From Analog to Algorithm: a comprehensive review of Artificial Intelligence's intersection with Management Literature and Actor-Network Theory

Wellington Rodrigo Monteiro; Eduardo Ayrosa

doi:10.36311/1981-1640.2024.v18.e024013

Authors

Wellington Rodrigo Monteiro Positive University
Eduardo Ayrosa Positive University

DOI:

https://doi.org/10.36311/1981-1640.2024.v18.e024013

Keywords:

Artificial intelligence, Actor-network theory, Data Science, Management, Systematic Literature Review

Abstract

In the digital age, the current Management literature remains heavily rooted in anthropocentric and analog perspectives, often overlooking the profound influence of algorithms and artificial intelligence (AI) in modern organizational dynamics. To this end, this systematic literature review seeks to bridge this gap by exploring the evolving landscape of AI in organizations and the role of the Actor-Network Theory (ANT) as a theoretical-methodological lens for understanding AI’s impact on Management. Through a systematic literature review, we address research on the utilization of AI in Management over the past decade, the applications of ANT in non-IT or non-AI management studies, and studies explaining IT or AI concepts via ANT. Our findings highlight a significant research gap in understanding human-machine interactions and Business Management within organizations that use AI. Additionally, we identify potential avenues for future scholarly contributions, emphasizing the need for a more integrated approach that considers human and machine actors in organizational contexts.

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Author Biographies

Wellington Rodrigo Monteiro, Positive University

Wellington Rodrigo Monteiro holds a Ph.D. in Industrial and Systems Engineering from PUCPR (Pontifical Catholic University of Parana), a Master's in Industrial and Systems Engineering from PUCPR, and a Bachelor's in Computer Engineering from PUCPR. He has over ten years of experience working as a data scientist in large international corporations and startups. His interests are rooted in the adoption and perception of artificial intelligence inside organizations.

Eduardo Ayrosa, Positive University

Eduardo Ayrosa holds a Ph.D. in Management from the London Business School (University of London), a Master's degree in Management from UFRJ (Federal University of Rio de Janeiro), and a Bachelor's in Civil Engineering from the UFRJ. Specializing in Management, his emphasis lies in Consumer Studies, Marketing, and Epistemology and Research Methodology. In the realm of Epistemology and Research Methodology, his interests are rooted in the philosophy of social sciences and interpretative research methods.

References

Akhtar, P., Frynas, J. G., Mellahi, K., & Ullah, S. (2019, APR). Big data-savvy teams’ skills, big data-driven actions and business performance. British Journal of Management, 30(2, SI), 252-271. doi: https://doi.org/10.1111/1467-8551.12333

Alonso Trillo, R., & Poliks, M. (2023). Debris: Machine learning, archive archaeology, digital audio waste. Organised Sound. doi: https://doi.org/10.1017/S1355771823000249

Andersen, L. B., Danholt, P., Halskov, K., Hansen, N. B., & Lauritsen, P. (2015). Participation as a matter of concern in participatory design. CoDesign, 11(3-4), 250 – 261. doi: https://doi.org/10.1080/15710882.2015.1081246

Andersson, R. (2016). Hardwiring the frontier? The politics of security technology in Europe’s ‘fight against illegal migration’. Security Dialogue, 47(1), 22 – 39. doi: https://doi.org/10.1177/0967010615606044

Armour, J., & Sako, M. (2020, Mar). AI-enabled business models in legal services: from traditional law firms to next-generation law companies? Journal of Professions and Organization, 7(1), 27-46. doi: https://doi.org/10.1093/jpo/joaa001

Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities . Technological Forecasting and Social Change, 163. doi: https://doi.org/10.1016/j.techfore.2020.120420

Bajde, D. (2013). Consumer culture theory (re)visits actor-network theory: Flattening consumption studies. Marketing Theory, 13(2), 227 – 242. doi: https://doi.org/10.1177/1470593113477887

Bartlett, L., & Vavrus, F. (2014). Transversing the vertical case study: A methodological approach to studies of educational policy as practice. Anthropology and Education Quarterly, 45(2), 131 – 147. doi: https://doi.org/10.1111/aeq.12055

Bartrolí, M. A. (2021). The university social responsibility framework by the international federation of catholic universities: A case of “intelligent” co-creation. Springer. doi: https://doi.org/10.1007/978-3-030-70013-3_2

Belanche, D., Casalo, L., V, Flavian, C., & Schepers, J. (2020). Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success. Journal of Service Management, 31(2, SI), 267-289. doi: https://doi.org/10.1108/JOSM -05-2019-0156

