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Understanding the interplay of artificial intelligence and strategic management: four decades of research in review

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Abstract

As artificial intelligence (AI) is enabling the automation of many facets of management and is increasingly used in a wide range of strategic tasks, it is necessary to better understand its relevance for strategic management. However, research on the interplay of AI and strategic management is unbalanced and lacks a coherent structure due to its multidisciplinarity. This article contributes to the emerging academic discussion by systematically reviewing and categorizing the substantial amount of research that has been conducted since the first article in the field was published in 1979. Furthermore, it introduces a comprehensive framework that integrates and synthesizes existing concepts. The framework displays the structure of the research field by classifying 58 relevant articles into two research scopes: condition-oriented research, which explores antecedents for leveraging the use of AI in strategic management, and outcome-oriented research, which studies the consequences of AI in strategic management at both the individual and the organizational level. Given the exponential potential of AI to reshape the field in its current form and the need for a realistic assessment of its impact, this review proposes promising research avenues for studying the quantifiable effects of the interplay of AI and strategic management based on the developed framework.

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Notes

  1. The Jourqual 3 is a magazine ranking on the basis of the judgments of VHB members. It is published by the German Academic Association for Business Research (VHB) and can be accessed online at http://www.vhbonline.org (retrieved on November 15th, 2018).

References

  • Acemoglu D, Restrepo P (2018) The race between man and machine: implications of technology for growth, factor shares, and employment. Am Econ Rev 108:1488–1542

    Google Scholar 

  • Agrawal A, Gans JS, Goldfarb A (2017) What to expect from artificial intelligence. MIT Sloan Manag Rev 58:23–26

    Google Scholar 

  • Ahn H, Clermont M, Schwetschke S (2018) Research on target costing: past, present and future. Manag Rev Q 68:321–354

    Google Scholar 

  • Akyuz GA, Gursoy G (2019) Strategic management perspectives on supply chain. Manag Rev Q. https://doi.org/10.1007/s11301-019-00165-6

    Article  Google Scholar 

  • Ashmore GM (1989) Applying expert systems to business strategy. J Bus Strategy 10:46–49

    Google Scholar 

  • Autor DH (2015) Why are there still so many jobs? The history and future of workplace automation. J Econ Perspect 29:3–30

    Google Scholar 

  • Ayoub K, Payne K (2016) Strategy in the age of artificial intelligence. J Strateg Stud 39:793–819

    Google Scholar 

  • Bader V, Kaiser S (2019) Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence. Organization 26:655–672

    Google Scholar 

  • Bani-Hani I, Tona O, Carlsson S (2018) From an information consumer to an information author: a new approach to business intelligence. J Organ Comput Electron Commer 28:157–171

    Google Scholar 

  • Barki H, Rivard S, Talbot J (1993) A keyword classification scheme for IS research literature: an update. MIS Q 17:209–226

    Google Scholar 

  • Barro S, Davenport TH (2019) People and machines: partners in innovation. MIT Sloan Manag Rev 60:22–30

    Google Scholar 

  • Berman S, Dalzell-Payne P (2018) The interaction of strategy and technology in an era of business re-invention. Strategy Leadersh 46:10–15

    Google Scholar 

  • Bettis RA (2017) Organizationally intractable decision problems and the intellectual virtues of heuristics. J Manag 43:2620–2637

    Google Scholar 

  • Bonczek RH, Holsapple CW, Whinston AB (1979) Computer-based support of organizational decision making. Decis Sci 10:268–291

    Google Scholar 

  • Bowman EH, Singh H, Thomas H (2002) The domain of strategic management: history and evolution. Handbook Strategy Manag 3:31–51

    Google Scholar 

  • Breitenmoser A, Bader B (2016) Repatriation outcomes affecting corporate ROI: a critical review and future agenda. Manag Rev Q 66:195–234

    Google Scholar 

  • Brocke JV, Simons A, Riemer K, Niehaves B, Platfaut R (2015) Standing on the shoulders of giants: challenges and recommendations of literature search in information systems research. Commun Assoc Inf Syst 37:205–224

