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Part III - Illustrative Examples and Emergent Issues

Published online by Cambridge University Press:  08 June 2023

Boyka Simeonova
Affiliation:
University of Leicester
Robert D. Galliers
Affiliation:
Bentley University, Massachusetts and Warwick Business School
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Publisher: Cambridge University Press
Print publication year: 2023

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References

Akhlaghpour, S., Wu, J., Lapointe, L. and Pinsonneault, A. (2013). The ongoing quest for the IT artifact: Looking back, moving forward. Journal of Information Technology, 28(2), 150166.CrossRefGoogle Scholar
Ashby, W. R. (1956). An Introduction to Cybernetics, 1976 edition. London and New York, NY: Methuen, distributed by Harper & Row.Google Scholar
Baecker, D. (2017). A calculus of negation in communication. Cybernetics and Human Knowing, 24(3–4), 1727.Google Scholar
Baecker, D. (2015). The be-ing of objects. Cybernetics and Human Knowing, 22(2–3), 4958.Google Scholar
Baecker, D. (2013). Form und Formen der Kommunikation, Suhrkamp-Taschenbücher Wissenschaft, 3rd edition. Frankfurt am Main, Germany: Suhrkamp, Vol. 1828.Google Scholar
Baecker, D. (2006). The form of the firm. Organization, 13(1), 109142.Google Scholar
Baecker, D. (1993). Im Tunnel. In Baecker, D. (ed.), Kalkül der Form, Suhrkamp Taschenbuch Wissenschaft. Frankfurt am Main, Germany: Suhrkamp, pp. 1237.Google Scholar
Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham, NC: Duke University Press.Google Scholar
Barad, K. (2003). Posthumanist performativity: Toward an understanding of how matter comes to matter. Signs, 28(3), 801831.Google Scholar
Barad, K. (1998). Getting real: Technoscientific practices and the materialization of reality. Differences, 10(2), 88128.Google Scholar
Barad, K. (1996). Meeting the universe halfway: Realism and social constructivism without contradiction. In Nelson, L. H. and Nelson, J. (eds), Feminism, Science, and the Philosophy of Science. Dordrecht, the Netherlands: Springer, Vol. 256, pp. 161194.Google Scholar
Baskerville, R. (2012). Reviving the IT in the IS. European Journal of Information Systems, 21(6), 587591.CrossRefGoogle Scholar
Baskerville, R. L., Myers, M. D. and Yoo, Y. (2020). Digital first: The ontological reversal and new challenges for IS research. MIS Quarterly, 44(2), 509523.Google Scholar
Bateson, G. (1979). Mind and Nature: A Necessary Unity, 2002 edition. Cresskill, NJ: Hampton Press.Google Scholar
Baudrillard, J. (2006). The System of Objects. London: Verso.Google Scholar
Behfar, K. and Okhuysen, G. A. (2018). Perspective – discovery within validation logic: Deliberately surfacing, complementing, and substituting abductive reasoning in hypothetico-deductive inquiry. Organization Science, 29(2), 323340.CrossRefGoogle Scholar
Carlile, P. R., Nicolini, D., Langley, A. and Tsoukas, H. (eds). (2013). How Matter Matters: Objects, Artifacts, and Materiality in Organization Studies. Oxford: Oxford University Press, Vol. 3.Google Scholar
Cecez-Kecmanovic, D., Davison, M., Fernandez, W., Finnegan, P., Pan, L. and Sarker, S. (2020). Advancing qualitative IS research methodologies: Expanding horizons and seeking new paths. Journal of the Association for Information Systems, 21(1), 246263.Google Scholar
Cecez-Kecmanovic, D., Galliers, R. D., Henfridsson, O., Newell, S. and Vidgen, R. (2014). The sociomateriality of information systems: Current status, future directions. MIS Quarterly, 38(3), 809830.CrossRefGoogle Scholar
Conant, R. C. and Ashby, W. R. (1970). Every good regulator of a system must be a model of that system. International Journal of Systems Science, 1(2), 8997.Google Scholar
Curtis, A. M., Dennis, A. R. and McNamara, K. O. (2017). From monologue to dialogue: Performative objects to promote collective mindfulness in computer-mediated team discussions. MIS Quarterly, 41(2), 559581.Google Scholar
Dennis, A. R., Fuller, R. M. and Valacich, J. S. (2008). Media, tasks, and communication processes: A theory of media synchronicity. MIS Quarterly, 32(3), 575600.CrossRefGoogle Scholar
Fuchs, P. and Hoegl, F. (2015). Die Schrift der Form. In Pörksen, B. (ed.), Schlüsselwerke des Konstruktivismus. Wiesbaden, Germany: VS Verlag für Sozialwissenschaften, pp. 165196.CrossRefGoogle Scholar
Garud, R., Jain, S. and Tuertscher, P. (2008). Incomplete by design and designing for incompleteness. Organization Studies, 29(3), 351371.Google Scholar
Glanville, R. (2001). An observing science. Foundations of Science, 6(1–3), 4575.Google Scholar
Glanville, R. (1990). The self and the other: The purpose of distinction. In Trappl, R. (ed.), Cybernetics and Systems ’90: Proceedings of the Tenth European Meeting on Cybernetics and Systems Research, Held at the University of Vienna, Austria. Singapore: World Scientific, pp. 349356.Google Scholar
Glanville, R. (1978). The nature of fundamentals, applied to the fundamentals of nature. In Klir, G. J. (ed.), Applied General Systems Research. Recent Developments and Trends, NATO Conference Series. Boston, MA: Springer, Vol. 5, pp. 401409.Google Scholar
Goffman, E. (1974). Frame Analysis: An Essay on the Organization of Experience. New York, NY: Harper & Row.Google Scholar
Hazelrigg, L. (1992). Reading Goffman’s framing as provocation of a discipline. Human Studies, 15(2–3), 239264.Google Scholar
Kallinikos, J., Aaltonen, A. and Marton, A. (2013). The ambivalent ontology of digital artifacts. MIS Quarterly, 37(2), 357370.Google Scholar
Kauffman, L. H. (2017). Foreword: Laws of form. Cybernetics & Human Knowing, 24(3–4), 515.Google Scholar
Kauffman, L. H. (2001). The mathematics of Charles Sanders Peirce. Cybernetics & Human Knowing, 8(1–2), 79110.Google Scholar
Lau, F. (2005). Die Form der Paradoxie: Eine Einführung in die Mathematik und Philosophie der ‘Laws of Form’ von George Spencer Brown, 5th edition. Heidelberg, Germany: Carl-Auer-Verlag.Google Scholar
Leonardi, P. M. (2013). Theoretical foundations for the study of sociomateriality. Information and Organization, 23(2), 5976.Google Scholar
Leonardi, P. M. (2011). When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly, 35(1), 147167.CrossRefGoogle Scholar
Luhmann, N. (1998). Die Gesellschaft der Gesellschaft. Suhrkamp-Taschenbuch Wissenschaft. Frankfurt am Main, Germany: Suhrkamp.Google Scholar
Luhmann, N. (1993). Die Paradoxie der Form. In Baecker, D. (ed.), Kalkül der Form. Suhrkamp-Taschenbuch Wissenschaft. Frankfurt am Main, Germany: Suhrkamp, Vol. 1068, pp. 197212.Google Scholar
Luhmann, N. (1987). Soziale Systeme: Grundriss einer allgemeinen Theorie, 1st edition. Frankfurt am Main, Germany: Suhrkamp.Google Scholar
March, S. T. and Smith, G. F. (1995). Design and natural science research on information technology. Decision Support Systems, 15(4), 251266.CrossRefGoogle Scholar
Markus, M. L. and Rowe, F. (2018). Is IT changing the world? Conceptions of causality for information systems theorizing. MIS Quarterly, 42(4), 126.Google Scholar
Maturana, H. R. and Varela, F. J. (1980). Autopoiesis and Cognition: The Realization of the Living. Dordrecht, the Netherlands: Reidel.CrossRefGoogle Scholar
McCulloch, W. S. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115133.CrossRefGoogle Scholar
Mingers, J. and Standing, C. (2020). A framework for validating information systems research based on a pluralist account of truth and correctness. Journal of the Association for Information Systems, 21(1), 117151.Google Scholar
Mingers, J. and Standing, C. (2017). Why things happen – developing the critical realist view of causal mechanisms. Information and Organization, 27(3), 171189.Google Scholar
Mutch, A. (2013). Sociomateriality – taking the wrong turning? Information and Organization, 23(1), 2840.CrossRefGoogle Scholar
Niedderer, K. (2007). Designing mindful interaction: The category of performative object. Design Issues, 23(1), 317.Google Scholar
Northoff, G. (2018). The Spontaneous Brain. From the Mind-Body to the World-Brain Problem. London and Cambridge, MA: MIT Press.Google Scholar
Northoff, G. (2011). Neuropsychoanalysis in Practice: Brain, Self, and Objects. Oxford: Oxford University Press.Google Scholar
Orlikowski, W. J. and Iacono, C. S. (2001). Research commentary: Desperately seeking the ‘IT’ in IT research – a call to theorizing the IT artifact. Information Systems Research, 12(2), 121134.Google Scholar
Orlikowski, W. J. and Scott, S. V. (2008). Sociomateriality: Challenging the separation of technology, work and organization. Academy of Management Annals, 2(1), 433474.Google Scholar
Østerlund, C., Crowston, K. and Jackson, C. (2020). Building an apparatus: Refractive, reflective, and diffractive readings of trace data. Journal of the Association for Information Systems, 20(1), 122.Google Scholar
