This document discusses how form auto-completion tools can negatively impact users' privacy calculus by making it too easy to disclose information without weighing risks and benefits. The researchers propose two new tools - Remove FormFiller and Add FormFiller - that allow users to manually remove or add filled fields, hypothesizing this will reinstate the privacy calculus. They conducted an experiment where participants used an auto-completion tool on forms for different websites (a blog, job site, health insurer). Results showed perceived risk was lower and relevance higher when the type of information matched the website purpose, supporting the role of purpose-specificity in disclosure decisions.
Consumer behavior
Week 2 of 13 of the 2007 Internet Marketing Course. Content is based in part on Dann, S and Dann S 2004 Strategic Internet Marketing 2.0, Milton: Wiley. Diagrams taken from the Dann and Dann text are copyright to their respective copyright holders.
Krugel's Assurance University Social Media & Law Presentation May 2014Charles Krugel
Materials from my May 2014 "Sink or Swim With Social Media: Legal Workplace Issues" presentation for Assurance University. It's a 56-slide PowerPoint analyzing court & regulatory agency rulings concerning who owns work-related accounts & content, & what employers can & should do to manager their employee & business' social media activities.
Consumer behavior
Week 2 of 13 of the 2007 Internet Marketing Course. Content is based in part on Dann, S and Dann S 2004 Strategic Internet Marketing 2.0, Milton: Wiley. Diagrams taken from the Dann and Dann text are copyright to their respective copyright holders.
Krugel's Assurance University Social Media & Law Presentation May 2014Charles Krugel
Materials from my May 2014 "Sink or Swim With Social Media: Legal Workplace Issues" presentation for Assurance University. It's a 56-slide PowerPoint analyzing court & regulatory agency rulings concerning who owns work-related accounts & content, & what employers can & should do to manager their employee & business' social media activities.
Employers in Utah can fire their employees for any reason or no reason at all. There are limitations to this rule - you can't fire an employee, for example, based on race, gender, religion, or age, or if doing so would breach a contract. You also can't fire an employee if doing so would violate "public policy." This presentation walks through this third limitation on Utah's at-will doctrine, its scope and its pitfalls, and the ways to potentially avoid its traps.
Article review of “Web 2.0 in Government“ by Francesca Barrientos and Elizabeth Foughty published on Interaction megazine (September + October 2009).
I wrote this article during my Master Degree course on Human-Computer Interactions as part of a project assignment.
Slide presentation from my social media & law seminar. 46 slides discussing court & regulatory agency cases concerning who owns an employer's social media content, & what an employer can or can't do relative to managing what's posted.
KDD 2016 tutorial on Algorithmic Bias, Parts I and II.
Video:
Part I: https://www.youtube.com/watch?v=mJcWrfoGup8
Part II: https://www.youtube.com/watch?v=nKemhMbaYcU
Part III: https://www.youtube.com/watch?v=ErgHjxJsEKA
By Sara Hajian, Francesco Bonchi, and Carlos Castillo.
http://francescobonchi.com/algorithmic_bias_tutorial.html
Employers in Utah can fire their employees for any reason or no reason at all. There are limitations to this rule - you can't fire an employee, for example, based on race, gender, religion, or age, or if doing so would breach a contract. You also can't fire an employee if doing so would violate "public policy." This presentation walks through this third limitation on Utah's at-will doctrine, its scope and its pitfalls, and the ways to potentially avoid its traps.
Article review of “Web 2.0 in Government“ by Francesca Barrientos and Elizabeth Foughty published on Interaction megazine (September + October 2009).
I wrote this article during my Master Degree course on Human-Computer Interactions as part of a project assignment.
Slide presentation from my social media & law seminar. 46 slides discussing court & regulatory agency cases concerning who owns an employer's social media content, & what an employer can or can't do relative to managing what's posted.
KDD 2016 tutorial on Algorithmic Bias, Parts I and II.
