Is ThIs Novel TechNology goINg To be a hIT? aNTecedeNTs PredIcTINg TechNologIcal NovelTy dIffusIoN

Documents de travail GREDEG GREDEG Working Papers Series

Michele PezzoniReinhilde VeugelersFabiana Visentin

GREDEG WP No. 2018-22https://ideas.repec.org/s/gre/wpaper.html

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Is This Novel Technology Going to be a Hit?

Antecedents Predicting Technological Novelty Diffusion

Michele Pezzoni1, Reinhilde Veugelers2, Fabiana Visentin3

GREDEG Working Paper No. 2018-22

Abstract: Despite the high interest of scholars in identifying breakthrough inventions, little

attention has been devoted to investigating how the technological content of those breakthrough

inventions is re-used over time. We overcome this limitation by focusing on the dynamics of

diffusion of the novel technologies incorporated in inventions. Specifically, we consider the factors

affecting the time needed for a technology to be legitimated as well as its technological potential.

We find that the dynamics of diffusion of a novel technology are affected by the characteristics of

its building blocks, i.e. the technological components that combined together for the first time

generate a novel technology. Combining similar technological components, components familiar

to the inventors’ community, and components with a high level of appropriability generates a

technology that requires a short time to be legitimated but with a low technological potential.

Combining technological components with a science-based nature generates technologies with a

longer legitimation time but also higher technological potential. Finally, when large firms are the

main innovative actors, novel technologies show a higher technological potential.

JEL code: O33

Keywords: Technological novelty, diffusion, combinatorial process, initial characteristics, patent

data

1 Université Côte d’Azur, CNRS, GREDEG, France; ICRIOS, Bocconi University, Milan, Italy; BRICK, Collegio

Carlo Alberto, Torino, Italy; email: michele.pezzoni@unice.fr 2 KULeuven; email: reinhilde.veugelers@kuleuven.be 3 School of Business and Economics Maastricht University and UNU-MERIT, Maastricht, Netherlands

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1. Introduction

While there is a rising interest in identifying individual breakthrough inventions, little work has

investigated how the novel technologies embodied in these inventions diffuse generating follow-

on inventions (Arts et al., 2013). We consider novel technologies as the result of a combination of

existing components (Schumpeter 1939; Nelson & Winter 1982; Strumsky and Lobo 2015) where

each component represents by itself a technology (Arthur 2009). We analyze the diffusion curve

of a novel technology by looking at all the patented inventions that build-up on that technology.

Looking at the diffusion curve of a novel technology, we are particularly interested in identifying

the time that a novel technology needs to be legitimated within the inventors’ community and its

technological potential defined as the number of follow-on inventions that the novel technology

can generate. In our analysis, we investigate how the characteristics of the combined components

influence both novel technology legitimation time and technological potential. By investigating

how the characteristics of the combined components impact on legitimation and technological

potential, we aim to reply to a series of what-if questions such as, what would have been the

diffusion of a novel technology if the combined components were more established (or less) within

the inventors’ community? What would have been the diffusion of a novel technology if the two

components were more science-based? Or more similar to each other? More generally, we ask,

what are the antecedents predicting a “hit” technology?

The discussion of a renowned invention and of the diffusion of its embodied novel technology

helps to illustrate the main idea of our paper. The “onco-mouse’’, a mouse largely used in

laboratory cancer research, is an example of a well-recognized breakthrough invention embodying

a novel technology. In the mid-80s, Philip Lader and Timothy Steward at the Harvard laboratories

had the revolutionary idea of isolating cancer-related genes and injecting them into a mouse eggs

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transplanted into a female mouse generating the first transgenic mammal likely to develop a

specific disease (Murray, 2010). The onco-mouse was both patented and published. The novel

transgenic mammal technology to mimic diseases was embodied in the onco-mouse invention and

was the result of an unprecedented combination of two existing technological components: “Gene

isolation” and “Injection of material into animals”. The novel technology generated a variety of

follow-on inventions represented by other transgenic mammals used in labs to experiment

treatments for a variety of diseases. Geneticists patented at least 110 transgenic mammals designed

to develop diseases from Alzheimer to cystic fibrosis (Murray, 2010). For instance, the “diabetic

mouse” created in 1996 by Seo Jeon Sun, a professor from Seoul National University is a follow-

on patented invention building up on the novel transgenic mammal technology. We look at the

patents’ technological content and we reconstruct the diffusion curve of the transgenic mammal

technology to mimic diseases, by tracing the reuse of the novel combination “Gene isolation” and

“Injection of material into animals” in follow-on patents4.

In this work we assume that the diffusion of the novel technology in the follow-up inventions

depends from the characteristics of the two technological components combined to generate the

novel technology. The two combined components at the base of the transgenic mammal

technology have each its own characteristics and so has their resulting combination. The “Gene

isolation” component was rather new within the technological community, as well as the “Injection

of material into animals” component at the time when they were recombined for the first time in

the onco-mouse. Furthermore, the two components belong to two different technological domains

4 Interestingly, we observed that patent citations do not connect all the patents belonging to the diffusion curve of the

transgenic mammal technology. Indeed, citations refer to the specific prior art used and not necessarily to the

technological content of an invention. For example, the onco-mouse patent does not appear in the backward citation

list of the diabetic-mouse.

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and they were both strongly science-based. Finally, the two components were characterized by a

low concentration of the actors conducting the innovative activities.

In this paper, we conduct an empirical study that traces the diffusion of 11,009 novel technologies

over the period 1985-2015. We consider the existing technological components as the

technological classes used in the European Patent Office classification, and novel technologies as

an unprecedented combination of technological classes in the history of patented inventions

(Strumsky and Lobo, 2015).

The contribution of our study is twofold. First, we provide insights to the economics of innovation

literature that is interested in identifying the key drivers of the evolution of a “technological

trajectory” (Dosi, 1985)5. Second, we provide managerial implications that support the decisional

process of practitioners asked to predict the success of a novel technology.

The remaining of the paper is organized as follow. Section 2 illustrates our novel technology

definition and develops the hypothesis on how the characteristics of the technological components

recombined affect the diffusion of the resulting technology. Section 3 illustrates the method used

in the analysis. Section 4 presents the results that are discussed in Section 5. Section 6 concludes.

2. Novel technology diffusion and antecedents

5 A neo-Schumpeterian scholar would define a novel technology as a new technological paradigm and the diffusion

of a novel technology as a technological trajectory. Over time, radical inventions introduce new technological

paradigms. Technology advancement is cumulative and new paradigms open new venues for further technological

developments shaping new technological trajectories. Technological trajectories can be traced over time by following

the patent paper trail related to the technological paradigm (Achilladelis, 1993).

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While a large number of studies has investigated the diffusion of an innovation looking at its use

by the relevant population of potential adopters (Hall, 2004), a limited number of studies has

investigated the diffusion path of a novel technology in the follow-on pool of inventions. Extant

works rely on case studies developed for specific technologies. For instance, Achilladelis (1993)

follows the diffusion of the novel technology related to Sulpholamide antibacterial drugs by tracing

all the patented inventions based on Sulpholamide appeared over 70 years. Differently from the

extant literature, our study adopts a systematic quantitative approach that allows us (i) to identify

the introduction of a novel technology, (ii) to trace its diffusion, and (iii) to assess the antecedents

impacting on its diffusion.

Identifying a novel technology

The first step in our analysis is to define a novel technology. In his seminal work on the origins of

innovation, Schumpeter claimed that “innovation combines components in a new way, or that it

consists in carrying out New combination” (1939, pp. 88, Schumpeter 1939). Fleming (2001)

elaborated on the concept of invention as a process of “recombinant search” (pp. 118) by arguing

that inventors search among existing technological components and recombine them to realize

something new. In the same vein, Arthur (2009) states that novel technologies are the result of the

combination of existing components and that these existing components are themselves

technologies. According to this “recombinant search” approach, we consider a novel technology

as the result of pre-existing technological components that are combined for the first time

(Verhoeven et al., 2016).

Not all the novel technologies diffuse. The aim of our study is to analyze the diffusion of successful

technologies. To do that, we consider those novel technologies that recombine existing

technological components and reach a minimum level of diffusion. Therefore, following Amabile

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(1996) and Fleming and co-authors (2007), we require to the result of creative efforts to be both

novel and useful. Novel technologies that do not show a minimum number of industrial

applications are considered not useful and are excluded from our analysis.

Tracing novel technology diffusion

In our study we assume that the diffusion of a novel technology is characterized by two distinct

phases: a legitimation phase and a phase of convergence towards the technological potential.

Initially a technology diffuses slowly since it needs time to gain legitimation within the inventors’

community. Inventors using the novel technology in follow-on inventions need time to learn and

familiarize with the novel technology and to abandon the established competing technologies.

Once legitimated, the diffusion of the novel technology accelerates since it becomes the dominant

technology. The second phase of asymptotical convergence to a ceiling level is determined by the

potential of a technology. Two different trends can lead a technology to reach its ceiling. First, all

the possible applications of the technology have been implemented exhausting the inventive

opportunities. Second, the technology loses its appeal in favor of emerging alternative

technologies.

