Vol. 1 No. 01 (2025)
Articles

Why Do We Understand Emergency Messages—or Not? Identifying Their Explanatory Conditions Using csQCA.

PDF
  • Cayetano Medina Molina,
  • Noemí Pérez-Macías,
  • María Eugenia López-Sanz
Cayetano Medina Molina
Centro Universitario San Isidoro, Spain
Bio
Noemí Pérez-Macías
Universidad Pontificia Comillas, Spain
Bio
María Eugenia López-Sanz
Centro Universitario San Isidoro, Spain
Bio
Why Do We Understand Emergency Messages—or Not? Identifying Their Explanatory Conditions Using csQCA

Published 2025-09-11

Keywords

  • csQCA,
  • Descriptive Inference,
  • Early Warning Systems,
  • Stimulus–Organism– Response
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Why Do We Understand Emergency Messages—or Not? Identifying Their Explanatory Conditions Using csQCA. (2025). JOINETECH (International Journal of Economic and Technological Studies), 1(01), 41-54. https://doi.org/10.65479/joinetech.17

Downloads

Download data is not yet available.

Abstract

Objective and significance of the study: In recent years, the frequency and intensity of natural disasters have significantly increased, prompting public managers to develop early warning systems. However, these systems have prioritized technological advancements over identifying the conditions that influence their adoption and use by individuals. Methodology: This study examines the factors that explain citizens’ understanding of emergency alert messages, applying crisp-set qualitative comparative analysis to a sample of 188 Internet users who have received such alerts. Key findings: Based on the stimulus–organism–response theory, the findings indicate that understanding the content of these messages can be attributed to stimuli or the combination of stimuli and organisms. Study limitations: The work focuses on a sample composed of people between 35 and 44 years old. Practical value of the findings: The study explores the conditions underlying the lack of comprehension of these messages, emphasizing the importance of recipients’ gender and highlighting how different combinations of stimulus and organism elements contribute to this outcome.
PDF

