MIS 696A Readings in Management Information Systems
Knowledge Diffusion in Nanotechnology
Jaebong Son
THE PROBLEM
Due to the nanotechnology’s substantial impact on numerous economic sectors, more than 60 countries have
invested in national projects or programs to stimulate research and innovation in nanotechnology (Dang, Chen,
Zhang, & Roco, 2009). In 2000, the National Nanotechnology Initiative (NNI) was established by the U.S.
government to encourage investment in nanotechnology (Huang, Chen, Li, & Roco, 2006). The cumulative NNI
investment now amounts to around $14 billion since its inception, including the 2011 budget of $1.8 billion
(Funding, 2011), and the investment is on the increase every year.
In order to make the NNI investment effective, it is necessary to identify what kinds of patents or research topics
will be dominant in the near future. According to Huang’s research (Huang, Chen, Li, & Roco, 2006), the topics of
nanotechnology changed significantly after the year of 2000, and it is also expected that this radical change of
topics will go on, meaning that the NNI investment must reflect this change in topics. Therefore, the purposes of
this research are to show that public funding from NNI has significantly contributed to the development of
nanotechnology, and to predict what research topics will be dominant in the near future.
LITERATURE REVIEW
KNOWLEDGE DIFFUSION IN NANOTECHNOLOGY
Patents have been used to assess the evolution and development of topics in nanotechnology (Dang, Chen, Zhang,
& Roco, 2009), so patents are considered to represent knowledge. Knowledge diffusion can be defined as the
adaptations and applications of knowledge which is documented in scientific publications and patents (Chen &
Hicks, 2004). In other words, the emergence and development of scientific knowledge is attributable to a diffusion
process in which new ideas are spread among researchers (Chen & Hicks, 2004). Citing means adopting knowledge
elements from cited scientific publications or patents, and human collaboration leads to knowledge evolution and
innovation by combining prior knowledge elements (Li, Chen, Zhang, Li, & Jay, 2009), so citations of research
papers represent the collaboration among researchers.
EPIDEMIC THEORY FOR KNOWLEDGE DIFFUSION
In 1964, Goffman adopted the Epidemic theory (Kermack & McKendrick, 1925) to explain the transmission of idea
based on the assumption that ideas are transmitted by personal communication or by publication (Goffman &
Newill, 1964). One advantage of the epidemic theory is the ability to estimate population (Bettencourt, ClintronArias, Kaiser, & Castillo-Chavez, 2006), so that it leads to more rational analysis of why and how infectious diseases
begin and continue and what factors lead to the decline and disappearance of diseases.
THE SIR MODEL
People are affected or unaffected to other peoples’ ideas or knowledge. When person is affected to ideas of other
people, he/she may try to talk about the ideas with other people, resulting in a knowledge ‘epidemic’ (Goffman &
Newill, 1964). The SIR model, which represents Susceptible, Infected, and Recovered, is devised to explain how an
infectious disease spreads (Kermack & McKendrick, 1925). Goffman (Goffman & Newill, 1964) described an
epidemic process as “The ‘epidemic’ process can be characterized as one of transition from on state (susceptible)
to another (infective) where the transition is caused by exposure to some phenomenon (infectious material).”

S: susceptibles - those who can become infectives given contact with infectious material.

I: infected - those members who are host to the infectious material.

R: Recovered - those who have been removed for one for a variety of reasons such as death, immunity,
hospitalization etc.
Figure 1: SIR Model (Goffman & Newill, 1964)
The table below clearly shows the analogy between infectious disease epidemic and knowledge diffusion in
nanotechnology.
Comparison
Compartments
of SIR Model
About
Susceptible
Infected
Recovered
Epidemics
Knowledge Diffusion
(Information Diffusion)
Disease
Knowledge / Topic (of Nanotechnology)
People who might make a contact with
an infective
Inventors who might conduct research on
specific topics of nanotechnology after reading a
certain patent information
People who have disease and might
infect others
Inventors who conduct research on specific
topics of nanotechnology and make citations on
their research works
People who are recovered from
disease
Inventors who no longer conduct research on
specific topics of nanotechnology
Infection Rate
The probability of disease transmission
from the susceptible to the infected
Recovery Rate
The probability that the infected is
recovered from disease
The probability of conducting research on
specific topics of on nanotechnology after
reading a certain patent information related with
one’s research interests
The probability that inventors who conducted
research on specific topics of nanotechnology
lose their research interests
Table 1: Analogy between disease epidemic and knowledge diffusion
RESEARCH QUESTIONS
From the prior studies, we were able to obtain clear evidence that the evolution and development of research
topics in nanotechnology can be explained by the epidemic model, that is, the SIR model. Then, our focus is placed
how well the model explains the lifecycle of topics.
The proposed SEIR-F model will be compared with the baseline SIR model in terms of predicting accuracy. They are
all designed to predict the population dynamics, and by using the dynamics, the epidemic model allows us to
estimate which topics will emerge, live, and die. Especially, the SEIR-F model takes into account public funding, so
it might be expected that the SEIR-F predicts better than the SIR model. Research questions are followed:

