In the Land of the Blind, the One-Eyed Man Is King: Knowledge Brokerage in the Age of Learning Algorithms

Published Online:https://doi.org/10.1287/orsc.2021.1544

References

  • Ajunwa I (2020) The “black box” at work. Big Data Soc. 7(2):1–6.CrossrefGoogle Scholar
  • Ancona DG, Caldwell DF (1992) Bridging the boundary: External activity and performance in organizational teams. Admin. Sci. Q. 37(4):634–665.CrossrefGoogle Scholar
  • Anthony C (2021) When knowledge work and analytical technologies collide: The practices and consequences of black boxing algorithmic technologies. Admin. Sci. Quart., ePub ahead of print June 4, https://doi.org/10.1177/00018392211016755.CrossrefGoogle Scholar
  • Bader V, Kaiser S (2019) Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence. Organ. 26(5):655–672.Google Scholar
  • Bailey DE, Barley SR (2020) Beyond design and use: How scholars should study intelligent technologies. Inform. Organ. 30(2):100286.CrossrefGoogle Scholar
  • Balzer D (2014) Curationism: How Curating Took over the Art World and Everything Else (Coach House Books, Toronto).Google Scholar
  • Barley SR (1986) Technology as an occasion for structuring: Evidence from observations of CT scanners and the social order of radiology departments. Admin. Sci. Quart. 31(1):78–108.CrossrefGoogle Scholar
  • Barley SR (1996) Technicians in the workplace: Ethnographic evidence for bringing work into organizational studies. Admin. Sci. Quart. 41(3):404–441.CrossrefGoogle Scholar
  • Barley SR, Bechky BA (1994) In the backrooms of science: The work of technicians in science labs. Work Occupations 21(1):85–126.CrossrefGoogle Scholar
  • Barredo AA, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, Garcia S, et al. (2020) Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inform. Fusion 58:82–115.CrossrefGoogle Scholar
  • Boari C, Riboldazzi F (2014) How knowledge brokers emerge and evolve: The role of actors’ behaviour. Res. Policy 43(4):683–695.CrossrefGoogle Scholar
  • Bolin G, Andersson Schwarz J (2015) Heuristics of the algorithm: Big Data, user interpretation and institutional translation. Big Data Soc. 2(2):1–12.CrossrefGoogle Scholar
  • Brayne S (2020) Predict and Surveil: Data, Discretion, and the Future of Policing (Oxford University Press, Oxford, UK).CrossrefGoogle Scholar
  • Brown JS, Duguid P (1991) Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation. Organ. Sci. 2(1):40–57.LinkGoogle Scholar
  • Brown JS, Duguid P (1998) Organizing knowledge. California Management Rev. 40(3):90–111.CrossrefGoogle Scholar
  • Brown JS, Duguid P (2001) Knowledge and organization: A social practice perspective. Organ. Sci. 12(2):198–213.LinkGoogle Scholar
  • Brynjolfsson E, McAfee A (2017) The business of artificial intelligence. Harvard Bus. Rev. (July 18), https://hbr.org/2017/07/the-business-of-artificial-intelligence.Google Scholar
  • Burgess N, Currie G (2013) The knowledge brokering role of the hybrid middle level manager: The case of healthcare. British J. Management 24(S1):S132–S142.CrossrefGoogle Scholar
  • Burrell J (2016) How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data Soc. 3(1):1–12.CrossrefGoogle Scholar
  • Burt RS (1992) Structural Holes: The Social Structure of Competition (Harvard University Press, Cambridge, MA).CrossrefGoogle Scholar
  • Callon M (1984) Some elements of a sociology of translation: Domestication of the scallops and the fishermen of St Brieuc Bay. Sociol. Rev. 32:196–233.CrossrefGoogle Scholar
