support
Social support is a human need that exists along with social relationships, and
defined as the accumulated experience of mutual assistance and obligations that
contribute to well-being lives (Wills 1991). Information support, instrumental and
emotional support are three norms of social support that can help to sustain social
relationships (Taylor et al. 2004). Facebook users are motivated to share information
and sustain close relationship on the platform (Waters and Ackerman 2011). Infor-
mation support and emotion support are believed to reinforce reciprocal motivation
to share information and receive the support from others, respectively (Liang et al.
2011). Facebook users may consider information support and emotion support as
the “metaphor” of switching costs if they leaving intimate members or losing social
identities on the platform. Information support is one of the motivations for Face-
book users that need advices or encounter problems. Emotion support is crucial to
those people that need the support from friends or the sharing of interests in online
connections. The reciprocity of social interactions or the norm of social support in
terms of either information support or emotion support would increase the benefit
beliefs of mutual trust (Ellison et al. 2007; Woolcock and Narayan 2000). In con-
verting social resources to meet the preference for benefits, “bridging” and “bond-
ing” influences of social support should be examined independently (Newell et al.
2004). We posit that:
H1 Information support of Facebook use would positively affect users’ perceived

benefits of online disclosure.
H2 Emotion support of Facebook use would positively affect users’ perceived ben-
efits of online disclosure.
3.2 Perceived benefits
The anticipated benefits of personalized services mostly encompass the support for
information and entertainment would promote users to access online personaliza-
tion (Xu et al. 2009). People that desire for intimate relationship, social identity,
and social support would perceive the stable benefits of social support. People may
trust a digital platform, depending on the promise that the support of positive social
outcomes can be converted to meet personal interests in against the uncertainty of
information misuse (Krasnova et al. 2010). Facebook users would perceive the ben-
efits of disclosure that come from information support and emotion support, such as
the ability to share thoughts and feelings with others (Acquisti et al. 2015). Com-
pared with privacy risks, the desire for personal interests, such as relationship inti-
macy and social identity, would encourage personal disclosure (Dienlin and Metzger
13
2016). We consider perceived benefits that develop on personal interests would play
the role as an enabler of information disclosure. We thus posit that:
H3 Perceived benefits of disclosure would positively affect Facebook users’ will-

ingness toward self-disclosure.
3.3 Privacy risks
Privacy is a concept of self-right that individuals perceived the control over personal
information in which others have limited access to the information. Facebook has
incentives to break the self-right of users and persuade them to build profiles online.
Online disclosure of private information may tempt opportunistic behavior because
of low costs of privacy invasion, such as unauthorized access, identity theft, and
selling consumer database. For individuals, the disclosure of private information is
judged on the benefits of a close relationship, as well as the potential of privacy
risks and privacy concerns that across the borders of privacy (Culnan and Arm-
strong 1999).
Privacy risks refer to general beliefs of vulnerability or loss in the misuse of per-
sonal information (Dinev and Hart 2006). Privacy concerns refer to specific beliefs
of personal control over private information based on technological capabilities
(Dinev and Hart 2006). Facebook users that perceive privacy risks in online con-
nections would perceive possible losses in information abuse, leading to mitigate
their benefit evaluation of the platform. According to Dinev and Hart (2006), pri-
vacy risks and privacy concerns are distinct factors, but work as related beliefs of
privacy calculus theory. Regarding the disclosure of private information, the risk
beliefs of privacy would increase Facebook users’ privacy concerns about the con-
trol over personal information. Facebook users that perceive the risks of privacy in
the abuse of personal information would less likely to disclose their profiles online.
In sum, we posit that:
H4 Privacy risks would negatively affect Facebook users’ perceived benefits of

online disclosure.
H5 Privacy risks would positively affect Facebook users’ privacy concerns of

online disclosure.
H6 Privacy risks would negatively affect Facebook user’ willingness to disclose

private information.
3.4 Privacy concerns
Facebook users that have concerns about their control over the spreading of private
information online are supposed to have low levels of ability to control their profiles
on the platform. The reason is obvious that most Facebook users want to minimize
the negative outcomes of information abuse or illegal access. According to privacy
13
calculus theory (Dinev and Hart 2006), privacy concerns mitigate consumers’ will-
ingness to disclose personal information online. From the phenomenon of privacy
paradox (Pavlou 2011), and the temptation of privacy invasion, this study examines
whether privacy concerns can mitigate the willingness to disclosure on Facebook.
We thus posit that:
H7 Privacy concerns would negatively affect Facebook users’ willingness to dis-