    Google Scholar 

  • Brynjolfsson E, McAfee A (2016) The second machine age: work, progress, and prosperity in a time of brilliant technologies. WW Norton & Co, New York

    Google Scholar 

  • Brynjolfsson E, Mitchell T (2017) What can machine learning do? Workforce implications: profound change is coming, but roles for humans remain. Science 358:1530–1534

    Google Scholar 

  • Carlsson C, Walden P (1997) Cognitive maps and a hyperknowledge support system in strategic management. Group Decis Negot 6:7–36

    Google Scholar 

  • Chi RT, Turban E (1995) Distributed intelligent executive information systems. Decis Support Syst 14:117–129

    Google Scholar 

  • Colson E (2019) What AI-driven decision making looks like. Harvard Bus Rev. https://hbr.org/2019/07/what-ai-driven-decision-making-looks-like. Accessed 30 Nov 2019

  • Constantiou ID, Kallinikos J (2015) New games, new rules: big data and the changing context of strategy. J Inf Technol 30:44–57

    Google Scholar 

  • D’Acunto F, Prabhala N, Rossi AG (2019) The promises and pitfalls of robo-advising. Rev Financial Stud 32:1983–2020

    Google Scholar 

  • Davenport TH (2013) Keep up with your quants. Harvard Bus Rev 91:120–123

    Google Scholar 

  • Davenport TH (2016) Rise of the strategy machines. MIT Sloan Manag Rev 58:13–16

    Google Scholar 

  • Davenport TH, Harris JG (2005) Automated decision making comes of age. MIT Sloan Manag Rev 46:83–89

    Google Scholar 

  • Davenport TH, Kirby J (2015) Beyond automation. Harvard Bus Rev 93:58–65

    Google Scholar 

  • Davenport TH, Kirby J (2016) Just How smart are smart machines? MIT Sloan Manag Rev 57:21–25

    Google Scholar 

  • Davenport TH, Mahidhar V (2018) What’s your cognitive strategy? In the eyes of many leaders, artificial intelligence and cognitive technologies are the most disruptive forces on the horizon. But most organizations don’t have a strategy to address them. MIT Sloan Manag Rev 59:19–23

    Google Scholar 

  • Davenport TH, Ronanki R (2018) Artificial intelligence for the real world. Harvard Bus Rev 96:108–116

    Google Scholar 

  • David FR, David FR (2017) Strategic management: a competitive advantage approach, concepts and cases. Pearson, Boston

    Google Scholar 

  • Dawes RM (1979) The robust beauty of improper linear models in decision making. Am Psychol 34:571–582

    Google Scholar 

  • Di Ciccio C, Marrella A, Russo A (2015) Knowledge-intensive processes: characteristics, requirements and analysis of contemporary approaches. J Data Semant 4:29–57

    Google Scholar 

  • Diakopoulos N (2016) Accountability in algorithmic decision making. Commun ACM 59:56–62

    Google Scholar 

  • Dias CSL, Ferreira JJ (2019) What we (do not) know about research in the strategic management of technological innovation? Innov Organ Manag 21:398–420

    Google Scholar 

  • Dietvorst BJ, Simmons JP, Massey C (2018) Overcoming algorithm aversion: people will use imperfect algorithms if they can (even slightly) modify them. Manag Sci 64:1155–1170

    Google Scholar 

  • Duan Y, Edwards JS, Dwivedi YK (2019) Artificial intelligence for decision making in the era of Big Data—evolution, challenges and research agenda. Int J Inf Manag 48:63–71

    Google Scholar 

  • Edwards JS, Yanqing D (2000) An analysis of expert systems for business decision making at different levels and in different roles. Eur J Inf Syst 9:36

    Google Scholar 

  • Eisend M (2011) Is VHB-JOURQUAL2 a good measure of scientific quality? Assessing the validity of the major business journal ranking in German-speaking countries. Bus Res 4:241–274