Can the epidemic theory (the SIR / SEIR-F models) explain knowledge diffusion in nanotechnology?

Have public funding facilitated research activities in nanotechnology?

Can the epidemic theory predict the evolution, development, and disappearance of specific topics?
METHODOLOGY AND RESEARCH APPROACH
EXPERIMENT STEPS
Figure 2: Experiment Steps

Data acquisition: The USPTO patent dataset is already acquired. The dataset covers data from 1976
through 2009.

Preparation: In order to construct citation network, graph-based algorithms will be used, so necessary
data will be extracted and prepared for constructing citation network.

Technique: To identify topics in patent content, keywords in patent content are extracted by Arizona
Noun Phraser and then they will be clustered for identifying topics by Self Organizing Map (SOM).

Analysis: The SIR and the SEIR-F models will be used to explain knowledge diffusion in nanotechnology,
and predict the evolution and development of specific topics.
TOPIC AND SOM ANALYSES
Topic analysis will be conducted for identifying the major research topics in nanotechnology. For example, Arizona
Noun Phraser (Li, Chen, Dang, & Lin, 2008) designed for extracting noun phrases will be performed for identifying
research topics represented by a set of keywords. Then Self Organizing Map (SOM) will produce clusters based on
extracted keywords. After getting clusters, we will label each cluster based on keywords.
THE SEIR-F MODEL
c (Bettencourt, Clintron-Arias, Kaiser, & Castillo-Chavez, 2006).
Figure 3: SIR Model
The second reason is that public funding facilitates idea adoption (research progress) by providing necessary
resources such as specific equipment, laboratory materials, human resources, or so. As a result, the SEIR-F model is
designed to take into account two reasons mentioned above.
RESULTS (EXPECTED)
Knowledge diffusion in nanotechnology is well described by the SIR and the SEIR-F models. As I expected, the SEIRF model outperformed the SIR model in terms of describing numbers of researchers who adopt, transmit, and
develop new ideas, meaning that public funding has significantly contributed to the nanotechnology fields. The
predictability of emerging and developing topics will allow NNI to invest on right topics in a timely fashion.
BIBLIOGRAPHY
Funding. (2011). Retrieved October 9, 2011, from National Nanotechnology Initiative:
http://www.nano.gov/html/about/funding.html
Bettencourt, L. M., Clintron-Arias, A., Kaiser, D. I., & Castillo-Chavez, C. (2006). The power of a good idea:
Quantitative modeling of the spread of ideas from epidemiological models. Physica A, 513-536.
Chen, C., & Hicks, D. (2004). Tracing knowledge diffusion. Scientometrics, 199-211.
Dang, Y., Chen, H., Zhang, Y., & Roco, M. C. (2009, 9). Knowledge sharing and diffusion patterns. pp. 9-21.
Goffman, W., & Newill, V. A. (1964). Generalization of Epidemic Theory. Nature, 225-204.
Huang, Z., Chen, H., Li, X., & Roco, M. C. (2006). Connecting NSF Funding to Patent Innovation in Nanotechnology
(2001-2004). Journal of Nanoparticle Research, 859-879.
Kermack, W., & McKendrick, A. (1925). Applications of mathematics to medical problems. Edinburgh Mathematical
Society, 1-34.
Li, X., Chen, H., Dang, Y., & Lin, Y. (2008). A longitudinal analysis of nanotechnology literature: 1976-2004. Journla
of Nanoparticle Research, 3-22.
Li, X., Chen, H., Zhang, Z., Li, J., & Jay, N. F. (2009). Managing knowledge in light of its evolution process: An
empirical study on citation netwokr-based patent classification. Journal of Management Information
Systems, 129-153.