Nudges to privacy behaviour : exploring alternative approaches to to privacy notices.
Date: 2015-09-07
Institutions: Institute for Prospective Technological Studies
Subjects: data processing, data protection, digital technology, personal data, protection of privacy, research report

Nudges to Privacy Behaviour:  
Exploring  an  Alternative  Approach  to 
Privacy Notices 

Shara Monteleone, René van Bavel, 

Nuria Rodríguez-Priego, Gabriele Esposito 

2 0 1 5  

Report EUR 27384 EN 

 
 
 
European Commission 
Joint Research Centre 
Institute for Prospective Technological Studies  

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This publication is a Science and Policy Report by the Joint Research Centre, the European Commission’s in-house science 
service. It aims to provide evidence-based scientific support to the European policy-making process. The scientific output 
expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person 
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All images © European Union 2015 

JRC96695 

EUR 27384 EN 

ISBN 978-92-79-50320-7 (PDF) 

ISSN 1831-9424 (online) 

doi:10.2791/142795 

Luxembourg: Publications Office of the European Union, 2015 

© European Union, 2015 

Reproduction is authorised provided the source is acknowledged. 

Abstract 

The report seeks to bring behavioural research methods for privacy to the attention of EU policy-makers. It argues that 
changes in web interface design can be a useful policy alternative to the traditional 'privacy notice' approach. Specifically, 
it examines whether  web interface design has effect on people's online privacy behaviour through an online experiment 
(n=3229)  in  four  European  countries.  Results  show that the  presence  of  an anthropomorphic  character  leads  to  greater 
disclosure of personal information, both directly and passively and the presence of a privacy notice leads to greater direct 
information disclosure. Additional psychological constructs (such as subjects' awareness that they were revealing personal 
information) were also recorded, and a demographic analysis according to gender, age, education and country of residence 
carried out.   

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Acknowledgments 

The authors are indebted to Alessandro Acquisti for advice and for leading the project Behavioural 
Response to Privacy Visceral Notices (contract no. 153752-2013-A08-IT), which collected the data; 
to Norberto Andrade for launching this project and guiding it through its early stages; to Ryan Calo 
for generous advice; to Aaron Martin for a thorough and constructive review; to Nestor Duch-Brown 
for comments on the statistical analysis; and to Ioannis Maghiros for continued support.  

1 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Acknowledgments ................................................................................................................................................................................. 1 

Table of Contents 

Executive Summary .............................................................................................................................................................................. 3 

I.  

Introduction .................................................................................................................................................................................... 5 

II.   Background..................................................................................................................................................................................... 5 

III.   Research design........................................................................................................................................................................... 9 

IV.   Results............................................................................................................................................................................................ 12 

V.   Discussion and conclusion .................................................................................................................................................. 28 

Annex 1: Screenshots of the experimental conditions .................................................................................................. 31 

Annex 2: Questionnaire ................................................................................................................................................................... 34 

Annex 3: Sample characteristics and socio-demographics ........................................................................................ 37 

References .............................................................................................................................................................................................. 40 

2 

 
 
 
 
 
 
 
 
 
 
 
Executive Summary  

This report is a contribution to the discussion on how best to ensure citizens’ on-line privacy while 
giving  them  the  freedom  to  benefit  as  much  as  possible  from  the  Internet.  It  explores  whether 
changes  in  the  design  of web  interfaces  (i.e. the  choice  architecture  according  to  the behavioural 
economics literature) lead to changes in privacy behaviour, and so merit attention as an additional 
policy  tool.  It  builds  on  two  premises:  (a)  the  predominant  model  of  informing  users  through 
'privacy notices' is ineffective, as people seldom read them and (b)  nudges, which are changes in 
the  choice  architecture  to  elicit  a  certain  behaviour,  have  been  shown  to  be  effective  in  other 
domains.  

An  on-line  experiment  (n=3,229)  across  four  European  countries  examined  the  effect  on  privacy 
behaviour  of  seven  nudges.  These  appeared  as changes  in the  design  of  a mock  search  engine's 
user  interface  (e.g.  including  an  anthropomorphic  character,  highlighting  prior browsing  history  or 
changing  the  look-and-feel  to  convey  greater  informality).  These  nudges  were  tested  and 
considered relevant in previous studies (particularly Groom and Calo, 2011). Two types of privacy 
behaviour  were  measured:  passive  disclosure,  when  people  unwittingly  disclose  personal 
information,  and  direct  disclosure,  when  people  make  an  active  choice  to  reveal  personal 
information. 

In  addition  to  directly  observing  privacy  behaviour,  the  on-line  experiment  also  included  a 
questionnaire  which  sought  to  capture  a  series  of  psychological  constructs,  such  as  participants' 
perception that the experiment was trying to get them to reveal personal information inadvertently 
or  their  feelings  of  being  observed  /  monitored.  It  also  tested  whether  participants  noticed  the 
privacy policy link. 

Selected results 

  Anthropomorphic  images  increase  subjects’  predisposition  to  disclose  personal  information, 
either wittingly or unwittingly. This could be due to users 'letting their guard down' following an 
increase  in  trust  due  to  the  presence  of  an  anthropomorphic  character  (Bente,  Dratsch, 
Rehbach, Reyl and Lushaj, 2014).  

  Actively  disclosing  personal  information  appears  to  be  a  strong  cultural  trait,  but  revealing  it 
inadvertently  less  so.  For  direct  disclosure  of  personal  information,  there  were  significant 
differences  between  countries;  but  for  passive  disclosure  only  Italy  stood  out  from  the  rest 
(participants there revealed the most personal information inadvertently). 

  Subjects  with  a  higher  level  of  education  felt  significantly  less  observed  or  monitored  than 
those  with  a  lower  level  of  education,  challenging  the  assumption  that  education  generates 
greater  awareness  of  privacy  risks.  However,  better-educated  participants  did  reveal  less 
personal information inadvertently than less educated ones (no difference in direct disclosure 
of information). 

  Approximately  73%  of  women  answered  'never'  to  the  stigmatized  questions,  compared  to 
27%  of  males.  This  large  difference  could  be  due  to  the  nature  of  the  questions  (e.g.  about 
alcohol consumption, which might be more acceptable for males). It could also suggest women 
feel  under  greater  social  scrutiny  or  simply  are  more  cautious  when  disclosing  personal 
information. 

By  showing  the  effect  of  nudges  and  demographic  variables  on  privacy  behaviour,  this  study 
highlights  the  value  of  a  behavioural  economics  approach  to  data  protection  regulation.  Further 
tests, either in laboratory or on-line experiments, or directly in the field (for example, when rolling 
out  a  new  government  website),  should  seek  to  confirm  the  effect  of  these  changes  and  test 
additional ones.  

3 

 
The implications for policy are that, while nudges are unlikely to solve all the challenges which on-
line  privacy  regulation  faces,  they  do  contribute  to  a  solution.  Good,  conscientious  and  evidence-
based  website  design  can  lead  to  more  aware  and  cautious  disclosure  of  personal  data.  Privacy 
enforcement  authorities  –  at  national  or  EU  level  –  can  work  together  with  major  web  service 
providers  (such  as  Google  or  Facebook),  who  have  vast  amounts  of  such  data  at  their  disposal, 
towards developing a series of 'safe practices in web design'. It is an opportunity to work together 
to  achieve  innovative  and  mutually-beneficial  solutions  to  privacy  challenges  in  the  online 
environment.  

4 

 
 
I.  

Introduction  

The  context  in  which  any  decision  is  taken  is  referred  to  as  the  choice  architecture  in  the  recent 
nomenclature of behavioural economics. A change in this choice architecture which is intended to 
encourage  certain  behaviour  is  considered  a  nudge,  and  is  distinct  from  a  direct  instruction  or 
demand  (Thaler  and  Sunstein,  2008).  Nudges  have  been  shown  to  influence  behaviour  across  a 
range of policy areas, including on-line privacy behaviour (Acquisti, 2009; Acquisti, Brandimarte and 
Loewenstein, 2015).  

Existing legal safeguards (such as privacy notices that inform users of how their personal data can 
be  used)  are  supposed  to  foster  a  privacy-protective  behaviour  among  Internet  users.  However, 
while  they  fulfil  legal  requirements,  they  have  been  relatively  ineffective  in  generating  more 
cautious approaches to personal data disclosure. A behavioural approach based on changes to the 
choice architecture cannot and should not replace them. It can, however help them to get citizens to 
make choices that are in their best interest.  

This  study  explored  alternative  ways  of  alerting  users  to  the  fact  that  their  behaviour  on-line 
revealed personal information about themselves. It measured their level of disclosure of personal 
information, as well as their replies to a questionnaire following exposure to different nudges.  

Two types of personal information disclosure were considered: passive, when the user inadvertently 
reveals personal information (by simply browsing the Internet carelessly, for example), and direct, 
when the user purposefully reveals personal information (Groom & Calo, 2011). The distinction has 
policy  implications.  In  passive  disclosure,  users  are  not  aware  they  are  disclosing  personal 
information,  and  therefore  do  not  take  steps  to  regulate  their  information  disclosure.  Disclosure 
occurs inadvertently, out of users' awareness and control. In this case, privacy notices have little or 
no effect at all. Instead, this may be the right domain for a behavioural economics approach, since 
behaviour may simply be automatic and not the result of a thoughtful process. 

The  report  first  provides  a  literature  review  on  current  privacy  policies  and  informed  consent 
requirements as legal tools, and discusses some literature on behavioural science applied to public 
policy. It then presents the results of the experiment which tested the impact of different nudges 
on privacy behaviour. It also examines the possible influence of demographic variables such as age, 
gender,  education  and  country  of  residence,  and  includes  an  analysis  of  self-reported  measures 
such as perceptions of disclosure and feelings of being observed.  