  • Carah N (2014) Curators of databases: Circulating images, managing attention and making value on social media. Media Internat. Australia 150(1):137–142.CrossrefGoogle Scholar
  • Carlile PR (2004) Transferring, translating, and transforming: An integrative framework for managing knowledge across boundaries. Organ. Sci. 15(5):555–568.LinkGoogle Scholar
  • Chiambaretto P, Massé D, Mirc N (2019) “All for one and one for all?” - Knowledge broker roles in managing tensions of internal coopetition: The Ubisoft case. Res. Policy 48(3):584–600.CrossrefGoogle Scholar
  • Christin A (2020) The ethnographer and the algorithm: Beyond the black box. Theor. Soc. 49:897–918.CrossrefGoogle Scholar
  • Christin A, Brayne S (2020) Technologies of crime prediction: The reception of algorithms in policing and criminal courts. Soc. Problems 68(3):608–624.Google Scholar
  • Ciuk S, James P (2015) Interlingual translation and the transfer of value-infused practices: An in-depth qualitative exploration. Management Learning 46(5):565–581.CrossrefGoogle Scholar
  • Cook SDN, Brown JS (1999) Bridging epistemologies: The generative dance between organizational knowledge and organizational knowing. Organ. Sci. 10(4):381–400.LinkGoogle Scholar
  • Czarniawska B, Joerges B (1996) Travels of ideas. Czarniawska B, Sevón G, eds. Translating Organizational Change (De Gruyter, New York), 13–48.CrossrefGoogle Scholar
  • Czarniawska B, Sevón G (2005) Translation is a vehicle, imitation its motor, and fashion sits at the wheel. Czarniawska B, Sevón G, eds. Global Ideas: How Ideas, Objects and Practices Travel in the Global Economy (Liber and Copenhagen Business School Press, Malmö, Sweden), 7–12.Google Scholar
  • Davenport T (2018) The AI Advantage: How to Put the Artificial Intelligence Revolution to Work (MIT Press, Cambridge, MA).CrossrefGoogle Scholar
  • DiMaggio P (1993) Nadel's paradox revisited: Relational and cultural aspects of organizational structures. Nohria N, Eccles R, eds. Networks and Organization (Harvard Business School Press, Boston).Google Scholar
  • Doorewaard H, van Bijsterveld M (2001) The osmosis of ideas: An analysis of the integrated approach to IT management from a translation theory perspective. Organ. 8(1):55–76.Google Scholar
  • Doran D, Schulz S, Besold TR (2017) What does explainable AI really mean? A new conceptualization of perspectives. Preprint, submitted October 2, https://arxiv.org/abs/1710.00794.Google Scholar
  • Dougherty D (1992) Interpretive barriers to successful product innovation in large firms. Organ. Sci. 3(2):179–202.LinkGoogle Scholar
  • Durán JM, Jongsma KR (2021) Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. J. Medical Ethics 47(5):329–335.Google Scholar
  • Edacott CG, Leonardi PM (2020) Keep them apart or join them together? How identification processes shape orientations to network brokerage. Comm. Res., ePub ahead of print August 14, https://doi.org/10.1177/0093650220947316.Google Scholar
  • Evers HD, Menkhoff T (2004) Expert knowledge and the role of consultants in an emerging knowledge‐based economy. Human Systems Management 23(2):123–135.CrossrefGoogle Scholar
  • Faraj S, Pachidi S, Sayegh K (2018) Working and organizing in the age of the learning algorithm. Inform. Organ. 28(1):62–70.CrossrefGoogle Scholar
  • Faraj S, von Krogh G, Monteiro E, Lakhani KR (2016) Online community as space for knowledge flows. Inform. Systems. Res. 27(4):668–684.LinkGoogle Scholar
  • Fernandez-Mateo I (2007) Who pays the price of brokerage? Transferring constraints through price setting in the staffing sector. Amer. Sociol. Rev. 72(2):291–317.CrossrefGoogle Scholar
