Big Questions of AI in Public Administration and Policy

Abstract

Technological advancements have created notable turning points throughout the history of humanity. Influential transformations in the administrative structure are the result of modern technological discoveries. The artificial intelligence (AI) revolution and algorithms now affect daily lives, communities, and government structures more than ever. Governments are the main coordinators of technological transition and supervisors of the activities of modern public administration systems. Hence, public administration and policies have crucial responsibilities in integrating, governing, and regulating AI technology. This article concentrates on the big questions of AI in the public administration and policy literature. The big questions discussion started by Robert Behn in 1995 draws attention to the big questions as the primary driving force of a public administration research agenda. The fundamental motivation of the big questions approach is shaped by the fact that “questions are as important as answers.” Integrating AI into public administration and the policy-making process allows numerous opportunities. However, AI technology also contains multiple threats and risks in economic, social, and even political structures in the long term. This article aims to identify big questions and discuss potential answers and solutions from an AI governance research agenda perspective.

Keywords

• Big Questions
• AI
• Public Administration
• Public Policy
• AI Governance
• AI Policy

References

1. Agarwal, P. K. (2018). Public Administration Challenges in the World of AI and Bots. Public Administration Review, 78(6), 917–921. https://doi.org/10.1111/puar.12979
2. Allen, B. G. C. (2019). Understanding China’ AI Strategy: Clues to Chinese Strategic Thinking on Artificial Intelligence and National Security. February.
3. Almeida, P., Santos, C., & Farias, J. S. (2020). Artificial Intelligence Regulation: A Meta-Framework for Formulation and Governance. Proceedings of the 53rd Hawaii International Conference on System Sciences, 3, 5257–5266. https://doi.org/10.24251/hicss.2020.647
4. Anastasopoulos, L.J., Whitford, A.B., 2019. Machine Learning for Public Administration Research, With Application to Organizational Reputation. Journal of Public Administration Research and Theory 29, 491–510. doi:10.1093/jopart/muy060
5. Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government Information Quarterly, 36(2), 358–367. https://doi.org/10.1016/j.giq.2018.10.001
6. Aoki, N. (2020). An experimental study of public trust in AI chatbots in the public sector. Government Information Quarterly, 37(4), 101490. https://doi.org/10.1016/j.giq.2020.101490
7. Arf, C. (1959). Makineler Düşünebilir mi ve Nasıl Düşünebilir? In Atatürk Üniversitesi 1958- 1959 Öğretim Yılı Halk Konferansları (Issue 1, pp. 91–103).
8. Barth, T. J., & Arnold, E. (1999). Artificial Intelligence and Administrative Discretion: Implications for public administration. American Review of Public Administration, 29(4), 332–351. https://doi.org/10.1177/02750749922064463

9. Berk, R. A., Sorenson, S. B., & Barnes, G. (2016). Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions. Journal of Empirical Legal Studies, 13(1), 94–115. https://doi.org/10.1111/jels.12098
10. Black, J., & Murray, A.D. (2019). Regulating AI and Machine Learning: Setting the Regulatory Agenda. Eur. J. Law Technol., 10.
11. Bostrom, N. (2016). Superintelligence: Paths, Dangers, Strategies, Reprinted.
12. Bostrom, N., Dafoe, A., & Flynn, C. (2019). Public Policy and Superintelligent AI: A Vector Field Approach. Ethics of Artificial Intelligence, 1(2018), 1–29.
