Team Communication and Handover Failures in Multi-Stage Cloud Recovery Exercises: A Mixed-Methods Study
Abstract
Multi-stage cloud recovery depends on accurate handover between teams responsible for infrastructure, applications, networking, identity, and business validation. We examined communication breakdowns during 18 recovery exercises conducted across six organisations using a convergent mixed-methods design. Event logs and message transcripts were coded for delayed, missing, duplicated, and ambiguous handovers, while 46 participants were interviewed after each exercise. Across 1,124 documented handover events, 14% contained at least one communication defect. Ambiguous ownership and unconfirmed task completion were the most frequent problems and were associated with longer recovery stages. Exercises using a shared incident timeline and explicit handover acknowledgements had 36% fewer repeated actions. Interviews showed that teams often assumed shared understanding of service dependencies that had never been documented. Seniority did not protect against handover failure when role boundaries were unclear. Communication quality was therefore not a secondary feature of recovery execution but a measurable determinant of coordination delay. Standardised handover fields, named ownership, and explicit confirmation of readiness can reduce avoidable recovery friction across organisational boundaries.
References
1. Nazir M. Scenario-based disaster recovery training for improving incident-response and RTO/RPO decision-making among enterprise cloud engineers. Journal of Computing, Intelligence and Information Sciences. 2024. Available from: https://jciis.com/index.php/jciis/article/view/2024-01-05
2. Rose S, Borchert O, Mitchell S, Connelly S. Zero trust architecture. Gaithersburg (MD): National Institute of Standards and Technology. 2020;NIST SP 800-207. doi:10.6028/NIST.SP.800-207.
3. Swanson M, Bowen P, Phillips AW, Gallup D, Lynes D. Contingency planning guide for federal information systems. Gaithersburg (MD): National Institute of Standards and Technology. 2010;NIST SP 800-34 Rev. 1. doi:10.6028/NIST.SP.800-34r1.
4. Grance T, Nolan T, Burke K, Dudley R, White G, Good T. Guide to test, training, and exercise programs for IT plans and capabilities. Gaithersburg (MD): National Institute of Standards and Technology. 2006;NIST SP 800-84. doi:10.6028/NIST.SP.800-84.
5. Petersen R, Santos D, Wetzel K, Smith MC, Witte G. Workforce framework for cybersecurity (NICE Framework). Gaithersburg (MD): National Institute of Standards and Technology. 2020;NIST SP 800-181 Rev. 1. doi:10.6028/NIST.SP.800-181r1.
6. Sweller J. Cognitive load during problem solving: effects on learning. Cogn Sci. 1988;12(2):257-285. doi:10.1207/s15516709cog1202_4.
7. Salas E, Tannenbaum SI, Kraiger K, Smith-Jentsch KA. The science of training and development in organizations: what matters in practice. Psychol Sci Public Interest. 2012;13(2):74-101. doi:10.1177/1529100612436661.
8. Sitzmann T. A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Pers Psychol. 2011;64(2):489-528. doi:10.1111/j.1744-6570.2011.01190.x.
9. Hart SG, Staveland LE. Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv Psychol. 1988;52:139-183. doi:10.1016/S0166-4115(08)62386-9.
10. Hattie J, Timperley H. The power of feedback. Rev Educ Res. 2007;77(1):81-112. doi:10.3102/003465430298487.
11. Freeman S, Eddy SL, McDonough M, Smith MK, Okoroafor N, Jordt H, et al. Active learning increases student performance in science, engineering, and mathematics. Proc Natl Acad Sci U S A. 2014;111(23):8410-8415. doi:10.1073/pnas.1319030111.
12. Salas E, DiazGranados D, Klein C, Burke CS, Stagl KC, Goodwin GF, et al. Does team training improve team performance? A meta-analysis. Hum Factors. 2008;50(6):903-933. doi:10.1518/001872008X375009.
13. Baldwin TT, Ford JK. Transfer of training: a review and directions for future research. Pers Psychol. 1988;41(1):63-105. doi:10.1111/j.1744-6570.1988.tb00632.x.
14. Arthur W Jr, Bennett W Jr, Edens PS, Bell ST. Effectiveness of training in organizations: a meta-analysis of design and evaluation features. J Appl Psychol. 2003;88(2):234-245. doi:10.1037/0021-9010.88.2.234.
15. Aguinis H, Kraiger K. Benefits of training and development for individuals and teams, organizations, and society. Annu Rev Psychol. 2009;60:451-474. doi:10.1146/annurev.psych.60.110707.163505.
16. Lakens D. Sample size justification. Collabra Psychol. 2022;8(1):33267. doi:10.1525/collabra.33267.
17. Nosek BA, Ebersole CR, DeHaven AC, Mellor DT. The preregistration revolution. Proc Natl Acad Sci U S A. 2018;115(11):2600-2606. doi:10.1073/pnas.1708274114.
18. Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013;4:863. doi:10.3389/fpsyg.2013.00863.
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