Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes

Dirk van der Linden; Irit Hadar; Matthew Edwards; Awais Rashid

doi:10.1007/978-3-030-55958-8_3

Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes

Dirk van der Linden, Irit Hadar, Matthew Edwards, Awais Rashid

Department of Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Understanding developers’ attitudes towards handling personal data is vital in order to understand whether the software they create handles their users’ privacy fairly. We present the results of a study adapting an existing user-focused privacy concern scale to a software development context and running it with a sample of 123 software developers, in order to validate it and develop a model for measuring the extent to which a software developer is (dis)favorable to ensuring their users’ privacy. The developed scale exceeds thresholds for internal reliability (α>.8), composite reliability (CR >.8), and convergent validity (AVE >.6). Our findings identified a model consisting of three factors that allows for understanding of developers’ attitudes, including: (1) informed consent, (2) data minimization, and (3) data monetization. Through analysis of results from the scale’s deployment, we further discuss mismatches between developers’ attitudes and their self-perceived extent of properly handling their users’ privacy, and the importance of understanding developers’ attitudes towards data monetization.

Original language	English
Title of host publication	Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers
Editors	Thomas Groß, Theo Tryfonas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	47-65
Number of pages	19
ISBN (Print)	9783030559571
DOIs	https://doi.org/10.1007/978-3-030-55958-8_3
State	Published - 2021
Event	9th International Workshop on Socio-Technical Aspects in Security and Trust, STAST 2019 - Luxembourg, Luxembourg Duration: 26 Sep 2019 → 26 Sep 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11739 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Workshop on Socio-Technical Aspects in Security and Trust, STAST 2019
Country/Territory	Luxembourg
City	Luxembourg
Period	26/09/19 → 26/09/19

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Attitude
Developer
Privacy
Scale development

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-55958-8_3

Cite this

van der Linden, D., Hadar, I., Edwards, M., & Rashid, A. (2021). Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes. In T. Groß, & T. Tryfonas (Eds.), Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers (pp. 47-65). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11739 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-55958-8_3

van der Linden, Dirk ; Hadar, Irit ; Edwards, Matthew et al. / Data, Data, Everywhere : Quantifying Software Developers’ Privacy Attitudes. Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers. editor / Thomas Groß ; Theo Tryfonas. Springer Science and Business Media Deutschland GmbH, 2021. pp. 47-65 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{bef06ac89e894d78b0b52881790bc9f5,

title = "Data, Data, Everywhere: Quantifying Software Developers{\textquoteright} Privacy Attitudes",

abstract = "Understanding developers{\textquoteright} attitudes towards handling personal data is vital in order to understand whether the software they create handles their users{\textquoteright} privacy fairly. We present the results of a study adapting an existing user-focused privacy concern scale to a software development context and running it with a sample of 123 software developers, in order to validate it and develop a model for measuring the extent to which a software developer is (dis)favorable to ensuring their users{\textquoteright} privacy. The developed scale exceeds thresholds for internal reliability (α>.8), composite reliability (CR >.8), and convergent validity (AVE >.6). Our findings identified a model consisting of three factors that allows for understanding of developers{\textquoteright} attitudes, including: (1) informed consent, (2) data minimization, and (3) data monetization. Through analysis of results from the scale{\textquoteright}s deployment, we further discuss mismatches between developers{\textquoteright} attitudes and their self-perceived extent of properly handling their users{\textquoteright} privacy, and the importance of understanding developers{\textquoteright} attitudes towards data monetization.",

keywords = "Attitude, Developer, Privacy, Scale development",

author = "\{van der Linden\}, Dirk and Irit Hadar and Matthew Edwards and Awais Rashid",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 9th International Workshop on Socio-Technical Aspects in Security and Trust, STAST 2019 ; Conference date: 26-09-2019 Through 26-09-2019",

year = "2021",

doi = "10.1007/978-3-030-55958-8\_3",

language = "English",

isbn = "9783030559571",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "47--65",

editor = "Thomas Gro{\ss} and Theo Tryfonas",

booktitle = "Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers",

address = "Germany",

}

van der Linden, D, Hadar, I, Edwards, M & Rashid, A 2021, Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes. in T Groß & T Tryfonas (eds), Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11739 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 47-65, 9th International Workshop on Socio-Technical Aspects in Security and Trust, STAST 2019, Luxembourg, Luxembourg, 26/09/19. https://doi.org/10.1007/978-3-030-55958-8_3

Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes. / van der Linden, Dirk; Hadar, Irit; Edwards, Matthew et al.
Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers. ed. / Thomas Groß; Theo Tryfonas. Springer Science and Business Media Deutschland GmbH, 2021. p. 47-65 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11739 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data, Data, Everywhere

T2 - 9th International Workshop on Socio-Technical Aspects in Security and Trust, STAST 2019

AU - van der Linden, Dirk

AU - Hadar, Irit

AU - Edwards, Matthew

AU - Rashid, Awais

PY - 2021

Y1 - 2021

N2 - Understanding developers’ attitudes towards handling personal data is vital in order to understand whether the software they create handles their users’ privacy fairly. We present the results of a study adapting an existing user-focused privacy concern scale to a software development context and running it with a sample of 123 software developers, in order to validate it and develop a model for measuring the extent to which a software developer is (dis)favorable to ensuring their users’ privacy. The developed scale exceeds thresholds for internal reliability (α>.8), composite reliability (CR >.8), and convergent validity (AVE >.6). Our findings identified a model consisting of three factors that allows for understanding of developers’ attitudes, including: (1) informed consent, (2) data minimization, and (3) data monetization. Through analysis of results from the scale’s deployment, we further discuss mismatches between developers’ attitudes and their self-perceived extent of properly handling their users’ privacy, and the importance of understanding developers’ attitudes towards data monetization.

AB - Understanding developers’ attitudes towards handling personal data is vital in order to understand whether the software they create handles their users’ privacy fairly. We present the results of a study adapting an existing user-focused privacy concern scale to a software development context and running it with a sample of 123 software developers, in order to validate it and develop a model for measuring the extent to which a software developer is (dis)favorable to ensuring their users’ privacy. The developed scale exceeds thresholds for internal reliability (α>.8), composite reliability (CR >.8), and convergent validity (AVE >.6). Our findings identified a model consisting of three factors that allows for understanding of developers’ attitudes, including: (1) informed consent, (2) data minimization, and (3) data monetization. Through analysis of results from the scale’s deployment, we further discuss mismatches between developers’ attitudes and their self-perceived extent of properly handling their users’ privacy, and the importance of understanding developers’ attitudes towards data monetization.

KW - Attitude

KW - Developer

KW - Privacy

KW - Scale development

UR - https://www.scopus.com/pages/publications/85111018674

U2 - 10.1007/978-3-030-55958-8_3

DO - 10.1007/978-3-030-55958-8_3

M3 - Conference contribution

AN - SCOPUS:85111018674

SN - 9783030559571

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 47

EP - 65

BT - Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers

A2 - Groß, Thomas

A2 - Tryfonas, Theo

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 26 September 2019 through 26 September 2019

ER -

van der Linden D, Hadar I, Edwards M, Rashid A. Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes. In Groß T, Tryfonas T, editors, Socio-Technical Aspects in Security and Trust - 9th International Workshop, STAST 2019, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 47-65. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-55958-8_3

Data, Data, Everywhere: Quantifying Software Developers’ Privacy Attitudes

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this