II.   Background 

In  parallel  to  its  key  enabling  role  for  economic  growth  and  productivity,  digital  technology  has 
spawned a new era in the disclosure of citizens' personal data. It represents a potential threat to 
privacy  and  data  protection  of  the  citizen,  but  also  offers  opportunities  for  strengthening  them1. 
Reinforcing trust in the online environment is essential for the realization of the Internet's potential 
as an engine for European economic growth and innovation2. European Commission President Jean-
Claude  Juncker  has  stressed  the  need  to  'make  Europe  more  trusted  and  secure  online,  so  that 
citizens and businesses can fully reap the benefits of the digital economy'.3 This objective is also 

1   See 

Charter 

of 

Fundamental 

rights 

of 

the 

European 

Union, 

Art 

7 

and 

8, 

http://www.europarl.europa.eu/charter/pdf/text_en.pdf.  

2   European Commission, 2012 
3   http://ec.europa.eu/about/juncker-commission/docs/oettinger_en.pdf 

5 

 
 
                                                 
recognised  by  the  Digital  Agenda  for  Europe  (DAE)4,  the  European  Union  flagship  initiative  on  all 
ICT-related activities. 

One way to reduce privacy concerns and increase trust is to provide users with good privacy policies 
which  increase  their  awareness  and  reassure  them  about  the  risks  involved  (Wu,  Huang,  Yen  & 
Popova, 2012). This should be done with caution, however, as offering greater privacy reassurances 
to  individuals  may  lead  to  increased  reluctance  to  reveal  personal  information  by  priming  the 
individuals  about  the  sensitivity  of  their  data  (Acquisti,  2010b).  The  role  of  privacy  policies, 
therefore, should be to enable a cautious willingness to disclose personal data while at the same 
time safeguarding privacy and personal data protection (Europoean Commission, 2012). 

The European Commission is addressing these challenges via the reform of the legal framework on 
privacy  and  data  protection  in  the  EU5.  Directive  95/46/EC  will  be  replaced  by  the  General  Data 
Protection  Regulation,  henceforth  Draft  Regulation,  which  aims  to  build  a  stronger  and  more 
coherent data protection framework in the EU (European Commission, 2012). 

Privacy notices 

Despite establishing these information obligations and consent requirements, the Draft Regulation 
contains few indications on how information should be provided to users or how they could exercise 
their  right  to  object  to  the  processing  of  their  data.  This  means  that,  as  far  as  the  information 
provision obligations are satisfied, i.e. the minimum of information is provided, the controller is free 
to choose how to provide this information.  

The  common  instruments  usually  adopted  by  data  controllers  to  be  compliant  with  the  law  are 
privacy  notices.  These  are  long,  detailed  and  highly  complex  statements  on  how  data  controllers 
will use their personal data. These notices also provide information about the data subjects' rights 
and  the  security  measures  adopted  for  the  safe  treatment  of  their  personal  data.  It  is  assumed 
that users read these texts, understand them and give their informed consent. 

Individuals are given control of their personal data and expected to weigh the costs and benefits of 
the  disclosure  of  their  data  themselves.  This  is  an  example  of  a  self-management  approach  to 
privacy issues, whereby users are provided with information and expected to act according to their 
best interests (Solove, 2013).  

These  privacy  notices  have  been  gradually  introduced,  either  through  mandatory  regulation  (the 
case in the EU) or as self-regulation practices by businesses in response to privacy concerns (the 
case in the US). However, there are a number of problems with this approach. 

Nobody reads privacy notices 

Studies conducted both in Europe (Lusoli, Bacigalupo, Lupiáñez-Villanueva, de Andrade, Monteleone 
&  Maghiros,  2012)  and  outside  Europe (Tsai,  Cranor,  Acquisti &  Fong,  2006; McDonald &  Cranor, 
2008)  have  shown  that  these  notices  are  not  effective.  They  are  hard  to  read  and  read 
infrequently,  least  of  all  by  young  people  (McDonald  &  Cranor,  2008;  Madden,  Lenhart,  Cortesi, 
Gasser,  Duggan,  Smith  &  Beaton,  2013).  Generally,  users  will  scroll  down  the  privacy  notice  and 
rush for the tick box, or simply tick the box without even looking at the notice (when this option is 
available).  This  habit  does  not  allow  them  to  give  their  meaningful,  informed  consent,  and  limits 
their ability to make 'rational' decisions. 

4   See  EC  Digital  Agenda  for  Europe,  available  at  http://ec.europa.eu/information_society/digital-

agenda/index_en.htm. 

5   The legal framework currently applicable in the field of privacy and data protection is represented mainly 
by Directive 95/46/EC on the protection of individuals with regard to the processing of personal data and 
on the free movement of such data, integrated by the Directive 2002/58/EC concerning the processing of 
personal  data and the protection of privacy in the electronic communications sector (so called e-Privacy 
Directive, as modified by the Directive 2009/136/EC, the e-cookies Directive). 

6 

 
                                                 
Information asymmetry 

There  is  insufficient  information  for  users  to  make  a  considered  decision  about  data  disclosure 
(Acquisti,  2010b).  This  is  also  referred  to  as  'information  asymmetry'  between  users  (they  are 
unaware  or  they  do  not  have  enough  information  on  what  happens  with  their  data)  and  data 
controllers (companies or government entities that collect  and process users' data). Even if users 
received  appropriate  and  clear  information  and  knew  how  their  data  would  be  treated,  they  still 
would ignore the consequences of future data use (Borgesius, 2013). This knowledge asymmetry is 
exacerbated by the rise of big data.  

Transaction costs 

Transaction  costs,  namely  the  time  needed  for  users  to  read  and  interpret  privacy  notices  where 
complete  information  is  provided,  make  information  asymmetry  even  more  difficult  to  overcome 
(Acquisti & Grossklags, 2007; McDonald & Cranor, 2008; Borgesius, 2013). 

In addition, users face increasing uncertainty in online environments due to  the new technological 
capabilities of tracking systems, which can be used in different ways by different actors to gather 
information6.  In  order  to  capture  these  changes,  privacy  policies  change  frequently  though  not 
always transparently, making the task of keeping abreast with the most recent version even more 
difficult for users (Martin, 2013). Transaction costs, therefore, increase. 

Even  well-informed  and  rational  individuals  sometimes  cannot  effectively  self-manage  their 
privacy  due  to  several  structural  problems:  (a)  there  are  too  many  entities  collecting  and  using 
personal  data  to  make  self-management  based  on  the  consent  model  feasible  and  (b)  often 
privacy  breaches  are  the  result  of  an  aggregation  of  pieces  of  data by different  entities  (Solove, 
2013).  

Privacy paradox 

Internet  users  usually  claim  they  are  worried  about  online  privacy  risks  and  are  aware  of  their 
privacy  rights.  Many  are  concerned  that  their  personal  data  held  by  companies  may  be  used  for 
purposes other than those for which it was collected (Lusoli et al., 2012). However, most users do 
not act accordingly. They do not read the privacy policies entirely or they find it difficult to obtain 
information  about  a  data  controller's  practices  (Tsai,  Cranor,  Acquisti  &  Fong,  2006;  Hoofnagle, 
King,  Li  &  Turrow,  2010;  Madden  et  al.,  2013).  Therefore,  providing  good,  clear  and  accurate 
information about the risks of disclosing private information is not enough to change behaviour. 

Favours the commercial use of data  

Criticisms of the self-regulation model of privacy policies, in particular in the U.S., point to the fact 
that this model has allowed a sectoral and weak approach to privacy (Solove & Hoofnagle, 2006) 
which favours the commercial use of personal data. As a result, privacy policies without substantial 
safeguards  have  proliferated.  Individual  protection  has  become  an  illusion  rather  than  a  reality: 
users may believe they have more privacy simply because a website has a privacy policy (Haynes, 
2007). 

In  sum,  current  privacy  policies  which  'take  refuge  in  consent'  do  not  provide  people  with 
meaningful control over their data (Solove, 2013). Consent in these circumstances is insufficiently 
informed and, therefore, generally not meaningful  (Borgesius, 2013). Current policies fall short of 
achieving  the  objectives  of  law,  namely  to  ensure  that  people  make  considered  decisions  about 
their privacy, and, ultimately, to increase trust in on-line services. 

6   See  Ashkan  Soltani’s  work  for an  excellent  overview  of  different  methods  that  are  used  to  track  users: 

http://ashkansoltani.org/work/online-tracking. 

7 

 
 
 
                                                 
Alternative mechanisms to traditional privacy policies  

As  users  very  often  do  not  read  privacy  policies,  other  strategies  might  be  more  successful  in 
encouraging  privacy-protective  behaviour. 
Instead  of  only  using  written  privacy  policies, 
organisations could embrace alternative ways and instruments  – more visual, explicit, simple and 
user-friendly – to inform Internet users so that they give better-informed consent, if they give it. 
- 

Transparency enhancing tools (TETs) would allow citizens to anticipate how they will be profiled 
and  what  the  consequences  of  this  would  be  (Hildebrandt,  2012).  Tools  such  as  Ghostery, 
Privacy Badger and other browser extensions make online tracking more transparent and give 
users the technological means to block trackers. However, not everyone knows about TETs, and 
not  everyone  can  use  them  or  is  interested  in  doing  so.  They  do  require  people's  conscious 
attention and they do require a certain amount of cognitive effort. This takes us back to some 
of the problems encountered with privacy notices. 

-  One  alternative  is  to  provide  simplified,  standardized  privacy  information.  These  are  notices 
which convey a simple message in a standardized format, such as the cookie alerts required by 
the  EU.  This  approach  has  some  benefits.  For  one,  the  messages  are  shorter  and  easier  to 
understand.  However,  they  can  also  prove  to  be  insufficient,  as  users  may  end  up  ignoring 
these alerts and simply accepting all the requests for consent with a click of the mouse button, 
as a matter of habit (Groom & Calo, 2011). 
Finally, a privacy by design approach (PbD) (Cavoukian, 2012) advocates good technical design 
which embeds privacy into IT systems and business practices from the outset (and doesn’t just 
add privacy measures ex-post). It proposes seven 'foundational principles' to offer the highest 
degree  of  privacy  to  individuals.  These  include,  for  example,  ensuring  that  personal  data  are 
automatically  protected  by  default,  being  preventative  and  not  remedial,  and  always  keeping 
the interests of the end user in mind (i.e. remaining user-centric). 