    Google Scholar 

  • Epstein SL (2015) Wanted: collaborative intelligence. Artif Intell 221:36–45

    Google Scholar 

  • Faraj S, Pachidi S, Sayegh K (2018) Working and organizing in the age of the learning algorithm. Inf Organ 28:62–70

    Google Scholar 

  • Ferràs-Hernández X (2018) The future of management in a world of electronic brains. J Manag Inq 27:260–263

    Google Scholar 

  • Fisch C, Block J (2018) Six tips for your (systematic) literature review in business and management research. Manag Rev Q 68:103–106

    Google Scholar 

  • Fosso Wamba S, Akter S, Edwards A, Chopin G, Gnanzou D (2015) How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int J Prod Econ 165:234–246

    Google Scholar 

  • Fountaine T, McCarthy B, Saleh T (2019) Building the AI-powered organization. Harvard Bus Rev 97:62–73

    Google Scholar 

  • Frank H, Hatak I (2014) Doing a research literature review. In: Fayolle A, Wright M (eds) How to get published in the best entrepreneurship journals. Edward Elger, Cheltenham, pp 94–117

    Google Scholar 

  • Furrer O, Thomas H, Goussevskaia A (2008) The structure and evolution of the strategic management field: a content analysis of 26 years of strategic management research. Int J Manag Rev 10:1–23

    Google Scholar 

  • Garfinkel S, Matthews J, Shapiro SS, Smith JM (2017) Toward algorithmic transparency and accountability. Commun ACM 60:5

    Google Scholar 

  • Geisler E (1986) Artificial management and the artificial manager. Bus Horiz 29:17–21

    Google Scholar 

  • George G, Haas MR, Pentland A (2014) Big data and management. Acad Manag J 57:321–326

    Google Scholar 

  • Ghasemaghaei M, Ebrahimi S, Hassanein K (2018) Data analytics competency for improving firm decision making performance. J Strateg Inf Syst 27:101–113

    Google Scholar 

  • Ginsberg A, Venkatraman N (1985) Contingency perspectives if organizational strategy: a critical review of the empirical research. Acad Manag Rev 10:421–434

    Google Scholar 

  • Grover V, Chiang RHL, Liang T-P, Zhang D (2018) Creating strategic business value from big data analytics: a research framework. J Manag Inf Syst 35:388–423

    Google Scholar 

  • Gunasekaran A, Papadopoulos T, Dubey R, Wamba SF, Childe SJ, Hazen B, Akter S (2017) Big data and predictive analytics for supply chain and organizational performance. J Bus Res 70:308–317

    Google Scholar 

  • Gutmann T (2019) Harmonizing corporate venturing modes: an integrative review and research agenda. Manag Rev Q 69:121–157

    Google Scholar 

  • Hambrick DC (2007) The field of management’s devotion to theory: too much of a good thing? Acad Manag J 50:1346–1352

    Google Scholar 

  • Hickson DJ, Miller SJ, Wilson DC (2003) Planned or prioritized? Two options in managing the implementation of strategic decisions. J Manag Stud 40:1803–1836

    Google Scholar 

  • Hirsch PB (2018) Tie me to the mast: artificial intelligence & reputation risk management. J Bus Strategy 39:61–64

    Google Scholar 

  • Hoffman R (2016) Using artificial intelligence to set information free. MIT Sloan Manag Rev 58:1–5

    Google Scholar 

  • Holloway C (1983) Strategic management and artificial intelligence. Long Range Plan 16:89–93

    Google Scholar 

  • Huang M-H, Rust RT (2018) Artificial intelligence in service. J Serv Res 21:155–172

    Google Scholar 

  • Huang M-H, Rust RT, Maksimovic V (2019) The feeling economy: managing in the next generation of artificial intelligence (AI). Calif Manag Rev 61:43–65

    Google Scholar 

  • Intezari A, Gressel S (2017) Information and reformation in KM systems: big data and strategic decision-making. J Knowl Manag 21:71–91