Video:
Part I: https://www.youtube.com/watch?v=mJcWrfoGup8
Part II: https://www.youtube.com/watch?v=nKemhMbaYcU
Part III: https://www.youtube.com/watch?v=ErgHjxJsEKA
By Sara Hajian, Francesco Bonchi, and Carlos Castillo.
http://francescobonchi.com/algorithmic_bias_tutorial.html
Page 579Assess the Constituent Data. What is included Omi.docxbunyansaturnina
Page 579
Assess the Constituent Data. What is included? Omitted? What are the data based on?
What assumptions are being made? Different retirement calculators give widely different
estimates of how much savings is needed for retirement because of factors they include or omit
(such as entertainment) and assumptions they make (such as inflation rate or healthiness of
annuities and mutual funds) in the calculations.
Two reputable sources can give different figures because they take their data from different
places. Suppose you wanted to know employment figures. The Labor Department’s monthly
estimate of nonfarm payroll jobs is the most popular, but some economists like Automatic Data
Processing’s monthly estimate, which is based on the roughly 20 million paychecks it processes
for clients. Both survey approximately 400,000 workplaces, but the Labor Department selects
employers to mirror the U.S. economy, while ADP’s sample is skewed, with too many
construction firms and too few of the largest employers. On the other hand, the government has
trouble counting jobs at businesses that are opening and closing, and some employers do not
return the survey. (Both organizations do attempt to adjust their numbers to compensate
accurately.)7
Check the Currency of the Data. Population figures should be from the 2010 census, not
the 2000 one. Technology figures in particular need to be current. Do remember, however, that
some large data sets are one to two years behind in being analyzed. Such is the case for some
government figures, also. If you are doing a report in 2014 that requires national education data
from the Department of Education, for instance, 2013 data may not even be fully collected. And
even the 2012 data may not be fully analyzed, so indeed the 2011 data may be the most current
available.
Hard to Quantify Sports Participation
How many people participate In sports, and which sports do they choose?
Governments and equipment makers want to know, but the data are fuzzy. Multiple
questions contribute to the lack of clarity.
What is a sport? One survey includes bird-watching.
Who should be counted? Do young children count?
How often do you have to participate in a sport to be counted? Is once a year enough?
How was the count made? Because younger and more active people tend to have only cell phones, a survey
made through landlines probably won’t be accurate.
In case you are curious, the National Sporting Goods Association survey says hiking is the most popular
participation sport in the United States, with over 40 million people.
Adapted from Carl Bialik, “Sports Results that Leave Final Score Unclear,” Wall Street Journal, June 9, 2012, A2.
Choosing the Best Data
Sometimes even good sources and authorities can differ on the numbers they offer, or on the
PRINTED BY: SHERIFAT EGBERONGBE <[email protected]>. Printing is for personal, private use only. No part of this book may be reproduced or transmitted witho.
Privacy in Mobile Personalized Systems - The Effect of Disclosure JustificationsBart Knijnenburg
Paper Presentation at the Workshop on Usable Privacy & Security for Mobile Devices (U-PriSM) at the Symposium On Usable Privacy and Security (SOUPS) 2012
Paper can be found here: http://appanalysis.org/u-prism/soups12_mobile-final11.pdf
Full journal paper (under review): http://bit.ly/TiiSprivacy
Explaining the User Experience of Recommender Systems with User ExperimentsBart Knijnenburg
A talk I gave at the Netflix offices on July 2nd, 2012.
Please do not use any of the slides or their contents without my explicit permission (bart@usabart.nl for inquiries).
At Techbox Square, in Singapore, we're not just creative web designers and developers, we're the driving force behind your brand identity. Contact us today.
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This Digital Transformation and IT Strategy Toolkit was created by ex-McKinsey, Deloitte and BCG Management Consultants, after more than 5,000 hours of work. It is considered the world's best & most comprehensive Digital Transformation and IT Strategy Toolkit. It includes all the Frameworks, Best Practices & Templates required to successfully undertake the Digital Transformation of your organization and define a robust IT Strategy.
