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A Cross-Sectional Study Using Health Behavior Theory to Predict Rapid Compliance With Campus Emergency Notifications Among College Students

Published online by Cambridge University Press:  07 February 2020

Christopher J. Rogers Open the ORCID record for Christopher J. Rogers [Opens in a new window] ,
Myriam Forster ,
Kaitlin Bahr  and
Stephanie M. Benjamin
Christopher J. Rogers*
Affiliation:
Department of Preventive Medicine, University of Southern California, Los Angeles, California
Myriam Forster
Affiliation:
Department of Health Sciences, California State University Northridge, Northridge, California
Kaitlin Bahr
Affiliation:
Department of Health Sciences, California State University Northridge, Northridge, California
Stephanie M. Benjamin
Affiliation:
Department of Health Sciences, California State University Northridge, Northridge, California
*
Correspondence and reprint requests to Christopher J. Rogers, Department of Preventive Medicine, University of Southern California, Keck School, 2001 N Soto Street, Los AngelesCA90089 (e-mail: [email protected]).
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Abstract

Objective:

Compliance with college emergency notifications can minimize injury; however, time is often wasted in alert verification. Building on prior research, this study assesses using health-behavior theory to predict rapid compliance to emergency notifications across a range of scenarios and within a diverse college population.

Methods:

Cross-sectional, student data were collected in 2017-2018 (n = 1529). The Theory of Planned Behavior and Protection Motivation Theory were used to explain intention to comply with emergency notifications in scenarios: robbery, shooter, fire, chemical spill, protest, health emergency, and air quality. Regression models assessed associations between constructs and intention to rapidly comply with each notification.

Results:

The most consistent predictors of rapid compliance were attitudes and subjective norms (adjusted odds ratio [AOR]: 1.057-1.118; 95% CI: 1.009-1.168). Scenarios prone to rapid developments such as robbery, shooter, and fire were associated with increased perceived threat and response efficacy (AOR: 1.024-1.082; 95% CI: 1.003-1.132) Slower developing situations such as air quality and health hazards were associated with increased perceived control (AOR: 1.027-1.073; 95% CI: 1.031-1.117).

Conclusions:

This study identified attitude and subjective norms as consistent predictors of rapid compliance and improves understanding of additional constructs across scenarios. Campuses may benefit from leveraging concepts from health-behavior theory to provide targeted intervention focusing on factors associated with rapid compliance.

Keywords

compliancecollegeemergency notification systemhealth behavior theory
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 15 , Issue 2 , April 2021 , pp. 198 - 207
Copyright
Copyright © 2020 Society for Disaster Medicine and Public Health, Inc.

