42,748
Views
145
CrossRef citations to date
0
Altmetric
Articles

Big data in the policy cycle: Policy decision making in the digital era

, &

References

  • Alfaro, C., J. Cano-Montero, J. Gómez, J. M. Moguerza, and F. Ortega. 2013, September. A multi-stage method for content classification and opinion mining on weblog comments. Annals of Operations Research 1–17. doi:10.1007/s10479-013-1449-6.
  • Andersen, S. S., and K. A. Eliassen. 1993. Making policy in Europe: The Europeification of national policy-making. London, UK: Sage Publications Limited.
  • Anderson, J. E. 1972. Public policymaking. New York: Praeger Publishing.
  • Asquer, A. 2013. The Governance of big data: Perspectives and issues. SSRN Scholarly Paper ID 2272608, Social Science Research Network, Rochester, NY. http://papers.ssrn.com/abstract=2272608.
  • Bannister, F., and R. Connolly. 2009. New problems for old? An exploration of e-governance. European Group of Public Administration 2:137–152.
  • Bannister, F., and R. Connolly. 2012. Forward to the past: Lessons for the future of e-government from the story so far. Information Polity 17(3):211–226.
  • Bannister, F., and R. Connolly. 2014. ICT, Public values and transformative government: A framework and programme for research. Government Information Quarterly 31(1):119–128. doi:10.1016/j.giq.2013.06.002.
  • Barkenbus, J. 1998, September. Expertise and the policy cycle. Energy, Environment, and Resources Center, University of Tennessee, Knoxville, Tennesse.
  • Bertot, J. C., U. Gorham, P. T. Jaeger, L. C. Sarin, and H. Choi. 2014. Big data, open government and E-government: Issues, policies and recommendations. Information Polity 19(1):5–16.
  • Bondi, A. B. 2000. Characteristics of scalability and their impact on performance. In Proceedings of the 2nd International Workshop on Software and Performance, 195–203. WOSP ’00, New York, NY, USA, ACM. doi:10.1145/350391.350432.
  • Brans, M. 1997. Challenges to the practice and theory of public administration in Europe. Journal of Theoretical Politics 9(3):389–415. doi:10.1177/0951692897009003007.
  • Bridgman, P., and G. Davis. 2003. What use is a policy cycle? Plenty, if the aim is clear. Australian Journal of Public Administration 62(3):98–102. doi:10.1046/j.1467-8500.2003.00342.x.
  • Bulmer, M. 1987. Social science research and government: Comparative essays on Britain and the United States. Cambridge, UK: Cambridge University Press.
  • Buskirk, E. V. 2009. How the Netflix prize was won. Blog of WIRED Magazine. http://www.wired.com/2009/09/how-the-netflix-prize-was-won/ ( accessed September 22).
  • Carpenter, D. P. 2001. The forging of bureaucratic autonomy: Reputations, networks, and policy innovation in executive agencies, 1862–1928. Princeton, NJ: Princeton University Press.
  • Chen, H., R. H. L. Chiang, and V. C. Storey. 2012. Business intelligence and analytics: From big data to big impact. MIS Quarterly 36(4):1165–1188.
  • Chen, M., S. Mao, and Y. Liu. 2014. Big data: A survey. Mobile Networks and Applications 19(2):171–209. doi:10.1007/s11036-013-0489-0.
  • Chen, Y.-C., and T.-C. Hsieh. 2014. Big data for digital government: Opportunities, challenges, and strategies. International Journal of Public Administration in the Digital Age 1(1):1–14. doi:10.4018/ijpada.2014010101.
  • Colebatch, H. K. 2005. Policy analysis, policy practice and political science. Australian Journal of Public Administration 64(3):14–23. doi:10.1111/ajpa.2005.64.issue-3.
  • Cordella, A., and N. Tempini. 2015. E-government and organizational change: Reappraising the role of ICT and bureaucracy in public service delivery. Government Information Quarterly 32:279–286. doi:10.1016/j.giq.2015.03.005.
  • Coronel, C., S. Morris, and P. Rob. 2012. Database systems: Design, implementation, and management. Stamford, Connecticut: Cengage Learning.
  • Crotty, M. 1998. The foundations of social research: Meaning and perspective in the research process. London, UK: Sage Publications.