close private information.
3.5 Antecedents of resistance
External and internal influences provide the reasons about why user resistance
occurs (Martinko et al. 1996). External influence refers to the threats that associ-
ate with behavioral change (Marakas and Hornik 1996), internal influence refers
to personal control over behavioral change (Martinko et al. 1996). The perspective
of status quo biases can provide the explanation for people’s user resistance toward
misuse of personal information (Samuelson and Zeckhauser 1988). People want to
maintain current status or situation because a change may increase the costs and lose
the control. Perceived benefits of switching did not provide the incentive to reduce
user resistance toward the implementation of a new information system (Kim 2011).
Hence, we consider that “perceived benefits” is not a significant antecedent of user
resistance. People resist to using a new technology that is considered a threat to pos-
sible losses or personal control over resources (Bhattacherjee and Hikmet 2007), or
because the new technology does against their habits or routines (Mani and Chouk
2017). The antecedents of user resistance include external and internal factors
(Hirschheim and Newman 1988). A general belief of privacy risks referring to the
unpredictable loss of information misuse that would play the role as an external fac-
tor of user resistance. A specific belief of privacy concerns referring to personal
control over information misuse that would play the role as an internal factor of user
resistance. We thus posit that:
H8 Privacy risks would positively affect Facebook users’ resistance toward infor-
mation disclosure.
H9 Privacy concerns would positively affect Facebook users’ resistance toward

information disclosure.
3.6 Outcomes of resistance
Consumers may resist to using an innovation because of functional and psychologi-

cal barriers (Ram and Sheth 1989). Compared with functional barriers, we consider
that psychological barriers are better to explain why people resist to disclosing pri-
vate information online. Knowing that self-disclosure behavior is irreversible and
out of personal control once exposed, people are supposed sensitive to the disclo-
sure. Bhattacherjee and Hikmet (2007) have emphasized, “resistance is not the
13
mirror opposite of IT acceptance, but a possible antecedent of IT acceptance” (p.

728). Resistance and adoption coexist in user behaviors (Mani and Chouk 2017).
Likewise, resistance and self-disclosure may coexist to shape Facebook users’ pri-
vacy choices. The psychological reaction to privacy as caused by psychological bar-
riers would likely inhibit Facebook users from disclosing personal information. We
thus posit that:
H10 Facebook users’ resistance toward information disclosure would negatively

affect their willingness to disclose private information on the platform.
4 Method
4.1 The instrument
The measurement items were mostly conducted by modifying prior measures with
wording change to meet the survey of Facebook users, but not benefit-oriented con-
sumers ("Appendix"). People’s beliefs of privacy risk and privacy concern differ
between online transactions and social interactions because the former is activated
by technology-push solutions, while the latter is activated by need-pull connections
(Debatin et al. 2009; Dienlin and Metzger 2016; Dinev and Hart 2006). We modified
previous scales of privacy risks adopted from Malhotra et al. (2004) and previous
scales of privacy concerns adopted from Awad and Krishnan (2006) for reflecting
the core elements of beliefs in online social interactions, but not the beliefs devel-
oped based on the type of information (Xu et al. 2009). The modified measures of
privacy concerns focus on the lose of confidence and control, and the threats to
information disclosure. The modified measures of privacy risks focus on risk beliefs
about the potential for losses and unexpected outcomes in privacy disclosure. Infor-
mation support and emotion support were measured by modifying previous scales
(Liang et al. 2011). We measured perceived benefits in terms of social rewards, such
as relationship intimacy, social identity (or acceptance), and social support (Ellison
et al. 2007; Jiang et al. 2013a, b). Self-disclosure was examined using the scales in
terms of users’ willingness to disclose specific private information. Two scales of
Bovey and Hede (2001) and one self-developed scale were adopted to measure user
resistance. All measurement items of the instrument were anchored using five-point
Likert scales and translated from English to Chinese, as well as improved before
lunching the formal survey.
4.2 The sample survey
According to NapoleonCat website (www.napoleoncat.com), Facebook users in Tai-