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References

REFERENCES

Fifolt, M, Burrowes, J, McPherson, T, et al. Strengthening emergency preparedness in higher education through hazard vulnerability analysis. Coll Univ. 2016;91(4):61.Google Scholar
Kapucu, N, Berman, E, Wang, XH. Emergency information management and public disaster preparedness: lessons from the 2004 Florida hurricane season. Int J Mass Emerg Disasters. 2008;26(3):169-197.Google Scholar
Rasmussen, C, Johnson, G. The Ripple Effect of Virginia Tech. Assessing the Nationwide Impact on Campus Safety and Security Policy and Practice. Minneapolis, MN: Midwestern Higher Education Compact; 2008.Google Scholar
Sulkowski, ML, Lazarus, PJ. Contemporary responses to violent attacks on college campuses. J School Violence. 2011;10(4):338-354 doi: 10.1080/15388220.2011.602601 CrossRefGoogle Scholar
Zdziarski, EL, Dunkel, NW, Rollo, JM. Campus Crisis Management: A Comprehensive Guide to Planning, Prevention, Response, and Recovery. New York: John Wiley & Sons; 2007.Google Scholar
Snyder, TD, de Brey, C, Dillow, SA. Digest of Education Statistics 2015, NCES 2016-014. Vol 51. Washington, DC: National Center for Education Statistics; 2016.Google Scholar
The Clery Center. What Happened to Jeanne Clery was a Tragedy. 2017. https://clerycenter.org/about-page/. Accessed January 12, 2018.Google Scholar
Janosik, SM, Gehring, DD. The impact of the Clery Campus Crime Disclosure Act on student behavior. J Coll Stud Dev. 2003;44(1):81-91. doi: 10.1353/csd.2003.0005 CrossRefGoogle Scholar
The Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act, 34, §1092 (1998).Google Scholar
Coombs, WT. Ongoing Crisis Communication: Planning, Managing, and Responding. Thousand Oaks, CA: Sage Publications; 2014.Google Scholar
Kapucu, N. Disaster Resilient Universities. In US Department of Education. Emergency Management for Higher Education Meeting; 2010; Philadelphia, PA.Google Scholar
Cohen, S. Using Social Networking in University Emergency Communications. Los Angeles, CA: UCLA School of Public Affairs, Department of Public Policy; 2008.Google Scholar
Catullo, LA, Walker, DA, Floyd, DL. The status of crisis management at NASPA member institutions. J Stud Aff Res Pract. 2009;46(2):301-324. doi: 10.2202/1949-6605.6043 Google Scholar
Fulmer, T, Portelli, I, Foltin, GL, et al. Organization-based incident management: developing a disaster volunteer role on a university campus. Disaster Manag Response. 2007;5(3):74-81. doi: 10.1016/j.dmr.2007.06.001 CrossRefGoogle ScholarPubMed
Han, W, Ada, S, Sharman, R, et al. Factors impacting the adoption of social network sites for emergency notification purposes in universities. Int J Bus Inf Syst. 2014;18(1):85-106. doi: 10.1504/IJBIS.2015.066129 Google Scholar
Johnson, T. Effect of a marketing program on freshman student registration for an emergency notification system. Management. 2012;9(1):5. doi: 10.1515/1547-7355.1938 Google Scholar
Kim, JK, Sharman, R, Rao, HR, et al. Efficiency of critical incident management systems: instrument development and validation. Decis Support Syst. 2007;44(1):235-250. doi: 10.1016/j.dss.2007.04.002 CrossRefGoogle Scholar
Maldonado, EA, Maitland, CF, Tapia, AH. Collaborative systems development in disaster relief: the impact of multi-level governance. Inform Syst Front. 2010;12(1):9-27. doi: 10.1007/s10796-009-9166-z CrossRefGoogle Scholar
Wu, PF, Qu, Y, Preece, JJ. Why an emergency alert system isn’t adopted: the impact of socio-technical context. In: Proceedings of the 22nd British HCI Group Annual Conference on HCI 2008.CrossRefGoogle Scholar
Han, W, Ada, S, Sharman, R, et al. Campus emergency notification systems: an examination of factors affecting compliance with alerts. MIS Q. 2015;39(4):909-929. doi: 10.25300/MISQ/2015/39.4.8 CrossRefGoogle Scholar
Lee, D, Chung, JY, Kim, H. Text me when it becomes dangerous: exploring the determinants of college students’ adoption of mobile-based text alerts short message service. Comput Human Behav. 2013;29(3):563-569. doi: 10.1016/j.chb.2012.11.014 CrossRefGoogle Scholar
Schneider, T. Mass Notification for Higher Education. National Clearinghouse for Educational Facilities. 2010. https://files.eric.ed.gov/fulltext/ED508002.pdf. Accessed January 14, 2020.Google Scholar
Sharpe, RT. Collaboration with IT & cops for emergency communications. In: Proceedings of the 37th Annual ACM SIGUCCS Fall Conference: Communication and Collaboration, St. Louis, Missouri, 2009.CrossRefGoogle Scholar