An insightful example of technology experiencing a first phase of legitimation followed by a

second phase of convergence to its technological potential, is represented by the Sulpholamide

drugs. The Sulpholamide is a class of synthetic antibacterial drugs introduced in 1935. The first

Sulpholamide-based drug, Prontosil, was developed and launched by Bayer. In the next 10 years

following the Prontosil introduction, other companies and public research laboratories gradually

developed and marketed more than 5,000 innovative Sulpholamide-based drugs. The success of

Sulpholamide-based drugs was exhausted by the mid- ‘50s for two reasons. First, additional

improvements of the therapeutic benefits of this class of drugs become more difficult reducing the

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opportunities to generate other inventions. Second, new antibiotics substituting the Sulpholamide-

based drugs appeared (for details on the case, see Achilladelis, 1993).

In reconstructing the two phases of the diffusion of a novel technology, we assume that a diffusion

process follows a sigmoid-curve (Griliches, 1957). The values of the length of the legitimation

period combined with the level of technological potential define the shape of the S-curve of the

corresponding novel technology. Figure 1 compares the diffusion curves of a technology having a

long legitimation phase and a high technological potential with another technology having a short

legitimation phase but a low technological potential.

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Figure 1: Comparison between diffusion curves

Technological, cognitive and institutional antecedents

There is general consensus among innovation scholars that novel technologies spur from an

unprecedented combination of existing components (Fleming, 2001). Recent studies look at the

antecedents of the probability of observing those new combinations (Curran, 2013; Caviggioli,

2016). Nevertheless, none of the previous studies consider the antecedents as predictors of

diffusion of the novel technology. In this paper we assume that the characteristics of the combined

components affect the resulting novel technology characteristics and therefore its diffusion

(Fleming, 2001). We identify technological, cognitive and institutional characteristics of the

combined components as relevant for the diffusion of the novel technology. Technological

characteristics are those related exclusively to the technological aspects of the combined

components. Cognitive characteristics are those related to the understanding of the combined

components by the inventors’ community. Finally, institutional characteristics are those related to

the level of appropriability of the combined components and to the characteristics of the industrial

sector.

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As technological characteristics we consider both the technological similarity and science-based

nature of the combined components. Novel technologies can result from the combination of

components that differ substantially or that are similar between each other. When combining

similar components, a large number of inventors might have already the competences to use the

resulting technology. On the contrary, combining dissimilar components requires cross-field

competences and inventors need to team-up to be able to use the novel technology or to expand

their competences. Since teaming-up and acquiring new competences are time consuming

activities, we expect that a novel technology resulting from the combination of similar components

is legitimated earlier than a novel technology resulting from the combination of dissimilar

components. At the same time, we expect that the combination of components that are based on

similar technological principles generates a novel technology that only marginally differs from the

existing components, harming its technological potential. On the contrary, combining dissimilar

components generates a novel technology with a greater original content (Verhoeven et al., 2016),

enhancing its technological potential.

We expect that:

Hypothesis 1 (combining similar components): A novel technology resulting from the

combination of two similar components (a) requires a shorter time to be legitimated and (b)

has a lower technological potential.

Novel technologies can result from the combination of science-based or applied components.

Science-based components are those components where the knowledge used has abstract content,

while applied components are those components where specialized knowledge is used for specific

applications (Breschi et al., 2000). For science-based components, the scientific knowledge

advancements need time to be incorporated in the population of potential inventors, requiring the

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education and training of cohorts of inventors employed in industrial R&D (Klevorick et al., 1995).

On the contrary, for applied components the inventors’ activities rely on stable set of knowledge

that is immediately available and does not need scientific updates. For these reasons, we expected

that a novel technology resulting from the combination of science-based components takes more

time to be legitimated in the inventors’ community. At the same time, the generality of the

scientific knowledge used in science-based components is expected to open up a broader set of

technological opportunities (Klevorick et al., 1995). On the contrary, novel technologies relying

on applied components are expected to have a limited set of possible technological applications.

For these reasons, we expect that a novel technology resulting from the combination of science-

based components has a higher technological potential while the combination of applied

components has lower technological potential.

Hypothesis 2 (combining science-based components): A novel technology resulting from the

combination of science-based components (a) requires a longer time to be legitimated and

(b) has a higher technological potential.

Cognitive characteristics are relevant since components are recombined by individuals who need

to receive and interpret information before being able to use it in a creative way (Cohen and

Levinthal, 1990). As cognitive characteristics, we consider the familiarity of the inventors’

community with the components. A novel technology can result from the combination of

components that have been frequently or rarely used within the inventors’ community. A

component frequently used is well-known and can be exploited, while a component rarely used is

unknown and has to be explored (March, 2001). Following Fleming’s argument (2001), we assume

that there is a greater chance that inventors build-up their inventions on components they are

familiar with. Exploiting familiar components reduces the risks for unexpected results and

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facilitates the legitimation of the resulting novel technology. However, familiar components have

been already largely exploited in the past so the novel technology resulting from their combination

is expected to have a lower technological potential.

We formulate the following hypothesis:

Hypothesis 3 (combining familiar components): A novel technology resulting from the

combination of familiar components (a) requires a shorter time to be legitimated and (b) has

a lower technological potential.

As institutional aspects we consider appropriability, defined as the level of protection from

imitation guaranteed to inventions spurring from the components. The level of protection largely

depends on extant intellectual property legislation. A broad scope of protection provides an

incentive to incumbent inventors and firms to invest in R&D activities. Higher investments in

R&D are expected to shorten the legitimation period. At the same time, a broad scope of protection

prevents outsider inventors from benefiting from the latest technological advancements generated

by the incumbent inventors (Breschi et al., 2000; Klevorick et al., 1995). Specifically, when the

scope of the protection is board, outsiders are deterred to start new R&D activities that build-up

on incumbent inventors’ results since the risk of infringing incumbents’ intellectual property rights

is high (Merges and Nelson, 1994). As a result of high entry barriers for new incumbents, the

community of inventors remains small. Due to its small size the community of inventors is not

able to exploit all the potential of the novel technology leaving several inventive opportunities

unexplored. As a consequence, we expect that the combination of components with high

appropriability leads to a novel technology with lower technological potential.

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Hypothesis 4 (combining components with high appropriability): A novel technology

resulting from the combination of components with high appropriability (a) requires less time

to be legitimated and (b) reaches lower technological potential.

Finally, we include as an additional institutional aspect the concentration of the innovative

activities of the firms characterizing the two combined technological components. Specifically,

for the two components, we consider if the innovative activity is conducted by a handful group of

large firms (the so-called Schumpeter Mark 1 model) or if the innovative activity is spread over a

large number of small firms (the so-called Schumpeter Mark 2 model) (Breschi et al. 2000). We

expect that when innovative activities are concentrated the legitimation time is reduced due to the

presence of an organized and formalized R&D activity within large firms. On the contrary, when

the innovative activity is spread over a large number of small firms, the R&D is less formalized

and conducted less systematically. The later approach to the R&D activity increases the

legitimation time. Moreover, concentrated environments are more stable and we expect firms

benefiting from the cumulativeness of their R&D efforts. R&D cumulativeness leads to a faster

legitimation of the novel technology. On the contrary, fragmented environments are unstable due

to a high firms’ turnover. Firms are not able to retain their R&D experience and this slows down

the legitimation process. Concerning the technological potential, we expect that large innovative

firms develop a greater number of industrial applications of the novel technologies for two main

reasons. First, large firms in a concentrated environment have established research labs and it is

easier for them to develop a stable flow of industrial applications of the novel technology (Malerba

et al., 1997). Second, large firms tend to patent for strategic reasons. They might aim to cover all

the industrial applications of their novel technology preventing the entry of other inventive firms.

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In a concentrated environment, both the availability of large R&D labs and strategic patenting,

lead to higher technological potentials.

Hypothesis 5 (combining components with high concentration): A novel technology resulting

from the combination of components with high concentration (a) requires shorter time to be

legitimated and (b) reaches higher technological potential.

3. Methods

Our analysis relies on a sample including all the patents filed to the European Patent Office (EPO)

in the period 1985-2015. Following Fleming and his coauthors (2007), we consider the technology

classes in which a patent is classified (International Patent Classification -IPC- codes) as the

technological components on which the patent build-up. We mark the appearance of a novel

technology as the first time ever6 appearance of a combination of IPC codes in a patent. Limiting

our definition of novel technology to the first time ever appearance of a combination, we might

consider as novel technologies, pairs of IPC codes that appears for the first time but that are never

re-used after their appearance7. Following Amabile (1996) and Fleming and co-authors (2007), we

require to the novel technology to be useful. Therefore, we restrict our novel technology definition

only to those novel combinations re-used in at least 20 patents in the 20 years following the novel

technology appearance8. We end up with a study sample that includes 11,009 successful novel

6 To flag a technology as novel, we would need the complete history of its component to be able to evaluate when a

pair of component appear together for the first time. We use the first available data period at EPO, 1978-1984, as a

buffer period to capture the history of our components and we track novel combinations starting from 1985. 7 The generation of pairs of IPC codes that are not re-used might be also the also be the result of the mechanical merge

of all IPC codes included in all the EPO patent applications. 8 Note that, as suggested by Jaffe (2002), “such selection does not create any selectivity bias” (pp. 25). We are

estimating the effects of our main variables of interest for a specific group of novel technologies, successful novel

technologies.

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technologies which generated 250,979 distinct follow-up patents9. In Appendix A we validate our

novel technology measure by applying a topic modelling algorithm to assess the coherence of the

patents included in the novel technology diffusion curves.

