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TitlePersonal information value from the user perspective
LanguageEnglish
File Size5.5 MB
Total Pages180
Table of Contents
                            Contents
List of the tables
List of the figures
List of the (ordinary) linear regressions
List of abbreviations
Introduction
1 Data as the currency of the 21st century
	1.1 The dawn of free services online with the Web 2.0
	1.2 Once an amusing experiment, now the main hub of personal information
	1.3 The value of personal information
2 Call to quantify the value of the personal information
	2.1 The previous attempts to frame the phenomenon
	2.2 The Ca’ Foscari University students as the target population
	2.3 The roadmap of the test
		2.3.1 The successful gathering of data and the 5 minute effortless survey mission
		2.3.2 The interviewing method
		2.3.3 The processing of the obtained data set
		2.3.4 The search for meaningful correlations between the variables
3 The survey
	3.1 Gauging the effects of time, how a long experience raises the awareness
		3.1.1 Checking whether longer experiences are worth more than the new adventures
		3.1.2 Measuring the dependency of the online activities with the intensities of use
	3.2 Observing the effects of a wide social network offer
	3.3 Monitoring the effects of different privacy management behaviors
		3.3.1 Partitioning the identity revealers with the level of public participation
		3.3.2 Probing the familiarity with the rules with the cognition of the terms of service
		3.3.3 Assessing the tracking acceptance, knowledge and annoyance
	3.4 Collecting the opinions about the free services revenue sources
	3.5 Collecting the value perceived of the personal information
		3.5.1 The maximum fee offer to retain the main social network functionality
		3.5.2 A pay or leave option to check the estimations consistency
	3.6 The layout of the survey and the opportunity for additional data
4 The survey results
	4.1 The survey duration
	4.2 A general overview
	4.3 Focus on the acquaintance of the web
		4.3.1 The Year of access, the indirect measure for the total permanence on the Web
		4.3.2 The Year of 1st social, the birthday in the socializing side of the Web
		4.3.3 The ratio between the years spent online with or without the social networks
		4.3.4 The Hours spent surfing and socializing, the intensities of the online activities
		4.3.5 The balance of the time-consuming activities online
	4.4 Focus on: The distribution of the social network profiles
		4.4.1 The Socials used and the extraction of the Numbers of socials used
		4.4.2 The social networks interoperability and ecosystems
	4.5 Focus on: The basic online privacy habits
		4.5.1 The Public participation and the choice to disclose the identity
		4.5.2 The Terms of service cognition, assessing an implicit consensus
		4.5.3 The Tracking acceptance between awareness and annoyance
		4.5.4 Choice patterns between the TOS cognition and the Tracking acceptance
	4.6 Focus on: The participants opinion about the free services income sources
		4.6.1 Income opinions, the first place source is clear
		4.6.2 A synthetic ranking for the remaining positions
	4.7 Focus on: How much students value their major profile
		4.7.1 The Maximum fee estimated to continue using the main social network
		4.7.2 The outcome of the Pay or leave option
		4.7.3 The coherence of the two estimation variables values
	4.8 The success of the optional fields
	4.9 Exit polls and visual analysis of the consistence of the results
5 The analysis of the survey results
	5.1 Effects of the two online permanences on the estimation variables
	5.2 Effects of intensity of use over the estimation variables
	5.3 The effects of a wide social network offer over the estimation variables
		5.3.1 Focus on the Socials used
		5.3.2 Focus on the Number of socials
	5.4 The effects of the Public participation over the estimation variables
	5.5 The effects of cognition of the rules over the estimation variables
	5.6 The effects of tracking acceptance over the estimation variables
	5.7 The effects of the income opinions over the estimation variables
	5.8 The effects of open sourcing over the estimation variables
	5.9 Generalizing the estimation variables with the framing variables
Conclusions
Appendices
	a) The survey
	I) Introduction block
		II) First block: Framing and first evaluation
		III) Second block: Exposition to real estimates and coherency check
		IV) Third block: Extra data retrieval, further acknowledgement and thanksgiving
	b) The landing page layout
References
Software used
                        