- 

Nudging privacy behaviour 

Empirical findings about human behaviour are increasingly being taken into consideration by policy-
makers  worldwide  and  incorporated  into  policy  initiatives  across  different  policy  areas  (Sunstein, 
2000; Shafir, 2013; van Bavel, Herrmann, Esposito and Proestakis, 2013; Lunn, 2014; World Bank, 
2015).  These  findings  are  commonly  applied  to  improving  the  background  against  which  people 
make their  decisions  (the  choice  architecture;  Thaler  &  Sunstein,  2008).  Policy-makers effectively 
become  choice  architects,  making  appropriate  and  small  changes  in  the  underlying  environment 
that  may  have  a  large  impact  on  people’s  behaviour.  In  an  on-line  environment,  the  choice 
architecture includes features such as website design, warnings, and defaults (Sunstein, 2013).  

An important behavioural insight is that the more our activities are routinized, repeated on a daily 
basis, the more people think fast (Kahneman, 2011). This is particularly interesting for daily digital 
activities, involving e-mail or Internet browsing, for example. Such activities are often repetitive and 
systematic gestures. This is also true of on-line terms and conditions, or privacy notices, which we 
very often accept without reading. Changing them requires the appropriate tools which tap into this 
automatic behaviour, not tools that require effort and deliberation by the user. 

This  study  aims  to  identify  and  test  privacy  nudges  (Acquisti,  2009;  Acquisti,  2010a;  Acquisti, 
Brandimarte & Loewenstein, 2015;  John, Acquisti & Loewenstein, 2009;  Wang, Leon, Scott, Chen, 
Acquisti  &  Cranor,  2013),  similar  to  visceral  notices  (Calo,  2012;  Groom  &  Calo,  2011),  as 
alternative  and  complementary  measures  for  personal  data  protection7.  Privacy  nudges  are  not 
meant to replace the notice and choice system  per se, but rather to improve it and provide more 
suitable, flexible and effective privacy-protective mechanisms.  

7   See  Calo  (2014)  for  a  detailed  discussion  of  the  difference  between  a  code,  a  nudge  and  a  notice  in 

privacy behaviour. 

8 

 
                                                 
Unlike  traditional  privacy  notices  that  rely  on  text  or  symbols  to  convey  information,  nudges  or 
visceral notices 'leverage a consumer's very experience of a product or service to warn or inform’ 
(Calo, 2012). Previous experiments on visceral notices (Groom & Calo, 2011) not only demonstrated 
the  weaknesses  of  traditional  explicit  notices,  but  also  that  other  notice  strategies  can  be 
successful at eliciting privacy-protective behaviour. This study builds on these results and follows 
the same empirical tradition. 

III. Research design  

The  study  is  based  on  an  on-line  experiment  inspired  by  Groom  and  Calo's  (2011)  research,  but 
with a much larger sample (3,229 participants) and in more countries (Germany,  Italy, the UK and 
Poland). This selection allowed us to get results from the north, south, east and centre of the EU.  

Data collection 

The data were collected between March and June 2014 by Harris Interactive under the guidance of 
University Tor Vergata. Harris first prepared a sample plan for recruiting a representative group of 
participants  from  four  European  countries.  Then,  based  on  the  sample  plan,  they  set  quotas  to 
balance demographic variables and performed real-time quota management during the run of the 
study. All participants were randomly assigned to one of the seven experimental conditions or the 
control group. In order to participate in the survey, participants had to: 
  Be at least 18 years old   
  Be connected to the Internet from the appropriate country, among the four countries chosen for 

the study 

  Have a reliable Internet connection  

A  pilot  with  263  participants  (assigned  randomly  to  the  various  conditions)  was  run  before  the 
actual experiment, using Amazon MTurk. This allowed for changes and adjustments in the design of 
the experiment.  

Experimental protocol 

Participants were assigned to one of the seven experimental conditions (or the control group) and 
asked  to  use  and  then  evaluate  a  mock  search  engine.  However,  this  was  a  pretext  –  the  real 
purpose of the experiment was to observe their behaviour. The study targeted around 400 subjects 
per experimental condition and around 100 subjects per experimental condition per Member State. 

The  internal  Evaluation  Committee  set  up  at  the  Institute  for  Prospective  Technological  Studies 
approached  this  study  as  an  on-line  split  ballot  questionnaire  and  sought  adherence  to  the 
appropriate  ethical  guidelines  for  conducting  surveys.  Informed  consent  was  obtained  from  all 
participants  in  the  study,  according  to  the  Terms  of  Services  and  Privacy  Policy  of  Harris 
Interactive8.  Participants  were  debriefed  about  the  purpose  of  the  study  at  the  end  of  the 
experiment. 

The  mock  search  engine  was  capable  of  searching  for  the  answers  to  a  set  of  sixteen  pre-
established questions. This mock search engine merely consisted of a website interface; no actual 
search  technology  was  created.  In  other  words,  the  search  engine  website  interface  simply 
connected to an existing search engine (Google).  

The mock search engine had an ad-hoc name ('Re-Search Engine'), a logo, a search box and, below, 
an area displaying search results. The search engine interface was translated into the languages of 

8   These  documents  cover  issues  ranging  from  confidentiality  to  consent  and  voluntary  participation: 

https://join.harrispollonline.com/?sid=068bbad9-0651-46dc-8083-08eecfcf7aed# 

9 

 
 
                                                 
the four countries selected. It was also adapted and modified according to the needs of the seven 
experimental conditions or control group described in the next section. 

The  mock  search  engine  could  direct  participants  to  existing  external  webpages.  However,  it  was 
ensured  that  the  subjects  returned  to  the  mock  search  engine  website  once  they  had  found  the 
answers to the search queries, so that they continued with the experiment. The questions that the 
participants were asked were displayed above the search box. Below the search box, another box 
was provided in which participants could type their responses. 

The  fact  that the  study's  setting  was  somewhat  artificial  might  have  had  an impact  on  absolute 
results. Participants were aware that they were participating in a study, and knew that their privacy 
would in fact always be guaranteed by the Privacy Policy of Harris Interactive, with whom they had 
signed  a  prior  agreement.  This  might  have  led  them  to  disclose  more  personal  information  that 
would have normally been the case. However, results in an experiment will never accurately reflect 
behaviour in the 'real world'. The objective, therefore, should be to observe the comparative impact 
of  different  treatments  on  behaviour,  not  their  absolute  impact.  This  comparison  should  not  be 
subject to bias, since all experimental conditions are subject to the same overall environment.  

Finally,  at  the  end  of  the  experiment  the  software  displayed  separate  pages,  with  the 
questionnaires  on  Internet  use  and  on  the  user  interaction  with  the  search  engine.  The 
questionnaires were also translated into all the languages of the four selected EU Member States. 

The  experiment  lasted  an  average  of  twenty-three  minutes.  Participants  were  asked  to  use  the 
search engine to find the answers to four general knowledge questions. These searches allowed for 
the collection of information on the IP address of participants' computers, the web  browser used 
and web pages that were visited (which would be relevant later on). Participation in the experiment 
could not be discontinued, otherwise it would be considered invalid.  

Experimental conditions 

The eight experimental conditions closely followed the experimental conditions first used by Groom 
and  Calo  (2011).  However,  unlike  Groom  and  Calo,  all  conditions,  except  for  the  control  group, 
included a link to a privacy  notice. This is more in line with the European Data Protection regime 
and with the current practices of existing websites, and allowed us to compare like and like. Had we 
included a privacy notice link in some conditions and not others, we would not have been able to 
assign  causality  to  a  single  variable.  This  would  also  allow  for  testing  users'  willingness  to  read 
privacy  policies,  whether  simplified  or  not,  after  a  treatment.  The  eight  experimental  conditions 
were as follows (see illustrations in Annex 1): 
  Control: The search engine did not include any privacy notice. Otherwise it displayed the same 
appearance  as  the  other  conditions (except  for the informality  condition). Nuances  of  blue  or 
grey were used throughout the webpages to transmit authority and seriousness. 

  Traditional: This experimental condition displayed a clickable privacy policy link at the top of the 
far-right  column.  Clicking  the  link  would  open  a  page  displaying  a  traditional  privacy  notice, 
consisting  of  written  text,  explaining  precisely  what  data  were  going  to  be  collected  by  the 
mock search engine and how these data would be used. 

  Simplified:  This  experimental  condition  displayed  the  same  link  to  a  privacy  notice  as  in  the 
traditional  condition,  but  which  led  to  a  much  simpler  version  of  the  same  notice.  The  same 
information was conveyed in simpler language and with the help of a table. 

  Static agent: An anthropomorphic character displayed from the shoulders up which appeared in 
the right column in line with the search box with a text written below.  Participants were able to 
see a static image of this agent with the words 'what would you like to search for?' beneath it. 
The agent had the appearance of a customer service agent. 

 

Interactive  agent:  Included  the  same  anthropomorphic  character  and  the  same  text  as  in  the 
static agent condition, but with moving head and eyes tracking cursor movements. 

10 

 
 

 

Informality:  The  overall  appearance  of  the  search  engine  website  was  adjusted  to  convey  a 
more informal and youthful look and feel than the others. It had bright yellow background with 
green and blue accents and red text, with softer lines in the logo and title, rounded shapes for 
the buttons and Comic Sans font. The content and structure were the same as the other seven 
conditions. 

