The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
How Artificial Intelligence Technology Affects Productivity and Employment: Firm-level Evidence from Taiwan
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Citations
Cites
Initial conditions and moment restrictions in dynamic panel data models
Blundell, R., & Bond, S. (1998) · Journal of Econometrics · doi:10.1016/s0304-4076(98)00009-8 · Cited by 22,571
Another look at the instrumental variable estimation of error-components models
Arellano, M., & Bover, O. (1995) · Journal of Econometrics · doi:10.1016/0304-4076(94)01642-d · Cited by 19,880
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727
Automation and New Tasks: How Technology Displaces and Reinstates Labor
Acemoglu, D., & Restrepo, P. (2019) · Journal of Economic Perspectives · doi:10.1257/jep.33.2.3 · Cited by 2,612
The productivity paradox of information technology
Brynjolfsson, E. (1993) · Communications of the ACM · doi:10.1145/163298.163309 · Cited by 2,604
Explaining Job Polarization: Routine-Biased Technological Change and Offshoring
Goos, M., Manning, A., & Salomons, A. (2014) · American Economic Review · doi:10.1257/aer.104.8.2509 · Cited by 2,111
The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms
Makridakis, S. (2017) · Futures · doi:10.1016/j.futures.2017.03.006 · Cited by 1,866
What can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131
Jovanovic, B., & Rousseau, P. L. (2005) · Handbook of Economic Growth · doi:10.1016/s1574-0684(05)01018-x · Cited by 708
What Can Machines Learn and What Does It Mean for Occupations and the Economy?
Brynjolfsson, E., Mitchell, T., & Rock, D. (2018) · AEA Papers and Proceedings · doi:10.1257/pandp.20181019 · Cited by 695
Cited by
Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024) · Journal of Organizational Behavior · doi:10.1002/job.2735 · Cited by 799
Fosso Wamba, S. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102544 · Cited by 269
AI-generated research paper fabrication and plagiarism in the scientific community
Elali, F. R., & Rachid, L. N. (2023) · Patterns · doi:10.1016/j.patter.2023.100706 · Cited by 197
Rawas, S. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00118-3 · Cited by 119
Anticipatory innovation of professional services: The case of auditing and artificial intelligence
Goto, M. (2023) · Research Policy · doi:10.1016/j.respol.2023.104828 · Cited by 92
Wang, Q., Qi, Y., & Li, R. (2026) · Sustainable Development · doi:10.1002/sd.70200 · Cited by 55
Metadata
- Authors
- Yang, C.-H.
- Venue
- Research Policy
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 51
- Issue
- 6
- Pages
- 104536
- ISSN
- 0048-7333 (ISSN-L)
- OpenAlex
- W4223898970
- Cited by
- 543
- References
- 12
- FWCI
- 76.05
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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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.