13. Boyd, M., & Wilson, N. (2017). Rapid Developments in Artificial Intelligence: how might the New Zealand government respond? Policy Quarterly, 13(4), 36–43. https://doi.org/10.26686/pq.v13i4.4619
14. Bozeman, B. (2009). Public values theory: Three big questions. International Journal of Public Policy, 4, 369-337.
15. Brooks, A. C. (2002). Can nonprofit management help answer public management’s “big questions”? Public Administration Review, 62(3), 259–266. https://doi.org/10.1111/1540-6210.00177
16. Brueckner, R. (2020, March 19). Inside HPC. https://insidehpc.com/2020/03/alibaba-cloud- offers-ai-cloud-services-to-help- battle-covid-19-globally/ (accessed 18 April 2021)
17. Brynjolfsson, E., & Mitchell, T. (2014). What can machine learning do? Workforce implications. Science, 358(6370), 1530–1534. https://doi.org/10.1126/science.aap8062
18. Buiten, M. C. (2019). Towards Intelligent Regulation of Artificial Intelligence. European Journal of Risk Regulation, 10(1), 41–59. https://doi.org/10.1017/err.2019
19. Busch, P. A., & Henriksen, H. Z. (2018). Digital discretion: A systematic literature review of ICT and street-level discretion. Information Polity, 23(1), 3–28. https://doi.org/10.3233/IP- 170050
20. Butcher, J., & Beridze, I. (2019). What is the State of Artificial Intelligence Governance Globally? RUSI Journal, 164(5–6), 88–96. https://doi.org/10.1080/03071847.2019.1694260
21. Callahan, R. F. (2001). Challenges of (dis) connectedness in the “big questions” methodologies in public administration. Public Administration Review, 61(4), 493–499. https://doi.org/10.1111/0033-3352.00052
22. Cath, C. (2018). Governing Artificial Intelligence: Ethical, Legal and Technical Opportunities and Challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133). https://doi.org/10.1098/rsta.2018.0080
23. Clarke, R. (2019). Regulatory alternatives for AI. Computer Law & Security Review, 35(4), 398-409.
24. Chen, T., Guo, W., Gao, X., & Liang, Z. (2020). AI-Based Self-Service Technology in Public Service Delivery: User Experience and Influencing Factors. Government Information Quarterly, May, 101520. https://doi.org/10.1016/j.giq.2020.101520
25. Chowdhury, G. G. (2003). Natural Language Processing. Annual Review of Information Science and Technology, 37, 51–89. https://doi.org/10.1002/aris.1440370103
26. Cihon, P. (2019). Standards for AI Governance: International Standards to Enable Global Coordination in AI Research & Development. In Future of Humanity Institute, University of Oxford (Issue April). https://arxiv.org/pdf/1802.07228.pdf
27. Cooper, T. L. (2004). Big questions in administrative ethics: A need for focused, collaborative effort. Public Administration Review, 64(4), 395–407. https://doi.org/10.1111/j.1540-6210.2004.00386.x
28. Copeland, B. (2020, August 11). Artificial intelligence. Encyclopedia Britannica. https://www.britannica.com/technology/artificial-intelligence Dafoe, A. (2018). AI Governance: A Research Agenda. In the Future of Humanity Institute, University of Oxford.
29. Denhardt, R. B. (2001). The Big Questions of Public Administration Education. Public Administration Review, 61(5), 526–534. https://doi.org/10.1111/0033-3352.00125
30. Desjairdens, J. (2019). How Much Data Is Generated Each Day? Retrieved 15 May 2021, from https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day- cf4bddf29f/ Dobbs, R., Manyika, J., & Woetzel, J. (2015). The Four Global Forces Breaking All the Trends. In McKinsey Global Institute. https://doi.org/10.18311/sdmimd/2015/2659
31. Duffy, G., & Tucker, S. A. (1995). Political Science: Artificial Intelligence Applications. Social Science Computer Review, 13(1), 1–20.
32. Dwivedi, Y. K. et al. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, July, 0–1. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
33. Fatima, S., Desouza, K. C., & Dawson, G. S. (2020). National Strategic Artificial Intelligence Plans: A Multi-Dimensional Analysis. Economic Analysis and Policy, 67, 178–194. https://doi.org/10.1016/j.eap.2020.07.008 Dye, Thomas R. (1984). Understanding public policy. Englewood Cliffs, N.J.: Prentice-Hall,
34. Dunleavy, P., Margetts, H., Bastow, S., & Tinkler, J. (2006). New public management is dead—long live digital-era governance. Journal of public administration research and theory, 16(3), 467-494.
