Maximización de los beneficios del aprendizaje automáticomejora de la teoría de efectos de redes de datos para mejorar la creación de valor y la apropiación

  1. Ricardo Costa Climent 1
  1. 1 Uppsala University y University of Economics and Human Sciences of Warsaw
Revue:
ESIC Digital Economy & Innovation Journal

ISSN: 2792-8721

Année de publication: 2023

Volumen: 2

Número: 1

Type: Article

DOI: 10.55234/EDEIJ-2-062 DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: ESIC Digital Economy & Innovation Journal

Résumé

La teoría recientemente propuesta de los efectos de la red de datos tiene como objetivo explicar cómo se crea el valor del usuario a partir del uso de la tecnología de aprendizaje automático. La teoría explica la capacidad de aprendizaje única del aprendizaje automático, que utiliza grandes conjuntos de datos para hacer predicciones y mejorar la toma de decisiones. Este artículo ofrece una evaluación de la teoría de los efectos de la red de datos, identificando algunas de sus fortalezas y limitaciones. En cuanto a las fortalezas, contribuye al éxito de las empresas, explica las características únicas de las tecnologías de ML y es un avance del cuerpo de la teoría de los efectos de red. Sus limitaciones luego se transforman en un conjunto de preguntas de investigación interrelacionadas que se centran en la relación del uso del aprendizaje automático y cuestiones tales como: captura de valor, una visión co-evolutiva, una perspectiva de múltiples actores y la dinámica de bases de datos. Este artículo describe un enfoque multiteórico para estudiar la creación de valor y la captura que permite el uso de tecnologías de aprendizaje automático.

