Generative mechanisms of AI implementation: A critical realist perspective on predictive maintenance

Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations

Disciplines :

Management information systems

Author, co-author :

Stohr, Alexander

Ollig, Philipp

Keller, Robert; Kempten University of Applied Sciences

RIEGER, Alexander ; University of Luxembourg

External co-authors :

yes

Language :

English

Title :

Generative mechanisms of AI implementation: A critical realist perspective on predictive maintenance

Publication date :

March 2024

Journal title :

Information and Organization

ISSN :

1471-7727

eISSN :

1873-7919

Publisher :

Elsevier, Oxford, United Kingdom

Peer reviewed :

Peer Reviewed verified by ORBi

Focus Area :

Security, Reliability and Trust

Development Goals :

9. Industry, innovation and infrastructure

Funding text :

This research was funded in part by PayPal, PEARL grant reference 13342933/Gilbert Fridgen. For the purpose of open access and in fulfillment of the obligations arising from the grant agreement, the author has applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission.

Available on ORBilu :

since 12 February 2024

Statistics

Number of views

146 (2 by Unilu)

Number of downloads

315 (2 by Unilu)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

Abbatemarco, N., Gaur, A., Meregalli, S., Stuck in pilot purgatory: Understanding and addressing the current challenges of industrial IoT in manufacturing. Hawaii International Conference on System Sciences (HICSS), Virtual Conference, 2022 Retrieved from http://hdl.handle.net/10125/80170.
Agrawal, A., Gans, J., Goldfarb, A., Economic policy for artificial intelligence. Innovation Policy and the Economy 19:1 (2019), 139–159, 10.1086/699935.
Anderson, J., Rainie, L., Luchsinger, A., Artificial intelligence and the future of humans. 2018, Pew Research Center Retrieved from website https://www.pewresearch.org/internet/wp-content/uploads/sites/9/2018/12/PI_2018.12.10_future-of-ai_FINAL1.pdf.
Andriopoulos, C., Lewis, M.W., Exploitation-exploration tensions and organizational ambidexterity: Managing paradoxes of innovation. Organization Science 20:4 (2009), 696–717, 10.1287/orsc.1080.0406.
Asatiani, A., Malo, P., Nagbøl, P.R., Penttinen, E., Rinta-Kahila, T., Salovaara, A., Sociotechnical envelopment of artificial intelligence: An approach to organizational deployment of inscrutable artificial intelligence systems. Journal of the Association for Information Systems 22:2 (2021), 325–352, 10.17705/1jais.00664.
Barkin, I., Davenport, T.H., Harnessing grassroots automation. MIT Sloan Management Review, 65(1), 2023 65124. Retrieved from https://sloanreview.mit.edu/article/harnessing-grassroots-automation/.
Benbya, H., Leidner, D., How Allianz UK used an idea management platform to harness employee innovation. MIS Quarterly Executive 17:2 (2018), 139–155 Retrieved from https://aisel.aisnet.org/misqe/vol17/iss2/7.
Berente, N., Gu, B., Recker, J., Santhanam, R., Managing Artificial Intelligence. MIS Quarterly 45:3 (2021), 1433–1450, 10.25300/MISQ/2021/16274.
Bhaskar, R., The possibility of naturalism: A philosophical critique of the contemporary human sciences. 1998, Routledge, New York, NY, US.
Bolino, M.C., Thompson, P.S., Norris, K., Kuo, S.-T., Research: When — And why — Employee curiosity annoys managers. Harvard Business Review, 2023 Retrieved from https://hbr.org/2023/11/research-when-and-why-employee-curiosity-annoys-managers?.
