Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-22T16:36:25.797Z Has data issue: false hasContentIssue false

Chapter 2 - A Philosophical Perspective on Qualitative Research in the Age of Digitalization

from Part I - Philosophical, Epistemological and Theoretical Considerations

Published online by Cambridge University Press:  08 June 2023

By
Allen S. Lee  and
Suprateek Sarker
Edited by
Boyka Simeonova  and
Robert D. Galliers
Boyka Simeonova
Affiliation:
University of Leicester
Robert D. Galliers
Affiliation:
Bentley University, Massachusetts and Warwick Business School
Get access

Summary

The digitalization of business organizations and of society in general has opened up the possibility of researching behaviours using large volumes of digital traces and electronic texts that capture behaviours and attitudes in a broad range of natural settings. How is the availability of such data changing the nature of qualitative, specifically interpretive, research and are computational approaches becoming the essence of such research? This chapter briefly examines this issue by considering the potential impacts of digital data on key themes associated with research, those of induction, deduction and meaning. It highlights some of the ‘nascent myths’ associated with the digitalization of qualitative research. The chapter concludes that while the changes in the nature of data present exciting opportunities for qualitative, interpretive researchers to engage with computational approaches in the form of mixed-methods studies, it is not believed they will become the sine qua non of qualitative information systems research in the foreseeable future.

Keywords

big dataqualitative researchinterpretive researchphilosophyinductiondeductionmeaninggrounded theoryhermeneuticscomputational approachesmixed-methods
Type
Chapter
Information
Cambridge Handbook of Qualitative Digital Research , pp. 15 - 27
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