To represent the diffusion curves of each novel technology we proceed in two steps. First, we

count the yearly number of patents that embodied the novel technology (yt). Doing so, we construct

the actual patent cumulated distribution over a window-period of 20 years (𝑌𝑡 = ∑ 𝑦𝑝𝑡𝑝=1 )

following the appearance of the novel technology10. Second, we use the actual cumulated

distribution of each novel technology to fit the corresponding trend function. The trend function

represents an algebraic approximation of the diffusion curve of the technology. Following the

arguments detailed in Section 2, we opt for an S-curve (or sigmoid curve) characterized by a slow

initial growth and by an asymptotic convergence to a ceiling level (Geroski, 2000). The use of an

estimated diffusion curve, instead of actual data, allows us to measure the technological potential

and the legitimation period also for those technologies that do not exhaust their innovative

potential during the observation period of 20 years. In fact, technologies with particularly slow

legitimation might not show any asymptotic convergence to a ceiling level after the 20 years

covered by the observed data. One example of a technology with a particularly long diffusion

process is the light bulb that was introduced in 1909 and that continued to generate additional

patented inventions until 1955 (Abernathy and Utterback, 1978). Sixty-eight percent of the novel

technologies included in our sample reaches the estimated ceiling level11 after 20 years.

9 58% of the generated 250,979 patents contain one novel technology, 18% two novel technologies, 8% three novel

technologies, and 4% 4 novel technologies. 10 We consider all the novel technologies until the patent cohort of 1996 to leave a 20-year window forward to this

last cohort. 11 We consider a novel technology reaching the ceiling level if the difference between the actual cumulated number

of patents after 20 years and the estimated value of the ceiling is less than 10% of the ceiling value.

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The technological diffusion S-curve is identified by three parameters, i.e, Midpoint, Alpha and

Ceiling and can be expressed by the following equation:

�̂�𝑡 =𝐶𝑒𝑖𝑙𝑖𝑛𝑔

1 + 𝑒(−

(𝑡−𝑀𝑖𝑑𝑝𝑜𝑖𝑛𝑡)𝐴𝑙𝑝ℎ𝑎

)

(Equation 1)

where �̂�𝑡 is the cumulated number of patents predicted at time t, t is the number of years elapsed

since the appearance of the novel technology, the parameter Ceiling is defined as the upper

asymptote of the S-curve, Midpoint is the required time to reach the fifty percent of the ceiling,

and Alpha is the inverse of the curve slope at the Midpoint. An increase of the value of Alpha leads

to flatter diffusion curves while a decrease of the value of Alpha leads to stepper diffusion curves.

We use the three parameters describing the S-curve as proxies of the concept of Legitimation and

Technological potential illustrated in Section 2. We assume that a technology is legitimated when

it is accepted by the community and we fix the “acceptance” point as the moment when the

technology reaches the ten percent of its ceiling (Griliches, 1957). Using the formula in Equation

1, legitimation is calculated as a linear combination of Midpoint and Alpha, Midpoint-ln(9)*Alpha

(See Appendix B for the details concerning the mathematical formulation) and is expressed in

number of years since the appearance of the novel technology until its legitimation (Griliches,

1957). The potential of a technology equals to ceiling and indicates the maximum number of

inventions that the novel technology is expected to generate.

In this paper, we conduct a set of regression exercises aiming to estimate the impact of

technological, cognitive and institutional characteristics of the components combined on the

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legitimation and technological potential of a novel technology. Specifically, we estimate with an

Ordinary Least Squares (OLS) the following two equations:

Legitimation =β0+ Component characteristics*β1 + Inventors’ characteristics*β2 +

Applicants’ characteristics*β3 + Other controls* β4 + ε

(Equation 2)

Technological potential =β0+ Component characteristics*β1 + Inventors’

characteristics*β2 + Applicants’ characteristics*β3 + Other controls*β4 +ε

(Equation 3)

where Component characteristics is a vector of variables that includes the characteristics of the

components that combined to generate the novel technology namely, Similarity, Science-based

content, Familiarity, Appropriability, and Concentration of the inventive activity. The other three

vectors of variables represent our controls. Inventors’ characteristics is a vector including the

inventors’ characteristics. Applicants’ characteristics is a vector accounting for applicants’

characteristics. As other controls we include a set of time-dummies representing the calendar year

when the novel technology appeared (Technology entry year) and a set of technology class

dummies (Dummy Technology class).

To measure Similarity between the components combined, we exploit the hierarchical structure of

the IPC code classification where each additional digit denotes a higher degree of refinement of

the technological classification. Precisely, we define two components being similar when they

have the first three digits of their IPC codes in common. To construct the variable Science-based

content, we consider the patent applications including the two combined components over a rolling

time window from t-1 to t-4. For each component we compute the average number of references

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to the non-patent literature per patent application (Meyer-Krahmer & Schmoch, 1998). Finally, we

calculate the Science-based content variable as the average number of references per patent

between the two components. To measure Familiarity, we refer to Fleming’s measure (2001). To

this end, we count the number of patent applications in a four-year rolling window for each of the

two combined components. Then, we calculate the average number of patents between the two

components. Similarly, to construct the variable Appropriability, we calculate the average number

of claims of the patents in a four-year rolling window for each of the two combined components

(Merges and Nelson, 1994; Tong and Frame, 1994)12. Then, we calculate the average between the

two components. We measure the Concentration of the inventive activity by calculating the

Herfindahl–Hirschman Index (HHI) of the patents owned by the applicants using the combined

components. For each component (limited at the first four digits) in the time window from t-1 to

t-4, we calculate the share of patents owned by each applicant. Based on these shares we calculate

the HHI of each component and we calculate the average of the HHI of the two components.

In our regression model we consider the logarithm transformation of the variables Science-based

content, familiarity, Appropriability, and Concentration to interpret the estimated effects as semi-

elasticities.

Several time-invariant unobserved characteristics of the components might bias our estimations of

the technological, cognitive and institutional characteristics such as, the unmeasurable propensity

of the component to generate additional inventions and its technological complexity. To take into

12 Our interpretation is that the larger the number of claims the higher the appropriability of the invention. However

other interpretations exist. An alternative interpretation is that a larger number of claims correspond to a lower

appropriability of the invention since the large number of claims protects very specific industrial applications. On the

contrary, a patent with a limited number of claims protects a less specific industrial application with a higher

appropriability.

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account these issues, we include as explanatory variables a set of 118 three-digit technological

class dummies referring to the combined components. A technology class dummy equals one if

the first three digits of the IPC codes of at least one of the two combined components equal to the

three-digit identifying the technological class dummy, zero otherwise. A complete list with the

detailed description of the 118 technological class dummies is reported in Appendix C.

As inventors’ characteristics we consider inventors’ experience and inventors’ team size. To build

the inventors’ experience variable, we proceed in three steps. First, we extract the inventors listed

in the patents filed during the first year when the novel technology appears. Second, we reconstruct

the patent history of each inventor and count the number of patents she filed. Finally, we define a

dummy that equals one if at least one of the inventors has a previous patented invention. The team

size is the number of inventors appearing in the patents filed during the first year when the novel

technology appears.

As applicants’ characteristics during the first year when the novel technology appears, we consider

their experience, type (university or public research center versus private company), being a single

applicant, and their countries. The applicants’ experience is computed similarly as for inventors.

However, instead of generating a dummy variable, we compute a count variable that records

applicants’ previous patented inventions. University applicant is a dummy that equals one if at

least one of the applicant is a university or a research center, zero otherwise. More than one

applicant is a dummy that equals one if there is more than one applicant, zero otherwise.

Applicant’s country is a set of dummies, one for each applicant’s country reported on the patent

documents, that identifies the geographical location of the applicant(s). As for the inventors’

characteristics, the set of variables that refer to applicants is computed on the patents filed during

the first year when the novel technology appears.

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Table 1 reports the descriptive statistics of all our dependent and independent variables.

Table 1: Descriptive statistics.

Obs. Mean Sd Min Max

Dependent variables

Technological potential [# patents] 11,009 65.97 93.84 16.32 998.29

Legitimation (10%) [# years] 11,009 5.86 3.13 0.00 16.78

Other sigmoid parameters

Midpoint (50%) [# years] 11,009 12.37 3.32 1.53 24.89

Alpha 11,009 2.96 1.16 0.21 7.47

Component characteristics

Similarity 3 digits (dummy) 11,009 0.33 0.47 0.00 1.00

Science-based content 11,009 3.50 7.41 0.02 283.99

Familiarity 11,009 346.24 427.84 0.50 5490.50

Appropriability 11,009 10.91 2.09 1.50 28.75

Concentration (HHI) 11,009 0.03 0.05 0.00 0.89

Inventor’s characterisics

Inventors' experience (dummy) 11,009 0.47 0.50 0.00 1.00

Inventors' team size 11,009 3.36 2.89 1.00 55.00

Applicant characteristics

Applicants' experience 11,009 637.19 1503.72 0.00 17279.00

University applicant (dummy) 11,009 0.05 0.22 0.00 1.00

More than one applicant (dummy) 11,009 0.25 0.43 0.00 1.00

The transgenic mammal technology used to mimic diseases provides an illustrative example of

how we identify the diffusion curve of a novel technology and of how we calculate the

characteristics of the combined components. We mark as transgenic mammal novel technology

the combination of the IPC codes “Introducing […] material into […] the body of animals” (IPC

code A01K67) and “[…] DNA or RNA concerning genetic engineering […]” (IPC code C07H21)

that appeared for the first time in onco-mouse patent. We observe the novel transgenic mammal

technology for a 20-year window. Figure 2 shows the actual cumulated distribution (Yt) of the user

patents embodying the combinations of the IPC codes A01K67-C07H21 (dotted line). Referring

to the patent distribution, we estimate the three parameters of the corresponding S-curve by using

20

a maximum likelihood estimation methodology. The solid line in Figure 2 represents the fitted S-

curve curve (�̂�𝑡). This curve is identified by an estimated ceiling equals to 267.38 patents, a

Midpoint of 15.49 years, and an Alpha of 3.45. In terms of legitimation and technological potential,

it results that the legitimation of the technology is reached after 7.91 years since the technology

appearance while 267.38 patents correspond to the technological potential reachable by the

technology. The use of a specific functional form allows us to predict the diffusion of a novel

technology at any point in time by means of the three estimated parameters. In our example, by

substituting the estimated parameters in Equation 1, we can predict that, after 10 years since the

transgenic mammal technology appearance (�̂�10), the cumulated number of user patents

embodying the transgenic mammal technology equals 45.24 (267.38

1+𝑒(−

(10−15.49)3.45

)=45.24). In the same

way, we can predict that after 30 years from its appearance (�̂�30), the cumulated number of user

patents embodying the transgenic mammal technology equals to 263.45 (267.38

1+𝑒(−

(30−15.49)3.45

)=263.45).

