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AI Strategic Managment Literature

Agrawal S, Chatterjee K, Novotný P. 2018. Lexicographic ranking supermartingales: an efficient approach to termination of probabilistic programs. Proceedings of the ACM on Programming Languages 2(POPL): 1–32. https://doi.org/10.1145/3158122.
Arduini M, Noci L, Pirovano F, Zhang C, Shrestha YR, Paudel B. 2020. Adversarial learning for debiasing knowledge graph embeddings. In MLG 2020: 16th International Workshop on Mining and Learning with Graphs—A Workshop at the KDD Conference, August 24, 2020, San Diego, CA: 7.
Avramov D, Cheng S, Metzker L. 2019. Machine learning versus economic restrictions: evidence from stock return predictability. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3450322.
Bail CA. 2014. The cultural environment: measuring culture with big data. Theory and Society 43(3/4): 465–482. http://www.jstor.org/stable/43694728.
Barney J. 1991. Firm resources and sustained competitive advantage. Journal of Management 17(1): 99–120. https://doi.org/10.1177/014920639101700108.
Barocas S, Selbst AD. 2016. Big data’s disparate impact. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2477899.
Baum JR, Wally S. 2003. Strategic decision speed and firm performance. Strategic Management Journal 24(11): 1107–1129. https://doi.org/10.1002/smj.343.
Biessmann F, Lehmann P, Kirsch D, Schelter S. 2016. Predicting political party affiliation from text. PolText, 14–19. https://ssc.io/pdf/poltext.pdf. Brynjolfsson E, Mitchell T. 2017. What can machine learning do? Workforce implications. Science 358(6370): 1530–1534. https://doi.org/10.1126/science.aap8062.
Castelvecchi D. 2016. Can we open the black box of AI? Nature 538(7623): 20–23. https://doi.org/10.1038/538020a.
Chalmers D, MacKenzie NG, Carter S. 2020. Artificial intelligence and entrepreneurship: implications for venture creation in the Fourth Industrial Revolution. Entrepreneurship Theory and Practice. https://doi.org/10.1177/1042258720934581.
Chan JT, Zhong W. 2018. Reading China: predicting policy change with machine learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3275687.
Cheung CF, Wang WM, Lo V, Lee WB. 2004. An agent-oriented and knowledge-based system for strategic e-procurement. Expert Systems 21(1): 11–21.
Cielen A, Peeters L, Vanhoof K. 2004. Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research 154(2): 526–532. https://doi.org/https://doi.org/10.1016/S0377-2217(03)00186-3.
Cockburn I, Henderson R, Stern S. 2018. The impact of artificial intelligence on innovation. NBER Working Paper Series 24449. National Bureau of Economic Research, Cambridge, MA. https://doi.org/10.3386/w24449.
Coletto M, Lucchese C, Orlando S, Perego R. 2015. Electoral predictions with Twitter: a machine-learning approach. CEUR Workshop Proceedings. http://eprints.imtlucca.it/3489/1/paper_19.pdf [1 August 2020].
Davenport TH, Ronanki R. 2018. Artificial intelligence for the real world. Harvard Business Review 96(1): 108–116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world [1 August 2020].
Dodge J, Gururangan S, Card D, Schwartz R, Smith NA. 2020. Show your work: improved reporting of experimental results. EMNLP-IJCNLP 2019: 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2185–2194.
Doyle G, Srivastava SB, Goldberg A, Frank MC. 2017. Alignment at work: using language to distinguish the internalization and self-regulation components of cultural fit in organizations. ACL 2017—55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) 1: 603–612.
Ferràs-Hernández X, Tarrats-Pons E, Arimany-Serrat N. 2017. Disruption in the automotive industry: a Cambrian moment. Business Horizons 60(6): 855–863. https://doi.org/10.1016/j.bushor.2017.07.011.