IP information: This condition displayed the participants' real IP, location and the browser that 
they  were  using  on  the  right  side  of  the  search  engine  webpage.  The  three  corresponding 
messages were: "Your IP is […]"; "Your current location is […]"; "Your current browser is […]". This 
information was collected for all participants and explained in the privacy policy, though it was 
only visibly displayed to the participants in this condition. 

  History:  This  condition  displayed  the  URL  of  each  external  website  visited  during  the  search 
experience on the right side of the search engine webpage. This information appeared in line 
with the search box. When participants visited a new site, the corresponding URL appeared at 
the top of the list. Click-stream data were collected for all participants and this was clarified in 
the privacy notices, though it was visibly displayed only to the participants in this condition. 

Behavioural output measures 

The experiment measured two types of personal data disclosure by participants: passive and direct. 
  Passive disclosure was measured by the number of 'sensitive' questions answered. Participants 
were shown four sets of three questions in sequence and asked to find the answer to one of 
the questions in each set. This led to a total of four questions being answered. In each of the 
four  sets,  the  answer  to  one  of  the  questions  would  reveal  (albeit  indirectly)  personal 
information, such as information on participants' bank, their home town or their year of birth 
(e.g. 'what year was your bank founded?'). The other questions did not require the user to reveal 
any personal information whatsoever. Participants’ choice of question to answer is therefore a 
measure of their passive disclosure of personal information.  

  Direct  disclosure  was  measured  by  the  number  of  questions  answered  about  engaging  in 
socially stigmatised behaviour9. Participants could answer never, once or twice, sometimes, or 
frequently,  but  –  crucially  –  they  also  had  possibility  to  not  answer  at  all.  In  other  words, 
responding  was  optional.  The  number  of  times  that  the  respondents  decided  to  answer  such 
questions, despite being optional, formed the basis for the measure of direct disclosure10.  

Questionnaire 

Complementary  measures  were  collected  through  the  responses  to  a  number  of  additional 
questions (see Annex 2), structured as follows: 
  Participants' usual internet usage11. 
  Participants' interaction with the search engine site. The possible answers here were structured 
in  a  7-point  Likert  scale  and  ranged  from  strongly  disagree  to  strongly  agree12.  The  purpose 
was to have additional results on perceived difference with a real search engine to see how it 
changed according to experimental condition.  

 

Items related to the search engine, aiming to verify users' level of awareness of online tracking 
practices and of privacy concerns. The possible answers were also structured in a 7-point Likert 
scale from strongly disagree to strongly agree13.  

9    E.g. ‘Have you ever looked at pornographic material?’ 
10   Providing  false  data  is  also  a  common  privacy  strategy,  but  since  responding  was  optional,  it  was 

assumed respondents did not have to resort to that. 

11   E.g. ‘What browser do you typically use?’ 
12   E.g. 'Do you think the search engine you tested is easier to use than the search engine that you typically 

use?'  

13   E.g. 'The search engine website was able to detect several pieces of information about my online activity.' 

11 

 
                                                 
 

Items  aimed  at  measuring  whether  participants  had  noticed  some  elements  during  the 
experiment,  even  in  the  case  where  these  elements  were  not  present  at  the  website14.    The 
purpose was to test whether noticing these elements may affect their predisposition to disclose 
personal information.  

  Socio-demographic data (e.g. age, education level, and current employment situation). 
  Exit questions relating to the goal of the study, the device they used to take the survey, etc.   

IV. Results  

Sample characteristics and socio-demographics are presented in detail in Annex 3. 

Behavioural output measures 

Passive disclosure  

The  experiment  originally included  four  sets  of  three  questions  for  measuring  passive  disclosure. 
However,  in  preliminary  analyses  one  of  these  sets  showed  a  disproportionate  number  of 
participants  choosing  to  answer  the  sensitive  question  compared  to  the  other  sets.  The  question 
was 'what is the street address of a post office in the town where you live?', and 64% of subjects 
chose to answer it. In the other sets, the personal questions were chosen by 24%, 30% and 19% of 
subjects.  This  was  puzzling,  and  might  have  been  due  to  the  fact  that  this  question  required  no 
search, as people are often familiar with the street of their local post office. For this reason, the set 
was omitted from subsequent analyses. 

A probit model was used to test differences in passive disclosure15. Choosing to answer at least one 
sensitive  question  scored  1;  not  choosing  any  sensitive  questions  scored  0.  In  the  model,  the 
dependent variable was passive disclosure, and the independent variables were treatment, country, 
gender, education level and age (see Table 1). 

Experimental treatments  

Participants assigned to the static anthropomorphic condition disclosed more personal information 
than  participants  in  the  rest  of  conditions.  Almost  60%  of  them  chose  to  answer  at  least  one 
personal  question (Figure 2). They were followed by participants in the  dynamic anthropomorphic 
condition (57% chose to answer at least one personal question). 

The probit regression confirms these results: the only experimental treatments that had an effect 
on passive disclosure were static and dynamic anthropomorphic characters. Subjects who visualized 
these  characters  were  more  likely  to  answer  questions  that  revealed  personal  information  than 
subjects in the control group (see Table 2). In the static anthropomorphic condition, this difference 
is significant at a 95% confidence level; in the dynamic anthropomorphic, at a 90% level (Table 2).  

This  result  may  be  explained  by  the  fact  that,  as  demonstrated  elsewhere  in  the  literature, 
anthropomorphic  images  increase  trust  in  on-line  transactions  (e.g.  Bente  et  al.,  2014).  And  with 
this  increased  trust  comes  less  vigilant  behaviour  which  leads  to  inadvertent  disclosures  of 
personal information.  

14   E.g. ‘While you were answering the quiz questions, did you notice an IP address?’ 
15   The  aim  of  the  probit  model  is  to  estimate  the  probability  that  an  observation  with  particular 

characteristics will fall into one of two categories. 

12 

 
 
 
                                                 
Table 1: Probit regression for passive disclosure with Control and Italy as baselines for 
treatment and country 

Treatment 

Country 

Other 

VARIABLES 
Traditional  
Simplified  
Anthropomorphic Dynamic  
Anthropomorphic Static  
Informal  
IP  
History  
Germany 
Poland 
UK 
Gender 
Education level 
Age 

Constant 

Passive Disclosure  
.1241855    
.1404511    
.1616479*    
.2036759 **   
.0327754    
.0758884    
.0562358    
-.3469802***    
-.3338886*** 
-.3709523*** 
.0225792    
-.1060172***    
.084415**    

-.0576963    

*** p<0.01, ** p<0.05, * p<0.1 
Treatment: Control = 1; Country: Italy = 1; Gender: Female = 1; Education level: College attendees = 1 
Age: >42 = 1 

Figure 2: Subjects’ passive disclosure by treatment in percentage 

Country of residence 

Participants in Italy revealed more personal information inadvertently: almost a 65% of them chose 
to  answer  at  least  one  of  the  sensitive  items.  On  the  other  hand,  in  the  other  three  countries 
(Germany,  Poland  and  the  UK)  between  48-49%  of  the  subjects  were  able  to  avoid  revealing 
personal information in this way (Figure 3). Table 3 shows information on how many participants 
chose to answer more than one of the sensitive items. In Italy, more subjects chose all 3 sensitive 
items. 

13 

 
  
 
 
 
 
 
 
 
In the probit model, taking Italy as the baseline country, there is a statistically significant difference 
between participants in this country and those in Germany, Poland and the UK (p < 0.001, Table 2). 
No other significant country differences were found. 

Figure 3: Subjects’ passive disclosure by country in percentage 

Table 2: Passive disclosure by Country: number of sensitive items answered  

0 

390 

282 

387 

404 

1 

293 

304 

302 

314 

1,463 

1,213 

2 

117 

169 

91 

87 

464 

3 

13 

44 

23 

9 

89 

Total 

813 

799 

803 

814 

3,229 

Country 

Germany 

Italy 

Poland 

UK 

Total 

Gender 

No significant differences were found for passive disclosure according to gender (Table 2).  

Education 
For analytical purposes, subjects were merged into two categories: those who had at least attended 
college  and  those  who  never  attended  college.  Participants  who  had  a  higher  level  of  education 
were  less  likely  to  answer  sensitive  items  (p<0.001;  Table  2).  48%  of  the  more  educated  group 
avoided answering sensitive questions, compared to 42% of less educated participants.  

14 

 
 
 
 
 
 
 
Figure 4: Subjects’ passive disclosure by education level in percentage 

Age 

Participants  were  split  into  two  groups  with  the  objective  to  compare  between  younger  vs.  older 
individuals).  The  'younger'  group  included  participants  between  18  and  41  years  old.  The  'older' 
group included subjects who were 42 years old or more. The option “I prefer not  to answer” was 
also registered. 

The results of the probit model show that older participants passively revealed more information 
than younger participants (p < 0.05; Table 2). Almost 56% of them chose to answer at least one 
personal question, compared to 53% of younger participants (Figure 5).  

Figure 5: Subjects’ passive disclosure by age in percentage 

15 

 
 
 
 
 
 
 
 
Direct disclosure 

The results of the impact of treatments on direct disclosure were measured with a Poisson model, 
in  which  the  dependent  variable  (direct  disclosure)  is  a  count.  The  possible  outcomes  were  to 
answer  positively  to  1  to  10  stigmatised  behaviours  or  not  to  answer  any  of  them,  or  answer 
negatively (i.e. 'never') to all of them, scoring 0. In the proposed model, the dependent variable is 
direct  disclosure,  and  the  independent  variables  are  treatments,  country,  gender,  education  level 
and age (see Table 3). 

