Integrating science and technology metrics into a competitive technology intelligence methodology

Authors

  • Marisela Rodriguez-Salvador
  • Pedro F. Castillo-Valdez

DOI:

https://doi.org/10.37380/jisib.v1i1.696

Keywords:

Competitive intelligence, competitive technology intelligence, patentometrics, science and technology metrics, scientometrics

Abstract

For years, the appropriate interpretation and application of metrics have enabled
scientists to assess science and technology dynamics. Consequently, diverse disciplines have
emerged, such as bibliometrics, scientometrics and patentometrics, offering important
theoretical and methodological contributions. However, the current accelerated technological
advances require researchers to implement a superior approach to detect continuous changes
in the external environment identifying opportunities and vulnerabilities to strengthen the
decision-making process regarding R&D and innovation. In this context, competitive technology
intelligence (CTI) offers a strategic approach based on a continuous cycle where information is
transformed into an actionable result. This research provides a broader scope to science and
technology metrics, incorporating them into a CTI global methodology of eight steps. Metrics
add value throughout the entire CTI process, from project planning to decision-making stages,
having the most significant role in the information analysis stage, mainly to process information
from sources such as scientific documents, patents, and social networks. Particularly, this
approach considers recent studies in CTI in which quantitative tools such as patentometrics
and scientometrics were successfully used. This proposal can be applied to predict upcoming
technologies, movements of competitors, disrupting activities, market changes, and future
trends. Accordingly, this research adds value to the assessment of science and technology
dynamics, aiming to improve the decision-making process of R&D and innovation.

References

Amidon Rogers, Debra M. (1996). The challenge

of fifth generation R&D. Research Technology

Management, 39(4), 33-41.

doi: 10.1080/08956308.1996.11671075.

Bollen, J., Van de Sompel, H., Hagberg, A.,

Chute, R., 2009. A Principal Component

Analysis of 39 Scientific Impact Measures.

PLoS ONE, 4(6): e6022.

doi:10.1371/journal.pone.0006022

Cronin, B., Sugimoto, C.R. (2014). Beyond

bibliometrics: Harnessing multidimensional

indicators of scholarly impact. Cambridge,

United States: The MIT Press.

Dou, H., Juillet, A., Clerc, P. (2019). Strategic

Intelligence for the Future 1: A New Strategic

and Operational Approach. Hoboken, United

States: Wiley.

Du Toit, A.B. (2015). Competitive intelligence

research: an investigation of trends in the

literature. Journal of Intelligence Studies in

Business. 5(2). doi: 10.37380/jisib.v5i2.127

European Union (2017). Next-generation metrics:

Responsible metrics and evaluation for open

science. Luxembourg: Publications Office of

the European Union. doi: 10.2777/337729.

Fitzpatrick, W., Burke, D. (2003). Competitive

Intelligence, corporate security and the virtual

organization. Advances in Competitiveness

Research, 11(1), 20-45.

Garcia-Garcia, L. A., Rodriguez-Salvador, M.

(2018a). Uncovering 3D bioprinting research

trends: A keyword network mapping analysis.

International Journal of Bioprinting, 4(2),

-154. doi: 10.18063/IJB.v4i2.147

Garcia-Garcia, L. A., Rodriguez-Salvador, M.

(2018b). Additive manufacturing knowledge

incursion on orthopedic devices: The case of

hand orthoses. Proceedings of the 3rd

International Conference on Progress in

Additive Manufacturing (Pro-AM 2018), 571-

doi: 10.25341/D4388H

Hernandez-Quintanar, L., Rodriguez-Salvador,

M. (2019). Discovering New 3D Bioprinting

Applications: Analyzing the Case of Optical

Tissue Phantoms. International Journal of

Bioprinting, 5(1), 178-189. doi:

18063/ijb.v5i1.178.

Huang, Y., Porter, A. L., Zhang, Y., Lian, X., Guo,

Y. (2018). An assessment of technology

forecasting: Revisiting earlier analyses on

dye-sensitized solar cells (DSSCs).

Technological Forecasting and Social Change,

, 831-843. doi:

1016/j.techfore.2018.10.031.

Luu, T. T. (2015). From cultural intelligence to

supply chain performance. The International

Journal of Logistics Management, 27(1). doi:

1108/IJLM-01-2014-0009

Michán, L., Muñoz-Velasco, I. (2013).

Cienciometría para ciencias médicas:

definiciones, aplicaciones y perspectivas.

Investigación En Educación Médica, 2(6), 100-

doi:10.1016/s2007-5057(13)72694-2

Niven, P. R. (2006). Balanced Scorecard Step-bystep:

Maximizing Performance and

Maintaining Results. Hoboken, United States:

Wiley.

Porter, A. L. (2019). Data Analytics for Better

Informed Technology & Engineering

Management. IEEE Engineering Management

Review, 47(3), 29-32. doi:

1109/EMR.2019.2928265.