Fethi MD, Pasiouras F. 2010. Assessing bank efficiency and performance with operational research and artificial intelligence techniques: a survey. European Journal of Operational Research 204(2): 189–198.
Fleder D, Hosanagar K. 2009. Blockbuster culture’s next rise or fall: the impact of recommender systems on sales diversity. Management Science 55(5): 697–712. http://www.jstor.org/stable/40539182.
Franz L, Shrestha YR, Paudel B. 2020. A deep learning pipeline for patient diagnosis prediction using electronic health records. In BioKDD 2020: 19th International Workshop on Data Mining in Bioinformatics, August 24, 2020, San Diego, CA.
Glikson E, Woolley AW. 2020. Human trust in artificial intelligence: review of empirical research. Academy of Management Annals 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057.
Goldfarb A, Tucker C. 2019. Digital economics. Journal of Economic Literature 57(1): 3–43.
Gomez-Uribe CA, Hunt N. 2015. The Netflix recommender system: algorithms, business value, and innovation. ACM Transactions on Management Information Systems 6(4): 1–19. https://doi.org/10.1145/2843948.
Haefliger S, Jäger P, von Krogh G. 2010. Under the radar: industry entry by user entrepreneurs. Research Policy 39(9): 1198–1213. https://ideas.repec.org/a/eee/respol/v39y2010i9p1198-1213.html.
Haenlein M, Kaplan A. 2019. A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. California Management Review 61(4): 5–14. https://doi.org/10.1177/0008125619864925.
Haki K, Beese J, Aier S, Winter R. 2020. The evolution of information systems architecture: an agent-based simulation model. Management Information Systems Quarterly 44(1). https://aisel.aisnet.org/misq/vol44/iss1/8.
Hamel G, Prahalad CK. 1994. Competing for the Future. Harvard Business School Press: Boston, MA. http://www.books24x7.com/marc.asp?bookid=2420.
Hamet P, Tremblay J. 2017. Artificial intelligence in medicine. Metabolism: Clinical and Experimental 69: S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011.
Hardt M, Price E, Srebro N. 2016. Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 3323–3331. https://arxiv.org/abs/1610.02413v1.
Harrington JE. 2018. Developing competition law for collusion by autonomous artificial agents. Journal of Competition Law and Economics 14(3): 331–363. https://doi.org/10.1093/joclec/nhy016.
He VF, Puranam P, Shrestha YR, von Krogh G. 2020 Resolving governance disputes in communities: a study of software license decisions. Strategic Management Journal 41 (10): 1837–1868.
Heinonen K. 2011. Consumer activity in social media: Managerial approaches to consumers’ social media behavior. Journal of Consumer Behaviour 10(6): 356–364. https://doi.org/10.1002/cb.376.
Hitt MA, Ireland RD, Hoskisson RE. 2015. Strategic Management: Competitiveness and Globalization--Concepts and Cases. Cengage Learning Asia Pte Ltd.: Boston, MA.
Hollebeek LD, Conduit J, Brodie RJ. 2016. Strategic drivers, anticipated and unanticipated outcomes of customer engagement. Journal of Marketing Management 32(5–6): 393–398.
Holstein K, Wortman Vaughan J, Daumé H, Dudik M, Wallach H. 2019. Improving fairness in machine learning systems: what do industry practitioners need? Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3290605.3300830.
Homburg C, Bucerius M. 2006. Is speed of integration really a success factor of mergers and acquisitions? An analysis of the role of internal and external relatedness. Strategic Management Journal 27(4): 347–367. https://doi.org/10.1002/smj.520
Huang AC, Jiang T, Liu YX, Bai YC, Reed J, Qu B, Goossens A, Nützmann HW, Bai Y, Osbourn A. 2019. A specialized metabolic network selectively modulates Arabidopsis root microbiota. Science 364(6440). https://doi.org/10.1126/science.aau6389.