  • Daniell, K. A., A. Morton, and D. R. Insua. 2015, June. Policy analysis and policy analytics. Annals of Operations Research. doi:10.1007/s10479-015-1902-9.
  • data4policy.eu. 2015. Data for policy: Big data and other innovative data-driven approaches for evidence-informed policy making. Presented at Policy-making in the Big Data Era: Opportunities and Challenges, Cambridge. http://media.wix.com/ugd/c04ef4_5ae7cee4ad3c46f39668de4f3a62d2df.pdf ( accessed June 16).
  • Davenport, T. H. 2014. Big data at work: Dispelling the myths, uncovering the opportunities. Boston: Harvard Business Press.
  • Davenport, T. H., and J. G. Harris. 2007. Competing on analytics: The new science of winning, 1st ed. Boston: Harvard Business Review Press.
  • Dearing, J. W., and E. M. Rogers. 1996. Agenda-Setting. Thousand Oaks, CA: Sage Publications.
  • Dunleavy, P., and H. Margetts. 2010. The second wave of digital Era Governance. Washington, DC: Oxford University Press.
  • Dunleavy, P., H. Margetts, S. Bastow, and J. Tinkler. 2006a. New public management is dead—Long live digital-Era Governance. Journal of Public Administration Research and Theory 16(3):467–494. doi:10.1093/jopart/mui057.
  • Dunleavy, P., H. Margetts, S. Bastow, and J. Tinkler. 2006b. Digital era governance: IT corporations, the state, and E-Government. Oxford, UK: Oxford University Press.
  • Edwards, M., C. Howard, and R. Miller. 2001. Social policy, public policy: From problems to practice. Sydney, Canberra, Australia: Allen & Unwin.
  • Evans, P. B. 1995. Embedded autonomy: States and industrial transformation. Princeton paperbacks. Princeton, NJ: Princeton University Press.
  • Everett, S. 2003. The policy cycle: Democratic process or rational paradigm revisited? Australian Journal of Public Administration 62(2):65–70. doi:10.1111/ajpa.2003.62.issue-2.
  • Executive Office of the President. 2014. Big data: Seizing opportunities, preserving values. Washington, DC: The White House. http://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf.
  • Fasenfest, D. 2010. Government, governing, and governance. Critical Sociology 36(6):771–774. doi:10.1177/0896920510378192.
  • Fawcett, S. E., C. Wallin, C. Allred, and G. Magnan. 2009. Supply chain information-sharing: Benchmarking a proven path. Benchmarking: An International Journal 16(2):222–246. doi:10.1108/14635770910948231.
  • Ferro, E., E. N. Loukis, Y. Charalabidis, and M. Osella. 2013. Policy making 2.0: From theory to practice. Government Information Quarterly 30(4):359–368. doi:10.1016/j.giq.2013.05.018.
  • Fukuyama, F. 2013. What is governance? Governance 26(3):347–368. doi:10.1111/gove.2013.26.issue-3.
  • Géczy, P. 2014. Big data characteristics. The Macrotheme Review 3(6). http://macrotheme.com/yahoo_site_admin/assets/docs/8MR36Pe.97110828.pdf.
  • Guba, E. G., and Y. S. Lincoln. 1989. Fourth generation evaluation. Thousand Oaks, CA: Sage Publications.
  • Gupta, M. P., and D. Jana. 2003. E-Government evaluation: A framework and case study. Government Information Quarterly 20(4):365–387. doi:10.1016/j.giq.2003.08.002.
  • Harris, S. 2015. The social laboratory. Foreign Policy. https://foreignpolicy.com/2014/07/29/the-social-laboratory/ ( accessed July 22).
  • Heinrich, C. J. 2007. Evidence-based policy and performance management: Challenges and prospects in two parallel movements. The American Review of Public Administration 37(3):255–277. doi:10.1177/0275074007301957.
  • Hertin, J., A. Jordan, J. Turnpenny, M. Nilsson, D. Russel, and N. Björn. 2009. Rationalising the policy mess? Ex ante policy assessment and the utilisation of knowledge in the policy process. Environment and Planning 41:1185–1200. doi:10.1068/a40266.
  • Heule, S., M. Nunkesser, and A. Hall. 2013. HyperLogLog in practice: Algorithmic engineering of a state of the art cardinality estimation algorithm. Proceedings of the EDBT 2013 Conference, Genoa, Italy, March.
