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-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-16T10:19:37.973Z Has data issue: false hasContentIssue false

Part I - Philosophical, Epistemological and Theoretical Considerations

Published online by Cambridge University Press:  08 June 2023

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

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Cambridge Handbook of Qualitative Digital Research , pp. 1 - 88
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

References

Baptista, J., Stein, M., Klein, S., Watson-Manheim, M. and Lee, J. (2020). Digital work and organisational transformation: Emergent digital/human work configurations in modern organisations. Journal of Strategic Information Systems, 29(2), Article 101627.Google Scholar
Bersin, J. (2015). The geeks arrive in HR: People analytics is here. Forbes Magazine. www.forbes.com/.Google Scholar
Bholat, D. (2015). Big data and central banks. Big Data & Society, 2(1), 1–6.Google Scholar
Brinkhues, R., Da Silva Freitas Jr, J. C. and Maçada, A. C. G. (2015). Information management capability as competitive imperfection in the strategic factor market of big data. Americas Conference on Information Systems, Puerto Rico.Google Scholar
Chatfield, A., Reddick, C. and Al-Zubaidi, W. (2015). Capability challenges in transforming government through open and big data: Tales of two cities. International Conference on Information Systems, Fort Worth, TX.Google Scholar
Cook, S. D. N. and Brown, J. S. (1999). Bridging epistemologies: The generative dance between organizational knowledge and organizational knowing. Organization Science, 10(4), 381–400.CrossRefGoogle Scholar
Daniel, E. M., Ward, J. M. and Franken, A. (2014). A dynamic capabilities perspective of IS project portfolio management. Journal of Strategic Information Systems, 23(2), 95–111.Google Scholar
Davenport, T. H., Harris, J. H. and Morison, R. (2010). Analytics at Work: Smarter Decisions, Better Results. Boston, MA: Harvard Business School Press.Google Scholar
Fecheyr-Lippens, B., Schaninger, B. and Tanner, K. (2015). Power to the new people analytics. McKinsey Quarterly, www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/power-to-the-new-people-analytics.Google Scholar
Finlay, L. (2002). ‘Outing’ the researcher: The provenance, process, and practice of reflexivity. Qualitative Health Research, 12(4), 531–545.Google Scholar
Forman, C., King, J. L. and Lyytinen, K. (2014). Special section introduction: Information, technology, and the changing nature of work. Information Systems Research, 25(4), 789–795.Google Scholar
Frické, M. (2015). Big data and its epistemology. Journal of the Association for Information Science and Technology, 66(4), 651–661.Google Scholar
Galliers, R. D. (2011). Further developments in information systems strategising: Unpacking the concept. In Galliers, R. D. and Currie, W. L. (eds), The Oxford Handbook of Management Information Systems: Critical Perspectives and New Directions. Oxford: Oxford University Press, pp. 329–345.Google Scholar
Galliers, R. D. (1995). A manifesto for information management research. British Journal of Management, 6(S1), S45–S52.Google Scholar
Galliers, R. D. and Huang, J. (2012). The teaching of qualitative research methods in Information Systems: An explorative study utilising learning theory. European Journal of Information Systems, 21(2), 119–134.Google Scholar
Galliers, R. D., Jackson, M. C. and Mingers, J. (1997). Organization theory and systems thinking: The benefits of partnership. Organization, 4(2), 268–278.CrossRefGoogle Scholar
Galliers, R. D., Newell, S., Shanks, G. and Topi, H. (2017). Datification and its human, organizational and societal effects: The strategic opportunities and challenges of algorithmic decision-making. Journal of Strategic Information Systems, 26(3), 185–190.Google Scholar
Galliers, R. D., Newell, S., Shanks, G. and Topi, H. (2015). Call for papers the Journal of Strategic Information Systems special issue: The challenges and opportunities of ‘datafication’ strategic impacts of ‘big’ (and ‘small’) and real time data – for society and for organizational decision makers. Journal of Strategic Information Systems, 24(2), II–III.Google Scholar
Gartner. (2013). Gartner predicts business intelligence and analytics will remain top focus for CIOs through 2017. Gartner Newsroom. www.gartner.com/newsroom/id/2637615.Google Scholar
Grant, A. M. and Parker, S. K. (2009). Redesigning work design theories: The rise of relational and proactive perspectives. Academy of Management Annals, 3(1), 317–375.CrossRefGoogle Scholar
Greenaway, K. E., Chan, Y. E. and Robert, E. C. (2015). Company information privacy orientation: A conceptual framework. Information Systems Journal, 25(6), 579–606.CrossRefGoogle 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), 1–4.Google 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), 268–291.Google Scholar
Günther, W. A., Mehrizi, M. H. R., Feldberg, F. and Huysman, M. H. (2017). Debating big data: A literature review on realizing value from big data. Journal of Strategic Information Systems, 26(3), 191–209.Google Scholar
Hansen, H. K. (2015). Numerical operations, transparency illusions and the datafication of governance. European Journal of Social Theory, 18(2), 203–220.CrossRefGoogle Scholar
Hedman, J., Srinivasan, N. and Lindgren, R. (2013). Digital traces or information systems: Sociomateriality made researchable. International Conference on Information Systems, Milan, Italy.Google Scholar
Hine, C. (2017). From virtual ethnography to the embedded, embodied, everyday internet. In Hjorth, L., Horst, H., Galloway, A. and Bell, G. (eds), The Routledge Companion to Digital Ethnography. Abingdon, UK and New York, NY: Routledge, pp. 21–28.Google Scholar
Hirschheim, R. (2021). The attack on understanding: How big data and theory have led us astray – a comment on Gary Smith’s Data Mining Fool’s Gold. Journal of Information Technology, 36(2), 176–183.Google Scholar
Huang, J., Baptista, J. and Newell, S. (2015). Communicational ambidexterity as a new capability to manage social media communication within organizations. Journal of Strategic Information Systems, 24(2), 49–64.Google Scholar
Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R. and Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86–94.Google Scholar
Jia, L., Hall, D. and Song, J. (2015). The conceptualization of data-driven decision making capability. Americas Conference on Information Systems, Puerto Rico.Google Scholar
Jones, M. (2019). What we talk about when we talk about (big) data. Journal of Strategic Information Systems, 28(1), 3–16.Google Scholar
Jones, S. (2016). Roberto Busa S. J. and the Emergence of Humanities Computing: The Priest and the Punched Cards. London: Routledge.Google Scholar
Kim, S., Lee, G., Sakata, N. and Billinghurst, M. (2014). Improving co-presence with augmented visual communication cues for sharing experience through video conference. IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Munich, Germany.Google Scholar
Leong, C., Pan, S., Ractham, P. and Kaewkitipong, L. (2015). ICT-enabled community empowerment in crisis response: Social media in Thailand flooding 2011. Journal of the Association for Information Systems, 16(3), 174–212.Google Scholar
Lightfoot, G. and Wisniewski, P. (2014). Information asymmetry and power in a surveillance society. Information and Organization, 24(4), 214–235.Google Scholar
Loebbecke, C. and Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. Journal of Strategic Information Systems, 24(3), 149–157.Google Scholar
Lycett, M. (2013). ‘Datafication’: Making sense of (big) data in a complex world. European Journal of Information Systems, 22(4), 381–386.Google Scholar
Madsen, A. K. (2015). Between technical features and analytic capabilities: Charting a relational affordance space for digital social analytics. Big Data & Society, 2(1), 1–15.CrossRefGoogle Scholar
Mai, J.-E. (2016). Big data privacy: The datafication of personal information. Information Society: An International Journal, 32(3), 192–199.CrossRefGoogle Scholar
Malgonde, O. and Bhattacherjee, A. (2014). Innovating using big data: A social capital perspective. Americas Conference on Information Systems, Georgia, USA.Google Scholar