Table 3: Poisson regression for direct disclosure with Control and Italy as baselines for 
treatment and country  

VARIABLES 
Traditional  
Simplified  
Anthropomorphic Dynamic  
Anthropomorphic Static  
Informal  
IP  
History  

Germany 
Poland 
UK 
Gender 
Education level 
Age 

Treatments 

Country 

Other 

Direct Disclosure  
.0670005*    
.0315333    
.0688383*     
.0639918       
.0201771    
.0206437    
.0555555    
.1450458***    
.3489893*** 
.2063947*** 
-.185638***     
-.0199921    
-.0816732***    

*** p<0.01, ** p<0.05, * p<0.1 
Treatment: Control = 1 
Country: Italy = 1; Gender: Female = 1; Education level: College attendees = 1; Age: >42 = 1 

Treatments  

The  results  reveal  that,  subjects  who  visualized  the  anthropomorphic  character  in  its  dynamic 
condition were more likely to answer stigmatised personal questions compared to the control group 
(p < 0.10; Table 3). This outcome partially corroborates the results obtained for passive disclosure. 
It would seem that anthropomorphic images increase subjects’ predisposition to disclose personal 
information, either wittingly or unwittingly.  

However, the traditional treatment (displaying a clickable privacy policy link) also shows a positive 
coefficient  which  is  significant  at  a  90%  level  of  confidence  (Table  3).  Subjects  exposed  to  a 
privacy link, without an additional nudge, in the search engine revealed more personal information 
than  subjects  who  did  not  see  a privacy link (control  treatment).  Perhaps,  seeing  a  privacy policy 
link  reassured  participants  and  made  them  believe  that  their  answers  would  not  be  shared,  as 
people often think of a privacy policy as some kind of guarantee of privacy (Hoofnagle and King, 
2008). However, this should have also applied, to a certain degree, to others who saw the privacy 
link alongside other nudges.  

16 

 
 
  
 
 
 
 
 
 
Country 

Even though participants in Italy revealed the most personal  information in  passive  disclosure, in 
direct disclosure they revealed less than the other countries. Approximately 75% of participants in 
Italy chose to answer positively to at least one stigmatised question, compared to 81% in Poland, 
83% in Germany and 92% in the UK (Figure 6).  

Figure 6: Subjects’ direct disclosure by country in percentage 

Gender 

Female  participants  directly  disclosed less information  about  personal  and  stigmatised  behaviour 
than  men (p<0.001;  Table  3).  Approximately  73%  of  women  answered  'never'  to  the  stigmatised 
questions (males  27%; Figure 7). Women were also more likely to avoid answering to this set of 
items: 57% compared to a 43% of men (Table 4). 

Figure 7: Subjects’ who answered “Never” to all the direct disclosure items 

17 

 
 
 
 
 
 
 
 
 
Table 4: Direct disclosure by gender: percentage of subjects by type of answer (%) 

Answered Never 

27.46 

72.54 

Answered Affirmatively 
(to at least one question) 
51.87 

48.13 

No Answer 

43.18 

56.82 

Male 

Female 

Education 

No significant differences were found for direct disclosure according to education (Table 3).   

Age 

The  Poisson  analysis  shows  that  the  group  of  participants  aged  42  and  above  revealed  less 
stigmatised information (p<0.001; Table 5) than the younger group. Although a greater number of 
older  participants  answered  positively  to  6  or  fewer  stigmatised  questions,  a  greater  number  of 
younger subjects answered 8 or more stigmatised questions (Figure 8). These results are contrary 
to those of passive disclosure, where older participants revealed more information.  

Figure 8: Subjects’ direct disclosure by age in absolute value 

The  results  confirm  that  passive  and  direct  disclosure  are  two  different  constructs,  intended  to 
capture different ways of revealing personal  information. Indeed,  the correlation between both is 
very low (Table 5).  

Table 5: Correlation between passive and direct disclosure 

Passive disclosure 

Direct disclosure 

Passive disclosure 

Direct disclosure  

1.0000 

1.0000 

0.0253 

18 

 
 
 
 
 
 
 
 
 
 
 
 
Self-reported measures 

In addition to the measures of behaviour outlined above, the experiment included a questionnaire. 
The  questionnaire  items  sought  to  capture (a)  disclosure  perception,  i.e.  subjects'  awareness  that 
the  quiz  questions  could  reveal  personal  information  about  themselves,  (b)  feeling  of  being 
observed / monitored, i.e. whether subjects felt their on-line behaviour was being tracked and their 
degree of comfort in answering questions as a consequence and (c) privacy policy link awareness, 
i.e. whether subjects saw the link to a privacy notice. Since the questionnaire was completed after 
exposure to the experimental treatments, these constructs could have also been affected by them.  

Disclosure perception 

This construct was measured by a single item ('my answers to the quiz questions revealed personal 
information about myself'). Subjects indicated their agreement with the statement on a seven-point 
Likert  scale  ranging  from  'strongly  agree'  to  'strongly  disagree'.  An  ordered  probit  model  tested 
differences  for  this  item  (Table  6)16.  Treatments,  country,  gender,  education  level  and  age  were 
introduced as independent variables in the model (see Table 6).  

Table  6:  Ordered  probit  regression  for  disclosure  perception  with  Control  and  Italy  as 
baselines for treatment and country 

Treatments 

Country 

VARIABLES 
Traditional  
Simplified  
Anthropomorphic Dynamic  
Anthropomorphic Static  
Informal  
IP  
History  

Germany 

Poland 
UK 
Gender 

Disclosure Perception  

.0278351       
-.0052195    
-.0024431    
-.0123343    
.0208297    
-.0796003    
.0652004    

.3857499***    

.3367445*** 
.0541955    
.1104099***     

Other 

Education level 
Age 
*** p<0.01, ** p<0.05, * p<0.1 
Treatment: Control = 1; Country: Italy = 1; Gender: Female = 1; Education level: College attendants = 1; 
Age: >42 = 1 

.0902366*** 
-.0157353    

Treatments  

No significant effect for disclosure perception was found according to experimental treatments. 

Country  

In  Germany,  subjects  perceived  more  information  disclosure  (M  =  5.16,  SD  =  1.79),  followed  by 
subjects in Poland (M = 5.05; SD = 1.91), the UK (M = 4.62; SD = 1.83) and Italy (M = 4.50; SD = 
1.89). Figure 9 compares means for disclosure perception. 

16   Ordered  probit  is  a  generalisation  of  the  probit  analysis  to  cases  with  more  than  two  outcomes  of  an 

ordinal dependent variable. 

19 

 
 
 
 
 
  
 
 
 
                                                 
The  ordered  probit  analysis  shows  that  subjects  in  Italy  were  least  likely  to  believe  they  had 
disclosed information compared to those in Germany and Poland (p < 0.01). However, there was no 
significant difference compared to those in the UK (Table 6).   

When the baseline country for the ordered probit regression was changed from Italy to Germany, 
subjects  from  the  UK  were  less  likely  to  perceive  they  had  disclosed  personal  information 
compared to subjects from Germany (p < 0.01). The same happened when Poland was taken as a 
baseline  country  (Table  7):  subjects  from  the  UK  were  less  likely  to  believe  they  had  disclosed 
personal  information  (p  <  0.01).  No  significant  differences  were  found  between  Germany  and 
Poland.  

Figure 9: Mean level of disclosure perception by country of residence 

Table  7:  Ordered  probit  regression  for  disclosure  perception  with  Germany  as  baseline 
country 

Country 

VARIABLES 

Italy 

Poland 
UK 

Disclosure perception  

-.3857499***    

-.0490054    
-.3315544***     

Table  8:  Ordered  probit  regression  for  disclosure  perception  with  Poland  as  baseline 
country 

Country 

VARIABLES 

Germany 

Italy 
UK 

Disclosure perception  

.0490054    

-.3367445*** 
-.282549***     

20 

 
 
 
 
 
 
 
  
 
 
 
  
 
Gender 

Women  were  more  likely  to  believe  their  answers  to  the  quiz  questions  revealed  personal 
information  about  themselves  (p  <  0.01,  Table  6).    Figure  10  shows  mean  level  of  disclosure 
perception by gender (women: M = 4.9; SD = 1.87; men: M = 4.7; SD = 1.87). 

Figure 10: Mean level of disclosure perception by gender 

Education 

Subjects with a higher level of education perceived they had disclosed more information than those 
with a lower level of education (p < 0.01; Table 6). Subjects who had attended college had a higher 
mean level (M = 4.86; SD = 1.90) than those who had not (M = 4.78; SD = 1.85; Figure 11). 

Figure 11: Mean level of disclosure perception by education level 

21 

 
 
 
 
 
 
Age 
No significant effect for disclosure perception was found according to age. 

Feeling of being observed / monitored  

This  construct  was  measured  by  three  items:  'I  felt  comfortable  answering  personal  questions 
during this study' (Q36, reversed), 'I felt observed during this study' (Q37), and 'I felt I was being 
monitored  during  this  study'  (Q38).  Participants  rated  each  item  on  a  seven-point  Likert  scale 
ranging from 'strongly disagree' to 'strongly agree'. The higher this construct scores, the more they 
felt their actions were noted while participating in the experiment.  

Table 9: Correlation between items of the feeling of being observed / monitored 

Q36 
Q37  
Q38 

Q36 
1.0000 
0.2878 
0.2984 

Q37 

1.0000 
0.8165 

Q38 

1.0000 

Table  10:  Ordered probit  regression  for the  feeling  of being  observed  /  monitored with 
Control and Italy as baselines for treatment and country 

VARIABLES 

Traditional  
Simplified  
Anthropomorphic Dynamic  
Anthropomorphic Static  
Informal  
IP  
History  

Germany 

Poland 
UK 
Gender 
Education level 
Age 

Treatments 

Country 

Other 

Feeling of being observed / 
monitored  

-.0176888    
.0507388    
-.060725    
.0399455    
-.0080207    
-.0411596    
.0734501    

.3933074***    

.0842225* 
.1820166***    
-.1661339*** 
-.2312539*** 
.1587669***    

*** p<0.01, ** p<0.05, * p<0.1 
Treatment: Control = 1; Country: Italy = 1; Gender: Female = 1; Education level: College attendants = 1; 
Age: >42 = 1 

The  latent  variable  feeling  of  being  observed /  monitored  presents  high  reliability  (α  =  0.7285)17. 
Eliminating item 36 increased the reliability of the construct (α = 0.8989). However, that item was 

17   Hinton,  Brownlow,  McMurray  &  Cozens  (2004)  suggest  four  cut-off  points,  which  involve  excellent 
reliability (0.90 and above), high reliability (0.70-0.90), moderate reliability (0.50 -0.70), and low reliability 
(0.50 and below). 

