Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
A Practical Grafting Model Based Explainable AI for Predicting Corporate Financial Distress Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Developing an Artificial Intelligence Capability: A Theoretical Framework for Business Value

verfasst von : Patrick Mikalef, Siw Olsen Fjørtoft, Hans Yngvar Torvatn

Erschienen in: Business Information Systems Workshops

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Despite the claim that Artificial Intelligence (AI) can revolutionize the way private and public organizations do business, to date organizations still face a number of obstacles in leveraging such technologies and realizing performance gains. Past studies in other novel information technologies argue that organizations must develop a capability of effectively orchestrating and deploying necessary complementary resources. We contend that if organizations aim to realize any substantial performance gains from their AI investments, they must develop and promote an AI Capability. This paper theoretically develops the concept of an AI capability and presents the main dimensions that comprise it. To do so, we ground this concept in the resource-based view of the firm and by surveying the latest literature on AI, we identify the constituent components that jointly comprise it.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Social Network Advertising Classification Based on Content Categories
Nächstes Kapitel Measuring Qualitative Performance Criteria with Fuzzy Sets
Literatur
1.
Davenport, T.H., Ronanki, R.: Artificial intelligence for the real world. Harv. Bus. Rev. 96, 108–116 (2018)
2.
Brynjolfsson, E., Rock, D., Syverson, C.: Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. The economics of artificial intelligence: An agenda. University of Chicago Press (2018)
3.
Jarrahi, M.H.: Artificial intelligence and the future of work human-AI symbiosis in organizational decision making. Bus. Horiz. 61, 577–586 (2018)CrossRef
4.
Pavlou, P.A., El Sawy, O.A.: From IT leveraging competence to competitive advantage in turbulent environments: the case of new product development. Inf. Syst. Res. 17, 198–227 (2006)CrossRef
5.
Bharadwaj, A.S.: A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Q. 169–196 (2000)
6.
Mikalef, P., Boura, M., Lekakos, G., Krogstie, J.: Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and moderating effect of the environment. Br. J. Manag. 30, 272–298 (2019)CrossRef
7.
Mikalef, P., Boura, M., Lekakos, G., Krogstie, J.: Big data analytics and firm performance: findings from a mixed-method approach. J. Bus. Res. 98, 261–276 (2019)CrossRef
8.
Gupta, M., George, J.F.: Toward the development of a big data analytics capability. Inf. Manag. 53, 1049–1064 (2016)CrossRef
9.
Mikalef, P., Boura, M., Lekakos, G., Krogstie, J.: Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and moderating effect of the environment. Br. J. Manag. (2019, in press)
10.
Mikalef, P., Krogstie, J., Pappas, I.O., Pavlou, P.: Exploring the relationship between big data analytics capability and competitive performance: the mediating roles of dynamic and operational capabilities. Inf. Manag. (2019)
11.
Mikalef, P., Pappas, I.O., Krogstie, J., Giannakos, M.: Big data analytics capabilities: a systematic literature review and research agenda. Inf. Syst. e-Bus. Manag. 16, 1–32 (2018)CrossRef
12.
Wade, M., Hulland, J.: The resource-based view and information systems research: review, extension, and suggestions for future research. MIS Q. 28, 107–142 (2004)CrossRef
13.
Barney, J.B.: Resource-based theories of competitive advantage: a ten-year retrospective on the resource-based view. J. Manag. 27, 643–650 (2001)
14.
Lockett, A., Thompson, S., Morgenstern, U.: The development of the resource-based view of the firm: a critical appraisal. Int. J. Manag. Rev. 11, 9–28 (2009)CrossRef
15.