Marabelli, M. and Newell, S. (2012). Knowledge risks in organizational networks: The practice perspective. Journal of Strategic Information Systems, 21(1), 18–30.Google Scholar
Markus, M. L. (2015). New games, new rules, new scoreboards: The potential consequences of big data. Journal of Information Technology, 30(1), 58–59.Google Scholar
Markus, M. L. and Topi, H. (2015). Big data, big decisions for science, society, and business. NSF Project Outcomes Report. Bentley University, Waltham, MA, http://dl.acm.org/citation.cfm?id=2849516.Google Scholar
McKenna, B., Myers, M. D. and Newman, M. (2017). Social media in qualitative research: Challenges and recommendations. Information and Organization, 27(2), 87–99.CrossRefGoogle Scholar
McKnight, D. H., Lankton, N. K., Nicolaou, A. and Price, J. (2017). Distinguishing the effects of B2B information quality, system quality, and service outcome quality on trust and distrust. Journal of Strategic Information Systems, 26(2), 118–141.Google Scholar
Mingers, J. (2001). Combining IS research methods: Towards a pluralist methodology. Information Systems Research, 12(3), 240–259.Google Scholar
Munford, M. (2014). Rule changes and big data revolutionise Caterham F1 chances. The Telegraph, www.telegraph.co.uk/technology/technology-topics/10654658/Rule-changes-and-big-data-revolutionise-Caterham-F1-chances.html.Google Scholar
Negash, S. (2004). Business intelligence. Communications of the Association for Information Systems, 13, article 15, http://aisel.aisnet.org/cais/vol13/iss1/15.Google Scholar
Newell, S. and Marabelli, M. (2015). Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of ‘datification’. Journal of Strategic Information Systems, 24(1), 3–14.CrossRefGoogle Scholar
Peppard, J., Galliers, R. D. and Thorogood, A. (2014). Information systems strategy as practice: Micro strategy and strategizing for IS. Journal of Strategic Information Systems, 23(1), 1–10.Google Scholar
Peppard, J. and Ward, J. (2004). Beyond strategic information systems: Toward an IS capability. Journal of Strategic Information Systems, 13(2), 167–194.Google Scholar
Pfeffer, J. and Sutton, R. I. 2006). Evidence-based management. Harvard Business Review, 84(1), 63–74.Google Scholar
Porter, M. E. and Millar, V. E. (1985). How information technology gives you competitive advantage. Harvard Business Review, 63(2), 149–160.Google Scholar
Power, D. J. (2002). Decisions Support Systems: Concepts and Resources for Managers. Westport, CT: Quorum Books.Google Scholar
Prasad, P. (2017). Crafting Qualitative Research: Working in the Postpositivist Traditions, 2nd edition. London and Armonk, NY: Routledge and M. E. Sharpe.Google Scholar
Rouibah, K. and Ould-ali, S. (2002). Puzzle: A concept and prototype for linking business intelligence to business strategy. Journal of Strategic Information Systems, 11(2), 133–152.Google Scholar
Seddon, P. B., Constantinidis, D., Tamm, T. and Dod, H. (2017). How does business analytics contribute to business value? Information Systems Journal, 27(3), 237–268.Google Scholar
Sharma, R., Mithas, S. and Kankanhalli, A. (2014). Transforming decision-making processes: A research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), 433–441.Google Scholar
Shollo, A. and Galliers, R. D. (2015). Towards an understanding of the role of business intelligence systems in organisational knowing. Information Systems Journal, 26(4), 339–367.Google Scholar
Sidorova, A. and Torres, R. R. (2014). Business intelligence and analytics: A capabilities dynamization view. Americas Conference on Information Systems, Georgia, USA.Google Scholar
Simeonova, B. (2018). Transactive memory systems and Web 2.0 in knowledge sharing: A conceptual model based on activity theory and critical realism. Information Systems Journal, 28(4), 592–611.Google Scholar
Simeonova, B., Galliers, R. D. and Karanasios, S. (2022). Power in organisations: The role of information systems. Information Systems Journal, 32(2), 233–241.Google Scholar
Simeonova, B., Galliers, R. D. and Karanasios, S. (2020). Strategic information systems and organisational power dynamics. In Galliers, R. D., Leidner, D. E. and Simeonova, B. (eds), Strategic Information Management: Theory and Practice, 5th edition. London and New York, NY: Routledge, pp. 221–238.Google Scholar
Starkey, K. and Madan, P. (2001). Bridging the relevance gap: Aligning stakeholders in the future of management research. British Journal of Management, 12(1), 3–26.Google Scholar
Stein, M., Newell, S., Galliers, R. D. and Wagner, E. L. (2013). Classification systems, their digitization and consequences for data-driven decision making: Understanding representational quality. International Conference on Information Systems, Milan, Italy.Google Scholar
Stein, M., Newell, S., Wagner, E. L. and Galliers, R. D. (2016). Crafting the ‘quantified professional’: Systems of accountability and job crafting. International Workshop on The Changing Nature of Work: The Impact of Digital Innovation on Work, IFIP WG 9.1, Dublin, Ireland.Google Scholar
Tambe, P. (2014). Big data investment, skills, and firm value. Management Science, 60(6), 1452–1468.Google Scholar
Tan, C., Sun, L. and Liu, K. (2015). Big data architecture for pervasive healthcare: A literature review. European Conference on Information Systems, Münster, Germany.Google Scholar
Thomsen, E. (2003). BI’s promised land. Intelligent Enterprise, 6(4), 21–25.Google Scholar
Van den Broek, T. and Van Veenstra, A. F. (2015). Modes of governance in inter-organizational data collaborations. European Conference on Information Systems, Münster, Germany.Google Scholar
Van der Vlist, F. N. (2016). Accounting for the social: Investigating commensuration and big data practices at Facebook. Big Data & Society, 3(1), 1–16.Google Scholar
Van Maanen, J. (2011). Ethnography as work: Some rules of engagement. Journal of Management Studies, 48(1), 218–234.Google Scholar
Van Maanen, J. (1979). Reclaiming qualitative methods for organizational research: A preface. Administrative Science Quarterly, 24(4), 520–526.Google Scholar
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118–144.Google Scholar
Waber, B. (2013). People Analytics: How Social Sensing Technology Will Transform Business and What It Tells Us about the Future of Work. Upper Saddle River, NJ: Pearson Education.Google Scholar
Walsham, G. (2006). Doing interpretive research. European Journal of Information Systems, 15(3), 320–330.Google Scholar
Whittington, R. (2014). Information systems strategy and strategy-as-practice: A joint agenda. Journal of Strategic Information Systems, 23(1), 87–91.Google Scholar
Woerner, S. L. and Wixom, B. H. (2015). Big data: Extending the business strategy toolbox. Journal of Information Technology, 30(1), 60–62.Google Scholar
Wu, L. and Brynjolfsson, E. (2009). The future of prediction: How Google searches foreshadow housing prices and quantities. International Conference on Information Systems, Phoenix, AZ.Google Scholar
Xu, J., Cenfetelli, R. T. and Aquino, K. (2016). Do different kinds of trust matter? An examination of the three trusting beliefs on satisfaction and purchase behavior in the buyer–seller context. Journal of Strategic Information Systems, 25(1), 15–31.Google Scholar

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.Google 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.Google 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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Hirsch, E. D. (1967). Validity in Interpretation. New Haven, CT: Yale University Press.CrossRefGoogle 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.Google 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.Google 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.CrossRefGoogle 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.Google Scholar
Nelson, L. (2020). Computational grounded theory: A methodological framework. Sociological Methods & Research, 49(1), 3–42.Google 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.Google 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

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), 78–108.Google Scholar
Benbasat, I. and Weber, R. (1996). Research commentary: Rethinking ‘diversity’ in information systems research. Information Systems Research, 7(4), 389–399.Google 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), 662–679.Google Scholar