Figure 3 plots all the estimated diffusion curves of the novel technologies in our sample. The black

line highlights the transgenic mammal technology diffusion curve.

Concerning the characteristics of the combined components generating the transgenic mammal

technology, we find that they are not similar (Similarity = 0 [sample average = 0.33]), they are

science based (Science-based content = 9.51 [sample average = 3.50]), the inventors’ community

is not familiar with the components (Familiarity=160.5 [sample average = 346.24]), and, that their

Appropriability is slightly higher than the average of the other components (Appropriability=13.11

[sample average = 10.91]). Finally, the Concentration of the innovative activities is lower than the

average (Concentration = 0.014 [sample average = 0.03]).

21

Figure 2: Diffusion curve of the novel onco-mouse technology

Figure 3: Novel technologies’ diffusion curves. The back curve identifies the onco-mouse technology

22

4. Results

Table 2 shows the results of our econometric exercise. Columns 1 and 2 report the estimation of a

baseline model including only the component characteristics and technology entry year dummies.

Columns 3 and 4 add as controls the inventors’ and applicants’ characteristics. Columns 5 and 6

add the technological class dummies. According to our most complete model specification,

represented in Equation 2 and 3 and estimated in columns 5 and 6, we find that combining two

similar components reduces significantly the technology legitimation time. Specifically, if the

novel technology results from the combination of two similar components the legitimation time is

15.12 months13 shorter than a technology resulting from the combination of two dissimilar

components. Component similarity significantly affects also the technological potential. Having

similar component decreases the technological potential by 14.8 patents. Then, hypothesis one,

combining similar components, is confirmed.

Combing components with a higher science-based content generates a novel technology that

requires more time to be legitimated but has a higher technological potential. Precisely 50% more

of science-based content of the technology increases the legitimation time by 1.38 months, while

it increases the technological potential by 9.1 patents. Therefore, hypothesis two, combining

science-based components, is confirmed.

A novel technology resulting from the combination of two familiar components requires a shorter

time to be legitimated. Increasing the level of familiarity by 50% decreases the time needed for a

novel technology to be legitimated by 1 month. The same technology has lower technological

13 15.12 is obtained multiplying the coefficient of Similarity in the regression with Legitimation as dependent variable

(-1.26) by the number of months in a year (12).

23

potential by 3.5 patents. These results are in line with hypothesis three, combining familiar

components.

Combining components with a high appropriability generates a novel technology that requires

shorter time to be legitimated and has lower technological potential. Precisely 50% more of

appropriability decreases the legitimation time by 5.94 months and decreases the technological

potential by 13.65 patents. Therefore, hypothesis four, combining components with high

appropriability, is confirmed.

Combining components with a higher concentration of the innovative activity generates a novel

technology with a higher technological potential. Precisely, 50% more of concentration increases

the technological potential by 5.5 patents. Legitimation time is not affected. Therefore, hypothesis

five, combining components with high concentration, is partially confirmed.

Concerning the controls, we find that the inventors’ experience reduces the time needed for a

technology to be legitimated while the inventors’ team size increases the technological potential.

The experience of the applicants, having multiple applicants, and the presence of a university

among the applicants promoting the novel technology lead to a technology with a higher potential.

The legitimation time is negatively affected by the presence of multiple applicants. The applicant

concentration in the sector fosters the technological potential.

24

Table 2: Regression results. OLS estimations for Equation 1 and 2.

(1) (2) (3) (4) (5) (6)

VARIABLES

Legitimation

(10%)

Technological

potential

Legitimation

(10%)

Technological

potential

Legitimation

(10%)

Technological

potential

Component characteristics

Similarity 3 digits (dummy) 0.039 18.9*** 0.077 18.1*** -1.26*** -14.8***

log(Science-based content) 0.46*** 23.7*** 0.49*** 22.3*** 0.23*** 18.2***

log(Familiarity) -0.10*** -4.90*** -0.092*** -4.96*** -0.17*** -6.91***

log(Appropriability) -2.71*** -52.6*** -2.51*** -54.4*** -0.99*** -27.3***

log(Concentration) 0.27*** 9.56*** 0.26*** 9.81*** 0.042 11.0***

Inventor’s characterisics

Inventors' experience (dummy) -0.30*** -3.08 -0.20*** -1.18

log(Inventors' team size) -0.17*** 4.03*** -0.063 5.55***

Applicant characteristics

log(Applicants' experience) -0.027** 0.14 -0.010 0.60*

University applicant (dummy) 0.10 7.62* 0.17 10.3***

More than one applicant (dummy) -0.77*** 7.76*** -0.86*** 5.71**

Dummy Technology class (3 digits) No No No No Yes Yes

Dummy Applicant’s country No No Yes Yes Yes Yes

Dummy Technology entry year Yes Yes Yes Yes Yes Yes

Constant 14.0*** 251*** 14.3*** 238*** 12.9*** 248***

Observations 11,009 11,009 11,009 11,009 11,009 11,009

R-squared 0.095 0.080 0.128 0.096 0.243 0.155

In Table 2 we present a set of regressions with legitimation and technological potential as

dependent variables. While the later variable has a direct correspondence in one of the parameter

of a sigmoid curve representing the diffusion curve, legitimation is a linear combination of the

remaining two parameters, Midpoint and Alpha. Table 3 reports a set of regressions with Midpoint

and Alpha as dependent variables in order to disentangle the determinants of the two parameters

that linearly combined define Legitimation. Note that the coefficients estimated for each variable

in Table 2, columns 5 and 6, can be calculated as follow:

�̂�𝐿𝑒𝑔𝑖𝑡𝑖𝑚𝑎𝑡𝑖𝑜𝑛 = �̂�𝑀𝑖𝑑𝑝𝑜𝑖𝑛𝑡 − �̂�𝐴𝑙𝑝ℎ𝑎 ∗ 2.2

25

(Equation 4)

Table 3: OLS estimation using as dependent variables Midpoint and Alpha and adopting the same

specifications reported in Equation 1 and Equation 2.

(1) (2)

VARIABLES Midpoint (50%) Alpha

Technological component characteristics

Similarity 3 digits (dummy) -0.48*** 0.35***

log(Science-based content) -0.20*** -0.20***

log(Familiarity) -0.12*** 0.022**

log(Appropriability) -0.83*** 0.074

log(Concentration) -0.069 -0.051***

Inventor’s characterisics

Inventors' experience (dummy) -0.13** 0.031

log(Inventors' team size) -0.078* -0.0072

Applicant characteristics

log(Applicants' experience) -0.028*** -0.0081**

University applicant (dummy) -0.30** -0.21***

More than one applicant (dummy) -0.27*** 0.27***

Dummy Applicant’s country yes yes

Dummy technology entry year yes yes

Dummy Technology Class (3 digits) yes yes

Constant 18.1*** 2.37***

Observations 11,009 11,009

R-squared 0.339 0.259

The results concerning Legitimation (time to reach 10%) and Midpoint (time to reach 50%), appear

consistent (see Table 2 column 5 versus Table 3 column 2). Indeed, Similarity, Familiarity,

Appropriability, and Concentration show qualitatively the same impact on Legitimation and

Midpoint. The level of Science-based content of the combined components represents the only

exception. While Science-based content increases the legitimation time, it decreases the time

needed to reach the Midpoint. The parameter Alpha explains this finding. A high Science-based

content of the combined components leads to a diffusion curve that grows slowly in its very first

26

part (that reflects in a positive and significant coefficient of the Science-based variable in the

Legitimation regression) but that catches up quickly due to its steepness (at the base of a negative

and significant sign of the coefficient of the Science-based variable in the Alpha regression) and

reaches faster the midpoint (with a negative and significant sign of the coefficient of the Science-

based variable in the Midpoint regression).

To test the reliability of our results we implemented a set of robustness checks that are reported in

Appendixes from D to I.

In Appendix D we check the robustness of the main results reported in Table 2. Specifically, we

change the functional form by using the logarithmic transformation of our dependent variables,

legitimation time and technological potential. In this regression exercise, the estimated coefficients

for the component characteristics can be interpreted as elasticities. Results are consistent across

model specifications.