wan have reached eighty percent of the population (19 million) in 2019. The sur-
vey targeted at experienced Facebook (Taiwan) users—those users with daily use
(Table 2), they may disclose their private information on the platform even though
they consider the abuse of disclosed information as potential concerns and risks
13
Table 2 Profiles of the Characteristics Count (%)

respondents (N = 311)
Gender
Male 139 (44.7%)
Female 172 (55.3%)
Age
18–24 5 (1.6%)
25–34 120 (38.6%)
35–44 124 (39.9%)
45–54 48 (15.4%)
55 or above 14 (4.5%)
Education level
High school 39 (12.5%)
Junior college 58 (18.7%)
College/University 172 (55.3%)
Graduate school 42 (13.5%)
Job categories
Students 5 (1.6%)
House wife/husband 15 (4.8%)
Public administrations 48 (15.4%)
Private sectors 235 (75.6%)
Others 8 (2.6%)
Daily use time (hours)
< 0.5 81 (26.0%)
0.5 ≤ ~ < 1 95 (30.6%)
1 ≤ ~ < 2 70 (22.5%)
2 ≤ ~ < 3 33 (10.6%)
3≤ 32 (10.3%)
of privacy. This study collected a general sample of voluntary Facebook users via
online questionnaire survey, and received a total of 311 completed questionnaires in
the survey (Table 2). Regardless of the levels of utilization experience in Facebook,
we consider those users or groups of users that have adopted and continued use of
Facebook as a general sample.
4.3 Reliability, validity, and goodness‑of‑fit
The factor analysis (Table 3) indicates all higher factor loadings for the measure-
ment items of the corresponding construct compared with lower cross-loadings for
other items, exceeding the 0.60 threshold (Hair et al. 2010), and thus achieving con-
vergent validity. The Cronbach’s alphas and the composite reliability (Table 4) of
each construct significantly exceeded the 0.70 threshold (Nunnally 1978), achieving
acceptable reliability (Fornell and Larcker 1981). From Table 4, the average vari-
ance extracted for each construct significantly exceeded the 0.50 threshold (Fornell
13
Table 3 Factor analysis—factor loadings and cross-loadings of measurement items

Item ES PR R IS PB PC SD
ES1 0.859 0.044 − 0.034 0.223 0.020 0.063 0.015

ES2 0.895 0.093 0.025 0.202 0.026 0.010 0.144
ES3 0.861 0.046 − 0.010 0.270 − 0.012 0.003 0.037
ES4 0.867 0.058 0.023 0.306 0.018 0.024 − 0.017
PR1 0.057 0.834 0.222 0.101 − 0.081 0.164 − 0.117
PR2 0.091 0.862 0.130 0.047 − 0.144 0.268 − 0.095
PR3 0.106 0.833 0.121 0.133 − 0.069 0.274 − 0.165
R1 0.028 0.053 0.830 − 0.023 0.050 0.092 − 0.020
R2 0.001 0.180 0.853 0.033 − 0.040 − 0.023 − 0.107
R3 − 0.030 0.157 0.882 0.043 − 0.091 0.045 − 0.094
IS1 0.400 0.077 0.020 0.813 0.092 0.104 0.035
IS2 0.461 0.122 0.038 0.826 0.056 0.037 0.176
IS3 0.452 0.121 0.012 0.827 − 0.051 0.072 0.182
PB2 0.001 − 0.168 − 0.036 0.033 0.929 − 0.035 0.149
PB3 0.044 − 0.068 − 0.031 0.102 0.916 − 0.090 0.215
PC2 0.090 0.296 0.075 − 0.016 − 0.061 0.873 − 0.065
PC3 0.025 0.364 0.045 0.072 − 0.074 0.839 − 0.120
SD1 0.010 − 0.159 − 0.212 0.007 0.198 − 0.066 0.851
SD3 0.085 − 0.150 − 0.021 − 0.006 0.175 − 0.104 0.882
Eigenvalue 3.651 2.546 2.336 2.335 1.839 1.683 1.674
Cumulative 19.215 32.614 44.909 57.197 66.877 75.736 84.548
variance (%)
It is better that the factor loading of bold items should exceed 0.7. The score of diagonal elements should
exceed that of off-diagonal elements
and Larcker 1981). The square root of the average variance extracted for each
construct significantly exceeded the correlation between that and other constructs
(Table 4), achieving acceptable discriminant validity (Fornell and Larcker 1981).
From the seven goodness-of-fit indexes associated with the cut-off scores (Bagozzi
et al. 1991), the measurement model and the structural model have passed the good-
ness-of-fit tests (Table 4).
4.4 Common method variance
To examine common method variance (CMV), we adopt a post-hoc approach to