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12.Google 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), 69–80.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, 67–80.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), 325–346.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), 28–40.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), 57–80.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), 651–661.Google 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), 1493–1507.Google Scholar
Gitelman, L. and Jackson, V. (2013). Introduction. In Gitelman, L. (ed.), Raw Data Is an Oxymoron. Cambridge, MA: MIT Press, pp. 1–14.Google Scholar
Golden-Biddle, K. and Locke, K. (1993). Appealing work: An investigation of how ethnographic texts convince. Organization Science, 4(4), 595–616.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), 1–4.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), 689–703.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
Jones, M. (2019a). What we talk about when we talk about (big) data. Journal of Strategic Information Systems, 28(1), 3–16.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. 227–250.Google Scholar
Kallinikos, J., Aaltonen, A. and Marton, A. (2013). The ambivalent ontology of digital artifacts. Management Information Systems Quarterly, 37(2), 357–370.Google Scholar
Kitchin, R. (2014a). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12.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), 1–10.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), 1203–1205.Google Scholar
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D. et al. (2009). Computational social science. Science, 323(5915), 721–723.Google Scholar
Leonelli, S. (2014). What difference does quantity make? On the epistemology of big data in biology. Big Data & Society, 1(1), 1–11.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), 1250–1255.Google Scholar
McAfee, A. and Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.Google Scholar
Mills, K. A. (2018). What are the threats and potentials of big data for qualitative research? Qualitative Research, 18(6), 591–603.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), 1–33.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, 1–28.Google 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. 75–80.Google Scholar
Pfeffer, J. (1993). Barriers to the advance of organizational science: Paradigm development as a dependent variable. Academy of Management Review, 18(4), 599–620.Google Scholar
Pfeffer, J. and Sutton, R. I. (2006). Evidence-based management. Harvard Business Review, 84(1), 62.Google Scholar
Powell, W. W., Horvath, A. and Brandtner, C. (2016). Click and mortar: Organizations on the web. Research in Organizational Behavior, 36, 101–120.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, 31–49.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), 1–6.Google Scholar
Robey, D. (1996). Research commentary – Diversity in information systems research: Threat, promise, and responsibility. Information Systems Research, 7(4), 400–408.Google Scholar
Strong, C. (2014). The challenge of ‘big data’: What does it mean for the qualitative research industry? Qualitative Market Research, 17(4), 336–342.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. 283–317.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), 525–547.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), 107–121.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), 687–692.Google Scholar
Van Maanen, J. (1979). Reclaiming qualitative methods for organizational research: A preface. Administrative Science Quarterly, 24(4), 520–526.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), 548–591.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

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
Ackoff, R. (1967). Management misinformation systems. Management Science, 14(4), B147–B156.Google Scholar
Alharthi, A., Krotov, V. and Bowman, M. (2017). Addressing barriers to big data. Business Horizons, 60(3), 285–292.Google Scholar
Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired.Google Scholar
Baesens, B., Bapna, R., Marsden, J., Vanthienen, J. and Zhao, J. L. (2016). Transformational issues of big data and analytics in networked business. MIS Quarterly, 40(4), 807–818.Google Scholar
Bell, G., Hey, T. and Szalay, A. (2009). Beyond the data deluge. Science, 323(5919), 1297–1298.Google Scholar
Bloom, B. (ed.) (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals. New York, NY: David McKay & Co.Google Scholar
Cohen, P. (2013). How Nordstrom uses WiFi to spy on shoppers. Forbes.Google Scholar
Constantiou, I. and Kallinikos, J. (2015). New games, new rules: Big data and the changing context of strategy. Journal of Information Technology, 30(1), 44–57.Google Scholar
Davenport, T. (2014). Big Data @ Work: Dispelling the Myths, Uncovering the Opportunities. Boston, MA: Harvard Business Review Press.Google Scholar
Dewey, J. (1933). How We Think. Buffalo, NY: Prometheus Books.Google Scholar
Dykes, B. (2016). Actionable insights: The missing link between data and business value. Forbes.Google Scholar
Ekbia, H., Mattioll, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T. et al. (2015). Big data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology, 66(8), 1523–1545.Google Scholar
Evenstad, L. (2018). The troublesome saga of Gov.uk Verify. ComputerWeekly, www.computerweekly.com/news/252450423/Govuk-Verify-a-troublesome-saga.Google Scholar
Feldman, D. (2016). When big data goes bad … CustomerThink, https://customerthink.com/when-big-data-goes-bad/.Google Scholar
Fricke, M. (2015). Big data and its epistemology. Journal of the Association for Information Science and Technology, 66(4), 651–661.Google Scholar
Galliers, R. D., Newell, S., Shanks, G. and Topi, H. (2017). Datification and its human, organizational and societal effects: The strategic opportunities and challenges of algorithmic decision-making. Journal of Strategic Information Systems, 26(3), 185–190.Google Scholar
Gandomi, A. and Haider, M. (2015). Beyond the hype: Big data concepts, methods and analytics. International Journal of Information Management, 35(2), 137–144.Google Scholar
Gartner. (2020). Gartner hype cycle. www.gartner.com/en/research/methodologies/gartner-hype-cycle.Google Scholar
Gent, E. (2016). Beware of the gaps in big data. Engineering & Technology, https://eandt.theiet.org/content/articles/2016/09/beware-of-the-gaps-in-big-data/.Google Scholar
Goes, P. B. (2014). Editor’s comments: Big data and IS research. MIS Quarterly, 38(3), iii–viii.Google 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), 268–291.Google Scholar
Harford, T. (2014). Big data: Are we making a big mistake? Financial Times Magazine, www.ft.com/content/21a6e7d8-b479-11e3-a09a-00144feabdc0#axzz2yQ2QQfQX.Google Scholar
Hirschheim, R. (2020). The attack on understanding: How big data and theory have led us astray – a comment on Gary Smith’s Data Mining Fool’s Gold. Journal of Information Technology, 36(2), 176–183.Google Scholar
Hirschheim, R. (1985). Office Automation: A Social and Organizational Perspective. Chichester, UK: Wiley.Google Scholar
Johnson, J. (2012). Big data + big analytics = big opportunity. Financial Executive, 28(6), 50–53.Google Scholar
Johnson, S., Gray, P. and Sarker, S. (2019). Revisiting IS research practice in the era of big data. Information & Organization, 29(1), 41–56.Google Scholar
Jones, M. (2019). What we talk about when we talk about (big) data. Journal of Strategic Information Systems, 28(1), 3–16.Google Scholar
Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12.Google Scholar
Kronz, A., Hare, J. and Krensky, P. (2020). Hype cycle for analytics and business intelligence. Gartner, www.gartner.com/doc/3988448.Google Scholar
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. and Kruschwitz, N. (2011). Big data, analytics, and the path from insights to value. MIT Sloan Management Review, 52(2), 21–31.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), 1203–1205.Google Scholar
Lee, L. and Sobol, D. (2012). What data can’t tell you about customers. Harvard Business Review, https://hbr.org/2012/08/what-data-cant-tell-you-about.Google Scholar
Lepore, J. (2020). If Then: How the Simulmatics Corporation Invented the Future. New York, NY: Liveright.Google Scholar
Lindstrom, M. (2016). Small Data: The Tiny Clues that Uncover Huge Trends. New York, NY: St Martin’s Press.Google Scholar