22 

 
 
 
 
 
 
 
 
 
  
 
 
                                                 
kept in order to ensure the robustness of results. Correlations were very high between item 37 and 
item 38 (0.8165), but lower between 37 and 36 (Table 9).  

An ordered probit model tested for differences in this construct, where treatments, country, gender, 
education level and age were introduced as independent variables (Table 10). The control condition 
and Italy were the baselines for treatment and country. 

Treatments  

No significant effect for feeling of being observed / monitored was found according to experimental 
treatments. 

Country 

In  Italy,  subjects  felt  less  monitored  while  using  the  website  (M  =  4.15,  SD  =  1.54),  followed  by 
Poland (M = 4.29; SD = 1.62), the UK (M = 4.42; SD = 1.34) and Germany (M = 4.89; SD = 1.55) 
(Figure 12).  

Figure 12: Mean feeling of being observed / monitored by country of residence 

Participants in Italy did not feel they were being observed (Table 10) as much as those in the rest 
of the countries (p < 0.01 for Germany and the UK; p < 0.1 for Poland).   

If  Germany  is  taken  as  baseline  country,  subjects  from  Poland  and  the  UK  seem  to  feel  less 
monitored or observed than participants from Germany (p < 0.01; Table 11).  

When  taking  Poland  as  the  reference,  there  are  also  significant  differences  showing  that  subject 
from the UK felt more monitored than those in Poland (p < 0.01; Table 12).  

23 

 
 
 
 
 
Table  11:  Ordered probit  regression  for the  feeling  of being observed  /  monitored with 
Germany as baseline country 

Country 

VARIABLES 

Italy 
Poland 
UK 

Feeling of being observed / 
monitored  

-.3933074***    
-.309085*** 
-.2112908***     

Table  12:  Ordered probit  regression  for the  feeling  of being observed  /  monitored with 
Poland as baseline country 

VARIABLES 

Germany 
Italy 
UK 

Feeling of being observed / 
monitored  

.309085*** 
-.0842225* 
.0977942*     

Country 

Gender 

The ordered probit regression reveals that females felt significantly less observed than males (p < 
0.01;  Table  10).  Their  average  score  in  this  item  was  4.38  (SD  =  1.59),  compared  to  4.51  (SD  = 
1.49) for males (Figure 13). 

Figure 13: Mean feeling of being observed / monitored by gender 

Education 

Subjects  with  a  higher  level  of  education  felt  significantly  less  (p  <  0.01;  Table  10)  observed  or 
monitored (M = 4.25, SD = 1.49) than those with a lower level of education (M = 4.66, SD = 1.54; 
Figure 14).  

24 

 
  
 
 
  
 
 
 
 
 
 
 
Age 

Younger participants felt significantly less (p < 0.01; Table 11) observed or monitored (M = 4.18, SD 
= 1.54) than older participants (M = 4.67, SD = 1.47; Figure 15). 

Figure 14: Mean feeling of being observed / monitored by education level 

Figure 15: Mean feeling of being observed / monitored by age 

Privacy policy link awareness 

For this construct, subjects were asked whether they had noticed a privacy policy link in the search 
engine  website  with  two  possible  answers  ('I  noticed  it'  or  'I  didn’t  notice  it').  Differences  were 
tested with a probit model. In this regression, the dependent variable can only take two values and 
the independent variables are treatments, country, gender, education level and age (see Table 13). 

25 

 
 
 
 
 
 
 
 
 
 
 
 
Treatments  

Subjects  exposed  to  the  dynamic  anthropomorphic,  IP  address  or  history  treatments  were  more 
likely to notice the privacy policy link (Table 13). 

Country 

Fewer subjects in the UK noticed the privacy policy link during the experiment (92% did not notice 
the link) compared to the other countries (88% in Germany, 86% in Italy and 87% in Poland; Figure 
16).  

Table  13:  Probit  regression  for  privacy  policy  link  awareness  with  Control  and  Italy  as 
baselines for treatment and country 

VARIABLES 

Traditional  
Simplified  
Anthropomorphic Dynamic  
Anthropomorphic Static  
Informal  
IP  
History  
Germany 
Poland 
UK 
Gender 
Education level 
Age 

Treatments 

Country 

Other 

Privacy policy link 
awareness  
.1991768    
.1976527    
.3314248***    
.1674936    
.0829997    
.2826691**    
.3226029***    
-.0755753    
-.051806    
-.3335011***     
-.1269112***    
.1117372   *** 
-.102267*    

*** p<0.01, ** p<0.05, * p<0.1 
Treatment: Control = 1 
Country: Italy = 1; Gender: Female = 1; Education level: College attendants = 1; Age: >42 = 1 

Participants in Italy were more likely to notice the privacy  policy link than subjects in the UK (p < 
0.01; Table 13).  

If Germany is taken as baseline country, subjects from the UK were less likely to have noticed the 
privacy policy link, although there were no differences with the other two countries (p < 0.01; Table 
14).  

When Poland is the reference, there are also significant differences showing that subjects from the 
UK  were  less  likely  to  see  the  privacy  policy  link.  No  differences  were  found  with  the  other  two 
countries (p < 0.01; Table 15).  

26 

 
  
 
 
 
Figure 16: Privacy policy link awareness by country in percentage 

Table 14: Probit regression  for privacy policy link awareness with Germany as baseline 
country 

Country 

VARIABLES 

Italy 
Poland 
UK 

Privacy policy link 
awareness 

.0755753    
.0237693    
-.2579258***    

Table  15:  Probit  regression  for  privacy  policy  link  awareness  with  Poland  as  baseline 
country 

VARIABLES 

Germany 
Italy 
UK 

Country 

Gender 

Privacy policy link 
awareness 

-.0237693    
.051806    
-.2816951***    

Females  were  less  likely  to  notice  the  privacy  link  than  males  (p  <  0.01;  Table  14).  A  lower 
percentage of women (Figure 17) noticed the privacy policy link (9%) than males (12%). 

Education 

Subjects  with  a  higher  level  of  education  were  more  prone  to  notice  the  privacy  policy  link  than 
those with a lower level of education (p < 0.01; Table 13). 54% of subjects who attended college 
reported noticing the link, compared with 46% of those who never attended college.  

Age 

Younger  participants  were  more  likely  to  notice  the privacy  policy  link  than  older  ones  (p  <  0.10; 
Table 14). 54% of younger participants reported noticing the link, compared to 46% of older ones. 

27 

 
 
 
 
  
 
  
 
 
Figure 17: Privacy policy link awareness by gender in percentage 

V.   Discussion and conclusion 

This report set out to explore whether changes in the choice architecture of a web interface had an 
effect  on privacy behaviour. It also observed  whether less tangible  psychological  constructs were 
affected by these changes, and also conducted a demographic analysis according to gender, age, 
education and country of residence. 

Effect of experimental treatments 

Results  suggest  people's  disclosure  of  personal  information  is  not  oblivious  to  changes  in  the 
choice architecture. Both dynamic and static anthropomorphic conditions had an effect on passive 
disclosure. Traditional and dynamic anthropomorphic treatments  also had a weak effect on direct 
disclosure.  In  both  cases,  the  anthropomorphic  characters  led  people  to  reveal  more  personal 
information  (not  less,  as  suggested  by  Groom  and  Calo,  2011).  Perhaps  this  was  due  to 
anthropomorphic  characters  increasing  levels  of  trust  and  inviting  greater  disclosure  of  personal 
information, as shown elsewhere in the literature (Bente et al., 2014). 

However, none of the treatments had an effect on psychological constructs such as the awareness 
that  the  answers  to  the  quiz  questions  revealed  personal  information,  or  the  feeling  of  being 
monitored and the degree of comfort when answering questions. This would seem to confirm that 
these  nudges  have  an  impact  on  automatic  behaviour  (the  kind  Kahneman,  2011,  refers  to  as 
System 1) rather than behaviour which is guided by in-depth thinking (i.e. System 2). If this is true, 
they are particularly relevant for habitual and instinctive on-line behaviour. 

Changes  in  the  choice  architecture  did  have  an  effect  on  whether  or  not  participants  noticed the 
privacy  policy  link.  The  presence  of  an  anthropomorphic  character  (the  'dynamic'  one),  a  line 
showing the participants' IP address and a listing of prior browsing history all made it more likely 
that  respondents  would  notice  the  link.  However,  noticing  the  link  was  not  correlated  with  either 
passive or direct disclosure.  

28 

 
 
 
 
 
 
 
Country differences 

The study revealed differences in observed behaviour and self-reported answers to a questionnaire 
according  to  demographic  variables.  With  regard  to  country  of  residence,  subjects  from  Italy 
revealed  the  most  personal  information  inadvertently.  In  addition,  they  were  (a)  the  least  aware 
that  quiz  questions  revealed  personal  information  about  themselves,  (b)  the  ones  who  felt  least 
observed / most comfortable answering questions, and (c) the ones least likely to disclose personal 
information directly. 

However, the most relevant finding with regard to country differences was that significant country 
differences  existed  between  all  countries  for  direct  disclosure  (while  for  passive  disclosure  only 
Italy  stood  out).  This  would  suggest  there  is  an  interesting  relationship  between  culture  and 
information disclosure, which merits further investigation. 