test the self-reported data. First, we adopted Harmon’s one-factor test, the largest
variance explained by one factor, i.e., the “emotion support” factor, was under 20%
(Table 3). Hence, the factor did not explain the majority of the variance (Podsakoff
and Organ 1986). Second, we examined the inter-construct correlations (Table 4),
the highest correlation (0.695) between information support and emotion support
was far below the threshold of 0.90 (Bagozzi et al. 1991). Third, we examined the
13
Table 4 Tests of reliability, validity and goodness-of-fit

Var Mean SD α CR IS ES PB PC PR R SD
IS 3.58 0.70 0.94 0.96 0.822

ES 3.54 0.65 0.93 0.96 0.695 0.871
PB 2.91 0.84 0.90 0.92 0.119 0.049 0.922
PC 3.53 0.72 0.84 0.90 0.115 0.125 − 0.190 0.856
PR 3.92 0.65 0.90 0.95 0.241 0.182 − 0.258 0.602 0.843
R 3.78 0.80 0.84 0.86 0.058 0.016 − 0.110 0.166 0.338 0.855
SD 2.26 0.89 0.80 0.81 0.015 0.068 0.411 − 0.259 − 0.349 − 0.240 0.867
Diagonal elements represent the square roots of the AVEs of the constructs, while the other matrix ele-
ments represent the inter-construct correlations
Goodness-of-fit indexes (the measurement model): χ2/df = 1.588; SRMR = 0.02; RMSEA = 0.04;
GFI = 0.93; AGFI = 0.90; NFI = 0.95; CFI = 0.98
Goodness-of-fit indexes (the structural model): χ2/df = 2.615; SRMR = 0.03; RMSEA = 0.07; GFI = 0.98;
AGFI = 0.94; NFI = 0.95; CFI = 0.97
Cut-off scores of goodness-of-fit indexes (Bagozzi and Yi 1988): Chi-square/degree of freedom (χ2/df ≦
3.00); standardized root mean square residual (SRMR ≦ 0.05); root mean square error of approximation
(RMSEA ≦ 0.08); goodness-of-fit index (GFI ≧ 0.90); adjusted goodness-of-fit index (AGFI ≧ 0.80);
normed fit index (NFI ≧ 0.90); comparative fit index (CFI ≧ 0.90)
α, Cronbach’s α; CR, Composite reliability
It is better that the factor loading of bold items should exceed 0.7. The score of diagonal elements should
exceed that of off-diagonal elements
correlation between a marker variable, i.e., a theoretically unrelated variable (e.g.,

“fake disclosure”), and the seven constructs (Lindell and Whitney 2001). The aver-
age correlation coefficient of the marker variable with other constructs was small
(0.075). In sum, we consider CMV is not a concern of this study.
Fig. 2 Empirical results of Facebook users
13
4.5 Hypothesis testing
From the empirical results (Fig. 2), we have found that the positive effect of infor-
mation support on perceived benefits (hypothesis H1), the positive effect of per-
ceived benefits on self-disclosure (hypothesis H3), the negative effect of privacy
risks on perceived benefits (hypothesis H4), the positive effect of privacy risks on
privacy concerns (hypothesis H5), the negative effect of privacy risks on self-disclo-
sure (hypothesis H6), the positive effect of privacy risks on resistance (hypothesis
H8), and the negative effect of resistance on self-disclosure (hypothesis H10) are all
significant. In contrast, hypotheses H2, H7, and H9 are not supported. Re-examine
the effect of perceived benefits on resistance, the path coefficient (− 0.017) is insig-
nificant in the study. The research model accounted for 25.7% of variances in self-
disclosure and 11.6% of variances in user resistance.
5 Discussion and conclusions
5.1 Implications for theory
The occurrence of privacy risks is inevitable on Facebook, but the anticipated