Humphreys P, McIvor R, Huang G. 2002. An expert system for evaluating the make-or-buy decision. Computers and Industrial Engineering 42(2): 567–585. Ireland RD,
Hitt MA, Sirmon DG. 2003. A model of strategic entrepreneurship: the construct and its dimensions. Journal of Management 29(6): 963–989. https://doi.org/10.1016/s0149-2063_03_00086-2.
Jarrahi MH. 2018. Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Business Horizons 61(4): 577–586. https://doi.org/10.1016/j.bushor.2018.03.007.
Jarzabkowski P, Balogun J, Seidl D. 2007. Strategizing: the challenges of a practice perspective. Human Relations 60(1): 5–27. https://doi.org/10.1177/0018726707075703. Jean N, Burke M, Xie M, Davis WM, Lobell DB, Ermon S. 2016. Combining satellite imagery and machine learning to predict poverty. Science 353(6301): 790–794. https://doi.org/10.1126/science.aaf7894.
Keding C. 2020. Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. Management Review Quarterly 71: 91–134. https://doi.org/10.1007/s11301-020-00181-x.
King WR, He J. 2006. A meta-analysis of the technology acceptance model. Information and Management 43(6): 740–755. https://doi.org/10.1016/j.im.2006.05.003.
Kleinberg J, Mullainathan S, Raghavan M. 2017. Inherent trade-offs in the fair determination of risk scores. Leibniz International Proceedings in Informatics (LIPIcs), 67. https://doi.org/10.4230/LIPIcs.ITCS.2017.43.
Kou G, Chao X, Peng Y, Alsaadi FE, Herrera-Viedma E. 2019. Machine learning methods for systemic risk analysis in financial sectors. Technological and Economic Development of Economy 25(5): 716–742. https://doi.org/10.3846/tede.2019.8740.
Kshirsagar V, Wieczorek J, Ramanathan S, Wells R. 2017. Household poverty classification in data-scarce environments: a machine learning approach. ArXiv: Machine Learning. https://arxiv.org/abs/1711.06813.
Kucukkeles B, Ben-Menahem SM, von Krogh G. 2019. Small numbers, big concerns: practices and organizational arrangements in rare disease drug repurposing. Academy of Management Discoveries 5(4): 415–437. https://doi.org/10.5465/amd.2018.0183.
Lawrence T. 1991. Impacts of artificial intelligence on organizational decision making. Journal of Behavioral Decision Making 4(3): 195–214. https://doi.org/10.1002/bdm.3960040306.
Lee C, Kwon O, Kim M, Kwon D. 2018. Early identification of emerging technologies: a machine learning approach using multiple patent indicators. Technological Forecasting and Social Change 127: 291–303. https://doi.org/10.1016/j.techfore.2017.10.002.
Liebeskind JP. 1996. Knowledge, strategy, and the theory of the firm. Strategic Management Journal 17(S2): 93–107.
March JG, Simon HA. 1958. Organizations. Wiley: New York, NY. McCarthy J. 1981. Epistemological problems of artificial intelligence. In Readings in Artificial Intelligence, Webber BL, Nilsson NJ (eds). Elsevier: Burlington, MA; 459–465. https://doi.org/10.1016/b978-0-934613-03-3.50035-0.
McCorduck P. 2004. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. AK Peters / CRC Press: Natick, MA.
Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. 2019. A survey on bias and fairness in machine learning. ArXiv, https://arxiv.org/pdf/1908.09635.pdf abs/1908.0.
Menon A, Choi J, Tabakovic H. 2018. What you say your strategy is and why it matters: natural language processing of unstructured text. Academy of Management Proceedings 2018(1). https://doi.org/10.5465/ambpp.2018.18319abstract.
Mithas S, Rust R. 2016. How information technology strategy and investments influence firm performance: conjecture and empirical evidence. Management Information Systems Quarterly 40(1). https://aisel.aisnet.org/misq/vol40/iss1/12.