Pask, G. (1970). The meaning of cybernetics in the behavioural sciences. (The cybernetics of behaviour and cognition; extending the meaning of ‘goal’). In Rose, J. (ed.), Progress of Cybernetics, Vol. 1: Main Papers, The Meaning of Cybernetics, Neuro- and Biocybernetics. London: Gordon & Breach, Science Publishers, pp. 1544.Google Scholar
Peirce, C. S. (1933). Collected Papers (Hartshorne, Charles and Weiss, Paul, eds). Cambridge, MA: Belknap Press of Harvard University Press.Google Scholar
Schönwälder-Kuntze, T., Wille, K. and Hölscher, T. (2009). George Spencer Brown: Eine Einführung in die ‘Laws of Form’: [Lehrbuch], 2nd edition. Wiesbaden, Germany: VS, Verlag für Sozialwiss.Google Scholar
Scott, S. V. and Orlikowski, W. J. (2013). Sociomateriality – taking the wrong turning? A response to Mutch. Information and Organization, 23(2), 7780.Google Scholar
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379423.Google Scholar
Shannon, C. E. and Weaver, W. (1949). The Mathematical Theory of Communication, 1963 edition. Urbana, IL: University of Illinois Press.Google Scholar
Sloman, S. A. and Lagnado, D. (2015). Causality in thought. Annual Review of Psychology, 66, 223247.Google Scholar
Spencer-Brown, G. (1969). Laws of Form. London: Allen & Unwin.Google Scholar
von Foerster, H. (1969). Laws of form. Whole Earth Catalog, spring: 14. Cited from: Die Gesetze der Form. In Baecker, D. (ed.), Kalkül der Form, Suhrkamp Taschenbuch Wissenschaft. Frankfurt am Main, Germany: Suhrkamp, Vol. 1068, pp. 911.Google Scholar
von Foerster, H. (2003). Understanding Understanding: Essays on Cybernetics and Cognition. New York, NY: Springer.CrossRefGoogle Scholar
von Foerster, H. and von Glasersfeld, E. (2010). Wie wir uns erfinden: Eine Autobiographie des radikalen Konstruktivismus, 4th edition. Heidelberg, Germany: Carl-Auer-Systeme.Google Scholar
von Glasersfeld, E. (2002). Cybernetics and the theory of knowledge. UNESCO Encyclopedia. Section on System Science and Cybernetics, p. 255.Google Scholar
Watzlawick, P., Weakland, J. H. and Fisch, R. (1974). Change: Principles of Problem Formation and Problem Resolution, reprint 2011. London and New York, NY: W. W. Norton.Google Scholar
Weber, R. (2003). Still desperately seeking the IT artifact. MIS Quarterly, 27(2), 183194.Google Scholar
Whorf, B. L. (1940). Science and linguistics. Technology Review, 42(6), 229231, 247–248.Google Scholar
Wittgenstein, L. (1963). Logisch-philosophische Abhandlung: Tractatus logico-philosophicus, 36th edition. Frankfurt am Main, Germany: Suhrkamp, Vol. 12.Google Scholar
Yoo, Y. (2010). Computing in everyday life: A call for research on experiential computing. MIS Quarterly, 34(2), 213231.Google Scholar
Zittrain, J. (2008). The Future of the Internet and How to Stop it. New Haven, CT: Yale University Press.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), ixxxii.Google Scholar
Agrawal, A., Fu, W. and Menzies, T. (2018). What is wrong with topic modeling? And how to fix it using search-based software engineering. Information and Software Technology, 98, 7488.Google Scholar
Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired, www.wired.com/2008/06/pb-theory/.Google Scholar
Avgerou, C. (2019). Contextual explanation: Alternative approaches and persistent challenges. MIS Quarterly, 43(3), 9771006.Google Scholar
Bao, Y. and Datta, A. (2014). Simultaneously discovering and quantifying risk types from textual risk disclosures. Management Science, 60(6), 13711391.Google Scholar
Bapna, S., Benner, M. J. and Qiu, L. (2019). Nurturing online communities: An empirical investigation. MIS Quarterly, 43(2), 425452.Google Scholar
Barthes, R. (1972). The Structuralist Activity: Critical Essays. Evanston, IL: Northwestern University Press.Google Scholar
Barrett, M. and Oborn, E. (2018). Bridging the research-practice divide: Harnessing expertise collaboration in making a wider set of contributions. Information and Organization, 28(1), 4451.CrossRefGoogle Scholar
Belford, M., Mac Namee, B. and Greene, D. (2018). Stability of topic modeling via matrix factorization. Expert Systems with Applications, 91, 159169.Google Scholar
Berry, M. W., Browne, M., Langville, A. N., Pauca, B. P. and Plemmons, R. J. (2007). Algorithms and applications for approximate nonnegative matrix factorization. Computational Statistics & Data Analytics, 52(1), 155173.CrossRefGoogle Scholar
Binkley, D., Heinz, D., Lawrie, D. and Overfelt, J. (2014). Understanding LDA in source code analysis. Proceedings of the International Conference on Program Comprehension, Hyderabad, India.Google Scholar
Bird, S., Klein, E. and Loper, E. (2009). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. Sebastopol, CA: O’Reilly Media, Inc.Google Scholar
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 7784.Google Scholar
Blei, D. M. and Lafferty, J. D. (2009). Topic models. In Srivastava, A. N. and Sahami, M. (eds), Text Mining: Classification, Clustering, and Applications. London: Chapman & Hall, pp. 7194.Google Scholar
Brynjolfsson, E. and McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 7, 120.Google Scholar
Busa, R. (1990). Informatics and the new philology. Computers and the Humanities, 24(5–6), 339343.Google Scholar
Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L. and Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. Advances in Neural Information Processing Systems, 22, 288296.Google Scholar
Chen, L., Baird, A. and Straub, D. W. (2019). An analysis of the evolving intellectual structure of health information systems research in the information systems discipline. Journal of the Association for Information Systems, 20(8), 10231074.Google Scholar
Christin, A. (2020). Algorithmic ethnography, during and after COVID-19. Communication and the Public, 5(3–4), 108111.CrossRefGoogle Scholar
Creswell, J. (2003). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 2nd edition. Los Angeles, CA: Sage.Google Scholar
Croidieu, G. and Kim, P. H. (2018). Labor of love: Amateurs and lay-expertise legitimation in the early U.S. radio field. Administrative Science Quarterly, 63(1), 142.Google Scholar
Dong, W., Liao, S. and Zhang, Z. (2018). Leveraging financial social media data for corporate fraud detection. Journal of Management Information Systems, 35(2), 461487.Google Scholar
Dreyfus, H. (1993). What Computers Still Can’t Do: A Critique of Artificial Reason, 2nd edition. Cambridge, MA: MIT Press.Google Scholar
Dubé, L. and Paré, G. (2003). Rigor in information systems positivist case research: Current practices, trends, and recommendations. MIS Quarterly, 27(4), 597636.Google Scholar
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T. et al. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 147.Google Scholar
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532550.Google Scholar
Evangelopoulos, N. (2016). Thematic orientation of the ISJ within a semantic space of IS research. Information Systems Journal, 26(1), 3946.Google Scholar
Evangelopoulos, N., Zhang, X. and Prybutok, V. R. (2012). Latent semantic analysis: five methodological recommendations. European Journal of Information Systems, 21(1), 7086.CrossRefGoogle Scholar
Galdas, P. (2017). Revisiting bias in qualitative research: Reflections on its relationship with funding and impact. International Journal of Qualitative Methods, 16(1), 12.Google Scholar
George, G., Haas, M. R. and Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321326.Google Scholar
Geva, H., Oestreicher-Singer, G. and Saar-Tsechansky, M. (2019). Using retweets when shaping our online persona: Topic modeling approach. MIS Quarterly, 43(2), 501524.Google Scholar
Giorgi, S. and Weber, K. (2015). Marks of distinction: Framing and audience appreciation in the context of investment advice. Administrative Science Quarterly, 60(2), 333367.Google Scholar
Giorgi, S., Maoret, M. and Zajac, E. J. (2019). On the relationship between firms and their legal environment: The role of cultural consonance. Organization Science, 30(4), 803830.Google Scholar
Glaser, V. L., Pollock, N. and D’Adderio, L. (2021). The biography of an algorithm: Performing algorithmic technologies in organizations. Organization Theory, 2(2), 127.Google Scholar
Goes, P. B. (2014). Editor’s comments: Big data and IS research. MIS Quarterly, 38(3), iiiviii.Google Scholar
Gong, J., Abhishek, V. and Li, B. (2018). Examining the impact of keyword ambiguity on search advertising performance: A topic model approach. MIS Quarterly, 42(3), 805829.Google Scholar
Günther, W. and Joshi, M. (2020). Algorithmic intelligence in research: Prevalent topic modeling practices and implications for rigor in IS and management research. International Conference on Information Systems, virtual conference.Google Scholar