benefits of social interactions and relationship intimacy can be used to encourage
users’ self-disclosure. The embeddedness of social support to explain the disclo-
sure of private information was less examined in the literature (Martin and Murphy
2017). The embeddedness of information support and emotion support is expected
to reduce the instability of privacy choices underlying changing privacy preferences
in rational calculus. People, including Facebook users, are not totally rational in pri-
vacy choices (Table 1). In this study, the embeddedness of “bridging” social support
in privacy calculus has been found to reshape the risk–benefit paradigms to foster
users’ willingness to disclose online by stabilizing the desire for information support
or inhibiting the change in privacy preferences (Farjoun 2010). The first implication
is that we adapt the risk–benefit paradigms to explain why privacy calculus should
be stabilized underlying the uncertainty of information misuse, and how the trade-
offs are rebalanced via the embeddedness of information support instead of rational
calculus-grounded theories (Adjerid et al. 2018).
From the empirical results, perceived benefits, privacy risks, and user resistance
are three antecedents of self-disclosure willingness, privacy risk is the antecedent
of user resistance. Hence, user resistance and self-disclosure are not located at the
opposite ends of the same conceptual spectrum according to the inconsistent ante-
cedents. User resistance and self-disclosure would coexist in the presence of privacy
risks. Perceived benefits can encourage self-disclosure, but cannot affect user resist-
ance. In contrast, privacy risks inhibit self-disclosure, but enable user resistance. A
possible explanation is that an enabler is better to encourage behavioral intention,
while an inhibitor is easy to cause psychological reaction. The appealing to antici-
pated benefits is a better route to enable self-disclosure willingness, while the threat-
ening to anticipated risks is more influential to enable user resistance toward infor-
mation disclosure. The second implication is the risk–benefit paradigms instead of
13
the imbalanced trade-offs would enable self-disclosure willingness and user resist-
ance via distinct routes, leading actual disclosure to become more unpredictable.
Previous studies verified that privacy risks and privacy concerns would reduce
consumers’ willingness to disclose private information in online transactions or
mobile services (Dinev and Hart 2006; Keith et al. 2013; Li et al. 2011). In contrast
to the information privacy framework (Ozdemir et al. 2017), the empirical results of
this study indicate that privacy risks rather than privacy concerns would reduce the
willingness toward self-disclosure on Facebook. The effect of privacy concerns is
probably reduced when individuals surrender partial control over personal informa-
tion. The change of context, from online transactions to SNSs, may provide stronger
incentives to loosen personal control over privacy. Facebook users tend to perceive
lower ability of control over personal information than shoppers because they need
intimate relationships. Pavlou (2011) explains that the presence of information pri-
vacy attitudes would mitigate the effect of privacy concerns on self-disclosure will-
ingness. The presence of an external factor, such as privacy risks, probably enables
the internal factor, such as privacy concerns, to become a weak inhibitor of self-dis-
closure. Another possible explanation is the potential losses of personal disclosure
that can be compensated by the benefits of that disclosure, coinciding with earlier
findings that trust can compensate privacy risks and thereby encourages self-disclo-
sure (Ozdemir et al. 2017). The third implication is that external and internal factors
differ in making the trade-offs of self-disclosure.
5.2 Implications for practice
Policymakers believe that “default settings” or “nudge” can help people make better
choices, such as organ donation or retirement saving (Thaler and Sunstein 2008).
Digital platforms, such as Facebook and Pinduoduo (China), are professional about
how to use “default settings” to encourage information disclosure and how to manip-
ulate the desire for free lunch. It is not surprising that “default settings” can help
digital platforms “legally” collect more visible profiles despite the fact that users are
either aware or unaware of the surrender of their privacy in exchange for free lunch
(Acquisti et al. 2015). We highlight the empirical findings of this study for Facebook
users and the likes of whom. Privacy paradox does always exist once perceived ben-
efits can satisfy the desire for free information and perceived risks are believed to be