Sherman-Morris, K. Tornado warning dissemination and response at a university campus. Nat Hazards. 2010;52(3):623-638. doi: 10.1007/s11069-009-9405-0 CrossRefGoogle Scholar
Kopel, DE, Sims, VK, Chin, MG. Taking emergency warnings seriously. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2014;58(1):1129-1133. doi: 10.1177/1541931214581236 CrossRefGoogle Scholar
Wang, X, Kapucu, N. Public complacency under repeated emergency threats: some empirical evidence. J Publ Adm Res Theor. 2007;18(1):57-78. doi: 10.1093/jopart/mum001 CrossRefGoogle Scholar
Gulum, MS, Murray, SL. Evaluation of the effectiveness of a mass emergency notification system. Paper presented at: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2009;53:1466-1470.CrossRefGoogle Scholar
Dow, K, Cutter, SL. Crying wolf: repeat responses to hurricane evacuation orders. Coast Manage. 1998. doi: 10.1080/08920759809362356 CrossRefGoogle Scholar
Drabek, TE. Shall we leave? A study on family reactions when disaster strikes. Emerg Manag Rev. 1983;1:25-29. doi: 10.1126/science.133.3462.1405 Google Scholar
Lachman, R, Tatsuoka, M, Bonk, WJ. Human behavior during the tsunami of May 1960. Science. 1961;133(3462):1405-1409. doi: 10.1126/science.133.3462.1405 Google Scholar
Sorensen, JH. When shall we leave? Factors affecting the timing of evacuation departures. Int J Mass Emerg Disasters. 1991;9(2):153-165.Google Scholar
Sorensen, JH. Hazard warning systems: review of 20 years of progress. Nat Hazards Rev. 2000;1(2):119-125. doi: 10.1061/(ASCE)1527-6988(2000)1:2(119)CrossRefGoogle Scholar
Paton, D, Smith, L, Johnston, DM. Volcanic hazards: risk perception and preparedness. NZ J Psychol. 2000;29(2):86.Google Scholar
Shaw, R, Shiwaku, Hirohide Kobayashi K, Kobayashi, M. Linking experience, education, perception and earthquake preparedness. Disaster Prev Manag. 2004;13(1):39-49. doi: 10.1108/09653560410521689 CrossRefGoogle Scholar
Ajzen, I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179-211. doi: 10.1016/0749-5978(91)90020-T CrossRefGoogle Scholar
Gielen, AC, Sleet, D. Application of behavior-change theories and methods to injury prevention. Epidemiol Rev. 2003;25(1):65-76. doi: 10.1093/epirev/mxg004 CrossRefGoogle ScholarPubMed
Ajzen, I. From intentions to actions: a theory of planned behavior. In: Kuhl, J, Beckmann, J, eds. Action Control. SSSP Springer Series in Social Psychology. Berlin: Springer; 1985:11-39.Google Scholar
Ejeta, LT, Ardalan, A, Paton, D. Application of behavioral theories to disaster and emergency health preparedness: a systematic review. PLoS Curr. 2015;7. doi: 10.1371/currents.dis.31a8995ced321301466db400f1357829 Google ScholarPubMed
Najafi, M, Ardalan, A, Akbarisari, A, et al. Salient public beliefs underlying disaster preparedness behaviors: a theory-based qualitative study. Prehosp and Disaster Med. 2017;32(2):124-133. doi: 10.1017/S1049023X16001448 CrossRefGoogle ScholarPubMed
Paek, H-J, Hilyard, K, Freimuth, V, et al. Theory-based approaches to understanding public emergency preparedness: implications for effective health and risk communication. J Health Commu. 2010;15(4):428-444. doi: 10.1080/10810731003753083 CrossRefGoogle ScholarPubMed
Terpstra, T, Lindell, MK. Citizens’ perceptions of flood hazard adjustments: an application of the protective action decision model. Environ Behav. 2013;45(8):993-1018. doi: 10.1177/0013916512452427 CrossRefGoogle Scholar
Nakagawa, K, Yamamoto, M. A study on factors related to earthquake preparedness by family of non-institutionalized individuals with severe motor and intellectual disabilities. Jpn J Soc Welf. 2015;55(5):1-12.Google Scholar
Maddux, JE, Rogers, RW. Protection motivation and self-efficacy: a revised theory of fear appeals and attitude change. J Exp Soc Psychol. 1983;19(5):469-479. doi: 10.1016/0022-1031(83)90023-9 CrossRefGoogle Scholar
Rogers, RW. A protection motivation theory of fear appeals and attitude change. J Psychol. 1975;91(1):93-114. doi: 10.1080/00223980.1975.9915803 CrossRefGoogle Scholar
Floyd, DL, Prentice-Dunn, S, Rogers, RW. A meta-analysis of research on protection motivation theory. J Appl Soc Psychol. 2000;30(2):407-429. doi: 10.1111/j.1559-1816.2000.tb02323.x CrossRefGoogle Scholar
Prentice-Dunn, S, Rogers, RW. Protection motivation theory and preventive health: beyond the health belief model. Health Educ Res. 1986;1(3):153-161. doi: 10.1093/her/1.3.153 CrossRefGoogle Scholar
Sutton, SR. Fear-arousing communications: a critical examination of theory and research. Soc Psychol Behav Med. 1982:303-337.Google Scholar