35. Engin, Z., & Treleaven, P. (2019). Algorithmic Government: Automating Public Services and Supporting Civil Servants in using Data Science Technologies. Computer Journal, 62(3), 448–460. https://doi.org/10.1093/comjnl/bxy082
36. Erdélyi, O.J., & Goldsmith, J. (2018). Regulating Artificial Intelligence: Proposal for a Global Solution. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society.
37. Fatima, S., Desouza, K. C., & Dawson, G. S. (2020). National Strategic Artificial Intelligence Plans: A Multi-Dimensional Analysis. Economic Analysis and Policy, 67, 178–194. https://doi.org/10.1016/j.eap.2020.07.008
38. Floridi, L. (2018). Soft ethics, the governance of the digital and the General Data Protection Regulation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180081.
39. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
40. French, P. E. (2009). Effective Leadership in Times of Public Health Crises. 485–487.
41. French, P. E., Spears, R. A., & Stanley, R. E. (2005). The Fifth Paradigm of Public Administration? Public Organizational Theory as a Possible Solution to The Perennial Big Questions of Public Administration. INTERNATIONAL JOURNAL OF ORGANIZATION THEORY AND BEHAVIOR, 8(2), 133–154.
42. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019
43. G20 Japan: AI Principles - G20 Insights. (2020). Retrieved 11 June 2021, from https://www.g20- insights
44. G7. (2018). Common Vision for the Future of Artificial Charlevoix Common Vision for the Future of. .org/related literature/g20-japan-ai-principles/
45. Gasser, U., & Almeida, V. A. F. (2017). A Layered Model for AI Governance. IEEE Internet Computing, 21(6), 58–62. https://doi.org/10.1109/MIC.2017.4180835
46. Gordon, I., Lewis, J., & Young, K. (1993). Perspectives on Policy Analysis. In M. Hill (Ed.), The Policy Process (pp. 5–9).
47. Hadden, S. G. (1989). The Future of Expert Systems in Government. Journal of Policy Analysis and Management, 8(2), 203–208. https://www.jstor.org/stable/3323379
48. Hawksworth, J., & Berriman, R. (2018). Will Robots Really Steal Our Jobs? An international analysis of the potential long-term impact of automation. In PwC. www.pwc.co.uk/economics
49. Head, B. W. (2008). Three Lenses of Evidence-Based Policy. Australian Journal of Public Administration, 67(1), 1–11. https://doi.org/10.1111/j.1467-8500.2007.00564.x
50. Hern, A. (2016). Stephen Hawking: AI will be 'either best or worst thing' for humanity. Retrieved 10 May 2021, from https://www.theguardian.com/science/2016/oct/19/stephen-hawking- ai-best-or-worst-thing-for-humanity-Cambridge
51. Ing, T. S., Lee, T. C., Chan, S. W., Alipal, J., & Hamid, N. A. (2019). An overview of the rising challenges in implementing industry 4.0. International Journal of Supply Chain Management, 8(6), 1181–1188.
52. Janssen, M., & Helbig, N. (2018). Innovating And Changing the Policy-Cycle: Policy-Makers Be Prepared! Government Information Quarterly, 35(4), S99–S105. https://doi.org/10.1016/j.giq.2015.11.009
53. Janssen, M., & Kuk, G. (2016). The Challenges and Limits of Big Data Algorithms in Technocratic Governance. Government Information Quarterly, 33(3), 371–377. https://doi.org/10.1016/j.giq.2016.08.011
54. Jiang, X., Coffee, M., Bari, A., Wang, J., & Jiang, X. (2020). Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity. 63(1), 537–551. https://doi.org/10.32604/cmc.2020.010691
55. Kankanhalli, A., Charalabidis, Y., & Mellouli, S. (2019). IoT and AI for Smart Government: A Research Agenda. Government Information Quarterly, 36(2), 304–309. https://doi.org/10.1016/j.giq.2019.02.003
56. Karnofsky, Holden (2016) Some background on our views regarding advanced artificial intelligence, Open Philanthropy, May 6, section 1. C
57. Kettl, D. F. (2002). Administrative Dilmmas. In Public Administration for Twenty-First Century America. The Johns Hopkins University Press.