controlled under privacy protection policies. Platform users need privacy protection
policies, but platform owners and creators define the rules of game—play or not.
Policymakers want people to believe the trade-offs of life or an exchange of privacy,
but the judgment on the risk and uncertainty of information disclosure is driven by
the conflicts of belief in one’s privacy choices. Yet, we echo the view that contexts
matter in privacy preferences and behaviors (Acquisti et al. 2015). We proceed one
more step that privacy choices are determined by the boundaries of the risk–benefit
paradigms, i.e., the conflicts of privacy beliefs can be used to examine the stability
and instability of privacy preferences and behaviors.
Previous studies focused on guiding firms to provide technological solutions and
develop information privacy policies (Bélanger and Crossler 2011), teaching people
13
to protect personal privacy (Chen and Rea 2004), or paying for privacy protection
(Rust et al. 2002). From the risk–benefit paradigms, one rule is derived that benefit
beliefs of information support rather than emotion support will win the war on per-
sonalization. In contrast to information support, the spreading of emotion support
mostly relied on intimate relationships that develop on trusting beliefs. Probably,
the high correlation between information support and emotion support reveals that
Facebook users might take the latter as a metaphor of social support, or consider it
as the connection with “advice” or “information”, but not the “bonding” connection.
Hence, Facebook users are more likely to perceive more benefits of information sup-
port than the benefits of emotion support. Facebook is easily to foster information
support via online connection strategies (Ellison et al. 2010). But online connections
indicate “perceived publicness” (Bateman et al. 2011), which is not a core element
of emotion support for Facebook users that need privacy protection. The effect of
emotion support may probably depend on social contexts or specific grouping. The
first implication is about the transparency and control of information sharing, which
are better than the spreading of emotion support to meet the preferences for privacy
choices (Acquisti et al. 2015).
In the context of cooperative work, “bonding” connection is considered as a pre-
requisite for converting “bridging” influence of social capital to benefit both indi-
vidual and collective goals (Newell et al. 2004). However, it is not true in our study
of privacy disclosure because Facebook users are motivated by the needs of the indi-
vidual. A trade-off between benefits and risks for judging Facebook users’ privacy
disclosure is unlikely to be balanced underlying the conflicts of interest in rational
calculus. However, the balance between personalization and privacy is crucial in
digital marketing, which would render a trade-off between convenience and control
for people in the coming era of artificial intelligence. Privacy protection and data
collection are believed to be balanced on the proposition that less privacy concerns
would encourage more disclosure of personal information (Liu et al. 2021). How-
ever, our study does not support the causal link. It is helpful for policymakers to
focus on the boundary of personal information disclosure instead of the restriction
of information disclosure in the design of privacy policy. The second implication is
that the connection strategies drawn upon information support and emotion support
should be aligned with external and internal ties of social networks, respectively.
Compared with a few (Wang et al. 2016), previous studies of privacy choices
widely examined the enabling and hindering forces of personal information disclo-
sure on Facebook were widely conducted in the United States and/or the sampling
survey of college students (Dinev and Hart 2006; Jiang et al. 2013a, b; Jiang et al.
2013a, b; Keith et al. 2013; Kordzadeh and Warren 2017; Li et al. 2011; Ozdemir
et al. 2017; Xu et al. 2011a, b; Xu et al. 2011a, b). Respond to the suggestion called
for sample diversity (Bélanger and Crossler 2011), the survey of general sample of
Facebook (Taiwan) exhibits different cultures from the United States. In the con-
text of Facebook, privacy risks tend to reinforce user resistance and reduce the will-
ingness toward self-disclosure. The strong effect (0.373) of privacy risks on user
resistance might verify the force-reaction patterns underlying the moral norms and
legal rights of privacy. The third implication is the risk–benefit paradigms that built
on privacy calculus and social support are better than the trade-offs that built on
13
rational calculus to examine the psychological-behavioral tension in terms of user