The arbitrariness of the assumption that the diffusion process follows an S-curve might raise a

concern. In Appendix E, to reply to this concern, we report a variant of the econometric exercise

of Table 2. Instead of using as dependent variables the estimated legitimation time and the

estimated technological potential, we use the actual values of the two parameters. Specifically, we

calculate the legitimation time as the time required to reach the ten percent of the actual ceiling.

We define the actual ceiling, i.e. technological potential, as the cumulated number of patented

inventions using the novel technology 20 years after its appearance. Results are consistent when

using actual legitimation time and actual technological potential.

In the same vein of Appendix E, we test our assumption on the validity of a S-curve as

representative diffusion curve by conducting two separated econometric exercises on two samples.

27

The first sample includes novel technologies reaching the technological potential within the 20

years observed, while the second sample includes the technologies not reaching the technological

potential. We adopt the same specification of the models estimated in columns 5 and 6 of Table 2.

Results are reported in Appendix F and are in line with those obtained in Table 2 for both samples.

Another possible concern regards the presence of multiple novel technologies within the same

patent. As reported in section 3 (Methods), 58% of the follow-up patents contains only one of the

11,009 novel technologies, while the 42% contains multiple novel technologies. The fact that a

patented invention embeds more than one novel technology, and therefore contributes to multiple

diffusion curves, is not in conflict with the hypotheses at the base of our analysis. However, in

order to test the robustness of our results, we restrict our sample to the novel technologies that

have diffusion curve where the large majority of the patents (at least the 90%) is mono-technology.

Then, we replicate the same econometric exercise as the one reported in Table 2, columns 5 and

6. Results are reported in Appendix G and are consistent with results in Table 2.

In Appendix H we conduct a similar robustness check as the one conducted in Appendix G.

Specifically, we limit the sample to those novel technologies having only one patent the first year

when they appear. Results are in line with those obtained in Table 2, columns 5 and 6.

In Appendix I, we consider a less stringent definition of novel technology avoiding imposing a

minimum number of reuse after its appearance and present a 2-step Heckman estimation strategy

that correct for possible selection biases. Results are coherent with those reported in Table 2,

columns 5 and 6.

5. Discussion

28

Novel technology diffusion and component characteristics

Ideally an attractive technology has a short legitimation period and a high technological potential.

To visualize the economic impact of the component characteristics on the novel technology

diffusion, we consider five scenarios where the novel technology is obtained by combining (1)

similar components (compared to dissimilar components), (2) components with a science-based

content increased by 50%, (3) components with familiarity increased by 50%, (4) components with

appropriability increased by 50%, and (5) components with concentration of the innovative

activities increased by 50%. The coefficients considered are those estimated in the econometric

models presented in Table 2, columns 5 and 6. Figure 4 and 5 show the increase/reduction of

legitimation time and technological potential obtained in the five scenarios, while Figure 6 reports

the complete diffusion curve corresponding to each scenario. We observe a window period of

twenty years after the introduction of the novel technology14.

The five scenarios show there is a trade-off when changing the values of each component

characteristics (Similarity, Science-based content, Familiarity, Appropriability, and

Concentration) to shorten the legitimation or increase the technological potential. Looking at

Figures 4 and 5, we observe that by augmenting the value of the variables Science-based content

and Concentration, the corresponding technology has higher technological potential. However, it

requires a longer time to be legitimated. On the contrary, augmenting the value of Familiarity, the

novel technology decreases the time needed to be legitimated and, as a drawback, reduces its

14 Although our model allows to predict technological potential for any time span, we selected twenty years because

it seems a reasonable period over which both managers and policy makers might be requested to predict the

technological potential of a novel technology.

29

technological potential. Higher Similarity as well as higher Appropriability decreases the

legitimation time and the technological potential.

Although each component characteristic shows a trade-off, the magnitude of these trade-offs

differs significantly according to the characteristic considered. Augmenting the Science-based

content and the Concentration increases to a considerable extent the technological potential,

augmenting only slightly the legitimation time. Familiarity exhibits limited effects both on

legitimation and technological potential. Appropriability and Similarity are characterized by the

largest trade-offs, namely shortening the legitimation time is obtained at the cost of significantly

lower technological potential and vice versa. We conclude that Familiarity has a limited effect,

Science-based content and Concentration increase significantly the technological potential with a

limited drawback in terms of legitimation time, Appropriability and Similarity show relevant trade-

offs between legitimation and technological potential.

Novel technology diffusion across different technological domains

The results discussed regard the average effects of the combined component characteristics in

different technological domains. However, the effects might differ across technological domains.

In order to further investigate the technological domain heterogeneity, we conduct two separated

regression exercises on the most representative sectors in our dataset: the Bio-pharmaceutical

sector and the Digital technology sector, respectively. Table 4 replicates the econometric exercise

as in Table 3, columns 5 and 6, for the bio-pharmaceutical and for the digital technology sectors15.

15 We identify bio-pharmaceutical and for the digital technology sectors according to Schmoch’s classification (2008)

30

Table 4: Regression results. OLS estimations for Equation 1 and 2 in the Bio-pharmaceutical sector and

Digital technology sector

Bio-pharmaceutical sector Digital technology sector

(1) (2) (3) (4)

Legitimation (10%) Technological potential Legitimation (10%) Technological potential

Technological component characteristics

Similarity 3 digits (dummy) -1.70*** -24.1*** -0.73** 23.8*

log(Science-based content) 0.13 24.7*** 0.083 3.86

log(Familiarity) -0.23*** -5.69*** -0.21*** -12.8***

log(Appropriability) -0.20 -8.66 -1.61*** -56.6***

log(Concentration) -0.83*** -22.6*** 0.14 35.9***

Inventor’s characterisics

Inventors' experience (dummy) -0.13 -1.74 -0.27** -1.06

log(Inventors' team size) -0.046 12.3*** 0.11 3.55

Applicant characteristics

log(Applicants' experience) 0.0062 1.61* -0.041** 0.21

University applicant (dummy) 0.21 4.62 -0.98*** -10.3

More than one applicant (dummy) -0.73*** 2.10 -0.82*** 9.52

Dummy Applicant’s country Yes Yes Yes Yes

Dummy technology entry year Yes Yes Yes Yes

Dummy Technology Class (3 digits) Yes Yes Yes Yes

Constant 8.19*** 58.9 15.8*** 389***

Observations 3,303 3,303 3,252 3,252

R-squared 0.295 0.151 0.321 0.244

We find substantial differences between the two sectors in the impact of the variables Similarity,

Science-based, Appropriability, and Concentration, respectively. In the bio-pharmaceutical sector,

consistently with our main results (Table 2), Similarity has a negative impact on the technological

potential. On the contrary, in the digital technology sector, the Similarity of the combined

components increases the technological potential of the novel technology by 23.8 patented

inventions. The different level of complexity across the two sectors might explain these different

results. In the digital sector, inventions are complex since they often result from assembling

31

numerous parts, while inventions in the bio-pharmaceutical sector are simpler being often

represented by a unique molecule or a chemical compound. In highly complex sectors, combining

similar components does not penalize the technological potential, on the contrary increase the

probability that a larger number of inventors engages in developing industrial applications that use

the novel technology. The Science-based content does not affect novel technologies diffusion in

the digital sector, while it increases significantly the technological potential of a bio-

pharmaceutical novel technology. This result might be explained by the strong connection between

laboratory scientific research and industrial applications in the bio-pharmaceutical sector. Greater

research on a molecule or chemical compound leads to a better knowledge of its functioning

biological mechanisms and to a larger number of applications. On the contrary in the digital sector,

laboratory scientific research and industrial applications are less connected and having a greater

number of scientific paper citations does not affect the potential of the resulting technology.

Appropriability does not show any effect on the diffusion of novel technologies in the bio-

pharmaceutical sector, while it is statistically significant and negative both for legitimation and

technological potential in the digital technology sector. In the bio-pharmaceutical sector, a patent

guarantees the protection of an invention regardless its form, i.e. the number of claims, since

property rights over molecules and chemical compounds are easily defined. Contrary to our

hypotheses, Concentration of the firms’ inventive activity reduces the technological potential in

the pharmaceutical sector. This result might be explained by the fact that few large firms can easily

exclude potential entrants and a limited number of actors in the sector limits the number of

industrial applications developed.

32

Strategical implications

Our general results can be used for practical purposes when managers or policy makers are asked

to decide the long terms R&D strategies of a company or a region. Table 5 reports an example of

a hypothetical situation. A firm is competing in a market where two emerging novel technologies

co-exist, A and B. The Chief Technological Officer (CTO) of the firm is asked to select the

technology that is more promising in terms of technological potential in order to plan the long run

R&D investment strategy of her firm. Moreover, she is asked to estimate if there is a significant

difference between the two novel technologies in terms of the time needed to generate a

“reasonable” number of industrial applications (legitimation time). Novel technology A is

characterized by a combination of dissimilar components with an average value of Familiarity,

Science-based content, Appropriability, and Concentration. Differently, novel technology B, is

characterized by the combination of similar components and has a significantly lower Science-

based content. Familiarity, Appropriability and Concentration of technology A equal to those of

technology B. Table 3 allows the CTO to compare the two technologies and to take a decision. It

shows that, according to the components characteristics, the novel technology A has higher

technological potential than B (+26.08 patents) although reaching a reasonable number of

industrial applications will require more than technology B (+1.4 years).