  • Fernandez RM, Gould RV (1994) A dilemma of state power: Brokerage and influence in the National Health Policy domain. Amer. J. Sociol. 99(6):1455–1491.CrossrefGoogle Scholar
  • Fisher G, Aguinis H (2017) Using theory elaboration to make theoretical advancements. Organ. Res. Methods 20(3):438–464.CrossrefGoogle Scholar
  • Fleming L, Waguespack DM (2007) Brokerage, boundary spanning, and leadership in open innovation communities. Organ. Sci. 18(2):165–180.LinkGoogle Scholar
  • Forsythe DE (1993) The construction of work in artificial intelligence. Sci. Tech. Human Values 18(4):460–479.CrossrefGoogle Scholar
  • Furusten S (1999) Popular Management Books: How They Are Made and What They Mean for Organizations (Routledge, London).CrossrefGoogle Scholar
  • Gal U, Jensen TB, Stein MK (2020) Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics. Inform. Organ. 30(2):100301.CrossrefGoogle Scholar
  • Glikson E, Woolley AW (2020) Human trust in artificial intelligence: Review of empirical research. Acad. Management Ann. 14(2):627–660.CrossrefGoogle Scholar
  • Gould RV, Fernandez RM (1989) Structures of mediation: A formal approach to brokerage in transaction networks. Sociol. Methodology 19:89–126.CrossrefGoogle Scholar
  • Grady R, Pratt J (2000) The UK technology transfer system: Calls for stronger links between higher education and industry. J. Tech. Transfer 25(2):205–211.CrossrefGoogle Scholar
  • Gray ML, Suri S (2019) Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass (HMH Books, Boston).Google Scholar
  • Haas A (2015) Crowding at the frontier: Boundary spanners, gatekeepers and knowledge brokers. J. Knowledge Management 19(5):1029–1047.CrossrefGoogle Scholar
  • Hargadon A, Sutton RI (1997) Technology brokering and innovation in a product development firm. Admin. Sci. Quart. 42(4):716–749.CrossrefGoogle Scholar
  • Heaphy ED (2013) Repairing breaches with rules: Maintaining institutions in the face of everyday disruptions. Organ. Sci. 24(5):1291–1315.LinkGoogle Scholar
  • Heimer CA, Stevens ML (1997) Caring for the organization: Social workers as frontline risk managers in neonatal intensive care units. Work Occupations 24(2):133–163.CrossrefGoogle Scholar
  • Henke N, Levine K, McInerney P (2018) You don’t have to be a data scientist to fill this must-have analytics role. Harvard Bus. Rev. (February 5), https://hbr.org/2018/02/you-dont-have-to-be-a-data-scientist-to-fill-this-must-have-analytics-role.Google Scholar
  • Heusinkveld S, Benders J (2005) Contested commodification: Consultancies and their struggle with new concept development. Human Relations 58(3):283–310.CrossrefGoogle Scholar
  • Huising R, Silbey SS (2011) Governing the gap: Forging safe science through relational regulation. Regulation Governance 5(1):14–42.CrossrefGoogle Scholar
  • Huysman M (2020) Information systems research on artificial intelligence and work: A commentary on “Robo-Apocalypse cancelled? Reframing the automation and future of work debate”. J. Inform. Tech. 35(4):307–309.CrossrefGoogle Scholar
  • Introna LD (2016) Algorithms, governance, and governmentality: On governing academic writing. Sci. Tech. Human Values 41(1):17–49.CrossrefGoogle Scholar
  • Johri A (2008) Boundary spanning knowledge broker: An emerging role in global engineering firms. Proc. 38th Annual Frontiers Ed. Conf. (IEEE, Piscataway, NJ), 7–12.Google Scholar
  • Karasti H, Baker KS, Halkola E (2006) Enriching the notion of data curation in e-science: Data managing and information infrastructuring in the long term ecological research (LTER) network. Comput. Supported Cooperative Work 15(4):321–358.CrossrefGoogle Scholar