resistance and self-disclosure. For policymakers, the connection between personal
benefits and social benefits may look as the boundary between “good” and “bad”
in a society (Leclercq-Vandelannoitte and Aroles 2020). People are often asked to
judge what’s the good or bad, as well as for whom to get it. From the study of user
resistance toward information disclosure, policymakers should learn that self-disclo-
sure willingness may not guarantee actual disclosure.
5.3 Concluding remarks and limitations
We surveyed experienced Facebook users to answer the two questions about pri-
vacy paradox and privacy choices, which were previously examined using distinct
theoretical frameworks, but exhibiting the conflicts of view (Table 1). Respond to
the first question, we have found that privacy concerns did not significantly mitigate
Facebook users’ willingness to disclose private information, exhibiting the phenom-
enon of privacy paradox that users express their privacy concerns (or psychological
reaction) but still intend to disclose. The phenomenon of privacy paradox can also
be explained by the reasoning that privacy concerns and self-disclosure willingness
are motivated by distinct factors. Our risk–benefit paradigms that built on a relative
weighing approach to the comparison between anticipated risks and anticipated ben-
efits in which the former is more likely to foster user resistance toward information
disclosure, while the latter is better to foster self-disclosure. Hence, privacy is often
exposed when the uncertainty of benefits is believed to be reduced.
The approach from the trade-offs between perceived risks and perceived benefits
is simplistic, without examining why causes the instability of privacy choices, such
as the co-existence between self-disclosure and user resistance toward information
disclosure. To examine the instability of privacy choices, we develop the risk–ben-
efit paradigms where risk and benefit beliefs are initially judged on imbalanced
trade-offs. The risk–benefit paradigms are theoretically conducted on the embedded-
ness of social support (e.g., information support and emotion support) to develop
the underlying mechanisms of privacy choices. Respond to the second question,
the underlying mechanisms of privacy choices that embed “bridging” social sup-
port in privacy calculus can be conducted upon a rebalanced trade-off by stabiliz-
ing the perceived benefits of self-disclosure. In contrast, privacy risks would cause
the instability of privacy choices. The risk–benefit paradigms help to address how
the resistance and willingness toward information disclosure are interlocked to judge
people’s privacy choices.
This study is subjected to few limitations that should be examined in future
research. First, user willingness (or intention) to disclose private information is not a
good measure of actual self-disclosure behavior (Keith et al. 2013). The average user
willingness toward self-disclosure ( u = 2.26) is far below the midpoint of ‘neutral’ in
the questionnaire. The descriptive statistics indicate that the average perceived ben-
efits ( u1 = 2.91) did not exceed the average privacy risks ( u2 = 3.92), which provide
a good explanation of lower average self-disclosure. Second, the measurement of
social support in terms of information support and emotion support is reasonable,
13
but might not meet the multi-dimensional framework of online social interactions
and thus should be extended for the future. Third, partial disclosure and full dis-
closure on SNSs should be examined in future study. Fourth, cultural difference
might shape people’s risk beliefs of privacy disclosure, suggesting a direction for
the future. Fifth, the extension of this study to other digital platforms (e.g., twitter
or WeChat) should consider contextual disparity between work and entertainment.
Sixth, slightly lower amount of variance explained in the model of self-disclosure
and user resistance should be improved. Last but not the least, our sample may not
represent the population of Facebook users in different nations. The limitation of our
sampling should be considered in explaining the empirical results.
Appendix: The measurement items of research constructs
Information support (IS)
IS1 On Facebook, some people would offer suggestions when I needed help.
IS2 When I encountered a problem, some people on Facebook would give me
information to help me overcome the problem.
IS3 When faced with difficulties, some people on Facebook would help me with
suggestions.
Emotion support (ES)
ES1 When faced with difficulties, some people on Facebook are on my side with
me.
ES2 When faced with difficulties, some people on Facebook would encourage
me.
ES3 When faced with difficulties, some people on Facebook listened to me talk
about my feelings.
ES4 When faced with difficulties, some people on Facebook expressed interests
and concerns in my well-being.
Perceived benefits (PB)
PB1 Self-disclosure on Facebook is beneficial to sustain intimate relationship.

