From Analog to Algorithm
a comprehensive review of Artificial Intelligence's intersection with Management Literature and Actor-Network Theory
DOI:
https://doi.org/10.36311/1981-1640.2024.v18.e024013Palabras clave:
Artificial intelligence, Actor-network theory, Data Science, Management, Systematic Literature ReviewResumen
En la era digital, la literatura actual sobre gestión sigue fuertemente arraigada en perspectivas antropocéntricas y analógicas, a menudo descuidando la profunda influencia de los algoritmos y la inteligencia artificial (IA) en las dinámicas organizativas modernas. Con este fin, esta revisión sistemática de la literatura pretende llenar este vacío explorando el panorama en evolución de la IA en las organizaciones y el papel de la Teoría del Actor-Red (TAR) como lente teórico-metodológica para comprender el impacto de la IA en la Gestión. A través de una revisión sistemática de la literatura, abordamos la investigación sobre el uso de la IA en la gestión durante la última década, las aplicaciones de la TAR en estudios de gestión ajenos a las TI y a la IA, y los estudios que explican conceptos de TI o IA a través de la TAR. Nuestras conclusiones ponen de manifiesto la existencia de una importante laguna en la investigación sobre la comprensión de las interacciones hombre-máquina y la gestión empresarial en organizaciones que utilizan IA. Además, identificamos posibles vías para futuras contribuciones académicas, haciendo hincapié en la necesidad de un enfoque más integrado que tenga en cuenta a los actores humanos y mecánicos en contextos organizativos.
Descargas
Métricas
Citas
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.
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2024 Wellington Rodrigo Monteiro, Eduardo Ayrosa
Esta obra está bajo una licencia internacional Creative Commons Atribución-CompartirIgual 4.0.
Al someter un artículo, los autores conservan los derechos de autor del artículo, otorgando todos los derechos para el Brazilian Journal of Information Science: research trends para publicar el texto.
El(los) autor(es) acuerdan que el artículo, si se acepta editorialmente para su publicación, tendrá licencia bajo Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) (http://creativecommons.org/licenses/by-sa /4.0).
Los lectores/usuarios son libres para:
- Compartir - copiar y redistribuir el material en cualquier medio o formato.
- Adaptar - remezclar, transformar y construir sobre el material para cualquier propósito, incluso comercialmente.
El licenciante no puede revocar estas libertades mientras siga los términos de la licencia. Bajo los siguientes términos:
- Atribución: debe otorgar el crédito apropiado, proporcionar un enlace a la licencia e indicar si se realizaron cambios. Puede hacerlo de manera razonable, pero de ninguna manera que sugiera que el licenciante lo respalda en su uso.
Compartir igual: si remezcla, transforma o desarrolla el material, debe distribuir sus contribuciones bajo la misma licencia que el original.
Sin restricciones adicionales: no puede aplicar términos legales o medidas tecnológicas que restrinjan legalmente a otros de hacer cualquier cosa que la licencia permita.
Avisos:
- No tiene que cumplir con la licencia para elementos del material de dominio público o cuando su uso esté permitido por una excepción o limitación aplicable.
- No se otorgan garantías. Es posible que la licencia no le otorgue todos los permisos necesarios para su uso previsto. Por ejemplo, otros derechos como publicidad, privacidad o derechos morales pueden limitar la forma en que usa el material.
Creative Commons Attribution-ShareAlike 4.0 International License