• Login
    View Item 
    •   Home
    • UA Faculty Research
    • UA Faculty Publications
    • View Item
    •   Home
    • UA Faculty Research
    • UA Faculty Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Augmenting Password Strength Meter Design Using the Elaboration Likelihood Model: Evidence from Randomized Experiments

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Paper with Appendix.pdf
    Size:
    1.122Mb
    Format:
    PDF
    Description:
    Final Accepted Manuscript
    Download
    Author
    Khern-am-nuai, W.
    Hashim, M.J.
    Pinsonneault, A.
    Yang, W.
    Li, N.
    Affiliation
    Eller College of Management, University of Arizona
    Issue Date
    2022
    Keywords
    password strength meter
    design science
    elaboration likelihood model
    randomized experiment
    
    Metadata
    Show full item record
    Publisher
    INFORMS
    Citation
    Khern-am-nuai, W., Hashim, M. J., Pinsonneault, A., Yang, W., & Li, N. (2022). Augmenting Password Strength Meter Design Using the Elaboration Likelihood Model: Evidence from Randomized Experiments. Information Systems Research.
    Journal
    Information Systems Research
    Rights
    © 2022 INFORMS.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Password-based authentication is the most commonly used method for gaining access to secured systems. Unfortunately, empirical evidence highlights the fact that most passwords are significantly weak, and encouraging users to create stronger passwords is a significant challenge. In this research, we propose a theoretically augmented password strength meter design that is guided by the elaboration likelihood model of persuasion (ELM). We evaluate our design by leveraging three independent and complementary methods: a survey-based experiment using students to evaluate the saliency of our conceptual design (proof of concept), a controlled laboratory experiment conducted on Amazon Mechanical Turk to test the effectiveness of the proposed design (proof of value), and a randomized field experiment conducted in collaboration with an online forum in Asia to establish proof of use. In each study, we observe the changes in users’ behavior in response to our proposed password strength meter. We find that the ELM-augmented password strength meter is significantly effective at addressing the challenges of password-based authentication. Users exposed to this strength meter are more likely to change their passwords, leading to a new password that is significantly stronger. Our findings suggest that the proposed design of augmented password strength meters is an effective method for promoting secure password behavior among end users.
    Note
    12 month embargo; published online: 23 March 2022
    ISSN
    1047-7047
    DOI
    10.1287/isre.2022.1125
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1287/isre.2022.1125
    Scopus Count
    Collections
    UA Faculty Publications

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.