(dropped)
PB2 Self-disclosure on Facebook is beneficial to build my social identity within
a group.
PB3 Self-disclosure on Facebook is beneficial to get social support from others.
Privacy risks (PR)
PR1 Disclose private information on Facebook is risky for me.
13
PR2 Disclose private information on Facebook would bring with the potential of
losses.
PR3 Disclose private information on Facebook would produce unexpected out-
comes.
Privacy concerns (PC)
PC1 Disclose private information on Facebook is a concern for me. (dropped)

PC2 Disclose private information on Facebook looks like a lose of control for
me.
PC3 Disclose private information on Facebook looks like a threat to me.
Resistance toward information disclosure (R)
Regarding the disclosure of private information on Facebook, my reaction to the

disclosure is …
R1 Very unlikely … Very Likely (opposition)

R2 Strongly unwilling to … Strongly willing to (obstruct)
R3 Very impossible … Very possible (resistance)
Willingness toward self‑disclosure (SD)
Regarding the use of Facebook, …
SD1 I am willing to disclose my mobile phone number on my profile.

SD2 I am willing to reveal very detailed thoughts and experiences in my profile.
(dropped)
SD3 I am willing to post my email information on my profile.
Acknowledgements The authors thank the two anonymous referees for their valuable comments and sug-
gestions on earlier versions of this paper.
Funding This research received partial financial support from Social Science Foundation of Fujian Prov-
ince, China, under Project No. FJ2021B166. The authors do not have any personal relationships with
other people or organizations that could inappropriately influence this research.
Declarations
Conflict of interest The authors declare that they have no known conflicts of interest or personal relation-
ships that could influence the work reported in this manuscript.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
13
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is
not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission
directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licen
ses/by/4.0/.
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Beyond The Trade Offs On Facebook: The Underlying Mechanisms of Privacy Choices

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Information Systems and e-Business Management (2023) 21:353–387

Beyond the trade‑offs on Facebook: the underlying

Hung‑Pin Shih1 · Wuqiang Liu1

Received: 7 September 2022 / Revised: 28 November 2022 / Accepted: 5 January 2023 /

Keywords Facebook · Privacy paradox · Privacy calculus · Social support ·

transaction convenience or social connection. The disclosure of personal informa-

choices as the taken-for-granted phenomenon to rethink the theory of privacy cal-

Question-1: What’s the alternative explanation of privacy paradox and why it

2 Competing views of information privacy

2.1 Social interactions on Facebook

The evolution of online connections, stemming from SNSs to multi-sided platforms

2.2 Instability of privacy choices

of privacy paradox reveals the presence of cognitive dissonance in privacy choices,

Theory Assumption Trade-off/context Privacy choice References

Theory Assumption Trade-off/context Privacy choice References

a rebalanced trade-off is also assumed to lie in the risk–benefit paradigms. In this

2.3 A trade‑off between risks and benefits

2.4 Embeddedness of social support

Social capital refers to public resources in a community, such as information, social

3 Research model and hypothesis development

We address self-disclosure willingness rather than actual disclosure because the

Fig. 1 Research model of self-disclosure

embedded in online social interactions, i.e., the structuration process of technology

antecedents of user resistance toward information disclosure. User resistance was

3.1 Information and emotion support

H1 Information support of Facebook use would positively affect users’ perceived

H3 Perceived benefits of disclosure would positively affect Facebook users’ will-

H4 Privacy risks would negatively affect Facebook users’ perceived benefits of

H5 Privacy risks would positively affect Facebook users’ privacy concerns of

H6 Privacy risks would negatively affect Facebook user’ willingness to disclose

H7 Privacy concerns would negatively affect Facebook users’ willingness to dis-

H9 Privacy concerns would positively affect Facebook users’ resistance toward

Consumers may resist to using an innovation because of functional and psychologi-

mirror opposite of IT acceptance, but a possible antecedent of IT acceptance” (p.

H10 Facebook users’ resistance toward information disclosure would negatively

4.2 The sample survey

According to NapoleonCat website (www.​napol​eoncat.​com), Facebook users in Tai-

Table 2 Profiles of the Characteristics Count (%)

4.3 Reliability, validity, and goodness‑of‑fit

Table 3 Factor analysis—factor loadings and cross-loadings of measurement items

ES1 0.859 0.044 − 0.034 0.223 0.020 0.063 0.015

4.4 Common method variance

To examine common method variance (CMV), we adopt a post-hoc approach to

Table 4 Tests of reliability, validity and goodness-of-fit

IS 3.58 0.70 0.94 0.96 0.822

correlation between a marker variable, i.e., a theoretically unrelated variable (e.g.,

Fig. 2 Empirical results of Facebook users

5 Discussion and conclusions

5.1 Implications for theory

The occurrence of privacy risks is inevitable on Facebook, but the anticipated

5.2 Implications for practice

rational calculus to examine the psychological-behavioral tension in terms of user

5.3 Concluding remarks and limitations

Appendix: The measurement items of research constructs

Information support (IS)

Emotion support (ES)

Perceived benefits (PB)

PB1 Self-disclosure on Facebook is beneficial to sustain intimate relationship.

2 Competing views of information privacy

2.1 Social interactions on Facebook

2.2 Instability of privacy choices

2.3 A trade‑off between risks and benefits

2.4 Embeddedness of social support

3 Research model and hypothesis development

3.1 Information and emotion support

4.2 The sample survey

According to NapoleonCat website (www.napoleoncat.com), Facebook users in Tai-

4.3 Reliability, validity, and goodness‑of‑fit

4.4 Common method variance

5 Discussion and conclusions

5.1 Implications for theory

5.2 Implications for practice

5.3 Concluding remarks and limitations