Document Text Contents
Page 1

Master's Degree programme – Second Cycle
(D.M. 270/2004)

in Economia e Gestione delle Aziende – International Management


Final Thesis


Personal information value from the user
perspective: an empirical study on the

students of Ca’ Foscari








Supervisor
Ch. Prof. Massimo Warglien




Graduand
Marco Byloos
Matriculation Number 842130

Academic Year
2016 / 2017

Page 2

I

I would like to thank my supervisor, for his wise advices during the creation of this document and for

holding the course on “Making decisions” that conflagrated my imagination and inspired me to do

my master degree thesis on this topic. Then, I would like to thank the over one thousand students of

the Ca’ Foscari University that took part in this survey, if this research has any merit, one thousandth

per participant is also yours and I want to share it with you!



Thank you to:



Marco B., Stela B., Luca P., Marco M., Myshi G., Ana T., Enrico C., Nicola B., Pierbarra, Omar R.,

Roberto P., Sam Haoliang H., Alessio B. Thuong N., Nicoló C., Alessia I., Lucrezia C., Benedetta B.,

Giulia Z., Michele S., Christian S., Riccardo P., Riccardo B., Alessia d. C., Virginia L., Alessandro

M., Naldy M., Silvia, Elisa C., Stefania M., Silvia, Hicham M., Antonietta S., Giada A., Beatrice F.,

Sara F., Marianna, Anna B., Federico D., Siderisgravehood, Riccardo d. A., Elisa G., Eda F., Elisa

M., Aleksandra G., Roberta G., Riccardo A., Alvise M., Veronica B., Eleonora d. R., Angela d. T.,

Thomas C., Quan P., Margherita R., Martina Z., Filippo L.P., Amos B., Silvia S., Riccardo C.,

Costanza S., Luna M.., Shahpara H., Chiara B., Laura M., Pier Alberto M., Giovanni O., William T.,

Michael B., Anna P., Marangoni G., Bianca V., Tania M., Martina C., Ilaria B., Francesca d. F.,

Emanuele B., Olga P., Michela, Veronica, Valentina M., Chiara M., Chiara S., Gaia, Tani, Arianna

B., Christian G., Linda P., Mattia B., Alberto F.., Tommaso F., Giorgia F., Nicoló P., Matteo, Daniela

M., Regina P., Sabrina d. P., Rossella Z., Clarissa P., Chiara, Giacomo C., Dawda J., Gianluca B.,

Sara Z., Elena d. M., Lorenzo C., Teresa Z., Federico B., Cecilia d. G., Paola R.B., Emma C., Maria,

Diletta C., Silvia G., Sebastiano A., Francesca, Luo F., Mattia T., Beatrice G., Luce, Alessandra V.,

Beatrice Z., Elisa P., Carol, Afnan, Vera L., Kuraishinju, Daniele, Giuliana, Chiara R., Elisabetta

Sofia P., Emilia R., Susan R., Martina B., Valentica C., Rita, Chiara M., Michelangelo S.,

Michelangelo M., Riccardo R., Francesca, Silvia C., Johnny Haze, Lucia T., Count of Oak, Spica,

Francesco R., Federico P., Marta B., Laura B., Arianna G., Lavinia S., Alessandro M., Lucia d. N.,

Max C., Miri L., Alessia B., Arianna, Alice, Mattia G., Michele B.C., Annalaura G., Nicola F., Andrea

R., Dario M.G., Martina S., Giacomo D., Diana B., Laura T., Laura F., Maria Teresa, Giorgia P.,

Serena V., BruceKetta, Firiel, Nicolo' G., LSP., Beatrice C., Emma R., Fundor333, EpicCiqui, Giulia

B., Giorgia, Lorenzo C., Mohamed A., Ilaria T., Raffaella M., Matteo M., Francesco M., Elena C.,

Gabriele C., Federica R., Enrico, Nicolo' S., Eva T., Stefano R., Anna P., Marco S., Giada P., Chiara,

Avril T., Berse, Elena C., Alessio M., Elena M., Eleonora C., Letizia, Alice, Francesca P., Giulia C.,

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Figure 70 - Correlations between the Year of 1st social and the outcome of the Pay or leave option

0% 10
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*2017

Option distributions in the same year

Ye
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oc
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Corr.: Year of 1st social Vs Pay or leave option outcome

Leave Free, but even less privacy Pay 80, keep ads Pay 100, no ads



















































The coefficients of influence of the Year of access over the two estimation variables are computed in,

Regression 3 and Regression 4. As visualized in Figure 71 and Figure 72, the Year of access

coefficients maintain a near flat trend, slightly descending for the first evaluation variable, slightly

rising for the second. Both variables have not a significative correlation with the Max fee estimation.