Références bibliographiques

  • Afuah, A. (2013). Are network effects really all about size? The role of structure and conduct. Strategic Management Journal, 34(3), 257–273. https://doi.org/10.1002/smj.2013 DOI: https://doi.org/10.1002/smj.2013
  • Afuah, A. & Tucci, C. L. (2003). Internet business models and strategies: Text and cases (Vol. 2). McGraw-Hill. https://www.researchgate.net/publication/215915163
  • Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Press. DOI: https://doi.org/10.3386/w24690
  • Al Dakhil, S. & Bayoumi, S. (2020). Reviews Analysis of Apple Store Applications Using Supervised Machine Learning. In R. Agrawal, M. Paprzycki & N. Gupta (eds.), Big Data, IoT, and Machine Learning (pp. 51–78). CRC Press. https://doi.org/10.1201/9780429322990 DOI: https://doi.org/10.1201/9780429322990-4
  • Amit, R. & Zott, C. (2001). Value creation in e-business. Strategic Management Journal, 22(6-7), 493–520. https://doi.org/10.1002/smj.187 DOI: https://doi.org/10.1002/smj.187
  • Arel, I., Rose, D. C., & Karnowski, T. P. (2010). Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE Computational Intelligence Magazine, 5(4), 13–18. https://doi.org/10.1109/MCI.2010.938364 DOI: https://doi.org/10.1109/MCI.2010.938364
  • Avison, D. & Elliot, S. (2006). Scoping the Discipline of Information Systems. In J.L. King (ed.), Information Systems: The State of the Field (pp. 3–18). Wiley. https://d1wqtxts1xzle7.cloudfront.net/32169300/Information_Systems_-_The_State_of_the_Field.pdf?1382868717=&response-content-disposition=inline%3B+filename%3DInformation_Systems_The_State_of_the_Fie.pdf&Expires=1688383913&Signature=FF3awMw5x6puPQFBf0~wacYbQJFATDdDhmb8SUv8G~vKClvv4mjK259VnnHLycsWo7GaQXEEUgt-idAgSpv1kUGodFi05-hJ9K8ckgEeTEiREYD2WScaR4jelwAaCPDGbLHIVDjS8DBXj4vfEoXnvS0dBxar5tfUIGE452XSeP-UtF9Y8OhH3dQPxI2TxuASOxehhLPMDvylMJNQ8iecd9KpxPz66OdpRwOio1DBMVQwCYJvfEgKQmpxDpuLEnfFMvG~1Ug6nr-ufJAc5Oy7wzGZZXNZwymras7~lbDxug0MWR6r9Rx43iQgORQgFO66GyuEiC5B4~T~zkD7hCAn6w__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
  • Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L. (2016). Transformational Issues of Big Data and Analytics in Networked Business. MIS Quarterly, 40(4), 807–818. https://www.jstor.org/stable/10.2307/26629677 DOI: https://doi.org/10.25300/MISQ/2016/40:4.03
  • Behfar, K. & Okhuysen, G. A. (2018). Perspective—Discovery within validation logic: Deliberately surfacing, complementing, and substituting abductive reasoning in hypothetico-deductive inquiry. Organization Science, 29(2), 323–340. https://doi.org/10.1287/orsc.2017.1193 DOI: https://doi.org/10.1287/orsc.2017.1193
  • Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Quarterly, 24(1), 169–196. https://doi.org/10.2307/3250983 DOI: https://doi.org/10.2307/3250983
  • Brynjolfsson, E. & Hitt, L. (1996). Paradox lost? Firm-level evidence on the returns to information systems spending. Management Science, 42(4), 541–558. https://doi.org/10.1287/mnsc.42.4.541 DOI: https://doi.org/10.1287/mnsc.42.4.541
  • Brynjolfsson, E., Hitt, L. M., & Yang, S. (2002). Intangible assets: Computers and organizational capital. Brookings Papers on Economic Activity, 2002(1), 137–181. https://doi.org/10.1353/eca.2002.0003 DOI: https://doi.org/10.1353/eca.2002.0003
  • Brynjolfsson, E., Jin, W., & McElheran, K. (2021a). The power of prediction: predictive analytics, workplace complements, and business performance. Business Economics, 56, 217–239. https://doi.org/10.1057/s11369-021-00224-5 DOI: https://doi.org/10.1057/s11369-021-00224-5
  • Brynjolfsson, E., Rock, D., & Syverson, C. (2021b). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333–72. https://doi.org/10.1257/mac.20180386 DOI: https://doi.org/10.1257/mac.20180386
  • Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., & Trench, M. (2017). Artificial intelligence: the next digital frontier? [discussion paper]. McKinsey Global Institute. https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx
  • Canhoto, A. I. & Clear, F. (2020). Artificial intelligence and ML as business tools: A framework for diagnosing value destruction potential. Business Horizons, 63(2), 183–193. https://doi.org/10.1016/j.bushor.2019.11.003 DOI: https://doi.org/10.1016/j.bushor.2019.11.003
  • Cennamo, C. (2020). Value Preserving Platform Regulation: Network Effects, Platform Value and Regulatory Remedies. Platform Value and Regulatory Remedies. https://doi.org/10.2139/ssrn.3901715 DOI: https://doi.org/10.2139/ssrn.3901715
  • Chae, H. C., Koh, C. E., & Prybutok, V. R. (2014). Information technology capability and firm performance: contradictory findings and their possible causes. MIS Quarterly, 38(1), 305–326. https://www.jstor.org/stable/26554879?seq=1&cid=pdf-reference#references_tab_contents DOI: https://doi.org/10.25300/MISQ/2014/38.1.14