    Google Scholar 

  • Jaderberg M et al (2019) Human-level performance in 3D multiplayer games with population-based reinforcement learning. Science 364:859–865

    Google Scholar 

  • Janssen M, van der Voort H, Wahyudi A (2017) Factors influencing big data decision-making quality. J Bus Res 70:338–345

    Google Scholar 

  • Jarrahi MH (2018) Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Bus Horiz 61:577–586

    Google Scholar 

  • Jordan J (2017) Challenges to large-scale digital organization: the case of Uber. J Organ Des 6:1–12

    Google Scholar 

  • Kahneman D, Rosenfield AM, Gandhi L, Blaser T (2016) Noise: how to overcome the high, hidden cost of inconsistent decision making. Harvard Bus Rev 94:38–46

    Google Scholar 

  • Kaplan A, Haenlein M (2019) Siri, Siri, in my hand: who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz 62:15–25

    Google Scholar 

  • Kiron D, Schrage M (2019) Strategy for and with AI. MIT Sloan Manag Rev 60:30–37

    Google Scholar 

  • Kolbjørnsrud V, Amico R, Thomas RJ (2017) Partnering with AI: how organizations can win over skeptical managers. Strategy Leadersh 45:37–43

    Google Scholar 

  • Kowalczyk M, Buxmann P (2014) Big data and information processing in organizational decision processes. Bus Inf Syst Eng 6:267–278

    Google Scholar 

  • Kurzweil R (1999) Spiritual machines: the merging of man and machine. Futurist 33:16–21

    Google Scholar 

  • Lado AA, Zhang MJ (1998) Expert systems, knowledge development and utilization, and sustained competitive advantage: a resource-based model. J Manag 24:489–509

    Google Scholar 

  • Lambrecht A, Tucker C (2019) Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Manag Sci 65:2966–2981

    Google Scholar 

  • Lau RYK, Liao SSY, Wong KF, Chiu DKW (2012) Web 2.0 environmental scanning and adaptive decision support for business mergers and acquisitions. MIS Q 36:1239–1268

    Google Scholar 

  • Lawrence T (1991) Impacts of artificial intelligence on organizational decision making. J Behav Decis Mak 4:195–214

    Google Scholar 

  • Lee MK (2018) Understanding perception of algorithmic decisions: fairness, trust, and emotion in response to algorithmic management. Big Data Soc 5:1–16

    Google Scholar 

  • Levina T, Levin Y, McGill J, Nediak M (2009) Dynamic pricing with online learning and strategic consumers: an application of the aggregating algorithm. Oper Res 57:327–341

    Google Scholar 

  • Levy Y, Ellis TJ (2006) A systems approach to conduct an effective literature review in support of information systems research. Inf Sci Int J Emerg Transdiscipl 9:181–212

    Google Scholar 

  • Li J, Lu X, Li J, Wu D (2019) Evaluating journal quality by integrating department journal lists in a developing country: are they representative? J Acad Librariansh 45:102067

    Google Scholar 

  • Lichtenthaler U (2018) Substitute or synthesis? The interplay between human and artifcial intelligence. Res Technol Manag 61:12–14

    Google Scholar 

  • Lichtenthaler U (2019) Extremes of acceptance: employee attitudes toward artificial intelligence. J Bus Strategy. https://doi.org/10.1108/JBS-12-2018-0204

    Article  Google Scholar 

  • Logg JM, Minson JA, Moore DA (2019) Algorithm appreciation: people prefer algorithmic to human judgment. Organ Behav Hum Decis Process 151:90–103

    Google Scholar 

  • Lu C-J, Lee T-S, Lian C-M (2012) Sales forecasting for computer wholesalers: a comparison of multivariate adaptive regression splines and artificial neural networks. Decis Support Syst 54:584–596

    Google Scholar 

  • Luconi FL, Malone TW, Scott Morton MS (1986) Expert systems: the next challenge for managers. Sloan Manag Rev 27:3–14