Brackenbury, W., Liu, R., Mondal, M., Elmore, A.J., Ur, B., Chard, K., Franklin, M.J., Draining the data swamp. Proceedings of the workshop on human-in-the-loop data analytics, 2018, ACM, New York, NY, US, 1–7, 10.1145/3209900.3209911.
Bunge, M., How does it work? The search for explanatory mechanisms. Philosophy of the Social Sciences 34:2 (2004), 182–210, 10.1177/0048393103262550.
Bygstad, B., Munkvold, B.E., Volkoff, O., Identifying generative mechanisms through affordances a framework for critical realist data analysis. Journal of Information Technology 31:1 (2016), 83–96, 10.1057/jit.2015.13.
Caserta, J., Harreis, H., Rowshankish, K., Srinidhi, N., Tavakoli, A., The data dividend: Fueling generative AI. Retrieved from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-data-dividend-fueling-generative-ai, 2023.
Christer, A.H., Wang, W., Sharp, J.M., A state space condition monitoring model for furnace erosion prediction and replacement. European Journal of Operational Research 101:1 (1997), 1–14, 10.1016/S0377-2217(97)00132-X.
Coleman, J., Leaders, make curiosity the Core of your organizational culture. Harvard Business Review, 2023 Retrieved from https://hbr.org/2023/11/leaders-make-curiosity-the-core-of-your-organizational-culture.
Danermark, B., Ekstrom, M., Jakobsen, L., Karlsson, J.C., Explaining society: Critical realism in the social sciences. Critical realism interventions. 2002, Routledge, London, UK.
Eisenhardt, K.M., What is the Eisenhardt method, really?. Strategic Organization 19:1 (2021), 147–160, 10.1177/1476127020982866.
Eisenhardt, K.M., Graebner, M.E., Theory building from cases: Opportunities and challenges. Academy of Management Journal 50:1 (2007), 25–32, 10.5465/amj.2007.24160888.
Elder-Vass, D., The causal power of social structures: Emergence, structure and agency. 2010, Cambridge University Press, Cambridge, UK, 10.1017/CBO9780511761720.
Fabri, L., Häckel, B., Oberländer, A.M., Rieg, M., Stohr, A., Disentangling Human-AI Hybrids. Business & Information Systems Engineering, 65, 623-641, 2023, 10.1007/s12599-023-00810-1.
Fleetwood, S., The ontology of things, properties and powers. Journal of Critical Realism 8:3 (2009), 343–366, 10.1558/jocr.v8i3.343.
Fleetwood, S., Powers and tendencies revisited. Journal of Critical Realism 10:1 (2011), 80–99, 10.1558/jcr.v10i1.80.
Fügener, A., Grahl, J., Gupta, A., Ketter, W., Will humans-in-the-loop become Borgs? Merits and pitfalls of working with AI. MIS Quarterly 45:3 (2021), 1527–1556, 10.25300/MISQ/2021/16553.
Gebre-Mariam, M., Bygstad, B., Digitalization mechanisms of health management information systems in developing countries. Information and Organization 29:1 (2019), 1–22, 10.1016/j.infoandorg.2018.12.002.
Grashoff, I., Recker, J., Design, development, and implementation of artificial intelligence technology: A scoping review. European conference on information systems (ECIS), Kristiansand, NO, 2023 Retrieved from https://aisel.aisnet.org/ecis2023_rp/305.
Henfridsson, O., Bygstad, B., The generative mechanisms of digital infrastructure evolution. MIS Quarterly 37:3 (2013), 907–931, 10.25300/MISQ/2013/37.3.11.
Hertzum, M., Bansler, J.P., Havn, E.C., Simonsen, J., Pilot implementation: Learning from field tests in IS development. Communications of the Association for Information Systems 30 (2012), 313–328, 10.17705/1CAIS.03020.
Hoerl, R.W., Redman, T.C., What managers should ask about AI models and data sets. MIT Sloan Management Review, 65(2), 2023, 65303 Retrieved from https://sloanreview.mit.edu/article/what-managers-should-ask-about-ai-models-and-data-sets/.