  • Chen, L., Tong, T. W., Tang, S., & Han, N. (2022). Governance and design of digital platforms: A review and future research directions on a meta-organization. Journal of Management, 48(1), 147–184. https://doi.org/10.1177/01492063211045023 DOI: https://doi.org/10.1177/01492063211045023
  • Chesbrough, H. (2007). Business model innovation: it’s not just about technology anymore. Strategy & Leadership, 35(6), 12–17. https://doi.org/10.1108/10878570710833714 DOI: https://doi.org/10.1108/10878570710833714
  • Chesbrough, H. & Rosenbloom, R. S. (2002). The role of the business model in capturing value from innovation: evidence from Xerox Corporation’s technology spin-off companies. Industrial and Corporate Change, 11(3), 529–555. https://doi.org/10.1093/icc/11.3.529 DOI: https://doi.org/10.1093/icc/11.3.529
  • Chhillar, D. & Aguilera, R. V. (2022). An Eye for Artificial Intelligence: Insights Into the Governance of Artificial Intelligence and Vision for Future Research. Business & Society, 61(5), 1197–1241. https://doi.org/10.1177/00076503221080959 DOI: https://doi.org/10.1177/00076503221080959
  • Churchman, C. W. (1961). Realism in management science: A report. Management Science, (3), 63–81. https://doi.org/10.1287/mantech.1.3.63 DOI: https://doi.org/10.1287/mantech.1.3.63
  • Clough, D. R. & Wu, A. (2022). Artificial Intelligence, Data-Driven Learning, and the Decentralized Structure of Platform Ecosystems. Academy of Management Review, 47(1), 184–189. https://doi.org/10.5465/amr.2020.0222 DOI: https://doi.org/10.5465/amr.2020.0222
  • Coglianese, C. & Lehr, D. (2019). Transparency and Algorithmic Governance. Administrative Law Review, 71(1), 1–56. https://www.jstor.org/stable/10.2307/27170531
  • Cooper, B. L., Watson, H. J., Wixom, B. H., & Goodhue, D. L. (2000). Data Warehousing Supports Corporate Strategy at First American Corporation. MIS Quarterly, 24(4), 547–567. https://doi.org/10.2307/3250947 DOI: https://doi.org/10.2307/3250947
  • Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to algorithms. MIT press. http://ir.juit.ac.in:8080/jspui/bitstream/123456789/5423/1/Introduction%20to%20algorithms%20by%20Thomas%20H.%20Cormen%2C%20Charles%20E.%20Leiserson%2C%20Ronald%20L.%20Rivest%2C%20Clifford%20Stein.pdf
  • Costa-Climent, R. & Haftor, D. M. (2021). Business model theory-based prediction of digital technology use: An empirical assessment. Technological Forecasting and Social Change, 173, 121174. https://doi.org/10.1016/j.techfore.2021.121174 DOI: https://doi.org/10.1016/j.techfore.2021.121174
  • Dąbrowska, J., Almpanopoulou, A., Brem, A., Chesbrough, H., Cucino, V., Di Minin, A., … & Ritala, P. (2022). Digital transformation, for better or worse: a critical multi-level research agenda. R&D Management, 52(5), 930–954. https://doi.org/10.1111/radm.12531 DOI: https://doi.org/10.1111/radm.12531
  • Davidovski, V. (2018). Exponential innovation through digital transformation. In Proceedings of the 3rd International Conference on Applications in Information Technology (pp. 3–5). New York. https://doi.org/10.1145/3274856.3274858 DOI: https://doi.org/10.1145/3274856.3274858
  • Deichmann, U., Goyal, A., & Mishra, D. (2016). Will digital technologies transform agriculture in developing countries? Agricultural Economics, 47(S1): 21–33. https://doi.org/10.1111/agec.12300 DOI: https://doi.org/10.1111/agec.12300
  • Devaraj, S. & Kohli, R. (2003). Performance impacts of information technology: Is actual usage the missing link? Management Science, 49(3), 273–289. https://doi.org/10.1287/mnsc.49.3.273.12736 DOI: https://doi.org/10.1287/mnsc.49.3.273.12736
  • Dewan, S. & Kraemer, K. L. (2000). Information technology and productivity: evidence from country-level data. Management Science, 46(4), 548–562. https://doi.org/10.1287/mnsc.46.4.548.12057 DOI: https://doi.org/10.1287/mnsc.46.4.548.12057
  • Dubosson-Torbay, M., Osterwalder, A., & Pigneur, Y. (2002). E-business model design, classification, and measurements. Thunderbird International Business Review, 44(1), 5–23. https://doi.org/10.1002/tie.1036 DOI: https://doi.org/10.1002/tie.1036
  • Economides, N. (1996). The economics of networks. International Journal of Industrial Organization, 14(6), 673–699. https://doi.org/10.1016/0167-7187(96)01015-6 DOI: https://doi.org/10.1016/0167-7187(96)01015-6
  • Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.5465/amr.1989.4308385 DOI: https://doi.org/10.5465/amr.1989.4308385
