research-article

This App Would Like to Use Your Current Location to Better Serve You: Importance of User Assent and System Transparency in Personalized Mobile Services

Authors:

S. Shyam SundarAuthors Info & Claims

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Paper No.: 537, Pages 1 - 13

https://doi.org/10.1145/3173574.3174111

Published: 21 April 2018 Publication History

Abstract

Modern mobile apps aim to provide personalized services without appearing intrusive. A common strategy is to let the user initiate the service request (e.g., "click here to receive coupons for your favorite products"), a practice known as ?overt personalization." Another strategy is to assuage users' privacy concerns by being transparent about how their data would be collected, utilized and stored. To test these two strategies, we conducted a 2 (Personalization: Overt vs. Covert) x 2 (Transparency: High vs. Low) factorial experiment, with a fifth control condition. Participants (N=302) interacted with GreenByMe, a prototype of an eco-friendly mobile application. Data show that overt personalization affects perceived control. Significant three-way interactions between power usage, perceived overt personalization and perceived information transparency was seen on perceived ease of use, trust in the app, user engagement and behavioral intention to use the app in the future. In addition, results reveal that perceived information transparency also promotes trust, which is negatively linked with privacy concerns and positively correlated with user engagement and product involvement.

Supplementary Material

ZIP File (pn4300.zip)

This zip file contains: -Questionnaire for pilot test -Questionnaire for main test -HTML file for each version of the prototype; to view each wireframe, simply open the folder and click on "index.html"

Download
9.05 MB

suppl.mov (pn4300-file5.mp4)

Supplemental video

Download
2.51 MB

References

[1]

Sherman R. Alpert, John Karat, Clare-Marie Karat, Carolyn Brodie, and John G. Vergo. 2003. User Attitudes regarding a user-adaptive ecommerce web site. User Modeling and User-Adapted Interaction 13, 4: 373--396.

Digital Library

[2]

James R. Averill. 1973. Personal control over aversive stimuli and its relationship to stress. Psychological Bulletin 80, 4: 286--303.

[3]

Rebecca Balebako, Florian Schaub, Idris Adjerid, Alessandro Acquisti, and Lorrie Cranor. 2015. The Impact of timing on the salience of smartphone app privacy notices. In Proceedings of the 5th Annual ACM CCS Workshop on Security and Privacy in Smartphones and Mobile Devices - SPSM '15, 63--74.

Digital Library

[4]

Hans Bauer, Tina Reichardt, Stuart Barnes, and Marcus Neumann. 2005. Driving consumer acceptance of mobile marketing: A theoretical framework and empirical study. Journal of Electronic Commerce Research 6, 3: 181--192.

[5]

William O Bearden, Donald R Lichtenstein, and Jesse E Teel. 1984. Comparison price, coupon, and brand effects on consumer reactions to retail newspaper advertisements. Journal of Retailing 60, 2: 11.

[6]

Vladlena Benson, George Saridakis, and Hemamaali Tennakoon. 2015. Information disclosure of social media users. Information Technology&People 28, 3: 426--441.

[7]

Henriette Cramer, Vanessa Evers, Satyan Ramlal, et al. 2008. The effects of transparency on trust in and acceptance of a content-based art recommender. User Modeling and User-Adapted Interaction 18, 5: 455-- 496.

Digital Library

[8]

T. K. Das and Bing-Sheng Teng. 1998. Between trust and control: Developing confidence in partner cooperation in alliances. Academy of Management Review 23, 3: 491--512.

[9]

Tamara Dinev and Paul Hart. 2006. An extended privacy calculus model for e-commerce transactions. Information Systems Research 17, 1: 61--80.

Digital Library

[10]

Motahhare Eslami, Aimee Rickman, Kristen Vaccaro, et al. 2015. "I always assumed that I wasn't really that close to {her}." In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI '15, 153--162.

Digital Library

[11]

Naveen Farag Awad and M S Krishnan. 2006. The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Quarterly 30, 1: 13--28.

[12]

David Gefen, Elena Karahanna, and Detmar W. Straub. 2003. Trust and TAM in online shopping: An integrated model. MIS Quarterly 27, 1: 51--90.

