Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,073

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Artificial intelligence and innovation management: A review, framework, and research agenda✰

Open access
Haefner, Naomi, Wincent, Joakim, Parida, Vinit, Gassmann, Oliver·2021·Technological Forecasting and Social Change
DOI 10.1016/j.techfore.2020.120392
Open this paper in CoChat — free account

Abstract

Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company's entire innovation process. In response, we review and explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explain what is important to consider in making the transformation to the digital organization of innovation. We conclude our study by exploring directions for future research.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.techfore.2020.120392

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 10 references

Research works printed but not linked: 62

  1. Adner, 2019, What is different about digital strategy? From quantitative to qualitative change, Strateg. Sci., 1
  2. Amabile, 2019, Creativity, artificial Intelligence, and a world of surprises, Acad. Manag. Discov, 10.5465/amd.2019.0075 · DOI · Paper page
  3. Augier, 2007, Historical roots of the “A Behavioral Theory of the Firm” model at GSIA, Organ. Sci., 18, 507, 10.1287/orsc.1070.0276 · DOI
  4. 2016, Airbus: reimagining the future of air travel [WWW Document], Autodesk.
  5. Bettis, 2018, Bounded rationality, heuristics, computational complexity, and artificial intelligence, Adv. Strateg. Manag., 39, 139
  6. Brynjolfsson, 2017, The business of artificial intelligence: what it can – and cannot – do for your organization, Harv. Bus. Rev. Jul, 10
  7. Bughin, 2017, Artificial intelligence: The next digital frontier?, McKinsey Global Inst.
  8. Byrnes, 2018, The importance of having an AI-powered business strategy, Forbes.
  9. Charrington, 2019, Trends in reinforcement learning with Simon Osindero [WWW Document], This Week Mach. Learn. Artifcial Intell. Pod.
  10. Chui, 2018, Most of AI’s business uses will be in two areas, Harv. Bus. Rev., 3
  11. Chui, 2018, Notes from the AI frontier: AI adoption advances, but foundational barriers remain, McKinsey Analytics
  12. Cooper, 1997, Portfolio management in new product development: lessons from the leaders—I, Res. Manag., 40, 16
  13. Elgammal, A., Liu, B., Elhoseiny, M., Mazzone, M., 2017. CAN: creative adversarial networks generating “art” by learning about styles and deviating from style norms. arXiv:1706.07068, 1–22. · arXiv
  14. Furman, 2020, Automation, research technology, and researchers’ trajectories: evidence from computer science and electrical engineering, Organ. Sci., 31, 330, 10.1287/orsc.2019.1308 · DOI
  15. Gary, 2005, Implementation strategy and performance outcomes in related diversification, Strateg. Manag. J., 26, 643, 10.1002/smj.468 · doi:10.1002/smj.468
  16. Gavetti, 2012, The behavioral theory of the firm: assessment and prospects, Acad. Manag. Ann., 6, 1, 10.5465/19416520.2012.656841 · doi:10.5465/19416520.2012.656841 · Paper page
  17. Gavetti, 2000, Looking forward and looking backward: cognitive and experiential search, Adm. Sci. Q., 45, 113, 10.2307/2666981 · DOI · Paper page
  18. George, 2014, Big data and management, Acad. Manag. J., 57, 321, 10.5465/amj.2014.4002 · DOI · Paper page
  19. Goldfarb, 2020, Artificial intelligence in health care? Evidence from online job postings, AEA Pap. Proc, 110, 400, 10.1257/pandp.20201006 · DOI
  20. Gupta, A., Mendonca, R., Liu, Y., Abbeel, P., Levine, S., 2018. Meta-reinforcement learning of structured exploration strategies. arXiv:1802.07245, 11. · arXiv
  21. Hajli, 2018, The impact of new ICT technologies and its applications on health service development and management, Technol. Forecast. Soc. Change, 126, 1, 10.1016/j.techfore.2017.09.015 · doi:10.1016/j.techfore.2017.09.015
  22. Hao, 2018, AI in China: how Uber rival Didi Chuxing uses machine learning to revolutionize transportation, MIT Technol. Rev
  23. Hill, 1987, Strategy and structure in the multiproduct firm, Acad. Manag. Rev., 12, 331, 10.2307/258539 · DOI · Paper page
  24. Johansen, N., Quon, G., 2018. scAlign: a tool for alignment, integration and rare cell identification from scRNA-seq data, bioRxiv. https://doi.org/10.1101/504944. · DOI
  25. Jones, 2016, Peering into the future of innovation management, Res. Manag., 59, 49
  26. Katila, 2002, Something old, something new: a longitudinal study of search behavior and new product introduction, Acad. Manag. J., 45, 1183, 10.2307/3069433 · DOI · Paper page
  27. Kijkuit, 2007, The organizational life of an idea: integrating social network, creativity and decision-making perspectives, J. Manag. Stud., 44, 863, 10.1111/j.1467-6486.2007.00695.x · DOI
  28. Lanzolla, 2018, Digital transformation: what is new if anything?, Acad. Manag. Discov, 4, 378, 10.5465/amd.2018.0103 · doi:10.5465/amd.2018.0103
  29. Lengnick-Hall, 1992, Innovation and competitive advantage: what we know and what we need to learn, J. Manage., 18, 399
  30. Lenka, 2017, Digitalization capabilities as enablers of value co-creation in servitizing firms, Psychol. Mark., 34, 92, 10.1002/mar.20975 · DOI · Paper page
  31. Lin, K., Zhao, R., Xu, Z., Zhou, J., 2018. Efficient large-scale fleet management via multi-agent deep reinforcement learning. arXiv:1802.06444, 10. · DOI · Paper page