Abbasi, A., Sarker, S. and Chiang, R. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), i–xxxii.Google Scholar
Abramson, C. M., Joslyn, J., Rendle, K. A., Garrett, S. B. and Dohan, D. (2018). The Promises of computational ethnography: Improving transparency, replicability, and validity for realist approaches to ethnographic analysis. Ethnography, 19(2), 254–284.Google Scholar
Alvesson, M. and Skoldberg, K. (2000). Reflexive Methodology: New Vistas for Qualitative Research. London: Sage.Google Scholar
Andrejevic, M. (2013). Infoglut: How Too Much Information Is Changing the Way We Think and Know. London and New York, NY: Routledge.CrossRefGoogle Scholar
Berente, N., Seidel, S. and Safadi, H. (2019). Research commentary: Data-driven computationally intensive theory development. Information Systems Research, 30(1), 50–64.Google Scholar
Boswell, J. and Corbett, J. (2015). Embracing impressionism: Revealing the brush strokes of interpretive research. Critical Policy Studies, 9(2), 216–225.CrossRefGoogle Scholar
Cesare, N., Lee, H., McCormick, T., Spiro, E. and Zagheni, E. (2018). The promises and pitfalls of using digital traces for demographic research. Demography, 55(5), 1979–1999.Google Scholar
Dibbern, J., Winkler, J. and Heinzl, A. (2008). Explaining variations in client extra costs between software projects offshored to India. MIS Quarterly, 32(2), 333–366.Google Scholar
DiMaggio, P. (2015). Adapting computational text analysis to social science (and vice versa). Big Data & Society, 2(2), 1–5.Google Scholar
Dubé, L. and Paré, G. (2003). Rigor in information systems positivist case research: Current practices, trends, and recommendations. MIS Quarterly, 27(4), 597–636.Google Scholar
Eck, A. and Uebernickel, F. (2016). Reconstructing open source software ecosystems: Finding structure in digital traces. International Conference on Information Systems, Dublin, Ireland.Google Scholar
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550.CrossRefGoogle Scholar
Ghazawneh, A. and Henfridsson, O. (2013). Balancing platform control and external contribution in third-party development: The boundary resources model. Information Systems Journal, 23(2), 173–192.CrossRefGoogle Scholar
Grover, V., Lindberg, A., Benbasat, I. and Lyytinen, K. (2020). The perils and promises of big data research in information systems. Journal of the Association for Information Systems, 21(2), 268–293.CrossRefGoogle Scholar
Hirsch, E. D. (1967). Validity in Interpretation. New Haven, CT: Yale University Press.Google Scholar
Howison, J., Wiggins, A. and Crowston, K. (2011). Validity issues in the use of social network analysis with digital trace data. Journal of the Association for Information Systems, 12(12), 767–797.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), 41–56.Google Scholar
Kaplan, A. (1964). The Conduct of Inquiry: Methodology for Behavioral Science. New York, NY: Chandler Publishing.Google Scholar
Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequence. London: Sage.Google Scholar
Klein, H. K. and Myers, M. D. (1999). A set of principles for conducting and evaluating interpretive field studies in information systems. MIS Quarterly, 23(1), 67–93.CrossRefGoogle Scholar
Latour, B. (1996). Aramis or the Love of Technology. Cambridge, MA: Harvard University Press.Google Scholar
Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D. et al. (2009). Life in the network: The coming age of computational social science. Science, 323(5915), 721–723.CrossRefGoogle Scholar
Lee, A. S. (2017). Philosophy and method: Making interpretive research interpretive. In Galliers, R. D. and Stein, M.-K. (eds), The Routledge Companion to Management Information Systems. New York, NY: Routledge, pp. 30–46.Google Scholar
Lee, A. S. (1994). Electronic mail as a medium for rich communication: An empirical investigation using hermeneutic interpretation. MIS Quarterly, 18(2), 143–157.Google Scholar
Lee, A. S. (1989). A scientific methodology for MIS case studies. MIS Quarterly, 13(1), 33–50.Google Scholar
Lee, A. S. and Baskerville, R. (2003). Generalizing generalizability in information systems research. Information Systems Research, 14(3), 221–243.Google Scholar
Lee, A. S. and Hubona, G. S. (2009). A scientific basis for rigor in information systems research. MIS Quarterly, 33(2), 237–262.Google Scholar
Lindberg, A. (2020). Developing theory through human and machine pattern recognition. Journal of the Association for Information Systems, 21(1), 90–116.Google Scholar
Lindberg, A., Berente, N., Gaskin, J., Lyytinen, K. and Yoo, Y. (2013). Computational approaches for analyzing latent social structures in open source organizing. International Conference on Information Systems, Milan, Italy.Google Scholar
Lohmeier, C. (2014). The researcher and the never-ending field: Reconsidering big data and digital ethnography. In Hand, M. and Hillyard, S. (eds), Big Data? : Qualitative Approaches to Digital Research. Bingley, UK: Emerald, pp. 75–89.Google Scholar
Markus, M. L. (1983). Power, politics, and MIS implementation. Communications of the ACM, 26(6), 430–444.Google Scholar
Mead, M. (1959a). Apprenticeship under Boas. In Goldschmidt, W. (ed.), The Anthropology of Franz Boas. Washington, DC: American Anthropological Association, Memoir 89, pp. 29–45.Google Scholar
Mead, M. (1959b). Introduction. In R. Benedict, An Anthropologist at Work. London and New York, NY: Routledge, pp. xv–xxii.Google Scholar
Mohr, J. W., Wagner-Pacifici, R. and Breiger, R. (2015). Toward a computational hermeneutics. Big Data & Society, 2(2), 1–8.CrossRefGoogle Scholar
Nelson, L. (2020). Computational grounded theory: A methodological framework. Sociological Methods & Research, 49(1), 3–42.CrossRefGoogle Scholar
Pelto, P. J. (1970). Anthropological Research: The Structure of Inquiry. New York, NY: Harper & Row.Google Scholar
Popper, K. (1968). Conjectures and Refutations: The Growth of Scientific Knowledge. New York, NY: Harper Torchbooks.Google Scholar
Rai, A. (2017). Avoiding type III errors: Formulating IS research problems that matter. MIS Quarterly, 41(2), iii–vii.Google Scholar
Ricoeur, P. (1991). The model of the text: Meaningful action considered as a text. In From Text to Action: Essays in Hermeneutics, II. Evanston, IL: Northwestern University Press, pp. 144–167.Google Scholar
Romele, A., Severo, M. and Furia, P. (2020). Digital hermeneutics: From interpreting with machines to interpretational machines. AI & Society, 35(1), 73–86.Google Scholar
Rosenberg, A. (1993). Hume and the philosophy of science. In Norton, D. (ed.), The Cambridge Companion to Hume. New York, NY: Cambridge University Press, pp. 64–89.Google Scholar
Sanday, P. (1979). The ethnographic paradigm(s). Administrative Science Quarterly, 24(4), 527–538.Google Scholar
Sarker, S. and Lee, A. S. (2006). Does the use of computer-based BPC tools contribute to redesign effectiveness? Insights from a hermeneutic study. IEEE Transactions on Engineering Management, 53(1), 130–145.Google Scholar
Sarker, S. and Lee, A. S. (2002). Using a positivist case research methodology to test three competing practitioner theories-in-use of business process redesign. Journal of the Association for Information Systems, 2(1), 1–74.CrossRefGoogle Scholar
Sarker, S., Xiao, X, Beaulieu, T. and Lee, A. S. (2018). The practice of qualitative research in the IS discipline: An evolutionary view and some implications for authors and evaluators (Part I). Journal of the AIS, 19(8), 752–774.Google Scholar
Schutz, A. (1962). Concept and theory formation in the social sciences. In Natanson, M. (ed.), Alfred Schutz: Collected Papers 1. Boston, MA: Kluwer Academic Publishers, pp. 48–66.Google Scholar
Strauss, A. and Corbin, J. M. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Beverly Hills, CA: Sage.Google Scholar
Taylor, C. (1971). Interpretation and the sciences of man. Review of Metaphysics, 25(1), 3–51.Google Scholar
Vaast, E., Safadi, H., Lapointe, L., and Negoita, B. (2017). Social media affordances for connective action: An examination of microblogging use during the Gulf of Mexico oil spill. MIS Quarterly, 41(4), 1179–1205.Google Scholar
Van Maanen, J. (1988). Tales of the Field: On Writing Ethnography. Chicago, IL: University of Chicago Press.Google Scholar
Venkatesh, V., Brown, S. A. and Sullivan, Y. W. (2016). Guidelines for conducting mixed-methods research: An extension and illustration. Journal of the Association for Information Systems, 17(7), 435–494.Google Scholar
Walsham, G. (2006). Doing interpretive research. European Journal of Information Systems, 15(3), 320–330.Google Scholar
Walsham, G. (1995). Interpretive case studies in IS research: Nature and method. European Journal of Information Systems, 4(2), 74–81.Google Scholar
Wolcott, H. (1975). Criteria for an ethnographic approach to research in schools. Human Organization, 34(2), 111–128.Google Scholar
Wood, A. (2000). Hume: The problem of induction. Stanford University. www.stanford.edu/∼allenw/Phil102/Hume%20-%20Induction.doc.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
×