Bellanova, R. (2017). Digital, politics, and algorithms: Governing digital data through the lens of data protection. European Journal of Social Theory, 20(3), 329 – 347. doi: https://doi.org/10.1177/1368431016679167

Bittner, C., Glasze, G., & Turk, C. (2013). Tracing contingencies: Analyzing the political in assemblages of web 2.0 cartographies. GeoJournal, 78(6), 935 – 948. doi: https://doi.org/10.1007/s10708-013-9488-8

Blok, A. (2013, NOV). Pragmatic sociology as political ecology: On the many worths of nature(s). European Journal Of Social Theory, 16(4), 492-510. doi: https://doi.org/10.1177/1368431013479688

Borch, C. (2023). Machine learning and postcolonial critique: Homologous challenges to sociological notions of human agency. Sociology. doi: https://doi.org/10.1177/00380385221146877

Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257. doi: https://doi.org/10.1017/jmo.2016.55

Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: lessons from tourism and hospitality. Journal of Service Management, 30(4, SI), 484-506. doi: https://doi.org/10.1108/JOSM-12-2018-0398

Burga, R., & Rezania, D. (2017). Project accountability: An exploratory case study using actor-network theory. International Journal of Project Management, 35(6), 1024-1036. doi: https://doi.org/10.1016/j.ijproman.2017.05.001

Campbell-Verduyn, M., Goguen, M., & Porter, T. (2017). Big data and algorithmic governance: the case of financial practices. New Political Economy, 22(2), 219 – 236. doi: https://doi.org/10.1080/13563467.2016.1216533

Caputo, F., Cillo, V., Candelo, E., & Liu, Y. (2019, SEP 12). Innovating through digital revolution the role of soft skills and big data in increasing firm performance. Management Decision, 57(8, SI), 2032-2051. doi: https://doi.org/10.1108/MD-07-2018-0833

Cecez-Kecmanovic, D., Kautz, K., & Abrahall, R. (2014). Reframing success and failure of information systems: A performative perspective. MIS Quarterly: Management Information Systems, 38(2), 561 – 588. doi: https://doi.org/10.25300/MISQ/2014/38.2.11

Contini, F. (2020). Artificial intelligence and the transformation of humans, law and technology interactions in judicial proceedings. Law, Technology and Humans, 2(1), 4–18. doi: https://doi.org/10.5204/lthj.v2i1.1478

Cooper, D. J., Ezzamel, M., & Qu, S. Q. (2017). Popularizing a management accounting idea: The case of the balanced scorecard. Contemporary Accounting Research, 34(2), 991 – 1025. doi: https://doi.org/10.1111/1911-3846.12299

Dery, K., Hall, R., Wailes, N., & Wiblen, S. (2013, SEP). Lost in translation? An actor-network approach to HRIS implementation. Journal of Strategic Information Systems, 22(3), 225-237. doi: https://doi.org/10.1016/j.jsis.2013.03.002

Domingo, D., Masip, P., & Costera Meijer, I. (2015). Tracing digital news networks: Towards an integrated framework of the dynamics of news production, circulation and use. Digital Journalism, 3(1), 53 – 67. doi: https://doi.org/10.1080/21670811.2014.927996

Elbanna, A. (2013, May). Top management support in multiple-project environments: an in-practice view. European Journal of Information Systems, 22(3), 278-294. doi: https://doi.org/10.1057/ejis.2012.16

Eze, S. C., Duan, Y., & Chen, H. (2014). Examining emerging ICT’s adoption in SMEs from a dynamic process approach. Information Technology and People, 27(1), 63–82. doi: https://doi.org/10.1108/ITP-03-2013-0044

Fleming, P. (2019, JAN). Robots and organization studies: Why robots might not want to steal your job. Organization Studies, 40(1), 23-37. doi: https://doi.org/10.1177/0170840618765568

Flyverbom, M. (2015). Sunlight in cyberspace? On transparency as a form of ordering . European Journal of Social Theory, 18(2), 168 – 184. doi: https://doi.org/10.1177/1368431014555258

Frauenberger, C. (2019). Entanglement HCI the next wave? . ACM Transactions on Computer-Human Interaction, 27(1). doi: https://doi.org/10.1145/3364998

Fuenfschilling, L., & Binz, C. (2018, MAY). Global socio-technical regimes. Research Policy, 47(4), 735-749. doi: https://doi.org/10.1016/j.respol.2018.02.003

Füller, J., Hutter, K., Wahl, J., Bilgram, V., & Tekic, Z. (2022). How AI revolutionizes innovation management – perceptions and implementation preferences of AI-based innovators . Technological Forecasting and Social Change, 178. doi: https://doi.org/10.1016/j.techfore.2022.121598