Lynn, B. (2020). The big tech extortion racket. Harper’s Magazine, https://harpers.org/archive/2020/09/the-big-tech-extortion-racket/.Google Scholar
Maass, W., Parsons, J., Purao, S., Storey, V. and Woo, C. (2018). Data-driven meets theory-driven research in the era of big data: Opportunities and challenges for information systems research. Journal of the Association for Information Systems, 19(12), 1253–1273.Google Scholar
Mamo, H. (2018). Taking a social media ‘nosedive’ and underrating common relationships in Black Mirror. https://medium.com/@heranmamo/taking-a-social-media-nosedive-and-underrating-common-relationships-in-black-mirror-9be23b3a5575.Google Scholar
Marr, B. (2016). Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. Chichester, UK: Wiley.Google Scholar
McAfee, A. and Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 1–9.Google Scholar
Mnookin, S. (2020). The bumbling 1960s data scientists who anticipated Facebook and Google. The New York Times, www.nytimes.com/2020/09/15/books/review/if-then-jill-lepore.html.Google Scholar
National Highway Traffic Safety Administration (NHTSA). (2019). 2018 fatal motor vehicle crashes: Overview. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812826.Google Scholar
Naughton, J. (2019). ‘The goal is to automate us’: Welcome to the world of surveillance capital. The Guardian.Google Scholar
Naughton, J. (2016). Death by drone strike, dished out by algorithm. The Guardian.Google Scholar
Pigliucci, M. (2009). The end of theory in science? EMBO Reports, 10(6), 534.Google Scholar
Ribes, D. and Jackson, S. (2013). Data bite man: The work of sustaining long-term study. In Gitelman, L. (ed.), ‘Raw Data’ Is an Oxymoron. Cambridge, MA: MIT Press, pp. 147–166.Google Scholar
Roose, K. (2017). Facebook’s Frankenstein moment. The New York Times.Google Scholar
Ryle, G. (1949). The Concept of Mind. Chicago, IL: University of Chicago Press.Google Scholar
Sandelands, L. (1990). What is so practical about theory? Lewin revisited. Journal for the Theory of Social Behavior, 20(3), 235–262.Google Scholar
Schrage, M. (2014). Learn from your analytics failures. Harvard Business Review.Google Scholar
Siegel, E. (2013). Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die. Hoboken, NJ: Wiley.Google Scholar
Simsek, Z., Vaara, E., Paruchuri, S., Nadkarni, S. and Shaw, J. (2019). New ways of seeing big data. Academy of Management Journal, 62(4), 971–978.Google Scholar
Smith, G. (2020). Data mining fool’s gold. Journal of Information Technology, 35(3), 182–194.Google Scholar
Smith, G. and Cordes, J. (2019). The 9 Pitfalls of Data Science. Oxford, UK: Oxford University Press.Google Scholar
Stephens-Davidowitz, S. (2017). Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us about Who We Really Are. New York, NY: Dey Street Books, HarperCollins.Google Scholar
US Department of Transportation (US DOT). (2018). U.S. Department of Transportation Transit, Highway, and Safety Funds. www.fhwa.dot.gov/environment/bicycle_pedestrian/funding/funding_opportunities.cfm.Google Scholar
US Department of Transportation Federal Highway Administration (FHWA). (2020a). Fact sheet – data-driven safety analysis. https://safety.fhwa.dot.gov/rsdp/factsheet/datadriven_safety_analysis.pdf.Google Scholar
US Department of Transportation Federal Highway Administration (FHWA). (2020b). Roadway Safety Data Program – data-driven safety analysis (DDSA). https://safety.fhwa.dot.gov/rsdp/ddsa.aspx.Google Scholar
US Department of Transportation Federal Highway Administration (FHWA). (2016). Guidance on state safety data systems. https://safety.fhwa.dot.gov/legislationandpolicy/fast/docs/ssds_guidance.pdf.Google Scholar
Veritas. (2016). Veritas global databerg report finds 85% of stored data is either dark or redundant, obsolete, or trivial (ROT). www.veritas.com/news-releases/2016-03-15-veritas-global-databerg-report-finds-85-percent-of-stored-data.Google Scholar
Walker, R. (2015). From Big Data to Big Profits: Success with Data and Analytics. New York, NY: Oxford University Press.Google Scholar
Wang, T. (2016). Why big data needs thick data. Ethnography Matters, https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7.Google Scholar
Weinberger, D. (2012). 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, NY: Basic Books.Google Scholar
Wigan, M. and Clarke, R. (2013). Big data’s big unintended consequences. IEEE Computer, 46(6), 46–53.Google Scholar
Wittgenstein, L. (1953). Philosophical Investigations. New York, NY: Macmillan.Google Scholar
Yampolskiy, R. (2017). What will happen when your company’s algorithms go wrong? Harvard Business Review.Google Scholar
Zimmermann, A. (2020). The A-level results injustice shows why algorithms are never neutral. The New Statesman.Google Scholar
Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York, NY: Public Affairs.Google Scholar

References

Acquisti, A., Brandimarte, L. and Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 348(6221), 509–514.Google Scholar
Alavi, M. and Leidner, D. E. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136.Google Scholar
Amershi, S., Cakmak, M., Knox, W. and Kulesza, T. (2014). Power to the people: The role of humans in interactive machine learning. AI Magazine, 35(4), 105–120.Google Scholar
Aversa, P., Cabantous, L. and Haefliger, S. (2018). When decision support systems fail: Insights for strategic information systems from Formula 1. Journal of Strategic Information Systems, 27(3), 221–236.Google Scholar
Azad, B. and Faraj, S. (2011). Social power and information technology implementation: A contentious framing lens. Information Systems Journal, 21(1), 33–61.Google Scholar
Bailey, D. E. and Barley, S. R. (2020). Beyond design and use: How scholars should study intelligent technologies. Information and Organization, 30(2), 100–286.Google Scholar
Barrett, M. and Oborn, E. (2010). Boundary object use in cross-cultural software development teams. Human Relations, 63(8), 1199–1221.Google Scholar
Barrett, M., Oborn, E. and Orlikowski, W. (2016). Creating value in online communities: The sociomaterial configuring of strategy, platform, and stakeholders. Information Systems Research, 27(4), 706–723.Google Scholar
Beaudry, A. and Pinsonneault, A. (2010). The other side of acceptance: Studying the direct and indirect effects of emotions on information technology use. MIS Quarterly, 34(4), 689–710.Google Scholar
Beaudry, A. and Pinsonneault, A. (2005). Understanding user responses to information technology: A coping model of user adaptation. MIS Quarterly, 29(3), 493–524.Google Scholar
Blackler, F. (2011). Power, politics, and intervention theory: Lessons from organisation studies. Theory and Psychology, 21(5), 724–734.Google Scholar
Blackler, F. (1995). Knowledge, knowledge work and organisations: An overview and interpretation. Organisation Studies, 16(6), 1021–1046.Google Scholar
Blackler, F. and McDonald, S. (2000). Power, mastery and organisational learning. Journal of Management Studies, 37(6), 833–852.Google Scholar
Bradley, R. V., Byrd, T. A., Pridmore, J. L., Thrasher, E., Pratt, R. M. and Mbarika, V. W. (2012). An empirical examination of antecedents and consequences of IT governance in hospitals. Journal of Information Technology, 27(2), 156–177.Google Scholar
Brooks, A. K. (1994). Power and the production of knowledge: Collective team learning in work organisations. Human Resource Development Quarterly, 5(3), 213–235.Google Scholar
Brown, A., Gabriel, Y. G. and Gherardi, S. (2009). Storytelling and change: An unfolding story introduction. Organisation, 16(3), 324–334.Google Scholar
Brynjolfsson, E. and McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York, NY: W. W. Norton.Google Scholar
Brynjolfsson, E. and Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530–1534.Google Scholar
Bunderson, J. (2003). Recognising and utilising expertise in work groups: A status characteristics perspective. Administrative Science Quarterly, 48(4), 557–591.Google Scholar
Bunderson, J. and Reagans, R. E. (2011). Power, status, and learning in organisations. Organisation Science, 22(5), 1182–1194.Google Scholar
Burton-Jones, A., Recker, J., Indulska, M., Green, C. P. and Weber, R. (2017). Assessing representation theory with a framework for pursuing success and failure. MIS Quarterly, 41(4), 1307–1334.Google Scholar
Busquets, J. (2010). Orchestrating smart business network dynamics for innovation. European Journal of Information Systems, 19(4), 481–493.Google Scholar
Cendon, V. and Jarvenpaa, S. (2001). The development and exercise of power by leaders of support units in implementing information technology-based services. Journal of Strategic Information Systems, 10(2), 121–158.Google Scholar
Chuang, C. H., Jackson, E. and Jiang, Y. (2016). Can knowledge-intensive teamwork be managed? Examining the roles of HRM systems, leadership, and tacit knowledge. Journal of Management, 42(2), 524–554.Google Scholar
Clegg, S. R. (1989). Frameworks of Power. London: Sage.Google Scholar
Clegg, S. R., Courpasson, D. and Phillips, N. (2006). Power and Organisations. London: Sage.Google Scholar
Constantinides, P. and Barrett, M. (2006). Large-scale ICT innovation, power, and organisational change: The case of a regional health information network. Journal of Applied Behavioral Science, 42(1), 78–90.Google Scholar
Contu, A. (2013). On boundaries and difference: Communities of practice and power relations in creative work. Management Learning, 45(3), 289–316.Google Scholar
Cook, D. and Brown, S. (1999). Bridging epistemologies: The generative dance between organisational knowledge and organisational knowing. Organisation Science, 10(4), 381–400.Google Scholar
Davenport, T. H. and Kirby, J. (2016). Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. New York, NY: HarperCollins.Google Scholar
Demetis, D. and Lee, A. S. (2018). When humans using the IT artifact becomes IT using the human artifact. Journal of the Association for Information Systems, 19(10), 929–952.Google Scholar
Deutsche Telekom. (2018). Deutsche Telekom’s guide for artificial intelligence. Digital Responsibility | Deutsche Telekom.Google Scholar
Dhillon, G., Caldeira, M. and Wenger, M. (2011). Intentionality and power interplay in IS implementation: The case of an asset management firm. Journal of Strategic Information Systems, 20(4), 438–448.Google Scholar
Díaz Andrade, A. and Techatassanasoontorn, A. A. (2021). Digital enforcement: Rethinking the pursuit of a digitally‐enabled society. Information Systems Journal, 31(1), 185–197.Google Scholar
Doolin, B. (2004). Power and resistance in the implementation of a medical management information system. Information Systems Journal, 14(4), 343–362.Google Scholar
Ekbia, H., Mattioll, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T. et al. (2015). Big data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology, 66(8), 1523–1545.Google Scholar
Elmes, M., Strong, D. and Volkoff, O. (2005). Panoptic empowerment and reflective conformity in enterprise systems-enabled organizations. Information and Organization, 15(1), 1–35.Google Scholar
Faraj, S., Pachidi, S. and Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70.Google Scholar
Fleming, P. and Spicer, A. (2014). Power in management and organisation science. Academy of Management Annals, 8(1), 237–298.Google Scholar
Forsythe, D. E. (1993). The construction of work in artificial intelligence. Science, Technology, & Human Values, 18(4), 460–480.Google Scholar
Foss, N., Husted, K. and Michailova, S. (2010). Governing knowledge sharing in organisations: Levels of analysis, governance mechanisms, and research directions. Journal of Management Studies, 48(5), 455–485.Google Scholar
Foucault, M. (1980). Power/Knowledge: Selected Interviews and Other Writings, 1972–1977. New York, NY: Pantheon.Google Scholar
Foucault, M. (1977). Discipline and Punish. Harmondsworth, UK: Penguin.Google Scholar
Fricke, M. (2015). Big data and its epistemology. Journal of the Association for Information Science and Technology, 66(4), 651–661.Google Scholar
Galinsky, A., Magee, J. C., Gruenfeld, D. H., Whitson, J. A. and Liljenquist, K. A. (2008). Power reduces the press of the situation: Implications for creativity, conformity, and dissonance. Journal of Personality and Social Psychology, 95(6), 1450–1466.Google Scholar
Galinsky, A., Magee, J. C., Inesi, M. K. and Gruenfeld, D. (2006). Power and perspectives not taken. Psychological Science, 17(12), 1068–1074.Google Scholar
Gherardi, S. (2010). Telemedicine: A practice-based approach to technology. Human Relations, 63(4), 501–524.Google Scholar
Göhler, G. (2009). ‘Power to’ and ‘power over’. In Clegg, S. R., and Haugaard, M. (eds), The Sage Handbook of Power. London: Sage, pp. 27–39.Google Scholar
Gray, P.H. (2001). The impact of knowledge repositories on power and control in the workplace. Information Technology and People, 14(4), 368–385.Google Scholar
Guinote, A. (2007). Power and goal pursuit. Personality and Social Psychology Bulletin, 33(8), 1076–1087.Google Scholar
Haas, M. (2006). Knowledge gathering, team capabilities, and project performance in challenging work environments. Management Science, 52(8), 1170–1185.Google Scholar
Hayes, N. and Walsham, G. (2001). Participation in groupware-mediated communities of practice: a socio-political analysis of knowledge working. Information and Organization, 11(4), 263–288.Google Scholar
Heizmann, H. (2011). Knowledge sharing in a dispersed network of HR practice: Zooming in on power/knowledge struggles. Management Learning, 42(4), 378–393.Google Scholar
Hicks, J., Nair, P. and Wilderom, C. P. (2009). What if we shifted the basis of consulting from knowledge to knowing? Management Learning, 40(3), 289–310.Google Scholar
Hirschheim, R. (2021). The attack on understanding: How big data and theory have led us astray – a comment on Gary Smith’s Data Mining. Journal of Information Technology, 36(2), 176–183.Google Scholar
Howcroft, D. and Wilson, M. (2003). Paradoxes of participatory practices: The Janus role of the systems developer. Information and Organization, 13(1), 1–24.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), 767–787.Google Scholar
Hussain, Z. I. and Cornelius, N. (2009). The use of domination and legitimation in information systems implementation. Information Systems Journal, 19(2), 197–224.Google Scholar
Huzzard, T. (2004). Communities of domination? Reconceptualising organisational learning and power. Journal of Workplace Learning, 16(6), 350–361.Google Scholar
Inkpen, A. C. and Tsang, E. W. (2005). Social capital, networks, and knowledge transfer. Academy of Management Review, 30(1), 146–165.Google Scholar
Jasperson, J. S., Carte, T. A., Saunders, C. S., Butler, B. C., Croes, H. J. and Zheng, W. (2002). Review: power and information technology research: A metatriangulation review. MIS Quarterly, 26(4), 397–459.Google Scholar
Kärreman, D. (2010). The power of knowledge: Learning from ‘learning by knowledge‐intensive firm’. Journal of Management Studies, 46(7), 1405–1416.Google Scholar
Kaufman, R. (2006a). Interdisciplinary perspectives on the ‘digital divide’ Part I: Economic perspectives. Journal of the Association for Information Systems.Google Scholar
Kaufman, R. (2006b). Interdisciplinary perspectives on the ‘digital divide’ Part II: Sociological perspectives. Journal of the Association for Information Systems.Google Scholar
Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12.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.Google Scholar
Koch, H., Gonzalez, E. and Leidner, D. E. (2012). Bridging the work/social divide: The emotional response to organizational social networking sites. European Journal of Information Systems, 21(6), 689–706.Google Scholar
Kumar, M., Jha, V. and Vaidya, D. (2007). Empirical investigation of impact of organizational culture, prosocial behavior and organizational trust on sharing mistakes in knowledge management systems. PACIS Proceedings.Google Scholar
La Roche, J. (2017). IBM’s Rometty: The skills gap for tech jobs is ‘the essence of divide’. Yahoo! Finance, https://finance.yahoo.com/news/ibms-rometty-skills-gap-tech-jobs-essence-divide.html.Google Scholar
Lawrence, T., Malhotra, N. and Morris, T. (2012). Episodic and systemic power in the transformation of service firms. Journal of Management Studies, 49(1), 102–143.Google Scholar
Lawrence, T., Mauws, M. K., Dyck, B. and Kleysen, R. (2005). The politics of organisational learning: Integrating power into the 4I framework. Academy of Management Review, 30(1), 180–191.Google Scholar
Lee, L. and Sobol, D. (2012). What data can’t tell you about customers. Harvard Business Review.Google Scholar
Leidner, D. E., Gonzalez, E. and Koch, H. (2018). An affordance perspective of enterprise social media and organizational socialization. Journal of Strategic Information Systems, 27(2), 117–138.Google Scholar
Leidner, D. E. and Tona, O. (2020). The CARE theory of dignity amid personal data digitalization. MIS Quarterly, 45(1), 343–370.Google Scholar
Leonardi, P. (2017). The social media revolution: Sharing and learning in the age of leaky knowledge. Information and Organization, 27(1), 46–59.Google Scholar
Leonardi, P. (2015). Ambient awareness and knowledge acquisition: Using social media to learn ‘who knows what’ and ‘who knows who’. MIS Quarterly, 39(4), 746–762.Google Scholar
Leonardi, P. (2014). Social media, knowledge sharing, and innovation: Toward a theory of communication visibility. Information Systems Research, 25(4), 786–816.Google Scholar
Leonardi, P. (2013). When does technology use enable network change in organizations? A comparative study of feature use and shared affordance. MIS Quarterly, 37(3), 749–775.Google Scholar
Leonardi, P., Huysman, M. and Steinfield, C. G. (2013). Enterprise social media: Definition, history, and prospects for the study of social technologies in organisations. Journal of Computer‐Mediated Communication, 19(1), 1–19.Google Scholar
Leong, C., Pan, S., Bahri, S. and Fauzi, A. (2019). Social media empowerment in social movements: Power activation and power accrual in digital activism. European Journal of Information Systems, 28(2), 173–204.Google Scholar
Leong, C., Pan, S., Ractham, C. P. and Kaewkitipong, L. (2015). ICT-enabled community empowerment in crisis response: Social media in Thailand flooding 2011. Journal of the Association for Information Systems, 16(3), 50–60.Google Scholar
Lightfoot, G. and Wisniewski, T. P. (2014). Information asymmetry and power in a surveillance society. Information and Organization, 24(4), 214–235.Google Scholar
Lindebaum, D., Vesa, M. and den Hond, F. (2020). Insights from ‘the machine stops’ to better understand rational assumptions in algorithmic decision making and its implications for organizations. Academy of Management Review, 45(1), 246–263.Google Scholar
Liyanage, C., Elhag, T., Ballal, T. and Li, Q. (2009). Knowledge communication and translation – a knowledge transfer model. Journal of Knowledge Management, 13(3), 118–131.Google Scholar
Markus, M. L. (1983). Power, politics, and MIS implementation. Communications of the ACM, 26(6), 430–444.Google Scholar
Marshall, N. and Rollinson, J. (2004). Maybe Bacon had a point: The politics of interpretation in Collective Sensemaking. British Journal of Management, 15(S1), 71–86.Google Scholar
McAfee, A. P. (2006). Enterprise 2.0: The dawn of emergent collaboration. MIT Sloan Management Review, 46(3), 21–28.Google Scholar
Messner, M., Clegg, S. and Kornberger, M. (2008). Critical practices in organisations. Journal of Management Inquiry, 17(2), 68–82.Google Scholar
Moe, C. E., Newman, M. and Sein, M. K. (2017). The public procurement of information systems: Dialectics in requirements specification. European Journal of Information Systems, 26(2), 143–163.Google Scholar
Muthusamy, S. and White, M. (2005). Learning and knowledge transfer in strategic alliances: A social exchange view. Organisation Studies, 26(3), 415–441.Google Scholar
Ngwenyama, O. and Nielsen, P. A. (2014). Using organisational influence processes to overcome IS implementation barriers: Lessons from a longitudinal case study of SPI implementation. European Journal of Information Systems, 23(2), 205–222.Google Scholar
Orlikowski, W. J. (2002). Knowing in practice: Enacting a collective capability in distributed organising. Organisation Science, 13(3), 249–273.Google Scholar
Pettigrew, A. (2012). Context and action in the transformation of the firm: A reprise. Journal of Management Studies, 49(7), 1304–1328.Google Scholar
Pozzebon, M. and Pinsonneault, A. (2012). The dynamics of client–consultant relationships: Exploring the interplay of power and knowledge. Journal of Information Technology, 27(1), 35–56.Google Scholar
Raisch, S. and Krakowski, S. (2020). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192–210.Google Scholar
Raman, R. and Bharadwaj, A. (2012). Power differentials and performative deviation paths in practice transfer: The case of evidence-based medicine. Organisation Science, 23(6), 1593–1621.Google Scholar
Ravishankar, M. N. (2015). The realignment of offshoring frame disputes (OFD): An ethnographic ‘cultural’ analysis. European Journal of Information Systems, 24(3), 234–246.Google Scholar
Ravishankar, M. N., Pan, S. and Myers, M. (2013). Information technology offshoring in India: A postcolonial perspective. European Journal of Information Systems, 22(4), 387–402.Google Scholar
Schuetz, S. and Venkatesh, V. (2020). The rise of human machines: How cognitive computing systems challenge assumptions of user-system interaction. Journal of the Association for Information Systems, 21(2), 460–482.Google Scholar
Simeonova, B. (2018). Transactive memory systems and Web 2.0 in knowledge sharing: A conceptual model based on activity theory and critical realism. Information Systems Journal, 28(4), 592–611.Google Scholar
Simeonova, B. (2015). Power and knowledge within activity theory: Applying activity theory to knowledge sharing. Conference: European Group for Organizational Studies, Athens, Greece.Google Scholar
Simeonova, B., Galliers, R. D. and Karanasios, S. (2020). Strategic information systems and organisational power dynamics. In Galliers, R. D., Leidner, D. E. and Simeonova, B. (eds), Strategic Information Management: Theory and Practice, 5th edition. London and New York, NY: Routledge, pp. 221–238.Google Scholar
Simeonova, B., Karanasios, S., Galliers, R. D., Kelly, P. R. and Mishra, J. (2018a). Where is power in information systems research? Towards a framework. International Conference on Information Systems, San Francisco, CA.Google Scholar
Simeonova, B., Karanasios, S., Galliers, R. D., Kelly, P. R. and Mishra, J. (2018b). New ways of organising in collaborative knowledge sharing: Examining the effects of power. Conference: European Group for Organizational Studies, Tallinn, Estonia.Google Scholar
Smith, P. K., Jostmann, N. B., Galinsky, A. D. and Van Dijk, W. W. (2008). Lacking power impairs executive functions. Psychological Science, 19(5), 441–448.Google Scholar
Stein, M. K., Newell, S., Wagner, E. and Galliers, R. D. (2015). Coping with information technology: Mixed emotions, vacillation, and nonconforming use patterns. MIS Quarterly, 39(2), 367–392.Google Scholar
Stein, M. K., Wagner, E., Tierney, P., Newell, S. and Galliers, R. D. (2019). Datafication and the pursuit of meaningfulness in work. Journal of Management Studies, 56(3), 685–718.Google Scholar
Szulanski, G., Cappetta, R. and Jensen, R. C. (2004). When and how trustworthiness matters: Knowledge transfer and the moderating effect of causal ambiguity. Organisation Science, 15(5), 600–613.Google Scholar
Tarafdar, M., Maier, C., Laumer, S. and Weitzel, T. (2020). Explaining the link between technostress and technology addiction for social networking sites: A study of distraction as a coping behavior. Information Systems Journal, 30(1), 96–124.Google Scholar
Townley, B. (1993). Performance appraisal and the emergence of management. Journal of Management Studies, 30(2), 221–238.Google Scholar
Trauth, E. (2017). A research agenda for social inclusion in information systems. The Data Base for Advances in Information Systems, 48(2), 9–20.Google Scholar
Trauth, E., Joshi, K. D. and Yarger, L. K. (2018). Social inclusion in the information systems. Information Systems Journal, 28(6), 989–994.Google Scholar
Tsoukas, H. (1996). The firm as a distributed knowledge system: A constructionist approach. Strategic Management Journal, 17, 11–25.Google Scholar
Vaast, E. and Pinsonneault, A. (2021). When digital technologies enable and threaten occupational identity: The delicate balancing act of data scientists. MIS Quarterly, 45(3), 1087–1112.Google Scholar
Van Kleef, G. A., Oveis, C., Van Der Löwe, I., LuoKogan, A., Goetz, J. and Keltner, D. (2008). Power, distress, and compassion: Turning a blind eye to the suffering of others. Psychological Science, 19(12), 1315–1322.Google Scholar
Wagg, S., Simeonova, B. and Cooke, L. (2019). An activity theory perspective on digital inclusion. Conference proceedings: European Group for Organizational Studies, Edinburgh, UK.Google Scholar
Wakabayashi, D. (2018). Uber’s self-driving trucks hit the highway, but not local roads. The New York Times, www.nytimes.com/2018/03/06/technology/uber-self-driving-trucks.html.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
Wang, S. and Noe, R. A. (2010). Knowledge sharing: A review and directions for future research. Human Resource Management Review, 20(2), 115–131.Google Scholar
Westcott Grant, K. (2018). Netflix’s data-driven strategy strengthens claim for ‘best original content’ in 2018. Forbes, www.forbes.com/sites/kristinwestcottgrant/2018/05/28/netflixs-data-driven-strategy-strengthens-lead-for-best-original-content-in-2018/?sh=13dcaa863a94.Google Scholar
Wilkesmann, U., Wilkesmann, M. and Virgillito, A. (2009). The absence of cooperation is not necessarily defection: Structural and motivational constraints of knowledge transfer in a social dilemma situation. Organization Studies, 30(10), 1141–1164.Google Scholar
Willem, A., Buelens, M. and Scarbrough, H. (2006). The role of inter-unit coordination mechanisms in knowledge sharing: A case study of a British MNC. Journal of Information Science, 32(6), 539–561.Google Scholar
Yang, T. and Maxwell, T. (2011). Information-sharing in public organisations: A literature review of interpersonal, intra-organisational and inter-organisational success factors. Government Information Quarterly, 28(2), 164–175.Google Scholar

References

Anduiza, E., Cristancho, C. G. and Sabucedo, J. M. (2013). Mobilization through online social networks: The political protest of the indignados in Spain. Information, Communication & Society, 17(6), 750–764.Google Scholar
Argyris, Y. A. and Ransbotham, S. (2016). Knowledge entrepreneurship: Institutionalising wiki-based knowledge-management processes in competitive and hierarchical organisations. Journal of Information Technology, 31(2), 226–239.Google Scholar
Aversa, P., Cabantous, L. and Haefliger, S. (2018). When decision support systems fail: Insights for strategic information systems from Formula 1. Journal of Strategic Information Systems, 27(3), 221–236.Google Scholar
Barrett, M., Davidson, E., Prabhu, J. and Vargo, S. L. (2016). Service innovation in the digital age: Key contributions and future directions. MIS Quarterly, 39(1), 135–154.Google Scholar
Bennett, W. L. and Segerberg, A. (2012). The logic of connective action. Information, Communication & Society, 15(5), 739–768.Google Scholar
Bernardi, R. (2017). Health information systems and accountability in Kenya: A structuration theory perspective. Journal of the Association for Information Systems, 18(12), 931–958.Google Scholar
Bernardi, R., Constantinides, P. and Nandhakumar, J. (2017). Challenging dominant frames in policies for IS innovation in healthcare through rhetorical strategies. Journal of the Association for Information Systems, 18(2), 81–112.Google Scholar
Bernardi, R., Sarker, S. and Sahay, S. (2019). The role of affordances in the deinstitutionalization of a dysfunctional health management information system in Kenya: An identity work perspective. MIS Quarterly, 43(5), 1178–1200.Google Scholar
Cardoso, A., Boudreau, M. C. and Carvalho, J. Á. (2019). Organizing collective action: Does information and communication technology matter? Information and Organization, 29(3), Article 100256.Google Scholar
Chen, J. V., Hiele, T. M., Kryszak, A. and Ross, W. H. (2020). Predicting intention to participate in socially responsible collective action in social networking website groups. Journal of the Association for Information Systems, 21(2), 341–365.Google Scholar
Chipidza, W. and Leidner, D. E. (2019). A review of the ICT-enabled development literature: Towards a power parity theory of ICT4D. Journal of Strategic Information Systems, 28(2), 145–174.Google Scholar
Cui, M., Pan, S. L. and Cui, L. C. (2019). Developing community capability for e‐commerce development in rural China: A resource orchestration perspective. Information Systems Journal, 29(4), 953–988.Google Scholar
Currie, W. and Lagoarde-Segot, T. (2017). Financialization and information technology: Themes, issues and critical debates – part I, pp. 1–6.Google Scholar
Currie, W. and Lagoarde-Segot, T. (2016). Financialization and information technology. Journal of Information Technology, 32(3), 211–216.Google Scholar
Davis, C. and Fonseca, F. (2006). Considerations from the development of a local spatial data infrastructure. Information Technology for Development, 12(4), 273–290.Google Scholar
Demetis, D. and Lee, A. S. (2018). When humans using the IT artifact becomes IT using the human artifact. Journal of the Association for Information Systems, 19(10), 5.Google Scholar
Díaz Andrade, A. and Doolin, B. (2019). Temporal enactment of resettled refugees’ ICT‐mediated information practices. Information Systems Journal, 29(1), 145–174.Google Scholar
Díaz Andrade, A. and Doolin, B. (2016). Information and communication technology and the social inclusion of refugees. MIS Quarterly, 40(2), 405–416.Google Scholar
Díaz Andrade, A. and Techatassanasoontorn, A. A. (2021). Digital enforcement: Rethinking the pursuit of a digitally‐enabled society. Information Systems Journal, 31(1), 185–197.Google Scholar
Díaz Andrade, A. and Urquhart, C. G. (2012). Unveiling the modernity bias: A critical examination of the politics of ICT4D. Information Technology for Development, 18(4), 281–292.Google Scholar
Ekbia, H., Mattioll, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T. et al. (2015). Big data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology, 66(8), 1523–1545.Google Scholar
Elmer, G., Langlois, G. and Redden, J. P. (2015). Compromised Data: From Social Media to Big Data. New York, NY: Bloomsbury.Google Scholar
Elmes, M., Strong, D. and Volkoff, O. (2005). Panoptic empowerment and reflective conformity in enterprise systems-enabled organizations. Information and Organization, 15(1), 1–35.Google Scholar
Eseryel, U. Y. (2014). IT-enabled knowledge creation for open innovation. Journal of the Association for Information Systems, 15(11), 805–834.Google Scholar
Faraj, S., Pachidi, S. and Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70.Google Scholar
Feller, J., Gleasure, R. C. and Treacy, S. (2017). Information sharing and user behavior in internet-enabled peer-to-peer lending systems: An empirical study. Journal of Information Technology, 32(2), 127–146.Google Scholar
George, J. J. and Leidner, D. E. (2019). From clicktivism to hacktivism: Understanding digital activism. Information and Organization, 29(3), 1–45.Google Scholar
Ghobadi, S. and Clegg, S. (2015). ‘These days will never be forgotten …’: A critical mass approach to online activism. Information and Organization, 25(1), 52–71.Google Scholar
Giddens, A. (1981). The Constitution of Society. Cambridge, UK: Polity Press.Google Scholar
Gozman, D., Butler, T. and Lyytinen, K. (2020). Regulation in the age of digitalization. Journal of Information Technology, 1–5.Google Scholar
Hong, Y., Huang, N., Burtch, G. and Li, C. (2016). Culture, conformity and emotional suppression in online reviews. Journal of the Association for Information Systems, 16(11), 736–758.Google Scholar
Howcroft, D. and Wilson, M. (2003). Paradoxes of participatory practices: The Janus role of the systems developer. Information and Organization, 13(1), 1–24.Google Scholar
Hussain, Z. I. and Cornelius, N. (2009). The use of domination and legitimation in information systems implementation. Information Systems Journal, 19(2), 197–224.Google Scholar
Iannacci, F. and Hatzaras, K. (2012). Unpacking ostensive and performative aspects of organisational routines in the context of monitoring systems: A critical realist approach. Information and Organization, 22(1), 1–22.Google Scholar
Joia, L. A. and dos Santos, R. P. (2019). ICT‐equipped bank boat and the financial inclusion of the riverine population of Marajó Island in the Brazilian Amazon. Information Systems Journal, 29(4), 856–886.Google Scholar
Kanungo, S. (2004). On the emancipatory role of rural information systems. Information Technology & People, 17(4), 405–422.Google Scholar
Kelly, S. and Noonan, C. (2017). The doing of datafication (and what this doing does): Practices of edification and the enactment of new forms of sociality in the Indian public health service. Journal of the Association for Information Systems, 18(12), 872–899.Google Scholar
Koch, H., Gonzalez, E. and Leidner, D. E. (2012). Bridging the work/social divide: The emotional response to organizational social networking sites. European Journal of Information Systems, 21(6), 689–717.Google Scholar
Lagoarde-Segot, T. and Currie, W. (2018). Financialization and information technology: A multi-paradigmatic view of IT and finance – part II. Journal of Information Technology, 33(1), 1–8.Google Scholar
Lawrence, T., Malhotra, N. and Morris, T. (2012). Episodic and systemic power in the transformation of professional service firms. Journal of Management Studies, 49(1), 102–143.Google Scholar
Lawrence, T., Winn, M. I. and Jennings, P. (2001). The temporal dynamics of institutionalization. Academy of Management Review, 26(4), 624–644.Google Scholar
Leidner, D. E., Gonzalez, E. and Koch, H. (2018). An affordance perspective of enterprise social media and organizational socialization. Journal of Strategic Information Systems, 27(2), 117–138.Google Scholar
Leidner, D. E. and Tona, O. (2020). The CARE theory of dignity amid personal data digitalization. MIS Quarterly, 45(1), 345–360.Google Scholar
Leonardi, P. (2015). Ambient awareness and knowledge acquisition: Using social media to learn ‘who knows what’ and ‘who knows whom’. MIS Quarterly, 39(4), 746–762.Google Scholar
Leonardi, P. (2014). Social media, knowledge sharing, and innovation: Toward a theory of communication visibility. Information Systems Research, 25(4), 786–816.Google Scholar
Leonardi, P., Leong, C., Pan, S., Bahri, S. and Fauzi, A. (2019). Social media empowerment in social movements: Power activation and power accrual in digital activism. European Journal of Information Systems, 28(2), 173–204.Google Scholar
Leong, C., Pan, S., Ractham, P. and Kaewkitipong, L. (2015). ICT-enabled community empowerment in crisis response: Social media in Thailand flooding 2011. Journal of the Association for Information Systems, 16(3), 174–212.Google Scholar
Li, L., Du, K., Zhang, W. and Mao, J. Y. (2019). Poverty alleviation through government‐led e‐commerce development in rural China: An activity theory perspective. Information Systems Journal, 29(4), 914–952.Google Scholar
Lightfoot, G. and Wisniewski, P. (2014). Information asymmetry and power in a surveillance society. Information and Organization, 24(4), 214–235.Google Scholar
Lin, C. I., Kuo, Y. and Myers, M. D. (2015). Extending ICT4D studies: The value of critical research. MIS Quarterly, 39(3), 687–712.Google Scholar
Linders, D. (2012). From e-government to we-government: Defining a typology for citizen coproduction in the age of social media. Government Information Quarterly, 29(4), 446–454.Google Scholar
Lyytinen, K., Nickerson, J. and King, J. L. (2020). Metahuman systems = humans + machines that learn. Journal of Information Technology, 36(4), 426–445.Google Scholar
Majchrzak, A., Faraj, S., Kane, G. C. and Azad, B. (2013). The contradictory influence of social media affordances on online communal knowledge sharing. Journal of Computer-Mediated Communication, 19(1), 38–55.Google Scholar
Martin, K. E. (2015). Ethical issues in the big data industry. MIS Quarterly Executive, 14(2), 67–85.Google Scholar
Mervyn, K., Simon, A. and Allen, D. (2014). Digital inclusion and social inclusion: A tale of two cities. Information, Communication & Society, 17(9), 1086–1100.Google Scholar
Miranda, M., Young, A. and Yetgin, E. (2016). Are social media emancipatory or hegemonic? Societal effects of mass media digitization in the case of the SOPA discourse. MIS Quarterly, 40(2), 303–329.Google Scholar
Naidoo, K. (2019). Surveillance giants. Amnesty International.Google Scholar
Pozzebon, M. and Pinsonneault, A. (2012). The dynamics of client-consultant power and knowledge. Journal of Information Technology, 27(1), 35–56.Google Scholar
Raisch, S. and Krakowski, S. (2020). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192–210.Google Scholar
Raman, R. and Grover, V. (2020). Studying the multilevel impact of cohesion versus structural holes in knowledge networks on adaptation to IT‐enabled patient‐care practices. Information Systems Journal, 30(1), 6–48.Google Scholar
Rane, H. and Salem, S. (2012). Social media, social movements and the diffusion of ideas in the Arab uprisings. Journal of International Communication, 18(1), 97–111.Google Scholar
Richards, N. G. (2013). The dangers of surveillance. Harvard Law Review, 126(7), 1934–1965.Google Scholar
Sæbø, Ø., Federici, T. and Braccini, A. (2020). Combining social media affordances for organising collective action. Information Systems Journal, 30(4), 689–722.Google Scholar
Sahay, S., Sein, K. and Urquhart, C. (2017). Flipping the context: ICT4D, the next grand challenge for IS research and practice. Journal of the Association for Information Systems, 18(12), 835–850.Google Scholar
Sandeep, M. and Ravishankar, M. N. (2018). Sociocultural transitions and developmental impacts in the digital economy of impact sourcing. Information Systems Journal, 28(3), 563–586.Google Scholar
Sandeep, M. and Ravishankar, M. N. (2016). Impact sourcing ventures and local communities: A frame alignment perspective. Information Systems Journal, 26(2), 127–155.Google Scholar
Schuetz, S. and Venkatesh, V. (2020). The Rise of Human Machines: How Cognitive Computing Systems Challenge Assumptions of User-System Interaction. Journal of the Association for Information Systems, 21(2), 460–482.Google Scholar
Simeonova, B. (2018). Transactive memory systems and Web 2.0 in knowledge sharing: A conceptual model based on activity theory and critical realism. Information Systems Journal, 28(4), 592–611.Google Scholar
Simeonova, B., Galliers, R. D. and Karanasios, S. (2020). Strategic information systems and organisational power dynamics. In Galliers, R. D., Leidner, D. E. and Simeonova, B. (eds), Strategic Information Management: Theory and Practice, 5th edition. London and New York, NY: Routledge, pp. 221–238.Google Scholar
Simeonova, B., Karanasios, S., Galliers, R. D., Kelly, P. R. and Mishra, J. (2018a). Where is power in information systems research? Towards a framework. International Conference on Information Systems, San Francisco, CA.Google Scholar
Simeonova, B., Karanasios, S., Galliers, R. D., Kelly, P. R. and Mishra, J. (2018b). New ways of organising in collaborative knowledge sharing: Examining the effects of power. European Group for Organizational Studies, Tallinn, Estonia.Google Scholar
Tim, Y., Pan, S., Bahri, S. and Fauzi, A. (2018). Digitally enabled affordances for community‐driven environmental movement in rural Malaysia. Information Systems Journal, 28(1), 48–75.Google Scholar
Trauth, E. (2017). A research agenda for social inclusion in information systems. Data Base for Advances in Information Systems, 48(2), 9–20.Google Scholar
Trauth, E., Joshi, K. and Yarger, K. (2018). ISJ editorial. Information Systems Journal, 28(6), 989–995.Google Scholar
Treem, J. W. and Leonardi, P. (2013). Social media use in organizations: Exploring the affordances of visibility, editability, persistence, and association. The International Communication Association, 36(1), 143–189.Google Scholar
Vaast, E. and Levina, N. (2015). Speaking as one, but not speaking up: Dealing with new moral taint in an occupational online community. Information and Organization, 25(2), 73–98.Google Scholar
Vaast, E. and Pinsonneault, A. (2021). When digital technologies enable and threaten occupational identity: The delicate balancing act of data scientists. MIS Quarterly, 45(3), 1087–1112.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), 1178–1206.Google Scholar
Wasko, M., Faraj, S. and Teigland, R. (2004). Collective action and knowledge contribution in electronic networks of practice. Journal of the Association for Information Systems, 5(11–12), 493–513.Google Scholar
Workman, M. (2009). A field study of corporate employee monitoring: Attitudes, absenteeism, and the moderating influences of procedural justice perceptions. Information and Organization, 19(4), 218–232.Google Scholar
Young, G. (2018). Using ICT for social good: Cultural identity restoration through emancipatory pedagogy. Information Systems Journal, 28(2), 340–358.Google Scholar
Zheng, Y. and Yu, A. (2016). Affordances of social media in collective action: The case of free lunch in China. Information Systems Journal, 26(3), 289–313.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
×