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Improving multiple-password recall: an empirical study

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Listed:
  • Jie Zhang
  • Xin Luo
  • Somasheker Akkaladevi
  • Jennifer Ziegelmayer

Abstract

As one of the most common authentication methods, passwords help secure information by granting access only to authorized parties. To be effective, passwords should be strong, secret, and memorable. While password strength can be enforced by automated information technology policies, users frequently jeopardize secrecy to improve memorability. The password memorability problem is exacerbated by the number of different passwords a user is required to remember. While short-term memory theories have been applied to individual-password management problems, the relationship between memory and the multiple-password problem has not been examined. This paper treats the multiple-password management crisis as a search and retrieval problem involving human beings’ long-term memory. We propose that interference between different passwords is one of the major challenges to multiple-password recall and that interference alleviation methods can significantly improve multiple-password recall. A lab experiment was conducted to examine the effectiveness of two interference alleviation methods: the list reduction method and the unique identifier method. While both methods improve multiple-password recall performance, the list reduction method leads to statistically significant improvement. The results demonstrate the potential merit of practices targeting multiple-password interference. By introducing long-term memory theory to multiple-password memorability issues, this study presents implications benefiting users and serves as the potential starting point for future research.

Suggested Citation

  • Jie Zhang & Xin Luo & Somasheker Akkaladevi & Jennifer Ziegelmayer, 2009. "Improving multiple-password recall: an empirical study," European Journal of Information Systems, Taylor & Francis Journals, vol. 18(2), pages 165-176, April.
  • Handle: RePEc:taf:tjisxx:v:18:y:2009:i:2:p:165-176
    DOI: 10.1057/ejis.2009.9
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    Cited by:

    1. Obi M. Ogbanufe & Corey Baham, 2023. "Using Multi-Factor Authentication for Online Account Security: Examining the Influence of Anticipated Regret," Information Systems Frontiers, Springer, vol. 25(2), pages 897-916, April.
    2. Warut Khern-am-nuai & Matthew J. Hashim & Alain Pinsonneault & Weining Yang & Ninghui Li, 2023. "Augmenting Password Strength Meter Design Using the Elaboration Likelihood Model: Evidence from Randomized Experiments," Information Systems Research, INFORMS, vol. 34(1), pages 157-177, March.

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