Haans, R. F. (2019). What’s the value of being different when everyone is? The effects of distinctiveness on performance in homogeneous versus heterogeneous categories. Strategic Management Journal, 40(1), 327.Google Scholar
Hannigan, T., Haans, R. F., Vakili, K., Tchalian, H., Glaser, V., Wang, M. et al. (2019). Topic modeling in management research: Rendering new theory from textual data. Academy of Management Annals, 13(2), 586632.Google Scholar
Heidegger, M. (1962). Being and Time. Oxford, UK and Cambridge, MA: Blackwell.Google Scholar
Heidegger, M. (1977). The question concerning technology. In Krell, D. F. (ed.), Basic Writings. New York, NY: HarperCollins, pp. 213238.Google Scholar
Hickman, L., Thapa, S., Tay, L., Cao, M. and Srinivasan, P. (2022). Text preprocessing for text mining in organizational research: Review and recommendations. Organizational Research Methods, 25(1), 114146.Google Scholar
Huang, A. H., Lehavy, R., Zang, A. Y. and Zheng, R. (2018). Analyst information discovery and interpretation roles: A topic modeling approach. Management Science, 64(6), 28332855.Google Scholar
Introna, L. and Ilharco, F. (2011). Phenomenology, screens, and screenness: Returning to the world itself. In Galliers, R. D. and Currie, W. (eds), The Oxford Handbook of Management Information Systems: Critical Perspectives and New Directions. Oxford: Oxford University Press.Google Scholar
Jarzabkowski, P., Bednarek, R. and , J. K. (2014). Producing persuasive findings: Demystifying ethnographic textwork in strategy and organization research, Strategic Organization, 12(4), 274287.Google Scholar
Jones, M. (2019). What we talk about when we talk about (big) data. Journal of Strategic Information Systems, 28(1), 316.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
Kaplan, S. and Vakili, K. (2015). The double‐edged sword of recombination in breakthrough innovation. Strategic Management Journal, 36(10), 14351457.Google Scholar
Karanović, J., Berends, H. and Engel, Y. (2020). Regulated dependence: Platform workers’ responses to new forms of organizing. Journal of Management Studies, 58(4), 10701106.Google Scholar
Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data Society, 1(1), 112.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), 6793.Google Scholar
Larsen, K. R. and Bong, C. H. (2016). A tool for addressing construct identity in literature reviews and meta-analyses. MIS Quarterly, 40(3), 529551.Google Scholar
Lash, M. T. and Zhao, K. (2016). Early predictions of movie success: The who, what, and when of profitability. Journal of Management Information Systems, 33(3), 874903.Google Scholar
Latour, B. (1990). Technology is society made durable. Sociological Review, 38(Supp. 1), 103131.Google Scholar
Lee, G. M., Qiu, L. and Whinston, A. B. (2016). A friend like me: Modeling network formation in a location-based social network. Journal of Management Information Systems, 33(4), 10081033.Google Scholar
Mackenzie, A. (2017). Machine Learners: Archaeology of a Data Practice. Cambridge, MA: MIT Press.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), 115.Google Scholar
Manovich, L (2011). Trending: The promises and the challenges of big social data. Manovich, http://manovich.net/index.php/projects/trending-the-promises-and-the-challenges-of-big-social-data.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), 314.Google Scholar
Nielsen, M. W. and Börjeson, L. (2019). Gender diversity in the management field: Does it matter for research outcomes? Research Policy, 48(7), 16171632.Google Scholar
Pääkkönen, J. and Ylikoski, P. (2021). Humanistic interpretation and machine learning. Synthese, 199(1), 14611497.Google Scholar
Patton, M. Q. (2002). Qualitative Research & Evaluation Methods, 3rd edition. Thousand Oaks, CA: Sage.Google Scholar
Ramsay, S. (2011). Reading Machines: Toward an Algorithmic criticism. Champaign, IL: University of Illinois Press.CrossRefGoogle Scholar
Ribes, D. and Jackson, S. J. (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. 147166.Google Scholar
Roberts, M. E., Stewart, B. M., Tingley, D. and Airoldi, E. M. (2013). The structural topic model and applied social science. Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation, Lake Tahoe, NV.Google Scholar
Rockwell, G. and Sinclair, S. (2016). Computer-Assisted Interpretation in the Humanities. Cambridge, MA: MIT Press.Google Scholar
Salge, T. O., Antons, D., Barrett, M., Kohli, R., Oborn, E. and Polykarpou, S. (2022). How IT investments help hospitals gain and sustain reputation in the media: The role of signaling and framing. Information Systems Research, 33(1), 110130.Google Scholar
Samtani, S., Chinn, R., Chen, H. and NunamakerJr, J. F. (2017). Exploring emerging hacker assets and key hackers for proactive cyber threat intelligence. Journal of Management Information Systems, 34(4), 10231053.CrossRefGoogle Scholar
Schmiedel, T., Müller, O. and vom Brocke, J. (2019). Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods, 22(4), 941968.Google Scholar
Schultze, U. and Avital, M. (2011). Designing interviews to generate rich data for information systems research. Information and Organization, 21(1), 116.Google Scholar
Shadish, W. R., Cook, T. D. and Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, MA: Houghton, Mifflin & Co., Vol. XXI.Google Scholar
Shi, Z., Lee, G. M. and Whinston, A. B. (2016). Toward a better measure of business proximity: Topic modeling for industry intelligence. MIS Quarterly, 40(4), 10351056.Google Scholar
Shi, D., Guan, J., Zurada, J. and Manikas, A. (2017). A data-mining approach to identification of risk factors in safety management systems. Journal of Management Information Systems, 34(4), 10541081.Google Scholar
Sidorova, A., Evangelopoulos, N., Valacich, J. S. and Ramakrishnan, T. (2008). Uncovering the intellectual core of the information systems discipline. MIS Quarterly, 32(3), 467482.Google Scholar
Suchman, L. (2006). Human-Machine Reconfigurations. Cambridge: Cambridge University Press.Google Scholar
Suchman, L. (1987). Plans and Situated Actions: The Problem of Human-Machine Communication (Learning in Doing: Social, Cognitive and Computational Perspectives). Cambridge: Cambridge University Press.Google Scholar
Tashakkori, A. and Creswell, J. W. (2007). Exploring the nature of research questions in mixed methods research. Journal of Mixed Methods Research, 1(3), 207211.Google Scholar
Tracy, S. J. (2013). Qualitative Research Methods: Collecting Evidence, Crafting Analysis, Communicating Impact. Chichester, UK: Wiley-Blackwell.Google Scholar
Tully, J. (1994). Philosophy in an Age of Pluralism. Cambridge: Cambridge University Press.Google Scholar
Van Maanen, J. (2011). Ethnography as work: Some rules of engagement. Journal of Management Studies, 48(1), 218234.Google Scholar
Van Maanen, J. and de Rond, M. (2017). The making of a classic ethnography: Notes on Alice Goffman’s On the Run. Academy of Management Review, 42(2), 396406.Google Scholar
Weber, M. (2003). The Protestant Ethic and the Spirit of Capitalism. New York, NY: Courier Dover.Google Scholar
Wendt, T. (2013). Designing for transparency and the myth of the modern interface. Ux Magazine, https://uxmag.com/articles/designing-for-transparency-and-the-myth-of-the-modern-interface.Google Scholar
Winner, M. (1999). Do artifacts have politics? In MacKenzie, D. A. and Wajcman, J. (eds), The Social Shaping of Technology, 2nd edition. London: Open University Press, pp. 2840. (Originally printed in Daedalus, 109(1), winter 1980.)Google Scholar
Winograd, T. and Flores, F. (1986). Understanding Computers and Cognition: A New Foundation for Design. Norwood, NJ: Ablex.Google Scholar
Yin, R. K. (1981). The case study crisis: Some answers. Administrative Science Quarterly, 26(1), 5865.Google Scholar
Yue, W. T., Wang, Q. H. and Hui, K. L. (2019). See no evil, hear no evil? Dissecting the impact of online hacker forums. MIS Quarterly, 43(1), 7395.Google Scholar
Zammuto, R. F., Griffith, T. L., Majchrzak, A., Dougherty, D. J. and Faraj, S. (2007). Information technology and the changing fabric of organization. Organization Science, 18(5), 749762.Google Scholar

References

Ackroyd, J. and O’Toole, J. (2010). Performing Research: Tensions, Triumphs and Trade-Offs of Ethnodrama. Stoke-on-Trent, UK: Trentham.Google Scholar
Antonovsky, A. (1987). Unraveling the Mystery of Health: How People Manage Stress and Stay Well. San Francisco, CA: Jossey-Bass.Google Scholar
Ayyagari, R., Grover, V. and Purvis, R. (2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831858.Google Scholar
Baptista, J., Stein, M.-K., Klein, S., Watson-Manheim, M. B. 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 101618.Google Scholar