[13]

Donna L. Hoffman and Thomas P. Novak. 1996. Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing 60, 3: 50.

[14]

Y. Hu and S. Shyam Sundar. 2010. Effects of online health sources on credibility and behavioral intentions. Communication Research 37, 1: 105--132.

[15]

Sriram Kalyanaraman and S. Shyam Sundar. 2006. The psychological appeal of personalized content in web portals: Does customization affect attitudes and behavior? Journal of Communication 56, 1: 110--132.

[16]

Rex B. Kline. 2015. Principles and Practice of Structural Equation Modeling (4th. ed.), The Guilford Press, New York, NY.

[17]

Yoon Jeon Koh and S. Shyam Sundar. 2010. Effects of specialization in computers, web sites, and web agents on e-commerce trust. International Journal of HumanComputer Studies 68, 12: 899--912.

Digital Library

[18]

Sherrie Y. X. Komiak, Weiquan Wang, and Izak Benbasat. 2004. Trust building in virtual salespersons versus in human salespersons: Similarities and differences. E-Service Journal 3, 3: 49--64.

[19]

Marios Koufaris and William Hampton-Sosa. 2004. The development of initial trust in an online company by new customers. Information and Management 41, 3: 377--397.

Digital Library

[20]

Chung Hun Lee and David A. Cranage. 2011. Personalisation--privacy paradox: The effects of personalisation and privacy assurance on customer responses to travel web sites. Tourism Management 32, 5: 987--994.

[21]

Sampada S. Marathe, S. Shyam Sundar, Marije Nije Bijvank, Henriette Van Vugt, and Jolanda Veldhuis. 2007. Who are these power users anyway? Building a psychological profile. In Annual Conference of the International Communication Association (ICA '07).

[22]

Roger C. Mayer, James H. Davis, and F. D. Schoorman. 1995. An integrative model of organizational trust. Academy of Management Review 20, 3: 709--734.

[23]

Sally J McMillan and Jang-Sun Hwang. 2002. Measures of perceived interactivity: An exploration of the role of direction of communication, user control, and time in shaping perceptions of interactivity. Journal of Advertising 31, 3: 29--42.

[24]

Kristopher Micinski, Daniel Votipka, Rock Stevens, Nikolaos Kofinas, Michelle L Mazurek, and Jeffrey S Foster. 2017. User interactions and permission use on android. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI '17, 362--373.

Digital Library

[25]

George R. Milne and Mary J. Culnan. 2004. Strategies for reducing online privacy risks: Why consumers read (or don't read) online privacy notices. Journal of Interactive Marketing 18, 3: 15--29.

[26]

Anthony D Miyazaki. 2008. Online privacy and the disclosure of cookie use: Effects on consumer trust and anticipated patronage. Journal of Public Policy&Marketing 27, 1: 19--33.

[27]

Jakob Nielsen and Rolf Molich. 1990. Heuristic evaluation of user interfaces. In Proceedings of the SIGCHI conference on Human factors in computing systems Empowering people - CHI '90, 249--256.

Digital Library

[28]

Jeeyun Oh, Saraswathi Bellur, and S. Shyam Sundar. 2015. Clicking, assessing, immersing, and sharing: An empirical model of user engagement with interactive media. Communication Research: 1--27.

[29]

Enrique Bonsón Ponte, Elena Carvajal-Trujillo, and Tomás Escobar-Rodríguez. 2015. Influence of trust and perceived value on the intention to purchase travel online: integrating the effects of assurance on trust antecedents. Tourism Management 47: 286--302.

[30]

Rashmi Sinha and Kirsten Swearingen. 2002. The role of transparency in recommender systems. In CHI '02 extended abstracts on Human factors in computing systems - CHI '02, 830.

Digital Library

[31]

Ji Hee Song, Hye Young Kim, Sahangsoon Kim, Sung Won Lee, and Jong-Ho Lee. 2016. Effects of personalized e-mail messages on privacy risk: Moderating roles of control and intimacy. Marketing Letters 27, 1: 89--101.

[32]

S Shyam Sundar, Haiyan Jia, T Franklin Waddell, and Yan Huang. 2015. Toward a theory of interactive media effects (TIME). The Handbook of the Psychology of Communication Technology: 47--86.