References

  1. Abbasi, G.A., Goh, Y.N., Iranmanesh, M., & Liébana-Cabanillas, F. (2924). Determinants of continuous intention to use retail apps: A hybrid PLS-ANN approach. Journal of Marketing Theory & Practice, 32(3), 413-434. https://doi.org/10.1080/10696679.2023.2218098
  2. Al-Debei, M.M., Dwivedi, Y.K., & Hujran, O. (2022). Why would customers continue to use mobile value-added services? Journal of Innovation & Knowledge, 7, 100242. https://doi.org/10.1016/j.jik.2022.100242
  3. Al-Rawas, G., Nonkoo, M.R., & Al-Wardy, M. (2024). A review of the prevention and control of flash flood hazards on a global scale: Early Warning Systems, vulnerability assessment, environmental, and public health burden. International Journal of Disaster Risk Reduction, 115, 105024. https://doi.org/10.1016/j.ijdrr.2024.105024
  4. Asociación para la Investigación de los Medios de Comunicación (AIMC) (2023). Navegantes en la Red. 25ª Encuesta AIMC a usuarios de Internet. https://www.aimc.es/a1mc-c0nt3nt/uploads/2024/05/Navegantes2022.pdf
  5. Asociación para la Investigación de los Medios de Comunicación (AIMC) (2024). Navegantes en la Red. 26ª Encuesta AIMC a usuarios de Internet. https://www.aimc.es/a1mc-c0nt3nt/uploads/2024/03/Navegantes2023.pdf
  6. Baumgartner, M. (2015). Parsimony and Causality. Quality & Quantity, 49, 839-856. https://doi.org/10.1007/s11135-014-0026-7
  7. Bonaretti, D., & Fischer-Preßler, D. (2021). The problem with SMS campuses warning systems: An evaluation based on recipients’ spatial awareness. International Journal of Disaster Risk Reduction, 54, 102031. https://doi.org/10.1016/j.ijdrr.2020.102031
  8. Brush, G.J., Guo, X., Hunting, A., & Frethey-Bentham, C. (2024). Using Qualitative Comparative Analysis to Identify Complex Solutions and Optimal Combinations of Conditions Influencing COVID Vaccine Acceptance: A Primer for QCA. Journal of Macromarketing, 44(2), 276-306. https://doi.org/10.1177/02761467231182300
  9. Chatfield, A.T., Scholl, H.J.(J.), & Brajawidagda, U. (2013). Tsunami early warnings via Twitter in government: Net-savy citizens’ co-production of time-critical public information services. Government Information Quarterly, 30, 377-386. https://doi.org/10.1016/j.giq.2013.05.021
  10. Chen, T., Gil-García, R., Burke, G.B., Dey, A., & Werthmuller, D. (2024). Characterizing technology affordances, constraints, and coping strategies for information dissemination to the public: Insights from emergency messaging in U.S. local governments. Government Information Quarterly, 41, 101910. https://doi.org/10.1016/j.giq.2024.101910
  11. Clarkson, G., Jacobsen, T.E., & Batcheller, A.L. (2007). Information asymmetry and information sharing. Government Information Quarterly, 24, 827-839. https://doi.org/10.1016/j.giq.2007.08.001
  12. Dargin, J.S., Fan, C., & Mostafari, A. (2021). Vulnerable populations and social media use in disasters: Uncovering the digital divide in three major U.S. hurricanes. International Journal of Disaster Risk Reduction, 54, 102403. https://doi.org/10.1016/j.ijdrr.2021.102043
  13. David-West, O., Iheanachor, N., & Umukoro, I. (2020). Sustainable business models for the creation of mobile financial services in Nigeria. Journal of Innovation & Knowledge, 5, 105-116. https://doi.org/10.1016/j.jik.2019.03.001
  14. Eom, S.-J., Hwang, H., & Kim, J.H. (2018). Can social media increase government responsiveness? A case study of Seoul, Korea. Government Information Quarterly, 35, 109-122. https://doi.org/10.1016/j.giq.2017.10.002
  15. Fischer-Preßler, D., Banaretti, D., & Bunker, D. (2024). Digital transformation in disaster management: a literature review. Journal of Strategic Information Management, 33, 101865. https://doi.org/10.1016/j.jsis.2024.101865
  16. Fischer-Preßler, D., Bonaretti, D., & Fischbach, K. (2022). A Protection-Motivation Perspective to Explain Intention to Use and Continue to Use Mobile Warning Systems. Bus Inf Syst Eng, 64(2), 167-182. https://doi.org/10.1007/s12599-021-00704-0
  17. Fiss, P.C. (2011). Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research. Academy of Management Journal, 54(2), 393–420. https://doi.org/10.5465/amj.2011.60263120
  18. Glaesser, J. (2023). Limited diversity and QCA solution types: assumptions and their consequences. Quality & Quantity, 57, 3485-3497. https://doi.org/10.1007/s11135-022-01483-w