  32. Lu, 2019, Artificial intelligence: a survey on evolution, models, applications and future trends, J. Manag. Anal., 6, 1
  33. Martin, 2016, Opportunity, discovery and creativity: a critical realist perspective, Int. Small Bus. J. Res. Entrep., 34, 261, 10.1177/0266242614551185 · DOI
  34. McNally, 2011, From the special issue editors: an introduction to the special issue on decision making in new product development and innovation, J. Prod. Innov. Manag., 28, 619, 10.1111/j.1540-5885.2011.00843.x · DOI
  35. Munos, 2009, Lessons from 60 years of pharmaceutical innovation, Nat. Rev. Drug Discov., 8, 959, 10.1038/nrd2961 · DOI · Paper page
  36. O'Cass, 2018, Contemporary issues and critical challenges on innovation in services, J. Prod. Innov. Manag., 35, 674, 10.1111/jpim.12464 · DOI
  37. Parloff, 2016, Why deep learning is suddenly changing your life, Fortune.
  38. Piezunka, 2019, Learning-by-participating: the dynamics of information aggregation in organizations, INSEAD Working Papers Collection
  39. Porter, 2001, Innovation: location matters, MIT Sloan Manag. Rev., 42, 28
  40. Posen, 2018, Renewing research on problemistic search—A review and research agenda, Acad. Manag. Ann., 12, 208, 10.5465/annals.2016.0018 · DOI · Paper page
  41. Prahalad, 1986, The dominant logic: a new linkage between diversity and performance, Strateg. Manag. J., 7, 485, 10.1002/smj.4250070602 · DOI · Paper page
  42. Puranam, 2018, Algorithmic induction through machine learning: using predictions to theorize, INSEAD Work. Pap. Collect, 1
  43. Puranam, 2015, Modelling bounded rationality in organizations: progress and prospects, Acad. Manag. Ann., 9, 337, 10.5465/19416520.2015.1024498 · DOI
  44. Raisch, S., Krakowski, S.,. Artificial intelligence and management: the automation-augmentation paradox. Acad. Manag. Rev. https://doi.org/10.5465/2018.0072. · doi:10.5465/2018.0072 · Paper page
  45. Ransbotham, 2017, Reshaping business with artificial intelligence, MIT Sloan Mangement Rev, 59, 1
  46. Restrepo, 2004, Problem structuring and information access in design, J. Des. Res., 4, 218
  47. Rhodes, 2015, Airbus’ newest design is based on bones and slime mold, Wired.
  48. Roose, 2019, The hidden automation agenda of the Davos elite, New York Times B1
  49. Samuel, 1959, Some studies in machine learning using the game of checkers, IBM J. Res. Dev., 3, 210, 10.1147/rd.33.0210 · DOI · Paper page
  50. Sbai, 2018, DeSIGN: design inspiration from generative networks, in: 15th European Conference on Computer Vision, ECCV 2018. Munich, 37
  51. Schuhmacher, A., Gatto, A., Hinder, M., Kuss, M., Gassmann, O.,. The upside of being a digital pharma player. Drug Discov. Today 6. https://doi.org/10.1016/j.drudis.2020.06.002. · DOI
  52. Schwab, 2018, A Google intern built the AI behind these shockingly good fake images [WWW Document], Fast Co.
  53. Spieth, 2014, Business model innovation - State of the art and future challenges for the field, R&D Manag, 44, 237, 10.1111/radm.12071 · DOI · Paper page
  54. Teece, 1980, Economies of scope and the scope of the enterprise, J. Econ. Behav. Organ., 1, 223, 10.1016/0167-2681(80)90002-5 · DOI · Paper page
  55. Unemyr, 2018, Monitor business data for anomalies with AI [WWW Document], Magnus Unemyr.
  56. van Riel, 2004, High-technology service innovation success: a decision-making perspective, J. Prod. Innov. Manag., 21, 348, 10.1111/j.0737-6782.2004.00087.x · DOI
  57. Ward, 2014, Applications of business analytics in healthcare, Bus. Horiz., 57, 571, 10.1016/j.bushor.2014.06.003 · DOI
  58. Warde-Farley, D., van de Wiele, T., Kulkarni, T., Ionescu, C., Hansen, S., Mnih, V., 2018. Unsupervised control through non-parametric discriminative rewards. arXiv:1811.11359v1, 1–17. · arXiv
  59. Williams, 2004, Focusing firm evolution: the impact of information infrastructure on market entry by U.S. telecommunications companies, 1984–1998, Manage. Sci, 50, 1561, 10.1287/mnsc.1040.0223 · DOI
  60. Yang, 2019, Machine-learning-guided directed evolution for protein engineering, Nat. Methods, 16, 687, 10.1038/s41592-019-0496-6 · DOI · Paper page
  61. Yun, 2019, The role of a business model in market growth: the difference between the converted industry and the emerging industry, Technol. Forecast. Soc. Change, 146, 534, 10.1016/j.techfore.2019.04.024 · DOI
  62. Zammuto, 2007, Information technology and the changing fabric of organization, Organ. Sci., 18, 749, 10.1287/orsc.1070.0307 · DOI · Paper page

Cited by

See all 1,073

Metadata

Authors
Haefner, N., Wincent, J., Parida, V., & Gassmann, O.
Venue
Technological Forecasting and Social Change
Publisher
Elsevier BV
Published
2021-01
Volume
162
Pages
120392
ISSN
0040-1625 (ISSN-L)
DOI
10.1016/j.techfore.2020.120392
OpenAlex
W3093821457
Cited by
1,073
References
10
FWCI
25.48
Type
journal article
Language
en
Open access
hybrid
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Artificial intelligence and innovation management: A review, framework, and research agenda✰. https://app.cochat.ai/papers/10.1016/j.techfore.2020.120392
The paper
Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda✰. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2020.120392

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.