In this paper, we build a model to calculate the TLC for an object technology based on multiple patent-related indicators.
and assessing patent-based TLC indicators. Then we choose some technologies (training technologies) with identified life cycle stages,
and finally compare the indicator features in training technologies with the indicator values in an object technology (test technology) using a nearest neighbour classifier,
Technology life cycle Patent Indicator Cathode ray tube Thin film transistor liquid crystal display Nano-biosensor 1. Introduction The rapidly changing economic environment
But using one indicator only to present technological performance would be problematic. A research team from MIT 11 studied the development trends of power transmission technology and aero-engine technology by S-curve modelling.
The results showed that the S-curve with a single indicator was not reliable and might lead the research in the wrong direction.
They suggested considering multiple indicators to measure technological development and to make business decisions. Usually, patent application activity is tracked as a TLC indicator for the S-curve analysis 10,12, 13.
But using patent application counts alone to represent the development of technology oversimplifies the situation. Accordingly, some multiple indicators are used to measure TLC.
Watts and Porter 14 have introduced nine indicators that look at publications of different types during the technology life cycle.
Reinhard et al. 15 tested seven indicators related to patents. Table 1 shows the indicators listed in the two papers.
These papers studied the indicators that would have different performance based on the changes of technology.
Separately the indicators can serve to measure technological changes. In this paper, we focus on combining multiple indicators to calculate the life cycle stages for an object technology
and hope that would help decision makers estimate its future development trends. 2. Methodology The model that we build to calculate the TLC for an object technology includes the following steps:
first, we focus on devising and assessing patent-based TLC indicators, then we choose some technologies (training technologies) with identified life cycle stages,
and finally we compare the indicator features in training technologies with the indicator values in an object technology Fig. 1. The S-curve concept of technology life cycle. 399 L. Gao et al./
/Technological forecasting & Social Change 80 (2013) 398 407 (test technology) via the nearest neighbour classifier,
which is used widely in pattern recognition, in order to measure the technology's life cycle stages. The research framework is designed as follows (Fig. 2). 2. 1. Indicators
and data source The most fundamental and challenging task is to select suitable indicators and data sources.
In a recent work 16, we have compiled candidate patent indicators from multiple sources. Thirteen indicators are selected for TLC assessment (Table 2). All the data of the indicators are extracted by priority year (the first filing date year for a patent application
except the first indicator. In this research, we choose the Derwent Innovation Index (DII) as the data source and Vantagepoint (VP) for data cleaning and extraction.
Matlab 2010b is used for implementing the algorithms. 2. 1. 1. Application and priority Usually, three kinds of dates are included in the DII database:
application year, priority year, and basic year. The basic year has no legal meaning, but only represents the year in
So in this paper, we choose the other two indicators to measure the development of technology:
we count the number of patents Table 1 Technology life cycle indicators by former researchers. Author Indicator Robert J Watts, Alan L Porter 14 Number of items in databases such as Science Citation Index number of items in databases such as Engineering
Index number of items in databases such as U s. patents Number of items in databases such as Newspaper Abstracts Daily Issues raised in the Business
/Technological forecasting & Social Change 80 (2013) 398 407 in DII by application year for the Application indicator and count the number of patents in DII by priority year for the Priority indicator
We count the respective numbers for each of these two indicators in DII by priority year. 2. 1. 3. Inventor This indicator indicates the amount of human resources invested in R&d of one particular technology.
Number of Inventors has been used as indicator to measure the TLC of RFID 21. We count the number of unique individual inventors of each year by priority year. 2. 1. 4. Citation Two major types of cited references are given in a patent:
IPC code has been used as an indicator to measure the technology life cycle 26. We count the number of IPCS 4-digit) in DII by priority year for the IPC indicator;
count the number of patents among the top 5 IPCS in DII by priority year for the IPC top 5 indicator;
and count the number of patents among the top 10 IPCS in DII by priority year for the IPC top 10 indicator. 2. 1. 6. MCS The Derwent manual code (MC
) system is developed a hierarchical classification system by Derwent. It is similar to the IPC classification system. Whereas the IPC is assigned by the examining patent offices,
We count the number of MCS in DII by priority year for the MC indicator;
count the number of patents among the top 5 MCS in DII by priority year for the MC top 5 indicator;
and count the number of patents among the top 10 MCS in DII by priority year for the MC top 10 indicator.
Table 2 Technology life cycle indicators. No. Indicator Indicator description 1 Application Number of patents in DII by application year 2 Priority Number of patents in DII by priority year 3 Corporate Number
of corporates in DII by priority year 4 Non-corporate Number of non-corporates in DII by priority year 5 Inventor Number of inventors in DII by priority year
and analyse indicator data. 2. 4. Data process First, we develop a map for 13 indicators of each training technology.