Editable Toolkit to help you reuse our content: 700 Powerpoint slides | 35 Excel sheets | 84 minutes of Video training
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Anny Serafina Love - Letter of Recommendation by Kellen Harkins, MS.AnnySerafinaLove
This letter, written by Kellen Harkins, Course Director at Full Sail University, commends Anny Love's exemplary performance in the Video Sharing Platforms class. It highlights her dedication, willingness to challenge herself, and exceptional skills in production, editing, and marketing across various video platforms like YouTube, TikTok, and Instagram.
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Counteracting the negative effect of form auto-completion on the privacy calculus
1. Counteracting the negative effect of form
auto-completion on the privacy calculus
Bart Knijnenburg, Alfred Kobsa, Hongxia Jin
2. Form auto-completion: the bright side
Modern browsers offer an
auto-completion feature
that reduces the effort of
filling out web forms
Bart
2
3. Form auto-completion: the bright side
Modern browsers offer an
auto-completion feature
that reduces the effort of
filling out web forms
!
Imagine such tools could
fill out any form, on any
website
3
4. Form auto-completion: the bright side
Modern browsers offer an
auto-completion feature
that reduces the effort of
filling out web forms
!
Imagine such tools could
fill out any form, on any
website
!
This would be particularly
useful for mobile browsers
4
5. Form auto-completion: the dark side
Preibusch et al. (2012) warn that such auto-completion tools
may cause users to complete more form fields than they
intended
6. Form auto-completion: the dark side
Preibusch et al. (2012) warn that such auto-completion tools
may cause users to complete more form fields than they
intended
!
Auto-completion tools make it so easy to submit a fully
completed form that users may skip weighing the benefits
and risk of disclosing a certain piece of information in a
specific situation (privacy calculus!)
7. Form auto-completion: the dark side
Preibusch et al. (2012) warn that such auto-completion tools
may cause users to complete more form fields than they
intended
!
Auto-completion tools make it so easy to submit a fully
completed form that users may skip weighing the benefits
and risk of disclosing a certain piece of information in a
specific situation (privacy calculus!)
!
Can we overcome these problems with a better
auto-completion tool?
8. Research outline
Please tell us more about yourself
BlogHeroes will assign a "guild" to you based on the information you provide below. Note that none
of the fields are required, but our classification will be better if you provide more information.
General info about me
We introduce two new tools
that we hypothesize will
reinstate the privacy calculus
Please provide some background info to get our matching process started.
Name (first):
> For employers
E-mail address:
> For inves tors
Gender:
> Contac t Age (years):
!
Address:
> About us
City:
(last):
John
Smith
Please enter your information
john@smith.com
I WRK will find jobs based on the information you enter on this form.
Male
None of the items on the form are required, but if you provide more
23
information the jobs will be a better match.
123 Main St.
State:
New York
G ENERA L
NY
Zip:
12345
A ND C ONTA C T I NFO
Specifically, we compare three
What I do for a living
auto-completion tools:
General and contact information
Some guilds write about their jobs. Tell us more about yours, and we can provide a better match.
FIRST NAME
Employment status:
– Auto FormFiller (automatically fills
fields, users can remove manually)
– Remove FormFiller (same but
users can click to remove eachMy health
field)
– Add FormFiller (no automatic
filling, users can click a button to fill
each field)
Experience (years):
Employed for wages
John
LAST NAME
clear
Smith
Enter your details, please
5
AGE
Your personal Codacare health insurance policy will be based on the
23
Current/previous job: information you provide. Please note that Education the items are
Researcher
Sector:
none of / training / library
required, but the insurance will be better tailored to your needs if you
Income level:
between $50K and $100K/year
GENDER
provide more information.
Education:
clear
Male
Doctoral
General information
E-MAIL ADDRESS
Please provide your general information.
john@smith.com
Name (first):
clear
(last):
Some guilds write about their health. Providing us with some info will help us match them to you.
ADDRESS
CITY
STATE
ZIP
Physical health:
Dietary restrictions:
Birth control usage:
clear
123 Main St.
About average
Address:
allergic to nuts
City:
ORK EXPERI ENC E
None
W
Gender:
Please
New York
State:
NY
12345
Zip:
tell us about your education and work experience, so that we
can find a suitable job for you.