Gender 

With  regard  to  gender,  approximately  73%  of  women  answered  'never'  to  the  stigmatized 
questions, compared to 27% of men. This is a very large difference, and could be because some of 
the questions (e.g. about alcohol  consumption)  were less problematic for men to answer. It could 
also  suggest  women  feel  they  are  under  greater  social  scrutiny,  or  simply  are  far  more  cautious 
when disclosing personal information. Further qualitative investigation  could help to shed light on 
the causes of this difference.  

Neither women nor men were more likely than the other to reveal personal information unwittingly, 
although  women  were  more  aware  that  the  answers  to  the  quiz  questions  revealed  personal 
information  about  them.  Men,  however,  felt  more  observed  and  less  comfortable  answering 
questions than women.  

Education 

With  regard  to  education,  subjects  who  had  attended  (though  not  necessarily  graduated  from) 
college  felt  significantly  less  observed  or  monitored  and  more  comfortable  answering  questions 
than those who never went to college. This is an odd result, which challenges the assumption that 
the  better  educated  are  more  aware  of  information  tracking  practices.  Further  investigation, 
perhaps of a qualitative nature, could help dig deeper into this issue. 

Also,  people  with  a  lower  level  of  education  were  more  likely  to  reveal  personal  information 
unwittingly  than  subjects  with  a  higher  level  of  education.  It  was  also  not  because  they  had  a 
greater willingness to reveal  personal information, since results for the  direct disclosure measure 
show no difference. It is more likely that this is due to the fact that non-college attendees simply 
were  less  aware  that  the  answers  to  the  quiz  questions  revealed  personal  information  about 
themselves. 

Age 

Finally,  with  regard  to  age,  older  people  (over  41)  tended  to  reveal  more  personal  information 
inadvertently. There was also a difference in direct disclosure, but in the opposite direction as older 
individuals  revealed  less  when  they  were  asked  directly.  No  differences  were  found  in  the  belief 
that the answers to personal questions revealed information about themselves. However, they did 
feel more observed / less comfortable when answering questions. 

These  differences  all  have  potential  policy  implications.  For  example,  recognising  that  there  are 
differences in cultural patterns of privacy behaviour could make differentiated implementation of 
data  protection  regulation  across  countries  possible.  The  fact  that  education  plays  such  an 

29 

 
 
 
 
important  role  has  implications  for  education  policy  and  the  promotion  of  digital  skills.  And 
differences according to age should inform social policy, which aims to ensure no generation is left 
behind in the information society. 

A final word… 

By  conducting  an  experiment  on  privacy  nudges,  this  report  has  attempted  to  bring  behavioural 
research  methods  for  privacy  to  the  attention  of  European  policy-makers.  It  has  considered  the 
opportunities  and  challenges  of  applying  behavioural  insights  to  privacy  legal  frameworks. 
However,  this  study  has  its  limits.  It  has  only  targeted  a  sample  of  users  in  four  countries  in  an 
experimental  setting  (even  though  every  effort  was  made  to  ensure  that  the  experiment  was  as 
realistic as possible). The question still remains: how would hundreds of thousands of people react 
to these changes in a real-life setting? 

The  major  web  service  providers  (e.g.  Google,  Facebook,  Twitter)  are  likely  to  have  extensive 
amounts  of  data  on  how  slight  changes  to  their  services’  privacy  controls  affect  users’  privacy 
behaviour.  One  policy  recommendation,  therefore,  is  that  national  and  European  privacy 
enforcement  authorities  should  work  with  these  providers  to  leverage  this  data  to  inform 
appropriate  privacy  and  data  protection  rules,  regulations,  laws,  and  legislation.  These  service 
providers  should  not  be  forced  to  hand  over  this  data;  rather,  they  could  work  together,  in 
partnership,  to  arrive  at  recommendations  for  web  interface  design  that  allow  for  an  aware 
disclosure of privacy information.   

30 

 
 
 
Annex 1  

Screenshots of the experimental conditions 

1.  Control  

2.  Traditional  

3.  Simplified  

31 

 
 
 
 
 
 
4.  Static agent 

5. 

Interactive agent 

6. 

Informality 

32 

 
 
 
 
 
 
7. 

IP information 

8.  History  

33 

 
 
 
Annex 2 

Questionnaire 

I.  Trivia Questions 

1.  What is the current Queen of Norway's birthday? 

2.  What is the high temperature today in Hammamet, Tunisia? 

3.  What is the name of the national opera house in Kiev? 

4.  Who is the current European Commissioner for Development? 

II.  Passive Disclosure 

Sensitive Questions in Italics 
Set 1: 

5.  Find a recipe for chocolate cake and list all the ingredients 

6.  Provide the name of the winner at the Eurovision song contest in 1978 

7.  Provide the name of a famous public figure or celebrity born the same year as you  

Set 2:  

8.  What is the street address of a post office in the town where you live? 

9.  What is the date of the first full moon in the year 2017? 

10. On what date was the highest temperature in the U.S.A. recorded? 

Set 3: 

11. In what year was the bank you use founded? 

12. Which years marked the beginning and the end of the Crimean War? 

13. Which Shakespeare play was first performed in 1604? 

Set 4: 

14. What are the names of three of the moons of Neptune? 

15. What is the full name of the 81st element in the periodic table? 

16. What is the address of your favourite restaurant located in the town where you were born? 

III.  Internet usage 

17. What browser do you typically use? 

18. What operating system do you typically use? 

19. What search engine do you typically use? 

20. Are you a member of a social networking site, such as Facebook? 

IV.  Search engine 

21. Do you think the search engine you tested is easier to use than the search engine that you 

typically use? 

22. Do you think the search engine you tested is more efficient in the result provided than the 

search engine that you typically use? 

23. Do  you  think  the  search  engine  you  tested  fits your  needs  better than  the  search  engine 

that you typically use? 

34 

 
V.  Direct Disclosure  

24. Have you ever claimed to have education that you didn't actually have? 

25. Have you ever pretended not to see a beggar to avoid being seen as stingy? 

26. Have you ever had sex with someone who was too drunk to know what they were doing? 

27. Have you ever looked at pornographic material? 

28. Have you ever had sex with the current husband, wife, or partner of a friend? 

29. Have you ever known about or witnessed a serious crime and failed to report it or stop it? 

30. Have you ever lied about your income to someone? 

31. Have you ever fantasized about having violent non-consensual sex with someone? 

32. Have you ever drunk so much that you got a hangover? 

33. Have you ever failed to tip a waiter in a country in which tipping is customary? 

VI.  Answers provided 

34. My answers to the quiz questions revealed personal information about myself 

35. I felt comfortable answering personal questions during this study 

36. I felt observed during this study 

37. I felt I was being monitored during this study 

VII. Personal information captured 

38. The  search  engine  website  was  able  to  detect  several  pieces  of  information  about  my 

online activity 

39. The websites you visited 

40. The web browser you used 

41. The search terms you used 

42. Your Internet Protocol (IP) address 

43. Your geographic location 

44. Your answers to the questions about web use 

45. The time you started the quiz questions 

46. The time it took for you to complete the quiz questions 

47. The year of your birth 

48. The town you are currently connected from 

VIII.  Noticing elements 

49. While you were answering the quiz questions, did you notice anything unusual in the right 

side of the screen? Please describe what you noticed, if anything 

50. While you were answering the quiz questions, did you notice a link to a privacy policy for 

the search engine? 

51. While you were answering the quiz questions, did you notice a series of links to URLs you 

had visited? 

52. While you were answering the quiz questions, did you notice an IP address? 

53. While you were answering the quiz questions, did you notice a geographical location? Was it 

the correct location from which you are currently connected? 

35 

 
54. While you were answering the quiz questions, did you notice a browser name? Was it the 

correct name of the browser you are currently using? 

55. While  you  were  answering  the  quiz  questions,  did  you  notice  an  image  of  a  female 

character? 

IX.  Socio-demographics 

56. You are: 

57. How old are you? 

58. Your education level: 

59. Your employment situation: 

60. Your personal income (net monthly salary) is: 

61. How many people live in your household, including you? 

62. Your household average income (net monthly salary) is 

63. For which political party did you vote at the last elections? 

X.  Other 

64. Did you complete this study alone or with others? 

65. Where did you connect from? 

66. What device did you use to take this survey? 

67. What was the goal of this study? 

68. Do you have any comment or suggestion for this study? 

36 

 
 
Annex 3 

Sample characteristics and socio-demographics 

The distribution of the sample corresponded to 49% of females (Table A  – Figure A). Participants 
were divided into groups of 7 years of range (e.g. 18-25, 26-33, etc.), though they were afterwards 
merged into two categories due to differences in the results of the statistical analysis conducted. 
The first group included participants between 18 and 41 vs. subjects over 42 years old, distributed 
as shown in Table B – Figure B. Participants were also allocated according to their education level 
depending on whether they had attended to college or not (Table C – Figure C).     