    Google Scholar 

  • Martínez-López FJ, Casillas J (2013) Artificial intelligence-based systems applied in industrial marketing: an historical overview, current and future insights. Ind Mark Manag 42:489–495

    Google Scholar 

  • Mazzei MJ, Noble D (2017) Big data dreams: a framework for corporate strategy. Bus Horiz 60:405–414

    Google Scholar 

  • Merendino A, Dibb S, Meadows M, Quinn L, Wilson D, Simkin L, Canhoto A (2018) Big data, big decisions: the impact of big data on board level decision-making. J Bus Res 93:67–78

    Google Scholar 

  • Merten PP (1991) Loop-based strategic decision support systems. Strateg Manag J 12:371–386

    Google Scholar 

  • Metcalf L, Askay DA, Rosenberg LB (2019) Keeping humans in the loop: pooling knowledge through artificial swarm intelligence to improve business decision making. Calif Manag Rev 61:84–109

    Google Scholar 

  • Mitchell M (2019) Artificial intelligence hits the barrier of meaning. Information 10:51

    Google Scholar 

  • Nag R, Hambrick DC, Chen M-J (2007) What is strategic management, really? Inductive derivation of a consensus definition of the field. Strateg Manag J 28:935–955

    Google Scholar 

  • Olszak CM (2016) Toward better understanding and use of business intelligence in organizations. Inf Syst Manag 33:105–123

    Google Scholar 

  • Orsini J-F (1986) Artificial intelligence: a way through the strategic planning crisis? Long Range Plan 19:71–77

    Google Scholar 

  • Orwig R, Chen H, Vogel D, Nunamaker JF (1997) A multi-agent view of strategic planning using group support systems and artificial intelligence. Group Decis Negot 6:37–59

    Google Scholar 

  • Parry K, Cohen M, Bhattacharya S (2016) Rise of the machines: a critical consideration of automated leadership decision making in organizations. Group Organ Manag 41:571–594

    Google Scholar 

  • Phan P, Wright M, Soo-Hoon L (2017) Of robots, artificial intelligence, and work. Acad Manag Perspect 31:253–255

    Google Scholar 

  • Phillips-Wren G, Mora M, Forgionne GA, Gupta JND (2009) An integrative evaluation framework for intelligent decision support systems. Eur J Oper Res 195:642–652

    Google Scholar 

  • Pinson S, Moraltis P (1997) An intelligent distributed system for strategic decision making. Group Decis Negot 6:77–108

    Google Scholar 

  • Plastino E, Purdy M (2018) Game changing value from Artificial Intelligence: eight strategies. Strategy Leadersh 46:16–22

    Google Scholar 

  • Pomerol J-C (1997) Artificial intelligence and human decision making. Eur J Oper Res 99:3–25

    Google Scholar 

  • Prahl A, Van Swol L (2017) Understanding algorithm aversion: when is advice from automation discounted? J Forecast 36:691–702

    Google Scholar 

  • Raghunathan S (1999) Impact of information quality and decision-maker quality on decision quality: a theoretical model. Decis Support Syst 26:275–286

    Google Scholar 

  • Ransbotham S, Gerbert P, Reeves M, Kiron D, Spira M (2018) Artificial intelligence in business gets real. MIT Sloan Manag Rev. https://sloanreview.mit.edu/projects/artificial-intelligence-in-business-gets-real/. Accessed 28 Nov 2019

  • Reeves M, Ueda D (2016) Designing the machines that will design strategy. Harvard Bus Rev. https://hbr.org/2016/04/welcoming-the-chief-strategy-robot. Accessed 5 May 2019

  • Reis J, Santo PE, Melão N (2019) Impacts of artificial intelligence on public administration: a systematic literature review. In: 2019 14th Iberian conference on information systems and technologies (CISTI), 19–22 June 2019, pp 1–7

  • Ronda-Pupo GA, Guerras-Martin LÁ (2012) Dynamics of the evolution of the strategy concept 1962–2008: a co-word analysis. Strateg Manag J 33:162–188

    Google Scholar 

  • Russakovsky O et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115:211–252

