Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-16T10:18:30.740Z Has data issue: false hasContentIssue false

Chapter 3 - Data as a Contingent Performance and the Limitations of Big Data

from Part I - Philosophical, Epistemological and Theoretical Considerations

Published online by Cambridge University Press:  08 June 2023

Boyka Simeonova
Affiliation:
University of Leicester
Robert D. Galliers
Affiliation:
Bentley University, Massachusetts and Warwick Business School
Get access

Summary

The proliferation of digital data has been presented as heralding a revolution in research methods for the study of social phenomena, such as IS. For some authors, this revolution involves the abandonment of the traditional scientific method in favour of purely inductive data-driven research. Others proclaim the emergence of a new, quantitative, computational social science that will displace qualitative methods, while others see digital data as potentially enriching qualitative research. All these claims, however, take the nature of data for granted, assuming that they straightforwardly instrument reality and that understanding of the world can therefore be gained through their analysis alone. This chapter presents a critical analysis of this ‘pre-factual’ view, arguing that data are not natural givens, but are performed, brought into being by situated practices that enact particular representations of the world. The implications of such a conceptualization of data for research methods in Information Systems and organizational research are discussed.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired.Google Scholar
Barley, S. R. (1986). Technology as an occasion for structuring: Evidence from observations of CT scanners and the social order of radiology departments. Administrative Science Quarterly, 31(1), 78108.Google Scholar
Benbasat, I. and Weber, R. (1996). Research commentary: Rethinking ‘diversity’ in information systems research. Information Systems Research, 7(4), 389399.CrossRefGoogle Scholar
Boyd, D. and Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Community, & Society, 15(5), 662679.Google Scholar
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 112.CrossRefGoogle Scholar
Callebaut, W. (2012). Scientific perspectivism: A philosopher of science’s response to the challenge of big data biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 43(1), 6980.Google Scholar
Chang, R. M., Kauffman, R. J. and Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 6780.Google Scholar
Conte, R., Gilbert, N., Bonelli, G., Cioffi-Revilla, C., Deffuant, G., Kertesz, J. et al. (2012). Manifesto of computational social science. European Physical Journal Special Topics, 214(1), 325346.Google Scholar
Coveney, P. V., Dougherty, E. R. and Highfield, R. R. (2016). Big data need big theory too. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2080), 20160153.Google Scholar
Cukier, K. and Mayer-Schönberger, V. (2013). The rise of big data: How it’s changing the way we think about the world. Foreign Affairs, 92(3), 2840.Google Scholar
Donaldson, L. (1995). American Anti-Management Theories of Organization: A Critique of Paradigm Proliferation. Cambridge: Cambridge University Press.Google Scholar
Elish, M. C. and Boyd, D. (2018). Situating methods in the magic of big data and AI. Communication Monographs, 85(1), 5780.Google Scholar
Fleck, L. (1981). Genesis and Development of a Scientific Fact. London and Chicago, IL: University of Chicago Press.Google Scholar
Flick, U. (2009). An Introduction to Qualitative Research, 4th edition. London: Sage.Google Scholar
Foucault, M. (1977). Discipline and Punish: The Birth of the Prison. London: Allen Lane.Google Scholar
Frické, M. (2015). Big data and its epistemology. Journal of the Association for Information Science and Technology, 66(4), 651661.CrossRefGoogle Scholar
Friedman, M. (1953). Essays in Positive Economics. London and Chicago, IL: University of Chicago Press.Google Scholar
Geertz, C. (1973). The Interpretation of Cultures. New York, NY: Basic Books.Google Scholar
George, G., Osinga, E. C., Lavie, D. and Scott, B. A. (2016). Big data and data science methods for management research. Academy of Management Journal, 59(5), 14931507.CrossRefGoogle Scholar
Gitelman, L. and Jackson, V. (2013). Introduction. In Gitelman, L. (ed.), Raw Data Is an Oxymoron. Cambridge, MA: MIT Press, pp. 114.Google Scholar
Golden-Biddle, K. and Locke, K. (1993). Appealing work: An investigation of how ethnographic texts convince. Organization Science, 4(4), 595616.Google Scholar
Grover, V. (2020). Do we need to understand the world to know it? Knowledge in a big data world. Journal of Global Information Technology Management, 23(1), 14.Google Scholar
Haag, S. and Cummings, M. (2013). Management Information Systems for the Information Age, 9th edition. New York, NY: McGraw-Hill Irwin.Google Scholar
Hammersley, M. (2008). Questioning Qualitative Inquiry: Critical Essays. London: Sage.Google Scholar
Hunter, S. D. (2010). Same technology, different outcome? Reinterpreting Barley’s technology as an occasion for structuring. European Journal of Information Systems, 19(6), 689703.Google Scholar
Johnson, S. L., Gray, P. and Sarker, S. (2019). Revisiting IS research practice in the era of big data. Information and Organization, 29(1), 4156.CrossRefGoogle Scholar
Jones, M. (2019a). What we talk about when we talk about (big) data. Journal of Strategic Information Systems, 28(1), 316.Google Scholar
Jones, M. (2019b). Examining materialisation work in data reuse in critical care. EGOS Colloquium: Enlightening the Future: The Challenge for Organizations. Edinburgh, UK.Google Scholar
Jones, M. (2019c). Beyond convergence: Rethinking pluralism in IS research. International Conference on Information Systems, Munich, Germany.Google Scholar