58. Kincaid, J., & Stenberg, C.W. (2011). “Big Questions” about Intergovernmental Relations and Management: Who Will Address Them? Public Administration Review, 71, 196-202.
59. Kirlin, J. J. (2001). Public Administration Review, Vol. 61(2), 140-143
60. Kirlin, J. J. (1996). The Big Question of Public Administration in a Democracy. Public Administration Review, 56(5), 416–423.
61. Kolberg, D., & Zühlke, D. (2015). Lean Automation Enabled by Industry 4.0 Technologies. IFAC-PapersOnLine, 28(3), 1870–1875. https://doi.org/10.1016/j.ifacol.2015.06.35
62. Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44(6), 101976. https://doi.org/10.1016/j.telpol.2020.101976
63. Linkov, I., Trump, B. D., Poinsatte-Jones, K., & Florin, M. V. (2018). Governance strategies for a sustainable digital world. Sustainability (Switzerland), 10(2), 1–8. https://doi.org/10.3390/su10020440
64. Lohmann, R. A. (2007). What Are the Big Questions of Nonprofit Management Today? Public Administration Review, 67(3), 437–444.
65. Maciejewski, M. (2017). To-Do More, Better, Faster and More Cheaply: Using Big Data in Public Administration. International Review of Administrative Sciences, 83(1_suppl), 120– 135. https://doi.org/10.1177/0020852316640058
66. Margetts, H., Dorobantu, C., 2019. Rethink government with AI. Nature 568, 163–165. doi:10.1038/d41586-019-01099-5
67. Makridakis, S. (2017). The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms. Futures, 90, 46–60. https://doi.org/10.1016/j.futures.2017.03.006
68. McClure, P. K. (2018). “You’re Fired,” says the Robot: The Rise of Automation in the Workplace, Technophobes, and Fears of Unemployment. Social Science Computer Review, 36(2), 139–156. https://doi.org/10.1177/0894439317698637
69. Mehr, H. (2017). Artificial Intelligence for Citizen Services and Government. Harvard Ash Center Technology & Democracy Fellow, August 19. https://ash.harvard.edu/files/ash/files/artificial_intelligence_for_citizen_services.pdf
70. Microsoft. (2018). The Future of Artificial Intelligence and its Role in Society. https://3er1viui9wo30pkxh1v2nh4w-wpengine.netdna-ssl.com/wp- content/uploads/2018/02/The-Future-Computed_2.8.18.pdf
71. Mingus, M. S., & Jing, Z. (2017). The Big Questions of Chinese Public Management Research. Administration and Society, 49(6), 775–797. https://doi.org/10.1177/0095399716636926
72. Misuraca, Gianluca; Van Noordt, C. (2020). Overview of the use and impact of AI in public services in the EU. https://doi.org/10.2760/039619
73. Misuraca, G., & Viscusi, G. (2015). Shaping public sector innovation theory: an interpretative framework for ICT-enabled governance innovation. Electronic Commerce Research, 15(3), 303–322. https://doi.org/10.1007/s10660-015-9184-5
74. Misuraca G, Mureddu F, Osimo D. (2014). Policy Making 2.0: Unleashing the power of big data for public governance. In: Mila Gasco-Hernandez, editor. Open Government: Opportunities and Challenges for Public Governance. New York / London (USA / UK): Springer / Heidelberg Dordrecht; 171-188. JRC85436