Table 5: Legitimation and technological potential of the novel technology A vs. B

A B A-B

∆Legitimation [years]

∆Technological Potential [patents]

Similarity 0 1 -1 +1.26 +14.8

log(Familiarity) 5.26 5.26 0 0 0

log(Science-Based content) 0.74 0.12 0.62 +0.14 +11.28

log(Appropriability) 2.37 2.37 0 0 0

log(Concentration) -3.91 -3.91 0 0 0

Total: +1.4 +26.08

33

Figure 4: Novel technologies’ legitimation time increase/reduction obtained by changing the values of the

technological component characteristics. The average legitimation time in the study sample equals 5.86 years

34

Figure 5: Novel technologies’ technological potential obtained by changing the values of the technological

component characteristics

35

Figure 6: Novel technologies’ diffusion curves obtained by changing the values of the technological

component characteristics

36

6. Conclusion

In this paper we investigate if the conditions at the time of the appearance of a novel technology

(technological antecedents) impact on its subsequent diffusion curve. To do that, we conduct an

empirical study that traces the diffusion of 11,009 successful novel technologies over the period

1985-2015. Using patent data, we identify a novel technology as an unprecedented combination

of existing technological components, as represented by the technological classes where a patent

is classified. Then, we trace all the patents incorporating that novel technology and we estimate

the corresponding diffusion curve. Under the assumption that a diffusion curve is S-shaped, we

look at the technological, cognitive and institutional characteristics of the combined components

affecting the duration of the legitimation phase (first part of the curve) and the potential of a

technology (ceiling of the curve).

We find that a high degree of similarity between the combined components, familiarity of

inventors with the components, and high level of appropriability shorten the legitimation time and

reduce the technological potential. The science-based nature of the combined components plays

an opposite role and increases the legitimation time, while positively affects the technological

potential of a novel technology. Concentration of innovative activities increases the technological

potential. By looking at the economic impact of our results, we find that increasing the science-

based nature of the combined components as well as the concentration of the innovative activities

significantly augment the technological potential with a limited increase in the legitimation time.

Familiarity has a limited effect both on technological potential and legitimation. Finally, similarity

and appropriability show a trade-off of a relevant extent: decreasing the legitimation period has a

significant cost in terms of reduction of technological potential (and vice versa).

37

Our results have strategical implications in predicting how the legitimation period and the

technological potential vary according to a set of characteristics of the novel technology observed

at its appearance. These predictions might be used in the private sector, when managers face the

choice between two emerging technologies for their businesses, as well as in the public sector,

when policy makers are asked to choose, for instance, the technological orientation of a region.

38

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Appendix A: Validation of the novel technology measure with topic modelling

A possible concern with the identification of a novel technology as the combination of two

different components (represented by the combination of two IPC codes) is that there is no proof

that the resulting combination is meaningful. It could be that the novel combination is a mere

artifact of our way of identifying novel technologies without any meaningful content. To

investigate the existence of a meaningful content, we implement a topic modelling analysis that

verifies the coherence of the content of the patents embedding the same novel technology. If the

patents associated to the same novel technology are coherent in terms of content, we can assume

that they embed a meaningful common content, i.e. the novel technology.

In detail, we proceed as follow. First, we pulled together the titles of all the patents included

in our study sample (250,979 patents) and we inferred the topics of each patent by using the Lantent

Dirichler Allocation (LDA) method. The idea of LDA is that each document is the results of a set

of (latent) topics, and topics generate words with a probability distribution (Steyvers and Griffiths,

2007). For example, if a document includes a unique topic, e.g., transgenic modification methods,

the words that are likely to appear in the document will be “protein”, “human”, “dna”,

“transgenic”. We used the Gibbs’s algorithm to estimate the LDA parameters and to classify each

patent according to 20 topics. We chose the number of topics according to the optimization method

proposed by Steyvers and Griffiths (2007) that aims at finding the number of topics that maximizes

the log-likelihood value of the Gibb’s estimation of the LDA parameters. The results of this

optimization method are reported in Figure A1. Figure A1 shows that the maximum value of log-

likelihood is obtained for 20 topics. Figure A2 illustrates the most likely four words generated by

each of the 20 topics.

42

Figure A1. Optimal topic selection number of topics identifying the patent titles text in our sample.

Figure A2: The most likely four words generated by each topic.

Once the 20 topics have been identified, we redefined each patent as a combination of topic shares.

Figure A3 shows an illustration of how topics contribute to the patent titled “Method and device

for preparing fibrous plant bodies.”. The patent is characterized by topic 5 and 12 that have the

highest shares.

Figure A3: Share contribution of topics in patent titles.

Ttitle 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Method and

device for

preparing

fibrous

plant

bodies.

5% 5% 5% 5% 8% 5% 5% 5% 5% 5% 5% 8% 5% 5% 5% 5% 5% 5% 5% 5%

43

To prove that each novel technology has a meaningful content, we need to show that patents

belonging to the same group are homogenous in terms of topic content. To do that, we grouped

the patents belonging to the same novel technology according to our definition reported in section

2: a patent is assigned to a technology if it has a specific pair of IPC codes in its IPC classification

list. Then, we evaluated the level of homogeneity of each topic within the group of patents

embedding the same novel technology. To do so, we calculate the standard deviation of each topic

share (see Figure A4). Then, we calculate the average of those standard deviations by group (novel

technology) (see Figure A5).

Figure A4: Standard deviation of each topic share by novel technology. A patent is assigned to a technology if

it has a specific pair of IPC codes in its IPC classification list.

IPC class IPC class 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #patents

A01D45 A01D43 2.8% 0.8% 0.6% 0.7% 0.7% 1.0% 0.6% 0.8% 0.5% 0.5% 0.3% 1.1% 0.1% 0.5% 0.7% 0.1% 0.7% 0.4% 0.4% 0.4% 26

Figure A5: Average standard deviation of topics shares by novel technology. A patent is assigned to a

technology if it has a specific pair of IPC codes in its IPC classification list.

PC class IPC class Average

standard devation

#patents

A01D45 A01D43 0.69% 26

To assess if topics are homogeneous or not within each group, as represented by the average

standard deviation calculate in Figure A5, we need to have a comparison value. We constructed a

44

comparison sample where each patent is assigned randomly to a group (and not grouped according

to its IPC codes). Each group maintains the same number of patents as in our original classification.

We computed the average standard deviation of topics shares of each of those groups where patents

are randomly assigned (see Figure A6).

Figure A6: Average standard deviation of topics shares by novel technology. A patent is assigned

randomly to a technology.

PC class IPC class Average standard

devation

#patents

A01D45 A01D43 1.04% 26

Finally, we test if the average standard deviations in the two samples, the original sample and the

one with patent randomly assigned to technologies, are statistically different. We find that the

average standard deviation of the topics share in the original sample is significantly lower than in

the sample with randomly assigned patents (0.008 vs. 0.012, P-value 0.000). This result shows that

topics are more homogeneous in a sample where patents are assigned to the novel technology

according to the IPC code combinations rather than when they are assigned randomly. In other

words, the method applied to identify a novel technology aggregates patents with coherent

contents.

45

Appendix B: Legitimation as a linear combination of midpoint and alpha of an S-curve

technology diffusion curve

In this appendix details the mathematical formulation relating legitimation (defined as the time

needed for diffusion curve to reach the 10% of its ceiling point) to the midpoint and alpha. Starting

from Equation 1, we extract t as follow:

�̂�𝑡 =𝐶𝑒𝑖𝑙𝑖𝑛𝑔

1 + 𝑒(−

(𝑡−𝑀𝑖𝑑𝑝𝑜𝑖𝑛𝑡)𝐴𝑙𝑝ℎ𝑎

)

1 + 𝑒(−

(𝑡−𝑀𝑖𝑑𝑝𝑜𝑖𝑛𝑡)𝐴𝑙𝑝ℎ𝑎

)=𝐶𝑒𝑖𝑙𝑖𝑛𝑔

�̂�𝑡

−(𝑡 − 𝑀𝑖𝑑𝑝𝑜𝑖𝑛𝑡)

𝐴𝑙𝑝ℎ𝑎= ln(

𝐶𝑒𝑖𝑙𝑖𝑛𝑔

�̂�𝑡− 1)

−𝑡 +𝑀𝑖𝑑𝑝𝑜𝑖𝑛𝑡 = 𝐴𝑙𝑝ℎ𝑎 ∗ ln(𝐶𝑒𝑖𝑙𝑖𝑛𝑔

�̂�𝑡− 1)

𝑡 = 𝑀𝑖𝑑𝑝𝑜𝑖𝑛𝑡 − 𝐴𝑙𝑝ℎ𝑎 ∗ ln (𝐶𝑒𝑖𝑙𝑖𝑛𝑔

�̂�𝑡− 1)

Then we set the 10% of the ceiling point, �̂�𝑡

𝐶𝑒𝑖𝑙𝑖𝑛𝑔=

1

10 and compute the corresponding t

𝑡10% = 𝑀𝑖𝑑𝑝𝑜𝑖𝑛𝑡 − 𝐴𝑙𝑝ℎ𝑎 ∗ ln(10 − 1)

𝑡10% = 𝑀𝑖𝑑𝑝𝑜𝑖𝑛𝑡 − 𝐴𝑙𝑝ℎ𝑎 ∗ 2.2

46

Appendix C: List of the technological class dummies of our sample (three digits)

