Big Data and Automation in Strategic Communication

References

• Ampofo, L., Collister, S., O’Loughlin, B., & Chadwick, A. (2015). Text mining and social media: When quantitative meets qualitative, and software meets humans. In P. Halfpenny, & R. Procter (Eds.), Innovations in digital research methods. Los Angeles, CA: SAGE Publications.
   Google Scholar
• Andersen, R. (2014). The emergence of content strategy work and recommended resources. Communication Design Quarterly Review, 2(4), 6–13. doi:10.1145/2721874.2721875
   Google Scholar
• Anderson, C. W. (2012). Towards a sociology of computational and algorithmic journalism. New Media & Society, 0(0), 1–17. doi:10.1177/1461444812465137
   Web of Science ®Google Scholar
• Banasiewicz, A. D. (2013). Marketing database analytics: Transforming data for competitive advantage. New York, NY: Routledge.
   Google Scholar
• Banerjee, A., Bandyopadhyay, T., & Acharya, P. (2013). Data analytics: Hyped up aspirations or true potential? Vikalpa, 38(4), 1–11.
   Google Scholar
• Borges-Rey, E. (2016). Unravelling data journalism. Journalism Practice, 10(7), 833–843. doi:10.1080/17512786.2016.1159921
   Web of Science ®Google Scholar
• Boxenbaum, E., & Jonsson, S. (2008). Isomorphism, diffusion and decoupling. In R. Greenwood, C. Oliver, R. Suddaby, & K. Sahlin (Eds.), Handbook of organizational institutionalism (pp. 78–98). London, UK: SAGE Publications.
   Google Scholar
• Boyd, D., & Crawford, K. (2012). Critical questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. doi:10.1080/1369118X.2012.678878
   Web of Science ®Google Scholar
• Boyd, D., Levy, K., & Marwick, A. (2014). The networked nature of algorithmic discrimination. In S. P. Gangadharan (Ed.), Data and discrimination: Collected essays (pp. 53–57). Washington, DC: New America.
   Google Scholar
• Buhl, H. U., Röglinger, M., Moser, F., & Heidemann, J. (2013). Big data: A fashionable topic with(out) sustainable relevance for research and practice? Business & Information Systems Engineering, 5(2), 65–69. doi:10.1007/s12599-013-0249-5
   Web of Science ®Google Scholar
• Campbell, C., Pitt, L. F., Parent, M., & Berthon, P. (2011). Tracking back-talk in consumer-generated advertising. Journal of Advertising Research, 51(1), 224–238. doi:10.2501/JAR-51-1-224-238
   Web of Science ®Google Scholar
• Che, D., Safran, M., & Peng, Z. (2013). From big data to big data mining: Challenges, issues, and opportunities. In B. Hong, X. Meng, L. Chen, W. Winiwarter, & W. Song (Eds.), Database systems for advanced applications: 18th International Conference, DASFAA 2013, international workshops: BDMA, SNSM, SeCoP, Wuhan, China, April 22-25, 2013. Proceedings (pp. 1–15). Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-40270-8_1
   Google Scholar
• Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
   Web of Science ®Google Scholar
• Child, J. T., Haridakis, P. M., & Petronio, S. (2012). Blogging privacy rule orientations, privacy management, and content deletion practices: The variability of online privacy management activity at different stages of social media use. Computers in Human Behavior, 28(5), 1859–1872. doi:10.1016/j.chb.2012.05.004
   Web of Science ®Google Scholar
• Cisco Systems. (2013). The Potential and Challenge of Data. Retrieved from http://www.cisco.com/c/dam/en/us/solutions/enterprise/connected-world-technology-report/Global-Data-CCWTR-Chapter3-Media-Briefing-Slides.pdf
   Google Scholar
• Collister, S. (2015). Algorithmic public relations: materiality, technology and power in a post-hegemonic world. In J. L’Etang, D. McKie, N. Snow, & J. Xifra (Eds.), The routledge handbook of critical public relations (pp. 360–371). New York, NY: Taylor & Francis.
   Google Scholar
• Couldry, N., Fotopoulou, A., & Dickens, L. (2016). Real social analytics: A contribution towards a phenomenology of a digital world. The British Journal of Sociology, 67(1), 118–137. doi:10.1111/1468-4446.12183
   PubMed Web of Science ®Google Scholar
• Couldry, N., & Hepp, A. (2016). The mediated construction of reality. New York, NY: John Wiley & Sons.