Table A: Participants’ distribution by gender 

Country 

Gender 

Germany 

Female 

Male 

I prefer not to answer 

Female 

Male 

I prefer not to answer 

Female 

Male 

I prefer not to answer 

Female 

Male 

I prefer not to answer 

Italy 

Poland 

UK 

Total  

Frequency 

% Country  % Sample 

398 

404 

11 

374 

408 

15 

394 

401 

7 

402 

405 

7 

48.95 

49.69 

1.35 

46.93 

51.19 

1.88 

49.13 

50.00 

0.87 

49.39 

49.75 

0.86 

12.34 

12.52 

0.34 

11.59 

12.65 

0.46 

12.21 

12.43 

0.22 

12.46 

12.55 

0.22 

3,226 

100.00 

Figure A: Gender distribution of the total sample 

37 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table B: Participants’ distribution by age  

Country 
Germany 

Italy 

Poland 

UK 

Total  

Age 
18-41 
42 and above 
I prefer not to answer 
18-41 
42 and above 
I prefer not to answer 
18-41 
42 and above 
I prefer not to answer 
18-41 
42 and above 
I prefer not to answer 

Frequency  % Country  % Sample 

332 
472 
9 
439 
346 
12 
433 
361 
8 
275 
529 
10 
3,226 

40.84 
58.06 
1.11 
55.08 
43.41 
1.51 
53.99 
45.01 
1.00 
33.78 
64.99 
1.23 

10.29 
14.63 
0.28 
13.61 
10.73 
0.37 
13.42 
11.19 
0.25 
8.52 
16.40 
0.31 
100.00 

Figure B: Age distribution of the total sample 

Participants’ distribution by level of education  

Country 
Germany 

Italy 

Poland 

UK 

Total 

Gender 
Non-college 
College 
I prefer not to answer 
Non-college 
College 
I prefer not to answer 
Non-college 
College 
I prefer not to answer 
Non-college 
College 
I prefer not to answer 

% Country  % Sample 

61.87 
33.33 
4.80 
44.79 
53.20 
2.01 
44.14 
53.62 
2.24 
32.19 
65.48 
2.33 

15.59 
8.40 
1.21 
11.07 
13.14 
0.50 
10.97 
13.33 
0.56 
8.12 
16.52 
0.59 
100.00 

Frequency 
503 
271 
39 
357 
424 
16 
354 
430 
18 
262 
533 
19 
3,226 

38 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure C: Education level distribution of the total sample 

39 

 
 
 
 
  
References 

Acquisti,  A.  (2009).  Nudging  privacy:  Behavioral  economics  of  personal  information.  Security  & 

Privacy, 7(6). 82–85. 

Acquisti, A. (2010a). From the economics to the behavioral economics of privacy: A note.  in Ethics 

and Policy of Biometrics, 6005 Springer. 

Acquisti,  A.  (2010b).  The  economics  of  personal  data  and  the  economics  of  privacy.  Background 
paper for the Joint WPISP-WPIE Roundtable The Economics of Personal Data and Privacy: 
30 Years after the OECD Privacy Guidelines. OECD. 

Acquisti,  A.  &  Grossklags,  J.  (2007).  What  can  behaviorual  economics  teach  us  about  privacy?  In 
Acquisti,  A.,  Gritzalis,  S.,  Lambrinoudakis,  C.,  and  di  Vimercati,  S.  (Eds.).  Digital  Privacy: 
Theory, technologies, and practices. CRC Press. 

Acquisti,  A.,  John,  L.  K.,  &  Loewenstein,  G.  (2012).  The  impact  of  relative  standards  on  the 

propensity to disclose. Journal of Marketing Research, 49(2), 160-174. 

Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of 

information. Science, 347(6221), 509-514. 

Bente, G., Dratsch, T., Rehbach, S., Reyl, M., & Lushaj, B. (2014). Do you trust my avatar? Effects of 
photo-realistic seller avatars and reputation scores on trust in online transactions. In HCI in 
Business (pp. 461-470). Springer International Publishing.  

Borgesius,  F.  (2013).  Consent  to  behavioural  targeting  in  European  law:  What  are  the  policy 
implications  of  insights  from  behavioural  economics?  Conference  paper  for  Privacy  Law 
Scholars Conference (PLSC), 6-7 June 2013, Berkeley,US. 

Calo, R. (2012). Against Notice Skepticism in privacy and elsewhere.  Notre Dame Law Review. vol. 

87.  

Calo, R. (2014). Code, Nudge or Notice? Iowa Law Review, vol. 99, no. 2 ; University of Washington 
SSRN: 

2013-04. 

Available 

Paper 

Law 

No. 

at 

of 

School 
http://ssrn.com/abstract=2217013, 773-802. 

Research 

Cavoukian,  A.  (2012).  Privacy  by  design  and  the  emerging  personal  data  ecosystem.  Privacy  By 

Design.  

European  Commission  (2012).  Proposal  for  a  Regulation  of  the  European  Parliament  and  of  the 
Council on the protection of individuals with regard to the processing of personal data and 
on the free movement of such data (General Data Protection Regulation), 25 January 2012, 
COM(2012) 11 final.  

Groom,  V.,  &  Calo,  M.  R.  (2011).  Reversing  the  privacy  paradox:  An  experimental  study.  TPRC 

Conference proceedings, available at SSRN: http://ssrn.com/abstract=1993125. 

Haynes,  A.  (2007).  Online  privacy  policies:  Contracting  away  control  over  personal  information? 

Penn State Law Review Vol 111: 3, 587-624. 

40 

 
Hildebrandt. (2012). The Dawn of a Critical Transparency Right for the Profiling Era. In M. C. Ed. J. 

Bus, Digital Enlightenment Yearbook 2012 . IOS Press, Amsterdam. 

Hinton,  P.  R.,  Brownlow,  C.,  McMurray,  I.  &  Cozens,  B.  (2004).  SPSS  explained.  Routledge,  East 

Sussex, UK. 

Hoofnagle, C. J., King, J., Li, S., & Turow, J. (2010). How different are young adults from older adults 

when it comes to information privacy attitudes and policies? Available at SSRN:   
http://ssrn.com/abstract=1589864  or   HYPERLINK "http://dx.doi.org/10.2139/ssrn.1589864"  

Hoofnagle, C.J. & King, J. (2008). What Californians understand about privacy online.  Available at 

SSRN 1262130. 

John,  L.,  Acquisti,  A.,  &  Loewenstein,  G.  (2009).  The  Best  of  Strangers:  Context  Dependent 
SSRN: 

Willingness 
http://ssrn.com/abstract=1430482 or http://dx.doi.org/10.2139/ssrn.14. 

Information. 

Available 

Personal 

Divulge 

at 

to 

Kahneman, D. (2011). Thinking, fast and slow. Penguin. 

Lunn, P. (2014). Regulatory Policy and Behavioural Economics, OECD Publishing. 

Lusoli, W., Bacigalupo, M., Lupiáñez-Villanueva, F., de Andrade, N.N.G., Monteleone, S. & Maghiros, I. 
(2012).  Pan-European  survey  of  practices,  attitudes  and  policy  preferences  as  regards 
personal  identity  data  management.  JRC  Scientific  and  Policy  Reports,  EUR  25295. 
Luxemburg: Luxemburg Publications Office. 

Madden, M., Lenhart,  A., Cortesi, S., Gasser, U., Duggan, M., Smith, A., & Beaton, M. (2013). Teens, 
social  media, 
at 
Pew 
http://www.pewinternet.org/files/2013/05/PIP_TeensSocialMediaandPrivacy_PDF.pdf. 

Available 

Research 

privacy. 

Center, 

and 

21. 

Martin, K.E. (2013). Transaction costs, privacy and trust: The laudable goals and ultimate failure of 

notice and choice to respect privacy online. First Monday Vol. 18, 12. 

McDonald, A., & Cranor, L. (2008). Cost of reading privacy policies. I/S: A Journal of Law and Policy 

for the Information Society, 4:543. 

Shafir, E. (2013). The Behavioral Foundations of Public Policy. Princeton University Press. 

Solove,  D. (2013).  Introduction:  Privacy  self-management  and the  consent  dilemma.  126  Harvard 

Law Review, 1880. 

Solove, D. & Hoofnagle, C. (2006). A model regime of privacy protection (Version 3.0). University of 

Illinois Law Review, Vol. 2006, No. 2. 

Sunstein, C.R. (2000). Behavioural Law and Economics. Cambridge University Press. 

Sunstein, C.R. (2013). Behavioral economics, consumption, and environmental protection. Regulatory 
Policy Program Working Paper RPP-2013-19. Cambridge, MA: Mossavar-Rahmani Center for 
Business and Government, Harvard Kennedy School, Harvard University. 

Thaler, R., & Sunstein, C. (2008). Nudge: Improving decisions about health, wealth, and happiness. 

Yale University Press. 

41 

 
Tsai,  J.,  Cranor,  L.,  Acquisti,  A.,  &  Fong,  C.  (2006).  What’s  it  to  you?  A  survey  of  online  privacy 

concerns and risks,. Preliminary Progress Report NET Institute Working Papers n. 06-29. 

van Bavel, R., Herrmann, B., Esposito, G., & Proestakis, A. (2013).  Applying Behavioural sciences to 
. 
EU  policy-making, 
http://ec.europa.eu/dgs/health_consumer/information_sources/docs/30092013_jrc_scientific
_policy_report_en.pdf. 

JRC  Scientific  and  Policy  Reports  EUR  26033  EN 

Wang,  Y.,  Leon,  P.,  Scott,  K.,  Chen,  X.,  Acquisti,  A.,  &  Cranor,  L.  (2013).  Privacy  nudges  for  social 
media:  an  exploratory  Facebook  study.  Proceedings  of  the  22nd  international  conference 
WWW '13 Companion. 

World  Bank 

(2015).  World  Development  Report  2015:  Mind,  Society,  and  Behaviour 

(http://www.worldbank.org/en/publication/wdr2015). 

Wu,  K.,  Huang,  S.Y.,  Yen,  D.,  &  Popova,  I.  (2012).  The  effect  of  online  privacy  policy  on  consumer 

privacy concern and trust. Computers in Human Behavior (28). 

42 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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European Commission 
EUR 27384 EN – Joint Research Centre – Institute for Prospective Technological Studies 

Title: Nudges to Privacy Behaviour: Exploring an Alternative Approaches to Privacy Notices 

Authors: Shara Monteleone, René van Bavel, Nuria Rodríguez-Priego, Gabriele Esposito 

Luxembourg: Publications Office of the European Union 
2015 – 42 pp. – 21.0 x 29.7 cm 

EUR – Scientific and Technical Research series – ISSN 1831-9424 (online) 
ISBN 978-92-79-50320-7 (PDF) 
doi:10.2791/142795 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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JRC Mission 

As the Commission’s  
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doi:10.2791/142795 
ISBN 978-92-79-50320-7