Priem, J., Piwowar, H., Hemminger, B. (2012).

Altmetrics in the wild: Using social media to

explore scholarly impact. arXiv:1203.4745

Qiu, J., Zhao, R., Yang, S., Dong, K. (2017).

Informetrics: Theory, Methods and

Applications. Singapore: Springer.

Rodriguez-Salvador, M. (2006). Innovación y

creatividad. En Ingeniería concurrente: Una

metodología integradora, 137-145. Barcelona,

Spain: Universitat Politècnica de Catalunya,

Edicions UPC.

Rodriguez-Salvador, M., Eddy-Valdez A., Garza-

Cavazos, R. (2002). Industry/university

cooperative research in competitive technical

intelligence: a case of identifying technological

trends for a Mexican steel manufacturer.

Research Evaluation, 11(3), 165–173. doi:

3152/147154402781776835

Rodriguez-Salvador, M., Garcia-Garcia, L. (2018).

Additive Manufacturing in Healthcare.

Foresight and STI Governance, 12(1), 47-55.

doi: 10.17323/2500-2597.2018.1.47.55

Rodriguez-Salvador, M., Lopez-Martinez, R. E.

(2000). Cognitive Structure of Research:

Scientometric Mapping in Sintered Materials.

Research Evaluation, 9(3), 189-200. doi:

3152/147154400781777214

Rodriguez-Salvador, M., Rio-Belver, R. M.,

Garechana-Anacabe, G. (2017). Scientometric

and patentometric analyses to determine the

knowledge landscape in innovative

technologies: The case of 3D bioprinting. PLoS

ONE, 12(6): e0180375. doi:

1371/journal.pone.0180375

Rodriguez-Salvador, M., Ruiz-Cantu, L. (2019).

Revealing Emerging Science and Technology

Research for Dentistry Applications of 3D

Bioprinting. International Journal of

Bioprinting, 5(1), 170-176. doi:

18063/ijb.v5i1.170

Rodriguez-Salvador, M., Villarreal-Garza, D.,

Alvarez, M., Trujillo-de Santiago, G. (2019).

Analysis of the Knowledge Landscape of 3D

Bioprinting in Latin America. International

Journal of Bioprinting, 5(2.2), 16-25.

doi:10.18063/ijb.v5i2.3.240

Rothberg, H. N., & Erickson, G. S. (2017). Big

data systems: knowledge transfer or

intelligence insights? Journal of Knowledge

Management, 21(1), 92–112. doi:10.1108/jkm-

-2015-0300

Shaitura, S.V., Ordov, K.V., Lesnichaya, I.G.,

Romanova, Y.D., Khachaturova, S.S. (2018).

Services and mechanisms of competitive

intelligence on the Internet. Revista

ESPACIOS. Vol. 39 (No 45).

Smirnova, K., Golkar, A., Vingerhoeds, R. (2018).

Competition-driven figures of merit in

technology roadmap planning. 2018 IEEE

International Systems Engineering

Symposium (ISSE), 1-6. doi:

1109/SysEng.2018.8544407.

Staudt, J., Yu, H., Light, R.P., Marschke, G.,

Börner, K., Weinberg, B. A. (2018) Highimpact

and transformative science (HITS)

metrics: Definition, exemplification, and

comparison. PLoS ONE, 13(7): e0200597. doi:

1371/journal.pone.0200597.

Verlander, E. G. (2012). The Practice of

Professional Consulting. San Francisco,

United States: Wiley.

Wilsdon, J., Allen, L., Belfiore, E., Campbell, P.,

Curry, S., Hill, S., Jones, R.A.L., Kain, R.,

Kerridge, S., Thelwall, M., Tinkler, J., Viney,

I., Wouters, P., Hill, J., Johnson, B. (2015).

The metric tide: report of the independent

review of the role of metrics in research

assessment and management. London, United

Kingdom: Higher Education Funding Council

for England. doi: 10.13140/RG.2.1.4929.1363.

Zhang, Y., Robinson, D.K.R., Porter, A.L., Zhu,

D., Zhang, G., Lu, J. (2016). Technology

roadmapping for competitive technical

intelligence. Technological Forecasting and

Social Change, 110, 175-186. doi:

1016/j.techfore.2015.11.029

Zhang, Y., Zhang, G., Chen, H., Porter, A. L., Zhu,

D., Lu, J. (2016). Topic Analysis and

Forecasting for Science, Technology and

Innovation: Methodology and a Case Study

focusing on Big Data Research. Technological

Forecasting and Social Change. 105, 179-191.

doi: 10.1016/j.techfore.2016.01.015.

Zeid, A. (2014). Business Transformation: A

Roadmap for Maximizing Organizational

Insights. Cary, United States: Wiley.

Downloads

Published

2021-04-28