Age:
HIGHEST DEGREE EARNED
Doctoral
E-‐mail:
fill
clear
fill
fill
fill
8
clear
9. Research outline
People base th
eir information
disclosure dec
the perceived
ision on
risk and relev
ance of the inf
ormation
e)
thus disclosur
(and
st
nd relevance
rceived risk a
y of the reque
Pe
cit
rpose-specifi
e pu
depend on th
The effect of ris
k and relevance
is moderated b
type: disclosure
y tool
is more “calcula
ted” when the a
remove tools ar
dd and
e used
15. What causes information disclosure?
Typical answer: privacy calculus (Laufer and Wolfe 1977)
15
16. What causes information disclosure?
Typical answer: privacy calculus (Laufer and Wolfe 1977)
!
Privacy calculus is like utility maximization (Li 2012): people
trade off the different aspects and then choose the option
that maximizes their utility (Bettman et al. 1998; Simon
1959)
16
17. What causes information disclosure?
Typical answer: privacy calculus (Laufer and Wolfe 1977)
!
Privacy calculus is like utility maximization (Li 2012): people
trade off the different aspects and then choose the option
that maximizes their utility (Bettman et al. 1998; Simon
1959)
!
So what are these aspects that people trade off?
17
18. What causes information disclosure?
Risk
!
Operationalization:
Providing my [item] to [site] is:
(-3 = very risky; +3 = very safe)
Risk
Disclosure
!
!
Relevance
Relevance
!
Operationalization:
The fact that [site] asked for my [item] was:
(-3 = very inappropriate; +3 very appropriate)
18
21. But what causes risk and relevance?
On web forms, users selectively disclose different types of
information in different extent to different types of websites
(Hsu 2006)
22. But what causes risk and relevance?
On web forms, users selectively disclose different types of
information in different extent to different types of websites
(Hsu 2006)
!
Social media users also share selectively with others,
depending on the purpose of the information (Olson et al.
2005)
23. But what causes risk and relevance?
On web forms, users selectively disclose different types of
information in different extent to different types of websites
(Hsu 2006)
!
Social media users also share selectively with others,
depending on the purpose of the information (Olson et al.
2005)
!
Does purpose-specificity play a role in commercial privacy as
well?
24. But what causes risk and relevance?
In our experiment, participants:
– entered a wide range of info into an auto-completion tool
– tested the tool on one of three websites
Create a Profile
Please create your profile by entering your information below.
Note that FormFiller will store the information locally on your device, and only for the duration of
this study. We will never submit any forms automatically or disclose this information to others
without your active involvement.
Please tell us more about yourself
BlogHeroes will assign a "guild" to you based on the information you provide below. Note that none
of the fields are required, but our classification will be better if you provide more information.
General info about me
Please provide some background info to get our matching process started.
Name (first):
John
E-mail address:
About you:
john@smith.com
Gender:
(last):
Age (years):
First name:
State:
NY
123 Main St.
City:
Gender:
23
Address:
Last name:
Smith
Male
New York
Zip:
12345
What I do for a living
Some guilds write about their jobs. Tell us more about yours, and we can provide a better match.
Age:
> For employers
Employment status:
Experience (years):
> For inves tors
Current/previous job:
Address:
City:
E-‐‑mail:
Phone:
> Contac t
Income level:
State:
Zip:
Education:
> About us
My health
Employed for wages
5
Please enter your information
I WRK will find jobs based on the information you enter on this form.
Sector:
Education / training library
None of the items on the form are required,/ but if you provide more
Researcher
between $50K andthe jobs will
information $100K/year
be a better match.
Doctoral
G ENERA L
A ND C ONTA C T I NFO
General and contact information
Some guilds write about their health. Providing us with some info will help us match them to you.