  19. Goyal, S., Chauchan, S., & Gupta, P. (2022). Users` response toward online doctor consultation platforms: SOR approach. Management Decision, 60(7), 1990-2018. https://doi.org/10.1108/MD-02-2021-0268
  20. Grofman, B., & Schneider, C.Q. (2009). An Introduction to Crisp Set QCA, with a Comparison to Binary Logistic Regression. Political Research Quarterly, 62(4), 662-672. https://doi.org/10.1177/1065912909338464
  21. Hazari, S., & Sethna, B.N. (2023). A Comparison of Lifestyle Marketing and Brand Influencer Advertising for Generation Z Instagram Users. Journal of Promotion Management, 29(4), 491-534. https://doi.org/10.1080/10496491.2022.2163033
  22. Hussain, A., Ting, D.H., Abbasi, A.Z., & Rehman, U. (2023). Integrating the S-O-R Model to Examine Purchase Intention Based on Instagram Sponsored Advertising. Journal of Promotion Management, 29(1), 77-105. https://doi.org/10.1080/10496491.2022.2108185
  23. Ingrams, A. (2023). Do public comments make a difference in open rulemaking? Insights from information management using machine learning and QCA analysis. Government Information Quarterly, 40, 101778. https://doi.org/10.1016/j.giq.2022.101778
  24. Kalpana, P.G.D. (2023). Factors influencing proactiveness in supply chain risk identification: A fuzzy-set qualitative comparative analysis. International Journal of Disaster Risk Reduction, 88, 103614. https://doi.org/10.1016/j.ijdrr.2023.103614
  25. Kang, W., Shao, B., & Zhang, Y. (2024). How does Interactivity Shape Users’ Continuance Intention of Intelligent Voice Assistants? Evidence from SEM and fsQCA. Psychology Research and Behavior Management, 17, 867-889. https://doi.org/10.2147/PRBM.S438465
  26. Li, J., Zhang, S., & Ao, W. (2023). Why is instant messaging not instant? Understanding users` negative use behaviour of instant messaging software. Computers in Human Behavior, 142, 107655. https://doi.org/10.1016/j.chb.2023.107655
  27. Li, S., Zhu, B., & Yu, Z. (2024). The Impact of Cue-Interaction Stimulation on Impulse Buying Intention on Virtual Reality Tourism E-commerce Platforms. Journal of Travel Research, 63(5), 1256-1279. https://doi.org/10.1177/00472875231183163
  28. Li, Z., Zhou, Y., Wong, Y.D., Wang, X., & Yuen, K.F. (2021). What influences panic buying behaviour? A model based on dual-system theory and stimulus-organism-response framework. International Journal of Disaster Risk Reduction, 64, 102484. https://doi.org/10.1016/j.ijdrr.2021.102484
  29. Lim, X-J., Cheah, J-H., Ng, S.I., Basha, N.K., & Liu, Y. (2021). Are men from Mars and women from Venus? Examining gender differences towards continuous use intuition of branded apps. Journal of Retailing and Consumer Services, 60, 102422. https://doi.org/10.1016/j.jretconser.2020.102422
  30. Lindenlaub, S., Matthes, K.S., & Thieken, A.H. (2024). Improving purchathe understability of flood warning messages- Explorative study on design preferences. International Journal of Disaster Risk Reduction, 111, 104685. https://doi.org/10.1016/j.ijdrr.2024.104685
  31. Lluch, J., Abad, F., Calduch-Losa, A., Rebollo, M., & Juan, M-C. (2024). Gender differences and trens in the use of warables in marathons. Sustainable Technology and Entrepreneurship, 3, 100063. https://doi.org/10.1016/j.stae.2023.100063
  32. Luht-Kallas, K., Laanemaa, E., Taukar, M., Hatšaturjan, A., Kapura, J., Kibar, T., & Põld, M. (2023). Assessing the comprehensibility of SMS warnings: an example of crisis communication in the Estonian trilingual landscape. International journal of Disaster Risk reduction, 97, 104014. https://doi.org/10.1016/j.ijdrr.2023.104014
  33. Lyle, T.S., Fang, L.L., & Hund, S.V. (2024). Implications of disclosure and non-disclosure of flood hazard maps- a synthesis for the Canadian context. Canadian Water Resources Journal, 49(3), 282-299. https://doi.org/10.1080/07011784.2023.2287462
  34. Lyu, Y., Yuan, H., & Wang, Y. (2023). Economic vulnerability of aquaculture households of elderly people to typhoon disasters and the factors influencing it in the island regions of southeast China. International Journal of Disaster Risk Reduction, 85, 103492. https://doi.org/10.1016/j.ijdrr.2022.103492
  35. Ma, R., Liu, J., & An, S. (2024). Early warning response in brainstorming: Designing a model with incentive and supervision mechanisms based on the principal-agent theory. International Journal of Disaster Risk Reduction, 11, 104683. https://doi.org/10.1016/j.ijdrr.2024.104683
  36. Mao, Y., Wang, X., He, W., & Pan, G. (2024). An investigation into the influence of gender on crowd exit selection in indoor evacuation. International Journal of Disaster Risk Reduction, 108, 104563. https://doi.org/10.1016/j.ijdrr.2024.104563
  37. Marzi, G., Balzano, M., Caputo, A., & Pellegrini, M.M. (2024). Guidelines for Bibliometric-Systematic Literature Reviews: 10 steps to combine analysis, synthesis and theory development. International Journal of Management Reviews. https://doi.org/10.1111/ijmr.12381
  38. Medina-Molina, C., Ribeiro Soriano, D., & Gonzáles-Tejero, C.B. (2023). Multi-level corporate entrepreneurship in SMEs: an intra-metropolitan analysis. Review of Managerial Science, 17, 2185-2213. https://doi.org/10.1007/s11846-022-00575-z
  39. Mehrabian, A., & Russell, J.A. (1974). An approach to environmental psychology. MIT Press.
  40. Meléndez-Landaverde, E.R., & Sempere-Torres, D. (2025). Design and evaluation of a community and impact-based site-specific early warning system (SS-EWS): The SS-EWS framework. Journal of Flood Risk Management, 18, e12860. https://doi.org/10.1111/jfr3.12860
  41. Mello, P.A. (2021). Qualitative Comparative Analysis. An Introduction to Research Design and Application. Georgetown University Press.
  42. Molinillo, S., Aguilar-Illescas, R., Anaya-Sánchez, R., & Liébana-Cabanillas, F. (2021). Social commerce website design perceived value and loyalty behavior intentions: The moderating roles of gender, age and frequency of use. Journal of Retailing and Consumer Services, 63, 102404. https://doi.org/10.1016/j.jretconser.2020.102404
  43. Neyazi, T.A., Nadaf, A.H., Tan, K.E., & Schroeder, R. (2024). Does trust in government moderate the perception toward the perception toward deepfakes? Comparative perspectives from Asia on the risks of AI and misinformation for democracy. Government Information Quarterly, 41, 101980. https://doi.org/10.1016/j.giq.2024.101980
  44. Nishant, R., Nguyen, T.(K)., Teo, T.S.H., & Hsu, P-F. (2024). Role of substantive and rhetorical signals in the market reactions to announcements on AI adoption: a configurational study. European Journal of Information Systems, 33(5), 802-844. https://doi.org/10.1080/0960085X.2023.2243892
  45. Oana, I.E., & Schneider, C.Q. (2024). A robustness test protocol for applied QCA: Theory and R software application. Sociological Methods & Research, 53(1), 57-88. https://doi.org/10.1177/00491241211036158
  46. Oana, I-E-. Schneider, C.Q., & Thoman, E. (2021). Qualitative Comparative Analysis Using R. A Beginner’s Guide. Cambridge University Press.
  47. Obeidat, Z.M., Alawan, A.A., Baabdullah, A.M., Obeidat, A.M., & Dwivedi, Y.K. (2022). The other customer online revenge: A moderated mediated model of avenger expertise and message trustworthiness. Journal of Innovation & Knowledge, 7, 100230. https://doi.org/10.1016/j.jik.2022.100230
  48. Ott, U.F., Williams, D., Saker, J., & Staley, L. (2019). A configurational analysis of the termination scenarios of international joint ventures: All is well that ends well. Journal of Innovation & Knowledge, 4(3), 202-210, https://doi.org/10.1016/j.jik.2019.01.004.
  49. Payal, R., Sharma, B., & Zhang, Y. (2024). Unlocking the impact of brand engagement in the metaverse on Real-World purchase intentions. Analyzing Pre-Adoption behaviour in a futuristic technology platform. Electronic Commerce Research and Applications, 65, 101389. https://doi.org/10.1016/j.elerap.2024.101381
  50. Rathim, M., Majumdar, A., & Rathl, S. (2024). Unraveling Prosumption Behavior for Online Reviews during Environmental Uncertainty: A Stimulus-Response-Perspective. Information Systems Frontiers. https://doi.org/10.1007/s10796-024-10557-z
  51. Rodríguez-Torrico, P., San-Martín, S., & San José-Cabezudo, R. (2019). What Drives M-Shoppers to Continue Using Mobile Devices to Buy? Journal of Marketing Theory & Practice, 27(1), 83-102. https://doi.org/10.1080/10696679.2018.1534211
  52. Roig-Tierno, N., González-Cruz, T.F., & Llopis-Martínez, J. (2017). An overview of qualitative comparative analysis: A bibliometric analysis. Journal of Innovation & Knowledge, 2, 15-23. https://doi.org/10.1016/j.jik.2016.12.002
  53. Roux, T., & Maree, T. (2021). Joy to the (Shopper) World: An S-O-R View of Digital Place-Based Media in Upmarket Shopping Malls. Journal of Promotion Management, 27(7), 1031-1060. https://doi.org/10.1080/10496491.2021.1888177
  54. Ruiz, C.D., & Nilsson, T. (2023). Disinformation and Echo Chambers: How Disinformation Circulates on Social Media Through Identity-Driven Controversies. Journal of Public Policy & Marketing, 42(1), 18-35. https://doi.org/10.1177/07439156221103852
  55. Sahid, A., Maleh, Y., & Ouazzane, K. (2025). Changing landscape of fake news research on social media: a bibliometric analysis. Qual Quant, https://doi.org/10.1007/s11135-024-02048-9
  56. Salgado, M.J.H., Alfonso, L., & Upegui, J.J.V. (2025). Towards integrating community and institutional flood early warning systems: A framework applied to an Andean tropical case. International Journal of Disaster Risk Reduction, 116, 105126. https://doi.org/10.1016/j.ijdrr.2024.105126
  57. Schneider, C.Q. (2024). Set-Theoretic Multi-Method Research. A Guide to Combining QCA and Case Studies. Cambridge University Press.
  58. Schneider, C.Q., & Wageman, C. (2013). Set-Theoretic Methods for the Social Sciences. Cambridge University Press.
  59. Shareef, A., Dwivedi, Y.K., Arya, V., & Siddiqui, M.Q. (2021). Does SMS advertising still have relevance to increase consumer purchase intention? A hybrid PLS-SEM-neural network modelling approach. Computers in Human Behavior, 124, 106919. https://doi.org/10.1016/j.chb.2021.106919
  60. Shareef, M.A., Dwivedi, Y.K., Kumar, V., & Kumar, U. (2017). Content design of advertisement for consumer exposure: Mobile marketing through smart messaging service. International Journal of Information Management, 37, 257-268. https://doi.org/10.1016/j.ijinfomgt.2017.02.003
  61. Shareef, M.A., Raman, R., Baabdullah, A.M., Mahmud, R., Ahmed, J.U, Kabir, H., Kumar, V., Kumar, U., Akram, M.S., Kabir, A., & Mukerji, B. (2019). Public service reformation: Relationship building by mobile technology. International Journal of Information Management, 49, 217-227. https://doi.org/10.1016/j.ijinfomgt.2019.03.007
  62. Sharma, V., Saharan, A., Gupta, H., Drave, V. A., & Singh, P. (2024). Assessing stimulus–organism responses in impulse buying among young consumers on online platforms using a hybrid BWMISM approach. Journal of Marketing Analytics, 1-20. https://doi.org/10.1057/s41270-024-00366-3
  63. Sievi, L., & Pawelec, M. (2025). (How) Should security authorities counter false information on social media in crisis? A democracy-theoretical and ethical reflection. International Journal of Disaster Risk Management, 116, 105093.
  64. Song, L., Yao, S., Liu, L., & Tso, G. (2024). Investigating binge-watching and its effect on paid subscription: A mixed-method study based on SOR theory. J Consumer Behav, 1-24. https://doi.org/10.1002/cb.2402
  65. Söderlund, M. (2023). Moderator variables in consumer research: A call for caution. Journal of Retailing and Consumer Services, 73, 103352. https://doi.org/10.1016/j.jretconser.2023.103352
  66. Sufri, S., Dwirahmadi, F., Phung, D., & Rutherford, S. (2020). A systematic review of community engagement (CE) in disaster early warning systems (EWSs). Progress in Disaster Science, 5, 100058. http://dx.doi.org/10.1016/j.pdisas.2019.100058
  67. Sultan, P., Wong, H.Y., & Azam, M.S. (2021). How perceived communication source and food value stimulate purchase intention of organic food: An examination of the situmulus-organism-response (SOR) model. Journal of Cleaner Production, 312, 127807. https://doi.org/10.1016/j.jclepro.2021.127807
  68. Tan, M. L., Vinnell, L. J., Valentin, A. P. M., Prasanna, R., & Becker, J. S. (2023). The public’s perception of an earthquake early warning system: A study on factors influencing continuance intention. International journal of Disaster Risk reduction, 97, 104032. https://doi.org/10.1007/s11846-024-00810-9
  69. Thomnan, E., & Magetti, M. (2020). Designing Research With Qualitative Comparative Analysis (QCA): Approaches, Challenges, and Tools. Sociological Methods & Research, 49(2), 356-386. https://doi.org/10.1177/0049124117729700
  70. Tong, X., Chen, Y., Zhou, S., & Yang, S. (2022). How background visual complexity influences purchase intention in the live streaming: The mediating role of emotion and the moderating role of gender. Journal of Retailing and Consumer Services, 67, 103031. https://doi.org/10.1016/j.jretconser.2022.103031