Jones, M., Blackwell, A., Prince, K., Meakins, S., Simpson, A. and Vuylsteke, A. (2019). Data as process: From objective resource to contingent performance. In Reay, T., Silber, T., Langley, A. and Tsoukas, H. (eds), Institutions and Organizations: A Process View. Oxford: Oxford University Press, pp. 227250.Google Scholar
Kallinikos, J., Aaltonen, A. and Marton, A. (2013). The ambivalent ontology of digital artifacts. Management Information Systems Quarterly, 37(2), 357370.CrossRefGoogle Scholar
Kitchin, R. (2014a). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 112.Google Scholar
Kitchin, R. (2014b). The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. London: Sage.Google Scholar
Kitchin, R. and McArdle, G. (2016). What makes big data, big data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1), 110.Google Scholar
Lazer, D., Kennedy, R., King, G. and Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 12031205.Google Scholar
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D. et al. (2009). Computational social science. Science, 323(5915), 721723.Google Scholar
Leonelli, S. (2014). What difference does quantity make? On the epistemology of big data in biology. Big Data & Society, 1(1), 111.Google Scholar
Leonelli, S. and Tempini, N. (2020). Data Journeys in the Sciences. Cham, Switzerland: Springer International.Google Scholar
Lohr, S. (2014). For big-data scientists, ‘janitor work’ is key hurdle to insights. The New York Times.Google Scholar
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Hung Byers, A. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.Google Scholar
Mayer-Schönberger, V. and Cukier, K. (2013). Big Data: A Revolution that Will Transform How We Live, Work and Think. London: John Murray.Google Scholar
Mazzocchi, F. (2015). Could big data be the end of theory in science? EMBO Reports, 16(10), 12501255.CrossRefGoogle ScholarPubMed
McAfee, A. and Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 6068.Google Scholar
Mills, K. A. (2018). What are the threats and potentials of big data for qualitative research? Qualitative Research, 18(6), 591603.Google Scholar
Moritz, M. (2016). Big data’s ‘streetlight effect’: Where and how we look affects what we see. The Conversation, http://theconversation.com/big-datas-streetlight-effect-where-and-how-we-look-affects-what-we-see-58122.Google Scholar
Olteanu, A., Castillo, C., Diaz, F. and Kiciman, E. (2017). Social data: Biases, methodological pitfalls, and ethical boundaries. Frontiers in Big Data, 2(13), 133.Google Scholar
Passi, S. and Jackson, S. J. (2018). Trust in data science: Collaboration, translation, and accountability in corporate data science projects. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 136, 128.CrossRefGoogle Scholar
Pentland, A. (2009). Reality mining of mobile communications: Toward a new deal on data. In Dutta, S. and Mia, I. (eds), The Global Information Technology Report 2008–2009. Geneva, Switzerland: World Economic Forum, pp. 7580.Google Scholar
Pfeffer, J. (1993). Barriers to the advance of organizational science: Paradigm development as a dependent variable. Academy of Management Review, 18(4), 599620.CrossRefGoogle Scholar
Pfeffer, J. and Sutton, R. I. (2006). Evidence-based management. Harvard Business Review, 84(1), 62.Google ScholarPubMed
Powell, W. W., Horvath, A. and Brandtner, C. (2016). Click and mortar: Organizations on the web. Research in Organizational Behavior, 36, 101120.Google Scholar
Power, M. (1997). The Audit Society: Rituals of verification. Oxford: Oxford University Press.Google Scholar
Prensky, M. (2009). H. sapiens digital: From digital immigrants and digital natives to digital wisdom. Innovate: Journal of Online Education, 5(3), online article.Google Scholar
Revellino, S. and Mouritsen, J. (2015). Accounting as an engine: The performativity of calculative practices and the dynamics of innovation. Management Accounting Research, 28, 3149.Google Scholar
Rieder, G. and Simon, J. (2016). Datatrust: Or, the political quest for numerical evidence and the epistemologies of big data. Big Data & Society, 3(1), 16.Google Scholar
Robey, D. (1996). Research commentary – Diversity in information systems research: Threat, promise, and responsibility. Information Systems Research, 7(4), 400408.CrossRefGoogle Scholar
Strong, C. (2014). The challenge of ‘big data’: What does it mean for the qualitative research industry? Qualitative Market Research, 17(4), 336342.Google Scholar
Tashakkori, A. and Teddlie, C. (2009). Integrating qualitative and quantitative approaches to research. In Bickman, L. and Rog, D. (eds), The SAGE Handbook of Applied Social Research Methods. Thousand Oaks, CA: Sage, pp. 283317.Google Scholar
Tonidandel, S., King, E. B. and Cortina, J. M. (2018). Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods, 21(3), 525547.Google Scholar
Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, Ann Arbor, MI.Google Scholar
Tuomi, I. (1999). Data is more than knowledge. Journal of Management Information Systems, 16(3), 107121.Google Scholar
Turban, E. (2006). Information Technology for Management: Transforming Organizations in the Digital Economy, 5th edition. Hoboken, NJ: Wiley.Google Scholar
Van Maanen, J. (1995). Fear and loathing in organization studies. Organization Science, 6(6), 687692.Google Scholar
Van Maanen, J. (1979). Reclaiming qualitative methods for organizational research: A preface. Administrative Science Quarterly, 24(4), 520526.Google Scholar
Weick, K. E. (1995). Sensemaking in Organizations. London: Sage.Google Scholar
Wenzel, R. and Van Quaquebeke, N. (2018). The double-edged sword of big data in organizational and management research: A review of opportunities and risks. Organizational Research Methods, 21(3), 548591.Google Scholar
Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. London: Profile Books.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×