Physical health:
Dietary restrictions:
FIRST NAME
About average
LAST NAME
John
Smith
Enter your details, please
Birth control usage:
clear
allergic to nuts
AGE
None
23
Your personal Codacare health insurance policy will be based on the
GENDER
information you provide. Please note that none of the items are
Male
required, but the insurance will be better tailored to your needs if you
E-MAIL ADDRESS
provide more information.
john@smith.com
clear
clear
clear
25. But what causes risk and relevance?
In our experiment, participants:
– entered a wide range of info into an auto-completion tool
– tested the tool on one of three websites
Enter your details, please
Your personal Codacare health insurance policy will be based on the
26. But what causes risk and relevance?
In our experiment, participants:
– entered a wide range of info into an auto-completion tool
– tested the tool on one of three websites
Websites correspond to a particular type of info:
– blogging community ⋍ personal interest items
– job search website ⋍ job skills items
– health insurer ⋍ health record items
Enter your details, please
Your personal Codacare health insurance policy will be based on the
27. But what causes risk and relevance?
Website
Item type
Risk
Relevance
Purpose-specificity will look
like an interaction effect
between Website and Item
type
!
!
When the type of information
requested matches the
purpose of the website:
– perceived risk will be lower
– perceived relevance will be higher
– people will be more likely to
disclose the item
28. But what causes risk and relevance?
Website
Item type
Risk
Relevance
Purpose-specificity will look
like an interaction effect
between Website and Item
type
!
!
Disclosure
When the type of information
requested matches the
purpose of the website:
– perceived risk will be lower
– perceived relevance will be higher
– people will be more likely to
disclose the item
29. But what causes risk and relevance?
Model tested with 543 Mechanical Turk participants
Website
Risk
see next
slide
Item type
odds: 0.818***
Disclosure
Relevance
odds: 1.079***
2
χ (12) = 11.929, p = .451; CFI = 1.00, TLI = 1.00; RMSEA < .001, 90% CI: [.000, .011]
29
30. But what causes risk and relevance?
Counteracting the negative privacy effect of form auto-completi
Counteracting the negative privacy effect of form auto-completi
Counteracting
negative privacy effect of form auto-complet
Contact info
Contact info
Contact info
33
3
Interests
Interests
Interests
Perceived risk
Perceived risk
Perceived risk
100%
100%
100%
95%
95%
95%
90%
90%
90%
Perceived relevance
85%
85%
85%
80%
80%
80%
22
2
Job skills
Health record
record
Health record
Job skills
3
3
Perceived relevance
Perceived relevance
2
2
11
1
1
1
0
00
#
#
#
#
#
0
0
-1
-1-1
#
#
-1
-1
-2
-2
-2
-3
-3
-3
"
"
"
BlogHeroes
BlogHeroes
BlogHeroes
"
"
"
I♡WRK
I♡WRK
I♡WRK
"
"
Codacare
Codacare
Codacare
-2
-2
-3
-3
-3
BlogHeroes
BlogHeroes
BlogHeroes
I♡WRK
I♡WRK
I♡WRK
Codacare
Codacare
Codacare
Figure 3. Perceived Risk and Perceived Relevance per Website and Item type. The
Figure 3. Perceived Risk and Perceived Relevance
Figure 3. Perceived Risk and Perceived Relevance per Website and Item type. The
and Item type.
When pointtype of item matches the purpose of±the website, The
the to the matching item types. Error bars are ± 1 Standard Error.
arrows point to the matching item types. Error bars are 1 Standard Error.
arrows
arrows point to the matching item types. Error bars are ± 1 Standard Error.
people perceive lower risk and higher relevance
2
egarding perceived Risk, we find that the interaction between Website and Item type is significant (χ2 2
egarding perceived Risk, we find that the interaction between Website and Item type is significant (χ (
garding perceived Risk, we find that the interaction between Website and Item type is significant (χ (
246.41, p < .0001). Moreover, for each website, the non-matching item types are perceived
246.41,
.0001). Moreover, for each website, the non-matching item types are perceived
246.41, pp << .0001). Moreover, for each website, the non-matching item types are perceived
gnificantly more risky than the matching item type. Also, for each item type, the non-matching websit
gnificantly more risky than the matching item type. Also, for each item type, the non-matching websi
nificantly more risky than the matching item type. Also, for each item type, the non-matching websit
31. But what causes risk and relevance?
Counteracting the negative privacy effect of form auto-completion
Contact info
3
Interests
3
Perceived risk
100%
95%
90%
85%
80%
2
2
1
1
0
Website
-3
"
BlogHeroes
"
I♡WRK
Health record
#
Perceived relevance
#
#
0
Risk
-1
-2
Job skills
-1
"
-2
-3
Codacare
Counteracting the negative privacy effect of form auto-completion
Contact info
100%
BlogHeroes
Interests
I♡WRK
!