Jöhnk, J., Weißert, M., Wyrtki, K., Ready or not, AI comes - an interview study of organizational AI readiness factors. Business & Information Systems Engineering 63:1 (2021), 5–20, 10.1007/s12599-020-00676-7.
Keller, R., Stohr, A., Fridgen, G., Lockl, J., Rieger, A., Affordance-experimentation-actualization theory in artificial intelligence research - A predictive maintenance story. International conference on information systems (ICIS), Munich, DE, 2019 Retrieved from https://aisel.aisnet.org/icis2019/is_development/is_development/1/.
Kim, Kankanhalli, Investigating user resistance to information systems implementation: A status quo Bias perspective. MIS Quarterly 33:3 (2009), 567–582, 10.2307/20650309.
Krishnan, H.A., Miller, A., Judge, W.Q., Diversification and top management team complementarity: Is performance improved by merging similar or dissimilar teams?. Strategic Management Journal 18:5 (1997), 361–374, 10.1002/(SICI)1097-0266(199705)18:5<361::AID-SMJ866>3.0.CO;2-L.
LaRiviere, J., McAfee, P., Rao, J., Narayanan, V.K., Sun, W., Where predictive analytics is having the biggest impact. Harvard Business Review, 2016 Retrieved from https://hbr.org/2016/05/where-predictive-analytics-is-having-the-biggest-impact.
Lebovitz, S., Levina, N., Lifshitz-Assa, H., Is AI ground truth really true? The dangers of training and evaluating AI tools based on Experts’ know-what. MIS Quarterly 45:3 (2021), 1501–1526, 10.25300/MISQ/2021/16564.
Lee, J.Y.H., Hsu, C., Silva, L., What lies beneath: Unraveling the generative mechanisms of smart technology and service design. Journal of the Association for Information Systems 21:6 (2020), 1621–1643, 10.17705/1jais.00648.
Lee, M.C., Scheepers, H., Lui, A.K., Ngai, E.W., The implementation of artificial intelligence in organizations: A systematic literature review. Information & Management, 60(5), 2023, 103816, 10.1016/j.im.2023.103816.
Levitt, B., March, J.G., Organizational learning. Annual Review of Sociology 14:1 (1988), 319–338, 10.1146/annurev.so.14.080188.001535.
Li, J., Li, M., Wang, X., Thatcher, J.B., Strategic directions for AI: The role of CIOs and boards of directors. MIS Quarterly 45:3 (2021), 1603–1644, 10.25300/MISQ/2021/16523.
Lou, B., Wu, L., AI on drugs: Can artificial intelligence accelerate drug development? Evidence from a large-scale examination of bio-pharma firms. MIS Quarterly 45:3 (2021), 1451–1482, 10.25300/MISQ/2021/16565.
Merhi, M.I., An evaluation of the critical success factors impacting artificial intelligence implementation. International Journal of Information Management, 69, 2023, 102545, 10.1016/j.ijinfomgt.2022.102545.
Mingers, J., Real-izing information systems: Critical realism as an underpinning philosophy for information systems. Information and Organization 14:2 (2004), 372–406, 10.1016/j.infoandorg.2003.06.001.
Mitchell, Knowledge integration and information technology project performance. MIS Quarterly 30:4 (2006), 919–939, 10.2307/25148759.
Mobley, R.K., An introduction to predictive maintenance. 2nd ed., 2002, Butterworth-Heinemann, Amsterdam, NL.
Myers, M.D., Newman, M., The qualitative interview in IS research: Examining the craft. Information and Organization 17:1 (2007), 2–26, 10.1016/j.infoandorg.2006.11.001.
Nijstad, B.A., Stroebe, W., How the group affects the mind: A cognitive model of idea generation in groups. Personality and Social Psychology Review 10:3 (2006), 186–213, 10.1207/s15327957pspr1003_1.
Orlikowski, W.J., Baroudi, J.J., Studying information Technology in Organizations: Research approaches and assumptions. Information Systems Research 2:1 (1991), 1–28, 10.1287/isre.2.1.1.