   Google Scholar
• Couldry, N., & Turow, J. (2014). Advertising, big data and the clearance of the public realm: Marketers’ new approaches to the content subsidy. International Journal of Communication. 8, 1710–1726. Retrieved from http://ijoc.org/index.php/ijoc/article/view/2166/1161
   Web of Science ®Google Scholar
• Cox, M., & Ellsworth, D. (1997, October). Application-controlled demand paging for out-of-core visualization. Paper presented at the VIS97 IEEE Visualization ‘97 Conference, Phoenix, AZ. Retrieved from https://www.evl.uic.edu/cavern/rg/20040525_renambot/Viz/parallel_volviz/paging_outofcore_viz97.pdf]
   Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. doi:10.1287/mnsc.35.8.982
   Web of Science ®Google Scholar
• De Sola Pool, I. (1983). Tracking the flow of information. Science, 221(4611), 609–613. doi:10.1126/science.221.4611.609
   PubMed Web of Science ®Google Scholar
• Denning, P. J. (1990). Saving all the bits. American Scientist, 78, 402–405.
   Web of Science ®Google Scholar
• Diakopoulos, N. (2016). Computational journalism and the emergence of news platforms. In S. Eldridge II, & B. Franklin (Eds.), The Routledge companion to digital journalism studies, (pp. 176–184). New York, NY: Routledge.
   Google Scholar
• DiMaggio, P. J., & Powell, W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48, 147–160. doi:10.2307/2095101
   Web of Science ®Google Scholar
• Dörr, K. N. (2015). Mapping the field of Algorithmic Journalism. Digital Journalism, 4(6), 1–23. doi:10.1080/21670811.2015.1096748
   Web of Science ®Google Scholar
• Erevelles, S., Fukawa, N., & Swayne, L. (2015). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904. doi:10.1016/j.jbusres.2015.07.001
   Web of Science ®Google Scholar
• Europe. (n.d.). Columbia Encyclopedia online. Retrieved from http://www.encyclopedia.com/doc/1E1-Europe.html
   Google Scholar
• European Union. (2016). About the EU: Countries. Retrieved from http://europa.eu/about-eu/countries
   Google Scholar
• Fairfield, J., & Shtein, H. (2014). Big data, big problems: Emerging issues in the ethics of data science and journalism. Journal of Mass Media Ethics, 29(1), 38–51. doi:10.1080/08900523.2014.863126
   Web of Science ®Google Scholar
• Fulgoni, G. (2014). Numbers, please: Big data: Friend or foe of digital advertising? Five ways marketers should use digital big data to their advantage. Journal of Advertising Research, 53(4), 372–376. doi:10.2501/JAR-53-4-372-376
   Web of Science ®Google Scholar
• Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. doi:10.1016/j.ijinfomgt.2014.10.007
   Web of Science ®Google Scholar
• Graefe, A., Haim, M., Haarmann, B., & Brosius, H.-B. (2016). Readers perception of computer-generated news: Credibility, expertise, and readability. Journalism. doi:10.1177/1464884916641269
   Google Scholar
• Grunig, J. E. (1983). Basic research provides knowledge that makes evaluation possible. Public Relations Quarterly, 28(3), 28–32.
   Google Scholar
• Guo, L., Vargo, C. J., Pan, Z., Ding, W., & Ishwar, P. (2016). Big social data analytics in journalism and mass communication: Comparing dictionary-based text analysis and unsupervised topic modeling. Journalism & Mass Communication Quarterly, 93(2), 332–359. doi:10.1177/1077699016639231
   Web of Science ®Google Scholar
• Heimbach, I., Kostyra, D. S., & Hinz, O. (2015). Marketing Automation. Business & Information Systems Engineering, 57(2), 129–133. doi:10.1007/s12599-015-0370-8
   Web of Science ®Google Scholar
• Hersh, E. D., & Schaffner, B. F. (2013). Targeted campaign appeals and the value of ambiguity. The Journal of Politics, 75(2), 520–534. doi:10.1017/S0022381613000182
   Web of Science ®Google Scholar
• Holtzhausen, D. R. (2016). Datafication: Threat or opportunity for communication in the public sphere? Journal of Communication Management, 20(1), 21–36. doi:10.1108/JCOM-12-2014-0082
   Web of Science ®Google Scholar
• Holtzhausen, D. R., & Zerfass, A. (2015). Strategic communication: Opportunities and challenges of the research area. In D. R. Holtzhausen, & A. Zerfass (Eds.), The Routledge handbook of strategic communication (pp. 3–17). New York, NY: Routledge.