  • Hilbert, M., and P. López. 2011. The world’s technological capacity to store, communicate, and compute information. Science 332(6025):60–65. doi:10.1126/science.1200970.
  • Hudson, J., and S. Lowe. 2004. Understanding the policy process: Analysing welfare policy and practice. Bristol, UK: Policy Press.
  • Jacobs, A. 2009. The pathologies of big data. Commun. ACM 52(8):36–44. doi:10.1145/1536616.1536632.
  • Johnston, E. W. 2015. Governance in the information Era: Theory and practice of policy informatics. London, UK: Routledge.
  • Joseph, R. C., and N. A. Johnson. 2013. Big data and transformational government. IT Professional 15:43–48. doi:10.1109/MITP.2013.61.
  • Kamal, M. M. 2006. IT innovation adoption in the government sector: Identifying the critical success factors. Journal of Enterprise Information Management 19(2):192–222. doi:10.1108/17410390610645085.
  • Kamateri, E., E. Panopoulou, E. Tambouris, K. Tarabanis, O. Adegboyega, D. Lee, and D. Price. 2015. A comparative analysis of tools and technologies for policy making. In Policy practice and digital science, ed. M. Janssen, M. A. Wimmer, and D. Ameneh, Cham: Springer International Publishing. http://link.springer.com/10.1007/978-3-319-12784-2.
  • Keller, K. L., and R. Staelin. 1987. Effects of quality and quantity of information on decision effectiveness. Journal of Consumer Research 14:200–213. doi:10.1086/jcr.1987.14.issue-2.
  • King, G., J. Pan, and M. E. Roberts. 2013. How censorship in China allows government criticism but silences collective expression. American Political Science Review 107(2):326–343. doi:10.1017/S0003055413000014.
  • Kjaer, A. M. 2004. Governance. Cambridge, UK: Polity.
  • Kogan, M. 1999. The impact of research on policy. In Speaking truth to power: Research and policy on lifelong learning, ed. F. Coffield, 11–18. Bristol, UK: Policy Press.
  • Laney, D. 2001. 3D data management: Controlling data volume, velocity, and variety. 949, META Group, Stamford, Connecticut. http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf.
  • Layne, K., and J. Lee. 2001. Developing fully functional E-government: A four stage model. Government Information Quarterly 18(2):122–136. doi:10.1016/S0740-624X(01)00066-1.
  • Lazer, D. M., R. Kennedy, G. King, and A. Vespignani. 2014. The parable of google flu: Traps in big data analysis. Science 343(6176):1203–1205. doi:10.1126/science.1248506.
  • Leetaru, K. 2011. Culturomics 2.0: Forecasting large-scale human behavior using global news media tone in time and space. First Monday 16(9). doi:10.5210/fm.v16i9.3663.
  • Leetaru, K., and P. A. Schrodt. 2013. GDELT: Global data on events, location, and tone, 1979–2012. Paper Presented at the ISA Annual Convention 2:4.
  • Lorentzen, P. 2014. China’s strategic censorship. American Journal of Political Science 58(2):402–414. doi:10.1111/ajps.2014.58.issue-2.
  • Malar, K. U., D. Ragupathi, and G. M. Prabhu. 2014. The hadoop dispersed file system: Balancing movability and performance. New York: International Journal of Computer Sciences and Engineering, Foundation of Computer Science.
  • Manyika, J., M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and H.-B. Angela. 2011. Big data: The next frontier for innovation, competition, and productivity. Washington, DC: McKinsey Global Institute. http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation.
  • Mayer-Schönberger, V., and K. Cukier. 2013. Big data: A Revolution that will transform how we live, work, and think, 1st ed. Boston: Eamon Dolan/Houghton Mifflin Harcourt.
  • McAfee, A., and E. Brynjolfsson. 2012, October. Big data: The management revolution. Harvard Business Review https://hbr.org/2012/10/big-data-the-management-revolution.
  • McCombs, M. E., and D. L. Shaw. 1972. The agenda-setting function of mass media. Public Opinion Quarterly 36(2):176–87. doi:10.1086/267990.
  • Meijer, A., and K. Löfgren. 2015. The neglect of technology in theories of policy change. International Journal of Public Administration in the Digital Age 2(1):75–88. doi:10.4018/ijpada.2015010105.
  • Micklethwait, J., and A. Wooldridge. 2014. The fourth revolution: The global race to reinvent the State. New York: The Penguin Press.