  • Kellogg KC (2014) Brokerage professions and implementing reform in an age of experts. Amer. Sociol. Rev. 79(5):912–941.CrossrefGoogle Scholar
  • Kellogg KC, Valentine M, Christin A (2020) Algorithms at work: The new contested terrain of control. Acad. Management Ann. 14(1):366–410.CrossrefGoogle Scholar
  • Kim B, Koopmanschap I, Mehrizi MHR, Huysman M, Ranschaert E (2021) How does the radiology community discuss the benefits and limitations of artificial intelligence for their work? A systematic discourse analysis. Eur. J. Radiology 136:109566.CrossrefGoogle Scholar
  • Kirsch A (2017) Explain to whom? Putting the user in the center of explainable AI. Proc. First Internat. Workshop Comprehensibility Explanation AI ML, Bari, Italy, November 16–17.Google Scholar
  • Kissling-Naf I (2009) From a learned society to a 21st-century broker: The Swiss Academy of Sciences as a partner in the dialogue with society. Internat. J. Tech. Management 46(1–2):120–131.Google Scholar
  • Langley A (1999) Strategies for theorizing from process data. Acad. Management Rev. 24(4):691–710.CrossrefGoogle Scholar
  • Langley A, Lindberg K, Mørk BE, Nicolini D, Raviola E, Walter L (2019) Boundary work among groups, occupations, and organizations: From cartography to process. Acad. Management Ann. 13(2):704–736.CrossrefGoogle Scholar
  • Latour B (1986) The powers of associations. Law J, ed. Power, Action and Belief: A New Sociology of Knowledge? (Routledge, London), 264–280.Google Scholar
  • Latour B (2005) Reassembling the Social: An Introduction to Actor-Network-Theory (Oxford University Press, Oxford, UK).CrossrefGoogle Scholar
  • Lave J (1988) Cognition in Practice (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Lave J, Wenger E (1991) Situated Learning: Legitimate Peripheral Participation (Cambridge University Press, New York).CrossrefGoogle Scholar
  • Law J (2002) Aircraft Stories: Decentering the Object in Technoscience (Duke University Press, Durham, NC).Google Scholar
  • Lebovitz S, Levina N, Lifshitz-Assaf H (2019) Doubting the diagnosis: How artificial intelligence increases ambiguity during professional decision making. Working paper, New York University, New York.Google Scholar
  • Lebovitz S, Levina N, Lifshitz-Assaf H (2021) Is AI ground truth really “true”? The dangers of training and evaluating AI tools based on experts’ know-what. MIS Quart. Forthcoming.CrossrefGoogle Scholar
  • Leonardi PM, Bailey DE (2017) Recognizing and selling good ideas: Network articulation and the making of an offshore innovation hub. Acad. Management Discoveries 3(2):116–144.CrossrefGoogle Scholar
  • Levina N, Vaast E (2005) The emergency of boundary spanning competence in practice: Implications for implementation and use of information systems. MIS Quart. 29(2):335–363.CrossrefGoogle Scholar
  • Lingo EL, O’Mahony S (2010) Nexus work: Brokerage on creative projects. Admin. Sci. Quart. 55(1):47–81.CrossrefGoogle Scholar
  • Lipton ZC (2018) The mythos of model interpretability. Queue 16(3):31–57.CrossrefGoogle Scholar
  • Meyer M (2010) The rise of the knowledge broker. Sci. Comm. 32(1):118–127.CrossrefGoogle Scholar
  • Michalski RS, Carbonell JG, Mitchell TM (2013) Machine Learning: An Artificial Intelligence Approach (Springer, Cham, Switzerland).Google Scholar
  • Miller T (2019) Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267:1–38.CrossrefGoogle Scholar
  • Mittelstadt B, Russell C, Wachter S (2019) Explaining explanations in AI. Proc. Conf. Fairness Accountability Transparency (ACM, New York), 279–288.Google Scholar