Job skills
Health record
Codacare
!
95%
!
Figure 3. Perceived Risk and Perceived Relevance per 90%
Website and Item type. The
arrows point to the matching item types. Error bars are ± 1 Standard Error.
85%
Contact info
3
Perceived risk
100%
95%
90%
85%
80%
2
1
Item type
0
-3
"
BlogHeroes
Interests
Job perceived Risk, we find that the interaction between Website and Item type is significant (χ2(6)
Health record
80%
Regarding skills
= 246.41, p < Perceived relevanceeach website, the non-matching item types are perceived as
.0001). Moreover, for
3
#
75%
significantly more risky than the matching item type. Also, for each item type, the non-matching websites
#
BlogHeroes
I♡WRK
Codacare
2
#
are perceived as significantly more risky than the matching website13. H3 is thus supported.
1
Likewise, the interaction between Website and Item type is also significant for Perceived Relevance (χ2(6)
0
= 913.47, p < .0001). For each website, the non-matching item types are perceived as significantly less
relevant than the matching item type, and for each item type, the non-matching websites are perceived as
-1
significantly less relevant than the matching website. H4 is thus supported as well.
-1
-2
"
I♡WRK
"
-2
H5+H6. Tool type ! Disclosure
-3
Codacare
BlogHeroes
I♡WRK
Codacare
The last row in Table 2 presents the effect of Tool type on Disclosure. Controlling for Perceived Risk,
Perceived Relevance, and Item type, the odds of Disclosure are 6.3% higher for users of the Add tool than
Figure 3. Perceived Risk and Perceived Relevance per Website and Item this effect not significant (p = .631). Surprisingly then, H5 is
for users of the Remove tool, although type. The
arrows point to the matching itemrejected:Erroris no significantStandard in Disclosure between the Add and Remove tools.
types. there bars are ± 1 difference Error.
The planned contrast between the traditional Auto tool and the alternative tools is significant (χ2(1) =
Regarding perceived Risk, we find that the interaction between Website and Item type is significant (χ2(6)
4.037, p = .045). Controlling for Perceived Risk, Perceived Relevance, and Item type, the odds of
= 246.41, p < .0001). Moreover, for each website, the non-matching item types are perceived as
Disclosure are 24.0% lower for users of the Remove tool compared to users of the Auto tool, a small (d =
significantly more risky than the matching item type. Also, for each item type, the non-matching websites
.165) but significant (p = .047) effect. The odds of Disclosure are 18.9% lower for users of the Add tool
are perceived as significantly more risky than the matching website13. H3 is thus supported.
compared to users of the Auto tool, a small (d = .107) effect that is not significant (p = .130). H6 is thus
partially is also significant for is indeed Relevance (χ2(6)
Likewise, the interaction between Website and Item typesupported: Disclosure Perceivedsignificantly higher for the Auto tool than for the Remove tool.
= 913.47, p < .0001). For each website, the non-matching item types are perceived as significantly less
32. But what causes risk and relevance?
When the type of information requested matches the purpose
of the website, people are more likely to disclose it.
Contact info
Interests
Job skills
100%
!
95%
!
Health record
!
90%
85%
80%
75%
BlogHeroes
I♡WRK
Codacare
32
34. ure?
ation disclos
auses inform
What c
perceived risk
and relevance
e?
and relevanc
es risk
ut what caus
B
purpose-spec
ificity
ence
at is the influ
Wh
tion
auto-comple
of the
tool?
35. Influence of tool type
Please tell us more about yourself
BlogHeroes will assign a "guild" to you based on the information you provide below. Note that none
of the fields are required, but our classification will be better if you provide more information.
General info about me
We tested three tools:
Please provide some background info to get our matching process started.