    Google Scholar 

  • Russell S, Norvig P (2009) Artificial intelligence: a modern approach. Prentice Hall Press, Upper Saddle River

    Google Scholar 

  • Schildt H (2017) Big data and organizational design—the brave new world of algorithmic management and computer augmented transparency. Innovation 19:23–30

    Google Scholar 

  • Schneider S, Leyer M (2019) Me or information technology? Adoption of artificial intelligence in the delegation of personal strategic decisions. Manag Decis Econ 40:223–231

    Google Scholar 

  • Schrader U, Hennig-Thurau T (2009) VHB-JOURQUAL2: method, results, and implications of the german academic association for business research’s journal ranking. Bus Res 2:180–204

    Google Scholar 

  • Shrestha YR, Ben-Menahem SM, von Krogh G (2019) Organizational decision-making structures in the age of artificial intelligence. Calif Manag Rev 61:66–83

    Google Scholar 

  • Silver D et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529:484–849

    Google Scholar 

  • Snyder H (2019) Literature review as a research methodology: an overview and guidelines. J Bus Res 104:333–339

    Google Scholar 

  • Sousa WGD, Melo ERPD, Bermejo PHDS, Farias RAS, Gomes AO (2019) How and where is artificial intelligence in the public sector going? A literature review and research agenda. Gov Inf Q 36:101392

    Google Scholar 

  • Tokic D (2018) BlackRock Robo-Advisor 4.0: when artificial intelligence replaces human discretion. Strateg Change 27:285–290

    Google Scholar 

  • Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14:207–222

    Google Scholar 

  • Uden L, He W (2017) How the Internet of things can help knowledge management: a case study from the automotive domain. J Knowl Manag 21:57–70

    Google Scholar 

  • Vidgen R, Shaw S, Grant DB (2017) Management challenges in creating value from business analytics. Eur J Oper Res 261:626–639

    Google Scholar 

  • von Krogh G (2018) Artificial intelligence in organizations: new opportunities for phenomenon-based theorizing. Acad Manag Discov 4:404–409

    Google Scholar 

  • Watson HJ (2017) Preparing for the cognitive generation of decision support. MIS Q Exec 16:153–169

    Google Scholar 

  • Webster J, Watson RT (2002) Analyzing the past to prepare for the future: writing a literature review. MIS Q 26:13–23

    Google Scholar 

  • Weizenbaum J (1966) ELIZA—a computer program for the study of natural language communication between man and machine. Commun ACM 9:36–45

    Google Scholar 

  • Wesche JS, Sonderegger A (2019) When computers take the lead: the automation of leadership. Comput Hum Behav 101:197–209

    Google Scholar 

  • Wilson HJ, Daugherty PR (2018) Collaborative intelligence: humans and AI are joining forces. Harvard Bus Rev 96:114–123

    Google Scholar 

  • Wilson HJ, Daugherty P, Bianzino N (2017) The jobs that artificial intelligence will create. MIT Sloan Manag Rev 58:14–16

    Google Scholar 

  • Wohlin C (2014) Guidelines for snowballing in systematic literature studies and a replication in software engineering. Paper presented at the 18th International conference on evaluation and assessment in software engineering, London, 13–14 May 2014

  • Wright SA, Schultz AE (2018) The rising tide of artificial intelligence and business automation: developing an ethical framework. Bus Horiz 61:823–832

    Google Scholar 

  • Yeomans M, Shah A, Mullainathan S, Kleinberg J (2019) Making sense of recommendations. J Behav Decis Mak 32:403–414

    Google Scholar 

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Acknowledgements

I am particularly thankful to the editor and the two anonymous reviewers for their constructive comments, which were very helpful for the revision of the manuscript. I would also like to thank Philip Meissner (ESCP Business School) for his valuable feedback on earlier versions of this paper.

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    Christoph Keding

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Keding, C. Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. Manag Rev Q 71, 91–134 (2021). https://doi.org/10.1007/s11301-020-00181-x

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