Palmatier, R.W., Dant, R.P., Grewal, D.: A comparative longitudinal analysis of theoretical perspectives of interorganizational relationship performance. J. Mark. 71, 172–194 (2007)CrossRef
16.
Sirmon, D.G., Hitt, M.A., Ireland, R.D., Gilbert, B.A.: Resource orchestration to create competitive advantage: breadth, depth, and life cycle effects. J. Manag. 37, 1390–1412 (2011)
17.
Melville, N., Kraemer, K., Gurbaxani, V.: Information technology and organizational performance: an integrative model of IT business value. MIS Q. 28, 283–322 (2004)CrossRef
18.
Grant, R.M.: The resource-based theory of competitive advantage: implications for strategy formulation. Calif. Manag. Rev. 33, 114–135 (1991)CrossRef
19.
Varian, H.: Artificial intelligence, economics, and industrial organization. National Bureau of Economic Research (2018)
20.
Maddox, T.M., Rumsfeld, J.S., Payne, P.R.J.J.: Questions for artificial intelligence in health care. JAMA 321, 31–32 (2019)CrossRef
21.
Mikalef, P., Framnes, V.A., Danielsen, F., Krogstie, J., Olsen, D.H.: Big data analytics capability: antecedents and business value. In: Pacific Asia Conference on Information Systems (2017)
22.
Mikalef, P., Pappas, I.O., Krogstie, J., Giannakos, M.: Big data analytics capabilities: a systematic literature review and research agenda. Inf. Syst. e-Bus. Manag. 1–32 (2017)
23.
Ransbotham, S., Gerbert, P., Reeves, M., Kiron, D., Spira, M.: Artificial intelligence in business gets real. MIT Sloan Manag. Rev. (2018)
24.
Balaraman, V., Brown, S., Duggirala, M., Moore, S., Nie, J.-Y.: Complexity sciences and artificial intelligence for improving lives through convergent innovation. In: Academy of Management Proceedings, pp. 17958. Academy of Management Briarcliff Manor, NY (2018)
25.
Li, B.-H., Hou, B.-C., Yu, W.-T., Lu, X.-B., Yang, C.-W.: Applications of artificial intelligence in intelligent manufacturing: a review. Front. Inf. Technol. Electron. Eng. 18, 86–96 (2017)CrossRef
26.
Lemley, J., Bazrafkan, S., Corcoran, P.: Deep learning for consumer devices and services: pushing the limits for machine learning, artificial intelligence, and computer vision. IEEE Consum. Electron. Mag. 6, 48–56 (2017)CrossRef
27.
Sousa, M.J., Rocha, Á.: Skills for disruptive digital business. J. Bus. Res. 94, 257–263 (2019)CrossRef
28.
Wilson, H.J., Daugherty, P., Bianzino, N.: The jobs that artificial intelligence will create. MIT Sloan Manag. Rev. 58, 14 (2017)
29.
Bloomfield, B.P.: The culture of artificial intelligence. In: The Question of Artificial Intelligence, pp. 59–105. Routledge (2018)
30.
Brynjolfsson, E., Mcafee, A.: The business of artificial intelligence. Harv. Bus. Rev. (2017)
31.
Zheng, N.-N., Liu, Z.-Y., et al.: Hybrid-augmented intelligence: collaboration and cognition. Front. Inf. Technol. Electron. Eng. 18, 153–179 (2017)CrossRef
32.
Shortliffe, E.H., Sepúlveda, M.J.: Clinical decision support in the era of artificial intelligence. Jama 320, 2199–2200 (2018)CrossRef
33.
Sterne, J.: Artificial Intelligence for Marketing: Practical Applications. John Wiley & Sons (2017)
34.
Heer, J.: Agency plus automation: Designing artificial intelligence into interactive systems. Proc. Nat. Acad. Sci. 116, 1844–1850 (2019)CrossRef
35.
Mikalef, P., Pateli, A.: Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: findings from PLS-SEM and fsQCA. J. Bus. Res. 70, 1–16 (2017)CrossRef
36.
Mikalef, P., Van de Wetering, R., Krogstie, J.: Big data enabled organizational transformation: the effect of inertia in adoption and diffusion. In: Business Information Systems (BIS) (2018)
Metadaten
Titel
Developing an Artificial Intelligence Capability: A Theoretical Framework for Business Value
verfasst von
Patrick Mikalef
Siw Olsen Fjørtoft
Hans Yngvar Torvatn
Copyright-Jahr
2019
Buch
Business Information Systems Workshops

Print ISBN: 978-3-030-36690-2

Electronic ISBN: 978-3-030-36691-9

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-36691-9_34

Premium Partner

    Bildnachweise