Mulilis, JP, Lippa, R. Behavioral change in earthquake preparedness due to negative threat appeals: a test of protection motivation theory. J Appl Soc Psychol. 1990;20(8):619-638. doi: 10.1111/j.1559-1816.1990.tb00429.x CrossRefGoogle Scholar
Grothmann, T, Reusswig, F. People at risk of flooding: why some residents take precautionary action while others do not. Nat hazards. 2006;38(1):101-120. doi: 10.1007/s11069-005-8604-6 CrossRefGoogle Scholar
California State University Office of the Chancellor. Total Enrollment by Sex and Student Level, Fall 2016. http://www.calstate.edu/as/stat_reports/2016-2017/f16_01.htm. Accessed January 20, 2018.Google Scholar
College Portrait. California State University, Northridge. 2016. http://www.collegeportraits.org/CA/CSUN. Accessed January 16, 2018.Google Scholar
California State University Northridge. Annual Security Report 2016. Northridge, CA: Department of Police Services; 2016. Google Scholar
Demuth, JL, Morss, RE, Lazo, JK, et al. The effects of past hurricane experiences on evacuation intentions through risk perception and efficacy beliefs: a mediation analysis. Weather Clim Soc. 2016;8(4):327-344. doi: 10.1175/WCAS-D-15-0074.1 CrossRefGoogle Scholar
Knowlden, AP, Sharma, M, Bernard, AL. A theory of planned behavior research model for predicting the sleep intentions and behaviors of undergraduate college students. J Prim Prev. 2012;33(1):19-31. doi: 10.1007/s10935-012-0263-2 CrossRefGoogle ScholarPubMed
Sturges, JW, Rogers, RW. Preventive health psychology from a developmental perspective: an extension of protection motivation theory. Health Psychol. 1996;15(3):158. doi: 10.1037//0278-6133.15.3.158 CrossRefGoogle ScholarPubMed
Xiao, H, Peng, M, Yan, H, et al. An instrument based on protection motivation theory to predict Chinese adolescents’ intention to engage in protective behaviors against schistosomiasis. Glob Health Res Policy. 2016;1(1):15. doi: 10.1186/s41256-016-0015-6 CrossRefGoogle ScholarPubMed
Ajzen, I. Constructing a TPB questionnaire: conceptual and methodological considerations. 2002 Google Scholar
Department of Homeland Security. Disasters and Emergencies. 2019. https://www.ready.gov/be-informed. Accessed January 15, 2017.Google Scholar
California State University Northridge. Emergency Communications. 2018. https://www.csun.edu/emergency/emergency-communications. Accessed January 28, 2018.Google Scholar
Faul, F, Erdfelder, E, Lang, A-G, et al. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175-191. doi: 10.3758/BF03193146 CrossRefGoogle ScholarPubMed
Peduzzi, P, Concato, J, Kemper, E, et al. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373-1379. doi: 10.1016/S0895-4356(96)00236-3 CrossRefGoogle ScholarPubMed
IBM SPSS Statistics for Windows Version 24.0 [computer program]. Version 24.0. Armonk, NY: IBM Corp; 2016.Google Scholar
Hanley, JA, McNeil, BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29-36. doi: 10.1148/radiology.143.1.7063747 CrossRefGoogle Scholar
Pearce, J, Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Modell. 2000;133(3):225-245. doi: 10.1016/S0304-3800(00)00322-7 CrossRefGoogle Scholar
Swets, JA. Measuring the accuracy of diagnostic systems. Science. 1988;240(4857):1285-1293. doi: 10.1126/science.3287615 CrossRefGoogle ScholarPubMed
Berry, WD, Feldman, S. Multiple Regression in Practice. United Kingdom: Sage; 1985.CrossRefGoogle Scholar
Vatcheva, KP, Lee, M, McCormick, JB, et al. Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiology (Sunnyvale). 2016;6(2):pii: 227. doi: 10.4172/2161-1165.1000227 CrossRefGoogle ScholarPubMed
Yavuz, N, Welch, EW. Addressing fear of crime in public space: gender differences in reaction to safety measures in train transit. Urban Stud. 2010;47(12):2491-2515. doi: 10.1177/0042098009359033 CrossRefGoogle ScholarPubMed
Ada, S, Sharman, R, Han, W, et al. Factors impacting the intention to use emergency notification services in campus emergencies: an empirical investigation. IEEE Trans Prof Commun. 2016;59(2):89-109. doi: 10.1109/TPC.2016.2527248 CrossRefGoogle Scholar
California State University Northridge. CSUN Office of Institutional Research. 2018. https://www.csun.edu/institutional-research. Accessed March 15, 2018.Google Scholar
Centers for Disease Control and Prevention. Zombie Preparedness for Educators. 2017. https://www.cdc.gov/phpr/zombie/educate.htm. Accessed March 18, 2018.Google Scholar
Southern California Earthquake Center. Shakeout College and University Guidelines and Resources. 2018. https://www.shakeout.org/colleges/. Accessed March 18, 2018.Google Scholar