A Proposal for an Intangible Resources Management System at the Organizational Level
Open accessAbstract
The intangible resources management (IRM) is a key area within organizations, not only in terms of theory, but also in practice. However, reality shows that organizations face unexpected challenges in developing and implementing strategies and processes of intangible resources management. This article seeks to contribute to the improvement of IRM at the organization level by building a model that describes the process followed by organizations seeking to implement an intangible resources management system. Our study emphasizes the need of three phases: the identification of critical intangible resources for creating value; the measurement of these resources through a set of indicators and, finally, the monitoring of the resources and intangible activities. However, the management, monitoring and reporting on intangible resources is very idiosyncratic and unique for each organization; there is not a universal recipe, each organization should develop its own process.
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Firm Resources and Sustained Competitive Advantage
Barney, J. (1991) · Journal of Management · doi:10.1177/014920639101700108 · Cited by 48,334 · see citing works
Firm resources and sustained competitive advantage
Barney, J. B. (2004) · Advances in strategic management · doi:10.1016/s0742-3322(00)17018-4 · Cited by 38,159 · see citing works
Value Driven Intellectual Capital: How to Convert Intangible Corporate Assets Into Market Value
Sullivan, P. H. (2000) · Cited by 647 · see citing works
Bladen, V. W., & Galbraith, J. K. (1969) · International Journal Canada s Journal of Global Policy Analysis · doi:10.2307/40200060 · Cited by 254 · see citing works
Management of intangibles – An attempt to build a theory
Sánchez, P., Chamináde, C., & Olea, M. (2000) · Journal of Intellectual Capital · doi:10.1108/14691930010359225 · Cited by 252 · see citing works
Anderson, K. K. (2009) · NSUWorks (Nova Southeastern University) · Cited by 15 · see citing works
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- [2]2. McGill T. P. (2006), Harnessing Intellectual Capital: A Study of Organizational Knowledge , UMI Dissertation Publishing, Number: 3235051, ProQuest LLC
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Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA
Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01056-1 · Cited by 1,800 · see citing works
The Incidental Pundit: Who Talks Politics with Whom, and Why?
Minozzi, W., Song, H., Lazer, D. M. J., Neblo, M. A., & Ognyanova, K. (2020) · American Journal of Political Science · doi:10.1111/ajps.12469 · Cited by 123 · see citing works
Barzagar Nazari, K., & Ebersbach, M. (2019) · Applied Cognitive Psychology · doi:10.1002/acp.3485 · Cited by 54 · see citing works
A generalized many-facet Rasch model and its Bayesian estimation using Hamiltonian Monte Carlo
Uto, M., & Ueno, M. (2020) · Behaviormetrika · doi:10.1007/s41237-020-00115-7 · Cited by 37 · see citing works
A Bayesian many-facet Rasch model with Markov modeling for rater severity drift
Uto, M. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01997-z · Cited by 19 · see citing works
- Authors
- Stan, S., Oprean-Stan, C., & Pele, A.
- Affiliations
- Lucian Blaga University of Sibiu (RO)
- Corresponding author
- Stan, Sebastian
- Oprean-Stan, Camelia
- Venue
- Management of Sustainable Development
- Publisher
- Lucian Blaga University of Sibiu
- Published
- 2018-06-01
- Volume
- 10
- Issue
- 1
- Pages
- 17-21
- ISSN
- 2066-9380 (ISSN-L)
- 2247-0220
- OpenAlex
- W2883167768
- Cited by
- 160
- References
- 6
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 9 · 2023: 11 · 2022: 21 · 2021: 26 · 2020: 31 · 2019: 44 · 2018: 11
- FWCI
- 46.79
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Management of Sustainable Development (publishedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Intellectual Capital and Performance Analysis
- Social Sciences › Business, Management and Accounting › Management Information Systems › Accounting and Organizational Management
- Social Sciences › Business, Management and Accounting › Accounting › Accounting Theory and Financial Reporting
- Keywords
- intangible assets, organizational management systems, value creation, resource measurement, performance indicators, resource monitoring, management reporting, implementation process model
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