The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Information Technology and Intangible Output: The Impact of IT Investment on Innovation Productivity
Open accessAbstract
Prior research concerning IT business value has established a link between firm-level IT investment and tangible returns such as output productivity. Research also suggests that IT is vital to intermediate processes such as those that produce intangible output. Among these, the use of IT in innovation and knowledge creation processes is perhaps the most critical to a firm's long-term success. However, little is known about the relationship between IT, knowledge creation, and innovation output. In this study, we contribute to the literature by comprehensively examining the contribution of IT to innovation production across multiple contexts using a quality-based measure of innovation output. Analyzing annual information from 1987 to 1997 for a panel of large U.S. manufacturing firms, we find that a 10% increase in IT input is associated with a 1.7% increase in innovation output for a given level of innovation-related spending. This relationship between IT, research and development (R&D), and innovation production is robust across multiple econometric methodologies and is found to be particularly strong in the mid to late 1990s, a period of rapid technological innovation. Our results also demonstrate the importance of IT in creating value at an intermediate stage of production, in this case, through improved innovation productivity. However, R&D and its related intangible factors (skill, knowledge, etc.) appear to play a more crucial role in the creation of breakthrough innovations.
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
Product development: Past research, present findings and future directions
Brown, S. L., & Eisenhardt, K. M. (1995) · The Academy of Management Review · doi:10.2307/258850 · Cited by 3,777
The interdisciplinary study of coordination
Malone, T. W., & Crowston, K. (1994) · ACM Computing Surveys · doi:10.1145/174666.174668 · Cited by 3,504
Garcia, R., & Calantone, R. (2002) · Journal of Product Innovation Management · doi:10.1111/1540-5885.1920110 · Cited by 3,470
The productivity paradox of information technology
Brynjolfsson, E. (1993) · Communications of the ACM · doi:10.1145/163298.163309 · Cited by 2,604
Does the “New Economy” Measure up to the Great Inventions of the Past?
Gordon, R. J. (2000) · Journal of Economic Perspectives · doi:10.1257/jep.14.4.49 · Cited by 1,206
Patents and Innovation: An Empirical Study
Mansfield, E. (1986) · Management Science · doi:10.1287/mnsc.32.2.173 · Cited by 1,128
The impact of information systems on organizations and markets
Gurbaxani, V., & Whang, S. (1991) · Communications of the ACM · doi:10.1145/99977.99990 · Cited by 960
Why Do Customer Relationship Management Applications Affect Customer Satisfaction?
Mithas, S., Krishnan, M. S., & Fornell, C. (2005) · Journal of Marketing · doi:10.1509/jmkg.2005.69.4.201 · Cited by 776
Cited by
Cocreation of Value in a Platform Ecosystem: The Case of Enterprise Software1
Ceccagnoli, M., Forman, C., Huang, P., & Wu, D. J. (2012) · MIS Quarterly · doi:10.2307/41410417 · Cited by 794
Benitez, J., Arenas, A., Castillo, A., & Esteves, J. (2022) · Information & Management · doi:10.1016/j.im.2022.103590 · Cited by 537
Khurana, I., Dutta, D. K., & Singh Ghura, A. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.06.048 · Cited by 443
Data Analytics, Innovation, and Firm Productivity
Wu, L., Hitt, L., & Lou, B. (2020) · Management Science · doi:10.1287/mnsc.2018.3281 · Cited by 368
Benitez, J., Castillo, A., Llorens, J., & Braojos, J. (2018) · Information & Management · doi:10.1016/j.im.2017.09.004 · Cited by 318
Innovating with enterprise systems and digital platforms: A contingent resource-based theory view
Sedera, D., Lokuge, S., Grover, V., Sarker, S., & Sarker, S. (2016) · Information & Management · doi:10.1016/j.im.2016.01.001 · Cited by 246
Information Technology Impacts on Firm Performance: An Extension of Kohli and 1
Sabherwal, R., & Jeyaraj, A. (2015) · MIS Quarterly · doi:10.25300/misq/2015/39.4.4 · Cited by 200
Lou, B., & Wu, L. (2021) · MIS Quarterly · doi:10.25300/misq/2021/16565 · Cited by 177
Huang, P., Tafti, A., & Mithas, S. (2018) · MIS Quarterly · doi:10.25300/misq/2018/13490 · Cited by 133
ICT as a general-purpose technology: The productivity of ICT in the United States revisited
Liao, H., Wang, B., Li, B., & Weyman-Jones, T. (2016) · Information Economics and Policy · doi:10.1016/j.infoecopol.2016.05.001 · Cited by 95
Griffith University, Amrollahi, A., Ghapanchi, A. H., Griffith University, Talaei-Khoei, A. T.-K., & University Technology Sydney (2013) · Pacific Asia Journal of the Association for Information Systems · doi:10.17705/1pais.05203 · Cited by 45
Neirotti, P., Pesce, D., & Battaglia, D. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121088 · Cited by 39
Metadata
- Authors
- Kleis, L., Chwelos, P., Ramirez, R. V., & Cockburn, I.
- Venue
- Information Systems Research
- Published
- 2012-03
- Volume
- 23
- Issue
- 1
- Pages
- 42-59
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2153807042
- Cited by
- 479
- References
- 8
- FWCI
- 42.73
- Type
- journal article
- Language
- en
- Open access
- green
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
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.