  • Mueller F, Whittle A (2011) Translating management ideas: A discursive devices analysis. Organ. Stud. 32(2):187–210.CrossrefGoogle Scholar
  • Muller MJ, Milien DR, Feinberg J (2009) Information curators in an enterprise file-sharing service. Proc. 11th Eur. Conf. Comput. Supported Cooperative Work (Springer, London), 403–410.Google Scholar
  • Nelson R, Winter S (1982) An Evolutionary Theory of Economic Change (Harvard University Press, Cambridge, MA).Google Scholar
  • Nielsen JA, Mathiassen L, Newell S (2014) Theorization and translation in information technology institutionalization: Evidence from Danish home care. MIS Quart. 38(1):165–186.CrossrefGoogle Scholar
  • Nonaka I (1994) A dynamic theory of organizational knowledge creation. Organ. Sci. 5(1):14–37.LinkGoogle Scholar
  • Nonaka I, von Krogh G (2009) Perspective—Tacit knowledge and knowledge conversion: Controversy and advancement in organizational knowledge creation theory. Organ. Sci. 20(3):635–652.LinkGoogle Scholar
  • Obstfeld D (2005) Social networks, the tertius iungens and orientation involvement in innovation. Admin. Sci. Quart. 50(1):100–130.CrossrefGoogle Scholar
  • Obstfeld DS, Borgatti P, Davis JP (2014) Brokerage as a process: Decoupling third party action from social network structure. Brass DJ, Labianca G, Mehra A, Halgin DS, Borgatti SP, eds. Research in the Sociology of Organizations (Emerald Books, Bingley, UK), 135–159.Google Scholar
  • O’Mahony S, Bechky BA (2008) Boundary organizations: Enabling collaboration among unexpected allies. Admin. Sci. Quart. 53(3):422–459.CrossrefGoogle Scholar
  • Orlikowski WJ (2002) Knowing in practice: Enacting a collective capability in distributed organizing. Organ. Sci. 13(3):249–273.LinkGoogle Scholar
  • Orr J (1996) Talking About Machines: An Ethnography of a Modern Job (ILP Press, Ithaca, NY).Google Scholar
  • Østerlund C, Carlile PR (2005) Relations in practice: Sorting through practice theories on knowledge sharing in complex organizations. Inform. Soc. 21(2):91–107.CrossrefGoogle Scholar
  • Pachidi S, Berends H, Faraj S, Huysman M (2021) Make way for the algorithms: Symbolic actions and change in a regime of knowing. Organ. Sci. 32(1):18–41.LinkGoogle Scholar
  • Pasquale F (2015) The Black Box Society: The Secret Algorithms That Control Money and Information (Harvard University Press, Cambridge, MA).CrossrefGoogle Scholar
  • Paul S, Whittam G (2010) Business angel syndicates: An exploratory study of gatekeepers. Venture Capital 12(3):241–256.CrossrefGoogle Scholar
  • Pawlowski SD, Robey D (2004) Bridging user organizations: Knowledge brokering and the work of information technology professionals. MIS Quart. 28(4):645–672.CrossrefGoogle Scholar
  • Preece A, Harborne D, Braines D, Tomsett R, Chakraborty S (2018) Stakeholders in explainable AI. Preprint, submitted September 29, https://arxiv.org/abs/1810.00184.Google Scholar
  • Reagans R, McEvily B (2003) Network structure and knowledge transfer: The effects of cohesion and range. Admin. Sci. Quart. 48(2):240–267.CrossrefGoogle Scholar
  • Rezazade Mehrizi MH, van Ooijen P, Homan M (2020) Applications of artificial intelligence (AI) in diagnostic radiology: A technography study. Eur. Radiology 31:1805–1811.CrossrefGoogle Scholar
  • Robbins S (2019) A misdirected principle with a catch: Explicability for AI. Minds Machines 29:495–514.CrossrefGoogle Scholar
  • Røvik KA (2016) Knowledge transfer as translation: Review and elements of an instrumental theory. Internat. J. Management Rev. 18(3):290–310.CrossrefGoogle Scholar
