The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Digital innovation and the effects of artificial intelligence on firms’ research and development – Automation or augmentation, exploration or exploitation?
Open accessAbstract
Digitalization has altered many assumptions underpinning research on innovation management. At the early innings of exploring how digital innovation management stands out, there is a need for further studies in this area. Previous research on how firms use artificial intelligence has distinguished between automation and augmentation of human activities. In this paper, we explore how firms implement artificial intelligence within research and development. Utilizing an international news database spanning 956 articles from 122 newspapers published in 2020, we find that artificial intelligence is primarily adopted to augment human activities (55%) within research and development, rather than to automate matters (11%). We observe differences across sectors where automation is more common in government, information and communication technology (ICT), and technology and software. Our systematic coding shows that artificial intelligence is primarily adopted for exploration research and development (64%), rather than exploitation (5%). Based on these findings, we conclude that research and development from artificial intelligence primarily focuses on novel markets and areas of operations, rather than enhancing existing product markets and activities. Moreover, it augments human labor rather than replaces it; hence, job losses related to artificial intelligence do not seem to be taking place within research and development.
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.
Integrity signals
Registry
Cites
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.
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
Exploration and Exploitation in Organizational Learning
March, J. G. (1991) · Organization Science · doi:10.1287/orsc.2.1.71 · Cited by 21,942
Henderson, R. M., & Clark, K. B. (1990) · Administrative Science Quarterly · doi:10.2307/2393549 · Cited by 8,357
Technological Discontinuities and Organizational Environments
Tushman, M. L., & Anderson, P. (1986) · Administrative Science Quarterly · doi:10.2307/2392832 · Cited by 6,112
The second machine age: work, progress, and prosperity in a time of brilliant technologies
(2015) · Choice Reviews Online · doi:10.5860/choice.184834 · Cited by 4,381
A dynamic model of process and product innovation
Utterback, J. M., & Abernathy, W. J. (1975) · Omega · doi:10.1016/0305-0483(75)90068-7 · Cited by 3,509
Kaplan, A., & Haenlein, M. (2019) · Business Horizons · doi:10.1016/j.bushor.2018.08.004 · Cited by 3,140
Doyle, T. (2017) · The Information Society · doi:10.1080/01972243.2017.1354593 · Cited by 3,071
Digital Innovation Management: Reinventing Innovation Management Research in a Digital World
Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017) · MIS Quarterly · doi:10.25300/misq/2017/41:1.03 · Cited by 2,967
Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making
Jarrahi, M. H. (2018) · Business Horizons · doi:10.1016/j.bushor.2018.03.007 · Cited by 2,772
Artificial Intelligence and Management: The Automation–Augmentation Paradox
Raisch, S., & Krakowski, S. (2021) · Academy of Management Review · doi:10.5465/amr.2018.0072 · Cited by 2,755
Cited by
Dwivedi, Y. K., Sharma, A., Rana, N. P., Giannakis, M., Goel, P., & Dutot, V. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122579 · Cited by 353
Zhang, W., Zhang, W., Daim, T., & Yalçın, H. (2025) · Journal of Knowledge Management · doi:10.1108/jkm-02-2024-0203 · Cited by 66
Metadata
- Authors
- Johnson, P. C., Laurell, C., Ots, M., & Sandström, C.
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 179
- Pages
- 121636
- ISSN
- 0040-1625 (ISSN-L)
- OpenAlex
- W4220795638
- Cited by
- 313
- References
- 51
- FWCI
- 19.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Digital innovation and the effects of artificial intelligence on firms’ research and development – Automation or augmentation, exploration or exploitation? https://app.cochat.ai/papers/10.1016/j.techfore.2022.121636
- The paper
- Johnson, P. C., Laurell, C., Ots, M., & Sandström, C. (2022). Digital innovation and the effects of artificial intelligence on firms’ research and development – Automation or augmentation, exploration or exploitation? Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2022.121636
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.