Nag R, Hambrick DC, Chen MJ. 2007. What is strategic management, really? Inductive derivation of a consensus definition of the field. Strategic Management Journal 28(9): 935–955. https://doi.org/10.1002/smj.615.
Nissen ME, Sengupta K. 2006. Incorporating software agents into supply chains: experimental investigation with a procurement task. MIS Quarterly: Management Information Systems 30(1): 145–166. https://doi.org/10.2307/25148721.
Pant G, Sheng ORL. 2015. Web footprints of firms: using online isomorphism for competitor identification. Information Systems Research 26(1): 188–209. https://doi.org/10.1287/isre.2014.0563.
Powell TC, Dent-Micallef A. 1997. Information technology as competitive advantage: the role of human, business, and technology resources. Strategic Management Journal 18(5): 375–405. https://doi.org/10.1002/(SICI)1097-0266(199705)18:5<375::AID-SMJ876>3.0.CO;2-7.
Ranaei S, Karvonen M, Suominen, A, Kässi, T. 2016. Patent-based technology forecasting: case of electric and hydrogen vehicle. International Journal of Energy Technology and Policy 12(1): 20–40. https://doi.org/10.1504/IJETP.2016.074490.
Randall C, Dent EB. 2019. Reconciling the historical divide between strategy process and strategy content. Journal of Management History 25(3): 401–427. https://doi.org/10.1108/JMH-11-2018-0062.
Reeves M, Levin S, Ueda D. 2016. The biology of corporate survival. BCG Henderson Institute. https://hbr.org/2016/01/the-biology-of-corporate-survival.
Robinson CV, Ahmad F, Simmons JEL. 2021. Consolidation and fragmentation in environmental scanning: a review and research agenda. Long Range Planning, forthcoming. https://doi.org/10.1016/j.lrp.2020.101997.
Shrestha YR. 2019. Bridging data science and organization science: leveraging algorithmic induction to research online communities. Doctoral dissertation, ETH Zurich. https://doi.org/10.3929/ethz-b-000332700.
Shrestha YR, Ben-Menahem SM, von Krogh G. 2019. Organizational decision-making structures in the age of artificial intelligence. California Management Review 61(4): 66–83. https://doi.org/10.1177/0008125619862257.
Shrestha YR, He VF, Puranam P, von Krogh G. Forthcoming. Algorithmic induction through machine learning: opportunities for management and organization research. Organization Science.
Shrestha YR, Krishna V, von Krogh, G. 2021 Augmenting organizational decision-making with deep learning algorithms: principles, promises, and challenges. Journal of Business Research 123: 588–603.
Shrestha YR, Yang Y. 2019. Fairness in algorithmic decision-making: applications in multi-winner voting, machine learning, and recommender systems Algorithms 12 (9): 199–227.
Simon HA. 1947. Administrative Behavior: A Study of Decision-Making Processes in Administrative Organization. Macmillan: New York, NY.
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Suominen A, Toivanen H, Seppänen M. 2017. Firms’ knowledge profiles: mapping patent data with unsupervised learning. Technological Forecasting and Social Change 115: 131–142. https://doi.org/10.1016/j.techfore.2016.09.028.
Treleaven P, Batrinca B. 2017. Algorithmic regulation: automating financial compliance monitoring and regulation using AI and blockchain. Journal of Financial Transformation 45: 14–21. https://econpapers.repec.org/RePEc:ris:jofitr:1586.
von Krogh G. 2018. Artificial intelligence in organizations: new opportunities for phenomenon-based theorizing. Academy of Management Discoveries 4(4): 404–409. https://doi.org/10.5465/amd.2018.0084.
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Yousfi-Monod M, Farzindar A, Lapalme G. 2010. Supervised machine learning for summarizing legal documents. Canadian AI 2010. In Lecture Notes in Artificial Intelligence, vol. 6085, Farzindar A, Keselj V (eds). Springer-Verlag: Berlin, Germany; 51–62. https://doi.org/10.1007/978-3-642-13059-5_8.
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