The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers
Open accessAbstract
Abstract This article aims at examining the mediating role played by Organizational Learning (OL) capabilities at different contextualization levels on the association between Industry 4.0 (I4.0) technologies and operational performance. For that, we gathered information from 135 firms that have initiated their digital transformation towards the fourth industrial revolution era. Data was analyzed by means of multivariate data techniques. Our results show that learning capabilities at an organization level positively mediate the impact of I4.0 for achieving higher operational performance levels. However, OL at a team and individual level may not present a significant effect on such mediation. As I4.0 is claimed to facilitate a faster and more efficient identification and solution of manufacturing problems, our research provides empirical evidence to indicate that companies that systematically foster learning and knowledge sharing at an organization level can obtain greater benefits from I4.0 technologies adoption.
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Cites
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
Tabachnick, B. G., & Fidell, L. S. (1983) · Cited by 79,866
Self-Reports in Organizational Research: Problems and Prospects
Podsakoff, P. M., & Organ, D. W. (1986) · Journal of Management · doi:10.1177/014920638601200408 · Cited by 17,102
Estimating Nonresponse Bias in Mail Surveys
Armstrong, J. S., & Overton, T. S. (1977) · Journal of Marketing Research · doi:10.2307/3150783 · Cited by 12,222
Applied Multivariate Statistics for the Social Sciences
Stevens, J. P. (2012) · Routledge eBooks · doi:10.4324/9780203843130 · Cited by 10,648
Estimating nonresponse bias in mail surveys
Armstrong, J. S., & Overton, T. S. (1977) · Journal of Marketing Research · doi:10.1177/002224377701400320 · Cited by 10,390
Senge, P. M. (1990) · Cited by 6,332
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014) · Business & Information Systems Engineering · doi:10.1007/s12599-014-0334-4 · Cited by 4,276 · cited 2×
“0) has been referred to as the new industrial paradigm that will possibly lead companies to superior performance results through an extensive adoption level of novel information and communication technologies (Lasi et al., 2014).”
Background
Research works printed but not linked: 4
- Belsley, 2005, vol. 571
- Rupčić, 2018, Intergenerational learning and knowledge transfer–challenges and opportunities, Learn. Organ., 25, 135, 10.1108/TLO-11-2017-0117 · doi:10.1108/tlo-11-2017-0117
- Tamás, 2016, Waste reduction possibilities for manufacturing systems in the industry 4.0, vol. 161
- Tvenge, 2016, Combining learning factories and ICT-based situated learning, Procedia CIRP, 54, 101, 10.1016/j.procir.2016.03.031 · doi:10.1016/j.procir.2016.03.031
Cited by
Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120420 · Cited by 910
Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.107599 · Cited by 889
Ghobakhloo, M., Iranmanesh, M., Grybauskas, A., Vilkas, M., & Petraitė, M. (2021) · Business Strategy and the Environment · doi:10.1002/bse.2867 · Cited by 462
Digital transforming capability and performance: a microfoundational perspective
Sousa-Zomer, T. T., Neely, A., & Martinez, V. (2020) · International Journal of Operations & Production Management · doi:10.1108/ijopm-06-2019-0444 · Cited by 417
Kumar, S., Raut, R. D., Nayal, K., Kraus, S., Yadav, V. S., & Narkhede, B. E. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126023 · Cited by 412
Metadata
- Authors
- Tortorella, G. L., Cawley Vergara, A. M., Garza-Reyes, J. A., & Sawhney, R.
- Venue
- International Journal of Production Economics
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 219
- Pages
- 284-294
- ISSN
- 0925-5273 (ISSN-L)
- OpenAlex
- W2955010130
- Cited by
- 386
- References
- 78
- FWCI
- 34.75
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
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- Tortorella, G. L., Cawley Vergara, A. M., Garza-Reyes, J. A., & Sawhney, R. (2020). Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2019.06.023
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