Numbers of inventors suggest very interesting changes in different stages. Fig. 3, which presents the emerging and growth stages, shows that the number of inventors is typically higher than that of all other indicators.
This declines in the mid-maturity stage (Fig. 4 but slightly increases in the following years.
The number of inventors is less than some other indicators, such as application numbers and priority application numbers in the maturity and decline stages.
Trends of other indicators also show different patterns. In the emerging and growth stages, indicators 1, 2, 4, 5, 9, 10,11, 12,
and 13 show similar trends; indicator 6 and 8 look similar; indicators 3 and 7 are different from the others and also different from each other.
In the maturity and decline stages, indicators 1, 2, 9 and 10 are similar. To make clear which indicators are similar with the others in the development trends,
we employ a cross-correlation analysis to measure the similarity among the 13 indicators in the four stages.
Table 4 provides the results of the cross-correlation analysis (r=0. 9). Emerging stage:
In group 1, indicators 1, 2, 3, 7, 9, 10,11, 12, and 13 have strong correlations.
Indicators 5, 6, and 7 are another group with strong correlations. Indicators 4 and 8 are uncorrelated.
Growth stage: 11 of the 13 indicators are correlated strongly. Indicators 6 and 7 form the other group with strong correlations.
Maturity stage: There are 5 groups in this stage. Indicators 1, 2, 3, 7, 8, 9, 10,11,
and 13 have strong correlations. Indicators 11,12, and 13 form another group. Indicators 4, 5,
and 6 are uncorrelated. Decline stage: There are 6 groups in this stage. Because CRT is still in its decline stage,
the indicator performance should be interpreted with great caution. Since the indicators show different trends in different stages, it might be better to combine all 13 indicators to measure the change of technology rather than using one single indicator.
It is common to process multidimensional data by matrix. The original data are extracted by Vantagepoint
and imported into MS Excel 13 rows of indicators, 30 columns (years) for TFT-LCD (from 1978 to 2007), 36 columns (years) for CRT (from 1972 to 2008),
and 24 columns (years) for NBS (from 1985 to 2008. Table 3 TLC stages of CRT and TFT-LCD.
Stage Emerging Growth Maturity Decline Period (year)( CRT) 1897 1929 1930 1972 1973 2000 2001 2020 Period (year)( TFT-LCD
) 1976 1990 1991 2007 2008 402 L. Gao et al.//Technological forecasting & Social Change 80 (2013) 398 407 We propose a normalisation method with two steps to pre-process the original data.
The first step is data smoothing by calculating three-year moving averages. The original data are defined as A A1;
-TOP10 Fig. 3. Development trends of 13 indicators (TFT-LCD. 0 500 1000 1500 2000 2500 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985
MC-TOP10 Fig. 4. Development trends of 13 indicators (CRT. 403 L. Gao et al.//Technological forecasting & Social Change 80 (2013) 398 407 A1 i;
vuutð13þ Table 4 Cross-correlation analysis for 13 indicators (r=0. 9). TLC stage Emerging Growth Maturity Decline Group 1 1, 2
it adopts 13 indicators that can be quantified to measure the TLC stages of an object technology.
since data of the all indicators can be downloaded from most patent databases. Certainly, our study possesses limitations.
if we can improve indicator performance. Many papers have pointed to the desirability of improving the accuracy of trend projection methods 36 39.
Change 56 (1997) 25 47.15 R. Haupt, M. Kloyer, M. Lange, Patent indicators for the technology life cycle development, Res.
, Study on indicator system for core patent documents evaluation, in: Proceedings of ISSI 2009-The 12th International Conference of the International Society for Scientometrics and Informetrics, Rio de janeiro, Brazil, 2009, pp. 154 164.17 C. Zhang, D. H
Public policy 27 (5)( 2000) 310 320.24 F. Narin, E. Noma, R. Perry, Patents as indicators of corporate technological strength, Res.
The figure shows the behavior over time for the outcome indicator‘fraction of new technologies of total energy generation'as well as the Gaussian Kernel Density Estimates (KDES) 56 of the end states.
although hardly improving the extremes, outperforms the‘basic policy'and‘no policy'ensembles on this key performance indicator:
the lower bound and the upper bound for each of the performance indicators can be calculated. These bounds are calculated across the 48 scenario generators and their associated parameter ranges.
The column‘static plan'in Table 5 shows the results of this analysis. Looking at the various outcome indicators,
Outcome indicators Static plan Adaptive plan Size of noise contour after 30 years (km2) 13.2 63.8 10.2 47.4 Cumulative Average Casualty Expectancy (ACE
which the option evolves during the time horizon of the simulation. 3. 3. 3. Analysis of results Fig. 5 shows a performance envelope for five outcome indicators.
