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      MIGRANT : Modeling Smartphone Password Manager Adoption using Migration Theory

      1 , 2
      ACM SIGMIS Database: the DATABASE for Advances in Information Systems
      Association for Computing Machinery (ACM)

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          Abstract

          Password manager applications have the potential to alleviate password pain and improve password strength, yet they are not widely adopted. Password managers are dissimilar to other kinds of software tools, given that the leakage of the credentials they store could give a hacker access to all the individual's online accounts. Moreover, adoption requires a deliberate switch away from an existing (manual) password management routine. As such, traditional technology adoption models are unlikely to model password manager adoption accurately. In this paper, we propose and explain how we validated a theoretical model of smartphone password manager adoption. We commenced by carrying out exploratory interviews with 30 smartphone owners to identify factors that influence adoption. These were used to develop a model that reflects the password manager adoption process, building on migration theory. The proposed model, MIGRANT (MIGRation pAssword maNager adopTion), was validated and subsequently refined in a week-long study with 198 smartphone owners, combining self-report and observation to measure constructs. This study contributes to the information security behavioral literature by isolating the main factors that encourage or deter password manager adoption, and those that moor smartphone owners in their current practices, hindering switching. With this investigation, we introduce migration theory as a reference theory for future studies in the information security behavioral field.

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              Multivariate Data Analysis

              For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. This text provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques. In this revision, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques. :Pearson New International Edition.
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                Author and article information

                Journal
                ACM SIGMIS Database: the DATABASE for Advances in Information Systems
                SIGMIS Database
                Association for Computing Machinery (ACM)
                0095-0033
                1532-0936
                April 22 2022
                April 22 2022
                : 53
                : 2
                : 63-95
                Affiliations
                [1 ]King Saud University, Riyadh, Saudi Arabia
                [2 ]University of Strathclyde & Rhodes University & University of South Africa, Glasgow, United Kingdom
                Article
                10.1145/3533692.3533698
                910ebd25-fc47-4253-903c-588def747f45
                © 2022
                History

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