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New Ways of Seeing Big Data

2019; Academy of Management; Volume: 62; Issue: 4 Linguagem: Inglês

10.5465/amj.2019.4004

1948-0989

Zeki Şimşek, Eero Vaara, Srikanth Paruchuri, Sucheta Nadkarni, Jason D. Shaw,

Privacy-Preserving Technologies in Data

Academy of Management JournalVol. 62, No. 4 From the EditorsNew Ways of Seeing Big DataZeki Simsek, Eero Vaara, Srikanth Paruchuri, Sucheta Nadkarni and Jason D. ShawZeki SimsekClemson University, Eero VaaraAalto University School of Business, Srikanth ParuchuriPennsylvania State University, Sucheta NadkarniUniversity of Cambridge and Jason D. ShawNanyang Technological UniversityPublished Online:22 Aug 2019https://doi.org/10.5465/amj.2019.4004AboutSectionsView articleView Full TextPDF/EPUB ToolsDownload CitationsAdd to favoritesTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail View articleREFERENCESAcquisti, A., Brandimarte, L., & Loewenstein, G. 2015. Privacy and human behavior in the age of information. Science, 347: 509–514. Google ScholarBowman, A. W. 2018. Big questions, informative data, excellent science. Statistics & Probability Letters, 136: 34–36. Google ScholarCalude, C. S., & Longo, G. 2017. The deluge of spurious correlations in big data. Foundations of Science, 22: 595–612. Google ScholarChan, J., & Moses, B. L. 2016. Is big data challenging criminology? Theoretical Criminology, 20: 21–39. Google ScholarCohen, J. 2013. What privacy is for. Harvard Law Review, 126: 1904–1933. Google ScholarCoveney, P. V., Dougherty, E. R., & Highfield, R. R. 2016. Big data need big theory too. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374: 20160153. Retrieved from https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0153. Google ScholarEinav, L., & Levin, J. 2014. The data revolution and economic analysis. Innovation Policy and the Economy, 14: 1–24. Google ScholarElragal, A., & Klischewski, R. 2017. Theory-driven or process-driven prediction? Epistemological challenges of big data analytics. Journal of Big Data, 4: 19. Retrieved from https://link.springer.com/article/10.1186/s40537-017-0079-2. Google ScholarFrické, M. 2015. Big data and its epistemology. Journal of the Association for Information Science and Technology, 66: 651–661. Google ScholarGalbraith, J. R. 2014. Organization design challenges resulting from big data. Journal of Organization Design, 3: 2–13. Google ScholarGitelman, L. (Ed.) 2013. "Raw data" is an oxymoron Cambridge, MA: MIT Press. Google ScholarJohnson, P., Gray, P., & Sarker, S. 2019. Revisiting IS research practice in the era of big data. Information and Organization, 29: 41–56. Google ScholarKitchin, R. 2014. Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1: 1–12. Google ScholarLeonelli, S. 2014. What difference does quantity make? On the epistemology of Big Data in biology. Big Data & Society, 1: 1–11. Google ScholarLocke, K., Golden-Biddle, K., & Feldman, M. 2008. Making doubt generative: Rethinking the role of doubt in research process. Organization Science, 19: 907–918. Google ScholarMayer-Schönberger, V., & Cukier, K. 2013. Big data: A revolution that will transform how we live, work, and think. Boston, MA: Houghton Mifflin Harcourt. Google ScholarMerton, R. K. 1973. The sociology of science: Theoretical and empirical investigations. Chicago, IL: University of Chicago Press. Google ScholarOswald, F. L., & Putka, D. J. 2016. Statistical methods for big data: A scenic tour. In S. TonidandelE. B. KingJ. M. Cortina (Eds.), Big data at work: The data science revolution and organizational psychology: 43–63. New York, NY: Routledge. Google ScholarSætra, H. K. 2018. Science as a vocation in the era of big data: The philosophy of science behind big data and humanity's continued part in science. Integrative Psychological & Behavioral Science, 4: 508–522. Google ScholarSaltz, J. S. 2015. The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness. In IEEE Computer Society (Ed.), 2015 IEEE international conference on big data: 2066–2071. Los Alamitos, CA: IEEE Computer Society. Google ScholarSucci, S., & Coveney, P. V. 2018. Big data: The end of the scientific method? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 377: 20180145. Retrieved from https://royalsocietypublishing.org/doi/full/10.1098/rsta.2018.0145. Google ScholarTonidandel, S., King, E. B., & Cortina, J. M. 2018. Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods, 21: 525–547. Google ScholarFiguresReferencesRelatedDetailsCited byLearning to Innovate with Big Data Analytics in Interorganizational RelationshipsRussell E. Browder, Hope Koch, Anna Long and James M. Hernandez14 March 2022 | Academy of Management Discoveries, Vol. 8, No. 1 Vol. 62, No. 4 Permissions Metrics in the past 12 months History Published online 22 August 2019 Published in print 1 August 2019 Information© Academy of Management JournalDownload PDF