Greenhalgh, T., Wherton, J., Shaw, S., Papoutsi, C., Vijayaraghavan, S., & Stones, R. (2019). Infrastructure revisited: An ethnographic case study of how health information infrastructure shapes and constrains technological innovation . Journal of Medical Internet Research, 21(12). doi: https://doi.org/10.2196/16093

Gregory, R. W., Henfridsson, O., Kaganer, E., & Kyriakou, S. H. (2021, JUL). The role of artificial intelligence and data network effects for creating user value. Academy of Management Review, 46(3), 534-551. doi: https://doi.org/10.5465/amr.2019.0178

Gunawong, P., & Gao, P. (2017). Understanding e-government failure in the developing country context: a process-oriented study. Information Technology for Development, 23(1), 153 – 178. doi: https://doi.org/10.1080/02681102.2016.1269713

Hansen, H. K., & Flyverbom, M. (2015). The politics of transparency and the calibration of knowledge in the digital age. Organization, 22(6), 872 – 889. doi: https://doi.org/10.1177/1350508414522315

Iskanderov, Y., & Pautov, M. (2020). Agents and multi-agent systems as actor-networks. In A. Rocha, L. Steels, & J. VanDenHerik (Eds.), ICAART: Proceedings of the 12th International Conference on Agents and Artificial Intelligence, vol 1 (p. 179-184). doi: https://doi.org/10.5220/0008935601790184

Islam, A. K. M. N., Mantymaki, M., & Turunen, M. (2019, Nov). Why do blockchains split? An actor-network perspective on bitcoin splits. Technological Forecasting and Social Change, 148. doi: https://doi.org/10.1016/j.techfore.2019.119743

Johnson, D. G., & Verdicchio, M. (2019, SEP). AI, agency and responsibility: the VW fraud case and beyond. AI & Society, 34(3), 639-647. doi: https://doi.org/10.1007/s00146-017-0781-9

Kaasinen, E., Anttila, A.-H., Heikkilä, P., Laarni, J., Koskinen, H., & Väätänen, A. (2022). Smooth and resilient human–machine teamwork as an industry 5.0 design challenge . Sustainability (Switzerland), 14(5). doi: https://doi.org/10.3390/su14052773

Karmakar, S. (2022). Artificial intelligence: the future of medicine, or an overhyped and dangerous idea? . Irish Journal of Medical Science, 191(5), 1991–1994. doi: https://doi.org/10.1007/s11845 -021-02853-3

Kitchenham, B. A., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering (Tech. Rep.). Keele, United Kingdom: School of Computer Science and Mathematics, Keele University.

Kumar, N., & Rangaswamy, N. (2013). The mobile media actor-network in urban India. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (p. 1989–1998). doi: https://doi.org/10.1145/2470654.2466263

Law, J., & Lien, M. E. (2013). Slippery: Field notes in empirical ontology. Social Studies of Science, 43(3), 363 – 378. doi: https://doi.org/10.1177/0306312712456947

Law, J., & Singleton, V. (2013). Ant and politics: Working in and on the world. Qualitative Sociology, 36(4), 485 – 502. doi: https://doi.org/10.1007/s11133-013-9263-7

Law, J., & Singleton, V. (2014). ANT, multiplicity and policy . Critical Policy Studies, 8(4), 379–396. doi: https://doi.org/10.1080/19460171.2014.957056

Lawrence, T. B., & Dover, G. (2015, SEP). Place and institutional work: Creating housing for the hard-to-house. Administrative Science Quarterly, 60(3), 371-410. doi: https://doi.org/10.1177/0001839215589813

Lee, F., & Bjorklund Larsen, L. (2019). How should we theorize algorithms? Five ideal types in analyzing algorithmic normativities. Big Data and Society, 6(2). doi: https://doi.org/10.1177/2053951719867349

Martin, D. M., & Schouten, J. W. (2014, FEB). Consumption-driven market emergence. Journal Of Consumer Research, 40(5), 855-870. doi: https://doi.org/10.1086/673196

Missonier, S., & Loufrani-Fedida, S. (2014). Stakeholder analysis and engagement in projects: From stakeholder relational perspective to stakeholder relational ontology . International Journal of Project Management, 32(7), 1108 – 1122. doi: https://doi.org/10.1016/j.ijproman.2014.02 .010

Modell, S., Vinnari, E., & Lukka, K. (2017, Jul). On the virtues and vices of combining theories: The case of institutional and actor-network theories in accounting research. Accounting Organizations and Society, 60, 62-78. doi: https://doi.org/10.1016/j.aos.2017.06.005