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Coeff. of Year of access over Pay or leave outcome

Full regression trend

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nt
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al
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Year of access

Coeff. of Year of access over Max fee estimation

Full regression trend

Figure 72 - Distribution of the coefficient of the Year of access influence over the Pay or leave outcomes

Figure 71 - Distribution of the Year of access influence over the Maximum fee estimations

Dependent variable: maximumfee


Coefficient Std. Error t-ratio p-value
const 74.1787 158.116 0.4691 0.6391
yearaccess −0.0332482 0.0788656 −0.4216 0.6734



Mean dependent var 7.520325 S.D. dependent var 8.058635
Sum squared resid 63826.04 S.E. of regression 8.062008
R-squared 0.000181 Adjusted R-squared -0.000837
F(1, 982) 0.177730 P-value(F) 0.673424
Log-likelihood −3449.003 Akaike criterion 6902.005
Schwarz criterion 6911.788 Hannan-Quinn 6905.726


Regression 3 - Regression of all the Year of access entries over the Maximum fee estimation. DV: maximumfee


















Dependent variable: payorleaveoutcome


Coefficient Std. Error t-ratio p-value
const −530.805 486.720 −1.091 0.2757
yearaccess 0.280725 0.242768 1.156 0.2478



Mean dependent var 32.01220 S.D. dependent var 24.82115
Sum squared resid 604792.3 S.E. of regression 24.81689
R-squared 0.001360 Adjusted R-squared 0.000343
F(1, 982) 1.337142 P-value(F) 0.247820
Log-likelihood −4555.375 Akaike criterion 9114.749
Schwarz criterion 9124.533 Hannan-Quinn 9118.471


Regression 4 - Regression of all the Year of access entries over the Pay or leave option out. DV: payorleaveoutcome

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O'Reilly, T. (2005, September 9). What Is Web 2.0. Retrieved from O'Reilly Media:

http://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html

Price, G. (2012, March 14). How Much Does The Internet Cost To Run? Retrieved from Forbes

website: https://www.forbes.com/sites/quora/2012/03/14/how-much-does-the-internet-cost-

to-run/#789e677e1157

Sarah Spiekermann, J. K. (2012). Psychology of ownership and asset defense: why people value their

personal information beyond privacy. Thirty Third International Conference on Information

Systems. Orlando.

Sarah Spiekermann, J. K. (2012). Psychology of Ownership and Asset Defense: Why People Value

Their Personal Information Beyond Privacy. Thirty Third International Conference on

Information Systems. Orlando.

Sören Preibusch, K. K. (2012). The privacy economics of voluntary over-disclosure in Web forms.

In Proceedings of the Workshop on the Economics of Information Security (WEIS). Berlin,

Germany.

Telegram LLP. (n.d.). Telegram F.A.Q. Retrieved from https://telegram.org/faq#q-how-are-you-

going-to-make-money-out-of-this

University Ca' Foscari of Venice. (2016, April 27). Ca' Foscari, occupazione sopra la media

nazionale: la 'fotografia' di Almalaurea. Retrieved from

http://www.unive.it/nqcontent.cfm?a_id=201385

Wallsten, S. (2013, October). What we're not doing when we are online. Retrieved from National

bureau of economic research: http://www.nber.org/papers/w19549

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Software used

Qualtrics® survey platform through the account of the Department of Management of Ca’ Foscari

Microsoft™ Excel® 2016, part of the student’s free copy of Office 365® suite

gretl© 2017c, x86_64bit edition for Windows®

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