   Google Scholar
• Hoy, M. B. (2015). If this then that: An introduction to automated task services. Medical Reference Services Quarterly, 34(1), 98–103. doi:10.1080/02763869.2015.986796
   PubMedGoogle Scholar
• Information explosion. (n.d.). Oxford English Dictionary online. Retrieved from http://www.oed.com/view/Entry/95568?rskey=Dcjq4x&result=1
   Google Scholar
• Kent, M. L., & Saffer, A. J. (2014). A Delphi study of the future of new technology research in public relations. Public Relations Review, 40(3), 568–576. doi:10.1016/j.pubrev.2014.02.008
   Web of Science ®Google Scholar
• Kollanyi, B., Howard, P. N., & Woolley, S. C. (2016). Bots and automation over Twitter during the First U.S. Presidential debate. Data Memo 1. Oxford, UK: Project on Computational Propaganda. Retrieved from http://politicalbots.org/wp-content/uploads/2016/10/Data-Memo-First-Presidential-Debate.pdf
   Google Scholar
• Kreiss, D., & Jasinski, C. (2016). The tech industry meets presidential politics: Explaining the democratic party’s technological advantage in electoral campaigning, 2004–2012. Political Communication, 33(4), 1–19. doi:10.1080/10584609.2015.1121941
   Web of Science ®Google Scholar
• Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387–394. doi:10.1016/j.ijinfomgt.2014.02.002
   Web of Science ®Google Scholar
• Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. Retrieved from http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
   Google Scholar
• LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 20–31.
   Web of Science ®Google Scholar
• Lewis, S. C. (2014). Journalism in an era of big data. Digital Journalism, 3(3), 321–330. doi:10.1080/21670811.2014.976399
   Web of Science ®Google Scholar
• Lewis, S. C., & Westlund, O. (2014). Big data and journalism. Digital Journalism, 3(3), 447–466. doi:10.1080/21670811.2014.976418
   Web of Science ®Google Scholar
• Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), 149–157.
   Web of Science ®Google Scholar
• Lokot, T., & Diakopoulos, N. (2016). News bots. Digital Journalism, 4(6), 682–699. doi:10.1080/21670811.2015.1081822
   Web of Science ®Google Scholar
• Marron, B. A., & De Maine, P. A. D. (1967). Automatic data compression. Communications of the ACM, 10(11), 711–715. doi:10.1145/363790.363813
   Web of Science ®Google Scholar
• Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Boston, MA: Houghton Mifflin Harcourt.
   Google Scholar
• Micu, A. C., Dedeker, K., Lewis, I., Moran, R., Netzer, O., Plummer, J., & Rubinson, J. (2011). The shape of marketing research in 2021. Journal of Advertising Research, 51(1), 213–221. doi:10.2501/JAR-51-1-213-221
   Web of Science ®Google Scholar
• Morris, R. J., & Truskowski, B. J. (2003). The evolution of storage systems. IBM Systems Journal, 42, 205–217.
   Google Scholar
• Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521–543. doi:10.1287/mksc.1120.0713
   Web of Science ®Google Scholar
• Newell, S., & Marabelli, M. (2015). Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of ‘datification’. The Journal of Strategic Information Systems, 24(1), 3–14. doi:10.1016/j.jsis.2015.02.001
   Web of Science ®Google Scholar
• Nickerson, D., & Rogers, T. (2014). Political campaigns and big data. Journal of Economic Perspectives, 28(2), 51–74.
   Web of Science ®Google Scholar
• Nunan, D., & Di Domenico, M. (2013). Market research and the ethics of big data. International Journal of Market Research, 55(4), 505–520. doi:10.2501/IJMR-2013-015
   Web of Science ®Google Scholar
• Parasie, S., & Dagiral, E. (2013). Data-driven journalism and the public good: “Computer-assisted-reporters” and “programmer-journalists” in Chicago. New Media & Society, 15(6), 853–871. doi:10.1177/1461444812463345
   Web of Science ®Google Scholar
• Parks, M. R. (2014). Big data in communication research: Its contents and discontents. Journal of Communication, 64(2), 355–360. doi:10.1111/jcom.12090
   Web of Science ®Google Scholar
• Phillips, D. (2015). The automation of public relations: A perspective on the development of automation affecting public relations. Woolbridge, UK: Blurb.
