IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0251964.html
   My bibliography  Save this article

Paths to social licence for tracking-data analytics in university research and services

Author

Listed:
  • Joshua P White
  • Simon Dennis
  • Martin Tomko
  • Jessica Bell
  • Stephan Winter

Abstract

While tracking-data analytics can be a goldmine for institutions and companies, the inherent privacy concerns also form a legal, ethical and social minefield. We present a study that seeks to understand the extent and circumstances under which tracking-data analytics is undertaken with social licence—that is, with broad community acceptance beyond formal compliance with legal requirements. Taking a University campus environment as a case, we enquire about the social licence for Wi-Fi-based tracking-data analytics. Staff and student participants answered a questionnaire presenting hypothetical scenarios involving Wi-Fi tracking for university research and services. Our results present a Bayesian logistic mixed-effects regression of acceptability judgements as a function of participant ratings on 11 privacy dimensions. Results show widespread acceptance of tracking-data analytics on campus and suggest that trust, individual benefit, data sensitivity, risk of harm and institutional respect for privacy are the most predictive factors determining this acceptance judgement.

Suggested Citation

  • Joshua P White & Simon Dennis & Martin Tomko & Jessica Bell & Stephan Winter, 2021. "Paths to social licence for tracking-data analytics in university research and services," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-19, May.
  • Handle: RePEc:plo:pone00:0251964
    DOI: 10.1371/journal.pone.0251964
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251964
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0251964&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0251964?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Moffat, Kieren & Zhang, Airong, 2014. "The paths to social licence to operate: An integrative model explaining community acceptance of mining," Resources Policy, Elsevier, vol. 39(C), pages 61-70.
    4. Bürkner, Paul-Christian, 2017. "brms: An R Package for Bayesian Multilevel Models Using Stan," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i01).
    5. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    6. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jack S. Gisby & Norzawani B. Buang & Artemis Papadaki & Candice L. Clarke & Talat H. Malik & Nicholas Medjeral-Thomas & Damiola Pinheiro & Paige M. Mortimer & Shanice Lewis & Eleanor Sandhu & Stephen , 2022. "Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence," Nature Communications, Nature, vol. 13(1), pages 1-21, December.
    2. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    3. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    4. Jian Lu & Raheel Ahmad & Thomas Nguyen & Jeffrey Cifello & Humza Hemani & Jiangyuan Li & Jinguo Chen & Siyi Li & Jing Wang & Achouak Achour & Joseph Chen & Meagan Colie & Ana Lustig & Christopher Dunn, 2022. "Heterogeneity and transcriptome changes of human CD8+ T cells across nine decades of life," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Jun Wang & Jinyong Huang & Yunlong Hu & Qianwen Guo & Shasha Zhang & Jinglin Tian & Yanqin Niu & Ling Ji & Yuzhong Xu & Peijun Tang & Yaqin He & Yuna Wang & Shuya Zhang & Hao Yang & Kang Kang & Xinchu, 2024. "Terminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    6. Elsner, James B. & Schroder, Zoe, 2019. "Tornado damage ratings estimated with cumulative logistic regression," Earth Arxiv k9wv6, Center for Open Science.
    7. Hongbo Guo & Enzai Du & César Terrer & Robert B. Jackson, 2024. "Global distribution of surface soil organic carbon in urban greenspaces," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    8. Marc Jan Bonder & Stephen J. Clark & Felix Krueger & Siyuan Luo & João Agostinho de Sousa & Aida M. Hashtroud & Thomas M. Stubbs & Anne-Katrien Stark & Steffen Rulands & Oliver Stegle & Wolf Reik & Fe, 2024. "scEpiAge: an age predictor highlighting single-cell ageing heterogeneity in mouse blood," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    9. David L. Miller & Richard Glennie & Andrew E. Seaton, 2020. "Understanding the Stochastic Partial Differential Equation Approach to Smoothing," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(1), pages 1-16, March.
    10. Erik Duijvelaar & Jack Gisby & James E. Peters & Harm Jan Bogaard & Jurjan Aman, 2024. "Longitudinal plasma proteomics reveals biomarkers of alveolar-capillary barrier disruption in critically ill COVID-19 patients," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    11. Paweł Teisseyre & Robert A. Kłopotek & Jan Mielniczuk, 2016. "Random Subspace Method for high-dimensional regression with the R package regRSM," Computational Statistics, Springer, vol. 31(3), pages 943-972, September.
    12. Barbara Emmenegger & Julien Massoni & Christine M. Pestalozzi & Miriam Bortfeld-Miller & Benjamin A. Maier & Julia A. Vorholt, 2023. "Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    13. Lauren P. Grant & Chris Gennings & Edmond P. Wickham & Derek Chapman & Shumei Sun & David C. Wheeler, 2018. "Modeling Pediatric Body Mass Index and Neighborhood Environment at Different Spatial Scales," IJERPH, MDPI, vol. 15(3), pages 1-19, March.
    14. Yang Yue & Yingjie Jiang & Fan Zhou & Yuantao Jiang & Yiting Long & Kaiyu Wang, 2022. "Reward Uncertainty and Expected Value Enhance Generalization of Episodic Memory," IJERPH, MDPI, vol. 19(21), pages 1-16, November.
    15. Patrick C Eschenfeldt & Uri Kartoun & Curtis R Heberle & Chung Yin Kong & Norman S Nishioka & Kenney Ng & Sagar Kamarthi & Chin Hur, 2018. "Analysis of factors associated with extended recovery time after colonoscopy," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-16, June.
    16. Rachel Sippy & Daniel F Farrell & Daniel A Lichtenstein & Ryan Nightingale & Megan A Harris & Joseph Toth & Paris Hantztidiamantis & Nicholas Usher & Cinthya Cueva Aponte & Julio Barzallo Aguilar & An, 2020. "Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(2), pages 1-20, February.
    17. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    18. Lindeløv, Jonas Kristoffer, 2020. "mcp: An R Package for Regression With Multiple Change Points," OSF Preprints fzqxv, Center for Open Science.
    19. Heather E Wheeler & Kaanan P Shah & Jonathon Brenner & Tzintzuni Garcia & Keston Aquino-Michaels & GTEx Consortium & Nancy J Cox & Dan L Nicolae & Hae Kyung Im, 2016. "Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues," PLOS Genetics, Public Library of Science, vol. 12(11), pages 1-23, November.
    20. Gaia Molinaro & Irene Cogliati Dezza & Sarah Katharina Bühler & Christina Moutsiana & Tali Sharot, 2023. "Multifaceted information-seeking motives in children," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0251964. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.