[33]

S. Shyam Sundar. 2008. Self as source: Agency and customization in interactive media. In Mediated interpersonal communication, 58--74.

[34]

S. Shyam Sundar and Sriram Kalyanaraman. 2004. Arousal, memory, and impression-formation effects of animation speed in web advertising. Journal of Advertising 33, 1: 7--17.

[35]

S. Shyam Sundar and Sampada S. Marathe. 2010. Personalization vs. customization: The importance of agency, privacy and power usage. Human Communication Research 36, 3: 298--322.

[36]

S. Shyam Sundar, Jeeyun Oh, Saraswathi Bellur, Haiyan Jia, and Hyang-Sook Kim. 2012. Interactivity as self-expression: A field experiment with customization and blogging. In Proceedings of the 2012 Annual Conference on Human Factors in Computing Systems (CHI'12), 395--404.

Digital Library

[37]

Janice Y. Tsai, Serge Egelman, Lorrie Cranor, and Alessandro Acquisti. 2011. The effect of online privacy information on purchasing behavior: An experimental study. Information Systems Research 22, 2: 254--268.

Digital Library

[38]

Ramaprasad Unni and Robert Harmon. 2007. Perceived effectiveness of push vs. pull mobile location based advertising. Journal of Interactive Advertising 7, 2: 28--40.

[39]

Max Van Kleek, Ilaria Liccardi, Reuben Binns, Jun Zhao, Daniel J Weitzner, and Nigel Shadbolt. 2017. Better the devil you know: Exposing the data sharing practices of smartphone apps. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI'17 5208--5220

Digital Library

[40]

Mark van Setten, Stanislav Pokraev, and Johan Koolwaaij. 2004. Context-aware recommendations in the mobile tourist application COMPASS. In Adaptive Hypermedia and Adaptive Web-Based Systems. 235-- 244.

[41]

Weiquan Wang and Izak Benbasat. 2007. Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. Journal of Management Information Systems 23, 4: 217--246.

Digital Library

[42]

Patricia M. West, Dan Ariely, Steve Bellman, et al. 1999. No title. Marketing Letters 10, 3: 285--300.

[43]

Heng Xu, Xin (Robert) Luo, John M. Carroll, and Mary Beth Rosson. 2011. The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Systems 51, 1: 42--52.

Digital Library

[44]

Heng Xu, Hock-Hai Teo, Bernard C. Y. Tan, and Ritu Agarwal. 2009. The role of push-pull technology in privacy calculus: The case of location-based services. Journal of Management Information Systems 26, 3: 135--174.

Digital Library

[45]

Judith Lynne Zaichkowsky. 1985. Measuring the involvement construct. Journal of Consumer Research 12, 3: 341--352.

[46]

Bo Zhang, Na Wang, and Hongxia Jin. 2014. Privacy concerns in online recommender systems: Influences of control and user data input. In SOUPS '14: Proceedings of the Tenth Symposium On Usable Privacy and Security, 159--173.

Cited By

De Vincenzi MMoore JSmith BSarma SMatteucci I(2025)Security Risks and Designs in the Connected Vehicle Ecosystem: In-Vehicle and Edge PlatformsIEEE Open Journal of Vehicular Technology10.1109/OJVT.2024.35240886(442-454)Online publication date: 2025
https://doi.org/10.1109/OJVT.2024.3524088
Snyder ESundar SLee J(2024)Reducing Reactance to Ads by Voice Assistants: The Role of Ad Customization, Ad Personalization, and Privacy CustomizationJournal of Interactive Marketing10.1177/10949968241261301Online publication date: 1-Aug-2024
https://doi.org/10.1177/10949968241261301
Mühl LStecker LHerter ESzczuka JWischnewski MKrämer N(2024)Integrating AI in Psychotherapy: An Investigation of Trust in Voicebot TherapistsProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685353(1-9)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685353
Show More Cited By

Index Terms

This App Would Like to Use Your Current Location to Better Serve You: Importance of User Assent and System Transparency in Personalized Mobile Services
1. Human-centered computing

Recommendations

Personalization-privacy paradox and consumer conflict with the use of location-based mobile commerce