Berntson, G. G., BiggerJr, J. T., Eckberg, D. L., Grossman, P., Kaufmann, P. G., Malik, M. et al. (1997). Heart rate variability: Origins, methods and interpretive caveats (Committee Report). Psychophysiology, 34(6), 623648.Google Scholar
Cavanaugh, M. A., Boswell, W. R., Roehling, M. V. and Boudreau, J. W. (2000). An empirical examination of self-reported work stress among U.S. managers. Journal of Applied Psychology, 85(1), 6574.Google Scholar
Charnas, D. (2016). Work Clean the Life-Changing Power of Mise-en-Place to Organize Your Life, Work and Mind. New York, NY: Rodale Books.Google Scholar
Cicourel, A. V. (1964). Method and Measurement in Sociology. New York, NY: Free Press.Google Scholar
Dan, C.-I., Roşca, A. C. and Mateizer, A. (2020). Job crafting and performance in firefighters: The role of work meaning and work engagement. Frontiers in Psychology, 11, 894.Google Scholar
Dreyfus, H. and Kelly, S. D. (2011). All Things Shining: Reading the Western Classics to Find Meaning in a Secular Age. New York, NY: Free Press.Google Scholar
Eriksson, M. and Lindström, B. (2007). Antonovsky’s sense of coherence scale and its relation with quality of life: A systematic review. Journal of Epidemiology and Community Health, 61(11), 938944.Google Scholar
Grønsund, T. and Aanestad, M. (2020). Augmenting the algorithm: Emerging human-in-the-loop work configurations. Journal of Strategic Information Systems, 29(2), article 101614.Google Scholar
Hahn, T., Ebner-Priemer, U. and Meyer-Lindenberg, A. (2019). Transparent artificial intelligence – a conceptual framework for evaluating AI-based clinical decision support systems, https://ssrn.com/abstract=330312.Google Scholar
Hesse-Biber, S. N. and Leavy, P. (2011). The Practice of Qualitative Research, 2nd edition. Los Angeles, CA: Sage.Google Scholar
Isak, C. (2016). Motivated self-endangerment: How indirect management can hurt you. Tech Acute, http://techacute.com/motivated-self-endangerment/.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 AIS, 18(12), 872899.Google Scholar
Krause, A., Baeriswyl, S., Berset, M., Deci, N., Dettmers, J., Dorsemagen, C. et al. (2014). Selbstgefährdung als Indikator für Mängel bei der Gestaltung mobil-flexibler Arbeit: Zur Entwicklung eines Erhebungsinstruments. Wirtschaftspsychologie, 4, 4959, www.psychologie-aktuell.com/index.php?id=184&tx_ttnews%5Btt_news%5D=3833&tx_ttnews%5BbackPid%5D=185&cHash=d51d629405#marker4.Google Scholar
Langer, E. J. (1989). Mindfulness. Reading, MA: Addison-Wesley.Google Scholar
Lepri, B., Oliver, N., Letouzé, E., Pentland, A. and Vinck, P. (2018). Fair, transparent, and accountable algorithmic decision-making processes. Philosophy & Technology, 31(4), 611627.Google Scholar
Lindström, B. (2012). Salutogenesis – an introduction. Folkhälsan Research Center, Helsinki, www.centrelearoback.org/assets/PDF/04_activites/clr-GCPB121122-Lindstom_pub_introsalutogenesis.pdf.Google Scholar
Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J. and Schwartz, P. J. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal, 17(3), 354381.Google Scholar
Mason, K. (2015). Participatory action research: Coproduction. Governance and Care: Geography Compass, 9(9), 497507.Google Scholar
Masood, K., Ahmed, B., Choi, J. and Gutierrez-Osuna, R. (2012). Consistency and validity of self-reporting scores in stress measurement surveys. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA.Google Scholar
McCraty, R. (2015). Science of the Heart: Exploring the Role of the Heart in Human Performance. Boulder Creek, CA: HeartMath Institute.Google Scholar
Ngwenyama, O. (2019). The ten basic claims of information systems research: An approach to interrogating validity claims in scientific argumentation, https://ssrn.com/abstract=3446798.Google Scholar
Perlow, L. A. and Kelly, E. L. (2014). Toward a model of work redesign for better work and better life. Work and Occupations, 41(1), 111134.Google Scholar
Pickering, A. (1995). The Mangle of Practice: Time, Agency, and Science. Chicago, IL: University of Chicago Press.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
Rosa, H. and Wagner, J. C. (2019). Resonance: A Sociology of Our Relationship to the World, English edition. Cambridge, MA: Polity Press.Google Scholar
Sonnentag, S. and Fritz, C. (2015). Recovery from job stress: The stressor-detachment model as an integrative framework. Journal of Organizational Behavior, 36(Supp. 1), S72S103.Google Scholar
Sturges, J. (2012). Crafting a balance between work and home. Human Relations, 65(12), 15391559.Google Scholar
Tims, M., Bakker, A. B. and Derks, D. (2015). Job crafting and job performance: A longitudinal study. European Journal of Work and Organizational Psychology, 24(6), 914928.Google Scholar
Tims, M., Bakker, A. B. and Derks, D. (2013). The impact of job crafting on job demands, job resources, and well-being. Journal of Occupational Health Psychology, 18(2), 230240.Google Scholar
Togo, F. and Takahashi, M. (2009). Heart rate variability in occupational health – a systematic review. Industrial Health, 47(6), 589602.Google Scholar
Venkatesh, V., Brown, S. A. and Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS Quarterly, 37(1), 2154.Google Scholar
Waardenburg, L., Sergeeva, A. and Huysman, M. (2018). Hotspots and blind spots. In Schultze, U., Aanestad, M., Mähring, M., Østerlund, C. and Riemer, K. (eds), IFIP Advances in Information and Communication Technology. Living with Monsters? Social Implications of Algorithmic Phenomena, Hybrid Agency, and the Performativity of Technology. Cham, Switzerland: Springer, pp. 96109.Google Scholar
Weisberg, D. S., Hirsh-Pasek, K., Golinkoff, R. M. and McCandliss, B. D. (2014). Mise en place: Setting the stage for thought and action. Trends in Cognitive Science, 18(6), 276278.Google Scholar
Wendsche, J. and Lohmann-Haislah, A. (2017). A meta-analysis on antecedents and outcomes of detachment from work. Frontiers in Psychology, https://doi.org/10.3389/fpsyg.2016.02072.Google Scholar
Wrzesniewski, A. and Dutton, J. E. (2001). Crafting a job: Revisioning employees as active crafters of their work. Academy of Management Review, 26(2), 179201.Google Scholar

References

Abbott, A. (1995). Sequence analysis: New methods for old ideas. Annual Review of Sociology, 21(1), 93113.Google Scholar
Abbott, A. (1990). A primer on sequence methods. Organization Science, 1(4), 375392.Google Scholar
Abbott, A. and Hrycak, A. (1990). Measuring resemblance in sequence data: An optimal matching analysis of musicians’ careers. American Journal of Sociology, 96(1), 144185.Google Scholar
Alvarez, R. M. (2016). Computational Social Science: Discovery and Prediction. New York, NY: Cambridge University Press.Google Scholar
Andersen, J. V. and Ingram Bogusz, C. (2019). Self-organizing in blockchain infrastructures: Generativity through shifting objectives and forking. Journal of the Association of Information Systems, 20(9), 247265.Google Scholar
Baldwin, C., MacCormack, A. and Rusnak, J. (2014). Hidden structure: Using network methods to map system architecture. Research Policy, 43(8), 13811397.Google Scholar
Bastian, M., Heymann, S. and Jacomy, M. (2009). Gephi: An open-source software for exploring and manipulating networks. Proceedings of the Third International Conference on Weblogs and Social Media, San Jose, CA.Google Scholar
Biemann, T. and Datta, D. K. (2014). Analyzing sequence data: Optimal matching in management research. Organizational Research Methods, 17(1), 5176.Google Scholar
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 7784.Google Scholar
Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(1), 9931022.Google Scholar
Borgatti, S. P., Mehra, A., Brass, D. J. and Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892895.Google Scholar
Chang, K., Yih, W. and Meek, C. (2013). Multi-relational latent semantic analysis. Conference on Empirical Methods in Natural Language Processing, Seattle, WA.Google Scholar
Choi, K. S., Im, I. and Yoo, Y. (2013). Liquid communication: An analysis of the impact of mobile micro-blogging on communication and decision-making. International Conference on Information Systems, Milan, Italy.Google Scholar
Cioffi-Revilla, C. (2014). Introduction to Computational Social Science: Principles and Applications. London: Springer.Google Scholar
Cioffi-Revilla, C. (2010). Computational social science. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 259271.Google Scholar
Cloutier, C. and Langley, A. (2020). What makes a process theoretical contribution? Organization Theory, 1(1), Article 2631787720902473.Google Scholar
Cornwell, B. (2015a). Network methods for sequence analysis. In Social Sequence Analysis: Methods and Applications. New York, NY: Cambridge University Press, pp. 155251.Google Scholar