Tech class Number of patents (whole Patstat) Legitimation Potential Class name

C12 226231 5.40 56.74 biochemistry; beer; spirits; wine; vinegar; microbiology; enzymology; mutation or

genetic engineering

C40 4502 6.28 74.89 combinatorial technology

B82 5427 6.19 62.41 nanotechnology

A61 972410 6.19 58.17 medical or veterinary science; hygiene

C07 593444 5.12 53.73 organic chemistry

B81 5593 5.44 59.87 microstructural technology

C13 1402 4.39 30.79 sugar industry

C30 11506 4.91 40.13 crystal growth

A01 100309 5.85 63.02 agriculture; forestry; animal husbandry; hunting; trapping; fishing

G06 333626 6.53 65.50 computing; calculating; counting

G01 344963 5.58 51.31 measuring; testing

A23 61675 5.48 46.07 foods or foodstuffs; their treatment, not covered by other classes

H04 568514 6.83 75.78 electric communication technique

G10 23284 5.76 56.73 musical instruments; acoustics

C01 41421 4.29 47.89 inorganic chemistry

G11 83155 5.11 58.21 information storage

G02 102565 5.86 48.71 optics

H01 424044 6.05 54.34 basic electric elements

C05 5273 4.57 30.47 fertilisers; manufacture thereof

H03 69422 5.77 44.52 basic electronic circuitry

C04 35183 4.63 46.52 cements; concrete; artificial stone; ceramics; refractories

B01 190188 5.30 46.96 physical or chemical processes or apparatus in general

A21 8172 4.82 42.78 baking; equipment for making or processing doughs; doughs for baking

C23 41382 5.25 40.21

coating metallic material; coating material with metallic material; chemical surface

treatment; diffusion treatment of metallic material; coating by vacuum evaporation,

by sputtering, by ion implantation or by chemical vapour deposition, in general;

inhibiting corrosion of metallic material or incrustation in general

C22 34422 4.76 34.53 metallurgy; ferrous or non-ferrous alloys; treatment of alloys or non-ferrous metals

G09 43751 6.19 56.42 educating; cryptography; display; advertising; seals

C08 337146 4.60 38.52 organic macromolecular compounds; their preparation or chemical working-up;

compositions based thereon

E21 29316 5.24 47.15 earth or rock drilling; mining

C03 28360 4.56 35.42 glass; mineral or slag wool

C09 132566 5.30 42.99 dyes; paints; polishes; natural resins; adhesives; compositions not otherwise

provided for; applications of materials not otherwise provided for

G07 33692 5.32 48.83 checking-devices

C02 23804 5.02 38.74 treatment of water, waste water, sewage, or sludge

G03 75366 5.70 43.36 photography; cinematography; analogous techniques using waves other than

optical waves; electrography; holography

G05 42106 5.67 47.14 controlling; regulating

D01 17333 4.65 36.77 natural or man-made threads or fibres; spinning

G21 15183 5.76 36.89 nuclear physics; nuclear engineering

C11 39725 5.19 43.40 animal or vegetable oils, fats, fatty substances or waxes; fatty acids therefrom;

detergents; candles

B09 4642 3.61 37.39 disposal of solid waste; reclamation of contaminated soil

A62 10026 3.53 30.05 life-saving; fire-fighting

C25 19093 5.50 42.70 electrolytic or electrophoretic processes; apparatus therefor

47

B06 2548 6.36 42.86 generating or transmitting mechanical vibrations in general

A63 29203 5.37 51.46 sports; games; amusements

C10 60809 5.02 44.54 petroleum, gas or coke industries; technical gases containing carbon monoxide;

fuels; lubricants; peat

B03 5096 6.76 45.44 separation of solid materials using liquids or using pneumatic tables or jigs;

magnetic or electrostatic separation of solid materials from solid materials or

fluids; separation by high-voltage electric fields

B22 25745 4.66 40.40 casting; powder metallurgy

B32 54028 5.47 44.85 layered products

G08 27261 7.00 60.63 signalling

C06 2871 4.76 44.14 explosives; matches

H05 74628 6.24 50.92 electric techniques not otherwise provided for

A24 5848 6.08 21.92 tobacco; cigars; cigarettes; smokers' requisites

B28 10629 3.89 32.62 working cement, clay, or stone

D21 30511 4.28 39.52 paper-making; production of cellulose

C21 18066 5.00 44.47 metallurgy of iron

B05 43991 5.20 38.17 spraying or atomising in general; applying liquids or other fluent materials to

surfaces, in general

F21 43450 7.27 65.79 lighting

B41 71628 5.32 42.43 printing; lining machines; typewriters; stamps

D04 14911 5.36 35.52 braiding; lace-making; knitting; trimmings; non-woven fabrics

B29 118121 4.36 36.21 working of plastics; working of substances in a plastic state in general

H02 117217 5.71 42.32 generation, conversion, or distribution of electric power

B44 6913 5.72 33.29 decorative arts

D02 4015 3.63 32.48 yarns; mechanical finishing of yarns or ropes; warping or beaming

D06 40523 4.71 41.65 treatment of textiles or the like; laundering; flexible materials not otherwise

provided for

B64 21484 5.76 38.66 aircraft; aviation; cosmonautics

B24 20637 5.42 50.81 grinding; polishing

B42 9003 5.36 36.31 bookbinding; albums; files; special printed matter

B31 6146 4.19 27.36 making articles of paper, cardboard or material worked in a manner analogous to

paper; working paper, cardboard or material worked in a manner analogous to

paper

F04 45443 5.94 41.67 positive-displacement machines for liquids; pumps for liquids or elastic fluids

B08 8716 5.09 43.42 cleaning

F17 6312 4.95 30.87 storing or distributing gases or liquids

A41 8219 5.05 37.20 wearing apparel

F41 9474 5.16 42.72 weapons

B27 9313 4.72 35.58 working or preserving wood or similar material; nailing or stapling machines in

general

B25 26692 4.96 37.78 hand tools; portable power-driven tools; handles for hand implements; workshop

equipment; manipulators

B04 4760 4.38 34.24 centrifugal apparatus or machines for carrying-out physical or chemical processes

C14 1182 1.81 29.61 skins; hides; pelts; leather

B23 73056 5.77 49.40 machine tools; metal-working not otherwise provided for

A43 10643 5.37 41.87 footwear

F01 64495 6.18 51.59 machines or engines in general; engine plants in general; steam engines

F03 14724 5.80 47.15 machines or engines for liquids; wind, spring, or weight motors; producing

mechanical power or a reactive propulsive thrust, not otherwise provided for

G04 9336 6.27 35.19 horology

B21 33691 4.14 37.22 mechanical metal-working without essentially removing material; punching metal

48

A46 4839 3.86 31.61 brushware

B02 6196 3.75 33.50 crushing, pulverising, or disintegrating; preparatory treatment of grain for milling

F25 32049 6.53 43.86 refrigeration or cooling; combined heating and refrigeration systems; heat pump

systems; manufacture or storage of ice; liquefaction or solidification of gases

F15 11656 4.17 37.40 fluid-pressure actuators; hydraulics or pneumatics in general

F23 26346 4.58 40.73 combustion apparatus; combustion processes

B67 9452 5.21 32.54 opening or closing bottles, jars or similar containers; liquid handling

F42 6421 5.56 38.51 ammunition; blasting

F27 11853 5.33 41.85 furnaces; kilns; ovens; retorts

F02 113956 6.27 50.83 combustion engines; hot-gas or combustion-product engine plants

B43 3051 5.02 39.64 writing or drawing implements; bureau accessories

B07 5771 3.38 32.54 separating solids from solids; sorting

F26 7296 3.87 26.95 drying

A45 12126 5.66 30.46 hand or travelling articles

E01 13889 4.20 34.12 construction of roads, railways, or bridges

B63 14208 4.62 29.88 ships or other waterborne vessels; related equipment

F28 23852 5.01 31.40 heat exchange in general

F22 3790 8.22 25.98 steam generation

D07 1360 2.72 24.97 ropes; cables other than electric

B65 165572 4.76 37.60 conveying; packing; storing; handling thin or filamentary material

E02 16012 5.45 50.15 hydraulic engineering; foundations; soil-shifting

A44 5202 5.14 48.71 haberdashery; jewellery

D05 2044 2.61 39.50 sewing; embroidering; tufting

B60 176856 6.27 58.58 vehicles in general

A47 59501 5.46 37.05 furniture; domestic articles or appliances; coffee mills; spice mills; suction cleaners

in general

E04 47926 4.74 37.33 building

D03 7892 3.08 47.50 weaving

B62 48784 7.01 53.75 land vehicles for travelling otherwise than on rails

F24 37270 6.76 37.86 heating; ranges; ventilating

F16 191793 5.81 45.69 engineering elements or units; general measures for producing and maintaining

effective functioning of machines or installations; thermal insulation in general

A22 3717 1.81 28.32 butchering; meat treatment; processing poultry or fish

B30 6065 6.81 31.84 presses

B61 9889 4.79 26.03 railways

B66 18098 5.22 41.57 hoisting; lifting; hauling

B26 14447 3.50 30.29 hand cutting tools; cutting; severing

E03 9308 4.55 42.68 water supply; sewerage

E06 15805 3.43 33.77 doors, windows, shutters, or roller blinds, in general; ladders