In this case both parametric and Fig. 5. Performance envelopes and distribution of end states for five outcome indicators.
the results of the case could be used to identify early indicators of, for example, cyclic pricing behavior.
and output indicators such as publications and patent applications 7, 8. More than ten years have passed since the U s. National science and Technology Council published its first vision for nanotechnology research
evidence refers to reliable documentation, such as statistics and indicators or forecasting of economic development through macroeconomic modelling.
Nevertheless, the Globalisation Council's process included both expertiseorieente foresight methods (expert panels, expert discussion papers), evidence-oriented foresight methods (indicators and fact reports) and interaction-oriented foresight methods
from an indicator to a network and process perspective, Technology analysis and Strategic management 13 (4)( 2001) 533 553.19 F. J. Contractor, P. Lorange (Eds.
PMS are based on performance indicators. These systems have been developed to allow an organisation to focus on results and to allocate resources rapidly and efficiently.
Finally, both the financial and nonfinancial indicators selected through the PMS should enable new targets
monitoring and evaluation of two new strategic indicators: community satisfaction and participation in decision making. The former increased from 46%into 72,
if the social responsibility indicators used by a higher education institution were aligned sufficiently to its strategic objectives.
The implementation of the proposed system enabled an understanding that indicators in use reflected only philanthropy instead of social inclusion.
The opening up of the organisation's strategic objectives into indicators, targets and initiatives or actions that reflect the true intentions of the institution enabled an overall reassessment of the institution's strategy (phase 1). Moreover, through a systematic consultation of key stakeholders
which lead to increased satisfaction reflected in indicators such as transparency, quality of life, among others. 3. 2. 4. Negative results Through the application of the proposed system it was possible to observe,
with a focus on long term sustainability indicators. These were generated through the development of strategy maps (phase 2), in conjunction with the city of Floriano'polis, SC, Brazil and relevant stakeholders,
which the horizon scan may be used as an indicator and scoping device for specific strategic forward looking activities (as scenario building,
in the present era of public funding accountability, remaining viable in the long term seems to be a critical indicator of success. In terms of critical success factors it was interestiin to note that all the studies showed that foresight delivery
it is insufficient as an indicator of success. Eight factors were identified as the critical keys to success in government-led foresight programs:
it is insufficient as an indicator of success. Taken together, the studies identified many similariities For example:
+R&d grants are more innovative than firms with only tax credits for 6 out of 8 innovation indicators Notes:+(
We use several innovation indicators in order to test the robustness of our results given that innovation is a latent phenomenon
and every single indicator measures only partly aspects of this complex phenomenon. In a sixth and last step we calculated a subsidy quotient for every subsidized firm by dividing the amount of the granted subsidy by the total R&d expendiiture in the period 2000 2002.
indicators, of the subsidized and the non-subsidized firms for four different matching methods. We calculated the differrenc of the means of the two categories of firms (subsidized, non-subsidized) for six innovation variables and four matching methods,
Hence, these results seem to be quite robust across various methods and innovatiio indicators. Having controlled for the size and age of the firms, sector affiliation, region, export propensity,
For the effectiivenes of CTI promotion policy is the result for the six output-oriented innovation indicators of particular interest.
For example, coluum 1 in Table A3 shows the mean value (score) for every innovation indicator for all available non-subsidized firms before matching.
For the outputorieente indicators the differences vary significantly between only 9 11%for the qualitative selfassesssmen of the technical importance of the innovattion introduced and a threefold to fivefold larger magnitude in the case of sales of products new to the market.
For five innovation indicators we found that the difference of the means of the‘high-subsidy
according to our results the larger the subsidy (in relative terms), the larger the impact effect for a series of indicators that measure the economic success of innovation (sales shares of products with different grades of innovativeeness reduction
This could be shown by four different matching methods (with the exception of the nearest neighbour method for the indicator‘importance of introduced innovations from an economic point of view'.
at the same time, increase and improve its current performance indicators. FINEP's mission entails promoting economic and social development in Brazil through public funding for the development of ST&I.
wild cards, SWOT analysis, roadmapping, web survey and interviews, expert panel, conference and workshops, multi-criteria and stakeholder analysis, indicator developmeent benchmarking,
China Science and Technology indicators (2008) and from<http://www. sts. org. cn>accessed 20 may 2011.264.
China Science and Technology indicators. 2008), p. 54. Beijing: Scientific and Technical Documents Publishing house. China Statistical Yearbook on Science and Technology.
and are thus a good indicator of ongoing developments in FTA. The analysis of the selected papers on FTA attests to the richness of approaches in relation to different types of transformatiions governance modes
2005), Anderson (2010), Chioncel and Cuntz (2012), European commission (2011), Seiser (2010) and authors'own expertise. 6. An indicator of the culture of openness may be the past and present openness
or explicit elements that could be used as indicators for the realisation of the storyline. These elements give clues for further scanning of signals that may support
which suggests that the engagement of a large number of scanners helps draw attention to phenommen that qualify as indicators of emerging policy issues.
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