Cornwell, B. (2015b). Theoretical foundations of social sequence analysis. In Social Sequence Analysis: Methods and Applications. New York, NY: Cambridge University Press, pp. 2156.Google Scholar
Csardi, G. and Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1965(5), 19.Google Scholar
Debortoli, S., Müller, O., Junglas, I. and vom Brocke, J. (2016). Text mining for information systems researchers: An annotated topic modeling tutorial. Communications of the Association for Information Systems, 39(1), 110135.Google Scholar
Deerwester, S., Dumais, S. T. and Landauer, T. K. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407.Google Scholar
Faulkner, P. and Runde, J. (2019). Theorizing the digital object. MIS Quarterly, 43(4), 12791302.Google Scholar
Gabadinho, A., Ritschard, G. and Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40(4), 137.Google Scholar
Gaskin, J., Berente, N., Lyytinen, K. and Yoo, Y. (2014). Toward generalizable sociomaterial inquiry: A computational approach for zooming in and out of sociomaterial routines. MIS Quarterly, 38(3), 849871.Google Scholar
Gaskin, J., Schutz, D., Thummadi, V., Weiss, A., Berente, N., Lyytinen, K. and Yoo, Y. (2010). Design DNA: A methodological artifact for sequencing of socio-technical design patterns. International Conference on Information Systems, St Louis, MO.Google Scholar
Glaser, B. G. (1978). Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Mill Valley, CA: Sociology Press.Google Scholar
Glaser, B. G. and Strauss, A. (1967). The Discovery of Grounded Theory. Chicago, IL: Aldine Publishing.Google Scholar
Godoe, H. (2000). Innovation regimes, R&D and radical innovations in telecommunications. Research Policy, 29(9), 10331046.Google Scholar
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 13601380.Google Scholar
Hedman, J., Srinivasan, N. and Lindgren, R. (2013). Digital traces or information systems: Sociomateriality made researchable. International Conference on Information Systems: Reshaping Society through Information Systems Design, Milan, Italy.Google Scholar
Heise, D. R. (1991). Event structure analysis: A qualitative model of quantitative research. In Fielding, N. and Lee, R. (eds), Using Computers in Qualitative Research. Newbury Park, CA: Sage, pp. 122.Google Scholar
Heise, D. R. (1989). Modeling event structures. Journal of Mathematical Sociology, 14(2–3), 139169.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), 767797.Google Scholar
Johnson, S. L., Safadi, H. and Faraj, S. (2015). The emergence of online community leadership. Information Systems Research, 26(1), 165187.Google Scholar
Jockers, M. L. (2013). Macroanalysis: Digital Methods and Literary History. Urbana and Champaign, IL: University of Illinois Press.Google Scholar
Kallinikos, J., Aaltonen, A. and Marton, A. (2013). The ambivalent ontology of digital artifacts. MIS Quarterly, 37(2), 357370.Google Scholar
Kitchin, R. (2014). The reframing of science, social science and humanities research. In The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences. London: Sage, pp. 128149.Google Scholar
Knoke, D. and Yang, S. (2008). Social Network Analysis: Mapping and Exploring the Network Society. London: Sage.Google Scholar
Landauer, T. K., Foltz, P. W. P. and Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2–3), 259284.Google Scholar
Landauer, T. K., McNamara, D. S., Dennis, S. and Kintsch, W. (eds) (2013). Handbook of Latent Semantic Analysis. London and New York, NY: Psychology Press.Google Scholar
Langley, A. (2007). Process thinking in strategic organization. Strategic Organization, 5(3), 271282.Google Scholar
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A., Brewer, D. et al. (2009). Computational social science. Science, 323(5915), 721723.Google Scholar
Lazer, D. and Radford, J. (2017). Data ex machina: Introduction to big data. Annual Review of Sociology, 43(1), 121.Google Scholar
Lindberg, A. (2020). Developing theory through integrating human & machine pattern recognition. Journal of the Association for Information Systems, 21(1), 90116.Google Scholar
Lindberg, A., Berente, N., Gaskin, J. and Lyytinen, K. (2016). Coordinating interdependencies in online communities: A study of an open source software project. Information Systems Research, 27(4), 751772.Google Scholar
Lindberg, A., Gaskin, J., Berente, N., 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
Marsland, S. (2014). Machine Learning: An Algorithmic Perspective. Boca Raton, FL: CRC Press.Google Scholar
Miles, M. B., Huberman, A. M. and Saldaña, J. (2014). Qualitative Data Analysis: A Methods Sourcebook, 3rd edition. Thousand Oaks, CA: Sage.Google Scholar
Moreno, J. L. (1934). Who Shall Survive? A New Approach to the Problem of Human Interrelations. Washington, DC: Nervous and Mental Disease Publishing Co.Google Scholar
Moretti, F. (2013). ‘Operationalizing’: or, the function of measurement in modern literary theory. Journal of English Language and Literature, 60(1), 319.Google Scholar
Müller, O., Junglas, I., vom Brocke, J. and Debortoli, S. (2016). Utilizing big data analytics for information systems research: Challenges, promises and guidelines. European Journal of Information Systems, 2(1), 289302.Google Scholar
Østerlund, C., Crowston, K. and Jackson, C. (2020). Building an apparatus: Refractive, reflective & diffractive readings of trace data. Journal of the Association for Information Systems, 21(1), 143.Google Scholar
Pentland, B. T., Liu, P., Kremser, W. and Hærem, T. (2020a). The dynamics of drift in digitized processes. MIS Quarterly: Management Information Systems, 44(1), 1947.Google Scholar
Pentland, B. T., Recker, J., Wolf, J. R. and Wyner, G. (2020b). Bringing context inside process research with digital trace data. Journal of the Association for Information Systems, 21(5), 12141236.Google Scholar
Prell, C. (2012). Social Network Analysis: History, Theory & Method. London, New Delhi, Singapore, Los Angeles, CA and Washington, DC: Sage.Google Scholar
Ritschard, G., Gabadinho, A., Muller, N. S. and Studer, M. (2008). Mining event histories: A social science perspective. International Journal of Data Mining, Modelling and Management, 1(1), 6890.Google Scholar
Rogers, R. (2013). Digital Methods. Cambridge, MA: MIT Press.Google Scholar
Rogers, R. (2009). New media & digital culture. Text prepared for the inaugural speech, Chair, University of Amsterdam, May, pp. 1–25.Google Scholar
Salganik, M. (2017). Bit by Bit: Social Research in the Digital Age. Princeton, NJ: Princeton University Press.Google Scholar
Salton, G., Wong, A. and Yang, C. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613620.Google Scholar
Scott, J. (1994). Social network analysis. Journal of the British Sociological Association, 22(1), 109127.Google Scholar
Sievert, C. and Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, Baltimore, MD.Google Scholar
Sinclair, B. (2016). Network structure and social outcomes: Network analysis for social science. In Alvarez, M. R. (ed.), Computational Social Science: Discovery and Prediction. New York, NY: Cambridge University Press, pp. 121140.Google Scholar
Steyvers, M. and Griffiths, T. (2007). Probabilistic topic models. Handbook of Latent Semantic Analysis, 427(7), 424440.Google Scholar
Svahn, F., Henfridsson, O. and Yoo, Y. (2009). Mangling the sociomateriality of technological regimes in digital innovation. International Conference Information Systems, Phoenix, AZ.Google Scholar
Trier, M. (2008). Research note – towards dynamic visualization for understanding evolution of digital communication networks. Information Systems Research, 19(3), 335350.Google Scholar
Urquhart, C. (2001). An encounter with grounded theory: Tackling the practical and philosophical issues. In Trauth, E. M. (ed.), Qualitative Research in IS: Issues and Trends. London and Hershey, PA: Idea Group Publishing, pp. 104140.Google Scholar
Urquhart, C., Lehmann, H. and Myers, M. D. (2009). Putting the ‘theory’ back into grounded theory: Guidelines for grounded theory studies in information systems. Information Systems Journal, 20(4), 357381.Google Scholar
Venturini, T. (2012). Building on faults: How to represent controversies with digital methods. Public Understanding of Science, 21(7), 796812.Google Scholar
Venturini, T. (2009). Diving in magma: How to explore controversies with actor-network theory. Public Understanding of Science, 19(3), 258273.Google Scholar
Venturini, T., Bounegru, L., Gray, J. and Rogers, R. (2018). A reality check(-list) for digital methods. New Media & Society, 22(2), 317341.Google Scholar
Venturini, T. and Latour, B. (2010). The social fabric: Digital traces and quali-quantitative methods. Proceedings of Future En Seine, Paris, France.Google Scholar
Wasserman, S. and Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.Google Scholar
Weltevrede, E. (2016). Repurposing digital methods: The research affordances of platforms and engines. PhD dissertation, University of Amsterdam.Google Scholar
Whelan, E., Teigland, R., Vaast, E. and Butler, B. (2016). Expanding the horizons of digital social networks: Mixing big trace datasets with qualitative approaches. Information and Organization, 26(1–2), 112.Google Scholar
Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10), 123.Google Scholar
Wild, F. and Stahl, C. (2007). Investigating unstructured texts with latent semantic analysis. In Decker, R. and Lenz, H.-J. (eds), Advances in Data Analysis. Berlin and Heidelberg, Germany: Springer, pp. 383390.Google Scholar
Yoo, Y., Henfridsson, O. and Lyytinen, K. (2010). The new organizing logic of digital innovation: An agenda for information systems research. Information Systems Research, 21(4), 724735.Google Scholar
Zachariadis, M., Scott, S. and Barrett, M. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed-method research in information systems. MIS Quarterly, 37(3), 855879.Google Scholar

References

Agrawal, A., Gans, J. and Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Boston, MA: Harvard Business Press.Google Scholar
Atkinson, P. and Hammersley, M. (2007). Ethnography: Principles in Practice. London: Routledge.Google Scholar
Barab, S. A., Thomas, M. K., Dodge, T., Squire, K. and Newell, M. (2004). Critical design ethnography: Designing for change. Anthropology & Education Quarterly, 35(2), 254268.Google Scholar
Baskerville, R. L. and Myers, M. D. (2015). Design ethnography in information systems. Information Systems Journal, 25(1), 2346.Google Scholar
Baumer, E. P. and Silberman, M. S. (2011). When the implication is not to design (technology). Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Vancouver, Canada.Google Scholar
Beckman, S. L. (2020). To frame or reframe: Where might design thinking research go next? California Management Review, 62(2), 144162.Google Scholar
Boland, R. J., Collopy, F., Lyytinen, K. and Yoo, Y. (2008). Managing as designing: Lessons for organization leaders from the design practice of Frank O. Gehry. Design Issues, 24(1), 1025.Google Scholar
Boland, R. J. and Lyytinen, K. (2004). Information systems research as design: Identity, process, and narrative. In Kaplan, B., Truex, D. P., Wastell, D., Wood-Harper, A. T. and DeGross, J. I. (eds), Information Systems Research. Boston, MA: Springer, pp. 5368.Google Scholar
Boyd, D., Levy, K. and Marwick, A. (2014). The networked nature of algorithmic discrimination. In Gangadharan, S. P. (ed.), Data and Discrimination: Collected Essays. Washington, DC: Open Technology Institute, pp. 5357.Google Scholar
Buchanan, R. (2015). Worlds in the making: Design, management, and the reform of organizational culture. She Ji: The Journal of Design, Economics, and Innovation, 1(1), 521.Google Scholar
Buchanan, R. (2001). Human dignity and human rights: Thoughts on the principles of human-centered design. Design Issues, 17(3), 3539.Google Scholar
Buchanan, R. (1992). Wicked problems in design thinking. Design Issues, 8(2), 521.Google Scholar
Cao, L. (2017). Data science: A comprehensive overview. ACM Computing Surveys, 50(3), 142.Google Scholar
Carlsson, S. A., Henningsson, S., Hrastinski, S. and Keller, C. (2011). Socio-technical IS design science research: Developing design theory for IS integration management. Information Systems and E-Business Management, 9(1), 109131.Google Scholar
Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P. and Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 110.Google Scholar
Chander, A. (2017). The racist algorithm. Michigan Law Review, 115(6), 10231046.Google Scholar
Churchill, E. F. (2017). Data, design, and ethnography. Interactions, 25(1), 2223.Google Scholar
Clegg, S., Kornberger, M. and Rhodes, C. (2007). Business ethics as practice. British Journal of Management, 18(2), 107122.CrossRefGoogle Scholar
Cossins, D. (2018). Discriminating algorithms: 5 times AI showed prejudice. New Scientist, www.newscientist.com/article/2166207-discriminating-algorithms-5-times-ai-showed-prejudice/.Google Scholar
Davis, D. (1992). Technological momentum, motor buses, and the persistence of Canada’s street railways to 1940. Material Culture Review, 36, 617.Google Scholar
Foucault, M. (1977). Discipline and Punish: The Birth of the Prison. London: Allen & Lane.Google Scholar
Friendly, M. (2008). A brief history of data visualization. In Chen, C.-H., Härdle, W. and Unwin, A. (eds), Handbook of Data Visualization. Berlin and Heidelberg, Germany: Springer, pp. 1556.Google Scholar
Gitelman, L. and Jackson, V. (2013). Introduction. In Gitelman, L. (ed.), Raw Data Is an Oxymoron. Cambridge, MA: MIT Press, pp. 114.Google Scholar
Haenlein, M. and Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 514.Google Scholar
Hamilton, M. (2019). The sexist algorithm. Behavioral Sciences & the Law, 37(2), 145157.Google Scholar
Harvey, L. J. and Myers, M. D. (1995). Scholarship and practice: The contribution of ethnographic research methods to bridging the gap. Information Technology & People, 8(3), 1327.Google Scholar
Hassan, N. R. and Mingers, J. (2018). Reinterpreting the Kuhnian paradigm in information systems. Journal of the Association for Information Systems, 19(7), 568599.Google Scholar
Hauskeller, C. (2020). Care ethics and care contexts: Contributions from feminist philosophy. East Asian Science Technology and Society – an International Journal, 14(1), 153161.Google Scholar
Heymann, M. and Nielsen, K. H. (2013). Hybridization of electric utility regimes: The case of wind power in Denmark, 1973–1990. RCC Perspectives, 2, 6974.Google Scholar
Hovorka, D. S. and Germonprez, M. (2010). Reflecting, tinkering, and tailoring: Implications for theories of information system design. In Isomäki, H. and Pekkola, S. (eds), Reframing Humans in Information Systems Development. London: Springer, pp. 135149.Google Scholar
Hughes, J., King, V., Rodden, T. and Andersen, H. (1994). Moving out from the control room: Ethnography in system design. Proceedings of the 1994 ACM conference on Computer Supported Cooperative Work, Chapel Hill, NC.Google Scholar
Hughes, T. P. (1994). Technological momentum. In Smith, M. R. and Marx, L. (eds), Does Technology Drive History? The Dilemma of Technological Determinism. London and Cambridge, MA: MIT Press, pp. 101113.Google Scholar
Hughes, T. P. (1969). Technological momentum in history: Hydrogenation in Germany 1898–1933. Past & Present, 44(1), 106132.Google Scholar
Ingold, T. (2015). Design anthropology is not and cannot be ethnography. Research Network for Design Anthropology, https://kadk.dk/sites/default/files/08_ingold_design_anthropology_network.doc.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), 872899.Google Scholar
Kim, P. T. (2016). Data-driven discrimination at work. William & Mary Law Review, 58(3), 857936.Google Scholar
Kimbell, L. (2012). Rethinking design thinking: Part II. Design and Culture, 4(2), 129148.Google Scholar
Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. London: Sage.Google Scholar
Lambrecht, A. and Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of stem career ads. Management Science, 65(7), 29662981.Google Scholar
Langley, A., Smallman, C., Tsoukas, H. and Van de Ven, A. H. (2013). Process studies of change in organization and management: Unveiling temporality, activity, and flow. Academy of Management Journal, 56(1), 113.Google Scholar
Ledford, H. (2019). Millions of black people affected by racial bias in health-care algorithms. Nature, 574(7780), 608610.Google Scholar
Lee, J. K. (2016). Reflections on ICT-enabled bright society research. Information Systems Research, 27(1), 15.Google Scholar
Leonardi, P. M. (2011). When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly, 35(1), 147167.Google Scholar
Li, M. and Sadler, J. (2011). Power and influence in negotiations. In Benoliel, M. (ed.), Negotiation Excellence: Successful Deal Making. Singapore: World Scientific Publishing Co., pp. 139160.Google Scholar
Liakos, K. G., Busato, P., Moshou, D., Pearson, S. and Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 129.Google Scholar
Lomborg, S. (2020). Disconnection is futile – theorizing resistance and human flourishing in an age of datafication. European Journal of Communication, 35(3), 301305.Google Scholar
Mayer-Schönberger, V. and Cukier, K. (2013). Big Data: A Revolution that Will Transform How We Live, Work, and Think. Boston, MA: Houghton Mifflin Harcourt.Google Scholar
Mehlenbacher, B. (2010). Instruction and Technology: Designs for Everyday Learning. Cambridge, MA: MIT Press.Google Scholar
Mejias, U. A. and Couldry, N. (2019). Datafication. Internet Policy Review, 8(4), 110.Google Scholar
Merry, S. E. (2016). The Seductions of Quantification: Measuring Human Rights, Gender Violence, and Sex Trafficking. Chicago, IL: University of Chicago Press.Google Scholar
Monteiro, E. and Parmiggiani, E. (2019). Synthetic knowing: The politics of the internet of things. MIS Quarterly, 43(1), 167184.Google Scholar
Myers, M. D. (1999). Investigating information systems with ethnographic research. Communications of the Association for Information Systems, 2(23), 119.Google Scholar
Myers, M. D. (1997). Critical ethnography in information systems. In Lee, A. S., Liebenau, J. and DeGross, J. I. (eds), Information Systems and Qualitative Research. New York, NY: Springer, pp. 276300.Google Scholar
Neyland, R. (2017). HPE: Stay on the cutting edge with software defined OT. CFOtech Australia, https://cfotech.com.au/story/hpe-stay-cutting-edge-software-defined-ot.Google Scholar
Norman, D. (2013). The Design of Everyday Things, revised and expanded edition. New York, NY: Basic Books.Google Scholar
Orlikowski, W. J. (2000). Using technology and constituting structures: A practice lens for studying technology in organizations. Organization Science, 11(4), 404428.Google Scholar
Owen, C. (2007). Design thinking: Notes on its nature and use. Design Research Quarterly, 2(1), 1627.Google Scholar
Razzouk, R. and Shute, V. (2012). What is design thinking and why is it important? Review of Educational Research, 82(3), 330348.Google Scholar
Reinsel, D., Gantz, J. and Rydning, J. (2018). Data age 2025: The digitization of the world from edge to core. IDC White Paper, pp. 1–29.Google Scholar
Scharre, P. (2018). Army of None: Autonomous Weapons and the Future of War. New York, NY: W. W. Norton.Google Scholar
Schön, D. A. (1984). Problems, frames and perspectives on designing. Design Studies, 5(3), 132136.Google Scholar
Sharda, R., Delen, D. and Turban, E. (2016). Business Intelligence, Analytics, and Data Science: A Managerial Perspective. Boston, MA: Pearson.Google Scholar
Thaler, R. H. and Sunstein, C. R. (2009). Nudge: Improving Decisions about Health, Wealth, and Happiness. New York, NY: Penguin.Google Scholar
Thompson, L. and Schonthal, D. (2020). The social psychology of design thinking. California Management Review, 62(2), 8499.Google Scholar
Tsoukas, H. (1989). The validity of idiographic research explanations. Academy of Management Review, 14(4), 551561.Google Scholar
Van de Ven, A. H. (1992). Suggestions for studying strategy process: A research note. Strategic Management Journal, 13(S1), 169188.Google Scholar
Van der Aalst, W. M. (2014). Data scientist: The engineer of the future. In Mertins, K., Bénaben, F., Poler, R. and Bourrières, J.-P. (eds), Enterprise Interoperability VI: Interoperability for Agility, Resilience and Plasticity of Collaborations. Cham, Switzerland: Springer, pp. 1326.Google Scholar
Verganti, R. (2017). Overcrowded: Designing Meaningful Products in a World Awash with Ideas. Cambridge, MA: MIT Press.Google Scholar
Viljoen, N. and Van Zyl, R. H. (2009). Design thinking – crossing disciplinary borders. Image & Text, 15, 6678.Google Scholar
Weber, R. (2004). The rhetoric of positivism versus interpretivism: A personal view. MIS Quarterly, 28(1), IIIXII.Google Scholar
Weiss, J. C., Natarajan, S., Peissig, P. L., McCarty, C. A. and Page, D. (2012). Machine learning for personalized medicine: Predicting primary myocardial infarction from electronic health records. AI Magazine, 33(4), 3345.Google Scholar
Williams, B. A., Brooks, C. F. and Shmargad, Y. (2018). How algorithms discriminate based on data they lack: Challenges, solutions, and policy implications. Journal of Information Policy, 8(1), 78115.Google Scholar
Williams, R. and Edge, D. (1996). The social shaping of technology. Research Policy, 25(6), 865899.Google Scholar
Wilson, H. J., Daugherty, P. R. and Morini-Bianzino, N. (2017). The jobs that artificial intelligence will create. MIT Sloan Management Review, 58(4), 1416.Google Scholar

References

Alavi, M. and Leidner, D. (2001). Knowledge management and knowledge management systems. MIS Quarterly, 25(1), 107136.Google Scholar
Allum, J. (2020). Introducing GOV.UK Accounts – Government Digital Service, GOV.UK, https://gds.blog.gov.uk/2020/09/22/introducing-gov-uk-accounts/.Google Scholar
Axelsson, A.-S. and Schroeder, R. (2009). Making it open and keeping it safe: E-enabled data-sharing in Sweden. Acta Sociologica, 52(3), 213226.Google Scholar
Baskerville, R. L., Myers, M. D. and Yoo, Y. (2020). Digital first: The ontological reversal and new challenges for information systems research. MIS Quarterly, 44(2), 509523.Google Scholar
Bellamy, C., Perri 6, and Raab, C. (2005). Joined-up government and privacy in the United Kingdom: Managing tensions between data protection and social policy: Part II. Public Administration, 83(2), 393415.Google Scholar
Benkler, Y. and Nissenbaum, H. (2006). Commons-based peer production and virtue. Journal of Political Philosophy, 14(4), 394419.Google Scholar
Blackler, F. (1995). Knowledge, knowledge work and organisations: An overview and interpretation. Organization Studies, 16(6), 10211045.Google Scholar
Busby, A., Mason, L., St Clair, C. and Williams, L. (2020). Motivations for and barriers to data sharing. Department for Digital, Culture, Media & Sport, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/895505/_Kantar_research_publication.pdf.Google Scholar
Cabinet Office. (2012). Government digital strategy. GOV.UK, www.gov.uk/government/publications/government-digital-strategy.Google Scholar
Cabinet Office and Central Data and Digital Office. (2021). Digital Economy Act information sharing powers and objectives register. GOV.UK, www.gov.uk/government/publications/register-of-information-sharing-agreements-under-chapters-1-2-3-and-4-of-part-5-of-the-digital-economy-act-2017.Google Scholar
Cabinet Office and Government Digital Service. (2016). Better use of data in government. GOV.UK, www.gov.uk/government/consultations/better-use-of-data-in-government.Google Scholar
Caldicott, D. F. (2016). Review of data security, consent and opt-outs. GOV.UK, www.gov.uk/government/publications/review-of-data-security-consent-and-opt-outs.Google Scholar
Cheliotis, G. (2009). From open source to open content: Organization, licensing and decision processes in open cultural production. Decision Support Systems, 47(3), 229244.Google Scholar
Clarke, R. (1988). Information technology and dataveillance. Communications of the ACM, 31(5), 498512.Google Scholar
Conservatives. (2009). Reversing the rise of the surveillance state: 11 measures to protect personal privacy and hold government to account. https://web.archive.org/web/20100328053242/http://www.conservatives.com/News/News_stories/2009/09/Reversing_the_rise_of_the_surveillance_state.aspx.Google Scholar
Davison, R. M. (2021). From ignorance to familiarity: Contextual knowledge and the field researcher. Information Systems Journal, 31(1), 16.Google Scholar
Dawes, S. S. (1996). Interagency information sharing: Expected benefits, manageable risks. Journal of Policy Analysis and Management, 15(3), 377394.Google Scholar
Department for Culture, Media and Sport. (2016). Digital Economy Bill: Explanatory notes. UK Parliament, https://publications.parliament.uk/pa/bills/lbill/2016-2017/0080/17080en.pdf.Google Scholar
Department for Digital, Culture, Media & Sport. (2021). Increasing access to data across the economy. GOV.UK, www.gov.uk/government/publications/increasing-access-to-data-held-across-the-economy.Google Scholar
Department for Digital, Culture, Media & Sport, Cabinet Office, Home Office, and UK Statistics Authority. (2018). Digital Economy Act 2017 Part 5: Codes of Practice. GOV.UK, www.gov.uk/government/publications/digital-economy-act-2017-part-5-codes-of-practice.Google Scholar
Douglass, K., Allard, S., Tenopir, C., Wu, L. and Frame, M. (2014). Managing scientific data as public assets: Data sharing practices and policies among full-time government employees. Journal of the Association for Information Science and Technology, 65(2), 251262.Google Scholar
Ducuing, C. (2020). Data as infrastructure? A study of data sharing legal regimes. Competition and Regulation in Network Industries, 21(2), 124142.Google Scholar
Duguid, P. (2005). ‘The art of knowing’: Social and tacit dimensions of knowledge and the limits of the community of practice. Information Society: An International Journal, 21(2), 109118.Google Scholar
Feller, J. and Fitzgerald, B. (2002). Understanding Open Source Software Development. London: Addison-Wesley.Google Scholar