75. Moynihan, D. P., & Pandey, S. K. (2010). The Big Question for Performance Management: Why Do Managers Use Performance Information? Journal of Public Administration Research and Theory, 849–866. https://doi.org/10.1093/jopart/muq004
76. Munne, R. (2016). Big Data in the Public Sector. In New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe (pp. 195–208). https://doi.org/10.1007/978-3-319-21569-3
77. Francis X. Neumann, Jr. (1996). What Makes Public Administration a Science? Or Are Its “Big Questions” Really Big? Public Administration Review, 56(5), 409–415. https://www.jstor.org/stable/977039
78. OECD. (2018). Open Government Data Report: Enhancing Policy Maturity for Sustainable Impact. In OECD Digital Government Studies. https://doi.org/10.1787/9789264305847-e
79. O’Flynn, Janine (2021). Confronting the big challenges of our time: making a difference during and after COVID-19, Public Management Review, 23:7, 961-980, DOI:10.1080/14719037.2020.182027
80. Onder, Murat, “Yapay Zekâ: Kavramsal Çerçeve”, Demir, İbrahim(ed.), Disiplinlerarası Politika Vizyonu ve Stratejiler 2020, Iksad Publishing House, 2020, ss. 91-102. Onder, M., & Uzun, M. M. (2021). Roles of Artificial Intelligence (AI) on COVID-19 Pandemic Crisis Management Policies. International Journal of Public Administration in the Digital Age (IJPADA), 8(2), 1-13.
81. Ojo, A., Zeleti, F. A., & Mellouli, S. (2019). A realist perspective on AI-Era public management. ACM International Conference Proceeding Series, 159–170. https://doi.org/10.1145/3325112.3325261
82. Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2018). Big Data and AI – A Transformational Shift for Government: So, What Next for Research? Public Policy and Administration. https://doi.org/10.1177/0952076718780537
83. Perc, M., Ozer, M., & Hojnik, J. (2019). Social And Juristic Challenges of Artificial Intelligence. Palgrave Communications, 5(1), 1–7. https://doi.org/10.1057/s41599-019-0278-x
84. Perry, B., & Uuk, R. (2019). AI Governance and the Policymaking Process: Key Considerations for Reducing AI Risk. Big Data and Cognitive Computing, 3(2), 1–17. https://doi.org/10.3390/bdcc3020026
85. Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
86. Shangraw, R. F. (1987). Knowledge Acquisition, Expert Systems, and. Social Science Microcomputer Review, 5(3), 163–173. https://doi.org/10.1016/0377-2217(87)90281-5
87. Sharma, R., Bhattarai, S., & Rupakheti, M. (2020). Lockdown caused by the COVID-19 pandemic reduces air pollution in cities worldwide. April. https://doi.org/10.31223/osf.io/edt4j
88. Smuha, N. A. (2019). From a “Race to AI” to a “Race to AI Regulation” - Regulatory Competition for Artificial Intelligence. SSRN Electronic Journal, 1, 1–27. https://doi.org/10.2139/ssrn.3501410
89. Sousa, W. G. de, Melo, E. R. P. de, Bermejo, P. H. D. S., Farias, R. A. S., & Gomes, A. O. (2019). How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly, 36(4), 1–14. https://doi.org/10.1016/j.giq.2019.07.004
90. Steuer, F. (2018). Machine Learning for Public Policy Making How to Use Data-Driven Predictive Modeling for the Social Good (Issue July).