   Google Scholar
• Place, K. R. (2015). Exploring the role of ethics in public relations program evaluation. Journal of Public Relations Research, 27(2), 118–135. doi:10.1080/1062726X.2014.976825
   Web of Science ®Google Scholar
• Rogers, D., & Sexton, D. (2012). Marketing ROI in the Era of Big Data: The 2012 BRITE/NYAMA Marketing in Transition Study. Retrieved from http://www8.gsb.columbia.edu/globalbrands/sites/globalbrands/files/images/2012-BRITE-NYAMA-Marketing-ROI-Study.pdf
   Google Scholar
• Sahlin, K., & Wedlin, L. (2008). Circulating ideas: Imitation, translation and editing. In R. Greenwood, C. Oliver, K. Sahlin, & R. Suddaby (Eds.), The SAGE handbook of organizational institutionalism (pp. 218–242). Los Angeles, CA: SAGE Publications.
   Google Scholar
• Sandhu, S. (2009). Strategic communication: An institutional perspective. International Journal of Strategic Communication, 3(2), 72–92. doi:10.1080/15531180902805429
   Google Scholar
• Schmeltz, L., & Kjeldsen, A. K. (2016). Naming as strategic communication: Understanding corporate name change through an integrative framework encompassing branding, identity and institutional theory. International Journal of Strategic Communication, 10(4), 309–331. doi:10.1080/1553118X.2016.1179194
   Google Scholar
• Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2013). Analytics: The real-world use of big data. How innovative enterprises extract value from uncertain data. Retrieved from http://www-935.ibm.com/services/multimedia/Analytics_The_real_world_use_of_big_data_in_Financial_services_Mai_2013.pdf
   Google Scholar
• Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent dirichlet allocation. Journal of Marketing Research, 51(4), 463–479. doi:10.1509/jmr.12.0106
   Web of Science ®Google Scholar
• Tufekci, Z. (2015). Algorithmic harms beyond facebook and google: Emergent challenges of computational agency. Colorado Technology Law Journal, 13(2), 203–217.
   Google Scholar
• Van den Driest, F., Sthanunathan, S., & Weed, K. (2016). Building an Insights Engine. Harvard Business Review, 94(9), 64–74.
   Web of Science ®Google Scholar
• Van der Haak, B., Parks, M., & Castells, M. (2012). The future of journalism: Networked journalism. International Journal of Communication, 6, 2923–2938.
   Web of Science ®Google Scholar
• Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208.
   Google Scholar
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
   Web of Science ®Google Scholar
• Wehmeier, S. (2006). Dancers in the dark: The myth of rationality in public relations. Public Relations Review, 32(3), 213–220. doi:10.1016/j.pubrev.2006.05.018
   Web of Science ®Google Scholar
• Weiner, M., & Kochhar, S. (2016). Irreversible: The public relations big data revolution (IPR white paper). Gainesville, FL: Institute for Public Relations.
   Google Scholar
• Wiencierz, C., & Röttger, U. (2017). The Use of Big Data in Corporate Communication. Corporate Communications: An International Journal, in press. doi:10.1108/CCIJ-02-2016-0015
   Google Scholar
• Woolley, S. C., & Howard, P. N. (2016). Automation, algorithms, and politics: Political communication, computational propaganda, and autonomous agents. International Journal of Communication, 10, 4882–4890.
   Web of Science ®Google Scholar
• Yang, K. C. C., & Kang, Y. (2015). Exploring big data and privacy in strategic communication campaigns: A cross-cultural study of mobile social media users’ daily experiences. International Journal of Strategic Communication, 9(2), 87–101. doi:10.1080/1553118X.2015.1008635
   Google Scholar
• Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., & Vasilakos, A. V. (2016). Big data: From beginning to future. International Journal of Information Management, 36(6), 1231–1247. doi:10.1016/j.ijinfomgt.2016.07.009
   Web of Science ®Google Scholar
• Zerfass, A., Verhoeven, P., Moreno A., Tench, R., & Vercic, D. (2016). European Communication Monitor 2016. Exploring trends in big data, stakeholder engagement and strategic communication. Results of a survey in 43 countries. Brussels: EACD/EUPRERA, Quadriga Media Berlin.
   Google Scholar
• Zerfass, A., Verčič, D., & Volk, S. C. (2017). Communication evaluation and measurement: Skills, practices and utilization in European organizations. Corporate Communications – An International Journal, 22(1), 2–18. doi:10.1108/CCIJ-08-2016-0056
   Web of Science ®Google Scholar
• Zikopoulos, P., de Roos, D., Parasuraman, K., Deutsch, T., Corrigan, D., & Giles, J. (Eds.). (2013). Harness the power of big data: The IBM big data platform. New York, NY: McGraw-Hill.
   Google Scholar