  71. Trahan, A., Walshe, R., & Mehta, V. (2023). Extreme heat, gender and access to preparedness measures: An analysis of the heatwave early warning systems in Ahmedabad, India. International Journal of Disaster Risk Reduction, 99, 104080. https://doi.org/10.1016/j.ijdrr.2023.104080
  72. Tsutsunashvili, A., Aránega, A.Y., & Urueña, R.C. (2024). Challenged global economics amid conflict in warring countries. Sustainable Technology and Entrepreneurship, 3(3), 100068. https://doi.org/10.1016/j.stae.2023.100068
  73. Trüdinger, E.-M., & Streckermeier, L.C. (2017). Trusting and controlling? Political trust, information and acceptance of surveillance policies: The Case of Germany. Government Information Quarterly, 34, 421-433. https://doi.org/10.1016/j.giq.2017.07.003
  74. United Nations for Disaster Risk Reduction. (2008). Disaster preparedness for effective response: Guidance and indicator package for implementing priority five of the Hyogo framework: Hyogo framework for action. Geneva: Building the Resilience of Nations and Communities to Disasters. UN Publication. Retrieved from: https://www.preventionweb.net/files/2909_Disasterpreparednessforeffectiveresponse.pdf
  75. United Nations. (2006). Global survey of early warning systems: An assessment of capacities, gaps and opportunities towards building a comprehensive global early warning system for all natural hazards. Bonn: United Nations (UN). Retrieved from: https://www.unisdr.org/2006/ppew/info-resources/ewc3/Global-Survey-of-Early-Warning-Systems.pdf
  76. Vailati, P.A., Cuiñas, A.A.F., & Larrañaga, J.R. (2025). Perceptions and consumption habits of print vs. digital newspaper readers. Esic Market Economic and Business Journal, 56(1), e367. DOI: 10.7200/esicm.56.367
  77. Velichety, S., & Shrivastava, U. (2022). Quantifying the impacts of online fake news on the equity value of social media platforms- Evidence from Twitter. International Journal of Information Management, 64, 102474. https://doi.org/10.1016/j.ijinfomgt.2022.102474
  78. Wang, J., Kim, J.U., & Kim, H-M. (2024). Purchase intention in TikTok streaming commerce: The role of recommendation accuracy, streamer’s attractiveness, and consumer-to-consumer interactions. Review of Managerial Science, 1-24. https://doi.org/10.1007/s11846-024-00810-9
  79. Wang, K-H., & Lu, W-C. (2025). AI-induced job impact: Complementary or substitution? Empirical insights and sustainable technology considerations. Sustainable Technology and Entrepreneurship, 4, 100085. https://doi.org/10.1016/j.stae.2024.100085
  80. Wang, G., & Musa, R. (2024). Does Green Advertising Always Effectiveness? A Multidimensional Perceived Value Analysis Based on the SOR Model. Journal of Promotion Management, 30(8), 1293-1321. https://doi.org/10.1080/10496491.2024.2393595
  81. Woszczynski, A., Green, A., Dodson, K., & Easton, P. (2020). Zombies, Sirens, and Lady Gaga- Oh My! Developing a Framework for Coordinated Vulnerability Disclosure for U.S. Emergency Alert Systems. Government Information Quarterly, 37, 101418. https://doi.org/10.1016/j.giq.2019.101418
  82. Yan, Y., Chen, H., Shao, B., & Lei, Y. (2023). How IT affordance influence customer engagement in live streaming commerce? A dual-stage analysis of PLS-SEM and fsQCA. Journal of Retailing and Consumer Services, 74, 103390. https://doi.org/10.1016/j.jretconser.2023.103390
  83. Zhang, C., Fan, C., Yao, W., Hu, X., & Mostafari, A. (2019). Social media for intelligent public information and warning in disasters: An interdisciplinary review. International Journal of Information Management, 49, 190-207. https://doi.org/10.1016/j.ijinfomgt.2019.04.004
  84. Zhang, C., Zhoum B., Wang, Q., & Jian, Y. (2024). The consequences of environmental big data information disclosure on hard-to-abate Chinese enterprises` green information. Journal of Innovation & Knowledge, 9, 100474. https://doi.org/10.1016/j.jik.2024.100474
  85. Zhao, Y., & Fan, B. (2021). Understanding the key factors and configurational paths of the open government data performance: Based on fuzzy-set qualitative comparative analysis. Government Information Quarterly, 38, 101580. https://doi.org/10.1016/j.giq.2021.101580
  86. Zhao, J., Krott, M., Liu, J., Giessen, L., Jiang, G., & Yao, H. (2025). How can a disaster trigger substantial policy? A power analysis of the 1998 floods and forest restoration in China. International Journal of Disaster Risk Reduction, 119, 105308. https://doi.org/10.1016/j.ijdrr.2025.105308