  • Misuraca, G., F. Mureddu, and D. Osimo. 2014. Policy-making 2.0: Unleashing the power of big data for public Governance. In Open Government, ed. M. Gascó-Hernández, 171–188. Public Administration and Information Technology 4. Springer New York. http://link.springer.com/chapter/10.1007/978-1-4614-9563-5_11.
  • Muller, P., G. Conlon, S. Devnani, and C. Bénard. 2013. Performance based policy. IP/A/IMCO/ ST /20 13 - 04 PE 507.457, European Parliament, Brussels. http://www.europarl.europa.eu/RegData/etudes/etudes/join/2013/507457/IPOL-IMCO_ET%282013%29507457_EN.pdf.
  • Nachmias, D., and C. Felbinger. 1982. Utilization in the policy cycle: Directions for research. Review of Policy Research 2(2):300–308. doi:10.1111/ropr.1982.2.issue-2.
  • Offe, C. 2009. Governance: An ‘empty signifier’? Constellations 16(4):550–562. doi:10.1111/j.1467-8675.2009.00570.x.
  • Orlikowski, W. J., and C. S. Iacono. 2001. Research commentary: Desperately seeking the ‘IT’ in IT research—A call to theorizing the IT Artifact. Information Systems Research 12(2):121–134. doi:10.1287/isre.12.2.121.9700.
  • Parsons, D. W. 1995. Public policy: An introduction to the theory and practice of policy analysis. Aldershot, UK: Edward Elgar Publishing.
  • Peled, A. 2014. Traversing digital babel: Information, E-government, and exchange. Boston: MIT Press.
  • Rhodes, R. A. W. 2007. Understanding governance: Ten years on. Organization Studies 28(8):1243–1264. doi:10.1177/0170840607076586.
  • Rogers, E. M., J. W. Dearing, and D. Bregman. 1993. The anatomy of Agenda‐setting research. Journal of Communication 43(2):68–84. doi:10.1111/jcom.1993.43.issue-2.
  • Russell Neuman, W., L. Guggenheim, S. Mo Jang, and S. Y. Bae. 2014. The dynamics of public attention: Agenda-setting theory meets big data: Dynamics of public attention. Journal of Communication 64(2):193–214. doi:10.1111/jcom.12088.
  • Sanderson, I. 2002. Evaluation, policy learning and evidence‐based policy making. Public Administration 80(1):1–22. doi:10.1111/padm.2002.80.issue-1.
  • Schaller, R. R. 1997. Moore’s Law: Past, present and future. IEEE Spectrum 34(6):52–59. doi:10.1109/6.591665.
  • Scheufele, D. A. 1999. Framing as a theory of media effects. Journal of Communication 49:103–122. doi:10.1111/jcom.1999.49.issue-1.
  • Schön, D. A., and M. Rein. 1995. Frame reflection: Toward the resolution of intractable policy controversies. New York: Basic Books.
  • Simon, P. 2014. Potholes and big data: Crowdsourcing our way to better government. WIRED. http://www.wired.com/2014/03/potholes-big-data-crowdsourcing-way-better-government/ ( accessed March 25).
  • Stockmann, R. 2007. Handbuch Zur evaluation. Münster, Deutschland: Waxmann Verlag.
  • TechAmerica Foundation. 2015. Demystifying big data—A practical guide to transforming the business of government, TechAmerica Foundation, Washington, DC. http://beautifuldata.net/2012/10/techamerica-publishes-big-data-a-practical-guide-to-transforming-the-business-of-government/ ( accessed June 22).
  • Tresch, A., P. Sciarini, and F. Varone. 2011. A policy cycle perspective on the media’s political Agenda-setting power. Reykjavik, Island.
  • Van der Aalst, W. M. P. 2011. Process mining: Discovery, conformance and enhancement of business processes, 1st ed. New York: Springer.
  • Warren, E. 2002. Market for data: The changing role of social sciences in shaping the law. Wisconsin Law Review 1:1–43.
  • Weinberger, D. 2011. Too big to know: Rethinking knowledge now that the facts aren’t the facts, Experts are everywhere, and the smartest person in the room is the room. New York: Basic Books.
  • Zou, H., T. Hastie, and R. Tibshirani. 2006. Sparse principal component analysis. Journal of Computational and Graphical Statistics 15(2):265–286. doi:10.1198/106186006X113430.