This study empirically explored consumers' response to the personalization-privacy paradox arising from the use of location-based mobile commerce (LBMC) and investigated the factors affecting consumers' psychological and behavioral reactions to the ...
Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model

Derived from the theory of reasoned action, the technology acceptance model (TAM) focuses on two specific salient beliefs-- ease of use and usefulness. It has been applied in the study of user adoption of different technologies, and has emerged as a ...
Disclosure Intention of Location-Related Information in Location-Based Social Network Services

Although location-based social network (LBSN) services have developed rapidly in recent years, the reasons why people disclose location-related information under this environment have not been adequately investigated. This study builds a privacy ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

April 2018

8489 pages

ISBN:9781450356206

DOI:10.1145/3173574

General Chairs:
Regan Mandryk
University of Saskatchewan, Canada
,
Mark Hancock
University of Waterloo, Canada
,
Program Chairs:
Mark Perry
Brunel University London, UK
,
Anna Cox
University College London, UK

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NSF USA

Conference

CHI '18

Sponsor:

SIGCHI

CHI '18: CHI Conference on Human Factors in Computing Systems

April 21 - 26, 2018

Montreal QC, Canada

Acceptance Rates

CHI '18 Paper Acceptance Rate 666 of 2,590 submissions, 26%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

30
Total Citations
View Citations
1,124
Total Downloads

Downloads (Last 12 months)130
Downloads (Last 6 weeks)11

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

De Vincenzi MMoore JSmith BSarma SMatteucci I(2025)Security Risks and Designs in the Connected Vehicle Ecosystem: In-Vehicle and Edge PlatformsIEEE Open Journal of Vehicular Technology10.1109/OJVT.2024.35240886(442-454)Online publication date: 2025
https://doi.org/10.1109/OJVT.2024.3524088
Snyder ESundar SLee J(2024)Reducing Reactance to Ads by Voice Assistants: The Role of Ad Customization, Ad Personalization, and Privacy CustomizationJournal of Interactive Marketing10.1177/10949968241261301Online publication date: 1-Aug-2024
https://doi.org/10.1177/10949968241261301
Mühl LStecker LHerter ESzczuka JWischnewski MKrämer N(2024)Integrating AI in Psychotherapy: An Investigation of Trust in Voicebot TherapistsProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685353(1-9)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685353
Loefflad CGrossklags J(2024)How the Types of Consequences in Social Scoring Systems Shape People's Perceptions and Behavioral ReactionsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658986(1515-1530)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658986
Asthana SIm JChen ZBanovic N(2024)"I know even if you don't tell me": Understanding Users' Privacy Preferences Regarding AI-based Inferences of Sensitive Information for PersonalizationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642180(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642180
Lee SMoon WSong Y(2024)Is It Transparent or Surveillant? The Effects of Personalized Advertising and Privacy Policy on Advertising EffectivenessJournal of Interactive Advertising10.1080/15252019.2024.2417422(1-21)Online publication date: 11-Dec-2024
https://doi.org/10.1080/15252019.2024.2417422
Chen YWu XJia FYang JBai XYu R(2024)Exploring the Impact of Social Robot Design Characteristics on Users’ Privacy Concerns: Evidence from PLS-SEM and FsQCAInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2402126(1-22)Online publication date: 17-Sep-2024
https://doi.org/10.1080/10447318.2024.2402126
Trinh NNguyen QDung LNguyen Q(2023)Tourism Apps Acceptance Among Generation Z in VietnamJournal of Law and Sustainable Development10.55908/sdgs.v11i12.159611:12(e1596)Online publication date: 1-Dec-2023
https://doi.org/10.55908/sdgs.v11i12.1596
Kim TMolina MRheu MZhan EPeng W(2023)One AI Does Not Fit All: A Cluster Analysis of the Laypeople’s Perception of AI RolesProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581340(1-20)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581340
Alhasani MOyebode OOrji R(2023)SereneMind: Design and Evaluation of a Persuasive Mobile App for Managing Stress Among Adults2023 IEEE 11th International Conference on Serious Games and Applications for Health (SeGAH)10.1109/SeGAH57547.2023.10253762(1-8)Online publication date: 28-Aug-2023
https://doi.org/10.1109/SeGAH57547.2023.10253762
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten