Everything Counts in Large Amounts: A Critical Realist Case Study on Data-Based Production

Abstract

Contemporary digital ecosystems produce vast amounts of data every day. The data are often no more than microscopic log entries generated by the elements of an information infrastructure or system. Although such records may represent a variety of things outside the system, their powers go beyond the capacity to carry semantic content. In this article, we harness critical realism to explain how such data come to matter in specific business operations. We analyse the production of an advertising audience from data tokens extracted from a telecommunications network. The research is based on an intensive case study of a mobile network operator that tries to turn its subscribers into an advertising audience. We identify three mechanisms that shape data-based production and three properties that characterize the underlying pool of data. The findings advance the understanding of many organizational settings that are centred on data processing.

Keywords

audience critical realism data-driven information actualization measurement mechanism

Get full access to this article

View all access options for this article.

References

Aaltonen

(2012). The Beauty and Perils of Metrics, Mercury Magazine 1(3): 56–59.

Aaltonen

, and Kallinikos

(2013). Coordination and Learning in Wikipedia: Revisiting the dynamics of exploitation and exploration, Research in the Sociology of Organizations 37: 161–192.

Archer

M.S.

(1982). Morphogenesis Versus Structuration: On combining structure and action, The British Journal of Sociology 33(4): 455–483.

Archer

M.S.

(1998). Introduction: Realism in the social sciences, in Archer

, Bhaskar

, Collier

, Lawson

, and Norrie

(eds.) Critical Realism: Essential Readings, New York: Routledge, pp. 189–205.

Baskerville

R.L.

, and Myers

M.D.

(2002). Information Systems as a Reference Discipline, MIS Quarterly 26(1): 1–14.

Bateson

(2000). Steps to an Ecology of Mind, Chicago: Chicago University Press.

Becker

H.S.

(2007). Writing for Social Scientists: How to Start and Finish Your Thesis, Book, or Article, Chicago: University of Chicago Press.

Benbasat

, and Zmud

R.W.

(2003). The Identity Crisis within the IS Discipline: Defining and communicating the discipline's core properties, MIS Quarterly 27(2): 183–194.

Benkler

(2006). The Wealth of Networks: How Social Production Transforms Markets and Freedom, New Haven: Yale University Press.

10.

Bermejo

(2009). Audience Manufacture in Historical Perspective: From broadcasting to Google, New Media & Society 11(1/2): 133–154.

11.

Bhaskar

(1998). Philosophy and Scientific Realism, in Archer

, Bhaskar

, Collier

, Lawson

, and Norrie

(eds.) Critical Realism: Essential Readings, New York: Routledge.

12.

Bhaskar

(2008). A Realist Theory of Science, New York: Routledge.

13.

Boyd

, and Crawford

(2012). Critical Questions for Big Data. Provocations for a cultural, technological, and scholarly phenomenon, Information, Communication & Society 15(5): 662–679.

14.

Bratich

J.Z.

(2005). Amassing the Multitude: Revisiting early audience studies, Communication Theory 15(3): 242–265.

15.

Brewer

J.D.

(2000). Ethnography, Buckingham: Open University Press.

16.

Bygstad

(2010). Generative Mechanisms for Innovation in Information Infrastructures, Information and Organization 20(3-4): 156–168.

17.

Carr

N.G.

(2008). The Big Switch, New York: W. W. Norton & Company.

18.

Cohen

C.M.

(2012). Aristotle's Metaphysics, in Zalta

E.N.

(ed.) The Stanford Encyclopedia of Philosophy (Summer 2012 Edition) [WWW document] http://plato.stanford.edu/archives/sum2012/entries/aristotle-metaphysics/ (accessed 11 December 2013).

19.

Crotty

(1998). The Foundations of Social Research: Meaning and Perspective in the Research Process, London: SAGE Publications.

20.

Day

G.S.

(2003). Creating a Superior Customer-Relating Capability, MIT Sloan Management Review 44(3): 77–82.

21.

Dourish

(2001). Where the Action Is, London: The MIT Press.

22.

Easton

(2010). Critical Realism in Case Study Research, Industrial Marketing Management 39(1): 118–128.

23.

Ekbia

H.R.

(2009). Digital Artifacts as Quasi-Objects: Qualification, mediation, and materiality, Journal of the American Society for Information Science and Technology 60(12): 2554–2566.

24.

Elder-Vass

(2005). Emergence and the Realist Account of Cause, Journal of Critical Realism 4(2): 315–338.

25.

Elder-Vass

(2007). For Emergence: Refining Archer's account of social structure, Journal for the Theory of Social Behaviour 37(1): 25–44.

26.

Ettema

J.S.

, and Whitney

D.C.

(1994). The Money Arrow: An introduction to audiencemaking, in audiencemaking: How the media create the audience, in Ettema

J.S.

, and Whitney

D.C.

(eds.) Sage Annual Reviews of Communication Research, London: SAGE Publications, pp. 1–18.

27.

Faulkner

, and Runde

(2009). On the Identity of Technological Objects and User Innovations in Function, Academy of Management Review 34(3): 442–462.

28.

Faulkner

, and Runde

(2010). The Social, the Material, and the Ontology of Non-Material Objects, in Judge Us Seminar, University of Cambridge, UK, (Unpublished manuscript).

29.

Faulkner

, and Runde

(2013). Technological Objects, Social Positions, and the Transformational Model of Social Activity, MIS Quarterly 37(3): 803–818.

30.

Flick

(2004). Triangulation in Qualitative Research, in Flick

, von Kardoff

, and Steinke

(eds.) A Companion to Qualitative Research, London: SAGE Publications, pp. 178–183.

31.

Kallinikos

(1999). Computer-Based Technology and the Constitution of Work: A study on the cognitive foundations of work, Accounting, Management & Information Technology 9(4): 261–291.

32.

Kallinikos

(2006). The Consequences of Information: Institutional Implications of Technological Change, Northampton, MA: Edward Elgar Publishing.

33.

Kallinikos

(2012). The Allure of Big Data, ParisTech REVIEW 16.11.2012 [www document] http://www.paristechreview.com/2012/11/16/allure-big-data (accessed 11 December 2013).

34.

Kallinikos

, Aaltonen

, and Marton

(2013). The Ambivalent Ontology of Digital Artifacts, MIS Quarterly 37(2): 357–370.

35.

Langley

(1999). Strategies for Theorizing from Process Data, Academy of Management Review 24(4): 691–710.

36.

Latour

(1999). Pandora's Hope: Essays on the Reality of Science Studies, Cambridge, MA: Harvard University Press.

37.

Lee

A.S.

(2010). Retrospect and Prospect: Information systems research in the last and next twenty-five years, Journal of Information Technology 25(4): 336–348.

38.

Leonardi

P.M.

(2010). Digital Materiality? How artifacts without matter, matter, First Monday (15:6) [www document] http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/viewArticle/3036/2567, (accessed 11 December 2013).

39.

Leonardi

P.M.

, Nardi

B.A.

, and Kallinikos

(2012). Materiality and Organizing: Social Interaction in a Technological World, Oxford: Oxford University Press.

40.

Lyytinen

, and Yoo

(2002). The Next Wave of Nomadic Computing, Information Systems Research 13(4): 377–388.

41.

McKinney

E.H.

, and Yoos

C.J.

(2010). Information about Information: A taxonomy of views, MIS Quarterly 34(2): 329–344.

42.

Mingers

(2004). Realizing Information Systems: Critical realism as an underpinning philosophy for information systems, Information and Organization 14(2): 87–103.

43.

Morley

(2006). Unanswered Questions in Audience Research, The Communication Review 9(2): 101–121.

44.

Mutch

(2010). Technology, Organization and Structure - A morphogenetic approach, Organization Science 21(2): 507–520.

45.

Napoli

P.M.

(2003). Audience Economics: Media Institutions and the Audience Marketplace, New York: Columbia University Press.

46.

Orlikowski

W.J.

(2007). Sociomaterial Practices: Exploring technology at work, Organization Studies 28(9): 1435–1448.

47.

Orlikowski

W.J.

, and Barley

S.R.

(2001). Technology and Institutions: What can research on information technology and research on organizations learn from each other? MIS Quarterly 25(2): 145–165.

48.

Redman

T.C.

(2008). Data Driven, Boston: Harvard Business Press.

49.

Runde

(1998). Assessing Causal Economic Explanations, Oxford Economic Papers 50(2): 151–172.

50.

Runde

, Jones

, Munir

, and Nikolychuk

(2009). On Technological Objects and the Adoption of Technological Product Innovations: Rules, routines and the transition from analogue photography to digital imaging, Cambridge Journal of Economics 33(1): 1–24.

51.

Russell

(1994). History of Western Philosophy, London: Routledge.

52.

Sayer

A.R.

(2000). Realism and Social Science, London: SAGE.

53.

Sismondo

(1993). Some Social Constructions, Social Studies of Science 23(3): 515–553.

54.

Simon

H.A.

(1996). The Sciences of the Artificial, Cambridge, MA: The MIT Press.

55.

Walsh

(1998). Doing Ethnography, in Seale

(ed.) Researching Society and Culture, London: SAGE Publications, pp. 217–232.

56.

Weick

K.E.

(1995). What Theory Is Not, Theorizing Is, Administrative Science Quarterly 40(3): 385–390.

57.

Wynn

, and Williams

C.K.

(2012). Principles for Conducting Critical Realist Case Study Research in Information Systems, MIS Quarterly 36(3): 787–810.

58.

Yin

R.K.

(2003). Case Study Research: Design and Methods, London: SAGE.

59.

Yoo

(2010). Computing in Everyday Life: A call for research on experiential computing, MIS Quarterly 34(2): 213–231.

60.

Yoo

, Boland

R.J.

Jr. , Lyytinen

, and Majchrzak

(2012). Organizing for Innovation in the Digitized World, Organization Science 23(5): 1398–1408.

61.

Yoo

, Henfridsson

, and Lyytinen

(2010). The New Organizing Logic of Digital Innovation: An agenda for information systems research, Information Systems Research 21(4): 724–735.

62.

Zuboff

(1988). In the Age of The Smart Machine: The Future of Work and Power, New York: Basic Books.