Fusi, F. (2020). When local governments request access to data: Power and coordination mechanisms across stakeholders. Public Administration Review, 81(1), 2337.Google Scholar
Ghobadi, S. and Mathiassen, L. (2017). Risks to effective knowledge sharing in agile software teams: A model for assessing and mitigating risks. Information Systems Journal, 27(6), 699731.Google Scholar
Gil-Garcia, J. R., Chengalur-Smith, I. and Duchessi, P. (2007). Collaborative e-Government: Impediments and benefits of information-sharing projects in the public sector. European Journal of Information Systems, 16(2), 121133.Google Scholar
Goldman, R. and Gabriel, R. P. (2005). Innovation Happens Elsewhere: Open Source as Business Strategy. London and San Francisco, CA: Morgan Kaufmann.Google Scholar
González, A. G. (2006). Open science: Open source licences in scientific research. North Carolina Journal of Law & Technology, 7(Spring), 321366.Google Scholar
Graham, F. S., Gooden, S. T. and Martin, K. J. (2016). Navigating the transparency–privacy paradox in public sector data sharing. American Review of Public Administration, 46(5), 569591.Google Scholar
Gruss, P. (2003). Berlin declaration on open access to knowledge in the sciences and humanities. Conference on Open Access to Knowledge in the Sciences and Humanities, https://openaccess.mpg.de/Berlin-Declaration.Google Scholar
Harris, T. L. and Wyndham, J. M. (2015). Data rights and responsibilities: A human rights perspective on data sharing. Journal of Empirical Research on Human Research Ethics, 10(3), 334337.Google Scholar
Involve. (2018b). Conclusions of civil society and public sector policy discussions on data use in government. https://web.archive.org/web/20180220012818/http://datasharing.org.uk/wp-content/uploads/sites/2/2015/03/20150327_Conclusions_OPM_paper_Data_final.pdf.Google Scholar
Involve. (2016a). What data should government bodies be allowed to share about us? www.involve.org.uk/our-work/our-projects/practice/what-data-should-government-bodies-be-allowed-share-about-us.Google Scholar
Involve. (2016c). Report from the External Advisory Group for the Better Use of Data in Government Consultation. https://web.archive.org/web/20161219034648/https://datasharing.org.uk/wp-content/uploads/sites/2/2016/07/External-Advisory-Group-Report-1.pdf.Google Scholar
Jetzek, T., Avital, M. and Bjorn-Andersen, N. (2019). The sustainable value of open government data. Journal of the Association for Information Systems, 20(6), 702734.Google Scholar
Kalkman, S., Mostert, M., Udo-Beauvisage, N., van Delden, J. J. and van Thiel, G. J. (2019). Responsible data sharing in a big data-driven translational research platform: Lessons learned. BMC Medical Informatics and Decision Making, 19(1), 283.Google Scholar
Kaye, J., Whitley, E. A., Lund, D., Morrison, M., Teare, H. and Melham, K. (2015). Dynamic consent: A patient interface for twenty-first century research networks. European Journal of Human Genetics, 23(2), 141146.Google Scholar
Ljunberg, J. (2000). Open source movements as a model for organising. European Journal of Information Systems, 9(4), 208216.Google Scholar
Mayernik, M. S. (2017). Open data: Accountability and transparency. Big Data & Society, 4(2), Article 2053951717718853.Google Scholar
Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 1437.Google Scholar
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716-1–aac4716-8.Google Scholar
Parliament. (2017). Digital Economy Act 2017. GOV.UK, www.legislation.gov.uk/ukpga/2017/30/contents/enacted/data.htm.Google Scholar
Perri 6, , Raab, C. and Bellamy, C. (2005). Joined-up government and privacy in the United Kingdom: Managing tensions between data protection and social policy: Part I. Public Administration, 83(1), 111133.Google Scholar
Phippen, A., Raza, A., Butel, L. and Southern, R. (2011). Impacting methodological innovation in a local government context – data sharing rewards and barriers. Methodological Innovations Online, 6(1), 5872.Google Scholar
Public Accounts Committee. (2019). Challenges in using data across government. GOV.UK, https://publications.parliament.uk/pa/cm201719/cmselect/cmpubacc/2492/2492.pdf.Google Scholar
Schultze, U. and Leidner, D. E. (2002). Studying knowledge management in information systems research: Discourses and theoretical assumptions. MIS Quarterly, 26(3), 213242.Google Scholar
Sexton, A., Shepherd, E., Duke-Williams, O. and Eveleigh, A. (2017). A balance of trust in the use of government administrative data. Archival Science, 17(4), 305330.Google Scholar
Stalla-Bourdillon, S., Carmichael, L. and Wintour, A. (2021). Fostering trustworthy data sharing: Establishing data foundations in practice. Data & Policy, 3, e4.Google Scholar
Stalla-Bourdillon, S., Thuermer, G., Walker, J., Carmichael, L. and Simperl, E. (2020). Data protection by design: Building the foundations of trustworthy data sharing, Data & Policy, 2, e4.Google Scholar
Stamper, R. (1985). Management epistemology: Garbage in, garbage out (and what about deontology and axiology). In Methlie, L. and Sprague, R. (eds), Knowledge Representation for Decision Support Systems. Amsterdam, the Netherlands: Springer, pp. 5577.Google Scholar
Stewart, K. J., Ammeter, A. P. and Maruping, L. M. (2006). Impacts of license choice and organizational sponsorship on user interest and development activity in open source software projects. Information Systems Research, 17(2), 126144.Google Scholar
Susha, I., Rukanova, B., Ramon Gil-Garcia, J., Tan, Y.-H. and Hernandez, M. G. (2019). Identifying mechanisms for achieving voluntary data sharing in cross-sector partnerships for public good. Presented at the ACM International Conference Proceeding Series, Dubai, United Arab Emirates.Google Scholar
Tsiavos, P. and Whitley, E. A. (2010). Open sourcing regulation: The development of the creative commons licences as a form of commons based peer production. In Bourcier, D., Casanovas, P., Rosnay, M. D. and Maracke, C. (eds), Intelligent Multimedia: Managing Creative Works in a Digital World. Florence, Italy: European Press Academic Publishing, pp. 89114.Google Scholar
Turnbull, B. H. and Marks, D. S. (2000). Technical protection measures: The intersection of technology, law and commercial licenses. European Intellectual Property Review, 22(5), 198213.Google Scholar
Verhulst, S. G., Young, A., Zahuranec, A. J., Aaronson, S. A., Calderon, A. and Gee, M. (2020). The emergence of a third wave of open data. Open Data Policy Lab, https://opendatapolicylab.org/images/odpl/third-wave-of-opendata.pdf.Google Scholar
Vezyridis, P. and Timmons, S. (2017). Understanding the care.data conundrum: New information flows for economic growth. Big Data & Society, 4(1), Article 2053951716688490.Google Scholar
von Krogh, G. and Spaeth, S. (2007). The open source software phenomenon: Characteristics that promote research. Journal of Strategic Information Systems, 16(3), 236253.Google Scholar
Wang, F. (2018). Understanding the dynamic mechanism of interagency government data sharing. Government Information Quarterly, 35(4), 536546.Google Scholar
Weber, S. (2004). The Success of Open Source. Cambridge, MA: Harvard University Press.Google Scholar
Welch, E. W., Feeney, M. K. and Park, C. H. (2016). Determinants of data sharing in U.S. city governments. Government Information Quarterly, 33(3), 393403.Google Scholar
Whitley, E. A. (2016). Can data-sharing improve public services? Lessons for Parliament. LSE blog, http://blogs.lse.ac.uk/politicsandpolicy/can-data-sharing-improve-public-services-some-lessons-for-parliament/.Google Scholar
Whitley, E. A. (2014a). REF impact case study: Scrapping costly and controversial proposals for identity cards. London School of Economics and Political Science, www.lse.ac.uk/Research/research-impact-case-studies/scrapping-costly-controversial-proposals-identity-cards.Google Scholar
Whitley, E. A. (2014b). The Privacy Impact Assessment undertaken for care.data isn’t clear on what opting out would mean for our data. LSE blog, http://blogs.lse.ac.uk/politicsandpolicy/the-impact-of-privacy-impact-assessments/.Google Scholar
Whitley, E. A. (2009). Informational privacy, consent and the ‘control’ of personal data. Information Security Technical Report, 14(3), 154159.Google Scholar
Whitley, E. A., Martin, A. K. and Hosein, G. (2014). From surveillance-by-design to privacy-by-design: Evolving identity policy in the UK. In Boersma, K., Brakel, R., Fonio, C. and Wagenaar, P. (eds), Histories of State Surveillance in Europe and Beyond. London: Routledge, pp. 205219.Google Scholar
Zhang, J., Dawes, S. S. and Sarkis, J. (2005). Exploring stakeholders’ expectations of the benefits and barriers of e‐government knowledge sharing. Journal of Enterprise Information Management, 18(5), 548567.Google Scholar

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