Paulus, P.B., Brown, V.R., Toward more creative and innovative group idea generation: A cognitive-social-motivational perspective of brainstorming. Social and Personality Psychology Compass 1:1 (2007), 248–265, 10.1111/j.1751-9004.2007.00006.x.
Rai, A., Constantinides, P., Sarker, S.S., Next-generation digital platforms: Toward human–AI hybrids. MIS Quarterly 43:1 (2019), iii–ix.
Raisch, S., Krakowski, S., Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review 46:1 (2021), 192–210, 10.5465/amr.2018.0072.
Redman, T.C., Davenport, T.H., The rise of connector roles in data science. MIT Sloan Management Review, 65(1), 2023, 65235 Retrieved from https://sloanreview.mit.edu/article/the-rise-of-connector-roles-in-data-science/.
Repenning, N.P., Sterman, J.D., Capability traps and self-confirming attribution errors in the dynamics of process improvement. Administrative Science Quarterly 47:2 (2002), 265–295, 10.2307/3094806.
Robey, D., Ross, J.W., Boudreau, M.-C., Learning to implement Enterprise systems: An exploratory study of the dialectics of change. Journal of Management Information Systems 19:1 (2002), 17–46, 10.1080/07421222.2002.11045713.
Roy, R., Stark, R., Tracht, K., Takata, S., Mori, M., Continuous maintenance and the future – Foundations and technological challenges. CIRP Annals - Manufacturing Technology 65:2 (2016), 667–688, 10.1016/j.cirp.2016.06.006.
Rubin, H.J., Rubin, I.S., Qualitative interviewing: The art of hearing data. 3rd ed., 2012, SAGE Publications, Thousand Oaks, CA, US.
Russell, S.J., Norvig, P., Artificial intelligence: A modern approach. Global ed., 2016, Pearson Education Limited, Harlow, UK.
Saldaña, J., The coding manual for qualitative researchers. 3rd ed., 2016, SAGE Publications, Los Angeles, CA, US.
Sarker, S.S., Chatterjee, S., Xiao, X., Elbanna, A., The sociotechnical Axis of cohesion for the IS discipline: Its historical legacy and its continued relevance. MIS Quarterly 43:3 (2019), 695–719, 10.25300/MISQ/2019/13747.
Sayer, A., Method in social science: A realistic approach. 2nd ed., 1992, Routledge, London, UK.
Shin, W., Han, J., Rhee, W., AI-assistance for predictive maintenance of renewable energy systems. Energy, 221, 2021, 119775, 10.1016/j.energy.2021.119775.
Shollo, A., Hopf, K., Thiess, T., Müller, O., Shifting ML value creation mechanisms: A process model of ML value creation. The Journal of Strategic Information Systems, 31(3), 2022, 101734, 10.1016/j.jsis.2022.101734.
Smith, M.L., Testable theory development for small-N studies. International Journal of Information Technologies and Systems Approach 3:1 (2010), 41–56, 10.4018/jitsa.2010100203.
Sterman, J.D., Business dynamics: Systems thinking and modeling for a complex world, 2000, (International ed.)., Boston, MA, US: Irwin/McGraw-Hill.
Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., Teller, A., Artificial intelligence and life in 2030 (one hundred year study on artificial intelligence: Report of the 2015–2016 study panel). Stanford, CA, US. Retrieved from Stanford University website http://ai100.stanford.edu/2016-report, 2016.
Strauss, A.L., Corbin, J.M., Basics of qualitative research: Techniques and procedures for developing grounded theory. 1st ed., 1998, SAGE Publications, Thousand Oaks, CA, US.
Strich, F., Mayer, A.-S., Fiedler, M., What do I do in a world of artificial intelligence? Investigating the impact of substitutive decision-making AI systems on Employees’ professional role identity. Journal of the Association for Information Systems 22:2 (2021), 304–324, 10.17705/1jais.00663.