  • Eisenhardt, K. M. & Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1), 25–32. https://doi.org/10.5465/amj.2007.24160888 DOI: https://doi.org/10.5465/amj.2007.24160888
  • Eisenhardt, K. M., Graebner, M. E., & Sonenshein, S. (2016). Grand challenges and inductive methods: Rigor without rigor mortis. Academy of Management Journal, 59(4), 1113–1123. https://doi.org/10.5465/amj.2016.4004 DOI: https://doi.org/10.5465/amj.2016.4004
  • Eisenhardt, K. M. (2021). What is the Eisenhardt Method, really?. Strategic Organization, 19(1), 147–160. https://doi.org/10.1177/1476127020982866 DOI: https://doi.org/10.1177/1476127020982866
  • Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734. https://doi.org/10.1007/s10796-021-10186-w DOI: https://doi.org/10.1007/s10796-021-10186-w
  • Farrell, J. & Saloner, G. (1986). Installed base and compatibility: Innovation, product preannouncements, and predation. The American Economic Review, 76(5), 940–955. https://www.jstor.org/stable/1816461
  • Gabriel, Y., Korczynski, M., & Rieder, K. (2015). Organizations and their Consumers: Bridging Work and Consumption. Organization, 22(5), 629–643. https://doi.org/10.1177/1350508415586040 DOI: https://doi.org/10.1177/1350508415586040
  • Goodall, N. J. (2016). Away from trolley problems and toward risk management. Applied Artificial Intelligence, 30(8), 810–821. https://doi.org/10.1080/08839514.2016.1229922 DOI: https://doi.org/10.1080/08839514.2016.1229922
  • Gregory, R. W., Henfridsson, O., Kaganer, E., & Kyriakou, H. (2021). The role of artificial intelligence and data network effects for creating user value. Academy of Management Review, 46(3), 534–551. https://doi.org/10.5465/amr.2019.0178 DOI: https://doi.org/10.5465/amr.2019.0178
  • Gregory, R. W., Henfridsson, O., Kaganer, E., & Kyriakou, H. (2022). Data network effects: Key conditions, shared data, and the data value duality. Academy of Management Review, 47(1), 189–192. https://doi.org/10.5465/amr.2021.0111 DOI: https://doi.org/10.5465/amr.2021.0111
  • Gregory, R. W., Kaganer, E., Henfridsson, O., & Ruch, T. J. (2018). IT Consumerization and the Transformation of IT Governance. MIS Quarterly, 42(4), 1225–1253. https://doi.org/10.25300/MISQ/2018/13703
  • Hitt, L. M., & Brynjolfsson, E. (1996). Productivity, business profitability, and consumer surplus: Three different measures of information technology value. MIS quarterly, 20(2), 121–142. https://doi.org/10.2307/249475 DOI: https://doi.org/10.2307/249475
  • Joshi, M. P., Su, N., Austin, R. D., & Sundaram, A. K. (2021). Why So Many Data Science Projects Fail to Deliver. MIT Sloan Management Review, 62(3). https://sloanreview.mit.edu/article/why-so-many-data-science-projects-fail-to-deliver/
  • Katz, M. L. & Shapiro, C. (1992). Product introduction with network externalities. The Journal of Industrial Economics, 40(1), 55–83. https://doi.org/10.2307/2950627 DOI: https://doi.org/10.2307/2950627
  • Katz, M. L. & Shapiro, C. (1985). Network externalities, competition, and compatibility. The American Economic Review, 75(3), 424–440. https://www.jstor.org/stable/1814809
  • Khoury, M. J. & Ioannidis, J. P. A. 2014. Big Data Meets Public Health. Science, 346(6213), 1054–1055. https://doi.org/10.1126/science.aaa2709 DOI: https://doi.org/10.1126/science.aaa2709
  • Kohli, R. & Devaraj, S. (2003). Measuring information technology payoff: A meta-analysis of structural variables in firm-level empirical research. Information Systems Research, 14(2), 127–145. https://doi.org/10.1287/isre.14.2.127.16019 DOI: https://doi.org/10.1287/isre.14.2.127.16019
  • Kohli, R., & Grover, V. (2008). Business value of IT: An essay on expanding research directions to keep up with the times. Journal of the Association for Information Systems, 9(1), 1. https://doi.org/10.17705/1jais.00147 DOI: https://doi.org/10.17705/1jais.00147
  • Kroener, I. & Wright, D. (2014). A Strategy for Operationalizing Privacy by Design. The Information Society, 30(5), 355–365. https://doi.org/10.1080/01972243.2014.944730 DOI: https://doi.org/10.1080/01972243.2014.944730
  • Lee, I. & Shin, Y. J. (2020). Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, 63(2), 157–170. https://doi.org/10.1016/j.bushor.2019.10.005 DOI: https://doi.org/10.1016/j.bushor.2019.10.005
  • Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging technology and business model innovation: the case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44. https://doi.org/10.3390/joitmc5030044 DOI: https://doi.org/10.3390/joitmc5030044
  • Lepak, D. P., Smith, K. G., & Taylor, M. S. (2007). Value creation and value capture: A multilevel perspective. Academy of management review, 32(1), 180–194 https://doi.org/10.5465/amr.2007.23464011 DOI: https://doi.org/10.5465/amr.2007.23464011