  • Sachs SE (2020) The algorithm at work? Explanation and repair in the enactment of similarity in art data. Inform. Comm. Soc. 23(11):1689–1705.CrossrefGoogle Scholar
  • Safadi H, Johnson L, Faraj S (2021) Who contributes knowledge? Core-periphery tension in online innovation communities. Organ. Sci. 32(3):752–775.LinkGoogle Scholar
  • Saka A (2004) The cross-national diffusion of work systems: Translation of Japanese operations in the UK. Organ. Stud. 25(2):209–228.CrossrefGoogle Scholar
  • Shestakofsky B, Kelkar S (2020) Making platforms work: Relationship labor and the management of publics. Theory Soc. 49(5):863–896.CrossrefGoogle Scholar
  • Soundarajan V, Khan Z, Tarba SY (2018) Beyond brokering: Sourcing agents, boundary work and working conditions in global supply chains. Human Relations 71(4):481–509.CrossrefGoogle Scholar
  • Stovel K, Shaw L (2012) Brokerage. Annual Rev. Sociol. 38(1):139–158.CrossrefGoogle Scholar
  • Sturdy A, Wright C (2011) The active client: The boundary-spanning roles of internal consultants as gatekeepers, brokers and partners of their external counterparts. Management Learning 42(5):485–503.CrossrefGoogle Scholar
  • Strauss A, Corbin J (1990) Basics of Qualitative Research (Sage Publications, Thousand Oaks, CA).Google Scholar
  • Suddaby R, Greenwood R (2001) Colonizing knowledge: Commodification as a dynamic of jurisdictional expansion in professional service firms. Human Relations 54(7):933–953.CrossrefGoogle Scholar
  • Teather JL (1990) The museum keepers: The Museums Association and the growth of museum professionalism. Museum Management Curatorship 9(1):25–41.CrossrefGoogle Scholar
  • Teece DJ (1998) Capturing value from knowledge assets. California Management Rev. 40(3):55–76.CrossrefGoogle Scholar
  • Tsoukas H (2003) Do we really understand tacit knowledge? Easterby-Smith M, Lyles M, eds. The Blackwell Handbook of Organizational Learning and Knowledge Management (Blackwell, Oxford, UK), 410–427.Google Scholar
  • Tushman ML, Katz R (1980) External communication and project performance: An investigation into the role of gatekeepers. Management Sci. 26(11):1071–1085.LinkGoogle Scholar
  • Van den Broek E, Sergeeva A, Huysman M (2021) When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quart. 45(3b):1557–1580.CrossrefGoogle Scholar
  • Van Maanen J (1973) Observations on the making of policemen. Human Organ. 32(4):407–418.CrossrefGoogle Scholar
  • Vogel A, Kaghan WN (2001) Bureaucrats, brokers, and the entrepreneurial university. Organ. 8(2):358–364.Google Scholar
  • Von Hippel E (1994) “Sticky information” and the locus of problem solving: Implications for innovation. Management Sci. 40(4):429–439.LinkGoogle Scholar
  • Von Krogh G (2018) Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Acad. Management Discoveries 4(4):404–409.CrossrefGoogle Scholar
  • Waardenburg L, Huysman M, Agterberg M (2021) Managing AI Wisely: From Development to Organizational Change in Practice (Edward Elgar, Cheltenham, UK).CrossrefGoogle Scholar
  • Wenger E (1999) Communities of Practice: Learning, Meaning and Identity (Cambridge University Press, Cambridge, UK).Google Scholar
  • Zarsky T (2016) The trouble with algorithmic decisions. Sci. Tech. Human Values 41(1):118–132.CrossrefGoogle Scholar
  • Zhang Z, Nandhakumar J, Hummel JT, Waardenburg L (2020) Addressing the key challenges of developing machine learning AI systems for knowledge-intensive work. MIS Quart. Executive 19(4):221–238.CrossrefGoogle Scholar
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