E05 37663 6.04 43.50 locks; keys; window or door fittings; safes

49

Appendix D: Elasticity estimation relying on an alternative functional form (log-log)

(1) (2)

VARIABLES Log(Legitimation time) Log(Technological potential)

Technological component characteristics

Similarity 3 digits (dummy) -0.28*** -0.12***

log(Science-based content) 0.027** 0.12***

log(Familiarity) -0.021*** -0.032***

log(Appropriability) -0.089* -0.17***

Inventor’s characterisics

Inventors' experience (dummy) -0.047*** 0.00077

log(Inventors' team size) -0.024** 0.036***

Applicant characteristics

log(Applicants' experience) -0.0030 0.0040

University applicant (dummy) 0.042 0.071**

More than one applicant (dummy) -0.21*** 0.070***

log(Concentration) -0.010 0.088***

Dummy Technology class (3 digits) yes yes

Dummy Applicant’s country yes yes

Dummy technology entry year yes yes

Constant 2.71*** 5.08***

Observations 11,009 11,009

R-squared 0.183 0.211

50

Appendix E: OLS estimation using actual data

(1) (2)

VARIABLES Actual Legitimation time Actual Technological potential

Technological component characteristics

Similarity 3 digits (dummy) -0.91*** -12.0***

log(Science-based content) 0.32*** 16.1***

log(Familiarity) -0.22*** -5.26***

log(Appropriability) -1.09*** -14.4***

log(Concentration) 0.14*** 8.32***

Inventor’s characterisics

Inventors' experience (dummy) -0.21*** -0.93

log(Inventors' team size) -0.077* 4.49***

Applicant characteristics

log(Applicants' experience) -0.0085 0.65**

University applicant (dummy) 0.021 9.74***

More than one applicant (dummy) -0.98*** 5.07**

Dummy Technology class (3 digits) yes yes

Dummy Applicant’s country yes yes

Dummy technology entry year yes yes

Constant 12.3*** 172***

Observations 11,009 11,009

R-squared 0.232 0.140

51

Appendix F: Splitting the sample between technologies that reach their ceiling within the

first 20 years from their appearance and the others

In Figure F1, we plot the actual cumulated number of patents at the end of our observation period

(20 years) against the estimated technology potential (the ceiling of the s-curve). The plot should

be interpreted as follows:

- The closeness of the points to the diagonal indicate that the estimated ceiling is close to

the actual number of patents;

- Staying on the diagonal means that the technology reaches its ceiling within 20 years.

Figure F1: Actual cumulated number of patents at the end of the observation period

(when t=20 years) against the estimated technology potential (the ceiling of the s-curve)

-

52

Table F1 apply the regression model reported in Table 2 to two different subsamples of novel

technologies. Subsample A includes the technologies reaching their ceiling within the first 20 years

(+/-10%), while subsample B includes technologies not reaching their ceiling within the first 20

years.

Table F1: Legitimation and technological potential estimations on two subsamples

(1) (2) (3) (4)

Subsample A: Technologies reaching their

ceiling within the first 20 years

Subsample B: Technologies NOT reaching their

ceiling within the first 20 years

68% of the cases 32% of the cases

Legitimation (10%) Technology potential Legitimation (10%) Technology potential

Component characteristics

Similarity 3 digits (dummy) -1.23*** -14.1*** -1.07*** -11.5*

log(Science-based content) 0.22*** 16.8*** 0.29*** 17.3***

log(Familiarity) -0.11*** -4.56*** -0.24*** -10.2***

log(Appropriability) -0.70*** -12.6* -1.01*** -21.6*

log(Concentration) -0.12** 4.17** 0.58*** 29.5***

Inventor’s characterisics

Inventors' experience (dummy) -0.23*** -2.41 -0.05 3.88

log(Inventors' team size) -0.076 5.44*** -0.063 4.54

Applicant characteristics

log(Applicants' experience) -0.0065 0.74* 0.00016 0.69

University applicant (dummy) 0.2 8.67** 0.17 14.6*

More than one applicant (dummy) -0.83*** 4.34 -0.94*** 5.96

Dummy Technology class (3 digits) Yes Yes Yes Yes

Dummy Applicant’s country Yes Yes Yes Yes

Dummy technology entry year Yes Yes Yes Yes

Constant 9.69*** 149*** 16.1*** 330***

Observations 7,540 7,540 3,469 3,469

R-squared 0.26 0.133 0.267 0.257

53

Appendix G: OLS estimations restricting the sample to novel technologies having a diffusion

curve for which the large majority of the patented inventions (at least the 90%) is mono-

technology (3,221).

(1) (2)

Legitimation (10%) Technological potential

Component characteristics

Similarity 3 digits (dummy) -1.91*** -33.2***

log(Science-based content) -0.042 14.1***

log(Familiarity) -0.17*** -7.50***

log(Appropriability) -1.84*** -43.6***

log(Concentration) -0.35*** 9.12**

Inventor’s characterisics

Inventors' experience (dummy) -0.43*** -1.01

log(Inventors' team size) -0.11 8.98***

Applicant characteristics

log(Applicants' experience) -0.016 1.38*

University applicant (dummy) -0.011 7.33

More than one applicant (dummy) -0.65*** 10.3*

Dummy Technology class (3 digits) yes yes

Dummy Applicant’s country yes yes

Dummy technology entry year yes yes

Constant 16.1*** 368***

Observations 3,221 3,221

R-squared 0.444 0.196

54

Appendix H: OLS estimations based on a restricted sample of 8,557 novel technologies

having only one patented invention the first year when they appear.

(1) (2)

Legitimation (10%) Technology potential

Component characteristics

Similarity 3 digits (dummy) -1.31*** -13.6***

log(Science-based content) 0.18*** 16.6***

log(Familiarity) -0.14*** -5.68***

log(Appropriability) -1.02*** -24.8***

log(Concentration) 0.080 9.97***

Inventor’s characterisics

Inventors' experience (dummy) -0.16** -2.48

log(Inventors' team size) 0.078 2.71*

Applicant characteristics

log(Applicants' experience) -0.0018 0.48

University applicant (dummy) 0.072 5.30

More than one applicant (dummy) -0.18 -6.32

Dummy Technology class (3 digits) Yes Yes

Dummy Applicant’s country Yes Yes

Dummy technology entry year Yes Yes

Constant 9.69*** 149***

Observations 8,557 8,557

R-squared 0.227 0.139

55

Appendix I: 2-step Heckman selection model

In the main text we consider the diffusion of successful novel technologies and, as a consequence,

we limit our observations to all the novel combination that reach a minimum level of diffusion. If

we eliminate this restriction and we consider all the novel technologies, our study sample includes

191,627 observations. However, only 11,009 technologies diffuse. Not including in our analysis

of the diffusion determinants the 180,618 (191,627-11,009) novel technologies that do not diffuse,

may generate a selection bias problem. To correct for the selection bias associated with the loose

definition of novelty we adopt a 2-step Heckman estimation strategy. In the first step, we assume

that the determinants of diffusing or not depends only on the characteristics of the patented

inventions embedding the novel technology at the first year of its appearance. In table I1, we

estimate a Probit model where the dependent variable is a dummy that equals 1 if the novel

technology diffuses, 0 otherwise. The independent variables are the characteristics of the patented

inventions during the first year when the technology appears. In the second step, we estimated a

model explaining legitimation and technological potential including as explanatory variables the

characteristics of the combined technological components and add the Inverse Mill’s ratio to

correct for the selection bias. Table I2 reports the results of our estimations without and with the

correction for the selection bias, respectively columns 1-2 and 3-4.

56

Table I1: Heckman first step, probability of a novel technology diffusion

(1)

Probit

VARIABLES Diffusion

Inventors' experience (dummy) -0.021**

log(Inventors' team size) 0.090***

log(Applicants' experience) 0.013***

University applicant (dummy) 0.23***

More than one applicant (dummy) 0.14***

Constant -1.81***

Observations 191,627

Dummy application year Yes

Dummy technology class (3 digits) Yes

Dummy applicant's country Yes

Pseudo-R2 0.034

Table I2: Heckman second step, estimation of legitimation and technological potential

equations correcting for selection bias

(1) (2) (3) (4)

OLS OLS OLS OLS

VARIABLES

Legitimation

(10%)

Technological

potential

Legitimation

(10%)

Technological

potential

Similarity 3 digits (dummy) -1.28*** -14.2*** -1.24*** -15.2***

log(Science-based content) 0.22*** 19.8*** 0.27*** 18.3***

log(Familiarity) -0.18*** -6.78*** -0.16*** -6.95***

log(Appropriability) -1.06*** -25.3*** -0.99*** -26.0***

log(HH-index) 0.036 11.2*** 0.043 11.0***

Inverse Mill's ratio

(nonselection hazard) 1.94*** -53.0***

Constant 12.4*** 264*** 8.68*** 363***

Observations 11,009 11,009 11,009 11,009

R-squared 0.217 0.139 0.231 0.150

Dummy application year Yes Yes Yes Yes

Dummy technology class (3 digits) Yes Yes Yes Yes

We find a substantial coherence between the results reported in Table I2, columns 3 and 4 and the

results reported in the main text in Table 2, columns 5 and 6. This allows us to claim that analyzing

successful novelty and restricting our sample to 11,009 novel combinations does not introduce any

57

selection bias on the estimated impact of the characteristics of the combined components on

diffusion.

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