Name (first):
> For employers
E-mail address:
> For inves tors
Gender:
John
(last):
Smith
Please enter your information
john@smith.com
– Auto FormFiller (automatically fills
I WRK will find jobs based on the information you enter on this form.
None of the items on the form are required, but if you provide more
fields, users can remove manually)
information the jobs will be a better match.
– Remove FormFiller (same but users
G
can click to remove each field)
What I do for a living
General and contact information
– Add FormFiller (no automatic filling,
Enter your details, please
users can click a button to fill each
Your personal Codacare health insurance policy will be based on the
field)
information you provide. Please note that none of the items are
> Contac t Age (years):
Address:
> About us
City:
Male
23
123 Main St.
New York
State:
NY
Zip:
12345
ENERA L A ND C ONTA C T I NFO
Some guilds write about their jobs. Tell us more about yours, and we can provide a better match.
FIRST NAME
Employment status:
Experience (years):
LAST NAME
Employed for wages
John
5
AGE
Current/previous job:
Income level:
Education:
23
Researcher
clear
Smith
Sector:
Education / training / library
clear
required, but the insurance will be better tailored to your needs if you
between $50K and $100K/year
GENDER
provide more information.
clear
Male
Doctoral
General information
E-MAIL ADDRESS
My health
Please provide your general information.
john@smith.com
Name (first):
clear
(last):
Some guilds write about their health. Providing us with some info will help us match them to you.
ADDRESS
CITY
STATE
ZIP
Physical health:
Dietary restrictions:
Birth control usage:
123 Main St.
About average
Address:
allergic to nuts
City:
ORK EXPERI ENC E
None
W
Gender:
Please
New York
State:
NY
12345
Zip:
tell us about your education and work experience, so that we
can find a suitable job for you.
fill
clear
fill
fill
Age:
HIGHEST DEGREE EARNED
Doctoral
E-‐mail:
CURRENT EMPLOYMENT STATUS
Employed for wages
fill
clear
fill
clear
36. Influence of tool type
Tool type
Website
Risk
Disclosure
Item type
Relevance
2
χ (86) = 99.443, p = .152; CFI = .990, TLI = .988; RMSEA = .007, 90% CI: [.000, .013]
2
χ (86) = 95.140, p = .235; CFI = .993, TLI = .992; RMSEA = .006, 90% CI: [.000, .012]
37. Influence of tool type
!
Tool type
Auto
0.863***
Remove
0.754***
!
Add
0.811***
!
Disclosure
!
Risk
!
!
!
Relevance
Auto
0.989
Remove
1.133***
Add
1.114***
Odds are closest to 1 for the Auto tool, so Risk and Relevance
influence Disclosure the least for users of the Auto tool
38. Influence of tool type
Results:
– Disclosure was not purpose-specific for users of the Auto tool
– Disclosure was purpose-specific for users of the Remove and Add tools.
These tools help users consider the website’s purpose in their disclosure
decisions
– Additional note: Users of the Add tool were more satisfied, despite
having to click more frequently on average!
Contact info
100%
!
Auto
100%
!
!
95%
90%
Job skills
Health record
Remove
!
!
95%
Interests
!
!
Add
!
!
!
!
!
90%
85%
85%
80%
80%
75%
BlogHeroes
I♡WRK
Codacare
BlogHeroes
I♡WRK
Codacare
BlogHeroes
I♡WRK
Codacare
39. ure?
ation disclos
auses inform
What c
perceived risk
and relevance
e?
and relevanc
es risk
ut what caus
B
purpose-spec
ificity
ence
at is the influ
Wh
moderates effe
tion
auto-comple
of the
ct of risk and re
levance
tool?
40. Take-home message
People base th
eir information
disclosure dec
the perceived
ision on
risk and relev
ance of the inf
ormation
e)
thus disclosur
(and
st
nd relevance
rceived risk a
y of the reque
Pe
cit
rpose-specifi
e pu
depend on th
The effect of ris
k and relevance
is moderated b
type: disclosure
y tool
is more “calcula
ted” when the a
remove tools ar
dd and
e used