  • Liebowitz, S. J. & Margolis, S. E. (1994). Network externality: An uncommon tragedy. Journal of Economic Perspectives, 8(2), 133–150. https://doi.org/10.1257/jep.8.2.133 DOI: https://doi.org/10.1257/jep.8.2.133
  • Meinhart, W. A. (1966). Artificial Intelligence, Computer Simulation of Human Cognitive and Social Processes, and Management Thought. The Academy of Management Journal, 9(4), 294–307. https://doi.org/10.5465/254948 DOI: https://doi.org/10.5465/254948
  • Melville, N., Kraemer, K., & Gurbaxani, V. (2004). Information technology and organizational performance: An integrative model of IT business value. MIS Quarterly, 28(2), 283–322. https://doi.org/10.2307/25148636 DOI: https://doi.org/10.2307/25148636
  • Merhi, M. I. (2023). An evaluation of the critical success factors impacting artificial intelligence implementation. International Journal of Information Management, 69, 102545. https://doi.org/10.1016/j.ijinfomgt.2022.102545 DOI: https://doi.org/10.1016/j.ijinfomgt.2022.102545
  • Pandey, A. & Mishra, S. (2021). Does the Executive Perception of the Value of Information Technology (IT) Influence the IT Strategy? A Case Study. Journal Of Information Systems Applied Research, 14(1), 24–35. https://jisar.org/2021-14/n1/JISARv14n1.pdf
  • Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023). Toward AI governance: Identifying best practices and potential barriers and outcomes. Information Systems Frontiers, 25(1), 123–141. https://doi.org/10.1007/s10796-022-10251-y DOI: https://doi.org/10.1007/s10796-022-10251-y
  • Porter, M. E. (2001). The value chain and competitive advantage. Understanding Business Processes, 2, 50–66.
  • Ragin, C. C. (2014). The comparative method: Moving beyond qualitative and quantitative strategies. Univ of California Press. DOI: https://doi.org/10.1525/9780520957350
  • Rai, A. & Tang, X. (2014). Research commentary—information technology-enabled business models: A conceptual framework and a coevolution perspective for future research. Information Systems Research, 25(1), 1–14. https://doi.org/10.1287/isre.2013.0495 DOI: https://doi.org/10.1287/isre.2013.0495
  • Rihoux, B. & Ragin, C. C. (2008). Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques. Sage Publications. DOI: https://doi.org/10.4135/9781452226569
  • Rosenblat, A. (2018). Uberland: How algorithms are rewriting the rules of work. Univ of California Press. DOI: https://doi.org/10.1525/9780520970632
  • Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019). Artificial intelligence and the implementation challenge. Journal of Medical Internet Research, 21(7), e13659. https://doi.org/10.2196/13659 DOI: https://doi.org/10.2196/13659
  • Sjödin, D., Parida, V., Jovanovic, M., & Visnjic, I. (2020). Value creation and value capture alignment in business model innovation: A process view on outcome-based business models. Journal of Product Innovation Management, 37(2), 158–183. https://doi.org/10.1111/jpim.12516 DOI: https://doi.org/10.1111/jpim.12516
  • Tallon, P. P., Kraemer, K. L., & Gurbaxani, V. (2000). Executives’ perceptions of the business value of information technology: a process-oriented approach. Journal of Management Information Systems, 16(4), 145–173. https://doi.org/10.1080/07421222.2000.11518269 DOI: https://doi.org/10.1080/07421222.2000.11518269
  • Tavory, I. & Timmermans, S. (2014). Abductive analysis: Theorizing qualitative research. University of Chicago Press. DOI: https://doi.org/10.7208/chicago/9780226180458.001.0001
  • Vargo, S. L. & Lusch, R. F. (2008). Service-dominant logic: continuing the evolution. Journal of the Academy of Marketing Science, 36(1), 1–10. https://doi.org/10.1007/s11747-007-0069-6 DOI: https://doi.org/10.1007/s11747-007-0069-6
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540
  • Windsor, D. (2017). Corporate citizenship: Evolution and interpretation. In J. Andriof & M. McIntosh (eds.), Perspectives on corporate citizenship (pp. 39–52). Routledge. https://doi.org/10.4324/9781351282369 DOI: https://doi.org/10.4324/9781351282369-3
  • Yang, A. & Ji, Y. G. (2019). The quest for legitimacy and the communication of strategic cross-sectoral partnership on Facebook: A big data study. Public Relations Review, 45(5), 101839. https://doi.org/10.1016/j.pubrev.2019.101839 DOI: https://doi.org/10.1016/j.pubrev.2019.101839
  • Zott, C. & Amit, R. (2008). The fit between product market strategy and business model: Implications for firm performance. Strategic Management Journal, 29(1), 1–26. https://doi.org/10.1002/smj.642 DOI: https://doi.org/10.1002/smj.642
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