Sturm, T., Gerlacha, J., Pumplun, L., Mesbah, N., Peters, F., Tauchert, C., Buxmann, P., Coordinating human and machine learning for effective organization learning. MIS Quarterly 45:3 (2021), 1581–1602, 10.25300/MISQ/2021/16543.
Teodorescu, M., Morse, L., Awwad, Y., Kane, G., Failures of fairness in automation require a deeper understanding of human-ML augmentation. MIS Quarterly 45:3 (2021), 1483–1500, 10.25300/MISQ/2021/16535.
Thiebes, S., Lins, S., Sunyaev, A., Trustworthy artificial intelligence. Electronic Markets 31:2 (2021), 447–464, 10.1007/s12525-020-00441-4.
Tiwana, A., An empirical study of the effect of knowledge integration on software development performance. Information and Software Technology 46:13 (2004), 899–906, 10.1016/j.infsof.2004.03.006.
Tushman, M.L., O'Reilly, C.A., Ambidextrous organizations: Managing evolutionary and revolutionary change. California Management Review 38:4 (1996), 8–29, 10.2307/41165852.
Vial, G., Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems 28:2 (2019), 118–144, 10.1016/j.jsis.2019.01.003.
Vial, G., Cameron, A.-F., Giannelia, T., Jiang, J., Managing artificial intelligence projects: Key insights from an AI consulting firm. Information Systems Journal 33:3 (2023), 669–691, 10.1111/isj.12420.
Volkoff, O., Strong, D.M., Critical realism and affordances: Theorizing IT-associated organizational change processes. MIS Quarterly 37:3 (2013), 819–834, 10.25300/MISQ/2013/37.3.07.
Vom Brocke, J., Maaß, W., Buxmann, P., Maedche, A., Leimeister, J.M., Pecht, G., Future work and Enterprise systems. Business & Information Systems Engineering 60:4 (2018), 357–366, 10.1007/s12599-018-0544-2.
Wagner, C., Hellingrath, B., Implementing predictive maintenance in a company: Industry insights with expert interviews. Proceedings of the 2019 IEEE international conference on prognostics and health management (ICPHM), 2019, IEEE, Piscataway, NJ, US, 1–8, 10.1109/ICPHM.2019.8819406.
Walz, D.B., Elam, J.J., Curtis, B., Inside a software design team. Communications of the ACM 36:10 (1993), 63–77, 10.1145/163430.163447.
Weber, M., Engert, M., Schaffer, N., Weking, J., Krcmar, H., Organizational capabilities for AI implementation—Coping with inscrutability and data dependency in AI. Information Systems Frontiers 25:4 (2023), 1549–1569, 10.1007/s10796-022-10297-y.
Wessel, L.K., Baiyere, A., Ologeanu-Taddei, R., Cha, J., Jensen, T.B., Unpacking the difference between digital transformation and IT-enabled organizational transformation. Journal of the Association for Information Systems 22:1 (2021), 102–129, 10.17705/1jais.00655.
Wuest, T., Weimer, D., Irgens, C., Thoben, K.-D., Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research 4:1 (2016), 23–45, 10.1080/21693277.2016.1192517.
Yan, J., Leidner, D.E., Benbya, H., Differential innovativeness outcomes of user and employee participation in an online user innovation community. Journal of Management Information Systems 35:3 (2018), 900–933, 10.1080/07421222.2018.1481669.
Yin, R.K., Validity and generalization in future case study evaluations. Evaluation 19:3 (2013), 321–332, 10.1177/1356389013497081.
Yin, R.K., Case study research: Design and methods. 5th ed., 2014, SAGE Publications, Thousand Oaks, CA, US.
Zarte, M., Wunder, U., Pechmann, A., Concept and first case study for a generic predictive maintenance simulation in AnyLogicTM. Proceedings of the 43rd annual conference of the IEEE industrial electronics society, 2017, IEEE, Piscataway, NJ, US, 3372–3377, 10.1109/IECON.2017.8216571.