Murray, A., Rhymer, J., & Sirmon, D. G. (2021, Jul). Humans and technology: Forms of conjoined agency in organizations. Academy of Management Review, 46(3), 552-571. doi: https://doi.org/10.5465/amr.2019.0186

Onno, J., Khan, F. A., Daftary, A., & David, P. M. (2023, Jun). Artificial intelligence-based computer aided detection (AI-CAD) in the fight against tuberculosis: Effects of moving health technologies in global health. Social Science & Medicine, 327. doi: https://doi.org/10.1016/j.socscimed .2023.115949

Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022, Mar 26). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. International Journal of Human Resource Management, 33(6, SI), 1125-1147. doi: https://doi.org/10.1080/09585192.2021.1879206

Piekut, B. (2014). Actor-networks in music history: Clarifications and critiques . Twentieth Century Music, 11(2), 191 – 215. doi: https://doi.org/10.1017/S147857221400005X

Pillai, R., & Sivathanu, B. (2020, Nov 9). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITES organizations. Benchmarking: an International Journal, 27(9), 2599-2629. doi: https://doi.org/10.1108/BIJ-04-2020-0186

Pinto, M. F., Leal, A., Lopes, F., Pais, J., Dourado, A., Sales, F., ... Teixeira, C. A. (2022). On the clinical acceptance of black-box systems for EEG seizure prediction. Epilepsia Open, 7(2), 247–259. doi: https://doi.org/10.1002/epi4.12597

Pollack, J., Costello, K., & Sankaran, S. (2013). Applying actor-network theory as a sensemaking framework for complex organisational change programs . International Journal of Project Management, 31(8), 1118–1128. doi: https://doi.org/10.1016/j.ijproman.2012.12.007

Prado, A. B., & Calani Baranauskas, M. C. (2014). Capturing semiotic and social factors of organizational evolution. In S. Hammoudi, J. Cordeiro, L. Maciaszek, & J. Filipe (Eds.), Enterprise information systems, ICEIS 2013 (Vol. 190, p. 264-279). doi: https://doi.org/10.1007/978-3-319 -09492-2

Raisch, S., & Krakowski, S. (2021, Jan). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192-210. doi: https://doi.org/10.5465/amr.2018.0072

Sarlak, M. A., Salamzadeh, Y., & Farzad, F. S. (2020). Actor-network theory and networked organizations, proposing a conceptual framework. Springer. doi: https://doi.org/10.1007/978-981-15-7066-7_11

Sayes, E. (2014). Actor-network theory and methodology: Just what does it mean to say that nonhumans have agency? . Social Studies of Science, 44(1), 134 – 149. doi: https://doi.org/10.1177/0306312713511867

Shmargad, Y. (2017). Network perspectives on privacy and security in the internet of things: From actor-network theory to social network analysis. In: 2017 AAAI Spring Symposium – No 3 - Computational Context: Why It Is Important, What It Means, and Can It Be Computed? Technical Report SS-17-03, p. 351 – 353. https://cdn.aaai.org/ocs/15243/15243-68241-1-PB.pdf

Sidorova, A. (2018). Interests and agency in ai: The case of image recognition with inception 3 model completed research papers. In AMCIS 2018 Proceedings. (24th Americas Conference on Information Systems (AMCIS) - Digital Disruption, New Orleans, LA, Aug 16-18, 2018)

Sperling, K., Stenliden, L., Nissen, J., & Heintz, F. (2022). Still w(AI)ting for the automation of teaching: An exploration of machine learning in Swedish primary education using actor-network theory . European Journal of Education, 57(4), 584 – 600. doi: https://doi.org/10.1111/ejed.12526

Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.

van Rijmenam, M., & Logue, D. (2021). Revising the ‘science of the organisation’: theorising AI agency and actorhood . Innovation: Organization and Management, 23(1), 127–144. doi: https://doi.org/10.1080/14479338.2020.1816833

Waeraas, A., & Nielsen, J. A. (2016, Jul). Translation theory ‘translated’: Three perspectives on translation in organizational research. International Journal Of Management Reviews, 18(3, SI), 236-270. doi: https://doi.org/10.1111/ijmr.12092

Warner, K. S. R., & Waeger, M. (2019, Jun). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326-349. doi: https://doi.org/10.1016/j.lrp.2018.12.001.

Williams, I. (2020). Contemporary applications of actor network theory. Springer. doi: https://doi.org/10.1007/978-981-15-7066-7.