91. Su, G. (2018). Unemployment in the AI age. AI Matters, 3(4), 35–43. https://doi.org/10.1145/3175502.3175511
92. Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368– 383. https://doi.org/10.1016/j.giq.2018.09.008
93. Thierer, A. D., Castillo, A., & Russell, R. (2017). Artificial Intelligence and Public Policy. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3021135
94. Thompson, S. E., & Parthasarathy, S. (2006). Moore’s Law: The Future of Si Microelectronics. Materials Today, 9(6), 20–25. https://doi.org/10.1016/S1369-7021(06)71539-5
95. Totschnig, W. (2019). The Problem of Superintelligence: Political, Not Technological. AI and Society, 34(4), 907–920. https://doi.org/10.1007/s00146-017-0753-0
96. Turner, J. (2019). Robot Rules Regulating Artificial Intelligence. In Jacop Turner (Ed.), Springer. https://doi.org/10.1126/science.343.6172.707-e
97. Valle-Cruz, D., Sandoval-Almazan, R., Ruvalcaba-Gomez, E. A., & Ignacio Criado, J. (2019). A Review of Artificial Intelligence in Government and Its Potential from A Public Policy Perspective. ACM International Conference Proceeding Series, 91–99. https://doi.org/10.1145/3325112.3325242
98. Valle-Cruz, J. Ramon Gil-Garcia, and Vanessa Fernandez-Cortez. (2020). Towards Smarter Public Budgeting? Understanding the Potential of Artificial Intelligence Techniques to Support Decision Making in Government. In The 21st Annual International Conference on Digital Government Research (dg. o '20). Association for Computing Machinery, New York, NY, USA, 232–242. https://doi.org/10.1145/3396956.3396995
99. Uzun, M. Metin, “Yapay Zekâ: Fırsat Ve Tehditler”, Demir, İbrahim(ed.), Disiplinlerarası Politika Vizyonu ve Stratejiler 2020, Iksad Publishing House, 2020, ss.137-153.
100. Wang, W., & Siau, K. (2018). Artificial Intelligence: A Study on Governance, Policies, and Regulations. Proceedings of the Thirteenth Midwest Association for Information Systems Conference, September 40. http://aisel.aisnet.org/mwais2018/40
101. Waldt, G.V. (2012). Contemplating the Big Five Questions in Public Administration and Management Curriculation.
102. Winfield, A. F. T., & Jirotka, M... (2018). Ethical governance is essential to building trust in robotics and artificial intelligence systems. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180085. https://doi.org/10.1098/rsta.2018.0085
103. Wirtz, B. W., & Müller, W. M. (2019). An Integrated Artificial Intelligence Framework for Public Management. Public Management Review, 21(7), 1076–1100. https://doi.org/10.1080/14719037.2018.1549268
104. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2018). Artificial Intelligence and the Public Sector— Applications and Challenges. International Journal of Public Administration, 42(7), 596– 615. https://doi.org/10.1080/01900692.2018.1498103
105. Wirtz, B. W., Weyerer, J. C., & Sturm, B. J. (2020). The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration. International Journal of Public Administration, 43(9), 818–829. https://doi.org/10.1080/01900692.2020.1749851
106. World Economic Forum. (2020). The Future of Jobs Report 2020 | World Economic Forum. The Future of Jobs Report, October 2020, 1163. https://www.weforum.org/reports/the-future- of-jobs-report-2018%0Ah
107. Yeung, K., Howes, A., Pogrebna, G., Yeung, K., Howes, A., & Pogrebna, G. (2020). AI Governance by Human Rights-Centered Design, Deliberation, and Oversight. In The Oxford Handbook of Ethics of AI. https://doi.org/10.1093/oxfordhb/9780190067397.013.5
108. Yıldız, M. (2020). Yeni Teknoloji ve İş Yapış Biçimlerinin Kamu Politikalarına Etkisi: Genel Bir Çerceve. In M. Yıldız & C. Babaoğlu (Eds.), Teknoloji ve Kamu Politikaları (pp. 1–8). Gazi Kitapevi.
109. Yildiz, M. (2007). E-government research: Reviewing the literature, limitations, and ways forward. Government Information Quarterly, 24(3), 646–665. https://doi.org/10.1016/j.giq.2007.01.002
110. Yildiz, M. (2013). Big questions of e-government research. Information Polity, 17(February), 343–355. https://doi.org/10.1145/2479724.
111. Zavattaro, S. M. (2018). What’s in a symbol? Big questions for place branding in public administration. Public Administration Quarterly, 42(1), 90-119.
112. Zimmerman, A. D. (1995). Toward a More Democratic Ethic of Technological Governance. Science, Technology, & Human Values, 20(1), 86 107. https://doi.org/10.1177/016224399502000105